{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Travel time prediction in Indian Metro cities using Uber Movement data and OpenStreetMap\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uber provides anonymized and aggregated travel time data through [Uber Movement](https://movement.uber.com/) platform for many citites across the world. For India, current and historic data is available for 5 cities - Bangalore, Hyderabad, New Delhi, Mumbai and Kolkata. It also provides the details on the ward boundaries in the form of JSON file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[OpenStreetMap](https://wiki.openstreetmap.org/wiki/About_OpenStreetMap) (OSM) is a free, editable map of the whole world that is being built by volunteers largely from scratch and released with an open-content license. OSM data includes a global navigable street network dataset. Several services exists that provide routing and network analysis on top of this data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we use the open travel time dataset from Uber and leverage open-source routing services for OpenStreetMap to build a fairly accurate model for travel time within each of the metro cities in India. We show that by using rich ecosystem of Python Geospatial libraries, we can easily consume, process, and visualize large amount of geospatial data easily and incorporate it easily into a machine learning model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Open datasets**\n",
    "- Uber Movement - Travel times and ward boundaries\n",
    "- OpenStreetMap\n",
    "\n",
    "**Python libraries**\n",
    "- geopandas\n",
    "- shapely\n",
    "- matplotlib\n",
    "- folium\n",
    "- scikit-learn\n",
    "\n",
    "**Services**\n",
    "- Open Source Routing Machine (OSRM)\n",
    "- OpenRouteService (ORS) API "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import requests\n",
    "import shapely\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import os\n",
    "import math\n",
    "import random\n",
    "import folium\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor \n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from matplotlib import pyplot\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Travel Times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = os.path.join('data', 'uber')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Uber Movement Travel Times data comes as a CSV file for each quarter. Here we are using the **Travel Times By Date By Hour Buckets (All Days)** dataset. This data set includes the arithmetic mean, geometric mean, and standard deviations for aggregated travel times between every ward in the city, for every day of the quarter and aggregated into time categories. This is a large dataset with over **7M rows**.\n",
    "\n",
    "We import the data as a Pandas DataFrame and call `convert_dtypes()` to select the best datatypes for each column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "travel_times_file = 'bangalore-wards-2020-1-All-DatesByHourBucketsAggregate.csv'\n",
    "travel_times_filepath = os.path.join(data_folder, travel_times_file)\n",
    "travel_times= pd.read_csv(travel_times_filepath)\n",
    "travel_times = travel_times.convert_dtypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7640967 entries, 0 to 7640966\n",
      "Data columns (total 17 columns):\n",
      " #   Column                                    Dtype  \n",
      "---  ------                                    -----  \n",
      " 0   sourceid                                  Int64  \n",
      " 1   dstid                                     Int64  \n",
      " 2   month                                     Int64  \n",
      " 3   day                                       Int64  \n",
      " 4   start_hour                                Int64  \n",
      " 5   end_hour                                  Int64  \n",
      " 6   mean_travel_time                          Float64\n",
      " 7   standard_deviation_travel_time            Float64\n",
      " 8   geometric_mean_travel_time                Float64\n",
      " 9   geometric_standard_deviation_travel_time  Float64\n",
      " 10  time_period                               int64  \n",
      " 11  travel_time                               Float64\n",
      " 12  src_lon                                   float64\n",
      " 13  src_lat                                   float64\n",
      " 14  dst_lon                                   float64\n",
      " 15  dst_lat                                   float64\n",
      " 16  distance                                  float64\n",
      "dtypes: Float64(5), Int64(6), float64(5), int64(1)\n",
      "memory usage: 1.1 GB\n"
     ]
    }
   ],
   "source": [
    "travel_times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ward Boundaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The travel times dataset contain details of travel between *Zones*. For Indian citites, the zones are **Wards** as defined by the local municipal corporation. This data comes as a **GeoJSON** file that contains the polygon representation of each ward. We use `geopandas` to read the file as a GeoDataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "wards_file = 'bangalore_wards.json'\n",
    "wards_filepath = os.path.join(data_folder, wards_file)\n",
    "wards = gpd.read_file(wards_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>WARD_NO</th>\n",
       "      <th>WARD_NAME</th>\n",
       "      <th>MOVEMENT_ID</th>\n",
       "      <th>DISPLAY_NAME</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Chowdeswari Ward</td>\n",
       "      <td>1</td>\n",
       "      <td>Unnamed Road, Bengaluru</td>\n",
       "      <td>MULTIPOLYGON (((77.59229 13.09720, 77.59094 13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Atturu</td>\n",
       "      <td>2</td>\n",
       "      <td>9th Cross Bhel Layout, Adityanagar, Vidyaranya...</td>\n",
       "      <td>MULTIPOLYGON (((77.56862 13.12705, 77.57064 13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Yelahanka Satellite Town</td>\n",
       "      <td>3</td>\n",
       "      <td>15th A Cross Road, Yelahanka Satellite Town, Y...</td>\n",
       "      <td>MULTIPOLYGON (((77.59094 13.09842, 77.59229 13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51</td>\n",
       "      <td>Vijnanapura</td>\n",
       "      <td>4</td>\n",
       "      <td>SP Naidu Layout 4th Cross Street, SP Naidu Lay...</td>\n",
       "      <td>MULTIPOLYGON (((77.67683 13.01147, 77.67695 13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>Basavanapura</td>\n",
       "      <td>5</td>\n",
       "      <td>Medahalli Kadugodi Road, Bharathi Nagar, Krish...</td>\n",
       "      <td>MULTIPOLYGON (((77.72899 13.02061, 77.72994 13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>172</td>\n",
       "      <td>Madivala</td>\n",
       "      <td>194</td>\n",
       "      <td>0 1st B Cross Road, Cashier Layout, 1st Stage,...</td>\n",
       "      <td>MULTIPOLYGON (((77.61399 12.92347, 77.61419 12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>26</td>\n",
       "      <td>Ramamurthy Nagar</td>\n",
       "      <td>195</td>\n",
       "      <td>Kalkere-Agara Main Road, Horamavu Agara, Kalke...</td>\n",
       "      <td>MULTIPOLYGON (((77.68336 13.05192, 77.68384 13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>25</td>\n",
       "      <td>Horamavu</td>\n",
       "      <td>196</td>\n",
       "      <td>0 Horamavu Agara Main Road, 1st Block, Mallapp...</td>\n",
       "      <td>MULTIPOLYGON (((77.64931 13.07853, 77.64993 13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>86</td>\n",
       "      <td>Marathahalli</td>\n",
       "      <td>197</td>\n",
       "      <td>0 3rd Cross Road, Manjunatha Layout, Marathaha...</td>\n",
       "      <td>MULTIPOLYGON (((77.68549 12.94121, 77.68539 12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>Hemmigepura</td>\n",
       "      <td>198</td>\n",
       "      <td>BGS Road, Kodipalya, Bengaluru</td>\n",
       "      <td>MULTIPOLYGON (((77.49854 12.92574, 77.49854 12...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    WARD_NO                 WARD_NAME MOVEMENT_ID  \\\n",
       "0         2          Chowdeswari Ward           1   \n",
       "1         3                    Atturu           2   \n",
       "2         4  Yelahanka Satellite Town           3   \n",
       "3        51               Vijnanapura           4   \n",
       "4        53              Basavanapura           5   \n",
       "..      ...                       ...         ...   \n",
       "193     172                  Madivala         194   \n",
       "194      26          Ramamurthy Nagar         195   \n",
       "195      25                  Horamavu         196   \n",
       "196      86              Marathahalli         197   \n",
       "197     198               Hemmigepura         198   \n",
       "\n",
       "                                          DISPLAY_NAME  \\\n",
       "0                              Unnamed Road, Bengaluru   \n",
       "1    9th Cross Bhel Layout, Adityanagar, Vidyaranya...   \n",
       "2    15th A Cross Road, Yelahanka Satellite Town, Y...   \n",
       "3    SP Naidu Layout 4th Cross Street, SP Naidu Lay...   \n",
       "4    Medahalli Kadugodi Road, Bharathi Nagar, Krish...   \n",
       "..                                                 ...   \n",
       "193  0 1st B Cross Road, Cashier Layout, 1st Stage,...   \n",
       "194  Kalkere-Agara Main Road, Horamavu Agara, Kalke...   \n",
       "195  0 Horamavu Agara Main Road, 1st Block, Mallapp...   \n",
       "196  0 3rd Cross Road, Manjunatha Layout, Marathaha...   \n",
       "197                     BGS Road, Kodipalya, Bengaluru   \n",
       "\n",
       "                                              geometry  \n",
       "0    MULTIPOLYGON (((77.59229 13.09720, 77.59094 13...  \n",
       "1    MULTIPOLYGON (((77.56862 13.12705, 77.57064 13...  \n",
       "2    MULTIPOLYGON (((77.59094 13.09842, 77.59229 13...  \n",
       "3    MULTIPOLYGON (((77.67683 13.01147, 77.67695 13...  \n",
       "4    MULTIPOLYGON (((77.72899 13.02061, 77.72994 13...  \n",
       "..                                                 ...  \n",
       "193  MULTIPOLYGON (((77.61399 12.92347, 77.61419 12...  \n",
       "194  MULTIPOLYGON (((77.68336 13.05192, 77.68384 13...  \n",
       "195  MULTIPOLYGON (((77.64931 13.07853, 77.64993 13...  \n",
       "196  MULTIPOLYGON (((77.68549 12.94121, 77.68539 12...  \n",
       "197  MULTIPOLYGON (((77.49854 12.92574, 77.49854 12...  \n",
       "\n",
       "[198 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "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": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "wards['geometry'].plot(color='grey',ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Travel Times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "travel_times = pd.concat([travel_times]*5, ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "travel_times['random'] = np.random.uniform(0, 1, len(travel_times))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "travel_times['travel_time'] = np.exp(travel_times['random']*np.log(travel_times['geometric_standard_deviation_travel_time']) + np.log(travel_times['geometric_mean_travel_time']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The source data contains the travel times grouped by blocks of time (peak/off-peak etc.), defined by `start_hour` and `end_hour` columns. To allow us to model this easily, we add a `time_period` columns and assign an integer category value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>sourceid</th>\n",
       "      <th>dstid</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>end_hour</th>\n",
       "      <th>mean_travel_time</th>\n",
       "      <th>standard_deviation_travel_time</th>\n",
       "      <th>geometric_mean_travel_time</th>\n",
       "      <th>geometric_standard_deviation_travel_time</th>\n",
       "      <th>time_period</th>\n",
       "      <th>travel_time</th>\n",
       "      <th>src_lon</th>\n",
       "      <th>src_lat</th>\n",
       "      <th>dst_lon</th>\n",
       "      <th>dst_lat</th>\n",
       "      <th>distance</th>\n",
       "      <th>random</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102</td>\n",
       "      <td>97</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>322.8</td>\n",
       "      <td>425.14</td>\n",
       "      <td>270.59</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3</td>\n",
       "      <td>362.063898</td>\n",
       "      <td>77.563817</td>\n",
       "      <td>12.982784</td>\n",
       "      <td>77.566287</td>\n",
       "      <td>12.970359</td>\n",
       "      <td>1778.9</td>\n",
       "      <td>0.548814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>306.71</td>\n",
       "      <td>200.99</td>\n",
       "      <td>256.58</td>\n",
       "      <td>1.84</td>\n",
       "      <td>5</td>\n",
       "      <td>396.842057</td>\n",
       "      <td>77.563817</td>\n",
       "      <td>12.982784</td>\n",
       "      <td>77.566287</td>\n",
       "      <td>12.970359</td>\n",
       "      <td>1778.9</td>\n",
       "      <td>0.715189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>97</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>282.94</td>\n",
       "      <td>206.01</td>\n",
       "      <td>233.29</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>354.279465</td>\n",
       "      <td>77.563817</td>\n",
       "      <td>12.982784</td>\n",
       "      <td>77.566287</td>\n",
       "      <td>12.970359</td>\n",
       "      <td>1778.9</td>\n",
       "      <td>0.602763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>294.18</td>\n",
       "      <td>183.97</td>\n",
       "      <td>258.09</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2</td>\n",
       "      <td>334.554688</td>\n",
       "      <td>77.563817</td>\n",
       "      <td>12.982784</td>\n",
       "      <td>77.566287</td>\n",
       "      <td>12.970359</td>\n",
       "      <td>1778.9</td>\n",
       "      <td>0.544883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102</td>\n",
       "      <td>97</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>263.55</td>\n",
       "      <td>149.79</td>\n",
       "      <td>232.89</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2</td>\n",
       "      <td>289.404809</td>\n",
       "      <td>77.563817</td>\n",
       "      <td>12.982784</td>\n",
       "      <td>77.566287</td>\n",
       "      <td>12.970359</td>\n",
       "      <td>1778.9</td>\n",
       "      <td>0.423655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38204830</th>\n",
       "      <td>162</td>\n",
       "      <td>83</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>3120.83</td>\n",
       "      <td>459.43</td>\n",
       "      <td>3089.7</td>\n",
       "      <td>1.15</td>\n",
       "      <td>4</td>\n",
       "      <td>3091.908238</td>\n",
       "      <td>77.587545</td>\n",
       "      <td>12.924545</td>\n",
       "      <td>77.599077</td>\n",
       "      <td>13.005976</td>\n",
       "      <td>11869.0</td>\n",
       "      <td>0.005112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38204831</th>\n",
       "      <td>8</td>\n",
       "      <td>127</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>2943.88</td>\n",
       "      <td>338.43</td>\n",
       "      <td>2924.38</td>\n",
       "      <td>1.12</td>\n",
       "      <td>3</td>\n",
       "      <td>3128.379559</td>\n",
       "      <td>77.672583</td>\n",
       "      <td>12.994474</td>\n",
       "      <td>77.548812</td>\n",
       "      <td>12.963703</td>\n",
       "      <td>17072.9</td>\n",
       "      <td>0.595019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38204832</th>\n",
       "      <td>34</td>\n",
       "      <td>95</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>2624.09</td>\n",
       "      <td>863.77</td>\n",
       "      <td>2493.34</td>\n",
       "      <td>1.37</td>\n",
       "      <td>5</td>\n",
       "      <td>3387.562808</td>\n",
       "      <td>77.506307</td>\n",
       "      <td>13.038050</td>\n",
       "      <td>77.632594</td>\n",
       "      <td>12.973725</td>\n",
       "      <td>19533.4</td>\n",
       "      <td>0.973561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38204833</th>\n",
       "      <td>56</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>2366.86</td>\n",
       "      <td>349.15</td>\n",
       "      <td>2340.33</td>\n",
       "      <td>1.16</td>\n",
       "      <td>3</td>\n",
       "      <td>2635.211006</td>\n",
       "      <td>77.603741</td>\n",
       "      <td>13.035174</td>\n",
       "      <td>77.547044</td>\n",
       "      <td>12.972093</td>\n",
       "      <td>13119.4</td>\n",
       "      <td>0.799564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38204834</th>\n",
       "      <td>128</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>2135.83</td>\n",
       "      <td>604.69</td>\n",
       "      <td>2033.07</td>\n",
       "      <td>1.39</td>\n",
       "      <td>5</td>\n",
       "      <td>2273.702852</td>\n",
       "      <td>77.551941</td>\n",
       "      <td>12.960545</td>\n",
       "      <td>77.553857</td>\n",
       "      <td>13.026029</td>\n",
       "      <td>10127.4</td>\n",
       "      <td>0.339695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38204835 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          sourceid  dstid  month  day  start_hour  end_hour  mean_travel_time  \\\n",
       "0              102     97      3   13          10        16             322.8   \n",
       "1              102     97      1   17          19         0            306.71   \n",
       "2              102     97      2    7          19         0            282.94   \n",
       "3              102     97      1   19           7        10            294.18   \n",
       "4              102     97      2    9           7        10            263.55   \n",
       "...            ...    ...    ...  ...         ...       ...               ...   \n",
       "38204830       162     83      3    2          16        19           3120.83   \n",
       "38204831         8    127      2    2          10        16           2943.88   \n",
       "38204832        34     95      2    1          19         0           2624.09   \n",
       "38204833        56    120      1   19          10        16           2366.86   \n",
       "38204834       128     25      1   24          19         0           2135.83   \n",
       "\n",
       "          standard_deviation_travel_time  geometric_mean_travel_time  \\\n",
       "0                                 425.14                      270.59   \n",
       "1                                 200.99                      256.58   \n",
       "2                                 206.01                      233.29   \n",
       "3                                 183.97                      258.09   \n",
       "4                                 149.79                      232.89   \n",
       "...                                  ...                         ...   \n",
       "38204830                          459.43                      3089.7   \n",
       "38204831                          338.43                     2924.38   \n",
       "38204832                          863.77                     2493.34   \n",
       "38204833                          349.15                     2340.33   \n",
       "38204834                          604.69                     2033.07   \n",
       "\n",
       "          geometric_standard_deviation_travel_time  time_period  travel_time  \\\n",
       "0                                              1.7            3   362.063898   \n",
       "1                                             1.84            5   396.842057   \n",
       "2                                              2.0            5   354.279465   \n",
       "3                                             1.61            2   334.554688   \n",
       "4                                             1.67            2   289.404809   \n",
       "...                                            ...          ...          ...   \n",
       "38204830                                      1.15            4  3091.908238   \n",
       "38204831                                      1.12            3  3128.379559   \n",
       "38204832                                      1.37            5  3387.562808   \n",
       "38204833                                      1.16            3  2635.211006   \n",
       "38204834                                      1.39            5  2273.702852   \n",
       "\n",
       "            src_lon    src_lat    dst_lon    dst_lat  distance    random  \n",
       "0         77.563817  12.982784  77.566287  12.970359    1778.9  0.548814  \n",
       "1         77.563817  12.982784  77.566287  12.970359    1778.9  0.715189  \n",
       "2         77.563817  12.982784  77.566287  12.970359    1778.9  0.602763  \n",
       "3         77.563817  12.982784  77.566287  12.970359    1778.9  0.544883  \n",
       "4         77.563817  12.982784  77.566287  12.970359    1778.9  0.423655  \n",
       "...             ...        ...        ...        ...       ...       ...  \n",
       "38204830  77.587545  12.924545  77.599077  13.005976   11869.0  0.005112  \n",
       "38204831  77.672583  12.994474  77.548812  12.963703   17072.9  0.595019  \n",
       "38204832  77.506307  13.038050  77.632594  12.973725   19533.4  0.973561  \n",
       "38204833  77.603741  13.035174  77.547044  12.972093   13119.4  0.799564  \n",
       "38204834  77.551941  12.960545  77.553857  13.026029   10127.4  0.339695  \n",
       "\n",
       "[38204835 rows x 18 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "travel_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories_to_hour = {\n",
    "    1: [0, 6],\n",
    "    2: [7, 9],\n",
    "    3: [10, 15],\n",
    "    4: [16, 18],\n",
    "    5: [19, 23]\n",
    "}\n",
    "\n",
    "def get_time_period(hour):\n",
    "    for category, (start_hour, end_hour) in categories_to_hour.items():\n",
    "        if hour >= start_hour and hour <= end_hour:\n",
    "            return category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "travel_times['time_period'] = travel_times['start_hour'].apply(get_time_period)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Travel time has a strong correlation with the day of the week. So we compute a new column `dow` from the day and month columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 2020\n",
    "\n",
    "def get_dow(row):\n",
    "    return datetime.date(year, int(row['month']), int(row['day'])).weekday()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "travel_times['dow'] = travel_times.apply(get_dow, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "travel_times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ward Boundaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For modeling purposes, we use centroid of each ward to represent the ward. We use GeoPandas `centroid()` function to get the point geometry representing the centroid.\n",
    "\n",
    "Our source data comes in the *EPSG:4326 WGS84 Geographic Projection* - which is not suitable forgeoprocessing operations. To get the accurate centroid computation, we must re-project the data to a *Planar Projection*. We use a UTM projection suitable for the region of the data - *WGS 84 UTM Zone 43N* - which is defined by the code [EPSG:32643](http://epsg.io/32643). Once computed, we transform it back to EPSG:4326 and add it to our GeoDataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroid_utm = wards.geometry.to_crs('EPSG:32643').centroid\n",
    "wards['centroid'] = centroid_utm.to_crs('EPSG:4326')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22370fe8108>"
      ]
     },
     "execution_count": 31,
     "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": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "wards['geometry'].plot(color='grey',ax=ax)\n",
    "wards['centroid'].plot(color='red',ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distance Computation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have the travel times for each pair of source and destination wards. The travel time is strongly correlated with the distance between the wards. We need to compute the actual distance along the road network for our ward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "ward_no = wards['MOVEMENT_ID']\n",
    "index = pd.MultiIndex.from_product([ward_no, ward_no], names = ['sourceid', 'dstid'])\n",
    "\n",
    "distancematrix = pd.DataFrame(index = index).reset_index()\n",
    "distancematrix = distancematrix.query('sourceid != dstid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_coordinates(row):\n",
    "    source_ward = wards[wards['MOVEMENT_ID'] == row['sourceid']].iloc[0]\n",
    "    dst_ward = wards[wards['MOVEMENT_ID'] == row['dstid']].iloc[0]\n",
    "\n",
    "    src_lon, src_lat = source_ward['centroid'].x, source_ward['centroid'].y\n",
    "    dst_lon, dst_lat = dst_ward['centroid'].x, dst_ward['centroid'].y\n",
    "    return src_lon, src_lat, dst_lon, dst_lat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "distancematrix[['src_lon', 'src_lat', 'dst_lon', 'dst_lat']] = distancematrix.apply(get_coordinates, axis=1, result_type='expand')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>sourceid</th>\n",
       "      <th>dstid</th>\n",
       "      <th>src_lon</th>\n",
       "      <th>src_lat</th>\n",
       "      <th>dst_lon</th>\n",
       "      <th>dst_lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>77.580422</td>\n",
       "      <td>13.121709</td>\n",
       "      <td>77.560037</td>\n",
       "      <td>13.102805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>77.580422</td>\n",
       "      <td>13.121709</td>\n",
       "      <td>77.583926</td>\n",
       "      <td>13.090987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>77.580422</td>\n",
       "      <td>13.121709</td>\n",
       "      <td>77.669565</td>\n",
       "      <td>13.006063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>77.580422</td>\n",
       "      <td>13.121709</td>\n",
       "      <td>77.715456</td>\n",
       "      <td>13.016847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>77.580422</td>\n",
       "      <td>13.121709</td>\n",
       "      <td>77.705502</td>\n",
       "      <td>13.022373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39198</th>\n",
       "      <td>198</td>\n",
       "      <td>193</td>\n",
       "      <td>77.505015</td>\n",
       "      <td>12.891903</td>\n",
       "      <td>77.594507</td>\n",
       "      <td>12.910882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39199</th>\n",
       "      <td>198</td>\n",
       "      <td>194</td>\n",
       "      <td>77.505015</td>\n",
       "      <td>12.891903</td>\n",
       "      <td>77.614418</td>\n",
       "      <td>12.920018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39200</th>\n",
       "      <td>198</td>\n",
       "      <td>195</td>\n",
       "      <td>77.505015</td>\n",
       "      <td>12.891903</td>\n",
       "      <td>77.676539</td>\n",
       "      <td>13.033613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39201</th>\n",
       "      <td>198</td>\n",
       "      <td>196</td>\n",
       "      <td>77.505015</td>\n",
       "      <td>12.891903</td>\n",
       "      <td>77.653272</td>\n",
       "      <td>13.044560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39202</th>\n",
       "      <td>198</td>\n",
       "      <td>197</td>\n",
       "      <td>77.505015</td>\n",
       "      <td>12.891903</td>\n",
       "      <td>77.691495</td>\n",
       "      <td>12.950743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39006 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sourceid dstid    src_lon    src_lat    dst_lon    dst_lat\n",
       "1            1     2  77.580422  13.121709  77.560037  13.102805\n",
       "2            1     3  77.580422  13.121709  77.583926  13.090987\n",
       "3            1     4  77.580422  13.121709  77.669565  13.006063\n",
       "4            1     5  77.580422  13.121709  77.715456  13.016847\n",
       "5            1     6  77.580422  13.121709  77.705502  13.022373\n",
       "...        ...   ...        ...        ...        ...        ...\n",
       "39198      198   193  77.505015  12.891903  77.594507  12.910882\n",
       "39199      198   194  77.505015  12.891903  77.614418  12.920018\n",
       "39200      198   195  77.505015  12.891903  77.676539  13.033613\n",
       "39201      198   196  77.505015  12.891903  77.653272  13.044560\n",
       "39202      198   197  77.505015  12.891903  77.691495  12.950743\n",
       "\n",
       "[39006 rows x 6 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distancematrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to get driving distance between approximately 40,000 coordinates. To do this efficiently, we ran the [Open Source Routing Machine (OSRM)](https://hub.docker.com/r/osrm/osrm-backend/) service locally using docker images provided by the project. OSRM holds the network graph in memory and the routing is extremely fast. We write and apply the following function and get the driving distance in meters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_distance(row):\n",
    "    \n",
    "    coordinates = '{},{};{},{}'.format(\n",
    "        row['src_lon'], row['src_lat'], row['dst_lon'], row['dst_lat'])\n",
    "    url = 'http://127.0.0.1:5000/route/v1/driving/'\n",
    "    response = requests.get(url + coordinates) \n",
    "    if response.status_code== 200:\n",
    "        data = response.json()   \n",
    "        distance = data['routes'][0]['distance']\n",
    "    \n",
    "    return distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting distance data is saved locally and used in the subsequent analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "osrm_data_folder = os.path.join('data', 'osrm')\n",
    "distancematrix_file = 'distancematrix.csv'\n",
    "distancematrix_filepath = os.path.join(osrm_data_folder, distancematrix_file)\n",
    "distancematrix = pd.read_csv(distancematrix_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "travel_times = pd.merge(travel_times, distancematrix, on=['sourceid', 'dstid']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>sourceid</th>\n",
       "      <th>dstid</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>end_hour</th>\n",
       "      <th>mean_travel_time</th>\n",
       "      <th>standard_deviation_travel_time</th>\n",
       "      <th>geometric_mean_travel_time</th>\n",
       "      <th>geometric_standard_deviation_travel_time</th>\n",
       "      <th>time_period</th>\n",
       "      <th>dow</th>\n",
       "      <th>src_lon</th>\n",
       "      <th>src_lat</th>\n",
       "      <th>dst_lon</th>\n",
       "      <th>dst_lat</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102</td>\n",
       "      <td>97</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>322.80</td>\n",
       "      <td>425.14</td>\n",
       "      <td>270.59</td>\n",
       "      <td>1.70</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>77.563817</td>\n",
       "      <td>12.982784</td>\n",
       "      <td>77.566287</td>\n",
       "      <td>12.970359</td>\n",
       "      <td>1778.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>306.71</td>\n",
       "      <td>200.99</td>\n",
       "      <td>256.58</td>\n",
       "      <td>1.84</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>77.563817</td>\n",
       "      <td>12.982784</td>\n",
       "      <td>77.566287</td>\n",
       "      <td>12.970359</td>\n",
       "      <td>1778.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>97</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>282.94</td>\n",
       "      <td>206.01</td>\n",
       "      <td>233.29</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>77.563817</td>\n",
       "      <td>12.982784</td>\n",
       "      <td>77.566287</td>\n",
       "      <td>12.970359</td>\n",
       "      <td>1778.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>294.18</td>\n",
       "      <td>183.97</td>\n",
       "      <td>258.09</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>77.563817</td>\n",
       "      <td>12.982784</td>\n",
       "      <td>77.566287</td>\n",
       "      <td>12.970359</td>\n",
       "      <td>1778.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102</td>\n",
       "      <td>97</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>263.55</td>\n",
       "      <td>149.79</td>\n",
       "      <td>232.89</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>77.563817</td>\n",
       "      <td>12.982784</td>\n",
       "      <td>77.566287</td>\n",
       "      <td>12.970359</td>\n",
       "      <td>1778.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7640962</th>\n",
       "      <td>162</td>\n",
       "      <td>83</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>3120.83</td>\n",
       "      <td>459.43</td>\n",
       "      <td>3089.70</td>\n",
       "      <td>1.15</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>77.587545</td>\n",
       "      <td>12.924545</td>\n",
       "      <td>77.599077</td>\n",
       "      <td>13.005976</td>\n",
       "      <td>11869.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7640963</th>\n",
       "      <td>8</td>\n",
       "      <td>127</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>2943.88</td>\n",
       "      <td>338.43</td>\n",
       "      <td>2924.38</td>\n",
       "      <td>1.12</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>77.672583</td>\n",
       "      <td>12.994474</td>\n",
       "      <td>77.548812</td>\n",
       "      <td>12.963703</td>\n",
       "      <td>17072.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7640964</th>\n",
       "      <td>34</td>\n",
       "      <td>95</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>2624.09</td>\n",
       "      <td>863.77</td>\n",
       "      <td>2493.34</td>\n",
       "      <td>1.37</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>77.506307</td>\n",
       "      <td>13.038050</td>\n",
       "      <td>77.632594</td>\n",
       "      <td>12.973725</td>\n",
       "      <td>19533.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7640965</th>\n",
       "      <td>56</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>2366.86</td>\n",
       "      <td>349.15</td>\n",
       "      <td>2340.33</td>\n",
       "      <td>1.16</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>77.603741</td>\n",
       "      <td>13.035174</td>\n",
       "      <td>77.547044</td>\n",
       "      <td>12.972093</td>\n",
       "      <td>13119.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7640966</th>\n",
       "      <td>128</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>2135.83</td>\n",
       "      <td>604.69</td>\n",
       "      <td>2033.07</td>\n",
       "      <td>1.39</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>77.551941</td>\n",
       "      <td>12.960545</td>\n",
       "      <td>77.553857</td>\n",
       "      <td>13.026029</td>\n",
       "      <td>10127.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7640967 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         sourceid  dstid  month  day  start_hour  end_hour  mean_travel_time  \\\n",
       "0             102     97      3   13          10        16            322.80   \n",
       "1             102     97      1   17          19         0            306.71   \n",
       "2             102     97      2    7          19         0            282.94   \n",
       "3             102     97      1   19           7        10            294.18   \n",
       "4             102     97      2    9           7        10            263.55   \n",
       "...           ...    ...    ...  ...         ...       ...               ...   \n",
       "7640962       162     83      3    2          16        19           3120.83   \n",
       "7640963         8    127      2    2          10        16           2943.88   \n",
       "7640964        34     95      2    1          19         0           2624.09   \n",
       "7640965        56    120      1   19          10        16           2366.86   \n",
       "7640966       128     25      1   24          19         0           2135.83   \n",
       "\n",
       "         standard_deviation_travel_time  geometric_mean_travel_time  \\\n",
       "0                                425.14                      270.59   \n",
       "1                                200.99                      256.58   \n",
       "2                                206.01                      233.29   \n",
       "3                                183.97                      258.09   \n",
       "4                                149.79                      232.89   \n",
       "...                                 ...                         ...   \n",
       "7640962                          459.43                     3089.70   \n",
       "7640963                          338.43                     2924.38   \n",
       "7640964                          863.77                     2493.34   \n",
       "7640965                          349.15                     2340.33   \n",
       "7640966                          604.69                     2033.07   \n",
       "\n",
       "         geometric_standard_deviation_travel_time  time_period  dow  \\\n",
       "0                                            1.70            3    4   \n",
       "1                                            1.84            5    4   \n",
       "2                                            2.00            5    4   \n",
       "3                                            1.61            2    6   \n",
       "4                                            1.67            2    6   \n",
       "...                                           ...          ...  ...   \n",
       "7640962                                      1.15            4    0   \n",
       "7640963                                      1.12            3    6   \n",
       "7640964                                      1.37            5    5   \n",
       "7640965                                      1.16            3    6   \n",
       "7640966                                      1.39            5    4   \n",
       "\n",
       "           src_lon    src_lat    dst_lon    dst_lat  distance  \n",
       "0        77.563817  12.982784  77.566287  12.970359    1778.9  \n",
       "1        77.563817  12.982784  77.566287  12.970359    1778.9  \n",
       "2        77.563817  12.982784  77.566287  12.970359    1778.9  \n",
       "3        77.563817  12.982784  77.566287  12.970359    1778.9  \n",
       "4        77.563817  12.982784  77.566287  12.970359    1778.9  \n",
       "...            ...        ...        ...        ...       ...  \n",
       "7640962  77.587545  12.924545  77.599077  13.005976   11869.0  \n",
       "7640963  77.672583  12.994474  77.548812  12.963703   17072.9  \n",
       "7640964  77.506307  13.038050  77.632594  12.973725   19533.4  \n",
       "7640965  77.603741  13.035174  77.547044  12.972093   13119.4  \n",
       "7640966  77.551941  12.960545  77.553857  13.026029   10127.4  \n",
       "\n",
       "[7640967 rows x 17 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "travel_times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `scikit-learn` library to build and train a linear regressor.\n",
    "\n",
    "The independent variables considered are `sourceid, dstid, day, time_period, dow, src_lon, src_lat, dst_lon, dst_lat, distance`. The dependnet variable is the travel time `geometric_mean_travel_time`.\n",
    "Of the independent variables we goes for one-hot-encoding of to categorical variables `time_period` and `dow`\n",
    "We sample the travel times to get a subset that will be used for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples = 500000\n",
    "samples = travel_times.sample(n=num_samples, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "sel_input=['sourceid', 'dstid', 'day', 'time_period', 'dow', 'src_lon', 'src_lat', 'dst_lon', 'dst_lat', 'distance']\n",
    "cat_ip=['time_period','dow']\n",
    "scale_ip= list(set(sel_input)-set(cat_ip))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['dow'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-cdb2a8455402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msel_input\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'travel_time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\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[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/spatial_data_science/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3028\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3029\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3030\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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[0m\n\u001b[0m\u001b[1;32m   3031\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3032\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/spatial_data_science/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1264\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1266\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\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   1267\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/spatial_data_science/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1314\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1315\u001b[0m                 \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1316\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{not_found} not in index\"\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   1317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1318\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\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: \"['dow'] not in index\""
     ]
    }
   ],
   "source": [
    "x = samples[sel_input].values\n",
    "y = samples['travel_time']\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.30, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data set contains categorical variable in case of 'time_period' and 'dow' and hence these are one_hot_encoded since these categorical values has no significance for linear model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_Trans= ColumnTransformer([('encoder',OneHotEncoder(categories='auto', sparse=False),[sel_input.index(i) for i in cat_ip]),\n",
    "                                   ('scaler',StandardScaler(),[sel_input.index(i) for i in scale_ip])],remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = Pipeline(steps=[('ct',category_Trans),('model',LinearRegression())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'regressor' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-923b4e1a10f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mregressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'regressor' is not defined"
     ]
    }
   ],
   "source": [
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy:  0.7442950836977186\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy: ', regressor.score(x_train,y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction Accuracy:  0.7441506633075343\n"
     ]
    }
   ],
   "source": [
    "print('Prediction Accuracy: ', regressor.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking Model Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the model performs well with the test partition, that dataset is not representative of real world data. We want to see how the model performs to routing requests that are not between centroids of wards. To achieve this, we create a dataset with random source and destination coordinates and check the model prediction against travel times predicted by commercial data providers such as Google Maps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Points within a Polygon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We generate random coordinate pairs within the bounds of the city. But to ensure that the points fall within the actual city geometry, we do a spatial join to select the points that intersect the wards. After the join, we select a subset of 100 points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_points = 200\n",
    "\n",
    "x_min, y_min, x_max, y_max = wards.total_bounds\n",
    "\n",
    "np.random.seed(0)\n",
    "src_x = np.random.uniform(x_min, x_max, n_points)\n",
    "src_y = np.random.uniform(y_min, y_max, n_points)\n",
    "dst_x = np.random.uniform(x_min, x_max, n_points)\n",
    "dst_y = np.random.uniform(y_min, y_max, n_points)\n",
    "\n",
    "src_gdf = gpd.GeoDataFrame(geometry=gpd.points_from_xy(src_x, src_y), crs='EPSG:4326')\n",
    "dst_gdf = gpd.GeoDataFrame(geometry=gpd.points_from_xy(dst_x, dst_y), crs='EPSG:4326')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "src_gdf = gpd.sjoin(src_gdf, wards, how='inner', op='intersects')\n",
    "dst_gdf = gpd.sjoin(dst_gdf, wards, how='inner', op='intersects')\n",
    "\n",
    "src_selected = src_gdf[:100].reset_index()\n",
    "dst_selected = dst_gdf[:100].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x200333bb488>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmoAAAI/CAYAAADHkfU7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZBrd3kn/O/Rvu9SS92S2mAbx/albco3uAyBmMgkvA6NJ3mLgYyTdyEJk6pQb4Y/pjIvZKEmw1RqZhLXVM0A5bDkxWNuwAuBnju2sYWxsY3Bd21f3+vr5V63tm4trX1pref9o+8RrW4t5+ico6X7+bhc9tWVjn696tHv9ywMy7IghBBCCCGzRzHtBRBCCCGEkP4oUCOEEEIImVEUqBFCCCGEzCgK1AghhBBCZhQFaoQQQgghM4oCNUIIIYSQGaWa9gKEcLlc7HXXXTftZRBCCCGEHHD69OkMy7JuKa85V4Haddddh1OnTk17GYQQQgghBzAMsyH1NenokxBCCCFkRlGgRgghhBAyoyhQI4QQQgiZURSoEUIIIYTMKArUCCGEEEJmFAVqhBBCCCEzigI1QgghhJAZRYEaIYQQQsiMokCNEEIIIWRGjQzUGIb5JsMwKYZhLuy57W8YhllnGOYcwzA/Yhhmke9jr93+JYZh4tcef45hmHvFfyiEEEIIIYcLnx21fwTwsX23/WeWZVdYlr0dwP8E8FcCHst5gGXZ26/9+7/4LJYQQggh5CgZGaixLPs8gOy+24p7/mgEwPJ9LCGEEEII4WfsHDWGYb7MMEwUwP0YvKM2zOeuHZ9+k2EY+7jrIIQQQgg5rMYO1FiW/SLLsgEADwP4nMCHfxXA9QBuB7AJ4O8G3ZFhmM8yDHOKYZhT6XR63OUSQgghhMwdKao+vwPgfxfyAJZlkyzLtlmW7QD4BwDvH3LfB1mWPc6y7HG32y1yqYQQQggh82OsQI1hmBv3/PETAF4X+Hjfnj/+DoALg+5LCCGEEHJUqUbdgWGYEwDuBuBiGCYG4K8B3MswzE0AOgA2APzJtfsuAvg6y7L3Dnosy7LfAPCfGIa5HbtFCO8A+NfSfliEEEIIIfOPYdm+BZsz6fjx4+ypU6emvQxCCCGEkAMYhjnNsuxxKa9JkwkIIYQQQmYUBWqEEEIIITOKAjVCCCGEkBlFgRohhBBCyIwaWfVJCJlfLMuiWq1CrVaj2WxCpVJBo9GAYZhpL40QQggPFKgRcohUq1XEYjHEYjHE43Ekk0lUKpWe+6hUKvh8Pnzyk5+E2Wye0koJIYTwQYEaIYdAq9XCV77yFeRyOV73jUajeOihh/CZz3wGOp1uAiskhBAyDspRI+QQUCgUvIK0vdLpNJ599lmZVkQIIUQKFKgRcgi8+eabYz3u1KlT2NjYkHg1hBBCpEKBGiFzLpPJ4PHHHx/rsZ1OBz/60Y8kXhEhhBCpUKBGyByrVqt4+OGH0Wg0xr5GIpFAKpWScFWEEEKkQoEaIXMsHA4jn8+Lvs6lS5ckWA0hhBCpUaBGyJxKJBI4c+aMJNd66623JLkOIYQQaVGgRsgcYlkWTzzxhGTXi8fjaLVakl2PEEKINChQI2QOra+vIxaLSXY9boIBIYSQ2UKBGiFzpl6v45lnnpH8urVaTfJrEkIIEYcCNULmzAsvvIByuSz5dbe3tyW/JiGEEHEoUCNkzly5ckWW6xYKBVmuSwghZHw065OQOcKyLDKZjCzXLhaLAHab4JbLZVQqFTSbTbAsC4Vi9z1drVZDPp9Hq9WCSqXCu971LrjdblnWQwghhAI1QuZKsVgU1dx2mNdeew2nT59Gs9nk/RiGYaBUKruBXKfT6f4/y7I991UoFGAYpvv/SqUSSqWye5vBYMD111+P2267DQ6HQ4oPiRBC5h4FaoTMkXQ6Ldu1S6USgsEgIpEI78ewLCtZW49cLod4PI7nn38edrsdCoWiG9zt/bfZbEKn08FsNuOuu+5CIBCQ5PkJIWQWUaBGyByR69iTs7m5CYPBMPVWHblcbuDfrWMdYYRRQAHWS1b8yQ1/gv/wqf8AlYp+nRFCDh/6zUbIHJGj2nOvZrMJr9crWaB2bH0doXAY1kIBBasV4VAIF1ZWxr7eOtaxhjU0sXs8W0ABf//W3yPz3zP4+//772GxWA485uFXH8YXw19EpBBB0BrEl0Nfxv3vvX/sNRBCyCRR1Schc2QSvc6i0agkOWLH1texurYGW6EABoCtUMDq2hqOra+Pfc0wwt0gjdNEE4/mH8XXv/51JJPJnr97+NWH8dm1z2KjsAEWLDYKG/js2mfx8KsPj70GQgiZJArUCJkjlUplIs/TbDbh9/tFXSMUDkOzrzBB02wiFA6Pfc0C+rcQKaCAUqmE73znO9jc3EQqlcLW1hb+/Ed/jmqzd3ew2qziC+EvjL0GQgiZJDr6JGSOZLPZiTxPqVRCqVSC0+mETqdDIpE4UMU5inVAX7ZBt/O6Jqx9gzUrrAB2pzZ84xvfQLvdBgDEEe97nUghgmQyiYWFhbHXQgghk0A7aoTMkUk3pd3e3kY8HofJZIJSqRT02ILVKuh2PkIIQQ11z21qqBFCCADgcrm6QRrwywBuPyusePDBB/HCCy+g0+mMvR5CCJEbBWqEzIlGoyFbD7VRSqUSfD6foMeEQyE01L1BVUOtRjgUGnsdK1jBKla7AZgVVqxiFSvYLVDYH0wOC+w6nQ7C4TC+9a1v0fgsQsjMoqNPQuZEqVSa6vML3VHjqjulrPoEdoM1LjDbb3+1Kne/bjsPWBFCqOfxsVgMX/va13DPPffg/e9/f7cBLyGEzAIK1AiZE5MqJBhk75EiXxdWVkQHZnzZ7fa+feaGBXacVquFJ598Eq+//jruu+8+2Gw2uZZJCCGCUKBGyAx55JFHoFAo4PV6sbCwgIWFBajVaqhUKuzs7Ex1bfV6farPP4rBYBjaKJePd955B1/96lfxW7/1W3jf+95Hu2uEkKmjQI2QGVEsFnHx4kUAwIULF3r+TqFQwOFwwO/3Q6FQoFgsIp/PT2xtOp1O8ufrmTDQ50hSCIvFgni8f4WnUI1GA2tra3j99dexuroKs9ksyXUJIWQcVExAyIzggrR+Op0OMpkMYrEYIpEI8vk8jEYjgsEgdDqd7GtzOByChrWPwk0Y4FptFFDAGtawjvGa4cpxVPnmm2/iK1/5Cl599VXBrUkIIUQqFKgRMiOGBWr9VCoVRCIRKBTz92M8aMJAGMKb4SqVSqRSKamW1mNnZwePP/44HnnkkannCBJCjqb5+w1PyCFULBYRjUbHeqzRaJR4NQe1Wi1JrzdswoBQXq9X9vy9S5cu4atf/SrefvttWZ+HEEL2o0CNkBkgdDdt0qQuJBjWiFaoSTWsrVQq+O53v4utra2JPB8hhAAUqBEyE2Y9UJN6x2pvI9rfWweuPgC0vwS88UBD0NB2o9GIzc1NSdc2TLPZxIkTJ1Aulyf2nISQo40CNUKmTMyxJ4CJTCuQOpmemzDwh+t6/MMacF1h95eRp1DD6toa72DN6XRKui4+isUivve970l+HEwIIf1QoEbIlIndTSsUClCp5O20I0fBwgpW8F/CGhj3FZNqmk2EwqOLCrRaLRKJhOTr4iMajeLkyZNUDUoIkR0FaoRM2auvvir6GhaLRYKVDCZX41frgCHzg27fy+PxTHVX69y5c3j55Zen9vyEkKOBAjVCpiiTyUxtV0gIh8Mhy3UL1v7FA4Nu32sW2pI8/fTTePPNN6e9DELIITb933SEHGHrAhLnp0WhUMjW/iIcCqGhVvfc1lCrEQ6FRj52Fo4dWZbFY4891nfGKCGESIECNUKmhGXZA6OixqVUKiW5zn56vR4LCwvY3t6W5foXVlawtrqKvNUKFkDeasXa6iqvQe7jDImXQ71ex4kTJ1Cr1aa9FELIIUSzPgmZklwuJ3qIOEer1Upynf3cbjcikYgs1+ZcWFnhFZjtN0tVl9lsFo888gjuv/9+2YJmQsjRRDtqhEzJO++8I9m1ZiFfa9KknD0qhatXr+Kpp56a9jIIIYfM0fvtTsiMkDJQk8ukuv6PYxZnb77yyis4derUtJdBCDlEKFAjZApYlp2LQE3uGZrj0uv1ko+1ksoTTzwxF19bQsh8oECNkCnY3t5GqVSa9jJGmsVdKwAwm83TXsJAnU4H3/ve9yTLPySEHG0UqBEyBfPSe6tWq8k+9WAcs56wX6vVcOLEiZnd9SOEzI/Z+w1MyBEgdaAm5xGlyWRCPp+X7fqDrGMdYYRRQAFWWBFCCCvYrQ6dpYrPQdLpNB5//HF86lOfOpLFHoQQadBvD0ImrF6vY2NjQ9JrFniMXBqXwWCQ7dqDrGMda1hDAbsfVwEFrGEN69htEDwvx4pvvPEGfvzjH097GYSQOUaBGiETduXKFcmrKev1OvR6vaTX5Ezj6DOMMJrobb/RRBNh7A5rb7VaM3/8yXnxxRfnYgIFIWQ2UaBGyIS98cYbslxXp9PJct1p9CvjdtKG3S7XoHg5PPfcc9NeAiFkTlGOGiETxLIs3nrrLVmurd43M1OoQTlhm5ubMJlMKJfLEq10NCusfYM1K3aHtU96PWK9+93vnvYSCCFzinbUCJmgRCIhW4AhZkj5qJwwu93eva/X60UwGJT16DGEENToDTzVUCOE0IH1zIObbrpp2ksghMwpCtQImaCf//znsl1bTCXkqJywaDQKl8sFYLdPWDKZhM/nk+34cQUrWMVqdwfNCitWsdqt+pynthcajQbXXXfdtJdBCJlTdPRJyIRks1lcuHBBtuvXarWxH8s3JywYDIJlWdjtdsRiMfh8PmSzWVkCp5Vr/+zncDiQSqV4X+fY+jpC4TCshQIKVivCodBYQ+DHdcMNN8xkLzpCyHyg3x6ETMiLL74o6nhyGI1GI6qX2qicMGC3L9h+m5ubMBqNsFqtgoKncalUKkG5eMfW17G6tgbNtYIIW6GA1bU1AJhYsPae97xnIs9DCDmc6OiTkAkolUo4f/68bNcXO1JpVE7YMJVKBZlMBl6vV9Qa+FCr1Ugmk7zvHwqHu0EaR9NsIhQOS720vhiGwY033jiR5yKEHE60o0bIBLz88stot9uyXV+r1Yp6PHfEOGgSwCidTgeVSgVKpVLWj1NoTpx1QCPgQbdLLRgMTqVhMCHk8KBAjRCZtVotnD17Vtbn4FOBqVQq4Xa7oVAokEgkDvz9oJwwvkqlEpaXlyWfurCX0Dy8gtUKW5+grGC19rm39OjYkxAiFgVqhMjs9ddfF5Xoz0e73YbH44FarQbDMGg2mweOCH0+H2KxGABgcXERxWJR8lYhGxsbcDqd2N7elvS6HK1Wi2azyXvXLhwK9eSoAUBDrUY4FJpIkQG15SCEiEWBGiEyO3PmjOzP0W+HTKFQwOFwwGAwgGVZRKPRnvvrdDqYzWaUSiVJ1yLXhASv14tmsykoCOQCr/0BGQDZiwxcLhecTqck1yKEHF0UqBEio2w2i6tXr07luTudDjKZzMC/39nZgdVqlTxQi8fjcLlcQ597HJ1OpxukDZqi0M+FlZUDwdefPfDAwCIDqQK1QCCAdrs9NzNJCSGziQI1QmQ0id00MZLJJILBICKRiKTXlSOBngt4uCkKXINebooCAN45dpMoMjh79izOnz8Pl8sFt9vd86/D4aAAjhDCCwVqhMik3W7j3Llz017GSJFIBIFAoOdodBYpFLvdhIZNUeAbqE2qyKDT6SCVSh3oMadQKCiAI4TwQoEaITJ54403UKlUpr0MXmKxGBYXF/vmuo2j2WxK3qqDC2D4TFEYZViRwSQICeA8Hg+cTqds47oIIbONAjVCZCJnmwqpsSyLra0tyYK1zc1NBINBbG1todFoiL6e0+lE4doOGJ8pCqMMKjKY5GipfvoFcAzD4C/+4i8oUCPkiKJAjRCZSJ2kL7dOp4OtrS34/f5uG49xmM1m1Ot1RCIRqFQq2O125HI50evjArUQQj05agD/KQp79SsymEUmk6l77EsIOXooUCNEJtMO1IRURnI6nQ5isZioAgOHw4FoNAq9Xo9Go9E3SNNqtXA4HN3K1FFHpM09R5RipyjMG7HjwQgh840CNUJkUiwWp/bcYisjI5EI/H4/EokEOp0O7+fVaDRot9vodDoDm/w6HA5ks1lsbm4C2D3WrFQqQ4fKN/e10hA7RWGemEymaS+BEDJFI/fTGYb5JsMwKYZhLuy57W8YhllnGOYcwzA/Yhhmke9jr93uYBjmaYZh3rz2X7v4D4WQ2dFqtaYaqA2rjORDr9cDAJaXl2E0Gkfe3+FwYHFxEe12e+Sx6f7AY3t7G+12GxaLBU6nEwsLC/D5fPD7/fD7/VhaWoJ1QiOfZhHtqBFytPHZUftHAP8NwLf33PafWZb9SwBgGOb/AfBXAP6E52MB4N8BCLMs+7cMw/y7a3/+c0ErJ0Qm1WoVGxsbyOVyYBgGCoUCSqUSGo0GGo0GWq0WGo0GBoMBVqu1b/5QJpMBy7JTWP0uoZWRarUabrcbKpUKpVIJuVyuG3AZDAa43W6k0+mBz1epVFAsFnlVebZarQO3NZvNA7tmZBcFaoQcbSMDNZZln2cY5rp9t+3dKjAC6PuK1O+x19wH4O5r////AfgJKFAjU9RsNvHMM8/g6tWrQwOS/RQKBaxWK+x2O2w2G+x2O+x2u+Rd+YXiUxnpdrthMBiw9Nxz+NXvf7+n+jG3J8m+Wq1iZ2dnaN5avV7nldfGMMzAz+8kZm/OIwrUCDnaxs5RYxjmywD+DwAFAB8R+PAFlmU3AYBl2U2GYTzjroMQsViWxcmTJ3H+/HnBj+10OsjlcoKrGtVqtaw7SIMqI/+l419iUbeITCaDdDqNY+vr+HUeMy87nc7IvDU+O4h2ux3ZbPbA7cfW12WfvTmvKFAj5Ggbu+abZdkvsiwbAPAwgM9Jt6ReDMN8lmGYUwzDnBKy00EIX9yon0nyeDzw+/2y5V6tYAWrWIX9WvqnXWHHKlaxlF1CIpHo9jYLhcMDZ172E4vF4Ha7odFoDvzdsGIATjabhUajweLiIoLBIJaWlqDT6QSv4yix2WzTXgIhZIqkqPr8DoCTAP5awGOSDMP4ru2m+QCkBt2RZdkHATwIAMePH59e0g85lKrVKp588smJP2+9XkcmkwHDMFhaWgIAbG1tierkb7fbYTQaoVQq0Wg0YCwasVK5ths1oHBznJmXyWQSDocD9Xq9O3nB6XTyPjJuNBo9TXUdDsdEZm/OoxtuuAFut3vayyCETNFYgRrDMDeyLPvmtT9+AsDrAi/xQwD/J4C/vfbfH4yzDkLEevnll6eSxM61rmBZFvF4HACgUqmwtLQEhmGQTCZ71qVUKmE0GqHX66HRaMAwDFiWBcMwqNVqyGazfY9gR+V9jTvzMpvNwmQywWq1olAowGAw4NntZ8fqbWYymSY2e3Pe3HPPPdNeAiFkykYGagzDnMBu4r+LYZgYdnfO7mUY5ibsvk/fwLWKz2ttOr7Osuy9gx7Lsuw3sBugfY9hmD8EEAHwSYk/LkJG2tnZwS9+8YuJPy/DMH1ngLZarW7QplQq4fP5oNVqAewOeGdZFp1OB81mE6VSaeQcUT55X2JmXpbLZej1eiwsLOCJ2BOC+rbZbDaYTKZuoDnt2Zuz6Pbbb8fCwsK0l0EImTI+VZ+/1+fmbwy4bwLAvSMeC5ZltwGB814IkdjPf/5z1Ov1iT+vyWQaObWg3W53G8L24/f7RwZqw/K+uEBN7MzLWq2GTqeDMBNGk+3ft21/oBYMBpFMJpHP57u3pWd09ua0qFQq3H333dNeBiFkBtBkAnIkVatV/OxnP5vKc+v1etHjpZRK5cj78M37Ejvzsl6vI4f+Va/9WoRUq9VugDwLLTlmYQ373XnnnUe6yS8h5Jdo0i85kl544YWp7KYB6FsxKRSfVhiD8rvkyPva259t1O2ZTAYOh6N7NGsrFMDgl0ezx9bXJV/fILOwhv30ej1+7dd+bWrPTwiZLRSokSOnUChMJTeN02+SgVBce41hwqEQGmp17+NkyvsKIQQ1ep9LDTVCAzIcTCbTTLTkmIU17PfhD38YOp1uas9PCJktdPRJjpyXXnpJVBuMWcCnZ5nY/DMhuDy0vVWfn3J+CovbfccAo1wuz0RLjllYw142mw3Hjx+fynMTQmYTBWrkSGm1Wlif4rEWAEmCxFGFBByx+WdCrFz7h7OkW0Ic8b73LRaLM9GSYxbWsNdv/MZvQKWiX8uEkF+io09ypFy6dInXbpSc+o1fEkKj0czFAPNha2y1Wnj+Yx+b2NHsIJM8Hh7F5/Ph2LFjE39eQshso0CNHCnnzp2b9hJEB2rzMvtxVB5d5jd/E2urq8hbrWAB5K1WrK2uTrTi8sLKytTXwPnoRz8KhmEm/ryEkNlGe+zkyMhms7hy5cq0l8GrYnMYvV4v0UrkNaqqtlarITrBo9lBJnk8PMgNN9yAd73rXVNdAyFkNtGOGjkyZmE3DRC/oyb28ZNiNBqH/n0mk0EwGJzQamYbjYoihAxCgRo5Elqt1tBO/5MkNtCal+Mxg8Ew8j7RaPTIDx2nUVGEkGEoUCNHwptvvom33nprJnZwxAZqfKYSzAI+HyfLsmg0GnMTfEqNRkURQkahQI0ceu12Gz/5yU8AAJFIBH6/f6rrMZlMM3ENOalUKqRSKV73LRQKU/+aTAuNiiKEjELFBOTQe/nll3uChq2tLTidTmxvb09lPfF4HIuLi0gkEmM9PhKJANgNhsxmM/R6PRQKBRqNBjqdDnQ6HRQKBViWRbPZRL1eR61Wm2hbEp/Ph2g0yvv+0xrnNU00KooQwgcFauRQO3fuHH784x/33NZqtVCv16HX61Gr1Sa+JpZlsbm5KSpYA3Y/jlwuh1yu/0D0/ex2O8xmM1qtFjKZDK8xVOOqVquC7l+Y0iQAsdax3jONIYRQT9PfYWhUFCGEDzr6JIdSu91GOBzGD37wg765UuVyGRaLZQor28WyLBKJBPx+/8TabeRyOUQiESQSCXg8Htmex+VyCd6trNfr8Hq9Mq1IHutYxxrWUMBukFlAAWtYwzr4Tb5IJBJ48cUXcfnyZWSz2bmp5iWETBbtqJGZEY1GkUgk8N73vpdXxeAgV65cwRNPPIFMJjP0fslkEsFgsHuUOA2xWAwajWbi65AzeX/cXaLt7W3odLqpT47gK4wwmuidvtBEE2GEee2qvfrqq3j11Ve7f1YqlXA6nXC5XLjvvvug0WgkXzMhZP5QoEZmAsuyePLJJ5FIJPD000/jpptuwp133imoSrNareKZZ57B2bNneT8mEolgeXkZGxsb4yx7JK1WOzL/qtFoTDRHS6/XizpyHcZgMIx17UAggGw2y3uG6SzgdtL43j5Ku91GKpVCKpXCJz7xCTFLI4QcIhSokZnw2muvdV/g2+02Ll68iNdffx3Ly8u45ZZbYDKZcN111x3YrWk0Gnj77beRSCRw+vTpsXLONjY2YLFYYLVaBSXAD+P3+9FoNJBKpbC4uNjNS+vHbrejXC5L8rx8eDwe2QJTt9st+NoKhQIKhWKugjQAsMLaNyizQnwVJw1mJ4Rw6LcBmbparYann376wO2dTgdXr17F1atXAezu1tx11124+eab4XQ6cfnyZfzwhz8UnLjeT7FYRLFYhNVqhdVq7RYcFAoFtFot3tdxOBwAdo80OVwAarFY4HQ6sbOzg1wuh52dHSwsLCCXy8ma2L9fo9GA3+/H9va2pMUUCoWCd0uOvWw2G6LRKPx+f8/nbdaFEMIa1nqOP9VQIwRxA92dTifeeust3HTTTWKXSAg5BChQI1PFsixOnjyJYrE48r7VahXhcBjFYhF33XUX/umf/kny9RQKhZ4KRL7Bg8FggNFoRDqdHnifSqWCer2Oer0Og8GA5eVlRKPRiSeR793ZczqdMBqNaDQaSCaTouaQ+nw+lEoluN1u7Ozs8A7aDAYDstksYrHY1HMGheDy0Mat+hxEoVDg8ccfx2c+8xmaWEAIoUCNTNf58+fx2muvCXpMJBKZWDsHhUIBh8PRPYoaFHwYDIahQRrH6XQikUigWq3KdvwoxPb2drdCU61Ww+12o91uI5lMCr5Wq9Xq7kwCgNlshs1mA8Mw6HQ6aDab2N7ePrBDqVD8svg8EolgcXERyWQS7XZbxEc2GSvX/pFKIBDoHr9/5zvfwR//8R/PfHNjQoi8GDHvoCft+PHj7KlTp6a9DCKRbDaLr33ta2g2m6PvvI/RaOQdHElFo9EcOKJUqVRwOp0jAxuTyQSdTjeyEnUWLC0tIR6PC3qMzWZDPp8feT+GYeByuaDVarsBWrlcRjab7bmf2+1GsVg8Uo1wVSoVtFptT67e0tISPvnJT+LKlSu4evUqSqUSdnZ2UK/X0Wg0oFQq8ad/+qdUIUrIjGAY5jTLsselvCbtqJGpaLVaeOyxx8YK0oDdY8RarTbRvCabzXZgR21paYnXzpjRaBxrl2rSFArFWMGvxWLhFaixLMvr+ul0Gna7HWq1eqKFFtPk9XoPfC/H43Gsra3h7bffHvi4ZrNJgRohhxgFamTiWJbFD37wA9EtIjqdDuLxOLxeL7a2tiRa3WCNRgPBYBCdTgf5fB61Wq1vkBYIBFCtVnuavmq1WlnWJKYzfj8ej0fw51KpVMoShOZyOZhMJt67dfNsWG7eqGP+QqEAo9Eox7IIITOAAjUyVK1Ww6OPPgqn04kPf/jDkuTLPPfcc7hw4YIEq9sN+gqFAlQqlaDqzHHk83leAUMmk0GtVutWkA5rzSEG1xmfqzrkOuMDGDtYG2dnZnFxUbK2JvuVy2UsLy8f6kBNo9GMLEIZZnt7G4uLi1IvixAyI2iEFBmIZVl8//vfx5UrV/DKK68cmJk5rjNnzkhyHU6tVuu2xZg2jUbTbXlRKBQQiUQQjUbHPuIdZlhn/HHxnRu6lxTtUYaZpzzacXi93qFtUmq12tBJEvvz+wghhwvtqJGBXnjhBbz55pvdP1+6dAl333236BmZBoMBpVJJ7PJ6zEqOzsLCgmy7S/tJ3Rnf4XAIftF3uVy8CiTEHNHOQ/XnuMxmM6/CDZPJNK7gdbQAACAASURBVPBnRuhcVULIfKEdNdLX5cuXD+yg7ezs4KGHHhJ9xOjz+UQ9vp+9LR6mxWQyCa6WFGNQB/xxO+ObzWbBj+EzUF7s8HKlUil4XfPCarXyCkSHfZ7noZKYEDK+6b+6kZmzubmJxx57rO/fZTIZhMPjH60Bu0c9UpNzyDhfJpNpos1rQwhBDXXPbaM6469jHQ/gAXwJX8IDeKAnWBI6pUCr1fIqCBFzRDtPDXCFslqtvCuWh+0Yb21tzc0ge0KIcBSokR65XA4nTpwYmlP18ssv4+WXXx47d0iOfK3DnsfUzwpWsIrV7g6aFVasYnXgkeKonS2dTgefz8c76PV6vbx2g8Y9onW73YcySON2f4WkEAz7mrAsOxPNkwkh8qAcNdJVrVbx0EMP8cofe+qpp7C+vo4Pf/jDuOmmmwTtaMlRwTfpMUz77e0oP0lCOuMP29lawUo3KNJoNHC73VAoFN0K1n74fh3HHV4uV0uTaVpeXsbGxgaUSqWg75fNzc2+DZc5V69epdmghBxSFKgRAL/sbSak6m9zcxPf/e534Xa7cfPNN+PGG2/E4uLiyHwxOQIauVtzjJLJZLCwsDDTTW357mw1Go2eXDu32w29Xt8zB1VI77pxhpcvLCxMNN9PKLPZjGaz2T1yDAaD2N7eHtpKIxgMdne+9u5E8im0aLVaQ4+Br1y5ApZlZyIFgBAiLQrUCADgmWeewRtvvDHWY9PpNNLpNJ5//nloNBp4vV7ceuutOHbsGAwGQ899i8Ui72HdQgzaaZiUWq2GWq2GYDCIeDw+k5WK4+5s7e3xxfWGExIY8x1efmx9HaFwGNZCAQWrFeFQCBdWpJujKSW1Wo1SqQStVguDwYBIJAKlUonl5WVsbW0dGH01KMgS0gsvEokg5ojhkewjBz6P6XQakUgEy8vLMn3EhJBpoVmfBGfOnMHa2prk11UoFLjxxhtx22234cYbb4RKpcJrr72GRx99VPLn0uv1gpPh5aBUKqFWq2cyuXt/UADs7mwNy2vrx2AwoFarSZoXeGx9Hatra9DsyV9sqNVYW12dyWBNqVQODMa1Wi0WFhaQTqfR6XTgdDoHFl08gAcGBs+fx+d7bhv19fP5fPj93//9A2+OCCGTQ7M+CW+tVgtXrlxBsViETqeD0WjE5uYm8vk8PB4P3vWud8HhcODq1as4efKkLGvodDq4fPkyLl++DJ1Oh1tvvVW2nk9yt3Dg2wdMzi79YvHd2RrF5XJJnuQfCod7gjQA0DSbCIXDUwvU9u7wlex2/OJf/AtEfu3XwDDM0I+/Xq93/16r1Q5tnyGk0GJUjuHm5iZefPFFfOhDH4JOp+PzIRJC5gAFaodUJpPBiRMnht5Hr9dDrVZPJBF/Z2cHp0+flu36Op1OtuHdQo6nRs1lnDYhxQf9MAwjS7BtHfB5G3S73Pbv8FlyOXz4f/wPrBUKggJHj8czNHAXchzNJ6h79dVX8fbbb+OP/uiPoFLRr3dCDgNqz3FI7c+R6adWq/FqWDoP5Gx4y7cPWCAQQLFYlG0ds2BxcXHk7MlxpK39vw8H3S63YTt8kj6PgF54doW97zW4oM7r9aJUKiGZTOJHP/qRpOskhEwPBWqHFN/k+sPSAkHOeZN8j6fkCGBmjRw98ADgCyGg0huvoKLevX0apNrhG/V9KaQX3n3G+4YGdfl8vvuG5ZVXXsHly5cFrZUQMptob/yQ4rvDlEwmYbVaZ/7IbpRyuQyj0ShLsMTneGpYuwqlUonFxUWUy+Wxhp7PCiEtOYT6xkoNVQD/MQwEC0DEuhuknVip4UuyPONwBasVtj4/EwWrsPFcfH6u+B5Hv1//fhRLxYE5hjs7O3C73d0q3aeeegrvec97qGUHIXOOArVDiu+Q8nq9DqPRCIZh5rK7v9fr7ebiCB0oztc4fcD2WlpaQiQSgUqlmtugWKVSybpraYUVJ1YKOLFy8PZpCIdCfatQwyH+W3xms5lX82i+tre3caf+TqzUBgd1e4tqcrkctra2ZJmtSwiZHDr6PKTUavXoO12TzWYRDAZlXI181Go1YrEYYrGYbIEEn+MpjUYDa5/dFoZhugFkq9USNDZolni9Xlnz70IIQcP0vrkQEgxL7cLKCtZWV1FyOMACyFutgluFmEwmSdfUbrdHvgHbXxh04cIFSddACJk82lE7pPjuqHE2NjZmvrP+NI06nuLaMXCzMrm+WUtLSz2Dt2u1GnQ63Uz2WRuEG3skpxWswG6149H8o6Jah0gp85u/ib8X0RpEyJslvkwm09Ad2f3NpC9evIh77rmHjj8JmWMUqB1S4xQJVCoVqNVq2RLGheLTu2zWXoA2NzcB7B57ORyOA8exmUwGFotlLgI1jUYDj8czsYHft3ZuRQCBiTwXH8P6n03LsF3jfj+7+Xwe6+vruO222+ReGiFEJnT0eUiNE8CUy+WZyWfhepdxSfxc77J1rPfcb1bz6rjcpP05Sj6fby5aeCwsLECj0fTsBsptloJXm80men6sHB/PsGruQTt4P/jBD3Dx4kXJ10IImQwK1A6pcZu/RiIReL1eiVcjHN/eZaVSSdYeauNaWFjoO9O00WggGAzC7XZDoVAgGAzCbDZDq9VCrVbDZrNNNY9NqVQiGAwimUzK1kB4EJ/PB6PRONHnHESK/DI5Pn/DdtQGpTuwLIvHHnsM//zP/4wXXngBly5dQiqVEh2IEkImg44+Dyk+DW8HKZVK0Gg0Ux10zrd3WT6fh8/nw9bW1szsrun1emQymb6zILe3t7ud/RUKxYFRRPl8HsFgcCq7bjabbeR4JDlxR6xerxdqtRqbm5t9gwmr1QqTyYR6vY56vS5pZSVHbPCv1WplKW4xm80DvzeG5cR1Oh2cP3/+wO02mw1OpxN+vx933HEHzGazZGslhEiDArVD6odXf9gd+Cw0MbtSqSAQCEx1ZqWQ0Tqbm5vw+/0TPabbz2QydXdQ+OZ1DRrdtb29PdF2KQqFovv5m8Q4sVG4Xm0qlQpLS0sAdr/G3NqsVmtPMMmtX8oAU+yxpcVi6fYzk9Kw3NNxRkbl83nk83m8/fbb+OlPf4pbbrkFH/3oR7u7uqVSCVeuXEEqlYJCoYDD4YDH44HH45GlWIIQchAFaofQw68+jH/703+LGmoAhs+mHCQajcLj8fQ9vpsEob3LYrHY1ILLpaUlxONx+Hw+KBQK0U1hK5VK95pys9vtfXf2ZkGr1ep+DrRaLTweD1qt1oGqx06ng0gkArvd3rehsNvtRrvdHtlnz2w2w2AwoNFoiA6y5ApihgWQe3uojaPT6eDChQt497vfjfe973146aWX8PTTTw+8Pxe0WSwW6HQ66HQ6HDt2jHblCJEYBWqH0BfDX0StVeu5jcvvEtLuoNFoTK0RLrfOUVWfe0Wj0Ym0ktiPe/HkKj6lIObomg+GYRAMBhGLxeD3+yUZtH5sfR2hcBjWQgEFqxXhUEhQ37Fh6vU6otEoFhYWBranMJvNBwI1s9mMdDoNhmEQCATQbDb7BtI6nQ4Mw0jWnkauyulhRUJS5WpmMhm88MILCI+Ya5rNZg8EvxsbG/j0pz8tyToIIbsoUDuEIoX+uyOD8r4GyefzUwl8OHxH6+y1sbEBu90OlmWh0+lkG3nEkWusUiaTke1zb7VaoVaru9fe2NiAz+dDNpsdO0A8tr7e08nfVihgdW13F1eqYA0YvlO1ubmJ5eVltNvt7jEplyfGsiyi0ShUKhWCwSAKhUL3PiqVCkqlUtJ2HLVabfSdxjAsGJOqVc3m5iauXr061mO54/NZLPAhZF7RT9MhFLT2nzIwzjieeDw+d0cZuVwO+Xx+Ijk0cu58bWxswGAwSHrNQCCASqVyICjZ3NyERqOB0+kc67qhcLhn3BIAaJpNhEbsygg1LABoNpvY2NhALBZDIpFAIpFAPp/vuU+r1UIkEkGhUECpVEKpVEIul5O8Z5pcFbPDPn6pdr77FcHwValU8I1vfIMaZxMiIQrUDqEvh74Mg7r3BX7ccTytVmvuAjVOMpmU/Z292LygUcxmc9/RVEJptVosLS0hGo0ObMtQKpW6VadCWQccRw66fVyzUOzAl16vl/yawwJ3KT43DMOIrjhOJBJ48MEHce7cOdHrIYRQoHYo3f/e+/Hg6oOwMTYA/WdTCpFIJGamEa4QjUajWzUol0wmM/YuFB/JZBJmsxmLi4tjH2253W5oNBpexQntdhuRSARLS0uCdiQLA4LJqkTBisFggF6vn3hvNzGk3g0Fhh9vShGoBYPBA7uQ4+h0Ojh79qzo6xBCKEft0Lr/vfej/kpdsirISqUCpVIp6lhkGiZRCGEwGEQn4w+rsI3FYlCr1VhcXES5XB4663EvrmBgnDy3eDwOq9UKpVI5sloSAMKhEO7753+Gal+woG00cGx9few8NY1GA7fbjWw2i1qtJlvulxyEztvlQ85ATepxYYlEgvLVCJEA/QQdYjabTbJrFYtF+P1+ya43KZMILMV2eNdqtUilUlhcXOz74h4IBNButxGPx3kHaVwjUzEvvIVCAYVCgdfX/cLKCup9enyp2u2x8tRUKhX8fn93J3CeAjTOOH3NxBD7fSj1sXKr1ZqZJtSEzDMK1A6pRqMxduXWINFoFG63W9JrykmtVk8kqVnsi9HCwgKA3R0IbpwUJxgMIhqNCnoRXVpaQrlcliRBvt1u855QoR8QTAnNU/P7/dDr9YjFYnN11LmfVFWYew1r+yGmJYhGo5FlCL0cnwNCjhoK1A6pM2fOSP4i1+l0UKvVeCfQ6/V6BIPBqc1v9Hg8E0k+l7KgYHt7G0ajEWazGQsLC4Ib0S4vLyMej0s6x5GblDDMOtYRHVDzMCh/bT+XywWXy4VYLCbLWKhJkyNIGfb9LCZQczgcYz92EIZhKFAjRAIUqB1CLMviF7/4hSzXLpfLvBL0XS4XVCoVIpEIKpUK7HY7lpaWugPJJ2FSuTFixg0FAoEDVXbZbBY6nU5wADhuPtoo7XYbdrt96H3CCOP/DQGVffUHVfVu/tooLpcL+Xxell2dw2RYOxgxs3nlaGVjMpkoUCNEAlRMcAglEom+o3SkEovFYLVa++ZL+f1+MAxzoIghl8v1rIkbXSRFR3zOOtZ7Jhl8bPNjuBk3S3b9Qba3t+FwOHgl3XP0ej2sVuvAYo9MJiMoJ9Dn88k6BspoNA79+Aoo4MS1eoH/GAaCBSBiBb4QAm7aV0jgcrm6AYdCoUCj0TiUAZpU+VnLy8tgWbZ7vUFVmWJ2UeV4U2MymSS/JiFHEQVqh9Brr70m6/U7nQ60Wm233xbXNNRisfAejJ7L5bpViVzSuhjrWO+ZDVpAAd9vfx9NNMduSyKEwWAQFKi53W5EIhHodDqYzWYoFAoUi8Vu0jzLslAoFLDZbCPbJWg0GklaKgwz6oXcCms3WDux0nv7TXvup1KpUK1WuxMDyHBKpZLXLqlerxdVcCFH0c289l8kZNbQ0echw7Ks7IEaAKRSKbAsi0gkglwuB5PJJPj4hHu8FA1dwwj3DHAHgAbbwLPMs6KvzYfQ/KB0Og21Wo2dnR2k02kkk0nUajX4fL7uTkQ8HucV0Ph8PtmrIkcdq4UQghq9X//9TZa5dh8UpPHH92dKTIW30+nk/QZLCNpRI0QaFKgdMrFYTHRncb5SqVS3+3q5XB7rGFOr1SKRSIhey6A5pjlWviPgvYTuSNRqtb7BHTfY3WKxoNVqjQyQDAaDLC+y+xWLRVgsloF/v4IVrGK1O6asX5NlvV4v+7D5WSL26FOj0cDlcvG6r5gdMTka8wIYmddICOGHjj4PmUnspnHq9ToCgYCoprparVaSwJI7etvPoXRAxagkq4LUaDTweDzdJOlOpyP5rMhyuQyj0QiLxTLyc+NyuWTNTeNUKhUAuwULg55v5do/g8jRAHaWia049nq9vL+2YsZVSfGzsT8/NIQQPrP8GdHXJYTQjtqhM+m2BtFoVNQIJamORwYdvd2ruVeSFyKz2QyNRgOdTodYLIZoNIpoNMr7eFKoSqWCTqcz9HMrJCeQD7fbjWAw2PcoOhgMIhgMdgM2obRa7ZEb1C1mR81kMiGdTvO+v5idSjFtPYBf5odyb5QKKGANa/hJ9ieirksI2UWB2iFzzz33TLwjupg+YlKtddDR2x2aO0Rfm2szYLFYZDsm6oc7Th40JN1sNkvaJ67VaiESiaBQKGBxcREejwfA7tFYJBJBJBIZu0p3YWHhSB17AuMHagqFAlqtVlDe4TR3K/vlhzbRxF8++5dTWhEhhwsdfR4ydrsdH/zgB/Hcc89N7DlTqRT8fv9Yuztiqz336nf0JraPk9lsBsMw3SNIu90uusJOqEgkcuDIcWFhgdeQdb6WlpZ6rsflDbrdbuj1etHHq+l0GsFgEFtbW6L6fR0FS0tLgtMJhOy+7Se2YfOg/NBIQf4jeUKOAgrUDqEPfvCDOHv27MSKCoDddhtqtVrwMcqgfmxiGI3Gbp82lmVhMplQLBaHfj4cDgcMBgMUCkVPcJdOp3uONrk2JBqNRvJ1DxOJROB0OmEymVAulyUPdgYdaYoJAPaq1WqIRCLwer1IJpNHYgbkuB/jOIUBOp1u7DcPYne1B+WHBq39d4IJIcJQoHYIqdVqXH/99Th79uzEnrNSqQxNNO/HaDRKtiu0uLiIUqmEarWKSqXSN/BQKpWwWq3dgAzYDSCy2Wz3Xz6KxSI0Gs2BXSi5bW9vY3t7G4uLi5JUynI8Hg9SqZRk1xtma2sLarUabrcbKpUKlUpF0qbHs2TcQC2RSMDn83UrgPkwmUxjNbm2WCyiv4dDCPX0MAR280P//a//e1HXJYTsokDtkJrGfM1IJCLoRV+v10Or1QpqFNsPwzAol8sjCyna7baggGyYRqOBeDwuODiVgtS7aalUqjv8fRI7Xc1msyfQNJvNsNvtyGQyh6rHmpjPpdDPw7iTSGw2m+iddy7dgKv6tDN23Ku9F6lwCi/XX8Ydd9why4gqQo4KCtQOqWkNQhdSYcm1tPB6vdja2hrr+dRqNbxer6gWIWL0yx+T07i5gKNEIhEcv3wZH3rySZhzORSsVoRDIVxYkX+qQ6lUQqlUgkKhmPgupZzEFE8UCgVeUyk4drsd5XJZ8POIrfjkcPmhNpsNjUZjd2cbFTz11FN48cUX8d73vhe33HILlpaWaP4nIQKNDNQYhvkmgI8DSLEse+zabX8D4D4AHQApAP8Xy7IHzmIYhvkYgP8KQAng6yzL/u21278E4I8BcAkwX2BZ9n+J/mhIlxwjYfgYp5/TuC0f7HY7Op3O1II0TiQSkfw4chC58g6Pra/jo2tr0Fx74bYVClhdWwOAiQRrwG7fsWq1imAw2B1LNq+4kWBiCMkdG2f3zu12Czpe5XO9SqVyYDewXC7jZz/7GX72s5/BZDLhPe95D2677baB1cyEkF582nP8I4CP7bvtP7Msu8Ky7O0A/ieAv9r/IIZhlAD+O4D/DcAtAH6PYZhb9tzlAZZlb7/2LwVpEsrlcqjX69DpdBN/bqEVZAaDQXDvN4Zh4Pf7US6XJ5rQP0wul4NWq5X1OQKBgGyBWigc7gZpHE2ziVA4LMvzDZLL5bpjySwWC4LB4FzuwNhsNtHHyFLtdg0ybpPcxcVFqFSq7uQEv98Pn893oPCmn3K5jDNnzuBb3/oWLly4MNbzE3LUjHzLxrLs8wzDXLfvtr2vFkYA/X4jvR/AWyzLXgEAhmH+Cbu7cBfHXSzh57XXXsNPf/pTALtJxk6nk9dgZ7E8Ho/gI0y73c47H8fhcECv1yOdTk9kbJIQtVpt6LFdv87tQobF63Q6Sacf7GcdEPAOun0SuEpdm802dw1zxUwKAHbnbwopsphUFW0gEECj0YDP50M0GhX1PfnKK6/g2LFjEq6OkMNp7Ia3DMN8mWGYKID70WdHDcASgL1nUrFrt3E+xzDMOsMw32QYhobCSej666/v7qaVy2VZfonvbYjKSaVS8Hq9gq7DdwduaWkJxWIR8Xh8ZvtwxeNxBAKBA7cP6ty+jnXe13Y6nbL2biv0mUYw7PZJyufzSCaTsFqtCAQCB77vZpHY3mRCf2bHaXzcbrfh9/uxtLQEs9k88v7Ly8tQKBRIJpOSpBtEIpFDVTxCiFzGDtRYlv0iy7IBAA8D+Fyfu/Q7r+B++3wVwPUAbgewCeDvBj0PwzCfZRjmFMMwp6Tq6XTY+Xy+nj/LcXTUarX65pYJHdc0KgFapVIhGAwiHo9LNq9TTtFo9ECwNqhzexj8jhX9fr/sCfbhUAiNfZV5TbUa4VBI1ucVolAoIBqNolaryX7MLJbYnzmhgVcmk+m2nOErHo8jFoshHo+jVCrB6/XC7/fDZrNBp9PBYrHA7XbD5/PB5/Oh3W5LvjM/bn4qIUeJFFWf3wFwEsBf77s9BmDvK5YfQAIAWJbtnmEwDPMP2M1z64tl2QcBPAgAx48fP/xdMiXAsix2dnZ6bjObzXA4HCgUCrwryYZptVpoNpsIBoNoNptIp9NotVqCKt1GVXt6vV6USqWJt78QK5FI9BxdDercPuj2vZRK5USS6rmCgVA4DGuh0K363PqN30DQYEA6nZ7oNIZhuJxGu92Oer0+k7syYnexjUYj1Go1yuUyr897s9mE0WgUFfjs/1nc+zvEZrONVVU6yix+7QiZNWMFagzD3Miy7JvX/vgJAK/3udsrAG5kGOZdAOIAPg3gX117vI9lWa7c6HcAUFbpNZ1OR/A74/32//JjWbbbAgEAXC4XDAJffPfnWH2S/ST8DX83iHI4HN0Xl0KhMHL3y+FwDN11MBqNSKfTU6teFaPdbqPRaEChUKDT6Qzs3M7NJR1maWlpYoHqhZWVgxWee3KQuO8b4JeBSK1WkzV3bphcLjeVPnZ8iJ3BuncH1ePxdHP0Bh37y111bLFYJHmDt9+sBP+EzDI+7TlOALgbgIthmBh2d87uZRjmJuy259gA8CfX7ruI3TYc97Is22IY5nMAnsJue45vsiz72rXL/ieGYW7H7lHoOwD+taQf1Rw7efIkLl++jKWlJdx88824+eabBR/zjHrny72wMgyDxcVFMAyDRCIxcBeAy7Hiju8KKODb+W/j4/h4NyF+byNZrVaLxcVFVKvVvi/iXM8npVLZHYm0n8vlmkgBhFxKpVI3iBjUuT2E4ceKWq127P5ychgUkE2qNUk/szqKSmygthfXQFqlUiEQCKBarU58moOYnnDD0I4aIaMxs/qLrp/jx4+zp06dmvYyZFEsFvH888/j0qVLPb+81Go1br31Vtxxxx28m0X+5Cc/6RnKzmfXQafTwePx9B3p8wAeGLgj9Hl8fuh19z63SqWCz+dDNpvtHtFotVq4XK4DOViDArh5olarodVqUS6Xx6r6XF5eHhmsajQaaDQaqNVqKJVKqNVqKBSK7sxShUKBVquFRqOBer0OjUYDvV4v2RQCi8WCUqk0lYDJ5XKhWCzOZHGJ0KpNofR6PdRqNTqdDtrttqw7U1qtFo1GQ5av8T333IMPfvCDkl+XkGlhGOY0y7LHpbwmTSaYEefPn8fp06cP3N5sNnHu3DmcO3cOGo0Gt956K+69996hzTAvXhTeAWVnZ6fnGNNkMnWPRsXkWDEMA5/Ph2aziUqlcqBarF6vd6slE4kE2u02AoHA1JvYSqHZbMLtdqNcLnc7t/OlUqmwtbUFk8kEq9UKpVIJhmHQbre7L8xckDIoUBmWA+h2u7tzUcWwWCyy9XYbxWg0Tu3YdZT9OaJSq9VqEzs2XFhYkO14We7PEyGHAQVqM4LPC2aj0cDZs2dx/vx5+Hw+XHfddbjxxhsRCAS6eW3Fr30N/+oLX+hJCC8tLwtaC3eMyQVZjpQD2fbB+Zh8cqz4Hl9Go9FutdmkBoRPQiKRGGvoud/vR7PZRDweH3tncViuYzqdhl6vFzWQ3WKxiOpnJ7a3XCwWm2qgOMxhqmaUs9p6HpsZEzJpFKjNiF//9V9Hs9nEmTNnRt630+kgHo8jHo/jxRdfhMPhwJ133onbL16E8d/8Gyiv5ZNwY4BesNux0ae/1ygsy2JzcxN34+6xcqyEyufzCAQC8Hq9c52ftl+lUoFGo+F9RKdWq5HNZkUFIAaDYWR+W61WQ6PRGHu+ppiB3v3yHtewO7KKb7DWbrdht9tlDdRcLpfgXTuDwTB3uVc6nQ4ulwsKhQLlcrmbb2q322WtOqZAjZDRxJUXEsno9Xr89m//Nux24b1/s9ksnnjiCdQ+//lukMbRNJs4/vjjota2ghWsYrW7g2aFFZ82fhofD35cdGPPvRYWFhCNRrGxsQG3282rCec8qFQqghoB+3w+0cGH2+3mldDebrcRj8e7zUyFEJNgLra3HEfO/LRAIIBMJiP4+1DsVIJJcbvd8Pv98Pv9aDQaiMViiEQiKBaLCAaD8Hq9yOVysh6xUqBGyGi0ozZDFAoFPvCBD+DkyZNjPd4yoHzenMuBYRhRycAHcqwqQKQSgcFggMvlQiwWE1XpplQqe/JV0uk0NBrNXOermUwmsCyLSqXC+3Mj9jiRI/S4dGNjA+9+97tRLBZ57yCJeQEXk/co1RqGWVhY6H4dzGYzOp0O7+PMeQk+9Hp939yzVqs1sZYnYlsREXIU0E/JjBGzizRsDJDb7R77usNUq1VEIhHo9XoEAoGxX6SWlpYODFhvNBqIRqNYWlqayoB5oVQqVXeHwmAwoFwuo1KpQKlUDi3+2IsLCsTa3t5GMBgU9Biu4jcYDPJ6ARVzvDcov/EP1/X4swcewF996Uv4swcewLH14WO28vm85N/bBoMBxWKx+8am2WwKytPKZDKwzsDorVFmIaCchTUQMutoR23GuN3usRO8w6EQVtfWoGn+8kipcW0MkNyBTqVSQaVSgVarhcfjQavVwubm5ugHYjcPaO87+ANJ5vEQT9cYUAAAIABJREFU7jLeNXKSwTRoNBq43W4wDINkMtl3N6zdbh8IQvvxeDySjYpaXFwU/D3EtWCIRHZ3Sp1OJ4Ddr20ul+sGLjabDWazWdROZ7/ecr+/rsB/W2tA19zdJeNyLAEcbMS7R6vVglarlazXl9VqPfC9uz9QW1xcRCaTGXj0arVa0W63YbPZkE6n0Ww24fF4oNFokEqlZqLacRZaM1GgRshoFKjNGIfDgV/91V8d6/hz0BigCysrUG9uTqSLe71e776A63Q6uN1uNJvNgQHW/iPZgUnmFWClsoJgMCj6mFUstVoNr9eLZrOJZDLJK7jK5/PdVhuD7i92CoPL5YJGo4FKpcLOzo6gYECpVPbskFWr1Z4/KxQK2Gw21Go15PN5WCwWUWvljtH3BuR/F/5lkMbRNJsIhcMDAzVu19Bms+Hq1aui1gTsBsv7g7R0Oo3la5XTnU4H1WoViUSi2+iXmxywvb3d/ZxxR957j6C5nwGlUjkTbzpmIVAzGo3TXgIhM48CtRkUDAahVCrHeuHuOwYIu8c3kUgEXq8X29vbaDabfR4trZ2dnW7QZjQauztne18ggsFgT4XnsCTzFawgEonA4XDw3qWSikKh6A6739raErybxLIsyuUyyuUyAoEAtra2er4GgUCgW2k3LpVKNfaEgH4Byl6dTqen+k+K3LD9eY/uwpf63s865Ou8vb3dzR3zeDzdnc1xDSqO6VeFzM103dnZQSqVgslk6v7csiw7MBBqt9tQq9Vjr1EqszCeTWzAT8hRQIHajOEGnLvdblnecSeTyakcN3BHo16vF6lUCp1Op2/iPJ8k82w2C41GA7/fL0ni/SBqtRoLCwtgWRapVEqyY8loNNqzdoVCgVwuB5PJNHb/Lb1eLypA4ZtDx0mn0z3NeDudDhiG6e6Q7v2zRqNBs9nsft/tDWC4gIZlWZQdDpj7BKuDci8NBkPP54s76jUajXA6nWi3290cwXq9PrLAwmg08j6u5+ydPlAul7G4uIh8Pj9yx5cr2BDbS04MOfuj8UWBGiGjUaA2Y6rVKh599FHZru/3+6daRbm1tdU9MjIajQfaUPAdYM61EwgEAojH45IfhRqNRmi1WtkCwc1rR9GNRgNarRYbGxsol8u8xkb14/F4RPWeGyd4L5fLsFgsI4/T3W430un0yOv96O67++ZY/mx1FcFg8MDYK6fT2beggXtTsN/i4iLq9frA0U5Op1N0o9pEIgGz2TxyZ1Or1eLntZ+L7iUnxiyM3qJAjZDRqOpzxly+fFnW68/Cu+hEIoHl5eW+uxchhKBG77HQsOa63EQDm80m2fqMRiOUSqXoo8hh2u02IpEIOp1OT+AciUTgcrkEX29zc3NilbHLy8uwWCxwu91IJBIwGAzwer0IBoMIBAI9lcuBQIBXkAbsHtuvra4ib7WCBZC3WrG2uopf3HBD9/Oi0WgA7AaWQqtOE4nE0EBMqsa5pVJp5H2USqVkveTGJdegdb4MBgO0Wu1U10DIPKAdtRnS6XT6zvuU0izkxgAYuAPWL8l81HFQNpuFWq2W5CjUZDJBrVbL2o2dw+Wq7f1csCzb3WUT8kLaarVE7SoKyVdqNps9Qc3+wgNgd4ePz3SE/QblWAK7x60LCwtIJpMHchv50uv1MBgMyGaz8Pl83TcL3HUnSapecuOadsWlx+OZ6vMTMi8oUJsRLMvipz/9qexzLovFoujmt1IY9iIhdIA5sBs8xGIxUVWhdrsdjUYDarVals+R2+2GTqcDwzAoFAoDj6CLxWL3eJgvo9HIaydnECHHYHx2nrjvY6m79HNBmtBcMk4ul4NWq0UwGEStVoPH4+k2V56k7e1t3sf8ctHpdFMddUWBGiH80NHnjIjH43juuedkf558Pg+/3y/784wi17v5SCQCu90+VuNgvV6PSqWCVCqFxcVFydem1+sRjUYRiUSGVqy6XC4olcpuSwg+SqUSTCbT2GvjG+RxsyD5GucYdxSVSiUqv6per6NarSKdTiOVSoFl2ankbQo95pfatHfXhXx/E3KUUaA2IzqdzsR2ufL5vOAqP6nJ+bFub2+jXq9jaWlJ0OP2Bk/xeFxwZ/9R+AanXEC3sbHBa9fB5XJheXl57CaqVquV92OFznZtNBqSB+WlUmnsawaDwe7O6bT1m6G7itWJVX1O83eA3W7Hr/zKr0zt+QmZJ3T0OSP4JlxLoVQqjT39QCpyB6WNRqMbbO2vFuxnf6sHYLdCVa/XSzZPcpzj2EqlApPJNHQXS6/XC8rX8vv9YBgGCoWi2xqDb086lUolqAdfMpnE0tKSZK1N9Ho90uk0XC4X75mkezEMM5H8Q764Y/5xq33FmOaczY985CM055MQnihQm7J6vY6NjQ08//zzE33eo/JLMhKJwO12o1qtDq34s9lsB/J1Go2GpC+gfAK1/TNPK5UKrFYrjEbjwPULCXr1er2ogguTySQ4cBW6CzeM0+lELBaDwWCQ7JrTZjQaUSgUYLfbJxpETut3gEajwbFjx6by3NNUrVbxzDPP4MqVK/B4PPid3/kdyXM4yeFEgdqUtFotPP/883jxxRenMg7pqARqwO5upV6v76ny22/QMVAikYBOp5NkNiOfgIVhmAPJ+oVCATabjVcl6KgiiJ2dHRgMBsG5ZpxxWoBUKhUEAgGwLIt0Oi2qLQT3fTvuZA2WZaFUKqFSqabengLYfYOQz+en8tzTKigymUxTrzidJJZlce7cOTz99NPdNzmFQgHr6+u48847p7w6Mg8oUJuCTqeDb3/721NtPDvNWZmAdD2r+KrVaqjVagN3yAb1l2s2m/D5fJLMSOXzwhiLxfoeFebzeSwsLCCdTh/42iWTSZhMJlSrVbAs253pqlarDwQ0LMuiWq1Cr9fD7XYjk8kIesEe54h+e3u7p8msXq+H2WzuCbp0Oh2USuXIxH5uV3Fzc3PkkXA/zWaz2/hWp9OhVqtNNO1gv2mOcZrWYHilUokrV66g0Wig2Wx28wWdTidcLheMRuOhCeRSqRROnjzZ9/fHpUuXKFAjvFCgNgXPPvvsVIM0YPwdCSkolcqJzunca2NjA16vF4VCoecIb9iuRjweh8FgEN3KgO+R4aBdP641xf5f+s1ms+frGYlEoFKp4HA4BvYG4wJXp9M5sFP/flL1GuOeG9hN7t/ba81oNEKlUkGlUqHVavUE0CqVqqcJsd1uFxyoFQqFnq+jWq0ee67uuAKBADqdDgqFArRarai2KmLI2dB5GJZl8dBDDw38e51OB7fbjY997GOyVF/LjWVZvPPOOzh9+jQuXrw48I0QN41ETLU2ORooUJuwfD6Pl156adrLmOqxj1qtnvhOwrH1dYTCYVgLBRSsVrxw772IfuhDSKVSI4OwdrvdHSgvRiaT6Rto7Wc2mwcGspFIZOgRLqfVaiGZTI4MxIxGI+9ATcou8larFXq9/sDngtsxa7VaCAQCPW9oPB5PT2+5aDQquKhg/9fZ6/VO9E2T0WhEMpns7iKNc/wslVarxXu8l5RGpV3s7OwgGo3iW9/6Fn73d38XN99884RWJs7Ozg7OnTuH06dP8/6efPvtt3HbbbfJvDIy7yhQm7CXXnpp6seOwPSOPYDdILHfsZxcjq2v98yQtBUK+M1HH8VavQ7dxz+OVqs1crdMql21WCw2Mmlcr9cP3XEsFou88+ZGNXIVcuzZb+dnfwAcDoX6Thaw2WzQ6XTd1ho7OzsDP0a73d53Xf3yCBuNBjQazdjtNia5s8wwDHQ6neh5olKaRjI73/zYVquF733ve7jnnnvwgQ98YOaOQ1mWRSqVwsbGBiKRCC5fvix4RN+VK1coUCMjUaA2YdM8cuQoFIqpzvxkWXaiOxmhcLhn0DcAaJpNhMJh/NeVFV4vVkJ21VwuF0qlUt9dy06nA6VSOTTpf1Qj0kqlwrvlxebm5tAjS74vfv128foFwKtru0PFL6ysdIfOA7vHbNzxMhdYqVQqmEwm6HQ6aDSa7visSqWCcrl84Di6X3A7zhQHjtVqFTziSoxAICBJrqOUIpEIHA4H9Hq9ZC1URhFayPTMM8+gWq3innvumYlgrVKp4Mc//jEuXrwo+g3vtI6fyXw5OqV/M+IjH/nI1Euy+VbuabVa3h3+uYHcfr+f12Mm2XDUOmDnhrudb+5YLBYb+rGpVCr4/X5kMpmhQ+K5I9B+PB4PrxYN8XgcFotl9KIxuFACEFfUMSwAttvtiEQi2NrawtbWVs/Xu9FodN8s5PN5bG1tIRKJIBqNIplM9j0OtFqtA3O5EonEWF3u+X7+pMCNDZtF2WwW8Xh8Yvlg4wRbL7300sRbGA0SDodx5swZSU4lZuGNO5l9FKhNmMViwR/8wR+M1eZAKsOCCGB3RycYDIJlWZRK/z97bxrryHmeCz5VJIssksWdh3sdS/ISy+2OnSiO4yWW0rKdcdROYNi6nrl3xgMEEwyCAE5+TSYBMsYMMggw90JzcTM/bjK4sRx4nNhCHKsl3yhKO5K8xJIlQzqR5EVqqQ/Jw+0c7vtWNT9Of2UuVcXauHQ3H0Fo6TQPWSwW63u+933e52mDpmkEg0HJ1V0OgiAgl8shn8+j3W7D4/EglUqB53nZGKFyuQy/fzOZhk2F11H6uRIEQVA8d263Gz6fT/IoI0avSiBRV7NwOBxotVqadUtKn8UiqtWqIjHUslD4fD5ZTZwaAV7leq+3qrLqWjk+PtZFNBiG2Vg1jaZpJBKJnSVqBJvSyxmtij311FP47ne/a/HR6EO5XMaLL75o2fPdTjZJexjH/irZAhKJBD73uc9ZKs7WA6VFlKZp8DwPu92ObDYrVUEEQUC9Xkc2m0W9XkcwGMTh4SEODg6kG81iVarb7eLk5ET6HTlsiqhdvXQJo4V24sjhwNVL+jMV8/m8LFnzer1LbYxn+8/iITyEL+ALeAgP4QhH0t+JojhHkvx+P8bjsa5dutYhAOC8iid3vWnRqCmRUzUC3O/3kclkZDckPM/rbr1r0XWenZ1pvqbi8fhGqhkURSEWixlqzW4apI28bphpX/7TP/0Tnn32WQuPRjum0ym+8Y1vWOo/tw+m30ML9kRtS4jH41ubZlrc2Xu9XhweHsLlciGbza5sBdbrdRwfH0sRVOFwWNXeQmmhL5VKlrrWK+Hlixdx5fJlNPx+iAAafj+uXL4sK3pfBVEUl1zxeZ5fiuM6whEerj2MJs7PdRNNXMEViawlEom5CoaREPlOp4N4PK7psb1eD36/HzzPw+v1IhwOa/Kromla0re53W6k02nE43GEQiE8/fGPKxLgTqcDiqJkp3v1Thm6XC5NpJS0VLWEjW/KZDaTyayc0N0l9Hq9tYe1m9WZ/cM//AN+8pOfWHQ02vHtb3/b8s9Sa1V8j9sb+2GCLUEUxY2Jd2fh9XrRvNGaSiQSGI1GirogLRAEYeUiqqTJG41GSKfTpiKNtOLlixcNETM5FAoF3HHHHRiNRhgOh7Kj+FdxFWPMV2zGGOMqruIiLi4tVkbtSvQEa1cqFfA8j06ng06nA5/Pt3ICMZlMSp/P7DBFJBJB4d57cWUyUZ36XKxaEZNZPYhEIpqvkXq9vvKacrlcGzNc3oX0Az1oNBqGhzO0woqBgG9961t4+9vfvrHhgmvXrq1FI+fxeCx/zj1uPeyJ2pZQKBS24ogeCAQQCoVQKpU2NnWppsPY9hTX4eEharWabtPR6XSqSrRJJU3u5xzHLS2ERokDGVzQWiEqFApSbqiW16RpGplMZinaimVZ5HI5NO65B8X77oPH44EoiqAoChlRRLfblW3j+nw+Xe1djuN0b2jy+byqPxjLspba0ySTSdhsNjQajaXraFtmtmZAhjM2HRKvB5VKBa+++ire9a53yf498RFstVpotVoYDoc4PDycGzoZj8f4yU9+gjfffBOCIOCd73wn3v72ty891yuvvIIXXnhhLZFbejZae9y+2F8lW8Krr766ldelaXrjFgGLVRWappFMJjEej6VJym0saB6PB8fHx7DZbOB5HsViUbNuqVQqqXrB+eGXJWsBnBPlxfcbiUQM+WsNBgMMBgO4XC4Eg0EwDIPxeKw4lDCZTJBMJjW/ltK10mg0YLfbzytrhcJSVVVpoT89PcUdd9yByWSCwWCAdrutSpqCwaCha0Otfed2uy0LPyfpD6QiGo1GpUxWl8u19QQSozg+Pl5btdsqwvP000/j7rvvBkVR6Ha7uHbtGvL5PE5OTlAqlZZ0jb/6q7+Kw8NDTCYTPPvss/j2t789V/F88cUX8bGPfQy/8iu/Ih3nt771LXznO99BKBSy5JgXsUueenvsLvZEbUswa5xqFNsIgCYLrc/nQzAYRLlcnlsA5IjLJkDI0XQ6RTabBcuymv3dRqORasrAJVzCFVyZa3864MCD4Qdl36tZX7vBYLCkn3G5XAgEApJHGXlf2Wx2ZXj7KpD3oNQiUyKwoihiOp3OnWOGYeD1eiXN3HQ6lXRxRiYzw+Gw6oSlVVVcm80mbTo6nQ7q9fpWc0OtRrFY1BUxphVGrzu/3w+GYaSIMYqi8P3vfx+lUgmvvPLKSvlAo9HA888/j+9973uKRP0f//Ef8dprr6HZbKLT6cz5AMbjccsnhbeZTLHHzYM9UdsSFrUrRzjCVVxFE0344cclXMJFWKOpIvD5fBsPQ6dpGhzHgeM4FItF2de3YiFgWVa39mmRMPX7feRyOcTjcfR6vZXnSo0MkM9u8TN9H/s+lEqlJZK3jrSKwWAwt7AkEgmp1WKz2XD9+nXLX5NAjXh3Oh34/X7p/I1GI9RqtbmpWbvdjkAgoCseCjhvQ56enqpWRq0iU6lUau4zjEajYFkWrVZrKxsiqzGdTqXKoJWtYiNELRAIYDQaLX3nhsOh5vzZo6MjHB0drXzcm2++KfvzVSkfRqD3+t7j9sSeqG0JsyTgCEdz1RcyIQjAUrIWCAQ2TtQEQVg5KdXpdDTlVyqBoiipsjEYDFa6fZOWndLuuFQqIZFIIBAILJm1zqLZbKq2hy7e+GcWg8EAk8lkabFa96QdsBz2vq7WFomKUkKtVgPP86pEdzKZ6FrEKIpCKpVa+X4YhkEkErFkkGeRjM0SwNlW9GQyQbvd3vh3Tw+UosDMfjfloHdwJhgMYjgcynYhNpmwsg6N2uuvv46XXnppHyO1hyr29hxbAHECJ1CbELQS24yNWgUzxo/pdBrdbheFQgG1Wg3pdHrJQmMWLMuqtjBomkav10M2m4XNZkMmk1F8rJ7WxWyFaPGmXyqVNi4szufzSCQSlj+vnJcZx3Fz6RVWL3qZTEYT6RyNRpILvxlrmIODA1XiRVrRx8fHODk5kTzKDg8Pd27Sj0SBBZpNUPhZFNiFG9WnYrGoaJhsBHqqc6FQCIPBQFEqsg7ypIR1pak8/vjjt1TLfA/rsSdqW8Cis7XahKCV2OWbQbFYNJzWsHgTz+fzmEwmODw8lCWAq5IZMpmMVO0h7dBgMCjrWdZoNJBOpzUd52xkUbfbnSOAo9FoLaRpFYrFIpLJpCmxdCaTwZ133gme58Hz/JKxLs/z8Hg8UkxUPp+3dIG12Wya218EhUJBNTliFYzEwPX7fdRqNfR6PWQymZ3x0FKLAiM4OTmx7Hi16nPD4TB6vZ5uScO6sK7jGI/H+NrXvraPk9pDEXuitgW89tprc//vh7ybutLPjSAcDu/0jUAQBEMu3QcHB7Iat9FohOPjY3g8niUipXYe/H6/7DBBvV6XWqKLFSOtVbXZSkK9Xl9qD26yOjCLQqEg+Y8ZQb1ex3g8Rjablf6dRaVSmatg2mw2SwXqyWTSkF9ZNps1nPdp5PXq9Tr8fr802FGv15FIJBCLxQwdg1VYlYULnLcraZo2PYjhcDg0nbtEIoFWq7VUfXM4HEin04jFYmBZdi3aTiV0Op21Vb1PT0/xzW9+cy3PvcfNjz1R2wIWb8yXcAkOzGuUHHDgEvRHHClh19otciiXy7qrSqu0Xe12W/LVikajANQ9yzwej+rNv1gsot1ug+d5qXWmparm8Xjm0gtisdiSxmmVtm6dEEUR+XxescXFcRwCgYBUVQyHw0gmk0gkEuh2u3MLOM/ziMfjCAQC4Hl+abGNRCKWitPNbECMxpgZ/awW5QfFYhHlchnhcBjpdNqwBODC0RE+/9BD+JMvfAGff+ghqW2pBVqzcKvVqqkqJKDtPsTzPEqlkuzn6vV6kc/nUS6X0e/3Nzq0IQiC5iQQI3jxxRfx0ksvre3597h5sR8m2AI+8YlPzBneKk0IWjlIcDMYbw6HQxSLRWQyGdRqtZUeQz6fT7MonJzrdDq9FPdEEI/HNTmyC4KAbDYLjuPg9XpRLBZXtnPC4fDc+yFB97M2Ab1eb2uecgTZbBZ33nmnVD04PT2Fz+eTWpfA+TmkKGqu8kgISCAQmKuoyS2k5XJ57tyZhRl9Yy6X033Ozdg0KOmcSIXR4/EgHA6rDrEsgmjMSPuSaMwAaErjuHrp0tzvA8pZuPl83tQQiiiKODw8hCAIshYxk8lE1edx2wbZ+XwemUxmbf54jz/+ONLpNMLh8Fqef4+bE3uitgUwDIMPf/jD+Lu/+zvpZ3ITglZBze9rF5HL5UDTNNLpNKbTqeJibmSKVW2B0VuZabfbaLfbyGQyKJfLqgv4YnuUvCeWZREOh6Xj8vl8WyfV0+l0jsw2Go25VmW/3186V0S/4/P5NFU5HA4HisUi0um0pNsyCq3tKCXvOD0+fmargavaft1uF91uFw6HAzzPo9ForLzG1TRmWogaeYxaFNgs8vk8ksmkdN4FQcB4PNakE2w2m6rTvmrgeX4nBqKsMkuWw3g8xve+9z1cvnx5ba+xx82HPVHbEjYlkOV5/qZ0RxcEQSIvLMsiEolgPB5LRIhlWdnqVzKZRKVSQSgUkoYTTk9PV55vM7vkXC6HSCSiqDGLRCKKVhOL7ZtNhNSvwmIlc9FOQU5fRhZQrQspx3Go1WrI5/OgaRqpVEqatq3X65q1R3psTTKZDKrVKjweD1wuF6bTKfr9vq7v4nQ6NdxuS6fTmivARPMHnF/Tk8lEsRKsRWO2CnqzcOW+e263e61G3t1u13LzXSPodDprMb8lWNd06R43L/ZEbUt45ZVXNvZa2xKpW4V+v49utwtBEOB2uxGJROaI3CzsdvvSosYwjERY5c4FwzCmjSfPzs6QTqdxcHCwtKCumhAURRGZTAalUslwtcEqOJ1OQ+eCVIq0/u7sgisIwhyBoSgKHMchGAyurAQvTiLKxXqRSsxoNJIqVrPQMsGZTCYBqJscr0Kr1TL0XSSkKBwOg2VZnJyczD1P0+9HQOa4lLRn64KZFrQWbMJrUCt6vR5oml7LMMObb76JyWSyzwHdQ8J+mGALqNVqG2tFZrNZJBIJw9YX2wZFUeB5HvV6HY1GQ/I3U2phyrWWRqMRstks/H6/rBg4Ho9bUuGU09TZ7faVO+9ut4tcLodEIrF1ombEgsFms2E0GsHr9Wqaokyn06qTsqIoot1uryQ1Xq8XlUoFgiAgGAxKhCyVSuHw8BCZTAaZTAbZbBaFQkGxtd3v91eStWq1ikKhYCqb0WzbrlqtIp/Pw+v1gud5ySn/6qVLGC2QGCWN2bqQTqcNTcLqwTqSAYyi1WoZmlLXgm63iyeeeGIn2rx77Ab2lH0LWPRRWzeKxSJisZilk3abQCgUAkVRukitmp6HtKze+ta3olgsotvtwu/3W+bOT+wWEokEJpOJZB+g5fntdvtOONcb2cV7PB4pBF7LhkBrFWI0GuHg4ACiKC55ANI0DZ/Ph06nI51foh1Sai+qkUOn06lI1r1eryWZjFYtvEQbSSrFWZ8PV6BdY2Y1WJaFKIprs/8JBALw+Xw7ZS9EBj7Wheeffx7tdhuf/exn1/Yae9w82BO1DUMUxa2MYFcqFdhsNt3xLdsAwzCIx+O6q45aNTLdblcKVZfLDzQDURTR6/VkyYUa4vH4WuKcNgGSbwlANRECOCffWiZrAUji9Gg0ing8DpvNhmKxCEEQkEwmdZ2vVZOdauTR7XZbQtSs1h6RSjEAlH7t1/D1Bx5AtVo1VfUzgn6/j5OTE1Utphm43e6dGoaKRCIb0ZHdCnmxe1iDPVHbMK5fv76VyokoiohEIisns5Qy/zYFn88HmqYN3ZgDgcBKouZyuVCtVufE2laiXC7DbrcjmUxqniRc57i/XhjRUM0mEYxGI8UF2+VyGXp+l8slnR+Xy4VEIqE7ZcPv96t+HmqtbytkA0Q7uS7Mnu90Oo1+v79x4f1iIoVZxONxOByOndlc0jSNTCaD4+PjjbyeUTPmPW497InahrHJIYJFEKNWEhK9uHCZ9WOyAhzHGQ7M1tK2C4fDlgRyK0EURUSjUZTLZTAMs3LnbbVLv1kYIVKzJIekN8ghEokYqhrOitQHgwHefPNNeL1eZDIZFAoF0wu5y+VSlQVYMYzjcDg2pjki5zgWi8Fms2muYJqFlcL6C0dH+Ph/+k/wVKtb2TAu4ghHeJp6GtXj6lp8LuXwoQ99aK3Pv8fNgz1R2yAEQcCPf/zjtT3/EY5UTXOJEB84j15aJGpm/ZisgJnJMS0tzFqthmAwCJfLBYfDsZaqmt1ux2AwwOHh4crddzKZ3JlqGqCflDAMs1TdkiMkZgi4HBHrdDrodDpgGGblOaQoSrUCFwwGVY13rRg02YbtCqme+3w+BAIBFAoFU2Rx1f3F7O86HA7E43Hc+f3v40OPPQb7jU3ONjaMi8d+BVcwnp7fG5to4grOj2ddZO2d73ynYkrIHrcf9lOfG0Qul1ubfoTcTEiQO7mZHEE+SqZSqcDr9c79zAo/JrMwStR8Pp8motbv91Gv11EsFucIwBGO8BAewhfwBTyEhxTPmxaQxfDk5GSlZuvs7GwunH3b0FudIrFcs5CrIgaDQcOVKbVrYjQsxIN4AAAgAElEQVQaIZ/Pq04ExuNx1YnEVZXYm10r1Gq1kM1mYbPZwPM8OI7T/Rxa7i9Kn5OW37XZbBLhfs9XvyqRNILFkPhN4iquYoz5DewYY1zFeo6Hpmncf//9a3nuPW5O7InaBvH888+v7bmN3ExCodDc/2vN/FOC3++fsw0wAqPtk0AgoPt3yMKil+SuAtHJTSaTlXYX/X4fuVwOwWBw6+HcgP6KGjmHNpsNNE2D4zjZ/EwtrvVKyOfzS9fqLERRVP381apZDodDdXqPZVlLWpbbjj4Czq1rstksBoOBbt2dlvuL0nnW8rscx0nV513YMM6C3Be0/tws3ve+96le77cLBEHAiy++uPWkll3AnqhtCNVqda36NCM3k8WWjlk/Jp/Ph2w2C1EUwfM8wuGw7gVhMBiA53kkk0mEQiFEIhGkUinwPA+3241gMIh0Or1kfqnXw8lms0nGtFbvmBuNBnieB8/zoGlak6FqvV5HuVxGOp3WFFy9LuglyqPRCMlkEqlUCoIgoN1uL7WT/X6/KY8tQRBWxvYokQSXy6Xa1kwkEqq2D7figjkej3WH0a+6v4TDYcXPWMu9abbybHbDaDX8kH9dpZ+bxfve9761PO/Nhqeffhrf+MY38NBDD+HJJ5/c9uFsFXuitiF85zvfWWtCgJGbCQncJnj54kVcuXwZDb8fIoCG348rly9r0oVwHCeJmMlEZbVa1V1dI7FChUIBtVoNZ2dnODk5QTableKFaJqeW1w5jtNdsZlth61jxzwYDJDNZpHL5eDxeDRrlPL5PIbDIXie34k4qVUol8srxeqLLXYjEEURDodDsU2p9PNYLKbazl3VLrfKxHXdrv16odflf9X9xePxKEdcrfjdxYnYXTDwncVHqY+Cwfx9zAEHLsH64wmFQoZMp281vPbaa3jmmWcAnH/3NxW5uKvYDxNsALVaDUdHxjVPWnAJl84FrzOVIS03k2AwOGcXojfzjyAQCMiWqFutli7D0Ol0imQyqbj4+3y+pb8LBoOmyuN++GVJmVU75rOzM9X3tIjJZIJsNguv14tAILBRf7VqtQqKouB0OuFyueByucAwDIrFomrlKZvNnpuvLlTT4vG4ZVO24/FY9jUA5dai2kRtMpnEYDBAJpOBKIooFApzFUWl11qFZDIJmqZB0zREUcR0OgVFUVtPnZiFXuK46v6SzWaleLdFLze532XA4MHQg0g4E6hWq3MkT29I/Lrg8XjOK/hIQ8gLhgcp9EBvpfNWRDabxde+9rW5n93uKQ17orZmlMtlfPnLX15LJtwsyE1D783k9PTUtBFuPB5Xnbrz+/3weDyaq15Kiy7DMKAoau5La2Sa8ODgYO53jJJcNSy2LwuFgu6Fn0w2JhIJtNttS0xXtUAURQwGgznLCo/HA6/Xq9qCXNz1UhRleRqG0uvLXTOrgrMnkwlqtRpqtRqA880GwzCoVCqGCbLL5ZIl5NtsZ1sBLfeXxanyRqOB0Wgk+7uf8n0KqVoKRci3pY1uGLXA7/fDbrdLHY7JZCJtVg8PD1GpVDCZTOZyYS/e+Gfd2KWYrG1gMpngK1/5ytKm8PT0FMViETRN74SWd9PYE7U1olQq4Ytf/OLaM/AIjNxMer2e4coBcC62XlXNIqQoFAqB4zhMJhP0ej0MBgOMRqMlkqi0GIdCoaWF1+Px6KqmOZ3OJVPcVYtQJBKBx+PRbHR5cHAgS6qy2awhc9tisfizuKAtObR3u13Y7XZVb7jF9iPJ2dSCZDKJ4XC40lOu3W4jHA4vPU5OVqDWOo5EIkutukajgWg0CrvdjlAoBJqmJRJHckwX3yMJ5iaVM7vdLvueN+FkrwcURSGRSIBhGHS7XU2JAlrvL6QiO/ueF3837o6j1FpfBJMa5CxliF3RpsxslaDXNNjIYMguo9/vy66XpVIJf/EXf4EPfvCDe6K2h3UYDAb46le/ujGSZgZmDFf9fr/mzLvZ6gVw3sbs9/uIRCIAzhc9coOXSxhYrEoaIT2xWExaSOPxODweD/r9PoL9IB7gHkA2m4XT6UQwGATDMJJOTsvUnsfjQTgcRj6fV7RAKJfLYFlWt+aCxAUdHByg3+9vZRKq2WwinU4rVprK5TL8fj+azSZomlYV8c+CYRgUCgUEAgHJkFmtTaxlOMPj8ag+h9LiRirMb7zxhvQ8oihK1U25Y9HyWY7HY00GyJsA8RCcJSVOpxPhcBh2ux3j8dhUBVcPQd8G5Fr4Svo6q8GyrFS57Xa70j2PpmlMJhNMJhMMBgNpAyAIAqbTqfTfFEWB4ziUSiX84Ac/wCuvvII/+IM/WGkDdLOA4zgcHh7i+vXrS39nt9tx3333bf6gdgB7orYmPPXUUysn1XYF4XBYt78bwzCIxWKmzFr9fj9arZbmfEBS1ZlMJgiHw4Yc1/P5PBKJhGR2y/O89Dz1eh0ejwe9Xm+JfKrtdIk2hww9AMoaIJIxanQhq1QqSKfTWxtZz+fzSKVSS+1mn88nfZ7AeYVCK4EPh8MoFotoNBqSZ1kgEADHcSgWi3OtbpqmNeW5hkIhxWuaEEMlzFZ41b4XNE3rItyBQACDwQBer1ex8rZOxGIxOBwOlMvlpZb0cDhcOieEFHAcJ1UnZ8kDqSKOx2MMh0MMh0PQNA2KopBOp2Gz2SAIAmq12sbzRwHlOLxNh7uHw2E4nU40m010u13Va+bk5ASvvvqqrucvFou46667zB7mzkCJdLpcrptiwGod2BO1NUAUxa1GRemB3t2vzWZDKpVCqVQy7aivVyDa7XaRTqfR7XbR6/UM6eoEQZAqPTzPL5FEpQVF6QZBYowWz6FaBa5UKpnWBW4TtVptLnYpk8mgVCrNDaXU63Wk02mMRqOV1Qq5CURC2kjyADn/drsdb7755tLjZ1ufoVBIVV/m8/ksCQ8XBGFl/NQsyHkg52lT10Amk0Gr1ZrTiGp5XVEU0Wq1dGUTC4KAk5OTpe92IpGAx+PBeDyGIAhrT+NQi8N77Zd+aa2vvQiv17vWlqreCV49INVkj8ezscllJT03y7IQBGHnJqg3gT1RWwOOj483Jvw2g0QioVswnUqlLKsEaMnmXMQ389/EU7anUJvWTE1fEdG3VrJYKBRw5513YjqdYjAY4OzsDAcHB8jn87L6KLXhkdFopNpCXIV12rxoQb/fl8iZUlX14OBA+nk0GoUoirLkaFW802g0mqv0JJPJlcfX6XTA8zyKxaJsq9HKiorb7TY0MLGJxSYcDkMURdnPZ11VJafTKVWXZ1EsFhEMBsFx3EYi05Ti8D7+9NP48S/8wtpfn4DjOEs2BWqwwv7m9ddfx2uvvQa32y2lt9RqNTQaDUwmE/h8Pjz44INIpVIAfjaAsQ6fQaWNAfFj1CJ9uNWwJ2oWolar4a/+6q80tWa2jUgkgrOzM8VFn+M4OJ1OMAwDu90OiqLgcDjw+uuvW/L6NE3rbg1blbmXTCalG5BWjMdjTKdTaWdM0zTK5bJh0rTuKeB1I5fLqbZwZ88tIWLJZBLj8XiOmKVSKV2ElWjZFmOdZiuYo9EIgiBgMpkgk8lgNBrNVZO63S58Pp+uSpESjE7pBQKBOb2m1eB5HrlcTvH6XJdW7uDgYO41KYqCx+OR2tj1el1TBq5ZKKUYeKrVjVk9RKNRqfq/Tvz0pz/FhQsX4Ha7DW8AisUinnvuOcW/b7VaePjhh/Gbv/mbeNe73oU33ngDX/nKV/COd7wDDzzwgCVkETi/byhV4D/+8Y/fliQN2BM1S6EkON41+P1+SVAsJ/gmi9iiDiqdTlt2DKlUSvfOWi1BQAtRs9vtSCaThiuCs4ubWaJVqVQMt762XVEjqNfrS2algLItBqmMkaktOa2UFtjtdjidzrlBHfLZpFIpyRB5tsUWDAallIh+vw+GYdDpdEx/jkYX/XVFStntdhwcHKy8xtdVUWu322BZFul0Wqo8j8fjufvM8fGx7OSuWfh8PjidTjgcDnTDYXhlnn+T6QYURW1k0/7EE0/giSeeAHDeKWBZFm63G4FAAL/+67++kkSJoqhJQzgej/HII4/AZrNJm6uf/OQnaLfb+O3f/m1LqsSvv/667HcqEAjckikhWrEnahaCLKBOpxPvec978PM///N46qmn8NOf/nTLR/YzsCwLURSlL1o0GoXT6USlUpEWO6/XK1ttsMqwM5PJGDJBNZsgQKaqVoHneclLrF6vS7+jJ5x71UI8mUyQSCQ0T0bOYpFcMAyDtz///MYNQtvttmxVbTKZqJJJUt2Kx+OGiBJpJbndbvh8Pqnqm0wmFa8rueqtWktWK4yK5Nclio5Go5qGbKz0t3O5XIhGo0sDIQRyk+9WvH+WZaVqP5lWJdXaJz7ykTmNGrD5dINtTPwT/8N6vY6TkxP86Ec/kjSe0+lUmiAl/02kHHoqrI888sjcBrNQKODP//zPcc899+ADH/iAqeNX+j6Nx+OdyMvdFvZEzULE43F87nOfQzqdlvRXn/3sZ/Gf//N/NhVKbRUcDgfcbvfcTpbc2BwOh7ToyolTtfilaUW73Ta0QCslCIRsIUCmMEV2eLOvpUUXt+isHggE4PF4dJFLLVUvoyLgxXP3nldfxUcfewz2GzfbQLOJT165Ap/Phx+9971rnT7OZrNz1RGPx6OZ+JCqWywWgyiKui0Ser0eer0eotGoqs5NCaenp8hkMgiFQtLnq/W6JDYLRoi21+tVrT5QFAWe5/Fk6Uk8Pnxcs4G1HrsaURThdrstqfhEIhHkcjm4XC7NFUYyvayl7U1RFCKRCNxuN6bTKdrtNprN5tL0ZDQaBcuyqFQqO5FusAth4oIgWJ5sItcFqNfrePLJJzEcDnH33Xcb9jpT+v51u13k83lkMhlDz3uzY0/ULITT6cRb3vKWuZ9dv37d0CJiNSiKQjgcVrRMIPmccvof4JysWJW3xrKsruoUgVKCwL3Te5ceG4/HpV0iRVFgWRbNZlMTgVq0K5GrEqyCloqBUQK1eDP7lStXJJJG4BiP8Utf/zq+95a3IBwOg6KotYmaZ3e6kUhEd5VptsI2nU51f1+MGn7OVgNrtRpYlsXBwQFqtdrKRZaQE71IJpM4OztTtS5JJpO4cnxl7lpvoonHqMdAg8YF8YLqe9EKn89naWtOb5WuXq/D4XAotmGJ7U2pVNJ0TZyenkokt1AorDXdYBWsIsE3G5555hmUy2V89rOfNfT7d999N5588knZa2JfUdtjbbhy5cpOCMe1iraVCImVI+BGhcxKUTSHrcO5xwUCAZyeni7t/CKRiKYvuxUVKC0O4+12G6FQSLeofPF6UhJOk5+TalcqlUKz2bRcR3l2dia1s81sSgh50WtkbKQlH4/Hl4hNv9+XRO7JZBKCIKhubPRiFZlyuVyIRCLI5/OyesyROMI/Uf+E3zj8jTkxvlFfPr2DEEc4kk3vMGox0u12ZY+dZVlEo9E5X0KtEEUR2WwWLMsiHo9vNCd3FhzH3ZZEDTAXl+bxeHDHHXfIyoX0pjbcStgTtTVjFy4uK6asrJyU8ng8hoXEi1E0KS6Fk9bPWpKJRAJ2u12WcKpVlHiel4TQRox0F6GV2HIcJy36nU5HU8Vv8TFNvx8BGbKyKJw+OTkBz/OSwayVk3/VahXpdNq0dQvHcboWfoZhdE9vEvNRNZBrwO/3w+/3L4XSl0olHBwcaGrXOhwOacAilUrBbrdDEASMx2P0+330ej2Mx2MMBgOJWCjpLhtiQwqS7/f7cLvdGzHOlSausTxxjSI0n4tF5PP5uTQLJV9Cvej3+8jn8/B6vRgMBhsP9b6dMzuj0ajp35cjas888wxyuRzuvvtuxONxvPvd775tqmx7orZmHB4eanZoNws5J+7WAw9YMgpvpd7CSpNPmqZB0zQSiQSm0ynK5bKhHR1ZGKwSWWutuDSbTencElE8TdPSnzabTfqXoihQFAWapsFxnHSTevHBB/Ghhx+ea3+qCaePj4/Bsqyl2aG9Xg8Mw4CiKFNTqcFgUNcxud1u3YQzGo1qrrQ0m000m01Jw9lqtXS1wTOZDGw2G4bDIabTqWado5Ie0w+/ZXpXPWL3VRPXNpsNiUQCp6enukgRMQ0m/l1W23ZoHSCyGrugT9sWrl69Co7j8K53vQuj0QgOh0MXoVLaxL/88ssAgH/5l3+B2+3GhQsX9kRtD2tw11134dlnn13768g5cX/yscfwKACY1GnQNL2VCBgtaDabkhs6AdlFayVLHo9Hen9W6fC06sEajYbU/hyNRoaqXMepFKoPPKBJOE1IVL/flwYBbDabJVmHo9EIkUgEvV7P0PXCsqxu3Zde02Q9JG0WRMMJnFdtKYpa+R7NVLKV9JiXYN3Uop4W+KqJazJUQTRiesj2Lgxa6QHLsmAYBsPhUHZjJxexdjthMpngueeew1NPPYVarYY77rgDH/7wh5HJZDRZeGj5zhweHt5WCQV7orZmHBwcbOR15Jy4HaMRLl29alpQKwgC7Ha7lFnX6/VMmXVa5QOmtEgXi0UEAgEIgqCpLeZ2uy0lorFYTNfi4/V6TZufahVOL557sntNp9NoNBqm9Guk2uR2u3WfA+CcROmt8Om9WVshRSgWiwiFQqrtU7NyAzk9ptEUDiXo2ZSoVfhmIYriRipYSnq5ReiJ+ALOvwezVRpRFCGKIgRBwGg0Qq/XQ7/fl84dRVFzoeqj0ei2JmkExEMPAK5du4Zr167B6XTi537u5/CBD3xAcV0cDoeWbZZvJeyJ2i2CVYJys5hMJpJQPBKJmHquRqMBmqYlUTzLsnC5XBAEQZcwXE0s3Gg04HK5NHmVWa0n2bQuMRgMYjAYKN7gZv3KlNoK+XweDocDyWTSsEav0WiA4zi02230ej0cHh4im82u1aBXb0XNKpNVpc+YBJK3221F41+tWNRjWg2Sm6hl2ElPhc9INJweqOnlFs+X1+vV1aoWBEHX9U/8FveYh9zGczgc4qWXXsJLL72EUCiEO+64A29729uQTCbh9XpBURR+/OMfa3p+LVFytxL2RG3N2FQ4u1ZBuVkwDGPa5qHX6821R8LhMPL5POLxOHw+H2q1miUVrsFggFKpBJ7nUSqVFNuKVpuP6r1xmxX193o9pNNpDIdDqQJAyJEgCGAYRlOlajwem54IDQaDkj7n+PgYgUBAU76jy+UyNDGq97PzeDxLXoJ6QCq1cuTf6XQiEAjMvdd4PI7hcLhWLzszcLlcmqYT9VT41pV6QKAnoURvxdWIbdAe5/D5fJhOp/B6vSur6bVaDbVaDS+88AKA8zUgmUzKRhS6XC7cd999cLvdKJfLYFkW73//+9fyHnYVK4kaRVH/BcADACqieG7gQ1HU/wHgNwEIACoA/kdRFJe2IRRF/TqA/wjABuD/FUXxz278PATgbwG8BcB1AA+KoribdzKTWFem3iKuXrqETz72GBwLgvIffvrT4Hle8qcyezxkYtAMaJqWxNkAJH3UbPVBS+tMy02YjOsr6WYikYjlRE3PomyFvxmJTLLCisDsoMfiIk086Px+P1iWVawwEVsKvSDXgM1mQyAQkITL/X5fcfI3HA7rfp3Z11t8XhI2Xq1Wl67ZUqm009E3WokaoL3CZ6WVjxzMJpQowUi7fo9zzJoXG9lkV6tVxc0Tx3H4xV/8RdhsNly4sOwheDtAy3bjiwB+feFn/5coihdFUXwPgMcA/MniL1EUZQPw/wD4bwDcDeC/pSjq7ht//YcAroqi+DYAV2/8/y2JD37wg6YWBq2o3H8/rly+jIbfDxFAw+/HlcuX8e1MBtlsFicnJ5a05KxoE4qiiGvXriGbzSKbzcpWoIbDIQ4PD+Hz+Zb+juM4ZG68L60gpXiWZZFKpcDzPILBIM7OzizV1LjdbhwcHIDneRwcHKwkgZFIxPTr0zRteW6iUZTLZdmFutlsStVN4LwlFYvF4Ha7dQezE5Ap2Wg0CkEQUK1WUSqVpCgju92OVCqFRCIx93tmfJ5arRbS6TQODw8Ri8XAMAzq9Tqy2aziAlWr1XbWUX0dpGrdk3iLuji1n+uZbN0FK6WbAX6/f+m6WZe84QhH+KPTP8J//6AduaANIk0Bb3kL8OUvr+X1dhUrK2qiKD5DUdRbFn42q9D2AJD7lN4H4HVRFN8AAIqi/gbnVbhXb/x5743HPQzgKQD/i64jv0ngcDjWvsNkGAbdbhf/+u5341/f/W7Zx1gVgmyFea8oimAYRrW6N5sGkEqlMBgMpOPXa+EAnE+4kbD5RbGvVRmm5Nhmn5+maYRCIdhsNula6Ha7qFarUoyPWdA0LeVuAjBluWH28xUEAYlEQlFQXalUJN+sTqcDl8tlSHxN2tnXrl1TfMxkMpGeOxaLodfrod1um8pgnEwmhkhlqVSC1+u13GzYLNYxObdug289ejk9rcxtZHPuKuSsnsiwksvlQqvVQigUgtPpxGQyWYsrANEifvpojL+8AnjGN66r42Pgd37n/L//7b+1/HV3EYY1ahRF/SmA/wFAE8B9Mg9JAZgVpuQB/PKN/46JolgEAFEUixRFbWY0cguoVCprL6cnEomVE2ZmdDmz0OO27XQ6pcy3RfIg5wyvhJOTE9hsNkQiEZydnRnesStNgLZaLbAsa8m00eKxCYIgK6ylaRrBYNDS1yTn04yXmRWterXFn4RGE4RCId3DC3qrqcB5pY9hmLlosU1iPB4jHo/vHFHT0/YPBoOS6Jv4k41GI8mmglxz4/F4roJIrk9RFA3Fbi1Cq16ObAi0Yld1hJuGnNXT5Svnwxo/vecenJ6eQhRF05Pqq0C0iP/nVcCzKHvs9YA//uM9UVsFURT/GMAfUxT1vwL4PQD/28JD5FZT3asHRVG/A+B3AEgVg5sF2WwWjz766Fqn3sLhsKaKRC6Xg8fjQTgcRqfTMfQlI0RJC4hTeTablXynZs9DNpvVFZ80nU5xdnaGeDxuuaYMsG5STevOUhAEdLtdS3bxi+TQ6XQankSbTqeaJwGVoEdzJ4qiNCkqh8VjSaVShhf70WgEm80GlmUN/b5Z5HI5yyrbVmHVpieZTEqt9Xq9ronMqE26WmWyrEUvZ7PZpDVDEAQIgoDpdIrxeCylQhDZgZEot1sVclZPzHiMS1evonL//ZZ4LmoB0RzySlx7A4kcuwIrVqf/D8DjWCZqeQCzwow0ALJ1LlMUlbhRTUvgfCBBFqIo/gWAvwCAe+65Z32Mx2K0Wi08/PDDa20DJJNJXU7g3W5XIhKxWAwulwvD4VCzhYDH49G8CM8GZReLRUSjUfT7fYzHY4mc1Go13XYQ1WoVTqdzzqTWCoTD4bkbt1HoIXzBYNCwfcNsa6IdDOLJ++6TWhNut1tKMRBFETabDX6/HzabDYVCQfY9Eo+8wWCA6XRqqh3c7/c1ExIymELI/OK14HK54PP54HA4QNO0KW+yZDIpbWqsTGXQg3XLIPRCrvrp9/sRCARwdnZmSZzaLE5OThAMBjdSvSKThUrIZDKoVCrwer1gWRZer1eSJdzOULN6WscmWfE4bnj3Zf3AW+QO6SYr3JiBIaJGUdTbRFF87cb/fhKAnPnJDwC8jaKoOwCcAPgsgP/uxt89CuBzAP7sxp/fMHIcu4zpdLrWSppZQ03SjrXZbJpDsKfTKQ4ODsAwDE5OTubeXyaTkUwO5UwvifWCy+WSnLt5ntc8QRoMBkHTNGq1Go6PjxGJRNDv9y0hwgcHB7h+/Tq8Xi8CgYCp6UmWZeHxeBAIBGC32zGZTEBRFGw2GzqdztwiUCqVpPawKIoYjUZot9sYDAaquZmLrQlfvS61Jl6+eFF2cSItN7mFORaLoVqtSp9ZLBYzrdvzer26FjxyHXi9Xul4HQ4Her0eer0eotGo4cB3juMQCoXmPle1SeB1olQqmfKqsxqz32G/3w+O45DP5y3Vbc5iOp3uDFklm8bFqrbL5TI8hbxLcLlcCAaD0gCYIAjS503uA5PJBIPBYK66r2b1tMkMU6JF/KNLRKM285duN/Cnf7qxY9k2tNhzfAXnwv8IRVF5nFfOPkFR1Dtwbs9xDOB/vvHYJM5tOD4hiuKEoqjfA/AEzu05/osoisRU7M8AfJWiqN8GkAXwGWvf1vYRDAbxW7/1W/j7v/97SwlbKpVCt9u1LBNvOp2iUChoamvO5lK63W6Ew2Gp+kKInsPhQCQSUSRgg8FA0giRYOpVyGQyGI1Gc1q/s7OzORfxwWAAt9uNfD6v22KCtMI6nQ46nY60sJ+enq7U5NntdoRCIbhcLkynUyk+Sana5/P5pGlTcnNcJAs2mw3ZbFaRQKu1JtTSCQhxnEU4HEa9Xp/7ucvlQiAQQL/fN9yaNarF6nQ6yGQyCAQCsNlsOD4+hs1mM2UbwjCM7Pclm80inU4vbTqshJwo+7rXu3KYZlOo1WpgWRbRaBS5XG5tBG0WlUplzs5hG0gkEora4cFgcNNlSM6SsslkglarhXa7rctKiWVZBAIBvPRv/g0+8MUvLlk9Pf+pT21Ux0da29+8eBX/E5r4s6sU0k0R43gczn//728bfRoAUOus+liNe+65R3z++ee3fRiakcvl8PDDD1sSQr44+Wg1KIpCPB5X/GJb2a4guhctN2o92hGO49Dr9XSf72g0CpfLhXw+P7dgUxSFcDgsBaQLgiCJqMfjMUajkSzR1FKpoShqpSjebrdLBsCz+JMvfEFRAPq/f+ELqs9HrCpIla9cLquSUZfLJS3ieuF0Oi3R4JmpfGlJpojH46hWq5YbtS5WPoHzBe/K5ctoPfDAVlqvsyDXYKVSMe2urzYlKAePx6P4/ZGD3++3ZLrQ5/MhEAionvtkMomzs7OdINJyIPclt9stpblYHQI/+3l2QiF874EH8P0777T0NYwgHA7j937v97Z9GKqgKOoFURTvsfI598kEa0K32zVN0jiOQzAYlLWUsBput1u1osZxnGVETb+jPtQAACAASURBVE/bx+PxaCZq7XZbdyAyx3HodDo4PT1FMBiE3W6XWmyiKBoyo81msyvbW6Iorpz4JFN16XQag8EADocDLpcLnVAInMw5WZVCMZlMJMLlcrmk3FY1DAYD5HI58DyPk5MTXdezz+cz3K4kCIfDpgiNFiJQKpUQiURkrTv0EpBZqFU+/+PFi6bauVbAyPSsHNSmBJXOVbfb1UXAfT6flF5it9sNVeNI9VQt/5dlWWkTtiuw2+2IxWKw2Wzo9/uo1WqmTbJXQWt28KZxs1U6rcKeqFkMURTxxhtv4NlnnzVM0niel0StRnZKWgOLZ7FqQdyGx1AkEtG9kPX7fWQyGZTL5ZU32wtHR/j400/DU61Ki3Dl/vvNHLKEWq2mWlHSSig7nQ5CoZB0Y2ZZFv94772ylZqrl5Z9pORgt9vBcZyuc5vNZqWp4Vwup6lVaNZAlKIo/GDwA3wD3zAUTM6yrOYF7ezsDMFgcG5jYISAzEJNlJ1IJLau1SoWi3C73bosd+RgtBU/GAwkCw21exbHcSgUChBFURq+IddirVbT3Ga32Wyy120mk5HsJkjo+i6B47h90PsNTCYTVCoVxVD3WxV7omYxfvSjH+FrX/ua4d9PJBKmdrl6AotnoUQqvV4vPB7Pxnf+LMtCEATN9iMEZNJrVgPD8zxGo5EkhJ1MJrjr2Wfx4ZnILbIIP+3zofH2t5veUQ8GA9WKgR4SP2tl0u/3cfaxj+EKYKjSQ9M0otGooRgwor2bJRkURYGmaWl4hqIoSfe4OLhAFleiNyTideB8mneRMJyETvCV6ld0X8sEw+FQV9WmXq9L7cBWq2WYgBAoibLbwaDpGDYrQLzdzBI1NUKqBK/Xi0qlgkgksvKeFQgEljas5FqkKAqpVAo0TWMwGKDVailujuRIms1mQ6lUWns+qRm0223Lp9xvVjQaDfzlX/4lPv/5z0uDR7cD9kTNYsiFyuqB2V22nsDiWShVHkKh0Fa0NNFoVHpd4p2mJ6s0n8+DYRgEAgGcnp4u7ZI/88gjc2JZ4HwR/qWvfx3f+4M/kLQsZJLVyHRpoVBQdaPXI6ietTux2WyGWhOrBj20Qu73Q6EQvF4v8vk8BEEAx3HSn8FgEIPBAJVKZW6xmU2fAM6nb10uF0qlEjweDx6qPmToWiYQBAHZbBbxeFyzDcqsKasRAjKLq5cu4Tcfewz2BVH2k/fJ+YOvB6tat7lcTpc/ohyUCGk3HAbP8xKBJ3Yxw+FQ+k6cnZ3hadvTGE/lP+f32t6r+tmJori0kXO73YhEIuj1enPvS86Wxufz7bzR7WQyQSKR2BO1G5hMJuj3+3uitocxkLanXvA8j8FggH6/b3oRNRJYrHajrlarcLlcpsXGejF78yQ3apZlkUgkUKlUNFWkRqORojnjqkW41WpJWhY9dgpzLZyJH5+2fxoZLOc8lkolhMNhuFwujMfjle+nUChI1aFyuazblJbYhqyrkkMqmV6vF8FgELlcDu12GyzLQhRFTVFF5LMik2tWhW8brY6qVcS0TKK+fPEi/H4/7vm7vzOkcTOLVa3baDQKh8Nhupp09dIl2Vb8Ex/5iKZNXnUqPyDVRBPJZFL3IEuv15Nel0ynA/KTyLMboF1GLpfTNBhzu+CVV17Bvffeu+3D2Bj2RM1C9Pt93ePtsxNIZqJ/CIhJoNzPlaCWN9ntdnFwcLBRoma322W1ef1+H/1+X/JiazabaLVahs6ZmlfQIpxOJ+LxuNTmI9UBImwdDocol8uyLZwvNb6Ef+f7dzhsHS4976zXGEVR4DgOTqcTDodDMs4VRVH6dzqdSnmlqVQKnU5Hejwxouz1ekuxVRzHSTqfdYNYnIRCIckmgCy0Ho8HkUgE1WpVVVc0Go0wGo0MXctyMGKsDCgTkCfvuw/T6RSBQAAcx4GiKHS7XbRarTnS43K58Mb734/vHi5/9puAUuv2Y089hfKlS3NyBq1einIgxPOj//zP4Op13YRU7XM2K0EgPnxKWEfW6bqwSwMO28Z3vvMdvPe974V/xQDVrYI9UbMQRryjvF6v1P6xwipFT2AxwSrxrFlRuF4kEgnVRWMwGMzt1DOZDE5PT3WRyR9++tP40Je+pEmQP51OVdsvJNdQqe389fbX8YeuP1w6vtnPWxRF1Wk04LzySh4z2+5ZnN7jeV4iakQDtulpqVqtBo7j5qqxi7qi6XSKcrkspSdwHDfXCpW7lhmKwSVR29DELIwMwxCiodQ6XGzdApDIG3Deat6mBYdS1dhbqy1pTs/OzkxVzl++eBGNT3wC5XJZd4VO6Z51P+5fuza22Wwik8nMtWbJ95L8OZlM0Gw2tz5kUK1WZb0Qb1fcTCTbLPZEzUIYCV+3OrBda2DxLMyKia0CRVFIp9O6y/uE1PE8L7Xb7Ha7pIdptVpLC9C1X/5l1Ot1w9YLi8cNKLfkGmJDNny8Xq/rMnNVIvKLJIy47rdaLUwmE3Q6HbTbbQQCgSVisU60223Zds2srshut8PpdErV6Fk92UVchNPpxDeH35Su5U9xn8IHuA+gWq3qIhXVatVQVU2vFlCOvAWDQXAcB1EU0e120Ww2LfFWXAU9VeN+v69LyyeHfD6PWCym+56mdM/6Df431k50SQVYC5S8JEmiCfCzCjipslr9fZPzVbwd8e53v1vaEN0O2BM1izAajfDP//zPun9vHTdsLYHFs1hVydtUNSYejxtuvwDnhFMpOJplWfh8PjidTlAUhVwuh4LGRXhVhYCcH7UWTqFQWGovtdttXQMFWokaANm0A47j0O12NzrhViwWVdtqxECYoNlszk24vc/1Prxj+A7p7w+D59FpLMuC53lUq9WdF1nLXZM+nw8ejwcOhwOiKKLX61luZv3DT38aH/7Sl+DQaONSKpVMJTWQCqoRkHuWw+GAx+OBx+NBqVSSJowJ8Vm3f5gSvF6v7H0lGAxiOBwqfoeNVPvVQFJUbnds29pm09gTNYvw1FNPGZoeWmdou1asKiFvSp9mlrSqLdhE3wac3zz1nPdV54cQjVVt51arBY7j5vR3+Xxec6VH6Zi1vJfpdIpcLge73Y50Oo1qtbqxVk4ul5MMO1e9z36/L/kIAssh96SF2e/3JTIaCoXgdDpXVmKtdm83g9lhFQKHw4FwOCzFSxmtbqXTaXQ6HXwbQP3yZV1V43w+j4ODA/R6Pd1SjkAgYDrabjweo9frSZWoxc80EomAoqiN2wUFg8Gl8xGJRNDpdFTvj7lcDi6XCzzPo1AomG5bLn4fblfs+ubMauw/dYuwbf2CGSgREWKMWq/XTYfAr4LZiSaapjV/BnoJIRHqK4HcwJVaOD9P/TzS6TTK5TKi0egSYdCqtVCqcui59kgI86avV9IOW9UaY1kW/X4fPp8PFEUthbvLDeuQVlAikUCv11Mc6Gm327qnZTeJ8Xg8R870CvwJQZut7hixcalUKnA4HLpf36rKu5ponlTU4vE4GIaRvhPdbnetLcF8Po9IJAK3243JZAKKolAulzURL6Kptdls0nHX63VDG4ddvXY3jU1IB3YJe6JmEe6++27867/+q6ELaNuLx+wNlgSSN5tNNBoN1Ot1qcy8TtPFVWRoFaLRqGZtjN7K56ppq2azCafTCYZhcK/jXvwa/Wtwu91otVp4xwsv4AOP/d9z03DTj31sbkFeNURAoHSN1Go1zRPDVkwWy0FrFiwxHV5EIpHA2dnZXOUTmCdmZOJVCcViERRFged5lMtl2QECu92+09Nzc/YuOT8eDD2IVC2l+juZTAbtdtvykHO997JN3sMWq43xeHytr2c0Tm4Wi0NJ4XAYTqdTl27yZi4IWInT01NJC3g74PYZm1gj8vk8/vZv/9Ywy1/HwqkFNpsNXq8XLpcLyWQSiUQC7XYbx8fHcyLY8XiM4+Nj9Pt9JBIJHB4eIh6PW+pBZLYtpbUqRSo2eqClBTQcDtFut6UcvslkgvQzz+DX/uZv4KvXQeFnPlaH3/3u3A1Gq+5ErfUZDAY1PQfRdVmJeDyORqOBZDK58rFy78Hr9YKiqJXaOZ/Pt/L5RVFENpuVEgYWscs7cWLvQnSOTTTxxdoXcYSjpcfSNA2e58FxHHK5nOWi9VgspnvwYpuL5s1YaapWqygUCkgmk+B5XhpIUINe+6dbFbVazZSe+WbDvqJmEqVSCV/+8pdNLQCbImo0TSMWi8HhcKDT6Ug5eVq1KIIgzLUnXS6XNEnIMAw8Ho8hnR7J+zMKjuM0t01DoZDu3DwjVUSaphV9rN7/6KMoPvSQpLFyuVzweDxSCDsxp3W5XNLiJ4oiHA6HojbH7XavbP0EAgHLb25+vx/1el2qOCxq8GZht9uXqp4kf1GLOFjPOD4Jk3/rW9+K69evYzKZSNqvXYWSvcu38C2prW6z2ZBKpXB2drbWiUgjFe5tbTi3/dpmMUuIGYZBMBiE0+mUpoTJ9ws43zQb2WyqYVV6xa7idqou7omaCdRqNfz1X/+1KbH9ulpRcojH45aang4GA8n4s9VqoV6vg+M4TKdTXZYfZomax+PRXJFjWVaXTYNR3yKKolTTDwqFguRb1Wg05shgv9+XPX/RaFTx9bQsrA6Hw9LKA8MwoChKajGORiP4fD50u13Z14nH40vtOb/fj1qtpokM6yVZqVQKr7/+OqLRKDqdDliWnSOzFEXB5XLtzA1f0d4FDdjtdiSTSZyenm7Em83IPcmIV51VMHsPTSaTa7PU0AOSgzsLiqLmNLzRaBSTyQTD4dB0iPyq9IpdBUVRt1Uw+771aQLPPfecaQ8yufbMOhAIBNbiTD8ej9FoNKSFmUxt6YGZm2wikdA1Hdfr9VAoFOD3+5FKqWt/AHMRM3J+VeTnk8kEkUhE+tls61LpfKidp1XnkOd5yyflwuHw0qJ2dnaGdDot+3g5wqukWZODnmrtLCk7PT2VUh14nkc8HofX64UoipraTZuCUuJC2BaWzHM3RSr1tjFJpX5bMHIP8Xq9SCQSODg4QL/fx8nJiab2+qaxGMGWzWZRKBSkyW2apuH1euF2u+F0OqVNG8dxuPPOO5HJZBRzMZWq/peuXl3fG7IALpdLs9zjVsC+omYCPM/jueeeM0w0SHbjJuDz+TayU4xGo7rbazRNI51Oy1oWqIFlWV2PB35WlWk2m2g2m4hGo6AoSjET1O12G0qcoChKMYKI+FjRNI1oNKqZQBltr7/GvoaHsg9pNkBWA6kCqk0EZrPZJX844ou1CKV2JkVRiMViEARBSsbQc12Fw+G51x+NRrh+/brm198GlJIYPjL9yMarflruabFYDE6nE3a7HdlsdquO+XrvwTabDQzDLEkmdlUDpnadCoIwd48KBALw+XyoVqtz2dM+nw9+vx+iKEokb1Xm8a5il763m8CeqJnA3Xffjd///d/HD3/4Q7zwwguaF/RQKCSJnq3E3MTYwoJstYBcDhRF6SZOwPnCTiZ40uk02u320g0zHo+j1+vNPX80GtV9DhcXPEKSkskkhsPh0nnSU/GZBUVRKyOIgPMqkdapXyML4RGO8Fj/MYxwg6CiiSs4b20YIWssy0qh62ooFAqIRCLSpFw4HJZtbyq1bOPxuGG7lmQyqXkCcpdu+Pcw9yDsDeORxiOoC3X44cdnAp9Bui5foVwnSKYuabMRjR9w/pk1m825Fp3X64Xf79et/7QKej/HVCole+9oNpsbT/DQgnq9rllj2el0ZI9/diNM7Jb0pFfsErrdLo6Pj3G4pRzdTWNP1EzC5/Ph3nvvxYc//GH86Ec/wnPPPae6iB0eHiKXy1k+pSQXCE4W5PsP7lesGFmJxcxJLXA4HNK0nyiKyOfzsNvtUkszHo9LbR+GYUz7rSm1ZQuFAnw+H+LxOJxOJ0ajkUSijIDs8NV8rKbTqS7yZUQL+bTtaYym8zf3Mca4iqu6iZqeeCBBENDtduH1ejGZTBTb7rMtttmQc6OSApfLpasqsgvj/bMDPqlaCp/H56W/Y7oMRIe40TQJ4HwD43A4NFcxyVBSMplEs9ncuCGpnu9pOp1WvU/5/f6dI2qdTgc2mw0ejwccxynKPci9axWILGBV1X+X8eijj+J3f/d3TVs73QzYEzWLYLPZcOHCBVy4cAGFQgHPP/88fvzjH4PjONjtdnS7XbAsuzbTWKWJsau4igdcD6zlNWfhcDgM+TglEomlm+ZkMkGpVILD4ZgjZcStnbTV9LaDVk1LMQyzdAOs1WpSjI0oihgMBqjX60gkEphOp3A4HJhMJmi1WhiNRpKgWksrhrQy1SYlZ6G2WMvptzKZDKo5+UqqknBdDXo1SP1+H6FQCJFIZKntaLPZEAwG4XK5EI/HUa/XZXMy9SIYDJoi8puC0+nEwcEBOp3OHPldHKwZjUZrN5tWghFyWCgU4HA41rYhVYIaUUskErDb7aBpGqIortTqbpoUa8V0OkW32wXDMIjH43Mh8tPpFI1GQ9NARyqVkiqfWqr+u4parYZKpYJEIrHtQ1k79kRtDUgmk/jkJz8JhmHw7LPPSj9fp/5BaeFtormREF+n06lby0XTtOJNUxRF2Z2hKIooFou6hwgArJzwkyMiSvYlNptNttpAURTcbrcmskAWsWAwqHlqVS7EXa7SxfM86vW6av6oHlAUZYhE1Wo10DSNVColWXF0Oh3U63WcnZ3h7OwMbrcb0WgUNE1Li89gMJAGEwRBwNnZ2cqKol4n/U2DoiikUikIgoBSqSR7rF6vd+k+USwWJW2gUbjdbmn4Y/ZaoygKFEVZSqiI76Lf7wfHcYazQ/VALVqJYRhdRJecH5ZlEQqFYLPZMB6PYbfbcXJysnXPNiMWSLNYrOIbSa/YBXi93rUbHe8K9kRtTRiPx3jppZc28lpHOAIFCiKWb4ZBejmjbh3o9/u6rUZWtSCUMJ1OUSwWwTAMIpEI+v2+ppvXqpw8h8Oh2Y5D6WZNvI+0gJwzPbYGDodjiajJvS9yXj9KfRSPio9KGjVgPn9UK5LJpGH90SpH916vJ9vqvPjyy7jvP/wHaaf/w09/Gm+8//0olUo4ODiA3W5HpVLBcDhEMpk0RNI2UT0JhULwer04PT1dWXWWa+OMRiNTg0c8z6NYLEq5k6lUCtVqVTJadjgca5FGkIEdv9+P0Wi01oEItfbX8fGx5uQM4Jyoker74jVPJg3NkqVtwoiOeBdxxx137IR0YRPYHSXtLQZRFPGhD31o7SPrRJsmR9IccOAzwc+s9fUJptMpYrGY5sermbdqxWg0QqFQkEbUV6Farap+HtlsVpNlh1Vot9tIpVK6bjaLpCwej6sSqAviBXwu9DmpguaHH5dxWbc+bdNThxeOjvAb3/gGAs2mlOrwoS99CcH/+l9BURSq1SpyuRzG4zEODw9BUZT02eo5n1ZXmymKgsfjQTQaBc/ziEQiqNVqmq01lDYJ+XzekB1BKBRCNpuVCOlgMMDJyYnUwicidZvNtrbBimazOWdFsw6obRC1pF4sQumzqtfr6Ha70iTyzYhwOLztQ7AESjZAtyL2FbU1gWEYHB4ern3HLqdNAwAKFC7jMt45eachPZIRrKpYzSISiVimJSJ2EZ1OB8PhEIPBQLbiJQjCSuK8aef6fD6/kljQNI2DgwMwDDNHyrS2m99jew9SME5At9FSVPN3mm3TCIIw19ZyOBxgGEZzVXM4HM5N+dE0DZ/PB7vdjul0imazqVg99fl8CAaDmE6nGA6H6HQ66Pf76Ha7hsX0StPZgiAYEk1riScjVT5S+SOgKAr9fh9nZ2em233rrnyoHZ/X64XH47GsszAajRCNRiXt8c1WodoVg2ez2Puo7WEJbDab6SnFVVAiYSJE/GrgVzc6vaTH5+vs7AxOp9MyN/NZIsHzPEajERiGwdnZmdRW0xIqr/V4rNTchEKhpQWaBIu7XC50u11ZPd7BwYEmAmXGtNfpdJoOozYCo/5O4/FYt9+cw+FAJBKBy+VCPp+f+85QFIVQKASPxyNpuUjlqVAoWL5ID4dDxTbd2dmZ7haoHnJXq9VkK4zhcNi0vY+ZDZDT6YTb7ZYGAlqtFhwOB3w+H9rtNux2O/L5vBRpR/zRyCY5GAxaboVEugGBQMBwesk2sO71aJN47bXX8La3vW3bh7ER7InaGpFIJPALv/ALePzxx9f2Gmpi8U2Z3BLoIS/j8XgtlRq32710U47FYmAYBjRNryRqoigiGo1CEATU63XFnTpN0wiHw5LFxmg0MrQYMQwjuziSiBg14qh1cWi1WobzAbWSQTWQyWc9uh4z/k7BYFAXsZj10vP5fBgMBtJnKYqiIoFZF7xer+K5Iu15tXa3w+FALBbTNOGoBWY3Uw6HY2nYRU++ZCwWW/pO9/t9iSR7PB4A55V1sqFhGAY8zytucqyCIAg3DUkDdnei1Qief/55JBIJvPe97932oawde6K2Zqw7j0zOzZyIxTc9naSXCAwGA8t3o6FQaEmYThYJlmXB8zwqlYriBN3sAqlWgVOaIrPb7eA4DvV6feVwBfENkyNCWj67YrGoqcLSbDbhcrkkvy5isKtkjEkQi8UsIdKiKKLZbEqttXq9vnLK1Yy/k5Z2nxwIqdn29OiqNmGxWJTNqyV5leVy2ZBVjhxomobH44HH49HsobeIUCg097t68iVpml6pZZX7jo5Go7WnvjAMc1O1PfVkHOsFx3EIBoPStUsMzMl/L0IURdTrdVPtaFEU8eijjyIajd7yerU9UVsz1IK0rQARhcslEmwyJFnOVmAVTk9PLfWIYhhGlQD0+31ks1nYbDbwPC9NpSnBSIVsMpmg2WyCZVnJZkIOd911F65du7axiudgMJA9Fr//vPK6SEzsdrvpHFuCTqcjed+RylQ0GgXLsnOt6VkY9XeS88LTi02btS5i1YZHEASUy2WpjUVyXNexCJPXcjqdS7FgWrEovNeqPwSMT4ZvAoFAYCNG4lZhnfrbQCCg+3Nyu92WvPbLL7+8J2p7mAPLspoNTY3i4o1/FrHJBScQCBjaHVlJJoPBoKZd/3Q6lW4qyWQSo9FoSYc1m5igF4IgoN/vK7aoGIZBpVJRbUdqHcwwK9ImZDUej6PRaEhkLplMWrpAKkV3AedVZ5fLhVqtNncNGfF3IvmpZqKMarXaWqsPq3B2drby+ptOpzg9PZWqf2qVW7VoOa0gE916LXiA5eqwmv7w4OAANpsN/X4fjUZjZ7M3AXPaz00jHo+vtQWs9z5kZUzXCy+8gLvuuuuW1qvt7Tk2gHW3P5WwSe2EkdH+TCZj6c3DyMh8oVCQNC6zCIVCpo9n9ubl8/nA8zxYlsVoNEK73VattmptW1s11FAqlaToLiO741WoVquK5pSVSgXZbBadTkeyteA4ztDrDAYDSyYMSfoEz/Mbny4TRRF+DVq8yWSiiaRdwRVJx0qi5Y5wpOuYBoOBlMmrF4ubVCWdYTsYRKVSQbFYRLPZRCwW22mitm4TXythNLNYK4yYMXMcZ4klzGQywdWrV00/zy5jX1HbAA4ODnDt2rWNv67D4diY3YSRiuGumBXK3XDL5TLC4TDcbjfy+byhmzKpaBId2aKeRe2cadUDWXkOZ/Ma19GS1XKss5U20h49PT3VpX+04pz0+330+32wLItoNAqv14tSqbQxMbZVnmZq0XJ6qmpm8hQXK/ty+sOxw4En77tP+n9RFA1r4jYFmqYRiURQr9d1TxpbiXwoj683v47qtKpYMV23JYfe+8Xs410uF1iWhdPphN1ulxJMBEHAeDzGcDhEv99X7b6Uy2W0223DG7xdx56obQDJZHIrr2vUbFfPRBYA3RN9BFYvemZuRqFQCJ1OZ47YVqtVqRI02xbUCjLtqFSdqtfrihYlHMdtJRh61UShGRSLRfh8Ps0C7FnSFovFJKuQVdo5s1YSBG63G+PxWPr8vF4vYrGYZUJ9NVgVNK0WLXeEI81kzWhFhqZp2cgi4Gf6w3YwiCfvu89UjJHee5YVmNXW+v1+eDwe0DQ9F4VGJsKJv57V+InzJ3ik9ohExpto4jHqMTjs561zqeV9aqzlrQVmpT1K+tlF2Gw2uN1uuFwuKUVm9jyXSqU9UdvDODbpdj8LPQa0BHomsgjC4bCh3a/VRMSoZoQsxEqVGHIDYFlWFyEdj8eqE2sURSEWi6Hdbi89r8fj0XR+rGy/MAyz9mgct9ttaFJu9voidiskPmoWVvpERSKROZJNKo6BQAA+n2+tIncj311gWY/GgkUf8huYKzj/Xq9avO12u+HqliiKiiTq5YsXLdECGrlnWY1Vg0kELMvOVY9I1ioJVyekrtPpaJI/PDF5YqliOhJHuDK+ggk1wUg833iSljew+vPWArfbjUAgIKU+rFODTTCdTtFut+deKxgMStW3w8PDtR/DtrAnahtAIBAw7GNlBkZ25Xomsgi2FadycHAgVaWIzQfP8yiVSoZavj6fT/FmS/L/9FSEFisJNpsNDMNgOp1iNBpBFEVks1kpQH32OLZxTuPx+NrIh9frhdvttkSTSEgDTdNIJBISkRiNRpZVoqLRqOKxNhoNNBoNqaXtdrshiqL0/c5kMnMLMAGxLBgMBiunBY20b4kebba6YoPt/2fv3WNlO+8qwbWf9X6/3/Z1wCE4xjGxnRASJ7lJepSxQ2JIuhEapoMCg9SDUDRIM92om0iIFjMMOK0eNVJnGCLUIzRgkia+QBxz82pCh0CCffFMguPXqVN16px6nqpT79q79vxx7rddj7137WdV3XtrWVe+Pq5Te1fVrv2tb/1+v7VAg8Yc64u+3hJoKpUyrbI+9OKLuKpBouyI2DNzz9oVSEldCatrhMfjgdvtBs/zckkQeP3aGA6HaDeU/f2GGGI1VXDx83a73WsqlpGhk1gstlMLG4KPfOQjiEajoGna8T68XeJA1LYAiqIQj8e3fmGb6XMx6ghPUZSpzM5cLmfZRHQ0ZHICoQAAIABJREFUGmE2m8kl1PPzc/A8b7qk6vF4NHfFo9EIHMcp3uSAy/c7n89jMpnA5XLJPROz2QzT6RSiKGI0Gq35dJXLZbmRv1qtotfrYTAY6PJIs8srL5PJOEbScrkcms2m7VYG8/lcVs9omkahULClbJvNZtFoNDZeR+VyWR6QOD09RSgUQiAQQK1W0xzkKRaLG8/BDFFT6kcTIWqqakql0WKxKDv+E6JgFu/84hc1SVS1WjVt+0FgNsVi37B6T9EidQRqhudq6KIrlyo5jkM2m8XR0ZEiyVdT4PaFpGWzWaTTacfztPcBh6nPLcFsKcMKzBA1tYkstZ+n02nDSmE4HEa1WoUgCMjlcqY9cJR6u7xer1waMwo9n9FkMkE0Gl0zVfX5fHIANlF8iLHrYvaomoGnIAiYz+fy44iFiNqiHolEZPNeqwgEAo4575dKJVSrVcc9/ebzOURRtERcPR4P8vk8WJbVTfZZlpVVs263i0qlYsu0tZmSttqCPcIIISh/f5V+PplM0Ol00Gg0cHx8jJdffhk+nw+lUslwuLpPpV+QkChBEFCpVHSRVzUYvWftI8zYngCXhucclokKBw4eKJs+hxCSS4ez2QxHR0coFov4MvVlxaGTrzFfQ7FYRD6fRyaTQTwe38lapoQ3v/nNe3MuTuPOeJU7xnw+d9TDRg1mykBGHeHN3FxItNVsNpMVkFgsBo/HI5PLyWSysS9GaTFd7OsKhULw+/2gaVrXxN50OpXTAmialvtGAMh/HwwGODk5QTAYBM/zso3AxcWF7MVGUZTq561l4KmkopTLZfj9/jWPOo7j1p5n9Wbv8/kwHA7ln6VSKbjdbtRqtaXSMFH/7ATLskgmk7aZGes9plkUCgU0Gg1UKhXwPA+v16vL8LfX6ynau+wCWnFyWgkmq1Da4C0GzRMX+sFgoDm4EY/HdUeBlctlQ20Fi7CSYrEvMDuhr2Z4DkD3510ul3EO5X7YltjaW8PhZ555BoPBAFdvoc/ZLA5EbQsol8tb708zCyOO8GYbt5XIndIN3+12I5FIgKIonJ+fIxQKycHYFEVtlN8XG3wZhpGHOmq1GubzOXieRzweB8dxsoowmUx0NfH3ej1QFIVAILBGKGOxmGqQuRZZVCt3RSKRJaIWCoUUlbRcLofz83NEo1EMBgMMh0O43W7E43GIoohOp4OzszM5B7HdbiMcDts+xcjzPILB4NYNY5vNJnK5HBiG2UgiCGKxGGiaXrqWptMpksmkbqLmRIyQGWVQi4xpJZisYlMpabGhm5C26XQqf38IfD6fIRI1m82QTCYNq8RmUyz2CSzLmrZSUjM8B/R93oA2yd9nnP+H/4DhT/80PM0mqGIR+I3fAH7mZ3Z9WrbjQNS2gFdeeWXXp2AIehzhGYYxndN2fHysq/9qPB4vLaCLC+Ldd99t6JiiKMrqXSQSgc/nQ61Ws0QmJElSVKK0olG0FmA1orbm7B4Kged5hEIhDAYDMAyD2WyGWq0GURTXPpdVQruYg0gURDsVtVgsZtvkpREMh8MlcuVyuRCLxcBxHMbjsUz2Cdxut+o1WKlUkEqlbPPyoihK7vfSo3SbKZ9uImNaC/oijPR4rk7hhcNhWcXu9Xo4MkCiRqMRYrGY7mMvwkyKxT6B53nbItsI9H7egDbJ31esTvvi6Aj4hV+4/PttRtYORG0LCAaDuz4F25HP5y2Vtcw4WQOQfcesGPm63W7HvLBYllUlfxRFrak8DMMgmUyCZVnV92SRoPI8j1qthtlsBq/Xa5kQVSoVsCyrizjrgV3PYwcmk8naZ9Fut3WToPF4DIZhTJmZ8jyPdDqN4XAoG6KS5nA9pVKzi7aexZmmac0NgxW/LzIRuwgjJMquqd1bDbueWDSiuO4LlKZ9MRwCv/qrB6J2gHFcuXIF6XQa8/kc3W4Xs9kMHMeB4zjZhZlMMNoJp5z/4/G45d4jM55nZHydDCOYQTKZRK1W2xgKbhbpdFqVBIbD4TWfsmAwuJFsXVxcIBQKoXszC5E8v10eaoIgoNls7hXJcgoul0s3Uet2u4bfk2g0Kqu1ar+nh/j1ej3wPG85WYTjOHkylViG9Pt9cBwHnuflVoLJZAKWZeFyufDqq69aOqYVbDOfeJ+wD5OLRhS4fYDqVO9teA87ELUtwO12rzWXTyaTtUk4lmVlI0GjsTlKcCKLjmEYW6bajPT1BINBXFxcIBKJYDQawe/3mzbLJQRx1fVezUDVCBKJhGapzOfzrRE1PWR6sdnfLjuOVSx6ulkha9vMlzUDnucNkQGSBav1OxRFIZ1OQ5IknJ6ebpyg1fseRaNRS0NIiUQC8/nckJUCCV/fBUjyxJ0EUuq9VXqY9wlqgyq4OUE8Ho9xcnKC0WiESCSys4QgO3AgaluA3mBhQRDk5mSWZVEoFDCZTExbMAwGA+TzedA0DVEU0e120e/3EY/H4Xa7MZ1ODT93LpezRXXRW+KIx+NoNptgWVZeAOfzOTiOM6xAUhSl+HoXDVSz2SxomsbZ2Zmh5ydkUut3lIiznvdBFEWZBK72kpkd61/FZDKRJ0mtxEg5nWxgFUaVC0EQkE6n14gaSZXgOA6NRsNQCXo2m6FQKMjPQ/5NDHGBy2vFqvWAy+UyXOLfRWwZQTKZ3At/rm3C5/Pd9iq2U1AaVJl7PJj8638Nsd/H7/7u78rVkocffhiJRGIvlEszOBA1BzGfz1Eul/GVr3zF8O8KgiDftMyqHGpTb2TXWiwWDRMeOybcjHxZvF6vYgRN9+1vhyRJiEQi8Hg8ut6fdDotL6hqsTakp4llWXlK9PT0FKIogqZpJBIJecINgBzaLQgCRFFU7TPzeDyK6oieBAJBEOTHLRI18po9Ho9c0jVbKnO73fKu3kog+Gg0ksu0+wgzvUCVSkWeRkwmk3C73Wg0GqbVrtlspouQkJKlWRhVN5VsYLaJer1u28bjVgDDMHsfPL/PUJ32rVRAP/nkUvXhW9/6Fu69915cuXJlV6drCQei5iCee+45PH0zLsUKrKocajg9PZUnBvWAqFtWYcQF/+6//mv8mEIEzdO4/KI2Go2lST4tEAKiJxtQEAT5/SakjWEY+bPodDpgGGYtdN3tdsuRYbPZDKIootlsWi5tdLtdVZ+p0WiEo6MjuN1uFItFVCoVwyXS1VxLK9CK4to1zDarUxQFr9dri8Gw3vL6YDAw3adG07RhE+NQKLRTojaZTBCPx+VS8+1eBs1kMo4NNe0z1DbJZqA2qKJ0/3vuueduWaJ2SCZwCM8//zyuXbtm2/M1m034/X5bnouiKBSLRfh8PkPTl6tu/FaOrwc8z+MtTz2lGkFDQGKZisUiisUiSqXS2qQty7Lo9/soFAr4wFe/uvE5F0FIGyEyFxcXKBaLyOVySwpXKpVCMpnEdDqVG7LJhKYalHqfaJpeU1O63S56vZ4m0RiPx3L2pFGn98XXIYqiY4Mou4aZCU6CxaETMtVZLBYNfy/n87ku1fLi4gLT6RQsyyIWi6FYLMrZppvg9Xo1bWKUsA8u781mE0dHR3tjIuwk9r2f0wmQTXK42wWF1zfJ99244fixnUpf2QZ2/828TfH888/bKuGT3aYdO14zfWZer9c2Ra9SqehS5zKZDIIqPTOLEz/T6VSxlBSLxeD3+zEej8FxHCqVCrrdLvwqX1i92YCrJqeBQAAcx62VMfSoWqIoLillDMPg7rvvxksvvbSWgZhMJtFqteByueDz+eDz+TCdTteO2+/3wbIsotEoeJ5fKtGRHMfFXrJsNrtkY3FycoJ8Po92u21qItYJA1i70Gg0TJXX6vU6PB6P3CowGo3k9zWfzxv+XjIMo1v1FAQBrVZLbmWgKArRaBRer1d+nul0itFohIuLC0iSJJ+PkbYJrfMhGbqL75udysgqrBDqWwVkwwxc3t97vd5tP1SgZKmxmP1qFNlsFo888ggkScK3vvUtTV9MvZWXfcSBqDkEI03Vem94Jycnso+YFZjxMEskErbFAYmiuNGeg6Zp1Ot13RE0Slhc3BYzCq085ypcLhcoilLcrVEUhVwuh16vp2oo2+v1wHEcCoUCKIrCZDKRF9ZKpQKPxwOe5zGfz+FyuWTz1slkIh8zFovJ0VDz+XyN4GWzWUwmE7k3SpIkFAoFtNttOcFgFZVKBQzDoFAo4OLiwlCT+T4vspPJBOl02nB/GbHQUVpIzfT0GWk5UDqXdrutqhD4/X74fD7wPA+aplGJVPAn3T9BZ97R9MfSUlHT6TSq1aoc9Vb8q7/Cu65dA3uzLKvUPmAFg8EAbrfbtN/irQClAZTb3SJHbTOsd5NMEIvF8Nhjj+Guu+6Sf3bffffhd37nd1Q3l7cyCT4QNQfQ6XR0L2x6+qUIJElCPB63pGzxPG9qMk8pSNwsisXixmZqMuVoR47f6sJ8/epVfOjpp8EZfE4lQn3+wQ/KisbqokI+J57nUSqVcHx8vKRaJBIJNBqNpebybDa71JPk8/kwmUxkB3ilkhAho6Qct6rukF1mqVSS1a7j42NwHKfZ+yiKIo6Pj5HNZg0RNeLJZUdph6ZpeUp5MV3CCuwu8ZnZODlp7Nrv9+Vr4AZuLDnOd9HFNeoaIMGQZxYZliE9eh9+6imZpBFYUUZW0el0bnvSogSrvnn7DqubZK/Xi3e9611461vfuvYdYhgGDzzwAF544QVZVZYkCTRN401vehPuueceW17DLnAgag7ASH+FUSnY6g0+lUoZHoGPRCJbt10gU4525PitlrmO3vEOfMHgcyoR6g9du4Yv3DxHrUVlOp3i6Oho6TGEIBUKBfnzSKfTa9L9anlYa2J2MRpqFR6PZ61xeTabbSQ+PM/rysxcfV4ri2yxWJQVrG63K5MDmqYtX4tONHC3223DxNTlcm3F3PU6ri/FAgHAVJriOq6vEbXZbAaKolAoFNDtdhEKhdBut9Hv99fIqF3KiBbulOnPRWzrPnsDN3aSQmBl4/2ud70LP/7jP655D3z/+9+P97///QAuN7DVahVveMMbDPdr7hsORM0BGCFT27jhWYWZFAEtlMvljQvu4k3aSo5fNBpd6+EKBoOGn1OJUHPTqUyoy+UyQqEQQqEQRqORogJZLpeRz+dxdnYmq2+tVgsMw4CmaV19TmZ9gJLJpKnStd/vRyAQMPy7JycniEajhht4KYpSJXikCd9ojxkpK8/nc0cWQpqm4XK5DPXmbatxXyloW+3ntVoNgUAA3W5X/lMoFNDv99emePUoI2a8DhfhhLnzPtt/qE112w0llfVyjt6YymoGZjfe73jHO/Ce97zH0LFisZjp7Nh9w4GoOQCGYeD1enU1YhuVgq3GTB0fHxv2uXLihrnJP0zpmCT2CbgsyxGTVo7j5JsvicmZz+fo9Xrw+XxrZKFarcp+WJ1OR1cg+SZCzXEcptMpqtWqPCAQDofXpj4rlQoikYisUg2HQ7nHTE9ZzwxRoyjKVOna7/fLvXD5fB7ValX3IicIgqnyFcuymtd4q9UypdY5WUIjfn5GFtltZVqGEFIkZSEo319WvwudTgeBQGBtE6GkjMx4Hn//0Y+iVCrJKQ1erxfxeBwnJyeGS+F2T0WWSiU0Gg3bw8/tgtvt3gpRU1JZZ5gpqqxOwGj268MPP4z3vve9Dp/VfuNA1BxCKBTSdUMwKgVbaYhkGAbhcNhwKctubyWXy7WR/CkRC73GtouPV5sCWvTDIs3XJB5rOBzK5riz2QwMw2gS6lgshm63K382PM+D53n0+33QNI18Po9OpyOXulZVHSPu5GYW+Hw+b7jc7XK5wLKs3JtWqVSQyWRwdnamm7hLkmRosfX5fHL+qhbK5bJsQKsH1WpVzol1AtPp1PB3altE7SquLqknAMBTPK5K+no81b77qspIPg8sqK+kHM/zPBKJhDw9quezszP7uFgsolqt7rUlxrZc842orLuEz+fDL/7iL9pmS3Ur40DUHEIoFNIVK2NUCrbiGm82/mkwGNi60GUyGTneiihgk8lkqUSpRAaM9vToLfctNl8DkE1jFzMetQi11+tdWqin0+kS2ahUKqqO+LFYTFfPVCAQQDQaNWW4auZzS6VSa9dKrVZDOp1Go9HQNdm5On2qB3o/4/F4rNsMdj6fmy796oVR37lt+NQxDIO3ud4GH3z4wvALcj/Shzwfwj1D643VRpSR6XQqb75I4scmWJ1u93g8iEQimM1mKJfLCIfDO43I2oR9VVmdht/vh9/vX5vEft/73ncgaTdxIGoOwYhni5EbHrlw/X7/UjkhlUrB5XJhNpuh3W4jkUiApmnZZ0uSJNNu8S6Xy1Y1YvGmTVAqlZb+O5vNrhEFo71yZpzNI5GIXL6cTqfyhKoWoQ5o3PyTySRcLpeiokXUBTWFKhqNwu/3y75nekq0qyCTpUbgcrlUlcjT01PE43FZMez3+4rnXygUDJG0xaEKPej1eshkMrozNuv1OhiGccw65OLiwtBmxkmiRqxXhsMhhsMh7sE9+K3sb8kbiEwog9pQfzapnVDL21WCmXsOGYbo9Xo4Pz9feg6v17vXRG1bvXNKKisHDlehf5LeDvj9fkSjURwfH2M4HMprGHDZr/cjP/IjWz2ffcaBqDkEJ831iIcSx3Fy6LokSTKxSSaTS4skz/MIBoOmI1lisZimkaBRKBHGxQW0UCgsLf6hUGgtaWATVk1c9YCiKDnAHrhUYhZNPhcJNTHTLQGaSk29Xl/ycFtEIpFY60sjN69erwe/32+pt8psM3c6ndZ8TavXUTAYRDAYlBv9KYqS0yKAyxJWo9HQLDuZWUBrtRpKpZLmuZIeqVXF1gkYea+dImrET2/1vT45OUEkEkEymdxpM300Gl0rE3McB4/HI5fbWZaVr6PF+9ompNNpOZ1DCfseyL0t/0HSh7aLqU/g8nNiGAYnJydyJUOSpKXv5z333HPbpqOYwYGoOYRwOOzI8y4SmNlspqharO5YV0txRmHXDY4smEoKjCRJCIfDchh9IBBAJpNBr9eTJ9CMQM9itDqi/k+j/xRSa/n3Tk9PFfuhXC6X7lLabDZbK9PdddddEAQBpVJJfj+I+kluXkbJ6SrS6bTh3jSWZQ2XVxeTGtQm1xiGQTabBUVRa+TUyrTb0dGRTEjJdCfBeDxGvV7fmheXkgq8bSSTScVzcLvd8Hg8GAwGO81hnUwmslfgZDLBaDTCbDbTJLmbSpapVEpe+LXgxFCUXTCjfFvB/Tf/2Ra8Xi8SiQTa7bYus+l9J9XbxoGoOQSrippaWoGdDbZ6sVpyY1kWoVAIHo9HVvNIALnL5YIoioolqYuLC8W+E1JqWyRXxODVDHw+38aSmNKI+u+3fh+P4/GlG5gkSaAoCn6/H6FQCCzLYj6fG1Lrut0u8vk8hsOh3M/22muvbfy94XCIYrFoSg0KhUKGSRpw2T+kl4AqXaMXjz+uSLpEUZTfM2L5QT6jXq+3NomsN62Dpmn5O1GpVHaqFhnpoXTqPNVUGYZh0Ov1dhq6Dqz3g+qBUskyHA7D5/Oh1+vp/m7sU8qB2+1GIpFAs9lUtfO5HZDJZEDTNKrV6tp9ZbEHeBF+vx8PP/zwtk7xlsCBqDkEK4qaVlrB8Tvfacv5GcFirmS/38dgMNg45RYMBmWvtF6vp7kr3lRqM4pIJLJ2A1hd+H/l6hSz+/WNqJ+dnckROmZB0zQ6nY4hX7FFFTSTyci2I4t/RFGEJEkIBAKgaVqOhTJqwQIYU9PUrtFn3W4c/eAPav4uWaxzuRza7fbaMIaRtA6v14tAIACWZU0RUzvRarUQCAR0bTBI4oMgCHLUE/C67cxgMDDVo6W2kWNZdqdKmhUolcAWlVm9ZsNmN35OIJFI7Px6dQqkfaPdbqtumAOBAD75yU+iXq/j5ZdflifkPR4P3vrWt260b7rTcCBqDkFtyk8PtNIK/v0DD1g9NcMwc0NZLIdls1nQNK1I1PL5vO25fquTgEoL/797GhgC+MMVkUZtRH06nZo2yzQ7bbuITQrhoooVjUZNlbqNlO7UrtF3/Nmf4e82EDWCarUKl8u19p4aSesgPVn7gkgkoosQSJKEk5MThMNhVXLMMAwCgQA8Hg9YlgVN0zJBFwQBs9kMgiDIf2dZVlWZCQaDtxVROz8/RzgcRq/X03Xd8jwvZ9wKgoB2u6042bwt7HMZ1gwoikImk4EkSajVahtV04uLC4iiiFQqhVQqtaWzvHVxIGoOYXHUWm8Zh0DLXHXfv+CFQgGSJC31zv3At76FB/74jxVf/+rQg9/vl8PHgUvTy2azaSgDr16vw+/3yzcLpYXfNwP+7fV1oqY2ot5utw1PJgLL04yJREImFolEwjGC4XK5FJU7lmXhcrkQjUblyVtyPXEcp7rIFwoFuURDYFeixmQysRRPZGXnbfR7qQdGNxuBQEBVaRZFEefn57oHLbTaIsbjMXK5HIbDIRiGkRv3e73e1uPh7IAkSfL7IknSRl89hmEQi8WWvr+7vJfuazqCUbjdbiSTSYzHY0PtIC6X6zAsYAAHouYQKIoCx3G499vf1l3GIVAzV72IRCyflxOL0yJI6Y0QlPtu3MA7rl0Dd5Noab3+6XSqSjCKxSKazaZuV/FoNCoTNbWFv7jy400j6t1uV1Y19CCbzaJeryOfz6Nery8RISdvUkp+TESxFARBLguHQiF4vV7UajVks1nF8nM2m5UXNzI+32q1LIcra8HIc/M8L6sk4/EY/X5fVx+nkfKqEdTrdd2pJMAluTJyTZmFFgm3k6g5kSG5yTvy+PgY0WhU8zGj0Wht8GqXPWvbmvDUg1KphNFoBI/Hg1qttnFTzLIsUqkUaJrGycmJYVXS6/XiZ37mZ7bmG3c7wLx76gGaINYOWmUcNVy/ehXTlamXKcfhOz/5k5bOiSxO4W4XFF5fnO67ccPS8y6CKBzHx8eIRCL4J1/7mkzSCDa9/lUIgoByuYzJZIJisahLRTk9PZVVOTXy0I74ZAUthNDaIMEqer0e8vm85nHD4TBKpRJisRjOz88RCARQqVQMKYJWwHGc4lRVIBBY+1m325VJmlpD9uIieXZ2hnK5jMFggL/5iZ+AsFLe1xuuvAlq17/Sc4uiiOPjY9RqNXQ6HXnCNhwOo1gsqh7DzPdSL9TsWJRwenqq2wDWCRjNYtUCGdAh7QMkQ/IG7Lu/LIKiKORyOfj9flOvYxtxTWrYl8pIPB7H0dER6vU6jo6OVO+tbrcb+XweuVwOkiShWq3i+PjYFOH8yZ/8SWSzWaunfkfhoKg5BFLOMVMiUjNX7TzyCGChod1I749ZkCxO4DIqyacydGAmdF4URZTLZdkbS6sPi5jVlstl1VSB//pPHscn8UZD59BoNJb8yTweD0KhEHieR7fbXSpVFYtFVXVHTVEjJIM8hni7EY+yxd9d7JmTJEn+O+kHXFyItMitVslCbQH85pUr6D/2GD7w1a/C326jH43iS+9+ty3XkZG0DqUm8ul0iul0Ku/8SWTZYDCQ1SO7SrdKMJoluasSkN1O/dvOkMxms4YGfFaVy+l06mi0mBZYdvdLbzweXyNai20IPp8PsVhMnjo3mjJygH3Y/dVym4I0FJstEa2mFVAUBdZEfNAinFycCFYblp0okQ2HQ12qBVGxlBb+r37gA3j+jcZIGjF29Xq9srfbaDRSvdGPx2PFNIVMJqPaTxOLxVCr1UDTtOmhAIJQKIRQKARBEEyVGTYt5OQadcIDSm9ah1b5avW9S6VScgLGRSSCoELJz47SbbPZNOQNty21dRV290k5lSGpRmSNEKxcLieTOpqmwbKs3LdGVFhyHFINGY/HGI1Gjnw+uyRqLMvKqR6rGx3yWskm12hsnx7sghjf6jgQNYdACIvR0HU1uN1uyxe4k31FBJFIZGnix67Xvwo970Wj0ZB30asLfygUAnQS1GQyifl8jmaziV6vB4Zh4PF4Nva41Ov1pZIjMX3VGkggO/58Pm95Im3RKJjjODmYWm+5QqvRfRGz2Uwex98miIqpF4vl3b//6Efx9t//fduvSwKv16ubqG1TUXO5XPB6vfJx9dqJ6IGTGZI8zyMUCsHtdoOiKMxmM0N+bIsl/Pl8vkS+NvmwURQFj8cDnufB87xM8iiKMv0d3WUjvZpXIk3TSKfTkCTJEYJGsE9+drcKDkTNIZAFxGjouhqm06mpWKRFOEWaFjEajZZ8jex6/Yvwer26FBwy/q10I9bT50ZyA1dvxqIoIhwO61ooFhfBTCazcWqUkCjijUYyJDmOU83V1AMSTE3KGbPZDGdnZ7b0ypyfn4NhGFNTsVaQSqVMH++r2SzOP/IRXL1+Hb6bwxF2Dtbo8fUisGLlYxQ+nw8ul8uRiWM7MySj0Sh8Ph8oikKr1VLMBzYCNWV606ACcPldJLmpi7CSHLKrqU+WZVU/e6NG3mZhtDXggANRcwyLk1RGQtfVIIqi5Z2IE6RpFc1mc60Utvr6s9kswsMhQqEQ2u224R19PB7XvZMdjUZLVh0EmxZHhmGQSCRUj9NoNNZiobTAsqyuHg9yA18kIKS8asbEdhWDwUDeLbMsi0gkApfLJSsE/X5fVsaMNFuTpv5CoYCTk5OdTLWR6TVigbJp+vO5N70Jz73pTXI5W0+0jV40m025fLQKhmGQz+fl1IttKpHkWE4ooHZmSPp8PttIv5Z1h5WNymg0MnQPWIRSQss2sA8xZ3b2Rd4pOBA1h+BEJIgdfQ12kEYtqC1OBIFAAI1GA7PZDOfn58jlcoaJmhHC2uv1kMlkIIriUrm02+3K2ZCrEU2JRAKSJGku3D6fz9BCFw6HN/abURS15tK/+hxkF78YyWQWgiAoHisUCiEYDJraGBCrhNls5rgTfK1WQ6FQAMMw6Ha7GA6H8veOlHp7vd7GhYGYM28KeDeKcrmsqDKSSbt0Oo1er7dVhSGfz8vTsU7ArgzJ4+MZVMaAAAAgAElEQVTjjfcSvVDqEyWwQphms5npc9z03XDC5oTjOEdLmnpxK/r27RoHouYQjAZb64GRcsouEA6HN960VntiFvvI9IDned0k+L4bN+SpxItIBF953/vw3A//MIDlLNF8Pg+appHNZtHpdHQ9v9HmfL/fj1arBUmSEIlEEAgE0O12EQwGIUkSaJrGYDDQVBEWSUQ4HJYJJfkjiuKST5pZkN42mqaRz+cNT3u12224XC6k02lbVapVCIKASqUiTxovEjJS6gX0B14fHR3ZRg4IqtXq2vH9fj84jkOlUpEd3efzueE8VzOgKGovFms9KJfLtnweWq/X6nvhhHKslEP8NC49/syStWAwCI/Hs5VrbBO23ct6O+BA1BzAZDJxJK7F6/Xu9UWutXMFLksQqyrQdDpdmshSgtfrlS0wGIbBK6+8onkcj8eDt774It65YLQb7HTw3/7pn0IQxTVFsVKpGCYkRtTNbDaL1157DQzDIB6P4/z8XN5VkuvEqFWC2mNDNg6GzOdzVCoVw2bDAGSV0m7is4p0Or2x32rRMmYTyuWy5V7QRcznc9lMdDQaIZvN4uWXX5b/P4nc2eTPpwSXywW32w2WZfGd2Xfwp4M/RVtsI8bE8EToCbzN9zaIooj5fC6XtndVcjMLqwNUm0rLw+FQd1aoEswqk1rDYXbYnLjdbqRSKUynU4xGI3S73Z36xi2i1+tBFMWD4a0BbFxtKIr6vwA8BqAuSdJ9N3/2WwAeBzAF8DKAj0uStLZyUBT1ywB+HgAF4DOSJH365s8/dfPnZJv5ryRJ+nPLr2ZPwDAM3vWud+Gv/uqvbDM2JDvwfUa73da86antPuv1umKJyOPxIB6Pg6ZpWU0i9gqrcLlcSKVSGA6HaDabePCpp9aMdtnpdM0zjgwMGH1ve70erly5Ir/W2WymShhIf5woiqo7Wm7F4NUsnDDSJEQrnU6D4zicnZ3p6suRJEku/1WrVdvPjeQ3bkK5XEYqlUKz2dSlgHS7XTAMY5ta0u/3kUql5LB1JZghJIFAAM1mc02BaYktfLb9WbTaLUf8y7YJq9YY4XB4Y6ktEAiYLseZJZI+n0/1mFZsTkh1YDwe21rG1wO9qTck/isWi231/G5l6JEFPgvg/wDwBws/exbAv5QkSaAo6n8F8C8B/M+Lv0RR1H24JGMP45LQfZGiqD+TJOn7Nx/ypCRJ/7vF899LsCyL97znPXC73fjSl75ky3OSPDW1/qV9gCAI4HlekahpTQTOZjNFtSYSiaz9jtLiWSwWUa/Xl5QbPZ5xgUAAPM+bUnwGgwEajYZcPiWN+as3X47j5H4vrUVnkxq5DyBlTNIML4qirunB4+NjxONxjEYjW8pumUwGDMNgPB7rLq2enZ0hkUjA4/GgXC6DZVnkcjnFYZbBYGC7Enh2dqb5Heh0OrpbAAKBAILBoKxCqykwn8fn8Tl8zrYeJyf6pjbBai+dFiEi8Hq9pomaEYuQRWjZc5i1OfF4PGg0GjtRTY1GsnU6nQNRM4CNs8mSJH0dQHvlZ1+SJImsxt8EoKTb/xCAb0qSNLz52K8B+IjF871l0O/38e1vf9u256tWq2i1WgiHw3t7gafTacUGdLWQ8EW0Wq21KB0laXx1B5vL5VAul5eOe9+NG5ir3AgHsRiSySSKxaJl4rs4ni8IAiiKQjAYRDqdln8+m81QqVTkxuNF3HfjBn75ySfxbz71KfzUr/yKrVFeTkIURVQqFdRqNQQCAV3mw2SQwkgZUgmBQAC1Wg2VSsWwGTDxwQMuyd7R0RH6/T4KhcLad8qJMhGZilXCfD7X9T7G43FMp9OlVgE1pUWCJP9/q1FO246HItBjn6EFPeU1KyU4SZJMXdNa1+5VXAWHZYV9k81JqVSC2+3eWWnbaCTbYaDAGOzoUfs5AP+Pws9fAPAbFEXFAIwAfBDA3y38//+Roqifvfmz/0mSpNvmk3v22Wfx7W9/25Evzfn5OWiatn1CzQ4ole/C4TAoitL1xWw0GkuRLkq7zsXePyWFguzsGIWS2JTj8KV3v1suyVlFtVpdKpEt2lrk83k5/kmSJDQaDfT7fdkqYHUHGux0bAkF3zYuLi4wHo91NewPBgP4/f4lawgyWEGUJIZhIAgC+v2+/FkXCgW5nMPzvOlp0kVPPKKESJIkX0MsyyIajcLtdquW462CkDWl592kqkYiEXS73TWVSU2BWYTVKKdtx0MReDwe06oVoE+Rs+pp5vP5DJdAR6ORal+qEZuTfD6Pbrdr+1pgVD01mnqzz73W+whLRI2iqF8FIAD4v1f/nyRJ371ZFn0WQB/A8zcfCwC/C+DXAUg3//3buCR8Ssf4BQC/AEAzZHmfEA6Hce+996LVauH09NT2yaD5fC6P9w+Hw71pEiUB3/P5XG5grtfrul8/yec8PT0Fz/MQRXGJuC36oQUCAcWyl9LODgBEisLTjz+O1gc+gLGFG/8itDyUKpUKwuEw/H6/3P+2eDNX24E+dP3z+Kn77StXbQMkbkdP6a7f74NhGORyOXmootPprA2UhEIh3HXXXWAYBoPBAK1Wy3IZjJRd1YiSIAjytLbf73csOFuNrFWrVdWkALfbDUEQFN8DJaNZJViJcnIqHkoLFEVZbjjXc2+0el3pMc9Wglbyh5rNCcMwSCaTmM1m8Hg8jhhMm5k6NZp6c/BSMwbTRI2iqP8el0MGVyWVLYkkSb8H4PduPv7fAqjc/PnZwvN8BsA1teNIkvQfAfxHAHjrW9+6Gztng3jooYfw0EMPAbi8+Z+enqJareLVV1/F97//fdsWgNPTU+Tz+b0hatPpFM1mE8lk0vTgQ71el0O1V3fSi7trr9eruKCp7eBoScIL99+P1HxuX2ROKKSpIp2fn6u6l6udZ767XK4CjI3k7yqa5uLiQndflyiKa1O+q9+JbrcLSZKWrm2fzyf3FRKz2MXSNbkNkQWeYRj4/X55MaMoCtFoVNci0e/3MZlMHMkxBZR9wkRRVL2uE4mE6qK8qsBQoOSy5yKsRDk5FQ/lcrng8XjgcrnAcZxc6pQkCZ1Ox5K9i9vt1qXGWfWxM+tvaaasS1o9nIQZ9dRo6s1BUTMGU1cYRVH/DS6HBx6VJEn1KqcoKilJUp2iqCKAJwC8/ebPM5IkkS7kj+CyTHpbgmVZ5PN55PN5PPLII+j3+/j0pz9tm8pG7BN27TZNMJ1OUalUNN3AtaBlskpuiFqvd9POzgqRWZ1q+vuPfhSNDbYKauVvtfMsL6x72ygt2YVSqYROpwOKogyXkjwej+KCHA6Hl4jaYqqCXnQ6HeTzeZyenmqmBShhNps5ZgwLvG4FQgygAahammxa2BYVmFVFBDAf5USgFg/1sejHkHFl0Gq1wLKsbKMzn88xmUwgiqKclUl8/hYDzyeTiWN9VZFIRNewy8XFhanrlsDs7xk1lF5U552EGfXUaOqNE/ZVtzP02HP8IYB3A4hTFFUB8Gu4nPJ0AXj25sL3TUmSfpGiqCyA/1OSpA/e/PU/udmjNgPwLxb60P43iqIewGXp8zUA/4N9L2m/YSQUWy/K5TJKpRKazSYYhgFN03K/2HQ6xXA4dHTBUYLL5bI18Bm4lP0DgYCmx9Wmnd3p6alhzzJAearpx//gD9B+7DHNnjK13jyl8xxwwL9aWUuNlpasNl8bRT6fR7/fl3tkzBjkxuNxRbXIqjUDASlDMwxjeHjk/Pzc0Y3QyckJYrEYxuOxTEKr1era+7iq2vA8D6/XC5fLBZZlQVEUzs/P0e/31xS2MBXGY+7HcO/0XtP3HvKcX6a+jHPpHCGE8CHvh5Br51DDJRki95p9gV7LG0mSEAwGTVcmzN5b1cg3SUwhxJaiKHAcJ0e8OQ2z6qmR1BtC4g9eavqwkahJkvTTCj/+PZXHnuByaID89ztVHvff6T3B2w0vvOCMeLipmdTr9cLv94OmaQyHQ0sB33pwfHwMiqLkhdyOngSapuH3+zXJ36adnVpI+yYo9ZRxCr5sq0gkEopmvqvneRyi8L9clfCHK09ltbTkFGiaRiqVWiNlw+HQUNIEeS4lnJ6ewuVy2aK4EMJlZsr37OzMtvNQQqvVgs/nQywWQ6vVkqdqY7EYRFHE+fk5otEoXC4XRqMRhsOh3B6wClJqf2T2CB6YPHD5OUgARgDN0chkMnI7hh4kEgmwLAuaplGgC7j/+PULNMyHcT7c314jI+U1n89nmqgZsVZZxGw2QzKZhMfjkX93PB470ndmBGrqqRVFVgnD4RCBQMDW57xdcUgm2CLm8zm+//3vb36gAxgOh2u7Xa/Xi1gshsFg4EjPgCRJqFQq4Hne0o6VgOM4XcqG1s6OpmnkcjnM53NwHIfpdCp7vzWbTVUVx+hUE0Gj0VB97YvnScpVsHhzdFJRo2kaPM+D53n4fD7FslK73UYul0OtVtO9cGmVjhKJhC3lHo7jTMe6TSYTRyZAFzEYDDCZTJZSEVqtFjweD8LhMLrdrq7vqNZ3jFjFAJeN7MPhUFFhS6VS4HkeFEWhUqmofo7BYHBvm8KN9hZaMZwej8e60yxYlkU4HAZN06AoChRF7d30vpGpUyuYTCYHoqYTB6K2RZTL5b2KcCHkrVgsOtrcOZ1O4fP5LLu9C4IAt9ttKiycQKtnhaIoxONx+Hw+DAYDNJtNOblgEIvBr6DGqE01EUynU/j9/o2vfa1chTDei/cavjlatRpYRSAQQDKZRLPZRLfbxXg8xng81iQE1WoVqVQK/X5fV0+Z1vtiV5tALBaz1JhOTHuNerfpgWyFIHQROgnhY9GPIde+9BQkk8JWrnklXFxcwOVyye8vcbQHLgmins+tXC7L9iokMmxfYNWvzyj0bJAKhQI6nY4j15DdUJs6tRNmp2XvRByI2haxKzVtE+zsI1NDp9NZa5w2isUYKUEQcHJyYisxkSQJzWZzyZyVBM0/8+ijaz1lM42ppkW0221diszizdFsqc3OcnaxWES1WsV0OjXc/Ht2diYP0mxSxLSuh3q9bqqstIrT01NcuXIFtVrNdOyP2ek+LShZIXy2/Vn88+g/R76ThyRJlvMu1UBIGsMwqNVqpkgxsVcBLlUsl8uFs7OzrfXE0vRlOZdlWXlzBagP8ajBbNYnwenp6Vp8XjKZlL3xBoPBzkua+4Ztk+lbGQeitiVIkoTvfe97uz4NRWwrvujk5MRUs/kiMpkMjo+PMZ/P4ff7wfO8Y2rgaDSSF0ml3re/e+IJHN9/PzJeLziOk3NGlVCpVAz1x5ktYYqiiGw2K0+xSZIEQRDQ7XYNNef7/X7LDfSCIKBWq21UE9vttupjRFE03Ve4isFgIPfWmXm+09NTxZgwK1CzQvij9h/h06VPO1oWC4VCEEXRtmlCUmrkOA6FQgHz+RytVst2NZAgEomAoqilHlCPxwOfz2e4zG2VDAuCIN/bwuEweJ43XWq/E8BxnCMbn9sVh3dqS6jVanvrHVOr1UzbaRjBakSUUWQyGZyensoqWr/fRygUsjVAWwurvW/FYhHdcnnJQV8NqVTKUulNLyRJUuyV8fl8mM1muhXIRXNh4HLKELhckIyoW6Iobry2BEHQnMS1YyNBUZQcyj4cDk33nPn9fluJmpYVQr/ft1zq1wIZrLC7z2w2m+H4+Bj33biBn7q5sRnEYnjm0UdtS90oFouK/XOLmyvyuPl8jtPTU03VzI6qwnw+RyqVQqfT2dvevX0BuZ8coA8HorYl2Jn7aSdcLhcSicRW+ksuLi5MDxT4fD602+01otHtdk3FaRWLRdk0FXi9t0sURcznc4iiiNlshsFgoFrGWb3xTyYTeXJvFUZvTEqKWiKRwGAwWHoPFpUzSZJUm6IHg4HcxDyfzyFJkvxaF5+P/H1RGVwkNOl02jDh1EO0tPztrKgdDMMgk8kAgKwakdgoM7YbJycntpInDzwYYf31hRCSp0H1NqrvE1btbPytFj709NOIxWL4xs3WBTNgWRbpdFr350Z654jKRyZeV+8jgiDA6/Vashch/a9294nejjgQNWM4ELUt4MUXX8R3vvOdXZ+GIuyaqtsEhmEsTX2qRUYBlwswGQDQi8WMRy2QUpnb7cZwOFyaJDs5OVkqo9XrdWSzWdXjGYESUXO5XBsn2bSm1+zY5TcaDWSzWTl3Uw9poygKoVAIgUAADMNgOp3i4uJiSbHTWiCbzabpnj2v16t6fZfLZcNkTRRF29zhb+AGplgvR9Og5WlfYvJLIreMGv7uCop2NrMZHvijP8Jzn/qUqsmxFiKRCAAYul+R94uofMDlpi8ej6Nery9tAvx+vyWidiBo+nEgasZwIGoO49VXX8VTTz2169NQRCaT2QpJSyQS8Hg8phe3TWUqURQRjUYNLWKrCs5q6gDxXpvP50tqI8dxcg8KwzBrr0ntHMgCoHacTfB4PLqIltOLhSiKS+pOsVhEo9HQVL1EUUS3210bSGBZFpFIBC6XS/M6JCUlM9eP2+3WLGuVy2Xk83lUq1Xd7x2ZBrb6Xl/HdYhYL9m74FqbuKtWq2AYBsViEfV63bFyqF3QsrMh10IsFgPP84qT2BzHLSnZaqVOLagFuhPyS9M0CoUChsMhWq3WYQpxizj0pxnD4d1yEIIg4Atf+MLWUwH0gOf5rWWECoJgmqR5PB5dZdnj42NDjd6LRE0pdeDxpy9zNldJ1Gw2U1W1WJZVnY6kKAo/Xi7j0WvXwN5s6g93u/jQtWsIBoP47lveIpchZ7MZBEFYKnkFg0Fd78O2d/Xkc00kEhBFUbEPU41UCIKg2+uq1WqZ6kXUs3OvVCqGyovD4XAtRN4M1PrTlEqhwCXhLZfL4DgOpVIJtVrNtvQGu6EnpJu0CORyOQwGA/j9fsxmM3m4hOM4cBwHr9dr6v4RDoc1NxDz+VzeACaTScuTnwfoh5Nm67cjtps3c4fhW9/61t42labT6a2UURiGsVROSCQSuhcjIzviRUKjVKbhZzNcvX5d9/MBUA1gBy5VmB/9kz+RSRoBN53ioc9/Hp1OB91uF71eD6PRCOPxWFaC3G637h5CQRDkgGuGYbYW0u52u1WHZezwDiTlP6NoNBpIJBJIJBKajzs5OTH0/HYs6mqpE5vSKGazGY6OjkBRFEql0l6qE9evXsV0pQyvFtJdrVZxfn6OSqUiW3uMx2MwDINut6srr1MJRspr9Xrd9HEOMI59FC/2Gfv3Db+NcP/992MwGODVV1/dq5uA3t4iOyBJkmmrCaO9QKenp7qVkcWF1mzqwCq8Xq/mZK+R4xQKBZTLZbAsi1QqpXtYQhRFVdXJ4/E45smlpYyNRiPTJd9FmFHVptMpzs/Pde3glTI21XB2dqY6OKIXVqN6JpMJjo6O4PF4kM1m5c3HaDTauamq0ZBuJVidxDyQgf3FQb00hgNRcxB+vx/vf//7AVzu2J9++umtESQtiKK4tf60VZsHvUgkEqbeq4uLizXjSSUsqjx6yjR6oERIY7EYvF4vBEHQnW7gcrnk155MJg1NtKqRGIqiHI2XIspdNBqF3+8HcEmSRqMRSt/4Bh67dg3cQslXrbSshcFgYGpSM5FI6C5rVioV3WTNqmGnXVE9o9Fo7T0xMwltN4yEdDsBp4y87dh0bBP7eL4HEm0MB6K2JWSzWXz84x/Hn//5n+P555/f6bmQ0OdCoYCzszNH+1xcLpfhXrhEIoFer2fKG+3i4kLXYr4YpHz96tW11AG1Mo1R+P1+ecH8uyeewI999rMbjyMIgtw8bZcSK0mSbD/gRB8bsXkpl8trquJ7nn1WJmkEpLRsdMGo1+vged7QNWu0NKi3Ub9arWr6v+mBU1E9R0dHhockbhfcwA18GV/G+cW57TmVRvpZ9wH7er4HRc0YDj1qWwTP8/jwhz+MD37wg7s+FQCXDfgcx8lj73aiUCggnU4bCkamaVqeIrTS11SpVDT7xYDLZtZoNArg8ob19OOP4zwUggTgPBTC048/bvhGRnI9k8kkCoUCisXikpr4ytvepus4pHxpxKBWDxqNhqYprxUEg0FVcmxXaRm4JFHEF00vjJYnm82mrn41SZL2OlS6UqkgEokgFouhWCwin8/v+pQcB4nkOsclee6ii6fxNG7ghi3Pb1c/67awr+d7UNSM4aCo7QAPPfQQotEovvnNb+Kll15a+/9+vx9vetObMJlM0O/30e/30Wg0HJmUGQwGiMfjtrqtA5cLqhGSxrIs4vG4Lf5UJF5Kj5JXKpXQbDZtKdPwPC9/XqsgvXPHOy4HETsKu8veWp+bXaVlAiOms2Yjn87Pz3Udg2wKtjVBbQbT6RQcx+29pYcdUIvkuo7rtqhqdm46toF9Pd/5fI75fO5oO8bthANR2xHuuece3HPPPXjllVfw9a9/Hb1eD5lMBqVSCW95y1uWjEvn8zl+8zd/07GRZidKn0YmML1eL0KhkK0DFycnJxv740gAu8/ns3y8YrGo2hPj8XhQq9UMqWN2TEqq4fT0FNFodGuRZkql5RnP47mPfQylUgnT6RSDwUA32ZnNZshkMrpIfTAYNEXUBoMBUqkUJpOJ5ucmSRJ4njdcjt0WxuMxotHoVvpR9wFakVy2PL/Nmw6nsavz1dMXN5lMDsHsOnEgajvGlStXcOXKFc3H1Ot1R6ViK5NraqhUKshkMkvky+/3w+12g2EYMAwjN/3XajVLFh5qSKfTiorlIlbP0QxCoRCOj49VF3TSu6UXZhrmjUAQBPR6PQQCAXnIgPybYRjQNA2PxyNnJCqRRp7nkUgkQNM0ms2m5jSp2gTg/1coQCqX5feNpmkEg0F4vV65r0zJKBe47A/bFPkTCoUsWdCcnZ3pUh+bzSbC4TBcLpdjDexGQcxca7XaHUPSgEtrEyVStsnyRC+c7Gd1Ars4X719cf/4j/+IBx54wLHzuJ1wIGq3AJy80QYCAUcsGxiGgdvtlp2/u92ualnQCeTzebz00ksymVDzIWMYxtJxOI4Dz/OaqgsZcGi1WrqIwzasFQRB0CQV8XgczWYToVAIfr9fJvPpdBq9Xg/hcBgnJyfy695EaJRKy6FAYImEzedznJ+frzXnK32GoigqlsoZhkEul0Oj0VA1HzaCSqWiizifn5/D7/cjEAjsnKzl83l0u92dT33uAlYtTzbBDtuRbWIX56vVF7d43G984xt485vfbPkefCfgQNRuAVh1QNfCxcUFeJ6XJyCtgGEYZLNZOXbp1VdfteksjaFUKsnEgvTJpdNpTCaTpTIYRVGo1+uWjjWbzRT7LJSk//kHP7iRqCWTScvnZAcIAet2u6AoCsViEaPRSLYNWVWyzBjr6i2Pk8+wWCyiWq3K08DHx8dL5W1Chu1WI/Vmgvb7fQQCAdOWNFYRDAY3xnHd7rgf9yMUDOFzvc9ZsjzRwq5tR4xi2+erty+u2WzixRdfxA/90A9t47RuaRyI2i0Ap2+8Xq/XcoJCIpHAZDLRFXTuNIh6t4jT01OZcJydnWEymSCRSFgmRTRNr5EvNen/v8bjqKiEthOYCSsmWagMwyAUCtnSe7aoEEqStJGkmJlONfpay+UywuEwGIZBq9WCJEmIRCKIRCLodruOlov1kDUymLKr4HSSX3mn453hd6LUK+36NO5Y6O2Lo2ka2Q33wwMucfhW7zlms5njpTCv12vp9/P5PFqt1t5Mvp2fnyvaUBDCQQib2+22dByPx4NYLLZGCtWk/wefekrz+dQCqhdB+skWByCImqWWtWkGRtVVM2qsmZIHKY0Sq4nj42P0er2tXHuErCkhn8/j6OhoZyQNuPz8SY/hnYw7zTfOKu67cQO//OST+Def+hR++ckncd8Na1YmeuPD8vk8Qns6hLFvuLO/0bcAjDb6m5misZIVWCqVUKlU9i5kV2v4Yjweo1wuYzQamfbB8nq94Hle0YJETfr3byBR8Xhc0+SXNPy73W4MBgMEg0EUCgVHymxGzYbNfP5m7SKIYXOxWARFUXISwjZQLpdRKq2rNU5FcxlFq9VyzCvvVsHZ2RkikQjS6bTsZ1goFBCLxXZ9ansHov6Hu11QeF39t0LW9PpSPvjgg9ZO/g7CofS559AqtaTTafA8j9FohEajAZfLhVgsZrhUapZkFYtFHB0dwe/3IxqNak4+bhunp6cbXeMbjQZ4njflKxYMBlUjrsyOxJNg8MFgoHjeqVQKp6ensqu3WSWJZVlIkiSrL2QKl6ZpRCIRXFxcGFbmzFxDVsvt5XIZyWQSrVZLHn7YBo6OjpbKoDzPOzI5bRZHR0dIpVKqAzS3O6bTqapVitfrRSwWw3Q6vWPfn0Xobfw3Cj19cftsFr1vOBC1PYfWAtDtduWd/CYnfi0YVQPIdBtZqMg0p9WQarsRCAQ2koHpdCqrM3qVwU29SlZG4qvVKnK5nOJ521XSkiRJNbzd5/OZIjxGFbhN1hp6Ua/X5Qb+bQaRL/asRSIRxUX/Bm5YzvE0i36/r9sU+E7CcDiUr7tkMgmKou5owrZLQ1wnvSJvNxxKn3uOe++9V/Hn6XR6iWARdcXM9J0RZaNQKGAymSj2Uu1b+dMIyuUyotHoxl1eJBLZGPBNpP9eJGIqkkqpFG0kWHwTtD4nM9cPYJyo2Wl0eXFxgXK5bDhayirK5TIKhQKm0+naYASJMiKeXnZHGW3CYDCQI9IOUEa9XsfZ2RkKhYIhg+7bCWoq/zYMfPXEtB1wiYOitufI5XLgOG6t50rpxuJ2uw1beRgxV00kEppTnftG1IySjmazKZdGlJRBj8cDQRB0BQr3HnsM//7BB3WHDxM/tsFgoPg+miU2gUAAkUgEFEVBkiT534s2F4swS9SMhixzK83GVjGfz3fSJ1av1zGZTNa+R05HGenBycmJPBGsF7FYDG63G5PJRC6R0zRta2rIvuH4+BjRaBQURd1xCqQdhrhmlONMJmOpCnSn4UDU9hxqvRZKpprxeHrGfM0AACAASURBVNxwr5WRG7CVoYNdwKjKA1yWRgRBWPMzI03revNL5/O5bvJSLBZRq9UwGAzg9/sxGAyQz+dB0zRmsxn6/T5arZZhr7tisQhRFBWJuNvtNvX+qMEoUXPiWjo/P3c81WEVk8kEkUgE4/F4qSfS6SgjvajVagiFQrIfXjQaxXA4VCW1giCsbfbuhOGEdruNSCQCmqYdSUnZV1g1xCXKMdmUEOUYgCZZU6sUHaCMW2vlvQNx/fr1NTUtGAwqNnubsTvQs8D6fD4kEgkIggCXy6XaW7Bv5YNqtSqHoRvBdDpFu91GOp2WBwby+bwhZaJSqWxUM1wuF6LR6BKxIBOcm5r5WZaFy+VCPB6XQ7clScJ4PIYgCOh2u6amcbdV+nTKQqJWq2leo06AGCl7PB45Q9XpKCO9EAQBoigikUjg/PwcrVYLDMMoXpvBYFAxzeFOsfvodDrwer07MyzeFawY4ppVjn/wB3/Q1PHuVByI2h5jPB7jhsKYdDgcVpz2MzpxuamhO5vNYjweo91uy/5QHMchn8+DoigIggCKosBxHERR3Mubm1GlZ/H36vU6CoUCaJo2FcezqYwSjUZNl5RICVbtORiG0SRPdk/nbosQbsJsNjNc7rMK8h0ajUZgWRahUAhXu1dxjbqGqfS6Is6Bw0/4fgL5SB6SJDme4UuwGt0miiKOj4/X1MdIJLJ0X5FLWkfbH4bYFYbDoWOWN7cjzCjH2WwW6XTaqVO6LXEganuM5557TnFBVcsSNLr4aj0+EomgVqutPWY2m91SETVmnP4J5vM5jo+PFX2z9EDLOsOuEp0a4dmkcKn9HjknlmXB8zwoipIfq7V4xeNx2YyXoih4PB5Z1Vo8Fvm7mvprR5TZ8fHxzqK4yHfzfcn3gW7QeBbPLvXuXBlcQWVQQTwe3wpJ00K5XJataWiaXurLNFvSuh1wfHxsiOwrxcXdShFTVrBJOaZpGnfffTfuvvtuFItFpNNp2/tT7wQciNqeotfr4W/+5m/Wfk5KK0ow6oqu1qfi8XgQiUSWcjFvVdhRthmNRshms2g0GoYW10WyQkCsN+zqozKrGG5StFaHJgKBAIrFIiRJUlzAVi0OSqWS5kK3SH4TiQQYhsHZ2RkYhkE8HrdMsuzsvzODer2O+27+owSzNih2QxRF+P3+NUV8H4YhdolqtbrU+qAGtbg4AHcEWbuKq0uEHrhUjv9Z/J/h8bc/jje+8Y2Wk28OOBC1vcXnPvc5RdsMv9+vSNR8Pp+p+KBgMLik/BQKBdTrdbzyyitbb8x2As1mE/l8HvV6XXUwQwvJZBKj0QgXFxeIRCIQRVGXyezqjpzneSSTSdvVSDOvyQwuLi5ktYgMOixCkiRZfZUkCRzHIZ1OQ5IkdDodxfMkEVyL7wnLsrYQGOLQvw/Zs0rYBw8prdL7vgxD7Arz+RztdnujN6RThrG3CghpJ1OfKVcKv/7uX8fPv+3nd3xmtxcORG0PIUmSagO82k0jGo2ayhkkRM3tdsvpAgTEW0wQhL3J8TQKYnBJ0zQymYzhnjCWZWWC0ul04Ha7N+600+n00uf3jqMjPPT5zyN4fq5aGjE64h6JRBAIBHRPoa7CitJolGwqvV8kCWH1ubxer202G81mEzzPb43MGoFa+8I2obbpAzaXtO4ETKdTjMdjzeGCXRrG7gPC4TB+7g0/h9/+4d9GqVRyrPf0TseBqO0h/uEf/kGxxKZFEMwsbl6vF263G6FQSJUcttttxONxw8+9b5jP53JflF5SpBQtNR6PcXp6imKxiJOTE8XS43w+l0tvj1Yq+LH/9J80SyNm+oHIhJpZUrPNST6lnrPpdKq4sVhUwnw+n6WQ89FotDeqcDAYlIduAOvxWVbBsqzmpkWtpHUV+v21bgcMBgOkUilVomY2Lu5WhcfjQalUwl133YU3vOENsv/cAc7iQNT2DJIk4dq1awAgT8YQcqbWhGm0cdrj8SCZTKJarWI4HGI6nWou+KR8SPpY9kENMIP5fK6bFKVSKU3lqFwuw+/3IxwOrz2OKDjZbBYPPPnkxtKI2X4gK8rTNm+uSv1imwYGCFkrFAqoVCqQJAkulwssyxoib+VyeWs5oCTKilil0DSNk5MTzGYzcBy3V/FqyWRS07aGXHdfob+Czrxzx0x9KkGr+d0Ow9hbAT6fDz/7sz+LRCJxIGY7wIGobQmSJC2pOmqo1WqymiYIgly+SSQSqpl0el3rfT4fYrEYTk5OcHR0hHg8jna7rcuNe7WPiCxIi8He+9oPtAi9pEjPtCixPYjH43C5XKjVakufMU3TukojZvuBrJjG7lpR0xrKWLyOjo+PQVEUAoEAJElCv98Hx3HIZDJot9uqSgfLssjlcjg6OrLdisTn88kKGUlEOD8/X+rjI/D7/YjH47omWf1+P/x+PyiKkr9XFEUt/e7FxQW63a7l16Tn2nlv/L24v3nnEbNFxGIxTeXRqmHsrYJcLodkMrnr07hjcSBqW0K9XsdnPvMZpFIp5HI53HPPPXjDG94g+12RxZ3nebjdbnAcJ6sA0+lUMxpq0yIQiUTg9/tRrVblMlA8Hkev1zPVvyMIwlrpplgswuPx7CTGRy8oitJNiowoMOSxPM8jlUoBuJzardfrukojZvuBrORlmjFHNgslRc3I9KwkSbi4uEA6nUa/38dsNpOv41gsBq/Xi+l0irOzM9neI5lM4ujoCKVSCZ1Ox1QJlBgS8zyP0WiE2WyGwWAg/9EDQuZjsRiKxSJOT0+XvnOxWAw+nw+j0QiNRkOXfxchrlaU7U2l14NqctkaMplMNk4QWzGMvVVg1qLoAHtwIGoO4tlnn8UjjzyCYDCI1157DaIo4uTkBCcnJ/jbv/1bhEIhfOITn8D3vvc9vPLKK3jwwQfBsixms5khlUytITiVSsk5fYtWG1amINVAFsHVKdJ9wnA4RIyJoSWul6AWSRGJ3DGK6XQqq0EulwvJZBJ/98QTeNdCjxqwXhpR6wf6tRs/gI9ff1J1p670+WUyGcxmM9nPjKIoWZkBIP+MYRj4fL613yePI4qNEXITCoXg9Xoxn8/lPr35fK54nmZsRZSep9VqodVqgaIoZLNZXFxcIBAIyIsrMSqeTCYbNxKxWAx+vx+CIKDf76Pb7dqWcdlqtWQbjHw+D+CS4JPzN4LFARczWIy6UkOxWNxo8mwm4/FWAfl+mB3Wud1w5cqVXZ/CHY0DUXMI/X4ff/3Xf43nn38eb3/72xVvet1uF8888wwef/xx/MVf/AW++93vyoqAXg8zn8+31p+WzWZlhWERxWIRrVbLUcNan8+3t0St0+ngUTy6sUnajiisyWSC4+NjHJdK4D7+cTz41FPwt9uKhGt1xD2EEH7txg/gXzz9vOYQgtICv6lJ3AgikYihx4dCId3EzozZayAQUN2ULA7DKJGYyWSiaNdB0zTy+TwuLi5MkSa9YBgG9Xodoiha/v5pvQ96EAwGNYmanszg290QN5PJbMXY+1Ygu16vV64UHLAbHIiaQ3j55ZcBXE4N/eVf/qXq41544QW0Wi25fGnEld3v9y+RtHw+j+FwuNYkHIvFQFHUVqbf9t11epUURegI3jN/z9LN0e6epn/80R/FV7NZAK/7qRUoCoPBQF5w77/5D8HHr2sPIWgZH9sFJ8tfZhQ1s+a+BKuJBblcDr1ebyvfi1wuZ9txrF6fWtdNKpVCq9XaWO67nQ1xS6WSqcg4o7hVyO7DDz98KIXvGAei5hBeeukl3Y81q4B4PB6Mx2Nks1l0Op21HSBN00ilUrYpLHpgN8lRQ7FYxHA4NDXNR0iRkv0GAFudtHmeX7JUmU6nS8ekaRrhcHjNq27TEIKaB5adN1QnBw4WiUAqlYLL5VJNPSCwY9pYEAQ57kqr79Nu2Dl1aiV1gaIoRKNRhEIhNBqNpXJyMplEs9nU9fy3qyGunpKvUaipZvtIdovFIvL5PBiGwXw+RzQaxVve8padnMsBr+NA1ByAKIr4/ve/7/hxGo0GeJ5X3akXCoWt7Ax3hWaziXg8Ljf9zmYzzGYzTKfTjc7vHMepetKZ6U9bRCwWg9vthiiKmE6nmgoGcUAvFotLRG3TEIKautRoNBzPEVWDXpJOURTy+Tzm8zkEQUC73ZZLoWrkOZVKqU49GwF5r7fprWbXuRMQg2oyeT2fz3FxcSGHw2tBkiT5tS+WghOJBDqdjm4SeLsZ4vI8j1gsZvt1oaWa7RPZ5TgOjz/+OO67776DeraHOBA1B/Dqq69uLSJGbSBAT5+JE9iWotZoNJYmY5WwaEFAIo5YlpWnYEl5ehFm0gsWoUY0NuH09HQpX3WTP5Pa6x6NRromB/XAiKIWDAY35iISaClnJCB8tQWA4zjb+nlOT0/h9Xp1ERs7YEfP4yrG4/GarU42m0Wr1dJ97yGkzOPxYDAYGOobvJ0McX0+HziOc6TyoKWa7QvZTSQS+OhHP4pEIrHV4x6gHwei5gC++93v7vT4hUIBJycnOwmm3tYggR7XeSXViQxZKPmkMQxjieQQ+wUzmE6nEAQBd911F1577TVNf6ZNBq5a0UBOIRwO2/bZZ7PZNbL7X87/i239PNPpFMlkcmtEzUjfqRWcnJwgEAggEAhsLLVSFCU/JhqNGi4DKw3A7KIR3gh5v+/GjbXv03ff8hZ4PB7HDJG1VLMn8MTOyW48HscnPvEJXb6RB+wOB6JmMwRBwPe+972dHT+TyezUePZWSS1YVYsoikKpVMIrr7xi+jnVIqX0gpSwSEnqhfvvx//7Iz8CmqaXSLfP59NcWOxSc40oaqIomrY1WcUqWfZ4PPjP/f9saz9PpVIxrX4axbaIGnD5/RsMBhs3MclkEmdnZ0in06Z79VYHYLYNI8349924saRQkynqWDSKrzm4odVSzXZNdimKwoc//OEDSbsFcCBqNuPGjRtb26mvgro5SbhL+Hw+20pvm2DWXJfn+SV7glwuJ4e3u91uuaTkdrsRj8cxmUwwHo8RCoUwGo0wGAwU0xysTiVms1mcnJxgOByC4zjMZjPk83lUq1UkEgl4PB4IgqBpIREMBm3rhzLSq1KtVpHJZGwhaufn5/LrBy57/roV+/t56vX6Vkqg2yRq5HjlchnpdBrdblfxe8LzPFiW1ZVK4iSUVC695rFGmvGvXr+uOEX9wB//Mb72yU9aexEa2FQi3iXZffDBB5HL5XZy7AOM4UDUbMaLL764s2M7MbFkFLFYbGtEzWyw9XQ6RTQaRTAYxHg8XlIUvF4vCoUCBEEAx3GyKrHoq6Tkx2WHmkT6+xbLuuPxGPP5XDbeJEkWaggEAraVIJ0aJtADhmFkokZRlCP9PIslUL/fj0AgIPc1kkgoO7BtokZAevFW+y6LxSIqlcrahmXbUFO5AOgia0aa8fVEuTmBXatmWnjjG9+461M4QCcORM1GNJvNrUx7KoHkGu4aJKzcabJmlRip9ZIRZW0VxB+OpmlFIuT3+y2dTygUWlpM+/2+YvlKFEUkEglVx3Q7o6EoikKxWARFUTIJI5m1ZDhj8b9dLhcikchaMoEoivLf9WJR6en1erY3r1MUhVQqBZZlZSKzes2SeKfBYGDJCNeq0moF5HouFos4Pj5GPp9HuVyW1dtdQk3lIl6Bm2CEvKtNUQ9iMbhcLkeHv3ZdIlbDwcT21sGBqNkESZLwxS9+cSe751wuZ7qJ3W5IkoRoNOo4UQsEAraU2fTA7/fLixrP8wiFQpcWE1//um1hzKvEs91uqypapKckGo3KsUSSJGE4HNqatbpo5WAWq83e78P78FjpMdRqtaV4K5ZlwbLsUuwV6ZGjaRofDHwQ0UEUn+99Hi2xhRgTwxOhJ/BjgR+DJElL0VcktoqokYvP5/F4wLIsGo0GTk9PUSwWVacdV5MKeJ4Hz/PgOA4cx4Hnefkc5/M5ZrMZJpMJhsPh0jS2mRQGu1EulxGJRGRV2EmPPL2wqnIZIe9qU9TPPPooaJpGLpfbqq/etkBRlHy9rv7x+/27Pr0DdOJA1GzCiy++qGj34CSi0Sjcbvfe3WDK5bK84G4irmYtF7YVKp5IJDCfz2XiOR6PUS6Xcd+NG/jQ00+DM1m2WcTiAroIn8+nqOScnJwgkUhgOp2uEal9GrFXavb+Ar6A6EUUuanx3pg88vgl/NLlf4gA2sBR25iKrBTN1mw2dQ0WTKdT3fm4PM8jEAjA5XKtRbypwek4oU6nA7fbjWg0unM1DdjsFbgJRsqKWlPUGI1QrVaRz+fRarVs3eyYgcvlgsfjgdvtXvrjcrmW/ntx07D4h2VZ+e/buk8e4CwORM0GCIKAZ555ZqvH3LZpp1GQspgWrESobEuluLi4QDKZBE3TS1FfV69fl0kagZGyDQFRkZQI7enpqWJPmiRJqqVPOz27rBpfqjV7f677udcJl4MwUt7r9XpLJV6rmE6nhsql24oT8ng8e0HSgM1egXpgpKz4wv33a343Sd9eqVRCpVLZib0RAPzSL/0SfD7fTo59wH7i/2/vzoPkrM970X9/ve/7Pt09ow1JaEUaBBKyQAyITWKTwHbhipx4yUnZ5VzfVG6c66pbvveWq3Kz1D3nVKrs2E5uchwfB5tj+1gGErAcBHGAAEYIgTFCoOlZNGv37L337/4x0+3umV7e9+337WXm+VAUTE+/S7/zzvTTv9/ze572j3+vAy+//LLgJupyKOaZdCKLxQKz2SwoYKi3aquRVtVrK46gTU5OVjQprzdtI6aZeSAQqFnzLJPJILTSI1QoOae0mg3UaiV7T+eVaXy+WjweRyAQEPTcubk5RCIRhc+otmZ+F8TopADg8t69OHfqFGbsdnAAM3Y7zp06JTl9QA6ZTAaDg4MwGAwIh8MtP77JZOqonxHpDDSi1qS5uTm89NJLLTue0+lsae9OMcSW5pDaQqU8f0lJ5VNRDjhw5/SdpU/vtaZt5lb6djai1WoRDAYbBtxip2Hambi+Wq1kb7favTx1qaDigpbx8fGqq3StVitMJlPF9R8fH4fRaGzL1Fer2gl1Qm5auUajXO2yuLjYlvpinZS6QDpHZ/3WdqHnn3++ZdNwJpOptIqu09jtdtE13GqVVmhUcqFQKCjSlqdccSqq+EY5gxmcwzlcwiUAy9M2mZWVoEUZrRZvPvZYw7IObrcb2WxW0Kjo1NQUotGo4POWsyNBsyNqAxiAFpXXSAstHrE/0tR+hSgmShfbVa2uF7W0tLTm9yidTsPtdit+btVI/V0Qq53lOLpNO/K7PB5Py49JOh8Fak0YHBzE5cuXW3Ks4qe7Vq10FEvKCqJab+SNSi6YTKaaOVpyaTQVVW3a5umHHsIb27c33LfBYBB1LrFYTNA0jMfjaXsB03J7sRencKoUbNhhxymcwmHzYcWPvXo0ZGRkBNFoFH6/H8FgEOFwuOq1Gh4ehs/nU/z8VpP6uyCG0+lsWcrAetCO0UcaUSPV0NSnRIVCAc8++2zLjuf3+9vaGqoexpikKTepxSDdbrfi1eSFTEWtnraJRqNYEDBKJuUNYHXT9mpMJpPo/dbT7IgaUD3ZW87CuLVUW5xRPoIZDAZrTtO3Y/q4FYVRbTYbei5ckK2kzHpHgRrpFBSoSfTGG2/I1qpHiHZVNxeimY4IUopBtmLqV0ol/KGhoVKR1GI9L7VavWaKU2i5hnK5XA6c84rWSqup1epSV4V0Oo2FhQXB5SSqKQ+o5Cwd0Yp7udH0s3bVtHW5eDzellXVchZGDYfDpam74s/R+/zzuLuJTgAbDQVqpFNQoCbB0tIS/vVf/7Wlx2ymMrrSpAQeUqnV6pYEyAMYwM/Yz5Dhvw10Gk1Fcc7XFEldnV9WrHQvxczMTN3CnIVCYc2oq06ng8VigV6vh0ajqSjQWizSmslkSv1MGWNQq9VQqVRQq9XQaDT4Ve5XspaOUDqn02AwNEwRaBTsj42NtW1hgRzi8fiaUedHnn66qU4AG00zgZrD4cChQ4fw0Ucf4YMPPhA0imwwGKgILamKAjUJfvGLXyj2B9xoNMLr9ZbeSFQqFbLZbMd0HljN5/NhYmJC8WKdRV6vV9ZrUasp9F7shcflwVOJpxAvxGGHHR93fxyhaeHlMtRq9ZoAe3FxsaLxu1jFXKtqoz3V3gwymYyoBQbFkUAA+OijjwDUztd7QfUC7vHfUwrs5ubmBCWrKx38uN3uukWgPR5PwyLRmUym6mrRbuByuar+zJXod9lMU/VOJ2Tqv9grtjiNrtPpcOzYMdxyyy3QaDQ4fPgw5ufn8fbbb+PixYt1c2u9Xq8s6QZk/aFATaTBwUG88cYbsu5TpVIhGAyCc47r1693bI20amZnZzHiGsG5uPLFOoG1SeL1NHoTadQUOjQdwpfwpdLzHXkHbNHlRu4TExMNG6T39PRU/Vkajcamkv5jsVhLW97UyteLF+IVpWKE9g5UOr+w0Wo9oSuGh4aGSh9EuonFYqkaqDXbCWC1Zpuqd7rVQZNOp0M4HEYkEkEkEkFPTw8MBgOSySSefvpp6PV6HD9+fM2omNVqxZEjR3D48GFcv34dFy9exNtvv73mbwCt+CS1UKAmwuzsLL7//e/Ltj+NRoOenh6Mj493XBsoodLpNH6Q/kHNFZJyB2pCE9GFvImIbQo9MzNTGjHS6/Xw+/01g2qdTqdovbuJiQnYbLaKVXxK5X4JzdcTmjtYKBSqjirK1RxbyIpgnU4nKH+vk+rSCVXrGsrRCaBcs03VO51Wq8W+fftKwZnX6606HWo0GnHmzJmG+2OMIRQKIRQK4cSJE3j//fdx8eLF0tQo5aeRWihQE+E//uM/ZHkjAZbb28zNzUlOwu8krSrWCdR+E1pNyJtIM1NB6XS6bqkDn89XtXek3W6XpZZVIBBAOp3G/Px8KXhVKqgQ2vxaTO6Zx+PB7OwsstkscrlcabFEs4QsApiYmEAul0M4HMbExETdgK1dCwuk0mq1NUcA6/a7lECJqdRyBoOhdG+0w+7du7F//35F9q3RaHDjjTfixhtvLE2NiqmXSDYWCtREkGPUy2g0wul0dky/PTlIWSEpldApQyFvIs1MBQUCgdI0bLXAq1bQYrFYmqqFxxhDOBwu5U6V51EpVUNNaOkIMfk1nPM1KzO1Wm1Tq1StVqug/MXiz2Z4eBiBQKDhNq3sWNBsrqfX6637t0XOTgByT6WWs9vtOHv2LKxWK2KxGK5evYoPP/ywpbm6W7ZsaclxilOjhNRCgZoIUj/Zeb1eGI1GcM4xMTGxroI0QNoKSakalV0oEvIm0sxUUHnZDavVCqfTifn5eSQSCVgslporU0dGRhAIBDA7Oyv6jd9sNsNkMlUkuA8NDaG3txeDg4OKFjMVUjpCTLeIVCoFlUpVMV1br2SGEE6nU/TI1/z8PDweD6ampmo+J51OIxwOVx0hLQqHw+CcQ61Wg3OO4eFh0SOEcjRm12ha9ydd7qnUIofDgbNnz8LhcAAANm/ejM2bNwNYXozz4Ycf4urVq7h69WrdlnXNBL2BQABWq7Wp10GIXChQEyiTyYiuhq/VauHz+bo2/0yovdgLcOAX7BeY4TNwMieO8+Oy5KdFo1HE4/HSaI3QQE3Im4jUqSCj0VjxM52fny+dVzAYhEajqfsGMj09DbfbLSpQ83q9WFxcrHoPDg4OwuVywWQyQavVlnLFcrlcSz8UiAkSpqen1wQ/zfRWtNlsosu2FEcjhZRLKXYsWD2tGI1GMT09jeHh4YpRN5PJBK/Xi6mpKcHlWOp1wxD6u9TKFlFyT6UCy8H22bNnYa8xKmc2m7Fnzx7s2bMHnHNMTk6WRtuuXbtW+jDdbNC7detWya+BELlRoCbQK6+8Impaxu12I5/Pr/sgrWgv9mIvX/4D6HHXH6EQIxaLScoREvomImUqyOPx1Czb0GgBgcVigU6nEzWFEw6Hcf369brJ+vF4HPF4HH6/vxSwlOe8FNtWVcv5MRqNpVyx1axWKxwOBzjnWFxcLHVGcDgcMBqNyGQypRIkYkdzhoeHK6Zu0+k0otEoCoUCcrkc8vk8VCoVtFot1Gp11Tpx5a9P7Iii2DpZ1a5PLpcrBWLlI2hLS0sYHBwsJZCrVCpkMhnMzc3VnKJuNtdz9eKSVpBzKtVqteLTn/40bDaboOczxuDz+eDz+XD48GHkcjkMDQ3h6tWr+K8v/1dkC9KDXgrUSCehQK2GTCZTqux+5coVXLhwQfC20WgUQ0NDLWmV04mE5CqJmZaIxWJrVgRaLBbYbDYkEomKkSmHw4HZ2VlwzmV9E5GDz+fD/Py8qLpmxWlNocqvffH+K289VW0Krzga5XQ6YTaboVKpMDs7i9nZWdhstlJwFAgESqNGNpsNsVgMKpUKbrcbGo2m6RpQxWPWU622mdVqlVRCQ+wq2WoLC8qDt3Q6DcZYxe8953zNqKbJZILZbC4FyFNTU8hms03netrt9q7t5ckYw5kzZwQHadVoNBps2rQJmzZtQuKX1VutCQl6i2U4COkUFKitwjnHq6++ip///OeSWhV10woxpTQKUKVMSxgMBhQKBfj9fhQKBYyOjpamFyORCGZmZipylKxWayngkNvqwHBhYaFh/qLP58PU1JTg4EClUiEUColeFVw+6lscsSoPzIaHh6HT6XD4ww+x/4c/hH12FgsuF5674w5c3ru3FND5/X54PJ6KabviKCBjrLTPQqFQUdRXaNmLIrHB3dDQUKnbgsFgKE31Cp0SB5Z/R1OplKBuH6tbdq3uWLB6VE7Ih7OlpaU1teQcDgceUz2G/5b4b5JzPTu5zVwjx48fl3XVY9QexeDs2t+doDGIowePYmpqCpOTk4jH42t+Zps3b25Yi4+QVqJAbZWf/vSnuHjxoqRtdTpd1xXHlFM0Gi0tmKhHSi4OYwz5fL5qQndxhKX8zXp+fr6UjCw3vV6PQCBQ+gOv1Wqh1+uhUqmQSCSqJ488fwAAIABJREFUBg1arVbwG6nRaITVaq14rUIrwJcHAJxzDA0NIRgMYn5+vhTY3vD66zhSlr9njcfX1JgbHx+HXq+vOkXLOa8ZkJjN5rqBWiAQqOiWIKWdlthuC+XUajUmJycF5Qeq1WrYbDZwzkutv+LxOMLhMOLxODweT8XPqJlVqzMzMwgjjJM4WRppdqvdeMT+CPZir6DX28lt5urZsmULjh49Kus+vz7wdXz+3OexlP3t74NJa8Jf3PcXGNjz28A3l8thenoaExMTmJycxNTUFHbt2iXruRDSLArUVmkmpywQCGzo0bRsNiuoyKuUXBwpSdI2m02R5OpEIlGzur7b7UYkEilNgTHGkEwmBSe6OxwOFAqFimBXTAV4nU63Jgdq9c9EyUKlxVy4WiYnJ5HP52E2m+FyuVreoqlWtwhgOdDy+/1gjJVqvM3MzCCfzyMej0On08HpdCKdTmNpaWnNfuQYhalYXZsHEAfMEXPDQK1b+5JaLBY88sgjsrdOemLPEwCAr57/KmKzMUTtUXx94Oulx4s0Gg38fr/grhqEtAMFaqtIXd7udrvXXdkNsYS+UbWq7ppSb1z1WiBZLJY105XBYFBQaZdAIIB4PL5mVEZMYGWxWBrmKQmpMRcIBCQ3T+/t7S31DF1aWqoIMux2O+LxOBYXF5vKR5KCMVaa2q3GZDLVLcGRyWSQyWRq5lcqVRpDyH2cTCa7rjcpYwyPPvoozGazIvt/Ys8TawIzQrpRw2VPjLG/Y4xNMMYulz32F4yx9xhjlxhjP2aMVZ1jYoz9IWPsMmPsHcbY/1L2uIsx9jxj7MrKf53yvJzmSfljWyyd0I3tZuQ0OzuLUCiESCSCaDRaM+dkAAPQorJmlhJ118Su6pPK7XaXXm+1ac+5uTk4nfVv8UgkgvHx8apTZ2IqwAu5f2sVJJ212xGJRGCz2TA2NiapBdb169cxODiIWCyG4eHh0hRhT08PAFT0QWymHIcU4XC4Zi5bOBwWlM9YzK8sftAo5ldewiW4XC5Zz7dI6N+VkZER+Hw+Rc5BCceOHcOmTZvafRqEdDwh72R/D+DeVY89D2A353wvgPcB/OnqjRhjuwF8DsAhAPsAnGSMbVv59lcAnOecbwNwfuXrjiC26GaxjpJSVeG7yfz8PEZHRzE0NISxsbGa0xl7sRencKo0gmaHHadwSva+oHJPp1QTDAZhsVgQi8UQi8WqTlEtLS3VzcXq7e2tu0q4XmC1mpBRsPMDA8isus+LNeby+bzsKwenpqYwMjICt9td8TNpZfJ7vd6rarW6bt27cvXyKxcWFhpO/Uoh9G9SoVDA3NxczRpknaSvrw/Hjh1r92kQ0hUafvzmnL/IGOtb9dhzZV++AqBaR9qdAF7hnC8BAGPsAoBHAPw5gIcA3LHyvH8A8AKAPxF15goROqLm9/uRyWQ2dE5aPV6vt+6KRSGV7pvVikAtlUohl8vVLaPh9XqrLrDQaDTw+XwNV3aKqQAvJOm8Xo25sIKjkNPT0xUJ71JWVUul0+ngcrmqFgEOhUKCpwzr5VdOT0+L6s4glJjct1QqBcZYRT29TmM2m/Hoo4+2bMSbkG4nR1LF7wF4ssrjlwF8nTHmBpAEcD+A11e+5+ecXwcAzvl1xljHjNffcMMNeP/992t+32AwwO12b5hCtlJ0SomSVkxFZzIZJBKJuvlWtUZZrFaroMLAQov3NmqFtHqf1RYOtCK4LaqX6ye3hYUFLCwsIBKJVDze09MjKq+rXn5lNBqVNF3ciNhR/mQyCa/XK/t5yOWRRx6h9kyEiNBUoMYY+yqAHIDvrf4e5/zXjLH/B8vTpAsA3lp5rthjfB7A5wHIWmenlgMHDuDtt9+uOsphsVjwZv5N/GzkZ5KbJm8EsVisYW/EVlB6Orq8kGy96cJa30skEnC5XEin0w3LVAgp3msymRqccWOtGuUwGo0tbXdUtPr1iam/BiznV5bXAAR+m1+p1IeTpaUl9PT0IJ/PN+xo0dPTg2w2q0j9QDkcPXq0Zc3OCVkvJP9VZoydBXASwBO8RnIN5/xvOecHOOfHAMQBXFn51jhjLLiynyCAmoW3OOff4pz3c877W/EpkTGGU6dOrZlucLvdeCP7Bn6Q/EHVRGJSaXR0tK25MowxxeqoFQkZFbBarXUDkng8Lktek81ma2qUt3i9WtVNw+PxtOQ4q5W/PrfbLTofr1Z+5cnoSVnPs9z09DRGRkYwNjYGt9td99qp1WpMTEx0ZKAWjUZx/Pjxdp8GIV1H0ogaY+xeLOeU3V7MQavxPB/nfIIxFgXwKIDDK9/6KYCzAP5s5b//U8p5KMXtduOOO+7A+fPnYTQa4fP5MDw8jGfyzzTdNHmj0Ov1LZ3aKlcsh6F0qYLylla16PX6uqM2Tqez6UKljDHo9fqmFgEU8+haNcoltTCsVFqtFk6ns6LUhclkknTtq+VXjo+Pw2AwKD6KOz09DYvFgkgkgoWFBczOzqJQKMBms8HhcHTsynOj0YjTp09TXhohEggpz/F9AC8D2M4YG2aMfQbAXwOwAnieMXaRMfbNleeGGGPPlG3+Pxhj7wI4B+ALnPNiEaM/A3A3Y+wKgLtXvu4ohw8fxq5du5BOpzE4OIh8Pt900+SNJJlMIhgMCn6+0WhENBqF3W6HzWZDNBqtKOUglMfjwfj4OCYnJ0VvK4bZbBbUhaJRjSg5aolFIhFJrzcajcLj8cDr9bb0Db7VHTyKNeGK1eeB5eBWzmT7dDrdstIYCwsLGBoaQiKRQKFQgFarhUajQSwW69hajg8//HDL6+YRsl4IWfX5ySoP/22N545iedFA8euP1XjeNCBz0SyZqdVq7Ny5E++8807pMbGFWotNkjdqc3YxbX5cLlcpx4cxVhodcjgcokZ5xLRqaobH4xHUh7PRz15o8n8toVBIcG6URqNBKBSCSqXCwsJC2xZ8eL3eli7GqVavzePxyB7Mx2IxUQs65JLNZhGPx2GxWASXGWmlw4cP44Ybbmj3aRDStWgcuo4bb7wRmzdvLn0tplCrz+fDF7/4RXzhC18QNbK0nmQyGcGrCMufVx7ciAm6HA6HIqvuVmOMCXozLm8SX43P55PU67LI7XYLDjYikQj0ej1isRjGxsZaHkyUa3XD62rBstiVlM0cq1UaFVVuh3A4jIEqZWQIIcJRoFZHcWFB8Y+60EKter0ejz/+ODQaDdxuNz796U9j69atLT//dstkMgiFQoKeW2vqzWKxlEaCGlGqFc1qoVBIUIBVXNUZiUSqBgbNLCIwm81IpVINC9y63W54vV4MDQ2VzjmVSiEQCEg+drPqtXGSSzHwj0aja6YDPR6PYlOE09PTLVmdXk08Hu+o6UWDwYDTp0+3PDAnZL2hXp8NOBwO3H333XjmmeXUu0aFWhljOH36NNxud+kxnU6HT3ziE/jRj36Ed999V/Fz7hRms1nwVEytN+9ikvbo6Ci8Xm+poChjDIwxzM/PI5FIwGq1tmw6TcwoXzweRzweh1qtRiQSQTKZhMlkQiwWE10aokin00Gv19edWtbr9fD5fDUXVCg1otSI3W5XdEUiY6y0MKJYM211YV2j0SjrMb1eb6mjQztbOC0uLsJisXTMFOhDDz2k+MprQjYCCtQE6O/vxzvvvCMoJ+nOO+/Etm3b1jyuVqvxwAMP4N1330VPTw8mJiYkN73uFslkUnBAkE6nodPp1qwGzOfzpZGgWtN8TqcTJpNJcuAjlpRpw3w+XxE0Wa3WNefLGGs4dabRaOB0OtckwkejURQKBaRSqdJCh3qrXluZzF/OZrOJCtR2X7rUsNBvuUgkUpHrWO13TO4RPc55KWhuVTK/y+WCxWJBoVCASqXC4uIipqensbCwALvdDpPJhKWlJeh0Orjdbuh0OqTTaTDGoNFooFKpkM/nMTMzA41GU/rQI9eikltuuQU7duyQZV+EbHQUqAnAGMODDz6Ib3zjG3X/kDkcDhw5cqTm900mE774xS/CarXi0qVLePrpp5U43Y5RHnQUyyNMTU3VHJGS2lIokUi0ZDoNWC6YKkeArdVqS4Ga1+sFYwwTExMwGAwwmUwwGAwV7cyK11KlUiGXy8Hn8yGdTpcC3FgsBrVaDb1eLyiQTKfT8Hq9iq+ObcbuS5cqWmc5Zmdx6tw5AKgZrJX/flbLDzSZTJJHm0KhEJLJJKxWaykoLo6OtlI0GsXQ0NCaEdVQKASNRoN0Og2Xy4VEIoGZmRlReZtms7l0/xWDuVQqhXg8LngkORgM4q677hL1mgghtVGgJpDL5cLAwAD+5V/+peZzjhw50rBOUHFK9ODBg7h27VrFqtJWuoRLOI/zinZYKF9Z5/V6MTo6imAwCMZYaRQgkUggHA5jbm5O9mbgSlCr1bKsKl1YWEBPTw/UajVGRkZKQWoqlRJdi6s4CpnP50XVrpOjk4FYYkY9B86fr+hvCgC6bBYD58/XbH/VKPB0Op2S6/vlcjlotdq2rZZVqVQIBoM1jy/HaN7i4mLV/EuTyQSHwwGtVotcLofFxcWqq7H1ej3OnDkjuGcyIaQx+m0S4dChQ7h8+XLVXCi1Wo3du3cL3hdjDA8//DDsdjtefvnllq4Wu4RLFW1wih0WAMgarBXfNN1ud+n1FT/dO51OZLNZaLXatreaEiObzVa0jpIqk8nIllNXKBSqTqU20uqpd51OJ6rUir3GFGmtx/V6PVwuF65fv17z96mZAGJiYgKhUAjhcLj0gSyTySAej7ekgG84HG5bkLi0tLQmwN2xYweOHTuGTCaDQqFQug9dLldbzpGQ9YoCNRFUKhUefvhh/M3f/M2aKdAdO3aITlLWaDS4++67YbVa647Uye08zlftsPAsnlVklK1a9XeLxaJ45wCl2Gy2lk21CmUwGEQHalNTUwiHw+Cciw4aI5EIGGNIJBKCj1vs8CHUrN0OR5WgbLZGa7JUKoV0Ol33Q08zOVgmkwmZTKbq1LLL5cLs7Kzk6ftGHA5H24K0Wlwu14YtPURIK1GgJpLH48GJEydKq0CL9u/fL3mfPT09zZ6WKLU6KSRX/ik+R4lRtqKRkREYjcaKlj7dotMKGDPGJK2kzGQyGB4eLq2ObCQajWJqaqo0pV382fn9fhgMBuTzeSSTSczOzlYNiMQGMecHBipy1AAgo9XifI26XMUp9XqaCdS8Xm/NBUULCwul16dEWoHFYpHc3kupNIdW9F4mhFCgJkl/fz+uXr2K3/zmNwCWR8b6+vok76/VdYZqdVhYTck+poVCAT6fT9BK2k6TSCSgUqla0gFBCL/fj7GxMcnbj46OwufzNVwJmk6nsbS0tGZkp1orJqPRCIvFAoPBAMYYcrmcqE4VwG8XDAhZ9Wk0GmG1Whu+hvHx8aqrixvR6XR1Rw71ej0ymYyktAKdTger1YrFxcWq+YlGoxHDw8OiV8ACyqY5+P3+prYnhAhDgZoExVWg3/zmNzE/P4/e3t6mcl+KgZrBYMC+ffvw2muvKRoEDGCg4o93PUr2MR0fH4darVZsukgp8/PziEajbZ2KCofDAJYDXqnJ8UX5fB4TExMIBoPQaDSYmpqqOtIpZvQzmUxWfb7ZbIbb7cbo6Kig0a3Le/c2DEaKDcqFlhyR8sHI7/fXnaov/r7WSiuo94HH7XaXcjd1Oh3sdjuMRiM451hYWIDVaoXtZz8TvQJW6vkIUaxXRwhRHgVqEplMJjz88MP47ne/29RoGrCcu/PZz34WXq8XOp0OfX19ePLJJ+U50SqKf6DLp0MyyJSmPcvV6mMqh1Qq1faAR6rJyUmYzeamWkA1I5vNytpUHPjtQg/GGILBILRaLRYWFrCwsIBMJoNsNitpVKdccVWhVqtFOBxGPp/H9evXS6sKNRoN5ubmRE3zFQoFUTmDUhL/V2+jUqlgMpnAOYfFYoFarcbi4mLNDzb1PvCUf8jLZDJrVq4mEgn8ocgVsI2O2+wHMI/HQys7CWkR+k1rwubNm3HHHXdU9AOVgjFWkae2Y8cO7Nq1S9HSHas7LKyeIgFq9zGVk5JV6pWUTCYRiURaHqgxxhCNRhUt7ss5X1N7y2g0ov/993HsZz+DZiVoETqqU002my0tLDCbzbDb7RXlJTweD0wmE6anpxteYzE/g0gkUiqibLFYkM1ma5akKLd6FM7v95euUfm2tdIK6n3gaZRXB4hfAdvM+QhB056EtA4Fak06duyYIvtt9bRCtVE2JWqrrTY7O4tQKNSyiu5ysdlsbWlszjlvS15fMpnEgaeeKgVpRUJGdRqpFiiVX1uPxwOz2VzzdQud+vV4PBgZGSlNU5Z/SNBoNKUSEw6HA0ajERqNBpxzLC4urpmmnZqaqpqnWC2toN4HHoPBIGiET+wKWKnnI1Q7W2URstFQoNakYvNnuW3ZsgUvvPBCU/sQO03VqI+pUuRqW9MqxUKx7VqxKiUZXg5SR3WaNTU1Vff3bPVol9vtLrVHKmc0GmvmfpbfgzMzMw2nXrPZLAKBwJpFHCejJ4FY/Q88jDGEQiEAy9PNQjoHiF0BW6TUBzAaUSOkdShQ61A9PT3weDySR22ktN9pl4mJiaZeq1arhd/vL00hKZnzptPpYDAYRK9glFOxnpeSqgX5Ukd15MA5h8FgKLXJKl5/o9EIn89XWpCSzWah0+mgUqkqgi2dTidbgeGian1sM5kMTgRO4ObEzUin02ue7/V6JdWtE7MCdjUlPoBRoEZI61Cg1macc7z55puwWq3YtGkTGGNQq9VgjGHbtm2Sgxex7XfazWAwiHq+0WiE1+tFJpPBxMRERSHVUCiE8fFx2VeTqtVqhMNhfPjhh7LutxGdTlcavZFaM02MWkH+m/v24aa33qq4r7I6XcNRHTmU/x6UJ7FbLJaqU6J+vx/RaBScc6hUKmg0Gly9elXWcxobG4PD4YBarYZarYbBYMDQ0BA453C73TCbzRUBfXG1q1RCVsC2gsFggM1ma/dpELJhUKDWAX75y18iHo+Xcl5cLhfMZnNTDbPbNU0l1cjICHw+X2lUjHMOzjnUajU0Gg1UKhU45ygUCshms5ienq7b89BqtcJisYhqSF2PSqWCx+PBhx9+CL/fj2w227JRtUKhgMXFRRQKhZZME9cK8rdfuYJzp05VjOpcfPxxXG5xwWabzVYKVmuNLK5eEdvb2yv7eWSz2ZpTpMVuHMV7JZVKNVXrrpP4/X7FUj4IIWtRoNZmjDH09vYiHo+X8mfi8XjTQcCS0QhzlRyqJZFtrlqFcy64DlYtFRXY5+0YmB/AfT33IZFINF1rLBgMlqarxsfHS6svW1FaJJfLQa1WI5fLQaVSQafTwWg0wmazKbKwoF6Qv3pUJxKJAC1uBVa+SlJonmC78gmnp6fh8XhgtVqb/lkp1WFALFpIQEhrUaDWZsWpO9KcmhXYR4B+XT8ikYjk3qLVAjLOOYaGhlqW2L+wsABgeXQtlUohlUrBYrEociwxuWhKlgmppZjIPzk5iUwmA8ZY3bZevb29bavV5/P5ZFnRrGSHAbGuXLmCp556Cj6fD36/H8FgkKZCCVEQBWptxDnHM888I3uSMwCYaowg1Hq829WtwJ7Zi6GhIYTDYYyPjyO7Mq0ndFWs1B6LcrHb7VXz0pSq4SZ0haHT6WxLc/pi4KNWq+H3+zE1NVU3HzGbzbatP6tc7eGU6jAgRXFVbLHOo06nw1e+8hWaDiVEIRSotQnnHM8++yzeeustRfbfzhV67SCkAvvw8HApd63nwgVBq2Lr1XgLBAKy5cAVud1upNPp0ggasFwQtlqgFo/HYTabYbVaUSgUMDs7u2aloRRCVxhaLJa2BGpF+XxeUHeGdgYQcl0fuToMNNtZohqfz0dBGiEKokCtTS5cuIDXXntNsf1LrbvUrYRWYJ+fn4fD4RC8KrZez1W5V5U6nc5SErrVaoXdbodKpao7ZVteLFbOnDkhKwzlbmGlFCX75tYj54ijHB0GlCrZY1+nH/4I6RSNe5cQWXHO8dJLL+HChQuKHufy3r04d+oUZux2cAAzdjvOnTrVEcv7lTCAAWhRWdeqVgX2mZkZQatinU5n3ZV6ExMTiEajEs94La1WWxqZmJ+fx/DwMGKxmOBpu+npaYTDYfT29iIajS4n+itIrmk9pbUrUDPKuHBHzP1dcx91Ppw0Q6fTNbU9IaQ+GlFrsampKfziF79oybE6pe5SK4itwD7ncMBeJfds1m6HXq8HsDy1dyFxoe4+iytAOedNTysVAz+xTcmB2lNaSrXnKq78a9dqSjEymQyi0ShGRkZkHwWtR85SKnJ0GFCqZI/D4Whqe0JIfRSotVh5sc5uwBjDZz7zGVy4cAFXrlxp9+nUJaYC+8/vvLPm1HAxz+vpoacbrrRLp9NwOBwIv/iiLNNKxalLh8MBi8VSUci3lnpTWjPhsOBjC2Gz2WCxWDA6OgqXyyXrvpWSSCSQSCTg9Xqbqk0oVrXOBc1otsOAXHmrVqsV+/btw+TkJCYnJ2nFJyEK666oYR1wOp246aab8Oabb7b7VAQ5ePAgenp68Pjjj+MnP/lJaaVXtxOSMC90pV0ul2uqE0TV+lgzezEzMwOLxVKxsKCaesf+1i231N1WqGJ3hKGhIczNzUGtVre1jZZYWq225efbaVPDcuWtzs/PY9u2bRhYp/muhHQaCtTa4P777wcAvPPOO21pri2UwWDA8ePHASyPBJ4+fRr79u3Da6+91vGja0I0mhoWutIuk8lInlZqVB+rvLhrrXph9Y6dTCbhcrmaClIikQimpqYqFiq43e6uqf+nVqsRDAZbXkttbm6uYY23VmqmX+hq58+fx6c//Wla7UlIC1Cg1gYajQYPPvgg7r//fgwPD2NhYQGpVArz8/MYHx/H1atXW9IqqJHjx4/DZDKVvi72H926dSt++tOf4uLFixXPV2LpfzsJXWmn0WgkTys1GrVbWFhAT08P1Go1CoVC1anQRseWOgXn8XgAoOqqU7G9WdtJo9G0pTDvzMxMy7pXCCVX3mosFsPVq1exdetWGc6KEFIPrfpsI41Gg76+PuzevRv9/f04fvw4PvGJT+DkyZPtPjW43W709/dX/R5jDPfee29FEFfMk3LMzoLht3lSuy9datEZy0/oSjuLxYLzAwPIrAqIhEwrNRq1KxQKGBkZQSwWq1kjrdGxpQZq6XS6ohl6kVqtrvp4p0qn06Uix60Wi8UUX33bLufPn++Y0UJC1jMK1DqQEv0bxbrzzjsrpt1W0+v12LZtW+lrpZb+t9Ne7MUpnCqNoNlhxymcWpPQbTQaJZdDqVUHq9rjtco9NDq23NPrfr+/6d6preRyuRTr4iDE2NjYulwZOTY2hmvXrrX7NAhZ92jqswOFQiFcu3atbVXfe3t7sXPnzobP27ZtW6mzglJL/1uh3pStkJV2s7OziEQiGPN48P277oJKpQJjbDmZvMGqzQEMVOSoAcujdnezuxEKhsAYK9Vyq5YT1tvbi8XFRcydPIn/UiMonJiYQDAYrNtFIRAIIJlMwmKxQK1WgzFWKr67WretXDabzW1d+JDNZmVfAdopXnzxRWzatKndp0HIutZdf3E3iP7+fhw8eBDvvfcefvCDH7T02Gq1GidPnhSUJLx161YYDAakUqmubVklR7V2rVZbs3uAz+erCLC0Wi30en1pOm4v9sLldOGHiR9WrPrczXeX6p/pdDro9fqqeVb5fL40DdnT04NUKoV4PI5wOIxcLgedTodCoQCVSgW1Wl21jpjX6y0Fg9VaVZWLRCKCSoa0k9lsht1uRzqdLiX0V1N1ta1CfTMnJycRiUTqdpkQopXnLMS1a9cwNjaGQCDQtnMgZL2jQK1DJZNJvPHGGy073s6dO3Hs2DFYLBZYLBZB2+j1etx99904d+5c17asaqasRlG9CvTz8/MIBoPIZDJYWFgoBWgWiwVOpxPxeBxbk1vxZXy55j4ymUzN6cvy0h0jIyOl/68WEFit1qplKoRU0DeZTHA4HE0HGq3gcrkqzrPaOTdabauEWtOvQoOvdpyzEO+99x4FaoQoiAK1DvWTn/wEV69elXWfOp0OR48eBeccnHOoVCr4fD5s2rRJchuYm266CdeuXcPbK19326pPpadsk8lk1er9CwsLWFhYgNVqbWpFophcseJxisntnHNkMpmGnQvC4TAmJycV6XCghNUJ7tUS3oXWyJNTPB6H2WyuCNjEBF/tOGchLl26hNtvv51KdRCiEArUOtSxY8dw9epVWfsUbtu2DR/72Mdk2x+wvAL05MmTuH79uixL/1UqFQKBAHw+H/x+P/x+PxwOB9RqNb7zne/IXmah3VO2zb4eKasZxYyK9fb2dsTiFjHqLYIpElojT25utxvZbLY0Qiom+GrXOTeSSCQwNTUFr9fb1vMgZL2iQK1DhcNh7Nq1C2+//XbjJwu0f/9+2fZVTqfT4fHHH8e3v/1tyWUQ1Go1Dhw4gNtuuw32GkGSEpXe5ZiybWX/yNXUanVH1NwDlhcZdMK5CBnZEVojT26xWKyitpqY4Ktd5yzE0NAQBWqEKIQCtQ7WbC9Fq9WKEydOwGazQa/Xw+/3y3Rma3m9Xpw8eRI//vGPRW2n1WrR39+Pw4cPw2q11n2uEg3A5ajW3q4pn9XTaEoYHBxEJBLB6Oho3YA0Go1iYWEBZrMZqVQKyWSyYesrpQip7VVrte3qGnlKKB8lFxN8tfOcGxkaGsKBAwfafRqErEsUqHWoVCqFX//619BqtXVHqex2O/bv34/e3l4Ay/ki7777LtxuNz75yU82DH7ktHfvXnzwwQeCRgH1ej0OHTqEW2+9taJwbj133nkn/vmf/1n2IpvNTNkyxtpWU6xVJR+Ghoag0WgQCoUwOztbERwyxtDT01MaISpfqNBs/p1UQurGFacV27GCsvwDR6PgS6VSwWAwIJ1OY2++fefcyAcffAAo2XlhAAAbB0lEQVTOOeWpEaIA1k2Vpfv7+/nrr7/e7tNoqWQyiQsXLpQSkTnnSCQSMJlMOHjwIDZv3rwmJ6dQKIAx1pY/mslkEt/4xjdqvkEbjUbceuutOHTokKQ2RDMzM3j11VfxxhtvtK3afLl25nAFAoFSWQ0ppLT8Kj+mRqOB1+utW59tdXmSIqXKTLjd7pr13+Qi5brV26batbhJfRP8fj9yuVzF9SvW5wuFQoJaUxkMBqhUKsU/TJw5cwa7du1S9BiEdAPG2Buc8+ptfSSiEbUOZzQace+994raRkgytVKMRiMefPBBfO9736t43Gw24/Dhw7j55pslrzAFAIfDgXvuuQe9vb148sknmz3dpvj9/rYm2jfzc5ZaP66YJ2gwGGCxWOoGacXnraZkmQkhpUaaIeW6NdqmWlFlu91edZUt5xy5XA5DQ0MNixgXg7RMJoNoNIqRkRFF8ilvvvlmCtIIURC1kCKy27p1K+644w6oVCq43W7cc889+MM//EPcdtttTQVp5Xbs2IEtW7bIsi+p2l1tvlbvTyGktvxSq9WwWCwwGAyC+n1WG8mpt9JxtZ6eHoRCIUQiEZjN5obHU2LBSTkp103INgaDoWIEvFEtQ8454vF43dQGp9OJpaUl5HI5xGIxGI1GhMPhuvsVKxqN4p577pF1n4SQSjSiRhRx++2342Mf+5iio3u7du2SvdacGEtLS1CpVLKWUBGjmR6eUuvHFdtHzczMCDpOPB6H0WisyMsSutLR5XLh+vXrpesbCoUaLp5Q+mch5brV28ZiscDhcGB+fh6pVApmsxkmk0nQlHY6nYbVasXCwkLVvM3Vo2fF2n0+nw/z8/NNL84xmUw4ffq04sExIRsdjagRxSg9BStXz8m+vj5JI33FVk3tInQRRjW16sTVetzpdCISieDDDz8UtZqzUCjA5/NVPCakEb1Op0Mmk6kIvBoFBF6vt6I7gxLEXrd635t3OrGwsIDh4eFS667FxUVMTk4KDsKnpqZgsVhgNpthMBhK92Nvb2/NHNWJiQlotVr09PQIOkYt9957L2w2W1P7IIQ0RiNqpGs1m29z6623IhAIoKenBwaDAZlMBnNzc4jH4zi3kkPUSPkIj5Qk82Y0M40spn6czWbD4uIiEomEpGMNDg5WLCoQUmYiGAyKzv8rFAqKj6hJqbtXa5vnjx+X5ZzKF+4kEgn09PRgeHgYer2+5jZzc3OYm5uD3W6HxWKRFODu3LlT0vkSQsShQI10rWZWfTLGYDKZsG/fvorHXS6XpNWocjR3F0On0zU1eiS0fpxGo4FGo2lqmhWoTPJvVBrDbrdX7Z6QTCYRDodLK5oZY8jn80ilUjAYDC1pFi+l7p4ctfqEWlxcLH14ELLSc3Z2FrOzswiFQpicnGz4O8UYQ19fH/bt20dTnoS0CAVqpGuFQiF4vV5MTk6K3nbXrl0122n5fD709fXh2rVrpemjajlAarW6FNTJ0dxdDI/H03TvTSH144SWgWhkfn4e0WgUnHMsLS1h7/TalY5FDoejNBVYTsjihVYQct2qja7+ly9/uUVnKN7o6ChCoVDNe4oxhv379+PYsWNwOBwtPjtCNjYK1EjXCgaD6OnpER2onThxou60jUqlwtmzZ5FIJPDWW29h3759+OUvf4nLly/D6XTCYrEgm80ikUiURrWUbu6+mlz5ea0Sj8dLxXB7enrg8/lQKBSQz+eRzWZLeW86nU7xOmhKa/XoqlxGR0fX1L3T6XTYtWsXjh492nSnFEKINN31156QFZxzPP3007h48aLobfft2ycoEd/pdOKOO+4AAJw8eRIPPPAAGGM4f/48/u3f/q3iua1u7l4oFOD1epHJZJDJZJBOpxXJz1rdm1IO1aZsi6M5NputY0bOpKo3uvp+f3/T08hK0mq1iEQi2LRpE7xeL2644QbZSuoQQqShQI10pRdeeAG/+tWvSl8LTeQ/deqU5KKoxWnQ8fHxNd+To7m7GKvzsfx+f9XzkoOQ4qrNmp2dLeXDdbuao6tzc/jjP/5jXL16Fa+88gquXbsmeJ9KdXJYLRqN4sSJE7LvlxAiXff/VSQbzuuvv44XX3yx9LWYqSatVttUa61sNouPPvpozeOtTBivRsnE7mLbMovFolij9cXFRUSjUUX23Wq1RldZNAqNRoPt27dDr9cLDtSU7ORQzm634/bbb5dtf4QQeVCgRrrKr371Kzz99NMVjwlJ5P/c5z6HycnJpoMBrVaL06dPV21f1Uxz92YpXbMulUrB5/MpFqgBkHV6tZ2qja4WDAaovv710tdOp1P4/up0cpAzUDt58mTdkh6EkPaggreka/z7v/971fpmjRL5o9EoQqEQ9u3bB3uTOWOFQgGvvPJKU/tQQiv6u05MTKC3t1fx43S7a0eO4MVPfQrzLhc4gEwwCNV3vgM88UTpOWKKFQvt5NCMAwcOYOvWrbLtjxAiHxpRI13hypUreP7556t+r1Eiv1yrCMfHx/Hkk0+KKvzaqtyiVhkcHITVakUul4PVaq1YIUiAI0eOYGBgoCJwrpaKL2b63Q571aCsVocHsRwOB+WlEdLBaESNdAW/31/ze+cHBpBZ1SC9mMh/33334ciRI00f/6WXXsI3v/lN0UHaOZwrvckWc4su4VLT59NOxT6RCwsLTbWxWm+CweCaIE0OAxiAFpX39+pODlIxxvDQQw/RlCchHYxG1EhXsNlsCAQCVZtV10rkZ088gUOHDsly/Pfee0/0Nq3KLWqXpaUluN1u2O12RVeEdiKdToc9e/YgGo3CYrHAaDTC7XYrMgXdqJNDMx555BH09fU1vR9CiHIoUCNdY9u2bVUDNWA5WIsdPYq5uTkAywVhv3T33bIcl3MuqfZVK3KLipTucVlLcVrZZrPB4XAgk8lgenq6qfZenc7j8eDjH/84PB6P5H2IXXm8F7U7OUh18uRJ7NmzR9Z9EkLkR1OfpGts27at7vePHTtWegM8ePAgrFarLMd97bXXJBVhrZVDJFduUblsNove3t7Sv5FIBKFQCB6PR3LdODHm5uYQi8UwNjbWVADT6cxmM37nd36n6ddY3n6sHe666y4cPHiwbccnhAhHI2qka/T09OD06dPQarUYGRnBW2+9VRpBs9vt2L9/PxYXF3Hp0iXcfPPNshxzcXERL7zwgqRtBzBQUf8KkC+3aLVGxW5VKhVMJhP0en2psKxKpYJKpUI+n5etoXlPT09TzeI7GWMMjz32mGwfAKxWK1KplCz7EnPM22+/nYI0QroIBWqka6hUKuzevRvA8uKC8jIZx48fh1qtxrFjx3Ds2DHZjvnWW28hmUxK2lbJ3CKxCoUCFhYWqtZBk6vQrE6nU7TOWrsdP35c1vIkFotFdJ/aZtx00004ceJEW0fyCCHiUaBGug7nHM8880wpD8rtdiuWa9Ns3TUlcovkJteojt/vx9DQkCz7ktNjjz2GYDAIrVaLfD6PXC6HbDaLXC6HXC6HdDqNVCqFZDKJZDKJmZkZTExMYHJyEvl8HsByMHvbbbfJel4Wi0XW/dWi1Wrx4IMPlj7kEEK6CwVqpOuk0+mK6bVIJKJYwdcbb7wRO3bskLTqsxvodDrZaqFxzmXZj5y2bNmCG2+8UdK2hUIB8XgciURCkXusFYGa3W7HJz/5ybrlbQghnY0WE5CuYzAY8Ad/8Afo7++HRqPB4uKiYsdijOHhhx+GdlWdtvVCzsT/dq08raeZ16dSqeDxeLBt2zZFpgvlynWrxmQy4eDBg/j93/99CtII6XI0oka6ksViwQMPPIC77rpLVBFaKVQq1botN6HRyPcnoNMCNaPRKPt0pZzkHFHT6XQIh8PYtGkTtmzZgkAgILoECCGkM1GgRrqaXq9HIBBQ9BgqlQoGg6HlK/RaobhqVg7FfK5O4HQ6cebMGUVHrZrVzLmZzWZs374dkUgEwWAQXq+3Jf1eCSGtR4EaIQ2o1WqcOnUKP/zhD9t9KrKyWCyYmZmRbX+dEKgFg0EcOXIEO3fuhFqtbvfp1CV1RG3nzp04ffp0x78+Qog8KFAjpAHOOWKxWLtPQ3ZOp1PWchq5XE62fUlx33334eabb+6aKT+pI2oDAwMUpBGygdBYOSENDA4O4tVXX233achO7lWa7QzUNBoN+vv7uyZIA5bzyoQuUunp6SlNbTYqbkwIWV8oUCOkASVXlbaLSqWSrSxHUTsXXOh0uq7L0WKMVYyq2Ww2PPTQQ3jiiSfgcDhKjx86dAif/exncddddwGApHZmhJDuRVOfhDTQTaM0Qvl8vpoN7qVqZ6DWiTXchHA6nYjH4wCA+++/H9u3bweA0kKIZ599FgMDyy3HDh06hHw+T43UCdlguusjKCEtlkql8Nxzz7X7NGSn0+lk3V+7c6a6bTStqFjnzePxlII0YHmq02az4fHHHy/9rNRqNY4ePdp0twxCSHdp+NeNMfZ3jLEJxtjlssf+gjH2HmPsEmPsx4wxR41tv8wYe4cxdpkx9n3GmGHl8a8xxkYYYxdX/r1fvpdEiHyuXr2K2dnZdp+G7OSoPed0OhGNRhEKheB0OmU4K+kikUhbjy9VX18f+vr68Nhjj1X9/noczSWEiCPkY+jfA7h31WPPA9jNOd8L4H0Af7p6I8ZYD4AvAejnnO8GoAbwibKn/L+c8/0r/z4j5eQJUVp54/f1wuVyYX5+vun9mM1mxGIxjI6Otj1vSs5m6a20detWnDlzBj6fr92nQgjpUA0DNc75iwDiqx57jnNeXOL1CoBwjc01AIyMMQ0AE4DRJs6VkJbbt2/fuhvVkKMivt/vx/DwsAxnI48tW7a0+xQk0Wg0MJvN7T4NQkgHkyOx4/cAPLv6Qc75CIC/BBADcB3ALOe8PNnniytTp3/HGGvvvAkhNfT396Ovr6/dpyGrZDLZ1PY6nQ5LS0synU3zbDabrD1LCSGkkzS16pMx9lUAOQDfq/I9J4CHAGwCMAPgh4yxT3HO/xHANwD83wD4yn//CssBX7VjfB7A5wEgGo02c7qEiHb9+nUMDg5W/Z5KpYLRaIRarUYmk+mKFlMmkwmTk5NN7cPn8zUcTduzZw9CoRCSySTm5uYwPz9f+rc8UGSMgTHWVJ/QnTt3rrtRT0IIKZIcqDHGzgI4CWCAV18bfxeAjzjnkyvP/xGAIwD+kXM+XrafbwP4Wa3jcM6/BeBbANDf39+da/BJ18pmszCZTMjn8/B6vYhEIggEAggGg3C5XBUBQjKZxOjoKM6fP4/r16+38axr83g8TXVZCIfDdYM0l8uF+++/v+5UZDabxfz8PDjnpXph8Xgck5OTmJqaQjweh1qths1mg9VqBWMM6XQaqVQKIyMj+OCDDyr2d+jQIcmvhxBCOp2kQI0xdi+APwFwO+e81hxIDMCtjDETgCSAAQCvr2wf5JwX38keAXC5+i4Iaa9oNIo/+qM/EvRco9GILVu2IBqN4qmnnsL777+v8NmJ10z3AJfLVTcADQaDOHv2LPR6fd39aLVauFyuise8Xi+8Xq+g85iYmMDPf/5zXLlyBTfccMOafRFCyHoipDzH9wG8DGA7Y2yYMfYZAH8NwArg+ZXyGt9ceW6IMfYMAHDOXwXwFIBfAXh75VjfWtntnzPG3maMXQJwHMCXZX5dhLSNVqvF6dOnBQceraLRaCS3H9Lr9cjn83Ubr2/evLlhkCYHn8+HRx55BAaDAbfccovixyOEkHZi3VTRu7+/n7/++uvtPg1CBInH4/j2t7/dMblrjaYta1GpVPB6vQ2DvFOnTuHAgQNST0+0jz76CH19fZSfRgjpGIyxNzjn/XLuszvLeRPSBVwuFx599NF2nwaA5WnZ6elpSdv29PQIGokr70/ZCps2baIgjRCy7lGgRoiCtm3bhjvvvLPdpwGv1yupLEc0GsXQ0JCg57Y6UCOEkI2AmrITorCjR49ienoab731VluObzKZMDIyImobn88HtVoteIUoY4x6UBJCiAJoRI0QhTHG2lpCwuPx1F0EUE06nRZVYsRut7e9MTshhKxHFKgR0gIqVXt+1ex2u+CpyyKn0ym6EX27m7ITQsh6RYEaIS3QrtXVdrtd9LFtNpvo41AtM0IIUQYFaoS0QLsCtampKdHbzM/Pi94mFAqJ3oYQQkhjtJiAkBZwuVzYsmULFhcXwTlHoVDAzMwMstmsYscMBAIYGxsTtY3NZkM8Hhd9rHA4LHobQgghjVGgRkgLGAwGfOpTn6p4LJVK4emnn8bly8p0UNPpdKK3cTqdmJubq/uc3ZcuYeD8edhnZzFrt+Ole+/tuC4MhBCyXtDUJyFtYjAY8Oijj+LUqVOyt15SqVSYmJgQvd3SUq3Wvct2X7qEU+fOwTE7CwbAMTuL+378Y7D//t8lnikhhJB6aESNkDZijOHAgQO44YYb8Nxzz+HXv/41XC4X+vr60NfXh97eXuh0OnDOkc/nkU6nkUqlkEqlkEgkMDQ0hFgstiYXze/3iyqvASyPwE1OTtZ9zsD589Ctmq7VZDLAV78KPPGEqOMRQghpjAI1QjqAxWJp2G5Kq9XCYDCUCsv29vZi//79AJZHwopBWywWk1QOxOVyNcxps9cq2yGwMC4hhBBxKFAjZB0wmUzYvn07tm/fDgDIZrMYHR1FLBbD8PAwhoeHG05rCpl+nbXb4agWrEWjks6bEEJIfRSoEbIOabVa9Pb2ore3F8ByeZCZmZlS0DY+Po6xsTGk0+nSNkJWoJ4fGMCpc+cqpj+50Qj29a/L/yIIIYRQoEbIRsAYg9PphNPpxJ49ewAA+Xwev/nNb/Dyyy9jeHgYuVyu4X4u790LABWrPm1//ddglJ9GCCGKYO0qxClFf38/f/3119t9GoSsOxcvXsQvf/lL0QVynU4nvvSlLyl0VoQQ0l0YY29wzvvl3CeV5yCEYP/+/Thz5ozonp1CRuEIIYRIR4EaIQTAckmPz3zmMwgEAnWf53Q6sXv3buzYsUPRzgqEEEIoR40QUsZsNuPs2bP4p3/6JwwODlZ8T6PR4PTp09ixY0fpsRiV5SCEEEXRiBohpILBYMATTzyBvXv3QqNZ/iwXiUTwu7/7uxVBGgBEqSwHIYQoihYTEEJqKhQKyOVykvqGEkLIRkOLCQghLaVSqShII4SQNqJAjRBCCCGkQ1GgRgghhBDSoShQI4QQQgjpUBSoEUIIIYR0KArUCCGEEEI6FAVqhBBCCCEdigI1QgghhJAORYEaIYQQQkiHokCNEEIIIaRDUaBGCCGEENKhKFAjhBBCCOlQFKgRQgghhHQoCtQIIYQQQjoUBWqEEEIIIR2KAjVCCCGEkA5FgRohhBBCSIeiQI0QQgghpENRoEYIIYQQ0qEoUCOEEEII6VAUqBFCCCGEdCgK1AghhBBCOhQFaoQQQgghHYoCNUIIIYSQDsU45+0+B8EYY5MABhs8zQNgqgWns97QdZOGrps0dN3Eo2smDV03aei6SbOdc26Vc4caOXemNM65t9FzGGOvc877W3E+6wldN2nouklD1008umbS0HWThq6bNIyx1+XeJ019EkIIIYR0KArUCCGEEEI61HoM1L7V7hPoUnTdpKHrJg1dN/HomklD100aum7SyH7dumoxASGEEELIRrIeR9QIIYQQQtaFjl71yRjbDuDJsoc2A/g/ABwGsH3lMQeAGc75/hr7UAN4HcAI5/zkymNfA/A5AJMrT/vfOefPyP4C2qTZ68YYuwZgHkAeQK648ocx5lrZbx+AawAe55wnFHkRLabgNfsa6F6rd90cAL4DYDcADuD3OOcvr+d7DVD0un0NdL9VvW61tuWc/+f1fL8peM2+BrrX6v2OfhnAZ7H8+/k2gN/lnKek3GtdM/W5EnCNALiFcz5Y9vhfAZjlnP9fNbb7XwH0A7CtCtQWOOd/qfiJt5mU67YSdPRzzqdWPf7nAOKc8z9jjH0FgJNz/ieKvoA2kPmafQ10r9W7bv8A4CXO+XcYYzoAJs75zEa51wDZr9vXQPdb3feEattulPtN5mv2NdC9VvW6McZ6APwbgBs550nG2A8APMM5/3sp91o3TX0OALi66iIxAI8D+H61DRhjYQAPYPmT50Yl+rrV8RCAf1j5/38A8LAsZ9h55LxmG4mo68YYswE4BuBvAYBznuGcz6x8e6Pca4C8120jaeb3dPW2G+V+k/OabSRSrpsGgJExpgFgAjC68rjoe62bArVPYO0F+RiAcc75lRrb/GcA/xuAQpXvfZExdokx9neMMaeM59lppFw3DuA5xtgbjLHPlz3u55xfB4CV//pkP9vOIOc1A+heq3XdNmN52uT/Y4y9yRj7DmPMvPK9jXKvAfJeN4Dut3q/p7W23Sj3m5zXDKB7rep145yPAPhLADEA17E86vbcyrdF32tdEaitDO0/COCHq771SdQeTTsJYIJz/kaVb38DwBYA+7F8Ef9KvrPtHFKu24rbOOcHANwH4AuMsWMKnWLHUeCa0b1W/xPnAQDf4JzfBGARwFcUO8kOpMB1o/utwchQnW3XNQWuGd1rteMPJ5ZHzjYBCAEwM8Y+JfUcuiJQw/Kb36845+PFB1aGEx9FZbJfudsAPLiSO/RPAO5kjP0jAHDOxznnec55AcC3ARxS8uTbSMp1A+d8dOW/EwB+jN9en3HGWHBlP0EAEwqddzvJes3oXqt73YYBDHPOX135+iksByDAxrjXAJmvG91v9X9Pa22LjXG/yXrN6F6re93uAvAR53ySc54F8CMAR1a+J/pe65ZArVrkeheA9zjnw9U24Jz/Kec8zDnvw/Kw5S84558CShen6BEAl+U/5Y4g+roxxsyMMWvx/wGcwG+vz08BnF35/7MA/qfsZ9x+sl4zutfq/o6OARhaWV0FLOeBvLvy/xvhXgNkvm50v9W+bg223Qj3m6zXjO61utctBuBWxphpJZdtAMCvV74n/l7jnHf0v1hOwpsGYF/1+N8D+E+rHgtheWXF6n3cAeBnZV9/F8vLZS+tXLRgu19np1w3LOe/vLXy7zsAvlr2PDeA8wCurPzX1e7X2QXXjO61Gtdt5ev9WC6fcwnAT7C8Amrd32sKXje63+pft1rbruv7TaFrRvda/ev2fwJ4D8sB7HcB6KXea11TnoMQQgghZKPplqlPQgghhJANhwI1QgghhJAORYEaIYQQQkiHokCNEEIIIaRDUaBGCCGEENKhKFAjhBBCCOlQFKgRQgghhHQoCtQIIYQQQjrU/w/4OkJ4erGmEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "wards['geometry'].plot(color='grey',ax=ax)\n",
    "src_selected.geometry.plot(color='green',ax=ax)\n",
    "dst_selected.geometry.plot(color='red',ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Days and Times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = datetime.date(2020, 1, 1)\n",
    "end_date = datetime.date(2020, 3, 31)\n",
    "days = (end_date - start_date).days\n",
    "\n",
    "random.seed(0) \n",
    "random_dates = [start_date + datetime.timedelta(days=random.randrange(days))\n",
    "                for _ in range(100)]\n",
    "months = [x.month for x in random_dates] \n",
    "days = [x.day for x in random_dates]\n",
    "dows = [x.weekday() for x in random_dates]\n",
    "random_time_periods = [random.choice(range(1, 6)) for _ in range(100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame({\n",
    "    'sourceid': src_selected['MOVEMENT_ID'],\n",
    "    'dstid': dst_selected['MOVEMENT_ID'],\n",
    "    'month': months,\n",
    "    'day': days,\n",
    "    'time_period': random_time_periods,\n",
    "    'dow': dows,\n",
    "    'src_lon': src_selected.geometry.x,\n",
    "    'src_lat': src_selected.geometry.y,\n",
    "    'dst_lon': dst_selected.geometry.x,\n",
    "    'dst_lat': dst_selected.geometry.y,\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Test Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting distance data is saved locally and used in the subsequent analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "osrm_data_folder = os.path.join('data', 'osrm')\n",
    "model_test_file = 'model_test.csv'\n",
    "model_test_filepath = os.path.join(osrm_data_folder, model_test_file)\n",
    "model_test = pd.read_csv(model_test_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>sourceid</th>\n",
       "      <th>dstid</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>time_period</th>\n",
       "      <th>dow</th>\n",
       "      <th>src_lon</th>\n",
       "      <th>src_lat</th>\n",
       "      <th>dst_lon</th>\n",
       "      <th>dst_lat</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91</td>\n",
       "      <td>184</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>77.637890</td>\n",
       "      <td>12.930561</td>\n",
       "      <td>77.590090</td>\n",
       "      <td>12.888096</td>\n",
       "      <td>9686.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>184</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>77.644817</td>\n",
       "      <td>12.944129</td>\n",
       "      <td>77.586162</td>\n",
       "      <td>12.885430</td>\n",
       "      <td>14531.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91</td>\n",
       "      <td>165</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>77.643652</td>\n",
       "      <td>12.941535</td>\n",
       "      <td>77.761146</td>\n",
       "      <td>12.935575</td>\n",
       "      <td>16056.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>165</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>77.655367</td>\n",
       "      <td>12.950985</td>\n",
       "      <td>77.713831</td>\n",
       "      <td>12.937901</td>\n",
       "      <td>10362.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>165</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>77.658390</td>\n",
       "      <td>12.949876</td>\n",
       "      <td>77.716294</td>\n",
       "      <td>12.927034</td>\n",
       "      <td>8051.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>185</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>77.556457</td>\n",
       "      <td>12.861758</td>\n",
       "      <td>77.572991</td>\n",
       "      <td>13.007320</td>\n",
       "      <td>20097.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>159</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>77.588550</td>\n",
       "      <td>12.909991</td>\n",
       "      <td>77.570795</td>\n",
       "      <td>12.997582</td>\n",
       "      <td>12067.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>76</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>77.648405</td>\n",
       "      <td>13.006604</td>\n",
       "      <td>77.661723</td>\n",
       "      <td>12.881504</td>\n",
       "      <td>20443.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>193</td>\n",
       "      <td>178</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>77.597410</td>\n",
       "      <td>12.915178</td>\n",
       "      <td>77.650824</td>\n",
       "      <td>12.889310</td>\n",
       "      <td>8032.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>11</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>77.557797</td>\n",
       "      <td>13.049415</td>\n",
       "      <td>77.526487</td>\n",
       "      <td>12.968088</td>\n",
       "      <td>12629.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sourceid  dstid  month  day  time_period  dow    src_lon    src_lat  \\\n",
       "0         91    184      2   19            2    2  77.637890  12.930561   \n",
       "1         91    184      2   23            1    6  77.644817  12.944129   \n",
       "2         91    165      1    6            1    0  77.643652  12.941535   \n",
       "3         10    165      2    3            5    0  77.655367  12.950985   \n",
       "4         10    165      3    6            4    4  77.658390  12.949876   \n",
       "..       ...    ...    ...  ...          ...  ...        ...        ...   \n",
       "95       185     49      3    1            1    6  77.556457  12.861758   \n",
       "96       159     49      1    9            5    3  77.588550  12.909991   \n",
       "97        76    178      1   12            4    6  77.648405  13.006604   \n",
       "98       193    178      3   27            2    4  77.597410  12.915178   \n",
       "99        11    115      1   17            1    4  77.557797  13.049415   \n",
       "\n",
       "      dst_lon    dst_lat  distance  \n",
       "0   77.590090  12.888096    9686.2  \n",
       "1   77.586162  12.885430   14531.8  \n",
       "2   77.761146  12.935575   16056.7  \n",
       "3   77.713831  12.937901   10362.1  \n",
       "4   77.716294  12.927034    8051.9  \n",
       "..        ...        ...       ...  \n",
       "95  77.572991  13.007320   20097.0  \n",
       "96  77.570795  12.997582   12067.3  \n",
       "97  77.661723  12.881504   20443.1  \n",
       "98  77.650824  12.889310    8032.5  \n",
       "99  77.526487  12.968088   12629.0  \n",
       "\n",
       "[100 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicted vs. Reference Travel Times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To validate our model against real-world data, we collected reference travel times from Google Maps. Google Maps allows one to set a specific departure time in the past and get a range of travel times. We used our randomly generated source and destimation pairs along with random departure times and collected reference data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "reference_data_folder = os.path.join('data', 'googlemaps')\n",
    "reference_file = 'googlemaps_traveltimes.csv'\n",
    "reference_filepath = os.path.join(reference_data_folder, reference_file)\n",
    "reference_data = pd.read_csv(reference_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>sourceid</th>\n",
       "      <th>dstid</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>time_period</th>\n",
       "      <th>dow</th>\n",
       "      <th>src_lon</th>\n",
       "      <th>src_lat</th>\n",
       "      <th>dst_lon</th>\n",
       "      <th>dst_lat</th>\n",
       "      <th>distance</th>\n",
       "      <th>goog_distance</th>\n",
       "      <th>goog_min</th>\n",
       "      <th>goog_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91</td>\n",
       "      <td>184</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>77.637890</td>\n",
       "      <td>12.930561</td>\n",
       "      <td>77.590090</td>\n",
       "      <td>12.888096</td>\n",
       "      <td>9686.2</td>\n",
       "      <td>10700</td>\n",
       "      <td>22</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>184</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>77.644817</td>\n",
       "      <td>12.944129</td>\n",
       "      <td>77.586162</td>\n",
       "      <td>12.885430</td>\n",
       "      <td>14531.8</td>\n",
       "      <td>15700</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91</td>\n",
       "      <td>165</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>77.643652</td>\n",
       "      <td>12.941535</td>\n",
       "      <td>77.761146</td>\n",
       "      <td>12.935575</td>\n",
       "      <td>16056.7</td>\n",
       "      <td>16200</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>165</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>77.655367</td>\n",
       "      <td>12.950985</td>\n",
       "      <td>77.713831</td>\n",
       "      <td>12.937901</td>\n",
       "      <td>10362.1</td>\n",
       "      <td>11000</td>\n",
       "      <td>20</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>165</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>77.658390</td>\n",
       "      <td>12.949876</td>\n",
       "      <td>77.716294</td>\n",
       "      <td>12.927034</td>\n",
       "      <td>8051.9</td>\n",
       "      <td>10300</td>\n",
       "      <td>24</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>185</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>77.556457</td>\n",
       "      <td>12.861758</td>\n",
       "      <td>77.572991</td>\n",
       "      <td>13.007320</td>\n",
       "      <td>20097.0</td>\n",
       "      <td>20300</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>159</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>77.588550</td>\n",
       "      <td>12.909991</td>\n",
       "      <td>77.570795</td>\n",
       "      <td>12.997582</td>\n",
       "      <td>12067.3</td>\n",
       "      <td>11900</td>\n",
       "      <td>22</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>76</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>77.648405</td>\n",
       "      <td>13.006604</td>\n",
       "      <td>77.661723</td>\n",
       "      <td>12.881504</td>\n",
       "      <td>20443.1</td>\n",
       "      <td>24000</td>\n",
       "      <td>45</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>193</td>\n",
       "      <td>178</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>77.597410</td>\n",
       "      <td>12.915178</td>\n",
       "      <td>77.650824</td>\n",
       "      <td>12.889310</td>\n",
       "      <td>8032.5</td>\n",
       "      <td>8000</td>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>11</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>77.557797</td>\n",
       "      <td>13.049415</td>\n",
       "      <td>77.526487</td>\n",
       "      <td>12.968088</td>\n",
       "      <td>12629.0</td>\n",
       "      <td>13400</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sourceid  dstid  month  day  time_period  dow    src_lon    src_lat  \\\n",
       "0         91    184      2   19            2    2  77.637890  12.930561   \n",
       "1         91    184      2   23            1    6  77.644817  12.944129   \n",
       "2         91    165      1    6            1    0  77.643652  12.941535   \n",
       "3         10    165      2    3            5    0  77.655367  12.950985   \n",
       "4         10    165      3    6            4    4  77.658390  12.949876   \n",
       "..       ...    ...    ...  ...          ...  ...        ...        ...   \n",
       "95       185     49      3    1            1    6  77.556457  12.861758   \n",
       "96       159     49      1    9            5    3  77.588550  12.909991   \n",
       "97        76    178      1   12            4    6  77.648405  13.006604   \n",
       "98       193    178      3   27            2    4  77.597410  12.915178   \n",
       "99        11    115      1   17            1    4  77.557797  13.049415   \n",
       "\n",
       "      dst_lon    dst_lat  distance  goog_distance  goog_min  goog_max  \n",
       "0   77.590090  12.888096    9686.2          10700        22        40  \n",
       "1   77.586162  12.885430   14531.8          15700        30        40  \n",
       "2   77.761146  12.935575   16056.7          16200        30        40  \n",
       "3   77.713831  12.937901   10362.1          11000        20        35  \n",
       "4   77.716294  12.927034    8051.9          10300        24        40  \n",
       "..        ...        ...       ...            ...       ...       ...  \n",
       "95  77.572991  13.007320   20097.0          20300        40        50  \n",
       "96  77.570795  12.997582   12067.3          11900        22        40  \n",
       "97  77.661723  12.881504   20443.1          24000        45        80  \n",
       "98  77.650824  12.889310    8032.5           8000        18        35  \n",
       "99  77.526487  12.968088   12629.0          13400        24        28  \n",
       "\n",
       "[100 rows x 14 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reference_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_time(row):\n",
    "    input = row[['sourceid', 'dstid', 'day', 'time_period', 'dow', 'src_lon', 'src_lat', 'dst_lon', 'dst_lat', 'distance']]\n",
    "    return round(regressor.predict([input])[0]/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>sourceid</th>\n",
       "      <th>dstid</th>\n",
       "      <th>distance</th>\n",
       "      <th>goog_distance</th>\n",
       "      <th>goog_min</th>\n",
       "      <th>goog_max</th>\n",
       "      <th>predicted</th>\n",
       "      <th>within_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91</td>\n",
       "      <td>184</td>\n",
       "      <td>9686.2</td>\n",
       "      <td>10700</td>\n",
       "      <td>22</td>\n",
       "      <td>40</td>\n",
       "      <td>29</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>184</td>\n",
       "      <td>14531.8</td>\n",
       "      <td>15700</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>26</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91</td>\n",
       "      <td>165</td>\n",
       "      <td>16056.7</td>\n",
       "      <td>16200</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>34</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>165</td>\n",
       "      <td>10362.1</td>\n",
       "      <td>11000</td>\n",
       "      <td>20</td>\n",
       "      <td>35</td>\n",
       "      <td>29</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>165</td>\n",
       "      <td>8051.9</td>\n",
       "      <td>10300</td>\n",
       "      <td>24</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>165</td>\n",
       "      <td>10610.8</td>\n",
       "      <td>10700</td>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>165</td>\n",
       "      <td>11898.9</td>\n",
       "      <td>11500</td>\n",
       "      <td>22</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>177</td>\n",
       "      <td>165</td>\n",
       "      <td>20806.9</td>\n",
       "      <td>21200</td>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "      <td>52</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17683.4</td>\n",
       "      <td>16800</td>\n",
       "      <td>30</td>\n",
       "      <td>65</td>\n",
       "      <td>40</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>20441.6</td>\n",
       "      <td>18800</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15</td>\n",
       "      <td>82</td>\n",
       "      <td>13335.8</td>\n",
       "      <td>16000</td>\n",
       "      <td>24</td>\n",
       "      <td>40</td>\n",
       "      <td>25</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>169</td>\n",
       "      <td>148</td>\n",
       "      <td>25992.0</td>\n",
       "      <td>26400</td>\n",
       "      <td>55</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>169</td>\n",
       "      <td>148</td>\n",
       "      <td>23628.8</td>\n",
       "      <td>23200</td>\n",
       "      <td>40</td>\n",
       "      <td>65</td>\n",
       "      <td>59</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>169</td>\n",
       "      <td>180</td>\n",
       "      <td>29579.8</td>\n",
       "      <td>28500</td>\n",
       "      <td>55</td>\n",
       "      <td>100</td>\n",
       "      <td>74</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>169</td>\n",
       "      <td>180</td>\n",
       "      <td>31056.3</td>\n",
       "      <td>29200</td>\n",
       "      <td>65</td>\n",
       "      <td>120</td>\n",
       "      <td>81</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>169</td>\n",
       "      <td>180</td>\n",
       "      <td>31774.2</td>\n",
       "      <td>30000</td>\n",
       "      <td>60</td>\n",
       "      <td>110</td>\n",
       "      <td>82</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>168</td>\n",
       "      <td>76</td>\n",
       "      <td>15065.9</td>\n",
       "      <td>19500</td>\n",
       "      <td>35</td>\n",
       "      <td>55</td>\n",
       "      <td>42</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>22362.3</td>\n",
       "      <td>23800</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>41</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>176</td>\n",
       "      <td>198</td>\n",
       "      <td>14221.3</td>\n",
       "      <td>14100</td>\n",
       "      <td>28</td>\n",
       "      <td>50</td>\n",
       "      <td>38</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>196</td>\n",
       "      <td>198</td>\n",
       "      <td>26543.1</td>\n",
       "      <td>33500</td>\n",
       "      <td>50</td>\n",
       "      <td>85</td>\n",
       "      <td>63</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>196</td>\n",
       "      <td>198</td>\n",
       "      <td>32441.9</td>\n",
       "      <td>34800</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>65</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>196</td>\n",
       "      <td>198</td>\n",
       "      <td>30500.7</td>\n",
       "      <td>30700</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>73</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5</td>\n",
       "      <td>198</td>\n",
       "      <td>34863.5</td>\n",
       "      <td>34000</td>\n",
       "      <td>55</td>\n",
       "      <td>80</td>\n",
       "      <td>78</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>194</td>\n",
       "      <td>144</td>\n",
       "      <td>9954.9</td>\n",
       "      <td>10800</td>\n",
       "      <td>26</td>\n",
       "      <td>50</td>\n",
       "      <td>31</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>198</td>\n",
       "      <td>35</td>\n",
       "      <td>21076.7</td>\n",
       "      <td>21300</td>\n",
       "      <td>35</td>\n",
       "      <td>50</td>\n",
       "      <td>51</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>198</td>\n",
       "      <td>188</td>\n",
       "      <td>9760.8</td>\n",
       "      <td>10200</td>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>24</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>198</td>\n",
       "      <td>188</td>\n",
       "      <td>14291.3</td>\n",
       "      <td>14300</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>198</td>\n",
       "      <td>55</td>\n",
       "      <td>20778.0</td>\n",
       "      <td>21500</td>\n",
       "      <td>40</td>\n",
       "      <td>75</td>\n",
       "      <td>49</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>198</td>\n",
       "      <td>13</td>\n",
       "      <td>33244.1</td>\n",
       "      <td>32800</td>\n",
       "      <td>55</td>\n",
       "      <td>70</td>\n",
       "      <td>67</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>32</td>\n",
       "      <td>13</td>\n",
       "      <td>19364.5</td>\n",
       "      <td>19600</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "      <td>43</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>32</td>\n",
       "      <td>13</td>\n",
       "      <td>21956.7</td>\n",
       "      <td>21500</td>\n",
       "      <td>40</td>\n",
       "      <td>75</td>\n",
       "      <td>48</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>180</td>\n",
       "      <td>13</td>\n",
       "      <td>27069.0</td>\n",
       "      <td>27500</td>\n",
       "      <td>45</td>\n",
       "      <td>80</td>\n",
       "      <td>63</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>15000.5</td>\n",
       "      <td>17400</td>\n",
       "      <td>28</td>\n",
       "      <td>50</td>\n",
       "      <td>37</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>81</td>\n",
       "      <td>13</td>\n",
       "      <td>22482.0</td>\n",
       "      <td>23600</td>\n",
       "      <td>40</td>\n",
       "      <td>65</td>\n",
       "      <td>58</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>78</td>\n",
       "      <td>192</td>\n",
       "      <td>22069.3</td>\n",
       "      <td>21800</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>41</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>78</td>\n",
       "      <td>192</td>\n",
       "      <td>20063.5</td>\n",
       "      <td>19400</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>41</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>78</td>\n",
       "      <td>166</td>\n",
       "      <td>17271.0</td>\n",
       "      <td>16500</td>\n",
       "      <td>28</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>195</td>\n",
       "      <td>166</td>\n",
       "      <td>16745.3</td>\n",
       "      <td>15100</td>\n",
       "      <td>26</td>\n",
       "      <td>40</td>\n",
       "      <td>43</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2</td>\n",
       "      <td>166</td>\n",
       "      <td>30049.5</td>\n",
       "      <td>31200</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "      <td>59</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>179</td>\n",
       "      <td>166</td>\n",
       "      <td>16929.2</td>\n",
       "      <td>16900</td>\n",
       "      <td>30</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sourceid  dstid  distance  goog_distance  goog_min  goog_max  predicted  \\\n",
       "0         91    184    9686.2          10700        22        40         29   \n",
       "1         91    184   14531.8          15700        30        40         26   \n",
       "2         91    165   16056.7          16200        30        40         34   \n",
       "3         10    165   10362.1          11000        20        35         29   \n",
       "4         10    165    8051.9          10300        24        40         31   \n",
       "5         10    165   10610.8          10700        18        35         30   \n",
       "6         10    165   11898.9          11500        22        40         37   \n",
       "7        177    165   20806.9          21200        40        60         52   \n",
       "8         15     20   17683.4          16800        30        65         40   \n",
       "9         15     20   20441.6          18800        35        60         45   \n",
       "10        15     82   13335.8          16000        24        40         25   \n",
       "11       169    148   25992.0          26400        55       110         70   \n",
       "12       169    148   23628.8          23200        40        65         59   \n",
       "13       169    180   29579.8          28500        55       100         74   \n",
       "14       169    180   31056.3          29200        65       120         81   \n",
       "15       169    180   31774.2          30000        60       110         82   \n",
       "16       168     76   15065.9          19500        35        55         42   \n",
       "17         1    154   22362.3          23800        40        50         41   \n",
       "18       176    198   14221.3          14100        28        50         38   \n",
       "19       196    198   26543.1          33500        50        85         63   \n",
       "20       196    198   32441.9          34800        60        75         65   \n",
       "21       196    198   30500.7          30700        50        90         73   \n",
       "22         5    198   34863.5          34000        55        80         78   \n",
       "23       194    144    9954.9          10800        26        50         31   \n",
       "24       198     35   21076.7          21300        35        50         51   \n",
       "25       198    188    9760.8          10200        18        35         24   \n",
       "26       198    188   14291.3          14300        24        28         26   \n",
       "27       198     55   20778.0          21500        40        75         49   \n",
       "28       198     13   33244.1          32800        55        70         67   \n",
       "29        32     13   19364.5          19600        30        60         43   \n",
       "30        32     13   21956.7          21500        40        75         48   \n",
       "31       180     13   27069.0          27500        45        80         63   \n",
       "32        66     13   15000.5          17400        28        50         37   \n",
       "33        81     13   22482.0          23600        40        65         58   \n",
       "34        78    192   22069.3          21800        40        50         41   \n",
       "35        78    192   20063.5          19400        40        50         41   \n",
       "36        78    166   17271.0          16500        28        45         45   \n",
       "37       195    166   16745.3          15100        26        40         43   \n",
       "38         2    166   30049.5          31200        45        60         59   \n",
       "39       179    166   16929.2          16900        30        55         44   \n",
       "\n",
       "   within_range  \n",
       "0             Y  \n",
       "1             N  \n",
       "2             Y  \n",
       "3             Y  \n",
       "4             Y  \n",
       "5             Y  \n",
       "6             Y  \n",
       "7             Y  \n",
       "8             Y  \n",
       "9             Y  \n",
       "10            Y  \n",
       "11            Y  \n",
       "12            Y  \n",
       "13            Y  \n",
       "14            Y  \n",
       "15            Y  \n",
       "16            Y  \n",
       "17            Y  \n",
       "18            Y  \n",
       "19            Y  \n",
       "20            Y  \n",
       "21            Y  \n",
       "22            Y  \n",
       "23            Y  \n",
       "24            N  \n",
       "25            Y  \n",
       "26            Y  \n",
       "27            Y  \n",
       "28            Y  \n",
       "29            Y  \n",
       "30            Y  \n",
       "31            Y  \n",
       "32            Y  \n",
       "33            Y  \n",
       "34            Y  \n",
       "35            Y  \n",
       "36            Y  \n",
       "37            N  \n",
       "38            Y  \n",
       "39            Y  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reference_data['predicted'] = reference_data.apply(predict_time, axis=1)\n",
    "results = reference_data[['sourceid', 'dstid', 'distance', 'goog_distance', 'goog_min', 'goog_max', 'predicted']].copy()\n",
    "results['within_range'] = np.where(\n",
    "    (results['predicted'] <= results['goog_max'])\n",
    "        & (results['predicted'] >= results['goog_min']), 'Y', 'N')\n",
    "results.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Real-time Routing and Prediction\n",
    "\n",
    "To demonstrate the use of our technique in a real-world application, we show how it can be used in a real-time routing application."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n",
    "\n",
    "ORS_API_KEY = os.getenv('ORS_API_KEY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_driving_route(source_coordinates, dest_coordinates):\n",
    "    parameters = {\n",
    "    'api_key': ORS_API_KEY,\n",
    "    'start' : '{},{}'.format(source_coordinates[1], source_coordinates[0]),\n",
    "    'end' : '{},{}'.format(dest_coordinates[1], dest_coordinates[0])\n",
    "    }\n",
    "\n",
    "    response = requests.get(\n",
    "        'https://api.openrouteservice.org/v2/directions/driving-car', params=parameters)\n",
    "\n",
    "    if response.status_code == 200:\n",
    "        data = response.json()\n",
    "        return data\n",
    "    else:\n",
    "        print('Request failed.')\n",
    "        return -9999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ward(coordinates):\n",
    "    df = pd.DataFrame({'x': [coordinates[1]], 'y': [coordinates[0]]})\n",
    "    src_gdf = gpd.GeoDataFrame(geometry=gpd.points_from_xy(df.x, df.y), crs='EPSG:4326')\n",
    "    src_gdf = gpd.sjoin(src_gdf, wards, how='inner', op='intersects')\n",
    "    return int(src_gdf['MOVEMENT_ID'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_route(source, destination, departure_time):\n",
    "    sourceid = get_ward(source)\n",
    "    dstid = get_ward(destination)\n",
    "    day = departure_time.day\n",
    "    time_period = get_time_period(departure_time.hour)\n",
    "    dow = departure_time.weekday()\n",
    "    driving_data = get_driving_route(source, destination)\n",
    "    summary = driving_data['features'][0]['properties']['summary']\n",
    "    distance = summary['distance']\n",
    "    input = [sourceid, dstid, day, time_period, dow, source[1], source[0], destination[1], destination[0], distance]\n",
    "    travel_time = round(regressor.predict([input])[0]/60)\n",
    "    ors_travel_time = round(summary['duration']/60)\n",
    "    route= driving_data['features'][0]['geometry']['coordinates']\n",
    "    \n",
    "    def swap(coord):\n",
    "        coord[0],coord[1]=coord[1],coord[0]\n",
    "        return coord\n",
    "\n",
    "    route=list(map(swap, route))\n",
    "    m = folium.Map(location=[(source[0] + destination[0])/2,(source[1] + destination[1])/2], zoom_start=13)\n",
    "    \n",
    "    tooltip = 'Model predicted time = {} mins, \\\n",
    "        Default travel time = {} mins'.format(travel_time, ors_travel_time)\n",
    "    folium.PolyLine(\n",
    "        route,\n",
    "        weight=8,\n",
    "        color='blue',\n",
    "        opacity=0.6,\n",
    "        tooltip=tooltip\n",
    "    ).add_to(m)\n",
    "\n",
    "    folium.Marker(\n",
    "        location=(source[0],source[1]),\n",
    "        icon=folium.Icon(icon='play',color='green')\n",
    "    ).add_to(m)\n",
    "\n",
    "    folium.Marker(\n",
    "        location=(destination[0],destination[1]),\n",
    "        icon=folium.Icon(icon='stop',color='red')\n",
    "    ).add_to(m)\n",
    "\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Live Demo\n",
    "\n",
    "We pick a set of coordinates within the city and show how to get turn-by-turn directions using OpenRouteService API and predict the travel-time using our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "source = 12.946538, 77.579975\n",
    "destination = 12.994029, 77.661008\n",
    "departure_time = datetime.datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
            L_DISABLE_3D = false;
        </script>
    
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.5.1/dist/leaflet.js"></script>
    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.5.1/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <meta name="viewport" content="width=device-width,
                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
            <style>
                #map_b37cdf3876b74b0f869a1de14fb0c011 {
                    position: relative;
                    width: 100.0%;
                    height: 100.0%;
                    left: 0.0%;
                    top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_b37cdf3876b74b0f869a1de14fb0c011" ></div>
        
</body>
<script>    
    
            var map_b37cdf3876b74b0f869a1de14fb0c011 = L.map(
                "map_b37cdf3876b74b0f869a1de14fb0c011",
                {
                    center: [12.9702835, 77.6204915],
                    crs: L.CRS.EPSG3857,
                    zoom: 13,
                    zoomControl: true,
                    preferCanvas: false,
                }
            );

            

        
    
            var tile_layer_1eccb6e75c8c4944aa373133e7cd728f = L.tileLayer(
                "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
                {"attribution": "Data by \u0026copy; \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
            ).addTo(map_b37cdf3876b74b0f869a1de14fb0c011);
        
    
            var poly_line_e770e8db12c046e0950f87d6fcfb5f5b = L.polyline(
                [[12.946538, 77.579964], [12.947332, 77.579948], [12.948013, 77.579943], [12.948078, 77.579944], [12.948191, 77.579947], [12.948287, 77.57995], [12.948394, 77.579951], [12.948587, 77.579954], [12.948608, 77.579954], [12.949052, 77.579953], [12.949203, 77.579953], [12.94952, 77.57995], [12.950515, 77.579949], [12.950603, 77.579948], [12.951929, 77.579952], [12.952338, 77.579958], [12.953208, 77.579951], [12.95348, 77.579954], [12.95362, 77.579956], [12.953973, 77.579959], [12.954267, 77.579963], [12.954623, 77.579973], [12.955231, 77.579995], [12.956091, 77.580945], [12.956273, 77.581097], [12.956751, 77.581471], [12.957135, 77.581752], [12.957746, 77.582133], [12.958924, 77.582793], [12.959287, 77.582977], [12.959482, 77.583085], [12.959805, 77.583253], [12.960343, 77.583532], [12.960378, 77.583549], [12.960485, 77.583603], [12.961223, 77.583967], [12.961474, 77.584091], [12.96299, 77.584832], [12.963147, 77.584909], [12.963438, 77.585047], [12.963824, 77.585232], [12.963845, 77.585242], [12.963992, 77.585282], [12.964101, 77.585435], [12.964581, 77.585889], [12.965447, 77.586617], [12.965602, 77.586664], [12.965792, 77.586666], [12.966011, 77.586755], [12.966253, 77.58699], [12.966665, 77.58734], [12.966849, 77.5874], [12.967036, 77.58742], [12.967266, 77.587371], [12.967471, 77.587347], [12.967608, 77.587349], [12.967717, 77.58742], [12.9678, 77.587558], [12.96783, 77.587713], [12.967827, 77.588129], [12.967809, 77.588291], [12.967838, 77.588459], [12.967906, 77.588773], [12.967985, 77.588989], [12.968085, 77.589165], [12.968321, 77.589481], [12.968427, 77.589613], [12.968439, 77.589628], [12.970151, 77.591685], [12.970517, 77.592125], [12.970545, 77.592158], [12.971818, 77.593537], [12.971973, 77.593702], [12.972061, 77.593796], [12.972185, 77.59393], [12.972286, 77.594009], [12.972399, 77.594098], [12.972444, 77.594137], [12.973081, 77.5947], [12.973515, 77.595053], [12.973815, 77.595392], [12.974054, 77.59569], [12.974272, 77.595969], [12.974665, 77.596435], [12.975039, 77.596935], [12.975164, 77.597102], [12.975497, 77.597566], [12.975722, 77.59788], [12.975879, 77.598099], [12.975959, 77.59821], [12.976557, 77.599046], [12.976603, 77.599115], [12.97662, 77.599141], [12.976652, 77.59919], [12.9767, 77.599331], [12.976715, 77.5995], [12.976713, 77.5996], [12.976692, 77.600291], [12.97665, 77.601658], [12.976647, 77.601727], [12.976644, 77.601772], [12.976637, 77.601882], [12.976628, 77.601942], [12.976083, 77.604255], [12.975621, 77.606103], [12.975208, 77.607836], [12.975186, 77.607954], [12.975157, 77.608078], [12.975118, 77.608305], [12.97502, 77.608793], [12.974462, 77.611036], [12.97445, 77.611085], [12.974432, 77.611167], [12.973832, 77.613568], [12.973768, 77.613802], [12.973741, 77.613894], [12.973644, 77.614394], [12.973377, 77.615531], [12.973319, 77.615779], [12.973233, 77.616147], [12.973184, 77.616358], [12.973131, 77.616602], [12.973055, 77.616947], [12.973038, 77.617025], [12.972888, 77.617713], [12.972802, 77.618106], [12.972667, 77.618706], [12.972631, 77.619189], [12.972645, 77.619396], [12.972665, 77.619565], [12.972677, 77.619783], [12.972703, 77.620247], [12.972707, 77.620294], [12.972714, 77.620374], [12.972776, 77.620863], [12.972888, 77.621339], [12.972963, 77.621555], [12.973023, 77.621728], [12.9731, 77.621916], [12.97314, 77.622017], [12.973154, 77.622053], [12.973237, 77.622252], [12.97339, 77.622565], [12.9737, 77.623112], [12.973886, 77.623404], [12.974004, 77.623591], [12.974121, 77.623776], [12.974212, 77.623937], [12.974493, 77.624388], [12.97473, 77.624773], [12.975004, 77.625224], [12.975049, 77.625296], [12.975119, 77.625404], [12.975252, 77.625609], [12.975304, 77.625694], [12.975461, 77.625877], [12.975712, 77.626098], [12.976006, 77.626313], [12.976595, 77.626748], [12.977003, 77.626951], [12.977468, 77.627275], [12.97753, 77.627352], [12.977669, 77.627548], [12.977917, 77.627969], [12.978046, 77.628186], [12.978121, 77.628313], [12.978263, 77.628475], [12.978403, 77.628643], [12.978441, 77.628666], [12.978534, 77.628722], [12.978667, 77.62882], [12.979065, 77.629095], [12.979197, 77.629236], [12.979781, 77.629663], [12.980166, 77.629936], [12.980564, 77.630268], [12.980898, 77.630536], [12.981114, 77.630743], [12.981625, 77.631325], [12.981708, 77.631412], [12.981843, 77.631618], [12.982162, 77.632152], [12.982318, 77.632614], [12.982447, 77.633738], [12.98255, 77.634728], [12.982562, 77.634841], [12.982681, 77.636299], [12.982776, 77.636792], [12.982937, 77.63735], [12.982952, 77.637407], [12.982962, 77.637438], [12.983645, 77.639235], [12.98381, 77.639687], [12.984144, 77.640603], [12.984815, 77.642465], [12.98507, 77.643116], [12.985466, 77.644113], [12.985625, 77.644516], [12.985829, 77.645052], [12.985856, 77.645118], [12.986227, 77.646044], [12.987029, 77.648052], [12.987157, 77.648297], [12.987186, 77.648381], [12.987232, 77.648513], [12.987397, 77.648872], [12.987595, 77.649336], [12.988733, 77.651786], [12.988757, 77.651837], [12.989001, 77.652362], [12.989229, 77.652858], [12.989273, 77.652953], [12.989531, 77.653514], [12.98968, 77.653821], [12.989721, 77.653904], [12.990055, 77.654581], [12.990125, 77.654728], [12.991153, 77.656898], [12.991251, 77.657105], [12.991324, 77.657274], [12.991429, 77.657514], [12.991524, 77.657733], [12.992847, 77.660375], [12.992922, 77.660525], [12.993158, 77.660412], [12.993564, 77.661246]],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "blue", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "noClip": false, "opacity": 0.6, "smoothFactor": 1.0, "stroke": true, "weight": 8}
            ).addTo(map_b37cdf3876b74b0f869a1de14fb0c011);
        
    
            poly_line_e770e8db12c046e0950f87d6fcfb5f5b.bindTooltip(
                `<div>
                     Model predicted time = 30 mins,         Default travel time = 14 mins
                 </div>`,
                {"sticky": true}
            );
        
    
            var marker_7644aa951d4b4f629643f713667c6c66 = L.marker(
                [12.946538, 77.579975],
                {}
            ).addTo(map_b37cdf3876b74b0f869a1de14fb0c011);
        
    
            var icon_0922422a861d4a9d948333b81568ae6c = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "play", "iconColor": "white", "markerColor": "green", "prefix": "glyphicon"}
            );
            marker_7644aa951d4b4f629643f713667c6c66.setIcon(icon_0922422a861d4a9d948333b81568ae6c);
        
    
            var marker_0ed01d844fef4e82a454efc1aa776c81 = L.marker(
                [12.994029, 77.661008],
                {}
            ).addTo(map_b37cdf3876b74b0f869a1de14fb0c011);
        
    
            var icon_9c51b21a809d46e7af52f04663cd88b1 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "stop", "iconColor": "white", "markerColor": "red", "prefix": "glyphicon"}
            );
            marker_0ed01d844fef4e82a454efc1aa776c81.setIcon(icon_9c51b21a809d46e7af52f04663cd88b1);
        
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x20047995ec8>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_route(source, destination, departure_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check how the model performs by comparing with the travel time predicted by Google Maps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import webbrowser\n",
    "\n",
    "url='https://www.google.com/maps/dir/{},{}/{},{}'.format(source[0],source[1],destination[0],destination[1])\n",
    "webbrowser.open(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}