12_geospatial_08_shortest_path.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shortest path of a network\n",
    "\n",
    "First, just check networkx package shortest_path. All functions are useful.\n",
    "\n",
    "\n",
    "Tutorial from: https://automating-gis-processes.github.io/site/develop/notebooks/L6/network-analysis.html#Shortest-path-analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "from geopandas import GeoSeries\n",
    "from shapely.geometry import Polygon\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import osmnx as ox\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "place_name = 'Kamppi, Helsinki, Finland'\n",
    "graph = ox.graph_from_place(place_name, network_type='drive')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = ox.plot_graph(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# retrive only edges\n",
    "edges = ox.graph_to_gdfs(graph, nodes=False, edges=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['osmid', 'oneway', 'lanes', 'name', 'highway', 'maxspeed', 'length',\n",
       "       'geometry', 'junction', 'bridge', 'access', 'u', 'v', 'key'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check columns\n",
    "edges.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4326>\n",
       "Name: WGS 84\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: World\n",
       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       "Datum: World Geodetic System 1984\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check crs\n",
    "edges.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>osmid</th>\n",
       "      <th>oneway</th>\n",
       "      <th>lanes</th>\n",
       "      <th>name</th>\n",
       "      <th>highway</th>\n",
       "      <th>maxspeed</th>\n",
       "      <th>length</th>\n",
       "      <th>geometry</th>\n",
       "      <th>junction</th>\n",
       "      <th>bridge</th>\n",
       "      <th>access</th>\n",
       "      <th>u</th>\n",
       "      <th>v</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23856784</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>Mechelininkatu</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>40.885</td>\n",
       "      <td>LINESTRING (24.92106 60.16479, 24.92095 60.164...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25216594</td>\n",
       "      <td>1372425714</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[29977177, 30470347]</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>Mechelininkatu</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>16.601</td>\n",
       "      <td>LINESTRING (24.92103 60.16366, 24.92104 60.163...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25238874</td>\n",
       "      <td>1372425713</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[372440330, 8135861]</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>25.190</td>\n",
       "      <td>LINESTRING (24.92129 60.16463, 24.92127 60.164...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25238944</td>\n",
       "      <td>25216594</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[25514547, 677423564, 30288797, 30288799]</td>\n",
       "      <td>True</td>\n",
       "      <td>[2, 3]</td>\n",
       "      <td>Mechelininkatu</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>242.476</td>\n",
       "      <td>LINESTRING (24.92129 60.16463, 24.92136 60.164...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25238944</td>\n",
       "      <td>319896278</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[30568275, 36729015, 316590744, 316590745, 316...</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fredrikinkatu</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>30</td>\n",
       "      <td>139.090</td>\n",
       "      <td>LINESTRING (24.93702 60.16433, 24.93700 60.164...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25291537</td>\n",
       "      <td>25291591</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               osmid  oneway   lanes  \\\n",
       "0                                           23856784    True       2   \n",
       "1                               [29977177, 30470347]    True       3   \n",
       "2                               [372440330, 8135861]    True       2   \n",
       "3          [25514547, 677423564, 30288797, 30288799]    True  [2, 3]   \n",
       "4  [30568275, 36729015, 316590744, 316590745, 316...    True     NaN   \n",
       "\n",
       "             name   highway maxspeed   length  \\\n",
       "0  Mechelininkatu   primary       40   40.885   \n",
       "1  Mechelininkatu   primary       40   16.601   \n",
       "2             NaN   primary       40   25.190   \n",
       "3  Mechelininkatu   primary       40  242.476   \n",
       "4   Fredrikinkatu  tertiary       30  139.090   \n",
       "\n",
       "                                            geometry junction bridge access  \\\n",
       "0  LINESTRING (24.92106 60.16479, 24.92095 60.164...      NaN    NaN    NaN   \n",
       "1  LINESTRING (24.92103 60.16366, 24.92104 60.163...      NaN    NaN    NaN   \n",
       "2  LINESTRING (24.92129 60.16463, 24.92127 60.164...      NaN    NaN    NaN   \n",
       "3  LINESTRING (24.92129 60.16463, 24.92136 60.164...      NaN    NaN    NaN   \n",
       "4  LINESTRING (24.93702 60.16433, 24.93700 60.164...      NaN    NaN    NaN   \n",
       "\n",
       "          u           v  key  \n",
       "0  25216594  1372425714    0  \n",
       "1  25238874  1372425713    0  \n",
       "2  25238944    25216594    0  \n",
       "3  25238944   319896278    0  \n",
       "4  25291537    25291591    0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges.head() # u, v are the two end points?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "residential      113\n",
      "tertiary          78\n",
      "primary           26\n",
      "secondary         17\n",
      "unclassified      10\n",
      "living_street      4\n",
      "primary_link       1\n",
      "Name: highway, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(edges['highway'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# project the map from lat&lon to UTM zones\n",
    "graph_proj = ox.project_graph(graph)\n",
    "fig, ax = ox.plot_graph(graph_proj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<networkx.classes.multidigraph.MultiDiGraph at 0x7f4218fa05d0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<networkx.classes.multidigraph.MultiDiGraph at 0x7f4219cdff10>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_proj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Edges and Nodes\n",
    "nodes_proj, edges_proj = ox.graph_to_gdfs(graph_proj, nodes=True, edges=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coordinate system: +proj=utm +zone=35 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +type=crs\n"
     ]
    }
   ],
   "source": [
    "print(\"Coordinate system:\", edges_proj.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>osmid</th>\n",
       "      <th>oneway</th>\n",
       "      <th>lanes</th>\n",
       "      <th>name</th>\n",
       "      <th>highway</th>\n",
       "      <th>maxspeed</th>\n",
       "      <th>length</th>\n",
       "      <th>geometry</th>\n",
       "      <th>bridge</th>\n",
       "      <th>junction</th>\n",
       "      <th>access</th>\n",
       "      <th>u</th>\n",
       "      <th>v</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23856784</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>Mechelininkatu</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>40.885</td>\n",
       "      <td>LINESTRING (384631.322 6671580.071, 384624.750...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25216594</td>\n",
       "      <td>1372425714</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[78537378, 8169098, 29081876, 78537375]</td>\n",
       "      <td>True</td>\n",
       "      <td>[2, 3]</td>\n",
       "      <td>Mechelininkatu</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>85.540</td>\n",
       "      <td>LINESTRING (384624.179 6671539.986, 384623.768...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>25238874</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29066052</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>20.591</td>\n",
       "      <td>LINESTRING (384624.179 6671539.986, 384627.284...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1372425714</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[29977177, 30470347]</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>Mechelininkatu</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>16.601</td>\n",
       "      <td>LINESTRING (384625.787 6671454.380, 384626.281...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25238874</td>\n",
       "      <td>1372425713</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57839670</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Itämerenkatu</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>40</td>\n",
       "      <td>20.414</td>\n",
       "      <td>LINESTRING (384627.187 6671437.809, 384646.250...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1372425713</td>\n",
       "      <td>568147264</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     osmid  oneway   lanes            name  \\\n",
       "0                                 23856784    True       2  Mechelininkatu   \n",
       "1  [78537378, 8169098, 29081876, 78537375]    True  [2, 3]  Mechelininkatu   \n",
       "2                                 29066052    True       2             NaN   \n",
       "3                     [29977177, 30470347]    True       3  Mechelininkatu   \n",
       "4                                 57839670    True     NaN    Itämerenkatu   \n",
       "\n",
       "    highway maxspeed  length  \\\n",
       "0   primary       40  40.885   \n",
       "1   primary       40  85.540   \n",
       "2   primary       40  20.591   \n",
       "3   primary       40  16.601   \n",
       "4  tertiary       40  20.414   \n",
       "\n",
       "                                            geometry bridge junction access  \\\n",
       "0  LINESTRING (384631.322 6671580.071, 384624.750...    NaN      NaN    NaN   \n",
       "1  LINESTRING (384624.179 6671539.986, 384623.768...    NaN      NaN    NaN   \n",
       "2  LINESTRING (384624.179 6671539.986, 384627.284...    NaN      NaN    NaN   \n",
       "3  LINESTRING (384625.787 6671454.380, 384626.281...    NaN      NaN    NaN   \n",
       "4  LINESTRING (384627.187 6671437.809, 384646.250...    NaN      NaN    NaN   \n",
       "\n",
       "            u           v  key  \n",
       "0    25216594  1372425714    0  \n",
       "1  1372425714    25238874    0  \n",
       "2  1372425714  1372425718    0  \n",
       "3    25238874  1372425713    0  \n",
       "4  1372425713   568147264    0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_proj.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze the network properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n': 124,\n",
       " 'm': 249,\n",
       " 'k_avg': 4.016129032258065,\n",
       " 'intersection_count': 116,\n",
       " 'streets_per_node_avg': 3.217741935483871,\n",
       " 'streets_per_node_counts': {0: 0, 1: 8, 2: 1, 3: 71, 4: 44},\n",
       " 'streets_per_node_proportion': {0: 0.0,\n",
       "  1: 0.06451612903225806,\n",
       "  2: 0.008064516129032258,\n",
       "  3: 0.5725806451612904,\n",
       "  4: 0.3548387096774194},\n",
       " 'edge_length_total': 19987.009000000016,\n",
       " 'edge_length_avg': 80.26911244979927,\n",
       " 'street_length_total': 13671.792999999998,\n",
       " 'street_length_avg': 74.70925136612021,\n",
       " 'street_segments_count': 183,\n",
       " 'node_density_km': None,\n",
       " 'intersection_density_km': None,\n",
       " 'edge_density_km': None,\n",
       " 'street_density_km': None,\n",
       " 'circuity_avg': 1.0244621366336473,\n",
       " 'self_loop_proportion': 0.0,\n",
       " 'clean_intersection_count': None,\n",
       " 'clean_intersection_density_km': None}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate network statistics\n",
    "stats = ox.basic_stats(graph_proj, circuity_dist='euclidean')\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"300\" height=\"300\" viewBox=\"384574.124221618 6671093.468717448 1330.1714883511304 1222.853895086795\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,13343409.791329984)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"8.86780992234087\" opacity=\"0.6\" d=\"M 384887.43520964065,6671142.734328128 L 384874.6134171016,6671144.953666562 L 384862.49321464007,6671149.703062389 L 384846.70817595173,6671156.250464548 L 384830.55097262113,6671163.678893966 L 384819.2577072033,6671169.817780623 L 384810.548805537,6671175.830884326 L 384799.8097661783,6671185.46288525 L 384787.5960553605,6671198.183697623 L 384743.85316015256,6671245.752737156 L 384735.2918974575,6671256.09654915 L 384727.49912411737,6671267.3857674645 L 384659.8583754331,6671369.992065999 L 384633.0996793158,6671411.455786523 L 384630.248874986,6671418.811690406 L 384628.10165789985,6671429.109849386 L 384627.1870488745,6671437.808982659 L 384624.5128871406,6671468.763145481 L 384623.92622339417,6671484.172020655 L 384623.3898322977,6671505.586139437 L 384624.16995266604,6671547.642789744 L 384629.5989824361,6671781.058284743 L 384629.84665722464,6671787.335923339 L 384630.9795318943,6671796.505516778 L 384633.3943610887,6671805.824194773 L 384997.3250063297,6672083.911938155 L 385005.4581702336,6672089.161916895 L 385285.23408373387,6672267.057001855 L 385431.07297831087,6672263.37285732 L 385453.99608750106,6672262.733936 L 385460.9719194115,6672262.437781863 L 385855.03009928943,6671721.810323974 L 385395.64087329013,6671421.571126892 L 385372.5008918552,6671406.570391574 L 384912.88220936153,6671147.774655972 L 384904.2688298981,6671144.4567097 L 384896.0935586088,6671142.885828212 L 384887.43520964065,6671142.734328128 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x7f421959dd10>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the Convex Hull of the network\n",
    "convex_hull = edges_proj.unary_union.convex_hull # sw: first-multilinestring; second-convex hull. \n",
    "# Show output\n",
    "convex_hull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n                                                                                    124\n",
       "m                                                                                    249\n",
       "k_avg                                                                            4.01613\n",
       "intersection_count                                                                   116\n",
       "streets_per_node_avg                                                             3.21774\n",
       "streets_per_node_counts                                 {0: 0, 1: 8, 2: 1, 3: 71, 4: 44}\n",
       "streets_per_node_proportion            {0: 0.0, 1: 0.06451612903225806, 2: 0.00806451...\n",
       "edge_length_total                                                                  19987\n",
       "edge_length_avg                                                                  80.2691\n",
       "street_length_total                                                              13671.8\n",
       "street_length_avg                                                                74.7093\n",
       "street_segments_count                                                                183\n",
       "node_density_km                                                                  148.885\n",
       "intersection_density_km                                                           139.28\n",
       "edge_density_km                                                                  23998.1\n",
       "street_density_km                                                                16415.5\n",
       "circuity_avg                                                                  1.2703e-05\n",
       "self_loop_proportion                                                                   0\n",
       "clean_intersection_count                                                            None\n",
       "clean_intersection_density_km                                                       None\n",
       "avg_neighbor_degree                    {25216594: 2.0, 1372425714: 1.0, 25238874: 2.0...\n",
       "avg_neighbor_degree_avg                                                          2.14315\n",
       "avg_weighted_neighbor_degree           {25216594: 0.04891769597651951, 1372425714: 0....\n",
       "avg_weighted_neighbor_degree_avg                                               0.0838867\n",
       "degree_centrality                      {25216594: 0.024390243902439025, 1372425714: 0...\n",
       "degree_centrality_avg                                                          0.0326515\n",
       "clustering_coefficient                 {25216594: 0, 1372425714: 0, 25238874: 0, 1372...\n",
       "clustering_coefficient_avg                                                     0.0954301\n",
       "clustering_coefficient_weighted        {25216594: 0, 1372425714: 0, 25238874: 0, 1372...\n",
       "clustering_coefficient_weighted_avg                                            0.0166188\n",
       "pagerank                               {25216594: 0.00870089854168712, 1372425714: 0....\n",
       "pagerank_max_node                                                               25416262\n",
       "pagerank_max                                                                    0.023918\n",
       "pagerank_min_node                                                             1008183915\n",
       "pagerank_min                                                                  0.00142892\n",
       "eccentricity                           {25216594: 1915.0230000000001, 1372425714: 187...\n",
       "diameter                                                                         2153.57\n",
       "radius                                                                           1007.07\n",
       "center                                                                      [1372376937]\n",
       "periphery                                                                    [319896278]\n",
       "closeness_centrality                   {25216594: 0.0009354029666749434, 1372425714: ...\n",
       "closeness_centrality_avg                                                      0.00143689\n",
       "dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the area\n",
    "area = convex_hull.area\n",
    "\n",
    "# Calculate statistics with density information\n",
    "stats = ox.basic_stats(graph_proj, area=area)\n",
    "extended_stats = ox.extended_stats(graph_proj, ecc=True, cc=True)\n",
    "\n",
    "# Add extened statistics to the basic statistics\n",
    "for key, value in extended_stats.items():\n",
    "    stats[key] = value\n",
    "\n",
    "# Convert the dictionary to a Pandas series for a nicer output\n",
    "pd.Series(stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shortest path analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT (385170.0891607622 6671717.293203074)\n"
     ]
    }
   ],
   "source": [
    "# Get the Convex Hull of the network\n",
    "convex_hull = edges_proj.unary_union.convex_hull\n",
    "\n",
    "# Centroid\n",
    "centroid = convex_hull.centroid # return centroid\n",
    "\n",
    "# Show\n",
    "print(centroid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>x</th>\n",
       "      <th>osmid</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>highway</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25216594</th>\n",
       "      <td>6.671580e+06</td>\n",
       "      <td>384631.322372</td>\n",
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       "      <td>24.921057</td>\n",
       "      <td>60.164794</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1372425714</th>\n",
       "      <td>6.671540e+06</td>\n",
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       "      <td>24.920951</td>\n",
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       "      <td>24.921028</td>\n",
       "      <td>60.163665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (384625.787 6671454.380)</td>\n",
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       "    <tr>\n",
       "      <th>1372425713</th>\n",
       "      <td>6.671438e+06</td>\n",
       "      <td>384627.187049</td>\n",
       "      <td>1372425713</td>\n",
       "      <td>24.921063</td>\n",
       "      <td>60.163516</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (384627.187 6671437.809)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25238944</th>\n",
       "      <td>6.671562e+06</td>\n",
       "      <td>384643.473274</td>\n",
       "      <td>25238944</td>\n",
       "      <td>24.921286</td>\n",
       "      <td>60.164631</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (384643.473 6671561.534)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       y              x       osmid        lon        lat  \\\n",
       "25216594    6.671580e+06  384631.322372    25216594  24.921057  60.164794   \n",
       "1372425714  6.671540e+06  384624.178763  1372425714  24.920951  60.164432   \n",
       "25238874    6.671454e+06  384625.787221    25238874  24.921028  60.163665   \n",
       "1372425713  6.671438e+06  384627.187049  1372425713  24.921063  60.163516   \n",
       "25238944    6.671562e+06  384643.473274    25238944  24.921286  60.164631   \n",
       "\n",
       "           highway                        geometry  \n",
       "25216594       NaN  POINT (384631.322 6671580.071)  \n",
       "1372425714     NaN  POINT (384624.179 6671539.986)  \n",
       "25238874       NaN  POINT (384625.787 6671454.380)  \n",
       "1372425713     NaN  POINT (384627.187 6671437.809)  \n",
       "25238944       NaN  POINT (384643.473 6671561.534)  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nodes\n",
    "nodes_proj.head() # sw: you need to have nodes and edges. Notice the use of node id here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "385855.03009928943\n"
     ]
    }
   ],
   "source": [
    "# Get the x coordinates of the nodes\n",
    "nodes_proj['x'] = nodes_proj[\"x\"].astype(float) \n",
    "\n",
    "# Retrieve the maximum x value (i.e. the most eastern)\n",
    "maxx = nodes_proj['x'].max()\n",
    "print(maxx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT (385855.0300992894 6671721.810323974)\n"
     ]
    }
   ],
   "source": [
    "# Retrieve the node that is the most eastern one and get the Shapely Point geometry out of it\n",
    "target = nodes_proj.loc[nodes_proj['x']==maxx, 'geometry'].values[0]\n",
    "print(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "301360197\n",
      "317703609\n"
     ]
    }
   ],
   "source": [
    "# Get the nearest centroid as the source node.\n",
    "\n",
    "# Get origin x and y coordinates\n",
    "orig_xy = (centroid.y, centroid.x)\n",
    "\n",
    "# Get target x and y coordinates\n",
    "target_xy = (target.y, target.x)\n",
    "\n",
    "# Find the node in the graph that is closest to the origin point (here, we want to get the node id)\n",
    "orig_node = ox.get_nearest_node(graph_proj, orig_xy, method='euclidean')\n",
    "print(orig_node) # sw: return source node id\n",
    "\n",
    "# Find the node in the graph that is closest to the target point (here, we want to get the node id)\n",
    "target_node = ox.get_nearest_node(graph_proj, target_xy, method='euclidean')\n",
    "print(target_node) # sw: return target node id\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve the rows from the nodes GeoDataFrame\n",
    "o_closest = nodes_proj.loc[orig_node]\n",
    "t_closest = nodes_proj.loc[target_node]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
       "      <th>osmid</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>highway</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>301360197</th>\n",
       "      <td>6.671721e+06</td>\n",
       "      <td>385166.707932</td>\n",
       "      <td>301360197</td>\n",
       "      <td>24.930617</td>\n",
       "      <td>60.166212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (385166.708 6671721.244)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317703609</th>\n",
       "      <td>6.671722e+06</td>\n",
       "      <td>385855.030099</td>\n",
       "      <td>317703609</td>\n",
       "      <td>24.943012</td>\n",
       "      <td>60.166410</td>\n",
       "      <td>traffic_signals</td>\n",
       "      <td>POINT (385855.030 6671721.810)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      y              x      osmid        lon        lat  \\\n",
       "301360197  6.671721e+06  385166.707932  301360197  24.930617  60.166212   \n",
       "317703609  6.671722e+06  385855.030099  317703609  24.943012  60.166410   \n",
       "\n",
       "                   highway                        geometry  \n",
       "301360197              NaN  POINT (385166.708 6671721.244)  \n",
       "317703609  traffic_signals  POINT (385855.030 6671721.810)  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a GeoDataFrame from the origin and target points\n",
    "od_nodes = gpd.GeoDataFrame([o_closest, t_closest], geometry='geometry', crs=nodes_proj.crs)\n",
    "od_nodes.head() # sw: Wait, you need to have the dataframe for OD? No."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[301360197, 1372441183, 1372441170, 60170471, 1377211668, 1377211666, 25291565, 25291564, 317703609]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the shortest path\n",
    "route = nx.shortest_path(G=graph_proj, source=orig_node, target=target_node, weight='length')\n",
    "# sw: weight is for distance. \n",
    "# sw: graph and graph_proj are basically directly downloaded from osm. \n",
    "# sw: Both seem some objects from networkx.\n",
    "\n",
    "# Show what we have\n",
    "print(route)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mUVVVpfP4BA2TkDAFDUrXrl0ZMGAAS5cu5fz584YO54Xi4ODAlClTaNKkCevXr+fkyZN1+vpPP/2U3Nxcli5dqqMI73FxcWHMmDFkZGSwdetWrKysGDt2LIWFhaxbt+6JCX706NHcvXuXrVu36jQ+QcP1pIQprJIVGKW4uDjN6SYODg6GDueFMWLECGbMmMGtW7f4+uuv65wsAVavXo23tzd2dnY6iPDeArE+ffowfvx4tm3bxoYNG2jXrh1vvPEGiYmJLFu27Kmj4a1btxIYGEirVq10Ep+g8RJGmAKj9vrrr+Pk5MR3331Xq5Kg4Pm0adOG8ePHI5PJWLduHadOnarX9aZNm4ZUKmXWrFlaivAeW1tbxowZQ1lZGWvWrEEkEhEdHY2ZmRmrVq3ixo0btbpOYGAgPXv2ZO7cuSiVSq3GKGj4hJKsoMH64IMPMDMz49y5c+Tm5pKYmGjokBoNqVTK2LFj8fX15fz58yxfvhyFQlHv61pbW/PBBx+wcuVKTp8+rYVIoUOHDvTr14+9e/eSmJioWehz5MgR9u3bh0qlqtP1JkyYQF5eHnv37tVKfILGQ0iYggbL1NSUpKQkWrRoAcDMmTP5448/DBxVwxcQEMCoUaNQKpUsX76czMxMrV5/7NixeHp68uWXX9brOpaWlkRFRdG0aVNWr15NSUkJQ4cOxdnZmVWrVpGbm/vc150+fTq//vor169fr1eMgsZFmMMUNFjW1taaZAkwaNAgA0bT8JmamvLGG28wduxYUlJS+PLLL7WeLAHWrFmDTCajX79+z32N0NBQpk+fzuXLl/npp5+wtLRkxowZlJWV8eOPPz53sgQoLi5m9+7dREVFIRI99vlRIKhBaFwgMHrXr18nLS0Nb29vAGQyGd7e3qSlpRk4soYnPDycgQMHcufOHebOncvVq1d1di+FQsGePXvo06cP+/fvr9M2E3Nzc0aOHIm1tTWLFi2ioKCAQYMG4efnx7p167S2j/Lo0aMEBQURHh5OfHy8Vq4paLyEkqygQbC2tmb06NF07tyZ77//nmHDhrF169Z6L055UVhaWjJlyhTs7OyIjY19agMCbavrNhN/f3+GDRvGsWPH2Lt3Ly1atGDMmDFcuXKFTZs2UVZWptX4mjdvzptvvsm8efMoLCzU6rUFDZMwhyloFLp27Yqnpyfbt2/n5ZdfRq1W07NnT4qLi3n//ffJy8szdIhGp1+/fkRGRlJQUMDixYu5ffu2Xu/v4eHB5MmT+e9//0tBQcETHyeXyxkyZAht27Zl1apV5OXl0aNHDzp16sSWLVtISUnRWYyRkZG4ubkJPWYFgJAwBY2EWCzmzTff5Pjx4yQnJ3P+/HnNsWAJCQkMHTq0zqslG6vqEzuaNm3K9u3bDdo16VnbTFq1asWYMWO4ePEiW7ZswdzcnJdeeony8nLWrl1LcXGxTuMTi8W8/fbbxMfHk5SUpNN7CYyfsOhH0CioVCpWr17NkCFDiIiIoEmTJprPOTs7M336dOFcTSA6Oppp06ZRWFjIv/71L4O3GFy+fDl2dnYEBAQ88rkePXowYcIEYmJiWLduHa6urrz11lucOnWKRYsW6TxZwr3fq3Xr1jFgwADMzc11fj9BwySMMAUNTosWLTh06BA2NjbcvXsXqVRKaWkpEyZMoEmTJrRp0+aFPe3Ezc2NV155BYlEwpo1a7S2B1IbHrfNpH///ri5ubFs2TJKSkro06cPoaGhrFixguzsbL3H2K9fP2xtbVmxYoXe7y0wHsIIU9Bo9OnTBxsbGwDMzMz4+OOPcXd3JyEhgdjYWNq2bUvTpk0NHKV+SaVSJk2axGuvvUZWVhZfffWVUSVLuLfNRCqVMmHCBHx8fOjWrRs+Pj4sXrwYhULBpEmTaN26NXPnzjVIsgTYu3cvjo6O+Pr6GuT+AuMmbCsRNDhpaWkolUokEglqtRqJRALcW125bt067OzskEqlfPzxxwaOVD+CgoKIioqiqqqKX3/9VXNOpbFRKBRcuXKFH374AYlEwtmzZxk0aBAODg6MGTOG5ORkdu3aZdA5aIVCwfr163nppZfIysqivLzcYLEIjI9QkhU0SHPmzGH8+PEAlJWVsW/fPtzc3DR7NQH8/Px0us/Q0MzMzJg0aRJOTk4cO3aMDRs2GDqkZ1q1ahV9+vTRvP/FF19gYWHB2rVrSU9PN2BkNQ0fPhyRSNQg/k0F2vekkqwwwhQ0eE2aNHmk+8/du3cpLS01UES617VrV/r3709xcTFz5swhPz/f0CHVyoULF2okzJkzZxIcHExJSYkBo3rUjh07ePfdd3F1dSUrK8vQ4QiMhJAwBQ2Oh4cHcrmcsrKyGqtkAS5fvszNmzdJTk42uidhbbC2tmby5MnY2tqyb98+9uzZY+iQ6uSrr75ixIgRODo6Avde7Bjjz6miooKNGzcycuRI5syZIxw2LQCERT+CBkQsFjN27FgmT57MgQMH8PHxISwsjE2bNgH3RpXr169nwIABZGdnExgYaOCItWvQoEF88MEHKBQKvv322waXLAEqKyuZOXMmd+/e1WwRMlZpaWnk5ubSu3dvQ4ciMBLCHKagQXB1dWX8+PGIxWJWrFjB+fPna3y+devWFBYWMnDgQJo2bUpCQgKjRo1ix44dpKamNujybMuWLZk4cSJmZmZs2bKFI0eOGDqkejM1NSUyMpJu3brx73//22h/Pk2bNmXmzJksWbJE6CL1AhE6/QgaJLFYzLhx4/Dz8+PcuXP8+eefT11FKRKJ6Nu3L5GRkbz00ktYWVmRnZ1N3759G9wRTmKxmFGjRhEcHMzFixdZsmRJnRqYNwQffPAB5eXlzJ0719ChPFFwcDCRkZHMnTtX6CL1ghD2YQoaHDc3Nz7//HPc3d1ZtGgRy5Yte+YTllqtZufOnVy5cgUrKysAXFxcGDBggB4i1h4PDw8+//xzfH19WbZsGb/++mujS5YAixYtwtHRkfbt2xs6lCdKTk6mqKiIyMhIQ4ciMDBh0Y/A6IjFYl555RV8fHw4c+YMK1eurPMr++PHj6NSqRCLxajVajIyMnQUrXbJ5XLGjx+Pu7v7c3/vDcnNmzfJzs7mr7/+QiwW89VXX7FkyRJDh/WIjRs3Mm3aNM6cOfPUBvKCxk0oyQqMipeXF+PGjUOlUrFs2bJ6LekfPHgwPXv2xMbGhmPHjjF//nwtRqp9Xl5evPLKK1RUVPDHH38YrNuNvi1fvpyBAwcC9xoHtG3bljt37hg4qkeFh4cTGBjIggULeNrzpqDhE+YwBUbtwVFlSkoKq1at0trIytramvfff5/4+Hi2b9+ulWtqy/vvv0/79u3JzMxELBaTlJTEmjVrDB2W3piamrJr1y78/f2Be9s52rZta5QddkQiEX/7299ITk7m8OHDhg5HoENC4wKB0fLw8NCcbamL1m6FhYWsXr2aMWPGkJmZ+cgKW0MZO3Ys//jHP4B7c69vvvnmC5UsO3bsyNChQ4mLi6O0tBQXFxejPhBcrVazbt063njjDVJTUykqKjJ0SAI9Exb9CAymelQ5efJkLly4wNdff62zPqinTp3ixIkTjB8/3miOb/L09NS8LRKJ9H6ws6GYmZnxzjvvMHz4cOLj4/n0008ZOHAg/v7+nDlzhilTphg6xCcqKCggPj6eESNGGDoUgQEICVNgEA+ugP3999+fuV1EG9atW8fNmzd55513dHqf2ujZsyempqaavX2XLl0iPj7ewFHpXnh4OJ9++ilmZmb88MMP7Nixo8bnf//9d5ycnAgODjZMgLVw8OBBmjVrZtQxCnRDmMMU6FV1tx5/f3/Onj3LihUr9LoKVC6X8+mnn3L58mUWLVqkt/s+yN3dnREjRvDLL79QVlaGjY0N3bp1Q6lUNtpm3+bm5kyZMgUHBwcOHjzIzp07n/jYESNGEBISwldffYVCodBjlLXn7OzMq6++yuzZs41ygZKgfoR9mAKDa9WqFZ999hkeHh4sWrSI5cuX633LRGVlJb/88gvu7u6PNGzXB5FIxODBg9m+fTslJSUoFAquX7/Oli1b8PT0xMvLS+8x6VrXrl355JNPkMvl/Oc//3lqsgTYsGEDCoWCl19+WU8R1l1ubi4nT55kyJAhhg5FoEdCwhToRdu2bXnjjTfIycnhq6++4sKFCwaL5erVq6xcuZKIiAhCQ0P1eu9WrVoBcPbs2Rofr6ioYM2aNYwcOfKRhvINlaWlJe+++y4DBgxg3759/Oc//6GwsLBWX7t8+XK8vb1p06aNboOsh927d+Ps7FzjSDlB4yYkTIHOSaVSJk2aRFpaGosWLTKKjfinT59m3759REdH4+Liorf7+vj4cO7cucd+Lisri7NnzzJ48GC9xaMr3bt356OPPkIsFvPtt9+yd+/eOn19VlYW6enpmjNPjVFVVRUbNmxg+PDhmJiYGDocgR4ICVOgcy+99BKVlZUsW7bM0KHUsGfPHlJTU5k6dareVs76+PiQmpr6xM/HxMTQpk2bBluatbKy4r333qNPnz7s3r2bH374geLi4ue61rJly5DL5QwbNkzLUWpPZmYm6enp9O/f39ChCPRASJgCnRKLxfj4+BATE2PoUB5r2bJl3Lp1ixkzZiAW6/bPwdraGnNzc3Jzc5/4mMrKStatW9cgS7O9evXiww8/RKVS8d1333HgwIF6XU+hULBhwwbCwsKwtbXVTpA6sH37dnx9fY26fCzQDiFhCrRCKpXy5Zdfap7sq/Xs2ROFQsHx48cNGN3TzZ07F4lEwltvvaXT+3h6epKenv7MtmpZWVmcOXOmwSwosba25oMPPqBnz55s27aNWbNmPfeo8mFJSUlcvXqVSZMmaeV6ulBeXs7mzZuJiopCKhV6wTRmQsIUaMXrr7/OtGnT6NGjBwsWLMDd3Z2xY8cyc+ZMoz9WS6FQMHfuXFq0aMHo0aN1dh8vL69adxmKiYnBxcUFX19fncWjDf379+eDDz6goqKCb775Rid7SRcvXoy1tTVdu3bV+rW15ezZs1y9epXevXtjZ2eHSPTYXQmCBk54OSTQCnt7e83bEomEn3/+mXbt2gH3RlZ//PEHJSUlhgrvmQoLC1m8eDFTpkzh6tWrxMXFafX6EokEV1dX1q5dW6vHV1VVsWbNGl5++WUuXbrE3bt3tRpPfdnZ2TF58mQsLCzYtGmTTg+1Li0tZd++ffTv359jx44ZZZ9ZuNfQYN++fdjZ2XH8+HGGDx+ulZ+bWCymY8eO+Pj4UFZWRnx8PDk5OVqIWFBXwghToBW///67prVbTEwMFRUVms81b96cN998Ew8PDwNFVzuZmZls27aNAQMGaD3WNm3acP369To9gV6+fJnk5GSjW/QyaNAg3n33XUpLS/l//+//6TRZVtu7dy8lJSVGXZodMGAAdnZ2ALRv314rZ7A6ODjw5ptv4u/vz5EjR7h48SKvvvoq7u7u9b62oO6EEaZAK65cucKqVav466+/OHXqFL1796Z9+/bI5XKysrJQKpVMmjQJlUrFlStXOHbsGCdOnDCKLSYPio+Px9HRkYkTJ/Lf//6XmzdvauW6dSnHPmjXrl1Mnz6dgIAATp8+rZVYnpeDgwOTJ0+madOmrF+/Xu/z0osXL2bGjBmanrPGRCQSIZfLa3ysPlMREomE7t2707lzZ3bu3MmxY8c0n7tx4wajR49m1qxZRjvabqyEEaZAKwIDA1EqlZrTJvbs2cPixYsBaN26NTdu3OCf//wn69evR6VSMXToUP71r3/x97//nd69e2NqamrI8GtYu3YtV65c4Z133nnkSfB5VS/4qSuFQsGaNWsYOnSo3pvGOzg4EBYWRpMmTRg6dCgzZsygqKiIf/3rXwZZxJWfn8/JkycZNWqUzlc015ZYLKZ79+58/PHHiEQiZs+eze7du9mwYQMSieS5runk5MQ777yDs7MzP/74Y41kCfcqIampqQbpVPWiE3rJCrRi6tSpyOXyGoc0X758GQsLCwDS09Pp1KlTja9xdXUlIiICV1dXTE1NKS4uJi0tjdjYWK2N7J6XWCzmk08+oby8nB9++KFe12rWrBnTpk3jm2++ee6Dh/v164e9vb3e9rIGBgayZcsWLCwsuH79Ohs2bGDNmjUkJSXp5f5PIhaL+eyzz8jIyODPP/80aCxOTk5ER0dTVFTE9u3ba4woq3/mv/32G/n5+bW6nlQqpXfv3rRr145t27aRnJz8xMeamJgwc+ZM1q1bR0ZGRn2/FcFDhF6yAp1ydnYmJSWlxscyMzM1bz/ujzorK4tly5bx5Zdf8v3335OWloa3tzfvv/8+X3zxBRMmTMDNzU3nsT+OSqVizpw5WFlZ8eqrr9brWl5eXly4cOG5kyXcG7Hb2trq7YSM6OhozYsde3t7du7cafBkCfd+Ln/99Rf+/v44OzsbJAapVEr//v2ZOHEiBw8eZMmSJY+UX4uKioiJiWH06NG1Gg23bt2aGTNmYGNjw5w5c56aLOFeK8X169cTFRWltSqI4NmEhCmoNzs7O+Ry+SOLPz7//HPOnj3Lzz///MwjtW7evMmGDRv497//zZdffsmhQ4do3rw5U6ZM4V//+hdvvvkm7du312sprrS0lAULFuDl5UXfvn2f+zrPW459kFKpZM2aNQwePFiTyHTF29u7xlmdlZWVBu39+7Dz58+TlZVV7xcyz6NNmza1TmzHjx+ntLSUHj16PPExcrmcIUOG8PLLLxMTE8OKFSsoLS2tVSwXLlwgMzNTK4uLBLUjlGQF9TZ8+HB8fHz497//XePj1dsO5syZ89zXFovFhIaG0qFDB5ycnBCLxRQUFJCSkkJiYqJetluEhoYyatQoVqxYUeeFNxKJhH/+8598//33WjkGqnfv3jg5ObF06dJ6X+thpqamTJw4kdatW5OSkoJIJCIsLIymTZsyZ84cgy86epBcLufzzz8nISGB7du36+V+/fv3x8/Pj82bNz/SPP9JLC0tmT59OosWLeLKlSs1Pufu7s7IkSO5ePEiW7dupaysrM5xNWnShJkzZ/LXX3/p7PD1F9GTSrLCKllBvVWXHB/Wpk2bZx7l9CwqlYrjx49rFpm4ubnRpUsXunTpQu/evbl79y5ZWVkcPny4RglYm5KSknB0dGTs2LHcuHGDq1ev1vprvby8uHbtmtbOTNy/fz9vv/02oaGhWi2RduvWjX79+lFSUsKPP/6o+R5XrFjByJEjGTVqFKmpqUZzPmVlZSVbtmxh2LBhJCQkaLY06YKHhwcjR44kMzOT2bNn1ymxFRcXs337dkaNGsX8+fNRKBSYmJgwaNAgPD09Wb9+fb2qD2VlZWzcuJGoqCjmzJlDVVXVc19L8GzCCFNQL1KplK+//rrGkyzcm5N54403+Oyzz3T2JGtpaUlERAS+vr7Y2tqiVqvJz8/nzJkzJCQkaH3J/ZQpU2jTpg3/+c9/at36bezYsWRmZnL06FGtxdGyZUsmTZrEnDlzal2+exI7OzsmTpyIlZUV+/bte+KpIp999hk5OTksWbKkXvfTtnfffReAWbNmaf3aTZo0YfDgwbi6urJ+/fp6laVHjx5NixYtuHjxIv7+/qSmprJjx44a+5XrY8yYMdy5c4etW7dq5XovuieNMIWEKaiXsLAwBg0axOeff17j4+PHj8fe3r7eK0zrIiAggA4dOuDi4oKJiQl37tzh4sWLJCYmkpWVpZV7vPvuu1hYWPDtt99SWVn51MeamJjw8ccf87//+7/PVW57mgEDBtCsWTP++uuv575GVFQU7dq1Iy8vj8WLFz+1vN2mTRv+9re/8fvvv+tsJP88rKys+PDDD9m8eTOHDx/W2nVdXFx4+eWXOXv2LDExMc/8WdeGj48PzZs3Jysri7y8PC1E+X/MzMyYOXMmycnJFBUV6aRF4YtEKMkKdCI0NPSRuRm4VzqNjY3VayynT5/WzLNZWVkRERGBj48Pr732GiqViuvXr5OSksLhw4efe/Q5d+5cPvroI37++WdMTExISkriv//972Mf6+npyeXLl7WeLOHeqtmZM2c+V0MEDw8Pxo0bh0QiYdWqVZq9s09z6dIlzp07xyuvvMKXX375vGFr3e3bt4mLi2Pw4MEkJSVpJbG1b9+e/v37s2bNmudqNvEkTzvWrb7u3r2Lqakpy5cvB+DPP/9k2rRpOrvfi0oYYQrq5euvv2bz5s01Nlc7Ojoyffp0vvjiC608gdWXWCwmICCAdu3a1Rh9ZmZmcvjw4TovlggODmbPnj2aFbuff/458+fPf2TbyKuvvsq5c+ce2XiuLe7u7kRFRTFr1qxa/TvL5XJeffVVXF1dOXPmDCtXrqxTpyWxWMwXX3zB2bNnWb16dX1C17pPP/2U69ev89tvvz33NWxsbBg5ciSmpqasXbuWa9euaTFC3YuPj8fHxwe414vYwcHBwBE1XMIIU6B1zs7OSKVSTpw4UePj3bt3p7Cw0CiSJdxbOHTq1CnNSMra2prw8HB8fHyYOnUqKpWK/Px8Tp8+/cjo08LCAkdHRzIzM1EqlZqvf3B7S+/evRGJRGzbto2srCzeeecd3nvvPcrLyxk4cKDOvq+MjAyysrLo168fW7Zseepjw8PDGThwIHfv3mX+/PlPPZPzSVQqFatWreKVV14hLi6uToufdG3JkiW88847zzXiFolERERE0KNHDw4ePMihQ4eMrmXjs/Tr16/G+8ZUNm9MhBGm4LmNHj2aNm3a8L//+781Pv75559z9OhRoz00+kHVo8/27dvTqlUrTExMKC0t5fLly9y5c4f//d//xdbWltjYWKKjo1EoFMhkMtasWUO3bt0oLCyke/fuWFlZ0b9/f2xsbJg6daomoR4+fJj/+Z//4cSJE5qEq03Vc1d//vknly9ffuTztra2TJo0CRsbGw4ePFjvVcsAb7zxBtbW1mRnZ9OmTRtWrlyptTni+hg3bhyenp589dVXtU549vb2mp/runXrDN5hqq4cHByYNGkS5ubm7Nq1C39/fwIDAykpKeHTTz81mhetDY2w6EegdZ9++imnT59m8+bNmo/Z2try/vvv8/XXXxvdkVS1YW1tTZcuXXB3d2fIkCE1Ouv0799fs9pVJBLRsmVL+vXrR1FREevWrQPuzQ8mJiZqEua2bdvYv38/vr6+ZGdns3HjRrKzs7Uas5+fHwMGDODHH3+s8QQ5bNgwwsLCuHbtGkuWLNHaoc6mpqasXr2aLl26APd6vHbo0KHeK3brqy4l4webm+/atYtjx47VqxOTIURHRxMaGkpOTg6LFy+uURn59NNPuXbtGgsXLjRghA2XUJIVaJWpqSnm5uaPrMbr0aMHxcXFDTJZwr1zMavLm8XFxZqEWV5eXmNlo1qtJi8vj+XLl/Pmm2/St29fdu3ahZubG8uWLSM8PJycnBz+/ve/ExERweTJkwF466236NSpk1b3DZ49e5bOnTuzY8cOysrKWL58OZ6enshkMtauXav1lnbl5eU1kq+DgwOOjo4G7wakUqk0Z4g+rWTs7OxMdHQ0hYWF/Pjjj1p7IaEvbdu2Zfz48UilUlauXPnYhhLLly/njTfewNXV1ShG/42FkDAFzyUiIoKKiopHSlje3t6P9JRtqBYtWoSZmRm+vr6sXLnysVsBKisr+f3335k0aRIBAQEMGzaM2NhYIiIiNPtPO3furHm8vb093t7eWt0CAdCzZ09CQkIACAkJ4aOPPuKvv/7S2R7YpUuX0qdPH2QyGYcPHzaaJ+UzZ86QnZ3NxIkTH+k8JZPJ6NOnDyEhIWzdurVWq4ONiVQqZdy4cfj4+JCWlsaff/75xJ/v5cuXSUtL45VXXuGrr77Sc6SNl1CS1ZEWLVowceJErl+/zpIlSxrcIoJnee+997h+/XqN0zMsLS355JNP+Pe//93gXrXXl4ODAydPntQcU7Zq1SrefPNN4N6eyaVLlyKVSrl+/Tp//vkn27Zt4+TJk7i4uFBUVFSvEaejoyPbt2+ndevWwL0Vki1bttTJnOmD3NzcaNWqFYmJiVrbgK8Npqam/POf/+TEiRMcPXqUvLw8XF1dGTlyJDk5OWzdulVrnZf0xd/fn9GjR6NUKlm2bFmtXqBIpVI+//xzkpOTWb9+vR6ibDyEkqweiUQiNm3ahIeHB3CvBNSYXuWJxWJsbW0f+SPs3r07d+7ceeGSJdxbTfvgmZ4jR47k/fff5x//+Aevv/46EomEY8eOMWbMGCwtLRk8eDDffPMNYWFh3L17l1deeYUDBw7U+n52dnZ06NCBkJAQzM3N2bNnDyNHjsTCwoJNmzbpPFnCvZWYxrgas7y8nHPnzjF//nxMTU05fPgwsbGxbNq0Sad7IXWhur+vi4sLJ0+eZM2aNbX+2uqFTGPGjCE+Pr7Wx4wJnkxImDpgYWGhSZZwb/HF8ePHOXToUKNIJh06dECpVD6yf9HPz4+0tDQDRWVYGRkZXL16FUdHR+Be+a99+/aaUSZAu3btcHV1BSAvL4+wsDDg3krXf//738ydO5eioiLu3r2LSqVCLBYjk8kwNTWlWbNmNGvWDGtra5o0aYJIJOLu3bukpqYSExNDaWkpH3zwAXK5nNdff52OHTtqtR1fQ9O1a1fNC5hOnTrx97//vcH9boaFhTFkyBDu3LnD3Llzn2sbz6lTp+jSpQsTJ07ku+++00GULxYhYepAcXExMTEx9O/fH5VKxaFDhwgNDaVr166UlZVx8eJFEhISjPLVeW20b9+enJycGh8zMzPD0tKS/fv3Gygqwxs7diwbNmzA2tqavXv3MnHixBqfr6iowNvbG6VSSWlpKUVFRTRr1gyAgoIC7O3tad26NVKpFJFIhFqtRqVSoVQquXPnDiUlJaSnp5OamvrIv3+1yspKVq9ezd/+9jcyMzMb3DYJbXB2dq6xab+8vLxBNSGwtLRk8uTJ2NvbExsbW+/tWYsXL+bTTz+lV69eT+wVLKgdIWHqyNdff63p0Xnu3DkAzM3NiYiIwM/PjylTpqBSqbh69SrJyckcOXLEaE6CeJaWLVs+0sO0W7dulJWVvZBP0NVSUlLw8vLi66+/5h//+AdSqRRHR0c6duxIaWkpr7zySo12gdnZ2bz99ttcu3aN7777Tmst9K5fv86+ffsYPXo0CxYsaHTz508il8vp27cvQUFBfPfdd6SkpNC+fXuuX7/eYP62evXqRa9evbh58yb/+c9/KCwsrPc17969y86dO+nXrx9Hjhwx+PafhkxY9KMjQ4cOpbi4+InzUmKxmMDAQDp06ICzszNyuZzbt2+TlpbGoUOHjDbx+Pr68vLLL/Ppp5/W+PiHH35ITk4OK1euNFBkxkEsFvPll1/yP//zPyiVSqZMmUJKSorO2uM9iUgkYvLkyWRmZtZpbrSh8vLyYvjw4WRlZbFt27Ya25o++OADysvLmTt3rgEjfDo7OzsmT56MpaUlMTExxMXFaf0e77//PpWVlfz4449av3ZjIyz60TMvLy/++OOPJ35epVKRnJysObHd0dFRc1RVp06dqKio4PLlyxw5ckQzQjUGnTt3fmTxgFwux9rausaK2ReVo6Mjt27dQqlUYmJigouLy1N/D3RFrVazZs0apk+fTnp6+mMb5DcG5ubmDB48mFatWrFu3ToyMjIeecyiRYt477336NChg95fuNRGdYOJvLw85s6dq7M9zIsXL+a9996jffv2mvNlBXUjJEwdsLe3RywW12lV2tWrV1m7di1wLwGFh4cTFBTEyy+/DNyb40pJSalxzmNERASWlpbs2rVLL6si4d6xR3v27KnxsfDwcCoqKoyqt6iheHh4aDbwe3h4cPnyZYMd6ltcXMyWLVsYM2YMc+fObTBlydpq164d/fv358SJE6xbt+6J/843b94kPj6eYcOGcerUKaNpF+fi4sKECRMwMTFhzZo1nDx5Uqf3u3nzJomJiQwbNoyUlBSj+XdoSISEqQPe3t71WpFXWVnJgQMHNKU0Hx8fwsLC6NatG3369KG0tBRXV1dGjBgBwNatW5kwYYI2Qn8qR0dH5HI5iYmJNT4eGBio9XZvDZWnp6fm51bf3wNtOHXqFD4+PvTv37/RHC5sa2vLiBEjMDU1ZfHixbUaPW/bto2goCAmTZrEggUL9BDlk4nFYsaOHYu/vz8XLlzgjz/+0NuLmS1bthAUFMT48eOFtnnPQUiYOuDt7c3Bgwe1dr3U1FTN/jFbW1u6dOnCuHHjNJ8fOHAgnTp14ujRozpd4FHdbPzhP24HBwetfr8NlYmJCU5OTly8eBGRSISnp6dRrBretGkTM2bMIC0t7bEly4ZCLBbTtWtXunXrxv79+0lISKjT7/vixYuZNm0a/v7+nDlzRoeRPpm3tzcvvfQScO+EFW2et1lbS5cu5a233nquk11edOJnP0RQF02aNKFly5Y62zJy8+ZNNm3axMaNGzUfy8nJYdCgQXzzzTd8+OGHDBs2DDs7O63f28PDg7Nnz9b4mJubGyKRqMG1GdMFNzc3srOzNZ12Htc60BDKyspYu3Yt0dHRNGnSxNDhPBdnZ2feeecdXF1dmTdv3nMdwXX16lWSkpIYPXo0Uql+xwpyuZypU6fy6quvcv78eb766iuDJaucnBzOnDnD2LFjaxxTJ3g2YYSpZZ6enly8eFHnJZavvvoKd3d34uLiWLp0KSUlJTg7O9O5c2f8/Pzo1KkTlZWV5ObmcuLECZKTk+s1+jQ3N6dp06aPrN7r1KkTt27dqu+30yh4eHiQnp4O3Fv0Zehy7IMyMjI4e/Ysw4YNe2RLkDF7cKvItm3bNIvkntfatWs1K72XLl2qnSCfoX379gwbNozy8vLnPotU21auXMkXX3zBqFGjWLVqlaHDaTCEhKll3t7eemm/5e7uzq5du2rMQ+Tm5mpaZ8nlcsLCwggMDGTkyJGa0xmqt63UdX9XZGQkd+/efaRTUdu2bXW+WKGh8PT01KyI9fb2ZteuXQaOqKaYmBimTZtGUFBQg6gIeHp6MmLECLKyspg1a5bWVo8uW7aM119/HTc3N502DzEzM2PKlCk4OjqSmJj4zEO+9an6MPDx48cTGxsrLNirJSFhapFYLMbT05MdO3bo/F4+Pj5PTcyVlZXExcVpRoQuLi507tyZwMBAzarWumxb8fPze+T4JlNTU5o2bfrIEV8vIltbW6RSKfn5+TRt2hR7e/tHWgcaWlVVFatWrWLSpElcvHjRaNs0Nm3alCFDhjx1q0h9XLx4kXPnzvHKK6/w9ddf62TePzIykr59+3L79m1++OEHoyjNP+zcuXNcunTpsSe7CB5PSJha1KpVK4qKinT+RCQSifD29q5Tm6vs7GzNSlZTU1NN8qzetnL9+nVOnTpFQkLCI8vNpVIp1tbWLF++vMbHqxOvNs92bKge3E7i6elJRkaG3rb61EVeXh4JCQmMGjWKRYsWGd2hydVbRZKSkp66VaS+/vzzT7744guio6Ofedh0XVhbWzNlyhSsra3Zs2ePUSz6epqlS5fyz3/+k/79+9e7Bd+LQEiYWlR9Tp2utWrVipKSkudum1VeXs7+/fs1f8zVzRK6d+9Ov379KC4uJj09ndjYWCIjI3n//fdRqVT861//0vQ4hXtHDgnbSe7x9PTUzK8Z++rDAwcO4O3tTadOnR7ZImQotra2DB8+nCZNmtR6q0h9PFiSjI+Pf+xZp3U1aNAgIiIiuHbtGv/+978bRAu68vJytm7dytChQzl8+LDw4vcZtJowZTKZwTZpGwNvb2/WrVun8/s8qxxbV+fOndOUZe3s7OjSpQteXl5ERkYybtw4zUq6Y8eOYWdnx4YNG/jkk0/w8vJi/vz5WoujoZJIJLi6urJ27VpNWX7btm2GDuuJVCoVq1ev5s033+T8+fMGXbT14FaRAwcOEB8fr7fet+fOnePixYtMnDiRb7755rmv07JlSyZOnIiZmRkbNmwwym5CT3P48GE6d+7MpEmTmDVrlqHDMWpaWVPs6upKcnIyV69efWFr4dbW1pibm+tlBZyvr6/OFhYVFBSwYcMGvv32W3755Zcay85btGiBRCIhOjpasyz9/fff10kcDUnr1q0pKCjg7t27uLi4cPv2bUpKSgwd1lPduHGD/fv3M2rUKESix7bN1LnqrSJubm7MmzePuLg4vTeKX7JkCaampgwZMqTOXysWixk9ejTvvPMON27c4KuvvmpwybLawoULsbOzIzw83NChGDWtJMzp06fj4uKCWCzmb3/7G2FhYSxfvpzk5GTeffddbdzC6FWX4XQ9J2RjY4OZmZleEvO5c+f45ZdfUCqVFBQU1PiciYkJAMOHD6d9+/Y6j8WYGfN2kqeJj49HJBLp/UlSLpczePBgXn31VWJjY1m0aJFWTuV4HpWVlWzatInOnTtja2tb669zc3Pjs88+w8/Pj+XLl/Prr7826FZzxcXFHDx4kIEDB9Y4CF1Qk1YS5oO/7Eqlkm+//ZaBAwfi4uKi+aVq7PTVBq16nlRfizX+8Y9/0KJFC/z8/Dhx4gQ5OTk1uvqUlZXx0ksv8dVXXzF16lSCgoL0Epcx8fT01CRMY2iHV1vVDdp79uxJ8+bN9XJPT09P3n33XZo0acKsWbPqva9SG44fP86VK1eYMmXKMx8rlUqZNGkSr732GpmZmXz55ZdGdThCfezcuZM7d+7w6quvGjoUo6WVhPn9999z+PBhzpw5w2uvvfZImzSJRKKN2xgtmUxG27ZtNU+auuTr66v3P1ClUolCoeDYsWOMHDmS0aNH891337FixQoGDhzIxx9/TExMDCYmJowePZr/9//+HzNmzKB79+7I5XK9xqpv5ubm2NjYkJOTQ7NmzbC0tHzi4c7G6ObNm+zdu1fnpdmmTZvy0ksvMWzYMNatW8eaNWt0dirH81i0aBHNmjWjV69eT3xMUFAQn3/+Oc7OzixYsIDly5c3urNGlyxZQuvWrfH19TV0KEZJK4t+7t69S2JiInPmzNEM7Xv27ImLiwsLFy4kJSVFG7cxWu7u7uTm5lJRUaHT+zRp0gQnJyeD9AMNCAhArVZrNnp/9913NT6fkJBAQkICcK8sGR4erll1W1RURHp6OvHx8XU6waUh8PDwIDMzE5VKhZeXF+np6Ua3VeNZEhMT8fPzo2vXrjUOuNYWfW0VqY+7d+8SExPDgAEDOHbsWI2tYaampkyePBlnZ2eOHTvGhg0bDBipbl29epWUlBRGjx7NV1991eheENSXVhJms2bNUCgUml+y27dvs3LlSnbu3ElWVpY2bmHU9NXdp7rtniGecKpPrq+N8+fPa7ZVODg40LVrVzw9PenQoQNVVVVcvXqVkydPcvz48QZ/5NTD5diG0EHnYWq1mrVr1/L222+TlpZW65/zs+h7q0h9xcXF0bFjR95+7TUObdrEkUuXaB8WxsCBAykpKWHOnDmN7gXf46xevVrTNm/79u1Gv4BNn7SSMK2trWssTZdIJDg6Ojao0lR9eHt76+SE9If5+PgYbL7ExcWFQ4cO1fnr8vPzNed8SqVS2rVrR0hICAMHDmTYsGEUFxdz4cIFEhISjP4J9WEikQgPDw927dqFVCrVbC1piAoLC9m9ezejRo3i559/rtfIwpBbRepDpFZzfeNGvh80iA8GDeKWiQkbmzVjV2ys0bU51CWVSsXx48f56aef+Pnnn1m0aJGwGv4+rSXMBze8Ojg4cOvWLaMsvWhby5Ytqaqq4saNGzq9jyH391laWmJqalrvTe4KhYIjR45w5MgR4N7vSXh4OJ6enrRr1w6FQkFubi7Hjh0jKSlJG6HrlKOjI2VlZRQWFuLh4cG1a9eMal6uro4cOYK/v78m0T0PJycnoqKiKC0tZd68eQZb/VpX9ioVA6qqGGNri+z+x2wqKri+ahW7Tp82aGyG0K9fP5o2bQrA5MmT+emnn16IauGzaCVhWlpaUlRUpHnfyclJK50zGgJ9rYps27YtN2/eNEh5JDw8nLt372o9GeTn52vmg8RiMR06dKB9+/aaZvH5+fkkJSWRmJholKXbhro69mnWrl3LtGnTSE1NrVP5US6X06dPH4KDg7Vyqoi+SNRqIhQKwhQKLJo2rbG1RKlUYpWejqNKxdUX6BgsMzMzbGxsNO9XVlbWeH5/kWklYcrl8hp7kGxtbR/Zt9dY6etUCm1396kLX19fLl26pNN7qFSqGqNPLy8vIiIi6NOnDwMHDuTmzZucPn2agwcPUl5ertNYasvT01OzItzb2/uRXrsNUVFRETExMYwePZr58+fXqpSqq1NFdM1ZqaR/VRW2ajU+Pj60bNmS7OxscnJycHZ2BkCtUjGgspKlJiYoDdTgQZ+CgoIYPHgw69atIz8/n5CQEHJzc7lz546hQzMKWkmYEomkxpOYlZVVo3i1/Szm5ubY2dnp5VQKHx8fli1bpvP7PE7z5s31Xgp+cOGQs7MzkZGRmn63xcXFnDt3jtjYWIOV/ORyOU5OTmRlZeHg4IBIJGo0RyQdP34cf39/evTo8dQG/7o+VURX5Go13auqCFEqsbKyIigoCLFYzOnTp7l+/TqVgPzYMUJDQwkODqY4NpZwhYI4meyZ126oLC0tGTFiBNbW1ixdupTc3FxNm8+PP/6YV199lQULFhg4SsPTSsJUKpU19lo2a9bshRjCe3l56eVUCnt7e8RiMdeuXdPpfR7H19cXtVpt0Gbiubm5/Pnnn8C96kVkZCT+/v506tSJsrIyLly4wIEDB/SasNzc3MjNzaWqqgo/P79Gs3m92vr165k+fTqpqamPXYzVELaKPI6bUkm/qiosgYDAQOzs7Lhx4wanT5+mWK1ml1zOBYmEgZWViJOTiYyMxMfHB1VqKukSCfmNrDQrEono0KEDffv2JTExkeXLlz/yfLZ06VKmT5/eaKYd6kMrCVOhUCCV/t+lrKysXpiEqa/uPoYqx3bo0MGoyus3b95k/fr1wL25lsjISAICApg+fTqVlZVcunSJQ4cOPXJ2p7Y92A7P19eX7du36/R++lZcXMz27dsZNWoUc+fO1ZRmbWxsGDFiRIPZKlLNTK2md1UVPkol9vb2mheCSUlJFBYWkiyRcEAup+J+2XWfTEbbigrOnj1LQEAAubm5DLxzh6VyOapGUpq1tbUlKioKqVTKr7/++sTtRNV7M6s7ejWEFc+6opWEWVVVpektCmBhYdHoE6ZEIsHDw4PNmzfr/F6+vr7s2bNH5/d5nNatW3P48GGD3PtZ7t69y44dO9ixYwdyuZzOnTsTEhLCpEmTUKlU5ObmcuTIEU6ePKn1e3t5ebF8+XKaNWuGtbW1zud4DSEpKYnIyEjWrl1LXl4eu3fvJigoqEFtFUGtxk+ppJdCgblYTHC7dlhZWXH16lXOnTtHoUjEDrmcnIe6kZWLROyUyYi6fp3bt28THBxMSVwcnRUK4ht4aVYsFtOlSxciIyPZt28fCQkJz2y2sWrVKr744guioqJYs2aNniI1PlobYcru/xKJRCKkUqlRHp6rTW3atKGgoEDnZ941bdoUe3t7gyzpNjc3p0mTJpoOPsassrKSgwcPcvDgQcRiMaGhoXTs2JGoqChGjRrFtWvXSE5Opnnz5nz99deUlJQwdepUTj/HloHWrVujUCi4evUqnTp14vz58w0jeTyHESNG4O/vD0BYWBj9+/c36HFgdWGpUtGvqgpXlQonJye8vLyorKzkyJEjFJeWclQqJV4qRfGEEWOGRMJZiQRJcjLdunXDy8sL1fnzpEskFDTQ0qyjoyNRUVGUlZXVaduPSqVizZo1vPzyy8THxzeYyoK2aW2EWZ0wG1pbsOfl7e2tl3k9b29vLly4YJAXIOHh4ZSVlTWIg3AfVL3x+vjx48C9knZ4eDi9evXib3/7G3K5HEdHR77//nv69etX5+uHhoZqRq1+fn5GOwLXhtatW2veFovFDSJZitRq2imVdK2qwlwuJyQkBHNzcy5fvkxGRgbXxGJiTExqNR+5935pNjU1FT8/v3ul2bIyljWw0qxUKqVnz5507NiRHTt2cOLEiTpf48yZM1y8eJFJkybV6/zQhkwrL5MqKytrNNl+MIE2Vvpqh6fLsy+fxc/Pr1GUGlNTU1m4cCFffPFFjdXc9vb2jB07FjMzs2deQyaT0a9fP82c6YkTJzA1NaVVq1Y6ny81BAcHB/7xj3+QnJyMSqVCqVTq5XD0+mquUvFKZSW9qqrwatuWrl27IhaLiY+PJy0jgwMyGX/I5bVevFN2vzR77do1ioqKCAkJoYVKRUcj3Bf8JC4uLkyfPh17e3vmzJnzXMmyWvX5ocOHD9degA2IVhJmRUVFjTnM8vLyGu83Ns2bN0cul+u8LCGVSnFzczPYCtXmzZs32ANxn+S1117jypUrZGRk8Morr6BUKundu/cTH29jY8PmzZvJyclh5cqVbNiwgSZNmlBSUkJAQACZmZkN+hzExxk6dCgzZsygsLCQUaNG4ePjQ/fu3RGLxZruL8ZGrFYTUVXFxIoK3E1NiYiIoG3btly4cIHExETSKytZZGLCEakUdR1HhukSCWkSCcnJycjlcjw9PemiUNDcyMvwcrmcIUOG8Morr7Br1y6WL19e78YnlZWVrF27lo4dO+Lg4KClSBsOrY0wH0yQFRUVjfoQUn2VY9u2bWuwdmteXl6IRCKDjW51Zc+ePQwaNIg1a9aQnp7Orl27GDx4MFOnTsXR0fGRx8+YMYMuXbrUqKB07dqVUaNGMX78eI4eParP8HXKzs6OTz75hI4dO7J27Vp+/vlnKisrKSgo4OzZsyQlJTF06FBDh/mIlioVEysq6KJQ4OvlRefOnamsrCQuLo4L2dnEyGSslMsprMe8426ZjGKlkrS0NFq1aoWFmRkDq6oQGekUlIeHB++++y6mpqbMmjWLM2fOaO3ap06dIjs7m8mTJ2vtmg2FVuYwHzfCbOwJUx8LYby8vAw2uuzYsaNRbSfRpsuXL2NiYsLFixeRSqXIZDKio6OZOXMm0dHRiMViWrRoQcuWLenYseMjX29jY6PZxJ2fn98o5nMGDRpEREQEubm5zJo167HdlHbv3s2MGTPw8/Pj7NmzBoiyJplaTTeFgnYKBc0sLQkODkYikXDu3DmuXr3KBYmE3aamlGhhrvGuSMRumQyzK1dwcnIiNDSUu4cO0VGp5IhUK0+jWtGkSRMGDRqEm5sb69ev19l0waJFi/j8888ZPHgwW7du1ck9jJHWEuaDr8AfTqCNiYmJCa1atdJLVxMvLy9Wrlyp8/s8Tps2bRrV6Olhffr0eWTu0tHRkQkTJmj6qCYnJ9OkSRNkMhkODg6cPXuWTZs2MXv2bM3XjB49mo0bN5Kent5gNu8/yM7OjilTpmBubs769es1C6UeR6FQsG7dOsaOHUtWVhZlZWV6jLSmtvcbEFgB/gEB2Nvbc+vWLU6dOkWpWs1uuZw0sRi0uDAnTSzGRyJBevIk3bp1w93dHUVGBhfEYm4ZwapZPz8/hg4dytmzZ5k1a5ZOpwoqKytZv3490dHRHDlypNG+uH6YMMKsIw8PDy5duqTzeSsbGxtMTEwM0m7NzMwMMzOzep9OYoykUimdO3emWbNmj3zu7t27rFy5kszMTE3vzLfeeosFCxawdu1azQrw1157jZCQEABycnIICwsjOjqapKQkWrZsyVtvvUVubi5Tp0416uX3AwYMoGvXruTl5TF79uxa9ei9dOkSZ8+eZfDgwQbZj9dEraZnVRX+SiW2trYEBAQAkJycfK/fsETCPrmccl2sYBWJ2CWT0aqigvPnz+Pt7U1eXh6DKipYLpfXeW5UW8zNzRk2bBgODg6sWLGCy5cv6+W+SUlJdO7cmSlTpvDtt9/q5Z6GppM5zMacMH18fPTS3cfLy4v09HSDbNPp3Lkz5eXlNU6dbwx8fHx47733aNOmDW+//TYpKSk1/n1v3bpFp06deP/993n77bdZvHgxY8aM0SRMuLeQwt7eXvM1bdu2Zf/+/ezdu5c2bdrwzTff0KpVKzp37swXX3yh9++xNmxtbfnoo4+IiIhgw4YNzJ8/v04N7WNiYmjbti2enp46jPIhajU+SiWvVVQQBJo+rzdu3ODgwYNk3brFKrmc7bpKlvfdEYnYI5WSl5dHSUkJoaGhtFSpaG+gfeehoaHMnDmTGzdu8OOPP+otWVZbuHAh5ubm9O/fX6/3NRStjDArKyuRSqWIRCLUanWjTZgikQgvLy+9dN3x8vKq1/Lv+ggICND7H54umZmZMXToUJydnVm/fj2tWrWiZ8+e/L//9/80jQ1UKhU//PADS5cuRSwW07ZtW6ZNm6a5Ro8ePfjss89o3749Tk5Omo9Xbze5devWI3sUg4KC+Oc//0lmZibx8fFkZ2fr7Xt+kv79+9OtWzeuXLnCnDlznuvkl+pyXFRUFLNnz6aiokIHkf4fC7WavlVVuCuVtGzZEi8vLxQKBceOHaOouJjjUilxUilVehrhnZNI8FapkJ08SdeuXXFzc0ORmUmGWFyvhUV1YWVlxYgRI7CwsGDRokUGq2SUl5ezefNmhg8fzrFjx7h586ZB4tAXrc1WV+/FrKioaLRzmM7OzpSWlur8hAypVEqbNm1YtWqVTu/zJPb29uzYscMg99a2gIAAhgwZwqlTp1i3bh3Dhw/nxo0bmi4nu3bt4tdff6WkpETTG1alUpGZmcmePXsYN26c5lqjRo3SHPsEUFpayoQJE2o0LpDJZHz44Yfk5eUxY8YM2rZti6+vL2+++aamO9CJEyc4ceKEXs/4tLW1ZcqUKTRr1oxNmzZpjlF7XhkZGWRkZNC/f382bdqkpSgfolYTolQSWVWFuUxGSLt2WFhYkJuby/nz5ykQidhhYqL/syofKM1euHABT09P8vLyGFhZyZ9yuVbnTR+9tYiwsDD69OlDXFwcsbGxBu8ydfToUcLCwpgyZQr/+7//a9BYdE0nCbO8vNxo92vVh7669bu6unL16lWDLKoICAgw+Okk2mBubs7w4cOxs7NjxYoV+Pr6Mm7cODZv3vxIO7wnjeRnzpxJSEgIPj4+AJpVtS1atADgxx9/fKTLz5IlS1iyZInm/XPnzrFt2zbEYjHt2rWjXbt2DB48mOHDh1NYWEhaWhpxcXE6fRHWt29funfvztWrV5k3b57Wtilt27aNd999l9OnT2u9daONSsWAqiqcVSratGmDq6sr5eXlJCQkUFpWRrxUyhGp1GDddkpFIvZKpZjm5NCyZUtCQ0MpT0ignVLJCR2tmm3evDlRUVGIRCJ++eUXo1pos3DhQj799FP69u2rl/OBDUVrP9nqUWVJSQnl5eU1Ti5vLLy9vdmyZYvO72PI7SRhYWEGOUZMm0JCQhg4cCDHjh0jMTGRqKgorly5wuzZs+t0EK5CoaB37968+uqr9OzenS/HjSPQzIxpb73FxqNH+XH//lqPJlQqFceOHdM0gnB2diYiIoKAgAA6d+5MRUUFFy9e5NChQ2RmZj7X9/0wa2trXnvtNaysrNi8ebPWW/iVl5ezceNGRo4cyZw5c7SySlisVtNRoaCLQoF5kyaEhIRgamrKxYsXuXjxInliMTtMTLhpBKtSz0gkeCuVyJKS6Nq1K23btiXy4kUyxWJuazE+sVhM165d6datG3v37iUxMdHoWpDevXuXbdu2MXjwYI4dO2awc2qfxEalIlSpxFmlQgTkiMWclEjq/Huk9YRZ/XZjm8O0tLTEyspKL/NQXl5emvMf9a1Vq1YGOxmlviwtLRk5ciTNmjVj2bJlBAUFMWbMGDZt2vTc+wbLyspY89NPhB8/ziQzM+7evUv+zp2IDh/mbWCHXE7aQydd1EZubq6m5G56vzNNYGAgU6ZMQalUkp2dTVxc3HNXNHr37k3Pnj25du0a33zzjc6aX6SmphIUFETfvn3rfch4i/ujSnuVCg8PD1xcXCgtLeXQoUOUVlZyUCYjSSLRacmzTkQidsrlOJeXa0qzV69eZUBlJSu1VJqtbpZ+9+7dOjVLN4SEhAQ6dOjAlClT+P777w0djkaAQkH/qqoaK1ztVSpCFQp2yWQk16EioNWSbHXCbIyLfry9vUlPT9f5fIGtrS1yudwg20kcHR2Ry+UNcjtJhw4d6NevH4mJiRw6dIgxY8Zw+fJlZs2aVa/StqlazZjKSq6npeHq6sr58+cR308+cmBIZSWlcjm5z5E0q5WXl7N371727t2LWCymQ4cOdOzYkQkTJqBSqcjJySEhIaFWJ6tYWVnx2muvYW1tzbZt24iPj3/uuGpry5YtzJw5k5SUFHJycur89VK1mq4KBR0UCiwtLAgODkYmk5GWlkZeXh6ZYjE7tdSAQNtKRCL2yWSY5OTg5ORESEgI5YmJhCiVnKxHaVYqldKrVy86dOjw3M3SDWHhwoV88skn9OzZk3379hk6HBxVqhrJUqxW01yt5rpYjAjoW1VFgVhMXi1HmjoZYTbGXrLe3t6cOnVK5/ep3k5iCBEREdy+fVuvi1Hqy9rampEjR2JqasrSpUtp164d0dHRbNiwQStl7QClEgu1moKCAto4OTHQ1RXz0lJyTE3JKS9HDHRSKFhbj4T5IJVKxZEjRzhy5IjmmLKwsDBeeuklXnrpJfLy8khMTHzsGZ+9evWiV69e5Ofn8+9//1tvp8zcuXOHLVu2EB0dzY8//link3VclEoGVFVhDfj6+dGiRQtu375NcnIyJSoVe+RyUrXcgEDbUu6XZuVJSXTp0oU2bdrQ/dIlMsViip+jNOvq6sqIESM00wgN6bSg0tJSYmJiGDBgAMePHzf41rT2CoUmWbYyN6ePiwuSqir2XrtGVkkJIqCDQkHeA413nkarCbO6209jG2FKpVJcXV31slHby8vLYA3P3d3dG8zJGyKRiE6dOtG7d29iY2O5du0a48aNIyMjo9ab8GvD5/6TfzOVCu+SElo2bw7W1gwzN+eXEydQiES4qlSYqNVUaPlJ/eFjyoKCgggPDyc6Opro6GjUajWtW7cmLS2NyspKrKys2LFjB3FxcVqNozZSUlIICgqiZ8+e7N69+5mPN1Wr6V5VRZBSiY2NDQEBAYhEIk6dOsWNGzc4K5Gw18SEMiNOlBr3D6FuWV5OZmYm7u7u90qzVVWsqkNptkmTJgwYMABPT082bdrUYPs4x8XF0b59e1577TX++9//GjQWV5UKiVpNG5WKfi4uWFhYANC/aVN+PXkShUiEWx1e4AlzmLWgr1Wr1dtJDNEOTywW06xZswZxtqOtra2m5+vixYsJCwujU6dOOumdaXJ/cUWVSETTB6omTWUyXIF0QMS98qxudyPea3pdXeWIiopiwYIFiMViBg0axKZNm5g2bZpBRyMbN25kxowZnDlz5qlTCp5KJX2rqrAQiQgKCcHGxoaCggJOnz5NEbBTLidLSyN2fSkRiTggk2Fy+TItW7YkJCSEisOHCVIqOVWL0qy/vz9Dhgzh3LlzzJo1S+d7W3Vt4cKFfPTRR0RGRnLw4EGDxWGnVOKtUmECNXKSHGilUnFRIqlT9x6tbyuBxjfC1NeZlK6urly5ckVro6O6CAwMRKlUkpeXp/d715ZIJCI8PJyePXuyf/9+bt68yfjx4zVPMrpoV1goEmGjVnNXJCL1zh3CzM0RiUSUlJRgp1CQL5FwXSxG3+fJVFZWIn6g3Hf+/HmDl+5KSkrYsWMH0dHRzJ8//5H5fnO1mj5VVXgqlTg4OODr64tSqeT48ePcLioiSSolViqlsiGMKh8j+X5p1uR+abZ169b0uHyZTImE0id8T5aWlgwbNozmzZvrta2drhUXF7N792769u3LiRMn9P67aaZW07uqijYqFdZmZtjb21NcXIy1tTWIRCQXFJB9/+/neh3K5jobYcrlck3nn4bO2dkZOzs7ysrKSEtLq/eZck/i5eWll32ejxMSEsL169cNcu/aaN68uaYMuWjRIiIiIujcuTOrVq3S+h7AB6VIpbjdT8Sxly6hvHYNW1NTLC0tMTMzw6OsjFiZDKUen+R79epFREQEFy9epG3btmRnZ7Ns2TK93f9pTpw4QVBQEN26dePAgQP3PqhWE6hU0kOhwEIqJTg0FEtLS/Ly8khLS+OGSESMiUmtF14YLZGIHTIZjhUVZGVl4ebmRl5eHr2qqtj00ByZSCSiY8eO9OnTh8OHD7NixYo6zf02BAcOHNCUZh88sECn1Gr8lEp6KRRYSCTYBgTgUFpKSUkJFy9epEIiIUMi4bZarSmVJ9WhmqHVEaa5ufn9mNVUVVUhk8kaxeG6CxcuxMvLCx8fHwYOHMiNGzdIS0sjNTVVqy2pDL2dxBhXx5qbm9OlSxc6dOjA3r17KS4u5tVXXyUlJYXZs2fr/ISQC2IxWWIxrioVapGIU+XlBN65g1gsxt7entuXL2vvj+gZLC0tef3117G2tmbz5s188skntGzZkoKCAqP6O1u/fj3Tpk3j7NmzVOXn07+qitYqFa1atcLDw4OKigoOHz5MyZ07JEqlJEqlen3BoUtFYjFxMhnyixdxcnIiODgYxfHjnFYqNWVmOzs7Ro4ciVgs5tdffzXqF6r19fvvv/Phhx8SERGh8xXbFmo1/SorcVOpcHJywsvLi8rKSlIyMmhaVkaeWEz2Q4O4CxIJZwyRMCsqKrCxsdG8X12WNaY/5OdVVlZGcnIyycnJmj6jPj4+jBs3DqlUqkmemZmZz73C1NbWFplMZrDTSZo0aWJUCdPGxoZu3boRFBTEyZMn+f333+nevTstW7bkzz//1FvpSi0SsV4up4dCQYBCQYlIxBWRCAoKaOHriyIgAN/UVE6qVDodIUVGRtK3b19u3LhRYwWsMZbQb9++zZ5du5g3dizmx45xOTOTli1bYmZmxqVLl8jMzOTq/QYEBQ19VPkYJyQS/JRKkpOTCQsLw8XFhVFFRcxRKAjv3p2IiAijbUCgbbdv32bfvn0MHDjw3tFruijN3m+h2L2qCnO5nJCQEMzNzbl8+TIZGRlcE4m4YmJCW5UK6/v/3rdFIk5KpRyTSOp0yoxOSrLV75uamhp8WbG2VfcZzczMZOvWrdjZ2eHj40P37t0ZO3YsmZmZpKamkpaWVqdfDkNuJ+nYsSMVFRUGnwODe+dwhoWF4enpydGjR/nhhx9wdXVl8uTJJCUlsWbNGr1ve1GKROyRyYiTSml1f0Vs76oqWt64QUhICFZXrjCguJjFJiZaHymZm5szdepU7Ozs2LVr1/+VOY2YvUpFdFERfQA6dKBjaCjHjx+/14CgooI4mYzjdXyiakjUIhE7ZTIcSktp0qQJL730EgA9JRJWFBby448/UlRUZOAo9WfPnj2EhIQwefJkfvzxR61e+0ktFOPj4yktL+eQTMbR6t81tRqL+19XAs+1VUknjQug8S38eZKCggIKCgqIjY3FzMxMU7odNGgQBQUFpKamkpqa+sx2c4bcTuLn52fwkUpwcDB9+vRBoVBw/PhxNm7ciImJCSNHjsTW1pY//vjjuTbFa1OFSETG/fJNqUjES7ducfPmTYKCgiiKjSVcoSBOJtPa/cLDwxk0aBCFhYV8++23Rv/iU6xWE65Q0FmhIOCBapNEIuHSpUucr6oixsSEokY4qnzYNbGY41Ip77Rujej+E7NPSQl7Fi9+Ib7/h/3+++988MEHhIeHk5CQUO/ridVqOiiVdKmqwuKBFopZWVlcunSJnPsVjBqnx4hE1Hf1ic5GmI2xecGz3L17l5MnT3Ly5EkkEommdDthwgREIpEmeWZlZdWY4Dc3N6dt27asWLHCIHG3aNGCzZs3G+TeNjY2jBw5kiZNmrBmzRouXbqERCIhPDycyMhIEhMT+fPPP41uQcRliYRTEgnilBQiIyPx8fFBlZrK+furZuvDzMyMKVOm4OjoyL59+xpEq0JHlYoBlZXYqdV4e3vTpEkTFAoFUqmU4qoqlty+zREdn+RhbC61bUvV/XUdAEW3b9O/spIlOqhEGLvCwkIOHjzIoEGDSE5OrlerRof7o0qHx7RQLKms5IBMRrKOWijqpHEBvDgjzCdRKpWaI5C2bNmCg4MDPj4+9O7dG3t7ezIyMkhNTaV37958+OGHlJWVsWXLFr2PMh0cHJBKpY/tHKNLIpGIiIgIevTowYEDB4iPj0d1f2FIVFQURUVFLFiwwKhOZHjYfpkMt4oKzp49S0BAANnZ2QwoK+MPufy5y40dOnRg2LBhlJSU8MMPPxj9+YIStZouCgVhCgXNLC0JDg5GIpFw7NgxDh48SLmbG01GjiTPzg5ycw0drl7I5XL69etHQEAAn+zaxRtmZtja2pKfn09ztZrOCgWHtFiJaCh27txJYGAgr7322nOVZiVqNRH3f9cszc0JCQmp0UIxQyJhl45bKOp8DlNwT35+Pvn5+Rw4cICmTZvi7e1NcHAwH330ESKRiKZNm/LVV18xYMAAvcbVqVMnSkpK9Dov6ODgQFRUFAqFgp9++ombN2/WeJLZtm2bXtoQ1lfF/XMRR16/TlFRESEhIdw5dIiOSiVH6thH1NTUlMmTJ+Ps7ExcXFyDOI/U6f6o0latxtfXF0dHR27dusWpU6coUavZKZNx4fx5gnfsICoqirlz5xr87EZdc3d3Z8SIEVy6dEnTx7ikspJgtZrIyEi8vb1RpaWRroVKRENUXZqt66pZJ5WKgZWV2Dzwu2aIForCHKYB3LlzhxMnTnDq1Ck+//xzLC0tgXt9Ub/55huuX7/O6dOnSUhI0FkTA4lEwmeffcaQIUP0tpBEIpHQvXt3OnfuzK5duzh27BhqtRpPT09GjBhBVlYWs2fP1tnJGrpwQSIhVSJBmpxMt27dcHd3R5GRQbpYXHP+5CmCg4OJjo7mzp07zJo1y6hH1XCvWXo3hYL2CgVWzZoRHByMWCwmJSXlXsceiYR9cjnl95+8kpOTCQ4Opnv37kbRkFsXTE1NGTRoEB4eHqxfv77GAr7qSsS5c+fw9/cnJyen3pWIhqqwsFCzavbkyZPP/FuX3/9da6dQYGVlRVBQEGKxmNOnT3P9+nXO3W+heFdP/446K8k+POIUPEqhUDB27Fg+/PBDbty4wccff4yDgwNhYWF069aNPn36UFpaSnp6OocOHdLqlpOpU6cyffp0AFxcXPj555/JyMjQ2vUf5uzsTFRUFLdv3+bHH3+kuLiYpk2bMnjwYFxcXFi3bp1O769Le2Qy2lRUcP78eby9vcnNzWVgZSV/PmPOTi6XM3HiRNq0acPhw4cNNo9cF63uN0u3Afz8/XFwcODmzZukpKRQpFYT84S2duvXr2f69OmcPXuW/Px8/QeuQz4+PgwfPpzU1NTHtrUrv79qdmR+Pq1atdJUIsIUCg6/gKXZPXv2EBoayuTJk5k3b94TH+eqVNKvqopmQEBgIHZ2dty4ceNeC0W1ml1yuWYRnr7opDUe3BthWllZaevyjVZiYiIjRozQvH/z5k3OnTsH3Btxdu3aFR8fH0JDQ6msrCQ7O5vExETNY56Xvb295m2JRELz5s11krBkMhm9e/cmNDSUrVu3akqtoaGhDBgwgKSkJL00INCluyIRe6RSmuTl4ezsTGhoKOUJCYQqlSQ9oTTr7+/PmDFjqKioYO7cuQbZf1sXMrWayPuv9K2trQkMDEQkEpGcnMzNmzdJlkg4IJc/sQF9cXExMTExjBo1ip9++qlRlGbNzMwYOnQozs7Oz+w49XAlwsPDA8WFC6RLJNx6AUuzCxcu5L333qN9+/aawwWqmarV9Kqqwl+pxM7ODj8/P9RqNUlJSRQWFnJSIuHgU37XdElrCVOlUqFSqZBKpSgUCqEkqwWFhYVs3ryZzZs3I5fL6dSpE8HBwbz88ssAXLt2jeTkZBITE+s8B7llyxYmTZpEs2bN2Lp1K0ePHtV6/K6urowcOZLc3Fxmz57NnTt3sLGxYfjw4Zibm7NkyRKDb2fRlnMSCb5KJfKTJzVHPEVeukTGQ0c8SaVSXn31Vdzd3Tlx4gRr1641YNS10/r+qNKK/3ulX90s/Tb3DtG+XItX+sePHycwMJAuXboQGxur46h1KzAwkMGDB3Pq1CnmzJlTqxd8e2QyWldUkJaWho+Pz71KREUFf76ApdmbN2+SkJDA8OHDSUlJ0TS4qW7MbykWExQairW1NdeuXePcuXPc4t7vWo4BG/NrtatXdRlWoVAIJVktq6ysJDY2VvNE4+/vT1hYGL1792bgwIHcvn2b1NRU4uLi8Pf3Z8aMGVy5coVPP/30kd63YrGY8PBwZsyYgbOzMz/99JNWYzUxMWHAgAH4+PiwceNGUlNTEYvFdOvWTXN6waFDhxrFKENDJGKnXI7z/SOe3NzcuHLlCv2qqlhzvzTr5eXFuHHjUCqV/PTTTwbfV/osJveP4ApWKrG1tSUgIKDGK/0TUikHpVKq6vBkv3HjRt5++23OnDnDrVu3dBi9blhYWDB8+HCaN2/OsmXL6vQzvHu/AYbZlSs4OzsTEhJCWUIC7ZRKjtfjsOmGauvWrQQFBfHKK6+wZuFCet9vzN+iRQt8fHxQKBQcPXqU4pISjkmlxEmlKAz8wkKrP6XqUeWdO3eEEaaOnTlzhjNnzgDg6OhI165dCQgIoGvXrowfP15THq+oqOCDDz6o8bVubm6IRCKys7Np2rSpVuPy8vJixIgRnD9/nv/+979UVFTg5OREVFQUpaWlzJ8/v0E+UdbGg0c8OTk5ERERQZu8PG4UFdFq+HB8fHxITk5m1apVhg71mVyVSvpXVdFMJCIwOFizLeLs2bPcArbL5eQ+xyv9W7ducfDgQYYPH86iRYu0H7gOtWvXjgEDBnDkyJHnbpaeKhbjI5EgO3mSrl270rZtW7pdvMgFsfiFbGjw5/LlzJs4kdeGDiUvKwuRSISlpSW5ubmcP3+e62IxMSYmXDWSfxudJMzqt4URpn5cvXqV1atXA/cW8EyZMkXzuQfnKqv5+/tz6tQpvLy8uHTpklZieHABz5o1a8jMzEQulzNo0CCCg4PZvn273vd6GkKyRIKPUolLbi5jxoyhY8eODFSrWdGsGfN+/ZWLFy8aOsSnMlWr6VlVRcD9+SN/f3/NQda3i4o4fv8Irvq80j906BDBwcGEhoaSlJSkxeh1w8rKipEjR9K0aVMWLlxYv/lmkYjdMhmtKiq4cOECnp6eXLlyhQFVVfz1gjV2sFapiCopodPNm4jbtsW7bVvOnz/P7t27Kblzh4T7jflVRvRvotW0XV5eTpMmTTRvCyNM/cvOzmbu3LnAvXmCWbNm1fi8SCTCx8dHs6Lz9OnTdbr+40akgYGBzJw5k9LSUmbPnk1mZiaenp68++67mJmZMWvWrBciWQIgErFdJsOpbVvNeZUmIhHla9dyUYfHkGmDh1LJaxUVBKnVhIaGEhgYSH5+PgcPHiSruJg/TUzYJ5PVuyymUqlYt24dAwcO1JxwZIxEIhGdOnVi2rRpZGVlMX/+fK0szioRidgvlZKTk8OdO3cIDQ2ltUpFkJF1s9IVkVpNmELB5IoKAps1q3Gua35+PhllZSwxMSFeJjOqZAk6HGEKjQsM54svvuD777+nrKzskcVAzs7OlJWVYWVlRX5+fq0brpuamrJmzRoiIiI4fvw4I0eOxMTEhEGDBmFjY6Pp9Wpubk5UVBStWrVq0FtF6qO5hwfKPn1QFxcjEonuLQjJzMRPqeSsEc5VNbl/sLPPQwc7Hzt2jKLiYo5KpRzS8vxRXl4ex44dY/jw4Sxfvlxr19WW5s2bExUVhUgk4pdfftH63tiU+5UIk/uLxFxcXOiRnU2WRKLTTjWG1lylYmBVFY4qFe7u7jg5OVFSUoKFhQXF5eUsyMkhxogXQWn1r/fBrSXCCNOwnnTIta+vL2fPnsXX17dWW1NcXV157733NHNyAO3bt+f333/n7NmzHDp0SNPrtX379vTv35/jx4+zbt26Br1V5HmIxWLGjRuHn58fG86c4XRCAiFWVpibmyMWi+mtUHBZIqHUWJ4M1Gq8VSr6VFVhIRYT0r49zZo10xzsXCASsUOH80d79+5l2rRpBAYGkpKSopN71JVYLKZLly5ERkbq9ggukYgYmYyW9w+bdnd35+rVq/StqmKdTNboSrPi+6PKCIUCCzMzQkNDkcvlpKamsnfvXu44OrK6rIxrZWVG/b3rLGFWVlYilUoRi8WNazVkA+fn58fq1asZP348v/322zMfv2rVKtzc3B75+O3btzWjWFtbW0aOHIlcLq//HE8D5eHhodnu8/vvv5OZmYm1SsXkigpaNG+Or68vxYcP089InhCb3h9VeimVODo64u3tjUKh4MiRIxSXlurlYGeFQsHatWuZMGECmZmZ3LlzR2f3qg0HBweio6OpqKjQy+K0IrGY2PuHTTs6OhISEkLV0aP4isWcM8JKxPN6sFm6l5cXzs7OFBcX3ztEXKlkr1TK2Zs3Df43URs6S5jwf9tMysrKtHkbwXNq3rx5jZL5jRs3nvn4Nm3aaN6/cuUKly5d0mxdWb16NWvXrsXFxYX9+/cTHx/f6A/EfZhYLOaVV17Bx8eHM2fOsHLlSs0LxML7T4g9b9zAwcEBd3d3VOnp+InFhivNqtX4KpX0ViiwkEgI6dABS0tLcnJySE9PJ18sZruJid76nObk5JCUlMTQoUNZuXKlXu75sAdbNu7cuVOvByCckEjwVio5efIk4eHhODk50fvKFS5LJNxpAAnkaST3j3vr9ECzdKlUyrlz57h69SrpEgm7TEwa1Pep04RZXZYVEqZx8PHxITU1FR8fn6eWY6v3Ufr7+7Nhwwaio6OprKzkn//8Jxs3buSjjz5i0KBBwL3ybGRkpMEOvzak6n2VKpWK33777bGdXo5LJHgplUjPn6dTp05cv36d3kVFBinNWqjV9K2qwl2pxMnJCS8vLyorK0lMTKTk7l3ipVKOGGBV4u7du5kxYwZ+fn6cPXtWr/d2cnIiOjq6RstGvRKJ2CGT0aKsjOzsbLy8vMjPz6d3VRWbHngubWha3m/M31ytxsfHh5YtW9Zolr5bLifNgA0InpfWGxdUNxKvfl/YWmI8PD09SUxMpE+fPmzcuPGxj3F3dycqKooLFy7w/fffU15ertm2cv36dUaMGMGYMWM0jzcxMXkhR5Xjx4/H29ublJQUVq1a9cRpB7VIxDaZjMn3e81Wl2b7VlWxXl+lWbWaAKWSngoFFlIpIe3aYWFhweXLl8nIyODq/VHlDQPtdasuzb788stcvHhRL833pVIpvXv3pl27dmzbto3k5GSd3/NJbonFHJLJkF64gIODA8HBwSiOHydVqSS9gSUV2YPN0ps1IygoCIlEwpkzZ8jPz+ecRMIeExPKGtCo8kFaT5gPjjCrqqqQvYDNhY2RTCbDxcWFrVu3Ym5uTnZ2do3Pm5iYMHDgQDw9PVm/fj0XLlzQfO7gwYNER0czadKkGi+I4F7nlgcf29g9OKr8tZb7KjWl2YIC7O3tNaVZfcxVWajV9K+sxPX+WaMeHh6Ul5eTkJBASVkZh2QyjkokBl+VePnyZVJSUhgyZIjOGzu0bt2a6Ohorly5omnZaGhHHyjNdurUCQcHB/pev062WKw59cXYPdhCsboxf/Vxb8X3m6VfaGAvAB6m9YT54MpYIWEajzZt2nDlyhVcXFzIyMioMSr08PBg5MiRpKenM3v27EdOW9i/fz8TJ058JFmePXuWyZMn6yV+Q5NKpYwfPx5PT0+Sk5NZs2ZNnRazVZdmZenpNUqz2boqzarVBCuVdK+qwkIuJzQ0lKZNm3Lx4kWysrLIuz+qNKbG3zt37mTGjBmaqQNtqz5z1d/fn82bN+u9/Ps06vv7dyfeuUNeXh6+vr4UFBTQs6qK7UZemjVRq+lRVUWQUomNjQ2BgYEAmsb8KRIJ+x847q0h0/o+zAdLsELCNB6urq5kZWXh5eWlmb80MTFh8ODBuLu7P3XPpEQieaRJulKpZM2aNTqP2xj4+Pjw0ksvoVQq+eWXX7h8+XKdr1H9hDjpfvNtXZZmm91fldhapaJ169a4ublRXl5OfHw8JeXlxMpknDCCUeXDqqqqWL9+PWPGjOHixYtaPQvW3d2dkSNHcvHiRWbPnm2U6yoKxGISpVLEaWnY29sTFBSE6uRJUpVKLhrpyMztfgtFywdaKF6/fp0zZ87UqTF/Q6H1RT9CwjRO7u7u7Nmzh4iICNavX4+npycjR44kLS2NWbNmaU4LeJizszPR0dGcPn2a6dOn4+fnR3x8PDk5OQwePBgLC4sn7vls6KRSKRMmTMDDw4OTJ09q2g8+r1tiMXEyGT0KCmqsmtVaaVatJlSpJLKqCgsTE0JDQ2nSpAlZWVlcunSJnPujyttGNKp8WFZWFufOnWPw4MFaOcml+mBnd3d3NmzYYPSL0xKkUjyVSpKTk+nQoQPNmzen/82bLBSLqTSiFzhN7h/B5adUYm9vj5+fH0qlUtNC8cT9Fop1aczfEOis0w/cS5jSRrSfqKFq1aoV/v7+DBo0CJFIxLBhw2jVqpWm5+vjPLgo4sFzLB9kb2/PiBEj+OOPP3T9Leidr68vL730ElVVVfz888+PzPk+r2MSCZ5KJbKHVs3WtzRrfX9U2Uqlom3btri6unLnzp17o8qKCg7IZJyUSBrEXrcdO3bw7rvv4unpWa8E5+vry7Bhwzh37txTXxQaE5VIxA65nPHFxVy7dg1/f39uHjxIpELBbiMZfHjdP4LLQiwmuF07rKysuHr1KufOnePm/WYXeUb8oqw+dLroR6FQCCNMA7Ozs2PYsGGMGDFC87O5desWkyZNeuITiIuLC9HR0Vy7do05c+Y8sX3evn37mD59OgEBAXXuSWusHjyvMikpSetlZ22XZkVqNe2VSrpWVWFhakpoaCimpqZcuHCB7OxsLonF7DAxqXEmp7GrrKxk3bp1REdHM2vWrEfm1J+ladOmDBkyBCcnJ/766y+jb3j/sKtiMcekUsTnztGtWzcCAwNRnzpFmlhs0LMgn9Ts4ujRoxSVlHBEKiVex80uDE1Y9NOI/f3vf+ef//wnCoWixkj/9u3bj02WMpmMvn37EhQUxObNmzXHhz2JUqlk3bp1vPLKK2RkZBjlvFBd+Pv7M3r0aKqqqpg/fz65ubk6uc+TSrN1bWhgc78vp5NKhZubG23atKG0tJRDhw5RUlnJfpmMUw1kVPmwjIwM0tPTGThwIBs2bKj11wUFBTF48GCSkpJYu3ZtnQ9WNxZxUinuSiUpKSmE3j9IecDt2ywSi/V/JqRajZ9SSa+Hml08eATXdhMT8hvQi7LnpZMDpKtVVlYKCdOAPvzwQ+DeqKm0tBRzc3Py8/OZN2/eI49t27YtUVFR5OTkMHv27FrvhcvOzubMmTMMHDiQdevWaTV+fZFKpUycOBE3NzdOnDihlbmzZ3m4NJufn0+v4uJaNTQQqdV0VCrpUlWFRZMmhIaGYmJiwvnz58nNzSVLLCbG1LTBN/Hetm0b7777Lm5ubk+cOqhmaWnJ8OHDsbGxYenSpTp7saMvivsNDV4uLKSgoIDAwEBux8bSVaFgvx6fUy3UavpVVuKmUj222UWCVMphIzuCS5e0mjAVCgUikQiJRIJSqXyk849Af5ycnCgtLcXGxgaAa9euMXLkSK5evVrj4Fu5XE7//v3x8/Nj48aNz7Wcf+fOnbV+YjM2AQEBjB49moqKCubNm/fIamBdebg06+fnR0ktSrPNVSoGVVXRQqXCw8MDFxcXSkpKOHr0KCUKBXtlMs400FHlwyoqKtiwYQNRUVHMnj37iVMI1U3/Dx8+rDkIoDHIlUhIkkoRnT5NZGQkfn5+qM6c4bxEwhVdj+bUaoKUSnrc35YUEhKCubm50TS7MBStr8ipXil79+5dKisrsbKy0vYtBE8hFovp2bMnnTp1YubMmYwePZpWrVrxxRdfPPKq283NjaioqHovta+oqGDTpk2MGDGC2bNnN4gymFwuZ+LEibRp04Zjx47VqeynLQ+XZt3c3FBduPDYVbNitZpOCgXhjznt4cqVK1y435fTaE5C0ZLz589z6dIl+vXrx5YtW2p8ztrampEjR9KkSRN+//13rl27ZqAodefg/dLsmTNnCAoKIjs7mwElJSwxMdHZXOHTtiWVlpcTJ5NxzAi3JemD1hNmRUWFpgz78CIggW7Z29szevRoSktLmTNnDm3atCE9PZ25c+fWaChtZmZGv3798PLyYv369VpZap+amkpwcDC9evVi586d9b6eLgUFBREdHU15eTnz5s3jypUrBovlsatmi4trrJq1vz9X+aTTHnbL5aSKxY1iVPk4W7Zs4d133yU3N5dbt26RnZ1N586d6dmzJ3FxccTFxTXaE5Eq75dmx9y4wa1btwgODqY4NpbOCgWHtF2aVatpd39bkvn9bUlmZmZkZWVx8eJFcu+PKgtfsFHlg7SeMB9cYCKUZPVDJBLRtWtXunXrxs6dOzl16hRDhw6lTZs2LF68WFNmFIvFdO7cmR49epCSkvJcKxCfZsuWLcycOZOUlBSjPOJLLpczadIkWrduzZEjR9i0aZOhQ3rsqtmSI0foW1XFJpmMzgoFnZ9w2sN5iYTdDey0h+dRVlZGYWEhu3btQiKREBsbS0xMDL/88sszT9xpDC5JJJyWSBCfOkVkZCTe3t6o0tJIl0i0dqqMzf1RpbNKhaurK23btuXOnTv3FpBVVHBQJiOpkZT660PrCVOlUiG+/0N8uJGBQPtsbW0ZNWoUKpWK+fPnY2lpyfTp07l48SI//vijZt7H09OTwYMHc/v2bX799VeuX7+u9VhKS0uJiYkhKiqKn376yahe9QcHBxMVFUV5eTlz5841qoT+pNLs+/fn4ry9vXFycqpx2sMuuZzzjaiDyrMMHz4cyf3vt0uXLowePbpB7KvUln0yGa4VFZw7dw5/f39yc3MZcPcuf8jl9SqNPryALCQkBFNTU9LT08nJyWmQ25J0SesJ08zMTDMXJpRkdUckEtGxY0f69u3Lvn37OHbsGH379iUwMJBNmzZp+mRaWVkxdOhQ7O3t2bZtm056dD7o+PHjBAcHExERQVxcnE7vVRtyuZzJkyfj4uLC4cOH2bx5s6FDeqxj1b1mz59n2LBhdO3alZs3b1JUVIRIJOL06dNcv369wZ/2UFcikYiIiAiaNm2q+Vh+fv4LlSwBykUidspkjMzPp1WrVgQHB1N66BAdlUqOPGdzGLv7pf6HF5DFxcVRcn81boowqqxBqwlTJpNhYmKi6f4vlGR1w9nZmaFDh6JWq/nll18wNzdn+vTpNbaEiEQiOnfuTK9evYiPj9fr6sENGzbw1ltv3ev8cfOmXu75OKGhoYwYMYKysjLmzJlDfn6+wWJ5lupjwD6wsMDV1RWRSISdnR0ZGRls2LCBErWanY3gtIe6sLOzIzo6GqVSycsvv8xrr71G27ZtuXz5MlKptEEsLtOmCxIJqRIJ0uRkunXrhoeHB4oLF7ggFtepib5Yrf6/Un/TpoSEhCCXy0lLSyMvL48L90v9DX1bki5oNWG6uLiQl5enOQnj4VZ5grozNTWlsrISlUpFs2bN6NOnD56ensTExHD27Fn69++Pr69vjS0hDg4OREVFoVQq+fnnn/U+z3Pz5k327dvHqFGjWLBggd7PyzQ1NWXSpEm0atWKxMTER1ZXGqtbYjEFfn6IHniiunHjBqdEIvY1ktMeakMkEtGlSxe6d+/O7t27OXLkCGq1ms8++wyAcePGNYjFZbqwRyaj9f35bh8fH3JzcxlYUcGftSzNtrg/V2n/wAKyoqIiDh8+TLFSyZ5GvoCsvrSaMFu3bl3jJIeysjKaNGmizVu8UD7++GM+/PBDbt68ybx587C0tOTo0aP88MMPtGrVihkzZpCVlaXZEiKRSOjZsydhYWHs2rWLY8eOGexw54SEBPz9/QkPDyc+Pl5v923fvj3Dhw/nzp07Rj+qfJBYLGbcuHHY+fiQWliIZ0UFd01NmVtYyI4XqEpTPapUKBTMnz+fW7duPfKYzZs3M2PGDE6fPm3QFc6GcFckYo9MhtmVKzg7OxMaGkpZfDztlEqOP6U0K1Gr6aJQEKZQYGlhQUhICBKJhLNnz3Lt2jXS7o8q7wqJ8qm0mjAdHR1r9BQVEubzs7Ky0nTqsbW1ZciQIQwbNgyAQYMGaQ56rt4S0rp1a6KioigoKODHH3+kuLjYYLEDqNVq1q5dy1tvvUVaWprOS7OmpqZMmTIFJycn4uPj2bZtm07vp01ubm6MHz8egEVLlvDJhQtI1GoCgoOJHDiQXfPmNZrN+E8iFovp0qULkZGR7Nq1i6NHjz7xxV5paSk7duwgOjqaefPmGdXiMn1IFYvxkUiQnTxJ165dcXV1pSoriwtiMUWPKc06qVQMqKzEVq3G19cXR0dHCgsL7x3sfH8BWfoLVOqvD60mTDs7OwoKCjTvq1QqFAoFcrn8hZukr6+KigpKSkqwsLAA7u0d/Oijj6isrOTUqVOaLSEmJiaasuyWLVue2f9Vn6pLs9HR0fz66686G+126NCBYcOGUVpayqxZs2r8Dhqz6lGln58fZ8+eZcWKFZonf6VIRPKpUwQFB9O9e3f27t1r4Gh1x97enujoaKqqqpg3bx6FhYXP/JqkpCSCgoLo1q0bBw4c0H2QxkQkYrdMhkt5ORcuXMDT05O8vDwGVFXxl1yuKafK1Gq6KRS0UyiwtrIiKCgIkUhESkoKBQUFnJFI2Gti8sKU+rVBawlTLBZja2v7yHxZWVkZZmZmQsKso7KyMl555RX+/PNPzM3NkUgkTJ06lcDAQE1C8Pb2Zvjw4aSnpxvtobgJCQkEBATQuXNnEhIStHrtB0eVhw4dYvv27Vq9vi55eHjw8ssvA/D7778/saXghg0bmD59uqZ01piIxWLN/uFnjSofZ8OGDUybNo2zZ882mBdJ2lIiErFPJsMkJwcnJydCQ0OpSEwkSKnklFRK6/sHO1sD/gEB2Nvbc+PGDU6fPk2RWk2MXE6WMKqsM60lTGtra0pKSqiqqqrx8eqy7O3bt7V1q0ZNIpHQunVrPDw88PDw4ObNm5ibmwP3Vh0PHz6cgwcP0r17dywsLFi9ejVZWVkGjvrJqkuzb775JufPn9daaTYsLIwhQ4ZQUlLCDz/8YNDVuHUhFot5+eWX8fX15cyZM6xcufKpJcXi4mJiYmKIjo42ur2t9WFvb8+oUaM03ZZqM6p82O3bt9m7dy9RUVEGWVxmaCkSCT5KJSYnT9KlSxdcXFzon52Nh1KJm0qFjY0NgYGBAJw8eZJbt25xUiLhgFxuVIdRNyRaS5j29vaPfZVXVlYmrJR9hqZNm+Ll5YW3tzceHh7cuHGD9PR0tmzZwubNm/n222/p0aMHFhYWfPfdd8THx/P5559z6tSpBvEEeuPGDfbv309UVBS//fZbvZ7YzMzMmDJlCo6OjpqOLw1F9ahSrVbz22+/1fqFzvHjxwkMDGwU5UexWEy3bt3o0qWLZlRZH4mJiQQGBtKpUycSExO1FGUDIRIRI5PhVFFBVlYWQUFBtG7dmjY5OVhZWWFra8v169c5c+YMhcAOuZxsYVRZL1pLmA/PX1YrLy9/4Rf+eHl58be//Y3y8nJ++eUXrly5gru7O+7u7ri5uWFlZUVGRgZpaWls3rxZc2CzqakpdnZ2fP/99/Tv319zvYqKCk6ePGmob+e5xMfH4+/vX6/SbKdOnRgyZAhFRUUNblQ5fvx4vL29azWqfJz169fzzjvvNOjyo4ODA9HR0ZSVlTFv3jytVJ3UajXr1q3jjTfeIDU19YWrZBWJxRyUyXAoKsLLywuJRIJarebcuXMcOnSIwtu3OSGVEiuVUiWMKutNKwnTycmJwYMHP3b7wIu+Utbb25v9+/drWgSOGzeO5cuXc/nyZS5cuMD69es1e1dtbW1p27YtLi4utG7dGgcHB65fv45SqeTEiRO0a9cOlUrVIPefPViaTUtLe+x2gScxMzPjtddeo0WLFhw8eLBBff9eXl6MGzcOlUrFr7/+ysWLF5/rOrdv32b37t1ER0fzyy+/NKjyo1gsJjIykoiICHbu3FnjIABtKCgoIC4ujhEjRrB48WKtXrshOCGRMN7dXdM6UCQScf36dTKLithhYkKe0NZOa+qdMJ2cnDhw4AC2tra8/PLLXLhwgXPnzmk+/6I3L+jUqVONfrqWlpZIJBIqKipo06YN3t7eWFhY0KxZM0pKSrhy5Qo5OTns2LGDnJwcTTeTX3/9lU6dOtG9e3fs7e0N9e3Uy40bNzhw4ADR0dG1Ls2Gh4czaNAgbt++zX/+85/nmusyBLFYzIQJE/Dy8iIlJYVVq1bVu3x+9OhRgoKCiIiI4NChQ1qKVLccHBwYNWoUd+/eZe7cuRQVFenkPrGxsQQGBhIaGkpSUpJO7mGsQtu1QzpsGBVFRZgoFFRUVbGhoICVOjwC7EVV74TZqVMnbG1tAWjSpAlTp07lww8/1Cz+edG3lBw+fLjGi4aMjAx+++03mjRpgkKhoLy8nNLSUoqKip7a6kulUpGQkMDx48f5/PPPGTBgADt27NDXt6E1hw4dwt/f/5lzTubm5kyZMgUHBwf279/P7t279Rhl/Xh7ezN27FhUKhULFizg0qVLWrludfnxrbfeIjU11ahL0mKxmO7duxMeHk5MTAzHjx/X6f1UKhVr165l0qRJpKena6Y1GrMHV4nvOnyY/xw6RB9PT+IvXyb12jWhW48OiJ72Kt/GxuaZQwBXV1fi4uJo0qQJVVVVzJo1izt37vDbb79RXl7OxIkTOXLkiM6bfhszb29v5syZw6FDh5g1a1aNP2YzMzO6d+9OdnZ2rfdQhoWFMWzYMP73f/+3Qc7ZNG/enDfffPOJnVwiIiIYOHAghYWFLFy4sMGMKqVSKePHj8fT05Pk5GTWrFmjk0VZERER+Pn51XsBla60aNGCUaNGUVpayvr163U2qnycfv36YWtry4oVK/R2T0Po1KkTgwcPprS0lIULFzbYeW1jdevWrce+2qh3cTsrK4t//OMfrFixgkGDBvHtt9/e20Q7YABwr/uPLo6SakjS0tKIj4/n4MGDNZKlTCZjy5YtLF++nP379zNkyJBaXe/IkSMUFBQwadIkXYWsUw+WZh/sm2pubs7MmTMZNGgQ+/bt4/vvv28wydLHx4fPPvuMVq1a8csvv2ilBPskCQkJSCQSwsLCdHL95yUWi+nVqxevvfYaCQkJLF68WK/JEmDv3r04Ojri5+en1/vqi7m5OTNmzGDo0KHExcXx7bffCslSj7Sy6EcqlTJ//nzNKDImJoaPPvqIkydPUllZadSlI31RKpWYmZnV+FibNm0ICQkB7u2/HDJkSK0bhS9cuJCPP/6Y8PBwrTcE0IdDhw7Rvn17fvrpJ/Lz8zl69CgRERHcunWrQY2cpVIpEyZMwMPDg+TkZFatWqXze1YvoHrjjTc4f/68UbyocHR0ZNSoURQXFxu0NaNCoWDdunWMHTuWrKwso2zm8by6d+9Onz59KCwsbFDz+Y1JvROmRCLBxcWFP//8U/Oxu3fvUlJSQvfu3Tl8+HB9b9EoKJVKzSo2uPdKcejQoZSWlmoaE9RlT1pxcTGxsbEMHDiQpKQkysvLtR6zLqnVarp06UKPHj0AuHLlCtOmTWP//v0Gjqz2fH19eemll6iqquLnn38mOztbb/cuKCggNjaWkSNHsnDhQr3d92ESiYTu3bvTuXNnduzYwYkTJwwWS7VLly5x7tw5Bg4cyLp16wwdTr1ZW1szZcoUrK2t2b17d4Pfi9uQ1Tthuri4cP369RpP2OPHj+ett97izp07tS4zNnYqlQrp/dMEevToQe/evSksLKR///707duXS5cusXHjxjpdMyYmhtDQUF599VUWLFigg6h168GymampaYNJllKplFdffRV3d3eSkpJYs2aNQeKIi4sjICCADh06aH2rRm0Yy6jycWJiYpg5cybu7u5kZGQYOpzn1q9fPyIjI8nPz+ff//73C7GYyZjVO2G6ubnV+IW0trbmhx9+QCqVYmVlxQcffMDbb79d39s8t549e9K7d2+DdoXp3bs377zzDgqFgj59+lBUVFTjleKD23DqasmSJUybNg0fH58Gs7DK0tKSqVOnkpmZqdki01AqEf7+/owePZqqqip++ukncnJyDBaLSqVizZo1TJ06lfT0dL3NF0okEnr06EGnTp3Yvn27UW7jqKioYMOGDYwcOZLZs2c3uJX6dnZ2TJ48GQsLC7Zu3dogp10aI60kzAdPUhCJRDUWcogMuLQ5ODiYVatWIZFIeP311xk6dKhBfvH++9//asquffr0oV27dlp7pXjlyhVOnTrFmDFj+Oqrr4y+VV737t3p27cvBQUFjBkzBicnJ9zc3OjatSutWrUyaAJ6GqlUysSJE3Fzc+PEiROsXbvW0CEBkJ+fT2JiIiNGjGDJkiU6v1/Lli0ZNWoUt2/fNrpR5cPS09O5dOkSffv2ZevWrYYOp9aGDh1Kp06dyMvLY86cOQ1uuqUxq9cqWZlMRsuWLWscGn3r1i0++OADSkpKSE5O5ptvvql3kM/L29tbM28oFovx9fXVewx2dnY0bdpU8/7Vq1e1XlZZvXo1AGPGjNHqdbXJ0tKSv//97/Tp04edO3dqttecP3+e7du3k5GRwauvvmroMB/L39+fzz//nBYtWjB//nyjSZbVDhw4QLNmzTQLyHRBIpHQp08fJk+eTFxcHEuXLjXqZFlt69atBAYG4uLiYuhQnqlly5Z8+umndOjQgbVr1zJ//nwhWRqZeiVMFxcXrl69+sgJJRKJhLy8PH744Qfy8vLqFWB97Nu3j9zcXODeQqQ9e/bo9f5Dhw7l3XffZfXq1aSkpHDixAmmTZum9fuoVCr++usvAgMDcXJy0vr166tbt2589NFHiEQivvvuOw4ePPjIY5YuXYqJiYnmkGxjIJfLmTp1KuPGjSMlJYV//etfmt8nY6JUKlmzZg2DBg3SnJ+qTU5OTrzzzju0bNmSOXPmGGUJ9knu3r3Lli1biIqKqrHozthER0fzzjvvUFBQwNdff92g/o1fJPVqXNChQwdcXFxqrESrPiwYoKqqim7dunH+/HkthVt3lpaW+Pn5MXjwYNLT01m6dKnO7+ns7Myrr76KqakpGzZs0Nsv/+uvv46dnZ1BR/UPMjc3Z+rUqTRv3rxWq/uCg4MZPXo0c+bMIT8/Xz9BPkFAQACjR4+moqKCxYsXG/SFX2317dsXBwcHli1bppXrSSQSevXqRceOHdm6dSvJyclaua4hjB8/nmvXrhldx6g2bdowYcIEpFIpq1evNqoD4F9kOmlc8Li2dz4+Ppq3ZTKZwUshxcXFJCYmsnDhQry9vWnTpo3O7iUWixkzZgxvv/02169f1/srxSVLltCkSRMGDhyot3s+SdeuXfnkk08Qi8V89913tVoKn5ycTG5urkEbMsjlcl5//XXGjh1LUlIS//rXvxpEsoR7m/abN29OQEBAva/l6OjItGnTcHBwYM6cOQ06WQJs3LiRsLAwHB0dDR0K8H/nov7tb38jJyeHr776SkiWDUC9EmanTp3o2LGjZrtEu3btsLS01GyoPXHihNE0ic7KyiI9PZ3x48fr5PoeHh589tln+Pr68scff/Dbb7/pfWVeZWUlW7ZsoUuXLlhbW+v13tXMzc159913GTBgAHv37uWHH36o01zXokWLMDc3p1+/fjqM8vGCgoL47LPPsLOzY968eWzYsEHvMdSHUqlk7dq1DBkypMa8eV2IRCK6devGlClTiI2NZdmyZZSUlGg5Uv0rKSkhJiaGqKgoxAY+vcPNzY3PP/8cDw8PlixZwuLFi5/aR1pgPJ67JPvVV1/xzjvvAPf2g1WvlF2zZg0VFRX8z//8D++9994j85uGJJVK+eKLLzh27BibN2/W2jWr+4eePXuWFStWGHyl6vTp05HL5Xz//fd6ve+DPWB///335+7WEx4ezuDBg/XW8UculzNp0iRat27N0aNH67wf1tgMGDCAZs2a8ddff9Xp66ysrBg9ejRwbyFZQ+m2VBevvfYa6enpxMbG6v3eYrGYV155BR8fH6N5rhA83pNKss+dMA8fPoynpydwb2TTpUsXMjMzUavVyOVyPv30U7744ov6xq117du3Z+TIkVppLRUQEMCoUaNQKBT88ccfWjuVor7Mzc355JNP9NYV5MGTRfbu3Vtjm9HzmjlzJhKJhB9++EELET5ZcHAwUVFRlJeXs2jRIq5evarT++mDVCplxowZ7Nixo1Z7fEUiEZ07d6ZXr17ExsYSGxtrlE3dtcHGxoa3336bn376Sa8tOx88F3XZsmVkZf3/9u48kOq07QP418FxSAuyixIpJVJE0qq02Je0J0zPTE9GTVPTM83onZaZmueZB83SNEWUCAclEkmWQVLKViQqRBuVfT3eP3qdt5MlnBX356/6Hef+XTXTuc59/+77ukp5dm9i4HpLmIM+h5mcnMxMmNevX2cpXtDZ2cnX85d9uX37NubNmwcXF5dBfxjTaDRs3boVEyZMwO3btxEREcHhKNlTX1+PhIQELFu2DLdu3UJjYyPX7sWtfpV+fn745ptvsGDBAq7MBqhUKlxcXKCqqorMzExcunSJ4/fglw/rqT5+/LjPeqpycnKwt7cHg8HAn3/+OewLedfU1ODGjRuwt7fnSbeXD1egONUXleCfQSfMffv2oaGhATQaDf/zP//D8trHdVMFjZ+fH7799luYmJggLS1tQO+dO3cuLC0t0dDQIBC7OXtz48YNzJkzBy4uLvjtt984Pr6EhATc3NygoKDAlX6VtbW1SExMhLm5OW7fvs3RpD9r1izY2dmhqalJoP8bsuPJkyfIz8+HhYVFj6X7PqzWEx8fj6ysrGE7q/xYWloaZs6cCUNDQ2RmZnLtPl21htvb2/Hnn3+ynFcnhqZBJ8zOzk5kZGSAwWB029zCYDCYtVMF8WF2fX09kpOTsXLlSty5c6dfh4M/XHbkZ5m9gfDz88Pu3bs53oXeyMgIlpaWHJ9Vfuz69evQ19eHq6srfv31V7bHo1KpcHV1xYQJE3Dz5k2OPccWVHFxcfDw8MCUKVPw8OFD5vUJEybAwcEBr1+/FvhqPdzQ1e3lH//4BwoLCzleUvDDqlD8rDVMcB5bpfGEhYV73Qna2toKMTExgUyYwPsPk/4WLl+yZAmWLl2KmpqaIdVWp7q6GpmZmbC1tUVubi7b/y0kJCTg6uoKRUVFJCcnIy4ujkOR9q4r6bNbYFxfXx92dnYCvzLASa2trYiIiMCePXtQU1OD1NRUAO9n2JcvX0ZeXh6fI+Sfly9fIj09HTY2Nhw9m62vrw9bW1u0tLTg999/F8hCF8TgsZ0wOzo6enytpaUFYmJiaGhoYOcWXBUQEAB3d3dMnToVhYWF3V6XkZGBi4sLxo0bh7i4OL7srGPXpUuXoKOjg82bN8PPz2/Q43QtRdfV1eGXX37h2YaJ6upqpKWlwdraGjk5OQM+qkOj0eDi4gIVFRVkZGT0u9/ocDF69Gg4ODiAQqHgH//4B7y8vODl5SXQ/y55JSkpCe7u7tDV1UVOTg5bY0lKSmLr1q1QVFQcds/Eif/HdsLs7QF2a2srqFQqO8NzXVfh8rVr13YrXG5hYYF58+ahsrISv//+O1c3znDb2bNn8cUXX0BTUxPFxcUDei+NRoObmxuUlJT4thQdExMDXV1dbN26dUBtzObMmQMbG5sRNav8mI6ODvPcoZCQEKqqqkiy/D9d51a3bNmCR48eDfrvxdTUFCtWrMDbt295+mWS4D22E2Zvy3xdM0xBFxoaih9++AEnTpxATk4Orl27hjVr1oBGoyEiIgK3b9/md4hsKysrQ35+PtavX49Dhw71e5eeoaEhrKyseD6r7MmZM2fg7u4OHR2dTy4l0mg0uLq6QllZGWlpaYiJieFRlIJlypQpmDJlCmpqaiAtLY36+nqBKw3HbxUVFbh79y4sLS0HfG5VUlISbm5ukJOT49hxKkKwsZUwRURE+lySFfQZJvB+g5K2tjYWL14MR0dHuLm54cSJEwgICBhyPfT6EhwcjAMHDsDBwYHZ3aQ3Hyac1NRUxMbG8ijK3lVVVSE7OxuOjo548OBBr1/UumaV9fX18PLyGvbHJHoiJiYGCwsLaGhowN/fH//5z38wY8YM6OrqQlNTE2VlZfwOUaBcu3YNHh4eA+op2zWrfPPmDc8KbBD8x7UZZtemn6FgxowZzF9LSkri1KlTfIyGOxgMBkJCQrBp0yakpqb2ekBfkBMOnU6HtrY2Nm7c2K33I5lVvjdlyhTY2dmhsLAQXl5ezC99aWlpyM/Ph7u7O/Ly8vDy5Us+Ryo42traEBERAScnJ5SWlqKlpaXXn+1qKCArK4uEhAQkJibyMFKC39gqqtifTT+CjEaj4fPPP2cprs3OTkxBd//+fTx+/LjH4uY0Gg3bt2+HnZ0dMjIycPToUYFKll3Onj0LLS0tTJ48mXnN0NAQ3333HSQlJeHl5TUikyWNRoO9vT1sbGwQFhaGixcvdlsheffuHRISEmBvby+whUX4pbS0FIWFhX02Lli0aBGzocDRo0dJshyBuLbpR9CXZD8sQLBu3TpoaGhAR0cHqqqqGDdu3LBdYvH394enpyfMzc2Zx0I+PHIhaLPKjz158gTNzc24du0aGhoacP36dTQ2NgrM0jE/fDir9Pb27vNRQmZmJnR1dWFkZISMjAweRin4rly5gl27dmHy5MkoKSlhXpeSkoKLiwukpaV5Vm6SEExsJUwKhdLrDFNQl2Q/LEDw4YfsixcvkJaWhl27dsHFxQX//e9/+Rwpd7S2tqKqqgpHjx6Fk5MTHjx4ACkpKaSnpyM6Oprf4fXLsmXLMG7cOIwbNw42NjaYPXv2iNyZSKPRYGFhAXV1dYSFhbF8yPems7MTERER+Pzzz3H//n2OH9ofylpaWhAZGQk3NzfU1taioKAAHR0dMDExwcuXL3Hs2LERV+SBYMVWwmQwGL2WwBPEGWZ/ChD4+vpi3759MDIyws2bN/kQJfd5eHhg/PjxUFNTw7Rp02BjYzNkjlzIyspCRkaG+ft3796NyGSppaUFW1tbPHjw4JOzyo+9evUKaWlpsLW17fYseKSrqqrC2rVrMXbsWADvS0weO3YMt27d4nNkhCBgK2G2t7f3mjBbW1shKSnJzvAcM5ACBLW1tUhNTYWFhQWys7OH1U5Z4H21HmlpaebvX79+PWSSpZWVFYyMjBAREYGFCxdCTk4O8fHx/A6LpwYzq+xJcnIyduzYwZFD+8OJuro6M1kCwLNnz0iyJJjY2vTTV5F1Qdn0Y2Vlhd27d6OpqQlHjhzpV7We2NhYNDU1ca3ZNL/MmTMH3377LVJSUsBgMNDS0jIklp5VVFSwf/9+GBgYgE6nY//+/Zg/fz5MTEzQ1taGWbNm8TtEntDS0sLOnTvR1tYGb2/vQSdL4P2/3fDwcFhYWEBCQoKDUQ5dWlpasLe3Zz7Db2trIxV7CBZszTA7OjogItLzEPxeklVWVoazs/OgCxCcO3cOX3zxRbcNAEORhIQEXFxcoKSkhPT0dHz33XeQkJDA7t27oa6ujhs3bvA7xB5RKBQ4OjpCT08PpaWl3c7Gvnr1CpmZmbCzs0NBQcGwWw3oIi4uDgsLC0ycOJGtWeXHKioqcO/ePVhYWHzybO5wJiIigo0bN0JLSwt5eXkwMDCAsbExnjx5gqKiIn6HRwgQri7J8muGaWtrC0NDwx4/ZPurrKwMDx48wIYNG3Dw4EEuRMkbJiYmWLVqFd69e8dSraexsRFnzpzBP//5z15r6fLT5MmTsXHjRlAoFJw9e7bXA+WXLl3CjBkzsGXLlmF5flZLSwt2dna4f/8+fHx8OP6lID4+Hjt37uzW0WSk+LAJ/MmTJ5lN4HnRWIAYerg6w+R1wpSXl4erqyvExcURFBTEdjeGoKAgHDhwANbW1kNuaWbMmDFwdXWFrKxsr2W7KioqkJOTg3Xr1uGHH34QiMa2H37bLygoQFBQ0Cfj8vf3x44dO6CtrY379+/zKFLu+nBWGRISgtLSUq7cp62tDZGRkbC3t2cpdDDcUalUbN26FWpqasjOzgadTud3SMQQwNYzzE/NMHm5JGtubg4PDw+8efMGhw4d4kjrovb2dkRGRmLu3LksOzMF3dKlS7Fv3z4AwNGjR/uscRkaGgoGg4H169fzKrxe6ejowNPTE6qqqvjrr78QGBjYryT+7NkzZGdnw8nJqdcvcEPJ1KlTsXPnTrS0tMDHx4drybLLo0ePUFpaCnNzc67eR1DMmTMHnp6eGD9+PH799VeSLIl+Y3tJVlRUtMfXOjs7eVJNhEqlYvv27ZCVlcXFixc5vqMtOzsb8+fPx9atW/Gf//yHo2NzmoyMDFxdXTF27FjExsYy+x/2hcFgICgoCC4uLpg0aRIeP37Mg0hZceLbflhYGKZNm4YNGzZwtL8hL/FqVtmTmJgY7Ny5Ezk5OcO21uzHz/KHyrljQnCwNcNsa2vrNWEKCQlxfYlPQkIC33zzDUaNGsXVs1J+fn6QkpLC0qVLuTI+J1haWmL37t1obGzEkSNH+pUsuxQXF6OoqIgvu4INDQ3h6ekJGRkZtr/tnzt3DlOnTmUpmzdUTJs2jaezyo81NjYiOjoa9vb2va4aDWUmJibYv38/Ro0aBS8vL5IsiUFha4bZ1taG0aNH9/gahUJBZ2cnO8N/koeHB5qbm+Hl5dVrEXhOqK+vx9WrV7Fy5UrcuXNHoMrmKSoqwsXFBeLi4my1IwsMDISnpyccHBx4skQlKSkJFxcXKCgocKxY+uPHj1FQUICNGzcOqI0ZP4mLi8PS0hJqamo8n1V+LDc3F3p6eli0aNGwaVX14bP8GzdukPZmBFuG7AzTysoKEhIS8PHx4Wqy7JKamopXr17B1dWV6/fqL0dHR7i7u+PVq1c4ePAgW70729vbERYWBn19fSgqKnIwyu4WLlyIf/3rXxATE8Mvv/zC0WLpQUFBEBISgpOTE8fG5JauWWVzczNfZpU9uXjxIoyNjSEnJ8fvUNi2ZMkS7Nu3DxQKBT///DNJlgTb2EqYra2tfSZMbs0wKRQK5s6di9jYWJ7u6jt16hSkpaWxZMkSnt2zJ5MmTYKnpyd0dHQQGBiIv/76iyN/D3l5eSgrK+uxmwknSElJ4euvv8by5ctx/fp1/Pvf/+Z4WTsGg4Hg4GDMnDkTqqqqHB2bU8TFxbFmzRpYWFjgwoULiIqKEpjdqbW1tbh27dqQ7mgiJSWFPXv2YOnSpbh69Sp++eUXgVoVIoYuthNmb0dHuJkwTU1N0dHRgfT0dK6M35uupVkzMzOMGTOGp/cG3n9R2LhxI7Zt24anT5/i4MGDHD9G4efnBwkJCVhZWXF03BUrVmDPnj1ob2/HTz/9xNXWSEVFRSguLsaWLVu4do/B+nBW6e3tzZdNVp9y69YtMBgMGBsb8zuUATM3N8eePXvQ0tKCn376qV+VvQiiv9hKmPX19b3Wi21ra+PasZK5c+fyrQJH19LsZ599xtP7amlp4cCBA5g8eTJOnz6NgIAArixFt7a2IiIiAkZGRpCXl2d7PHl5efzrX//C/PnzERUVBW9vb9TX13Mg0r6dPXsWoqKisLW15fq9+qOnWWVbWxu/w+pRZ2cnwsPDsXTpUpa6qoJMRkYG33zzDRYsWICoqCgcP36cJ/+fESMLWwmzoaEBo0aN6vG15uZmrhUukJKS4ms5N19fX8jIyGDu3LlcvxeFQsGWLVvg7OyMwsJCHDp0iOul+rKzs1FeXs7281pbW1t4eHjg7du3OHz4ME+7v7S3tyM8PBwGBgZcfyb7KV2zyqamJoGdVX7s9evX+PvvvwXmC0dfVq1ahd27d6OhoQFHjhwZtl2GCP5jK2FqampixYoVPf6jampqgri4ODvD92jixIno7OxEZWUlx8fur9raWqSlpcHCwoKrB+W7ZpUTJ07EyZMnERwczLOdn76+vhAXFx/U0qyamhq+//576OvrIyQkBCdOnEBzczMXouxb15lCZ2dnnt8beH/sycnJCatXr8aFCxdw+fJlgZ1V9iQ5ORljx46Fnp4ev0PpkaysLPbt24d58+bh4sWL+O2339DY2MjvsIhhbNAJU1VVFXQ6HbNmzYKvr2+3D9auGSanNw6MHz9eID50YmJi0Nraig0bNnB87J5mlV01LnllMEuzFAoF69evx+eff45nz57hhx9+4HvrKH9/f4waNQqrV6/m6X21tbWxc+dONDQ0wMfHZ0jMKj/GYDBAp9OxevVqgWnV18XKygq7du1CbW0tDh8+TFpwETwx6OmRhoYGS1ug9evXo7q6GqWlpXj+/DlsbW1haWkJCQkJ7Nu3Dy0tLRwJeMyYMTw5RtIfQUFBcHNzw4QJE1BeXs6RMTU1NbFhwwZ0dnbir7/+4usH7d27dzF37ly4urrixx9/7PNntbS0sG7dOgDvk5SgdHlobm5GVFQUbGxscPPmTa43m5aQkIClpSUmTJiAoKAgnn/R4bRnz57hzp07sLKyQlBQEL/DYZ47ptFooNPpyM7O5ndIxAgy6BlmVlYW80OxoaEBoaGhkJOTw/r16/Hzzz/j5MmTUFFRwZYtW+Du7s6xgBkMhsBsdy8pKUFpaSlHKuRQKBRs3rwZLi4uKCoqwqFDhwRiVuLn59fn0qyIiAhcXFzg7OyMhw8f4uDBgwKTLLvcunULVVVVcHFx4ep9Pp5VDvVk2SUhIQEKCgqYMWMGX+Ows7Njnjs+dOgQSZYEzw16hllXVwczMzN88803uHbtGkspNlNTU5YNI0ZGRuxF+YHm5mZQKGw9euWogIAAeHp6YtmyZYM+GC1Is8qPdS3NOjo6IjMzEy9evGC+pqurCwcHB7S0tOCPP/7g2CybG3x9fbF//34sXbqU41Vsuo7hqKio4Pz583j69ClHx+e3rg1UGzZsQGlpKc+fE6qqqmLLli0QFRVFcHAwRxorEMRgsLVjpaGhAbm5ud02dDQ0NLD8nlPLsQBQXV3da7EEfmhtbUV8fDzMzc2RlpY2oA+TrnOV06ZNQ15eHi5cuCCQ5dy6lmYPHTqE/Px8JCYmYuHChVBWVsatW7dw8eJFfof4SY2NjczyhllZWaitreXIuNOnT4e1tTVycnLg4+MjEM/XueHp06fIzc3labNpCoWCNWvWQFdXF8XFxTh79qzAPI4hRia2EqaSkhK2bdsGMTExfPPNN2hqaoKhoSGUlJRQVVXF3M4fGxvLkWAB4MWLFwI1wwSAlJQUGBsbY8uWLThx4kS/3vPhrPLUqVMCURatL5WVlfjxxx/h4OCAffv24fTp0/Dx8WGZcQq61NRUGBgYwNXVFV5eXmyN1TWrVFZWHpazyp7ExcVh586d0NLS4vqye1cDcSEhIYF6Jk6MbGxlnv/85z+YM2cOdHR0EBYWhtmzZ+PmzZs4fPgwFi5cCD8/Pxw6dAiBgYGcipc5MxC0XXv+/v5QVVXF1KlT+/y5D59VFhcX49ChQwKfLAFg/vz5zF/TaDRcu3ZtSCXLLqdPn4asrCxMTEwGPcb06dOxc+dO1NXV4fjx4yMiWQLvi5FERETA1taWa2esu/59uLm5oaSkRCCfiRMjF1szTCkpqf8fSEQEZ86cYZbDe/36NSIiIiArK8tehD1gMBiQl5cXqEoeL168QF5eHpycnPDDDz/0+DNDbVbZZfXq1ZCVlUV7eztERETw5s0bvh8XGaza2lokJiZi1apVuHv37oCW0EfirPJjJSUlKCwsxKpVqxAZGcnRsadNm4a1a9eCwWAMqX8fxMjB1gzz8OHDqK2tRUdHB7777rtutWPfvn3LldJabW1tGD9+PMfHZdeFCxcgLCzcrZADhULBpk2bhtysUllZGfv374exsTH++OMPzJ8/H59//jkuXboEHR0dfoc3aAkJCXj37t2Ads2O1FllT2JjY6GlpQUNDQ2OjEelUuHm5obNmzfj/v37Q+bfBzHysDXDTEtLg4GBAbZu3QpfX99ur7979w7jxo1j5xY9amlpEciEyWAwEBkZCUdHRyQlJeHNmzeYPHkyNm3aNKRmlR9utigtLUVAQACzm8bDhw/x9u1bZm9QflTw4QRfX1/s3r0bc+bM6bMtGplVdtfS0oLIyEjY2dnB29ubrU4r+vr6sLW1RUtLC37//XdUVFRwMFKC4Cy267q9efMGkpKSEBER6baDrbq6GjIyMhzvXNLU1MSVRMwJd+/exaJFi7B582bU1NRAW1sb+fn5PC1rx44PCxAEBASgsLCw28/Ex8dj9uzZ2LJlC06ePMnrEDmiuroaaWlpsLGxQW5ubo8f+h/ugD1+/Piw3QE7GEVFRXj8+DHMzc1x+fLlAb+fRqPBxcUFKioquHnzJqKiorgQJUFwFtvbTdvb2/H69eseC1w3NzejubmZ48mtrq4Oo0eP5uiYnHT27FkoKChAXV0dp06dwvnz5wU+WVKpVLi6urIUIOgpWXbx9/fHxIkTP7nJSZDFxMSgpaUFlpaWLNclJCSwdu1arFixAufPn0dMTAxJlj2Ijo6Gjo4O1NTUBvS+OXPm4LvvvsPYsWPh4+NDkiUxZHCkcnh5eXmv5eGeP38OJSUlvHnzhhO3AvB+48ZA/5HyUnV1Nf773/+iurpa4BMlwLos1t8CBFVVVcjNzcXatWtx8ODBIfHn7ElhYSEmTZrE/L2Ojg4sLS3JrLIfmpqacOnSJTg4OMDHx+eTZyQlJCSwdetWKCsrIy0tDTExMTyKlCA4gyMHGisqKnrtbv/gwQPMnDmTE7dhevPmDWg0GkfH5LRXr14JfBKRkJDAjh074ODggKysLBw+fHhA1XpCQkIAAA4ODtwKkeumT5+OJUuWYObMmdiwYQOWLVuGwMBAMqvsp4KCAlRWVmLZsmV9/pypqSn2798PSUlJeHl5kWRJDEkcSZgPHjyAlpZWjw2jc3JyMGXKFI62+qqpqeFac+qRYtGiRdi/fz/ExcXxyy+/DGpZjMFgIDQ0FHp6enzvOTkYzs7O+PLLL2FkZIQrV66gsbERx48fR1lZGb9DG1KioqKgr68PFRWVbq+NGzcOu3fvxsqVK3Hjxg0cO3YMr1694kOUBME+jiTM+vp6lJaWQldXt9trTU1NyM/PZ+ug+Mdev37N1T6Uw5mUlBS+/vprLFu2DAkJCfj3v//NVgeP+/fvo6ysDFu2bOFglLzxYQNwCQkJFBcXk9Jrg9DQ0IDo6Gg4ODhAWFiYed3c3Bx79+5FR0cHjh49ioSEBD5GSRDs41iNuYyMDCxcuLDHRHbjxg0YGxtzbJb58uVLgelYMpSsXr0ae/bsQWtrK3766SfcuHGDI+P6+/tj9OjRWLp0KUfG4wV9fX2Wnd13797Fw4cP+RzV0JWTkwNVVVXk5eUxZ5KmpqaIioqCt7c3x2r3EgQ/cWya9ujRI1RWVmLp0qWIi4tjea2mpgYPHjyAiYkJR75ldp39GzduHN6+fcv2eMOdsrIynJ2dIS4ujosXL3K82W5zczOuXr2KFStWIDMzU6AqMH1s7NixsLW1xdixY3HgwAEcPXoUampqyMjIYOs84UgnKiqKdevWQUxMDAoKCqBSqVi6dOmQPadLED3haBXzqKgoGBgYQElJqdtriYmJHJ1lMhgMrpTdG04oFAqcnJywY8cOvHz5EgcPHuRaZ/rU1FS8ffsWzs7OXBmfEwwNDeHu7o6ysjL89ttvqKysRElJCRITE9HU1MTv8Ia0j1d8ysvLSbIkhh2OJsz6+npcuXIF9vb23TqK1NTUoKCgAAsXLuTIvdra2iAjI8ORsYajyZMn4/vvv4e2tjbOnj2LU6dOcX0G5efnByUlJejp6XH1PgMlJSUFNzc3GBgY4K+//kJiYiI6Ojr4Hdaw0traCnd3d1RVVSE/Px+enp78DokgOI7jO2eys7Ohp6cHU1NTJCcns7yWkJAADw8PZGRk4N27d2zdp7W1laX4O/EehULBhg0boK2tjYKCAgQFBfHseEt1dTWysrJgZ2eH/Px8vm+gERISgrGxMZYsWYKUlBT8/fffAn/UZyij0+mg0+n8DoMguIYrjSUjIyOxYMGCbjPA2tpa3Lp1C2ZmZmzfQ5DL4/HL1KlTceDAAairq+P06dMIDAzkeYKIjIxEe3s7s7wev4wfPx7btm3DzJkz8eeffyIlJYUkS4Ig2MKVhPnmzRskJibC3t6+27ONpKQkTJ06FXJycmzdo6amRiALsPOSvLw8FBQUICIiAldXV2zZsgUPHjzAoUOHUFJSwre4zp8/D21tbb5UYxISEoKpqSm++OIL5OXl4eTJk3j9+jXP4yAIYvjhSsIEgPT0dIiIiMDAwIDlektLC5KTk7Fy5Uq2xi8vLx/RS7LOzs7Iz89Hfn4+6HQ6lJSU8Mcff+DChQt8n0mVlJSguLgYmzZt4ul95eTk8MUXX2Dq1Kn4/fffkZ6eztGi/wRBjGxcS5idnZ0IDw/H8uXLMWbMGJbXMjIyIC8vz1LDc6Bu3boFcXHxEblTlkKhYO/evRAWFgaFQoGuri4OHTo0oLJ23BYYGAgxMTFYWFhw/V4UCgWLFi3Ctm3bcPv2bZw+fRo1NTVcvy9BECML1xImALx48QI3b96EjY0Ny/WOjg7Ex8ezNcusr69HdXV1t04Tw528vDy++OILNDY2Mq8VFRXxMaKetba2IioqCvPmzePqSoCioiL++c9/YtKkSfj1119x69YtMqskCIIruJowgfdVfmRkZKCjo8NyPScnB8LCwpgxY8agx758+TI0NTVHxNJs1yzqs88+w507d3DixAn8/fffOHfuHLZu3crv8HqUlZWFly9fciU+YWFhmJmZwdXVFenp6Thz5gzbO68JgiD6wvWE2dHRgfDwcFhaWkJCQoJ5vbOzE7GxsTA3N+92ZrO/ioqK8OLFC4FNGJwiLy+P7du3Q11dHeHh4TAxMUF9fT2cnJzg4eGBqqoqfofYKz8/P4wfPx5GRkYcG1NZWRnu7u5QUlKCj48P7ty5w7GxCYIgesP1hAkAZWVlyM3NxerVq1muP3r0CDU1NTA1NR302F0fyJws7i4oKBQKlixZgs8++wxZWVl49uwZ7OzsEBcXh5CQkCFRnaa2thYpKSmwsLBgu8OMiIgIVqxYAWdnZ9y4cQNnz55FXV0dhyIlCILoG08SJgDEx8dj0qRJ0NTUZLl+8eJFmJqaDnrzTm1tLW7cuIFVq1axzGCHOgUFBWzfvh1qamoICQmBkZER5OTk4OPjg/z8fH6HNyBXr15FU1MTWx1NVFVV8eWXX0JGRgY+Pj7IycnhYIQEQRCfxrOE2draisjISNja2rLMNN68eYOEhAQ4ODgMugPJtWvXUFtbOyyWZrtmlW5ubsjMzMSTJ0/g5OSE1NRUnDt3TqALm/clICAA6urq0NLSGtD7REVFsXr1amzcuBHx8fE4f/78kP07IAhiaONZwgSA4uJilJSUYNWqVSzXMzMz0d7eztayqp+fH5SVlaGvr89umHyjoqKCY8eOQV9fH+fPn4ehoSFz92d2dja/w2NLRUUF8vPzsXbt2n4/s540aRI8PDwgKSkJb2/vITezJghieOFpwgSA6OhoaGlpQUNDg3mt68zm4sWLB12959WrV8jMzOw2gx0KKBQKli5dikuXLsHV1RV79+7FgQMHcPv2bfj5+Q2b3Z/BwcGgUCiws7Pr8+eoVCqsra2xdu1axMTEICQkhOUYDUEQBD/wPGG2tLQgPDwcDg4OEBMTY16vqanB9evXeyyn11+XLl1CS0sLzyvMsENRURE7duzAjBkzmIUcREREIC4ujszMTD5Hx1kMBgN0Oh36+vqQl5fv8Wc0NDSwa9cuiIqKwtvbGw8ePOBxlARBED3jecIE3u+OLSws7FYFJiMjAwBgbGw86LH9/f2hoaGBqVOnshUjt314jjAtLQ337t1j2fEpiMUIOCEvLw/Pnj3r1jdTTEwMdnZ2cHBwQGRkJOh0+pDYBUwQxMjBl4QJAFeuXMHkyZNZNoF0dnaCTqdj6dKlkJaWHtS4FRUVyMnJGdCzMl7rqk6joqKCwMBAGBgYQEtLCzY2Njh+/Diio6OH9RLkmTNnIC8vjz/++AMuLi6YPn06du3aBQaDAS8vLzx8+JDfIRIEQXQj1FcZMWlpaa7WGFNXV4eTkxO8vb1ZZhOmpqaYOnUqTp8+PagyZxQKBZ6eniguLsb58+c5GTJbhIWFsXjxYhgZGSE2NhajRo3CwoULkZCQgJs3bzL/rDQaDd9//z2SkpJw7do1PkfNHffu3YOqqiqA95u+duzYwdcOKwRBEF1qamp6fC7I1ylYaWkp8vPzu9WD/fvvvyEiIoK5c+cOalwGg4Hz589jxowZfGkx1RMlJSXs2LEDysrKCAoKgpGREaZMmYLffvsNGRkZLF8MmpubceXKFSxevLhb4frhQFxcnJksgfcrCyRZEgQh6Pi+Znn16lWoqqpCW1ubea1radbMzGzQdWKLi4vx8OFDbN68mVOhDoqwsDCWLVsGFxcXpKamoqysDOvXr0dWVhZOnz6NN2/e9Pi+tLQ0vH79Gi4uLjyOmLukpKTwz3/+E6WlpcxriYmJfIyIIAiif/ieMNva2hAWFgYbGxuWSj2vXr1CamrqJ48g9OXcuXMQFRWFra0tJ0IdMGVlZezYsQNKSkoIDg6GiYkJJk6cyOyq8Sl+fn6Qk5Mb9Exb0KxYsQJ79uxBa2srlixZAjs7Ozg7O0NISGjIHQUiCGLk4XvCBICnT58iJycH1tbWLNdTU1NBo9FgaGg4qHHb29sRHh4OAwODXo8xcIOwsDCWL1+OrVu3IjU1Fc+ePcO6deuQkZExoK4ab9++RUpKCiwtLUGj0bgcNffIyspi3759mD9/Pi5duoTjx4+jtrYWSUlJiIqKQmlpKU/6ZhIEQbBDIBImAMTFxUFRUZGlDRiDwUBYWBiWL1+OsWPHDmrcnJwclJeX86xsXlcnDQUFBYSEhMDU1BTKyso4fvw4bt++PeDxrl69ioaGBrbqsPKTlZUVdu3ahdraWhw+fLjHs6XR0dFD4igQQRAjm8AkzPb2doSFhcHKygqSkpLM6y9fvkR6ejpby6pnzpyBpKQkVqxYwYlQe9Q1q3R2dkZKSgqeP3+ONWvWIDk5GQEBAaitrR302P7+/lBTU2OrdyivKSkpYf/+/TAwMACdTscff/yB5ubmHn+2paUFYWFhsLOzg7i4OI8jJQiC6B+BSZgAUF5ejtu3b8PGxoblelJSEsaMGYNZs2YNatyuXacLFixgScac0jWrlJeXR1hYGBYuXMjsLHLv3j22x6+qqsLdu3fh6OgosGdLP+To6Ah3d3e8fPkShw4d6lcd3MePHyM/P79bCziCIAhBIXCfvtevX4ecnBzLbKqrpNqqVasGnfDS09Px9u1bbNy4kVOhQlhYGObm5sxZ5evXr+Ho6Ijr168jMDCQo101wsLCwGAwsGHDBo6NyWkTJ06Ep6cndHR0cP78eZw6dQqtra39fv/Vq1ehoaHBUmeYIAhCUAhcwuzaqGNlZcWyPFdZWYmsrKxuG4MGIjAwEGpqapg2bRpERETYilNFRQVffvkl5OTkmIXjpaSk4O3tjdzcXLbG7k1gYCC0tbUxceJErow/WBQKBRs2bMA//vEPlJWV4eDBg4PqLNLa2oqLFy/C1tYWoqKiXIiUIAhi8AQuYQLvd83m5+d3awOWmJgIeXn5QT/Lq6yshLy8PFJTU1FSUoJFixYNeAwRERGsWLECW7ZsQVJSEt68eQM7OzvExcUhKCgIDQ0Ng4qtP0pKSlBUVCRQxeU1NTXh6ekJTU1N+Pn5wd/fH+3t7YMer7CwEOXl5TAzM+NglARBEOwTyIQJ9Lw8197eDjqdDisrK5Yzm/0lJCSElStXgkKhYPTo0fj2228H9H4VFRW4u7tj/PjxiIyMxNKlSzFq1Cie9moMDAzk69nSLiIiInB2doaLiwuKi4tx8OBBFBcXc2Tsy5cvQ19fH0pKShwZjyAIghMENmG2trYiMjKy2/JcWVkZcnJyupXT6w8lJSWWZ2o1NTX9fq+qqiqcnZ2RnJyM2tpaWFtb86VXI7/Oln5IW1sbnp6eUFVVxcmTJ3H+/HkwGAyOjd/Q0IDY2FjY29sPiU1OBEGMDAL9afTw4UM8ffoUy5cvZ7keHx+PCRMm9PvcXtcyqrOzM77++mtcv34dRUVFn6y2IyMjg5MnT+LSpUvYu3cvCgoKsHz5clCpVL72aszJyUFFRUW3FlncRqVSsW3bNmzatAm5ubk4ePAgnjx5wpV7ZWdno7GxEfPnz+fK+ARBEAMl0AkTeH+oXVdXFxMmTGBea2trQ0REBGxsbD5ZAWfixInw8PCAtLQ0fHx8EBYWBkdHRzg5OUFWVrbPDTTHjh2Do6MjTE1NYWtrCxqNhsDAQIHo1ejn54cxY8Zg2bJlPLmfvr4+vv/+e8jKyuLXX38FnU7n+j0jIyOxcOFCyMjIcP1eBEEQnyLwCbOxsRHR0dGwt7eHsLAw83ppaSkKCwu7bQzqQqVSYWVlhXXr1uHq1asICgpiOeZRVlaG4uLiPo9pfFgQXkxMDBEREaioqODAn4p9vOpoQqPRsH37djg4OCArKwtHjhxBZWUl1+73oZqaGiQlJfH9eS1BEAQwBBImAOTm5qKmpqbbrtbY2Fhoamp2O7enoaGBXbt2MZdOCwoKehw3MDAQ4uLi3ZZ81dXV4enpifv376O+vh4MBgNHjhxBS0sLR/9c7OJ2R5O5c+fiu+++w5gxY+Dl5YWoqCiu3KcvaWlpoNFomD17Ns/vTRAE8SG+NpAeiDFjxuDLL7/EX3/9hZcvXzKvT5kyBW5ubqiqqsLt27cxevRo6OrqIjw8HA8fPvzkuKampvjss8/w+PFjxMfHM2uaFhYWMptPi4mJcfW4CDvGjRuHvXv3IioqCjdv3uTImJKSknBxcYGCggJSUlJw9epVjow7WIqKinB1dYW3tzdHi0EQBEH0pLcG0kMmYQKAoaEh5syZgxMnTjAbLquoqCAzMxPi4uLo7OzEhQsXsGfPnn7vXDU2NkZ0dDSEhITQ0dGB4OBgeHt7s/RrFHQrVqzA/Pnzcfjw4V7rtfbXggULYG5ujrdv3/bZr5PXzM3NIS0tjeDgYH6HQhDEMNdbwhwSS7JdsrKy0N7eDhMTE+Y1XV1dZkUgISEhWFpaDujgvK6uLoSE3v/dCAsLIzU1dUglS+D/O5qws2t23Lhx2L17N8zNzXH9+nX8+9//FphkCbwvmaisrIxp06bxOxSCIEaoIZUwOzs7QafTcfDgQeTk5ODUqVPQ1dVFW1sb82ckJSUH1AosLi6OeR6zrq4OKSkpHI+bF/z9/aGqqjqoKkjLli3D3r170dHRgWPHjiExMZELEbKnvb0dERERsLa2hpiYGL/DIQhiBBpSCRN4v4Sqp6eHCRMmwN7eHm1tbdixYwczad66dQuvXr3q93iPHz+GiYkJ1q5dCzqdPmR7MlZVVSE7O3tAHU1kZGSwd+9eLFq0CDExMfD29marDRm3lZaW4uHDh3BwcICBgQFpBUYQBE8NuYT5cdH0yspKhIWFYdasWQgJCUFCQgJLE+r+ePHiBeLj43Ht2jWYmZmxXZidX+h0er87mlhaWmL37t1oaGjAkSNHkJaWxoMI2VdSUoKff/4ZcXFxiI+Px6hRo/gdEkEQI8SQS5iXL1/GhQsX8PbtWzx8+BA3btwAANTX16O8vBwXLlyAtbU1xo0bN+Cxr169ira2Njg5OXE4at7p6mgyadKkHl+fMGECvvvuOxgaGiIiIgK///47T0v7sWv16tXMmeX06dNhZGTE54gIghgphlzCZDAY2L59O9TV1eHh4QFbW1tQKBTIycmhuroaFRUVSElJwdq1awdVhzQ0NBQzZsyArKwsF6LnvpKSEhQWFnbr+9nVgmv79u2oqqrCoUOHcPv2bT5FOXiFhYXMXzc3N6OkpISP0RAEMZIMqWMlPfnss8+gpaUFaWlpxMTEIDw8HEJCQnBxccHTp0+RkJAw4DHd3d0hKiqK//73v1yImPtEREQQGhoKLS0t3Lt3D7///jvMzc3R2dmJ4OBgFBUV8TtEtqxbtw4bNmxAQEAAwsLC+B0OQRDDTG/HSobmw7oPTJs2jXmcYvr06QgPD0dnZydCQ0Ph7u6OkpISPH78eEBjBgQEYN++fTAyMuJYMQBeUlBQwIIFC0ChUKCoqAglJSX8+eefCAsL42hXEX4JDg4GlUrttYITQRAENwy5JdmPfXguT1NTk3nkoK6uDuHh4XBycvpkgfaP1dbWIjU1FRYWFqBSqRyNlxdWrVrFshx9584dhISEDItkCbw/L6usrIwXL17wOxSCIEaQIZ8wL1y4wEwEycnJLPVei4qKUFhYCAsLiwGPGxsbi8bGRmzZsoVjsXKblpYWDhw4ABUVFQQGBqKurg4vX75EXFwcv0PjGCEhIaxYsQJVVVWorq7mdzgEQYwgQ/4ZJvC+q4i6ujr09PS61RulUqnYuXMnLl26NOBnd8rKytixYwf8/f0F+rkfjUbD5s2bMWnSJOTn5yM4OJj5JcLa2hpz5szBDz/8MKAKSIJo7NixcHBwAJVKhb+/P99brBEEMTwNi1qyn7JixQpISUl1qzeqrq4OJycn+Pj4DPgIxfr16zFlyhQcPHhQIJc0TUxMsHLlSjQ2NuLs2bM9th/7/vvvUVZWhoCAAD5EyBmzZ8/GypUr8ffffyMlJUUg/1sQBDE8DItasp+SkJAABQUF6OrqslwvLS1FXl4erK2tBzzmhQsXICQkBAcHB06FyREyMjL4+uuvsXr1aqSmpuLHH3/stVdnYGAgpk6d2uvZTEEmKSmJzZs3Y/78+fD19UVSUhJJlgRB8MWwSpjt7e0IDQ2FpaVlt6bKV69ehaKiImbOnDmgMRkMBkJCQjBr1iwoKipyMtxBs7Gxwe7du9Ha2ooff/zxk88oHz9+jKKiom5nMwWdjo4OPDw88Pz5c/z222+oqqrid0gEQYxgwyphAsCzZ8+Qnp4OBwcHZhcSgDWZjh49ekBj3r9/H0+fPuX7BqDJkyfD09MTs2fPBp1Ox/Hjx/vdHzIwMBBUKnVQs2xeExcXx9q1a7Fs2TIEBAQgPj4eHR0d/A6LIIgRbtglTABISkqCmJgYFi5cyHK9oqICmZmZsLe3H/CY/v7+kJSUxPLlyzkVZr9RqVS4urrCzc0NT548wQ8//IDs7OwBjdHV7WPu3LmQkZHhUqSDQ6VSmbP3KVOmYOfOnaivr8evv/7a6zIzQRAErw3LhMlgMHD+/HkYGxtDW1ub5bXExERISkrC0NBwQGM2NzcjNjYWixYtgqSkJCfD7dPcuXPh6ekJBQUFnDhxAmfPnh30bte7d++isrISLi4uHI5y8FRVVXH79m0UFBQgOTkZdnZ2CA0NRXR0NEvbNoIgCH4blgkTeF984Ny5c7Czs4OCggLzOoPBQGhoKJYvXw5paekBjZmWloaamhqeJBwpKSl89dVXsLa2Rnp6Oo4cOYKysjK2x/X398e4ceO6zb75xdnZGSoqKgDeP7O8efMmqQ9LEIRAGrYJE3i/BHv58mVs3ryZpQ3Uy5cvkZSUhHXr1kFUVHRAY/r6+kJBQQFz5szhaKxTpkzB5s2boampCSsrK+zZswcMBgNHjx7FlStXOHaf+vp6JCYmYvny5ZCQkODYuIMxfvx4TJw4kfn79vZ2sgRLEITAGlbnMHtjZmaGqVOn4tSpUyyVgNasWQMxMTEEBgair7+Hj1lZWcHCwgL3799HYmIicnNz2YpPS0sLCQkJGDVqFFpbWxEWFoazZ88iKyuLrXH78s0336Curg5//PEH1+7RGwqFAlNTUyxYsAA3btzA/PnzMWPGDISGhiIqKorn8RAEQXxoRBQu6Iu1tTXk5eXh5+fHfAYoLCwMZ2dnvHr1akAf1Hp6erh27RqEhYXR3NyMxYsXs1UJyNnZmaUzyldffQV/f/9Bj9cf8vLy8PDwQHBwMPLy8rh6r4/v6+joiKamJkRERODNmzc8uzdBEER/jIjCBX2JiopCXV0d1q1bxyxM3tHRgcDAQEyaNAmmpqb9HsvAwADCwsIA3pelmzVrFlux3bx5k1mBqKGhAenp6WyN1x8vXrxAdnY2HB0dB9U3dKCEhYVhZmaGzz77DDdv3oSvry9JlgRBDCkjJmF2tfwSERGBnZ0d84xmS0sL/P39mcuC/ZGUlIS6ujoA73fPDrR92McKCwthZmaGr776CmZmZnj48CFb4/UXnU5HR0cH1q9fz9X7dNXkVVZWxvHjx4dk42qCIIgRsyTbRVRUFG5ubigrK0NMTAzzupKSElxcXBAQEIDy8vJPjjNp0iQcPXoUERER0NDQgJ+f35CsRKOpqQkXFxecOHGCI7twPyQiIgIzMzPMnj0b0dHRyMnJ4ej4BEEQ3DDil2S7tLW1wd/fH5qamli0aBHzemVlJcLDw7Fp0yZISUl9cpxZs2ahsLAQISEhiIqKwtatWzF+/HguRs4dxcXFePToETZt2sTRcdXU1ODh4QFpaWn4+PiQZEkQxJA34hImADQ1NcHX1xcGBgYsBQwePHiApKQkODs7Q1xcvNf329vbY86cOaDT6QCAvLw8xMXFwdnZecDHVATBuXPnQKPRsHr1arbHolKpsLS0xIYNGxAXF4egoKB+l+8jCIIQZCMyYQJAXV0d/Pz8sHTpUpZnl+np6SguLsbGjRuZG3u6UKlU7Nq1C7NmzUJAQADL7tI7d+6gvLycL6Xz2NXa2oqoqCiYmJj0a3bdm8mTJ8PDwwM0Gg1eXl7Iz8/nYJQEQRD8NWITJgBUV1fD398fNjY20NDQYF6PiYlBc3Mz7OzsICEhAXFxcSgpKeHbb7+FuLg4fv755x6PkVy+fBl6enqYMGECL/8YHJGVlYUXL15g69atA36vmJgYbG1t4ejoiKioKISFhZHmzgRBDDsjOmECQFVVFQIDA7F27Vpmouvs7MSFCxdgbm6OJ0+e4MmTJ/Dy8kJ5eTmOHj2K2traHsdqbGzE5cuX4eDg0G12OhScOXMG48ePx7x58/r9Hi0tLezatQsA4OXlxdZ5VIIgCEE24hMmADx58gR0Oh2bN2+GnJwcgPebg+bPnw8RERGIiopCR0cHvr6+n2xenJubi+rqaixevJgXoXNUbW0tkpOTsWrVKtBotD5/VkJCAmvWrIGVlRXCwsIQGRnJUkWJIAhiuCEJ8/8UFhYiJiYGX331FXx9fXH8+HGW158/f97vsSIjI2FkZMRS9H2oiIuLQ0NDQ5+9P2fMmIGdO3eisbER3t7epFg6QRAjwog7h/kp9+7dg6qqKoD3M8/CwkKIiYnh0aNHOHr0aL+r03TtwD1x4sQnZ6WCRlFREV9++SXOnTuH+/fvM6+PHj0a1tbWkJOTQ3h4OJ4+fcrHKAmCILhjxNeS7a/y8nJmZ5MHDx7AxMQEADBv3jxmAuzv0qObmxuKioqQmprKtXi5xcnJCfPnz0d2djbS0tIwbtw4LF++HJmZmbhx48age3ISBEEIOlK4oJ9++eUXAO9bTWVnZzM376Snp+Px48dYu3Yts6zep0RERGDRokWQkZHhWrzcUlBQACcnJ/zyyy9ITk6Gubk5Tp06hWvXrpFkSRDEiEQS5ke8vb2hra0NbW1t3Lp1C46OjswEefnyZYiJicHMzKxfY9XU1CAxMRH29vb9TrKCYt68eRAREQHw/thIaWkpXrx4weeoCIIg+IckzB48f/4cr1+/RnBwMMaOHQtLS0sAAIPBwPnz56Gvr4/p06f3a6z09HQICwuzVBQSdMrKylBTU0NbWxuA94UNMjIy+BwVQRAEf5FnmJ9Ao9Hg5uaGR48e4erVqwDeJ5StW7fCz88PlZWVnxxDTk4O27Ztw6+//op3795xO+RBExUVxbJlyzBr1izExMSgpaUFRkZG0NfXR01NDXJzc3Hp0iWyJEsQxLBGNv2wQUJCAtu2bUNubi4SExMBANOnT4etrS2CgoJQWlr6yTGWLFkCVVVVrjeGHqxJkybB3t4eFRUVuHz5MhoaGpivnTp1Cvb29gDe9xV1dnbmU5QEQRDcRzb9sKGxsRGnT5/GrFmzmI2mCwoKEBQUhPXr1/erj2ZycjLGjh0LXV1dboc7IFQqFVZWVli7di2uXLmCCxcusCRLANDT02P+ur/PbwmCIIYbkjD7qb6+HqdOnYKRkRGzdFxpaSl8fX1haWkJY2PjPt/f0dEBOp0OCwsL5rEVftPQ0MCuXbtApVLh7e3NcuYSAISEhGBiYsJSCjApKYnHURIEQQgGsiQ7QNLS0nB1dUV2djauX78OAJCSkoKzszMqKipw6dIltLa29vr+lStXYsyYMQgJCeFVyN2IiYlh9erV0NTUREREBIqLi7v9jLS0NBwdHQG8Px5jamoKGo0GOp3e55+PIAhiqCPPMDlIUlISW7duZSZIBoMBUVFRWFtbQ1VVFSEhIXj27FmP7xUVFYWHhweio6NRWFjI48jfF0u3tbVFYWEhYmNjeyzCMHfuXCxbtgxJSUlIS0tDX/+PEARBDDckYXIYlUrFxo0b0dbWhuDgYObOUT09PaxevRo5OTm4du1ajwlJXV0da9asgZeXF88KlouLi8PS0hJqamoIDw/vcaPSmDFjYG9vDwkJCYSGhuLVq1c8iY0gCEKQkITJBcLCwrC3t4eioiJCQkKYBdolJCSwcuVKTJkyBXFxcbh37163erJ2dnZgMBi4ePEi1+OcPn06rK2tkZubi7i4OOb5yg/p6enBwsIC6enpSEpKGnL1bwmCIDiFJEwumj17NlauXImUlBSkpqYylzAnTpwIMzMzjB8/Hn///Tdu3brFfP5Ho9Gwa9cuXLhwAY8fP+ZKXKNGjYK1tTUUFRVBp9N7LJY+atQo2NraYvz48QgNDe3XuVKCIIjhjCRMLpOSkoKjoyOoVCpiY2NZWl4pKytjwYIF0NDQQH5+PvLy8lBaWgotLS1YWloiNDQULS0tqKqq4lg8urq6sLCwwJ07d5CQkNBjsQFtbW3Y2Njg7t27pEYsQRDE/yEJkweEhISgo6MDc3NzvHr1CrGxsSz1V8eNG4eZM2dCR0cHUlJSuH//PszNzZlFAb799lv89ddfbMUwevRo2NraQlpaGnQ6HRUVFd1+hkajMZ9nhoWFkTZdBEEQHyAJk4eEhYUxd+5cLF68GCUlJUhJSem21CklJYUZM2bg7NmzGDt2LADg1atX0NbWRkdHx6Du27U0fPPmTdy4caPHcTQ0NODg4IAHDx4gNjaWHBEhCIL4CEmYfCAmJgZDQ0PMnz8fL1++RHJyMh49esTyM5cvX2b23Hzy5AkuXbqEoqIiFBQUoKioqF8JTUFBAStXroSkpCTodHqPS7vi4uJYsWIFtLS0EB4e3uPZS4IgCIIkTL4SFhaGnp4eFixYgI6ODqSkpCAvLw8dHR2Ql5fHvn37ICQkhGPHjqGurg7a2tqYPn061NTUUFpaiocPH6K8vBzPnz9nzhrFxMSgqqoKY2NjqKioICUlBenp6T3ubtXT08OqVatQUFCAuLg4NDc38/qvgCAIYsggCVMACAkJYcqUKViwYAFkZWWRlZWFzMxMltJzH6LRaJg6dSrU1dUxYcIEyMjIoLm5Ge3t7Rg1ahSePXuGvLw8ZGVl9bhhR1ZWFjY2NhAXF0dERESPzzMJgiAIViRhChg5OTkYGRlBT08PpaWlyMjIYNlZ2xNRUVGIi4tDVFQUb9++7fVZp4iICBYvXgwjIyMkJiYiIyODnKskCILoJ5IwBRSVSsWsWbNgbGwMYWFhZGVlITs7G/X19QMeS0hICFOnToWFhQWePXuG6OjoXmevBEEQRM9IwhwCVFVVYWhoiOnTp6OkpAR3795FRUXFJ5tOS0tLY9asWZgzZw6ampoQFxeHoqIiHkVNEAQxvJCEOYSIiYlBV1cXM2bMgIKCAqhUKl68eIEXL16gpaUFFAoFwsLCkJGRgZKSEjo6OpCfn4/bt2+TSj0EQRBsIglzCJOQkIC8vDzk5eUhKioKBoOBjo4OvHnzBpWVlairq+N3iARBEMMGSZgEQRAE0Q+9JUwKrwMhCIIgiKGIJEyCIAiC6AeSMAmCIAiiH0jCJAiCIIh+IAmTIAiCIPqBJEyCIAiC6Ic+j5UQBEEQBPEemWESBEEQRD+QhEkQBEEQ/UASJkEQBEH0A0mYBEEQBNEPJGESBEEQRD+QhEkQBEEQ/fC/HpUS+0kPZ2kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the shortest path\n",
    "fig, ax = ox.plot_graph_route(graph_proj, route) # sw: graph and route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'length'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-61-a4419bbd3daa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgraph_proj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'length'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/networkx/classes/graph.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, n)\u001b[0m\n\u001b[1;32m    463\u001b[0m         \u001b[0mAtlasView\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m         \"\"\"\n\u001b[0;32m--> 465\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    467\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_for_adding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/networkx/classes/coreviews.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mAdjacencyView\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_atlas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'length'"
     ]
    }
   ],
   "source": [
    "graph_proj['length']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges = ox.graph_to_gdfs(streets_graph, nodes=False, edges=True,\n",
    "                                   node_geometry=False, fill_edge_geometry=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(249, 14)\n"
     ]
    }
   ],
   "source": [
    "print(edges.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "edges.plot(figsize = (10,10), linewidth = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>osmid</th>\n",
       "      <th>oneway</th>\n",
       "      <th>lanes</th>\n",
       "      <th>name</th>\n",
       "      <th>highway</th>\n",
       "      <th>maxspeed</th>\n",
       "      <th>length</th>\n",
       "      <th>geometry</th>\n",
       "      <th>junction</th>\n",
       "      <th>bridge</th>\n",
       "      <th>access</th>\n",
       "      <th>u</th>\n",
       "      <th>v</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23856784</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>Mechelininkatu</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>40.885</td>\n",
       "      <td>LINESTRING (24.92106 60.16479, 24.92095 60.164...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25216594</td>\n",
       "      <td>1372425714</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[29977177, 30470347]</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>Mechelininkatu</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>16.601</td>\n",
       "      <td>LINESTRING (24.92103 60.16366, 24.92104 60.163...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25238874</td>\n",
       "      <td>1372425713</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[372440330, 8135861]</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>25.190</td>\n",
       "      <td>LINESTRING (24.92129 60.16463, 24.92127 60.164...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25238944</td>\n",
       "      <td>25216594</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[25514547, 677423564, 30288797, 30288799]</td>\n",
       "      <td>True</td>\n",
       "      <td>[2, 3]</td>\n",
       "      <td>Mechelininkatu</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>242.476</td>\n",
       "      <td>LINESTRING (24.92129 60.16463, 24.92136 60.164...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25238944</td>\n",
       "      <td>319896278</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[30568275, 36729015, 316590744, 316590745, 316...</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fredrikinkatu</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>30</td>\n",
       "      <td>139.090</td>\n",
       "      <td>LINESTRING (24.93702 60.16433, 24.93700 60.164...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25291537</td>\n",
       "      <td>25291591</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               osmid  oneway   lanes  \\\n",
       "0                                           23856784    True       2   \n",
       "1                               [29977177, 30470347]    True       3   \n",
       "2                               [372440330, 8135861]    True       2   \n",
       "3          [25514547, 677423564, 30288797, 30288799]    True  [2, 3]   \n",
       "4  [30568275, 36729015, 316590744, 316590745, 316...    True     NaN   \n",
       "\n",
       "             name   highway maxspeed   length  \\\n",
       "0  Mechelininkatu   primary       40   40.885   \n",
       "1  Mechelininkatu   primary       40   16.601   \n",
       "2             NaN   primary       40   25.190   \n",
       "3  Mechelininkatu   primary       40  242.476   \n",
       "4   Fredrikinkatu  tertiary       30  139.090   \n",
       "\n",
       "                                            geometry junction bridge access  \\\n",
       "0  LINESTRING (24.92106 60.16479, 24.92095 60.164...      NaN    NaN    NaN   \n",
       "1  LINESTRING (24.92103 60.16366, 24.92104 60.163...      NaN    NaN    NaN   \n",
       "2  LINESTRING (24.92129 60.16463, 24.92127 60.164...      NaN    NaN    NaN   \n",
       "3  LINESTRING (24.92129 60.16463, 24.92136 60.164...      NaN    NaN    NaN   \n",
       "4  LINESTRING (24.93702 60.16433, 24.93700 60.164...      NaN    NaN    NaN   \n",
       "\n",
       "          u           v  key  \n",
       "0  25216594  1372425714    0  \n",
       "1  25238874  1372425713    0  \n",
       "2  25238944    25216594    0  \n",
       "3  25238944   319896278    0  \n",
       "4  25291537    25291591    0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shortest path between two nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "osmid                                                23856784\n",
      "oneway                                                   True\n",
      "lanes                                                       2\n",
      "name                                           Mechelininkatu\n",
      "highway                                               primary\n",
      "maxspeed                                                   40\n",
      "length                                                 40.885\n",
      "geometry    LINESTRING (24.9210566 60.1647939, 24.9209527 ...\n",
      "junction                                                  NaN\n",
      "bridge                                                    NaN\n",
      "access                                                    NaN\n",
      "u                                                    25216594\n",
      "v                                                  1372425714\n",
      "key                                                         0\n",
      "Name: 0, dtype: object\n",
      "osmid              [21081120, 332402667, 316713564, 36729011]\n",
      "oneway                                                  False\n",
      "lanes                                                     NaN\n",
      "name                                                Annankatu\n",
      "highway                                           residential\n",
      "maxspeed                                                   30\n",
      "length                                                139.057\n",
      "geometry    LINESTRING (24.939344 60.1651348, 24.9393282 6...\n",
      "junction                                                  NaN\n",
      "bridge                                                    NaN\n",
      "access                                                    NaN\n",
      "u                                                    25291565\n",
      "v                                                  1377211666\n",
      "key                                                         0\n",
      "Name: 10, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# connect node 0 and 10\n",
    "print(edges.loc[0,:])\n",
    "print(edges.loc[10,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "no numeric data to plot",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-7680c3970f6b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0medges\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    947\u001b[0m                     \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 949\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    950\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    951\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/__init__.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ax\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"left_ax\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    270\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    416\u001b[0m         \u001b[0;31m# no non-numeric frames or series allowed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"no numeric data to plot\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[0;31m# GH25587: cast ExtensionArray of pandas (IntegerArray, etc.) to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: no numeric data to plot"
     ]
    }
   ],
   "source": [
    "edges.loc[0,:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shortest path between all the nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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