From e7ed9bc6dd50345bd4074868072bd69109f07a58 Mon Sep 17 00:00:00 2001 From: Luciano Gervasoni Date: Fri, 1 Dec 2017 15:32:25 +0100 Subject: [PATCH] Sprawl v1.1 --- README.md | 51 +- examples/approximation_evaluation.ipynb | 585 -------- examples/images/Grenoble.png | Bin 349570 -> 0 bytes examples/images/Grenoble_Accessibility.png | Bin 424053 -> 0 bytes .../images/Grenoble_Activity_densities.png | Bin 64972 -> 0 bytes examples/images/Grenoble_Activity_points.png | Bin 251646 -> 0 bytes .../images/Grenoble_Bubble_land_use_mix.png | Bin 710253 -> 0 bytes examples/images/Grenoble_Buildings.png | Bin 613299 -> 0 bytes examples/images/Grenoble_Dispersion.png | Bin 231879 -> 0 bytes .../Grenoble_Dispersion_scatter_plot.png | Bin 624028 -> 0 bytes examples/images/Grenoble_Land_use_mix.png | Bin 299703 -> 0 bytes .../images/Grenoble_Residential_densities.png | Bin 183439 -> 0 bytes .../images/Grenoble_Residential_points.png | Bin 312891 -> 0 bytes examples/images/Lyon_Accessibility.png | Bin 0 -> 278565 bytes examples/images/Lyon_Dispersion.png | Bin 0 -> 314519 bytes examples/images/Lyon_Landusemix.png | Bin 0 -> 361418 bytes examples/images/Lyon_activities.png | Bin 0 -> 439907 bytes examples/images/Lyon_activities_densities.png | Bin 0 -> 306793 bytes examples/images/Lyon_densities.png | Bin 0 -> 338067 bytes examples/images/Lyon_graph.png | Bin 0 -> 397241 bytes examples/images/Lyon_residential.png | Bin 0 -> 465294 bytes .../zagreb_croatia_osm_point.dbf | Bin 37952561 -> 0 bytes .../zagreb_croatia_osm_point.prj | 1 - .../zagreb_croatia_osm_point.shp | Bin 786060 -> 0 bytes .../zagreb_croatia_osm_point.shx | Bin 224660 -> 0 bytes .../zagreb_croatia_osm_polygon.dbf | Bin 64038185 -> 0 bytes .../zagreb_croatia_osm_polygon.prj | 1 - .../zagreb_croatia_osm_polygon.shp | Bin 8849288 -> 0 bytes .../zagreb_croatia_osm_polygon.shx | Bin 370252 -> 0 bytes examples/land_use_mix.ipynb | 175 --- examples/osm_cities.ipynb | 219 +++ examples/overview.ipynb | 884 ------------ examples/sprawl_analysis.ipynb | 300 ---- examples/sprawl_batch.ipynb | 213 +++ examples/sprawl_overview.ipynb | 962 +++++++++++++ sprawl/accessibility.py | 368 ----- sprawl/core.py | 82 -- sprawl/dispersion.py | 178 --- sprawl/landusemix.py | 254 ---- sprawl/osm_shapefile_parse.py | 423 ------ sprawl/osm_tags.py | 81 -- sprawl/parameters.py | 35 - sprawl/storage.py | 569 -------- sprawl/utils.py | 343 ----- sprawl/visualization.py | 587 -------- {sprawl => src}/__init__.py | 0 src/core.py | 449 ++++++ src/osm/__init__.py | 0 src/osm/osm_data.py | 237 ++++ src/osm/osm_overpass.py | 1260 +++++++++++++++++ src/osm/osm_surface.py | 265 ++++ src/osm/osm_tags.py | 142 ++ src/osm/osm_utils.py | 158 +++ src/parameters.py | 57 + src/sprawl/__init__.py | 0 src/sprawl/accessibility.py | 233 +++ src/sprawl/accessibility_parallel.py | 236 +++ src/sprawl/dispersion.py | 173 +++ src/sprawl/landusemix.py | 196 +++ sprawl/graph_utils.py => src/sprawl/utils.py | 142 +- 60 files changed, 4897 insertions(+), 4962 deletions(-) delete mode 100644 examples/approximation_evaluation.ipynb delete mode 100644 examples/images/Grenoble.png delete mode 100644 examples/images/Grenoble_Accessibility.png delete mode 100644 examples/images/Grenoble_Activity_densities.png delete mode 100644 examples/images/Grenoble_Activity_points.png delete mode 100644 examples/images/Grenoble_Bubble_land_use_mix.png delete mode 100644 examples/images/Grenoble_Buildings.png delete mode 100644 examples/images/Grenoble_Dispersion.png delete mode 100644 examples/images/Grenoble_Dispersion_scatter_plot.png delete mode 100644 examples/images/Grenoble_Land_use_mix.png delete mode 100644 examples/images/Grenoble_Residential_densities.png delete mode 100644 examples/images/Grenoble_Residential_points.png create mode 100644 examples/images/Lyon_Accessibility.png create mode 100644 examples/images/Lyon_Dispersion.png create mode 100644 examples/images/Lyon_Landusemix.png create mode 100644 examples/images/Lyon_activities.png create mode 100644 examples/images/Lyon_activities_densities.png create mode 100644 examples/images/Lyon_densities.png create mode 100644 examples/images/Lyon_graph.png create mode 100644 examples/images/Lyon_residential.png delete mode 100644 examples/input_data/Zagreb_Croatia/zagreb_croatia_osm_point.dbf delete mode 100644 examples/input_data/Zagreb_Croatia/zagreb_croatia_osm_point.prj delete mode 100644 examples/input_data/Zagreb_Croatia/zagreb_croatia_osm_point.shp delete mode 100644 examples/input_data/Zagreb_Croatia/zagreb_croatia_osm_point.shx delete mode 100644 examples/input_data/Zagreb_Croatia/zagreb_croatia_osm_polygon.dbf delete mode 100644 examples/input_data/Zagreb_Croatia/zagreb_croatia_osm_polygon.prj delete mode 100644 examples/input_data/Zagreb_Croatia/zagreb_croatia_osm_polygon.shp delete mode 100644 examples/input_data/Zagreb_Croatia/zagreb_croatia_osm_polygon.shx delete mode 100644 examples/land_use_mix.ipynb create mode 100644 examples/osm_cities.ipynb delete mode 100644 examples/overview.ipynb delete mode 100644 examples/sprawl_analysis.ipynb create mode 100644 examples/sprawl_batch.ipynb create mode 100644 examples/sprawl_overview.ipynb delete mode 100644 sprawl/accessibility.py delete mode 100644 sprawl/core.py delete mode 100644 sprawl/dispersion.py delete mode 100644 sprawl/landusemix.py delete mode 100644 sprawl/osm_shapefile_parse.py delete mode 100644 sprawl/osm_tags.py delete mode 100644 sprawl/parameters.py delete mode 100644 sprawl/storage.py delete mode 100644 sprawl/utils.py delete mode 100644 sprawl/visualization.py rename {sprawl => src}/__init__.py (100%) create mode 100644 src/core.py create mode 100644 src/osm/__init__.py create mode 100644 src/osm/osm_data.py create mode 100644 src/osm/osm_overpass.py create mode 100644 src/osm/osm_surface.py create mode 100644 src/osm/osm_tags.py create mode 100644 src/osm/osm_utils.py create mode 100644 src/parameters.py create mode 100644 src/sprawl/__init__.py create mode 100644 src/sprawl/accessibility.py create mode 100644 src/sprawl/accessibility_parallel.py create mode 100644 src/sprawl/dispersion.py create mode 100644 src/sprawl/landusemix.py rename sprawl/graph_utils.py => src/sprawl/utils.py (58%) diff --git a/README.md b/README.md index b8c8325..bffec96 100644 --- a/README.md +++ b/README.md @@ -10,8 +10,9 @@ It uses OpenStreetMap data to calculate its sprawling indices, divided in Access * Gervasoni Luciano, Bosch Martí, Fenet Serge, and Sturm Peter. 2017. "[LUM_OSM: une plateforme pour l'évaluation de la mixité urbaine à partir de données participatives](https://hal.inria.fr/hal-01548341)." GAST Workshop, Conférence Extraction et Gestion de Connaissances (EGC 2017). -* Gervasoni Luciano, Bosch Martí, Fenet Serge, and Sturm Peter. 2017. "[Calculating spatial urban sprawl indices using -open data](https://hal.inria.fr/hal-01535469)." 15th International Conference on Computers in Urban Planning and Urban Management. +* Gervasoni Luciano, Bosch Martí, Fenet Serge, and Sturm Peter. 2017. "[Calculating spatial urban sprawl indices using open data](https://hal.inria.fr/hal-01535469)." 15th International Conference on Computers in Urban Planning and Urban Management. + +NOTE: The implementation for the previous publications can be found at version 1.0 ## Dependencies @@ -19,47 +20,53 @@ urbansprawl works with Python 2+3. - Python dependencies: ```sh -osmnx utm scipy statsmodels h5py +osmnx scikit-learn ``` * Using anaconda: ```sh conda update -c conda-forge --all -conda install -c conda-forge osmnx utm scipy statsmodels h5py +conda install -c conda-forge osmnx scikit-learn ``` ## Example -PostGIS-enabled PostgreSQL databases are used in this framework (refer to [OSM2PGSQL tools](https://mapzen.com/data/metro-extracts/)): +OpenStreetMap is retrieved using the Overpass API. + +Results are depicted for the city of **Lyon, France**: + +- Locations of residential and activity land uses are retrieved + +* Activity uses: + + + +* Residential uses: -Results are depicted for the city of **Grenoble, France**: + -- Locations and densities of residential and activity land usages +- Densities for each land use are estimated: - - - - + -- Street network +* Activity uses can be further classified: - + -- Buildings +- Street network: - + **Sprawling indices:** -- Land use mix indices +- Land use mix indices: - - + -- Accessibility indices +- Accessibility indices: - + -- Dispersion indices +- Dispersion indices: - + diff --git a/examples/approximation_evaluation.ipynb b/examples/approximation_evaluation.ipynb deleted file mode 100644 index 51ab4c4..0000000 --- a/examples/approximation_evaluation.ipynb +++ /dev/null @@ -1,585 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "autoscroll": "json-false", - "collapsed": true, - "ein.tags": [ - "worksheet-0" - ] - }, - "outputs": [], - "source": [ - "# automatically update imports\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "# For graphs\n", - "%matplotlib inline\n", - "\n", - "# snippet for relative imports\n", - "import os\n", - "import sys\n", - "module_path = os.path.abspath(os.path.join('..'))\n", - "if module_path not in sys.path:\n", - " sys.path.append(module_path)\n", - "\n", - "from sprawl.storage import get_osm_data, get_stored_cities\n", - "from sprawl.dispersion import _apply_polygon_closest_distance_neighbor\n", - "\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Evaluate approximations\n", - " * Closest neighbor distance computation for each building\n", - " * Land use inference for uncertain buildings given polygons with defined land use" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Closest neighbor distance \n", - "* Retrieve the processed OpenStreetMap data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Zagreb_Croatia\n" - ] - } - ], - "source": [ - "# Test on a given city\n", - "city_ref = get_stored_cities()[0]\n", - "print( city_ref )" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Retrieve data\n", - "df_osm = get_osm_data( city_ref )\n", - "\n", - "# Drop columns, keep geometries\n", - "columns_drop = [ col for col in df_osm.columns if col not in ['geometry'] ]\n", - "df_osm.drop(columns_drop,inplace=True,axis=1)\n", - "\n", - "from shapely.geometry import Point\n", - "# Drop rows, keep polygons (buildings)\n", - "geometries = df_osm['geometry'].map( lambda x: type(x) == Point )\n", - "df_osm.drop( df_osm[geometries].index, inplace=True )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Calculate the closest neighboring distance with different approximation values" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "K neighbors: 20\n", - "Time: 91.25518894195557\n", - "K neighbors: 40\n", - "Time: 161.61907291412354\n", - "K neighbors: 60\n", - "Time: 231.69448232650757\n", - "K neighbors: 80\n", - "Time: 302.65931844711304\n", - "K neighbors: 100\n", - "Time: 374.8525826931\n", - "K neighbors: 120\n", - "Time: 438.05286955833435\n", - "K neighbors: 140\n", - "Time: 508.8374490737915\n", - "K neighbors: 160\n", - "Time: 577.8181850910187\n", - "K neighbors: 180\n", - "Time: 634.7658643722534\n", - "K neighbors: 200\n", - "Time: 681.8726420402527\n" - ] - } - ], - "source": [ - "import time\n", - "\n", - "# Test K neighboring values\n", - "K_nearest_values = [20, 40, 60, 80, 100, 120, 140, 160, 180, 200]\n", - "time_values = []\n", - "\n", - "for K_nearest in K_nearest_values: # For each K neighbors value, calculate the closest distances\n", - " print ( \"K neighbors:\",K_nearest )\n", - " \n", - " start = time.time()\n", - " \n", - " _apply_polygon_closest_distance_neighbor(df_osm, K_nearest = K_nearest)\n", - " df_osm['closest_d']\n", - " df_osm.rename(columns={'closest_d': 'K_'+str(K_nearest)}, inplace=True)\n", - " \n", - " end = time.time()\n", - " time_values.append( end-start )\n", - " \n", - " print ( \"Time:\",str(end-start) )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Verify difference by comparing against the best approximation made" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Maximum K_nearest (best approximated value): 200\n", - "\n", - "K_nearest: 20\n", - "Mean closest distance calculated: 5.461420863307704\n", - "Sum of differences (compared to best approximated value): 593.9413317380911\n", - "Number of differing rows (compared to best approximated value): 62\n", - "Mean difference encountered (compared to best approximated value): 0.01536121380416633\n", - "\n", - "K_nearest: 40\n", - "Mean closest distance calculated: 5.4546539454212875\n", - "Sum of differences (compared to best approximated value): 332.2984516597659\n", - "Number of differing rows (compared to best approximated value): 20\n", - "Mean difference encountered (compared to best approximated value): 0.008594295917749021\n", - "\n", - "K_nearest: 60\n", - "Mean closest distance calculated: 5.452642046354884\n", - "Sum of differences (compared to best approximated value): 254.5083742573038\n", - "Number of differing rows (compared to best approximated value): 9\n", - "Mean difference encountered (compared to best approximated value): 0.006582396851346277\n", - "\n", - "K_nearest: 80\n", - "Mean closest distance calculated: 5.44648669977693\n", - "Sum of differences (compared to best approximated value): 16.511898820646277\n", - "Number of differing rows (compared to best approximated value): 5\n", - "Mean difference encountered (compared to best approximated value): 0.0004270502733905671\n", - "\n", - "K_nearest: 100\n", - "Mean closest distance calculated: 5.446293323926101\n", - "Sum of differences (compared to best approximated value): 9.035021548346888\n", - "Number of differing rows (compared to best approximated value): 3\n", - "Mean difference encountered (compared to best approximated value): 0.00023367442256166788\n", - "\n", - "K_nearest: 120\n", - "Mean closest distance calculated: 5.446243366974095\n", - "Sum of differences (compared to best approximated value): 7.103435999032767\n", - "Number of differing rows (compared to best approximated value): 2\n", - "Mean difference encountered (compared to best approximated value): 0.00018371747055561275\n", - "\n", - "K_nearest: 140\n", - "Mean closest distance calculated: 5.446216695413422\n", - "Sum of differences (compared to best approximated value): 6.072180105611736\n", - "Number of differing rows (compared to best approximated value): 1\n", - "Mean difference encountered (compared to best approximated value): 0.00015704590988262603\n", - "\n", - "K_nearest: 160\n", - "Mean closest distance calculated: 5.446059649503539\n", - "Sum of differences (compared to best approximated value): 0.0\n", - "Number of differing rows (compared to best approximated value): 0\n", - "Mean difference encountered (compared to best approximated value): 0.0\n", - "\n", - "K_nearest: 180\n", - "Mean closest distance calculated: 5.446059649503539\n", - "Sum of differences (compared to best approximated value): 0.0\n", - "Number of differing rows (compared to best approximated value): 0\n", - "Mean difference encountered (compared to best approximated value): 0.0\n", - "\n", - "K_nearest: 200\n", - "Mean closest distance calculated: 5.446059649503539\n" - ] - } - ], - "source": [ - "# Best approximation:\n", - "MAX_K = max(K_nearest_values)\n", - "print ( \"Maximum K_nearest (best approximated value):\",MAX_K )\n", - "print ()\n", - "\n", - "diff_mean = []\n", - "diff_sum = []\n", - "distance_mean = []\n", - "for K_nearest in K_nearest_values:\n", - " print ( \"K_nearest:\",K_nearest )\n", - " \n", - " print ( \"Mean closest distance calculated:\", df_osm['K_'+str(K_nearest)].mean() )\n", - " distance_mean.append( df_osm['K_'+str(K_nearest)].mean() )\n", - " \n", - " if (K_nearest == MAX_K): continue\n", - " \n", - " # Compute difference between approximated and 'most' approximated real value\n", - " diff = df_osm['K_'+str(K_nearest)].subtract(df_osm['K_'+str(MAX_K)]).abs()\n", - " \n", - " diff_mean.append( diff.mean() )\n", - " diff_sum.append( diff.sum(axis=0) )\n", - " \n", - " print ( \"Sum of differences (compared to best approximated value):\",diff.sum(axis=0) )\n", - " print ( \"Number of differing rows (compared to best approximated value):\",len(diff[diff != 0 ]) )\n", - " print ( \"Mean difference encountered (compared to best approximated value):\", diff.mean() )\n", - " print ()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Plot the mean of the computed closest distances for each approximation" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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3r3yPbocdG3WkUqs03AeqzGtRqyMrt84kFiukZxIRERERkVLm/nEvcse4i6mW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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plots\n", - "fig, axes = plt.subplots(1,2, figsize=(14,7))\n", - "marker, marker_size = '^', 10\n", - "\n", - "### Approximation\n", - "ax = axes[0]\n", - "ax.set_xlabel(\"K nearest neighbors approximation\")\n", - "ax.set_ylabel(\"Mean closest neighboring distance (meters)\")\n", - "ax.set_title(\"Mean closest distance approximation\")\n", - "\n", - "ax.plot( K_nearest_values, distance_mean, K_nearest_values, distance_mean, 'g', marker=marker, markersize=marker_size)\n", - "\n", - "### Time\n", - "ax = axes[1]\n", - "ax.set_xlabel(\"K nearest neighbors approximation\")\n", - "ax.set_ylabel(\"Running time (seconds)\")\n", - "ax.set_title(\"Closest distance approximation: Time\")\n", - "\n", - "ax.plot( K_nearest_values, time_values, K_nearest_values, time_values, 'g', marker=marker, markersize=marker_size)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Land use inference" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from sprawl.core import get_input_shapefiles\n", - "from sprawl.osm_shapefile_parse import process_shapefiles\n", - "\n", - "city_refs, poly_shapefiles, point_shapefiles = get_input_shapefiles()\n", - "polygon_shapefile, point_shapefile = poly_shapefiles[0], point_shapefiles[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Perform land usage inference using different approximation values" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "K neighbors: 10\n", - "Total number for each land usage: residential 35716\n", - "activity 7776\n", - "mixed 2\n", - "Name: classification, dtype: int64\n", - "Number of inferred land usages: 35148\n", - "Number of inferred land usages per classification:\n", - "Number of residential inferred land usages: 34077\n", - "Number of activities inferred land usages: 1071\n", - "Time (seconds): 140.0178153514862\n", - "\n", - "K neighbors: 20\n", - "Total number for each land usage: residential 35696\n", - "activity 7786\n", - "mixed 2\n", - "Name: classification, dtype: int64\n", - "Number of inferred land usages: 35138\n", - "Number of inferred land usages per classification:\n", - "Number of residential inferred land usages: 34057\n", - "Number of activities inferred land usages: 1081\n", - "Time (seconds): 152.96601152420044\n", - "\n", - "K neighbors: 30\n", - "Total number for each land usage: residential 35683\n", - "activity 7791\n", - "mixed 2\n", - "Name: classification, dtype: int64\n", - "Number of inferred land usages: 35130\n", - "Number of inferred land usages per classification:\n", - "Number of residential inferred land usages: 34044\n", - "Number of activities inferred land usages: 1086\n", - "Time (seconds): 163.18894267082214\n", - "\n", - "K neighbors: 40\n", - "Total number for each land usage: residential 35678\n", - "activity 7794\n", - "mixed 2\n", - "Name: classification, dtype: int64\n", - "Number of inferred land usages: 35128\n", - "Number of inferred land usages per classification:\n", - "Number of residential inferred land usages: 34039\n", - "Number of activities inferred land usages: 1089\n", - "Time (seconds): 173.62510323524475\n", - "\n", - "K neighbors: 50\n", - "Total number for each land usage: residential 35674\n", - "activity 7798\n", - "mixed 2\n", - "Name: classification, dtype: int64\n", - "Number of inferred land usages: 35128\n", - "Number of inferred land usages per classification:\n", - "Number of residential inferred land usages: 34035\n", - "Number of activities inferred land usages: 1093\n", - "Time (seconds): 184.28256368637085\n", - "\n", - "K neighbors: 60\n", - "Total number for each land usage: residential 35674\n", - "activity 7798\n", - "mixed 2\n", - "Name: classification, dtype: int64\n", - "Number of inferred land usages: 35128\n", - "Number of inferred land usages per classification:\n", - "Number of residential inferred land usages: 34035\n", - "Number of activities inferred land usages: 1093\n", - "Time (seconds): 194.82000970840454\n", - "\n", - "K neighbors: 70\n", - "Total number for each land usage: residential 35674\n", - "activity 7798\n", - "mixed 2\n", - "Name: classification, dtype: int64\n", - "Number of inferred land usages: 35128\n", - "Number of inferred land usages per classification:\n", - "Number of residential inferred land usages: 34035\n", - "Number of activities inferred land usages: 1093\n", - "Time (seconds): 203.43622088432312\n", - "\n", - "K neighbors: 80\n", - "Total number for each land usage: residential 35674\n", - "activity 7798\n", - "mixed 2\n", - "Name: classification, dtype: int64\n", - "Number of inferred land usages: 35128\n", - "Number of inferred land usages per classification:\n", - "Number of residential inferred land usages: 34035\n", - "Number of activities inferred land usages: 1093\n", - "Time (seconds): 212.43647408485413\n", - "\n", - "K neighbors: 90\n", - "Total number for each land usage: residential 35674\n", - "activity 7798\n", - "mixed 2\n", - "Name: classification, dtype: int64\n", - "Number of inferred land usages: 35128\n", - "Number of inferred land usages per classification:\n", - "Number of residential inferred land usages: 34035\n", - "Number of activities inferred land usages: 1093\n", - "Time (seconds): 222.0151674747467\n", - "\n", - "K neighbors: 100\n", - "Total number for each land usage: residential 35674\n", - "activity 7798\n", - "mixed 2\n", - "Name: classification, dtype: int64\n", - "Number of inferred land usages: 35128\n", - "Number of inferred land usages per classification:\n", - "Number of residential inferred land usages: 34035\n", - "Number of activities inferred land usages: 1093\n", - "Time (seconds): 231.5161440372467\n", - "\n" - ] - } - ], - "source": [ - "# Test K neighboring values\n", - "K_nearest_values = [ 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 ]\n", - "time_values = []\n", - "inferred_residential_values = []\n", - "inferred_activities_values = []\n", - "\n", - "for K_nearest in K_nearest_values: # For each K neighbors value, calculate the closest distances\n", - " print ( \"K neighbors:\",K_nearest )\n", - " \n", - " start = time.time()\n", - " \n", - " df_osm_processed, bbox = process_shapefiles(polygon_shapefile, point_shapefile, K_nearest = K_nearest)\n", - " \n", - " print ( \"Total number for each land usage:\",df_osm_processed.classification.value_counts() )\n", - "\n", - " df_osm_processed['inferred'] = df_osm_processed.key_value.apply(lambda x: 'inferred' in x.keys() )\n", - " print ( \"Number of inferred land usages:\",len(df_osm_processed[ df_osm_processed['inferred'] ]) )\n", - " \n", - " inferred_value_counts = df_osm_processed[ df_osm_processed['inferred'] ].classification.value_counts()\n", - " print ( \"Number of inferred land usages per classification:\" )\n", - " print ( \"Number of residential inferred land usages:\",inferred_value_counts.residential )\n", - " print ( \"Number of activities inferred land usages:\",inferred_value_counts.activity )\n", - " \n", - " inferred_residential_values.append( inferred_value_counts.residential )\n", - " inferred_activities_values.append( inferred_value_counts.activity )\n", - " \n", - " end = time.time()\n", - " time_values.append( end-start )\n", - " \n", - " print ( \"Time (seconds):\",str(end-start) )\n", - " print ()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Plot the different land usages inferences made" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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4w4UMkubDzKVwtNmxeXl51JrqlBpT1cxjfR9j9PmjeXTlo0zfMN3vOCLiEzsa\noP7OORgh6u+cXW7VqcmTJ9OtW7dj3z/22GOMHTuWlJQUWrRowcyZM8tlOyIiUn3NWLOIyUf7ErRM\nyGkER5v/YH5tqk6pMVXNmBmTB07mVxf+ir6n9PU7joj4pNFXEyH/Ab4uVC7VqV27drF8+XJGjBjh\nrdY53n33XQYNGgTA0KFDWbJkSZm3IyIi1dfiLYsZvXQwRB8CK/y2k9pSndJzpqqhulF1eTrhaQBC\nLsS2jG10bdnV51QiUl4abR5PdNamwhcIBYnOXI/hncTMBam/YxpRBzZCnXoFviW36TlknzmhyO2O\nHz+ehx9+mIMHDwKwf/9+mjdvTnS0d6po164du3fvLsUe1Qxm1hGYAcQCIeBfzrlJZvY34FogCHwF\njHbOZYTf8wAwBsgDfu2ce9OX8CIi5WDp1qXcmHQj9TPP5vCMZXCkRaHLBoPw/vuVGM4nqkxVc79f\n/nsu+vdFbNm3xe8oIlJJ6hzeUcBUV8j04nnzzTdp3bo1559//vdrLGCgGzMr9TZqgFzgXufcmcDF\nwN1m9hNgOXC2c+5c4EvgAYDwvKHAWUA/4Dkzi/IluYhIGb319Vtc/9L1nBlzJql/Xo473BLnKPL1\n0Ud+p654qkxVc2N7jGXGxzPoN6sf79/2vh7uK1IDFFVBsqMBWq7qcawqdWw6jjq5mWSeNwVXv+TH\ngXXr1rF06VJWrFjB0aNHycrKYvz48WRmZpKbm0t0dDS7du0iNja2xOuuKZxzu4Hd4a+zzGwz0N45\ntyxisTXAkPDX1wEvOeeOAt+Y2VbgQmB1JcYWESkXnVt05qpTr2LaddNo1bCV33GqDFWmqrnTWp3G\nopsXETgYYMDsARwMHvQ7kohUoB/cK3W8Mtw79cc//pFNmzbx0Ucf8a9//YuePXsyefJkevbsycKF\nCwF46aWXSEhIKG30GsXMugA/BdYeN+s2IP/GsvZAZLkwNTytoPXdaWYfmNkHaWlp5RtWRKQMPt/7\nOc45Tmt1Gq8Pe52TGp3kd6QqRY2pGuDC9hcyd8hcNgQ2MHLBSL/jiEgFOTaCnyt4eHJzwXId2Q/g\noYce4vnnnycuLo709HSGDx9ebuuursysCTAPuMc5dyBi+ni8roCz8icV8PYC79Z2zv3LOdfDOdcj\nJiamvCOLiJRK8rZkfjb5Z/zlvb/4HaXKUje/GmJA9wH857r/cEqLU/yOIiIVpMiqVL5wderQT54s\n9XZ69uwBhDUqAAAgAElEQVRJz549AejSpQvLly8v9bpqGjOri9eQmuWcmx8xfRQwELjCfX+zWSrQ\nMeLtHYBdlZVVRKQsVm5byYDZAzil5Snc/rPb/Y5TZakyVYPcct4t9OrcC4ANgQ0+pxGR8hadkVJo\nVSqfuSDRGesqKVHtYt7oGy8Am51zf4+Y3g+4DxjknMuOeMtCYKiZ1TezU4BugH45IlLlvbv9XQbM\nHkDn5p15+5a3ObnxyX5HqrJUmaqBlmxZQv/Z/Xl+wPOM7THW7zgiUk4yL032O0JtdxkwEthkZvlX\nrB4EngbqA8vDox2ucc6Ndc59amZzgc/wuv/d7ZzL8yG3iEixZR3N4oakG+jYvCNvj3pbg5udgBpT\nNdBVp17FgG4DuHvx3bRt0pbrzrjO70giItWec+49Cr4PanER75kAFP2ALxGRKqRp/aa8NOQlzoo5\ni9gmtXcE1+JSN78aKLpONElDkujRrgdD5w3l/R214IlpIiIiIlJqa1LXMGfTHACu7HolbZu29TlR\n9aDGVA3VuF5jFg1bRIdmHbh2zrXsy97ndyQRERERqYLW7VzHNTOv4dGVjxLMK/reXPkhNaZqsJjG\nMbw54k2euuopPRNApAYKHApw7fxr2XOo/IZCFxGR2iVlZwpXv3g1MY1iWHHLCupF1fM7UrWixlQN\n17VlV0b/dDQAH+7+kANHD5zgHSJSXUxMmcjaXWuZmFK6B/UW5Kc//Sm9evWiT58+XHHFFQCkp6cz\nePBg4uLiGDx4MBkZGeW2PRER8c/6Xeu5eubVtGrYindGvUOHZh38jlTtqDFVS6QfTqfv9L7cmHSj\nyrciNUDgUIA5m+cQIsTszbPLtTr16quvkpyczFtvvQXApEmTiI+PJyUlhfj4eCZNmlRu2xIREf8s\n+2oZLRq04J1R79CxeccTv0F+pMIaU2bWwMzWmdnHZvapmT163PxnzOxgxPf1zSzJzLaa2Voz6xKe\nPtzMNkS8QmZ2fnjeBWa2Kfyep8PPAJECtGzYkqf7Pc1b37zFba/dRuhED/4UkSptYsrEY/+PQy5U\nrtWp4y1ZsoTExEQAEhMTWby40MHrRESkGsgLeU9peKDXA3z0i4/o3KKzz4mqr4ocGv0ocLlz7mD4\nifHvmdkS59waM+sBtDhu+TFAunPuNDMbCvwVSHTOzQJmAZjZOcBrzrn853s8D9wJrMEbmrYfsKQC\n96laG3X+KHZm7WT82+Np37Q9f73qr35HEpECjH93PJvSNhU6P5gXZP2e9Tic930oyLRPprExbWOh\nfd3PiTmHCb1OPEK3mTFkyBDMjFGjRjFq1CjS0tKIjfWGx42NjWXv3r2l2CsREakKPg58TOIriSQN\nSeK82PNo0eD4j+RSEhXWmHLOOSC/8lQ3/HJmFgX8DbgZuCHiLdcBj4S/fgV41swsvJ58w4A5AGbW\nFmjmnFsd/n4GcD1qTBXpgZ4PkHoglSfff5LLOl3GoNMH+R1JREpoR9aOH01zOHZk7eDUFqeWad1v\nvPEGbdu2JS0tjSFDhtCtW7cyrU9ERKqOjXs2csWMK2hYtyFN6zf1O06NUKEP7Q03nNYDpwH/cM6t\nNbNxwELn3O7jeuW1B3YAOOdyzSwTOAmIvASaiNfoyl8+NWJeanhaQTnuxKtg0alTp7LuVrVmZjyT\n8AzntTmP/t36+x1HRApQVAUpcChAjxk9jlWl8jkcmUczmXLNFNo0Lv3T6tu29Z4rEhMTQ//+/fnw\nww+JiYkhEAgQGxtLIBCgdevWpV6/iIj445PvPuGKGVfQILoByaOS6dqyq9+RaoQKHYDCOZfnnDsf\n6ABcaGbxwM+BZwpYvKD7nY59WjCzi4Bs59wnxVn+uBz/cs71cM71iImJKdE+1ERRdaL4RY9fEF0n\nml1Zu1iTusbvSCJSTJH3Sh2vrPdOHTp0iKysrGNfJycnc+aZZ9KvXz+SkpIASEpKIiEhodTbEBGR\nyrd1/1Yun3459aLq8c6odzi1Vdl6Mcj3KmU0P+dcBpAM9MWrUm01s21AIzPbGl4sFegIYGbRQHNg\nf8RqhhLu4hexfOT4jR2AXRUQv0a77bXbSJiVwKfffep3FBE5gfwR/IKhgkfkDIaCZRrZLy0tjYED\nB9K7d2+uvvpqrrrqKq644grGjRtHcnIycXFxJCcnM27cuLLshoiIVLL2TduT0C2Bd0a9Q7eT1H27\nPFVYNz8ziwFynHMZZtYQuBL4q3MuNmKZg86508LfLgRGAauBIcDb+fdLmVkdvIpWfP57w90Es8zs\nYmAtcAsFV7ykCJMHTuaSFy6h36x+rB6zWs8XEKnCiqpK5cuvTj3Z58kSr79Lly6sXLnyR9NbtWrF\nggULSrw+ERHx15Z9W2jdqDUtG7Zk+vXT/Y5TI1VkZaot8I6ZbQRSgOXOuUVFLP8CcFK4UvVb4P6I\nefFAqnPu6+Pe80vg38BW4Cs0+ESJdW7RmcXDF5N5JJOEWQlkHNHDOEWqqpRASqFVqXzBUJB1gXWV\nlEhERKqqL/Z+Qfy0eEYuGOl3lBqtIkfz2wj89ATLNIn4+ghe9amg5ZKBiwuY/gFwdpmCCufHns+C\nxAUkzErgvuX3MfnayX5HEpECJA9N9juCiIhUA1v2baHv9L7khfJ48qqS91SQ4qvQ0fyk+rii6xW8\nPux1Lmx/od9RRERERKSUtu7fSt/pfckJ5fDOqHf4ScxP/I5Uo1XKABRSPVxz2jW0bNiSI7lHmLlx\npt9xRERERKQEnHOMfm00R3KP8PYtb3P2yerAVdFUmZIf+df6fzFu6TjSDqXxm0t+43ccERERESkG\nM+PFG17kwNEDnNPmHL/j1AqqTMmP3B13N4PPHMxvl/2WpE+S/I4jIoXIyQnw1VfXkpNTuqHQRUSk\nZtiWsY3xb40n5EJ0adGFc9uc63ekWkONKfmRqDpRzLxxJr069eKWV28heVuy35FEpADffTeR7Oy1\nfPdd6R/Ue7zMzExGjx7NxRdfzCWXXEJKSgrp6ekMHjyYuLg4Bg8eTEaGRv0UEfFTVlYW06ZN4+DB\ng2zP2E7f6X15/oPn2Zaxze9otY4aU1KgBtENeG3oa5zW6jRue+02cvJy/I4kIhFycgKkp88BQqSn\nzy636tSDDz7I5Zdfzpo1a1i5ciXdu3dn0qRJxMfHk5KSQnx8PJMmTSqXbYmISOmsWrWKb7/9lpeX\nvUzf6X3JOJLB8pHL6dqyq9/Rah01pqRQLRu2ZOnwpbxx8xvUjarrdxwRifDddxNx4Qf4Ohcql+pU\nVlYWq1evZsSIEQDUq1eP5s2bs2TJEhITEwFITExk8eLFZd6WiIiUTlZWFhs2bCDDZXDvpnvZf3g/\ny0Ys44J2F/gdrVbSABRSpI7NOwLe6DD//OCfDD17KC0btvQ5lUjNtmvXeI4c2VTo/FAoyOHD6wEX\nnhJk//5pHD68kTp16hX4ngYNzqFduwlFbnfbtm2cdNJJ/OpXv+LTTz/l3HPP5c9//jNpaWnExsYC\nEBsby969e0uzWyIiUg5WrVqFc4797CeHHB495VHi2sf5HavWUmVKiuWLfV9wz5v3cN1L13Ek94jf\ncURqtZycHQVMdYVML77c3Fw2btzI6NGjeeedd2jcuDFPP/10mdYpIiLlJysri3UfrSMvL49TOIVf\n82sObz3MwYMH/Y5Wa6kyJcVyRuszmH79dIbNG8bIBSN5afBLRNWJ8juWSI1UVAUpJyfAF1/04Puq\nVD5HXl4mHTtOoW7dNqXcbjvatWvHBRd4XUWuvfZaJk2aRExMDIFAgNjYWAKBAK1bty7V+kVEpGzm\nLJvD03lP05e+nMd51KMezjlWrlzJgAED/I5XK5WoMmVmLc1MYy3WUkPPHsrEqyfyymev8Js3f4Nz\nx3+YE5GKFnmv1PHKeu9UmzZtaN++PVu2bAG8riSnn346/fr1IynJe0xCUlISCQkJpd6GiIiUzpqv\n13DvJ/cSJMjJnHxsel5eHhs2bFB1yicnrEyZWTIwKLzsBiDNzFY6535bwdmkCvrtJb8l9UAqT699\nmtt/drueYyBSib4fwS9YyBJB0tNnc/LJ95a6OvWXv/yFsWPHkpOTQ+fOnXnmmWcIhUKMGTOGmTNn\n0qFDB/7zn/+Ueh9ERKTk1u1cx1Wzr6IOdRjFKGKI+cF8Vaf8U5xufs2dcwfM7HZgqnPuYTPbWNHB\npOp66uqnGHb2MDWkRCpZUVWpfPnVqfbtnyzVNs455xzeeuutH01fsGBBqdYnIiJls/PATq6ccSV1\n8+pyC7fQkh8PBJZfnerduzdNmjTxIWXtVZxuftFm1ha4CVhUwXmkGqhjdY6NGrPwi4Ws+HqFz4lE\naofs7BQKr0rlC5Kdva4y4oiISCVo36w9I2NHcnud2wtsSOXLr05J5SpOZeox4E3gv865FDPrCmyp\n2FhSHeSF8ng4+WG27t/KqltX8dO2P/U7kkiN1q1bst8RRESkkszfPJ8OzTpwYfsLOTd4LoFQoMjl\n8/LySE1NraR0ku+EjSnn3MvAyxHffw0MrshQUj1E1Yli0bBFXPLCJfSf3Z/VY1bTpUUXv2OJiIiI\nVGszPp7B6NdG079bf14f9jq/+MUv/I4khThhNz8z625mb5nZJ+HvzzWzP1R8NKkO2jdrz5LhSziS\ne4R+M/uxL3uf35FEREREqq3nUp5j1Kuj6NulL3MGz/E7jpxAce6ZmgI8AOQAOOc2AkMrMpRUL2ed\nfBYLhy5kW8Y2Xtz4ot9xRERERKqlJ//7JHcvvptru1/LopsX0aSeBpOo6opzz1Qj59w6M4uclltB\neaSa6tW5FxvGbuD0k073O4pIrZEeyuN/j+zjNw1OoqUeoi0iUq2FXIj/7vgvQ88eyozrZ1A3qq7f\nkaQYilOZ2mtmpwIOwMyGALsrNJVUS2e0PgMz49PvPuWR5Ef0UF+RCvZy8ACbQ0FeCR4ol/Vt2bKF\nPn36HHt16dKFf/7zn6SnpzN48GDi4uIYPHgwGRkZ5bI9ERHxGlGZRzKpY3WYO2QuM2+YqYZUNVKc\nxtTdwGTgDDPbCdwD/LJCU0m19vJnL/Poykd54r0n/I4iUmOlh/J4J/cQDngn9xDpobwyr7Nbt24k\nJyeTnJzMW2+9RaNGjRgwYACTJk0iPj6elJQU4uPjmTRpUtl3QEREyAvlccfCO4ifFs+h4CHqR9cn\nSj0NqpUTNqacc187564EYoAznHM9nXPbKjyZVFsP9X6IEeeO4MG3H2T6hul+xxGpkV4OHiC/9huC\ncqtO5Vu1ahVdunShY8eOLFmyhMTERAASExNZvHhxuW5LRKQ2CuYFuXn+zfxnw3+4/vTraVS3kd+R\npBROeM+UmY0DpgJZwBQz+xlwv3NuWUWHk+qpjtXhhUEvEDgY4PbXbye2SSzXnHaN37FEqo2pRzP4\nJq/wh/PmOMdWl3OsMZULLMs9xDeHgkT/8P7WY06Jqsfo+i2KnWHBggXceOONAKSlpREbGwtAbGws\ne/fuLfZ6RETkxw7nHObnL/+cN7a8wZNXPsnvLvud35GklIrTze8259wB4GrgZGA0oP5bUqR6UfWY\nd9M8zj75bJ5NedbvOCI1yl6Xx/F3JDogzZW9qx9AMBhk6dKlDBo0qFzWJyIiPzRu6TgWb1nM8wOe\nV0OqmivOaH75lzn7A1Odcx+bFXLpUyRCs/rNWDZiGc3qN/M7iki1UlQFKT2Ux13ZBY8BdIhQuYzs\nt2LFCs4991xOPvlkAGJiYggEAsTGxhIIBGjdunWZ1i8iUts93Pthrup6FT8/6+d+R5EyKk5lar2Z\nLcNrTL1pZk3xuuiLnFBM4xjqR9dn/+H9jHp1FHuz1T1IpCwi75U6XnndOzV//vxjXfwA+vXrR1JS\nEgBJSUkkJCSUeRsiIrVN2qE0/vD2H8gL5dG+WXs1pGqI4jSmxgD3A3HOuWygHl5XP5Fi27JvC3M/\nncvA2QPJzsn2O45ItZQ/gl9hD/rLpewj+2VnZ7Ny5UoGDhx4bNq4ceNITk4mLi6O5ORkxo0bV+r1\ni4jURjsP7CR+Wjx/X/13PvnuE7/jSDkqTje/nuF/z1XvPimtizpcxJzBcxg8dzBDXxnK/MT5RNcp\nzp+fiOQrqiqVL786dUeDlqXaRqNGjdiyZcsPprVq1YoFCxaUan0iIrXd1+lfc+WMK9mbvZelI5Zy\nXux5fkeSclScT7ORd8U1AC4E1gOXV0giqbGuP+N6nk14lrsW38Vdb9zF5IGTUQNdpPi+DAULrUrl\nywW+CBU+EqCIiFSezWmbufLFKzmSe4S3R71Nj3Y9/I4k5eyEjSnn3LWR35tZR+DJCkskNdov435J\n6oFUZn8ym73Ze4lpHON3JJFq46lGbfyOICIiJbD/8H4aRjfkzRFvcvbJZ/sdRypAce6ZOl4qoL8G\nKbU/Xf4n1t+5Xg0pERERqZECBwMAXNbpMjbfvVkNqRrshI0pM3vGzJ4Ov54F3gU+rvhoUlOZGa0a\ntiI3lMsvF/2SN758w+9IIiIiIuVixdcrOO3p05i5cSYAdaPq+pxIKlJxKlMf4N0jtR5YDdznnBtR\noamkVjiSe4R1u9Zx0ys3sW7nOr/jiIiIiJTJ61+8zoDZA+jasitXdr3S7zhSCU7YmHLOTY94zXLO\n/bcygknN16ReE964+Q3aNG7DgNkD2Lp/q9+RRKqVQ4cOMX/+fA4dOuR3FBGRWu+lT17ixrk3cn7s\n+STfmkxsk1i/I0klKM09UyLlJrZJLEtHLMU5xzUzr2HPwT1+RxKpNlJSUti1axcpKSnlsr7nn3+e\nyy67jJ49e3LHHXdw5MgRtm/fztVXX01cXBxjxowhGNRIgSIix/t87+cMnz+cSzteyoqRK2jVsJXf\nkaSSqDElvut+UncW3byIrKNZfL73c7/jiFQLhw4dYvPmzQBs3ry5zNWp3bt3M2XKFFasWMF7771H\nKBRiwYIFPPbYY4wdO5aUlBRatGjBzJkzyyO+iEiNckbrM0gaksSS4UtoWr+p33GkEqkxJVXCxR0u\n5ptx39C7S28AnDvRo0lFareUlJRj/0+cc+VSncrNzeXIkSPk5uaSnZ1NmzZtePfddxk0aBAAQ4cO\nZcmSJWXejohITeCc44n3nuC9b98DYMhPhtCobiOfU0llK/Q5U2b2OlDoJ1rn3KAKSSS1VuN6jQF4\nLuU5NgQ26KG+Umu9++67pKWlFTo/Ly+PPXu+7xIbCoX45JNPSEtLIyoqqsD3xMTE0KtXr0LX2bZt\nW+6++27OP/98GjRoQJ8+fTjvvPNo3rw50dHeqaJdu3bs3r27lHslIlJzOOe4b8V9/O39v3F33N30\n7NTT70jik6IqU08BE4FvgMPAlPDrIPBJxUeT2uq7Q98x5cMpPJL8iN9RRKqkrKysEk0vjoyMDJYs\nWcL69ev55JNPyM7O5q233vrRcrrAISK10b7cHMYFtrI/L4eQC3HXG3fxt/f/xl097uLphKf9jic+\nKrQy5ZxbCWBmjzvn4iNmvW5mqyo8mdRaD/d+mNQDqTy26jHaN2vPnRfc6XckkUpVVAXp0KFDzJgx\no8B5R48e5ZprrqFx48Yl3ubKlSvp3LkzrVu3BmDgwIGsW7eOzMxMcnNziY6OZteuXcTGanQqEal9\nZmTuYdPRQ0zdv5MN7/2RmRtnct9l9/GXK/6ii0y1XHHumYoxs67535jZKUBMxUWS2s7M+OfAf9K/\nW39++cYvef2L1/2OJFJlRN4rdbyy3DvVoUMHPvjgA7Kzs3HOsWrVKk4//XR69uzJwoULAXjppZdI\nSEgodXYRkepoX24OSw/txwHLDqVzMPcIEy6fwBNXPqGGlBSrMfUbINnMks0sGXgHuKdCU0mtF10n\nmrlD5hLXLk7PnxIJyx/BLxQKFTg/FAqVemS/Cy64gGuvvZbLL7+cXr16EQqFuOWWW3jooYd4/vnn\niYuLIz09neHDh5d1N0REqpUZmXvIycnmaHYaIatDr74TebDXg37Hkiqi0G5++ZxzS82sG3BGeNLn\nzrmjFRtLxBuQ4t3R71I3qi4AIReijmkASqm9iqpK5cuvTvXp06fE67///vu5//77fzCtS5cuLF++\nvMTrEhGpCfbl5vD6/m2sX/ErcoNZ9Bj0EsuyM7g1ry2twp9PpHYr7ifTC4CzgPOARDO7peIiiXwv\nvyG1avsqLvjXBQQOBnxOJOKfQCBQaFUqXygUIhDQ/xMRkfLw9M4NrFs8mqy9n9DlvDupU6cuec4x\nI2PPid8stcIJK1Nm9iJwKrAByAtPdkDBd0CLVIBGdRuxZd8W+s/qz8pbV+qBeFIrDR061O8IIiK1\nxprAJp58+TpyjmZy7pX/oGW7iwDIBZYe2s8tLdqoOiXFqkz1AC5zzt3lnPtV+PXrig4mEqlHux68\n/POX2bhnI0NeHkIwL+h3JBEREanBRi2+m1DeUc5PeOFYQyqfqlOSrziNqU8AjYUrvkvolsCUa6ew\n7Ktl3L7w9hPeOyIiIiJSUs459uXm0PbSRzi//zSannTmj5bJr07tz8up/IBSpZywmx/QGvjMzNYB\nxwaecM4NqrBUIoUY/dPR7MzayWdpn5Ebyj12T5WIiIhIWc3aOIv5n8/nkr4TqdvwJKzhSYUum1+d\nuuekDpWYUKqa4jSmHqnoECIlMb7XeByOOlaHnLwcNaik1goEjDvuaMq//51Fmzaq1IqIlMX/rfk/\nfvPmb+jTpQ+bDu0j14r+mJwLfHq05I+ikJqlOEOjr6yMICLFZWYYxs4DO7l65tU83vdxbjzzRr9j\niVS6iRMbsXZtNBMnNuLJJ8t+Qp88eTIvvvgizjlGjhzJ2LFjSU9P5/bbb+fbb7+lU6dOvPDCC7Ro\n0aIc0ouIVA3OOR546wH++t+/MvjMwcy8cSYNohv4HUuqiRPeM2VmF5tZipkdNLOgmeWZ2YHKCCdS\nlJYNW9KsfjNunncz7337nt9xRCpVIGDMmVOfUMiYPbs+e/ZYmda3efNmXnzxRZYtW8bKlStZtmwZ\nX331FZMmTSI+Pp6UlBTi4+OZNGlSOe2BiEjV8P+W/T/++t+/MvaCsSQNSVJDSkqkOANQPAsMA7YA\nDYHbw9NEfNWobiNeH/Y6nVt0ZtCcQXyW9pnfkUQqzcSJjch/5FQo5H1fFl9++SUXXHABjRo1Ijo6\nmksvvZQ33niDJUuWkJiYCEBiYiKLFy8ua3QRkSpl6NlDmXD5BJ4b8BxRdaL8jiPVTHHumcI5t9XM\nopxzecBUM3u/gnOJFEvrRq1ZOnwpl7xwCf1m9mP1mNW0b9be71giZTJ+fCM2bSr88BwMwvr10Thn\n4e+NadPqs3FjFPXqFfyec87JZcKE7ELXeeaZZzJhwgT2799PgwYNWLFiBeeffz5paWnExnoDusbG\nxrJ3797S71gVY2YtgXbAYWCbc67oJyKLSI2x//B+FmxewJifjSGufRxx7eP8jiTVVHEqU9lmVg/Y\nYGZPmtlvgMYVnEuk2E5peQqLhy/m1FanEl2nWNcHRKq1HTt+fOh2ruDpxdW9e3d+/etfM3jwYG66\n6SbOOussoqJq3hVaM2tuZg+a2SZgDTAZmAtsN7OXzazvCd7f0czeMbPNZvapmY0LT29lZsvNbEv4\n35bh6WZmT5vZVjPbaGY/q+h9FJGipR5IpdfUXty1+C6+Tv/a7zhSzRXnk+dIIAr4H+A3QEdgcEWG\nEimpn7X9GW/f8jZmRk5eDg5HvahCLtGLVHFFVZACAaNHj5bHqlL5nDMyM+swZUpmqUf2GzFiBCNG\njADgT3/6E+3atSMmJoZAIEBsbCyBQIDWrVuXat1VyCvADKCXcy4jcoaZXQCMNLOuzrkXCnl/LnCv\nc+5DM2sKrDez5cCtwFvOuSfM7H7gfuA+IAHoFn5dBDwf/ldEfLA5bTPXzLyGzKOZvDniTbq27Op3\nJKnmTngZ0zm33Tl32Dl3wDn3qHPut865rZURTqQkzIyQC3FD0g2Mfm00IfXYkRoo8l6p45X13qm0\ntDQAUlNTWbRoETfeeCP9+vUjKSkJgKSkJBISEkq9/qrAOXeVc+7F4xtS4XnrnXP3FNGQwjm32zn3\nYfjrLGAz0B64DpgeXmw6cH346+uAGc6zBmhhZm3LcZdEpJjWpK6h59SeBPOCrLx1JX269PE7ktQA\nhVamwl0gCr286Zw7t0ISiZRBHatDz049eeCtB2jXpB1/u/pvfkcSKTf5I/gFgwWP3BcMeiP73Xtv\ndqmqU6NHj2b//v3UrVuXJ598khYtWjBu3DjGjBnDzJkz6dChA//5z3/KuhtVgpldBmxwzh0ysxHA\nz4BJzrntJVhHF+CnwFqgjXNuN3gNLjM7ObxYe2BHxNtSw9N2H7euO4E7ATp16lSaXRKRE9i6fyut\nGrZi6fClnNrqVL/jSA1RVDe/gZWWQqQc3XfZfaQeSOWp1U/Rvll77rn4Hr8jiZSLoqpS+fKrU6V5\n7tSiRYt+NK1Vq1YsWLCgxOuqBp4HzjOz84DfAy/gdf/rXZw3m1kTYB5wj3PugFmhQ9MXNONHLV3n\n3L+AfwH06NFDT2AWKUepB1Lp0KwDI84dwZCfDNHQ51KuCu3mF+7eV+irMkOKlISZManfJG444wZ+\n++ZvefnTl/2OJFIuUlKiC61K5QsGjXXrNBBLMeQ65xxeN7xJzrlJQNPivNHM6uI1pGY55+aHJ+/J\n774X/ve78PRUvHuN83UAdpVDfhEphqfef4rTnj6NlJ0pAGpISbnTGVdqpKg6Ucy6cRYjFozQzaVS\nYyQnZ/odoSbJMrMHgBFAvJlFAXVP9CbzSlAvAJudc3+PmLUQGAU8Ef73tYjp/2NmL+ENPJGZ3x1Q\nRCpOyIW4b/l9PLX6KYb8ZAjnttHdKVIx1JiSGqth3YbMu2nese/3H95Pq4atfEwkIlVIInAzMMY5\nFzCzTkBxbrK8DG+U201mtiE87UG8RtRcMxsDfAv8PDxvMdAf2ApkA6PLbxdEpCA5eTnc/vrtzPh4\nBrMD0KsAACAASURBVHf1uIunE57Ww3ilwqgxJbXCE+89wT9S/sHqMavp0KyD33FExGfOuQDw94jv\nv8W7Z+pE73uPgu+DAriigOUdcHcpY4pIKcz4+P+zd5/hUZZpG8f/V0LvICDVBoK6qKyACiigiFQp\nKsWCCgq6FlDXsrZFN6ur+No7FooCUgSlLzZERBGsFBuCCJIAIr2kzfV+mIlGNiQDZPJkkvN3HM8x\nM888MznzASbX3Pd93WMY89UY/tXuX9zd5m5yWdMocsgOppufEf580HipxI3ODTvzwIcP0HlsZz4c\n8CFVylQJOpKIBMDMdpB7p9pKBRhHRGJgwF8HcFSVo2h/zP98vyGS73LbZ6obcF4OR9Z5kbhxcq2T\nmdp3Kt/9+h09X+9JakZq0JFEDtmulF1MOW8KuzYceOe+4srdK0YKpscJb6xbl3BTiNuBfweZTUQO\n3tptazlnzDms3rKaBEtQISUFRt38pNhof0x7RvUcxQdrPuDyNy8nPPtGJH4tfmQx6xetZ/Eji/Pl\n/YYMGcJxxx3HGWec8fu5LVu2cMEFF9CiRQsuuOACtm4N73Xr7txxxx20aNGCNm3a8NVXX+VLhgLU\n0d2fdfcdkU3pnwMuCDqUiBy4FZtW0OqVVixev5j1O9QsUwpWbiNTAJjZ6Wa22Mx2mlmamWWa2faC\nCCeS3y4+8WIePfdRejTuoTnUEtd2pezim/HfQAi+GfdNvoxO9evXjwkTJvzp3BNPPEGbNm1YvHgx\nbdq04YknngDgnXfeYdWqVXz66ac8+uij3HrrrYf88wtYppldYmaJZpZgZpcAmUGHEpED8/Hajznj\nlTPICGUw/4r5tD6iddCRpJjJs5gCngYuAn4AygJXAU/FMpRILN3U8iYuOvEiAH7Z/kvAaUQOzuJH\nFuOh8OiqhzxfRqdatWpF1apV/3Ru9uzZ9O3bF4C+ffsya9as38/36dMHM6N58+Zs27aNlJSUQ85Q\ngC4G+gAbIkfvyDkRiRMf/fwR7ce057Byh7Fw4EJOrnVy0JGkGIqqm5+7rzSzRHfPBEaa2cIY5xKJ\nuQ/XfEiHVzswssfI34srkcLgw7s+ZNPSTft9PjMtkw2fbfi9jUIoLcSyUcvY9PUmEkvl3P63xok1\nOPP+Mw84y6ZNm6hVqxYAtWrV4tdffwUgOTmZunXr/n5dnTp1SE5O/v3aws7dfyK8Ya+IxKmTDj+J\ni0+8mAfaP0DN8jWDjiPFVDTF1G4zKwV8aWbDgWSgfGxjicRei7otOK3eaVz+5uUcXuFwzj767KAj\niURlx9od/3vSw+erNCiYTpU5rTmMp6mzZlYDGAQcRbbPQncfGFQmEcmbu/Pq169ywfEXULF0RV7q\n/lLQkaSYi6aY6g8kAtcDNwH10SJdKQLKlCjDm33f5MyRZ9JrQi/mXzFfUwSkUMhtBGlXyi7GNB/z\nv829HVK3pdLxxY6UPzz/vu+qUaMGKSkp1KpVi5SUFKpXrw6ER6J++eWPabLr16+Pm1GpiLeAD4F3\n0FopkbgQ8hC3zr2VRz95lI27NnJLq1uCjiSS95qpSPe+PZFuR/e5+83uvrIgwonEWtWyVZl9yWwq\nlqpI57Gd2bBzQ9CRRHKVfa3UvvJr7VR2nTp1+r0pxYQJE+jcufPv5ydOnIi7s2TJEipVqhRvxVQ5\nd7/d3Se6+xtZR9ChRCQsNTWZL75oS2pqeC1mWmYal029jEc/eZQbTr2Bm1veHHBCkbD9FlNmNjFy\nu9TMvt73KLiIIrFVv3J95lw6h0GnDKJG+RpBxxHZr6wOfqG0UI7Ph9JCh9TZb9CgQXTq1ImVK1dy\n4okn8tprrzF06FDmzZtHixYtmDdvHkOHDgWgQ4cOHHnkkbRo0YKbbrqJ4cOHH/TvFZAZZtYl6BAi\nkrOffkpi27YFrFmTxM60nXQf352xS8fywNkP8ESnJ0iwaHqoicRebtP8hkZuuxVEEJEgNanZhCY1\nmwCwZusaapavSdmSZQNOJfJnuY1KZckanWo3vN0Bv/+LL76Y4/mpU6f+zzkzi8cCKruhwJ1mlgak\nR855ZENfEQlQamoyGzaMBEKkpIwkVPlylm5cyovnvchVp1wVdDyRP9lvMeXuyZG717r77dmfM7OH\nCO8WL1Kk7EjdQcuXW3J6vdOZ1HsSiQk5d0YTCULK4pT9jkplCaWFSPk0rlqUB8LdKwadQURy9tNP\nSbiH2JIGVUtlkLBtNN9f/z3lS6n/mRQ+0TSg6MD/Fk6dczj3J2ZWBpgPlI78nMnuPizb808BA9y9\nQuRxaWAM0AzYDPSNtK7FzE4CXgAqASGghbvvNbNmwCjC+1/NAoZ6Ti2mRKJUsXRFbm99Ozf+90aG\nzB7C012ejqsOZVK09ZvXL+gIRYqZdQfaRB7Oc/cZQeYRkT9GpVbvTOPWr6FL7XSuTBjJkUfeg5pJ\nS2GU25qpv5nZUqDxPuulVgPRrJlKBc5295OBpkAnMzs98t7NgX37914JbHH3hsBjwEORa0sArwHX\nuPtfgHb8MSXjOWAwcGzk6BRFLpFcDT19KLe0vIVnlzzLQx89FHQcKUaK4ndB7l4ofy8ze5DwVL8V\nkWNo5JyIBOinn5JI3pPJLV+Hvz1vUx3cM1mzJinoaCI5ym313jjgPGBa5DbraObul+b1xh62M/Kw\nZORwM0sEHgZu2+clPYDRkfuTgfYWHhI4F/ja3b+KvO9md880s9pAJXf/ODIaNQbomedvLBKFhzo8\nxEVNLuKOd+/grW/fCjqOFANlypRh+/bthbLwOFjuzo4dO0hNTQ06Sk66AB3c/RV3f4Xwl3FqSCES\noNTUZL5f9wq3fpVOagj+7yRoUAHc00hJGfl7Zz+RwiS3NVPbgG3ARZEC6PDI9RXMrIK7/5zXm0de\n9xnQEHjG3ReZ2VBgmrsn7zN9qi6wNvKzM8xsG3AY0IhwEfZfoAbwursPj1y/Ltvr10XO5ZRjMOER\nLI444oi8YouQYAmM7DGShtUa0v6Y9kHHkWKgXr16fP7555gZCQlFo0uVu5Oamsq6deH/qhMTC90a\nxCrAb5H7lYMMIiKwevW/uGtpGil7w4XU0dlm9WWNTjVq9ExwAUVykOeaKTO7HrgX2EB4xBXC20We\nlNdr3T0TaGpmVYCpZtYG6E14qt7//Kic3iKS8QygBbAbeNfMPgO27+f6nHKMAEYANG/evOh87Ssx\nVbpEaf511r8A2Jm2k7Xb1nJ8jeMDTiVFVcmSJTnssMOYNm0a5cuXL4yFx0Fxd3bt2kWdOnUoV65c\n0HGy+w/whZm9T/jzpw1wR7CRRIqv1NRkNm4cRb96Tgg4aZ/FIFmjU0ceeQ+lS8fVnnZSxEXTgOJG\noLG7bz7YH+LuW81sHnAW4VGqlZFRqXJmtjKyTmodUB9YF1knVZnwN4brgA/c/VcAM5sFnEJ4HVW9\nbD+mHrD+YDOK5ObyNy9n4dqFLBy4kKOrHh10HCmiGjZsSLdu3VixYgVpaWlBx8kXZkbDhg1p2bJl\noRpxc/fxkc+lFoSLqdvdXXOIRALg7sz8fAiHeYhW1XO7TqNTUvhEU0ytJTzd74CYWQ0gPVJIlQXO\nAR5y91rZrtkZKaQgvDbrcuBj4ELgPXfPmt53m5mVA9KAtsBjkWmCOyJNLRYBlwFPHWhOkWj8+6x/\n0/qV1nQa24mPBn5E9XK5/G8vcggaNWpEo0aNgo5R5JlZL8KfM9Mij6uYWU93fzPgaCLFzt3v3c2D\nCybz7CnQOJdNC9zT2LZtYcEFE4lCNMXUKmCemc0k3KEPAHd/NI/X1QZGR9ZNJQAT82g7+zLwqpmt\nJDwi1S/yc7aY2aPAYsLT+Ga5+8zIa/7GH63RZ0cOkXx3fI3jmX7RdM559Ry6j+/OO5e9Q7mShWrK\nkogcmGHu/vtuxJEv/oYBKqZECtDTnz7NAwseYPApgxnc7XltRyJxJ5pi6ufIUSpyRMXdvwb+msc1\nFbLd30t4PVVO171GeFrfvueXAE2izSRyKFof0Zpx54/jgokXMGT2EF7q/lLQkUTk4OU05zCaz0QR\nySeTlk9iyOwh9Gjcg2e6PqNCSuJSnh8c7n4fgJmVd/ddsY8kUnj1Or4Xr/Z6lVb1WwUdRUQOzZLI\nrIdnCM96uIFw91kRKQDfb/6eS6deSsv6LRl/wXhKJOi7DIlPea4GNrOWZrYC+Cby+GQzezbmyUQK\nqUtOuoSjqx5NyEO8u+rdoOOIyMG5gfA63AnARGAPcF2giUSKkWOrHcsTnZ5g+kXTKVuybNBxRA5a\nNK2VHgc6ApsBIpvntollKJF48OJnL3LOq+cw6stRQUcRkQPk7rvc/R9AO3dv7u53avaFSOz9tPUn\nVmxagZlxTfNrqFa2WtCRRA5JVH1q3X3tPqcyY5BFJK4M+OsAOhzTgaumXcXsH9T7RCSemFmryKyL\nFZHHmnUhEmO/7v6Vjq915Lzx55GemR50HJF8EU0xtdbMWgFuZqXM7BYiU/5EirNSiaV4o88bnHT4\nSfSe1Jsl65cEHUlEovcYmnUhUmB2pe2i27hurNm6hlE9RlEysWTQkUTyRTTF1DWE55HXJbyBblM0\nr1wEgIqlKzLrklnUKF+D8yecT2pGat4vEpFCQbMuRApGemY6fSb3YfH6xbx+4euceeSZQUcSyTfR\ndPP7FbikALKIxKVaFWox55I5JO9MpnSJ0kHHEZHo/GnWBTAEzboQiYnHP3mcWT/M4vmuz9PzuJ5B\nxxHJV/stpszsKcLtYnPk7kNikkgkDjWu3pjG1RsDMPP7mbQ7qh3lS5UPOJWI5OIa4An+mHUxF826\nEImJG067gaOqHEXvv+S4nahIXMttmt8SwntulAFOAX6IHE3RVAiRHK3asooer/egz+Q+ZIQygo4j\nIvvh7r+6+yXufri713T3S919c9C5RIqSySsm89ue3yhToowKKSmy9ltMuftodx8NHAuc5e5PuftT\nQHvCBZWI7OOYqsfwTJdnmPXDLK6ZcQ3u+x3cFZEAmdlwM6tkZiXN7F0z+9XMLg06l0hRMXH5RPpM\n6sO/5/876CgiMRVNA4o6QMVsjytEzolIDq5ufjV3n3k3L3/xMvd9cF/QcUQkZ+e6+3agG+Fpfo2A\nW4ONJFI0vL/6ffpP7U+r+q24/+z7g44jElN5NqAAHgS+MLP3I4/bAvfGLJFIEfCvs/7Fuh3ruO+D\n++h6bFda1G0RdCQR+bOsvsxdgPHu/puZBZlHpEj4KuUrek7oScNqDZl20TTKliwbdCSRmIqmm99I\nM5sNnBY59Q93T4ltLJH4ZmaM6DaCno17qpASKZymm9m3wB7gWjOrAewNOJNIXHN3rp11LZVKV2LO\nJXOoVrZa0JFEYm6/0/zM7LjI7SmEp/WtjRx1IudEJBclE0vS47geACxZv4RF6xaFn0hOhrZtIUXf\nSYgExd3/AbQEmrt7OrAb6BFsKpH4ZmZM6j2JuZfOpX7l+kHHESkQua2Zujly+0gOx//FOJdIkRHy\nEFdNu4pu47vx/ebvISkJFiwI34pIgTKzM7Luu/sWd8+M3N/l7imRphRNgksoEn92pe3ioQUPkRHK\noE7FOhxf4/igI4kUmP1O83P3wZHbswoujkjRk2AJTO4zmVYvt6LT6A58PGEDh4dCMHIk3HMP1KoV\ndESR4uQCMxsOzCG8/ccmwluANATOAo4E/h5cPJH4kp6ZTp/JfZizcg5nHnkmreq3CjqSSIHKs5uf\nmfU2s4qR+3eb2RQz+2vso4kUHQ2rNWTGxTPYsH09XXqnsaMUkJmp0SmRAubuNwFdgWSgN5BEeCbG\nscAL7t7G3RcHGFEkbrg7g2cMZtYPs3iu63MqpKRYiqY1+j3uviMyNaIjMBp4PraxRIqeUxPqM3Gy\n8dXhzuOnA2lp4dEprZ0SKVCR6X0vuvsV7t7R3Xu6+x3uviDobCLx5K737mLUl6O4t+29DG42OOg4\nIoGIppjKjNx2BZ5z97eAUrGLJFJEJSXR9QfjvdFwR9afbBqdEhGROPTztp95YtETDD5lMP9s+8+g\n44gEJppi6hczewHoA8wys9JRvk5EsiQnh0eh0tJoswZKhGBjefi2kkanREQk/hxR+QgWD1rMs12f\nRXu0SXEWTVHUB/gv0MndtwLV0C7xIgcmKQlCod8fOtDxUrigD+wlQ6NTIiISF95f/T7PLwmv9jih\nxgkkJiQGnEgkWHkWU+6+G9gIZLWTzQB+iGUokSIl26hUFgMefAdW1IS7z0zX6JRIATOzcmZ2j5m9\nGHl8rJl1CzqXSGH2ZcqX9Hi9B09/+jR7M7THtQhE181vGHA7cEfkVEngtViGEilS9hmVytLxR7hm\nMTzaEubXSdfolEjBGgmkEt64F2Ad8O/g4ogUbqu3rKbz2M5ULlOZOZfOoUyJMkFHEikUopnm1wvo\nDuwCcPf1QMVYhhIpUj7++E+jUtk9/DYcvQUu75bBjk8/LOBgIsVaA3cfDqQDuPsewoPGIrKPTbs2\n0fG1jqRmpPLfS/9LvUr1go4kUmhEU0ylubsTXuaBmZWPbSSRIuaLL8A9x6NCqjPm7wto3vpC0ufP\nCzqpSHGSZmZl+eOzrQHhkSoR2ceclXNYt30d0y+azgk1Tgg6jkihUiKKayZGuvlVMbNBwEDgxdjG\nEik+Wh/RmtZHtA46hkhxMwyYA9Q3s7FAa+CKQBOJFFL9T+7P2UefTd1KdYOOIlLoRNOA4v+AycAb\nQGPgn+7+VKyDiRQ3K39bSa8Jvdi8e3PQUUSKPHd/GzifcAE1Hmju7vOCzCRSmLg7Q2YPYf6a+QAq\npET2I9diyswSzewdd3/b3W9191siH0Aiks92pu1k5vczuW7WdUFHESku6gKJhDeib2Nm5wecRyQw\nyTuSaTuqLSk7w51l73z3Tp769KnfiykRyVmu0/zcPdPMdptZZXffVlChRIqjprWacm+7e7nrvbvo\neVxP+jXpF3QkkSLLzF4BTgKWA1ntNh2YElgokQAlzU9iwc8LSPogicbVG/PgRw9ydbOruevMu4KO\nJlKoRbNmai+w1MzeJtLRD8Ddh8QslUgxdVvr25j+/XSunXktbY5sQ52KdYKOJFJUne7uWkkvQnhU\nauSXIwl5iBc/f5GMUAY9j+vJM12ewUxNLkVyE003v5nAPcB84LNsh4jksxIJJRjdczR7M/aS9IH2\nnRKJoY/NTMWUCOFRqZCHB2gzPZNaFWox7vxxJCYkBpxMpPDLc2TK3UcXRBARCWt0WCPmXDqH5nWa\nBx1FpCgbTbigSiHcEt0Ad/eTgo0lUrCyRqXSMsP7IYY8xJY9W9iWuo2yJcsGnE6k8ItmZEpEClib\nI9tQrmQ5dqfvJnlHctBxRIqiV4D+QCfgPKBb5FakWEman0RmKPNP50KENDtCJEoqpkQKKXen3ah2\n9Jnc538+6ETkkP3s7tPcfbW7r8k6gg4lUpCSdyTzyhevkB5K/9P5tMw0Rn458vfOfiKyfyqmRAop\nM+P6U69nwc8LeOyTx4KOI1LUfGtm48zsIjM7P+sIOpRIQRo2b9jv0/v2lemZGp0SicJ+10yZ2XTC\nbWJz5O7dY5JIRH7X/6T+TP12Kne9dxedGnaiSc0mQUcSKSrKEl4rdW62c2qNLsXGum3rePmLl/H9\n/KmXNTp1T9t7qFWhVgGnE4kfuY1M/R/wCLAa2AO8GDl2AstiH01EzIwXur1A5dKVuWzqZfv9BlFE\nDoy7D8jhGBh0LpGCMmzeMNz3+505oNEpkWjsd2TK3T8AMLMkd2+T7anpZqbtsEUKSM3yNRlx3giG\nfzScLXu2cHiFw4OOJBK3zOw2dx9uZk+Rw+wL7aEoxYG783nK5/sdlcqSlpnGwnULCyiVSHyKZtPe\nGmZ2jLuvAjCzo4EasY0lItn1PK4n3Rt3J8G0zFHkEH0TuV0SaAqRgIz5agyTVkxiwYAFlC9VPug4\nInEvmr/MbgLmmdk8M5sHvA/cGNNUIvI/EiyBjbs2MnT2UHan7w46jkhccvfpkbu73X109gPQPywp\n0ub+OJcrp13JnvQ9lEwsGXQckSIhmk1755jZscBxkVPfuntqbGOJSE6WbVzGk58+iZnxeKfHg44j\nEs/uACZFcU6kSPg8+XMumHgBf6nxF6b0nUKpxFJBRxIpEvIspsysHHAzcKS7DzKzY82ssbvPiH08\nEcnu7KPP5voW1/PEoifo3rg7Zx99dtCRROKKmXUGugB1zezJbE9VAjKCSSUSW6u3rKbL2C5UK1uN\nWZfMolLpSkFHEikyopnmNxJIA1pGHq8D/h2zRCKSq4c6PESjwxox4K0BbNu7Leg4IvFmPeH1UnuB\nz7Id04COAeYSiZmte7dStWxV5lwyhzoV6wQdR6RIiaaYauDuw4F0AHffA1hMU4nIfpUrWY7RPUez\nbvs67nn/nqDjiMQVd/8qsj6q4T5rpqa4+5ag84nkp4xQeLD1r7X/yrK/LeP4GscHnEik6Immm1+a\nmZUl0kLWzBoQ3uhQRAJyer3TGXv+WE3zEzlI7p4edAaRWMoIZXDBxAtoUqMJ97e/n8SExKAjiRRJ\n0YxMDQPmAPXNbCzwLnBbTFOJSJ76NelHzfI1yQxlsiN1R9BxRIoFM3vFzDaa2bJs55qa2Sdm9qWZ\nLTGzUyPnzcyeNLOVZva1mZ0SXHIpTtyd62ddz7TvplG7Yu2g44gUabkWU2ZmwLfA+cAVwHigubvP\ni3kyEclTyEN0eLUDV7x1RZ472YtIvhgFdNrn3HDgPndvCvwz8higM3Bs5BgMPFdAGaWYe+DDB3jh\nsxf4R+t/cP2p1wcdR6RIy3Wan7u7mb3p7s2AmQWUSUSilGAJdGrYidvfuZ2xS8dy6UmXBh1JJC6Y\n2XQi09ez2Ua4OcUL7r43p9e5+3wzO2rf04S7AQJUJtzkAqAHMMbD33R8YmZVzKy2uyfnw68gkqNR\nX47i7vfvpv9J/Xmg/QNBxxEp8qKZ5veJmbWIeRIROSh/b/l3WtdvzfWzrmfttrVBxxGJF6uAncCL\nkWM7sAFoFHl8IG4EHjaztcD/Ed6vCqAukP0f5brIuf9hZoMjUwSXbNq06QB/vMgfSiWWouuxXXmp\n+0uEJxiJSCxFU0ydBXxsZj9G5nwvNbOvYx1MRKKTmJDI6J6jyQhlMHDaQEIeCjqSSDz4q7tf7O7T\nI8elwKnufh1woGub/gbc5O71gZuAlyPnc/pLNsf5uO4+wt2bu3vzGjVqHOCPF4G9GeHB1ItPvJjp\nF03XprwiBSSaYqoz0AA4GzgP6Ba5FZFCokG1Bjxy7iP8tPUnNuzcEHQckXhQw8yOyHoQuV898jDt\nAN/rcmBK5P4k4NTI/XVA/WzX1eOPKYAi+ebH336k0VONePPbNwE0IiVSgHJdM2VmCcBMd29SQHlE\n5CANbjaYy06+jLIlywYdRSQe/B1YYGY/Eh5BOhq41szKA6MP8L3WA22BeYS/ePwhcn4acL2ZvQ6c\nBmzTeinJb5t2baLT2E7sTt/N8dW1j5RIQcurAUXIzL4ysyPc/eeCCiUiB87MKFuyLLvTd/Pc4ucY\nevpQSiREs5WcSPHj7rPM7FjgOMLF1LfZmk48vr/Xmdl4oB1Q3czWEd4+ZBDwhJmVAPYS7twHMAvo\nAqwEdgMDYvCrSDG2K20X3cZ3Y932dbx32Xs0rt446EgixU40f2nVBpab2afArqyT7t49ZqlE5KDN\nWTmHW96+hbTMNO448468XyBSfDUDjiL8WXiSmeHuY3J7gbtflMt77XutA9cdakiRnGSEMug7uS9L\n1i9hSp8ptKzfMuhIIsVSNMXUfTFPISL5ptdxveh9Qm+GzRtGl2O7cHKtk4OOJFLomNmrhNcDfwlk\nRk47kGsxJVJYJFgCf6nxF7o16kaP43oEHUek2MqzmHL3D8zsSOBYd3/HzMoBibGPJiIHw8x4tuuz\nzF8zn/5T+7N40GJKlygddCyRwqY5cIJrt2uJQ9v2bqNymco81OGhoKOIFHt5dvMzs0HAZOCFyKm6\nwJuxDCUih6Z6ueq81P0llm5cyr8++FfQcUQKo2VAraBDiByolz5/ieOeOY4ff/sx6CgiQnTT/K4j\n3OZ1EYC7/2BmNWOaSkQOWbdG3Xi4w8N0b6zljSI5qA6siKwHTs06qfXAUpjN/H4m18y4hg4NOnBE\n5SPyfoGIxFw0xVSqu6dl7VkQ6VakaREiceCWVrcA4O5khDIomVgy4EQihca9QQcQORCf/vIpfSb3\noWmtpkzqPUn/n4sUEtEUUx+Y2Z1AWTPrAFwLTI9tLBHJLyEP0XdyX2qWq8kzXZ8JOo5IoeDuHwSd\nQSRaq7asouu4rhxe/nBmXjyTCqUqBB1JRCLyXDMF/APYBCwFria8b8bdsQwlIvknwRKoX6k+zy55\nlrk/zg06jkigzGxB5HaHmW3Pduwws+1B5xPJSc3yNenYoCNzLp3D4RUODzqOiGRj+2tkZGbvunt7\nM3vI3W8v4Fwx07x5c1+yZEnQMUQK1J70PTQb0YztqdtZ+relVC1bNehIUsyZ2Wfu3jzoHIWRPqck\ny860nQAaiRIJQLSfU7mNTNU2s7ZAdzP7q5mdkv3Iv6giEmtlS5ZlTK8xpOxMYcicIUHHESkUzCzR\nzOqY2RFZR9CZRLKkZ6bTZ1IfzhlzDpmhzLxfICKByG3N1D8JT/GrBzy6z3MOnB2rUCKS/5rXac49\nbe7hmcXPkLIzhVoV1BVaii8zuwEYBmwAQpHTDpwUWCiRCHfn6hlXM3vlbF7o9gKJCdreU6Sw2m8x\n5e6Tgclmdo+7JxVgJhGJkTvPvJO/tfgbNctrdwMp9oYCjd19c9BBRPY1bN4wRn45kn+2+SeDmw0O\nOo6I5CLPBhTunmRmdc2slZm1yToKIpyI5K+SiSWpWb4mmaFMJi6fyP7WTIoUA2uBbUGHENnXyC9G\nkjQ/iYFNB3Jvu3uDjiMiecizNbqZPQj0A1YAWZN2HZgfw1wiEkMTlk/gkimXsLP7Tgb+dWDQ0wtH\nhAAAIABJREFUcUSCsAqYZ2Yz+fOmvftOaxcpUO2Oasd1La7jsY6PkbXHp4gUXtG0Ru9FeCpEF3c/\nL3Joh3iRONavST/aHdWOG+fcyE9bfwo6jkgQfgbeBkoBFbMdIgVjTzK83Rb2pACwestqQh7i6KpH\n83SXp7Upr0iciKaYWgXoX7RIEZJgCYzsMRKAK968gpCH8niFSNHi7vfldASdS4qRpUmwaQEsS+L7\nzd/T4sUW3PHOHUGnEpEDlOc0P2A38KWZvcufp0Kov7JIHDuqylE83ulxrpx2JU8uepIbT78x6Egi\nBcbMGgG3AEeR7bPQ3dWpVmJvTzKsHgmESPn+FTp9NIMES2BQs0FBJxORAxRNMTUtcohIETOg6QC+\nTPmS0+qeFnQUkYI2CXgeeIk/1gOLFIylSeAhdoSg67pUNmSs5/0BH9GwWsOgk4nIAcqzmHL30QUR\nREQKnpnxZOcnf3/s7lrwLMVFhrs/F3QIKYayRqVCaVySAl+lOm/VS+DUatozWiQe7XfNlJlNjNwu\nNbOv9z0KLqKIxFpmKJMhs4dw/4f3Bx1FpKBMN7Nrzay2mVXLOoIOJcVAZFQK4KYq8PLh0LU8sExb\neorEo9xGpoZGbrsdzBubWRnC7dNLR37OZHcflu35p4AB7l4h8rg0MAZoBmwG+rr7T2Z2FPAN8F3k\npZ+4+zWR1zQDRgFlgVnAUNfGOSIHLDEhkc17NvPckufo3LAzzeo0CzqSSKxdHrm9Nds5B44JIIsU\nF5FRqc92p9GsDJxVLnI+lAarRkKTe6BsrUAjisiB2e/IlLsnR27X5HRE8d6pwNnufjLQFOhkZqcD\nmFlzoMo+118JbHH3hsBjwEPZnvvR3ZtGjmuynX8OGAwcGzk6RZFLRHLwdOenqVm+Jpe9eRl7M/YG\nHUckptz96BwOFVISW0uTeHhzOs3Xwnu793nOMzU6JRKHommNflA8bGfkYcnI4WaWCDwM3LbPS3oA\nWeuzJgPtLZfFG2ZWG6jk7h9HRqPGAD3z83cQKU6qlq3KK91fYcWmFdz93t1BxxGJKTO7LKcj6FxS\nhO1JZvSXL3Hbpkz6VoB2Zfd5Pmt0KrLvlIjEh5gVUwBmlmhmXwIbgbfdfRFwPTAta+Qrm7rAWgB3\nzwC2AYdFnjvazL4wsw/M7Mxs16/L9vp1kXM55RhsZkvMbMmmTZvy5XcTKYo6NuzINc2u4fklz7Nx\n18ag44jEUotsx5nAvYA2pJeYmfHulVyZnM45ZWH04ZCQ09fFGp0SiTv7XTNlZu+6e3sze8jdbz+Y\nN3f3TKCpmVUBpppZG6A30C6nH5nTWwDJwBHuvjmyRupNM/tLLtfnlGMEMAKgefPmWlMlkouHz32Y\nm1veTM3yNYOOIhIz7n5D9sdmVhl4NaA4UsSt3baWPl/MoWlpmFIHSu/vq+xQGvy6sECzicihya0B\nRW0zawt0N7PX2ad4cffPo/0h7r7VzOYBZwENgZWRGXzlzGxlZJ3UOqA+sM7MSgCVgd8iU/hSI+/z\nmZn9CDSKXF8v24+pB6yPNpOI5KxCqQoce9ixuDuLflnE6fVODzqSSEHYTXjtrUi+q1+5Ps+e9wpd\nju1CRX1RJVKk5FZM/RP4B+Ei5dF9nnMg113izawGkB4ppMoC5wAPuXutbNfsjBRSEN4Y+HLgY+BC\n4D1398j7/ObumWZ2DOEPu1Xu/puZ7Yg0tVgEXAY8Fd2vLSJ5Gbt0LP2n9mfmxTPpcmyXoOOI5Csz\nm84fsxkSgBMIb+Qrkm9+3vYzG3dtpHmd5lzR9Iqg44hIDOy3mHL3ycBkM7vH3Q9mAm9tYHSk4UQC\nMNHdZ+Ry/cvAq2a2EvgN6Bc53wb4l5llEN6l/hp3/y3y3N/4ozX67MghIvmg9wm9Gf7RcK6cdiXL\n/raMw8odlveLROLH/2W7nwGscfd1+7tY5EBt3r2Zjq91ZEfqDn4c8iOlS5QOOpKIxEBuI1MAuHuS\nmXUnXNQAzMujKMp63dfAX/O4pkK2+3sJr6fa95o3gDf28/olQJO8sojIgStdojRjeo3h1BdP5bpZ\n1/H6ha8HHUkk37j7B9kfRxomXeLuY4PKJEXHrrRddB3XldVbVjO3/1wVUiJFWJ7d/MzsP4Q38F0R\nOYZGzolIEde0VlPubXcvE5ZP4PVlKqYk/plZJTO7w8yeNrNzLex6YBXQJ+h8Ev/SM9PpPak3i9cv\n5vULX6fNkW3yfpGIxK08R6aArkBTdw8BmNlo4AvgjlgGE5HC4bbWt7Hol0WUL1k+6Cgi+eFVYAvh\n9blXAbcCpYAe7v5lkMGkaHh28bPMXjmbF897kZ7HaftLkaIummIKoArhdUwQ7rInIsVEiYQSvNXv\nraBjiOSXY9z9RAAzewn4lfD2GzuCjSVFxXWnXkeDag3o1qhb0FFEpABEs2nvf4AvzGxUZFTqM+CB\n2MYSkcImM5TJ8I+G8/LnLwcdReRQpGfdieyFuFqFlOSH0V+OJmVnCiUSSqiQEilG8iym3H08cDow\nJXK0dHctnhApZsyMuT/OZeicofz4249BxxE5WCeb2fbIsQM4Keu+mW0POpzEp9FfjuaKt65g+EfD\ng44iIgUsmpEp3D3Z3ae5+1vunhLrUCJS+CRYAiN7jKREQgmueOsKMkOZQUcSOWDunujulSJHRXcv\nke1+paDzSfyZ8f0Mrpx2Jecccw7/aa/+XCLFTVTFlIgIQP3K9Xmy85Ms+HkBj368717eIiLFy8K1\nC+kzqQ9NazVlSp8paoEuUgypmBKRA9L/pP70Oq4Xw+YN49fdvwYdR0QkEO7ObW/fRr1K9Zh1ySwq\nlq4YdCQRCUCu3fzMLAH42t21Ma6IAOG1Uy90e4EffvuB6uWqBx1HRCQQZsZb/d5iZ9pOapavGXQc\nEQlIriNTkb2lvjKzIwooj4jEgRrla9CqfisA1mxdE3AaEZGCs3n3Zm57+zZSM1I5rNxhHFnlyKAj\niUiAopnmVxtYbmbvmtm0rCPWwUSk8Bu/dDwNn2rIp798GnQUEZGY25W2i67juvLkoidZvml50HFE\npBCIZtPe+2KeQkTiUudjO1O7Qm0um3oZn1/9OeVKlgs6kohITKRnptN7Um8Wr1/MG33e4JTapwQd\nSUQKgWj2mfoA+AkoGbm/GPg8xrlEJA5UKVOFkT1G8t3m77jjnTuCjiMiEhMhDzFw2kBmr5zN812f\np+dxPYOOJCKFRJ7FlJkNAiYDL0RO1QXejGUoEYkf7Y9pzw2n3sCTnz7Je6vfCzqOiEi+W7VlFdO/\nm07SWUkMajYo6DgiUohEM83vOuBUYBGAu/9gZmpbIyK/e/CcB/nw5w9Zt31d0FFERPJdw2oNWX7t\ncupUrBN0FBEpZKIpplLdPc3MADCzEoDHNJWIxJVyJcuxZNASEhMSg44iIpJvxnw1hvU71nN769up\nW6lu0HFEpBCKppvfB2Z2J1DWzDoAk4DpsY0lIvEmMSERd2fs12OZ+f3MoOOIiBySmd/PZOBbA3ln\n1TtkembQcUSkkIqmmPoHsAlYClwNzALujmUoEYlPmZ7JIx8/wsBpA9m0a1PQcUREDsrCtQvpPak3\nTWs1ZWrfqZRIiGYij4gUR9F08wsBo4Ekwm3SR7u7pvmJyP8okVCCMb3GsHXvVq6ecTX6r0JE4s3y\njcvpNq4b9SrVY9Yls6hYumLQkUSkEIumm19X4EfgSeBpYKWZdY51MBGJT01qNuHfZ/2bqd9O5bWv\nXws6jojIAfk8Obxn3n8v/S81y6vflojkLpppfo8AZ7l7O3dvC5wFPBbbWCISz25ueTNnHHEGN8y+\ngc27NwcdR0QkT1kj6f1P7s+313/L0VWPDjiRiMSDaIqpje6+MtvjVcDGGOURkSIgMSGRUT1G8XL3\nlzms3GFBxxERydWutF2c8+o5zPphFgAVSlUIOJGIxIv9rqg0s/Mjd5eb2SxgIuGW6L2BxQWQTUTi\nWINqDWhQrQEAO1J3aN2BiBRK6Znp9J7Um3k/zeOGU28IOo6IxJncRqbOixxlgA1AW6Ad4c5+VWOe\nTESKhCnfTOHIx4/kh80/BB1FRORPQh5i4LSBzF45m+e7Pk/P43oGHUlE4sx+R6bcfUBBBhGRoum0\nuqfhOJe9eRkfDvhQLYZFpNC47e3beO3r10g6K4lBzQYFHUdE4lA03fyONrNHzWyKmU3LOgoinIjE\nv7qV6vJsl2f5ZN0nPPzRw0HHEZFibOfmFF7/+3ns+m0DIQ+xI3UH17e4nrvOvCvoaCISp6L5ivhN\n4GVgOhCKbRwRKYr6NenH1G+nMmzeMLoc24WTa50cdCQRKYY+HvsI65Yt4oPXhtNlyCM83+15HMfM\ngo4mInEqmm5+e939SXd/390/yDpinkxEigwz49muz1KzfE0W/Lwg6DgiUgzt3JzCsrnj+abKXi5P\nfoJlqz/FzEiwaP4UEhHJWTQjU0+Y2TBgLpCaddLdP49ZKhEpcqqXq843132jrn4iEoiPxz7C6nJ7\nebXxDg7fU5Ifp4+jyZBTg44lInEummLqRKA/cDZ/TPPzyGMRkahlFVLvr36fciXLcVq90wJOJCLF\nwc7NKbwzfwwvHfcbldMSuHJFBVZ/M5ldl95B+WqHBx1PROJYNMVUL+AYd0+LdRgRKfrSM9O5avpV\nGMZX13xF+VLlg44kIkXc1DH/5PnGv1LSYdDySlRIT8BLhFg49hE63DA86HgiEseimSj8FVAl1kFE\npHgomViSV7q/wqotq7jt7duCjiMiRdzOzSmsnTeDejsTGbS8EtVSEwHIzEhj+X/Hseu3DQEnFJF4\nFk0xdTjwrZn9V63RRSQ/tD2qLTedfhPPLnmWuT/ODTqOiBRRW/du5YPXHqJsOgz4phK1dv95Qo6H\nwqNTIiIHK5ppfsNinkJEip3729/P7JWzGfjWQJZfu5zKZSoHHUlEipBdabvoNPpctq75miszymP8\nb/vzrNGpVpf8XWunROSg5FlMqQ26iMRCmRJleLXXqyz4eYE6/IlIvkrLTOPCSReyOGUJ/TdWzrGQ\nypI1OqW1UyJyMPIspsxsB+HufQClgJLALnevFMtgIlL0NavTjGZ1mgGQGcokMSEx4EQiEu8yQ5lc\nNvUy5qycw4BtjThh4+bcr89IY/2KTwsonYgUNdGMTP3pK2Mz6wloYwYRyTdzVs5hyOwhzB8wn1oV\nagUdR0Ti2J3v3smE5RMYfs5wbm19a9BxRKSIO+Btv939TbTHlIjkoyMrH8nP235m8PTBuHveLxAR\n2Y8rml7Bg+0fVCElIgUiz2LKzM7PdlxoZg/yx7Q/EZFDdnyN4/lP+/8w/fvpjPpyFBBuZ/z6389T\n22IRicrCtQtxd46vcTy3n3F70HFEpJiIZmTqvGxHR2AH0COWoUSk+Bl6+lDaHtmWoXOG8tPWn/h4\n7COsW7ZIbYulUDGzV8xso5kt2+f8DWb2nZktN7Ph2c7fYWYrI891LPjExcPIL0bS+pXWjP5qdNBR\nRKSYiWbN1ICCCCIixVuCJTCq5yhOeu4kXl38MhXmjgcPqW2xFDajgKeBMVknzOwswl8ynuTuqWZW\nM3L+BKAf8BegDvCOmTVy98wCT12ETf1mKldNv4pzG5zLxSdeHHQcESlm9ltMmdk/c3mdu3tSDPKI\nSDF2VJWjWHbtMr4b/RTLQiFAbYulcHH3+WZ21D6n/wY86O6pkWs2Rs73AF6PnF9tZisJN3D6uIDi\nFnnvrnqXfm/047S6pzGlzxRKJZYKOpKIFDO5TfPblcMBcCWgycgiEhPVMkqxbO54fim1m41lM3/f\nVFNrp6QQawScaWaLzOwDM2sROV8XWJvtunWRc//DzAab2RIzW7Jp06YYxy0atu7dyoWTLqTRYY2Y\ncfEMypcqH3QkESmG9ltMufsjWQcwAigLDABeB44poHwiUsx8PPYRMjyTl07YwYi/bGdD2YzfR6dE\nCqkSQFXgdOBWYKKZGeS4U2yODZzcfYS7N3f35jVq1Ihd0iKkSpkqjDt/HHMvnUu1stWCjiMixVSu\nDSjMrJqZ/Rv4mvCHxSnufnu2KQwiIvlm5+YUls0dj6WnM3BFRTLNee7E7awpvVujU1KYrQOmeNin\nQAioHjlfP9t19YD1AeQrUn7a+hMzvp8BQOdjO1O7Yu2AE4lIcbbfYsrMHgYWE+7ed6K73+vuWwos\nmYgUOx+PfQQia6Xq7C7BtUsrUzLTeKHJdlaW36PRKSmsft9/0cwaAaWAX4FpQD8zK21mRwPHAp8G\nlrII2LBzAx1e7cCAtwawI3VH0HFERHIdmfo74e5DdwPrzWx75NhhZtsLJp6IFBdZo1KZGWm/n6ux\nN5HrllaiUloCn1TfpdEpCZyZjSfcQKKxma0zsyuBV4BjIu3SXwcuj4xSLQcmAiuAOcB16uR38Lbu\n3UrH1zqyfsd6pvWbRsXSFYOOJCKy/25+7h7NHlQiIvki+6hUdlXSErl2aSVKZxqeGOKD14bTZYhG\nqCQY7n7Rfp66dD/X3w/cH7tExcPu9N10G9eNFZtWMOPiGbSs3zLoSCIiQHSb9oqIxNz6bxb/aVQq\nu/IZCZRwYzt7GbzxeUZ8NqKA04lIkMZ+PZaFaxcy9vyxnNvg3KDjiIj8Ls9Ne0VECsLlz83L85rd\n6bv5dFJvrp5xNVv3buW21rfFPpiIBO6qU67ilNqn0KxOs6CjiIj8iUamRCRulCtZjql9p9KvST9u\nf+d27nz3Ttxz7DQtInHO3fnn+/9k+cblmJkKKREplDQyJSJxpVRiKV7r9RqVS1fmPwv+Q+XSlbn9\nDO0jLlLUDJs3jKT5SSRYAn+p+Zeg44iI5EjFlIjEncSERJ7r+hwNqzWk/0n9g44jIvnssY8fI2l+\nElf+9UqGtR0WdBwRkf3SND8RiUtmxi2tbuHwCoeTnpnOgwseZE/6nqBjicghGv3laG6eezMXnnAh\nL3R7ATMLOpKIyH6pmBKRuDd/zXzufPdOOo/tzPZUbYMnEq/cnbFLx9LhmA681us1EhMSg44kIpIr\nFVMiEvfaH9OeseeP5aO1H9F+THt+3f1r0JFE5CCYGdMumsaUvlMoXaJ00HFERPKkYkpEioSLTryI\nN/u+ybKNy2gzsg2/bP8l6EgiEqUl65fQ6bVObNmzhTIlylChVIWgI4mIREXFlIgUGV0bdWXOJXPY\nnrqdlJ0pQccRkSh8s+kbOr3Wie82f8eeDK17FJH4om5+IlKktD2qLSuHrKRMiTIAbNq1iRrlawSc\nSkRysmbrGjq82oESCSV4u//b1KlYJ+hIIiIHRCNTIlLkZBVSIz4bQeOnG/PJuk8CTiQi+9q4ayMd\nXu3ArvRdzO0/l4bVGgYdSUTkgKmYEpEi69wG51KtbDXOGXMO76x6J+g4IpLNjtQdlEwsyYyLZnDS\n4ScFHUdE5KComBKRIuuoKkfx4YAPOabqMXQd15Wp30wNOpJIsZeakYq706BaA76+5mtaH9E66Egi\nIgdNxZSIFGm1K9Zm3hXzOKX2KfSZ3IdVW1YFHUmk2ErPTOeCiRdwzYxrALSPlIjEPRVTIlLkVStb\njbf7v82ECydwTNVjgo4jUiyFPMQVb13BzB9mckrtU4KOIyKSL1RMiUixUKFUBc4//nwA3lv9HvfP\nvx93DziVSBGXnAxt2+LJyQydPZRxS8fxwNkPcHXzq4NOJiKSL9QaXUSKnSnfTOGZxc+wec9mHjn3\nEcws6EgiRVNSEixYQNKjPXi6wmJuaXkL/zjjH0GnEhHJNyqmRKTYebLzkyRaIo998hhb925lxHkj\nKJGg/w5F8lVyMowcCaEQp876iuvuG8DwDsP15YWIFCn660FEip0ES+DxTo9TtWxV7vvgPralbmP8\nBeMplVgq6GgiRUdSEmvLZ1J/L3RaCZ3eLwsXqpASkaJFa6ZEpFgyM+5tdy+PdXyMiqUqamRKJD8l\nJ7Nw7ss0/Fs60xoDaWnhUaqUlKCTiYjkK/31ICLF2o2n34i7Y2as2bqGSqUrUbVs1aBjicS1Lf++\nm4vPS6PODmj7U+RkZmZ4DdUzzwQZTUQkX2lkSkSKPTMjI5RBl3FdaDe6HRt2bgg6kkjc8vXrGfTb\nKH6pCK9PhsqpkSc0OiUiRZCKKRERoERCCR7r+Bgrf1vJGSPPYM3WNUFHEolLIx65iDeOC3H/e3Da\nL/s8mTU6JSJSRKiYEhGJOLfBubzd/21+3f0rZ4w8g29//TboSCLxJTmZvZ8upNt3cMvCHJ7X6JSI\nFDExK6bMrIyZfWpmX5nZcjO7b5/nnzKzndkelzazCWa20swWmdlR+1x/hJntNLNbsp3rZGbfRV6j\njStE5JC1qt+KeZfPIy0zjZv/e3PQcUTiS1ISQz9NYNp4SNjfntganRKRIiSWDShSgbPdfaeZlQQW\nmNlsd//EzJoDVfa5/kpgi7s3NLN+wENA32zPPwbMznpgZonAM0AHYB2w2MymufuKGP5OIlIMnFzr\nZD4a+BFVyuz735SI7M9tb99G69Vz6JGWRq4N0NPSYGFOw1YiIvEnZiNTHpY18lQycnikCHoYuG2f\nl/QARkfuTwbaW2RnPzPrCawClme7/lRgpbuvcvc04PXIe4iIHLKG1RpSvVx1UjNS6TWhF7N+mBV0\nJJFCa+LyiTy88GE++XtfcM/7+OKLoCOLiOSLmK6ZMrNEM/sS2Ai87e6LgOuBae6evM/ldYG1AO6e\nAWwDDjOz8sDtwH37uz5iXeRcTjkGm9kSM1uyadOmQ/21RKQY2ZW+i5+3/UyP13swYdmEoOOIFDqr\nt6xm0PRBnF7vdP511r+CjiMiUqBiWky5e6a7NwXqAaeaWRugN/BUDpfnNCvACRdRj2Ub5crr+pxy\njHD35u7evEaNGtH/AiJS7FUrW433LnuPlvVactEbF/HiZy8GHUmk0EjPTOeiNy7CMMadP46SiSWD\njiQiUqAKZNNed99qZvOAs4CGwMrIDL5yZrbS3RsSHlmqD6wzsxJAZeA34DTgQjMbTnidVcjM9gKf\nRa7PUg9YXxC/j4gUL5XLVGbOpXPoPak3g2cMJi0zjetOvS7oWCKBm7xiMot+WcTECydydNWjg44j\nIlLgYlZMmVkNID1SSJUFzgEecvda2a7ZGSmkAKYBlwMfAxcC77m7A2dmu/5eYKe7Px0puI41s6OB\nX4B+wMWx+n1EpHgrV7IcU/tO5YZZN3DGEWcEHUekUOjXpB9HVD6C1ke0DjqKiEggYjnNrzbwvpl9\nDSwmvGZqRi7Xv0x4jdRK4GYg11bnkXVV1wP/Bb4BJrr78txeIyJyKEolluKF817g5FonA/Dmt28S\n8lD4yT3J8HZb2KP9c6To27BzA9/++i1mpkJKRIq1mI1MufvXwF/zuKZCtvt7Ca+nyu36e/d5PAtQ\niy0RKXDzfppHrwm9uPjEixnVYxQllybBpgWwLAlaPBN0PJGYCXmI/lP780XKF/w09CfKlyofdCQR\nkcDEtAGFiEhR1e6odvyn/X8Yt3Qc54/vwp4fXwFCsGqkRqekSPu/hf/H26ve5v6z71chJSLFnoop\nEZGD9I8z/sGzXZ5l5o/v0HltGtszAc8Mj06JFEGL1i3irvfuovcJvRl0yqCg44iIBE7FlIjIIfhb\nk56MrVOSj/Y4X6cBoTSNTkmRtG3vNvq90Y+6Fesy4rwRRLryiogUayqmREQOxdIkLqpoLDsSzigb\nPvXY5jTmfXBNsLlE8lnpEqXp3qg74y8YT5UyVYKOIyJSKKiYEhE5WHuSYfVICKXRuFT41N4QPL01\nk7MWvUWvcZ35YfMPwWYUyQchD1GmRBme6PwELeu3DDqOiEihoWJKRORgLU2CrNboEWUSYNkR8ED1\nRN5Z9S4nPHsCN865kd/2/BZQSJFDs2LTCk5+/mSWblgadBQRkUJHxZSIyMHINiq1r7IJcEfVTFYe\nncjAE/sx4rMRbNmzJYCQIodmT/oe+k7uy8ZdG6lRvkbQcURECh0VUyIiByOHUal9HZ4Y4oW6lVhz\n4xoaVGsAwPWzrufNb9/E3Qsipcghufm/N7Ns4zLG9BxDrQq1go4jIlLoqJgSETkYmz/OcVTqT0Jp\n8OvC37/R37JnC++tfo9eE3rRbnQ7Plv/WQEEFTk4b6x4g+c/e55bW91Kx4Ydg44jIlIolQg6gIhI\nXOr8xQG/pOr/t3fn8XZN9//HX+/kRiJEIqQIKoYYS9NIg5pSU02l+jWPIX7G0m8fVf366ldpWlVz\n0fI1Jaa2CEkNpUIFjTFkFiVITSHxNURSZPr8/ljr6Ml1zp1yb07uOe/n43Efd589fvbae6+1117r\n7LPiqkw6eRLXPX8dPx/zcwZcN4CjtjqKS/a4xF2obLlzy6RbGLj2QH65yy8rHYqZ2XLLlSkzs2Wo\nrkMdJ3/zZA7f8nB+/fdfc9vk21ih4wqVDsvsS0YcPIIPP/3Q56eZWQPczc/MrAK6d+nOBbtdwPTT\nptO9S3cWLV7Ed279Dje8cAOLFi+qdHhWw26eeDPvzX2Pug51bjE1M2uEK1NmZhXUua4zALP/NZtP\nPv+E4+89nv7X9ufh1x6ucGRWix557REGjxrMhWMvrHQoZmbtgitTZmbLgTVXXpOxx43l9gNvZ87n\nc9j9lt3Z9w/78v6/3q90aFYjZs2bxZEjj2TT1Tdl6C5DKx2OmVm74MqUmdlyQhIHb3Ew006dxoW7\nXcgHn35Ajy49ANz1z9rU4ljMMaOO4cNPP+T2A2+na6eulQ7JzKxdcGXKzGw506WuCz/Z/ieMPW4s\ndR3qmPP5HDb//eZcNPYiPl/4eaXDsyp09XNX8+D0B7nsO5ex5RpbVjocM7N2w5UpM7PllCQAPvn8\nE/r27MuZD5/JZr/bjDun3ukf/bVWdejXDuWi3S/ipAEnVToUM7N2xZUpM7Pl3NqrrM19h9/H6KNG\n061zNw4ecTA7DNuBOZ/PqXRo1s7Nmz+PBYsWsFrX1TjjW2d8UYE3M7OmcWXKzKyd2G3wxbc3AAAa\n7UlEQVSD3XjhhBe4/rvXs1HPjVil8yoAzJ0/t8KRWXsUERx/7/F8+6Zv+zt5ZmYt5MqUmVk70rFD\nR4b0H8JN37sJgBkfzWCdS9fhrIfPckuVNcuwCcP405Q/sXffvenYoWOlwzEza5dcmTIza8c6d+zM\nfpvsxwVjL2CjKzbimnHXsHDxwkqHZcu5abOncdoDp7HL+rvw0+1/WulwzMzaLVemzMzasbW6rcXN\nB9zMc//vOTbrtRkn338yW1+7td/6Z2V9uuBTDr3rULp26sotB9ziVikzs6VQV+kAzMxs6Q3oPYAx\nx4xh1EujmDp7Kp3rOgPw5sdvsm73dSscnS1P3pv3HgsWLeCm791E7269Kx2OmVm75pYpM7MqIYkD\nNjuAn+30MwCefPNJ1v/t+px474m8N/e9Ckdny4s+Pfow6eRJ7N1370qHYmbW7rkyZWZWpTZdfVN+\nMPAH3DjhRja6ciPOf+J8Pl3waaXDsgp54+M3OP2B05k3fx51HdwxxcysNbgyZWZWpXqu2JPL97yc\nqadMZbcNduPsv53NNtdvw+JYXOnQbBlbuHghh991OMMmDOPdue9WOhwzs6rhR1NmZlVu49U2ZuQh\nIxkzYwxvzXmLDurA4ljM+Jnj2br31pUOz5aB88acx9g3x3Lb929jw54bVjocM7Oq4ZYpM7MaMajP\nII7c6kgARrw4ggHXDeCgOw/i1Q9erXBk1pYeff1RfvXErxjcbzCHb3l4pcMxM6sqrkyZmdWgfTfe\nl18M+gV/eeUvbPa7zTjjoTP46LOPKh2WtbLFsZhT/nIKG6+2MVfudWWlwzEzqzquTJmZ1aCunbry\nPzv/D6+c9gpHbXUUlz51KbvfsjsRUenQrBV1UAfuP/x+7jr4LlZeYeVKh2NmVnX8nSkzsxrWu1tv\nbtj/Bk7b5jQ++uwjJPHpgk95dMaj7LXRXkiqdIjWQhPfnchWa2zFBqtuUOlQzMyqllumzMyMfmv2\nY1CfQQDcMP4G9vnDPux6866Mnzn+S/OOemAsJ614Dfc8+OQyjtIaM3PmTHbeeWcenPwg37zum1z8\n5MWVDsnMrKq5MmVmZks4cesTuWqvq5j03iS2vnZrjvvzcbzzyTtfTB95/Mus8dks7jr+5QpGWRmS\nbpQ0S9KUEtPOkBSSVs+fJekKSdMlTZLUv63jGzp0KE88+wSH3HEIa668JkP6D2nrTZqZ1TRXpszM\nbAmdOnbi1IGnMv306fx4ux9z2+TbGDxqMJBapdZ95x06EKz79tu12Do1HNiz/khJ6wK7A28Ujd4L\n6Jv/TgCubsvAZs6cyY3DbiT2DuZ0mMMVg66g54o923KTZmY1z5UpMzMrqUeXHly0x0VMO3Ual+95\nOQB/OO05pm45AQARNdc6FRGPAx+UmHQZcCZQ/AaP/YGbI3ka6CFprbaKbejQoSzcYiFsBR2f6Mjo\n60e31abMzCxzZcrMzBq0waobsHmvzRn1wFhm9b6f97/yLgB1LKrV1qklSNoPeDsiJtabtDbwZtHn\nt/K4Vjdz5kyGDRvGog8XwRRYNGYRw4YN4913322LzZmZWebKlJmZNcnI419mh7/vyKAxg74YV4ut\nU8UkdQXOBs4pNbnEuJLvnpd0gqRxksbNnj272XEMHTqUxYsXw2vAiLSVRYsWMXTo0Gavy8zMms6V\nKTMza1Thu1KdIqhb9O9f1XDrFBsC6wMTJc0A1gFekLQmqSVq3aJ51wHe+dIagIi4NiIGRMSAXr16\nNSuAQqvU/Pnzlxg/f/58t06ZmbUxV6bMzKxRI49/GZVuVKnp1qmImBwRX4mIPhHRh1SB6h8R7wL3\nAEfnt/ptC3wcETNbO4YvWqVKcOuUmVnbcmXKzMwaVGiVqmNRyem11Dol6Y/AU8Amkt6S1NC7x/9C\n6ng3HbgOOKW14ynXKlXg1ikzs7blypSZmTWooVapglppnYqIwyJirYjoFBHrRMQN9ab3iYj383BE\nxKkRsWFEbBkR41o7noZapQrcOmVm1nZcmTIzswZ1mzWvbKtUQR2L6Pbe3GUUkRU89dRTZVulCubP\nn8+TT1Z/q6GZWSXUNT6LmZnVsqsW/KDSIVgZ48ePr3QIZmY1zS1TZmZmZmZmLeDKlJmZmZmZWQu4\nMmVmZmZmZtYCrkyZmZmZmZm1gCtTZmZmZmZmLeDKlJmZmZmZWQu4MmVmZmZmZtYCrkyZmZmZmZm1\ngCtTZmZmZmZmLeDKlJmZmZmZWQsoIiodwzIlaTYwD3i/0rEs51bHadQUTqfGOY2aptbSab2I6FXp\nIJZHuZz6Z6XjaEW1dm43h9OmYU6fhjl9Gra06dOkcqrmKlMAksZFxIBKx7E8cxo1jdOpcU6jpnE6\nWbXyuV2e06ZhTp+GOX0atqzSx938zMzMzMzMWsCVKTMzMzMzsxao1crUtZUOoB1wGjWN06lxTqOm\ncTpZtfK5XZ7TpmFOn4Y5fRq2TNKnJr8zZWZmZmZmtrRqtWXKzMzMzMxsqbgyZWZmZmZm1gJVU5mS\ndKOkWZKmFI3rKWm0pFfy/1Xz+J9ImpD/pkhaJKlnnranpH9Imi7pvyq1P22hFdNohqTJedq4Su1P\nW2hmGnWXdK+kiZKmSjq2aJlj8vyvSDqmEvvSlloxnRYVnWf3VGJf2koz02hVSSMlTZL0rKSvFS1T\ntXmSVRdJ60p6VNK0fK3/MI8ved7XKkkdJY2XdF/+vL6kZ3L63C5phUrHWCmSekgaIemlfB5t5/Mn\nkfSjfF1NkfRHSV1q+dxpZhkrSVfkcnSSpP6tGUvVVKaA4cCe9cb9F/BIRPQFHsmfiYiLIqJfRPQD\nzgIei4gPJHUEfgfsBWwOHCZp82W1A8vAcJYyjYqW+3aeXm2/bzCcJqYRcCrwYkR8HRgEXCJphVzp\n/DmwDTAQ+HkVZv7DWcp0ytM+LZxnEbFf24e9TA2n6Wn038CEiNgKOBr4LaSbLqo7T7LqshD4cURs\nBmwLnJrP13Lnfa36ITCt6PNvgMty+nwIDKlIVMuH3wIPRsSmwNdJ6VTz54+ktYHTgQER8TWgI3Ao\ntX3uDKfpZexeQN/8dwJwdWsGUjWVqYh4HPig3uj9gZvy8E3A90osehjwxzw8EJgeEa9FxHzgT3kd\nVaGV0qiqNTONAugmScDKebmFwHeA0RHxQUR8CIzmyxd8u9ZK6VTVmplGm5MyfiLiJaCPpDWo8jzJ\nqktEzIyIF/LwJ6Qb4bVpWjlTEyStA+wDXJ8/C9gFGJFnqdn0kbQKsBNwA0BEzI+Ij/D5U1AHrCip\nDugKzKSGz51mlrH7AzdH8jTQQ9JarRVL1VSmylgjImZCyuSBrxRPlNSVdJN7Vx61NvBm0Sxv5XHV\nrLlpBOnm+CFJz0s6YZlFWjnl0ugqYDPgHWAy8MOIWExtnkfQ/HQC6CJpnKSnJdVCIVAujSYC3weQ\nNBBYD1iH2j2XrJ2T1Af4BvAMjZQzNeZy4EygkAeuBnwUEYUHTLV8jW8AzAaG5W6Q10taCZ8/RMTb\nwMXAG6RK1MfA8/jcqa/cudKmZWm1V6Ya811gbFH3NZWYp9bfHV8/jQC2j4j+pGbTUyXtVJnQKu47\nwASgN9APuCo/WfN5tKRy6QTw1dxV9HDgckkbVijGSrsAWFXSBOA0YDyp9c7nkrU7klYmPYD7z4iY\nU+l4lheS9gVmRcTzxaNLzFqr13gd0B+4OiK+AcyjBrv0lZK/KrA/sD6pLF2JdA9WX62eO41p0+us\n2itT7xWa8fL/WfWmH8qS3dfeAtYt+rwO6Wl6NWtuGhER7+T/s4CRpK5I1axcGh0L3J2bjacDrwOb\nUpvnETQ/nYrPpdeAMaQn2dWsZBpFxJyIODZ/R/FooBcpnWr1XLJ2SlInUkXqtoi4O49urJypFdsD\n+0maQeqyuwuppapH7roFtX2NvwW8FRHP5M8jSJUrnz+wG/B6RMyOiAXA3cC38LlTX7lzpU3L0mqv\nTN0DFN6kdgzw58IESd2BnYvHAc8BffPbUVYgVSSq6g1jJTQrjSStJKlbYRjYA/jiTSpVqlwavQHs\nCpC/37IJ8BrwV2APpTe0rUpKo78u04gro1nplNOncx6/OulG48VlGvGyVzKN8husCi/lOB54PD/R\nr8U8ydqp/P2fG4BpEXFp0aSy5UwtiYizImKdiOhDupb/FhFHAI8CB+bZajl93gXelLRJHrUrqUzw\n+ZPK0W0ldc3XWSFtfO4sqdy5cg9wdH6r37bAx4XugK0iIqrij9R6MhNYQKqBDiH1RX4EeCX/71k0\n/2DgTyXWszfwMvAqcHal92t5SyNSn+aJ+W9qLacRqan9IdL3gKYARxat5zhgev47ttL7tTymE+mp\n2uR8Lk0GhlR6vyqYRtvlcS+RnjiuWrSeqs2T/Fddf8AOpK4zk0hdeyfk87dsOVOrf6Q3m96XhzcA\nns3lxZ1A50rHV8F06QeMy+fQKGBVnz9fpM15uYyYAtwCdK7lc6eZZaxIb8Z9Nd9vDGjNWJQ3YmZm\nZmZmZs1Q7d38zMzMzMzM2oQrU2ZmZmZmZi3gypSZmZmZmVkLuDJlZmZmZmbWAq5MmZmZmZmZtYAr\nU7bUJIWkS4o+nyHp3FZa93BJBzY+51Jv5yBJ0yQ9Wm98b0kjWrp8W5A0SNJ9rbCewZJ6F32+XtLm\njSzzZP7fR9Lhzdxeq8Sd1zVY0lWtsS4zaxlJc4uG95b0iqSvVjKmciT1k7R3K66vKfllyfKrNfPC\nBrbdanmkpHMlndEa62rCtn4habdG5uks6WFJEyQdsizias+akqZNXE8PSacUfW7S/VEtcGXKWsPn\nwPfzD68uNyR1bMbsQ4BTIuLbxSMj4p2IaEplruTyTY0t/5Dcsr4eB5N+AwqAiDg+Ihr80dyI+FYe\n7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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plots\n", - "fig, axes = plt.subplots(1,2, figsize=(14,7))\n", - "import itertools\n", - "# Default colors\n", - "colors = ['black','red','saddlebrown','orange','green','y','turquoise','gray','blue','purple','magenta']\n", - "marker, marker_size = '^', 10\n", - "\n", - "####### Approximation\n", - "ax = axes[0]\n", - "ax.set_ylabel(\"Number of inferred residential land uses\")\n", - "ax.set_xlabel(\"Number of inferred activity land uses\")\n", - "ax.set_title(\"Quantity of inferred land usages\")\n", - "\n", - "color = itertools.cycle(colors)\n", - "\n", - "for activities, residential, K_ in zip(inferred_activities_values[:-1], inferred_residential_values[:-1], K_nearest_values[:-1]):\n", - " ax.plot( activities, residential, color=next(color), marker=marker, markersize=marker_size, label=str(K_) )\n", - "# Plot last one with different color\n", - "ax.plot( inferred_activities_values[-1], inferred_residential_values[-1], color=next(color), marker=marker, markersize=marker_size, label=str( K_nearest_values[-1] ) )\n", - "\n", - "# Legend\n", - "ax.legend(loc='upper right', shadow=True)\n", - "\n", - "# Draw lines\n", - "ax.plot( inferred_activities_values, inferred_residential_values, 'g--' )\n", - " \n", - "####### Time\n", - "ax = axes[1]\n", - "ax.set_xlabel(\"K nearest neighbors land use inference approximation\")\n", - "ax.set_ylabel(\"Running time (seconds)\")\n", - "ax.set_title(\"Land use inference: Time\")\n", - "\n", - "color = itertools.cycle(colors)\n", - "\n", - "for K_, time in zip(K_nearest_values[:-1], time_values[:-1]):\n", - " jet = plt.cm.jet\n", - " ax.plot( K_, time, label=str(K_), color=next(color), marker=marker, markersize=marker_size )\n", - "# Plot last one with different color\n", - "ax.plot( K_nearest_values[-1], time_values[-1], color=next(color), marker=marker, markersize=marker_size )\n", - "\n", - "# Draw lines\n", - "ax.plot( K_nearest_values, time_values, 'g--' )\n", - "\n", - "plt.show()" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.3" - }, - "name": "closest_distance_approximation.ipynb" - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/examples/images/Grenoble.png b/examples/images/Grenoble.png deleted file mode 100644 index c28f19e48c72a5c0211b1c2e9decddeaf94473d4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 349570 zcmcGW1y@|rvaOro?rsS#!3pjj+}(n^yE_CAmf-I04#C~s-Q6AD&bjxz_X{3lFtF~1 zz1FI#IlrpSPkC8!L^wP+5D0|$T|z_=1Om?nfj()$Kmp&uB#+wyK0r7MeOHD7-n?Lp 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