collect_repositories/analyze_repositories.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of repository collection\n",
    "\n",
    "In this notebook, the retrieved results of the repository collection is analyzed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date\n",
    "import os\n",
    "import statistics\n",
    "from pathlib import Path\n",
    "from pprint import pprint\n",
    "\n",
    "import pandas as pd\n",
    "from pandas.plotting import table \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.table as table\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = [12, 8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Folders and constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset = \"all\"  # \"user\", \"group\", \"all\"\n",
    "\n",
    "# file paths\n",
    "fp_repos = Path(\"results\", \"repositories_filtered.csv\")\n",
    "fp_figs = Path(\"figs\")\n",
    "\n",
    "# create output folder if not exists\n",
    "fp_figs.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load filtered repositories\n",
    "\n",
    "Load (manually) filtered repositories into notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "repos = pd.read_csv(fp_repos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering for research groups or private users only (manual annotation not yet done)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/80/92jv0gdd74n_p57srnsh1wcr0000gp/T/ipykernel_15968/1328199163.py:1: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  users = pd.read_csv(\"../collect_users/results/annotated.csv\", error_bad_lines=False,  delimiter=';')\n"
     ]
    }
   ],
   "source": [
    "users = pd.read_csv(\"../collect_users/results/annotated.csv\", error_bad_lines=False,  delimiter=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: replace by join?\n",
    "def add_research_group_filter(dataset):\n",
    "    user_is_research_group = []\n",
    "    for row in dataset.iterrows():\n",
    "        try:\n",
    "            owner = row[1]['owner']\n",
    "        except:\n",
    "            owner = row[1]['html_url_repository'].split('/')[-2]\n",
    "            \n",
    "            \n",
    "      \n",
    "        row_users = users.loc[users['user_id'] == owner]\n",
    "        if len(row_users['is_research_group']) == 0:\n",
    "            user_is_research_group.append(False)\n",
    "        for i in row_users['is_research_group']:\n",
    "            if i == 1:\n",
    "                user_is_research_group.append(True)\n",
    "                break\n",
    "            else:\n",
    "                user_is_research_group.append(False)\n",
    "                break\n",
    "                    \n",
    "    return user_is_research_group\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "repos['is_research_group'] = add_research_group_filter(repos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subset repositories\n",
    "\n",
    "Make a subset of repositories on \"user\", \"group\", or \"all\".\n",
    "- \"user\" - Account of individual researcher\n",
    "- \"group\" - Account of research group\n",
    "- \"all\" - Both individual researcher or research group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if subset == \"user\":\n",
    "    repos = repos[~repos['is_research_group']]\n",
    "elif subset == \"group\":\n",
    "    repos = repos[repos['is_research_group']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the github API, metadata on the repositories was collected. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The repository data from Github contains the following columns:\n",
      "['id',\n",
      " 'node_id',\n",
      " 'name',\n",
      " 'full_name',\n",
      " 'private',\n",
      " 'owner',\n",
      " 'html_url',\n",
      " 'description',\n",
      " 'fork',\n",
      " 'url',\n",
      " 'forks_url',\n",
      " 'keys_url',\n",
      " 'collaborators_url',\n",
      " 'teams_url',\n",
      " 'hooks_url',\n",
      " 'issue_events_url',\n",
      " 'events_url',\n",
      " 'assignees_url',\n",
      " 'branches_url',\n",
      " 'tags_url',\n",
      " 'blobs_url',\n",
      " 'git_tags_url',\n",
      " 'git_refs_url',\n",
      " 'trees_url',\n",
      " 'statuses_url',\n",
      " 'languages_url',\n",
      " 'stargazers_url',\n",
      " 'contributors_url',\n",
      " 'subscribers_url',\n",
      " 'subscription_url',\n",
      " 'commits_url',\n",
      " 'git_commits_url',\n",
      " 'comments_url',\n",
      " 'issue_comment_url',\n",
      " 'contents_url',\n",
      " 'compare_url',\n",
      " 'merges_url',\n",
      " 'archive_url',\n",
      " 'downloads_url',\n",
      " 'issues_url',\n",
      " 'pulls_url',\n",
      " 'milestones_url',\n",
      " 'notifications_url',\n",
      " 'labels_url',\n",
      " 'releases_url',\n",
      " 'deployments_url',\n",
      " 'created_at',\n",
      " 'updated_at',\n",
      " 'pushed_at',\n",
      " 'git_url',\n",
      " 'ssh_url',\n",
      " 'clone_url',\n",
      " 'svn_url',\n",
      " 'homepage',\n",
      " 'size',\n",
      " 'stargazers_count',\n",
      " 'watchers_count',\n",
      " 'language',\n",
      " 'has_issues',\n",
      " 'has_projects',\n",
      " 'has_downloads',\n",
      " 'has_wiki',\n",
      " 'has_pages',\n",
      " 'has_discussions',\n",
      " 'forks_count',\n",
      " 'mirror_url',\n",
      " 'archived',\n",
      " 'disabled',\n",
      " 'open_issues_count',\n",
      " 'license',\n",
      " 'allow_forking',\n",
      " 'is_template',\n",
      " 'web_commit_signoff_required',\n",
      " 'topics',\n",
      " 'visibility',\n",
      " 'forks',\n",
      " 'open_issues',\n",
      " 'watchers',\n",
      " 'default_branch',\n",
      " 'permissions',\n",
      " 'date',\n",
      " 'is_research_group']\n"
     ]
    }
   ],
   "source": [
    "print(\"The repository data from Github contains the following columns:\")\n",
    "pprint(repos.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stargazers\n",
    "\n",
    "Stargazers represent the amount of people that have 'starred' a Github project. Starring a project can indicate that a user likes the project. It can also be used to bookmark a project, since starred projects are saved. The amount of stargazers can be used as a metric to measure popularity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "repos['stargazers_count'].plot(kind = 'hist', fontsize = 12, title = 'Number of stargazers per repository', bins = 100)\n",
    "plt.savefig(Path(fp_figs, f'stargazers_{subset}.png'))\n"
   ]
  },
  {
   "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>id</th>\n",
       "      <th>node_id</th>\n",
       "      <th>name</th>\n",
       "      <th>full_name</th>\n",
       "      <th>private</th>\n",
       "      <th>owner</th>\n",
       "      <th>html_url</th>\n",
       "      <th>description</th>\n",
       "      <th>fork</th>\n",
       "      <th>url</th>\n",
       "      <th>...</th>\n",
       "      <th>web_commit_signoff_required</th>\n",
       "      <th>topics</th>\n",
       "      <th>visibility</th>\n",
       "      <th>forks</th>\n",
       "      <th>open_issues</th>\n",
       "      <th>watchers</th>\n",
       "      <th>default_branch</th>\n",
       "      <th>permissions</th>\n",
       "      <th>date</th>\n",
       "      <th>is_research_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>12091112</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkxMjA5MTExMg==</td>\n",
       "      <td>sqlpp11</td>\n",
       "      <td>rbock/sqlpp11</td>\n",
       "      <td>False</td>\n",
       "      <td>rbock</td>\n",
       "      <td>https://github.com/rbock/sqlpp11</td>\n",
       "      <td>A type safe SQL template library for C++</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/rbock/sqlpp11</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>public</td>\n",
       "      <td>345</td>\n",
       "      <td>23</td>\n",
       "      <td>2263</td>\n",
       "      <td>main</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>58459878</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnk1ODQ1OTg3OA==</td>\n",
       "      <td>clingo</td>\n",
       "      <td>potassco/clingo</td>\n",
       "      <td>False</td>\n",
       "      <td>potassco</td>\n",
       "      <td>https://github.com/potassco/clingo</td>\n",
       "      <td>🤔 A grounder and solver for logic programs.</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/potassco/clingo</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>['answer-set-programming', 'declarative-progra...</td>\n",
       "      <td>public</td>\n",
       "      <td>75</td>\n",
       "      <td>8</td>\n",
       "      <td>523</td>\n",
       "      <td>master</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>65741668</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnk2NTc0MTY2OA==</td>\n",
       "      <td>spacy-api-docker</td>\n",
       "      <td>jgontrum/spacy-api-docker</td>\n",
       "      <td>False</td>\n",
       "      <td>jgontrum</td>\n",
       "      <td>https://github.com/jgontrum/spacy-api-docker</td>\n",
       "      <td>spaCy REST API, wrapped in a Docker container.</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/jgontrum/spacy-ap...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>['docker', 'microservice', 'natural-language-p...</td>\n",
       "      <td>public</td>\n",
       "      <td>98</td>\n",
       "      <td>25</td>\n",
       "      <td>251</td>\n",
       "      <td>master</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1846</th>\n",
       "      <td>290899681</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkyOTA4OTk2ODE=</td>\n",
       "      <td>odbc2parquet</td>\n",
       "      <td>pacman82/odbc2parquet</td>\n",
       "      <td>False</td>\n",
       "      <td>pacman82</td>\n",
       "      <td>https://github.com/pacman82/odbc2parquet</td>\n",
       "      <td>A command line tool to query an ODBC data sour...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/pacman82/odbc2par...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>['odbc', 'parquet']</td>\n",
       "      <td>public</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>164</td>\n",
       "      <td>main</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>131267949</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkxMzEyNjc5NDk=</td>\n",
       "      <td>rainymotion</td>\n",
       "      <td>hydrogo/rainymotion</td>\n",
       "      <td>False</td>\n",
       "      <td>hydrogo</td>\n",
       "      <td>https://github.com/hydrogo/rainymotion</td>\n",
       "      <td>Python library for radar-based precipitation n...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/hydrogo/rainymotion</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>public</td>\n",
       "      <td>53</td>\n",
       "      <td>5</td>\n",
       "      <td>163</td>\n",
       "      <td>master</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>9429665</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnk5NDI5NjY1</td>\n",
       "      <td>after-effects-script-snippets</td>\n",
       "      <td>ff6347/after-effects-script-snippets</td>\n",
       "      <td>False</td>\n",
       "      <td>ff6347</td>\n",
       "      <td>https://github.com/ff6347/after-effects-script...</td>\n",
       "      <td>this is a loose collection of js snippets for ...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/ff6347/after-effe...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>public</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>157</td>\n",
       "      <td>master</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>151979487</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkxNTE5Nzk0ODc=</td>\n",
       "      <td>chor-js</td>\n",
       "      <td>bptlab/chor-js</td>\n",
       "      <td>False</td>\n",
       "      <td>bptlab</td>\n",
       "      <td>https://github.com/bptlab/chor-js</td>\n",
       "      <td>An editor for BPMN 2.0 choreography diagrams b...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/bptlab/chor-js</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>public</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>138</td>\n",
       "      <td>master</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>12629151</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkxMjYyOTE1MQ==</td>\n",
       "      <td>sqlpp11-connector-mysql</td>\n",
       "      <td>rbock/sqlpp11-connector-mysql</td>\n",
       "      <td>False</td>\n",
       "      <td>rbock</td>\n",
       "      <td>https://github.com/rbock/sqlpp11-connector-mysql</td>\n",
       "      <td>A C++ wrapper for mysql meant to be used in co...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/rbock/sqlpp11-con...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>public</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>127</td>\n",
       "      <td>archive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>58460549</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnk1ODQ2MDU0OQ==</td>\n",
       "      <td>clasp</td>\n",
       "      <td>potassco/clasp</td>\n",
       "      <td>False</td>\n",
       "      <td>potassco</td>\n",
       "      <td>https://github.com/potassco/clasp</td>\n",
       "      <td>⚙️ A conflict-driven nogood learning answer se...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/potassco/clasp</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>public</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>101</td>\n",
       "      <td>master</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>236913315</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkyMzY5MTMzMTU=</td>\n",
       "      <td>rainnet</td>\n",
       "      <td>hydrogo/rainnet</td>\n",
       "      <td>False</td>\n",
       "      <td>hydrogo</td>\n",
       "      <td>https://github.com/hydrogo/rainnet</td>\n",
       "      <td>RainNet: a convolutional neural network for ra...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/hydrogo/rainnet</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>public</td>\n",
       "      <td>58</td>\n",
       "      <td>6</td>\n",
       "      <td>94</td>\n",
       "      <td>master</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                           node_id  \\\n",
       "2003   12091112  MDEwOlJlcG9zaXRvcnkxMjA5MTExMg==   \n",
       "1916   58459878  MDEwOlJlcG9zaXRvcnk1ODQ1OTg3OA==   \n",
       "1346   65741668  MDEwOlJlcG9zaXRvcnk2NTc0MTY2OA==   \n",
       "1846  290899681  MDEwOlJlcG9zaXRvcnkyOTA4OTk2ODE=   \n",
       "1186  131267949  MDEwOlJlcG9zaXRvcnkxMzEyNjc5NDk=   \n",
       "694     9429665      MDEwOlJlcG9zaXRvcnk5NDI5NjY1   \n",
       "323   151979487  MDEwOlJlcG9zaXRvcnkxNTE5Nzk0ODc=   \n",
       "2004   12629151  MDEwOlJlcG9zaXRvcnkxMjYyOTE1MQ==   \n",
       "1913   58460549  MDEwOlJlcG9zaXRvcnk1ODQ2MDU0OQ==   \n",
       "1185  236913315  MDEwOlJlcG9zaXRvcnkyMzY5MTMzMTU=   \n",
       "\n",
       "                               name                             full_name  \\\n",
       "2003                        sqlpp11                         rbock/sqlpp11   \n",
       "1916                         clingo                       potassco/clingo   \n",
       "1346               spacy-api-docker             jgontrum/spacy-api-docker   \n",
       "1846                   odbc2parquet                 pacman82/odbc2parquet   \n",
       "1186                    rainymotion                   hydrogo/rainymotion   \n",
       "694   after-effects-script-snippets  ff6347/after-effects-script-snippets   \n",
       "323                         chor-js                        bptlab/chor-js   \n",
       "2004        sqlpp11-connector-mysql         rbock/sqlpp11-connector-mysql   \n",
       "1913                          clasp                        potassco/clasp   \n",
       "1185                        rainnet                       hydrogo/rainnet   \n",
       "\n",
       "      private     owner                                           html_url  \\\n",
       "2003    False     rbock                   https://github.com/rbock/sqlpp11   \n",
       "1916    False  potassco                 https://github.com/potassco/clingo   \n",
       "1346    False  jgontrum       https://github.com/jgontrum/spacy-api-docker   \n",
       "1846    False  pacman82           https://github.com/pacman82/odbc2parquet   \n",
       "1186    False   hydrogo             https://github.com/hydrogo/rainymotion   \n",
       "694     False    ff6347  https://github.com/ff6347/after-effects-script...   \n",
       "323     False    bptlab                  https://github.com/bptlab/chor-js   \n",
       "2004    False     rbock   https://github.com/rbock/sqlpp11-connector-mysql   \n",
       "1913    False  potassco                  https://github.com/potassco/clasp   \n",
       "1185    False   hydrogo                 https://github.com/hydrogo/rainnet   \n",
       "\n",
       "                                            description   fork  \\\n",
       "2003           A type safe SQL template library for C++  False   \n",
       "1916        🤔 A grounder and solver for logic programs.  False   \n",
       "1346     spaCy REST API, wrapped in a Docker container.  False   \n",
       "1846  A command line tool to query an ODBC data sour...  False   \n",
       "1186  Python library for radar-based precipitation n...  False   \n",
       "694   this is a loose collection of js snippets for ...  False   \n",
       "323   An editor for BPMN 2.0 choreography diagrams b...  False   \n",
       "2004  A C++ wrapper for mysql meant to be used in co...  False   \n",
       "1913  ⚙️ A conflict-driven nogood learning answer se...  False   \n",
       "1185  RainNet: a convolutional neural network for ra...  False   \n",
       "\n",
       "                                                    url  ...  \\\n",
       "2003         https://api.github.com/repos/rbock/sqlpp11  ...   \n",
       "1916       https://api.github.com/repos/potassco/clingo  ...   \n",
       "1346  https://api.github.com/repos/jgontrum/spacy-ap...  ...   \n",
       "1846  https://api.github.com/repos/pacman82/odbc2par...  ...   \n",
       "1186   https://api.github.com/repos/hydrogo/rainymotion  ...   \n",
       "694   https://api.github.com/repos/ff6347/after-effe...  ...   \n",
       "323         https://api.github.com/repos/bptlab/chor-js  ...   \n",
       "2004  https://api.github.com/repos/rbock/sqlpp11-con...  ...   \n",
       "1913        https://api.github.com/repos/potassco/clasp  ...   \n",
       "1185       https://api.github.com/repos/hydrogo/rainnet  ...   \n",
       "\n",
       "     web_commit_signoff_required  \\\n",
       "2003                       False   \n",
       "1916                       False   \n",
       "1346                       False   \n",
       "1846                       False   \n",
       "1186                       False   \n",
       "694                        False   \n",
       "323                        False   \n",
       "2004                       False   \n",
       "1913                       False   \n",
       "1185                       False   \n",
       "\n",
       "                                                 topics visibility forks  \\\n",
       "2003                                                 []     public   345   \n",
       "1916  ['answer-set-programming', 'declarative-progra...     public    75   \n",
       "1346  ['docker', 'microservice', 'natural-language-p...     public    98   \n",
       "1846                                ['odbc', 'parquet']     public    12   \n",
       "1186                                                 []     public    53   \n",
       "694                                                  []     public    34   \n",
       "323                                                  []     public    24   \n",
       "2004                                                 []     public    63   \n",
       "1913                                                 []     public    13   \n",
       "1185                                                 []     public    58   \n",
       "\n",
       "     open_issues watchers default_branch permissions        date  \\\n",
       "2003          23     2263           main         NaN  2023-09-09   \n",
       "1916           8      523         master         NaN  2023-09-09   \n",
       "1346          25      251         master         NaN  2023-09-09   \n",
       "1846           4      164           main         NaN  2023-09-09   \n",
       "1186           5      163         master         NaN  2023-09-09   \n",
       "694            0      157         master         NaN  2023-09-09   \n",
       "323            4      138         master         NaN  2023-09-09   \n",
       "2004           0      127        archive         NaN  2023-09-09   \n",
       "1913           2      101         master         NaN  2023-09-09   \n",
       "1185           6       94         master         NaN  2023-09-09   \n",
       "\n",
       "     is_research_group  \n",
       "2003             False  \n",
       "1916              True  \n",
       "1346             False  \n",
       "1846             False  \n",
       "1186             False  \n",
       "694              False  \n",
       "323               True  \n",
       "2004             False  \n",
       "1913              True  \n",
       "1185             False  \n",
       "\n",
       "[10 rows x 82 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repos.nlargest(10, 'stargazers_count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Watchers\n",
    "\n",
    "Watchers receive  notifications about project activity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "repos['watchers_count'].plot.hist(fontsize = 12, title = 'Number of watchers per repository', bins = 128)\n",
    "plt.savefig(Path(fp_figs, f'watchers_{subset}.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>node_id</th>\n",
       "      <th>name</th>\n",
       "      <th>full_name</th>\n",
       "      <th>private</th>\n",
       "      <th>owner</th>\n",
       "      <th>html_url</th>\n",
       "      <th>description</th>\n",
       "      <th>fork</th>\n",
       "      <th>url</th>\n",
       "      <th>...</th>\n",
       "      <th>web_commit_signoff_required</th>\n",
       "      <th>topics</th>\n",
       "      <th>visibility</th>\n",
       "      <th>forks</th>\n",
       "      <th>open_issues</th>\n",
       "      <th>watchers</th>\n",
       "      <th>default_branch</th>\n",
       "      <th>permissions</th>\n",
       "      <th>date</th>\n",
       "      <th>is_research_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>12091112</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkxMjA5MTExMg==</td>\n",
       "      <td>sqlpp11</td>\n",
       "      <td>rbock/sqlpp11</td>\n",
       "      <td>False</td>\n",
       "      <td>rbock</td>\n",
       "      <td>https://github.com/rbock/sqlpp11</td>\n",
       "      <td>A type safe SQL template library for C++</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/rbock/sqlpp11</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>public</td>\n",
       "      <td>345</td>\n",
       "      <td>23</td>\n",
       "      <td>2263</td>\n",
       "      <td>main</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>58459878</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnk1ODQ1OTg3OA==</td>\n",
       "      <td>clingo</td>\n",
       "      <td>potassco/clingo</td>\n",
       "      <td>False</td>\n",
       "      <td>potassco</td>\n",
       "      <td>https://github.com/potassco/clingo</td>\n",
       "      <td>🤔 A grounder and solver for logic programs.</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/potassco/clingo</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>['answer-set-programming', 'declarative-progra...</td>\n",
       "      <td>public</td>\n",
       "      <td>75</td>\n",
       "      <td>8</td>\n",
       "      <td>523</td>\n",
       "      <td>master</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>65741668</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnk2NTc0MTY2OA==</td>\n",
       "      <td>spacy-api-docker</td>\n",
       "      <td>jgontrum/spacy-api-docker</td>\n",
       "      <td>False</td>\n",
       "      <td>jgontrum</td>\n",
       "      <td>https://github.com/jgontrum/spacy-api-docker</td>\n",
       "      <td>spaCy REST API, wrapped in a Docker container.</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/jgontrum/spacy-ap...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>['docker', 'microservice', 'natural-language-p...</td>\n",
       "      <td>public</td>\n",
       "      <td>98</td>\n",
       "      <td>25</td>\n",
       "      <td>251</td>\n",
       "      <td>master</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1846</th>\n",
       "      <td>290899681</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkyOTA4OTk2ODE=</td>\n",
       "      <td>odbc2parquet</td>\n",
       "      <td>pacman82/odbc2parquet</td>\n",
       "      <td>False</td>\n",
       "      <td>pacman82</td>\n",
       "      <td>https://github.com/pacman82/odbc2parquet</td>\n",
       "      <td>A command line tool to query an ODBC data sour...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/pacman82/odbc2par...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>['odbc', 'parquet']</td>\n",
       "      <td>public</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>164</td>\n",
       "      <td>main</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>131267949</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkxMzEyNjc5NDk=</td>\n",
       "      <td>rainymotion</td>\n",
       "      <td>hydrogo/rainymotion</td>\n",
       "      <td>False</td>\n",
       "      <td>hydrogo</td>\n",
       "      <td>https://github.com/hydrogo/rainymotion</td>\n",
       "      <td>Python library for radar-based precipitation n...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/hydrogo/rainymotion</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>public</td>\n",
       "      <td>53</td>\n",
       "      <td>5</td>\n",
       "      <td>163</td>\n",
       "      <td>master</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>9429665</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnk5NDI5NjY1</td>\n",
       "      <td>after-effects-script-snippets</td>\n",
       "      <td>ff6347/after-effects-script-snippets</td>\n",
       "      <td>False</td>\n",
       "      <td>ff6347</td>\n",
       "      <td>https://github.com/ff6347/after-effects-script...</td>\n",
       "      <td>this is a loose collection of js snippets for ...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/ff6347/after-effe...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>public</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>157</td>\n",
       "      <td>master</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>151979487</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkxNTE5Nzk0ODc=</td>\n",
       "      <td>chor-js</td>\n",
       "      <td>bptlab/chor-js</td>\n",
       "      <td>False</td>\n",
       "      <td>bptlab</td>\n",
       "      <td>https://github.com/bptlab/chor-js</td>\n",
       "      <td>An editor for BPMN 2.0 choreography diagrams b...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/bptlab/chor-js</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>public</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>138</td>\n",
       "      <td>master</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>12629151</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkxMjYyOTE1MQ==</td>\n",
       "      <td>sqlpp11-connector-mysql</td>\n",
       "      <td>rbock/sqlpp11-connector-mysql</td>\n",
       "      <td>False</td>\n",
       "      <td>rbock</td>\n",
       "      <td>https://github.com/rbock/sqlpp11-connector-mysql</td>\n",
       "      <td>A C++ wrapper for mysql meant to be used in co...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/rbock/sqlpp11-con...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>public</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>127</td>\n",
       "      <td>archive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>58460549</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnk1ODQ2MDU0OQ==</td>\n",
       "      <td>clasp</td>\n",
       "      <td>potassco/clasp</td>\n",
       "      <td>False</td>\n",
       "      <td>potassco</td>\n",
       "      <td>https://github.com/potassco/clasp</td>\n",
       "      <td>⚙️ A conflict-driven nogood learning answer se...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/potassco/clasp</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>public</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>101</td>\n",
       "      <td>master</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>236913315</td>\n",
       "      <td>MDEwOlJlcG9zaXRvcnkyMzY5MTMzMTU=</td>\n",
       "      <td>rainnet</td>\n",
       "      <td>hydrogo/rainnet</td>\n",
       "      <td>False</td>\n",
       "      <td>hydrogo</td>\n",
       "      <td>https://github.com/hydrogo/rainnet</td>\n",
       "      <td>RainNet: a convolutional neural network for ra...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://api.github.com/repos/hydrogo/rainnet</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>public</td>\n",
       "      <td>58</td>\n",
       "      <td>6</td>\n",
       "      <td>94</td>\n",
       "      <td>master</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                           node_id  \\\n",
       "2003   12091112  MDEwOlJlcG9zaXRvcnkxMjA5MTExMg==   \n",
       "1916   58459878  MDEwOlJlcG9zaXRvcnk1ODQ1OTg3OA==   \n",
       "1346   65741668  MDEwOlJlcG9zaXRvcnk2NTc0MTY2OA==   \n",
       "1846  290899681  MDEwOlJlcG9zaXRvcnkyOTA4OTk2ODE=   \n",
       "1186  131267949  MDEwOlJlcG9zaXRvcnkxMzEyNjc5NDk=   \n",
       "694     9429665      MDEwOlJlcG9zaXRvcnk5NDI5NjY1   \n",
       "323   151979487  MDEwOlJlcG9zaXRvcnkxNTE5Nzk0ODc=   \n",
       "2004   12629151  MDEwOlJlcG9zaXRvcnkxMjYyOTE1MQ==   \n",
       "1913   58460549  MDEwOlJlcG9zaXRvcnk1ODQ2MDU0OQ==   \n",
       "1185  236913315  MDEwOlJlcG9zaXRvcnkyMzY5MTMzMTU=   \n",
       "\n",
       "                               name                             full_name  \\\n",
       "2003                        sqlpp11                         rbock/sqlpp11   \n",
       "1916                         clingo                       potassco/clingo   \n",
       "1346               spacy-api-docker             jgontrum/spacy-api-docker   \n",
       "1846                   odbc2parquet                 pacman82/odbc2parquet   \n",
       "1186                    rainymotion                   hydrogo/rainymotion   \n",
       "694   after-effects-script-snippets  ff6347/after-effects-script-snippets   \n",
       "323                         chor-js                        bptlab/chor-js   \n",
       "2004        sqlpp11-connector-mysql         rbock/sqlpp11-connector-mysql   \n",
       "1913                          clasp                        potassco/clasp   \n",
       "1185                        rainnet                       hydrogo/rainnet   \n",
       "\n",
       "      private     owner                                           html_url  \\\n",
       "2003    False     rbock                   https://github.com/rbock/sqlpp11   \n",
       "1916    False  potassco                 https://github.com/potassco/clingo   \n",
       "1346    False  jgontrum       https://github.com/jgontrum/spacy-api-docker   \n",
       "1846    False  pacman82           https://github.com/pacman82/odbc2parquet   \n",
       "1186    False   hydrogo             https://github.com/hydrogo/rainymotion   \n",
       "694     False    ff6347  https://github.com/ff6347/after-effects-script...   \n",
       "323     False    bptlab                  https://github.com/bptlab/chor-js   \n",
       "2004    False     rbock   https://github.com/rbock/sqlpp11-connector-mysql   \n",
       "1913    False  potassco                  https://github.com/potassco/clasp   \n",
       "1185    False   hydrogo                 https://github.com/hydrogo/rainnet   \n",
       "\n",
       "                                            description   fork  \\\n",
       "2003           A type safe SQL template library for C++  False   \n",
       "1916        🤔 A grounder and solver for logic programs.  False   \n",
       "1346     spaCy REST API, wrapped in a Docker container.  False   \n",
       "1846  A command line tool to query an ODBC data sour...  False   \n",
       "1186  Python library for radar-based precipitation n...  False   \n",
       "694   this is a loose collection of js snippets for ...  False   \n",
       "323   An editor for BPMN 2.0 choreography diagrams b...  False   \n",
       "2004  A C++ wrapper for mysql meant to be used in co...  False   \n",
       "1913  ⚙️ A conflict-driven nogood learning answer se...  False   \n",
       "1185  RainNet: a convolutional neural network for ra...  False   \n",
       "\n",
       "                                                    url  ...  \\\n",
       "2003         https://api.github.com/repos/rbock/sqlpp11  ...   \n",
       "1916       https://api.github.com/repos/potassco/clingo  ...   \n",
       "1346  https://api.github.com/repos/jgontrum/spacy-ap...  ...   \n",
       "1846  https://api.github.com/repos/pacman82/odbc2par...  ...   \n",
       "1186   https://api.github.com/repos/hydrogo/rainymotion  ...   \n",
       "694   https://api.github.com/repos/ff6347/after-effe...  ...   \n",
       "323         https://api.github.com/repos/bptlab/chor-js  ...   \n",
       "2004  https://api.github.com/repos/rbock/sqlpp11-con...  ...   \n",
       "1913        https://api.github.com/repos/potassco/clasp  ...   \n",
       "1185       https://api.github.com/repos/hydrogo/rainnet  ...   \n",
       "\n",
       "     web_commit_signoff_required  \\\n",
       "2003                       False   \n",
       "1916                       False   \n",
       "1346                       False   \n",
       "1846                       False   \n",
       "1186                       False   \n",
       "694                        False   \n",
       "323                        False   \n",
       "2004                       False   \n",
       "1913                       False   \n",
       "1185                       False   \n",
       "\n",
       "                                                 topics visibility forks  \\\n",
       "2003                                                 []     public   345   \n",
       "1916  ['answer-set-programming', 'declarative-progra...     public    75   \n",
       "1346  ['docker', 'microservice', 'natural-language-p...     public    98   \n",
       "1846                                ['odbc', 'parquet']     public    12   \n",
       "1186                                                 []     public    53   \n",
       "694                                                  []     public    34   \n",
       "323                                                  []     public    24   \n",
       "2004                                                 []     public    63   \n",
       "1913                                                 []     public    13   \n",
       "1185                                                 []     public    58   \n",
       "\n",
       "     open_issues watchers default_branch permissions        date  \\\n",
       "2003          23     2263           main         NaN  2023-09-09   \n",
       "1916           8      523         master         NaN  2023-09-09   \n",
       "1346          25      251         master         NaN  2023-09-09   \n",
       "1846           4      164           main         NaN  2023-09-09   \n",
       "1186           5      163         master         NaN  2023-09-09   \n",
       "694            0      157         master         NaN  2023-09-09   \n",
       "323            4      138         master         NaN  2023-09-09   \n",
       "2004           0      127        archive         NaN  2023-09-09   \n",
       "1913           2      101         master         NaN  2023-09-09   \n",
       "1185           6       94         master         NaN  2023-09-09   \n",
       "\n",
       "     is_research_group  \n",
       "2003             False  \n",
       "1916              True  \n",
       "1346             False  \n",
       "1846             False  \n",
       "1186             False  \n",
       "694              False  \n",
       "323               True  \n",
       "2004             False  \n",
       "1913              True  \n",
       "1185             False  \n",
       "\n",
       "[10 rows x 82 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repos.nlargest(10, 'watchers_count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Has issues\n",
    "\n",
    "Boolean stating whether a repository allows users to address issues. An issue is way to keep track of the tasks, enchantments and bugs of the project. They can be discussed in a thread by users and developers. Each repository can enable their own issue page. An issue can be open, for example when a new bug is found, or closed, when it is solved. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "repos['has_issues'].value_counts().plot.bar(fontsize = 12, title = 'Repository has issues')\n",
    "plt.savefig(Path(fp_figs, f'has_issues_{subset}.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open issues\n",
    "\n",
    "The amount of open issues a repository has"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "repos['open_issues'].value_counts().sort_index().plot.bar(fontsize = 12, title = 'Repository has open issues')\n",
    "plt.savefig(Path(fp_figs, f'open_issues_{subset}.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fork count\n",
    "\n",
    "A fork is a copy of a repository for another user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "repos['forks'].plot.hist(fontsize = 12, title = 'Number of forks', bins = 25)\n",
    "plt.savefig(Path(fp_figs, f'forks_{subset}.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Default branch\n",
    "\n",
    "The default branch is the standard branch of a repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "repos['default_branch'].value_counts().plot.bar(fontsize = 12, title = 'barplot default branch ')\n",
    "plt.savefig(Path(fp_figs, f'default_branch_{subset}.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Homepage\n",
    "\n",
    "The homepage is often the personal website of the research group or user. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAABOKMgAAAAAAJhRlAAAAAABMKMoAAAAAAJhQlAEAAAAAMKEoAwAAAABgQlEGAAAAAMCEogwAAAAAgAlFGQAAAAAAE4oyAAAAAAAmFGUAAAAAAEwoygAAAAAAmFCUAQAAAAAwoSgDAAAAAGBCUQYAAAAAwISiDAAAAACACUUZAAAAAAATijIAAAAAACYUZQAAAAAATCjKAAAAAACYUJQBAAAAADChKAMAAAAAYEJRBgAAAADAhKIMAAAAAIAJRRkAAAAAABOKMgAAAAAAJhRlAAAAAABMKMoAAAAAAJhQlAEAAAAAMKEoAwAAAABgQlEGAAAAAMCEogwAAAAAgAlFGQAAAAAAE4oyAAAAAAAmFGUAAAAAAEwoygAAAAAAmFCUAQAAAAAwoSgDAAAAAGBCUQYAAAAAwISiDAAAAACACUUZAAAAAAATijIAAAAAACYUZQAAAAAATCjKAAAAAACYUJQBAAAAADChKAMAAAAAYEJRBgAAAADAhKIMAAAAAIAJRRkAAAAAABOKMgAAAAAAJhRlAAAAAABMKMoAAAAAAJhQlAEAAAAAMKEoAwAAAABgQlEGAAAAAMAkVUV53bp1ev3111WyZEllyZJFAQEBatq0qXbs2JFW+QAAAAAAcKpUFeWvvvpKx48f1zvvvKMVK1Zo4sSJunDhgqpVq6Z169alVUYAAAAAAJwmY2o2/vLLL5UrV65Ey15++WUVK1ZMI0eOVJ06dRwaDgAAAAAAZ0vVEeV/lmRJ8vb2VunSpXXq1CmHhQIAAAAAwCqpOqKcnJiYGO3cufOBR5Pj4uIUFxdnvx8bG/u4LwsAAAAAQJp47KL81ltv6fr16/rwww/vu82oUaM0fPjwx30pALBcoQHLrY7wSI6Pbmh1hEfGPnc+9rnzsc8B4N/lsaaHGjJkiGbPnq0JEyaocuXK991u4MCBiomJsd84TRsAAAAA8G/1yEeUhw8frhEjRujjjz9W7969H7ith4eHPDw8HvWlAAAAAABwmkc6ojx8+HANGzZMw4YN06BBgxydCQAAAAAAy6S6KH/00UcaNmyYBg8erJCQkLTIBAAAAACAZVJ16vW4ceM0dOhQvfzyy2rYsKG2bt2aaH21atUcGg4AAAAAAGdLVVH+6aefJEmrVq3SqlWrkqw3DMMxqQAAAAAAsEiqivL69evTKAYAAAAAAP8OjzU9FAAAAAAAroaiDAAAAACACUUZAAAAAAATijIAAAAAACYUZQAAAAAATCjKAAAAAACYUJQBAAAAADChKAMAAAAAYEJRBgAAAADAhKIMAAAAAIAJRRkAAAAAABOKMgAAAAAAJhRlAAAAAABMKMoAAAAAAJhQlAEAAAAAMKEoAwAAAABgQlEGAAAAAMCEogwAAAAAgAlFGQAAAAAAE4oyAAAAAAAmFGUAAAAAAEwoygAAAAAAmFCUAQAAAAAwoSgDAAAAAGBCUQYAAAAAwISiDAAAAACACUUZAAAAAAATijIAAAAAACYUZQAAAAAATCjKAAAAAACYUJQBAAAAADChKAMAAAAAYEJRBgAAAADAhKIMAAAAAIAJRRkAAAAAABOKMgAAAAAAJhRlAAAAAABMKMoAAAAAAJhQlAEAAAAAMKEoAwAAAABgQlEGAAAAAMCEogwAAAAAgAlFGQAAAAAAE4oyAAAAAAAmFGUAAAAAAEwoygAAAAAAmFCUAQAAAAAwoSgDAAAAAGBCUQYAAAAAwISiDAAAAACACUUZAAAAAAATijIAAAAAACYUZQAAAAAATCjKAAAAAACYUJQBAAAAADChKAMAAAAAYEJRBgAAAADAhKIMAAAAAIAJRRkAAAAAABOKMgAAAAAAJqkuylevXtUHH3ygunXrKmfOnLLZbBo2bFgaRAMAAAAAwPlSXZSjo6P1zTffKC4uTs2aNUuDSAAAAAAAWCdjah/w1FNP6a+//pLNZtOlS5f03XffpUUuAAAAAAAskeqibLPZ0iIHAAAAAAD/CgzmBQAAAACASaqPKD+KuLg4xcXF2e/HxsY642UBAAAAAEg1pxTlUaNGafjw4c54KQAAAOChCg1YbnWER3J8dEOrIzwy9rnzsc8fnVNOvR44cKBiYmLst1OnTjnjZQEAAAAASDWnHFH28PCQh4eHM14KAAAAAIDHwmBeAAAAAACYPNIR5ZUrV+r69eu6evWqJGn//v368ccfJUkNGjRQ5syZHZcQAAAAAAAneqSi3LNnT504ccJ+f/78+Zo/f74k6c8//1ShQoUcEg4AAAAAAGd7pKJ8/PhxB8cAAAAAAODfgWuUAQAAAAAwoSgDAAAAAGBCUQYAAAAAwISiDAAAAACACUUZAAAAAAATijIAAAAAACYUZQAAAAAATCjKAAAAAACYUJQBAAAAADChKAMAAAAAYEJRBgAAAADAhKIMAAAAAIAJRRkAAAAAABOKMgAAAAAAJhRlAAAAAABMKMoAAAAAAJhQlAEAAAAAMKEoAwAAAABgQlEGAAAAAMCEogwAAAAAgAlFGQAAAAAAE4oyAAAAAAAmFGUAAAAAAEwoygAAAAAAmFCUAQAAAAAwoSgDAAAAAGBCUQYAAAAAwISiDAAAAACACUUZAAAAAAATijIAAAAAACYUZQAAAAAATCjKAAAAAACYUJQBAAAAADChKAMAAAAAYEJRBgAAAADAhKIMAAAAAIAJRRkAAAAAABOKMgAAAAAAJhRlAAAAAABMKMoAAAAAAJhQlAEAAAAAMKEoAwAAAABgQlEGAAAAAMCEogwAAAAAgAlFGQAAAAAAE4oyAAAAAAAmFGUAAAAAAEwoygAAAAAAmFCUAQAAAAAwoSgDAAAAAGBCUQYAAAAAwISiDAAAAACACUUZAAAAAAATijIAAAAAACYUZQAAAAAATCjKAAAAAACYUJQBAAAAADChKAMAAAAAYEJRBgAAAADAhKIMAAAAAIBJqovytWvX1KdPH+XLl0+enp6qUKGCvv/++7TIBgAAAACA02VM7QNatGihyMhIjR49WsWLF9ecOXPUrl07JSQkqH379mmREQAAAAAAp0lVUV6xYoV+/vlnezmWpNq1a+vEiRN6//331bZtW2XIkCFNggIAAAAA4AypOvV60aJF8vb2VuvWrRMt79Kli86ePatt27Y5NBwAAAAAAM6WqqL8+++/q1SpUsqYMfGB6PLly9vXAwAAAADwJEvVqdfR0dEqUqRIkuU5cuSwr09OXFyc4uLi7PdjYmIkSbGxsal5+VRJiLuRZs+dltJyn6Q19rnzsc+dj33ufOxz52OfOx/73PnY587HPnc+9nnyz2sYxkO3TfVgXjabLdXrRo0apeHDhydZXqBAgdS+vMvL9pnVCdIf9rnzsc+dj33ufOxz52OfOx/73PnY587HPne+tN7nV69eVbZs2R64TaqKsp+fX7JHjS9fvizp/48s/9PAgQP17rvv2u8nJCTo8uXL8vPze2Dx/jeKjY1VgQIFdOrUKfn4+FgdJ11gnzsf+9z52OfOxz53Pva587HPnY997nzsc+d7Uve5YRi6evWq8uXL99BtU1WUy5Urp7lz5yo+Pj7Rdcp79+6VJJUtWzbZx3l4eMjDwyPRsuzZs6fmpf91fHx8nqhvClfAPnc+9rnzsc+dj33ufOxz52OfOx/73PnY5873JO7zhx1JvidVg3k1b95c165d04IFCxItnz59uvLly6fnnnsuNU8HAAAAAMC/TqqOKNevX1/BwcHq2bOnYmNjVaxYMc2dO1erVq3SrFmzmEMZAAAAAPDES/VgXgsXLtSHH36ooUOH6vLlyypZsqTmzp2rV155JS3y/et4eHgoJCQkyankSDvsc+djnzsf+9z52OfOxz53Pva587HPnY997nzpYZ/bjJSMjQ0AAAAAQDqRqmuUAQAAAABwdRRlAAAAAABMKMoAAAAAAJhQlAEAAAAAMKEoAwAAAABgQlG+j927d1sdAQCAJ05CQoLVEQAAeGxMD3Ufbm5uKlKkiFq2bKlWrVqpSpUqVkcCnObs2bOKjo6Wn5+f8uXLZ3UcAE8Qf39/NW/eXC1btlRQUJDc3d2tjpRuXb16VVu2bFF0dLT8/f1VrVo1Zc2a1epYLu/QoUO6dOmSKlSooCxZslgdB8Ajoijfx549e7RgwQItWLBA+/fvV/78+dWiRQu1atVKL7zwgmw2m9URXVpUVJT27t2r6Ohoubm5KSAgQJUrV3bpSc3/DRYuXKiBAwfqjz/+sC8rWrSoRo4cqVatWlmYDHCs1atXa/369bp06ZKGDBmiggULKjIyUoUKFVLOnDmtjvdE+/zzz7Vw4UL98ssvypo1qxo1aqRWrVqpXr168vT0tDpeujF27FgNHz5cN27ckGEYstlsypw5s4YPH653333X6nguacaMGRo0aJDOnTsnSYqMjFSlSpXUpk0bBQcHq1u3bhYndD2zZs3SnDlzdOLECd28eTPROpvNpqNHj1qUzDVt3Ljxvuvc3NyUPXt2lSxZUhkzZnRiqrRDUU6BgwcP2kvzrl27lDt3bjVr1kytWrVSYGCgMmTIYHVEl7Ft2zb169dPmzdvTrIuS5YsevPNNxUaGqrMmTNbkM61zZs3T+3atVPJkiXVtm1b5cmTR+fOndO8efN0+PBhzZkzR23btrU6ZrqxYMECtWnTRnfv3rU6iku5ceOGmjZtqvDwcPsHnvf+mG3btq0KFCigsWPHWpzSNVy4cEELFy7UwoULtX79enl4eKh+/fpq1aqVGjZsyJG2NDRjxgx17txZ9evXV+fOnZUvXz6dPXtW06dP18qVKxUWFqbXXnvN6pguZf78+Wrbtq0aNWqk+vXr66233tKvv/6qSpUqafTo0QoPD9fPP/9sdUyX8sknn2jgwIEqXbq0ypcvn+zBlGnTplmQzHW5ubk99GCht7e33n33XYWEhDgpVdqhKKfSn3/+qfnz52vhwoWKjIyUr6+vmjRpolatWqlBgwZWx3uibd68WUFBQcqaNatq1qwpDw8Pbdu2TSdOnND777+v6Oho/fDDDypRooQiIiLk5eVldWSXUqZMGRUqVEg//fST3Nz+f/iChIQENWzYUCdPntS+ffssTJi+UJTTRt++fRUWFqapU6cqODhYPj4+9j9mp0yZov/+97/atWuX1TFdzuXLl7V48WItWLBA4eHhcnNzU3BwsFq1aqXGjRsre/bsVkd0KRUrVlSZMmU0a9asJOs6dOig/fv3a+fOnRYkc12VKlVSxYoVNWXKFN29e1fu7u7295YlS5aoV69eOnPmjNUxXUrRokXVoEED/fe//7U6SrqxfPlyvf322ypRooTatWun3Llz6/z585o7d64OHTqk0NBQbdq0SVOnTtWECRP09ttvWx35sVCUH8OpU6e0YMECLVy4UFu2bNGdO3esjvREq1OnjuLi4rR69Wp5e3tLku7evavu3bvr4MGD+uWXX3Ty5Ek9++yz6tWrl4YNG2ZtYBfj6empBQsWqGHDhknW/fTTT2rdurVu3bplQTLXMmPGjBRtFxkZqUmTJlGUHSx//vwaMGCAevfuneSP2Z9//llt27bV5cuXrY7p0mJjY7V06VItWLBAq1ev1t27dxUXF2d1LJfi5eWlRYsW6eWXX06ybtWqVWrevHmS01TxeLy8vLR06VIFBwcneW/ZtGmTgoOD+R3qYFmyZNFPP/2kOnXqWB0l3Xj77bd19epVhYWFJVnXqVMneXp6avLkyXrzzTe1detW7dmzx/khHcg1TiC3SIECBdSnTx/16dNHUVFRVsd54kVGRmrWrFn2kixJGTJkUEhIiAoVKqTTp0+rYMGC6t+/v7755huKsoM99dRTunHjRrLrbty4oQIFCjg5kWvq3LmzbDabUvIZJWMhON7FixdVpkyZZNe5ublRHpzAx8dHHTp0UIcOHXT9+nWtWLHC6kgux8vL674f+Fy+fJkzstJA5syZFRMTk+y6M2fOyNfX18mJXF/lypV19OhRirITzZs3TzNnzkx2Xfv27dWhQwdNnjxZjRo1uu92TxKKcgqk9MJ1PB6bzZbolN97MmTIIMMwFBsbK0mqUKGCTp486ex4Lu+9995TaGioateuLX9/f/vyCxcuaMSIEerXr5+F6VxHtmzZ1LBhQ/Xt2/eB24WHh2vgwIFOSpV+BAQEaO/evapdu3aSdXv27FHhwoUtSOW6zp49q6tXr6pEiRKSpPj4eI0fP147d+5U3bp19frrr6t169YWp3Q9L774ooYNG6bAwMBEMxecP39eoaGhqlmzpoXpXNMLL7ygL774Qi1btkyyLiwsTIGBgc4P5eLGjx+vDh06qFKlSqpcubLVcdKF69ev6+LFi8mui4qKsh9wyZo1q0sM6PXk/w+cIDAwMF1duG6V559/XuPHj1fdunUTDcjw8ccfK2vWrHr66aclSXFxcYmOOuPR/ec//0l0PzY2VoUKFVJQUJDy5Mmj8+fPKzw8XP7+/tq/f79FKV3LM888o4sXLz70l/rx48edEyidadGihT7++GO9+OKLKl++vKS/P6Q7ceKEJkyYoC5dulic0LV0795dBQsW1JdffilJGjFihEJDQ5U9e3bNnz9fmTJlUocOHSxO6XpGjhyp6tWrq1ixYgoKClLevHl17tw5rVu3Tu7u7lq4cKHVEV3O0KFDVaNGDVWtWlXt27eXzWbTwoULFRISoo0bN2r79u1WR3Q5Xbp0UXR0tKpWrao8efLIz88v0Xqbzabdu3dblM41vfDCCxoyZIiqVKli/wBU+nvg43s/A5J07Ngx5c+f36qYDsM1yimQ3i5ct8r27dtVq1Yt+fr6KjAwUB4eHtq6dasOHz6s0aNH6/3335ckhYaGat26dVq/fr21gV1Ackfw78dms3G9rAP06dNHc+fOfejlGgsWLFDr1q2VkJDgpGTpw9WrV1WzZk39/vvvKlu2rPbs2aNy5crp6NGjKlGihDZt2sRpqQ4UEBCgiRMn2qeXCwgI0CuvvKJx48apZ8+e2rVrl7Zs2WJxStd0+PBhhYSEKCIiQtHR0fLz81NQUJBCQkJUvHhxq+O5pIiICPXq1UuHDh2yL3v66ac1efJkjiingZQcyIqIiHBSmvRh//79qlmzpq5cuaKyZcsqd+7cioqK0u+//y5fX19t3LhRpUqV0kcffSR3d3cNGDDA6siPhaKcAuntwnUrRUZGasiQIdq8ebNu376t0qVL6+233050lGffvn3KlCmT/Qgz8CQ5c+aM/vjjD9WqVcvqKOnWzZs3NXHiRC1fvlxRUVHy9/dXo0aN1KdPH6aeczBPT0+tXbtWNWrU0IEDB1SmTBnt3r1b5cqV09q1a9WmTRsGT4PLOXr0qP29hQ8l4GrOnz+v8ePHa+PGjfYP4WrVqqU+ffoob968VsdzKIpyCuTKlUszZ85UvXr1kqxbvXq1OnTooIsXL2rp0qVq27Ytg8EAACApd+7c+uqrr9SiRQtNnjxZH374oS5duiTp7+vwmzZtqmvXrlmcEgCApLhGOQXS24XrAAA4QtWqVfXJJ5/I3d1dEydOVN26de3rjh07lmigKTye0NBQde3aVfny5VNoaOgDt7XZbBoyZIiTkrm+2NhYrVq1Snv27FF0dLTc3NwUEBCgmjVr2q/ZRNowDEORkZE6ceJEsgeqOnbsaEEq13fr1i3t3LnTfkS5UqVK8vT0tDqWw3FEOQXq1q2rI0eOaNWqVUkuXH/55ZdVokQJrV69WlOmTNHYsWN14MABC9O6vgMHDmj+/PkaOnSo1VFcipub20Ov9eEaZce5c+eOLl++rFy5ciW7369evarffvuN0Wkd7EHTiNybxaBKlSp6/fXXlTNnTicmc027du1ScHCwoqOj7dev3Zueq2HDhvL19dWsWbMsTuka3NzctHXrVlWtWvWh408w5oTjfPbZZxo6dKiuX78uwzDs7+f3/l2xYkXNmjWL2VHSwOHDh9WkSRMdOXIk2SkX+T5PG+PHj9dHH32k2NhY+/d51qxZNWTIEL333ntWx3MoinIK7Nu3T7Vq1Uo3F67/2y1YsEBt2rThzc/Bhg0blqSwXbx4UWvWrNHdu3fVsWNHRnV3kBEjRuiTTz7RjRs3lDNnTg0ZMkRvvfVWom22bdum6tWr833uYIGBgTpy5IjOnTunQoUK2QdnPHHihPLly6ecOXPqwIEDyp49uzZt2sRYCA5w48YNHTx4UMWKFZOPj499+fLly/X0009zDSeeWFOnTlXXrl1Vt25dNWrUSB4eHtq0aZN++OEHjR8/Xr6+vpo4caKOHTumHTt2qECBAlZHdinBwcE6cuSIxo4dq/LlyyeaMeWep556yoJkruu///2v3nnnHQUHB6t9+/b2GVJmz56t8PBwTZgwIcmMKk8yinIKpacL1//tKMrOdfv2bdWrV09t2rRRz549rY7zxLs3mnXDhg1VtWpVrVu3Ths2bFCbNm00a9Ys++UbFOW0sXbtWnXt2lXz5s3Tc889Z1++detWtW3bVl9//bVKliyp4OBgVa5cWfPmzbMwLYB/s2eeeUaVK1fW1KlTEy3/8ssv9emnn+rPP//UnTt39Nxzz6ly5cr67rvvLErqmrJly6Zvv/1Wbdq0sTpKulG0aFG98MILmjFjRpJ1HTp00JYtW3T06FELkqUNivJD3Lp1S6GhoWrZsiWTmaexIkWKpGi7Gzdu6OLFixQIJ1q4cKH69++vI0eOWB3liVejRg0VL1480R9W3333nXr37q169erpxx9/lLu7O0U5jVStWlU9evTQ66+/nmTdlClT9MUXX+i3337T5MmTNWTIEF24cMGClE+2kydPKm/evHJ3d9fJkycfuK3NZpOfnx+jjeOJ5OXlpaVLlyo4ODjR8suXL8vf318HDhxQiRIlNHXqVA0ZMkRnzpyxKKlrCggI0JQpU/Tyyy9bHSXd8PLy0uLFi+87wHGzZs1calBjRp56CE9PT02YMIEfQic4ceKEihYtmug68OScP3/+voOrIW14eXnp3LlzVsdwCQcOHEgyiE7Xrl0VEBCgli1bqlmzZlq0aJFF6Vzf3r1773v6Y4ECBXTw4EFJUsmSJRUTE+PMaC6jcOHC2rJli6pWrapChQo9dOwDSSpXrpzCwsJUoUKFtA+YznXr1k0JCQmaMmWK1VGeeD4+Pjp//nyS5efPn5fNZrOfIVS0aFFFR0c7O57L69Kli+bMmcPf6E5UvHhxRUVFJbvu3LlzKlasmJMTpS2KcgqUKlVKf/75J4PqpLGnn35azz33nKZPn/7A7X788Ue1bdvWSalw8eJFffrppw/9AAMpk5CQkOzo+PXr19eiRYvUvHlzNWnSRAMHDrQgnevLnTu3Fi1alOQIkCQtWrRIuXLlkvT3KLbZs2d3cjrXMHXqVBUtWtT+74cV5StXrmj69Onq1auXNm/e7IyI6dq0adNkGAZF2QHq16+vwYMHq1y5cvYPeU6fPq0ePXqoQIEC9p+D6Oho+fv7W5jUNZUtW1Zz585VkyZN1LhxY/n5+SXZpkWLFhYkc13Dhw9X3759ValSJZUtW9a+fM+ePRo+fLjGjx9vYTrHoyinwJAhQ/TBBx+oRo0a9jc9OF7FihW1a9euh26XkqMTSL3ChQsn2bdxcXG6cOGC3NzctHTpUouSuZaiRYvq119/VVBQUJJ19erVs5dlTnNPG2+++aYGDx6smJgYtW7d2j4447x58/TDDz9oxIgRkqTNmzerfPnyFqd9MnXq1Mn+786dO6foMSVKlOAPWic5duxYsiMEI/VGjx6tF154QZUrV1bu3Lnl4eGh06dPK2PGjFq8eLF9u/Xr16tKlSrWBXVR7du3lyT9+eefWrZsWZL1jHrteFOnTlV8fLwqVKigMmXK2Afz2rdvn/Lly6dp06Zp2rRpkv7e/0uWLLE48ePhGuUUaNKkiXbs2KGLFy+qfPnyyps3b6JC4QrfCP8GM2fO1MyZM7VmzZoHbnfgwAH98MMPjMDsYJ07d05SlD09PVWoUCG1bdtWhQoVsiaYi+nXr59Wrlypffv23XebNWvWqFmzZoqLi+OXfBoYNGiQJkyYoNu3b0v6exoXDw8Pvfvuu/r4448l/f3peJYsWfhw1EliY2P122+/qVatWlZHAVIlNjZWkyZN0ubNm3X79m2VLl1ab775JtNBOcGGDRseug3vKY6V0stppL/70bFjx9I4UdqiKKfAw74pXOEbAYBzHDlyRGvWrFG7du2UI0eO+273yy+/KDw8nA+E0siVK1e0detW+ywGzz33nHx9fa2O5dJ2796trVu3ys3NTdWqVVO5cuWsjgQAwH1RlAEAgMP06tVL/fv3t89fmpCQoC5dumjWrFn2U35tNpu6dOnCdDkOFBoamuJtbTZbkkEFAQCJUZTxr5SQkKBjx44pOjpaNptNOXLkUJEiReTm5mZ1NJd2/Phx/fDDDzpx4kSS4f1tNhuDv8BlxMTE6PDhw8lOY8HAjY8nQ4YM9lGvJWn8+PHq16+fevfurQ4dOsgwDE2fPl2TJ0/WV199pTfffNPixK4hNb8fuXbTceLi4rRgwQKdOnVKZcqUUaNGjZJsc+zYMY0YMSLJfMt4fEeOHNHkyZN14MCBZP9uCQ8PtygZXAFFOYXu3LmjGTNmKDw83D564UsvvaQOHTrI3d3d6ngu48iRIxo6dKh++umnJG94Xl5eatq0qUJCQlS8eHGLErqu5cuXq0WLFrp7965y5colDw+PROu5xMBxjhw5otmzZ8vd3V2dO3dWQECADhw4oEGDBunw4cN6+umnFRISoooVK1od1eXEx8erR48emjFjxn2LAgXi8bi5uWnr1q32olymTBlVrlxZM2bMSLRdu3bt9Oeff2rr1q1WxAQeW0xMjGrUqGEfc8Jms6lKlSqaN2+e/YwKSdq2bZuqV6/Oe4uD/f7776pWrZoCAgL0xx9/qHz58rp06ZLOnDljH3V83bp1Vsd84pk//HRzc3voNcp+fn6qXr26Pvvssyd+fBuKcgrExMQoKChIO3fuVJYsWewjvF2/fl2VK1dWeHi4fHx8rI75xPvtt98UGBgoDw8PtWjRQuXLl7dfw3n58mXt2bNHixYtUlxcnDZs2KBnnnnG4sSupUKFCsqRI4e+//57+xQ5cLz9+/frueee0/Xr1yVJefLkUUREhAIDA2UYhkqWLKm9e/fq9u3bioyMZEAYB/v00081duxYjRs3Th07dtSXX34pd3d3ffvtt4qJidHnn3+uunXrWh3zifbPopwpUyYtWLBAjRs3TrTdokWL1LFjR129etWKmMBjGzhwoL755ht98803qlq1qsLDwzVw4EDZbDb9/PPPKlOmjCSKclpp0qSJ3N3dNW/ePGXKlEm//vqrKlWqpOXLl+v111/XokWLVL16datjPvGGDx+ubt26KV++fBo2bNhDi3JMTIwWLlyoEiVKaPXq1U5KmUYMPNRbb71leHt7Gz/88EOi5fPnzzeyZs1q9O7d26JkriU4ONh48cUXjdjY2PtuExsba9SsWdOoW7euE5OlD5kzZzZWrVpldQyX98orrxglSpQwDhw4YFy8eNFo2rSpUbJkSaNKlSrG1atXDcMwjIsXLxqlSpUyOnXqZG1YF1SuXDlj4sSJRnx8vGGz2YwdO3bY19WtW9cYMGCAhelcg81mM7Zv326/nzVrVmPTpk1Jtlu3bp3h4eHhzGjpzpEjR4zJkycbI0eONL755hvjyJEjVkdyKaVKlTImTJiQaNnp06eN8uXLGzlz5jR2795tGIZhbN261XBzc7MgoWsLCAgwlixZYty9e9ew2WxGZGSkfd2YMWOMmjVrWpgufVu8eLHh7e1tdYzHxgWfKbB48WKFhoaqdevWiZa3atVKw4YN06JFiyxK5lq2bNmiQYMGKWvWrPfdJmvWrBowYIA2b97sxGTpw1NPPaVr165ZHcPlbd68WQMHDlTJkiXl7++v0aNH69ChQ+rbt6+8vb0lSf7+/vrPf/6jiIgIi9O6nmPHjumZZ56xX89569Yt+7oePXpo9uzZVkVzKe+9956aNGmiJk2ayM3NTUePHk2yzYkTJ+Tn52dBOtdnGIZ69+6tkiVLqkePHvrwww/VvXt3lSxZUv/5z3+sjucyTpw4oUqVKiVaFhAQoPXr16tAgQKqU6eOfvvtN4vSub6//vpLOXLkkJubm9zd3fXXX3/Z1z377LPauXOnhenSt+rVq+uLL76wOsZjoyinwL35k5PzzDPP6NKlS05O5JoyZsyouLi4h253+/ZtZcyY0QmJ0pdBgwZp7NixunHjhtVRXFpUVJSKFCliv3/v3/+cr7dEiRI6f/68U7OlB1myZNHt27ftgwSeOHHCvs7Ly0vR0dEWpnMNBQsW1KlTp7R3717t3btXvr6++vXXX5Nst3z5cvupqXCsCRMmaNKkSerevbu2bdumU6dOadu2berRo4cmTZqkCRMmWB3RJeTIkSPZvwF9fX0VHh6up556Si+99JIiIyMtSOf6AgIC7Pu/WLFi2rhxo33dnj177B8+wzlOnTqlkydPSpJy5sypTp06WZzo8dE2UiAgIEC//PKLgoKCkqz73//+p3z58lmQyvXUrl1bQ4YMUeXKlZU/f/5ktzlz5oxCQkJUp04dJ6dzTf88shAVFaVixYqpdu3aSY702Gw2TZw40ZnxXFLWrFkTfeqdMWNGeXh4KFOmTIm2u3nzpjw9PZ0dz+WVLFlSf/75p6S/P/EeP368XnzxRWXKlEljxoxRiRIlLE745Dt+/HiKtmvRokWiD43gON99953efvvtRO/ZAQEBqlKlijJkyKBvv/1Wffv2tTChayhbtqzWrl2rFi1aJFmXPXt2hYeHKygoSH369HF+uHSgRo0a2rx5s5o1a6ZXX31VISEhOnfunDJlyqSwsDB16NDB6ojpSpEiRWQYhuLj462O4jAU5RRo27atRo4cqaxZs6pTp07y8/NTdHS0Zs2apZEjR+rdd9+1OqJLGDdunGrUqKFixYqpTp069sG8bDaboqOjtXfvXq1bt05+fn6c7u4g9zstZu7cuUmWUZQdo1ixYtq9e7eaNGki6e+Bj5KboujIkSMqUKCAs+O5vLZt2+rw4cOS/h6gpGbNmvbRad3d3bVw4UIr46Ur7dq1szqCyzp27Fiy0xRJUqNGjTR58mQnJ3JNwcHBGj58uD7++GP5+vomWX+vLNetW1c7duywIKFr+/DDD3X27FlJUv/+/XX+/HnNnj1bNptNbdq00dixYy1OmL507NhRCQkJVsdwKEa9ToG4uDg1bdpUa9askc1mU8aMGRUfHy/DMFSvXj0tWbIkydEgPJpLly5pzJgxWrx4sY4ePap73542m03FihVT8+bN1a9fP/n7+1ucFHg0//3vfxUdHa1hw4Y9cLvnnntOFSpU4A/aNHbq1CktXrxYNptNwcHBHFFOI6dPn9bGjRsVHR0tPz8/1axZ875nDuHx5c6dWx9//LG6du2aZN13332nQYMG6cKFCxYkcy0JCQm6efOmvLy8HjiP9a1btxQVFZVoyigA/34U5VRYvXq1IiIi7L/og4KCFBwcbHUsl3Xr1i37Kaq+vr6chprGTp48qbx58yY7L3h8fLzOnj2rggULWpAsfYqNjZWnpycfwjnIuXPnFBYWphMnTihXrlxq06aNypYta3Usl5eQkKA+ffroq6++SjQ1ToYMGdSjRw9NnDjxgQUDj6ZDhw5au3atli9frsqVK9uX79q1Sw0bNlSdOnU0c+ZMCxMCwL8fRRmApMQTyv/Tjh07VLVqVeaAxBPp4MGDqlGjhi5fvmxfljFjxmTn9oVjDR06VCNGjFC3bt3Uvn175cmTx3565HfffafBgwcrNDTU6pgu59SpU3r++ed17tw5lS5dWnnz5tW5c+e0f/9+5cuXT1u2bOGIvgNs375dVapUeei8skg758+f14IFC3TixIlEsxhIXDKWFu4N1pUcNzc3ZcuW7YGz1zxpKMp44ty5c0fnzp3j6KaDubm5aevWrckW5e3bt6t69eouNUDDv826deu0c+dOubm5qVq1aqpevbrVkVxGmzZt9PPPP2v8+PGqVq2ajhw5ovfee0+GYeiPP/6wOp5Ly58/v1q3bp3sKMt9+vTRjz/+qNOnT1uQzPVdunRJ48aNS3ImXN++fbl8yUHc3NxUoEABdenSRV26dOHUaidbvXq1mjdvnqQg32Oz2fiA38Hc3Nwe+sHQ008/rYEDB7rEqNcU5ccUHByshIQEhYeHWx3FJcyaNUuhoaE6deqUypYtq5CQkCQDkmzbtk3Vq1fnzc/B3NzctG3bNlWpUiXR8ri4OI0ePVpfffUV0xU5wMcffyzDMDR48GBJ0tWrV9WwYUP973//S3RNfuPGjTV//vxkT4VH6uTJk0cDBgxINPLspk2bFBgYqJMnTyogIMC6cC4uc+bMWrp0qV566aUk637++Wc1bdqUKenwxHJzc5O/v78uXbokNzc3vfTSS3rjjTfUrFkz3rudoHz58vL29tbXX3+tUqVKsc+d4LvvvtPIkSOVOXNmtWnTRrlz59a5c+c0f/583bx5Uz179tTPP/+s8PBwzZo164kftJELgx5TRESE1q9fb3UMlxAREaGOHTsqc+bMat++vWJiYtS0aVP179/f6mgua/jw4cqQIYMyZMggm82matWq2e/fu2XOnFmhoaFq2rSp1XFdwvTp0xNNKffBBx9ox44dmjBhgg4dOqSDBw9q7NixWrNmDaekOsilS5f07LPPJlpWpUoVGYaR7ByocJxnnnnGPsr4Px0+fJjrxNPY1atXtWbNGs2dO1dr167V1atXrY7kcpYtW6aIiAi1b99emzZt0iuvvKKAgAC999572r9/v9XxXNrRo0cVEhKi8uXLU5Kd5OTJkypTpoz27t2roUOHqnv37ho2bJh+//13lS5dWpcvX9aaNWvUrFkz15iv3QD+JV566SWjUaNGxt27dw3DMIz4+Hjjww8/NGw2m9GzZ0/7dlu3bjXc3NysiulSVqxYYfTu3dt46623DJvNZrRu3dro3bt3ott7771nhIWFGbdv37Y6rkvw8vIy1q9fb7+fK1cuY+zYsUm2GzVqlFGoUCFnRnNZNpvN2LZtW6Jl8fHxhs1mM3bs2GFRqvRh06ZNRv78+Y1ly5YlWr506VIjf/78xsaNGy1K5vo+/fRTw9vb23BzczNsNpths9kMb29vY9y4cVZHcxn/fG+JiYkxvvrqK+PZZ581bDab4ebmZjz//PPGlClTjGvXrlmY1DVVqlTJ+OGHH6yOka4k935+z9KlS418+fIZhmEYCxYsMLJkyeLMaGmCeZTxr7F3715NmTLFPgJqhgwZNGLECBUqVEg9evTQnTt39O2331qc0rXUr19f9evXlyRdv35dQ4cOVeHChS1O5do8PT117do1+/3Lly8nOd1d+vuIZ0hIiDOjubT169cnuhY2ISFBNptNEREROn78eKJtW7Ro4eR0rqtnz566deuWmjRpoqxZsyp37tyKiorS1atX5efnp7feesu+rc1m0+7duy1M6zpmzJihDz74QPXr11fnzp2VL18+nT17VtOnT9f777+vnDlz6rXXXrM6psvx8fFRjx491KNHD+3du1ffffedZs+era5du6pv376KiYmxOqJLCQ0N1YcffqiaNWsqd+7cVsdJFy5duqSbN28mu848W42fn5/9crInGdcop0J4eLjCw8MVHR0tf39/vfTSS6pdu7bVsVyGj4+Pli5dqsDAwCTrpk2bpm7duqljx47q1q2batSowTXKeCI1bNhQvr6+mjVrliSpYsWKateunT744INE23388cf69ttvk5Q4pF5qph9i8BfHCgwMTNWIwBEREWmYJv2oWLGiypQpY3+fMevQoYP279+vnTt3WpDMtTxoEMx7bt++rUWLFmnq1KlavXq1E9OlDx999JE++eQTVahQQTly5Ei0zmazacmSJRYlc03PP/+84uLiFB4eLl9fX/vyy5cvq06dOsqcObM2b96sWbNmKSQkREePHrUw7ePjiHIK3L59Wy1bttSKFStkGIYyZsyo+Ph4jR49Wg0bNtSCBQu4NsIBChUqpN27dydblLt06SJJ6tatm/bu3evkZK5r48aNqlSpkry9vbVx48aHbl+zZk0npHJtAwcOVGBgoHx9ffXBBx/ok08+UZs2bWSz2VS3bl1J0sqVK/Xxxx8nGnwKj47yZR3G8LDGwYMHNWrUqGTXdejQQc2bN3dyovQrU6ZMatu2rdq2bWt1FJcTFhamkJAQZciQQX/++afOnDmTaD3Tdjnep59+qrp16+qpp55SnTp17GcJrVu3TvHx8Vq7dq0k6bfffnON6RetPfP7yfDhhx8a7u7uxpgxY4wLFy4YhmEYFy5cMD799FMjU6ZMxuDBgy1O6Breeusto2rVqg/cZtq0aUaGDBm4RtlBzNdX3bueKrnbvXVwjNmzZ9uvHfT39ze8vLyS7O8mTZoYt27dsjoq4DR37941ChcubPz+++9WR3ni+fr6GrNnz0523ezZsw1fX18nJ3JNnTt3No4dO2Z1jHSrUKFCRpMmTYzLly9bHSVd2b17t9G6dWsjd+7cRsaMGY3cuXMbbdq0MXbv3m11NIfj1OsUKFq0qF577TUNGzYsybphw4ZpxowZOnbsmPODuZidO3dq7ty56t+//wPneJw/f75WrFihadOmOTGda9qwYYMqV64sb29vbdiw4aHb16pVywmp0oezZ8/q22+/1aZNm3TmzBklJCTIz89P5cuXV8uWLRUcHGx1RJd36NAhXbp0SRUqVFCWLFmsjpPu3b17V+7u7vr1119VqVIlq+M80Zo2baoDBw5o/fr1iUbZP3/+vAIDA1WyZEktXrzYuoCAAzxo+jnAESjKKeDh4aEVK1YoKCgoybrw8HA1aNBAcXFxFiQDAKTWjBkzNGjQIJ07d06SFBkZqUqVKqlNmzYKDg5Wt27dLE6YPlGUHWffvn2qXr267ty5o6CgIOXNm1fnzp3TunXr5O7urs2bN6t06dJWxwQeywsvvKBu3bqpc+fOVkeBi2Ie5RTImTPnfa+L3bt3r3LmzOnkROnL4cOHtWXLFh05csTqKMBjq1ixoj7++GMdOHDA6ijp0vz589W5c2dVqlRJX3zxRaJROStVqqQffvjBwnSAY5QpU0aRkZFq2rSpIiMjNW3aNEVGRqpZs2bavn07JdnBzpw5o23bttlH/P2nS5cuacaMGU5O5frGjRunMWPGaNeuXVZHSVd++eUX9erVSw0bNlSdOnUS3ZI7qPgk44hyCvTq1UuzZs1SWFhYomlDlixZok6dOunVV1/Vl19+aWFC1zR//nz169cv0ZQu+fPn17hx49SqVSsLk7mm119//b7r3NzclD17dlWpUkXNmzdXpkyZnJjMtbz77rtatGiRTp48qRIlSqhly5Zq2bKlKlSoYHW0dKFSpUqqWLGipkyZkuQI5pIlS9SrV68kA8LAOTiijCdNQkKC3nzzTfulYBkzZtR//vMfjRw5MtEgr9u2bVP16tUZUd/BypUrp/Pnz+vy5cvKmzdvsqNeM+WcY02bNk1vvPGGcuTIoeLFi8vDwyPJNq40gCZFOQX++usvBQYG6vfff1eWLFmUJ08eRUVF6dq1aypXrpzWr1+v7NmzWx3TpaxYsUKNGzdWmTJl9Nprrylfvnw6c+aMZs2apf379+unn36yz/8LxyhcuLBiYmJ05coVZcyYUX5+foqOjlZ8fLyyZ88uwzAUExOjEiVKaP369cxZ+JgiIyO1YMECLVy4UH/88YcKFy6sli1bqlWrVg+cagSPx8vLS0uXLlVwcHCSYrZp0yYFBwfr1q1bVsdMlyjKeNJ8++236tmzp3r27KmqVasqPDxcs2bNUvXq1bV8+XJlzZpVEkU5raRk+jlXKm3/BqVKldIzzzyj6dOnJ1uSXQ1FOYXi4uIUFhamiIgIRUdHy8/PT0FBQerYsWO6+EZxthdeeEE+Pj5avnx5ojlQDcNQ/fr1dfXqVf3vf/+zMKHr2blzp5o3b64xY8aoVatWypAhg+7evav58+erf//+mj9/vuLj49WiRQs1bNhQU6ZMsTqyy9i9e7cWLFigBQsW6MCBAwoICFCLFi3UqlUr1ahRgykuHMjPz0+TJ09Wq1atkhSz77//Xn379rVfuwznoig71i+//KI5c+boxIkTunnzZqJ1NptN4eHhFiVzHc8++6wCAwM1duxY+7I1a9aobdu2KlGihFavXq1s2bJRlOEy0tsAahRl/CtlyZJF33//fbJzsC1dulTt27fXtWvXLEjmugIDA9WyZUu9/fbbSdZNnDhR8+fP1y+//KIJEyZo7NixnJ6aRg4cOGAvzbt371bu3Lkpbg7UpEkTxcbGKiIiQgkJCYmK2csvvyxfX1/NnTvX6pjpEkXZcdLb6ZFWyZYtmxYuXJjkusydO3eqbt26Kly4sNasWaPDhw9TlOESKleurHfffVevvvqq1VGcIqPVAZ4Ehw8f1rlz55KdGmfDhg3Kly+fnn76aQuSua4MGTLo9u3bya67c+dOoqPMcIzIyEgNGTIk2XVly5bVoEGDJEkVKlTQpUuXnBktXSlVqpQGDx6swYMH6+jRo1q4cKHVkVzK0KFDVaNGDVWtWlXt27eXzWbTwoULFRISoo0bN2r79u1WR0y3bDabatWqZT9dFY9uzJgxatOmTbo5PdIqmTJl0p07d5Isr1Spkn7++WcFBwcrKChII0aMsCCdazp58mSqti9YsGAaJUmfRo4cqX79+ikwMFABAQFWx0lzHFFOgUaNGql48eIaP358knX9+vXT4cOHtXTpUguSua6goCBdu3ZN69evl5eXl315XFycAgMD5e3trZ9//tnChK4nb968euONN5L9hf7hhx9q6tSpOnfunFasWKGOHTtSlh3o4MGD2rBhgy5duqQ33nhDefLk0dmzZ+Xr65vo+x+OERERoV69eunQoUP2ZU8//bQmT56swMBA64IBDpLeTo+0SvXq1RUcHKzhw4cnu/63335TcHCwbDabLl++zBFlB3Bzc0vV5Ujsc8dq0qSJdu3apejoaFWoUEF+fn6J1ttsNi1ZssSidI7HEeUUiIyMVNeuXZNdV6tWLc2ePdvJiVzf8OHDFRQUpCJFiqh169bKkyePzp07p4ULFyo6Olrr1q2zOqLLad++vT755BMZhqHWrVsrd+7cioqK0rx58zRu3Di98847kqQdO3aoVKlSFqd1DXfv3tWbb76psLAwGYYhm82m+vXrK0+ePOrevbsqVqyo0NBQq2O6nNq1a+vAgQM6evSooqKi5O/vr+LFi1sdy2XUqVMnxdtyrWzaKFWqlKKioqyO4fLq1Kmj6dOna+jQocqQIUOS9RUrVtTatWv5wMKBpk6dyrgdFtqzZ48yZMigXLly6ezZszp79myi9a72teGIcgp4enpq2bJlyb7RrV27Vo0aNWKU1DSwceNG9e/fX9u3b5dhGHJzc9Nzzz2nUaNGqWbNmlbHczm3b99W586d9f333yd6ozMMQ+3atVNYWJjc3d21du1a+fj4MDKzA4SGhmrUqFH6+OOP9fLLL6ts2bL26zMnTZqksLAwTgXGEyclI9Gaca2s461evVr9+vXTqlWr0sXpkVY5f/68duzYoRo1aihbtmz33e7w4cPasmWLOnXq5MR0AB4XR5RTICAgQNu3b0+2KG/fvl158+a1IJXrunXrlmbMmKEXX3xRW7Zs0Y0bN/TXX3/J19dXmTNntjqey8qUKZPmzJmjIUOGaMOGDfbR3WvWrKnSpUvbt+OTcccJCwvTkCFD9O677yY5Paxw4cL6888/LUrm2uLi4rR27VqdOHEiyYecNptNffv2tSiZa1i/fr3VEdKlJk2aJLofExOj4sWLp4vTI63Sq1cvjRkzRtmyZdPGjRtVqVIleXt7J9muePHinLWSxm7evKnLly8rd+7cypiRegPH4IhyCrz33nv69ttvtWTJEtWuXdu+fP369WratKneeOONZK9fxqPz9PTU6tWrkx1ADXAVHh4eWrVqlWrXrp1kxN+IiAjVr1+fs1UcbMeOHWrcuLGioqKU3K8/m83GNW14IhUqVCjFR/JtNpuOHTuWxolcn5ubm7Zu3aqqVasqQ4YM2rJlC2dbOVlERIQGDRqkyMhISX8fwKpUqZLeeustBQUFqUWLFhYndC0pGUzNlQZQ4yOXFBg6dKhWr16tl156ScWLF1f+/Pl1+vRpHT58WKVLl9awYcOsjuhyihQpovPnz1sdI91KSEjQSy+9pMmTJzOiexrKlSuXjh07lugDuHsOHTqk/PnzW5DKtfXq1Us+Pj76+uuvVapUKWXKlMnqSC4vLi5OYWFhWr9+vS5duqRJkybp6aef1pIlS1SuXDkVKVLE6ogu4fjx41ZHSHdy5sypY8eOqWrVqvZxJuA869atU7169VS2bFn169dPY8aMsa/z9/dXWFgYRdnBUvKBnCt92MwR5RS6du2aJkyYoFWrVunixYvKmTOn6tevrz59+iR7mg0ez+TJk/X1119rw4YN8vHxsTpOusN8ps7RvXt3/fzzz9q0aZPy5Mkjd3d37dixQ0WKFNHzzz+vevXqacKECVbHdCne3t6aM2dOktNUkTYuXbqk2rVra9++fcqTJ4+ioqIUGRmpSpUqqUuXLvLy8tKkSZOsjgk8kldeeUVLlixR0aJFtX//fhUtWvS+MxXYbDbt3r3byQldW40aNeTn56clS5YoPj5emTJlsv/dsnDhQvXp0yfV00nhwcLCwpIU5UuXLmnp0qU6ffq0Bg8erC5duliUzvE4opxC3t7eGjJkyH3nmYVj7du3T5cuXVKhQoVUp04d5c2bN9EPps1m08SJEy1MCDy+0NBQrVy5UqVLl1bt2rVls9k0aNAg/f7773J3d+f9Jg240ilhT4IPPvhAV65c0a+//qry5csnOoJfu3ZtffLJJxamc13Lli3T8ePH1bt37yTrvvzySxUuXFgNGjSwIJlr+eqrr5Q3b17t27dPBw4ckI+PDx/uO9Fvv/2m+fPnS0o62nLOnDl14cIFK2K5tM6dOye7/L333lPr1q116tQp5wZKYxTlR7Rt2zb99ttvqlWrFlPlpIEvvvjC/u+FCxcmWU9RhivInTu3IiMjFRISouXLlytDhgzavXu3GjVqpNDQUOXIkcPqiC7ngw8+0NixY1WvXj15eHhYHcflLVu2TJ988okqVaqU5HS8e5cxwfE+/vhjNW3aNNl1169f18iRIynKDuDr62s/68fNzU1fffUV1yg7UcaMGXXnzp1k1124cEFZs2Z1cqL0rXPnzurZs6eGDh1qdRSHoSinQNeuXRUfH6+wsDBJ0vfff6/27dtL+nuk4IiICD3//PMWJnQ9CQkJVkdI19zc3NSpUyf5+/tbHcXl5c6dW19//bXVMdKNzp076/jx4ypatKgCAwOTfBjBh3COFRsbq6eeeirZdXfu3FF8fLyTE6UPBw8e1PDhw5NdV7FiRY0ePdrJiVzfn3/+qXz58lkdI12pUqWKZs6cmeyHQj/++CN/mztZfHy8rly5YnUMh6Iop0BERIRCQkLs9+/NeTp69Gj16dNHI0eO1E8//WRhQtdz8uRJ5c2bV+7u7knWxcfH6+zZs5xCmYZsNpumTZtmdQyXd+fOHd2+fVtZsmRJsu769evKlClTsj8DeHTLly/XqFGjdOfOHc2ZMyfJeoqyYxUuXFhbtmxRnTp1kqzbvn27SpQoYUEq1xcXF6fbt2/fd93NmzednMg1XbhwQbly5ZKk+34ghLQzYMAA1atXT82bN1fHjh1ls9m0bds2TZ06VT/++CNztDvJnTt3tGfPHoWEhOiZZ56xOo5DuVkd4Elw/vx5+xvg2bNntW/fPg0cOFDly5fXO++8o19//dXihK6ncOHC+u2335Jdt3v3bhUuXNjJidKPq1evas2aNZo7d65+/vlnXb161epILqtbt27q2rVrsuvefPNN9ezZ08mJXN/777+vSpUqadeuXYqLi1NCQkKimyuN1vlv8Oqrr+qTTz7RkiVL7NNx2Ww2RUZGauLEiXrttdcsTuiaSpQooWXLliW7btmyZczp6yB58+ZVjRo1FBYWphs3blgdJ9156aWXNH36dG3atEktW7aUYRh66623NGfOHIWFhalGjRpWR3Q5bm5uypAhQ6Kbp6enqlatqtOnT+uzzz6zOqJDcUQ5Bdzd3e1zmf7vf/+Tp6enqlWrJunv61Nc7TSDf4MHDcZ+9+5dpmBII2PHjtXw4cN148YN+1QXmTNn1vDhw/Xuu+9aHc/lRERE3PcUyMaNG2vgwIFOTuT6jh8/rkWLFql8+fJWR0kX+vfvr//9739q3ry5fH19JUn16tVTdHS0Xn75Zb3zzjsWJ3RNr7/+uvr27avcuXOrV69eyp07t6KiovTVV1/pu+++0/jx462O6BIMw1BkZKQ2b96sd955R+3atdMbb7yhKlWqWB0t3ejQoYNatmypzZs3KyoqSv7+/nrhhReSPVMLj2/o0KFJ/gb39PRUoUKF1KBBA5e7LpyinAIlS5bUzJkzVb16dU2ZMkUvvPCC/XTI06dPK2fOnBYndE3JleG4uDitXLmSa2fTwIwZM/TBBx+ofv366ty5s/Lly6ezZ89q+vTpev/995UzZ06O/jhYVFSU8ubNm+y6PHnyMJd4GihZsqRiY2OtjpFuuLu7a8WKFZo3b56WL19u/0O2UaNGeuWVV+TmxoltaaF3796KjIzURx99pBEjRihDhgy6e/euDMPQa6+9pv/85z9WR3QZq1ev1okTJzRlyhR98803+vbbb1WmTBl169ZNHTp0sH9AhLTj5eWloKAgq2O4vLt376p9+/bKlSuXsmfPbnUcp2Ae5RSYP3++XnnlFfv9xYsXq3HjxpKkN954Q1FRUfc9xQkpN3z4cIWGhqZo265du2ry5MlpnCh9qVixosqUKaNZs2YlWdehQwft379fO3futCCZ68qTJ49GjBiR7OnX3333nQYNGsT0Fg62fPlyDRo0SKtXr1aePHmsjgOkqU2bNmnVqlW6ePGicubMqfr163M6qgO5ublp69at9pGu//jjD3333XeaMWOGzp8/Lw8PDzVv3lxdu3ZN9jp9OMa+fft04sQJ+9mfZi1atLAgkWuKj4+Xp6enfvrpJ9WvX9/qOE5BUU6hLVu2aMuWLapSpYpefPFF+/KQkBA999xzTLPgACtXrtSKFStkGIYmTZqkVq1aKXfu3Im28fDwULly5dS+fXsGOXIwLy8vLVq0SC+//HKSdatWrVLz5s0ZAMbB2rVrp+3btysyMjLR6MuXL19W1apV9eyzz+r777+3MKHradKkiXbt2qXLly+rQoUKyY56vWTJEovSua6DBw9qw4YNunTpkt544w3lyZNHZ8+ela+vr7y8vKyOBzySfxble+7evavly5drypQpWrlype7evavChQvrjz/+sCipazp69KhatWqlPXv2SEp62Z7NZmPcCQfLnz+/Jk+erIYNG1odxSkoyvhX6tKli4YOHcqgXU6UI0cOffHFF/apz8zmzJmj3r176/LlyxYkc12HDh1SlSpV5O7urrZt2yogIECnT5/W/PnzdefOHUYFTgOFChV64BgHNptNx44dc2Ii13b37l29+eabCgsLs497EBkZqUqVKqlx48aqWLFiis8kQsqdPXtWV69etb9/3L17V+PGjdPOnTtVt25dvf766xYndA33K8pmUVFRCgsL07Rp03Tw4EEnpnN9QUFB2r9/vwYOHKhSpUopU6ZMSbapVauWBclcV79+/XTq1CnNmzfP6ihOQVFOgX/+womPj9f48eP5hWOBW7duydPT0+oYLqlp06Y6cOCA1q9fn2guyPPnzyswMFAlS5bU4sWLrQvoonbv3q13331XGzdu1N27d5UhQwbVqlVL48ePZ8ApPPFCQ0M1atQo+7SKZcuW1a+//qpKlSpp0qRJCgsL0/bt262O6XIaN26sggUL6ssvv5QkDRs2TKGhocqePbtiYmI0ffp0dejQweKUT76UFGWknaxZs+rbb79NdHkk0tasWbM0cOBAPf3002rRooXy5s2b5MNnVzrdnaKcAvzCcb558+YpOjpavXr1kvT3dT9NmjTRoUOHVL16dS1dupQBMhxs3759ql69uu7cuaOgoCDlzZtX586d07p16+Tu7q7NmzerdOnSVsd0WTdv3tRff/2lHDly8GEQXEaRIkXUtWtXDRo0SHfv3pW7u7u9KK9cuVIdO3bUxYsXrY7pcgICAjRx4kS1atXKfv+VV17RuHHj1LNnT+3atUtbtmyxOOWTr3bt2vrqq69UsmRJq6OkS0WKFNGXX36Zbq6X/Td42ACMrna6O6Nep8DOnTvVqVMn+/1vv/1Wffv2tf/C+fLLLynKDjZ27Fi1adPGfv/999/XX3/9pXfeeUczZ87UyJEj9emnn1qY0PWUKVNGkZGRCgkJUUREhKKjo+Xn56dmzZopJCSEeTfTmJeXF9dqOsmdO3c0Y8YMhYeHKzo6Wv7+/nrppZfUoUMHxj5wsDNnzuj5559Pdp2npyfztKeR6Oho+2B1Bw4c0Llz59S5c2dJUsuWLdPNaZNpbeLEiSpUqJDVMdKtnj176ttvv6UoO1FERITVEZyKopwC/MJxvmPHjqls2bKS/j7devXq1fr666/VsWNHlShRQmPHjqUop4HixYtr7ty5VsdIV+7evauVK1fqwIEDSQZLs9lsGjJkiEXJXFNMTIyCgoK0c+dOZcmSRXny5NHmzZs1d+5cTZo0SeHh4fLx8bE6psvIlSuXjh07ptq1aydZd+jQIeXPn9+CVK4vW7Zs9hHzN27cqBw5cqhcuXKS/n5fuX37tpXxXEbFihW1ZcsWVa1aVXXq1NGkSZM4uuxE77//vt577z1VrlxZ9evXT3Zwxr59+1qUzjWlt2u+KcopwC8c57tx44Z9svht27YpLi7O/olh6dKldebMGSvjuaQH/ZI/fPiwevTooXXr1lmQzHVFR0frxRdf1MGDB2Wz2ewjdpqv96EoO9aHH36oQ4cOad68eWrdurV9+Y8//qjXX39dH374of773/9amNC1NGjQwH598r0PnG02m2JiYvT555/bp1qEY1WtWlWffPKJ3N3dNXHiRNWtW9e+7tixY4nGocCj8/DwsP8NuH79euZod7Jt27Zp+vTpunz5sn777bck6ynKaScmJkZbt27VpUuX1KBBA5e9HJKinAL8wnG+vHnzateuXapZs6ZWrVqlEiVKKGfOnJKkv/76S5kzZ7Y4oet50C/5q1evasOGDU5O5Po+/PBDeXp66sSJE3rqqae0bds25ciRQ19//bWWLVumtWvXWh3R5SxevFihoaGJSrIktWrVSidPntT48eMpyg4UGhqqlStXqnTp0qpdu7ZsNpsGDRqk33//Xe7u7nwQlEY++ugjBQcHq2nTpvL19dWHH35oX7d48WIGn3KQIkWKaNy4cTp//rykv3+Pnj59+r7bu9IgR/8GvXv3lr+/v6ZOnXrfUa/heB999JFGjx6tmzdv2mcy8PX1VVBQkIKDgzVgwACrIzqOgYf67bffDH9/f8Nmsxk5cuQwfv/9d/u6Bg0aGK+++qqF6VzTe++9Z3h7exstWrQwvLy8jGHDhtnXffTRR0bVqlUtTOeabDabsW3btmTX/fTTT4a3t7eTE7m+YsWKGTNnzjTi4+MNm81m/Prrr/Z1vXv3Nl555RUL07mmTJkyGWvXrk123dq1aw0PDw8nJ3J958+fN7p3727kz5/fcHd3N/LmzWt069bNOHfunNXRXNr169eNHTt2GDExMYmWL1u2zDh06ND/sXfncTWm///AX+e0KtFGi6WyZWuRkb2dpLK0oKwtZsLMWMdOMWQd24ydFlpsRSlrdSoisg8iYylSoUhSUZ3fH37OV59zEM45d929n49Hj0fd1/3Ha5qcc973dV3vi6FU7LJv3z6+vLw8n8Ph8LlcLp/D4Xz2i8vlMh2XdRo1asSPiYlhOkaDsnnzZr6MjAz/t99+4x87dozP4XD4ly9f5vP5fP66dev4/fr1YziheNGMci2YmpoiOzsbd+7cQbt27WrsX5s8eTLat2/PYDp2+vPPP/HmzRucO3cOnp6emD17tmAsLi4OdnZ2DKZjj5iYGMTExAh+/vPPPwUz9x+VlZUhOTkZ3bp1k3Y81nvy5An09fUhIyMDLpeL0tJSwZizs7PIM63Jj2nRogXOnj0LW1tbobG0tDRaISQBWlpa2LZtG9MxGhwlJSWYmZkJXXd0dGQgDTuNHDkStra2uHv3Lvr374/NmzfT6RBS1Lp1a8GWJSId//zzD2bMmIHVq1cLdbdu37497t27x1AyyaBCuZboDUe6GjVq9NkPVunp6VJOw163b9/GwYMHAXzYy5OUlCTU+l9BQQFGRkbYuHEjExFZTVNTE8XFxQAAXV1d3Lx5ExYWFgCAoqIiVFZWMhmPlUaOHInAwECoqKhg/Pjx0NDQQGFhIcLCwhAYGIgZM2YwHZEQsXj+/DnWrVuH5ORkFBYW4vDhw+jSpQu2b98Oc3NzevgpJpqamtDU1MT48eMxaNAgGBgYMB2pwZg7dy7Wrl0Le3t7OlZRSh48eAB7e3uRYyoqKnj16pV0A0kYFcrfoLi4GFlZWUKdaQEIPtwSyXr58iXCwsIQFBQksnED+Tbz5s3DvHnzAHw4G4/H49HeNSnq3r07bt26BUdHRwwePBhLly5FkyZNIC8vj/nz56NXr15MR2SdgIAAXL16FbNmzcIff/wBWVlZVFZWgs/nw97eHgEBAUxHZJ2zZ88iIiIC2dnZIju7JyYmMpSMvR4+fIg+ffrg9evXMDExwf3791FRUQEAuHHjBtLT0xEcHMxwSnah36f0XblyBbm5uWjbti2sra1Fdr2mh/zi1bRpUxQUFIgce/ToEZo3by7lRJLF4dOaha+qrKyEn58f9uzZ89lDtNl0uHZddPr0aezevRsxMTGoqKhAy5YtkZOTw3QsQn5IQkIC7t+/j19++QXPnz/HgAEDcOPGDQBA27ZtERcXB0NDQ4ZTstPJkyeRlJSEoqIiaGhoCJqQEPEKDg6Gj48P1NXV0aFDBygoKAjd09DO5ZQGd3d33Lp1CwkJCWjevDnk5eVx6dIlmJmZITIyEv7+/sjKymI6JitUVlbi7NmzkJWVRd++fcHhcFBeXo7t27cjKysL7du3h4+PD1RUVJiOyjr/uwLuf3E4HPp8Lmaenp64ffs20tLSoKioCDk5OVy+fBlGRkbo168fjIyMsHPnTqZjig0VyrWwZs0arF27Fn/99RfGjRuHzZs3Q05ODjt37hQccfFpJ2wiHtnZ2QgODkZISAgeP34M4MMRRlOnTsXgwYO/+gJJvh+fz4ePjw8CAgLQunVrpuM0GHw+Hzdv3gSHw0HHjh0hK0uLfkj91qlTJ5iYmCA0NFRkkUwkQ11dHVu3bsXIkSNRVVUFOTk5QaGcnJwMJycnvHnzhumY9V5hYSGsrKxw+/ZtAED//v0RHx8POzs7XLhwAVwuF9XV1ejQoQPS09OhqqrKbGBCftB///2HHj16oEmTJhg+fDj+/vtvTJgwAVevXkVOTg6uXLnCqs+NVGnUwt69e7FgwQJ4eHgAAHr27AlfX19cuHABenp69DRcjN69e4d9+/Zh4MCBaNu2LZYuXQo5OTlBq/lFixbBycmJimQJq66uRmhoKF68eMF0lAaFw+HAyMgIXbt2pSJZjNq0aVPrr7Zt2zIdl1Wys7Ph6+tLRbKUlZeXCy1D/ai0tJTeQ8Vk1apVePz4MTZv3oz9+/fj2bNnGDVqFHJycpCeno53794hISEBL168wKpVq5iOS8gPa9euHdLS0tCpUyds2bIFfD4fe/bsgaamJs6cOcOqIhmgPcq18uDBA5iYmAjeWMrLywVjfn5+mDp1KlasWMFUPNb4/fffER4ejlevXkFRURGenp7w8fGBpaUliouL6XcsZbTYRDpev36NzZs3g8fjobCwEBoaGrC2tsakSZNo9kFMOnfuDA6HI/iZz+fj2LFj6NevH5o2bcpgMvbr1KnTZ/ezEckxNDREQkKCyO0Eqamp6Nq1KwOp2CcmJgYLFy6En58fAEBHRwf9+/fHpk2bBP0+Pq6E27dvH32OkaC3b9/W+Hz+0eceGJHv17lzZ5w4cQIVFRUoLCyEmpoaGjVqxHQsiaBCuRaUlZXx7t07cDgcqKurIzs7G3369AHwoTtzYWEhwwnZ4Z9//gGHw4GjoyNCQ0OhpqbGdCRCJOrhw4ewtrZGTk4O9PT0oK2tjXv37iEhIQHbtm0Dj8dDmzZtmI5Z78XFxdX4ubKyEvLy8tiwYYPI0wyI+AQGBmLWrFmwsrJCixYtmI7TYEycOBEzZsyArq4uRo8eDeDDiq1Dhw5hy5Yt+OeffxhOyA6PHz/GTz/9JPj5YydxU1PTGvf16NEDK1eulGa0BuHt27eYP38+wsPDUVRUJPIe2qMsOQoKCqw/UpEK5Vro2LEjHj58CADo06cP1q1bh/79+0NeXh6rV6+mZjtiMmzYMMTHxyM+Ph5dunTB+PHj4eXlhQ4dOjAdrcHhcDiwtLSk5iMSNnXqVJSXlyMtLQ29e/cWXD937hxcXFwwbdo0xMbGMpiQnT6dXSbiN2TIkBo/FxcXo0OHDjA1NYWGhkaNMQ6HU+MsdyIekydPxrVr1zB9+nTMnDkTANCvXz/w+XxMnDgR48ePZzghOygoKNSYxfy4xUBJSanGfXJyclLN1VD8+uuv2Lt3L5ydndGpUyfIy8szHYmV9uzZA0dHR2hoaGDPnj1fvX/cuHFSSCUd1MyrFrZs2YIHDx5g7dq1uHr1KiwsLPD27VsAH178oqOjMXjwYIZTssPz588RGhqKoKAg3LlzBxwOB3379oW7uzumTZsGHo9HR3ER1mjcuDE2btwIHx8fobFdu3Zh2rRp1HBHAv63uRERL319/Vo/jOBwOHjw4IGEEzVc6enpiI+PR0FBATQ1NeHk5CRYEUd+nJGREX755Rf8+uuvgmsXLlyAsbFxjaWoQUFBWLZsGf2ti1mzZs0wZ84czJo1i+korMblcpGeng5zc/MG12mcCuXv8PjxYxw5cgQcDgcDBgygGWUJOXfuHHbv3o2DBw/izZs34HA4cHNzw+zZs9G9e3em4xHywzQ0NBAZGSmya/6pU6fg4eFBWzskgAplQog4+Pr6oqysDOHh4V+8b9iwYVBQUMD+/fullKxhaNasGfbt2wdbW1umo7BadnY2dHR0IC8vj+zs7K/er6enJ4VU0kGFci3k5ORAR0dH5NKZyspKPH36lHVd3uqS0tJS7Nu3D0FBQTh//jw4HA66deuGS5cuMR2NVd6/f49Vq1YhIiIC2dnZQk0xOBwOKisrGUrHTt7e3pCRkRF55uDEiRPx7t07hIaGMpCM3ahQJoRIU3x8PNq1a0cTK2L2yy+/QFVVlTqKE4mhQrkWZGRkcP78eUEHw09dvnwZ5ubmrFpmUJfduXMHu3btQlhYGPLz85mOwyqzZs3C+vXr4eDgAGNjY5HHufj7+zOQjL0SEhLg4+MDc3NzeHp6QltbG/n5+QgPD8elS5ewe/fuGk3tqKj7PleuXKnxc1VVFXr27ImwsDB07NhR6H76PYtPjx49MGDAANja2qJfv350TJQEcbncb9p/T59bSH335s0buLq6okuXLhg8eLDIDtf0ei55Fy5cwNWrV2FpaYlOnToxHUesqFCuhU/X5v+vixcvok+fPjTTJgbNmjWDtbU1rKysYG1t/cV/bJWVlXTOrJi1bt0aPj4+VAxL0ad7ff73+KJPr/H5fNbt+5EmUQXEx9+pqGv0exafIUOGIDU1Fa9fv4aioiL69u0LOzs72NnZ0RYaMQsICKjxNx0cHIw3b97A2dkZ2trayMvLQ1xcHJSVleHt7U2v9aTee/78OUaNGgUej0ev51Li6+uLyspKhISEAAD27duH0aNHg8/nQ15eHjwer0Zz0vqOKo1aEvWUtqKiAsePH4empiYDidjH2NgY8fHxOHToEDgcjqBw/vjVvn17wb1UJIvfy5cvqVGalAUHBzMdoUGg3zNzYmNjUVVVhQsXLiAhIQGJiYlYvHgx5s+fDzU1NdjY2ODAgQNMx2SFgIAAwfd//fUXtLW1kZCQgMaNGwuul5SUwM7OTqgrM/l+Z86cwe7duyErK4tff/0VpqamSE1NxdSpU5GVlYX27dsjMDCQmr5KgI+PDzIyMjBt2jTqei0lPB6vxkO25cuXw97eHitXrsS0adMQGBiIo0ePMphQvGhG+TOWLFmCpUuX1upeX19fbN++XcKJGob379/jwoUL4PF4SE5ORnp6OsrKysDhcKCjoyMomq2srOh8WTFzdHSEnZ0dpk+fznQUQgiLXbx4EYsXL8apU6doxkdC2rZtizVr1sDFxUVoLCoqCrNmzRIce0m+3/nz52FhYQEulwtFRUUAwIkTJzBo0CDo6enBzMwM6enpePDgAc6cOYOePXsynJhdVFRUsG7dOkycOJHpKA2GsrIyjh07BktLSzx9+hQtW7ZESkoK+vfvj5iYGPj5+SEvL4/pmGJD03KfYW5ujsmTJ4PP52PLli1wc3ODlpZWjXsUFBRgZGQET09PhlKyj5ycHPr164d+/fph0aJFeP/+Pc6fPw8ej4eUlBRERUUhIiKCGktJwKZNmzB06FDo6enBycmJnswy5PHjx7h16xZ69OghdOYsIfVRfn4+EhIScPr0aSQmJiIvLw+tWrWCl5cX7OzsmI7HSrm5uZ9deSUrK0s9PsRkxYoVMDMzw+nTp9G4cWNMmjQJ7u7u6NmzJ44fPw4ZGRmUl5fDysoKa9aswaFDh5iOzCoqKirQ19dnOkaDIicnJ2j2mpaWBkVFRfTq1QsAoKamhlevXjGYTvxoRrkWvLy8sHjxYhgYGDAdpcG6ffs2kpKScOLECRw/fhwANSIRNxUVFbx//x7v378Hh8MRWprH4XBQXFzMUDp2WrhwIUpLS7F+/XoAH5p7OTs7o6KiAqqqqjhz5gy6dOnCcEpCvp+RkRFu374NNTU1WFlZwc7ODra2tjW20hDx69atG5o2bYrTp0/XOLHj3bt3sLOzQ0lJCa5evcpgQnZo0aIF1q9fjxEjRgAAHj16hDZt2uDQoUM1ZvNDQkKwYMEC5ObmMhWVlebOnYvnz59j9+7dTEdpMHr16oV27dph69atcHd3R1VVFU6fPg0AiIiIwNy5c5GTk8NwSvGhGeVaoP1t0nfnzh0kJycLlmC/ePECGhoasLCwwIYNG2Bpacl0RNZxdXX9po6p5MdFRUVhxowZgp8XLlwIY2NjLFiwAAEBAVi2bBkiIyMZTEjIj7l16xYaNWoENzc3DBo0CDY2NmjSpAnTsVhv2bJlGDZsGNq0aQMXFxdBR/3o6Gjk5+fjyJEjTEdkhcLCQujo6Ah+btmyJQAIHRlqYGCAFy9eSDVbQ2BiYoIFCxZg+PDhcHR0FNn1WtT2A/L9Zs6ciVGjRgk+m3z6WpKYmAhjY2OGkkkGFcq1dP/+fQQEBCAxMRGFhYXQ1NSEnZ0dFi9ejLZt2zIdjxV27twpKIzz8/Ohra0NCwsLBAQEwNLSEp07d2Y6Iqt97GBIpCc3Nxft2rUD8OEDV0ZGBo4dOwZ7e3uUl5dj5syZDCck5MdcunQJCQkJSEhIgKenJyorK/HTTz9hwIABGDBgAHr37g0ZGRmmY7KOo6MjTpw4gQULFmDz5s2orq4Gh8OBubk5goODacm7mKiqqtYogLlcLgwNDaGsrFzjvuLiYqFr5MeNHj0awIeZ/JiYGKFx6oEgfu7u7mjVqhXOnTuHHj16oH///oKxli1bwtXVlcF04kdLr2vhzp076N27N8rLy2FjYwNdXV08ffoUSUlJUFJSQlpamsizOMm34XK5gj0+vr6+tDSPsJ6KigqOHDkCW1tbxMbGws3NDcXFxWjUqBHOnDmDgQMHoqysjOmYhIhFRUUFzp49i9OnT+P06dO4du0aGjduTFs6JOzt27d4+fIl1NTUqNu1mFlaWqJfv35Yvnz5F+9btWoV9u/fL3SmO/kxKSkpX72HViCSH0EzyrUwf/58aGhoIDk5WbCsBgCePHkCGxsbLFiwAFFRUQwmZAdnZ2ecPXsWa9asQUREBCwtLWFlZQVLS0sqmqWEVk5IV9u2bREXFwdbW1vs27cP5ubmaNSoEQAgLy8PampqDCckRHzy8/Px6NEjZGdn4/Hjx+Dz+SgtLWU6FutxuVzBFxGvSZMmobCw8Kv3xcbG0iy+BFARTCSNZpRrQUNDA5s2bRIs8fhUWFgYfv/9dxQVFTGQjH34fD6uX78u2J985swZFBcXQ0tLq0bhTDP44kcrJ6Rv69atmDJliqBTZFBQEMaPHw8AmDp1Km7duoWEhASGUxLy/aKiogRLrx88eAA+n48OHToImnrZ2NigadOmTMdkpXPnzmHOnDlIT09HdXU1uFwu+vTpg5UrV6J3795MxyOE1BNt2rTB4cOHYWJiAgMDgy/2s+FwOLh//74U00kWzSjXwtu3bz97TIumpiYtjRQjDocDU1NTmJqaYtq0aeDz+bh69SqSk5ORnJyMuXPn4vXr12jevDmrzmmrC2jlhPRNmjQJampqOHfuHMzNzTFmzBjBWFlZmaBoJqS+cnd3h46ODmxtbbFw4ULY2dmhRYsWTMdivfT0dNjY2EBVVRU///wzdHV1kZubi+joaNjY2CA5OZnO9CX1kre3NxYtWgQDAwN4e3t/8V4Oh0MdscXA0tJS0ITR0tKyQTV+pRnlWjA1NYWRkRH27t0rNDZ+/HjcuHGDjlmQoKqqKmRkZCA5ORmJiYlISkoSXCfiQysn6p6KigooKCgwHYOQ73b79u1vasSYk5MDXV3dz54BTGpn4MCBePXqFXg8Xo0mUqWlpbC2toaamhpOnjzJYEJ2ef/+PWRkZGosb79+/TqysrLQrl07dOvWjcF07GJgYIAjR47AxMQE+vr6X53dfPDggRTTEbahd6Ja+P333+Hr64vi4mKMHz8eOjo6yMvLQ1hYGGJjY7Fr1y6mI7JKdXU1Ll26JOiAnZaWhtLSUvD5fDRr1gxubm6wtrZmOibr0MoJ6VuyZAn8/f1Fjr1//x4uLi6Ij4+XcipCxOdbiuSqqioYGBggIyMDZmZmEkzFfunp6QgKChLqtKysrIw//vgDPj4+DCVjl6qqKkyaNAmhoaHgcrmYNWsW/vzzT/z+++/YvHkz+Hw+OBwORowYgfDwcNonLgYPHz4UfP/o0SPmgpAGgQrlWvD29kZBQQGWLVsm+NDK5/PRqFEjLF++HF5eXgwnZIc1a9aAx+MhLS0Nb968AZ/Ph7q6OgYMGABra2tYW1ujS5cuTMdkLUNDQ4SHh2PQoEFCY5GRkbQ/WQKWLVuGNm3aYOzYsTWuV1dXw8PDA2fPnmUoGSHMoEVu4lFVVfXZ1SiKioq0IktMtm3bhl27dmHEiBHQ1NTEhg0bwOFwsGPHDqxYsQLm5uZISkrCihUr0L9/f0yePJnpyIR8s5ycnG+6/3/PEa/PaOn1NyguLsa5c+dQVFQEDQ0N9O7dm5qQiBGXy0XTpk1hYWEhKIxNTEyYjtVgBAUFwdfXF05OTp9dOUEPhcRry5YtmDFjBk6cOAErKysAHwqFcePGITo6GsePH4eFhQWzIQmRkqqqKsjJyeHSpUs0o/yD+vTpA1VVVRw7dkxozMnJCUVFRTh37hwDydjFzMwM/fv3x8aNGwEA+/fvx+jRozF79mwEBgYK7ps0aRKuXbuG8+fPMxWV9Z4/fy5y5RubijamcLncb9qXzKYHcTSj/A2aNm0KBwcHpmOw1sfldg2pSUBdQisnpG/y5Ml48OABXFxckJaWhk6dOmHSpEk4cOAAYmJiqEgmhHyXuXPnYtiwYejWrRvGjBkjePAZERGBa9eu4ciRI0xHZIUHDx5gxYoVgp/t7e1RXV0NGxubGvc5ODggIiJC2vFYr6SkBNOnT0dkZCTKy8tF3sOmoo0pQUFBDfazOc0o19Lr16+xefNm8Hg8FBYWQkNDA9bW1pg0aRJUVVWZjkeI2BQXF+P8+fOCv3NaOSF5bm5uuHTpEhwcHLBr1y7s27cPrq6uTMciRKpoRlm8IiIiMHv2bDx9+lRwrUWLFli9ejU8PDwYTMYejRs3xtGjRwV9Uz73N5yamooBAwagoqKCqais5OPjg4iICPj4+MDY2FjkdgM6PYL8CJpRroWHDx/C2toaOTk50NPTg7a2Nu7du4eEhARs27YNPB4Pbdq0YTpmvVddXY3Q0FC0adNGcIg8n8/H0KFDa9zXtGlTQeMMIn5NmzYVuU+ZSE5YWBhsbGywc+dOBAcHU5FMCPlhnp6e8PDwwN27dwUPPg0NDRvszJAkaGlp1di/KSMjgxUrVtQ4YhEAcnNzP9ssk3y/+Ph4rFy5ElOnTmU6CmEpKpRrYerUqSgvL0daWhp69+4tuH7u3Dm4uLhg2rRpiI2NZTAhO8THx+OXX37BlStXBNeqq6sRFxcHHR0dyMvLAwAKCgrg4OAAT09PpqKyRk5ODnR0dCAnJ1erZg201+fHDRkyROR1DocDNTU1HDx4EAcPHhRci4mJkWY8QgiLcDgcasQoQSYmJkhOTq4xazlnzhyh+1JTU2FkZCTNaA1CeXk5/V4ZUFVVhePHjyMzM1NoXziHw8GiRYsYSiZ+tPS6Fho3boyNGzeKPE5h165dmDZtGt68ecNAMnZxdXWFvLw8IiMjBddELWOaOXMm7t+/T3usxEBGRgbnz5+Hubl5rZo10F6fH/e1cx8/RWdAkoaEll6LV0lJCY4fP47s7GzWf5hlypMnT1BaWgpDQ8Mv3hcQEICffvoJTk5OUkrWMHh6esLQ0PCzxywS8SssLET//v1x584dcDgcwUkFn36uYdNnRZpRrgUFBQW0atVK5Fjr1q0/ewQD+TYXL17EqlWrvnqfhYUFDhw4IIVE7BcUFIS2bdsKvqcleZJH5z6ShqKsrAw+Pj6YPHky+vXr99X7uVwu/P39oaurK4V07HbhwgU4OjqiqKhI5DgVyuLxv0usPycgIECyQRqQT/+mFy5cCDc3N6ioqMDZ2Vnk8nZ1dXVpxmO9BQsWQFFREdnZ2dDT08OFCxegrq6Obdu2IS4uDgkJCUxHFCuaUa4Fb29vyMjIYOfOnUJjEydOxLt37xAaGspAMnZRUFBAYmJijQ9UfD4fM2bMwKxZs9CiRQsAwNmzZ2FnZ/fZDoeE1AffWkQQUh8pKyvTMWcM6NOnD0pLS7F7924YGxsLti4RUt/97+o3Pp//xYf8bJrdrAvat28Pf39/eHh4QE5ODhkZGejevTsA4LfffsOLFy9qrAyt72hGuRY8PT3h4+MDd3d3eHp6QltbG/n5+QgPD8elS5ewe/fuGvtqacnY91FUVBRaws7hcLB+/foa1968eUNv+hLg7e2NRYsWwcDAQGgsOzsbS5YsQVBQEAPJ2KlRo0aIiYmBn58f01EIkRhTU1PcvHmTCmUp+/fffxEREYGffvqJ6SgEwOXLl7F582Z6DxWDxYsX0+o3Bj158gT6+vqQkZEBl8tFaWmpYMzZ2Zl1/YOoUK6FgQMHAgAeP36M6OhowfWPk/Efxz8+1aKnV9+nTZs2SE9P/2rH5fT0dMFyYSI+ISEh8PPzE1kov3jxAqGhofQmL2ZURBC2W7lyJcaOHYsuXboITjMgktesWTOmI5BPPHr0iN5DxYSWsTNLU1MTxcXFAABdXd0an2GKiopQWVnJZDyxo0K5FoKDg5mO0CA4ODhg69atmDx5Mpo3by7ynvz8fGzduhUTJ06UcrqGraioiPbiSwAVEYTtJk+ejDdv3sDGxgZqamrQ0dGpMRvE4XBw/fp1BhOy02+//YZt27bBycmJZt9Ig1JeXo5Hjx6hffv2kJGRYToO63Tv3h23bt2Co6MjBg8ejKVLl6JJkyaQl5fH/Pnz0atXL6YjihXtUf6M6upqOqdXyp49e4auXbuiadOmWL16NRwcHKCoqAjgwwvfsWPHMHv2bJSUlODmzZv0xFwMUlNTkZycDODDU1pfX1+h5iRlZWWIiYmBkpISLl26xEBK9jIyMkJeXh5evnxJRQRhJSsrq68WajweT0ppGo6//voLW7ZsgYqKChwdHYWaHHE4HEyfPp2hdOzxrYUYrTgUr7///huvXr0SNKa7fPkyBg0ahKKiIujr6yM5OfmzzXjJ90lISMD9+/fxyy+/4Pnz5xgwYABu3LgBAGjbti3i4uK+2gW+PqFC+TM0NTUxfPhwuLq6wtbWFnJyckxHahDS09MxbNgwPH/+HDIyMoJi+Pnz56iqqkLz5s1x5MgR9OzZk+Gk7LBkyRIsWbIEAGq0+f9fenp6CAoKgrW1tTTjsR4VEYQQSfjag37aJiYe8vLyMDEx+eos2v3793Hy5En6nYuZiYkJfH198dtvvwEA7Ozs8PjxY0yZMgUbNmzAgAEDsH37doZTshufz8fNmzcFZ7bLyrJrsTIVyp+xadMmREdH4+zZs1BRUYGTkxPc3Nxgb28vmOUkklFcXIwdO3YgISEBjx8/BvDhGK4BAwbA19cXTZs2ZTghe5SVleHt27fg8/lo3rw5Tpw4Iehe+JGCggIaN27MUEJCCCHfKjs7+6v36OnpSSEJuxkbG6NLly5f7fIbFRWFESNGUKEsZmpqajhw4AAGDBiAkpISaGhoYN++fXBxccHevXuxePFiPHz4kOmYpB6jQvkrnj17hujoaERHRyM5ORkKCgpwcHCAm5sbHB0doayszHRE1igsLBR5Bh6RjuzsbOjq6tLqCUKIWBUVFWH9+vVITExEYWEhNDU1YWdnh2nTpkFNTY3peKxx/fp1mJiYMB2jQRk/fjwuXLiAO3fufPG+qKgouLu7o7q6WkrJGgYlJSUcO3YMVlZWOHXqFAYPHoyioiI0adIEZ86cwcCBA1FWVsZ0TFZJTU396j1salDKrvlxCWjevDn8/Pzg5+eHoqIiHDlyBFFRURg3bhy4XC4GDBgANzc3ODs7Q1VVlem49Zq2tjYsLS3h5uaGYcOGQVtbm+lIDUpFRQXOnTsnsqlUSkoKdHV10b59ewaSsRsVEYTNcnNz0bdvX+Tk5KBTp05o3bo1nj59ij///BN79uxBWloadHV1mY7JCt26dUObNm3g6uoKNzc39OjRg+lIrDdixIhaPVzu0aMHNYaVgNatW+PMmTOwsrJCTEwMTE1N0aRJEwAftux9/J6IT222jLFp5QTNKH+n169fIzY2FlFRUYJ9JxUVFUzHqtdiY2MRHR2N2NhYvH79Gr1794abmxuGDx+O1q1bMx2P9ZycnNChQwesW7dOaGzWrFnIyspCbGwsA8nY63+LCF1dXTx9+hSZmZnQ09OjIoLUe+PHj8eJEycQFxdXo3DLyMiAs7MzBg0ahJCQEOYCssiNGzcQFRWFqKgo3L59Gy1btoSLiwvc3NzQt29f6n5NWGfZsmVYsmQJjI2Ncf36daxduxbTpk0DAMyfPx9nzpzBmTNnmA3JMikpKULXXrx4gZiYGKSlpWHz5s1fPea1PqFC+TNiYmIwaNCgWh2J8/btW8THx8Pd3V0KydivsrISCQkJiI6OxpEjR1BYWIju3bvDzc0NLi4uaNeuHdMRWUlLSwvbt2/HsGHDhMaOHj2Kn3/+GXl5edIPxmJURBC209LSwrJly0Qe6bdjxw4sWrQIBQUFDCRjtzt37giK5mvXrkFLSwvDhg2Dm5sbrKys6Ngcwgp8Ph+rVq3CuXPnYG5ujvnz5wsa2Q0ZMgQ2NjaCwplInp+fHxQVFbFhwwamo4gNFcqfIS8vDwUFBQwePBhubm4YPHgw7UdmQHV1NZKTkxEVFYXDhw+joKAAXbt2FRTNXbp0YToiaygqKiIuLg52dnZCYwkJCXByckJ5eTkDydiLigjCdoqKijh69CgGDBggNHb69Gk4OzvT64qEPXz4EAcPHkR0dDQyMjKgpqaGIUOGCD7bEPHj8/nw8fFBQEAArYgjDcbJkycxfvx45OfnMx1FbOig4M8oKCjApk2bUFpairFjx6JZs2YYNmwYwsLCUFxczHS8BoPL5cLGxgabN29Gbm4uUlJSYGNjg127dsHY2JjpeKzSokULXLx4UeTYxYsXoaOjI+VE7FdcXAx9fX2RYwYGBvRaQ+o9AwMDxMfHixw7fvw4DAwMpJyo4TEwMMDs2bORnp6O7OxsLFy4EP/99x+GDh3KdDTWqq6uRmhoKF68eMF0FEKk5uXLl6zbhkozyrVQUlJSYz9yZWUlbGxs4ObmhqFDh0JTU5PpiKxSVlYGHx8fTJ48Gf369fvsfRkZGdSsRIxmzpyJnTt3IiYmpsZ5ycnJyRg6dCh8fHxE7l8m369Tp06wt7cXuUxpxowZOH78ODIzM6UfjBAxWb16NebOnYtff/0V48ePh46ODvLy8hAWFoa///4bK1euxKxZs5iOyQrTp08X7EeujYKCAmhpaUk4VcNUVVUFOTk5XLp0CWZmZkzHYbV79+5h+/btyMzMFOpwzeFwkJiYyFAydsrJyRG6VlFRgRs3bmDWrFno2LEjjh8/zkAyyaBC+Ru9ffsWcXFxiI6OxrFjx1BWVob+/fvDzc0NkydPZjoeaygrK+P48eOsajFf1xUXF6Nv377IzMxEhw4d0LJlSzx58gRZWVno3Lkz0tLSqIOkmFERQdiOz+fDz88PO3furNFMis/n4+eff8a2bdsYTMcuZmZmgv3Iw4cPF+xH/rhnk0gPFcrScfPmTfTq1QstWrTAf//9B2NjY7x48QK5ublo1aoV2rZti6SkJKZjsgqXyxXZGJDP58PQ0BBxcXFo27YtA8kkgwrlH1BRUYHjx48jKioK8fHxKCoqYjoSa/Tt2xejR4+mhw9S9ubNG6xfvx4nTpzA8+fP0axZMzg4OGDatGlo3Lgx0/FYh4oI0lDcvXsXPB4PhYWF0NDQgI2NDTp06MB0LNZ59OiRYD/yxYsXoaamhqFDh8LNzQ12dna1OsqI/Ljq6mrY2tpix44ddKyiBA0ZMgRycnLYv38/5OXlBQ8m4uPj4e3tjcOHD6NPnz5Mx2SVkJAQoUJZUVER+vr66NGjB+sezFGhLCbv37+nNyAxOnPmDMaOHYvQ0FCR5/oSwiZURBBCxC03NxeHDh1CdHQ00tLS0LhxYzg5OcHNzQ2DBg2CoqIi0xEJ+SEtW7bEli1b4OTkBFlZWVy8eBE//fQTAGDNmjWIi4sTeZwRIbVFhTKpk4yMjJCXl4eXL19CTU0NOjo6NZ5gcTgcXL9+ncGEhBBCSP1QUFCA6OhoREdHIyUlBQoKCnBwcMCBAweYjsZKWVlZKCwshKamJs0oS5CysjJOnjyJfv36QUFBAXFxcYIO+zweD0OGDEFJSQnDKdnvwoULuHr1KiwtLdGpUyem44iVLNMB6gsDAwPIy8vj7t27Na63b98e1dXVuH//PkPJ2ElDQ4OapEmZjY3NF8epKYb49ejRAwMGDICtra3gjZ6Q+u5ze9hE4XA4qKyslHAioqWlhUmTJmHSpEkoKirC4cOHER0dzXQs1jl48CBmzZqFJ0+eCK61bNkSf/31F9zc3BhMxk4tWrQQdBZv164dUlNTBYXyjRs3aMuYBPj6+qKyshIhISEAgH379mH06NHg8/mQl5cHj8dD7969mQ0pRlQo1xKfz0d1dXWtr5Mfk5yczHSEBqe6ulrow+2LFy9w9+5dNG/enJYCS4COjg62bNmClStXQlFREX379oWdnR3s7OzQvXt3puMR8l0WL15c60KZSJ+6ujp8fHzg4+PDdBRWOXbsGEaNGoUuXbrg119/ha6uLnJzcxEWFoZRo0bh6NGjcHBwYDomq/Tr1w/nzp3DsGHDMHr0aPj7+yMvLw/y8vIICQnBmDFjmI7IOjweD/7+/oKfly9fDnt7e6xcuRLTpk1DYGAgjh49ymBC8aKl14SQL8rKysLQoUOxbds22i8uAVVVVbhw4QISEhKQmJiI9PR0VFZWQk1NDTY2NrQ0khDyXXJycsDhcNCqVasa1x8/fgw+n4/WrVszlIyd+vbtiyZNmiA+Pr5GQyM+nw8HBweUlJQgLS2NwYTsc//+fTx9+hT9+/dHVVUVpk+fjvDwcHA4HDg5OWHTpk10WoeYKSsr49ixY7C0tMTTp0/RsmVLpKSkoH///oiJiYGfnx/y8vKYjik2VCiTOuv58+dYt24dkpOT8eLFCxw5cgRdunTB9u3bYW5ujm7dujEdscEICgrC9u3bceHCBaajsN7FixexePFinDp1ChwOB1VVVUxHIoTUQ1wuF3JycqioqKhxXU5ODnw+n5a8i5mysjL27dsHZ2dnobHY2Fh4enrizZs3DCQjRHxUVVWxf/9+2Nvb4+DBgxg/fjyKi4shJyeH1NRU2NvbC51nXZ/R0uvv9PjxY9y6dQs9evSAhoYG03FY5+HDh+jbty+Ki4thYmKCBw8eCN7sb9y4gfT0dAQHBzOcsuHQ19fHzZs3mY7BSvn5+UhISMDp06eRmJiIvLw8tGrVCl5eXrCzs2M6HiE/7P79+wgICEBiYqKgwZGdnR0WL17MqvM265px48ZBVlb4Y964ceNoy5gEyMjI4N27dyLH3r9/z7pjc0jD1LFjR+zduxd9+vTB7t270bdvX8GpP0+ePEGzZs0YTiheNKNcCwsXLkRpaSnWr18PAEhISICzszPevXsHVVVVpKamokuXLgynZBd3d3fcunULCQkJaN68eY3z8SIjI+Hv74+srCymYzYYU6ZMwalTp3Dv3j2mo7CKkZERbt++DTU1NVhZWcHOzg62trbUJZWwxp07d9C7d2+Ul5fDxsYGurq6ePr0KZKSkqCkpIS0tDR07NiR6ZiE/DBbW1u8efMGycnJaNSokeB6RUUFrKys0LhxY5w+fZrBhOywZ88eODo6QkNDA3v27Pnq/ePGjZNCqobj4MGDGDVqlODnI0eOCFZR+Pj4oKCgAHFxcUzFEzsqlGuhU6dOmDFjBiZOnAgA6NWrF/h8PhYsWICAgAAYGhoiMjKS4ZTsoq6ujq1bt2LkyJGoqqqCnJycoFBOTk6Gk5MTLWESM29vb6FrFRUVuHHjBm7fvo3Vq1dj5syZDCRjLy6Xi0aNGmHs2LEYNGgQbGxsaD8VYRUXFxfcuHEDycnJaNmypeD6kydPYGNjAyMjI0RFRTGYkBDxOHv2LGxtbaGurg53d3doa2sjLy8P0dHRKCwsRFJSEvr06cN0zHqPy+UiPT0d5ubmX52lp+1LkpGeno5z586hR48e6N+/v+C6v78/evbsicGDBzOYTrxo6XUt5Obmol27dgCAwsJCZGRk4NixY7C3t0d5eTkVDxJQXl4OdXV1kWOlpaW0hEkCkpKShDrVKioqQl9fH/PmzYOnpydDydjr0qVLSEhIQEJCAjw9PVFZWYmffvoJAwYMwIABA9C7d2/IyMgwHZOQ75aSkoJNmzbVKJKBD0fmLF68GL///jtDyRqGJ0+eIDU1FYWFhdDQ0ICFhYXQ/wsiHv369cOpU6cwd+5cbN68GXw+H1wuFz179kRkZCQVyWLy8OFD6OjoCL4n0terVy/06tVL6PqSJUsYSCNZVCjXwqdHQKWlpUFGRgYWFhYAPhzv8vEMNyI+hoaGSEhIEJyH96nU1FR07dqVgVTs9ujRI6YjNDhmZmYwMzPD7NmzUVFRgbNnz+L06dOIi4vDsmXL0LhxYxQXFzMdk5Dv9vbt28/28dDU1GRV05e6pLq6GtOmTcPWrVtrzKjJyMjAz88PGzdupAfOEmBpaYnz58/j7du3ePnyJdTU1KCkpMR0LFbR09MT+T0hkkCvkrXQtm1bwXr7ffv2wdzcXLD/JC8vD2pqakzGY6WJEydi48aN2LhxI16+fAkAePfuHQ4dOoQtW7bgl19+YTghIeKVn5+PR48eITs7W3B8S2lpKdOxCPkhhoaGCA8PFzkWGRlJ+5MlJCAgAP/88w+8vb3B4/GQmZkJHo8HLy8vbN68GQEBAUxHZB1vb2/BDKeSkhJatGghKJKzs7NFbm8ipD5o06YNrl+/DgAwMDBAmzZtPvvFtgaNtEe5FrZu3YopU6ZATU0Nr169QlBQEMaPHw8AmDp1qqDpFBGvn3/+Gbt27QKXy0V1dTW4XC74fD4mTpyIbdu2MR2PFVJTU7/p/o8rKYh4REVFCZZeP3jwAHw+Hx06dBA09bKxsUHTpk2ZjknIdwsKCoKvry+cnJwwfvx46OjoIC8vD2FhYYiNjcWuXbvg5eXFdEzWadmyJdzd3QVNSD81bdo0HDp0CE+ePGEgGXt9unf2f12+fBnm5ua0X1YCwsLCEBERgezsbKEVKhwOB/fv32coGXt4eXlh8eLFMDAwwIQJE4S26f0vNp1KQ4VyLe3btw/nzp2Dubk5xowZI7j+888/w8LCosY1Ij7p6emIj49HQUEBNDU14eTkRPt8xIjL5Qpe8Ph8/ldf/OhNXry4XC50dHRga2sLW1tb2NnZoUWLFkzHIkSsVqxYgWXLlqG8vBzAh9eaRo0aYdGiRZg7dy7D6dhJSUkJsbGxIo+YO336NIYOHYq3b98ykIy9vlQo0+9cMlatWoV58+ahc+fOMDY2hoKCgtA9bCraiPRRoUzqDDMzM+zduxddunSp0f6fSE5KSorg+5KSEvz6668wNDSEp6cntLW1kZ+fj/DwcNy9exebN2+Gk5MTg2nZ5/bt2+jcuTPTMQiRuOLiYpw/f17QVKp37960WkKCevfujbFjx2Ly5MlCY5s3b0ZoaCguXrzIQDJ2SU1NRXJyMoAPy919fX2FmqWVlZUhJiYGSkpKuHTpEgMp2att27YYPHgw/v77b6ajsJqpqSl8fX0xevToBrfdlArlb0DdIyVLVlYWaWlp6NmzJ2RkZHD+/HmRT2aJZEyePBlv3rwReS7h2LFjoaSkhO3btzOQjF26desGNzc3uLi4oFOnTkzHIYSw0NmzZ+Hh4YFt27bB0dFRcP3o0aOYPHkyIiIiahzrQr7PkiVLBJ1+ORwOPveRWk9PD0FBQbC2tpZmPNZTVlbG0aNHYWNjw3QUVmvdujWePHkCBQUFDBs2DD4+PiJXq7ARFcq1QN0jpUNXVxeLFi3CpEmTwOVyceHCBfTo0YPpWA1Gs2bNEBYWBnt7e6GxEydOYOzYsXj+/DkDydhlxowZOHz4MHJycmBoaAhXV1e4urrC1NSU6WiEiF1wcDCys7NFNo8KCAhAmzZtMG7cOOkHYzkjIyPk5+ejqKgIKioq0NLSQkFBAUpKSqChoQFtbW3BvRwOR9Coh3ybsrIyvH37Fnw+H82bN8fJkydhZmZW4x4FBQU0btyYoYTsZmFhgbFjx2LixIlMR2E1Pp+P06dPIygoCLGxsaioqECrVq3g5eWFCRMmsLr7OBXKtbB48WIsW7YMEydOFFqSumvXLixcuBBLly5lOma95+fnhx07dkBJSQllZWVo1KjRZx9AcDgcOjZHzJSVlbFt2zaMHTtWaCw0NBSTJ0+mLsxilJGRgaioKERHR+O///6DgYEBXF1d4ebmRispCGt069YNEyZMwNSpU4XG/vnnH4SEhNByVAmwsrL6as+JT/F4PAmmaRiys7Oho6MDeXl5pqM0GJcuXcKYMWMQHh6O7t27Mx2nQSguLkZ4eDiCgoJw5coVcLlc2NrawtvbG8OHD2fd3z8VyrVA3SOl4/3799iyZQtu3ryJoKAgODg4oFmzZp+9nxo0iNegQYNw+/ZtHDt2rMY51f/++y8GDx6MLl264MSJEwwmZK/r168jKioKUVFRyMzMRIsWLeDi4gI3Nzf069fvmz7wElKXqKio4MiRI7C1tRUaS0pKwvDhw+mhJyGk1oyNjWv8nJeXh6KiImhrawv1taHVEpL18fN6eHg4Xrx4AVVVVYwePRqbNm1iOprYUKFcC9Q9Uvq+1D2SSMbdu3dhYWGBwsJCdOnSRbBy4tatW9DQ0EBqaioMDQ2Zjsl6mZmZgqL5+vXr0NLSQl5eHtOxCPkuKioqCA0NhYuLi9DY4cOHMWbMGFqpIgFfaohZVFSEuLg4WvIuBjY2NtiyZQs6duz41X2yHA4HiYmJUkrGXrRaou6prKzEwoULsWbNGgDsOiFFlukA9YGJiQmysrJEFspZWVk1Zt/I91NXV0dCQgLMzMzg7+9PjdKkzNDQEP/++y/WrVuH5ORkPHjwABoaGvjjjz8wbdo0aGlpMR2xQejUqRMWLlyIhQsX4v79+4iOjmY6EiHfzcjICPv27RNZKEdGRsLIyIiBVOzn5eWF8+fPiyyUHz58CC8vLyqUxeDTuabq6uovFnA0LyUeH7uME+Y9f/4ce/fuRXBwMG7fvg1ZWVnWnY5CM8q1QN0jpUNOTg5nzpxBr169qOs1IYSwQEREBMaMGSM4qqhly5Z48uQJtm7dir1792LPnj0YPXo00zFZ50urstLS0mBtbY13794xkIwQ8aGVE9JXXV2N+Ph4BAUF4dixY3j//j06deoEb29vjBs37otbJusjKpRrgbpHSoeenh4mTJiA3377Dc2bN8epU6eEukd+Sl1dXYrpGo6SkhLBeafNmjVDz549oaKiwnQsVjIwMPjsDASXy4Wqqip69OiB33//nY6SIvXW4sWLsWLFClRXVwuucblczJ8/X3C0DvlxOTk5ePToEYAPy1O3bt0q9LpRVlaGHTt24Pr16/jvv/8YSEmI+HxpUuXy5cswNzdn1TJgJt25cwdBQUHYu3cvnj17BmVlZYwYMQI+Pj7o3bs30/EkhgrlWqD9ENKxcOFCBAYG1vp3TS9+4rd27VosWbJEcNwF8KEb9pIlSzBjxgyG07HPhAkTkJKSgqdPn6Jv377Q0tJCfn4+zp07B11dXZiamuLcuXN48+YNUlJS8NNPPzEdmZDv8ujRI5w+fRrPnz9Hs2bNMHDgQFYfKcKEj2f61mb578aNG/Hbb79JK1qD8uzZM2RnZ6OsrExozMLCgoFE7EUrJ6SjT58+uHDhAvh8Pnr37g0fHx+MHDkSysrKTEeTONqjXAu0H0I6li1bBnNzc/z7779YtGgRfHx8aJ+yFO3ZswezZ8+Gg4MDJkyYAF1dXTx9+hShoaH4448/0KxZM5FHR5HvZ29vj/T0dPz3339o1aqV4HpOTg4GDhyIYcOGISQkBFZWVvD390d8fDyDaQmpnY0bN8LV1bXG67e+vj6ddSphI0aMQNeuXcHn8zFixAgEBgaiffv2Ne5RUFBA165doa+vz0xIFsvLy8PYsWNFTpbw+XxwOBx6wC8Gn66cAICrV6+ivLy8xj0fV060bt1ayunY6cGDB5gxYwZ8fHzQsWNHpuNIFc0o1wLtgZA+AwMDHDlyBCYmJkxHaTC6deuGLl26ICwsTGhszJgxuH37Nq5cucJAMvbq2rUrFi5ciFGjRgmNRURE4M8//0RmZib27NmDadOmoaioiIGUhHwbLS0tvHjxAj/99BPc3Nzg4uKCtm3bMh2rQQkNDYWTk5PIzy1EMlxdXZGcnIx58+bB2NgYCgoKQvdYWloykIxdaOWE9FVWVkJWtmHOrVKhXAu0B4I0BI0aNcLhw4cxaNAgobETJ05g+PDhIpeSke+nqKiIw4cPw8HBQWjs+PHjcHFxQVlZGVJTUzFw4EChp+aE1EXV1dVISUlBVFQUDh8+jPz8fBgZGQmK5s6dOzMdkfU+Pbbof2VlZcHPzw9JSUkMJGMvTU1NrFmzBl5eXkxHYbXMzEzcvn2bVk4w4NWrVzhx4gRu3ryJwsJCcDgcqKuro2vXrhg0aBBUVVWZjih2DfPxwDf60rOE8vJyyMjISDENe+Xk5EBHRwdycnLIycn56v20pEa8GjVq9NkZy6KiIjRq1EjKidhPT08PISEhIgvloKAgwd94YWEhNa8j9QaXy4W1tTWsra3xzz//IC0tDYcOHcKuXbvg7++PDh06wNXVFa6urujWrRvTcVkpOTkZr1+/FjlWUlKClJQUKSdiPw6HU2MLDZGMTp06CZrUBQcH08oJKVm5ciWWLVuGt2/fAgDk5eUBQLAHXElJCYsWLcKcOXMYyygJVCh/Bu2BkD4DAwPBzL2+vv5Xm3rRLL549e/fHwEBAbCysoKurq7gen5+PpYuXUpNSCRg1qxZ+OWXX/DkyRO4u7sLOuofOHAAFy5cwI4dOwB8aBBIjbxIfdW3b1/07dsX69evx8WLFxEVFYX9+/djxYoV0NPTg5ubG1avXs10zAYjLy8PSkpKTMdgHXd3d8TFxcHOzo7pKA1GaGgoevbsKbJQppUT4vPPP/9gwYIF8PX1hZeXF4yNjQWvIW/fvsW///6L4OBgLFiwACoqKpg8eTLDicWHll5/Bu2BkL5P91SFhIR8tVAeP368lJI1DDdv3kSfPn1QWVkJW1tb6OjoIC8vD0lJSZCTk8O5c+doyaQE7NixAwEBAcjPzxdc09bWxpIlSwTNjwoKCqCgoMDKZU2k4bp27RqioqIQHR2NW7duMR2n3ouJiUFMTAwAICQkBI6OjkJnmpaVlSE5ORnt27dHamoqEzFZKzk5GRMnToSDgwOcnZ1FFm9fOvKSfLsvdb2mrZHi06lTJ7i4uGD58uVfvG/BggWIjo5GZmamlJJJHhXKn0F7IEhDlJWVBX9/f/B4PBQWFkJDQwO2traC5ZJEfKqqqnD//n00b94cTZs2xd27dwW/c0NDw286ko6Q+qC8vBwvX76EmpoaFBUVmY7DOitWrEBgYCCAD7M8ioqK4HK5Ne5RUFCAkZERNm7cCGNjYyZistanv+v/ff2mrteS8aVCOS4uDh4eHigpKWEgGbsoKiri1KlTX11ZmJqaCnt7e1b1s6Gl159BeyDqjrKyMhQVFUFLS6vBdt2TtPLycixduhSurq6IjIxkOk6DwOfz0blzZxw9ehQODg4N7sgF0nCcO3cOc+bMQXp6Oqqrq8HlctGnTx+sXLkSvXv3Zjoea8ybNw/z5s0D8KGA4PF4IgsIIhnBwcFMR2gQPl05AQB//vnnZ1dOUA8E8dDR0cGlS5e+WihnZGRAW1tbSqmkg6qOWqA9EMzg8XiYP38+MjIyAAAXL16EmZkZpkyZAltbW7i4uDCckD0UFRWxfv16kR2viWTIyspCW1sb1dXVTEchRGLS09NhY2MDVVVV/Pzzz9DV1UVubi6io6NhY2OD5ORk9OzZk+mYrPPp68rz589FzvBQfxXxou1g0nH79m0cPHgQwIeZ+6SkpC+unCA/bsKECVi4cCEqKysxYcIENG/evMb4s2fPEBoaCn9/f8ydO5ehlJJBS69rgfZASF9SUhLs7e3RtWtX2NvbY/Xq1bh06RLMzMzg7++Pq1evIjY2lumYrGJmZoapU6fSm70UzZo1C48fP8b+/fuZjkKIRAwcOBCvXr0Cj8eDsrKy4HppaSmsra2hpqaGkydPMpiQnUpKSjB9+nRERkZ+9lg5+txC6rsvfT4n4lNZWYlJkyYhKCgIANCsWTOoq6uDw+GgsLAQz58/BwD4+Phg69atrDoNiGaUfxB1j5SMxYsXY/DgwYiJiUFlZWWNjqgmJia0xEkCFi1ahNmzZ6Nfv35o27Yt03EaBFNTU+zfvx82NjZwcXGBjo6O0N42WjlB6rP09HQEBQXVKJIBQFlZGX/88Qd8fHwYSsZu06ZNQ0REBHx8fGBsbAwFBQWmI7Get7f3Z8e4XC5UVVXRo0cPDB8+XHC0DvkxtCJLOmRlZbFz505Mnz4dhw8fxq1bt1BYWAjgw2dyIyMjDB06lJUNX6lQ/gzaA8Gsq1ev1lha86lmzZrh2bNnTMRiteDgYLx9+xadOnWCsbGxUNHG4XBq/JsgP27cuHEAgNzcXCQnJwuNU/MXUt9VVVV9tkhTVFSkv28JiY+Px8qVKzF16lSmozQYPB4PxcXFePXqFWRlZaGhoYHCwkJUVlZCVVUVfD4f69atg6GhIZKTk6GlpcV0ZEK+SefOnVlZDH8JFcqfQXsgmCUrK4v379+LHHv27BlUVFSknIj9bty4AXl5ebRo0QKFhYWCp4VEcng8HtMRCJEoExMTbN26Fc7OzkJj27dvh4mJCQOp2K+8vBxGRkZMx2hQoqKiMHz4cGzduhVubm6QkZFBVVUVDh48iDlz5uDgwYOorKyEi4sL5s+fj927dzMdmRXCwsKwYcMGZGZmitxmQA/jyI+gPcq1QHsgpM/Ozg6qqqo4dOgQqqqqICcnJ9ij7OHhgdLSUtqjTAghdVxsbCyGDRsGExMTjBkzRnA+e0REBK5du4YjR46ILKLJj/H09IShoSH8/f2ZjtJgWFlZwdXVFb/99pvQ2MaNG3Hw4EGcPXsW69evx9q1a5Gbm8tASnaJjY2Fq6srJkyYgN27d8Pb2xvl5eWIjY2Frq4uPDw86N+AGJ07dw6PHz9Gly5d0LVrV6Hx3Nxc7N69G4sXL2YgnWRQofyNqHukdCQkJMDe3h5DhgzBuHHj4Obmhn/++Qe3bt3C9u3bwePx0K9fP6ZjskpqairMzMzQuHFjobHS0lJcvnz5q0cDkO9TUlKC8+fPo7CwEJqamujVqxetmiCsERERgdmzZ+Pp06eCay1atMDq1avh4eHBYDJ2KSoqEnyfn58PNzc3+Pr6wtnZWeSpHerq6tKMx3rKysqIjY2Fra2t0FhiYiKGDBmC0tJS8Hg8DBo0CBUVFQykZJc+ffqgf//+CAwMrDGhkp+fj/79+2PevHlf3DtOaqeiogLOzs5ITEwUnAnu4uKCnTt3QlVVVXDfhQsX0KdPH3bN4vPJV71+/Zrv4+PDV1JS4nO5XJFfRPz27t3L19DQ4HM4HMGXmpoaPywsjOlorMTlcvkXLlwQOXbp0iX6O5eQNWvW8Bs3bszncrmCv/PGjRvz//rrL6ajESI21dXV/MzMTP7Zs2f5mZmZ/OrqaqYjsQ6Hw6nxueR/f6bPLZKlra3NX7Bggcix+fPn87W1tfl8Pp8fHx/P19DQkGY01lJXV+efOnWKX1VVxedwODU+w4SEhPC7dOnCYDr2WLVqFb9Ro0b8tWvX8lNTU/n+/v58JSUlfqdOnfhPnz4V3Jeens661xbao1wL1D1SOl6/fg0VFRVBA6kxY8bA1dUV586dQ0FBATQ1NdG3b1+h7qlEPPhfWFzy/v17oT365Mft2bMHs2fPhoODAyZMmABdXV08ffoUoaGh+OOPP9CsWTOMHTuW6ZiE/DAOh4OOHTsyHYPVFi9eLNT8kkiPp6cnVq1aBT6fD3d3d2hpaaGgoAD79+/HX3/9JWisdvnyZXTq1InhtOxQVVUFeXl5cLlcKCsrIz8/XzDWunVrPHjwgMF07BEWFoZ58+Zh5syZAID+/fvDxcUFTk5OsLCwAI/HQ8uWLRlOKRm09LoWtLW1MW/ePOoeKWEyMjI4f/48zM3N4e3tjUWLFsHAwIDpWKz2+vVrvHr1CgCgr6+PI0eOwNTUtMY9ZWVl2LBhA+Li4vD48WPph2Sxbt26oUuXLggLCxMaGzNmDG7fvo0rV64wkIyQ7/fpNo7U1NSv3k9bOggbvHv3DhMmTMC+fftqPLDg8/nw8PBASEgI5OTkkJCQgCZNmlDfGzHo1q0bZs+eDQ8PD/Tr1w+tWrVCZGQkAGDKlCk4ceIE7t+/z3DK+k9FRQVHjx6FlZVVjeuPHj2CtbU1uFwukpKSkJ+fz7ql1zSjXAvUPVI6ZGVlBf+4QkJC4OfnR4WyhK1fvx5Lly4F8GHGZ/jw4SLv4/P5mD9/vjSjNQh37tzBihUrRI6NGTPms/8/CKnLrKysBA0wraysPjvLyf//e93Y9KGKNFzy8vKIiIjAokWLkJycjKKiImhoaMDCwqLGkTp2dnYMpmQXW1tbJCQkwMPDA1OnTsXIkSORkZEBeXl53L17FytXrmQ6Iis0btwYb968Ebqur6+P5ORkWFlZwcrKCoGBgQykkywqlGth8ODBOHPmDGxsbJiOwmqtW7dGaGgo5OTkAAB3796FrOzn/0TNzMykFY21Bg4ciMaNG4PP52P27Nn47bffhBrTfTwGzdLSkqGU7NWoUaMaDXg+VVRUhEaNGkk5ESE/jsfjCQoDOgKNNDSdOnWipdVSsnz5ckFTNHd3d8jIyCA8PBwcDgezZ8/GhAkTmA3IEh07dkRKSgqcnJyExvT09ATFso+PDwPpJIuWXn8GdY+Uvr///htTp0796h4rmoWQjCVLlmDixInQ1dVlOkqDMXToUGRmZiI5ObnG7z0/Px9WVlbo2LEjjhw5wlxAQgghtfb+/Xvs2bMHiYmJglMM7OzsMGbMGMEkACH1zfLly7Fp0yY8evTosw/wc3JyYG1tjUePHrHq8zkVyp/B5XKF9ph8qYBj0x8Fk65fv46bN29i7NixWLhwIdq2bfvZe8ePHy/FZISI361bt9CnTx+8f/8etra2gjNmk5KSICcnh3PnztVYskcIIaRuKi4uhq2tLa5cuQJlZWVoa2sjPz8fpaWl6N69OxITE9GkSROmYxLyzUpKSpCTk4M2bdp8caXb8+fPcfv2bVatQKRC+TMCAgK+qXskHWguXtbW1ti6dSt1SZWyR48e4cCBA8jOzhY6L5zD4WD37t0MJWOvrKws+Pv7g8fjobCwEBoaGrC1tYW/vz86dOjAdDxCJCYsLAzV1dUYN24c01EI+WG//vorQkNDERQUBHd3d8H1Q4cOwdvbG+PHj8fff//NYEJ2OnLkCMLDw5GdnY3y8vIaYxwOB9evX2coGWEDKpQJIQCA+Ph4uLi4oKqqCs2bNxc6Bo3D4dBRC4QQsZGTk0N1dTWtyCKs0LJlS8ycORPTp08XGlu3bh3WrVuHJ0+eMJCMvdasWYM5c+agWbNmaNeuHeTl5YXuoT4JkpWSkoJjx46Bz+fDwcEB1tbWTEcSKyqUSZ316NEjrFixosZMm7W1NebOnUvdsCXA1NQU6urq2LdvH5o3b850HEIIy+3Zswd8Pp+20RBWUFBQwLFjx2Brays0lpiYCEdHR6EZT/JjDAwMYGtri+3bt0NGRobpOA3O9u3bMWvWLAwcOBBv3rxBUlIS1q9fj19//ZXpaGJDhTKpk65duwZra2u8ffsWffr0Eez1OXfuHBo1aoTk5GSh837Jj1FWVkZ0dDTs7e2ZjkJAy1IJIaQ+adOmDcaPHy9yK97SpUsREhJCq7LErEmTJjhy5AidSsOQ9u3bIzIyEj/99BMAYOPGjVi/fj0ePXrEbDAx4jIdgBBRpk2bhmbNmuHevXvg8XiIjIwEj8dDVlYWmjdvLnJpE/kxenp6Is/JI8zw8vKCl5cX0zEIIYTUwsiRIxEYGIh169ahsLAQAFBYWIiNGzciMDAQo0aNYjgh+/Tt2xeZmZlMx2A9a2tr3Lt3T+h6cXFxjV5CHTp0wOvXr6UZTeJoRpnUSUpKSti9ezc8PDyExiIiIuDr64u3b98ykIy9wsLCsHnzZiQmJkJJSYnpOA0eLUslbEHbaEhDUFFRgaFDh+LUqVPgcDiQlZVFZWUl+Hw+7O3tERMTI3IPLfl+d+/exfDhw7Fy5UoMGjSIfr8SMmXKFISEhGDu3LmYO3eu4Kiz0aNH49WrV5g8eTJKS0uxdOlSdOnSBfv372c4sfhQoUzqJB0dHezevRuDBw8WGjt27Bi8vb2Rn5/PQDL2+v333xEfH4+ysjJYW1sLnRfO4XCwceNGhtIRQuoj2kZDGpqTJ08KnWIwYMAApmOxUlVVFaZPn47NmzeDw+EIPeTncDgoLi5mKB27pKen45dffsH79++xc+dO9O3bFy9fvsTvv/+OEydOCB4Ibdy4EZqamkzHFRsqlEmdNHPmTDx8+BDR0dFCY8OHD4e+vj7Wr1/PQDL24nK/vBODw+FQd1pCyDexsrLC06dPkZCQgNatWwuuZ2dnY8CAAWjRogV1pSWEfJeZM2di/fr1MDU1RadOnUTOKAcHBzOQjJ0qKyuxZs0aLF++HKNHj8aaNWtYfzY4FcqkTgoPD8f8+fOhpaUFDw8PwSxEZGQknj17hsDAQCgqKgrud3FxYTAtId+voqICCQkJnz0Dkvbjk/qMttGQhiI9PR05OTkYMWKE0NiBAwegp6eHnj17MpCMvTQ0NPDzzz9jxYoVTEdpUO7fvw8/Pz/cvHkTGzduFPk3zxZUKJM6qTazmx//dGmmk9RXly9fhrOzMwoKCiDqpZj+tkl9R9toSENhY2ODvn374s8//xQaCwgIQFpaGk6fPs1AMvZSVVVFdHQ0db2WAj6fj3v37qG8vByGhoZQUFDA3r17MWvWLPz000/YunVrjVVDbCHLdABCRKGleKQhmDx5Mpo0aYJt27Z9dtkYIfWZp6cndu3aJbJQ3rlzp8iZZkLqoxs3bmDmzJkix3r27IktW7ZIORH7DRw4EOnp6VQoS1hmZiZcXV1x584dAICmpiaCg4MxduxYDB48GDNnzkSXLl2wZMkSTJs27auTXfUJFcqkTrK0tGQ6QoPQpk0bHD58GCYmJjAwMACHw/nsvRwOB/fv35diOva7desWIiIiMGTIEKajECI2n/aW6N69Ow4dOgRzc3OR22jc3d0ZTEqI+JSWlkJWVvTHai6Xi5KSEiknYr9FixZh5MiRUFZWhqOjI9TV1YXuEXWNfBs/Pz+0a9cOiYmJUFZWRmBgIMaOHYuCggJoaGggJCQE48ePh5+fH8LDw3H58mWmI4sNLb0mpAHz8vLC4sWLYWBggAkTJnyxUAaoKYa4de7cGStXrqRCmbAKl8sVbI/5dJuMKLS9gLBF586dMWTIEKxcuVJobO7cuThy5IhgRo6Ix8eZyy99dqHXlx/XpEkTHD58GLa2tgA+nJ+spqaGO3fuoEOHDoL7KioqsHz5cixdupSpqGJHM8qkzgoLC0NERASys7NRVlZWY4xmN8Xj08I3JCSEuSAN1OzZs7F27VrY29tDQUGB6TiEiAVtnSEN0ahRo7B8+XIYGhrCy8tLcD0kJAQbNmzAvHnzGEzHTosXL/7qA37y49q1a4dDhw7B2toaXC4XkZGRkJeXR6tWrWrcp6CgwKoiGaAZZVJHrVq1CvPmzUPnzp1hbGwssoig2c0fN336dLi5uaFv375MR2mwAgICsGvXLlhZWQktEaOzqwkhpH549+4dBg0ahOTkZDRq1Ai6urp4+vQpysvLYWVlhePHj1MfClIvpaamwsXFBe/evYOCggJKSkqwceNG/PLLL0xHkzgqlEmd1LZtWwwePBh///0301FYzczMDNeuXYOWlhaGDx8ONzc3WFlZsaoRQ10WHx8PFxcXvH//XuQ4LUslhJD6o6qqChEREThx4gSeP3+OZs2awcHBAR4eHpCRkWE6Hit069YNbm5ucHFxQadOnZiO02AUFxfj/PnzKC8vx08//YSWLVsyHUkqqFAmdZKysjKOHj1KnQyl4NGjRzh48CCio6Nx8eJFqKmpYejQoXBzc4OdnR3k5OSYjshanTt3RtOmTbF9+3Z06tSJfteEdb72Gs7hcJCYmCilNISQ+m7GjBk4fPgwcnJyYGhoCFdXV7i6usLU1JTpaISFqFAmdZKFhQXGjh2LiRMnMh2lQcnNzcWhQ4cQHR2NtLQ0NG7cGE5OTnBzc8OgQYOgqKjIdERWUVJSwuHDh2Fvb890FEIkwsrKSmgP4YsXL3D37l00b94cHTp0QFJSEkPpCJEMPp+PP//8Ez///DO0tbWZjsNKGRkZiIqKQnR0NP777z8YGBjA1dUVbm5uMDc3Zzoeq/H5fGRkZIjsIQQA48aNYyCVZFChTOqkS5cuYcyYMQgPD0f37t2ZjtMgFRQUIDo6GtHR0UhJSYGCggIcHBxw4MABpqOxhpmZGebNm0dH5JAGJysrC0OHDsW2bdvoOEDCOlVVVZCXl0dGRgbMzMyYjsN6169fR1RUFKKiopCZmYkWLVrAxcUFbm5u6NevHzX8EqOsrCwMGTIE9+7dE3miAdu2jFGhTOoMY2PjGj/n5eWhqKgI2tra0NDQqDHG4XBw/fp1acZr0IqKinD48GFER0cjPj6e6TisER8fj/nz5+PkyZM060AanKCgIGzfvh0XLlxgOgohYlVVVQU5OTlcunSJCmUpy8zMFBTN169fh5aWFvLy8piOxRoDBgzAvXv3sHbt2s8229XT02MgmWRQoUzqDFFL9L6EjiAh9d2QIUNw7do1FBUVwdTUVGTX65iYGIbSESJZSUlJcHZ2RmlpKdNRCBErKpTrhvv37yM6Ohp//PEH01FYo2nTpti5cydGjBjBdBSpoHOUSZ2RnJzMdIQGzdvb+7NjXC4Xqqqq6NGjB4YPH05HXIjJjRs3ICMjg2bNmiE3Nxe5ubk1xmm5GGGzqKgo6OrqMh2DELHjcDjQ09MTOdtGpKdt27ZUJItZ48aN0aRJE6ZjSA3NKJM6g870ZZaBgQGKi4vx6tUryMrKQkNDA4WFhaisrISqqir4fD6Ki4thaGiI5ORkaGlpMR2ZEFLHiXoAV1FRgRs3buD27dtYvXo1Zs6cyUAyQkh9V1VVhb///hvh4eHIzs5GeXl5jXEOh4Pi4mKG0rHTwoULkZOTgz179jAdRSqoUCZ1Bp3py6wrV65g+PDhWL16Ndzc3CAjI4OqqiocPHgQc+bMwcGDB1FZWQkXFxc4Ojpi9+7dTEcmhNRx+vr6QisjFBUVoa+vj7Fjx8LT05OhZIRI3oULF3D16lVYWlrSmb8S8Mcff+Cvv/6CqakpOnXqJHK1W3BwMAPJ2Gvfvn1YsGABunTpAmdnZ6EeQgDg4uLCQDLJoEKZ1Cl0pi9zrKys4Orqit9++01obOPGjTh48CDOnj2L9evXY+3atULLhMn3ef/+Pfbs2YPExEQUFhZCU1MTdnZ2GDNmDP29E0JIPeHr64vKykqEhIQA+FBQjB49Gnw+H/Ly8uDxeOjduzezIVlGV1cXEyZMQGBgINNRGoyvTV5R12tCpITO9JUuZWVlxMbGwtbWVmgsMTERQ4YMQWlpKXg8HgYNGoSKigoGUrJLcXExbG1tceXKFSgrK0NbWxv5+fkoLS1F9+7dkZiY2KD2AhFCSH3Vtm1b+Pv7C86QNTIyQqtWrbBy5UpMmzYNysrKOHr0KMMp2UVNTQ1RUVGwsbFhOkqDkZKS8tV72HTkHzXzInVWixYtMHXqVEydOrXGmb4jRoygM30loEmTJuDxeCIL5aSkJEHBVlZWBhUVFWnHY6UFCxbg7t272L9/f42zlA8dOgRvb28sWLAAf//9N4MJCflxfD4fGRkZyM7ORllZmdD4x8KCkPosPz9fcCzO06dPcevWLWzZsgXGxsaYOnUq/Pz8GE7IPgMHDsSFCxeoUJYiNhXBtUGFMqkXtLS0MGnSJEyaNKnGmb5EfDw9PbFq1Srw+Xy4u7tDS0sLBQUF2L9/P/766y9MnToVAHD58mXaayUmR44cwdKlS2sUyQDg5uaGnJwcrFu3jgplUq9lZWVhyJAhuHfvHkQtYONwOFQoE1aQk5MTNJNKS0uDoqIievXqBeDDzOerV68YTMdOmzZtgqOjI5SVlTF48GChIxYBiLxGSG3R0mtS5929excvXryAqakplJWVmY7DWu/evcOECROwb9++Gs13+Hw+PDw8EBISAjk5OSQkJKBJkyYwNzdnMC07KCgo4NixY59d7u7o6CjUxZOQ+mTAgAG4d+8e1q5dC2NjY5HH5XychSOkPuvVqxfatWuHrVu3wt3dHVVVVTh9+jQAICIiAnPnzkVOTg7DKdmlrKwMvr6+2Ldv32fvYdN+WaZ4e3tj0aJFMDAw+OJRosCHh59savZKM8qkztqzZw/mz5+PvLw8AEBGRgbMzMwwYsQIDBgwABMnTmQ4IbvIy8sjIiICixYtQkpKCgoLC6GhoQELCwt07txZcJ+dnR2DKdmlRYsWOHv2rMhCOS0tjc6YJfXexYsXsXPnTri5uTEdhRCJmjlzJkaNGoXIyEgAH1YMfZSYmAhjY2OGkrGXn58fDhw4gKFDh3626zX5cTweT7CqMCkpSegkg099aaw+okKZ1EkHDx7EhAkT4OTkBAcHB0yZMkUwZmZmhgMHDlChLCGdOnWipdVSMnLkSAQGBkJFRQXjx48XnF0dFhaGwMBAzJgxg+mIhPyQxo0bU0M60iC4u7ujZcuWOH/+PHr06IH+/fsLxlq2bAlXV1cG07HTkSNHsGLFCsyaNYvpKKz28OFDwfePHj1iLggDaOk1qZPMzMzQrVs37N69G1VVVZCTk8OlS5dgZmaGmJgYTJ48mY4nkpBnz559tumOhYUFA4nYq6KiAkOHDsWpU6fA4XAgKyuLyspK8Pl82NvbIyYmhp6Qk3pt4cKFyMnJwZ49e5iOQghhGU1NTezfv1/kqixCxIFmlEmdlJmZiVWrVokcU1dXR2FhoZQTsV9eXh7Gjh0LHo8HAILGOxwOB3w+n3Vn49UFCgoKOHHiBE6ePAkejydY7m5ra4sBAwYwHY+Q7/Jpo0UjIyNERERgyJAhcHZ2hoaGhtD9Li4u0oxHiES4u7tjwIABsLW1Rdu2bZmO0yC4uLjg5MmTVCgTiaFCmdRJSkpKKC4uFjmWm5sLNTU1KSdiv19//RVXr17FqlWrPtt0h0iGvb097O3tmY5BiFi4ubnVeMDG5/Px6NEjxMXFCd1LD+AIW9y9e1fwkKh169aws7ODnZ0dbG1toampyXA6dvLw8MDEiRPx/v17ODo6iuxwbWZmxkAydgsLC8OGDRuQmZkpsuEom17Taek1qZOGDBmC169fg8fjobq6usbS60GDBkFNTU3QMIOIh6amJtasWQMvLy+mozQY6enpyMnJwYgRI4TGDhw4AD09PfTs2ZOBZIR8v5SUlG+6v6Gdy0nY6/nz5zh9+jQSEhKQlJSEnJwccLlcGBkZYcCAAVi9ejXTEVmFy+UKvv/fJlK0Ek4yYmNj4erqigkTJmD37t3w9vZGeXk5YmNjoaurCw8PD/j7+zMdU2yoUCZ10qVLl9CvXz8YGRnB09MTs2bNwrx583D9+nUkJibi4sWL6Nq1K9MxWaVZs2aIjIykrtZSZGNjg759++LPP/8UGgsICEBaWprgeBFCCCH1y6VLl7Bw4UJBHwoq2sQrNDT0q/eMHz9eCkkajj59+qB///4IDAysMYmVn5+P/v37Y968eV89Qqo+oUKZ1Fk8Hg+TJ0/G3bt3Bdfat2+P7du3w8rKirlgLDV58mTIy8tjw4YNTEdpMDQ1NREaGgpHR0ehsePHj2P8+PF49uwZA8kIEY82bdrg8OHDMDExERq7efMmhgwZggcPHjCQjBDxq6qqwvnz5wWzyhkZGZCRkUGfPn1gZ2eHefPmMR2RkB+ioaGBffv2wdbWFrKyskhPT4e5uTmADw8u1qxZg5s3bzKcUnxojzKps6ytrZGZmYn79++joKAAmpqa6NChA9OxWGvEiBGYOHEiqqurP9t0h/b6iFdpaSlkZUW/DHO5XJSUlEg5ESHi9ejRI1RUVIgcKy8vR3Z2tpQTESIZzs7OSElJQVlZGUxMTGBnZ4eAgAD0798fioqKTMdjvaysLBQWFkJTUxPt27dnOg5rVVVVQV5eHlwuF8rKysjPzxeMtW7dmnUPPqlQJnVe27ZtqYOkFNjY2AAA/vnnH2zevLnGGO31kQwDAwPweDyRjbx4PB709PQYSEWIeP3v3sGPHjx4ABUVFSmnIUQy4uPjoaSkhJkzZ2LUqFEwNTVlOlKDcPDgQcyaNQtPnjwRXGvZsiX++usvuLm5MZiMnQwMDPD06VMAgImJCSIjIzFkyBAAwKFDh6Cjo8NkPLGjQpnUGd96zua4ceMklKRhCg4OZjpCgzNq1CgsX74choaGNZqohYSEYMOGDbRMj9RLoaGhNfYOTpo0CU2aNKlxT1lZGa5fv06NvAhrHDp0CAkJCYiOjsaaNWugqakpOOrPzs4OrVq1Yjoi6xw7dgyjRo1Cly5d8Ouvv0JXVxe5ubkICwvDqFGjcPToUTg4ODAdk1VsbW2RkJAADw8PTJ06FSNHjkRGRgbk5eVx9+5drFy5kumIYkV7lEmd8Wn3QuD/ZiE+/RP9dGaCZjdJfffu3TsMGjQIycnJaNSoEXR1dfH06VOUl5fDysoKx48fh7y8PNMxCfkmW7duxZYtWwAAt2/fhoGBARo1alTjHgUFBRgZGWHJkiVo3bo1EzEJkZjs7GwkJCTg9OnT4PF4ePHiBTp06IDMzEymo7FK37590aRJE8THx9f4DMnn8+Hg4ICSkhKkpaUxmJB9KioqUFFRIXj4GR0djfDwcHA4HDg5OWHChAnMBhQzKpRJnfHpXrX8/HyMHDkS9vb28PT0hLa2NvLz8xEeHo5Tp05h//79dGwOYYWqqipERETgxIkTeP78OZo1awYHBwd4eHhARkaG6XiE/BADAwMcOXJEZDMvQtiqoqICZ8+eRUJCAk6dOoWrV6/S9iUJUFZWxr59++Ds7Cw0FhsbC09PT7x584aBZIQtqFAmdZKHhwe0tbWxfv16obHp06fj6dOn2L9/PwPJ2GXp0qXw9fWFrq4uli5d+sV7ORwOFi1aJKVkhBBCSP1x+fJlJCQkICEhAWlpaaioqICysjIsLCxga2sLOzs7GBkZMR2TVZo0aYLg4GC4uroKjUVFRcHLywuvX79mIBlhCyqUSZ2kqqqKQ4cOiTzT9/Tp03B3d8erV6+kH4xluFyuoLX//y59/1/0NJwQQggRjcvlQk5ODj179hQUxj179vzsyQbkx9na2uLNmzeC7UsfVVRUwMrKCo0bN8bp06cZTMg+Hxu/isLlcqGqqooePXrA29sbzZo1k2IyyaB/vaROqq6uxr1790QWyvfu3QM93xGP6upqkd8T6Xj//j1WrVqFiIgIZGdno7y8vMY4h8NBZWUlQ+kI+T4yMjI4f/684AHc57peA/Q3TtgjLi4OlpaWUFZWZjpKg7FkyRLY2tqiTZs2cHd3h7a2NvLy8hAdHY3CwkIkJSUxHZF1Pn4+z8vLg76+PrS0tJCfn4/s7Gzo6uqiWbNmiIuLw/r163HmzJl6f1QXFcqkTho0aBAWLFiA1q1bw9HRUXA9Li4OCxcuFHmcDiH1zbx587B+/Xo4ODhg2LBhUFBQYDoSIT9s8eLFaNmypeD7LxXKhLDF4MGDmY7Q4PTr1w+nTp3C3LlzsXnzZvD5fHC5XPTs2RORkZHo06cP0xFZZ+HChfD19cX58+dr9ApKT0/HyJEjERgYiI4dO2LAgAFYuHBhvd8mSUuvSZ2Ul5cHW1tb3L17FyoqKtDS0kJBQQFKSkrQvn178Hg81p3VRhqe1q1bw8fHB/7+/kxHIYQQIgY3b95EZmYmysrKhMboWEvJefv2LV6+fAk1NTUoKSkxHYe1zM3N4efnB29vb6Gx3bt3459//sHVq1exfft2LFq0CM+ePWMgpfjQjDKpk3R0dHDlyhWEhIQgOTkZhYWF6NatG6ytrTFu3Diho0bIj6NlwNL38uVLWFhYMB2DEELID3r79i2GDBmCpKQkcDgcwRaxT1dUUKEsOUpKSlQgS8G///772TPBW7VqhTt37gAAOnbsiOLiYmlGkwgqlEmdpaioCD8/P/j5+TEdpUGgZcDSZ2FhgWvXrsHa2prpKIRIxJ49ez479rHxi5mZGXR1daWYihDx+/PPP/Ho0SOkpKTA0tIS0dHRUFFRwbZt2/Dvv//W+yWoddGMGTNQUFCA8PBwobExY8ZAR0cHa9asYSAZe2lpaeHw4cMYMGCA0Njhw4fRvHlzAMDr16+hqqoq5XTiR4UyIQQAcODAASxevJiWAUvRpk2bMHToUOjp6cHJyQny8vJMRyJErCZMmCCYUft0p9en17hcLsaOHYudO3dSh2BSb8XExGDOnDmCfbGtW7eGmZkZbG1t4enpia1bt2Lbtm0Mp2SX2NjYzx5bOXDgQCxbtowKZTH7+eefsXDhQhQXF8Pd3V2wNXL//v04cOAAli1bBgA4d+4cjI2NGU774+gdidQ7EydORHV1NXbv3s10FFahZcDSZ2pqivfv38Pd3R0cDkdo2RiHw2HF0iXScF28eBEjR47EwIED4eHhIeiQGhkZiVOnTmHr1q24fPkyli5dCn19fXpQR+qtR48eoWPHjpCRkQGHw8Hbt28FY6NHj4aPjw8VymKWm5sLfX19kWN6enp48uSJdAM1APPnz8ebN2+wfv167Nu3D8CHB54KCgqYO3cu5s2bBwDw8PCAr68vk1HFggplUu8EBweDz+dToSxmtAxY+lxdXakjMGG1jRs3Yvjw4Vi7dq3gmqGhISwtLTFz5kwEBQVh//79ePnyJcLDw6lQJvWWqqoqSktLAQDNmzfHvXv30K9fPwAfeoB8HCPio6ysjMePH4scy8nJgaKiopQTNQyBgYGYPXs20tPTUVhYCA0NDfTs2RNqamqCe9gwmwxQ12tSD+Xk5IDP50NPT4/pKKxy//59DB06FEuXLqVlwIQQsVBTU8PBgwdhZ2cnNJaQkAA3Nze8evUKx48fx/Dhw4WaCBJSX9jb28PR0RG///47PD09kZGRgeDgYMjLy8PPzw9KSko4e/Ys0zFZxdnZGU+ePMHFixchJycnuP7+/Xv07NkTurq6iIuLYzBhw1JYWIi9e/di2rRpTEcRG5pRJvVO69atmY7ASrQMmBAiblVVVbh//77IQvm///4T7FuWl5enBoKkXvPx8cG9e/cAAMuXL0e/fv1gaWkJ4MNs87Fjx5iMx0oLFy6EhYUFunbtCh8fH7Ro0QJPnjxBUFAQsrOzaam7FPD5fJw8eRJBQUGIjY3F+/fvqVAmhLAPLQOWjtTUVJiZmaFx48ZITU396v20b5zUZwMHDsTChQvRpk2bGl1ST548iUWLFmHgwIEAgDt37nx2ryEh9cGIESME3xsYGCArK0twVFSfPn2grq7OYDp26tmzJ2JjYzFlyhTMnTtXcL1t27aIjY2Fubk5g+nY7eHDhwgODkZISAhyc3MhKysLZ2dn/Pzzz0xHEytaek3qJFEHmX/08UiRHj16YPjw4bREmNQrXC4X6enpMDc3B5fL/ezDCT6fDw6Hg6qqKiknJER8cnNzYWVlhQcPHkBFRUXQIbWkpARt27YFj8dDixYtsHnzZigpKcHLy4vpyISQeujevXt4/vw5mjVrhvbt2zMdh5UqKioQFRWF3bt3IyUlBdXV1dDX10d2djaOHTsGe3t7piOKHRXKpE4yMDBAcXExXr16BVlZWWhoaKCwsBCVlZVQVVUFn89HcXExDA0NkZycDC0tLaYjE1IrKSkpMDMzg4qKClJSUr56/8ele4TUV2/fvkVISAhSU1MFjV8sLS0xfvx4oS0ehLBBdXU17OzssH37diraSL135coV7N69G5GRkXj16hWUlJTg7u4Ob29vGBkZQV1dHcnJyaxcAUeFMqmTrly5guHDh2P16tVwc3ODjIwMqqqqcPDgQcyZMwcHDx5EZWUlXFxc4OjoSB2wvxMtA5a+6upqcLlcpmMQQgiRkKqqKsjJyeHSpUswMzNjOk6D9PjxY/D5fOprIwYfV7/16NEDvr6+GDVqFBo3bgwAKC4uhpqaGmsLZdqjTOqkGTNmYNasWRg5cqTgmoyMDEaNGoWCggLMmDEDZ8+exZw5c2ocO0K+jZWVlWAZsJWVFS0DloLmzZtj+PDhcHV1ha2tbY1OnYQQQgj5cW3atAGfz0dlZSXTUeo9OTk5vH//HtnZ2bh37x5yc3NhaGjIdCypoEKZ1EkZGRlYtGiRyLGuXbti/vz5AD50an7x4oU0o7EKj8dD586dBd8TyVu8eDGio6Ph5OQEFRUVODk5wc3NDfb29nTmI2lQJk6ciOrqaloRRAgRu3HjxqG6uprpGKzw9OlT7NmzB8HBwVizZg3Wrl2LXr16wcfHh5X7kj9FS69JnaSjowMfHx8sW7ZMaGzBggUICgpCXl4ejh07hnHjxlGxTOqdZ8+eITo6GtHR0UhOToaCggIcHBzg5uYGR0dHKCsrMx2REImSlZUFn8+nlSqEdfh8Pry9vbFkyRJa+ktY5cKFC9i9ezcOHDiAkpISKCoqory8HLt27WJlM0YqlEmdNHPmTGzatAmzZ8+Gu7u7oFPq/v378ddff2Hq1KlYs2YN/vzzT5w6dQpnzpxhOjIh362oqAhHjhxBVFQUEhMTweVyMWDAALi5ucHZ2RmqqqpMRyRE7HJycsDn86Gnp8d0FEIIId+grKwM+/fvx+7du5GWlgYOh4NOnTrBz88Pv/76K9PxxIYKZVInvXv3DhMmTMC+fftq7Jvl8/nw8PBASEgI5OTkkJCQgCZNmtBZeWJSVFSEiIgIZGZmoqysrMYYh8OhJZJS8Pr1a8TGxiIqKgonT55EVVUVKioqmI5FCCGE1DlVVVU4cOAAeDyeoKu+tbU13N3dIStLO0yl4d69e9i1axf27NmDZ8+esWqVEBXKpE7LzMxEcnIyioqKoKGhAQsLC8GeWiJeOTk56NGjB96+fYu3b99CU1MTRUVFqKqqgpqaGpo2bYoHDx4wHbPei4mJwaBBg6CgoPDVe9++fYv4+Hi4u7tLIRkhhJBv8bEbcG2xqYCoC168eIFBgwbhypUrQkeJduvWDSdPnoSmpibTMRuMqqoqxMfHY8iQIUxHERsqlAkhAABPT0/k5+cjLi4OjRs3xqVLl9C1a1fs3LkTgYGBSEhIQKdOnZiOWe/Jy8tDQUEBgwcPhpubGwYPHkz7kQmreHt71/peWqlC6rOAgIAahXJwcDDevHkDZ2dnaGtrIy8vD3FxcVBWVoa3tzf8/f0ZTMs+Y8eOxZEjR7Bjxw6MGDFCcJTo/v374efnh6FDh2Lv3r1Mx2SV6upqPHjwAIWFheBwOFBXV0ebNm1Ye+wlrUkgdRYtp5Gu8+fPY/Xq1YLOy3w+H/Ly8pgyZQoKCgrwxx9/IC4ujuGU9V9BQYFgP/LYsWPB5XIxcOBAwX7kpk2bMh2RkB+SlJRUo3h49eoViouLhWZ8mjZtCjU1NQaTEvJjAgICBN//9ddf0NbWRkJCguCMWQAoKSmBnZ0dlJSUGEjIbkePHsWyZcvg4eEhuCYjIwNPT088e/asxv8f8mPu3buHxYsX4+jRo0Jb8xo1aoShQ4fC398fHTp0YCihZNCMMqmTaDmN9CkpKeHUqVPo168f5OTkcOrUKVhbWwMAEhMT4erqilevXjEbkmVKSkpq7EeurKyEjY0N3NzcMHToUPobJ/VeRkYGXFxcsHr1aqEZn9mzZyM6Opp6TBBWaNu2LdasWQMXFxehsaioKMyaNQsPHz5kIBl7NW3aFFFRUbCzsxMaS0hIgKurK4qLixlIxi5Xr16FlZUVFBQU4OLiAmNjY6irqwP40Nvmxo0bOHz4MCoqKpCSkgITExOGE4sPO+fJSb03ffp03L17F+Hh4SgrK0NeXh7KysoQFhaGe/fuYfr06UxHZB0tLS0UFRUBAPT19XHp0iXB2KNHj2gWXwJUVFQwevRoREdH4/nz59i7dy+aNm2K6dOnQ0dHBzY2NtiyZQvTMQn5bjNnzsSsWbPg4eEBGRkZAP834zNz5kx6LSeskZub+9n3SVlZWeTn50s5EfvZ2dkhISFB5Njp06dhZWUl3UAsNWfOHJiYmOD+/fvYtm0bJk+ejFGjRmHUqFGYPHkytm3bhv/++w8mJiaYPXs203HFimaUSZ2kqqqKJUuWYOrUqUJjGzZsQEBAAM1uipmHhwc6duwIf39/zJkzB1u2bMGcOXMgLy+PNWvWwNraGgcOHGA6ZoNQUVGB48ePIyoqCvHx8YIHGITUN8rKyoiNjYWtra3QWEJCAoYOHYrS0lIGkhEiXt26dUPTpk1x+vRpyMnJCa6/e/cOdnZ2KCkpwdWrVxlMyA6fvh/m5OTAxcUFrq6u8PT0hLa2NvLz8xEeHo7o6GhER0fD1NSUubAsoaKigoMHD2LQoEFfvO/48eMYMWIESkpKpJRM8miKiNRJfD4fXbp0ETnWtWtX0PMd8Zs1axYePXoEAFi8eDEyMzPh7+8PPp8PCwsLbNy4kdmADYiCggKGDRuGYcOG4f3790zHIeS7NWnSBAkJCZ8tlJs0acJAKkLEb9myZRg2bBjatGkDFxcXQdEWHR2N/Px8HDlyhOmIrKCpqSl0bOhff/2FdevW1bgGAN27d6dO42IgKytbq2Mq3717x7rVh+z6ryGs8XE5jah9J7ScRjK6d++O7t27A/i/WaDXr1+Dw+FARUWF4XQN16czE4TUN2PHjsWaNWtQWVkpNOOzYcMGzJgxg+mIhIiFo6MjTpw4gQULFmDz5s2orq4Gh8OBubk5goODRX6eId9u8eLF33QkF/lx1tbWWLRoEbp3746WLVuKvCc3Nxf+/v6wsbGRcjrJoqXXpE66du3aV5fTtG7dWnD/x6YC5PuUlZWhXbt22LZtG5ydnZmO06AYGBhAXl4ed+/erXG9ffv2qK6uxv379xlKRsiPq6yshK+vL/bs2SM0CzRmzBgEBQWxbgaCkLdv3+Lly5dQU1Ojbtek3nv48CH69euHwsJC2NjYCJp5cTgcFBYW4t9//0VSUhI0NDRw5swZGBgYMB1ZbKhQJnXSp+ex/e+Hq/+9BoCW1oiBuro6Dh06xLqngXWdvr4+5OTkcO/evRrX27Vrh6qqKuqSSljh7t27NY76s7KyQseOHZmORQghpBZevHiB1atX48iRI7h//36Nz+Pt2rXD8OHDMWvWLNad1kGFMqmTAgICvmlpjb+/vwTTNAzu7u5o3749AgMDmY5CCCGE1DuPHj3CgQMHkJ2dLXTWLIfDwe7duxlKxk5Lly794jiHw8GiRYuklKbhKC8vx8uXLwEAampqUFRUZDiR5FChTAgB8OGcPFdXV0yYMAEuLi7Q0dERelhBS9wJId/j2bNnIosHALCwsGAgESHiFR8fDxcXF1RVVaF58+ZQUFCoMc7hcPDgwQOG0rHTp6sPReFwOLTikPwQKpQJIQA+v9z9U/SGI3mPHz/GrVu30KNHD2hoaDAdh5AfkpeXh7Fjx4LH4wGouX2Gz+fTB1nCGqamplBXV8e+ffvQvHlzpuM0WEVFRThy5Ag2bNiA+Ph4tGrViulIDcb79++Rl5dXo4dQfUcdNEidlZ+fj6ioKGRnZ6O8vLzGGIfDoeOKxIw6SUrfwoULUVpaivXr1wP4cFyOs7Mz3r17B1VVVaSmpn72mDRC6oNff/0VV69exapVq2BsbCw0y0YIW9y7dw/R0dFUJDNMXV0d3t7eePbsGX7//XccPnyY6UisEBYWhqVLl+Lx48fo2rUr/P394eTkVOOeK1euoE+fPqx6+EkzyqROOnnyJIYPHy5UIH9EsxCEDTp16oQZM2Zg4sSJAIBevXqBz+djwYIFCAgIgKGhISIjIxlOScj309TUxJo1a+Dl5cV0FEIkqnPnzvjzzz/h6urKdBQCICkpCcOGDcPr16+ZjlLv8Xg82NrawtjYGN27d8eZM2dw//59zJo1C6tWrRLcd+HCBdYVyl9e3E8IQ/744w+Ympri2rVrqKioQHV1dY0vNv0jrCu8vb0/22E5Ozsb3t7eUk7Efrm5uWjXrh0AoLCwEBkZGVi6dCmGDBmCuXPn4uzZswwnJOTHcDgcWvpIGoT58+dj7dq1ePv2LdNRCIDr16+jcePGTMdghcDAQDg6OuLKlSvYvXs3MjMzMW/ePKxZswaTJ09mOp5E0dJrUifdv38f0dHRMDY2ZjpKgxESEgI/Pz+R59+9ePECoaGhCAoKYiAZe/H5fFRXVwMA0tLSICMjI2hspKOjgxcvXjAZj5Af5u7ujri4ONjZ2TEdhRCJunjxIp49e4Z27drB2tpaqMcEbRkTvz179ghdq6iowI0bNxAUFIQxY8YwkIp9/v33X+zevVvQy0ZGRgbLli2Dvr4+/Pz88P79e+zcuZPhlJJBhTKpkzp27EjLZeqQoqIi2lsoAW3btkVcXBxsbW2xb98+mJubo1GjRgA+NEFSU1NjOCEhP2bEiBGYOHEiqqur4ezsLLJBnZmZGQPJCBGvf/75R/C9qC0zVCiL34QJE0ReV1RUxJgxY7B27VrpBmKpt2/fQllZWei6r68vZGRkMHHiRFRVVQm2kbEJFcqkTlq6dCkWLFgACwsLaGlpMR2HtVJTU5GcnCz4edeuXThx4kSNe8rKyhATE4POnTtLOR37/fLLL5gyZQr27NmDV69e1ZixT0tLo985qfdsbGwAfCgiNm/eXGOMul4TNvm4OohIj6jtYoqKivS5Ucz09fVx/fp1WFlZCY197D8xceJE/Pvvv1JOJnlUKJM66eNeiLZt2wqOXPgUh8NBTEwMQ+nYg8fjYcmSJQA+/E537dol8j49PT2hD7nkx02aNAlqamo4d+4czM3NaywTKysr++zTckLqi+DgYKYjEEJYSk9Pj+kIDYKFhQUiIiIwdepUkeNeXl7gcDjw9fWVcjLJo67XpE4KCQmBt7c3ZGRk0Lx5c8jLy9cY53A4ePDgAUPp2KOsrAxv374Fn89H8+bNcfLkSaFlkAoKCtQQgxBCCCF11vPnz1FWViZ0nU1n+jLlypUriIyMxJw5c6CpqfnZ+w4ePIhjx46x6gEpFcqkTjIwMICxsTFCQkJon6aUZGdnQ0dHR+ihBJG8J0+eIDU1FYWFhdDQ0ICFhQVatmzJdCxCCCHfICwsDBs2bEBmZqbI4y1pm4F4lZSUYPr06YiMjPzscaL0Oyc/gpZekzqpoKAAv/32GxXJUvRxCRMVbdJTXV2NadOmYevWrTXezGVkZODn54eNGzcKukwSUl8sXboUvr6+0NXVxdKlS794L4fDwaJFi6SUjBDJiY2NhZeXFyZMmIArV67A29sb5eXliI2Nha6uLjw8PJiOyDrTpk1DREQEfHx8YGxsTE1HGVBVVYWsrCzw+XwYGhpCRkaG6UhiRTPKpE7q27cvJk6cSHs0pYiKNulbvHgxli1bhokTJ8LT0xPa2trIz89HeHg4du3ahYULF3610CCkruFyuUhPT4e5uflXXzOomRdhiz59+qB///4IDAyEnJwcLl26BDMzM+Tn56N///6YN28evL29mY7JKtra2pg3b95n984Sybp16xaGDx+O//77D8CH1aCHDx9m1dGu9KmX1El//fUXVq9ejWvXrjEdpcEICAjAP//8A29vb/B4PGRmZoLH48HLywubN29GQEAA0xFZJygoCFOnTsX27dthaWkJQ0NDWFpaYseOHfj999/p3GpSL1VXV8Pc3Fzw/Ze+qEgmbHH37l3Y2dmBw+EAACorKwF8KOYWLlyIdevWMRmPlcrLy2FkZMR0jAbr119/xciRI1FcXIwnT56ga9eumDRpEtOxxIqWXpM6aeLEiXj+/Dm6d+8OHR0dkV2vr1+/zlA6dvpYtK1fv15w7WPhpqSkhKCgIJrdFLOioiI4OjqKHHN0dMSOHTuknIgQQsj3qKqqgry8PLhcLpSVlZGfny8Ya926NTUglYDBgwfjzJkzgmPoiGSEhISIXOF5/fp1nDhxAgoKClBRUcHvv/+OYcOGST2fJNGMMqmTNDQ00LVrV1hYWKB9+/bQ0NCo8fW/hTP5cV8r2oqKiqSciP1MTEyQlZUlciwrKwtdu3aVciJCCCHfw8DAAE+fPgXw4bU9MjJSMHbo0CHo6OgwFY1VioqKBF8LFy7E/v37sW7dOty7d6/G2Mcv8uMWLFgAW1tbwRLrj9q2bYuIiAgAH1YPHTx4EG3btmUiosTQjDKpk5KTk5mO0OB8LNrs7OyExqhok4w1a9bAw8MDenp6NR5SHD16FCtXrhS8ARFSn3zL7A6Hw0FiYqIE0xAiHba2tkhISICHhwemTp2KkSNHIiMjA/Ly8rh79y5WrlzJdERW0NTUFCxvBwA+n48//vgDf/zxh8j7aXvHj8vMzMTcuXNhamqKefPmYc6cOZCVlcXatWsxfPhwzJ49GxUVFeByuYiOjmY6rlhRMy9CCADg7Nmz8PDwwLZt24SKtsmTJyMiIgL9+/dnMCH7GBkZIT8/H0VFRVBRUYGWlhYKCgpQUlICDQ0NaGtrC+6l7QakvrCysqrxQfZreDyeBNMQIh0VFRWoqKhAkyZNAADR0dEIDw8Hh8OBk5MTNScVk4CAgG96ffH395dgmobl/Pnz+OWXX1BVVYWdO3eiT58+KCoqwvnz58Hn89G7d29oaGgwHVOsqFAmpAH7386EeXl5Ios2dXV16OrqUqEmZlRQEEIIIaS+qKysxOrVqxEYGIixY8di1apVgodDbESFMql35OTkwOfzBR0lyfejQo0QQggRvz179sDZ2RlqampMR2Etb29vLFq0CAYGBkJj2dnZWLJkCZ0eISH//fcf/Pz8cPv2bWzcuBHu7u5MR5IIKpRJvWNra4vq6moq2ki9t2fPHjg6OopcqlRUVIS4uDiMGzeOgWSEiE9VVRUOHDgAHo+HwsJCaGhowNraGu7u7pCVpVYphH0+dsDOyMiAmZkZ03FY69Mz2//X5cuXYW5uTnuUxeD9+/dYs2YN4uLiUF5ejr59+8Lf3x+amprYs2cPZs2ahZ49e2LLli1o1aoV03HFirpek3onMTGRimTCCl5eXrh//77IsYcPH8LLy0vKiQgRrxcvXqBnz54YPXo0QkJCcO7cOYSEhGD06NHo2bMnXrx4wXREQiSC5qGYVVRUBAUFBaZjsMKsWbOwadMmODs7w8vLCykpKRg6dCgAYNy4ccjMzIS6ujq6dOlS44hRNqBHuYQQAEBqaupX77GwsJBCkobjSx+kysvLISMjI8U0hIjf9OnTcffuXYSHh2PEiBGQkZFBVVUV9u/fDz8/P0yfPh179+5lOiYhYvct25pI7aWmptY4GWXXrl04ceJEjXvKysoQExODzp07SzkdO+3btw9btmyBq6srAGDIkCFo06YNHj9+jFatWkFDQwOhoaEYP348Jk2ahOnTpzOcWHyoUCaEAKjdfmVawvTjcnJy8OjRI8HPV69eRXl5eY17ysrKsGPHDrRu3VrK6QgRr6NHj2LZsmXw8PAQXJORkYGnpyeePXuGgIAA5sIRIkE0oywZPB4PS5YsAfDhYcSuXbtE3qenp4fNmzdLMxprycnJ4e3bt4KfS0tLwefzhbbO2NjY4MaNG9KOJ1FUKJM6o02bNjh8+DBMTExgYGDwxaKNw+F8dskq+T6ilrO/ePECMTExSEtLozccMQkODsaSJUvA4XDA4XAwefJkoXs+fsDauHGjtOMRIlZ8Ph9dunQROda1a1cqJggrycjIoLq6mukYrDR79mz8+uuv4PP5aN68OU6ePCm0D1xBQQGNGzdmKCH7+Pj4YMqUKTh37hyUlZVx8OBBODg4QEdHR+heti13p0KZ1BmWlpaCFvOWlpa0bEnKLC0tRV53dXWFn58fTpw4gUGDBkk5FfuMGDFCUCCMGDECgYGBaN++fY17FBQU0LVrV+jr6zMTkhAxsbOzQ0JCAuzs7ITGTp8+DSsrK+mHIoTUW40aNUKjRo0AfOjloaOjA3l5eYZTsduSJUvQtm1bHDt2DHl5eZgyZQp+++03pmNJBXW9JoR81cmTJzF+/Hjk5+czHYVVQkND4eTkJLLrNSH1VVFRkeD7nJwcuLi4wNXVFZ6entDW1kZ+fj7Cw8MRHR2N6OhomJqaMheWkB+wdOlS+Pr6QldXF0uXLv3ivRwOB4sWLZJSMkKIOFChTAj5qn379mHSpEl4+fIl01FYxcbGBlu2bEHHjh2FxrKysuDn54ekpCQGkhHy/bhcbo0VQR8/ZnzuGvU+IPXVp8cTcblfPkiG/tbF49P3TRsbmy/ey+FwkJiYKKVkDcudO3eQkpKCFy9ewMfHB9ra2nj69CnU1NQEM/5sQEuvSZ31/PlzrFu3DsnJyXjx4gWOHDmCLl26YPv27TA3N0e3bt2YjsgqOTk5QtcqKipw48YNzJs3D7169WIgFbslJyfj9evXIsdKSkqQkpIi5USE/LjFixfT1hnSIHy6D5n2JEvHp/N71dXVX3ytoblA8auqqsLPP/+MkJAQ8Pl8cDgcODg4QFtbG7/88gu6dev21dUV9QkVyqROevjwIfr27Yvi4mKYmJjgwYMHqKioAADcuHED6enpCA4OZjglu+jr64t8w+Hz+TA0NMQ///zDQKqGKy8vD0pKSkzHIOSbUSdrQoikfNp49NNjooh0LF++HBEREVizZg0GDRqErl27CsYcHBwQEhJChTIhkjZ79myoqqri0qVLaN68eY1GDf369YO/vz+D6dgpKChIqFBWVFSEvr4+evTo8dVlZaR2YmJiEBMTI/j5zz//RLNmzWrcU1ZWhuTkZFo1QVilrKwMRUVF0NLSEjpWhBC2ePLkCVJTU1FYWAgNDQ1YWFigZcuWTMciRCxCQkKwaNEizJgxQ2grgYGBAR4+fMhQMsmgdypSJyUmJmLr1q3Q1dUV+oeoo6ODp0+fMpSMvSZMmMB0hAbh9u3bOHjwIIAP+6eSkpKEHkIoKCjAyMiIjocirMDj8TB//nxkZGQAAC5evAgzMzNMmTIFtra2cHFxYTghIT+uuroa06ZNw9atW2t8bpGRkYGfnx82btxID5zFTNSWsY+4XC6aNm0KFRUVKSZiv9zcXPTu3VvkmKKiIkpKSqScSLLoXyypk8rLy6Guri5yrLS0lN5sSL01b948lJSUoKSkBHw+HzweT/Dzx68XL16Ax+PB2NiY6biE/JCkpCQMHDgQ5eXlmDVrVo19nJqamggJCWEuHCFiFBAQgH/++Qfe3t7g8XjIzMwEj8eDl5cXNm/eTFsSJEBfXx8GBgYiv/T09KCqqoqOHTsiNDSU6ais0bx5czx48EDk2N27d1m3eoJmlEmdZGhoiISEBAwYMEBoLDU1tcaeCCI+YWFhiIiIQHZ2NsrKymqMcTgc3L9/n6Fk7PRp0fD8+XOh3zkAtG7dWpqRCBGrxYsXY/DgwYiJiUFlZSVWr14tGDMxMaFeE4Q1goKCMHXqVKxfv15wzdDQEJaWllBSUkJQUBCr9m7WBTt27EBgYCCUlJQwYsQIaGlpIS8vDwcPHkRZWRkmTZqE06dPw9vbG/Ly8vDw8GA6cr03ePBgLF++HIMGDYK2tjaAD58Pi4uLsWnTJjg7OzOcULyoUCZ10sSJEzFjxgzo6upi9OjRAIB3797h0KFD2LJlCzWWkoBVq1Zh3rx56Ny5M0xMTKCgoMB0JNYrKSnB9OnTERkZifLycpH30HEipD67evVqja0Gn2rWrBmePXvGRCxCxK6oqAiOjo4ixxwdHbFjxw4pJ2K/nJwcdOnSBbGxsTVeX/z9/eHs7IyioiKcOnUKrq6uWL9+PRXKYrB06VIcP34cnTt3hrW1NTgcDubPn4+bN29CTk6OdWeFU6FM6qTJkyfj2rVrmD59OmbOnAngQxMvPp+PiRMnYvz48QwnZJ8dO3ZgypQp+Pvvv5mO0mBMmzYNERER8PHxgbGxMT2cIKwjKyuL9+/fixx79uwZ7R8krGFiYoKsrCzY2dkJjWVlZdFKOAkIDg7Gtm3bhB7CcTgc/PLLL/Dz88OKFSswevRojBs3jqGU7KKlpYWMjAz4+/sjPj4eMjIyuH79OpycnLB06dLPbpusr6hQJnXWjh074O3tjfj4eBQUFEBTUxNOTk7o06cP09FYKT8/H8OHD2c6RoMSHx+PlStXYurUqUxHIUQievTogb1792Lo0KFCY4cOHfpsUxhC6ps1a9bAw8MDenp6NWaWjx49ipUrVyIiIoLBdOz04sULkVuWgA+9bl6+fAkA0NDQoDOVxUhLSwvbtm1jOoZUUKFM6rRevXqhV69eTMdoELp374779+/DxsaG6SgNRnl5OYyMjJiOQYjEzJ07F/b29hg+fDjGjRsHDoeDCxcuICgoCIcOHapxJioh9dmkSZNQXl6OIUOGQEVFBVpaWigoKEBJSQk0NDQwZcoUwb0cDgfXr19nMC07mJqaIjAwELa2tlBTUxNcLyoqwvLly2FqagoAePz4sWA/LfkxNjY22LJlCzp27Cg0lpWVBT8/PyQlJTGQTDKoUCaEAADWrVuHMWPGwMzMDN27d2c6ToMwePBgnDlzhh5OENays7NDaGgopk2bJjg/fMqUKVBVVUVISAj69evHcEJCxENDQwOampo1runq6jKUpmFYs2YNBg4cCD09PdjY2AgeTiQlJaGyshIJCQkAPvRKYFuTKaYkJyfj9evXIsdKSkqQkpIi5USSxeHTWgRSR4WFhWHDhg3IzMwU2eiImhz9uP89figvLw9FRUXQ1taGhoZGjTF6Ai4eRUVFgu/z8/Ph5uYGX19fODs7C/3OAbBuvw9pmMrKynDu3DnBNpq+fftCWVmZ6ViEkHruxo0bWLZsGVJTU1FYWAgNDQ1YWlpiwYIFdMSiBHC5XKSnp8Pc3FxoLC4uDh4eHqw6S5kKZVInxcbGwtXVFRMmTMDu3bvhfuufeAAAg8VJREFU7e2N8vJyxMbGQldXFx4eHvD392c6Zr1nZWUl1ATjS2iZ5I/jcrk1fud8Pv+L/w/ogRAhhBDyQXV1NbhcLtMxGpSYmBjBiqCQkBA4OjqiWbNmNe4pKytDcnIy2rdvj9TUVCZiSgQtvSZ10sqVKzFjxgwEBgZi9+7dmDx5MszMzJCfn4/+/fujVatWTEdkheTkZKYjNDiLFy/+pocThNR3fD4fGRkZIs9nB0DdaAmrFBcXIysrS+TfuoWFBQOJ2KV58+YYPnw4XF1dYWtrCzk5OaYjsd7t27drHPOXlJQk9LBCQUEBRkZG2LhxIxMRJYZmlEmdpKGhgX379sHW1haysrI1lnmEhoZizZo1uHnzJsMp2WXPnj1wdHQUufy3qKgIcXFx9IGWEPJNsrKyMGTIENy7d09k11kOh0OrJggrVFZWws/PD3v27Pns3zT9rf+4TZs2ITo6GmfPnoWKigqcnJzg5uYGe3t7KCoqMh2P9b609JqNaO0CqZOqqqogLy8PLpcLZWVl5OfnC8Zat26NBw8eMJiOnby8vHD//n2RYw8fPoSXl5eUExFC6rspU6agvLwc+/fvx507d/Dw4cMaX/RaTthi/fr1OHr0KIKC/h97dx5VdbX/f/x1DgJOqIgKmoloaho4ldR1FpxBTUFNM3Ms01tqZeXNTM1Ms5tRqQ3mlEMOkKg4JM455JBpqUXlgJqiggPKkMD5/eE3fnFBU8/wgcPzsVbry/ns/V3r1V2G5/3Ze7/3bFksFn388cf69NNP9cgjj6hGjRpau3at0RGdwgsvvKAtW7bojz/+0DvvvKOEhAR1795d5cuXV48ePbR06VJdv37d6JhOKysrq9AUyRKFMvIpPz8//fHHH5KkevXqafHixdljy5cvV8WKFY2K5rRut7kkLS1NLi4uDkwDwBns2bNH7777rsLDw1WzZk35+vrm+gdwBl9++aVef/119erVS5L06KOPatCgQfruu+/k6+tLjw8bq1ChgoYMGaJvvvlG586dU0REhK5fv66+ffuqfPny6tKli7788ktdvnzZ6KgowDijjHwpODhYsbGx6tWrl4YPH66ePXtq7969cnNz0y+//KLJkycbHdEpxMfH68SJE9mfDxw4kKvDeGpqqj777DNVqVLFwekAFHQlS5ZUqVKljI4B2N2xY8dUr1697LObf/+7dMiQIRo+fLjeeecdo+I5tbJly2rAgAEaMGCArl69qpUrVyoyMlLPPvusBg0apPT0dKMjOpXCdCsNhTLypbfffjv7F1v37t3l4uKihQsXymQy6ZVXXlG/fv2MDegk5syZo/Hjx8tkMslkMmno0KG55vy10uxsDRoA2F///v21aNEitW/f3ugogF2VKFFCf/75p0wmk8qWLauTJ0+qcePGkqRixYopMTHR4ITOITo6Wu3bt5e7u3ue46VKlVKfPn3Up08fpaSkKCYmxsEJndvKlSvVv39/9evXT99//32et9I4E5p5AYXY0aNHdeTIEVksFvXo0UOTJk1SjRo1csxxd3eXv7+/qlatakxIAAVKVFRU9s83btzQ6NGj5e/vf8u7wrt16+bIeIBdtGjRQk8++aSeeeYZde7cWWfPntXXX38tNzc3PfHEE7p8+bK+//57o2MWeG5ubnJ3d1fHjh0VHh6ujh07cie7AzVu3FjNmjXTpEmT5Orqqn379uW4lWb06NEaMGCA0TFthkIZgKSb3cRDQ0Pz/CILAHfqr7vC/7oj/HZfM+h6DWcxY8YMHTt2TO+9954OHDig5s2bKyUlRZLk6uqqqKgodezY0eCUBd+lS5e0YsUKRUZGKjY2VmazWW3btlV4eLg6deqk0qVLGx3RqRW2W2kolFHgTJw4URaLRW+88YbRUQAA/2Pr1q13Nb9FixZ2SgIY59SpU1qxYoVMJpPatGmjWrVqGR3J6SQnJ2efR16/fr0yMjIUFBSk8PBwdenSReXKlTM6otMpU6aMoqOj1aJFC3l4eGjhwoXq3LmzJGnz5s0KCQnJfkHkDCiUUeC4uroqKyuLVQgbmDBhggYNGqRKlSppwoQJt51rMpl4OQEAQB7i4+NVsWJFubq65hrLyMjQH3/8QVNMO0pJSdHq1asVFRWlNWvWKDU1Vc2aNVN4eHie/Vdwbxo0aKBXXnlFvXr1UtOmTXX//fdn30wzbNgwrVu37pZXjRZEFMoocLZt2yaLxcIqhA38/eL4vzp13gpbJAHcrWrVqunrr79WvXr1co399NNP6ty5M3cpwym4uLho165ded4xu3//fgUGBvJ3qIOkp6dr7dq1ioyMVExMjJKSkoyO5DRefvllXbp0SV988YWWLVumnj17qlq1ajlupRk1apTRMW2GQhkAANjF31/G/a99+/bp0UcfpXiAU7jdn/U9e/aocePGysjIMCBZ4Xbjxo08V/lxb9LT05Wenp597V9UVFT2rTShoaFOdyvN7ZeQAIMMGDBAx48fz3Ps5MmTTtVRDwCcmclkyvP5sWPH5OHh4eA0gP3k9Wf9r9VNzssagyLZttzd3bOLZOnmrQWRkZFavny5evbsqfj4eAPT2R4rysiXbvdmli1MAJB/zZs3T/PmzZMkbdmyRQ0bNszxxUqSUlNTdfDgQbVo0UJr1641IiZgtfHjx/9jf4+/DBo0SJ9++qmdExU+fn5+2dt+/65GjRrKyspyqvOy+V1kZKR69OjhVN/PixgdALhbSUlJt7xoHvfOz8/vlis/ZrNZZcqUUaNGjfTCCy+odu3aDk4HoKBISUnRhQsXJN1cYbt8+bLS09NzzHF3d1fPnj01fvx4IyICNhEYGKihQ4fKYrFoxowZCg8Pl7e3d4457u7uCggIUO/evQ1K6dwsFouysrLu+DlwN1hRRr6xbds2bdmyRZI0btw4DRo0SJUrV84xJzU1VdHR0SpevLj27dtnQErn1a9fP23dulV//PGHmjRpIm9vb507d047d+5UpUqVVL9+fe3cuVPXrl3T1q1b9cgjjxgdGUA+5+fnpxUrVuTZzAtwJv3799fYsWPl5+dndBTAEKwoA3a0efPm7NUFk8mkWbNm5TnP19dX06dPd2S0QqFdu3bavXu3fvvtN91///3Zz+Pj49W2bVs9/vjjmjt3rlq2bKk333xTMTExBqYFUBDcqtcE4GzmzJmT5/O0tDQVLVrUwWkA2AIrysg3UlNTlZKSIovFogoVKmj9+vVq2LBhjjnu7u4qWbKkQQmdm7+/v8aMGaMnnngi19iiRYv01ltv6ejRo5o/f75GjBjBdQsA7tj58+d18uRJpaam5hpr3ry5AYkA21qyZIkSExOz7+z97bff1LlzZ/3yyy9q3LixVq5cKU9PT4NTOr9Tp07p8OHDatSokby8vIyOU6iwogzYUbFixVSsWDFJN1chKlasKDc3N4NTFR6//fabSpcuneeYp6enTpw4IUmqWrWqUlJSHJgMQEF19uxZPfXUU9q8eXOuMYvFwv3scBrvvfeeevTokf151KhRunTpkoYPH64vv/xSkyZN0tSpUw1M6HzGjBmj69eva9q0aZKk2NhYderUSX/++afKlCmjbdu26aGHHjI4ZcH3/fff39G8Y8eO2TmJ41EoI1/y9fU1OkKh4+vrq7lz56pDhw65xmbPnq0qVapIkhITE1W2bFlHxwNQAP373//WgQMHNGXKFNWtW5dGjHBax44dk7+/v6Sb263Xr1+vTz75RH379lWtWrX03nvvUSjbWGRkpF588cXsz2PGjFHdunX1+uuva9y4cZo4caIWL15sYELn8Mgjj9yy2evf/fXy05lQKCPfCAoK0owZM/Tggw8qKCjotnNNJpM2btzooGSFw8svv6xnn31Wp0+fVvfu3eXt7a2EhAQtXbpU3333nT777DNJN8+S08gLwJ3YunWr3nvvPfXv39/oKIBdpaSkqESJEpKk7777Tunp6dkvnuvUqaMzZ84YGc8pnTlzRg888ICkmy/x9+7dqzVr1qhdu3ZKS0vTSy+9ZHBC5zB79mynK4DvFIUy8o2/H5fPysq67X+UHK23vcGDB8tisWjcuHE53tD6+Pjok08+0cCBAyVJr7/+OqtCAO6IyWTK0RwQcFYVK1bUDz/8oObNm2vdunWqVauWypcvL0m6dOmSihcvbnBC5/P3K6B27NghFxeX7J4HFStW1MWLF42M5zT69etndATDUCgj3/j7Gba/romCYz3zzDMaPHiwfvnlFyUmJsrLy0u1atXK8dLif++IBIBb6d69u1avXq3WrVsbHQWwq27duun111/X1q1btXbtWr366qvZY4cOHVL16tUNTOecqlevrtWrVys4OFhfffWVAgMDs3vdnD17luZpNtKgQQOFh4crLCxMDz74oNFxHIpCGSjEunbtqvDwcIWGhmY38jKZTIXuFyEA++jRo4cGDx6srKwsderUKc8utP97uwFQEL311lu6du2adu7cqd69e+uVV17JHuNlkX08++yzGjZsmObPn6/Lly9r9uzZ2WM7duxQnTp1DEznPFq1aqVZs2Zp7NixqlWrlsLCwhQWFqb69esbHc3uuB4K+VJ8fPwtx8xms0qXLi0PDw8HJnJO3bp10/r165WZmamgoCCFh4erS5cuXKkAwCbMZnP2z/97nIau1wCs9dVXX2nnzp0KDAxUnz59sp8/88wzat68eY5nsM7evXsVGRmpqKgo/fbbb/Lz81NYWJjCw8MVGBhodDy7oFBGvmQ2m/+xcUCNGjU0evRoPf300w5K5ZxSUlIUExOjqKgoxcTEKC0tTc2aNVN4eLgef/xxVaxY0eiIAAqoefPm/eMcfocDQMFy8OBBRUZGKjIyUkePHtV9992nbt26KTw8XE2bNnWa5l8UysiXZs2apUmTJql48eLq0aOHvL29dfbsWS1btkypqal67rnntGHDBm3cuFELFixQr169jI7sFNLT07Vu3TpFRUVp1apVunr1qh577DGFh4era9euXNsFAMAtJCUladGiRTp69KhSU1NzjJlMJn3xxRcGJXNup0+f1rZt27J7qzRv3lyVK1c2OlahcfTo0eyi+eDBg9nf2Z0BhTLypbFjx+rAgQNauXJljrdSFotFnTp1UkBAgN555x2FhYXp1KlT2rNnj4FpnVNGRoY2bNigqKgoRUdHKzExUQ8//DD/WwMA8D/i4+PVqFEjpaSkKCUlReXKlVNSUpIyMzPl6emp0qVL69ixY0bHdCpZWVkaMWKEZs6cmeMIh4uLi4YMGaKIiIgcxz9gf8eOHVNkZKRGjRpldBSboFBGvnT//ffrk08+UUhISK6xVatWaciQITpz5oyioqLUt29fXbt2zYCUhUdWVpY2b96sqKgoTZ8+3eg4AAoQVtlQGPTu3Vvnzp3T6tWrVbJkSe3bt0/+/v76/PPPNWnSJMXGxqp27dpGx3QqY8eO1cSJEzV48GD17t1bPj4+OnfunBYuXKhZs2ZpzJgxmjBhgtExncrGjRsVHBx8y/EPP/xQL7zwggMT2Rddr5EvXbx4MdcXqr+kpaXp0qVLkiQvLy/uVHYAs9ms4ODg2/5yBID/dSerbIAz2LVrl959910VLVpU0s0dcG5ubho2bJgSEhI0atQorV692uCUzmX27NkaPny4pk2blv2sVq1aatGihYoXL67Zs2dTKNtYu3btNGbMGL355ps5dnxeuXJF/fr108qVK52qUGY/AvKl+vXra9KkSdkF8V+SkpL09ttvZ7ekP3XqlHx8fAxI6JxWrFih7t27KzAwUHXr1s3xT7169YyOB6CAee211/TQQw8pISFBFotFa9eu1fXr1/XRRx+paNGiiomJMToiYBMJCQmqWLGizGazXFxcdPXq1eyxFi1a6NtvvzUwnXNKSkrKc+ehJIWEhCgpKcnBiZzf66+/rokTJ6p169ZKSEiQdLMbdoMGDbRlyxYtXbrU4IS2xYoy8qWpU6eqbdu28vX1VVBQkLy9vZWQkKBNmzYpIyNDsbGxkqQDBw6oU6dOBqd1DlOnTtWrr76q8uXL64EHHlCJEiWMjgSggGOVDYWFt7d3dmFWtWpV7du3T61atZIknThxQkWK8JXb1urVq6e4uLg876iOi4uTv7+/Aamc2/jx49WiRQv17t1b9evX15NPPqmPP/5YAQEB2rhxo/z8/IyOaFP8V4t8qWnTptq9e7cmTpyYo5Nhhw4d9Prrr6tu3bqSpP/+978GJ3UeM2bM0IABA/Tpp5/KxcXF6DgAnMA/rbJ9+OGHBqYDbOexxx7TgQMH1LlzZ3Xr1k0TJkxQenq63NzcNHXqVAUFBRkd0elMnTpVvXr1kq+vb46V5VWrVmny5MlatGiRgemcV1BQkLZs2aKGDRtq2rRpCgwM1Pbt253yZZDz/RuhwMrKysrRnbBu3bpOt4UjP0tMTFTv3r0pkgHYDKtsKCxefvllnThxQtLNJlNHjx7Vm2++KYvFoubNmysiIsLYgE7oueeeU1pamjp37iwPD4/s3YfJycny8vLSsGHDsueaTCYdPHjQwLTO4/vvv1ePHj3k5uam4OBgxcTE6JlnntH06dNVrFgxo+PZFH9DId+oUKGCunbtqrCwMAUHB8vV1dXoSIVKkyZNdPToUd56A7AZVtngzA4dOqSaNWuqaNGievjhh/Xwww9LkkqUKKGVK1fq6tWrMplM8vDwMDipc/Ly8lK5cuVyPKtUqZJBaQqHGTNm6KWXXlKdOnW0fv16Va9eXbNmzdLw4cO1b98+LVu2TLVq1TI6ps1wPRTyjQ8//FBRUVH69ttv5eHhodDQUIWHh6tdu3bZ59tgP7/88ou6du2qyZMnq3379nJzczM6EoACbv/+/Tpx4oTCwsJ0/fp19erVSzExMdmrbIsXL1bFihWNjgncExcXF+3atUuBgYEKCgrSjBkz9OCDDxodC7Abs9msIUOG6IMPPsjxPfHHH39Ujx49dPr0aSUnJxuY0LYolJHvnD9/XlFRUYqKitKWLVvk7u6uDh06KDw8XCEhITSZspPMzEyNHDlS06dPl8lkUvHixXOMm0wmXblyxaB0AJwFq2xwFsWLF9c333yjpk2bymw2a/fu3QoMDDQ6VqExf/58hYSEyMvLK9dYUlKSVq9erb59+xqQzHktWbJEPXv2zHPs+vXreu655zR//nwHp7IfCmXka0lJSVqxYoUiIyO1ceNGmc1mtWnTRuHh4erUqZPKlCljdESn8dJLL2natGmqX7++ateuneeK8pw5cwxIBgBA/uPv768aNWroySefVI8ePTR58mQ98MADt5zfrVs3B6Zzfn9f0f9f+/fvV2BgoDIzMw1IBmdBoYwC4+rVq1q5cqUiIyO1fv16ZWZmKj093ehYTsPLy0vPPPOM3nnnHaOjACjAtm3bpoYNG6pkyZLatm3bP85v3ry5A1IBtrdkyRL17dtXN27ckMlk0u2+UptMJoo2G7vdKv6OHTvUqlUr/fnnnwYkc15nz57V4cOHlZiYKJPJpLJly8rf318+Pj5GR7MLCmXkG9HR0Wrfvr3c3d3/cW5KSopiYmLUvXt3ByQrHMqUKaOoqCia6wCwyt+/vJrNZplMpjznWSwWigcUeBcvXtQvv/yiZs2aafr06apTp84t57Zo0cKByZxTfHx8dnfxli1baubMmapdu3aOOampqfrss8908OBB/fbbbwakdD7bt2/XK6+8oj179khS9kuhv36/P/bYY5oyZYqaNm1qWEZ7oFBGvuHm5iZ3d3d17NhR4eHh6tixI+eRHahHjx6qX7++/vOf/xgdBUABtnXrVj388MMqWbKktm7d+o/zKR7gDPr376+xY8fKz8/P6ChObfz48Ro/fvwtX8BJ/7+Ii4iI0PPPP++oaE5r06ZNat++vWrWrKk+ffqobt26Klu2rKSbRyQPHTqkBQsW6Ndff9W6deuyrwB0BhTKyDcuXbqUfR45NjZWZrNZbdu2zT6PXLp0aaMjOrUff/xRPXv21LPPPquQkJDsX4J/l9czAACQU3x8vCpVqsRd4TZ29OhRHTlyRBaLRT169NCkSZNUo0aNHHPc3d3l7++vqlWrGhPSyTRu3Fg+Pj5atmyZXFxc8pyTmZmp7t27KyEhQTt27HBwQvuhUEa+lJycnOM8ckZGhoKCghQeHq4uXbrkujcP1jObzZJ027e0bJEEcK/++OMPJSYmysvLi7tO4dQyMzPl5uamvXv3qmHDhkbHcVrz5s1TaGhonl2vYTt/3QseHBx823kbN25U586ddf36dQclsz9ecyFf8vDw0JNPPqknn3xSKSkpWr16taKiojRy5EgNGTJEzZo1U3h4uIYOHWp0VKcxduzY2xbJAHAvoqKiNHr06BxnBatXr65JkyYpPDzcwGSA/bAOZX/z5s3To48+mmehHBcXpyFDhmjTpk0GJHMuxYsX14ULF/5x3vnz53NdLVrQsaKMAiU9PV1r165VZGSkYmJilJSUZHQkAMAtLFmyRL169dKDDz6onj17ysfHR2fPntWSJUsUFxenRYsW3fJOTqCgyszMlKurq/bt28eKsh3drus110PZTr9+/bR582YtW7bslveE7927V927d1erVq2c6ipRCmUUWDdu3JCrq6vRMZxSWlqaLl26JE9PTxUtWtToOAAKqIceekhVq1bVqlWrso93SFJWVpZCQkIUHx+vw4cPG5gQsD0KZce4XaG8evVq9erVS8nJyQYkcy4XLlxQUFCQjhw5olq1amU38zKZTEpMTNSPP/6on3/+WXXq1NGmTZtUvnx5oyPbDIUygGw7d+7Uq6++qt27dysrK0tms1mNGzfW5MmT9a9//cvoeAAKmKJFiyoyMlIhISG5xlatWqXu3bsrLS3NgGSAff29+ztsJzo6WtHR0ZKkuXPnKiQkJFdhlpqaqi1btqhGjRp3dJc7/tmff/6pWbNmacWKFfrpp5+UmJgoSfLy8lJAQIC6du2qAQMGyM3NzeCktsUZZeRbfn5+cnNz0y+//JLjeY0aNZSVlaXff//doGTOaffu3QoKClKZMmX0zDPPqFKlSjpz5kz23cpbtmzRo48+anRMAAWIr6+vUlJS8hxLSUnR/fff7+BEgGNw7Zl9HDlyRMuWLZN0s/nopk2bcuxWkW52vQ4ICFBERIQREZ2Sm5ubhg4dWuh6A7GijHyratWqcnV11a+//prj+QMPPKDMzEwdP37coGTOqW3btrp8+bI2b96c4/7q69evq1WrVvL09NT69esNTAigoPnss8/00UcfafPmzTluKzh//ryCg4P173//W88++6yBCQHbSU5O1tq1a3Xy5EmlpqbmGDOZTHrjjTcMSuacbrf1GrAFVpSRb504cSLP53/vnArb2b17t2bPnp2jSJZuXgswatQoDRw40KBkAAqSF154Icfnq1evqmrVqgoODpaPj4/OnTunjRs3qly5cjpy5IhBKQHb+u677xQSEnLLJqMUyraXlZWV/fOFCxdyvZyQpCpVqjgyUqFQmF4IsaIMQNLNgvirr75Sp06dco2tWrVKTzzxhFPdjQfAPv53G+TtmEwmutLCKTRu3FjXr1/XF198obp16zrdWc38KDk5WSNHjtTixYtv2euA3y+2dScvhJzpf3MKZRQYp06d0uHDh9WoUSMul7eDxo0bq0yZMlqzZk2usdDQUCUlJWnnzp0GJAMAIH/z8PDQokWL8nzZDPsYOHCgFi1apIEDB6pu3bpyd3fPNefpp582IJnzKmwvhNh6jXxpzJgxun79uqZNmyZJio2NVadOnfTnn3+qTJky2rZtmx566CGDUzqX1157TY8//rgaNGigPn36qGLFijp79qwWLVqkH374QStWrDA6IgAA+ZIzXYlTUMTExGjy5MkaPny40VEKjR9//FGLFi3SI488YnQUh7jz/VGAA0VGRqpOnTrZn8eMGaO6devq66+/lq+vryZOnGhgOufUuXNnLViwQBcuXNCoUaPUp08fjRo1SufPn9eCBQt4Sw4AwC08//zz+uSTT8RGTcdJS0tTQECA0TEKlcL2QogVZeRLZ86c0QMPPCBJSkxM1N69e7VmzRq1a9dOaWlpeumllwxO6Jx69+6tXr166ZdfflFiYqK8vLxUq1YtmUwmo6MBKKAWLFigDz74QEePHs3zHKEznWdD4fL+++/n+Hz06FE1aNBAISEhuY6ImUwmjRw50pHxnF7Hjh21fft2BQUFGR2l0PjrhVBoaGih+G5IoYx8yWKxZHcz3LFjh1xcXNS8eXNJUsWKFXXx4kUj4zk1k8mkBx980OgYAJzAypUr1b9/f/Xr10/ff/+9BgwYoLS0NK1cuVKVKlVSr169jI4I3LOXX345z+eHDh3K9YxC2Tb+3kRqzJgxCg8Pl4eHhzp16pRn/5qyZcs6Mp7Ty8rK0s8//1xoXgjRzAv5Uv369dWqVStNmzZNvXv3Vnx8vL799ltJ0tKlSzVixAj98ccfBqd0LlOmTNHp06f10Ucf5Rp7/vnn5evre8svBQCQl8aNG6tZs2aaNGmSXF1dtW/fPjVs2FDnzp1Ts2bNNHr0aA0YMMDomMA9OXny5F3N9/X1tVOSwsNsNudYybRYLLdd2WTHim39060Gztb1mhVl5EvPPvushg0bpvnz5+vy5cuaPXt29tiOHTtynF+GbcybN++WDTHq1auniIgICmUAd+WXX37R+PHjs7/IZmRkSJJ8fHw0ZswYTZ06lUIZBRaFr+ONHTu2UGz5za+OHz9udASHolBGvvTcc8/J09NTO3fuVGBgoPr06ZM9lpqaqn79+hkXzkmdPHlSNWvWzHPsgQce0IkTJxwbCECBl5mZKTc3N5nNZpUoUULnzp3LHqtSpYqOHTtmYDrAdlxcXLRr1y4FBgbmGtu/f78CAwOdaqXNKOPGjTM6QqFW2F4OUSgj33riiSf0xBNP5Hr+2WefGZDG+bm6uur8+fN5jiUkJPAGF8Bd8/Pzyz4mU69ePS1evFidO3eWJC1fvlwVK1Y0Mh5gM7c7yZiVlcXfoXAKWVlZOnbsmBITE2UymVS2bFlVq1btH7dkF1TO+W8Fp3H69GktWrRIH330kRYtWqTTp08bHclpPfLII/r888/zHPv8888LzZ15AGwnODhYsbGxkqThw4dryZIleuCBB1SnTh198sknGjJkiMEJAdu5VTG8f/9+lS5d2sFpANv59ddf1atXL5UqVUq1atVS48aN9a9//Uu1atVSqVKl9OSTTyouLs7omDZHMy/kS1lZWRoxYoRmzpyZY6uSi4uLhgwZooiICKd9e2WUdevWKSQkRM2aNdPQoUN133336fTp0/rkk0+0bdu27Ou5AOBOpaenKz09XaVKlZIkRUVFaeHChTKZTAoNDeUYDQq0iIgIRURESLp5fMnHx0fu7u455qSmpur8+fMKDw/XkiVLjIgJWOXAgQNq2bKl3N3d1a1bN9WtWze7m3hSUpIOHTqkr7/+Wunp6dq6davq1atncGLboVBGvjR27FhNnDhRgwcPVu/eveXj46Nz585p4cKFmjVrlsaMGaMJEyYYHdPpfPHFF3rppZd09epVmUwmWSwWlS5dWv/9739puAPgjrz44osaOXKk7r//fsXHx6tixYpydXU1OhZgc4sXL9aiRYskSTExMWrSpInKlCmTY467u7sCAgI0fPjwXGNAQdC2bVulpaUpJiZGHh4eec5JTk5WaGioihYtqvXr1zs4of1QKCNfqly5srp3765p06blGhsxYoSWL1/ONmw7uX79unbu3KkLFy6ofPnyaty4sUqUKGF0LAAFhNls1u7duxUYGHjbBkeAM2nVqpVmzpypBx980OgogE15eHho2bJlat++/W3nrV27Vj169FBycrKDktkfzbyQLyUlJSkkJCTPsZCQEBp62VGJEiXUpk0bo2MAKKA8PT2VkJAg6Z/vOAWcxebNm42OANhFkSJFlJ6e/o/z/vzzTxUp4lylpXP928Bp1KtXT3FxcWrdunWusbi4OPn7+xuQCgDwTx577DENHDgwexX5pZdeuuWWU5PJpOjoaAemA2zn70cL4uPj/3F+lSpVHJAKsK1WrVrpjTfe0MMPP6zKlSvnOefMmTN68803FRQU5OB09sXWa+RL3377rXr16qVPPvkkx8ryqlWrNHToUC1atEjNmjUzMGHh4urqKovFooyMDKOjAMjnTp48qREjRujw4cP6/fff5e3tnavB0V9MJhN3KaPA+vvRArPZ/I+7J7hHGQXR8ePH1bRpUyUmJiooKCi7mZfJZFJiYqJ+/PFHbdq0SV5eXtq+fbv8/PyMjmwzFMrIlwICAnTu3DklJSXJw8ND3t7eSkhIUHJysry8vOTj45M912Qy6eDBgwamdX7BwcHKyspiaxmAu/L388qAs5k3b55CQ0Pl5eWluXPn/mOh/PTTTzsoGWBbFy9e1LvvvqsVK1bo999/z7433GQy6YEHHlDXrl318ssvq1y5cgYntS0KZeRLLVu2vKtzbRRwAJA/HDx4MPt6kK1bt+rhhx9WyZIlDU4FALCFtLQ0Xbp0SdLNnhRFixY1OJH9UCgDAACbMZvNqlatmsLCwhQWFsZqMgqFw4cP66GHHjI6BmCojIwMp2roZTY6AJCX+fPnKzExMc+xpKQkzZ8/38GJCo9du3Zp0qRJeumllzRp0iTt3LnT6EgACpAffvhBTz75pGJiYvTYY4+pSpUqGjFihL799lvxbh7OKiAgQJUqVdJTTz2lefPm6cyZM0ZHAmzu6aefVmpqap5jJ06cUNOmTR2cyL5YUUa+dLu7N/fv36/AwECaYthYamqqnnjiCa1evTrHl1mTyaSOHTtq6dKlKlasmIEJARQ0P//8syIjIxUZGakffvhB3t7eevzxxxUeHq6WLVvKxcXF6IiATURFRWnjxo2KjY3Vr7/+KpPJpJo1a6p169Zq3bq1WrVqpVKlShkdE7BKyZIlVbVqVS1dulR16tTJfr5ixQoNGDBApUuX1vHjxw1MaFusKCNfut37m7S0NL5c2cErr7yitWvXauLEiTp+/LhSU1N1/PhxvfXWW1q/fr1eeeUVoyMCKGAefPBBvf766/r+++/1+++/a+TIkTpw4IDatm0rb29vDRgwQGvWrDE6JmC1bt26afr06frll1908uRJzZo1Sw0bNtTy5cvVrVs3p2tyhMJpz549slgsCgwM1Ny5c5WRkaHhw4erW7duatasmb7//nujI9oUK8rIN+Lj43XixAlJN5t5zZw5U7Vr184xJzU1VZ999pkOHjyo3377zYCUzqtChQp6/vnn9cYbb+QaGz9+vD7++GNduHDBgGQAnM3p06e1fPlyRUVFadeuXbpx44bRkQCbSUtL0/bt2/XNN98oNjZWBw8eVIkSJZScnGx0NMBqqampeu655/Tll1/Kx8dHiYmJmjRpkl588UWjo9kchTLyjfHjx2v8+PG37Xb91x/XiIgIPf/8846KViiULFlS0dHRCg4OzjUWGxurxx9/XNeuXTMgGYCCZOTIkQoPD1eTJk3uaH5CQoK8vb3tnAqwr/3792vDhg2KjY3Vjh07lJmZqUaNGmVvvf7Xv/7lVE2OULhFR0erV69eSktLU4MGDbR27VpVqFDB6Fg2R6GMfOPo0aM6cuSILBaLevTooUmTJqlGjRo55ri7u8vf319Vq1Y1JqQT++sv89deey3X2DvvvKMNGzZo06ZNBiQDUJA0bNgw+zxy165ds88jm82c9oLzMpvNKl68uPr376927dqpZcuWXIsGp5OZmalXX31V06ZNU0hIiJ5++mkNGzZMLi4uWrhwoVq2bGl0RJuiUEa+NG/ePIWGhsrLy8voKIXGkSNHFBISopdfflm9e/eWp6enLl26pIULF+q///2vVq9ezdUXAO7IiRMntGzZMkVFRWnPnj3y9PRUly5dFB4ertatW8vV1dXoiIBNBQQE6PDhwypVqpRatmyptm3bqk2bNrle+AMFWZMmTbR37169/fbbGjVqlCTp7Nmz6tWrl3bs2KGxY8fmeYSvoKJQRr4UFBSkGTNm6MEHH8w1FhcXpyFDhrC6aQMeHh45trr/+eef2WcFixQpooyMDEmSq6ur3N3ddeXKFUNyAii4zpw5k30eeceOHSpZsqRCQ0MVHh6u9u3bq2jRokZHBGzi/Pnzio2N1YYNG7Rx40adPn1a999/v9q0aaM2bdqoZ8+eRkcErFKlShV99dVXaty4cY7nWVlZGjt2rKZMmeJUPScolJEvmc1m7d69m+uh7Kxfv363PRP+v+bMmWPHNACcXUJCgqKiohQVFaWtW7fK3d1dHTp00NKlS42OBtjc3r179cYbb+ibb76RyWTiewsKvMTExNvu9oyNjVXr1q0dmMi+KJSRL92uUF69erV69epF90gAKMCSkpL09ddfKyoqSjExMUbHAayWmZmp3bt3KzY2VrGxsdqzZ49u3LghT09PtWzZUpGRkUZHBHAXKJSRb0RHRys6OlqSNHfuXIWEhKh8+fI55qSmpmrLli2qUaOGtm3bZkRMAACAHDp16qRt27bp2rVrcnd3V5MmTRQcHKzWrVvr4YcfvqvdW0BBFB8fry1btqhv375GR7EZ+tQj3zhy5IiWLVsmSTKZTNq0aVOuLqnu7u4KCAhQRESEERGd2vz58/9xjjP98gPgGPHx8TKZTLr//vtzPD916pQsFouqVKliUDLAdhISEjRs2DC1bt1aTZo0kbu7u9GRAIfau3ev+vfv71TfFVlRRr50u63XsI9bXd3y97fgnK8CcLfMZrNcXV2Vnp6e47mrq6ssFkt200AAQMEVGRmpHj16ONV3RVaUkS9lZWVl/3zhwgWlpqbmmsMqhG0dP34817OLFy8qOjpaS5Ys0VdffWVAKgAFXd++fVWkSO6vG3379s3xux5wFhaLRW+99ZaeeeYZ+fj4GB0HsEpQUNAdzbtw4YKdkzgeK8rIl5KTkzVy5EgtXrxYaWlpec5xpjdW+d3o0aN19uxZzZ071+goAADka5mZmXJzc9PevXvVsGFDo+MAVjGbzSpfvrwqVKhw23lXr17V6dOnner7OSvKyJdGjBihRYsWaeDAgapbty5nfQwWHBysHj16GB0DAIACgXUoOIuqVasqODhYn3/++W3nLV++3OnuCqdQRr4UExOjyZMna/jw4UZHgaSTJ0/KxcXF6BgACqjTp09r27Zt2XdwNm/eXJUrVzY6FgDgHzRo0EA//PDDP85zxs7uFMrIl9LS0hQQEGB0jEIlr+u20tPTdejQIb3zzjsKDg42IBWAgiwrK0sjRozQzJkzc2zHc3Fx0ZAhQxQREXHLRoJAQWUymeTr68tuODiFZs2a6dNPP/3HeVWrVnWqjtcSZ5SRT/Xu3Vu1atXSm2++aXSUQsNsNud6G/jXr4fWrVtrwYIF/3g+BQD+buzYsZo4caIGDx6s3r17y8fHR+fOndPChQs1a9YsjRkzRhMmTDA6JgAAuVAoI99ISkrK/vncuXMKDw/XoEGD1KlTJ3l5eeWaX7ZsWUfGc3pbt27N9axo0aKqWrWqvL29DUgEoKCrXLmyunfvrmnTpuUaGzFihJYvX67Tp08bkAwAcCcyMjLyvLmgMKBQRr7xvyuaFovltucdnKmrHgA4o+LFi2vlypVq3bp1rrENGzaoS5cuSklJMSAZYHsLFizQokWLdPLkyVzXWppMJv3+++8GJQPuXYUKFdS3b1/1799fDz30kNFxHKpwvh5AvjR27FinbAQAAIVVvXr1FBcXl2ehHBcXJ39/fwNSAbY3ZcoUjR49WnXq1FG9evU4nwynUaNGDb3//vuaNm2aGjVqpEGDBumJJ55QyZIljY5md6woA5Ak3bhxQ1OmTMl+G/6/91ebTCZlZGQYlA5AQfTtt9+qV69e+uSTTxQSEpL9fNWqVRo6dKgWLVqkZs2aGZgQsI3q1aurY8eO+uijj4yOAtjcr7/+qi+++EILFizQH3/8oRIlSig8PFwDBw5U06ZNjY5nNxTKACRJL7/8sqZNm6YOHTrc8u5qmqsBuBsBAQE6d+6ckpKS5OHhIW9vbyUkJCg5OVleXl7y8fHJnmsymXTw4EED0wL3rkSJElq1apWCgoKMjgLYTVZWltatW6fZs2dr9erVunHjhh544AENGDBAffv2VcWKFY2OaFMUygAkSVWqVNHAgQMphgHYTMuWLe/qSM3mzZvtmAawn+bNm+upp57S4MGDjY4COERSUpIWLFigOXPm6ODBgypSpIjat2+vlStXGh3NZiiUAUiSPDw8tHLlSrVq1croKAAAFCj79u1Tnz59tHDhQj388MNGxwEc5s8//9TYsWM1depUSc7VbJdmXgAk3Xwb/sMPP1AoA7CZ+fPnKyQkJM8r/pKSkrR69Wr17dvXgGSAbfXv31+JiYkKDAyUj49Prj/zHC2Aszlw4IDmzJmjRYsW6dKlS/Ly8tJTTz1ldCybYkUZgCTp999/V5cuXTRhwgSFhobKzc3N6EgACjgXFxft2rVLgYGBucb279+vwMBAp1p9QOF1J8cMOFqAgu7SpUtasGCBZs+erUOHDslkMqlt27YaOHCgOnfuLFdXV6Mj2hQrykAh5uHhkeMv9j///FPdu3eXyWRS8eLFc8w1mUy6cuWKoyMCKMBu9y4+LS1NLi4uDkwD2M+WLVuMjgDYhcVi0fr16zV79mytWrVK6enp8vPz0/jx49W/f3/dd999Rke0GwploBALCwvj7moANhUfH68TJ05kfz5w4ECu6+ZSU1P12WefqUqVKg5OBwC4G1WqVNEff/whd3d3devWTQMHDiw03d3Zeg0AAGxm/PjxGj9+/G1fwv311SMiIkLPP/+8o6IBdpWUlKRp06Zp48aNSkxMVLly5dS6dWuNGDFCnp6eRscD7kmDBg00aNAgPfnkkypTpozRcRyKQhmAJGnChAkaNGiQKlWqlGvs7Nmz+vzzzzV27FgDkgEoSI4ePaojR47IYrGoR48emjRpkmrUqJFjjru7u/z9/VW1alVjQgI2dubMGTVp0kTx8fGqXbu2KlWqpD/++ENHjx6Vr6+vduzYkeffrwDyLwplAJJougPA9ubNm6fQ0NA8u14DzuTpp5/WunXrtHr1ajVq1Cj7+d69e9WpUye1b99ec+fONS4gYGfx8fHasmWLU91kYDY6AID84XbvzK5du+Z0nQwB2N+8efN04cKFPMfi4uIKzTk3OL9169Zp4sSJOYpkSWrUqJEmTJigtWvXGpQMcIy9e/eqf//+RsewKZp5AYXYoUOH9MMPP2R/XrNmjX7++eccc1JTU7Vw4UJVr17dwekAFHRbtmzR1atX8xxLTk7W1q1bHZwIsI8rV67c8iiBn58ft0YABRCFMlCIff311xo/frykm9c/TZgwIc95xYoV05w5cxwZDYCTO3v2bK5r6ICCys/PTzExMWrTpk2usbVr18rPz8+AVID17nTnz612DxVkFMpAIfbMM88oNDRUFotFgYGBmjNnjvz9/XPMcXd3V/Xq1VWsWDGDUgIoSKKjoxUdHZ39+a233lL58uVzzElNTdWWLVvUoEEDR8cD7KJ///567bXXlJWVpaeffloVK1bU2bNntWDBAn300UeaPHmy0RGBe7JlyxaVL19eFSpUuO28W+0eKsho5gVAkrR161Y1bNhQHh4eRkcBUIC98847mjRpkiQpJSVFRYsWldmcsyWKu7u7AgICFBERobp16xoRE7Api8WiIUOG6PPPP89xNZrFYtEzzzyjTz75xMB0wL2rVq2agoOD9fnnn9923vLly9WzZ0+navxKoQwAAOzCbDZr9+7deXbTB5zRL7/8os2bNysxMVFeXl4KCgpSzZo1jY4F3LOwsDDFx8dr7969t50XGRmpHj16OFWhzNZroBAbMGCA3njjDfn5+WnAgAG3nWsymfTFF184KBkAZ5CVlZX984ULF5SampprTpUqVRwZCbCrWrVqqVatWkbHAGymWbNm+vTTT/9xXtWqVZ3qaiiJFWWgUPPz89OKFStUr149Va1aNcd2sf9lMpl07NgxB6YDUNAlJydr5MiRWrx4sdLS0vKc40yrDwAA58GKMlCIHT9+PPvnEydOGBcEgFMaMWKEFi1apIEDB6pu3bpyd3c3OhJgMy4uLtq1a5cCAwNlNpv/8WVzRkaGA9MBsBaFMgAAsIuYmBhNnjxZw4cPNzoKYHNjx45V5cqVs3++XaEMoOBh6zUAALCLMmXKKCoq6o7v4QQA5C+3+/3t6uoqb29vtWnTRr169VKRIs61Bmv+5ykACgOz2SwXF5c8/ylSpIjKlSun9u3ba/PmzUZHBVBAdOzYUdu3bzc6BmB3EyZM0B9//JHn2NmzZzVhwgQHJwJs4/z587pw4UKe/8THx2vlypV6+umn1bRpU6WkpBgd16ZYUQYgSRo3bpzmz5+v5ORkderUSd7e3jp79qxiYmLk4eGhzp07KzY2Vj///LPWrl2rNm3aGB0ZQD6UlJSU/fO5c+cUHh6uQYMGqVOnTvLy8so1v2zZso6MB9jF388r/6/9+/crMDCQxnVwWmvXrlXPnj31/PPP6+233zY6js041/o4gHtWtmxZ+fj46Mcff1SJEiWyn1+7dk1t2rTRfffdpx9++EFt2rTR22+/TaEMIE/lypXLcVbTYrFo1KhRGjVqVJ7zKR7gDG637nTt2jW5uro6MA3gWB06dNCrr76qL7/8kkIZgPP58MMP9e677+YokiWpZMmSevnll/Xyyy9r1KhRGjJkiAYPHmxQSgD5HU2NUFgcOnRIP/zwQ/bnNWvW6Oeff84xJzU1VQsXLlT16tUdnA5wrMDAQE2cONHoGDZFoQxAknT69OlbvvEuUqSIzp07J0mqWLGibty44choAAqQcePGGR0BcIivv/5a48ePl3Tz+qdbnUMuVqyY5syZ48hogMOlpqY6XTMv5/q3AXDPatWqpYiICHXo0CHHL7qMjAxFRESoVq1akm42JSlfvrxRMQEAyBeeeeYZhYaGymKxKDAwUHPmzJG/v3+OOe7u7qpevbqKFStmUErAMdasWaM6deoYHcOmKJQBSLrZsTMsLEwPPPCAHn/8cXl7eyshIUErVqzQmTNnFBkZKUnasGGD/vWvfxmcFgAAY1WsWFEVK1aUJG3evFkPP/ywSpYsaXAqwLb+3qDxf2VmZurMmTNasmSJZs2apRkzZjgwmf3R9RpAtnXr1mns2LHav3+/LBaLTCaTHnnkEU2YMEHt2rUzOh4AAAAcyGw231HfiX//+9+KiIhwQCLHoVAGkEtKSoouXbokT09PFS9e3Og4AADke7/++qs+/fRTHT16VKmpqTnGTCaTNm7caFAy4N6NGzfuloVykSJF5O3traCgIFWrVs3ByeyPQhkAAACwwk8//aTHHntM9913n3777TfVrVtXFy9e1JkzZ3T//ferevXq2rRpk9ExAdwFs9EBAOR/27dv17Zt24yOAQBAvvSf//xH7dq10+HDh2WxWPTFF1/o1KlTWrVqldLS0pzu2hygMKBQBvCPgoKC1KpVK6NjAACQL33//fd6+umnZTbf/GqdlZUlSQoJCdHLL7+s0aNHGxkPsNqZM2f03Xff6dKlS3mOX7x4UfPnz3dwKvuiUAbwj9544w2NHTvW6BgAAORLly5dUtmyZWU2m+Xq6pqjmHjkkUf0/fffG5gOuHdZWVkaNGiQqlSposaNG8vHx0ejRo3SjRs3csz7/fff1b9/f4NS2gfXQwH4RxTJAADc2n333aeLFy9Kkh544AFt27ZNbdq0kSQdOnSIa6NQYH3xxReaO3euhg4dqsDAQG3cuFHTpk3Td999p5iYGHl4eBgd0W4olAEAAAArNG3aVDt37tTjjz+uJ598Um+++abOnj0rNzc3zZ07V3369DE6InBPPv30U40YMULvvfeeJOmpp55S79691bNnT7Vp00br169X6dKlDU5pH3S9BpDt6tWrmj59ujZv3qzExER5eXmpVatWeu6551SmTBmj4wEAkC/9/vvv+uOPP9SsWTNlZmZq5MiRWrhwoUwmk0JDQ/Xhhx+qVKlSRscE7lrp0qUVFRWl4ODgHM+///57tW3bVn5+fvrmm28UFxenxo0bKzMz06CktkehDECSdPz4cbVq1Urx8fHy9fWVj4+Pzp07p5MnT+r+++/X5s2bnfKOPAAAAOStfPny+vLLL9W+fftcYwcOHFCbNm1UpUoVTZw4UZ06dXKqQplmXgAkScOHD1daWpp27Nih48ePa9euXTp+/Li+/fZbpaena8SIEUZHBAAAgAPVqFFDu3btynOsQYMG2rBhg+Lj4/X00087OJn9USgDkCRt2rRJb7/9tv71r3/leN64cWNNnDhRmzZtMigZAAD524svvqgnn3wyz7E+ffpo1KhRDk4E2EZQUJDmzZt3y5XiBg0aKDY2Vs64SZlCGYAkyd3dXffff3+eY1WqVJG7u7uDEwEAUDCsXLlSbdu2zXOsbdu2io6OdnAiwDb+/e9/a/r06bp27dot59SvX187d+7U7NmzHZjM/uh6DUCS1KVLFy1btizPv+iXLVum0NBQA1IBAJD/nTlzRlWrVs1zzNfXV6dPn3ZsIMCGQkJC/nFOzZo1VbNmTQekcRwKZaAQ+/7777N/7t27twYOHKju3burd+/e2c28Fi5cqH379umLL74wMCkAAPlXiRIldOrUqTzH4uPjVbRoUQcnAmzD399fM2fOVPfu3Y2O4nB0vQYKMbPZLJPJlP35r18Ht3rmTJ0MAQCwlU6dOun06dPas2ePXF1ds5/fuHFDjz76qCpVqqTVq1cbmBC4N+3atVNsbKx69OihGTNmyNPT0+hIDkOhDBRi8+bNu6v5ztjREAAAa3333Xdq3ry5qlatqoEDB+q+++7T6dOnNXv2bJ08eVLbtm1TYGCg0TGBe/LZZ59p1KhRKlmypGbNmqUOHToYHckhKJQBAAAAK61fv17Dhg3TsWPHsp9Vr15d06dPv2WjL6CgOHnypAYOHKjNmzdr0KBBGj9+vNzc3HLNK1u2rAHp7INCGQAAALCRX3/9VRcuXFD58uVVo0YNo+MANjVx4kSNHTs2xzG9v3OmY3o08wKQLSkpSYsWLdLRo0eVmpqaY8xkMtHQCwCAf1CjRg0KZDilr7/+Wh999JE8PT317LPPOv3VoRTKACTd7MrZqFEjpaSkKCUlReXKlVNSUpIyMzPl6emp0qVLGx0RAIB8Y9u2bWrYsKFKliypbdu2/eP85s2bOyAVYHuXL1/WsGHDtHjxYrVv315ffPGFKlasaHQsu2PrNQBJN6+HOnfunFavXq2SJUtq37598vf31+eff65JkyYpNjZWtWvXNjomAAD5gtls1u7duxUYGJjrFom/s1gs3ByBAmv16tV69tlnlZycrPfee0/PPPOM0ZEchhVlAJKkXbt26d13382+69FiscjNzU3Dhg1TQkKCRo0axdUWAAD8n82bN6tOnTqSpE2bNt2yUAYKss6dO6tp06aaN2+e/Pz8jI7jUBTKACRJCQkJqlixosxms1xcXHT16tXssRYtWujDDz80MB0AAPlLixYtsn9u2bKlcUEAO3r33Xf10ksvFcoXQWajAwDIH7y9vZWUlCRJqlq1qvbt25c9duLECRUpwns1AADyEhQUpJ9//jnPsbi4OAUFBTk4EWAbL7/8cqEskiVWlAH8n8cee0wHDhxQ586d1a1bN02YMEHp6elyc3PT1KlT+UseAIBb2LJlS46dWH+XnJysrVu3OjgRYDvp6emKjIzUqVOn9NBDDyk0NDTXnGPHjmnixImaPXu2AQntg0IZgKSbbwxPnDghSRo7dqyOHj2qN998UxaLRc2bN1dERISxAQEAKIDOnj2r4sWLGx0DuCdXrlxR06ZNdfjwYUk3rwtt1KiRlixZIl9f3+x5Fy5c0Lx58yiUATifhx9+WA8//LAkqUSJElq5cqWuXr0qk8kkDw8Pg9MBAJC/REdHKzo6OvvzW2+9pfLly+eYk5qaqi1btqhBgwaOjgfYxOTJk/XHH39o2bJlCgwM1MaNGzV69Gj961//0oYNG/TQQw8ZHdFuKJQB3FKpUqWMjgAAQL505MgRLVu2TNLNVbZNmzbJbM7Z/sfd3V0BAQHsykKBFR0drTfeeENhYWGSpH79+qlNmzbq2LGjWrVqpdjYWNWtW9fglPbBPcpAIbZt27a7mt+8eXM7JQEAoOD6+53KgDMpUaKE1q5dm+s74KVLl9S6dWudPHlSGzZs0J9//qnGjRs71X3hrCgDhVjLli3vqJOhxWKRyWRyql9+AADYSlZWltERALsoW7asLl68mOu5p6enNm7cqODgYLVu3Vrjx483IJ19saIMFGJbtmy5q5b/f78zEgAA5HbhwgWlpqbmel6lShUD0gDW6dChg/z8/DRjxow8xy9fvqzg4GAdPHhQFovFqRZVKJSBQiwrKyvXeSoAAHB3rl69qhdffFGLFy9WWlpannOcqYBA4fH+++9r/PjxOnHihDw9PfOcc/nyZbVt21b79+93qj/nFMpAIVauXDl17dpVYWFhCg4Olqurq9GRAAAocAYOHKhFixZp4MCBqlu3rtzd3XPNefrppw1IBlgnKytLqampKlas2G0XV9LS0pSQkJDjyqiCjkIZKMQ+/PBDRUVF6dtvv5WHh4dCQ0MVHh6udu3aqWjRokbHAwCgQPDx8dHo0aM1fPhwo6MAsBH2XAKF2AsvvKAtW7bojz/+0DvvvKOEhAR1795d5cuXV48ePbR06VJdv37d6JgAAORraWlpCggIMDoGYFerV6/Wxx9/nOfY9OnTtWbNGgcnsi8KZQCqUKGChgwZom+++Ubnzp1TRESErl+/rr59+6p8+fLq0qWLvvzyS12+fNnoqAAA5DsdO3bU9u3bjY4B2NXbb7+ta9eu5Tl2/fp1TZo0ycGJ7Iut1wBu6erVq1q5cqUiIyO1fv16ZWZmKj093ehYAADkK0eOHFF4eLgGDRqkTp06ycvLK9ecsmXLGpAMsB1PT08tWbJEbdu2zTW2YcMG9ezZU0lJSQYksw8KZaAQi46OVvv27fNsOvK/UlJSFBMTo+7duzsgGQAABcdfTY5ud+WiM3UDRuFUvHhxLV26VKGhobnGVq9ere7du+d5NVpBVcToAACM0717d7m7u6tjx44KDw9Xx44dVaJEiTznFi9enCIZAIA8jB079rZFMuAMatWqpdWrV9+yUK5Zs6YBqeyHQhkoxBISErRixQpFRkbqqaeektlsVtu2bRUeHq5OnTqpdOnSRkcEACDfGzdunNERALsbMGCARo4cKW9vbw0dOlTe3t5KSEjQzJkzNWvWLL3//vtGR7Qptl4DkCQlJyfnOI+ckZGhoKAghYeHq0uXLipXrpzREQEAyPdSU1OVlJQkb29vFSnCmhSch8Vi0dNPP60FCxbIZDLJxcVFmZmZslgseuqppzRv3jyjI9oUhTKAXFJSUrR69WpFRUVpzZo1Sk1NVbNmzRQeHq6hQ4caHQ8AgHxn8+bN+s9//qO9e/dKkvbs2aOGDRtq2LBhCg4OVrdu3QxOCNjG9u3btXbtWl28eFHly5dXhw4d1LRpU6Nj2RyFMoDbSk9P19q1axUZGamYmBin6mYIAIAtbNq0Se3atZO/v7/atWund999V/v27VPDhg315ptv6sCBA1q5cqXRMQHcBQplAHfsxo0bcnV1NToGAAD5StOmTeXl5aXo6GhlZGTIzc0tu1COiorSiBEjFB8fb3RMwCpnz57V4cOHlZiYKJPJpLJly8rf318+Pj5GR7MLDk4AuGMUyQAA5HbgwAEtW7ZMUu4rosqXL6/z588bEQuwie3bt+uVV17Rnj17JN08q/wXk8mkxx57TFOmTHG67dcUygCy+fn5yc3NTb/88kuO5zVq1FBWVpZ+//13g5IBAJB/FSlSRDdu3Mhz7Pz58/Lw8HBwIsA2Nm3apPbt26tmzZp6++23VbduXZUtW1aSlJSUpEOHDmnBggUKDg7WunXr1KpVK4MT2w5brwFkq1q1qlxdXfXrr7/meP7AAw8oMzNTx48fNygZAAD5V+vWrVWmTBktX75cmZmZcnV1zd563atXL12/fp0zyiiQGjduLB8fHy1btkwuLi55zsnMzFT37t2VkJCgHTt2ODih/VAoAwAAAFaIjY1Vu3bt1LlzZ/Xt21fh4eH6+OOPdfjwYX366afavHmz021LReFQokQJrVy5UsHBwbedt3HjRnXu3FnXr193UDL7Y+s1AAAAYIXWrVtr3rx5GjFihKKjoyVJw4YNU5kyZTR37lyKZBRYxYsX14ULF/5x3vnz51W8eHEHJHIcVpQB3NKpU6d0+PBhNWrUSF5eXkbHAQAgX0tNTdXOnTuVkJCgcuXKqUmTJipRooTRsYB71q9fP23evFnLli1TYGBgnnP27t2r7t27q1WrVpozZ46DE9oPhTIASdKYMWN0/fp1TZs2TdLNbWSdOnXSn3/+qTJlymjbtm166KGHDE4JAAAAR7lw4YKCgoJ05MgR1apVK7uZl8lkUmJion788Uf9/PPPqlOnjjZt2qTy5csbHdlmKJQBSJJq166tF198UYMHD5YkPfbYY7JYLHr99dc1btw41apVS4sXLzY4JQAA+ZPFYtHevXt18uRJpaam5hrv27evAakA6/3555+aNWuWVqxYoZ9++kmJiYmSJC8vLwUEBKhr164aMGCA3NzcDE5qWxTKACRJpUqVUnR0tFq1aqXExERVqFBBa9asUbt27bR06VK99NJLOnXqlNExAQDId+Li4tS5c2f9+uuvyuurtclkUmZmpgHJANwrmnkBkHTzTXhWVpYkaceOHXJxcVHz5s0lSRUrVtTFixeNjAcAQL41bNgwpaWlacmSJapbt67c3d2NjgTY3E8//SR/f3+jYzgMhTIASVL16tW1evVqBQcH66uvvlJgYKCKFSsmSTp79qw8PT0NTggAQP60Z88eff755woPDzc6CmA3devW1SOPPKL+/furV69eKlOmjNGR7MpsdAAA+cOzzz6riIgIeXl5acmSJdlnlaWbK8x16tQxMB0AAPlXyZIlVapUKaNjAHY1ffp0mc1mDRs2TJUqVVLv3r21YcMGo2PZDWeUAWT76quvtHPnTgUGBqpPnz7Zz5955hk1b948xzMAAHDTmDFjFB8fr/nz5xsdBbC7o0ePas6cOVq4cKHOnj2rypUrq1+/fnr66adVvXp1o+PZDIUyAAAAcJeioqKyf75x44ZGjx4tf39/derUSV5eXrnmd+vWzZHxALvLysrS2rVrNW/ePK1cuVIZGRnKyMgwOpbNUCgDyOH06dPatm2bEhMT5eXlpebNm6ty5cpGxwIAIF8xm80ymUyyWCzZ//dW6HoNZ/XLL79ozpw5mjt3ri5cuOBUf85p5gVA0s23giNGjNDMmTNz/JJzcXHRkCFDFBERIbOZtgYAAEjS5s2bjY4AGCI5OVlLlizRnDlztHv3brm7u6tr167q37+/0dFsikIZgCRp3Lhx+vjjjzV48GD17t1bPj4+OnfunBYuXKjp06fL09NTEyZMMDomAAD5QosWLYyOADjU5s2bNWfOHEVFRSklJUWNGjXS9OnT1atXL5UuXdroeDbH1msAkqTKlSure/fumjZtWq6xESNGaPny5Tp9+rQByQAAyN+qVaumr7/+WvXq1cs19tNPP6lz5846duyYAckA2zGbzapQoYL69OmjAQMGOP2NKKwoA5AkJSUlKSQkJM+xkJAQffbZZw5OBABAwXDixAmlp6fnOZaWlqaTJ086OBFge19//bVCQ0Pl4uJidBSH4MAhAElSvXr1FBcXl+dYXFyc/P39HZwIAICCw2Qy5fn82LFj8vDwcHAawPa6dOlSaIpkiRVlAP9n6tSp6tWrl3x9fXOsLK9atUqTJ0/WokWLDEwHAED+Mm/ePM2bNy/783PPPadSpUrlmJOamqqDBw9ynhkF3vr16/X111/rp59+UmJiokwmk8qWLSt/f3+FhYWpTZs2Rke0Oc4oA5AkBQQE6Ny5c0pKSpKHh4e8vb2VkJCg5ORkeXl5ycfHJ3uuyWTSwYMHDUwLAICxZs6cqRkzZkiSjhw5Ij8/PxUrVizHHHd3dwUEBGj8+PGqUqWKETEBq1y/fl1hYWH65ptvVLJkST344IMqW7aspJvH9n7++Wddv35dbdu2VWRkpIoXL25wYtuhUAYgSWrZsuUtt43lhWsxAAC4yc/PTytWrMizmRdQkI0cOVJz587VjBkz1L17dxUpknNDcmZmppYtW6ahQ4eqX79+ev/99w1KansUygAAAACAXO677z6NGzdOgwcPvu28zz//XOPGjdOZM2cclMz+OKMMQJI0f/58hYSEyMvLK9dYUlKSVq9erb59+xqQDACA/C0+Pv4f57D1GgVRUlKSatas+Y/zatasqaSkJAckchxWlAFIklxcXLRr1y4FBgbmGtu/f78CAwOVmZlpQDIAAPI3s9n8j8eX+DsUBVGDBg306KOP6pNPPrntvCFDhui7777TgQMHHJTM/lhRBiBJut07s7S0tEJ1HQAAAHdj9uzZuQrlixcvauXKlTp9+rTGjBljUDLAOq+++qp69+6t+Ph49e/fXwEBASpbtqxMJpMSExP1448/au7cuVq3bp3T3ZBCoQwUYvHx8Tpx4kT25wMHDigtLS3HnNTUVH322WdsGQMA4Bb69euX5/OXXnpJ3bt316lTpxwbCLCRJ554QpmZmXrllVfUs2fPXC+ELBaLKlasqPnz56tnz54GpbQPtl4Dhdj48eM1fvz4224X++tXREREhJ5//nlHRQMAwCnExMToueeeu6NzzEB+lZWVpd27d2ffoyxJXl5eCggI0KOPPiqz2WxwQttjRRkoxHr06CF/f39ZLBb16NFDkyZNUo0aNXLMcXd3l7+/v6pWrWpMSAAACrCMjAxdvnzZ6BiAVcxmsxo3bqzGjRsbHcVhWFEGIEmaN2+eQkND8+x6DQAA7s6NGzd06NAhDRw4UB4eHtq+fbvRkQCrpKWl6c8//1SpUqWyny1dulTff/+92rRpo+DgYAPT2Z7zrZEDuCfz5s3ThQsX8hyLi4tTUFCQgxMBAFAwmM1mubi45PinaNGiCgwM1OnTp/XBBx8YHRGw2lNPPaUXXngh+/OHH36oJ554Qu+++67atm2rNWvWGJjO9lhRBiDp5l/yu3fv5nooAADu0rhx43L1+yhatKiqVq2qjh07ysPDw6BkgO34+vpqypQpeuKJJyRJDzzwgBo3bqyPP/5YAwcOVGJiojZt2mRwStvhjDKAf3T27FkVL17c6BgAAORL48aNMzoCYHcXLlzQfffdJ0k6fvy4jh07psWLF6tUqVIaOHCg+vbta3BC26JQBgqx6OhoRUdHZ39+6623VL58+RxzUlNTtWXLFjVo0MDR8QAAAJBPFC9eXFeuXJEkbd++XSVLltQjjzwi6eYOimvXrhkZz+YolIFC7MiRI1q2bJkkyWQyadOmTbna+7u7uysgIEARERFGRAQAoEBISkrSokWLdPToUaWmpuYYM5lM+uKLLwxKBthGQECApk+fLl9fX82YMUOtWrXKPnIQHx8vHx8fgxPaFmeUAUi6/RllAABwa/Hx8WrUqJFSUlKUkpKicuXKKSkpSZmZmfL09FTp0qV17Ngxo2MCVtm0aZNCQ0OVnp4uNzc3xcbGqkmTJpKknj17KjMzU8uXLzc4pe1QKAPI5cKFC7nehktSlSpVDEgDAED+1rt3b507d06rV69WyZIltW/fPvn7++vzzz/XpEmTFBsbq9q1axsdE7DayZMntX//ftWvX1/VqlXLfv7pp5+qfv36evTRRw1MZ1sUygAkScnJyRo5cqQWL16stLS0POfQ9RoAgNz8/Pz07rvvKiwsTEWKFNHevXv18MMPS5LGjh2r77//XqtXrzY4JYC7wRllAJKkESNGaNGiRRo4cKDq1q0rd3d3oyMBAFAgJCQkqGLFitn3KV+9ejV7rEWLFvrwww8NTAfYR2Jioo4dO6Zq1arJy8vL6Dg2R6EMQJIUExOjyZMna/jw4UZHAQCgQPH29lZSUpIkqWrVqtq3b59atWolSTpx4oSKFOErNwquSZMm6bPPPlORIkX02muvadCgQXr//fc1evRoZWRkyGw26+WXX9Y777xjdFSb4r9aAJKktLQ0BQQEGB0DAIAC57HHHtOBAwfUuXNndevWTRMmTMhueDR16lQFBQUZHRG4JwsXLtSYMWP06KOPqly5cho2bJgyMzP1yiuv6JlnnlFgYKA2bdqkd999V3Xr1lWvXr2MjmwznFEGIOlmI5JatWrpzTffNDoKAAAFyv79+3XixAmFhYXp+vXr6tWrl2JiYmSxWNS8eXMtXrxYFStWNDomcNcaN24sPz8/LVy4UJI0Y8YMvfTSSxowYICmT5+ePe+JJ57Q+fPntWnTJqOi2hyFMlCI/bVNTJLOnTun8PBwDRo0SJ06dcrzrEnZsmUdGQ8AgALr6tWrMplM8vDwMDoKcM+8vLz05ZdfqmPHjpKkixcvqkKFClqzZo3at2+fPS8qKkqDBw9WYmKiUVFtjq3XQCFWrly57IviJclisWjUqFEaNWpUnvPpeg0AwJ0pVaqU0REAq6WmpqpEiRLZnz09PSVJFSpUyDGvXLlyunbtmkOz2RuFMlCIjR07NkehDAAArJOZmSk3Nzft3btXDRs2NDoOYJXy5cvrzJkz2Z/NZrOeffbZXIVyQkKCypQp4+B09kWhDBRi48aNMzoCAABOh5ONcBYBAQH69ttv1bt3b0mSyWTSzJkzc83bs2ePateu7eh4dkWhDAAAAADI5e2339bly5f/cd7Vq1fVv39/+wdyIJp5AQAAADaSmZkpV1dX7du3j63XQAFmNjoAAAAA4CxcXFw0Z84c+fn5GR0FgBVYUQYAAACsMH/+fIWEhOR5tWJSUpJWr16tvn37GpAMsM6ECRPueK7JZNIbb7xhxzSORaEMAAAAWMHFxUW7du1SYGBgrrH9+/crMDCQKxZRIJnNOTcgm0ymXM3q/n6DijP9OWfrNQAAAGCF2607paWlycXFxYFpANvJysrK/ueXX36Rn5+f3n77bR0/flypqak6fvy43nrrLfn5+ennn382Oq5N0fUaAAAAuEvx8fE6ceJE9ucDBw4oLS0tx5zU1FR99tlnqlKlioPTAbY3fPhw9e3bV6NHj85+5uvrq//85z+6ceOGXnjhBa1du9bAhLZFoQwAAADcpTlz5mj8+PEymUwymUwaOnRorjl/rTRHREQ4Oh5gc9u3b9dLL72U51iTJk303nvvOTiRfVEoAwAAAHepR48e8vf3l8ViUY8ePTRp0iTVqFEjxxx3d3f5+/uratWqxoQEbMjd3V379u1TcHBwrrF9+/bJzc3NgFT2Q6EMAAAA3KXatWurdu3akm6uLoeGhubZ9RpwFl27dtX48eNVsmRJ9e7dW56enrp06ZIWLlyoCRMm6MknnzQ6ok3R9RoAAACwQkxMjFq2bKkSJUoYHQWwm+TkZHXp0kVbtmyRyWRSkSJFlJGRIYvFoubNm2vVqlXy8PAwOqbNUCgDAAAAVjCbzXJ1dVVgYKBat26t1q1b67HHHqPbNZzSunXrtGXLFiUmJsrLy0utWrVS27Ztc1wT5QwolAEAAAArfP/994qNjdXGjRv17bffKjU1VR4eHmrevHl24fzQQw8ZHRPAXaBQBgAAAGzkzz//1I4dOxQbG6sNGzZo//79MplMysjIMDoaYHMXLlyQl5eXzGaz0VFszvn+jQAAAACDnDlzRr/99pvi4uJ07NgxWSwWVa9e3ehYwD1btGiRWrZsqdatW2vdunWSpCVLlqhcuXLy8fFR+fLl9emnnxqc0vZYUQYAAACsEBkZqQ0bNig2NlbHjh2Tt7e3goKCsrdd33///UZHBO7J2rVrFRISovvuu09lypTRr7/+qiVLlqhHjx4KDg5WYGCgtmzZou3btysmJkbt27c3OrLNUCgDAAAAVjCbzSpevLief/559enTh/PIcBrBwcFyc3PTqlWrVKRIEb355pv68MMP1a5dO3311VfZ89q1aycXFxetWbPGwLS2xdZrAAAAwAohISFycXHRu+++qyeffFKvvPKKNmzYoLS0NKOjAVb56aefNGTIEBUpUkSSNGTIEF25ckV9+vTJMa9///7at2+fERHthkIZAAAAsMKqVauUlJSkbdu2qWvXrtq1a5dCQkLk6emp1q1ba8qUKUZHBO7JlStXVLZs2ezPFSpUkCRVrFgxx7xKlSrp8uXLjoxmdxTKAAAAgJVcXFzUpEkTvfnmm9q+fbt27NihFi1aaNOmTfrPf/5jdDzgnnh5eencuXPZn81ms9q1a6cyZcrkmJeYmKhSpUo5OJ19FTE6AAAAAFDQJSQkKDY2Nvs+5TNnzkiSAgIC1Lp1a4PTAffmoYce0q5du9S9e3dJkslk0tq1a3PN++GHH/TAAw84Op5dUSgDAAAAVvD399fRo0dlsVhUtWpVtWvXTsHBwQoODlb58uWNjgfcs1dffVWJiYn/OO/gwYMKDw93QCLHoes1AAAAYIUePXpkXwVVrVo1o+MAsAEKZQAAAABALl27dlV4eLhCQ0NVunRpo+M4FM28AAAAAAC5mEwmPfPMM/L29lbHjh01e/bsO9qK7QxYUQYAAADuUrVq1fT111+rXr168vPzk8lkuuVck8kkLy8vNW7cWGPHjs1x3Q6Q36WkpCgmJkZRUVGKiYlRWlqamjVrpvDwcD3++OO5ropyFhTKAAAAwF3q37+/xo4dKz8/P/Xr1++2hbJ08z7azZs3q3Xr1lq2bJmDUgK2lZ6ernXr1ikqKkqrVq3S1atX9dhjjyk8PFxdu3aVr6+v0RFthkIZAAAAcICvvvpKQ4cOVVJSktFRAKtlZGRow4YNioyM1MqVK5WYmKiHH35Ye/bsMTqaTXBGGQAAALhLI0eO1I4dO+7q/6dJkyYaPny4nRIBjlWkSBF16NBBs2bN0rlz5/TNN9+oUaNGRseyGVaUAQAAgLvUsGFD/fDDD/L29s7uDNyyZUuZzaxDofD47rvvdODAAbVs2VIPPvig0XFsikIZAAAAuAcnTpzQsmXLFBUVpT179sjT01NdunRReHi4WrduLVdXV6MjAjYzaNAgZWRkaO7cuZJuHiV48sknZbFY5Obmps2bN+tf//qXsSFtiEIZAAAAsNKZM2e0fPlyRUVFaceOHSpZsqRCQ0MVHh6u9u3bq2jRokZHBKxSvXp1vfnmm+rbt68kKSAgQPfff78mT56sESNGqESJElq1apXBKW2HQhkAAACwoYSEBEVFRSkqKkpbt26Vu7u7OnTooKVLlxodDbhnJUqU0Jo1a9SiRQv98ccfqly5srZu3apmzZopOjpaQ4YM0dmzZ42OaTMcogAAAABsyNvbW88995w2bNigc+fO6YMPPtD169eNjgVYxdXVVWlpaZKkHTt2qGjRonrsscckSZ6enrp8+bKB6WyPQhkAAACwk7Jly2rgwIGKiYkxOgpglQcffFBffvmlkpOT9cUXX6hJkybZ5/BPnz6t8uXLG5zQtooYHQAAAAAo6A4cOKBFixbp5MmT2atufzGZTIqOjjYoGWAbL730kp544gktXrxYkrRixYrssY0bN6pu3boGJbMPCmUAAADACvPnz1f//v1lNptVoUIFubm55Rg3mUwGJQNsp3v37rr//vu1c+dONWrUSM2aNcseq1y5ssLCwgxMZ3s08wIAAACsUKtWLdWqVUvz5s2Tp6en0XEA2AArygAAAIAVzpw5o+nTp1Mko9BISUnJdcRAunkm31lQKAMAAABWaNCggc6cOWN0DMCuUlJS9J///EcLFy5UUlJSnnMyMzMdnMp+KJQBAAAAK0ydOlX9+/dXgwYNnK6hEfCXf//73/ryyy/VqVMn1a5dO9dZfGfDGWUAAADASq+//romT56sihUr5tp+ajKZdPDgQYOSAbZRvnx5vfrqq3r55ZeNjuIQrCgDAAAAVpgyZYreeecdlS9fXr6+vk6/0obCq0GDBkZHcBhWlAEAAAArVKpUSR07dtSnn34qFxcXo+MAdvHss8+qTJkymjJlitFRHIJCGQAAALBCyZIltXLlSgUFBRkdBbCba9euKSwsTA899JA6duyYZ4frhg0bGpDMPth6DQAAAFihadOmOnLkCIUynFpqaqoyMjL0wQcfKCIiIseYxWKRyWSi6zUAAACAmyIiIhQWFqb7779fHTp04IwynNLAgQO1d+9ejRgxgq7XAAAAAG7Pw8NDN27c0I0bN2QymVS8ePEc4yaTSVeuXDEoHWAbHh4eev/99zV48GCjozgEK8oAAACAFcLCwmQymYyOAdiVh4eHqlatanQMh2FFGQAAALhHmZmZ+v3331W+fHl5enoaHQewm9dee00XLlzQF198YXQUh6BQBgAAAO5RRkaGihYtqlWrVqlDhw5GxwHsZvHixXr99ddVr149hYSE5Nn1ulu3bgYksw8KZQAAAMAKlStX1qeffqqQkBCjowB2YzabbztO12sAAAAA2Z544gnNnz+fQhlObfPmzUZHcChWlAEAAAArLFiwQKNHj1aNGjXUrVs3VaxYMVdzL2fakgoUBhTKAAAAgBUK25ZUFG5XrlzR7t27dfHiRXXs2NFpm9ix9RoAAACwwqZNm7geCoXCW2+9pcmTJys1NVUmk0l79+6Vp6engoOD1aZNG7322mtGR7QZCmUAAADACi1btjQ6AmB3M2bM0Pjx4zV06FB16NAhx5n80NBQRUVFOVWhfPt9IgAAAABuq1q1ajp48GCeYz/99JOqVavm4ESA7X388cd68cUX9eGHH6pt27Y5xmrUqKFff/3VoGT2QaEMAAAAWOHEiRNKT0/PcywtLU0nT550cCLA9o4dO6Z27drlOebh4aHLly87NpCdUSgDAAAAVrrVGeVjx47Jw8PDwWkA2ytdurQSEhLyHDtx4oQqVKjg4ET2xRllAAAA4C7NmzdP8+bNy/783HPPqVSpUjnmpKam6uDBg2rRooWj4wE2FxwcrHfffVddunRR0aJFJd18QZSRkaGZM2fecrW5oKJQBgAAAO5SSkqKLly4IOlmsXD58uVc26/d3d3Vs2dPjR8/3oiIgE1NmDBBjRo1Up06ddS1a1eZTCZ9/PHHOnDggOLj47V06VKjI9oU9ygDAAAAVvDz89OKFStUr149o6MAdnXkyBG9+OKL2rRpkzIyMuTi4qJWrVopIiJCtWvXNjqeTVEoAwAAAADuWHp6uhITE+Xp6alixYoZHccuKJQBAAAAGzh//rxOnjyp1NTUXGPNmzc3IBFgOwMGDNAbb7whPz+/XGMnT57U+PHjNXv2bAOS2QeFMgAAAGCFs2fP6qmnntLmzZtzjVksFplMJmVmZhqQDLAds9ms3bt3KzAwMNfY/v37FRgY6FR/zmnmBQAAAFjh3//+tw4cOKApU6aobt26cnd3NzoS4FBJSUlO9+eeQhkAAACwwtatW/Xee++pf//+RkcBbGrbtm3asmVL9udZs2Zp3bp1OeakpqYqOjpaderUcXA6+6JQBgAAAKxgMpl0//33Gx0DsLnNmzdnX29mMpk0a9asPOf5+vpq+vTpjoxmd5xRBgAAAKwwdOhQubm56YMPPjA6CmBTqampSklJkcViUYUKFbR+/Xo1bNgwxxx3d3eVLFnSoIT2w4oyAAAAYIUePXpo8ODBysrKUqdOneTl5ZVrzv8WF0BBUKxYsezrn44fP66KFSvKzc3N4FSOwYoyAAAAYAWz2Zz9s8lkyjFG12ugYGJFGQAAALDCnDlzjI4A2EVQUJBmzJihBx98UEFBQbedazKZtHHjRgclsz8KZQAAAMAKTz/9tNERALv4++bjrKysXDsmbjXXGbD1GgAAAACAv2FFGQAAALDCgAEDbjlmNptVpkwZNWrUSF27di00jZDgHCIiIhQWFqbKlSsbHcXhWFEGAAAArODn56crV67o8uXLKlKkiLy8vJSYmKiMjAyVKVNGFotFV65cUa1atbRlyxZ5e3sbHRm4I97e3rp48aIeeeQRhYeHq1u3bqpevbrRsRzC/M9TAAAAANxKZGSkPDw8tHjxYqWmpurs2bNKTU3VokWL5OHhofXr1+vbb7/VpUuX9J///MfouMAdO3v2rGJjY9WoUSN98MEHqlmzpurXr6+JEyfqyJEjRsezK1aUAQAAACu0bNlSYWFhev7553ONRUREaNmyZfr22281bdo0vffeezpz5owBKQHr7dixQ8uXL9fXX3+tU6dOqWbNmgoLC1NYWJgaNGhgdDybYkUZAAAAsMLevXtVp06dPMf8/f114MABSVL9+vV18eJFR0YDbKpJkyaaNm2aTpw4oV27dqlz585asmSJHnnkEVWrVk2vvPKK0RFthkIZAAAAsEKpUqW0efPmPMc2bdqkUqVKSZJSU1Pl4eHhyGiA3QQGBmrKlCn69ddftX//fj355JOKiYkxOpbN0PUaAAAAsELv3r01ZcoUWSwWde/eXd7e3kpISNCSJUv03//+V8OHD5ck7d+/X7Vr1zY4LXBvIiMj1bVrV5nNudda69evr/r16+utt94yIJl9cEYZAAAAsMKff/6pfv366auvvpLJZMp+brFY1KtXL82dO1eurq6KjY1VqVKlFBgYaGBa4N6YzWbdd999GjJkiAYPHqwKFSoYHcmuKJQBAAAAGzh69Ki2bt2qxMREeXl5qXnz5rc8uwwUNNu2bdPHH3+sFStWyGw2q3v37ho2bJgee+wxo6PZBYUyAAAAAOCOnD17VjNnztSsWbOUkJCg+vXr6/nnn1evXr3k7u5udDyboVAGAAAAbODYsWPavHmzLly4oHLlyql169aqWrWq0bEAu8jIyNCyZcv0/vvv6/vvv1fZsmU1aNAgvfDCC6pYsaLR8axG12sAAADACmfPnlXnzp0VEBCgRYsW6dChQ5o/f75q167tVNflAH93/Phxfffdd/r111/l4uKigIAARUREqGbNmlq1apXR8axG12sAAADgHh07dkzBwcFq0aKFTp06pbJly2aPxcXFqUOHDqpUqZJGjBhhXEjARiwWi1atWqXp06dr48aNKlu2rP79739r6NChqlSpks6fP6+BAwdq5MiR6tSpk9FxrcLWawAAAOAeZGVl6bHHHtPDDz+smTNn5jknNjZWffv21R9//KF27dpp4cKFKleunIOTAtabMmWKPvnkE508eVL16tXTCy+8oN69e+c6l7xt2za1atVKmZmZBiW1DVaUAQAAgHuwcOFCnT9/Xh988IGysrIUHByc43ooScrMzFRCQoJOnDihkiVL6t1339W7775rUGLg3o0ZM0adO3fW3Llz1aJFi1vOq169usaOHevAZPbBGWUAAADgHixYsEBPP/203N3dZTabFRISol27dslkMikwMFDly5fXrl27NGrUKJUpU0Z9+/bVokWLjI4N3JPffvtNkZGRty2SJem+++7Tm2++6aBU9sPWawAAAOAe+Pj4aM6cOerQoYMkKTw8XJUrV9YHH3yQPWfBggWaOHGifv75Z509e1aVK1fWqVOnVKlSJYNSA7gTbL0GAAAA7kFiYqJKly6d/XnNmjVau3ZtjjmdO3dW3759FRcXJ19fX1ksFiUlJVEoo0CYMGHCHc81mUx644037JjGsSiUAQAAgHtQtmxZnTp1Ksfnn376KcfW1EOHDmWPXbp0SZJyFNdAfjZu3Lg7nkuhDAAAAECNGjXSvn371LNnT0nSM888o9GjRys5OVkNGjTQiRMn9Pbbb6tr164qV66cVq5cKS8vL91///0GJwfuTFZWltERDMMZZQAAAOAezJ8/Xy+++KJOnTqlYsWKyWKx6KOPPtJnn32m48ePy9vbWz169NAbb7yhEiVKqFOnTqpUqZI+/fRTo6MD+AcUygAAAMA9yMzMVEBAgIKCgvTxxx/fdu6yZcvUr18/HTlyRL6+vg5KCOBesfUaAAAAuAcuLi5asmSJmjVrpmLFiumdd95RkSK5v17Pnz9fzz33nD7//HOKZDilwYMHKysrS1988YXRUWyGFWUAAADACocPH1bPnj2VkpKifv36qW7duipatKji4uK0ePFi/fbbb/rkk08UFhZmdFTALooUKSKLxaLMzEyjo9gMhTIAAABgpaysLC1evFjR0dGKi4vTjRs3VKVKFbVt21YDBgyg0zWcWnx8vCwWi1PtmKBQBgAAAADgb8xGBwAAAAAKmgYNGujtt9/W0aNHjY4CwA5YUQYAAADu0osvvqivv/5a8fHxqlWrlsLCwhQWFqb69esbHQ2wmQkTJtzxXJPJpDfeeMOOaRyLQhkAAAC4R3v37lVkZKSioqL022+/yc/PT2FhYQoPD1dgYKDR8QCrmM13vgHZZDLRzAsAAABATgcPHlRkZKQiIyN19OhR3XffferWrZvCw8PVtGlTmUwmoyMCuEMUygAAAICNHT16NLtoPnjwoLy9vXX27FmjYwG4QxTKAAAAgB39/vvvioqK0qhRo4yOAtyzuLg4nT17Vi1atMg1tnXrVlWqVEk1atQwIJl9UCgDAAAAAG4rNDRUNWvW1Pvvv59r7OWXX1ZcXJxWrlxpQDL74HooAAAAwAqZmZn64IMP1KhRI1WoUEGlSpXK8U/p0qWNjghYbe/evWrevHmeYy1atNDevXsdnMi+ihgdAAAAACjIXnvtNf33v/9V/fr11aZNG7m5uRkdCbC5K1euqGTJknmOFStWTJcuXXJwIvuiUAYAAACssHDhQr322muaNGmS0VEAu7nvvvu0Z88etW7dOtfYnj17VLFiRQNS2Q9brwEAAAArpKam5lk8AM7k8ccf1+TJk7V58+Ycz7ds2aIpU6aoa9euBiWzD5p5AQAAAFbo2bOn6tevr9GjRxsdBbCbK1euqEmTJjp69Khq1qypypUr6/Tp04qLi1OdOnW0Y8cOlSpVyuiYNkOhDAAAAFghISFBISEh6tu3rzp27KiyZcvmmpPXM6CguXbtmqZNm6Z169bpwoULKl++vDp06KARI0bc8vxyQUWhDAAAAFghNTVVgwYN0ldffXXLOZmZmQ5MBMBaNPMCAAAArDBkyBAtXbpUXbp0Ue3atel6DTgBVpQBAAAAK5QuXVpvvPGGXn75ZaOjAIaYOHGiLBaL3njjDaOj2AyFMgAAAGCFcuXKacmSJQoODjY6CmAIV1dXZWVlOdURA66HAgAAAKzQrVs3rV+/3ugYgM29+OKLOnXqlCQpPj5eN27cyHPexo0btWnTJkdGsztWlAEAAAArbN68WYMHD1anTp0UEhKSZ4frhg0bGpAMsI7ZbNbu3bsVGBgoFxcX7dq1S4GBgUbHcggKZQAAAMAKZvP/36RpMplyjFksFplMJqfakorCw8vLS3PnzlWnTp1kNpv13XffqVGjRkbHcgi6XgMAAABWmDNnjtERALt47LHHNHDgwOxV5JdeekllypTJc67JZFJ0dLQD09kXK8oAAAAAgFxOnjypESNG6PDhw/r999/l7e0td3f3POeaTCYdO3bMwQnth0IZAAAAsJG4uDglJiaqXLlyqlGjhtFxAJv5+3nlwoCu1wAAAICVli1bJl9fX9WuXVtNmzbVgw8+KF9fXy1fvtzoaMA9O3jwYPbPmzdvVp06dQxM41isKAMAAABWWLNmjTp16qSHHnpITz31lCpVqqQzZ85owYIFOnLkiFatWqUOHToYHRO4a2azWdWqVVNYWJjCwsIKzWqyRKEMAAAAWKVJkyYqVaqUYmJicnTAtlgs6tChg5KTk7Vjxw4DEwL35tChQ4qMjFRkZKSOHDmiypUrq1u3bgoPD1eTJk1ydXl3JhTKAAAAgBVKlCihr776Sp06dco1tnLlSvXu3VvXrl0zIBlgOz///HN20fzDDz/I29tbjz/+uMLDw9WyZUu5uLgYHdGmOKMMAAAAWMHFxUV//vlnnmM3btzIscoMFFQPPvigXn/9dX3//ff6/fffNXLkSB04cEBt27aVt7e3BgwYoDVr1hgd02ZYUQYAAACsEBwcrGvXrmnLli0qVqxY9vP09HS1bNlSJUuW1IYNGwxMCNjP6dOntXz5ckVFRWnXrl26ceOG0ZFsgkIZAAAAsMK3336r4OBglS1bVt27d5ePj4/Onj2rqKgoJSYmatOmTWrcuLHRMYG7NnLkyOzzyHciISFB3t7edk7lGBTKAAAAgJW2bt2q1157TXv27JHFYpHZbNajjz6qd955R82bNzc6HnBPGjZsmH0euWvXrtnnkQvDcQIKZQAAAMBGUlJSdOnSJXl6eqp48eJGxwGsduLECS1btkxRUVHas2ePPD091aVLF4WHh6t169ZydXU1OqJdUCgDAAAAAP7RmTNnss8j79ixQyVLllRoaKjCw8PVvn17FS1a1OiINkOhDAAAAFjhxRdfVEJCghYuXJhrrE+fPqpYsaKmTp1qQDLAfhISEhQVFaWoqCht3bpV7u7u6tChg5YuXWp0NJtw/s3lAAAAgB2tXLlSbdu2zXOsbdu2io6OdnAiwP68vb313HPPacOGDTp37pw++OADXb9+3ehYNsOKMgAAAGCFYsWKad26dWrRokWusa1bt6pDhw5KSUkxIBmAe8WKMgAAAGCFEiVK6NSpU3mOxcfHO9W5TRRu8fHxef5ZP3XqlOLj4w1IZD+sKAMAAABW6NSpk06fPq09e/bk6AB848YNPfroo6pUqZJWr15tYELANsxms1xdXZWenp7juaurqywWizIyMgxKZntFjA4AAAAAFGRjxoxR8+bN5e/vr4EDB+q+++7T6dOnNXv2bJ08eVKffPKJ0REBm+jbt6+KFMldQvbt21dZWVkGJLIfVpQBAAAAK61fv17Dhg3TsWPHsp9Vr15d06dPv2WjLwD5F4UyAAAAYCO//vqrLly4oPLly6tGjRpGxwFwjyiUAQAAAAB35PTp09q2bZsSExPl5eWl5s2bq3LlykbHsjnOKAMAAAB2curUKVksFlWpUsXoKIBVsrKyNGLECM2cOVOZmZnZz11cXDRkyBBFRETIbHaeS5UolAEAAAA7qVatmtN1A0bhNG7cOH388ccaPHiwevfuLR8fH507d04LFy7U9OnT5enpqQkTJhgd02bYeg0AAADYycCBA5WVlaU5c+YYHQWwSuXKldW9e3dNmzYt19iIESO0fPlynT592oBk9kGhDAAAAAC4reLFi2vlypVq3bp1rrENGzaoS5cuSklJMSCZfTjPJnIAAAAAgF3Uq1dPcXFxeY7FxcXJ39/fwYnsizPKAAAAgA1cu3ZN8fHxSktLyzXWsGFDAxIBtjN16lT16tVLvr6+CgkJyX6+atUqTZ48WYsWLTIwne2x9RoAAACwwoULFzR48GCtWrUq15jFYpHJZMrRJRgoiAICAnTu3DklJSXJw8ND3t7eSkhIUHJysry8vOTj45M912Qy6eDBgwamtR4rygAAAIAVnn32WW3atEnDhw9X7dq15ebmZnQkwOa8vLxUrly5HM8qVapkUBr7Y0UZAAAAsEKZMmU0depUDR482OgoAGyEZl4AAACAFUqUKCFfX1+jYwB2NX/+fCUmJuY5lpSUpPnz5zs4kX1RKAMAAABWeOqpp7Rs2TKjYwB21b9/f/3+++95jh0/flz9+/d3cCL74owyAAAAYIWJEydq4MCB6tq1q0JCQlS2bNlcc7p162ZAMsB2bndiNy0tTS4uLg5MY38UygAAAIAVjh8/ru+++05xcXGKjo7ONU7XaxRU8fHxOnHiRPbnAwcO5Lr+LDU1VZ999pmqVKni4HT2RaEMAAAAWOGZZ57RlStX9MEHH9D1Gk5lzpw5Gj9+vEwmk0wmk4YOHZprzl8rzREREY6OZ1d0vQYAAACsULx4cX3xxRfq1auX0VEAmzp69KiOHDkii8WiHj16aNKkSapRo0aOOe7u7vL391fVqlWNCWknrCgDAAAAVvD29laZMmWMjgHYXO3atVW7dm1JN1eXQ0ND5eXlZXAqx6DrNQAAAGCF5557Tp9++qnRMQC7mjdvni5cuJDnWFxcnIKCghycyL5YUQYAAACsYDabdejQITVs2FAdO3bM1fXaZDJp5MiRBqUDbGPLli26evVqnmPJycnaunWrgxPZF2eUAQAAACuYzbffpEnXazgDs9ms3bt3KzAwMNfY6tWr1atXLyUnJxuQzD5YUQYAAACscPz4caMjAHYRHR2d48qzt956S+XLl88xJzU1VVu2bFGDBg0cHc+uKJQBAAAAK/j6+hodAbCLI0eOaNmyZZJu7ozYtGlTrh0U7u7uCggI4HooAAAAAEDhcrut186IQhkAAACwgp+fn0wm0y3HTSaTfv/9dwcmAuzrwoULSk1NzfW8SpUqBqSxD7ZeAwAAAFZo0aJFrkL54sWL2rlzp0qVKqUWLVoYlAywneTkZI0cOVKLFy9WWlpannOcqWkdhTIAAABghblz5+b5PDExUW3atFFISIhjAwF2MGLECC1atEgDBw5U3bp15e7ubnQku2LrNQAAAGAnixcv1qRJk/Tjjz8aHQWwio+Pj0aPHq3hw4cbHcUhbn/pGwAAAIB7Vq5cOR07dszoGIDV0tLSFBAQYHQMh6FQBgAAAOzgxo0b+vzzz+Xn52d0FMBqHTt21Pbt242O4TCcUQYAAACsEBQUlOtZenq64uLilJSUpHnz5hmQCrBeUlJS9s9jxoxReHi4PDw81KlTJ3l5eeWaX7ZsWUfGsyvOKAMAAABWaNmyZa6u10WLFlXVqlX11FNPqXHjxgYlA6xjNptz/Nm2WCy3vQqNrtcAAAAAJElbtmwxOgJgF2PHjr1tYezMWFEGAAAAAOBvWFEGAAAA7tK2bdvUsGFDlSxZUtu2bfvH+c2bN3dAKgC2wooyAAAAcJfMZrN2796twMDAXOc4/+6vM53OdHYTKAxYUQYAAADu0ubNm1WnTp3snwE4F1aUAQAAAAD4G7PRAQAAAAAAyE/Yeg0AAADcpQEDBtzxXJPJpC+++MKOaQDYGoUyAAAAcJc2bdqUo4HX5cuXdeXKFRUpUkReXl5KTExURkaGSpcuLU9PTwOTArgXbL0GAAAA7tKJEyd0/PhxHT9+XEuXLlXJkiW1cOFCpaam6uzZs0pNTdWCBQtUokQJffXVV0bHBXCXaOYFAAAAWKF58+YKCwvT8OHDc41NmzZNy5cv144dOwxIBuBesaIMAAAAWGH//v3y9/fPcywgIEA//PCDYwMBsBqFMgAAAGCFUqVKKTY2Ns+x2NhYlSpVysGJAFiLZl4AAACAFZ566ilNnTpVGRkZ6t27t3x8fHTu3DktXLhQH3zwgV588UWjIwK4S5xRBgAAAKyQkZGhQYMGaf78+Tk6YVssFvXp00ezZ89WkSKsTwEFCYUyAAAAYAO//PKLNm3apKSkJHl5eally5Z68MEHjY4F4B5QKAMAAAAA8Dc08wIAAACslJ6erk8//VS9evVS27Zt9euvv0qSoqOjdezYMYPTAbhbHJYAAAAArHDx4kW1atVKhw8flo+PjxISEpScnCxJWrFihdavX68ZM2YYnBLA3WBFGQAAALDCK6+8osuXL2vfvn2Kj4/X3082tmrVSlu3bjUwHYB7wYoyAAAAYIXVq1drypQpatiwoTIzM3OMVa5cWadPnzYoGYB7xYoyAAAAYIWrV6/K19c3z7EbN24oIyPDwYkAWItCGQAAALCCn5+fdu3alefYnj17VKtWLQcnAmAtCmUAAADACk8++aSmTJmi6Ojo7PPJJpNJe/fuVUREhJ566imDEwK4W9yjDAAAAFjhxo0b6ty5s9avXy9PT09dunRJ5cqVU2Jiotq3b69Vq1bJbGZ9CihIKJQBAAAAK1ksFi1ZskQxMTFKSEhQuXLlFBoaqieeeIIiGSiAKJQBAAAAAPgbXm8BAAAAAPA33KMMAAAAWCEzM1MfffSRFi5cqJMnTyotLS3HuMlk0pUrVwxKB+BeUCgDAAAAVnjttdf03//+V/Xr11ebNm3k5uZmdCQAVuKMMgAAAGCFSpUqqV+/fpo0aZLRUQDYCGeUAQAAACukpqaqdevWRscAYEMUygAAAIAV2rZtq++++87oGABsiK3XAAAAgBUSEhIUEhKivn37qmPHjipbtmyuOXk9A5B/USgDAAAAVkhNTdWgQYP01Vdf3XJOZmamAxMBsBZdrwEAAAArDBkyREuXLlWXLl1Uu3Ztul4DToAVZQAAAMAKpUuX1htvvKGXX37Z6CgAbIRmXgAAAIAVXF1d1aBBA6NjALAhCmUAAADACt26ddP69euNjgHAhjijDAAAAFihV69eGjx4sG7cuKGQkJA8O1w3bNjQgGQA7hVnlAEAAAArmM3/f5OmyWTKMWaxWGQymeh6DRQwrCgDAAAAVpgzZ47REQDYGCvKAAAAwF3KysrKsZIMwLnwXzcAAABwlypUqKDBgwdr3bp1unHjhtFxANgYhTIAAABwl8aOHatff/1VoaGhqlChgp566ilFR0crLS3N6GgAbICt1wAAAMA9On/+vKKiohQVFaUtW7bI3d1dHTp0UHh4uEJCQlSiRAmjIwK4BxTKAAAAgA0kJSVpxYoVioyM1MaNG2U2m9WmTRuFh4erU6dOKlOmjNERAdwhCmUAAADAxq5evaqVK1cqMjJS69evV2ZmptLT042OBeAOUSgDAAAAdyk6Olrt27eXu7v7P85NSUlRTEyMunfv7oBkAGyBQhkAAAC4S25ubnJ3d1fHjh0VHh6ujh07ch4ZcCJ0vQYAAADuUkJCgj788ENdv35dTz31lMqXL6/HH39cCxYs0JUrV4yOB8BKrCgDAAAAVkhOTs5xHjkjI0NBQUEKDw9Xly5dVK5cOaMjArhLFMoAAACAjaSkpGj16tWKiorSmjVrlJqaqmbNmik8PFxDhw41Oh6AO0ShDAAAANhBenq61q5dq8jISMXExCgpKcnoSADuEIUyAAAAYGc3btyQq6ur0TEA3CEKZQAAAAAA/oau1wAAAICV/Pz8VKtWrVzPa9SooerVqxuQCIA1KJQBAAAAK1ksFmVlZd3xcwD5G1uvAQAAAAD4G1aUAQAAAAD4GwplAAAAwMZOnTqldevWKTEx0egoAO4BhTIAAABghTFjxmjkyJHZn2NjY1WzZk2FhISoZs2aOnz4sIHpANwLCmUAAADACpGRkapTp0725zFjxqhu3br6+uuv5evrq4kTJxqYDsC9KGJ0AAAAAKAgO3PmjB544AFJUmJiovbu3as1a9aoXbt2SktL00svvWRwQgB3ixVlAAAAwAp/vwJqx44dcnFxUfPmzSVJFStW1MWLF42MB+AeUCgDAAAAVqhevbpWr14tSfrqq68UGBioYsWKSZLOnj0rT09PI+MBuAdsvQYAAACs8Oyzz2rYsGGaP3++Ll++rNmzZ2eP7dixI8f5ZQAFA4UyAAAAYIXnnntOnp6e2rlzpwIDA9WnT5/ssdTUVPXr18+4cADuiclisViMDgEAAAAAQH7BijIAAABgA6dPn9a2bduUmJgoLy8vNW/eXJUrVzY6FoB7QKEMAAAAWCErK0sjRozQzJkzlZmZmf3cxcVFQ4YMUUREhMxmeugCBQmFMgAAAGCFcePG6eOPP9bgwYPVu3dv+fj46Ny5c1q4cKGmT58uT09PTZgwweiYAO4CZ5QBAAAAK1SuXFndu3fXtGnTco2NGDFCy5cv1+nTpw1IBuBesQcEAAAAsEJSUpJCQkLyHAsJCVFSUpKDEwGwFoUyAAAAYIV69eopLi4uz7G4uDj5+/s7OBEAa3FGGQAAALDC1KlT1atXL/n6+uZYWV61apUmT56sRYsWGZgOwL3gjDIAAABghYCAAJ07d05JSUny8PCQt7e3EhISlJycLC8vL/n4+GTPNZlMOnjwoIFpAdwJVpQBAAAAK3h5ealcuXI5nlWqVMmgNABsgRVlAAAAAAD+hmZeAAAAgBXmz5+vxMTEPMeSkpI0f/58BycCYC1WlAEAAAAruLi4aNeuXQoMDMw1tn//fgUGBiozM9OAZADuFSvKAAAAgBVut+6UlpYmFxcXB6YBYAs08wIAAADuUnx8vE6cOJH9+cCBA0pLS8sxJzU1VZ999pmqVKni4HQArEWhDAAAANylOXPmaPz48TKZTDKZTBo6dGiuOX+tNEdERDg6HgArcUYZAAAAuEtHjx7VkSNHZLFY1KNHD02aNEk1atTIMcfd3V3+/v6qWrWqMSEB3DMKZQAAAMAK8+bNU2hoqLy8vIyOAsBGaOYFAAAAWGHevHm6cOFCnmNxcXEKCgpycCIA1qJQBgAAAKywZcsWXb16Nc+x5ORkbd269f+1d8eqqYNhGIC/ZAk4thScvIMILl6E0K13oLt1dHHxXtrBKTcgQpdeQW+hBaFLh2SRnk3i6eFATjwG4Xmm/OEf3vUlH18unAhoS1EGAID/5P39PXq9XtcxgIZsvQYAgIaKooiiKI7n9Xodd3d3J3fKsozdbhej0ejS8YCWFGUAAGjo7e0tNptNREQkSRLb7TbS9HRYM8uyyPPc76HgCtl6DQAALaRpGq+vrzEej7uOApyJogwAAGey3++jLMsf7weDQQdpgH9l9BoAAFr4+vqKxWIRz8/PUVXVH+8cDocLpwLaUJQBAKCFx8fHeHp6iul0GsPhMLIs6zoS0JLRawAAaKHf78dyuYz5fN51FOBM/EcZAABaqKoq8jzvOgZwRooyAAC0MJlM4uXlpesYwBkZvQYAgIY+Pz+Pzx8fH/Hw8BCz2Szu7+/j9vb2x/2bm5tLxgNaUpQBAKChNE0jSZLj+fv7++T8O1uv4brYeg0AAA2tVqu/FmPguvmiDAAAADWWeQEAAECNogwAAAA1ijIAAADUKMoAAABQoygDAABAjaIMAAAANYoyAAAA1CjKAAAAUPMLhcG+nmjaLbQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "repos['homepage'].value_counts().nlargest(10).plot.bar(fontsize = 12, title = 'barplot 10 most occuring homepages ')\n",
    "plt.savefig(Path(fp_figs, f'homepage_{subset}.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most recent commits \n",
    "\n",
    "If a repository is really active, we expect them to be more FAIR. As stated, a higher FAIR score implies a repository that is easier to find and work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "months_ago_most_recent_commit =[]\n",
    "today = str(date.today())\n",
    "split_today = today.split('-')\n",
    "for date_item in repos['updated_at']:\n",
    "    split_date = date_item.split('-')\n",
    "    years_ago = int(split_today[0]) - int(split_date[0])\n",
    "    months_ago = 12 * years_ago + (int(split_today[1]) - int(split_date[1]))\n",
    "    months_ago_most_recent_commit.append(months_ago)\n",
    "repos['months_ago_most_recent_commit'] = months_ago_most_recent_commit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "repos['months_ago_most_recent_commit'].plot(kind = 'hist', fontsize = 12, title = 'histogram for amount of months since the last commit') #Add x-axis months ago\n",
    "plt.savefig(Path(fp_figs, f'most_recent_commit_months_{subset}.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean contributors: 36.6\n",
      "median contributors: 25\n"
     ]
    }
   ],
   "source": [
    "print('mean contributors: {:.1f}'.format(statistics.mean(repos['months_ago_most_recent_commit'])))\n",
    "print('median contributors: {}'.format(statistics.median(repos['months_ago_most_recent_commit'])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}