knn_cities.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Unnamed: 0',\n",
       " 'lat',\n",
       " 'lng',\n",
       " 'name',\n",
       " 'population',\n",
       " 'district',\n",
       " 'subject',\n",
       " 'density',\n",
       " 'ivl_per_100k',\n",
       " 'ivl_number',\n",
       " 'ekmo_per_100k',\n",
       " 'ekmo_number',\n",
       " 'life_quality_place_rating',\n",
       " 'ecology',\n",
       " 'cleanness',\n",
       " 'public_services',\n",
       " 'neighbourhood',\n",
       " 'children_places',\n",
       " 'sport_and_outdoor',\n",
       " 'shops_and_malls',\n",
       " 'public_transport',\n",
       " 'security',\n",
       " 'life_costs',\n",
       " 'infected_3003',\n",
       " 'died_3003',\n",
       " 'recovered_3003',\n",
       " 'sick_3003',\n",
       " 'infected_1503',\n",
       " 'died_1503',\n",
       " 'recovered_1503',\n",
       " 'sick_1503',\n",
       " 'inf_rate',\n",
       " 'avg_temp_min',\n",
       " 'avg_temp_max',\n",
       " 'avg_temp_std',\n",
       " 'avg_temp_median',\n",
       " 'humidity_min',\n",
       " 'humidity_max',\n",
       " 'humidity_std',\n",
       " 'humidity_median',\n",
       " 'pressure_min',\n",
       " 'pressure_max',\n",
       " 'pressure_std',\n",
       " 'pressure_median',\n",
       " 'wind_speed_ms_min',\n",
       " 'wind_speed_ms_max',\n",
       " 'wind_speed_ms_std',\n",
       " 'wind_speed_ms_median',\n",
       " 'region_x',\n",
       " 'urban_50-54_years',\n",
       " 'urban_55-59_years',\n",
       " 'urban_60-64_years',\n",
       " 'urban_65-69_years',\n",
       " 'urban_70-74_years',\n",
       " 'urban_75-79_years',\n",
       " 'urban_80-84_years',\n",
       " 'urban_85-89_years',\n",
       " 'urban_90-94_years',\n",
       " 'rural_50-54_years',\n",
       " 'rural_55-59_years',\n",
       " 'rural_60-64_years',\n",
       " 'rural_65-69_years',\n",
       " 'rural_70-74_years',\n",
       " 'rural_75-79_years',\n",
       " 'rural_80-84_years',\n",
       " 'rural_85-89_years',\n",
       " 'rural_90-94_years',\n",
       " 'work_ratio_15-72_years',\n",
       " 'work_ratio_55-64_years',\n",
       " 'work_ratio_15-24_years',\n",
       " 'work_ratio_15-64_years',\n",
       " 'work_ratio_25-54_years',\n",
       " 'num_patients_tubercul_1992',\n",
       " 'num_patients_tubercul_1993',\n",
       " 'num_patients_tubercul_1994',\n",
       " 'num_patients_tubercul_1995',\n",
       " 'num_patients_tubercul_1996',\n",
       " 'num_patients_tubercul_1997',\n",
       " 'num_patients_tubercul_1998',\n",
       " 'num_patients_tubercul_1999',\n",
       " 'num_patients_tubercul_2000',\n",
       " 'num_patients_tubercul_2001',\n",
       " 'num_patients_tubercul_2002',\n",
       " 'num_patients_tubercul_2003',\n",
       " 'num_patients_tubercul_2004',\n",
       " 'num_patients_tubercul_2005',\n",
       " 'num_patients_tubercul_2006',\n",
       " 'num_patients_tubercul_2007',\n",
       " 'num_patients_tubercul_2008',\n",
       " 'num_patients_tubercul_2009',\n",
       " 'num_patients_tubercul_2010',\n",
       " 'num_patients_tubercul_2011',\n",
       " 'num_patients_tubercul_2012',\n",
       " 'num_patients_tubercul_2013',\n",
       " 'num_patients_tubercul_2014',\n",
       " 'num_patients_tubercul_2015',\n",
       " 'num_patients_tubercul_2016',\n",
       " 'num_patients_tubercul_2017',\n",
       " 'volume_serv_household_2017',\n",
       " 'volume_serv_chargeable_2017',\n",
       " 'volume_serv_transport_2017',\n",
       " 'volume_serv_post_2017',\n",
       " 'volume_serv_accommodation_2017',\n",
       " 'volume_serv_telecom_2017',\n",
       " 'volume_serv_others_2017',\n",
       " 'volume_serv_veterinary_2017',\n",
       " 'volume_serv_housing_2017',\n",
       " 'volume_serv_education_2017',\n",
       " 'volume_serv_medicine_2017',\n",
       " 'volume_serv_disabled_2017',\n",
       " 'volume_serv_culture_2017',\n",
       " 'volume_serv_sport_2017',\n",
       " 'volume_serv_hotels_2017',\n",
       " 'volume_serv_tourism_2017',\n",
       " 'volume_serv_sanatorium_2017',\n",
       " 'num_phones_rural_2018',\n",
       " 'num_phones_urban_2019',\n",
       " 'bus_march_travel_18',\n",
       " 'bus_april_travel_18',\n",
       " 'epirank_avia',\n",
       " 'epirank_bus',\n",
       " 'epirank_train',\n",
       " 'epirank_avia_cat',\n",
       " 'epirank_bus_cat',\n",
       " 'epirank_train_cat',\n",
       " 'whole_population',\n",
       " 'urban',\n",
       " 'rural',\n",
       " 'has_metro']"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname = 'data/covid_data_train.csv'\n",
    "df = pd.read_csv(fname, sep=';')\n",
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>name</th>\n",
       "      <th>lng</th>\n",
       "      <th>ivl_number</th>\n",
       "      <th>volume_serv_post_2017</th>\n",
       "      <th>population</th>\n",
       "      <th>humidity_median</th>\n",
       "      <th>ivl_per_100k</th>\n",
       "      <th>pressure_std</th>\n",
       "      <th>work_ratio_15-24_years</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Абдулино</td>\n",
       "      <td>53.655701</td>\n",
       "      <td>542.0</td>\n",
       "      <td>358862.30</td>\n",
       "      <td>20663.0</td>\n",
       "      <td>85.29</td>\n",
       "      <td>27.7</td>\n",
       "      <td>8.391038</td>\n",
       "      <td>23.2</td>\n",
       "      <td>15.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Абинск</td>\n",
       "      <td>38.157819</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2281604.00</td>\n",
       "      <td>34926.0</td>\n",
       "      <td>67.43</td>\n",
       "      <td>17.6</td>\n",
       "      <td>3.166792</td>\n",
       "      <td>23.5</td>\n",
       "      <td>73.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Агрыз</td>\n",
       "      <td>52.997251</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>394884.40</td>\n",
       "      <td>19299.0</td>\n",
       "      <td>76.86</td>\n",
       "      <td>28.2</td>\n",
       "      <td>8.129898</td>\n",
       "      <td>29.4</td>\n",
       "      <td>57.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Азнакаево</td>\n",
       "      <td>53.079228</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>394884.40</td>\n",
       "      <td>34859.0</td>\n",
       "      <td>76.86</td>\n",
       "      <td>28.2</td>\n",
       "      <td>8.129898</td>\n",
       "      <td>29.4</td>\n",
       "      <td>57.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Азов</td>\n",
       "      <td>39.424075</td>\n",
       "      <td>643.0</td>\n",
       "      <td>1119143.29</td>\n",
       "      <td>82882.0</td>\n",
       "      <td>66.43</td>\n",
       "      <td>15.3</td>\n",
       "      <td>3.638419</td>\n",
       "      <td>25.0</td>\n",
       "      <td>41.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name        lng  ivl_number  volume_serv_post_2017  population  \\\n",
       "2   Абдулино  53.655701       542.0              358862.30     20663.0   \n",
       "3     Абинск  38.157819      1000.0             2281604.00     34926.0   \n",
       "4      Агрыз  52.997251      1100.0              394884.40     19299.0   \n",
       "5  Азнакаево  53.079228      1100.0              394884.40     34859.0   \n",
       "6       Азов  39.424075       643.0             1119143.29     82882.0   \n",
       "\n",
       "   humidity_median  ivl_per_100k  pressure_std  work_ratio_15-24_years  \\\n",
       "2            85.29          27.7      8.391038                    23.2   \n",
       "3            67.43          17.6      3.166792                    23.5   \n",
       "4            76.86          28.2      8.129898                    29.4   \n",
       "5            76.86          28.2      8.129898                    29.4   \n",
       "6            66.43          15.3      3.638419                    25.0   \n",
       "\n",
       "   density  \n",
       "2    15.95  \n",
       "3    73.73  \n",
       "4    57.27  \n",
       "5    57.27  \n",
       "6    41.87  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "important_features = ['name', 'lng', 'ivl_number', 'volume_serv_post_2017', 'population', 'humidity_median', \n",
    "                      'ivl_per_100k', 'pressure_std', 'work_ratio_15-24_years', 'density']\n",
    "X = df[important_features].dropna()\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "XX = X.drop(['name'], axis=1).values #returns a numpy array\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "X_scaled = min_max_scaler.fit_transform(XX)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "knn experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbrs = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     name        lng  ivl_number  volume_serv_post_2017  population  \\\n",
      "3  Абинск  38.157819      1000.0              2281604.0     34926.0   \n",
      "\n",
      "   humidity_median  ivl_per_100k  pressure_std  work_ratio_15-24_years  \\\n",
      "3            67.43          17.6      3.166792                    23.5   \n",
      "\n",
      "   density  \n",
      "3    73.73  \n"
     ]
    }
   ],
   "source": [
    "town = X.loc[X.name == 'Абинск']\n",
    "print(town)\n",
    "x = town[important_features].drop(['name'], axis=1)\n",
    "x = x.values.flatten().tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 26, 176, 311, 327,  17]])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_, towns = nbrs.kneighbors([x], 5)\n",
    "towns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>name</th>\n",
       "      <th>lng</th>\n",
       "      <th>ivl_number</th>\n",
       "      <th>volume_serv_post_2017</th>\n",
       "      <th>population</th>\n",
       "      <th>humidity_median</th>\n",
       "      <th>ivl_per_100k</th>\n",
       "      <th>pressure_std</th>\n",
       "      <th>work_ratio_15-24_years</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Армавир</td>\n",
       "      <td>41.129406</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2281604.0</td>\n",
       "      <td>188897.0</td>\n",
       "      <td>67.43</td>\n",
       "      <td>17.6</td>\n",
       "      <td>3.166792</td>\n",
       "      <td>23.5</td>\n",
       "      <td>73.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>Ейск</td>\n",
       "      <td>38.274768</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2281604.0</td>\n",
       "      <td>87771.0</td>\n",
       "      <td>67.43</td>\n",
       "      <td>17.6</td>\n",
       "      <td>3.166792</td>\n",
       "      <td>23.5</td>\n",
       "      <td>73.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>Кропоткин</td>\n",
       "      <td>40.575027</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2281604.0</td>\n",
       "      <td>80743.0</td>\n",
       "      <td>67.43</td>\n",
       "      <td>17.6</td>\n",
       "      <td>3.166792</td>\n",
       "      <td>23.5</td>\n",
       "      <td>73.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Лабинск</td>\n",
       "      <td>40.724218</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2281604.0</td>\n",
       "      <td>62822.0</td>\n",
       "      <td>67.43</td>\n",
       "      <td>17.6</td>\n",
       "      <td>3.166792</td>\n",
       "      <td>23.5</td>\n",
       "      <td>73.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Анапа</td>\n",
       "      <td>37.316887</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2281604.0</td>\n",
       "      <td>58983.0</td>\n",
       "      <td>67.43</td>\n",
       "      <td>17.6</td>\n",
       "      <td>3.166792</td>\n",
       "      <td>23.5</td>\n",
       "      <td>73.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name        lng  ivl_number  volume_serv_post_2017  population  \\\n",
       "31     Армавир  41.129406      1000.0              2281604.0    188897.0   \n",
       "218       Ейск  38.274768      1000.0              2281604.0     87771.0   \n",
       "389  Кропоткин  40.575027      1000.0              2281604.0     80743.0   \n",
       "411    Лабинск  40.724218      1000.0              2281604.0     62822.0   \n",
       "21       Анапа  37.316887      1000.0              2281604.0     58983.0   \n",
       "\n",
       "     humidity_median  ivl_per_100k  pressure_std  work_ratio_15-24_years  \\\n",
       "31             67.43          17.6      3.166792                    23.5   \n",
       "218            67.43          17.6      3.166792                    23.5   \n",
       "389            67.43          17.6      3.166792                    23.5   \n",
       "411            67.43          17.6      3.166792                    23.5   \n",
       "21             67.43          17.6      3.166792                    23.5   \n",
       "\n",
       "     density  \n",
       "31     73.73  \n",
       "218    73.73  \n",
       "389    73.73  \n",
       "411    73.73  \n",
       "21     73.73  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.iloc[towns[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "352"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TSNE visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(352, 2)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.manifold import TSNE\n",
    "X_embedded = TSNE(n_components=2, learning_rate='auto',\n",
    "                  init='random', random_state=10).fit_transform(X_scaled)\n",
    "X_embedded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "X['tsne_0'] = X_embedded[:, 0]\n",
    "X['tsne_1'] = X_embedded[:, 1]\n",
    "X['inf_rate'] = df['inf_rate']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как будто видны какие-то кластеры"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tsne_0', ylabel='tsne_1'>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "sns.scatterplot(\n",
    "    x=\"tsne_0\", y=\"tsne_1\",    \n",
    "    data=X,    \n",
    "    hue=\"inf_rate\",\n",
    "    alpha=0.9\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tsne_0', ylabel='tsne_1'>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "sns.scatterplot(\n",
    "    x=\"tsne_0\", y=\"tsne_1\",    \n",
    "    data=X,    \n",
    "    hue=\"lng\",\n",
    "    alpha=0.9\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tsne_0', ylabel='tsne_1'>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7YAAAJNCAYAAAAF2On2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACK6UlEQVR4nOz9eZRc9X3n/z8/997aq7p6X9Td2ncJIUAGDDbGxoC3QOw4NomT4CXxxM7i8Tcnx5lv8ptx5nvmG2cm35nJwixOHJvMjE08Hts4NobY2HgDLAQIkISE9qXV6n2rrvXe+/n90aKRkARauru6ul+PczBdn7rL+2JB16s+m7HWIiIiIiIiIlKrnGoXICIiIiIiInIlFGxFRERERESkpinYioiIiIiISE1TsBUREREREZGapmArIiIiIiIiNc2rdgEzpbm52S5fvrzaZYiIiIiIiMgsePrppwettS3ne2/BBNvly5ezY8eOapchIiIiIiIis8AYc/RC72kosoiIiIiIiNQ0BVsRERERERGpaQq2IiIiIiIiUtMWzBxbERERERGRhaRSqXDixAmKxWK1S5lT8Xicrq4uIpHIRZ+jYCsiIiIiIjIPnThxgkwmw/LlyzHGVLucOWGtZWhoiBMnTrBixYqLPk9DkUVEREREROahYrFIU1PTogm1AMYYmpqaLrmXWsFWRERERERknlpMofZll/PMCrYiIiIiIiJS0xRsRUREREREpKYp2IqIiIiIiNSIm2666TXff+yxx3jPe94zR9W84siRI2zevHnO7/syBVsREREREZEa8fjjj1e7hFnh+/4Vna9gKyIiIiIiUiPS6TQA99xzD9/5znem2z/84Q/zta997XXP/+xnP8tHP/pRbr31VlauXMlf/dVfAef2uP7FX/wFn/3sZwG49dZb+fSnP822bdvYsGEDTz31FO973/tYs2YNf/InfzJ9ju/7fOhDH2LDhg28//3vJ5/PA/D000/zlre8heuuu44777yT3t7e6ev+y3/5L9m2bRt/+Zd/eUX/XBRsRUREREREaswHP/hBvvrVrwJQLpd59NFHefe7331R5+7du5dHHnmE7du386d/+qdUKpXXPScajbJjxw5++7d/m7vvvpv77ruPXbt28aUvfYmhoSEA9u3bxyc/+UlefPFF6urq+C//5b9QqVT4vd/7Pb72ta/x9NNP89GPfpQ//uM/nr5uuVxmx44d/MEf/MFl/FN4hXdFZ4uIiIiIiMice+c738mnPvUpSqUSDz/8MLfccguJROKizn33u99NLBYjFovR2tpKX1/f655z1113AXDVVVexadMmOjo6AFi5ciXHjx+nvr6e7u5ubr75ZgB+7dd+jb/6q7/iHe94B7t27eL2228HIAiC6XNhKqDPBAVbERERERGRGhOPx7n11lt55JFH+Md//Efuueeeiz43FotN/+y6Lr7v43keYRhOtxeLxfOe4zjOWec7jjM9P/bV+88aY7DWsmnTJp544onz1pJKpS667teiocgiIiIiIiI16IMf/CBf/OIX+clPfsI73vGOK7pWW1sb/f39DA0NUSqV+Pa3v33J1zh27Nh0gP3yl7/Mm970JtatW8fAwMB0e6VSYffu3VdU6/ko2IqIiIiIiNSgO+64gx/96Ee8/e1vJxqNXtG1IpEI//pf/2uuv/56br/9dtavX3/J11i3bh333XcfGzZsYGRkhE984hNEo1G+9rWv8ZnPfIarr76arVu3zsrKzsZaO+MXrYZt27bZHTt2VLsMERERERGRGfHiiy+yYcOGapdRFed7dmPM09babec7Xj22IiIiIiIiUtO0eJSIiIiIiMgC88UvfvGcvWFvvvlm7rvvvipVNLsUbEVkwQpKRcrjo/iTOSKZOiLpLE7EwwYBxvUwjgatiIiIyML0kY98hI985CPVLmPOVDXYGmO6gX8A2gALfN5a+5fGmEbgH4HlwBHgA9bakWrVKSK1J/QrTJ44SlCaWqrez09SyU3gxOM4GMIgwLgukXQdXjJ1zvL0IiIiIlI7qt1d4QN/YK3dCNwI/I4xZiPwR8Cj1to1wKOnX4uInJe1lspkjsLAKYpD/fiFPEGpOB1qTx9FZWwENxKj0H+K0mAfxf5eckcP4E/mqla7iIiIiFy5qvbYWmt7gd7TP08YY14EOoG7gVtPH3Y/8BjwmSqUKCLzXOhXqOTGmTx+BIzBGAdcl1TnsrMPtBbjRfDzk2DD6TYslIYH8VLpqTBcyBMGPm40jpdK4biasSEiIiIy31W7x3aaMWY5cA3wc6DtdOgFOMXUUOXznfNxY8wOY8yOgYGBuSlUROaV8sQYpcH+qZAahtjAhyAgKORxE8kzjjREUump91/FBj5BuURlfIzS0ACloQEquTEqE+Nz9yAiIiIi81CxWOT666/n6quvZtOmTfybf/NvADh8+DA33HADq1ev5oMf/CDlchmAUqnEBz/4QVavXs0NN9zAkSNH5qTOeRFsjTFp4P8A/9Jae9YnSTu10e55N9u11n7eWrvNWrutpaVlDioVkfnAhgGVyRzl8VEq42OEQfDqI7BhQGpJN/GWNrxkinhrB14qgxtLvHKY44AxxBqaCIoFCqd68PM5wlKR0mA//mSOsFKZ02cTERERmU9isRg/+MEPeO6559i5cycPP/wwTz75JJ/5zGf49Kc/zYEDB2hoaOALX/gCAF/4whdoaGjgwIEDfPrTn+Yzn5mbgbdVD7bGmAhTofZ/WWu/frq5zxjTcfr9DqC/WvWJyPxiraU0Mkzu6EEquXEqkxNEMnXnHBdJZ3BjcRIt7WSWrybR0kYk24CXSpHsXIqXTOPGkyQ7uvDSdYSlEq/+Dq08Nkx4nh5eERERkflq/PAxjnzzuxz48tc58s3vMn742BVdzxhDOp0GoFKpUKlUMMbwgx/8gPe///0A3HvvvXzzm98E4MEHH+Tee+8F4P3vfz+PPvooU32Vs6uqwdZMLUP6BeBFa+1/POOtbwH3nv75XuDBua5NROansFyi0D81U6GSmyCWbSAsFYk1teFEorixOMnOZXjJ9DnnupEI0UyWeFMr6RVryKxYQ6yhCcfzMO6r/nPoOBjXIygWqOQn5+Q/yCIiIiJXYvzwMQa2P4ufLwDg5wsMbH/2isNtEARs3bqV1tZWbr/9dlatWkV9fT2eN7UWSVdXFz09PQD09PTQ3d0NgOd5ZLNZhoaGruj+F6PaPbY3A78OvM0Ys/P0X+8CPgfcbozZD7z99GsREWwQTM2nBazvE/o+bjxJ6FeIt3aQWrqSWH3j6+5R67gujutOv46kMjjRGBgzNUQ5DIlmG8j39pA7cgA/r5WTRUREZH4bfm731GelM9ggYPi53Vd0Xdd12blzJydOnGD79u3s3bv3iq43G6q9KvJPgQttHnnbXNYiIvNfWCljwwDHixD6ZcDgT+bw83nqVq7FOAa/MEkxP4kbi+ElUpgzwutrceMJ0stW40+O45dLuNEEldzY9ArKpaGB0/vdVvv7QBEREZHze7mn9mLbL1V9fT1vfetbeeKJJxgdHcX3fTzP48SJE3R2dgLQ2dnJ8ePH6erqwvd9xsbGaGpqmpH7vxZ9QhORmuAXC+SOHiB37AjR+gacSBSsxYlGSXcvwzgOk70nmDxxjELfSXLHDlMaGbqkIcReIkEk24jrRamMDeO4LrHGZsAQ+v4FlrETERERmR+8ZOKS2i/GwMAAo6OjABQKBb73ve+xYcMG3vrWt/K1r30NgPvvv5+7774bgLvuuov7778fgK997Wu87W1vY2oG6uzSBo0iUhMqYyME5akViouD/UTq6km0d+JEY3ixOJXcOH5u4qxzCgN9RDJ1uLH4Rd3D2pDiwClKQ6e3D8o7GM8jWt+Al0i+7vBmERERkWpqvHoTA9ufPWs4snFdGq/edNnX7O3t5d577yUIAsIw5AMf+ADvec972LhxI/fccw9/8id/wjXXXMPHPvYxAD72sY/x67/+66xevZrGxkYeeOCBK36ui6FgKyLznrWWSn5y+nUk2wC+T+7oQZxojERrx9Tc2HNODLHhxXez+vn81J64L3fN2hDr+7ixOJFM9gqfQkRERGR21a1YCkzNtfXzBbxkgsarN023X44tW7bw7LPPntO+cuVKtm/ffk57PB7nf//v/33Z97tcCrYiMu8ZY4jWZSkU8hgvAkFAeWwY47hY3yd/8jjp7hVgnOk5sQBeMokTjV70fax/nq19bIhxXRxP/7kUERGR+a9uxdIrCrK1SuPqRKQmRDJZonVZvGRqalEn44DzSi+tXyqQ6uzGjcYAQzSTJdHeddbKxxdibYhfLBDakGi2/qz3jOdd9FBmEREREakOdUGISE1wozGSS7oJSmXCUpGgVDzjXQvWMnniKF4qTTSZIpKuw89NEBTzU6sZux5huYJxHdxojKBUwi/kCCsVjOtSGhnGSyaxjkusuY0gP4kTjRHJ1uMlklV7bhERERF5fQq2IlIzjOPiJRIk2jrIHTs83e5EooSVqYWl/Mkc2JDy2DCRTD2V8VHiLe2Ux0cJS0VwHFJLuikO9E2FYxtig4B4Sxvl8XGidVmsMUSy9bjxpEKtiIiISA1QsBWRmuOlMmSWr54Kpo4DxpA/cfT0u3ZqJcAgwLgubjJFeXSIsFyaGr4chviTOYJiYWrBqdPrRJWGB4nUN1EaGQLPI7tizdSWQiIiIiIy7ynYikjNMcbgJVN4yRQAfj53zjFONEboV3BjcUrjoxjHhekpuVNDlzFmus0GwfR2Pl40NnW8iIiIiNQELR4lIjXPjSdJtC85veWPwURjxBqb8XMTWN+f6nk9czugMxeeMgaMQ6Qui5+fxLguidYOzEUsOiUiIiKyGIyOjvL+97+f9evXs2HDBp544gmGh4e5/fbbWbNmDbfffjsjIyPA1DaNv//7v8/q1avZsmULzzzzzJzUqGArIjXPOA6xhmbqVqwls2wVmWWrsaE9vR1QhURH11SYPc0GPunuFXiJJMb1iDe3Em9uJ9naTmbFmumeYBERERGBT33qU7zjHe9g7969PPfcc2zYsIHPfe5z3Hbbbezfv5/bbruNz33ucwB897vfZf/+/ezfv5/Pf/7zfOITn5iTGjUUWUQWBGMMbvyVbXnceJx4UzM4LsZxcGMJgnIRx3Vx40kcz8NNpiEMMF4Ec2aProiIiEiNOrVzP4e+t53SWI5YNs3K26+nfeuay77e2NgYP/7xj/nSl74EQDQaJRqN8uCDD/LYY48BcO+993Lrrbfy53/+5zz44IP8xm/8BsYYbrzxRkZHR+nt7aWjo2MGnu7C1GMrIguSMQYnEsVx3ak5uYkEsWwDkXQdjjf1nZ7jujiRqEKtiIiILAindu5n34M/pjQ2tf5IaSzHvgd/zKmd+y/7mocPH6alpYWPfOQjXHPNNfzmb/4mk5OT9PX1TYfV9vZ2+vr6AOjp6aG7u3v6/K6uLnp6eq7gqS6Ogq2IiIiIiMgCcOh72wkr/lltYcXn0Pe2X/Y1fd/nmWee4ROf+ATPPvssqVRqetjxy4wxVe8oULAVERERERFZAF7uqb3Y9ovR1dVFV1cXN9xwAwDvf//7eeaZZ2hra6O3txeA3t5eWltbAejs7OT48ePT5584cYLOzs7Lvv/FUrAVERERERFZAGLZ9CW1X4z29na6u7vZt28fAI8++igbN27krrvu4v777wfg/vvv5+677wbgrrvu4h/+4R+w1vLkk0+SzWZnfX4taPEoERERERGRBWHl7dez78EfnzUc2Yl4rLz9+iu67l//9V/zoQ99iHK5zMqVK/niF79IGIZ84AMf4Atf+ALLli3jq1/9KgDvete7eOihh1i9ejXJZJIvfvGLV3Tvi6VgKyIiIiIisgC8vPrxTK6KDLB161Z27NhxTvujjz56Tpsxhvvuu++K7nc5FGxFREREREQWiPata644yNYizbEVERERERGRmqZgKyIiIiIiIjVNwVZERERERERqmoKtiIiIiIiI1DQFWxEREREREalpCrYiIiIiIiJyQX/5l3/J5s2b2bRpE//5P/9nAIaHh7n99ttZs2YNt99+OyMjIwBYa/n93/99Vq9ezZYtW3jmmWfmpEYFWxERERERETmvXbt28bd/+7ds376d5557jm9/+9scOHCAz33uc9x2223s37+f2267jc997nMAfPe732X//v3s37+fz3/+83ziE5+Ykzq1j62IiIiIiMgC8dJPd/HEA4+RGxon3VTHG++5lbVv2nzZ13vxxRe54YYbSCaTALzlLW/h61//Og8++CCPPfYYAPfeey+33norf/7nf86DDz7Ib/zGb2CM4cYbb2R0dJTe3l46Ojpm4vEuSD22IiIiIiIiC8BLP93FDz//ELnBcbCQGxznh59/iJd+uuuyr7l582Z+8pOfMDQ0RD6f56GHHuL48eP09fVNh9X29nb6+voA6Onpobu7e/r8rq4uenp6ruzBLoJ6bEVERERERBaAJx54DL/sn9Xml32eeOCxy+613bBhA5/5zGe44447SKVSbN26Fdd1zzrGGIMx5rLrngnqsRUREREREVkAckPjl9R+sT72sY/x9NNP8+Mf/5iGhgbWrl1LW1sbvb29APT29tLa2gpAZ2cnx48fnz73xIkTdHZ2XtH9L4aCrYiIiIiIyAKQbqq7pPaL1d/fD8CxY8f4+te/zq/+6q9y1113cf/99wNw//33c/fddwNw11138Q//8A9Ya3nyySfJZrOzPr8WNBRZRERERERkQXjjPbfyw88/dNZwZC/q8cZ7br2i6/7SL/0SQ0NDRCIR7rvvPurr6/mjP/ojPvCBD/CFL3yBZcuW8dWvfhWAd73rXTz00EOsXr2aZDLJF7/4xSu698VSsBUREREREVkAXp5HO5OrIgP85Cc/OaetqamJRx999Jx2Ywz33XffFd3vcijYioiIiIiILBBr37T5ioNsLdIcWxEREREREalpCrYiIiIiIiJS0xRsRUREREREpKYp2IqIiIiIiEhNU7AVERERERGRmqZgKyIiIiIiIhf00Y9+lNbWVjZvfmW15T/8wz9k/fr1bNmyhfe+972Mjo5Ov/dnf/ZnrF69mnXr1vHII49Mtz/88MOsW7eO1atX87nPfW5Ga1SwFRERERERkQv68Ic/zMMPP3xW2+23386uXbt4/vnnWbt2LX/2Z38GwJ49e3jggQfYvXs3Dz/8MJ/85CcJgoAgCPid3/kdvvvd77Jnzx6+8pWvsGfPnhmrUfvYioiIiIiILBBPPLKdr/+3bzHUP0xTayPv++27eOOd11/RNW+55RaOHDlyVtsdd9wx/fONN97I1772NQAefPBB7rnnHmKxGCtWrGD16tVs374dgNWrV7Ny5UoA7rnnHh588EE2btx4RbW9TD22IiIiIiIiC8ATj2zn/s99maG+YbAw1DfM/Z/7Mk88sn1W7/v3f//3vPOd7wSgp6eH7u7u6fe6urro6em5YPtMUbAVERERERFZAL7+375FuVQ+q61cKvP1//atWbvnv/t3/w7P8/jQhz40a/e4GBqKLCIiIiIisgAM9Q9fUvuV+tKXvsS3v/1tHn30UYwxAHR2dnL8+PHpY06cOEFnZyfABdtngnpsRUREREREFoCm1sZLar8SDz/8MP/+3/97vvWtb5FMJqfb77rrLh544AFKpRKHDx9m//79XH/99bzhDW9g//79HD58mHK5zAMPPMBdd901Y/Uo2IqIiIiIiCwA7/vtu4jGome1RWNR3vfbVxYgf+VXfoU3vvGN7Nu3j66uLr7whS/wu7/7u0xMTHD77bezdetWfvu3fxuATZs28YEPfICNGzfyjne8g/vuuw/XdfE8j7/5m7/hzjvvZMOGDXzgAx9g06ZNV1TXmYy1dsYuVk3btm2zO3bsqHYZIiIiIiIiM+LFF19kw4YNl3TObKyKXA3ne3ZjzNPW2m3nO15zbEVERERERBaIN955fU0G2SulocgiIiIiIiJS0xRsRUREREREpKYp2IqIiIiIiEhNU7AVERERERGRmqZgKyIiIiIiIjVNwVZEREREREQu6KMf/Sitra1s3rx5uu2zn/0snZ2dbN26la1bt/LQQw9Nv/dnf/ZnrF69mnXr1vHII49Mtz/88MOsW7eO1atX87nPfW5Ga1SwFRERERERkQv68Ic/zMMPP3xO+6c//Wl27tzJzp07ede73gXAnj17eOCBB9i9ezcPP/wwn/zkJwmCgCAI+J3f+R2++93vsmfPHr7yla+wZ8+eGatRwVZERERERGSB+M43v8c7bv4gW1e8lXfc/EG+883vXfE1b7nlFhobGy/q2AcffJB77rmHWCzGihUrWL16Ndu3b2f79u2sXr2alStXEo1Gueeee3jwwQevuLaXKdiKiIiIiIgsAN/55vf4t//qL+jt6cNaS29PH//2X/3FjITb8/mbv/kbtmzZwkc/+lFGRkYA6Onpobu7e/qYrq4uenp6Ltg+UxRsRUREREREFoC//g9/R7FQOqutWCjx1//h72b8Xp/4xCc4ePAgO3fupKOjgz/4gz+Y8XtcCq+qdxcREREREZEZcepk/yW1X4m2trbpn3/rt36L97znPQB0dnZy/Pjx6fdOnDhBZ2cnwAXbZ0LVe2yNMX9vjOk3xuw6o+2zxpgeY8zO03+9q5o1ioiIiIiIzHftS1ovqf1K9Pb2Tv/8jW98Y3rF5LvuuosHHniAUqnE4cOH2b9/P9dffz1veMMb2L9/P4cPH6ZcLvPAAw9w1113zVg986HH9kvA3wD/8Kr2/2St/Yu5L0dERERERKT2/N4f/ib/9l/9xVnDkeOJGL/3h795Rdf9lV/5FR577DEGBwfp6uriT//0T3nsscfYuXMnxhiWL1/Of//v/x2ATZs28YEPfICNGzfieR733XcfrusCU3Ny77zzToIg4KMf/SibNm26orrOZKy1M3axyy7CmOXAt621m0+//iyQu5Rgu23bNrtjx47ZKVBERERERGSOvfjii2zYsOGSzvnON7/HX/+Hv+PUyX7al7Tye3/4m7z7F2+fpQpnz/me3RjztLV22/mOnw89thfyu8aY3wB2AH9grR2pdkEiIiIiIiLz2bt/8faaDLJXqupzbC/gvwKrgK1AL/D/ne8gY8zHjTE7jDE7BgYG5rA8ERERERERmS/mZbC11vZZawNrbQj8LXD9BY77vLV2m7V2W0tLy9wWKSIiIiIiMsvmw9TRuXY5zzwvg60xpuOMl+8Fdl3oWBERERERkYUoHo8zNDS0qMKttZahoSHi8fglnVf1ObbGmK8AtwLNxpgTwL8BbjXGbAUscAT4F9WqT0REREREpBq6uro4ceIEi23aZTwep6ur65LOqXqwtdb+ynmavzDnhYiIiIiIiMwjkUiEFStWVLuMmjAvhyKLiIiIiIiIXCwFWxEREREREalpCrYiIiIiIiJS0xRsRUREREREpKYp2IqIiIiIiEhNU7AVERERERGRmlb17X5EREQWK79cwTgOrudOt+WGJ+g9cJL8eJ6GjgY6VncSiUWqWKWIiMj8p2ArIiIyh/yKT9+hU/Qf7aOcLzE6MML6GzfSvXEZhVyB733hYY7uOkJTVzNLNy6j70gfHSs7aF3RRjQeq3b5IiIi85KCrYiIyBwJ/IDnH32WR+//HuXJEsYxXH/3TTx6/z9z52+9m3KxxNFdR6hrrqN9RTs//ccfgYFEJsmNv3gTb7jrRiJR9d6KiIi8moKtiIjIHBnuGeIn//gjKsUyADa07PinJ7n+F2/i2J6jNLQ3ArDi6lU8+c3Hp06yU//zxDd+Rqa5jkqxwtJNy2jubqnOQ4iIiMxDCrYiIiKzbPjkEEMnBqmUK4R+gMFgpxIrgR9gbYgXcWnqbMKNuFgbEvoBAG7EJQwspcki/Uf6eOGHz5HIJPilz3xQ4VZEROQ0rYosIiIyi3peOsEDf/o/+c593+LUgZOEQUgk/spw4lgyhmMclm5aTnN3C7/wqfcSiUdJ1adwIx7RRIxKsUw0EcUAXjRCuVjmxN7j1XsoERGReUY9tiIiIrOkUqpwYMdLbHzzZvyKTyyd4Kb3v5lnHt6BMYZoIsotv/pW2lcuoW1FOwDLr1pB+4p2ujcs5Yf/4/uMnhol3ZThhrtvolIss3rbmulzRUREZIqCrYiIyCzJjeY4daCXAztemmowhrf86lt524dvp761gXR9mkxz3TnnxdMJlm9ZyQf/f7/G5GiOgRMDjPYO8+Mv/5AgmBrK/NLP99K4pIn2lR1z/FQiIiLzj4Yii4iIzJKx/lGOvHDojBbLzx98gkQ6QcfqJecNtWdK1iVpWdrKso3L6Nl7gjAMcRyHSDxCMVfgwFMvze4DiIiI1Aj12IqIiMySYq5AJBbFGAMWrLWEQUAkdmnDiCtln4nhcVzPnXpdqoCFieHx2ShbRESk5qjHVkREZJY0dDROrXIcWiqlCn7Zp769gb4jp6bC6UU4daiXl558kXVv3IDjOoRBeHoLIFi6afnsFS8iIlJD1GMrIiIyS1q6W3n7R+/k+3/3MLlyjvbVHay7cSPH9xzDi3gEvk+5UMY4Dl3rumlf1YHjvvKd88n9PXz933+VSrFM24p2tt5xHYeePYANLRvfvJnWZa1VfDoREZH5Q8FWRERkljiuQ6Yxw9obNxBLxRnpHebQs/vJttQzeHyA5x99lsnRSYzrkKxLcPf/9UusuHoVuZEJRnqHefFnu6kUp4Jv74Feeg/2cv0v3Ejg+7Sv6qCpS/vYioiIgIKtiIjIrGpob+TE3mNMjk4CsOW2rTzz3afY8rat0202CPHLAU9+83HqWrJ856+/BQYiUY9Crkg8FT89pDnk5P4e3vXJXyDT9NoLT4mIiCwmmmMrIiIyizJNdfzCp97Lss3Licaj1DVlSWQSlIvnzrEtjOc5daCX4ZNDjJwcpnNt9+nQO3WscRy23LZVoVZERORV1GMrIiIyy9pXdvCe3/9FipMFovEYA8f6SDdkpt83jsFxHTbfejWHdk5tD2StpWf/CW765Tez78m9RBNRtr3relZcvapajyEiIjJvKdiKiIjMgUgsQiQWAeCm99/CoZ0Heeu9b2fXY88T+gFb77iODW/aRDQW4eDTU/vT9h06xXDPEG/78O0s37KSRDpRzUcQERGZtxRsRURE5li2Jcs1t19LpVzhmjuuIwhC0vVpAFZeu5qTB07y0va9YGHZ5uUsWdOlUCsiIvIaFGxFRESqJBKNEIlGzmqra85y+8fewbZ3XQ9YGjoaicSi1SlQRESkRijYioiIzDORWITW5W3VLkNERKRmaFVkERERERERqWkKtiIiIiIiIlLTFGxFRERERESkpinYioiIiIiISE1TsBUREREREZGapmArIiIiIiIiNU3BVkRERERERGqagq2IiIiIiIjUNAVbERERERERqWkKtiIiIiIiIlLTFGxFRERERESkpinYioiIiIiISE1TsBUREREREZGapmArIiIiIiIiNU3BVkRERERERGqagq2IiIiIiIjUNAVbERERERERqWkKtiIiIiIiIlLTFGxFRERERESkpinYioiIiIiISE1TsBUREREREZGapmArIiIiIiIiNU3BVkRERERERGqagq2IiIiIiIjUNAVbERERERERqWkKtiIiIiIiIlLTFGxFRERERESkpinYioiIiIiISE1TsBUREREREZGapmArIiIiIiIiNU3BVkRERERERGqagq2IiIiIiIjUNAVbERERERERqWkKtiIiIiIiIlLTFGxFRERERESkplU92Bpj/t4Y02+M2XVGW6Mx5nvGmP2n/95QzRpFRERkZtkwJD84wuihHib7hwmDsNoliYhIDat6sAW+BLzjVW1/BDxqrV0DPHr6tYiIiCwA1lqG9h1lz1f+mf3/9GNe/Md/ZuCFAwq3IiJy2aoebK21PwaGX9V8N3D/6Z/vB35xLmsSERGRmVUczZE7NUQlX6Q4OsHRHzyFDaeCrA0tx3/yDMWh0eoWKSIiNcurdgEX0Gat7T398ymg7XwHGWM+DnwcYOnSpXNUmoiIiFysMAgZPnCMoz94irDsE6/PsPSt19G8eRWEFr9QYuTgcWxoKU8WSFa7YBERqUlV77F9PdZaC9gLvPd5a+02a+22lpaWOa5MRERELqQ8WWBg10H2f+tHjB44QfOGlTixCOnOVvqfO8Cpp/cycaKfUi5P2zXrMa5DNJOqdtkiIlKj5muPbZ8xpsNa22uM6QD6q12QiIjIXCnn8pQmCkSSMeLZdLXLuWRhEHLqqT30PfcSoR8QlMrEG+rovPEqcr2D1HW3kWzOEpR9jOfgRSOsetebSTTWVbt0ERGpUfM12H4LuBf43Om/P1jdckRERObG6NFT7P3WzyiN5ogk46x59400r12KcUy1S7topbEJ+l/YD4AxBuM4lEZzlEbGiWaSHHzoZxhjwEA0naR+dRfJtkaMM+8HkomIyDxV9WBrjPkKcCvQbIw5AfwbpgLtV40xHwOOAh+oXoUiIiKzy4YhY8f7KQyPc+Cfn8Ivlkk219O0ppORQydxox71S9txPLfapV4cC/blSUQGHM8lKFWIZtP0bt8N1mKtxYl4lMZyuJ5H7uQg2aUdVS1bRERqV9WDrbX2Vy7w1m1zWoiIiEiVjB7t44Uvf4/2a9dSGJ4gUZ+mflkbh37wDFjoeepF1rzjRpZsW4fjzv9wG6tP07JxBQO7DwFTPc2RVBxjDJV88ZzjrQ1xIlX/SCIiIjVMv0VERESqpDA8wdjxPnp3HsAvVbDhVMBr2byCYz974YylEw0Hv/cUmc5msl2t1Sz5ojiuS8cNm4k31TO89wixxgzxbIaeJ1+gYVUXg7sPTQ2tthYMePE4dTXwXCIiMn8p2IqIiFRBpVjipe8+gV8oEVQC/FKFvucPsfSmzYRBgA1CMAYvHiEoVagEIYMvHsUvlmlc1Tk1R3Uei2VStF+zjrataylPFpjsHaRp3TIcz6PtuvWM7j+BE4vQsW0D6c4WUm1N1S5ZRERqmIKtiIhIFeT7x4imEiQbs8TqkmTaGznx1F6GDpxg2ZuuIpZNEfoBfrGMDS1uNEJQ9tn91R9y7cfeTbqtsdqPcFGMMcTSSWJrltK4ZilhEGAch/LNBRzPJZKIVbtEERFZABRsRUREqiAMfAZfOk5haBzHcYhlU6y583oG9x0jkkyw6f23svdbP6OSLxFJxVh28xZ6n3kJG4TkB8dqJti+2stzhGOZZJUrERGRhUTBVkREpArGewYpjeamXjhTe9fm+kZov24t8bokmSXNbPrlKOMn+gkrPn6pQrlQAmvVyykiIvIqCrYiIiJVkOsbwYtHCf2AMAgJyj7jJwfwiyVObt/Lirddy+6v/oDKZBHjujiew9KbtzBxcpBkc7ba5YuIiMwr2gldRESkCl5eAMrxXEI/AGOoX9rGZP8oLRuX0//8IcoTBWxoCSv+6SHIoySb6wj8oNrli4iIzCsKtiIiIlXQuGoJS96wfmrbGwMt65diHAf/9HDjUi7/8hawAARln+LIBGEQaiiyiIjIq2gosoiISBXEMklW3b6NjmvXMt4zSO8z++h/4RAAfqlCZkkTg3uPvnKCMTStW0rj6i4FWxERkVdRsBUREakSx3VJtzYQTcYxxjCYOkKqtZHG1Z0cf3I3y2/ZyslnXiIMfLpvuoq2q1aQ1n6vIiIi51CwFRERqbJoOkHH1tV0bF093RZJxBg/3s+6u24iXpemrqsFN6Jf2yJSPaVcgeFDJ8mdGiZWl6R+eTt17fqyTeYH/YYUERGZh1It9aRa6qtdhojItBM/38NLD2+ffl3f3cqWX72NVHN99YoSOU2LR4mIiIiIyGsaOznAoR88c1bb6PF+Ro/1V6kikbMp2IqIiIiIyGsKKwF+2T9P+7ltItWgYCsiIiIiIq8p2dJA87rus9qciEeqraFKFYmcTXNsRURERETkNcWSMdbceT2xTJL+PYdJtdSz6rZtNC7vqHZpIoCCrYiIiIiIXIT67lbS772FlW+9hmgqTjSVqHZJItMUbEVERERE5KJ4UY90q4Yfy/yjObYiIiIiIiJS0xRsRUREREREpKYp2IqIiIiIiEhNU7AVERERERGRmqZgKyIiIiIiIjVNqyKLiIiIiMi8F5QrFPoHKQ2PgjHEmxtItrdijKl2aTIPKNiKiIiIiMi8l+8dYGDHs1RyeQCciEfHW24k3dlR5cpkPtBQZBERERERmdememsHpkMtQFjxGXvpMGEQVLEymS8UbEVEREREZH4z4BeK5zSXJ3KEFb8KBcl8o2ArIiIiIiLzmhuJkGxvOac93b0ELx6rQkUy3yjYioiIiIjIvJdsb6Hp6o040QjGc6lbs5zM8u5qlyXzhBaPEhERERGReS9al6Hxqg2kl3ZiQ0usPoPjKc7IFP1JEBERERGRmuC4DvHG+mqXIfOQhiKLiMiiZK0lqFSwYVjtUkREROQKqcdWREQWncLAIIW+AcoTOWKN9cQbG0i0NFe7LBEREblMCrYiNSAMAgxgXLfapYjUvOLIKAM7duLnJgHInzhJZtUKTCRCvD5b5epERETkcmgossg8Fvo+pZEhJo7sJ3fsEOXxUQ2bFLlC5dGx6VD7styRYwT5c/dHFBGR11YplCiMTBBc5F6yNgwpjk9SKZRmuTJZbNRjKzKPVXLj5HtPABAC/omjpLpXEM3UVbcwkVpm7blN+sJIROSSlCbyjB49RX5wlPGeIVzPYfmt15Bua7zgOYWRcY4/uYf+Fw4RyyRY8bbraFzdheOqr02unP4UicxTNgwpjQye016ZGJ37YkQWkEi2DicWPast1bUENxWvUkUiIrUl1zfMzi89xPP/85858PB2KpN5nIjHSw89SaVYPu85pckCgy8dx3EMicYM+aFxdn/tMSZ6z/2sI3I5FGxF5isDjnvuoArH0UALkSuRaGqk9frrSHV1Es3WkV2/hrpVK4hnNb9WROT12DCkZ/uL5IfGp9tGDvXixaPkeocojk6cc85k/wgv/K/vsfcbP+HwY8/ixaM0ru4Ca5nsG5nL8mUB0ydkkSoIy2UwBicSueAxxjjEGpup5CbObCRSpw/fIlcq2d5KrLmRoFTGi8dwtDCbiMhF8UsVRo+ewpiz24sjOWLZFG7klXgR+gH5oTFOPLmb3KlhjGOwoaV/12GW33oNGIMXv/BnIZFLoWArMoeCSpny0CDFwT6M45JoX0I023DB1Y69VIb0slX4kxNToTaVwUum5rhqkYXJ9TxcT78GRUQuhReL0riqk5MjEzieQ+hPrVGQaEzTuLaTROPUOiB+scyJ7S+S6xti5MBJ/GIJNxbBL0wNVS6O5sh2tVDX2VK1Z5GFRb/RReZQeWSYwqkeACw+k8cOY1Z4RLP15z3eGEMklSaSSs9hlSIiIiLnZxzDkm3rGT/RT+7UMKEX0rxhGc0blpNZ0owxhsLIBEP7j1MYGiO7tA0bhPTvOkRYCfDiUbCW+mXtNK9fSrw+U+1HkgVCwVZkjoRBQGlo4Jz2ysTYBYOtiIiIyHyTaqnnqg/dQWFwDOM6JFvq8aJTQ4qL45Ps/j+PMX6sj6BUYeVt15FszhJJximNTRJWfFo2r6Bl4zLiWX1xLzNHwVZkjpjTc2rD0tl7Zb7WPFsRERGR+SiajBNdeu5q8mNHTjF66CSO6xBvyJDrH2FgzxGWbFuPG/UwjoMb9YjVzc7UqvLYOKXRUcAQq88SzWqLxMVCwVZkjhjHIdHawUQuB0zto2lcj0imvqp1iYiIiMyE/NAYxfFJbBASAl4sQljxsUHIqWf3g4Gg7NOyfunURyHzele8NMWhYfp//hRhoQSAm0jQcv11xJsvvLeuLBwKtiJzyMvUkVmzDn8yj3EMXiqDl0hc1rWCUgnrVzCehxvT/psiIiJSXRMnB4nEozgRj7DiMzk4SuvmFQwf6MG4Dn6xjOM6dGzbgHFmONUCk8d6pkMtQFAokO/tVbBdJBRsReaQOb2ycSR1ZQslVCbGmOw5jg0DcBxSHV1E6uoxr157X0RERGSO2NAyOTzOyrdfR/8LhyiMTFApllh395s49dwBsJauGzbRtKZr5u9tLeXxsXPaK2Pn7qsrC5OCrUiNCcolJnuOYcOp5fUJAyZ7jpGORIgktQiDSGFsnMrIGJXJApFUgmh9HfF67f8sIjLbUm0NHPz+DjquXUv9iiXUdQe4kQgNK5bQce1asJy1z+1MMsaQaG+jNDh8Vnu8TdsJLRYKtiI1JqxUXgm11mLt1M/+ZI4gn8dLpfESySpWKFI9pYkc4/sOMbL7pem2xqvWY1YvI6ZwKyIyqzLtTWz+wFs58eQeLJaW9cvILm8nnpmdhaJeLdnRRmUix+SxEwCkly8l0dY6J/eW6lOwFakxjueCMWeFWqzFuC6V8TFCv4INfLxURkOTZdEpj46fFWoBhnftI9HWrGArIguGDe2szFGdCdnuNjJLmrFBiBud250fonV1NF69mcyKZQBEMmncaHROa5DqUbAVmWXWWvz8JH5+ajXkqQWjUpcdOp1onGRHJ/mTx08vrmyJN7dT7D9FWC5RGR+lMjFGoq2TaJ0+yMviEpYr5zZaS1Aqz30xIiIzaOzUECPHBijlikRTMWLpBJF4lJZVS6pd2jkc1wXXrcq93UgEt0mLRS1GCrYiM8RaS1AqEvoVjOPhxeMYx8HP56jkJgjLZYzr4ufzYCGSurz5sMYYotlG3FgCfzIHjqE40EdYPnMVwDx+Pocbj+NGYzP1iCLznpdK4sSihGcEWTcew0te3urjMjsmB0aZHBillCuQbKyjYWUHjuNUuyyReaswPknf3uMc3f4SffuO47gOK2/eRKa9gfJkkc4tK6tdokjVKdiKzJBKbpziQB/+5ARONEaitYNotgE/n6c4cApenhfruKS6l1/RvYwxeIkkYeATFIuElfP0RlmLDYIruo9IrYk3NdB+8zYGdrxAZXyCaDZD8zWbidZpYbX5YvzkIKeeO8jBHzwD1uJEPDa//y10Xreu2qWJzFsTfaP0v9RD377jAIRByIEfv8C2X30rO/7xMRqWtZLM6r9zsrgp2IrMgKBYpHDqJEFhEoCwVGTyxBGcWIzS6PAroRYgDPAnc0RSGRzvyv4VjKQyGMfBS6TwJ19Zzt6JxU9/YJzbuS0i1eZGPOqWdeGlEoTFMm40SrShDlf/Lswb+aFxDj769PTrsOKz958eJ9XWQH2XFnkROS9rGThw8pzmsd5hbBhSHJ1UsJVFT8FWZAYEfnk61E6zlrBSwZwZal8WBoSV8hUHW2MMkWQap3MZpZEhKrkx3HgCNxYnkq7D8fRhXhanZHNTtUuQCyhN5M9pK+cKlMbPbReRKYmGNPVdzUwOjb/SaCBZn8bxXJyox+TQOIn6NI6rYf2yOCnYiswAx/XAcc7umQUMhlhjE/neE2e3RqKYGVxUwY1PLSjll5shCHAiEYVaEZmX4tn09MruL4tlUyTq1dskciGZlnrWv/1aho/2U5zIY4yhcVkr5XyJTe+4nu3/4/tMDoyx7Pp1rH3bNaSb6qpdssicU7AVmQFeIkmyvZPy+Ch+IQ9BgJfJ4iaTuIkEidBSHh3CuB6RTBYTi83Kok6eFooSkXku2VLHunffyP6HtxP6AZFUnA13v4m6Jc3VLk1kXuvYuIy3/+EvM3J8AGMMsfTUong7vvIDShMFAA4/8SKu57Hl7pvm7XZAIrNFwVZkBlQKeTAObjSGl6rDSyZxYwncyNTeabGmFrx0hjAIcFwXL5GscsUiItWRaW0kkkhQt6SFSr5IvDFDw9K2apclUhOy7Y1k21/ZymbfD3dOh9qXHX36Jda+7WoSmnMri4yCrcgVCiplKmOjFPtfWdTBRKKkl62c3hTc8TwcT79gREQA4pkE8UxXtcsQqXmxVPyctkR9Gi+q6Uiy+Gh2ucgVCopFSsMDZ7XZSpmgULjAGSIiIiJXrnlFB5nW+unXxnHY/M43EEloapIsPuqxFbliFhueu1+stedZDflSrhqGBMUCYaWCE4vhxuIYo/kyIiIiMiXdkuWm33wXw8f68ItlskuaaFiqbbNkcZrXwdYYcwSYAALAt9Zuq25FIudyojGi2QbKI0NnNDq4sXOHB10sGwYUhwbx83nAUpmYINW1lFh9w5UXLCIiIgtGurmOdLNWQRaZ18H2tLdaawerXYTIhXixOLHGZozrURkbxUSjJJpb8VKZS76WtRZjDH4+jz+Zozw0CAZijS2UBgdwEwm8KwjMIiIiIiILUS0EW5F5L5LK4CXTBE0tOK6H413av1r+ZI7y2AjlsVG8VAY3GqU00Df9fnHgFIn2JQTFooKtiIiIiMirzPdga4F/NsZY4L9baz9/5pvGmI8DHwdYunRpFcoTeYUx5rJCZ1AqkT/VQ3n49FBma/Ed95zj/PwkkYamKy1TRERERGTBme+rIr/JWnst8E7gd4wxt5z5prX289babdbabS0tLdWpUOQKBcXCK6EWCH0fx/Mwrwq3bjwJ1s51eSIiIiIi8968DrbW2p7Tf+8HvgFcX92KRGbJGasdh6UibiIBjjP9l4lEiGTqNAxZREREROQ85u1QZGNMCnCstROnf74D+LdVLktkRtkwxESjxJqaKQ2+shduaWSYzKq1BPlJLODGE3iJJE40Wr1iRUREpOb45QoD+0/S88IhYukknVctp3FZW7XLEplx8zbYAm3AN07v2+kBX7bWPlzdkkRmjp+fpNDXiz85QaypldTSFZRHR3CTKWINjUTSGajLVrtMkTnjFwoE5TJOJEIkmax2OSIiC8LJXUd46n8+Ov360M92ccsn76KhW9P4ZGGZt8HWWnsIuLradYjMhqBcYuLwAWylDECx7yROIkF65Vq8iHplZfEpDg4yfvAglfFxIpkMmVWrSGjtBBGRK1IplNj3/WfOavNLFfoP9CjYyoIzr+fYiixUYbE4HWqn2woFbKlUpYpEqqc8Ps7Inj1UxsbAWirj44zu3k1pdLTapYmI1LQwtARl/5x2G4RVqEZkdinYilSDc/5/9cwF2kUWMj+fJywWz2oLy2X8fL5KFYmILAyxVJw1t549ANI4Ds2rllSpotoRhgr/tWbeDkUWWcjcRIJItoHK2Mh0W7SxGVerHssi5Hjn/1V0oXYREbl4XVtX4UY8Dj2+m1hdktVvvoomLR51QeXxUcqjw4SVCpFsPZFUHV4iUe2y5CLoU4NIFTiuR7JzKX59A0GxgBtP4qUzGNd9/ZNFFhivro5UdzeTx49PtyWWLMGrq6tiVSIiC0MsnWD5Devpvm41xjg4rkaHXUh5Yozc0UNgp3pr/ckJbNsSBdsaoWArUiVuNIobbap2GSJV50WjJJcuJVpfT1As4sTjROrqiMQ1gkFEZKa4GgXzuoJCYTrUvqw0NEA0W48X12r9853+hIuISNVFUymiqVS1yxAREZEapbEIIiIiIiKy6LnJxDkLfMabW9VbWyPUYysiIiIiIoteNJ0lvWwV5dERwkqFaDaLl9J6D7VCwVZERERERASIZrJEM9lqlyGXQUORRUREREREpKYp2IqIiIiIiEhN01BkEREREZFZMDEwQjlXwI1GyXY0YRxT7ZJEFiwFWxERERGRGVQplRg+1Effi0c4+rPdhNay6pYtrHrrNSSy2tpMZDYo2IqIiIiIzKDRE4OMHDnFwR/snG7b//1nSNSnWXXr1iu69njvEBOnRnAiLvXdLSSy6SsrVmSBULAVEREREZlBtuIzdKj3nPaenQdY8earcFz3sq47dKiXHfc/QlgJAMi0N3DNh95Oqklb0oho8SgRERERkZlkHBIN5/akplsbMM7lffz2yz4HfvDMdKgFmDg1wvChk5ddpshComArIiIiIjKDks11tK7rJpZJTLdFU3GW3bgRYy5vAamgXGFyYOyc9sLo5GXXKbKQaCiyiIiIiMgMSjdlAbju1+8g1z+CcRwalrfRsLTtsq8ZTcXp2LKSIz/bfVZ7fXfLFdUqslAo2IqIiIiIzLB0U5Z0U5a2jctm5HrGGJbeuJFirsCp5w/jRT1W33YNjSvaZ+T6IrXusoOtMSZtrc3NZDEiIiIiInJ+qaY6trzvzax52zU4rkOy8coWjbLWkh8aJ/QDEg0ZvFhkhioVmXtX0mO7B1g6U4WIiIiIiFwJv1SmMlnAWoikE0Ri0WqXNOPciEe6pf6Kr+OXyhzfvpcDP9hJUPFpXr2E9e+6gXRrw5UXKVIFrxlsjTH/14XeArRploiIiIjMCxM9Axz+/lOMHz9FZkkzS27cTDSVILusg6BcYeLkIBMnB4imEtQtbSPRsLi3yBk50se+R3ZMvx48cJLDP3mBTb9482VvRyRSTa+3KvL/CzQAmVf9lb6Ic0VEREREZl1xNMe+r/+QsSM9GCB3cpDDj/yc/uf2c/zx5xg53MPwvqNgLYWhMV76+g8pjk5Uu+yqmugbPqet78VjlHKFKlQjcuVebyjyM8A3rbVPv/oNY8xvzk5JIiIiIiIXrzgyTmXydCAzhrDiUxrL4UQjHP/xTjq2baDnyV1gLeklzTSuXUaud4h4faa6hVdRvC51TlumtZ5IfOEN35bF4fV6XT8CHL3Ae9tmuBYRERERkUvmRiNgDMZxCINgqtExGGOwQUhhaIzY6SCXOzlIJBkj9P0qVlx9DcvbaVjWOv3ajXisfvt1eAtwXrIsDq/ZY2ut3fca7/W9/LMx5q+ttb83k4WJiIiIiFyMRHM9bVvX0rvjRQhDLJbWq1YzdvQkoR8QTSfwi6Xp40M/INmyuBdJStSn2XrP2xg7OURQqpBuayDTtrj/mUhtm6l9bG+eoeuIiIiIiFwSLxah+81bqVvWQWl0gkqhyETPAKXRSeINGYzrEpQqADgRj0RLPam2xipXXX2xTJLWdclqlyEyI2Yq2IqIiIjMqcD3KU+WiCZjuBF9pFnsIsk4zeuXAZAfGCXV2ojZtoFIPEbvs/uIZJLE6zN0XLeOus5WjDFVrlhEZpJ+C4iIiMi8NTk0ztDBk0wOjdGwtI3GlR1EEzHGegY5+NizDB/uo3F5G6veupVsZ0u1y5V5ItlST/KMvV6jdSnar1mLE4mQbM5qHqnIAjRTwVZfeYmIiMiMKoxN8uz/+j4Tp0YAOMIu1tx+LV3XrWPnAz+gMJIDoH/vcSZ6h7n+4+8mkU1Xs2SZpxKNdSQaF/e+tSIL3SXtRWuMudAg/L+cgVpEREREpo33DEyH2pcdfOw5cgOj06H2ZYWxSSYHx+ayPBERmUcuKtgaY24yxuwB9p5+fbUx5r+8/L619kuzU56IiIgsVoEfnNMW+sEFh4l5kcjsFiQiIvPWxfbY/ifgTmAIwFr7HHDLbBUlUmuCIMDPT+KXi9UuRURkwci0N+LFzg6rbZuWkelopOu6tWe1d16zmrS2KhERWbQueo6ttfb4q1aPO/drVJFFqJKboDw6THl8BCcSI97ShpfJ4rputUsTEalpmdYGtt17Bwd/9BwTp0Zov2oFS29YTzQZZ83t19GytovcwCiplnoalrWeE4JFqsEvVagUSkRTca3WLTKHLvbftuPGmJsAa4yJAJ8CXpy9skRqg+/7lEYGKY8MAVNbT0weP0x62WrcumyVqxMRqX0Ny9u5pqsZv1QhmoxPb9ESSydo27SctirXJ+dnraU4PEp5dBy/UCKSSZJsa8aLx6td2qwp5fLkhyY48rMXGDxwkqaVHax+2zXULWmudmkii8LFBtvfZmqBqE6gB/hn4HdmqyiRWmFLBcqjw69qtASlAqBgK3IlSqNjlCcmcaMRYg31uFH1xi1Wrufheur5qiX5vkHGDx5lZO9BAIxxqF+7gtbrt+BGF9ZWO365wqkXDvPS93ZQHM3RcfUqmtd00rf7KJND49zwsXcRTSeqXabIgndRvyWstYPAh2a5FpHaYxyM42ID/+xmc0kLjovIq0z29jHw1E783CQ4hoaN66hbtZxI6kKL84vIfBGUKxT6hxh56fB0m7UhY4eOkV2znGRbbfdgFsZyTJwcIgwtde0NTA6Os+sbPyX0AyqFMseefJEVb76KSCLG5MAYk0PjCrYic+Cigq0xpgX4LWD5medYaz86O2WJ1IZIMkW8rYPCyePTbU40hpvQLzCRy1XO5Rh6bs9UqAUILSO79hKtzyrYitSAsOITlisQhme/Yafeq2W5/lGe/fKj01tLpVvraVjWDsDLS9EYxzB06CSb7n4jpXwJL76weqhF5quLHdfzIPAT4Pto0SiRs3iZelLdHkGxgPE8vESKSCpT7bJEalZQKFEeHjmn3Z/MV6EaEblUXiKGl0oQrUtTHn9lv2E3FiFal6piZRcnqASUJvIYx5AbHKU4OkmiIUN9ZzP9e4+dtV9yYTRH0yoHsBjHwY16GMcQTcR46XtPk+sfZcN7biTZmAEDxbFJvGiEWEZf0onMtIsNtklr7WdmtRKRGhWJxSAWq3YZIguGE48SqctQGZ84q91LLNxFZ0QWEuM4pDvbMY7D6P4jFPqHSLY00vKGLUTr5vcXv7n+EfZ//xn69x4llk6w5Jo1nHj6Jcq5IhvefSMTvUNnHR+UfSKJGNFUgvJkEddzCfyA5jVd7H3452ANL/zvH5FubeDEU/voe/EY0VSc9e+8nvbNy3G0g4LIjLnYYPttY8y7rLUPzWo1IiKy6MUyGZqu3kTfEzuw/tSwxcyKpcSatEepSK2I1qXxkgmS7S3YICCSTs37BeD8UoW9D21n8EAPNggY6xli4tQIV/3yW8gPjnHy+YN0bl1N7wuHzzrPuA7Xf+xdjB7vpzxZpDiW4/BPXgA7NTa5bkkzRx/fzeD+HgDKuQLPf+1HJLIpGpa3z/lziixUFxtsPwX838aYElABDGCttXWzVpmIiCxa6e4luPGbqIxP4sSixBrqiKTm/xBGEXmF47nEsvO7h/ZMhbEcgwemwqe1U3Nljesw2T/KgR/upHV9N8nmOpbdtJFjT76ItdBx1XI6tqwg2VBHurWesZODbP+7hyjlCtPXbVzZQc/TL2HO7J21U73DCrYiM+diV0Wunf8qiYjIgpBoaSbRUturp4pI7XAjHl48il8sYxyD67n4pcpUyrWWgb3HSTZm2PTeN9G9bR02tCSb687aiirT1sj6d9/Azv/5KGEYYszUnsvJxjoKY5Nn3S+S1DQmkZl0sasi3wzstNZOGmN+DbgW+M/W2mOzWp2IiIiIyBxINmRYd+c2dj/4OMZxwBjql7YxOTyO4zp4sQijPUPk+kdxHIdIPIrjnD1HNvQDGpa2cfOn3keub4RIKk7jsjay3a08948/xIYWgIalrWS7W6vxmCILlrHWvv5BxjwPXA1sAb4E/B3wAWvtW2a1ukuwbds2u2PHjmqXISIiIiI1Kqj4jB0fINc/CgaGDp3k1AuHMcbQsr4bJ+LRt+swfqFE57VraVjRTtvGZSTqM4yfHGTvQz9n+Egf6ZYsa+98A/mhcfxSmYbl7biRCLm+YbxEjGxXM4lsutqPK1JzjDFPW2u3ne+9i51jG1hrrTHmbuBvrLVfMMZ8bOZKFBERERGpLjfi0biyg8aVHRRGc/Q8ux/jODieS7wuxdHHd1MplgE4tn0vkVQcv1hh6Y0b2fWNnzLeOwxA0+pOdtz/CMaY6d7faz90G13b1lXz8UQWtIsNtuPGmH8F/BpwizHGuYRzRURERERqSqI+zdZfeRtjJwYxBo48vptXj3QcOdpHsrGO3MDIdKh1ox6VQolKvkQkEZ0KttZy6EfP0biyA2+erw4tUqsuNpzuA0rAx6y1p4wxSwEtTykiIiIiC1Yim54eMjxytI/B/SfOej/ZkMHxHCKJGG7UIyj7OJ5LUK4AYIyZPrY8WcT6IUTnrn6RxcS5yOO2WWv/o7X2JwCnF43Kz15ZIiIiIiLzx5KrV5HIpjDOVFiNpuKk2xrofsN6Mq0NrLtzatpfJV8i0ViHG3GnemtPW3rDBq2ELDKLXrPH1hjzCeCTwMrTC0i9LAP8bDYLExERERGZL+qWNHPDx9/DyNF+/FKFeDZJIpumrnNqW7Il164h3drA5NA48Wya7JJmDv34efximaU3bqDj6pVVfgKRhe01V0U2xmSBBuDPgD86460Ja+3wLNd2SbQqsoiIiIjMJ36xTBiGRJPxapcisiBc9qrI1toxYAz4ldkoTGQulfOTEPiE5TIYgxNLEE1pqriIiIjMDi+uCbUic0UrG8uiUCnkoVwm33ucsDK1TL+bSGKWLCWS0j5yIvL6KoUildEx/GKJSCZNvKnhrIVhREREpHoUbGVRCCs+lYnR6VALEBTyVHITCrYi8roqk5OMHzjK8At7wVqM59H6xmupW95d7dJEREQEBVtZJIyxBIVzF/IOilrcW6TW2SAkPzhMZWKS0A+I1WdItbfM6D1KI2PToRbA+j6D23cSa6gnls3M6L1ERETk0inYyuLguHipDEGxcFZzJKneWpFaZq1lsrefoV37mTh8HAA3HqPrthvJLO2csfv4+eJ0qH1ZUK4QFAqgYCsiIlJ1F7uPrUhNcxyXSF0WL/XKB9BIfSNuWh9IRWpZeXyC0tjEdKgFCIolBp59kXJu5kZkRNJJeNV8WjcWw0slZ+weIiIicvnUYyuLgpdIEFZcEh1dhH4FYwxuIoXr6V8BkVoWVHz8ycI57YWBYfxCkWh6ZoJnrKmR5muvYmjnbmwQ4MSitN6wlWhGoz5ERETmA32ql0XDiURxIlp2X2Qh8aJRIplzt+1KdbTgphMzd59YlOz6VcRbGwkKZSJ1ac2tFRERmUcUbEVEpGZF69JEJ/M0bFrDyJ4DYC3R+jqar95ALDFzwRbAcRwSzU0zek0RERGZGQq2IiJS09IdrUSSCeqWd2GDgEh9hkRDttpliYiIyBya18HWGPMO4C8BF/g7a+3nqlySiIjMQ7FsRkODRUREFrF5G2yNMS5wH3A7cAJ4yhjzLWvtnupWJiIiMn+EQUBxaJTKxCRuIkY0myGq1ZpFRGSRmbfBFrgeOGCtPQRgjHkAuBtQsBUREQFCP2B4zwH6tj+H9X2M59J63VVklnUSb9RwbBERWTzmc7DtBI6f8foEcMOZBxhjPg58HGDp0qVzV5mIiMg8UBoZo3/HC1jfB8D6AYM79+BEI8SyGYyr7epF5ov8RJ5ju49yfM8xmjqbWXH1ChraG6tdlsiCMZ+D7euy1n4e+DzAtm3bbJXLERERmVN+vgBheFZbUCrj54v45TKRRLxKlYnImcIgZMdD29n+Tz+fbmt+rIVf+sP3k2msq2JlIgvHfA62PUD3Ga+7TreJiIjMqMLAMKXxCQgtTjRCsq0ZLx6rdlmvy0snwXXAf6XNTcSJJON4Ue3bLTJfjPSNsOOhp85qGzw+QN/hPgVbkRkyn4PtU8AaY8wKpgLtPcCvVrckERFZaPIDQ4wfPE5xcITc8V4AsquX0fbGrUTTKWwYYq3Fcd0qV3queH2WjhuvofeJZwnLZdxYlJatG0l2tGgYssg8EgYB4atGV7zcLiIzY94GW2utb4z5XeARprb7+Xtr7e4qlyUiIgtMcWiUsFyZDrUAYwePkmhvJt7UwMju/QTlCg3rV5LqaseLzZ+eUOM61K9fSaKtifLEJG40SiybwUtqCLLIfFLf2sCa69ayf8dL023xVJzm7tYqViWysMzbYAtgrX0IeKjadYiIXAxrLZXxCWwY4KbTeJFItUuSi2CMoTg0enajhdzxXnLHTlIcnHqv96dP03HztWRXL5vzGl+LMYZ4Yz3xxvpqlyIiFxCJRbjlV26lsbORl36+j9blbWx7xxto7NDiUSIzZV4HWxGRWuGXSkweO07u8BGs7xNvbSWzehWxem25Mt9FUkmi2Qz5UwPTbcZ1iTfWM3H05FnHDu85QHpZJ25Evz5F5NI0tDXw5l9+C2949w1EohFcb/5NbxCpZZqAIyIyA0pDw0zsPzC97Uqxv5/J4yfOO6dK5pdEaxN1q7qJZjPA6VDbXE+8uYGgWDrrWOM6GGOqUaaILBDxZFyhVmQW6CtnEZEZUBkfP6et2N9PsHI5TipVhYrkYrnRCHXLOollM5RGx6d7a/18HuM62OCVLyeaNq/F0QdSERGReUfBVkRkBrjxcxfriaRTGE/zbGtFrL6OWP0r225EUgm673gTuaM9BGWfzLJOku3NVaxQRERELkTBVkRkBsQaG/Dq6vBP99wa1yW9csW8WkFXLl2ytYlka1O1yxAREZHXoWArUkV+qYgtl8ExeMkUxmjae62K1tXRuHULlbFxbBAQyaSJNTRUuywRERGRRUHBVqRKyhMTVMaGKI+OgGOIN7cRqavHiyeqXZpcpmg6TTSdrnYZsyLwfUrDowTFIpF0inijQruIiIjMHwq2IlUQVCpUJkYpDQ9ONYRQONWD40UUbGXeCcplRvceYGTPPggtJhKh7cZrSXd3Vrs0EREREUDb/YhUhfXL+BNj57RXJnNYa6tQkciFFQdHGNm9F8KpP5u2UmHwmRcojZ27ErSIiIhINSjYilSD52Ei5y4q5ES0gq7MP34+D6/6vsWfzBMUSuc/QURERGSOKdiKVIEXiRFvagFjptuMFyWSTmPOaBOZD7xUEl71x9JLp3DjseoUJCIiIvIqmmMrUiWRunrSy1cTFAsY4+DE43ipTLXLEjlHvLmRxqs2MrzrRQgtTjxG8zVXnbXnq4iIiEg1KdiKVIkxhmgmC5lstUsReU1uJEL9+tUkWpsJiiW8TIpYvf7cioiIyPyhYCsiIq/L8TwSrc3VLkNERETkvDTHVkRERERERGqagq2IiIiIiIjUNAVbERERERERqWkKtiIiIiIiIlLTFGxFRERERESkpinYioiIiIiISE1TsBUREREREZGapmArIiIiIiIiNU3BVkRERERERGqagq2IiIiIiIjUNAVbERERERERqWkKtiIiIiIiIlLTFGxFRERERESkpinYioiIiIiISE1TsBUREREREZGapmArIiIiIiIiNU3BVkRERERERGqagq2IiIiIiIjUNAVbERERERERqWkKtiIiIiIiIlLTFGxFRERERESkpinYioiIiIiISE1TsBURWaRsEBKUK9UuQ0REROSKedUuQERE5l6+b4jBXS9RGpkgu6qL+tXLiGZS1S5LRERE5LIo2IqILDLFkXGO/vPPCCs+AAPP7qWSK7DkpmswrgbyiIiISO3RJxgRkUUiDAIAisOjp0OtnX5v9MAxyhO5Oa3HzxepTBbm9J4iIiKyMKnHVkRkgSuNTjB+/CR+vki8qZ6w4hNWKtggBAOO52EiHuWJPPn+ISKpJInWJtzI7PyKCMoVxg4dZ+DZvdgwpGnTaurXLieSjM/K/URERGThU7AVEVnA/EKJscMnmOwdIChXcKNRYtk09WuXM/rSUbAhNghpvnoVJ3+2g+LQGMY4NG1ZS9u2zXjx2IzXNNnbT+/jO6df9z+zBzcWoXHDqhm/l4iIiCwOCrYiIgtYaTzH2MHjuLEIXjxK7+PPApZkWwtL3nwdE0d7iNXXEWvIEpSn5txaGzLy4kHqli4hs7RjxmuaONZ7TtvIviPUr12O47ozfj8RERFZ+DTHVkRkAQvLFfJ9A8Qb6xk7cAysBQvFwRHGDx3HL5Tof2YPp558jkxX+/R51kJ5Mj8rNUVSiXPbMimMMbNyPxEREVn4FGxFRBYwLxnHSybxC8Vz3sv3DxFJJsBa/HwR54w5tcYxRNPJWakps3QJbjTyyr1ch6aNqzCOfiWJiIjI5dFQZBGRBSzeUEfzVWsISpXpNuM4WGuJN9VTzk3ixqNEkglsGIIxuNEIzVvWkWxtmpWaEs0NLH/3WygMDGFDS7y5nmRz46zcS0RERBYHBVsRkQXMOA7NV60l3z9MaWycyZ5+jGNwolHSnW0MPv8SXjxO51vegJeIk121FC8eJ9aQmdX5rvGGOuINdbN2fREREVlcFGxFRBY4NxYl091OsrWR4sgYoR8QrcsQViqkOlqJZpJE69IAxBuzVa5WRERE5NIp2IqILBJuLEqqvaXaZYiIiIjMOK3UISIiIiIiIjVNwVZERERERERqmoKtiIiIiIiI1DTNsa0yv1QiLJUAcGMx3FisyhWJLG7WWowx1S5jXiuNTVAYHCEoVYg3ZUm2NumfmYiIiFSVgm2V2DCkODhA7tBBglKJWFMTXipNvLmZSGZqCwx/cpJyLkcwmcO4LtFsPZFsVh8gRWZBeWyc8ugopaFhvGSCWEsL8SbtrfpqhaFReh9/loljJwEwnsuyO95EdkVXlSsTERGRxUzBdo5ZawGoTIwz/tJLWL8CQGlwEIxD7kQP2TVJrF+hODTI5JHD2DAEwHgeDVdtIZqtr1b5IpckDEPCchknEpnVPVGvVLlYJN97ktyhI9Nt+ZO9NF57DbF6bX9zpnzf0HSoBbB+wKknd5JsbyaSiFexMhEREVnMFGzniA0CSqMjFHpPYRyINjbhxGMEucr0MeXhIRLtHYSVCn6hgJ/LTYdaAOv7FHp7FWxlXvMLBSrj42AMhVN9lMfGiDU1kujoIN7QUO3yzivM55k8evystqBYpDI+rmD7KpXJwjltpZEJglJZwVZERESqRsF2jpRGRxnft/eV18PDJLu6mczlptucWAwcg/E8jIHQr5x9EWMIyiXKE+O4Uc3HldnhF4v4uRwYQySdvqQ/Z+WxcYafe45oYwOlgUGCUgljDJO5HJXxCdwtVxFJJmex+stk7VlfIp3ZLmeL12fOaUt3txNJzcP/X0VERGTR0KrIc8Avlymc7Jn64Hz6g7IxBj+Xw0unOd1Aor2DeHMrbiSCG4sTrTujp8hxTgeNDKO7XmDkheepTExU4WlkIStPTDC042mGn3+B4eeeZ/j5F/Dz5/bQnY8NQ3JHj04NPfY8gmJx6s/76Tnh5dFR/MnJ2Sz/sjnxBMnOJWe1Gc8jUldXpYrmr2R7M+03XI0TmfpeNNnaTNsbrsKN6HtSERERqR59EpllfqFA7vhRbBBgfR8LmEgEYwxOLEaio4OwVMKJxfCSKbxEAgAvmSTa3EzadSn0ngQcYk2NVPKTYC1hucTk8WPUrVs/r+cuSu2w1pLv6SEol6fbKhMTlIaG8JKvvzBQ6Pvn/bLl5XnlzOPVhqOpJMnubtx4nEL/AJFUkmRXF7GG+mqXNu9EMylartlApruD0A+INdQRSWoIsoiIiFSXgu0sq0yMURoYINXZRXl0BJiab2s8j0RLK9H6+gueG01niKYzJNraKY+Okjt8kGhTM7GGJqxfoTw2hvV9ULCVGWCDkPLo2DntFzsywIlEiDU3kz9xAr9QJFqfpTw6hjEGCySWLMHLnDuMdb6IN9QTy9aRWrYUNxqdtyF8PnBcl2RbU7XLEBEREZk2L4OtMeazwG8BA6eb/m9r7UPVq+jy+ZN5bKVCoe8UqWXLp+YuOg7JJUuIZC9uURo3GsVxXVLLVlAeGaYw2kMknSHW2PhKb5jIFXI8l3hLM7mjx85qj15kr6UxhlRXJ34uR3l4iHh7O7GWFoJ8gUhdHdGmRrx5Pi/cOM68r1FEREREzjUvg+1p/8la+xfVLuJKhL6POd2bGhQKTB49gptIEG9tI1rfcEk9Qk4ywfgLBwjyU3MU/VyOSF0dbjKFF9cwQJkZiY4OKuMTlEamRhckOzqINV78Xq6RVIrGLVfhFwpgDE40iuN5Gi4vIiIiIrNqPgfbmmeDgEouR2LJEgqnTkEY4sZieOn0JQ9zDPMFgkL+rLbK+DhhuTSTJcsiF0kmabhqM34+jzEGL5mc/nLmYjmRCNFIZJYqFBEREbky5UKZkRMD5EdzGMfgFys0LW+jobO52qXJFZjPwfZ3jTG/AewA/sBaO/LqA4wxHwc+DrB06dI5Lu/1OdEoXjxOcXiIZGcXxnVxIhGCYon8yR68uizRl1dFvoDKxDjlsXGM502tLvuqocfGm8//F0otcjyPqFYDrlk2DLHWqpdcRETkPApjkzzz9Z9yePs+yvkiyfo0a2/ZwvPf/jlv+cR7aFraSnEiz/ipEYznkG1vIpqIVrtsuQhVS0XGmO8D7ed564+B/wr8P4A9/ff/D/joqw+01n4e+DzAtm3b5t1kU2MMyY4l2CCgODhAsr2D3KGD09v+eOk02Q2biKRSVHITlIaHCSsVIpkMlXweL5Fg4uABDBBtbJxqHx+fDrex5hYirxOMRWRxCCs+uZ4+xg8fxy+UqFvZTaqrjVg6Ve3SRERE5o3+Ayc5+vR+gnIFLORHcvQf6MGNevS8cATXc3n8/n9m5PggNgzpunolW+++ifolWjRxvqtasLXWvv1ijjPG/C3w7VkuZ9a4iQSZVauJt7UztnfPWXvZ+rncdFAd3b1rakugICAfBKRXrKQ0OIitVDCeR3l4mHhrG7GGJvz8JJG6LLHGRrx4ospPKCLzQe5kP8e//zOsH0y9PnaSjjddR2zz2ipXJiIiMnPCMMRgMM75p/WFYch43yiliQLppgypprNHoY2dGgYLYRBOtw0fG2DJ5mWUcnkO/GwXw0f7KRemtj889ORe6pc0se7Wq0lk9WXxfDYvx7EaYzqstb2nX74X2FXNeq6UcRwwEBSL5wwltkFAZWwMGwRg7dTfgUJ/H7GGxqm20/t/Fvv7iDY2Ur9xUzUeQ0TmsULf4HSofdnQrpeoW9FFJJWsUlUiIiIzo1Isc3LPMQ78dBeReIQ1t1xF25ouHNeZPsYv+xx8Yjc7H3yc0A+JpePcdO+dtK/rmj6mfkkTxjG4EQ+/VAGgeUUboz1DrLppIzu/+TiVYvmsew8fH2C0d1DBdp5zXv+Qqvj3xpgXjDHPA28FPl3tgq6UG08Qb2o5u9EYvFRyqhf31YIAN3Xuvzzx5pZzjxUROR8LoP14RUSk9p3cfYTHv/QI/Qd66Nl1hB/9128zcPDkWceMnhzkma//lNCf+mxdyhXZ/pUfUBifnD6mdfUSVt28ETfm4UY86toaaF7ZQdfVK2le0UHrms5X+qGsBWtJN9WRGxifq0eVyzQve2yttb9e7RpmmhuJkOzuBgeKAwM40SiZFSuJZusxjju1MBRgXBcbBMSamykPD5Natpzy8DAYSHZ2Ea1vqPKTyHxRyU0SBgFeKomrRcQWvURbE8Z1sGcMrWrcvIZIStMVRESktgVln30/fO6sNmstPS8coW3tK72xucHx01/qvmJyeILCWJ5EXYpKsczYqRFaVi2h66qVU+HWc3EiLvUdTTiOw5o3X8XgkT6KY5OM943Qtq6b/HieyNA4Y73DZDsufhtEmVv6NDyHonV1eKn1pJYux/Fc3NjU/rORujqy6zdSOHmCoFwm1txCWCrhRiLEmppJdnUDU+FYJPR9Jo6eYHj3Pqzvk2hpounqTUTrMtUuTaoo1dVO9+1vYnT/EYJiiezqZaSXdVS7LBERkStmDefdftDxzh58mmo497NQvC5JPJPAL5bZ9fBT7P3BTmxoCcOQ6973JrqvXU268ZV5uEHFp2N9N+N9o6y/7RpKuTxOxOPw9r00dLUo2M5jCrZzzHFdnFcNMTbGEGtoIJrNTm/TYYMAHOeS97uVha80PMLQc7unXxcGhhh5cT+t27Zi3NeeXWDDkKBUAsfBi8Vmu1SZQ67rUre8k7rlndPz8kVERBYCL+Kx4W1b+ckXeqfbHNeh86rlZx1X39XM5ndez+6Hn8Jaixv1uP6et5KsTzNwqHcq1AYhpXwJG1qe+uqPKIznWXrNarIdjfS+eIyffuERSrk8rudxfOcBNt25jaM7XqI4lj9rPq/MPwq284hxnOnZcOf7VkoEoDyRO6ct39uHXyy+5iJBfrHA5PHjFAf6Ma5LZuUqIukMbjyuELTA6P9PERFZaNo3LuUt/+LdHH16P5FElKXXrKF5xdkjkyKxCBvefg1LNi6jlCuQbq6jrm1qGl9xIg9MLTA1tUsJ+MUK5UKJl378PEs2LWfgUO8Zx1XwYhH2PvosK27cwODhUzR2t87tQ8slUbAVqTHueXpaI+kUzmsMVbfWUujtpdjfh/EiJNvbyR05QuhXSLS2kVyyBC+hlXNFRERkfvIiHks2LWfJpuWve1zTsnMDaLq5Hsdzprb5OT0PN9WYoTRRAAMnnj9MLBV/5QRjCP0AJxljyaZlbLz9WtLNdedcV+YP9aeL1Jh4YwPx5jM2CXccGq/agBu9cLANKxWKgwNT57e0MHHkMH5uAlupUDjVS+7o0emtpkREREQWmvqORm66905SjVPzcDOt9ay99WqO7NhPpqWeWCqG4zl4sVc+TxnXYeMd17Hs2jVk2zW3dr5Tj61IjfGSCVrfsJXy6BiB7xPNpIlmX/sbROO6uPE4Ybk8tXT9y1tMnR6yWhoaJFi6DC+pXlsRERFZeIxj6L56JZnmX6J37zFO7T3O8w9tZ9m2NUwMjrHyhvXsfPAJrv6FGxg42Et+JMeaN29m+fXrq126XCQFW5Ea5CXieIn46x94muO6pLqXMrrnlUWnMGZ6LqZxXXBeGcARVMoEhQLGOLjJJI7mfIuIiMgCUN/ZhJeI4kU9Mq1Z+g+cpHPzClpWdnDLx9/F0JE+skuaqV/SSH1H0+tfUOYNY619/aNqwLZt2+yOHTuqXYbIvGWtxZ/MERRL5I4dIcjnp3ts0ytWklrSCUBlcpLxl/bh56c2M4+3tpFeuuy8c3trRVAuUx6fICgUcWIRvHSaqHqnRUREFq3QD8gNjWOMId2cxThaeLEWGGOettZuO9976rEVWSSMMUTSGSLpDF46TWV8jKBUIpJOE8lMDWW21pI/eXI61AIU+/uI1teTaKnNlQCttRT6Bxja+QJhsYQTiVC/eQOmo42Iwq2IiMii5Hju9IrJsjAo2IosQl48jhc/dyhz6PuUR0fOafdzOZhnwTaoVKiMTxCWK3jpJNHMuZuyAxSHhhh+9gXCUgmYWkhr+PndRFIpBVsRERGRBULBVmSRqOQLUwtOlcpE69LEGhvO2e/UcV2idVmKg/1ntXup1FyW+rqCcpnRF/cxebwHmNoDunnbNSRaW846zoYhfr5AcDrUvnKBAL9QmKtyRURERGSWabsfkUWgPJ5jdO9+Rl/cT2l4lImjJ5js6T3nOOM4JDs7cWKv9ObGGhqJ1mXnstzXVR4dmw61MBVgh3e9SFAqn3VcUCpjjIMTOfc7vEtZfEtERERE5jf12IosAqWRUQgtoe8zunc/GENQLBHJpIm9aqugSDpN41Vb8PN5jOPgJZM4kQvvkVsNQbF4bls+T1Ap48ai021OxMMvFsmuW8PInn3T2xxlN6wl2qT96EREREQWCgVbkUUgKJfxi0WKg8NTDdYycfgYyY62c4ItgBuLzetVkM83NDpanz2nZsfzSDQ1Mtl7ipZt1xCUy7jxGLFsFm+ehXURERERuXwaiiyyCEQy6VdC7cuMoTw2UZ2CrlA0W0f9xvWY03vveqkUDZs34p4nrMYaG8gsX4oTjRDNZkm0tBDJpOe6ZBERERGZReqxFVkEonUZ4k0N5CbzUw1maphuNHv+lYTnO8fzyKxYRrylGev7eMnkWUOQXy2SShGZZwtgiYiIiMjMUbAVWQQiyQT1G9ZSHBkjLJUwxiHe3EiitbnapV02YwxR9byKiIiICAq2IotGormRrre9ifL4BMZ1iGazePH5O49WRERERORiKdiKLCKRdIpIWkNyRURERGRh0eJRIiIiIiIiUtMUbEVERERERKSmKdiKiIiIiIhITdMcWxGRGVYeHyMoljCOwU2liSQS1S5JREREZEFTsBWRGRGUy5THxqnkchhjiNTVEW9sqHZZc644PMzk4YNUJiYAiDU1kVq+gmimrsqViYiIiCxcCrYicsWstZSGRhh+/gWCQhEANx6jadu1JJqbqlzd3PELBcojw/iTk+A4EIaUhoaIZOsVbEVERERmkebYisgVq+QLFPoHpkMtQFAsUTjZW8Wq5pa1Fr+QpzwyjA1DCEOM6wJQGR+vcnUiIiIiC5uCrYhcMWPM9NDbM5XHz21bqIJ8ntyhg3ipV/YJtkEAjkMkk6liZSIiIiILn4KtiFwxLxYl3tp8TnuirbUK1VSHXywQlss40RhuMjndHqnLEm1orGJlIiIiIguf5tiKyBUzrkuitQU/l2fy+AkAUt1dJDraqlzZ3HEiUQCKfaeINjYRa2rGGEO8pVU9tiIiUlWBH5AfzxNLxYjGotUuR2RWKNiKyIyI1dcTuTpDevlSjIFIXR3O6Tmmi4GXTJJY0knhZA/l4SEAMitX46XTVa5MREQWs4O7D/Pz7/yc/c8eoHNlB2//1dtYvml5tcsSmXEKtiIyYxzXXZRb/AA4nkeqq5tYQwNhuYIbj+OlUhhjql2aiIgsUscO9/DN//IgR/ccBWDo1BA9h3r5nb/4bZo7z51CJFLLFGxFRGaI43lEs/XVLkNERASAU0dPTYfal40MjNJ7uFfBVhYcLR4lIiIiIrIABYGPFzl7WpC1IW5EfVuy8CjYioiIiIgsQPWtDWy9/Zqz2tZdu5al65ZWqSKR2aOva0REREREFqA1G1ZSLpVpaG9gfGCc9qXtbHjDOtL1qdc/WaTGKNiKiIiIiCxAnudx9bbNrN24inLZJ1uv7edk4VKwFRERERFZwBLJBIlktasQmV2aYysiIiIiIiI1TcFWREREREREapqCrYiIiIiIiNQ0zbEVERERERG5TJVShcGTg9jQ0rSkiVgiVu2SFiUFWxERERERkcswNjjGo1/5AS/8dBdYWHvdGu689w4a2xurXdqio6HIIiIiIiIil2H/M/t54SdToRbgpaf3s/uJPdUtapFSsBUREREREbkM+589cE7biz/fSxAEVahmcVOwFRERERERuQxda7vOaVu2YSmu61ahmsVNwVZEREREROQybLh+Pa3dLdOv69vq2frWq6tY0eKlxaNEREREREQuQ3NnM7/2Jx/i1JE+bGhpW9ZKtjlb7bIWJQVbERERERGRy5RpyJBpyFS7jEVPwVZEREREROS0kwd76T3ci+M6dK/tIgwtXsSjoa0eY0y1y5MLULAVEREREREBju45yv/8f7+MX/a55m3XsPvxPSQzCWLJGC3dzWx50xbiqfhZ5xRyBfqO9RP4Aa1dLWQa1XtbDQq2IiIiIiKy6AVBwBPf+Tl+2aeuqY5IzCPTkObpR5/BL/lcfevVNLQ2suaa1dPnjA2O8e3Pf4e+o31svnkze57YQ1N7I8s2L6NzVWcVn2bxUbAVEREREZFFLwxCRvtHAViyagmO4/Lj//MTAIwxPPXwUzS0Zs8KtgefO8jB5w5x8y/exMNfeoTQD/GiHsm6JL/xJ7/GiqtWVONRFiVt9yMiIiIiIoteJBrhmrdNbdXjeQ75iTyO43DmtNoXt++jXCxPvz556BRdazvZ/fgeQj8EIPQDCuMF9jz5IqViaU6fYTFTj62IiIiIiAiw8caNROMxju89Tj6X5x0fuYNYMsZgzxDRRJSgEuB67vTxXWs6GRsco+9Y/3Sb47mEYcj40Dj5iQKxeKwaj7LoKNiKiIiIiIgAgR/y02/8jMGTg9jQUswXue62aznw3EFG+kZ43++/96xgu3LLCo7tPcb6bet4vHeYrrVdhGFIz0s9LFnVQTyhUDtXNBRZREREREQE6D3Uy/CpYRzHwVpL6Ic888Nn2XzzJpKZJD/62o8Z7huZPr6usY47772DN9z5Bn7pU+8lEvWoa8hwz2c+yMotK0mkE1V8msVFPbYiIiIiIiJA4AfTP1trAQj9kEg0guu5lAtlChN5Dg+MUsgViCfjYAyHdx3m4S8+ghf18CIeJ/b38JF/++EqPcXipGArIiIiIiICtC9vI5aMUcqXcBwHDGy8cQPH9x0HYMnKJfQePsW3/us/USlVuOFd11PXnOXp7z2DDS2VYgUsuJ7LoRcOsXR9d5WfaPHQUGQRERERERGgubOZX/vjX2XTTRtpW9bKHb9+Ox0rOjh1pI+VW1bw5vfdzD/+xVfJj+eplCr8/KHtuK6D476ydHKlXMGGdioYy5xRj62IiIiIiMhpXWu6eO/vdeCXfWKJGJPjk7zhHdvINGb4zt9+FxvYVw42sOOfn+a6t1/Hd/72odNNBi/iag/bOVa1YGuM+WXgs8AG4Hpr7Y4z3vtXwMeAAPh9a+0jVSlSRETkNYR+wPjxUwzuPoRxHJo3rSTT1Ybj6lt6EZFa5roubmJq9eNUXYpUXQqApo5GHNchDKb2rMVCpjHDwIkB7viN2zm29xgNbQ1su2Mb3Wu7qlX+olTNHttdwPuA/35mozFmI3APsAlYAnzfGLPWWhucewkREZHqGT9+igP/9OPp1yMHj7Pm7reQXdpRxapERGS2rH/DOn7+0M8ZPDlE4Ae4nsOb33szEyM5hk+N8LZ73krn6k5iSW3zM9eqFmyttS8CGGNe/dbdwAPW2hJw2BhzALgeeGJuKxQRkVoUVHyKQ2OU80VsGDLw/H6M69B29Toy3TPXm2qtZeD5A69uZOjFIwq2IiILVOvSVj7y/3yYEy/1UClV6FjZTtca9czOB/Nxjm0n8OQZr0+cbhMREXlNlckCJ554nsFdB7F+QCSTpHHtMk4++QJjh3pYfddbCEplrLWkWhpJtjZUu2QREakxzUuaaV7SXO0y5FVmNdgaY74PtJ/nrT+21j44A9f/OPBxgKVLl17p5UREpIYVx3JMHO9jaPchbBgSBgHFoTEKgyMkmrKUJybp37mP8mSB0ugEjuey9hffSnpJy2XdzxhDy5bVjB09eWYjTeuXz8wDiYiIyEWb1WBrrX37ZZzWA5y54VPX6bbzXf/zwOcBtm3bZs93jIiIVJe1lspkgaDiE00lcKORGb1+6AcM7T3Cyad2kWpuwC+WcCKv/HrL9Q5S191OaSyHnSoIjMGNRRl44SCpjubzTYu5KHXd7az+hbcwuPsgxnFo2byKdGfrzDyYiIiIXLT5OBT5W8CXjTH/kanFo9YA26tbkoiIXI6xY72Ux/OcemYv+f4RMl2tLH3LtaQ7Zm4I12T/MEd/sB2MIbK8c6q3tuLjeC5BEJJqbaQwPA5Aur2JyT5DdvkSisPjuDGP4vA4iabsZd3b8VzqVyyhfsWSGXseERERuXRV24/AGPNeY8wJ4I3Ad4wxjwBYa3cDXwX2AA8Dv6MVkUVEas/EyQGGXjzC4e/9nPGjp/ALJcaPnmL/gz+mNJ6fsfuURiamfrCWSr5AdsVUuDWOQ6IpS8OapSRb6ln1C29mvKcfLx6l5/HnGNp7mFNP7+Xgd39KaWJyxuoREZErY63lxL7jvPTUPvoO91a7HKkR1VwV+RvANy7w3r8D/t3cViQiIjMpd3IQYwyVXGG6Laj4lHN5SqPjxOqSM3IfLxmf/nn04AnSXa1033IdqY4m4g11uLEI7ddtmBqibAwvfevHgCHekMHxpnps8/3DxDKpGalHREQuXyFX4MXHd/O9v3+EwniB+vYG3vkv3s26G9ZXuzSZ5+bjUGQREVkAwiCAV89dtVPLIczkPNtUeyONpxdsisRjxLJpIukkyaZ6Ytn09HFePEaiuZ54Q5rmDSspDI4CkGhePmO1iIjIlel56QTf/utvEQYhAKOnRnjov32bTHMdS1Zp2odcWNWGIouIyMKWbm9m7FgfzRtXTLc5EY+2a9cRv8w5recTScRp2bya8aO99D61h0MPP87wvqMM7DrA6OFX1h4MfB9cl84br+bET3cyuOcQg3sOcfwnOy978SgREZlZo30j06H2ZWN9o4z3j1WpIqkV6rEVEZFZUbe0jaW3XMPooRN033INxp2a85pdvgQ3MnO/foKyT++Tu6hMFrF26sNQ/859dN1yDfm+YRJNWYJyhZ7HX6A0kSOSiON4HqHvY4zBiXgM7ztG/cquGatJREQuT+qMkTYviyVjJDKJKlQjtUTBVkREZoUxhsY13WRXdBBWAiKJ2Kzcxy8WmejtB2uxfogNp8Ktny9x8rnnSXe1cuInz5IfGCHeOBVyg3IFLxHDOFM9tUGlMiu1iYjIpWlZ1sr1v3Aj2//pSQCMY7jtw7fTsUbDkOW1KdiKiMiscj0P15u9XzdePE66o5nxo6emQy1MbcUT+iGVXIH8wAgAxeEx2q5Zz9ihHmwQYowDxtC8adWs1SciIhevubOZ6++6geVblpMbyVHf1kDHmiVEY9FqlybznIKtiIjUNDfq0XnTVgpDP8YvlsBC4/rlTPYN48YiuPEoxnWwQYgNQob3HqHz5qvJ9fQTBiEdb9hE3dK2aj+GiIic1tzZQnNnS7XLkBqjYCsiIjUv3d7EhnvuYOxwL4XhMXI9/eQHRsguX0KypYEl119Fz+PPEVZ88oMjeJkkmc5WJvuHCX0fNzJzqzSLiIjI3FOwFRGRBSGWSdG0fjm5U4PE69NEknHSHS1EUwlatqwh0ZRl7MhJjOtQHp+k/7mXgKnVm0VERKS2KdiKiMiC4UY9skvbyS5tP6vdi0XIrljC+Ik++nfuO+u9VIeCrYiISK3TPrYiIrIoGGNovWo12eWdU69dZ2p+bWdrlSsTERGRK6UeWxERWTTiDXWsfOdNlEYnMK5LvCGDMabaZYmIiMgVUrAVEZFFxY14JFsaql2GiIiIzCANRRYREREREZGapmArIiIiIiIiNU3BVkRERERERGqagq2IiIiIiIjUNAVbERERERERqWkKtiIiIiIiIlLTFGxFRERERETmSLFYwvf9apex4GgfWxERERERkVnW3zvEUz95hh89/Dh19XXc8Ytv4bqbr8Z13WqXtiAo2IqIiIiIiMyyn37vCT7/F/9j+vX2H+3gs3/zR1z7xquqWNXCoaHIIiIiIiIi///27jy+rrLe9/j3t3d2kp2pmacm6TzPaTpRQNAiAiIIIjjhRY9chcvVc9R7vIoXr/dw5R6PF/WoiAqCHlCryHC0HARUWloLnULpSOcxbdKkmZOdnezn/JFNaEg6UNqsvcLn/XrtV9d61lp7/Vaf1+rqbz/Pep7zqLbmmP6w9Nl+ZV3Rbm2p3u5RRMMPiS0AAAAAnEfBYEDB4MDUa7AynB3+JgEAAADgPMorzNW1H7+qX1lqOEXTKid7FNHwwzu2AJBgerp71LK/Rp0NzQqEkhQuyFZmaaHXYQEAgLfhwiULlJaWqhefe0nZeSN04ZIFmlk11euwhg1zznkdwzlRVVXl1q5d63UYAPC21W/do91/WC4X6/33Ob04X+XvmaessiKPIwMAAPCOma1zzlUNto2uyACQQDqON+vQyuq+pFaS2o4cU0ftcQ+jAgAASGwktgCQQHoiXYo0tQ4o7+6MeBANAACAP5DYAkACSc0boezx5QPKU3KyPIgGAADAHxg8CgASSFIopKLKyXLRbjXuOqikcIpKL5il9OJcr0MDAABIWCS2AJBgssqLlZKTpeKGZllSUBkl+TIzr8MCAABIWCS2AJCAUjLSlJKR5nUYAAAAvsA7tgAAAAAAXyOxBQAAAAD4GoktAAAAAMDXeMcWAHDGYt09ijQ2S4GAwrkj+sqdc+psaFJXS5tCaakK5+fIAvx2CgAAhgaJLQDgjHTUN6p+0w41bN2tQFJQhXOnKmfiGCWFU9SwfY8Or1inWDSqQCik0gsrlT2uQsGUZK/DBgAA7wAktgCAM9K4Y5/qN70mSerp6VbNqg0KpYWVmpfdl9RKUiwaVc2qDUrOylBmWbGXIQMAgHcIElsAwGlF2zvUtHP/gPLWw3UKJIcUi3b3K++JdCna0jZU4QEAgHc4XoACAJxWIBRSKCt9QHlyRppCGWmyYPBN+ycplJmutqPH1HK4Vu31DUMVKgAAeAeixRYAcFrBUJIKZk1W2+E6uZ7e1tlQRroyyosVzs1W6eLZOvTieikWkwWDKrt0gaIt7Wo5UKPGnfsUDIVUWDVdOZPHKBRO9fhqAADAcENiCwA4qe6uqGKRLoXSw8oaVaqxH7hUnccaZUFTWmGewgW5kqS8qRMUzs9VV3OrQpnp6jzerM5jx9X42l5JvV2Ta1auV3JGmrInjPLwigAAwHBEYgsAGFTLgRrVVW9TZ0OTMitKlDd9gjJKC5VRWthvPxeLKRaLKb2kQOklBZKkSFOLmvccGPCd7bX1JLYAAOCcI7EFAAzQdvSY9j2zUj2RiCSpJ9qttsO1SsnK6DeFT3ttvRq27FJHfZNGjCtX9rhyJWemKyktrOSsDEVbO/p9byg9PKTXAQAA3hkYPAoAMEBnQ5N6IhGl5ueo/D2L1NMR0fHte9Swbbe64qMdRxqbte+ZlWrafVBdTS2qW79FddXb5HpiCudkKW/GJFnwjcdMSnam0t/U2gsAAHAu0GILABggEB/lOH/mZB3482op5iRJHbUNck4qnD1ZnfVNA6b5ady5X/kzJiglO0vBcKrGXHWJOhubFUhKUrgwV2n5OUN+LQAAYPgjsQUADBDOz1FWRak66xv7klpJkkkNm3coZ3yFFBzY6ccCAcl6y4OhJGVWlCizomSIogYAAO9UdEUGAAyQmjtCRQtnKZAUHLDNkoJSIKBwXraS3zS3bf6MCQPKAAD+1xPt0aGt+7V73Q4dr6n3OhxgAFpsAQCDSivIVU8kqmMbtysWjfaV58+crFBa71y0FUsWqeXgUXU1Niu9tEjppQUyM69CBgCcB63Hm/Xqnzbopd+/qJ5oj/IrCnTpp9+nihljvA5t2HLOKdIZUeop5n7ftWOvdr+2Vy0tbRo7YZSmzpio5OTkk+4/3JHYAgBOKrOsSKOvuFjN+w6ppzOirFEjlVFW3Lc9JTtLKdlZHkYIADjfDm87qFW/eaFv/dj+Oq3+7QrljsxXRm6mh5H5WzTarcMHj6i5sVnNzS1KSwtrwuRxOlZbr2VPPqd1L2/U/Avm6H1Xv1ujxpT3O/aV9Zv1y58t1XNP99ZLckqyvvrNL+jaD1/pxaUkBBJbAMApZZYXK7O8+PQ7AgCGpaajjQPKDmzeq5b6ZhLbs1R7pE6PPvR7Pb50mTraOnTltUsU6Yxo7oJZWvbkc9q3+6Akae+u/apet1n3fO9OjYj/kNzS3Kr1azb2JbWS1BXp0kP3/0aLLp6nouICT67Ja7xjCwAAAOCk0rIHjp2QX16g1IyTd5PFqb3w/N/05G+fVnNji7q6uvTE0mUqKinU0cO12rl9T799X9uyU/v2HOxbb21pU2NDU7993n/de7Vg8Rz901e/owfve1R7d+8fkutIJCS2AAAAAE6qeHypJiya0rceCod0wU2XKqckz8Oo/Ks72q3n/2O5YjEn52J95a9t3aUROSMUO3E2grjgCTMR5OSOUMnIIoWSQ5KkmXOmqr7uuH77yL9r08bteuTB3+l/ffn/qe7osfN/MQmErsgAAAAATiqvrEAX37xEky+crkhbh3JK8zVySvnpD4Sk3hbWjRu2aPvWnSotLdKceTM1fuIYbd+6S8Fgknp6eueELy4t1J6d+zVv4Wxt3LCl7/hFF8/T6LEVfeup4VTNWzhHd3z5M/q3B36rmXOm6dGHf6+MrHQF4gM4Htpfo1079ioa7VbtkWPKyR2h8tEjFQgM33ZNElsAwJCJdnapvaZWLQeOyPX09M5zW16iYPxXZwBAYsouylF2UY7XYfiOc05P/u5pPfijR/vKpsyYpFvv+IRWvvCSYj09am1pU15BrgqL8zVm3ChNnzVZa1dXa9PGbZo5Z6rmL6pUekZav+8dN3G08gvzNG/hbLU0t2nZU8/rzXMSNB5v1v/9+pfU1tqu5JRk3fGlv9N733+JkpKGZwo4PK8KAJCQ2g8f1b5nXpTr6e161bB5pyouv1A5E0Z5HBkAAOfewf2H9cgDv+tXtvXV7eps79T3H/iWdmzfo1hPj4pLi5Sbl6PSsiJJ0sjyEl1zwxWn/O4R2ZkakZ2pzs6ILr1ssf767Mq+baXlxdq8cZvaWtvl5NTZ0anv3nO/Jk4Zp/GThuc0TSS2AIAh0dXSppb9NX1J7esaNu9U1qhSWm0BAMNOtCuqSKRrQHlbW7vKR41U+aiRb/scqakpuvWOmzV1xiStWv6yps+aolmV0/SPd3xT0Wi3Oto71N3To+TkZO3dvZ/EFgCAt8PFYop1dw8oj3V3y7mBA2UAAOB3OXnZqpw/U2tXV/e93xpOS9WY8f17Km3bvEOrlq9RS3OLFl08T7Mrpys5Jblve0P9cbU0tSq/KE/p6f27JUtSUUmBrv/I+3X9R94vSTp8sEbJKSEdq6vve8ZGu7p0rK5BHe0dCqeFz9cle4bEFgAwJEKZ6cooL9Hxbf2nMciZPFZJJzy8AQAYDja/sk3f/eefaMq0CZp/QaW2vPqaJk4Zq0/eepNGj31j8K3tW3bqi5+7S50dnZKkp373jO6650u6+D0XKBaLacPaTdrw8iuqr29UQ/1xffpzH9PEKeNOee7SshJ98c7btWbVeh06UKOXVq7TDR+/Rk8/+bzmLZytcROHX6vt8B0WCwCQUAKBgMIFOSpfskjppYUKF+Vp5LvmKbOixOvQAAA4p2qP1Olbd31Pa1at1y9++htteXWb5s6fqc994RbNnDO1377rXnqlL6l93aMPP66O9g5temWbfvSdB/WD7zygPz7+J5WVl+oXP1uq5qaWU55/9469WvbEs3rqsWdUV9egO+/+ona9tldHj9QpNEx/TKbFFgAwZMK52QrnZiu9tEAWCCg5I93rkAAAOOd279qnTa9s7Vs/sO+wDu6v0bwLKge849rZGRlwfEd7hyJdUT3688e07uVqSVJzU4se+NG/6bOf/y86crhWWSMyBz13S3Orvv1/fqhtm3co0hnR1le36+6v36uP3XK9Fl5UpbLy4fmDsmcttmZ2g5ltNrOYmVWdUD7azDrMrDr++bFXMQIAzo+UrEySWgDA8OVMI7Kz+hc5p4yMge/Hzl0wU4Fg/7TsQx+9Wh3tHXpp1boB+3d2RgZM/yNJ3d3d2r5lp/78pxc1beZkzVtUqYysDKVnpMvMVDGmXNfdeNWwncvWyxbbTZKuk3T/INt2OedmD204AAAAAPD2FZcU6qZPXqeffP/hvsGbLrlssaZMnzBg32kzJ+vue7+mpb98Ui1NLbrmw1do8SULJCeVlZeqvbVDkcgbrbqjxpRp5CCtri/+9SXdfee9inZF1Xi8WQsvqtLCxXO15m8bJEll5cXKzMo4T1fsPc8SW+fcVkkye/NUwgAAAADgX6PGlmnB4rkKh1PVUN+ogqI8LbigUoXFBQP2TUpK0vxFc1RZNUPdPT1KTU3p23bbP3xKX//SPUruTFYsFlPl/JladFHVgO+oq63XD/7lAcV6YgoGg0pLD2v1ijX6u9s/rjV/26DFlyzQhMmnHnDK7xL1HdsxZrZBUrOkO51zKwbbycxulXSrJFVUVAxheAAAAAAwODNT5bwZGj9htFpa25SXn9MvYR1MUihJSaH+6dmCxZX64c/v0b49B5SRma5JUyYoryBnwLFtrW06Xt/Yt56amqKkpKDyCnL1jX/+H5o6Y+JJ38kdLs5rYmtmz0kqHmTT15xzT57ksBpJFc65ejObK+kJM5vmnGt+847OuZ9I+okkVVVVMQkiAAAAgISRlZ2prOyBCWVdbb3Wv/yKmptaZWYqKS3UjDlTBySfgUBAk6aO16Sp4095noKifN34iWt0vKFJ1es2qfbIMSUnJ2tW5TRNnTHpnF5Tojqvia1zbslZHBORFIkvrzOzXZImSlp7jsMDAAAAgHPKOadN1Vv1l2dXqq2tXZdedqEq581Qcnyanc7OiH5+36PKL8zTg/c9otaWNgWTkvTeK9+lL955m/IL8t7S+boiXVq9Yp3+/MyLOlJTq4vfc4Eq58/U7Lkzzqj7ce3RY6peu0n79hzQ+EljNbtymnLyss/m0j2VcF2RzaxAUoNzrsfMxkqaIGm3x2EBAAAAwGlt3rhNX779G4pGuyVJzy17Qf/72/+oCy9ZIEk6sPeQjhyu1cYNW9Ta0iZJ6unu1osvvKzLr363LlmyWLFYTI3HmxROCyscTh1wjv17Dur5Z1Zo/ZqNmjV3uhrrG1VXW69AIKAXnl+lT3zqBl125btOG2tzU4u+d8/9Wv3iG6MvX3395brt72/pS8T9wrPE1sw+KOlfJRVI+qOZVTvnLpd0saRvmllUUkzSZ51zDV7FCQAAAABn6m8r1vYlta9b+ssnNG/RHKWkJKsn1qOikoKBU/k46ejhOh06WKMnlj6tvzyzQqVlJbrlto8oe0SWNlZvVaSzU9NmTNa937pfe3btkyStXV2tSVPHq3L+TK1/eaOSgkE9u+yvuv6j71dObvYpY929c1+/pFaS/vj4s7rq2iW+G2zKy1GRH5f0+CDlj0l6bOgjAgAAAIC3p/tNSa0kRbu7+6b9qRhVptRwimZVTteq5S9LkgKBoJKSgqoYM1IP3f9r/fk/esfOPd7QpNUr1mnZE8+pva1dknTth6/U9m07lRwKxY8NqHrtq/r0bR/X+pc3SpLyCnNPO1iVJEUiXQPKYrGYIpHoWVy5t4bn7LwAAAAA4IGFF1UpEOyfZn3oI1f3JZpp6WF96KMf0LU3XqnJ0ycqNTVVOXnZuvkzN6q4tFB/fXZl33Fp6WE1NjTqeMPxN77MOXW0d8qpN1EOhZIUCLxxvmAwqE9+5kaF08KnjXX02HLlF+T2Kxs3aYwqRo18y9fttYR7xxYAAAAA/GrG7Cn61vfu1BNLl6mttUMfuP5yLVg8t98+I8tLNLK8RFULZutoTa1Sw6kqH1Wq+roGhcOpamvtbZ1NSU1RS3ObJOs79lhdg8ZPHK3aI8ck601kL7vyEs2pmqGyUaWaOHmsJk+bcEaxFhUX6Jv/8hX96uHHtWXjNlUtnK0bPvaBQUdyTnT2epO431VVVbm1axk4GQAAAID3YrGYYrGYkpLeWlvi40v/qB98+4G+9ZtvvVE///GvFDyhVfar//T3Onq4VtXrNmn+BXO0+F0LVDKy6KxjjUajamtpV0ZW+luOdyiZ2TrnXNVg2xI3agAAAADwqUAg0K+L8Jm6/Kp3a2RZibZu2qHC4nzNmD1FZeUleuj+X6ujo1MfvPEqzZk7XbmX5+ijt1x/TmINhULKzh1xTr7LK7TYAgAAAECCa25qUXd3t3LzcrwOxTO02AIAAACAj2WN8N97r0OJUZEBAAAAAL5GYgsAAAAA8DUSWwAAAACAr5HYAgAAAAB8jcQWAAAAAOBrJLYAAAAAAF8jsQUAAAAA+BqJLQAAAADA10hsAQAAAAC+RmILAAAAAPA1ElsAAAAAgK+R2AIAAAAAfI3EFgAAAADgayS2AAAAAABfI7EFAAAAAPgaiS0AAAAAwNdIbAEAAAAAvkZiCwAAAADwNRJbAAAAAICvkdgCAAAAAHzNnHNex3BOmFmdpH1ex4FTypd0zOsg8JZRb/5DnfkT9eY/1Jk/UW/+Q535z/mqs1HOuYLBNgybxBaJz8zWOueqvI4Dbw315j/UmT9Rb/5DnfkT9eY/1Jn/eFFndEUGAAAAAPgaiS0AAAAAwNdIbDGUfuJ1ADgr1Jv/UGf+RL35D3XmT9Sb/1Bn/jPkdcY7tgAAAAAAX6PFFgAAAADgayS2AAAAAABfI7HFeWdmN5jZZjOLmVnVCeWjzazDzKrjnx97GSfecLI6i2/7n2a208y2m9nlXsWIUzOzb5jZoRPuryu9jgmDM7P3xe+nnWb2Fa/jwZkxs71m9mr8/lrrdTwYyMweNLNaM9t0QlmumT1rZjvif+Z4GSMGOkm98UxLYGZWbmZ/MbMt8f8/fj5ePqT3G4kthsImSddJWj7Itl3Oudnxz2eHOC6c3KB1ZmZTJd0kaZqk90n6kZkFhz48nKF7T7i/lnkdDAaK3z8/lHSFpKmSPhK/z+APl8bvL+bXTEwPqfdZdaKvSHreOTdB0vPxdSSWhzSw3iSeaYmsW9IXnXNTJS2UdHv8WTak9xuJLc4759xW59x2r+PAmTtFnV0j6dfOuYhzbo+knZLmD210wLAyX9JO59xu51yXpF+r9z4D8DY555ZLanhT8TWSHo4vPyzp2qGMCad3knpDAnPO1Tjn1seXWyRtlTRSQ3y/kdjCa2PMbIOZvWBmF3kdDE5rpKQDJ6wfjJchMf03M9sY79ZFd7vExD3lX07Sn8xsnZnd6nUwOGNFzrma+PIRSUVeBoO3hGeaD5jZaElzJL2kIb7fSGxxTpjZc2a2aZDPqVoeaiRVOOfmSPoHSY+aWdbQRIyzrDMkkNPU4X2Sxkmard577TtexgoMQxc65yrV2438djO72OuA8Na43jkvmffSH3im+YCZZUh6TNIXnHPNJ24bivst6Xx+Od45nHNLzuKYiKRIfHmdme2SNFESg3AMgbOpM0mHJJWfsF4WL4MHzrQOzeynkv5wnsPB2eGe8inn3KH4n7Vm9rh6u5UPNpYEEstRMytxztWYWYmkWq8Dwuk5546+vswzLTGZWUi9Se0jzrnfx4uH9H6jxRaeMbOC1wceMrOxkiZI2u1tVDiNpyTdZGYpZjZGvXX2sscxYRDxB8jrPqjeAcGQeNZImmBmY8wsWb2Dsz3lcUw4DTNLN7PM15clvVfcY37xlKRPxpc/KelJD2PBGeKZltjMzCQ9IGmrc+7/n7BpSO83620VBs4fM/ugpH+VVCCpUVK1c+5yM7te0jclRSXFJN3lnPt3zwJFn5PVWXzb1yR9Sr0j4H3BOfe0V3Hi5Mzsl+rtsuUk7ZX0X094zwUJJD5txXclBSU96Jy729uIcDrxH2Mfj68mSXqUeks8ZvYrSZdIypd0VNJdkp6QtFRShaR9kj7snGOgogRyknq7RDzTEpaZXShphaRX1ft/ekn6qnrfsx2y+43EFgAAAADga3RFBgAAAAD4GoktAAAAAMDXSGwBAAAAAL5GYgsAAAAA8DUSWwAAAACAr5HYAgAAAAB8jcQWAAAPmFm2md02BOcxM/u+me00s41mVnm+zwkAwFAjsQUAwBvZks57YivpCkkT4p9bJd03BOcEAGBIkdgCAOCNeySNM7NqM/upmS2PL28ys4skycxazexuM3vFzFabWVG8vMDMHjOzNfHP4lOc5xpJv3C9VkvKNrOS8395AAAMHRJbAAC88RVJu5xzsyVtk/RMfHmWpOr4PumSVjvnZklaLukz8fLvSbrXOTdP0vWSfnaK84yUdOCE9YPxMgAAho0krwMAAABaI+lBMwtJesI5Vx0v75L0h/jyOkmXxZeXSJpqZq8fn2VmGc651iGKFwCAhEKLLQAAHnPOLZd0saRDkh4ys5vjm6LOORdf7tEbP0gHJC10zs2Of0aeIqk9JKn8hPWyeBkAAMMGiS0AAN5okZQpSWY2StJR59xP1dut+HQjF/9J0h2vr5jZ7FPs+5Skm+OjIy+U1OScq3k7gQMAkGjoigwAgAecc/VmttLMNqn3Xdo2M4tKapV086mP1n+X9EMz26jeZ/lySZ89yb7LJF0paaekdkm3nIv4AQBIJPZGDycAAAAAAPyHrsgAAAAAAF+jKzIAAMOAmd0i6fNvKl7pnLvdi3gAABhKdEUGAAAAAPgaXZEBAAAAAL5GYgsAAAAA8DUSWwAAAACAr5HYAgAAAAB87T8BsaskxH0GJtoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "sns.scatterplot(\n",
    "    x=\"tsne_0\", y=\"tsne_1\",    \n",
    "    data=X,    \n",
    "    hue=\"ivl_number\",\n",
    "    alpha=0.9\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 ('mlenv')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "b501fadf3fcd54fad1d558131fed1dc11f5a6e7b57e5dc1336698e936ed52168"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}