Clustering/le clustering des données avec k-Means.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "conda install -c conda-forge python-utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "random-state=200 dans sickit-learn pour fit va fixer nos resultats."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Clustering avec scikit-learn\n",
    "\n",
    "<br><br></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dans ce notebook, nous allons appliquer k-means en utilisant scikit-learn. \n",
    "\n",
    "Nous allons utiliser l'analyse de clusters pour avoir une vue d'ensemble concernant la méteo dans une station.\n",
    "\n",
    "Dans ce dataset, nous avons des milliers d'enregistrements. comment peut-on en créer 12 clusters?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Importing the Necessary Libraries<br></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "import python_utils\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from itertools import cycle, islice\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.tools.plotting import parallel_coordinates\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Créer un Pandas DataFrame à partir du fichier CSV en question<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./weather/minute_weather.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\">\"Minute Weather Data\"</p>\n",
    "<br>\n",
    "\n",
    "\n",
    "Chaque ligne dans **minute_weather.csv** contient des données méteorologiques en une minute, les variables sont:\n",
    "\n",
    "\n",
    "* **rowID:** \tunique number for each row\t(*Unit: NA*)\n",
    "* **hpwren_timestamp:**\ttimestamp of measure\t(*Unit: year-month-day hour:minute:second*)\n",
    "* **air_pressure:** air pressure measured at the timestamp\t(*Unit: hectopascals*)\n",
    "* **air_temp:**\tair temperature measure at the timestamp\t(*Unit: degrees Fahrenheit*)\n",
    "* **avg_wind_direction:**\twind direction averaged over the minute before the timestamp\t(*Unit: degrees, with 0 means coming from the North, and increasing clockwise*)\n",
    "* **avg_wind_speed:**\twind speed averaged over the minute before the timestamp\t(*Unit: meters per second*)\n",
    "* **max_wind_direction:**\thighest wind direction in the minute before the timestamp\t(*Unit: degrees, with 0 being North and increasing clockwise*)\n",
    "* **max_wind_speed:**\thighest wind speed in the minute before the timestamp\t(*Unit: meters per second*)\n",
    "* **min_wind_direction:**\tsmallest wind direction in the minute before the timestamp\t(*Unit: degrees, with 0 being North and inceasing clockwise*)\n",
    "* **min_wind_speed:**\tsmallest wind speed in the minute before the timestamp\t(*Unit: meters per second*)\n",
    "* **rain_accumulation:**\tamount of accumulated rain measured at the timestamp\t(*Unit: millimeters*)\n",
    "* **rain_duration:**\tlength of time rain has fallen as measured at the timestamp\t(*Unit: seconds*)\n",
    "* **relative_humidity:**\trelative humidity measured at the timestamp\t(*Unit: percent*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1587257, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rowID</th>\n",
       "      <th>hpwren_timestamp</th>\n",
       "      <th>air_pressure</th>\n",
       "      <th>air_temp</th>\n",
       "      <th>avg_wind_direction</th>\n",
       "      <th>avg_wind_speed</th>\n",
       "      <th>max_wind_direction</th>\n",
       "      <th>max_wind_speed</th>\n",
       "      <th>min_wind_direction</th>\n",
       "      <th>min_wind_speed</th>\n",
       "      <th>rain_accumulation</th>\n",
       "      <th>rain_duration</th>\n",
       "      <th>relative_humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2011-09-10 00:00:49</td>\n",
       "      <td>912.3</td>\n",
       "      <td>64.76</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-09-10 00:01:49</td>\n",
       "      <td>912.3</td>\n",
       "      <td>63.86</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>215.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-09-10 00:02:49</td>\n",
       "      <td>912.3</td>\n",
       "      <td>64.22</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>143.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>324.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-09-10 00:03:49</td>\n",
       "      <td>912.3</td>\n",
       "      <td>64.40</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-09-10 00:04:49</td>\n",
       "      <td>912.3</td>\n",
       "      <td>64.40</td>\n",
       "      <td>185.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>260.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rowID     hpwren_timestamp  air_pressure  air_temp  avg_wind_direction  \\\n",
       "0      0  2011-09-10 00:00:49         912.3     64.76                97.0   \n",
       "1      1  2011-09-10 00:01:49         912.3     63.86               161.0   \n",
       "2      2  2011-09-10 00:02:49         912.3     64.22                77.0   \n",
       "3      3  2011-09-10 00:03:49         912.3     64.40                89.0   \n",
       "4      4  2011-09-10 00:04:49         912.3     64.40               185.0   \n",
       "\n",
       "   avg_wind_speed  max_wind_direction  max_wind_speed  min_wind_direction  \\\n",
       "0             1.2               106.0             1.6                85.0   \n",
       "1             0.8               215.0             1.5                43.0   \n",
       "2             0.7               143.0             1.2               324.0   \n",
       "3             1.2               112.0             1.6                12.0   \n",
       "4             0.4               260.0             1.0               100.0   \n",
       "\n",
       "   min_wind_speed  rain_accumulation  rain_duration  relative_humidity  \n",
       "0             1.0                NaN            NaN               60.5  \n",
       "1             0.2                0.0            0.0               39.9  \n",
       "2             0.3                0.0            0.0               43.0  \n",
       "3             0.7                0.0            0.0               49.5  \n",
       "4             0.1                0.0            0.0               58.8  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "échatillonnage de données<br></p>\n",
    "\n",
    "Plusieurs ligne, nous allons prendre chaque 10ième élément \n",
    " <br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158726, 13)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_df = data[(data['rowID'] % 10) == 0]\n",
    "sampled_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Statistiques \n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rowID</th>\n",
       "      <td>158726.0</td>\n",
       "      <td>793625.000000</td>\n",
       "      <td>458203.937509</td>\n",
       "      <td>0.00</td>\n",
       "      <td>396812.5</td>\n",
       "      <td>793625.00</td>\n",
       "      <td>1190437.50</td>\n",
       "      <td>1587250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>air_pressure</th>\n",
       "      <td>158726.0</td>\n",
       "      <td>916.830161</td>\n",
       "      <td>3.051717</td>\n",
       "      <td>905.00</td>\n",
       "      <td>914.8</td>\n",
       "      <td>916.70</td>\n",
       "      <td>918.70</td>\n",
       "      <td>929.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>air_temp</th>\n",
       "      <td>158726.0</td>\n",
       "      <td>61.851589</td>\n",
       "      <td>11.833569</td>\n",
       "      <td>31.64</td>\n",
       "      <td>52.7</td>\n",
       "      <td>62.24</td>\n",
       "      <td>70.88</td>\n",
       "      <td>99.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_wind_direction</th>\n",
       "      <td>158680.0</td>\n",
       "      <td>162.156100</td>\n",
       "      <td>95.278201</td>\n",
       "      <td>0.00</td>\n",
       "      <td>62.0</td>\n",
       "      <td>182.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>359.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_wind_speed</th>\n",
       "      <td>158680.0</td>\n",
       "      <td>2.775215</td>\n",
       "      <td>2.057624</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3.80</td>\n",
       "      <td>31.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_wind_direction</th>\n",
       "      <td>158680.0</td>\n",
       "      <td>163.462144</td>\n",
       "      <td>92.452139</td>\n",
       "      <td>0.00</td>\n",
       "      <td>68.0</td>\n",
       "      <td>187.00</td>\n",
       "      <td>223.00</td>\n",
       "      <td>359.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_wind_speed</th>\n",
       "      <td>158680.0</td>\n",
       "      <td>3.400558</td>\n",
       "      <td>2.418802</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.70</td>\n",
       "      <td>4.60</td>\n",
       "      <td>36.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_wind_direction</th>\n",
       "      <td>158680.0</td>\n",
       "      <td>166.774017</td>\n",
       "      <td>97.441109</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>180.00</td>\n",
       "      <td>212.00</td>\n",
       "      <td>359.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_wind_speed</th>\n",
       "      <td>158680.0</td>\n",
       "      <td>2.134664</td>\n",
       "      <td>1.742113</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.60</td>\n",
       "      <td>3.00</td>\n",
       "      <td>31.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rain_accumulation</th>\n",
       "      <td>158725.0</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.011236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rain_duration</th>\n",
       "      <td>158725.0</td>\n",
       "      <td>0.409627</td>\n",
       "      <td>8.665523</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2960.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relative_humidity</th>\n",
       "      <td>158726.0</td>\n",
       "      <td>47.609470</td>\n",
       "      <td>26.214409</td>\n",
       "      <td>0.90</td>\n",
       "      <td>24.7</td>\n",
       "      <td>44.70</td>\n",
       "      <td>68.00</td>\n",
       "      <td>93.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count           mean            std     min       25%  \\\n",
       "rowID               158726.0  793625.000000  458203.937509    0.00  396812.5   \n",
       "air_pressure        158726.0     916.830161       3.051717  905.00     914.8   \n",
       "air_temp            158726.0      61.851589      11.833569   31.64      52.7   \n",
       "avg_wind_direction  158680.0     162.156100      95.278201    0.00      62.0   \n",
       "avg_wind_speed      158680.0       2.775215       2.057624    0.00       1.3   \n",
       "max_wind_direction  158680.0     163.462144      92.452139    0.00      68.0   \n",
       "max_wind_speed      158680.0       3.400558       2.418802    0.10       1.6   \n",
       "min_wind_direction  158680.0     166.774017      97.441109    0.00      76.0   \n",
       "min_wind_speed      158680.0       2.134664       1.742113    0.00       0.8   \n",
       "rain_accumulation   158725.0       0.000318       0.011236    0.00       0.0   \n",
       "rain_duration       158725.0       0.409627       8.665523    0.00       0.0   \n",
       "relative_humidity   158726.0      47.609470      26.214409    0.90      24.7   \n",
       "\n",
       "                          50%         75%         max  \n",
       "rowID               793625.00  1190437.50  1587250.00  \n",
       "air_pressure           916.70      918.70      929.50  \n",
       "air_temp                62.24       70.88       99.50  \n",
       "avg_wind_direction     182.00      217.00      359.00  \n",
       "avg_wind_speed           2.20        3.80       31.90  \n",
       "max_wind_direction     187.00      223.00      359.00  \n",
       "max_wind_speed           2.70        4.60       36.00  \n",
       "min_wind_direction     180.00      212.00      359.00  \n",
       "min_wind_speed           1.60        3.00       31.60  \n",
       "rain_accumulation        0.00        0.00        3.12  \n",
       "rain_duration            0.00        0.00     2960.00  \n",
       "relative_humidity       44.70       68.00       93.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157812, 13)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_df[sampled_df['rain_accumulation'] == 0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157237, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_df[sampled_df['rain_duration'] == 0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "supprimer  rain_duration et rain_accumulation\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "del sampled_df['rain_accumulation']\n",
    "del sampled_df['rain_duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows_before = sampled_df.shape[0]\n",
    "sampled_df = sampled_df.dropna()\n",
    "rows_after = sampled_df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "combien de lignes supprimées ?\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows_before - rows_after"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['rowID', 'hpwren_timestamp', 'air_pressure', 'air_temp',\n",
       "       'avg_wind_direction', 'avg_wind_speed', 'max_wind_direction',\n",
       "       'max_wind_speed', 'min_wind_direction', 'min_wind_speed',\n",
       "       'relative_humidity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    " Features qui nous intéressent pour le Clustering\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['air_pressure', 'air_temp', 'avg_wind_direction', 'avg_wind_speed', 'max_wind_direction', \n",
    "        'max_wind_speed','relative_humidity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "select_df = sampled_df[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['air_pressure', 'air_temp', 'avg_wind_direction', 'avg_wind_speed',\n",
       "       'max_wind_direction', 'max_wind_speed', 'relative_humidity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>air_pressure</th>\n",
       "      <th>air_temp</th>\n",
       "      <th>avg_wind_direction</th>\n",
       "      <th>avg_wind_speed</th>\n",
       "      <th>max_wind_direction</th>\n",
       "      <th>max_wind_speed</th>\n",
       "      <th>relative_humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>912.3</td>\n",
       "      <td>64.76</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>60.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>912.3</td>\n",
       "      <td>62.24</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>167.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>912.2</td>\n",
       "      <td>63.32</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>58.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>912.2</td>\n",
       "      <td>62.60</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>57.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>912.2</td>\n",
       "      <td>64.04</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>57.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>912.1</td>\n",
       "      <td>63.68</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>119.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>51.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>912.0</td>\n",
       "      <td>64.04</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>51.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>911.9</td>\n",
       "      <td>64.22</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>911.9</td>\n",
       "      <td>61.70</td>\n",
       "      <td>67.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>71.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>911.9</td>\n",
       "      <td>61.34</td>\n",
       "      <td>67.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>75.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>72.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>911.8</td>\n",
       "      <td>62.96</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>106.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>63.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>911.8</td>\n",
       "      <td>64.22</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>59.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>911.8</td>\n",
       "      <td>63.86</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>911.6</td>\n",
       "      <td>64.40</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>203.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>50.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>911.5</td>\n",
       "      <td>65.30</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>911.4</td>\n",
       "      <td>64.58</td>\n",
       "      <td>154.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>176.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>50.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>911.4</td>\n",
       "      <td>65.48</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>46.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>911.5</td>\n",
       "      <td>65.66</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>109.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>45.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>911.4</td>\n",
       "      <td>65.66</td>\n",
       "      <td>155.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>167.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>42.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>911.4</td>\n",
       "      <td>67.10</td>\n",
       "      <td>157.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>36.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>911.4</td>\n",
       "      <td>68.00</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>911.3</td>\n",
       "      <td>67.64</td>\n",
       "      <td>167.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>196.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>911.4</td>\n",
       "      <td>67.82</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>34.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>911.4</td>\n",
       "      <td>66.74</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>192.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>37.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>911.4</td>\n",
       "      <td>66.56</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>41.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>911.4</td>\n",
       "      <td>65.66</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>51.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>911.5</td>\n",
       "      <td>65.66</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>101.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>41.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>911.4</td>\n",
       "      <td>66.92</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>174.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>911.3</td>\n",
       "      <td>64.76</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>43.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>911.3</td>\n",
       "      <td>64.94</td>\n",
       "      <td>164.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>176.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>43.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1586960</th>\n",
       "      <td>914.7</td>\n",
       "      <td>76.46</td>\n",
       "      <td>247.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>43.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1586970</th>\n",
       "      <td>914.8</td>\n",
       "      <td>76.28</td>\n",
       "      <td>208.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>43.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1586980</th>\n",
       "      <td>914.8</td>\n",
       "      <td>76.10</td>\n",
       "      <td>209.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>43.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1586990</th>\n",
       "      <td>914.9</td>\n",
       "      <td>76.28</td>\n",
       "      <td>339.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>350.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>43.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587000</th>\n",
       "      <td>914.9</td>\n",
       "      <td>75.92</td>\n",
       "      <td>344.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>352.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>43.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587010</th>\n",
       "      <td>915.0</td>\n",
       "      <td>75.56</td>\n",
       "      <td>323.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>348.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>45.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587020</th>\n",
       "      <td>915.1</td>\n",
       "      <td>75.56</td>\n",
       "      <td>324.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>347.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587030</th>\n",
       "      <td>915.1</td>\n",
       "      <td>75.74</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>45.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587040</th>\n",
       "      <td>915.2</td>\n",
       "      <td>75.38</td>\n",
       "      <td>355.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>46.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587050</th>\n",
       "      <td>915.3</td>\n",
       "      <td>75.38</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>45.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587060</th>\n",
       "      <td>915.4</td>\n",
       "      <td>75.38</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>45.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587070</th>\n",
       "      <td>915.5</td>\n",
       "      <td>75.38</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>46.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587080</th>\n",
       "      <td>915.6</td>\n",
       "      <td>75.20</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>46.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587090</th>\n",
       "      <td>915.6</td>\n",
       "      <td>75.20</td>\n",
       "      <td>356.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>47.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587100</th>\n",
       "      <td>915.7</td>\n",
       "      <td>75.38</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>46.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587110</th>\n",
       "      <td>915.7</td>\n",
       "      <td>75.02</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>46.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587120</th>\n",
       "      <td>915.7</td>\n",
       "      <td>74.84</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>46.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587130</th>\n",
       "      <td>915.8</td>\n",
       "      <td>74.84</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>46.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587140</th>\n",
       "      <td>915.8</td>\n",
       "      <td>74.84</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>45.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587150</th>\n",
       "      <td>915.8</td>\n",
       "      <td>75.20</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>45.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587160</th>\n",
       "      <td>915.8</td>\n",
       "      <td>75.38</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>46.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587170</th>\n",
       "      <td>915.7</td>\n",
       "      <td>75.38</td>\n",
       "      <td>347.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>48.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587180</th>\n",
       "      <td>915.8</td>\n",
       "      <td>75.74</td>\n",
       "      <td>326.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>337.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>48.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587190</th>\n",
       "      <td>915.9</td>\n",
       "      <td>75.92</td>\n",
       "      <td>289.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>309.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>48.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587200</th>\n",
       "      <td>915.9</td>\n",
       "      <td>75.74</td>\n",
       "      <td>335.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>348.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>47.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587210</th>\n",
       "      <td>915.9</td>\n",
       "      <td>75.56</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>47.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587220</th>\n",
       "      <td>915.9</td>\n",
       "      <td>75.56</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>341.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587230</th>\n",
       "      <td>915.9</td>\n",
       "      <td>75.56</td>\n",
       "      <td>344.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>352.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587240</th>\n",
       "      <td>915.9</td>\n",
       "      <td>75.20</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>46.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587250</th>\n",
       "      <td>915.9</td>\n",
       "      <td>74.84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>46.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158680 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         air_pressure  air_temp  avg_wind_direction  avg_wind_speed  \\\n",
       "0               912.3     64.76                97.0             1.2   \n",
       "10              912.3     62.24               144.0             1.2   \n",
       "20              912.2     63.32               100.0             2.0   \n",
       "30              912.2     62.60                91.0             2.0   \n",
       "40              912.2     64.04                81.0             2.6   \n",
       "50              912.1     63.68               102.0             1.2   \n",
       "60              912.0     64.04                83.0             0.7   \n",
       "70              911.9     64.22                82.0             2.0   \n",
       "80              911.9     61.70                67.0             3.3   \n",
       "90              911.9     61.34                67.0             3.6   \n",
       "100             911.8     62.96                95.0             2.3   \n",
       "110             911.8     64.22                83.0             2.1   \n",
       "120             911.8     63.86                68.0             2.1   \n",
       "130             911.6     64.40               156.0             0.5   \n",
       "140             911.5     65.30                85.0             2.2   \n",
       "150             911.4     64.58               154.0             1.3   \n",
       "160             911.4     65.48               154.0             0.9   \n",
       "170             911.5     65.66                95.0             1.1   \n",
       "180             911.4     65.66               155.0             1.1   \n",
       "190             911.4     67.10               157.0             1.2   \n",
       "200             911.4     68.00                53.0             0.3   \n",
       "210             911.3     67.64               167.0             1.5   \n",
       "220             911.4     67.82                 4.0             0.6   \n",
       "230             911.4     66.74               172.0             1.3   \n",
       "240             911.4     66.56                39.0             0.2   \n",
       "250             911.4     65.66                56.0             1.9   \n",
       "260             911.5     65.66                74.0             0.8   \n",
       "270             911.4     66.92               147.0             0.9   \n",
       "280             911.3     64.76                73.0             1.0   \n",
       "290             911.3     64.94               164.0             1.3   \n",
       "...               ...       ...                 ...             ...   \n",
       "1586960         914.7     76.46               247.0             0.6   \n",
       "1586970         914.8     76.28               208.0             0.7   \n",
       "1586980         914.8     76.10               209.0             0.7   \n",
       "1586990         914.9     76.28               339.0             0.5   \n",
       "1587000         914.9     75.92               344.0             0.4   \n",
       "1587010         915.0     75.56               323.0             0.3   \n",
       "1587020         915.1     75.56               324.0             1.1   \n",
       "1587030         915.1     75.74                 1.0             1.3   \n",
       "1587040         915.2     75.38               355.0             0.9   \n",
       "1587050         915.3     75.38               359.0             1.4   \n",
       "1587060         915.4     75.38                11.0             1.1   \n",
       "1587070         915.5     75.38                13.0             1.4   \n",
       "1587080         915.6     75.20                18.0             1.0   \n",
       "1587090         915.6     75.20               356.0             1.7   \n",
       "1587100         915.7     75.38                13.0             1.5   \n",
       "1587110         915.7     75.02                19.0             1.2   \n",
       "1587120         915.7     74.84                25.0             1.4   \n",
       "1587130         915.8     74.84                23.0             1.3   \n",
       "1587140         915.8     74.84                32.0             1.4   \n",
       "1587150         915.8     75.20                23.0             1.1   \n",
       "1587160         915.8     75.38                16.0             1.2   \n",
       "1587170         915.7     75.38               347.0             1.2   \n",
       "1587180         915.8     75.74               326.0             1.2   \n",
       "1587190         915.9     75.92               289.0             0.7   \n",
       "1587200         915.9     75.74               335.0             0.9   \n",
       "1587210         915.9     75.56               330.0             1.0   \n",
       "1587220         915.9     75.56               330.0             1.1   \n",
       "1587230         915.9     75.56               344.0             1.4   \n",
       "1587240         915.9     75.20               359.0             1.3   \n",
       "1587250         915.9     74.84                 6.0             1.5   \n",
       "\n",
       "         max_wind_direction  max_wind_speed  relative_humidity  \n",
       "0                     106.0             1.6               60.5  \n",
       "10                    167.0             1.8               38.5  \n",
       "20                    122.0             2.5               58.3  \n",
       "30                    103.0             2.4               57.9  \n",
       "40                     88.0             2.9               57.4  \n",
       "50                    119.0             1.5               51.4  \n",
       "60                    101.0             0.9               51.4  \n",
       "70                     97.0             2.4               62.2  \n",
       "80                     70.0             3.5               71.5  \n",
       "90                     75.0             4.2               72.5  \n",
       "100                   106.0             2.5               63.9  \n",
       "110                    88.0             2.5               59.1  \n",
       "120                    76.0             2.4               63.5  \n",
       "130                   203.0             0.7               50.4  \n",
       "140                    92.0             2.5               58.0  \n",
       "150                   176.0             2.1               50.2  \n",
       "160                   208.0             1.9               46.2  \n",
       "170                   109.0             1.6               45.2  \n",
       "180                   167.0             1.6               42.8  \n",
       "190                   172.0             1.6               36.8  \n",
       "200                    69.0             0.5               33.4  \n",
       "210                   196.0             2.2               34.4  \n",
       "220                    25.0             0.7               34.2  \n",
       "230                   192.0             1.9               37.8  \n",
       "240                   145.0             0.3               41.6  \n",
       "250                    67.0             2.2               51.8  \n",
       "260                   101.0             1.2               41.1  \n",
       "270                   174.0             1.1               36.0  \n",
       "280                    82.0             1.2               43.3  \n",
       "290                   176.0             1.7               43.0  \n",
       "...                     ...             ...                ...  \n",
       "1586960               264.0             0.7               43.4  \n",
       "1586970               216.0             0.9               43.7  \n",
       "1586980               216.0             0.9               43.9  \n",
       "1586990               350.0             0.7               43.4  \n",
       "1587000               352.0             0.6               43.9  \n",
       "1587010               348.0             0.5               45.5  \n",
       "1587020               347.0             1.5               46.0  \n",
       "1587030                13.0             1.7               45.8  \n",
       "1587040                 1.0             1.1               46.1  \n",
       "1587050                11.0             1.5               45.8  \n",
       "1587060                21.0             1.3               45.7  \n",
       "1587070                24.0             1.6               46.6  \n",
       "1587080                24.0             1.2               46.5  \n",
       "1587090                 1.0             1.9               47.2  \n",
       "1587100                24.0             1.7               46.7  \n",
       "1587110                28.0             1.4               46.7  \n",
       "1587120                35.0             1.6               46.5  \n",
       "1587130                30.0             1.5               46.9  \n",
       "1587140                41.0             1.7               45.5  \n",
       "1587150                31.0             1.4               45.7  \n",
       "1587160                28.0             1.5               46.3  \n",
       "1587170               353.0             1.4               48.1  \n",
       "1587180               337.0             1.6               48.3  \n",
       "1587190               309.0             0.9               48.1  \n",
       "1587200               348.0             1.1               47.8  \n",
       "1587210               341.0             1.3               47.8  \n",
       "1587220               341.0             1.4               48.0  \n",
       "1587230               352.0             1.7               48.0  \n",
       "1587240                 9.0             1.6               46.3  \n",
       "1587250                20.0             1.9               46.1  \n",
       "\n",
       "[158680 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "metter à l'echelle les Features (caractéristiuques) en utilisant StandardScaler\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.48456281,  0.24544455, -0.68385323, ..., -0.62153592,\n",
       "        -0.74440309,  0.49233835],\n",
       "       [-1.48456281,  0.03247142, -0.19055941, ...,  0.03826701,\n",
       "        -0.66171726, -0.34710804],\n",
       "       [-1.51733167,  0.12374562, -0.65236639, ..., -0.44847286,\n",
       "        -0.37231683,  0.40839371],\n",
       "       ...,\n",
       "       [-0.30488381,  1.15818654,  1.90856325, ...,  2.0393087 ,\n",
       "        -0.70306017,  0.01538018],\n",
       "       [-0.30488381,  1.12776181,  2.06599745, ..., -1.67073075,\n",
       "        -0.74440309, -0.04948614],\n",
       "       [-0.30488381,  1.09733708, -1.63895404, ..., -1.55174989,\n",
       "        -0.62037434, -0.05711747]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = StandardScaler().fit_transform(select_df)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Utiliser k-Means Clustering\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model\n",
      " KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
      "    n_clusters=12, n_init=10, n_jobs=1, precompute_distances='auto',\n",
      "    random_state=89, tol=0.0001, verbose=0)\n"
     ]
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=12)\n",
    "model = kmeans.fit(X)\n",
    "print(\"model\\n\", model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ajoutez le paramètre random_state, qu'est ce que vous osbservez ?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "les centroides des 12 clusters formés  ?\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.83975946, -1.19886028,  0.37519108,  0.36047832,  0.47351689,\n",
       "         0.34852918,  1.36175966],\n",
       "       [ 0.13260307,  0.84409639,  1.41071601, -0.63863579,  1.67475786,\n",
       "        -0.58934083, -0.71418953],\n",
       "       [-1.18120169, -0.87429246,  0.44697635,  1.97971426,  0.53880996,\n",
       "         1.94097864,  0.91231634],\n",
       "       [ 1.36746942, -0.08180255, -1.20726888, -0.04806476, -1.07621861,\n",
       "        -0.02771725, -0.97747632],\n",
       "       [ 1.19057401, -0.25522069, -1.15497259,  2.1248264 , -1.05348098,\n",
       "         2.24173171, -1.13466563],\n",
       "       [ 0.24737672, -0.99434819,  0.65898733, -0.54739738,  0.85010733,\n",
       "        -0.53010795,  1.16020147],\n",
       "       [-0.6906401 ,  0.5519542 ,  0.17927558, -0.58458793,  0.34908892,\n",
       "        -0.59797627, -0.11924938],\n",
       "       [-0.21251243,  0.63113549,  0.40847277,  0.73592499,  0.51654339,\n",
       "         0.67376289, -0.14966829],\n",
       "       [ 0.05827029, -0.78686628, -1.19603792, -0.57109581, -1.04193927,\n",
       "        -0.58563345,  0.87704715],\n",
       "       [ 0.2335848 ,  0.31908666,  1.88785423, -0.65198911, -1.55170741,\n",
       "        -0.57683313, -0.28271609],\n",
       "       [ 0.73667723,  0.42555986,  0.28587683, -0.53159164,  0.47325425,\n",
       "        -0.53788159, -0.7723346 ],\n",
       "       [-0.16272324,  0.86341173, -1.31081098, -0.58988081, -1.16644317,\n",
       "        -0.60516545, -0.6418177 ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers = model.cluster_centers_\n",
    "centers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Ploting\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us first create some utility functions which will help us in plotting graphs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# source: python for data science \n",
    "# Function that creates a DataFrame with a column for Cluster Number\n",
    "def pd_centers(featuresUsed, centers):\n",
    "\tcolNames = list(featuresUsed)\n",
    "\tcolNames.append('prediction')\n",
    "\n",
    "\t# Zip with a column called 'prediction' (index)\n",
    "\tZ = [np.append(A, index) for index, A in enumerate(centers)]\n",
    "\n",
    "\t# Convert to pandas data frame for plotting\n",
    "\tP = pd.DataFrame(Z, columns=colNames)\n",
    "\tP['prediction'] = P['prediction'].astype(int)\n",
    "\treturn P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that creates Parallel Plots\n",
    "## islice('ABCDEFG', 2) --> A B\n",
    "    # islice('ABCDEFG', 2, 4) --> C D\n",
    "    # islice('ABCDEFG', 2, None) --> C D E F G\n",
    "    # islice('ABCDEFG', 0, None, 2) --> A C E G\n",
    "\n",
    "def parallel_plot(data):\n",
    "\tmy_colors = list(islice(cycle(['b', 'r', 'g', 'y', 'k']), None, len(data)))\n",
    "\tplt.figure(figsize=(15,8)).gca().axes.set_ylim([-3,+3])\n",
    "\tparallel_coordinates(data, 'prediction', color = my_colors, marker='o')"
   ]
  },
  {
   "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>air_pressure</th>\n",
       "      <th>air_temp</th>\n",
       "      <th>avg_wind_direction</th>\n",
       "      <th>avg_wind_speed</th>\n",
       "      <th>max_wind_direction</th>\n",
       "      <th>max_wind_speed</th>\n",
       "      <th>relative_humidity</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.839759</td>\n",
       "      <td>-1.198860</td>\n",
       "      <td>0.375191</td>\n",
       "      <td>0.360478</td>\n",
       "      <td>0.473517</td>\n",
       "      <td>0.348529</td>\n",
       "      <td>1.361760</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.132603</td>\n",
       "      <td>0.844096</td>\n",
       "      <td>1.410716</td>\n",
       "      <td>-0.638636</td>\n",
       "      <td>1.674758</td>\n",
       "      <td>-0.589341</td>\n",
       "      <td>-0.714190</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.181202</td>\n",
       "      <td>-0.874292</td>\n",
       "      <td>0.446976</td>\n",
       "      <td>1.979714</td>\n",
       "      <td>0.538810</td>\n",
       "      <td>1.940979</td>\n",
       "      <td>0.912316</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.367469</td>\n",
       "      <td>-0.081803</td>\n",
       "      <td>-1.207269</td>\n",
       "      <td>-0.048065</td>\n",
       "      <td>-1.076219</td>\n",
       "      <td>-0.027717</td>\n",
       "      <td>-0.977476</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.190574</td>\n",
       "      <td>-0.255221</td>\n",
       "      <td>-1.154973</td>\n",
       "      <td>2.124826</td>\n",
       "      <td>-1.053481</td>\n",
       "      <td>2.241732</td>\n",
       "      <td>-1.134666</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.247377</td>\n",
       "      <td>-0.994348</td>\n",
       "      <td>0.658987</td>\n",
       "      <td>-0.547397</td>\n",
       "      <td>0.850107</td>\n",
       "      <td>-0.530108</td>\n",
       "      <td>1.160201</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.690640</td>\n",
       "      <td>0.551954</td>\n",
       "      <td>0.179276</td>\n",
       "      <td>-0.584588</td>\n",
       "      <td>0.349089</td>\n",
       "      <td>-0.597976</td>\n",
       "      <td>-0.119249</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.212512</td>\n",
       "      <td>0.631135</td>\n",
       "      <td>0.408473</td>\n",
       "      <td>0.735925</td>\n",
       "      <td>0.516543</td>\n",
       "      <td>0.673763</td>\n",
       "      <td>-0.149668</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.058270</td>\n",
       "      <td>-0.786866</td>\n",
       "      <td>-1.196038</td>\n",
       "      <td>-0.571096</td>\n",
       "      <td>-1.041939</td>\n",
       "      <td>-0.585633</td>\n",
       "      <td>0.877047</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.233585</td>\n",
       "      <td>0.319087</td>\n",
       "      <td>1.887854</td>\n",
       "      <td>-0.651989</td>\n",
       "      <td>-1.551707</td>\n",
       "      <td>-0.576833</td>\n",
       "      <td>-0.282716</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.736677</td>\n",
       "      <td>0.425560</td>\n",
       "      <td>0.285877</td>\n",
       "      <td>-0.531592</td>\n",
       "      <td>0.473254</td>\n",
       "      <td>-0.537882</td>\n",
       "      <td>-0.772335</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.162723</td>\n",
       "      <td>0.863412</td>\n",
       "      <td>-1.310811</td>\n",
       "      <td>-0.589881</td>\n",
       "      <td>-1.166443</td>\n",
       "      <td>-0.605165</td>\n",
       "      <td>-0.641818</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    air_pressure  air_temp  avg_wind_direction  avg_wind_speed  \\\n",
       "0      -0.839759 -1.198860            0.375191        0.360478   \n",
       "1       0.132603  0.844096            1.410716       -0.638636   \n",
       "2      -1.181202 -0.874292            0.446976        1.979714   \n",
       "3       1.367469 -0.081803           -1.207269       -0.048065   \n",
       "4       1.190574 -0.255221           -1.154973        2.124826   \n",
       "5       0.247377 -0.994348            0.658987       -0.547397   \n",
       "6      -0.690640  0.551954            0.179276       -0.584588   \n",
       "7      -0.212512  0.631135            0.408473        0.735925   \n",
       "8       0.058270 -0.786866           -1.196038       -0.571096   \n",
       "9       0.233585  0.319087            1.887854       -0.651989   \n",
       "10      0.736677  0.425560            0.285877       -0.531592   \n",
       "11     -0.162723  0.863412           -1.310811       -0.589881   \n",
       "\n",
       "    max_wind_direction  max_wind_speed  relative_humidity  prediction  \n",
       "0             0.473517        0.348529           1.361760           0  \n",
       "1             1.674758       -0.589341          -0.714190           1  \n",
       "2             0.538810        1.940979           0.912316           2  \n",
       "3            -1.076219       -0.027717          -0.977476           3  \n",
       "4            -1.053481        2.241732          -1.134666           4  \n",
       "5             0.850107       -0.530108           1.160201           5  \n",
       "6             0.349089       -0.597976          -0.119249           6  \n",
       "7             0.516543        0.673763          -0.149668           7  \n",
       "8            -1.041939       -0.585633           0.877047           8  \n",
       "9            -1.551707       -0.576833          -0.282716           9  \n",
       "10            0.473254       -0.537882          -0.772335          10  \n",
       "11           -1.166443       -0.605165          -0.641818          11  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P = pd_centers(features, centers)\n",
    "P"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# jours secs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PC\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: 'pandas.tools.plotting.parallel_coordinates' is deprecated, import 'pandas.plotting.parallel_coordinates' instead.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parallel_plot(P[P['relative_humidity'] < -0.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# jours chauds "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PC\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: 'pandas.tools.plotting.parallel_coordinates' is deprecated, import 'pandas.plotting.parallel_coordinates' instead.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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JiIiIHklMS8S49ePQ0qElNk3ZBAtTC52+/tzucxGTEoMvjn2Bto5tsbDPQp2+PhGRGjF0EhEREQHIzM3E2PVjocnTYH/AfjSyaSTLdT4b9hli78fi1d2vonWD1hjTdows1yEiUgtOryWiOiHkXAjcgtwAAG5Bbgg5F6JsQUSkKlpRi4CtAYhKjsL6SevRoXEH2a5lIphgzYQ16N68O6Zvmo6zt8/Kdi0iIjVg6CQioxdyLgSB2wMRnxYPAIhPi0fg9kAGTyIq8kH4B9h0aRO+HP4lfDx8ZL+erYUttk3fhgbWDTBm3RjcTL8p+zWJiJTC0ElERu/tfW9Dk6cp8ZgmT4NF+xYpVBERqcn68+vx8aGPMafrHLzS5xW9XbeFfQtsn74dD7IeYOy6scjMzdTbtYmI9Imhk4iM0oOsB1h3bh38NvshIS2h3OdU9DgR1R0nb5zE7D9mo79Lf/zs+7POdqqtqq7NumLdxHX4+9bfmLllJrSiVq/XJyLSB24kRERGQRRFXL53GaExoQiNDcXRhKMoEAvQ2KYxbM1tkZlXdgTBpZ6LApUSkVrceHgD49ePRzO7ZrLsVFtVY9qOwTfPfINXdr+Ct/58C4uHL1akDiIiuTB0EpHBysnPwaH4Q0VB89qDawAAr6ZeeNP7Tfh6+qJni55Yf2E9ArcHlpliO6L1CCXKJiIV0ORpMG79OKTnpmPPzD1obNtY0Xpe7v0you9F44tjX8CzkSfmdp+raD1ERLrE0ElEBiU5Ixk7YncgNDYUe67uQUZuBqzMrDC01VC80e8NjPYYDed6ziW+x6+zHwBg0b5FiEc8XBxc4GDlgBVnVmBE6xGY1GGSEj8KESlEK2oxa+ss/H3rb2ybvg2dmnRSuiQIgoAlo5bgWuo1LAhbAPcG7hjcarDSZRER6YQgiqLSNZRLEARRrbWR+giCALYX4ySKIs7ePls0mnnyxkkAQEv7lvD19IWvpy+GtBoCG3ObKr1eYVvJzM3EiF9H4NSNU9g+fTueafOMnD8GGSj2Lcbpw/AP8cHBD/DFsC/wxtNv6OQ1ddVW0rLT0O+XfriZfhPH5x5HW8e2OqiO1IZ9C1WVIAinRVF8Suk6aouhk4wCO2/josnTYN+1fQiNCUVYbBhupN+AAAG9WvYqCppeTb1qtOHH420lNTsVg1cPRvS9aOyZuQfeLt66/lHIwLFvMT4bL2zElN+nIMArACvHrdTZxkG6bCvXH1xH7+W94WDpgOPzjsPRxlEnr0vqwb6FqoqhU2YMnVQd7LwNX2JaIsJiwxAaE4p91/chOz8bdhZ2eKb1M/D19MWoNqPQ1K5pra9Tuq3cybyD/iv743bGbYQHhKNb8261vgYZD/YtxuX0zdPov7I/ujfvjn3++2BpZqmz19Z1W4lIjMDg1YPRq2Uv7J25V6e1kvLYt1BVMXTKjKGTqoOdt+Ep0Bbg1M1T0rTZmFBEJkcCANwbuGOM5xj4evqiv0t/nX/QKq+tJKYlwnulNzR5GhyefRjtHNvp9JpkuNi3GI+b6TfRc1lPmJuY4+T8k2hi20Snry9HW1l3bh1mbJ4Bfy9/rBq3Su/HuZB82LdQVTF0yoyhk6qDnbdheJjzEHuu7kFoTCh2xO7AXc1dmAqm8HbxLpo227ZRW1k/WFXUVmJSYtB/ZX9YmFrgyOwjcK3vKlsNZDjYtxiHrLwsDFw1EBfvXsSxucfQpWkXnV9Drrby0cGP8H74+/jP4P9g0YBFOn99Ugb7Fqoqhk6ZMXRSdbDzVq8r968UjWYeij+EPG0eGlg1gI+HD3w9ffFM62fQwLqB3uqprK1EJUdh4KqBcLRxxJHZR3QynZcMG/sWwyeKImZsnoEN5zdg67StGNt2rCzXkautiKKImVtmIuRcCDZM2oApHafo/Bqkf+xbqKoYOmXG0EnVwc5bPfIK8nA08WhR0IxOiQYAdGjcAb4e0mhmX+e+MDNR5sSmJ7WViMQIDFszDG0atkF4QLheAzGpD/sWw/fxwY/xXvh7+Hzo5/i3979lu46cbSU7PxvDgofh9K3TCA8IR2+n3rJch/SHfQtVFUOnzBg6qTrYeSsrRZOCnVd2IjQmFLuu7EJaThosTC0wyG0QfD18MdpzNNwbuCtdJoCqtZW9V/fCd50vujfvjr0z98LOwk5P1ZHasG8xbJsubsKkjZMws8tMrB6/WpGp+7pyN/Mu+qzog4zcDJyYdwJu9d1kuxbJj30LVRVDp8wYOqk62HnrlyiKuHD3QtFoZkRSBLSiFk1tm2K0x2j4evpimPsw2FvaK11qGVVtK1subcHkjZMxyG0QQmeEwsrMSg/VkdqwbzFcZ26dgfdKb3Rp2gUHAg7I/m9YH23l0t1L6LuiL5zrOePonKNwsHSQ9XokH/YtVFUMnTJj6KTqYOctv+z8bByMO4jtMdsRGhOK+LR4AED35t2Lps32aNEDJoKJwpVWrjptJTgyGAFbAzC+3XhsnLxRsSnBpBz2LYbpVvot9FreCwIEnJp/Si/rs/XVVv689idG/joSw1sPx/bp29kvGSj2LVRVDJ0yY+ik6mDnLY9b6bewI3YHQmNDsffqXmTmZcLazBrDWw+Hr4cvfDx80NKhpdJlVkt128r3J77HS7tewswuM7Fq/CrVh2rSLfYthic7PxuDVg3CuTvncHTOUXRt1lUv19VnW1l2ehkCQwPxfM/n8YPPD3q5JukW+xaqKmMJnbw9RkRFtKIWZ26dkabNxobir5t/AQCcHZwR4BUAX09fDHIbBGtza4Ur1Z8Xe7+ItJw0vHvgXdSzrIclo5bwrDwilRJFEXO3zcWJGyewecpmvQVOfZvfYz6iU6LxdcTXaNuoLV7s/aLSJRERVYqhk6iOy8zNxJ/X/kRoTCjCYsNwK+MWBAjo69wXnw75FL6evujUpFOdDlqL+i9CanYqvo74GvWt6uPjIR8rXRIRleOzI59h7bm1+GTIJ5jQfoLS5chq8bDFuHL/ChbuXgj3Bu4Y7Tla6ZKIiCrE6bVkFDhNpXriUuMQFhOG0NhQHLh+ADkFOXCwdMDINiPh6+GLkW1GorFtY6XLlEVN24ooigjcHojlZ5bjy+Ff4vV+r8tQHakN+xbDseXSFjz727OY0XkGfp3wq95vlCnRVjJzM9F/ZX/E3o/F0TlH0aVpF71en2qOfQtVlbFMr2XoJKPAzrtyBdoCHE86XjRt9vyd8wAAj4YeGOM5Br6evvB28Ya5qbnClcqvNm2lQFuAGZtn4LcLv2Gp71LM7zFfx9WR2rBvMQyRtyPx9C9Po2OTjggPCFdkCYBSbeXGwxvotbwXTAVTnJx/Es3smum9Bqo+9i1UVQydMmPopOpg511WanYqdl/ZjdDYUOyM3YmUrBSYmZihv0t/+HpKu816NvJUuky9q21byS3Ixfj147Hryi6sm7gOUztN1WF1pDbsW9QvOSMZPZf1hAgRJ+edRHP75orUoWRb+fvW3+i/sj86Nu6I8FnhsDG3UaQOqjr2LVRVDJ0yY+ik6mDnLU3/jEmJKRrNPBx/GAViARpZN4KPhw98PX0xovUI1Leqr3SpitJFW9HkaTDy15GISIrAH9P+gI+Hj46qI7Vh36Ju2fnZGLJ6CM7ePosjc46ge/PuitWidFv54/IfmLBhAiZ2mIgNkzZwp22VU7q9kOFg6JQZQydVR13tvHMLcnE4/nBR0Lxy/woAoHOTzkWjmb1b9oapianClaqHrtrKw5yHGLJ6CC7cvYBdfrsw0G2gDqojtamrfYshEEURAVsDsCZqDX6f/DsmdpioaD1qaCtfH/sar+99HW95v4VPh36qaC1UOTW0FzIMDJ0yY+ik6qhLnfedzDvYGbsTobGh2H1lN9Jz02FpaokhrYbA19MXoz1Gw7W+q9JlqpYu28o9zT0MWDkASQ+TsD9gP55qYfDvCVRKXepbDM3iI4vx5r438dGgj/DuwHeVLkcVbUUURSwIXYClfy/FynErMavrLEXroYqpob2QYWDolBlDJ1WHMXfeoigiKjmqaDTzRNIJiBDR3K550Wjm0FZDYWthq3SpBkHXbeXGwxvwXumN9Jx0HJp9CB0ad9DZa5PyjLlvMWTbordh/PrxmNppKtY+u1YVRzqppa3kFeTBZ60PDsYdxN6ZezkLQ6XU0l5I/Rg6ZcbQSdVhbJ13Vl4WDsQdkIJmTCgSHyYCAHq26FkUNLs166aKD1qGRo62cvX+VXiv9IaJYIIjs4+gVYNWOn19Uo6x9S3GICo5Cv1W9EP7xu1xaNYhRXaqLY+a2kpqdir6ruiL5IxkHJ93vE5uGqd2amovpG4MnTJj6KTqMIbO+8bDGwiLDUNoTCj+vPYnsvKzYGtuixGtR8DX0xc+Hj7cCl8H5Gor5++cx4CVA9DAugEOzz6MFvYtdH4N0j9j6FuMyZ3MO+i1rBfytHk4Nf+Uqv6dqa2tXL1/FX1W9EEDqwY4Pu84Glo3VLokeoza2gupF0NndS8kCL8A8AVwRxTFTlV4PkMnVZkhdt5aUYu/bv5VNJp55vYZAIBbfbeiszMHug6EpZmlwpUaFznbyskbJzE0eChc67ni4KyDaGTTSJbrkP4YYt9irHLyczA0eCj+vvU3Ds0+pLo11GpsK0cTjmJI8BD0deqLPTP3wMLUQumS6BE1thdSJ4bO6l5IEAYAyAAQzNBJumYonXd6Tjr2XtuL0JhQhMWG4U7mHZgIJujn3K8oaLZ3bM9pszKSu60cuH4Ao0JGoUvTLtjnvw/2lvayXYvkZyh9i7ETRRFzts3BqrOrsGHSBkzpOEXpkspQa1sJiQrBc1uew6yus/DL2F/4/qISam0vpD7GEjrN9HUhURQPCYLgpq/rEanFtQfXikYzw+PCkafNQ32r+hjZZiR8PXwxss1IjogZkcGtBmPj5I2YsGECxq4fix0zdqhmzRmRofo64musOrsK7w98X5WBU838uvghJiUGHx36CG0btcWb3m8qXRIR1UF6XdP5KHSGcqSTdE1Ndwzztfk4lnisKGheuncJANDOsR18PaRNgPo594O5qbnCldZN+mora8+txXObn4Ovpy82TdnE/98GSk19S10VGhOKsevGYlKHSVg/aT1MBBOlSyqXmtuKKIrw2+yHdefXYePkjZjUYZLSJdV5am4vpC4c6ZSBIAiBAAIL/xweHq5cMWRwlGwvD/Me4uT9k4i4H4GT908iIz8DZoIZvOp54fnWz6Nvo75oad0SACDGiTgad1SxWkk/baUFWuBlj5cRFBOEkUtH4u12b8NUMJX9uqR7fC9SzvXM63j+zPNoY9cGcxrOwaGDh5QuqVJqbiuzGsxClEMU/Db54e6Vu2jv0F7pkuo8NbcXIl3jSCcZBX3fMRRFEZfuXSoazTyaeBRaUYvGNo0x2nM0fD18Mbz1cDhYOuitJqoafbeVz498jrf2vYUFPRbgp9E/cT2VgeFohHLuZt5Fr+W9kJOfg5PzT8LJwUnpkiplCG3lTuYd9F7eG1l5WTg5/yRc6rkoXVKdZQjthdSBI51EdUxOfg4Oxh8sCprXU68DALo264q3vd+Gr6cverbsqdqpX6SMN73fRFp2Gj4/+jnqW9XHZ8M+U7okItXLLcjFpI2TcDvjNg7OOqj6wGkomtg2QdiMMPRd0Re+a31xZM4R3hwlIr3QW+gUBGEdgEEAHAVBSALwviiKK/R1faKauJ1xGztidyA0JhR7ru5BZl4mrMysMMx9GP799L/h4+ED53rOSpdJKvfp0E+Rmp2Kz49+jnpW9biRB1ElRFHEv8L+hUPxh7D22bXo1bKX0iUZlQ6NO2Dj5I3wCfHB9E3T8ce0P2BmwjEIIpKXPnevna6vaxHVlCiKOHP7TNFo5qmbpwAATg5OmNllJnw9fTG41WDYmNsoXCkZEkEQ8OPoH/Ew9yHe2vcW6lnWwz97/lPpsohUKeh4EFacWYF3+r+D6Z350UEOI1qPwA8+P+CfYf/Ea7tfw3ejvlO6JCIycry1RXWeJk+Dfdf2SUEzNhQ3029CgIDeTr3xn8H/ga+nL7o07cK1eFQrJoIJVo1bhfScdDy/43k4WDrAr4uf0mURqcrO2J14fe/rmNh+Ij4c/KHS5Ri1BU8tQExKDL5FR7HsAAAgAElEQVQ9/i08G3ni+V7PK10SERkxvW4kVB3cSIiqo7oL8hPSEhAWE4bQ2FDsv74f2fnZsLewxzNtnoGvhy9GeYxCE9smMlZMSlF684bs/Gz4hPjgUPwhbJm6BWPajlGsFnoypdtLXXLx7kX0XdEX7g3ccWT2Edha2CpdUrUYYlsp0BZgwoYJCIsNQ9iMMIxsM1LpkuoMQ2wvpAxj2UiIoZMMWkgIsGgREB8vwNVVxCefAH7lDB4VaAtw8sbJotHMqOQoAIB7A3eM8RwDX09fDHAdAAtTCz3/BKRvanijT89Jx9DgoYhKjsIOvx0Y0mqIovVQxdTQXuqCe5p76L28NzJzM3Fq/imDXCtvqG0lIzcD/Vf2x9X7V3Fs7jF0avLEAwZIBwy1vZD+MXTKjKGTniQkBAgMBDQaABAAiLCxAZYulYJnWnYa9lzdg9DYUOyI3YF7mnswFUzh7eINX09f+Hr6om2jtpw2W8eo5Y0+RZOCQasH4fqD69jnvw+9nXorXRKVQy3txZjlFuRixJoROJ50HOGzwtHHqY/SJdWIIbeVpIdJ6LWsFyxMLXBi3gk0tWuqdElGz5DbC+kXQ6fMGDrpSVxdgYR6IcDQRUBQPLDQFTj+Iho0MEG3aaE4FH8I+dp8NLRuiFFtRsHX0xfPtH4GDawbKF06KUhNb/S30m/Be6U3HmQ9wKHZhzjCoEJqai/GSBRFLAhdgKV/L8WvE3416HXOht5WTt88jQGrBqBzk844EHAA1ubWSpdk1Ay9vZD+MHTKjKGTKqLVAr//Dkz9TwgwJhCw0AAfQPp6pGPjjkWjmX2c+nA7eCqitjf66w+uw3ulN7SiFodnH0abhm2ULokeo7b2YmyWnFiCl3e9jLe838KnQz9VupxaMYa2suXSFkz8bSImd5yMdRPX8dxpGRlDeyH9YOiUGUMnlWfvXuCtt4DTCReBuX0Bq4fSX3yAotBpmtkC+V/cUKhCUjs1vtFfvHsRA1YOgJ2FHY7MOQInByelS6JH1NhejMXuK7vhs9YHY9uOxaYpmww+4BhLW/ni6Bf495//xjv938HHQz5WuhyjZSztheRnLKHTsHt4qjNOnQIGjLqLEe98j3N9egLPdywOnKUU2N7Sc3VEtdOhcQfsfm437mfdx/A1w3E3867SJRHJ6vK9y5j6+1R0btIZayasMfjAaUze6PcG5nabi/8c/g+CI4OVLoeIjAR7eVK1cxdz0G/uZvT6ZhwO92wB+LyE9h3z8c2Ib+BkX/5okGWWC7Kz9VwoUS31aNEDoTNCEZcah5EhI5GWnaZ0SUSyuJ91H2PWjYGlmSX+mPYH7CzslC6JHiMIAn4a/RMGuw3GvG3zcCj+kNIlEZERYOgk1RFFEdvOHEeHN/6FLsHNEeEyEXZtT+KlXgsRuSASZ/95Bq/0fQWfD/8cNuY2Jb7XQrBBzo5PEBAgrf0kMiQDXAdg05RNiEqOwph1Y6DJ0yhdEpFO5RXkYfLGyUhIS8CWqVvgWt9V6ZKoHBamFtg0ZRPcG7hjwoYJuHL/itIlEZGBY+gk1YhPjcc7ez5Bow/aYdy2vrhkuQptTUZh/ehdePBeIr7z/RJdmnYper5fZz8sHbMUrvWkDy2u9Vzxy4Sl+NLfD7/9Brz+ulI/CVHN+Xj44NcJv+JIwhFM+m0ScgtylS6JSGde3vUy9l/fj2VjlqGfcz+ly6FKNLBugNAZoQCA0WtH40HWA4UrIiJDxo2ESFEPcx5i08VNWHUmGIcSw6UH4wair40//vvSJHRp61Cl13l8Qb4oAgsXAkuWAN98A7zyikzFk0EylM0blp1ehsDQQEzpOAVrn10LUxNTpUuqW0JCgEWLIMTHQ3R1BT75RDoAmGrsx5M/4oWdL+D/+v0fFg9frHQ5OmcofUt1HY4/jKHBQ+Ht4o1dz+2ChamF0iUZBWNtL6R7xrKREEMn6V2BtgD7ru/D6sjV2HJpC7Lys2Ca6oGCv/0xuNFzCPrADV26PPl1Hle68y4oAKZOBTZvBjZsACZP1vEPQQbLkN7ovz72NV7f+zrmdZuHpWOWQhAEpUuqG0JCgMBAQKOBAEAEABsbYOlSBs8a2nt1L0aFjIKPhw+2TN1ilDdRDKlvqa7gyGAEbA3A3G5zsWzMMvZFOmDM7YV0i6FTZgydxuf8nfMIjgzGr1G/4lbGLdia1IdZ9DSkHfRHX5c+WPy5gP79a/ba5XXeWVnA8OHSzrd79wIDBujghyCDZ2hv9O/ufxf/OfwfvNb3NXw5/Et+2JObKALOzsAN6dilotAJAC4uQHy8UpUZrJiUGPRe3hvODs44Ouco7C3tlS5JFobWt1RXYV+0eNhi/N/T/6d0OQbP2NsL6Y6xhE4zpQsg43Yn8w7WnluL4MhgnLl9BmYmZuhZ3wfWf/nj2k5fdGxnieAfgTFjAF1/lra2BrZtA55+Ghg3Djh6FOjQQbfXIJLbR4M/QlpOGr6O+Br1rerjnQHvKF2ScRBFKVheuFD2KyOj/O9JSACaNQOcnKRgWvrLyQlo0QIwN9fvz6JiD7IeYMy6MTA3Mce26duMNnDWBR8O/hAx92Pw5p9vok3DNni2/bNKl0REBoQjnaRz2fnZ2B69HcFRwdgZuxMFYgF6NO+BQQ38ceqX6Ti0qzFcXICPPgKeew4w1cEsq8ruGMbFAX37AhYWQESE9JmQ6i5DvLusFbWY/cdsBEcGY8nIJXix94tKl2Q4RBFITpbC5PnzJcNl2mPH0jRpAnTsKH2tXQvcvw+g1EhnvXrAlClAYmLx18NS5wWbmEjB9PEgWjqcNmumm45P5fIK8uCz1gcH4w5if8B+eLt4K12SrAyxb6murLwsDF49GFHJUTg0+xCeamHwgy+KqQvthXTDWEY6GTpJJ0RRRERSBIIjg7HhwgakZqeihX0LzOwyE/3rzcTqLzti40bA0RFYtAj45z8BS0vdXf9JnfeZM9L02tatgUOHAIeq7U9ERshQ3+jztfmYvHEytl7eitXjV8Pfy1/pktTn7t3iQPl4wHwUIAEAjRoVh8vHvxo3Ln5OddZ0PnwIJCWVDKKJiSUfy8ws+T2mptLdr/JGSgt/36SJFGAN2As7XsCPp37EL2N/wexus5UuR3aG2rdUV3JGMnov743cglycnH8STg7ln5lNFeAmZVRNDJ0yY+g0DNcfXMeaqDUIjgzG1QdXYWNug2fbPwv/Lv5oZzkE//nYFCtWAFZWwGuvSV9yBL6qvNnv3g34+gKDBgFhYdLIJ9U9hvzBMCc/B77rfLH/+n78Pvl3TGg/QemSlHH/fskRy8KAefdu8XPq1QM6dSobLps2rdpcfl19MBRFIDW1/DD6+GPZ2SW/z8ICaNmy4tFSJyfpLp5K1/j+fOpn/GvHv/Ba39fw1YivlC5HLwy5b6mu83fOo9+KfnBv4I4jc47AzsJO6ZIMAzcpoxpg6JQZQ6d6pWWnYePFjQiODMbhhMMQIGBwq8Hw7+KPZ9s/i3yNPRYvlo4syc8HFiyQRjebNpWvpqq+2a9aBcyeDcycCaxerdrPayQjQ/9gmJGbgRFrRuD0rdMInR6K4a2HK12SfNLSyq63PH8euH27+Dn29tJi7dIBs0ULnfwD10t7EUUgJaXikdLCP+fllfw+K6uSgbS8cFq/vt47uv3X92PEmhEY2WYk/pj2h1HuVFseQ+9bqmvXlV0YvXY0fDx8sHXq1jrz/7lcBQXSv+G7d4F790r++vjvDx4s+ndcYuq+q6u0FoioHAydMmPoVJd8bT72Xt2L4KhgbL28Fdn52WjbqC38vfzxXJfn4FLPBVlZwPffA59/Lt3YnzFDWrfp7i5jYTUYjfj4Y+C994C335aeTnWLMXwwfJD1AINWD8KV+1fw58w/0de5r9Il1U5GBnDxYtk1l0lJxc+xsZHCZceOJQOms7OsoUo17UWrBe7cqXwq740b0offx9naVr7xkbOzTqefxKbEovfy3mhh3wLH5h6Dg2XdWcugmraiRz+d+gnP73ger/R5Bd88843S5eiORlM2MFYWJh88kG4elcfBQZq+7+gInDhR9HCJ0AlIsx10ue6IjAZDp8wYOtUh8nYkgiODEXIuBMmZyWho3RDTO02Hv5c/erboCUEQkJ8PrFwJfPABcPMm4OMDfPop4OUlc3E1nKYiisA//gEsWwb8/LM0Ekt1h7F8MEzOSIb3Sm/c09xDeEA4vJrJ/Q9OBzQa4NKlsmsuHz+GxMoKaN++5Khlp07SSIACaxwNqr0UFEijwJVN5b11q+yHYweHykdLnZ2lvvUJUrNT0Wd5H9zT3MPJ+Sfh3kDOO47qY1BtRYde3vkylpxcgp9H/4wFT6nwDVWrlUJhRYGxvMeyssp/LVNTKTwWhsjGjUv+vvRjjo4l1/K4uRX1d2VCZ9Om0oeTBQuA5s1l+o9BhoihU2YMncq5nXG76JiTyORImJuYY7TnaPh38cdoz9GwMJU6UFEENm2Sps7GxEg7xH7+uZ7Ow8zJkdY7paQAKNV5W1oCkydLZ+qV/rKXtuvPzwfGjwd27gS2bAHGjtVDzaQKxvTBMD41Ht4rvZFbkIvDsw/Ds5Gn0iVJsrOBy5fLrrm8fr048FhYAO3alV1z6e6uqp1djam9AJCm9t26VfnGR8nJZb+vQYNKNz7Kb9EMozdPxIHrB/Cn/58Y4Fr3DkY2urZSRQXaAoxbPw67ruzCDr8dGNF6hLwXzM6u2uhj4a8pKVLwLI+dXfVCZG2nq1d0s/yFF6Q+cscOqf+bPBl46SWgd2+uAyKGTrkxdOpXVl4W/oj+A8GRwdh9dTe0oha9WvaCfxd/TO00FY42jiWev28f8OabwF9/STPePv1UCm6y94137gD/+x/w008l1nWVuWPo5iZ9iMrPL/n99etL4dPZGXnNXfC/XS74K9kFb3zvgo6jXKR1YGY8vtaYGdsHw+h70ei/sj+szKxwZM4RuNRz0d/Fc3OB6Oiyay6vXi3+kGdmBnh6ll1z2aaNQfxbM7b2UiU5OdJU3co2Prp3r8S3vDwSWNIHWH7WBXO1XcsPqC1bGvUObnWyrTySnpMO75XeiEuNw7E5x9CxSceqfaMoSmu3qzL6WPhrRefomphIu1NXNUQ2aiQd6K1vlS0LunIF+PFH4JdfpJ2xn3pKCp9TpnDqbR3G0Ckzhk75iaKIIwlHEBwZjN8u/oaHOQ/h7OCMmV1mYqbXTLRzbFfme06fBt56C9i7V/oc8dFH0qY8sg9MnDsHBAVJnXVODjBqlFTMnTsAKliQXzjVLCGh4q/Hj1IApDetli2LR0adncuOliqwMQfpjjF+MDxz6wwGrx6MpnZNcXj2YTSxbaLbC+TlSR+GSq+5jIkpXkNoaioFydJrLj08DDpoGGN70YmsrKJAuvTiGvwjZRUWZnTGtxedi8NpamrJ7xEEaQphRcfEODtL0woN4GZEeep6W0lIS0CvZb1gbWKBE/1Xo8lD7ZND5L17ZW8OF7K2rnzUsfRj9eurapbEk1TaXtLTgTVrpI0yLl+WjlAqnHrLw8brHIZOmTF0yufq/asIjgzGmqg1uJ56HbbmtpjUYRL8vfwxyG0QTISy66ZiY4F33gF++026OVh41qaVlYyFarXSVJOgIGlo1doamDVLuuvXrp1uth7PzETc4US8MTUBHpYJWDQzAbYpj4XSxERpNOdxdnZlg+jj4dTJyaA/ZBs7Y/1geDThKIavGY62jm1xIOAA6lvVr/6LFBRIo5Sl11xGRxfvnCoI0oG3j6+37NgRaNvWKO/EG2t70ZXwuHAMXzMcw9yHYfv07TAzeSwwZmQ8+QzT9PSSL2hiIgXPyjY+atZMlWeYGl1bEUXp/09VRh8f/f6UbRoGzga63gb2rwasHs+TDRuWHxgrCpO2tor96PpQpfYiisCff0rHAYSFSaF60iTgxRelNU28AV4nMHTKjKFTt1KzU/Hbhd8QHBmMo4lHIUDAUPehCPAKwIR2E2BrUX7nfvOmNJq5fLkUMF99FXj9dXnO2iySkSGdbbJkiZR2nZyk9Q7z50tvWo/T0Vl6J04AgwdLn58PHHjsvU6rld5MKxstfTTaWkQQpA9F5a0pLQynKj5fz9gZ3QfDx+y+shtj1o1Bz5Y9see5PRX+u4ZWK62vLL3m8vJlaSZBoVatyq65bNeuSpvKGAtjbi+1dfX+VfRa3gtNbZsiYm4E6lnVq/6LpKVVvvFRYmLZTV3MzCo/w9TZWQoueu5jVd9W8vOl9Y1VCZGFv5a+6VrIwqLCUcff7RIw+eFyTGsyBGv7fwehSRPpvdtAR7DlUu32cvVq8dTbtDSgRw/pJvzUqUZ5w4+KMXTKjKGz9vIK8rD76m4ERwZjW/Q25BTkoL1jewR4BcCvix+cHJwq/N7UVGDxYuC776QBjn/8A3j3XXnP2kR8PPDDD9K2smlp0gL6V14Bnn0WMDcv91uSk0Nw7doi9OsXj2PHXOHu/gmaNq3ZAcvbtgETJkgzd7durcb742PTzCoMpqU/NFlZlR9KHx8tVWKtSR2g+g+GtbTp4iZM+X0KhrkPw7YpW2F5M7nsmstLl0q2SWfnsmsu27eXRvXrOGNvLzWVlp2Gviv6IjkzGSfnnUTrhq3luZAoSjuPVjZampRU8mYJIIWi0oG09J8bNtRpMNV7W8nMrPqRHvfulV1O8rh69aq+mU7jxlLfUMl/u8+PfI639r2F9wa8hw8HfyjDD2/4atxeMjKKp95euiT9/wgMlKaftWyp+0JJcQydMmPorBlRFHH29lkERwZj7fm1uJN5B442jpjeaToCvALQvXl3CJW8UWRlSbnvs8+k9/kZM6RzLWU7a1MUgYgI4Ntvgc2bpTexSZOAhQuBPn0q/dbk5BBERwdCq9Vg8GBphNLExAZt2y6tcfD873+lfnv+fGm/Ip18Hik8+P3xKbulQ2l5xxg0blx5MG3SRJVTzNTOKEOEKEobvzwasVwZtxVzHI9gYrQp1m8ogFnhxo0tWpRdc9mhg8xTFwybUbaXWirQFmDMujHYe20v9s7ci0Fug5QtSBSlYFXRSGlSUvkby1lbP/kM03r1nvxGoIsZN1qtFAqreqTHvXsVH+thZla9HVkbNdL5khBRFDF321ysPLsSv074FX5davaebMxq3beIorT0aMkSIDRUmnr77LPS6Ge/fpxNZUQYOmXG0Fk9N9NvIiQqBMFRwTh/5zwsTC0wxnMM/L38MbLNyKJjTiqSny/NaP3gA+mz66hR0o60XbvKVHBeHrBxo7Re89QpaTv+wEDg+eelN/oqiIhwRU5OAgAUhU4AsLBojj594mFiUv7o6JMsWiT97B9/LK1j1YvcXOk/fEJC+eE0Pr7sjn2Fd/IrCqXOzhypKodBhwhRlDbHKr3m8sIFaafDQk2b4rvhDljYJhazrPtiRb/FMOnYSfp3RtVi0O1FJq/ufhXfHv8W//P9HwJ7BCpdTtVotdJRMJVN5b15s+zRGnZ2lY+WRkRI6+tK7y3w44/A0KFVXw95/37Fx3rY21cvRFYlKOtBbkEuRqwZgYikCOzz3wdvF2+lS1IVnfYt165JbW7FCmmmWPfuUrucNk3mzTdIHxg6ZcbQ+WSaPA22Xt6K4Mhg7L22F1pRiz5OfRDgFYApHaegoXXDJ76GKEoDjIsWSXuF9OkjnbU5cKBMRaekSBv9/PCD9Abv6SmNavr7V2vTgOzsJBw/XhxOHw+dACAIFrC17Qg7Oy/Y2XWFra0X7Oy8YG7+5A/doggEBEizV1aulPYuUlzhtvIVTd9NTJRCa+FOooUaNqx8J97mzQ1qtz9dMJgQcedO2TWXFy5IUxAKNWpUctSy8PeNGgEAPgz/EB8c/AAv9XoJQSODKp3lQOUzmPaiJyv+XoF52+fhxV4vYsmoJUqXo1v5+dJNncqm8t6+XXZWyiNlju4qj4mJFAyrEiILn2fAoeF+1n30Wd4HD7If4Pjc4/JNwzZAsvQtGRnAr79KU28vXpTaT+HUW6eKl1SRujF0yoyhs3xaUYvD8YexOnI1fr/4O9Jz0+Faz7XomJPqHA6/f7901uapU9LyrU8/BcaNk+kG6cWL0gLRNWukKUHDh0thc+TIak0RFUUtbt78H65d+zcKCjJQ+Bb/eOg0M3NE8+azkZERiYyMs8jLK97ox9LStSiIFv5qZeUGodSOvbm5wOjRQHi4tGHcCJnPutaJ/Hxpmm5lmx6VPsLA1LTkaGl5wbReDTYHUTHVhYiUlLJrLi9cKHkOYv36ZddcduwoTbGu5B+sKIp4bc9r+Pb4t3h/4Pv4YNAH8v88RkZ17UVBh+IPYVjwMAxuNRhhM8JK7lRbV+TmSjdMC4PojBlFf1UmdC5dWjZENmhQ55ZFxKbEos+KPmhi2wQRcyNqtrO2EZK1bxFF6UPe999LG1aYmBRPvX36aVWMhFPVMXTKjKGzpJiUGKyJXIM1UWsQnxYPewt7TO4wGf5e/ujv2r/cY04q8vff0lmbe/ZIGePDD6WBRp0PeGm10kWCgoDdu6W7tTNnSp1ep07VfjmNJhrR0fORlnYYDRoMQ8OGPrh+/Z0nrunMybmNjIyzyMyUQmhGRiQ0mmgA0lQmU1P7UiOiXWFr2xGZmdYYMEDaMO7gQWm2isFLT698w6OkpOKjMQo5OFR+REzLlhVu9KRGioWI1NSS4bIwYCYnFz/H3r7smsuOHaUR6Rp+SBBFEfO2zcMvZ3/BNyO+wSt9X9HRD1Q3MHRKrj+4jl7Le6GRdSMcn3ecwaGQm5u0/AEVnBdNAICDcQcxfM1wDHAdgJ1+O2FuajjvGXLRW99y/Trw00/SMQSpqdK6qZdeAqZPN+hR9LqEoVNmDJ3StJQN5zcgOCoYx5OOw0QwwXD34fD38sf4duNhY169YwuuXJHWKG7YIM26XLQI+Ne/ZOhzNBppRDMoSDqCoXlzaa1mYKB0t7eatNo8JCZ+ibi4j2BqaoPWrb9Bs2YBEAShxrvXFhRokJl5vmg0NCMjEpmZkY9GTwHABDY27WBq6oVly7ri+nUv/PRTV7RuLef2vSpQuO6pstHSx0fgACkMtWhR+RExOt4lsjZkf6NPT5dG9kuvubxxo/g5trbSBj6lA6aTkyz/nQq0BZi2aRp+v/g7VoxdgTnd5uj8GsaKoRN4mPMQ/Vb0w830mzgx7wQ8GnkoXZJ66OK86Dpi1dlVmP3HbMzvPh//8/1fnZ/ur8huxyEh0sZDFy5IyzAKp95WcS8NUgZDp8zqaujMLcjFriu7EBwZjO0x25FbkItOTTohwCsAMzrPQAv7FtV+zVu3is/atLCQTiF54w0ZZk0mJUkL2ZculTZF6NFDutjkyTXeGe/hw78QHT0XmZlRaNx4Mtq0WQJLy2ZlnqeLzlsUtcjOvv4ohJ4tCqQ5OYlFzzEzawZ7+5LTc21sPCEIdWhdpEZTvL6pomBa+vgCG5snHxGjp3PGdPZGn5kpbVdfes1lQkLxc6ytpbnrpddcurjofYpdTn4Oxq0fh73X9mL9xPWY3HGyXq9vqOp66CzQFmDc+nHYdWUX9szcgyGthihdkvro6LzouuDtfW/jsyOf4avhX+G1fq8pXY6iFOtbRFGaGlY49VYQpPPiXnoJ8PZWzQ1iKsbQKbO6FDpFUcTpW6cRHBmMdefX4Z7mHhrbNIZfZz/4e/mja7OuNbojmJoKfPmlNOCYmyvd0Hr3XaBZ2cxWOydPShfZuFEaKZswQVqvWYt1AwUFGly//h6Skr6FhUUzeHr+BEfHcRU+X87OOy/vPo4di8TPP0eiV6+z8PaORFbWBYiiNA3VxMQKtradH1sr2hW2tl1gZmYvSz2qV3h8QUU78SYkSJtxlNa0aeXBVEeHvVe7rWRlSSP2pddcxsUVbyhiYVEyXBZ+tWqlqo2aNHkaPPPrMziRdALbpm/DyDYjlS5J9ep66Hxjzxv4KuIr/OTzE/7Z859Kl6Nqdb2tVIVW1GLq71Ox6eImbJm6BePaVfy+buxU0V7i4oqn3j54AHh5FU+95VnhqsHQKbO6EDqTHiYVHXNy8e5FWJpaYmzbsQjwCsCI1iNqvOYhK0sacPzsM2nAcfp06fiP1rrcNC4/X9r2NihI2jLewQGYNw944QXpg3YtPHiwD9HRgcjOvobmzQPh7r4Y5uaVrx/SR+e9cSMwZQowcSKwbl0ucnIulZiem5FxFvn5xYdvW1m1LrNpkaWlc52fUgRAGglNSip/F97CI2I0mpLfY2lZ8WZHhY/bVDLl/EmjETk50hbOpddcXrtWfJSBubm043LpNZetW0tn4xmAtOw0DF49GJfvXcbu53ajv2t/pUtSNVV8MFRI4XTI53s+jx98flC6HNWry22lOjR5GgxaNQgX7l7A4dmH0b25MWyYUH2qai8aTfHU2/Pnpam38+dLU29dXJSurs5j6JSZsYbOjNwMbLm0BcFRwdh3bR9EiHja+Wn4e/ljcofJaGBd83P08vOB1aulszaTkqSNYT/7TMdnbT54IN0R+/57KSC0bg28/LJ0roh97Ub28vIe4OrV13H79i+wtm6Dtm2Xo379qp3doq/O+9tvgVdflW4EBgWVHHgTRRE5OTdKbVp0FllZV4qeY2bWoJxNizrAxES3B3MbPFGU2lplR8SUd6aeo2P5mx1dugQsXgxkZRWvu7KwkLYoNjGRAmZsbPGRM6amgIdH2TWXHh4GtWlSRe5k3sGAlQNwK+MWDgQcqLMf+qpCVR8M9ehIwhEMWT2EG79UQ11tKzVxO+M2ei/vjXxtPk7OO4mWDi2VLknvVNleRFHaOXHJEuCPP6THJkyQzvwcMIBTbxXC0CkzYwqdWlGL8LhwrI5cjU0XNyEzLxOt6reCv5c/nuvyHNo0bFOr1xdFYMsWaWOgy5eB3r2lszYHDdJN/QCkEaAlS4BVq6Q7YoMHS1NoR4/WyfTBu3c3ITb2BSHvmUIAACAASURBVOTm3oWz8+twc3sfpqZVn9qhz8771Vel8PnVV8BrVViSkp+fjszMc6U2LYqCVpsFABAEM9jYdCgxPVc6U7SRzD+JgcvLk4JnZcE0La3Mt5U51qAwXD4eMD099bbGVCmJaYnwXukNTZ4Gh2cfRjvHdkqXpEqq/GAos7jUOPRa1gv1rerjxLwTtboZWpfUxbZSG1HJUXj6l6fh0dADh2Yfgp2FndIl6ZXq20t8fPHU2/v3gS5dpPA5Y0bls4pI5xg6ZWYMofPyvcsIjgzGr1G/IvFhIhwsHTClwxT4e/nD28VbJ9Msw8OlszZPnADatZPO2hw/Xkc3o0QR2LdPGtILC5NGhmbMkMKml5cOLgDk5NxEbOwLuHdvC+zsuqFt2+Wwt6/+qIs+O2+tFpg2TZpuu26d9PvqEsUCZGVdKbNpUW7uraLnWFo6FY2GFgZSa+vWZc4UpUqkpUnhs0uXovWXJUKnIJQdLa1DYlNi0X9lf5iZmOHInCNwq++mdEmqo/oPhjqWnpOOp395GokPE3F87nG0dWyrdEkGo661FV3YEbsDY9aNga+nLzZP2QxTE/WsgZebwbQXjQZYu1aa4RYVJe1GP2+edPyBq6vS1dUJDJ0yM9TQmaJJwfrz6xEcFYyTN07CVDDFM22egX8Xf4xtOxbW5rpZmH3mjHTW5u7d0safhWdt6mRZWVaW1MEEBUlz+5s0kTqXBQukzV50QBRF3Lq1Alevvg5RzIGb2wdwcnoNJjU8bFzfnXd2NjBihBT2d+/W3ahybu6dRwG0eHquRnMZgDTt08TEFnZ2XUoEUVvbzjA15V3HSvEsvQpFJUdh4KqBcLRxxOHZh9HMTtc7jRk2g/lgqANaUYsJGyYgLCYMO/12Ynjr4UqXZBBqenQXSb4/8T1e2vUSXuv7Gr4a8ZXS5eiNwfUtoggcOiSFzy1bpMfGjZPWGw0cyKm3MmLolJkhhc7cglyExYQhOCoYYTFhyNPmwaupF/y9/DGj8wydfoi7ckXagXb9eulm09tvS3lQJ5uM3bolTaX473+lsxi9vKRRzenTdTrVUKO5gpiYQKSmHkD9+oPg6bkUNja1O/dNic77/n1pd/GbN4EjR6SZmXIoKMiGRnOhzKZFBQUPHz1DgLW1Z4kgamfnBQuL5ty0qBDP0qvU8aTjGBY8DK0atMLBWQfR0Lqh0iWphsF9MKyFN/98E4uPLsb3o77HC71eULocg5CcHILo6EBotRoMHiydRGFiYoO2bZcyeFbDCztewI+nfsT/fP+HwB6BSpejFwbdtyQkAD//XHxEXufO0tRbPz9OvZUBQ6fM1B46RVHEqZunio45uZ91H83smsGvsx9mdpkJr2a6mX5a6PZtaQfapUulWa4LF0pnbdavfFPXqvn7b2lUc/16aTeisWOlC+j4zpVWm4+kpG8QF/c+BMECrVt/hebN5+pkuqhSnXdCAtCnj7Ss9fhxoKWe9kIQRRHZ2fFFo6GFGxdlZ8cVPcfcvHGZTYtsbNrCxKSObgjCs/Qq9ee1PzF67Wh0a9YNf/r/WefWV1XEoD8YVkNwZDACtgZgQY8F+Gn0T7xhVUUREW7IyZFmURSGTgCwtHRF375xyhVmYPK1+Rizbgz2Xt2LXc/twjD3YUqXJDuj6FsKZ8Z9/z0QGQk0aFA89dbNTenqjAZDp8zUGjoT0hLwa9SvCI4MRnRKNKzMrDC+3XgEeAVgmPswmNVwemhF0tKksza//VY6a3P+fGmks3nzWr5wQYG0M1lQEHD4MGBnB8yZI92palO7jY3Kk55+FtHRc5GR8TccHcfDw+NHWFq20NnrK9l5nz0rberm5ib9p6xXT5EyAAB5eanIzIwqtWnReYhiDgBAECxha9ux1PTcLk88ksaYGMUbvUy2Xt6KSb9NwiC3QQidEQorMyulS1JcXWgvxxKPYfDqwXja+Wnsfm43d6qthvBwExRO2H88dAICBg2qu+vFa+JhzkNpPXFaIiLmRqB94/ZKlyQro+pbRFH6AFQ49VYUpQGMl16S1h/xJlatMHTKTE2hMz0nHZsvbcbqyNUIjwuHCBEDXAfAv4s/JnWYhHpWuk8Z2dnSWZuffirNXJg2TRrprHUeTEsDfvlF2ok2Lk5a0/bSS8DcubKkpYKCLMTHf4SEhC9hbu4ID48f0LjxRJ3fRVe68967F/DxkcLnzp3SaLRaaLV50GiiHzvGRfo1L+9u0XOsrNwe27RICqRWVm5GOdqhdFtRuzWRa+C/1R/j2o7Dxskb63wAMfb2Ep8aj17Le8Hewh4n5p1AIxvuml0VmZmXkJDwGZKT1xQ99njoNDGxRrduh2Fv30OhCg1TYXu0NbfFiXkn0Ni2sdIlycZo+5bExOKptykp0tqjwqm3trZKV2eQGDplpnToLNAWYP/1/QiOCsbmS5uhydOgdYPWRcecuDdwl+W6+flAcDDw/vvSWZvPPCOdtdmtWy1f+OpV6Q7UL78A6elA//7SFNqxY2U71D419RCio+chKysWzZrNRuvWX8HcXJ61YmrovNeskTZz8vOT/h+aqHiTWVEUkZt7u0QIlc4UjUHhXXtT03plNi2ysekIU1PDHv1SQ1tRux9P/ogXdr6A5/6fvfMOb6p++/Cd7t20pSNdYdeFmykbQQRRkSHIFFpcqLgQBXGwHD+cL6KWLVMElCFO9kYRcGDZ6Up30515zvvHKXUBtqXJSdJzX1evq9Qk56l98uR8vs+6fiRL712KRyOemOzO/lJuLqfzos6cM5zjwPgDbp9ZagjKyo6Snj6L/Px1eHj4ExrajZKSHQhCVY3oVKm8AS9EsYrw8H5otS8RGtpBbtNdhoOZB+m+tDu3aG7h+9Hfu23FhTvHFkAqvV29WkpyHD0q9YNdKL1t1kxu61wKRXTaGblE5+/5v9esOckqy0Ltp+b+a+9n9A2j6Rjf0W6ZH1GEL76Qdm2eOAHt2km7Nnv0uMIX3blTKqHduFESl/ffL4nNW+x3+mq1lnDmzPPo9R/j59eM1q0/ITzcvv0ZzhK8Z8+W/oZTpkiHBa6GzVZBRcWv/xhadAxBqKh+hCcBAVddZGhRlKx21wVn8RVnZ/bu2UzdNpXH2j7GB3d+4JZZ79rgrv4iiAKDPhvExrSNbHlgC31b9pXbJKempGQ/Ot0sioq24OkZQlzc48THT8LHp8lFp9dGRAwgK+v/yMh4G6u1kLCw3mi1L6FWd5H7V3EJPvvtM+7//H4eaPMAywcud8v4466x5V+IIuzdK4nP9eulNWV33y1lP3v2VEpva4EiOu2MI0VnfkU+q35dxbJjy/hJ/xOeKk/ubHUno68fzYCkAXY/Zdu5UxIpBw5AUpIkXAYOvIL3ockknS69+650utSkibTu5JFHILbh+igvRkHBRk6efBSzWU98/CSaNXsNT0/7l1M4S/AWRekQ76OPpPLoRx+V26IrRxQFqqrO1AjRC2W6JlNmzWN8fDR/EaHS4KKAgFaoVM63c81ZfMXZEUWR579/nrf2vcXULlOZ2XOm3CbJgrv6y9QfpjJ7z2zeveNdnuzwpNzmOCWiKGIw7ECnm4nBsA0vrwgSEp4iNvaxi/bBX8xXrNZysrPnk5HxPyyWPNTq7tXis4dbCqmGZNauWUzbPo1Xu7/K9G7T5TanwXHX2HJZMjP/LL0tKIBrr5XE58iRSuntZVBEp52xt+g0WU1sPrmZZceX8dWpr7AKVm7W3Mzo60czvM1wogLtn7k5elTatfn119LU01dfhTFjrqDaNTdXUjsffgh5edKbedIkqd6zQXaqXBqzOZdTp54gP/8zAgPbkJS0kJCQtna95l9xpuBttcJ998HmzdKh3r33ym2RfbBYCi+yU/R3RNEKSD1NgYFt/tYnGhh4PV5e8k5FdSZfcXZEUeShzQ+ReiSVN29/k+due05ukxyOO/rLiuMrGLlhJCk3p/DxXR8r4ucfiKJIUdFWdLqZlJbux8cnhoSE59BoJlw2fl3OV2y2SvT6VNLT38Bs1hMS0ommTacTFtZH+f9/CURRZOyXY1l2bBkr71vJ8DbD5TapQXHH2FJrjMY/S29//lkqvR03Dh57DJrbp33NlVFEp52xh+gURZEDmQdYdmwZa35bQ7GxGE2QhpHXj2T0DaO5LspOixb/wZkz0gTaVauk6dIvvii9z+qtC48dg/fek1ZCmM3SRJunnoJevexetiCKIrm5yzh9+ilstgq02pdITJyMh4djJ+k4W/CurJSqRo4dg23boGNHuS1yDIJgoqLixL+GFlmtxdWPUOHv3+Jva1yCgm7A1zfeYTdezuYrzo5NsDFi/QjW/LamUe3Qu4C7+cvBzIN0W9KNDvEd+HbUt/h4OtHUM5kRRYGCgg3odLMoL/8ZX99EEhOnEBPzYK162WvjKzabkZycRaSnv47JlEFwcFu02peIiLhLEZ8XwWQ10fvT3hzKOsS2MdvolNBJbpMaDHeLLfVCFGHfPkl8rlsnld7edZc04NIB97CugiI67UxDis7zhvN8euxTlh1fxumi0/h7+XPf1fcx+obR9GrWC08Px5QA5uTAzJnw8cfg7S0lISdPrueuTUGQUmnvvitNLggIgLFjpTdqUlJDm35RqqrOcfLkwxQXf0tIyG0kJaUSGCjPIApnDN75+dCpExQXSzG1dWu5LZIHURQxmTJrsqEXhKjReKbmMV5e4f/qEw0IuNouhxfO6CvOjsVmYeCagXx16itWDlrJsOuGyW2Sw3Anf8koyaBtalsCfaTJoE0CmshtklMgCFby89eg082msvJ3/P1bkZj4AtHRI+u017guviIIZnJylpKePgej8RxBQTeh1U6jSZN7G2R3tTtRWFlIh4UdKDGWcDD5IM3C3GMIjTvFlgYhM1Oq1vvkE+kG6pprYOJEGDVKWuvXiFFEp525UtFZairl898/Z9mxZezU7QSgR9MejL5hNIOuHkSwb3BDmfqflJTA//4n7do0GqVdm9On13PXZlkZLFkinQqdPg3x8VI9fEqKlDZ1AKJoIzPzfc6dm4ZK5UHz5m8QG/uwrB+Uzhq8z5yRspxBQbB/P0RHy22R82C1ll1kp+hxBMEISBMgAwKu+ZcYvdIJyM7qK85OlaWKviv6si9jH1/c/wX9W/eX2ySH4C7+UmGuoPPizpwpOsP+8fu5NupauU2SHUEwkZOzjPT01zEazxIYeB2JiVOJihpSr370+viKIFjIzV1BevpsqqpOERh4HVrtNCIjBztlT7xcpBWk0WFhB2KDY9k3bp9dVtU5GneJLQ2O0Qhr1kj3uUeOSOv8LpTetmght3WyoIhOO1Mf0WkTbHx/9nuWHlvKhj82YLQaaRXeijE3jGHk9SPRqrV2svbiGI1Se+Xs2dKqovvvl3ZttmpVjxc7fx7+7/9gwQJJxXboIJXQDhwopU0dRHn5r6SlJVNWdpDw8H60bj0fP79Eh13/Ujhz8D58WNqNfPXVsGNHoz+wuyyCYKWq6tS/hhaZzTk1j/H1TfibEA0MvAF//+a1PvRwZl9xdkpNpfRc2pPf8n/j6xFf061pN7lNsjvu4C+CKDB07VA2/LGBTcM30a9VP7lNkhWbrQq9fgEZGW9iMmUSHHwrWu00IiIGXNHh6ZX4ipRt/QydbiaVlScICLiqWgAPw8PDPmvNXI3t57bTZ3kfejTtwZYHtrj8DmF3iC12RRSl0/oPPoDPPwebDfr3lyr6br+9UZXeKqLTztRFdP6S+wvLji1jxS8r0JfrCfMLY9h1wxhzwxjaxbVzeJ+Ezfbnrs2MDOjTRxKedd5ScmHM9LvvwoYN0htsyBB48klJdDoQQTCh080mPX0OXl6htGz5PlFRw5ymB8XZg/fmzXDPPdLe1QvbaxRqj9mc+481LkeprEwDbAB4egYRGHj9P4YWXYenZ0DNa1xsrUF09AiZfiPXpaCygG5LupFeks620dtoG+e4gWFy4OyxpTZM3z6dGbtmMLfPXJ7u+LTc5siG1VpWPUl2LhZLHqGhXdBqpxEW1rtBPssawldEUSA/fx063QwqKn7Bz68FWu3UOpf6uiuLfl7E+I3jefiWh/mw/4dOcw9SH9whtjiM7Gyp9Pajj6TS26uukqr8Ro9uFCf5iui0M/8lOnPLc1n16yqWHlvK0ZyjeHl40b9Vf0bfMJr+rfrj6+XrQGslRFESFC++CL//Dm3bSrs2e/as4wuZzbB2rSQ2f/xRKpudMEEqLUhIsIvtl6OkZB9paclUVp4gOnokLVq8g4+Pc/UCuULw/uQTeOghGD8eUlMb1SGdXbDZqqio+O1fQ4tstrLqR3gQENCaoKAbEQSBwsIvEUVTzQJ3D48AkpI+UYRnPcgqzaLL4i6UmErYNXaXW5dqukJsuRyrf13N8HXDGXfjOBbcvcClb9Lri8VSRFbWB2RmvofVWkxYWB+02qmo1V0b9DoN6SvSUKON6HQzKC8/gp9f0+qhRmPx8HD8/Y0zMeX7Kbyx9w3eueMdJnWYJLc59cbVY4ssmEzw2WfS8MyffoKQkD9Lb1u2lNs6u6GITjtzMdFptBrZmLaRZceW8fXpr7GJNm6NvZUxN4zh/mvvJzIwUiZrYdcuadfm/v3SHJ9Zs6S1GXX6fC8okKYMzZsHer30QpMmSU3UMuwvslrLOHfuRbKy5uHrm0Dr1h8REXGnw+2oDa4SvF96SRom9corUiZcoWERRQGj8fy/sqImk67mMRdEJ4Cvr5aOHc/LY6yLc7b4LJ0XdQZgz7g9NA9zzzH3rhJbLsahrEN0W9KNtrFt+X70941uUq3ZnEdGxttkZ8/DZisnIuIetNqpdlvnZQ9fkda3fMX58zMoKzuIr288CQnPo9Ek12qirjsiiAJD1g5hw4kNfDnsSwYkDZDbpHrhyrFFdkRRWm7/wQdSksZmkzY3XCi99XCvYVyK6LQzKpVK1L6jZVbPWTRVN2XpsaV89ttnlJhKiAuOY9T1oxh1wyiuibxGVjuPHZN2bW7dKu3afOUVaYhsnconf/tNOrX59FOpEbRPH6lfs08f2d44hYVbOXnyIUymTOLiJtKs2Sy8vBw3fKmuuErwFkV48EFYuhQWLpQO6BTsz44dHoDkH38VnQBt2mwhPPwOZWhHPfgt7ze6LulKqG8oe8btITY4Vm6TGhxXiS3/JKs0i7apbfH18uVQ8iFZD2UdjdGYSUbGW+j1qQiCkaio+0lMfJGgoDZ2va49fUUURYqLv0ene42Skj3Vu0MnExv70N/aCBoLlZZKui7uyh8Ff7Bn3B5ujLlRbpPqjKvGFqcjO1tK2Hz0kbSjPinpz9LbYOe9b60Liui0MyqVSuQVUKFCRCTQO5BB1wxi9PWj6d60u8PWnFyKs2elCbQrV0qDtV54QfLxWu/aFAT45htppO1334Gfn/QGeeIJuFa+UjWzuYDTpyeRl7eCgICrSUpaSGio8y+ZdKXgbbFIa6h++AE2bYI7nTN57Fbs39+0Jtv5d9HpAQj4+iYQEzMOjWacUwzGciUOZx2m57KeJIYmsmvsLiICIuQ2qUFxpdhygQs35GmFaewfv99hO6jlpqrqLOnpb5CTsxgQiY4eSWLiFAICHLNGzBG+IooiBsNOdLrXMBi24+0dSULCs8TGPuLUB8P2ILssm/YL2iOKIodSDrncoZcrxhanxmSSsp7vvy9NcAwJkU75H3usnhM8nQdFdNqZC6ITIMI/gvOTzhPkI3+zcG7un7s2vbykmT6TJ9dhW0lFhTRl6L33IC1N2psycaLUs9lEvj5JURTJy1vF6dNPYrWWkJj4IlrtCy7TO+JqwbusDLp1g5MnYefOegyZUqgTubkrSEubgCBU/q2ns1WrD/H0DESvT6W4+DsAwsP7otGkEBFxlzK4o5bsOL+Dvsv70ia6DT+M/oEQ3xC5TWowXC22CKLAsM+H8fnvn7Nx+Ebuan2X3CbZnYqKE6SnzyE3dyUqlRcazTgSEibj79/UoXY42ldKSvZy/vwMiou/wcsrnPj4p4iPfxwvL9dfJ1JbjuUc47ZFt3FVk6vYOXYngT6Ob0WqL64WW1yKgwcl8bl2rXTS36+flBmSsYLwSlBEp535q+hUoUJ4WZDVntJSadfm229LFbDJyVKmM7a2B2sZGVKv5iefQHEx3HqrVEI7eDD4yNtnYzSmc/LkIxQVfUVwcHuSkhYQFORaJ+OuGLz1emmHp9Eo9QI3c499107Lf02vrao6R07OIvT6RZjN2fj4xBATMxaNJhl//8a5G6wubD65mYFrBnJbwm1sHbEVf+/aln04N64WW17d8Sqv7HyFN29/k+due05uc+xKWdlR0tNnkZ+/Dg8Pf2JjHyYh4Rl8feXJeMnlK6Wlh9DpZlBYuBlPz1Di458kPv7JK95p7CpsPrmZe1bfw91Jd7Nu6Do8ZNwZXhdcLba4JHr9n6W3ubnQurWU6BkzRsqEugiK6LQzfxWd2lAt5yedl8UOoxHmz5cGAxUWwtCh0q7N1q1r+QIHDkhTaD//XGrou+8+aThQp06yjy8VRYHs7PmcPTsFURRo1mwW8fGPu2Rvm6sG7xMn4LbbIDIS9u2DCPeqTHRK/stXBMFKUdFW9PpUCgu3AAJqdU9iYyfQpMm9LpP9l4NVv6xixPoR9GvVjw33b3D5PXrgWrFl7W9rGfr5UMbcMIbF9yx220m1JSX70elmUVS0BU/PEOLiHic+fpLsU9Xl9pWysiPodDMpKNiAp2cwcXETiY9/Wvb/L47g3QPv8tQ3TzG502Te6P2G3ObUCrn9pVFxYSvEBx9IWdDgYGkAy8SJdbihlw9FdNqZC6IzwDuATwZ8wog2jl1rYLNJc31efhnS06F3b2nX5q21+ZNbLLB+vdSvefCgdJqSkiKl9rVau9teGyoqTpCWlkxp6T7CwvrQuvXHDi9FakhcOXjv2SMNW7v5ZqnPs9Z9wQr1oi6+YjJlodcvRq9fgMmkw8srgpiYMWg0yQQGXm1nS12Tj3/8mIe3PMyw64axfOBy2fvvrxRXiS0/Zf9El8VduFlzMz+M/kGWtWH2ROpl3IFONxODYRteXhEkJDxFbOxjeHur5TYPcB5fKS//BZ1uJvn5a/Hw8Ccu7lHi45/B1zdGbtPshiiKPPbVY8z/cT6pA1JJvjlZbpP+E2fxl0bHoUOS+FyzRrpf79tXmqdyxx1OW3qriE47UzO9ttcshwpOUZSGu7z4ojRU9tZbpV2bvXrV4slFRdICxv/7P8jMlHYGPfmklMZ3kglagmAmPf0NdLqZeHoG0bLlO0RHj3L5E3FXD97r1sGQIXDvvdJhnKdr36c7NfXxFVEUKC7+Hr0+lYKCLxBFK6GhndFoUoiMHNwop0dejjf3vsnz3z/PQ7c8xPz+8106vrhCbMkuy6Ztalu8Pbw5lHKIqMAouU1qMKSVIVvR6WZSWrq/emrrs2g0D+HlJf+ch7/ibL4i9brOJjd3JR4ePmg0E0hMnIyvb5zcptkFq2Cl/8r+bDu3jW9GfkPPZnVdku5YnM1fGh05OVLL2/z50vetWkmZz7Fjna70VhGdduZiezrtze7d0q7NffukbPusWTBoUC2qYP/4Q2pYXroUKiuhZ0+phLZ/f6c6NSktPURaWjIVFb8QGXk/rVq9h49PtNxmNQjuELzff186o5g4Ufrehe/TnZor9RWzOY+cnKXo9alUVZ3C0zOU6OgRaDQpBAe73th+e/HiDy8yZ88cJneazOu3v+6ywtPZY0uVpYpuS7rxe/7v7Bu/j+ujr5fbpAZBFAUKCjag082ivPxnfH0TSUycQkzMg067n9JZfaWy8hTp6XPIyVmGSuWJRjOexMQpbjmpu8RYQqdFncguy2b/+P1c1eQquU26JM7qL40Os1lqgfvgA6klLijoz9LbJMdMvv4vFNFpZxwpOo8flzKbW7ZIg4FeeUWasnzZXZuiCN9/L5XQbt0Kvr4wYoSkGq53rg99m62Cc+deIjPzPXx8NLRu/SFNmtwtt1kNirsE72efhblz4Y03pKnICg1PQ/mKKIqUlOwiOzuV/PzPEUUTwcFt0WhSiIoa1ujWF/wTURSZ+NVEPvzxQ2b3nM0LXV6Q26R64cyxRRRFHlj/AGt+XcOG+zdwz1X3yG3SFSMIVvLz16DTzaay8nf8/VuRmPgC0dEjnX6atDP7CkjD0tLTX69eKQMxMWNITHwBf//mMlvWsJwrPkf7Be0J8Q3hQPIBmgQ4Z0+rs/tLo+TwYUl8rl4tld7ecYdUetu3r6xJJEV01vVCKlVf4D3AE1ggiuLr//F4u4vOc+ekCbQrVki7NqdMkdouAy5XKVdVBcuXS8OBfv8doqPh0Ufh4YchyvlKmoqKvufkyQkYjeeIjX2Y5s1fd8tx6u4SvAVBOrtYvVryywcekNsi98MevmKxFJGbuxy9PpWKil/x8AgkOnp4dfazrctm+a4UQRQY88UYlh9fzrx+83i07aNym1RnnDm2zNw1k5e2v8ScXnOY0nmK3OZcEYJgIidnGenpr2M0niUw8DoSE6cSFTXEZYbbObOv/BWjMYP09DfQ6xcgilaio0ei1b5IQIDzD1SpLfsz9tNjaQ/axbXju1HfOWWPs6v4S6MkN/fP0lu9XmqXu1B6G+r4e2hFdNblItInxkmgN5AJHAaGi6L4+2WeYzfRmZcn7dr86COpd+7JJ+H55/9j12Z2Nnz4ofSkwkK48UaphHbYMCnL6WRYLEWcOfMMOTlL8PdvTVJSKmp1V7nNshvuFLxNJulwbd8++PprqVpboeGwp6+Iokhp6UH0+lTy8lYjCJUEBl6PRpNCdPQIvL1ru9DXfbDYLAxeO5iNaRv5dOCnjLx+pNwm1QlnjS3rfl/H4LWDGXX9KJbeu9RlDzZstir0+gVkZLyJyZRJcPCtaLXTiIgYgMpFVl9cwFl95VKYTNlkZPyP7OyPEAQTUVH3Y5HGvAAAIABJREFUo9VOJTDwWrlNaxBW/7qa4euGO+17xNX8pVFiNkuDQd9/X9ptFxQkzWmZOBGuclzptiI663IRlaoj8IooindU//sFAFEU51zmOQ0uOktLpdLFuXOlVSjjx0uZzrjL9dT/9JNUQrtmjTTS9p57JLHZtatTNt2Jokh+/uecOvU4FksBiYmT0WqnO20PTEPhbsHbYIDOnaX1rnv2QJs2clvkPjjKV6zWUvLyVpGd/Qnl5Ufw8PAjMnIIGk0KoaGdne4GyJ4YrUb6rejHLt0u1g1d51JloM4YW37W/0znxZ25Pvp6to/Zjp+X68V3q7WM7Oz5ZGTMxWLJIzS0C1rtNMLCervse8MZfaU2mM15ZGTMJStrHoJQSWTkILTaaQQF3SC3aVfMjJ0zmL5jOjN7zGRq16lym/M3XNVfGi0//vhn6a3ZDH36SOWR/frZvfRWEZ11uYhKNRjoK4picvW/RwHtRVGc+I/HTQAmVP/zlu3btzfI9c1mFRs3xrF8eSIlJT5065bHuHHnSEysuri9NhsRe/YQv24d6l9+wervT06/fmQOHIjxsgpVbvKRKpj3Aq2AyUBLWS1yFD169KCh/MVZyMvz5bHHbgZg3rwjREWZZLbIPZDHV04CW4DvgUogEegP9AGcY92Dvam0VvLs8Wc5XX6aOW3mcEvYLXKbVCucLbYUmgp55OdHUKFi/s3zCfcJl9ukOlIKbADWAWXArcBIwPUFjrP5St0pAT5H+vtUALcBowDnGKZSH0RRZM4fc/gu7zumXz2dHlE95DapBtf3l8aJd3ExsZs3E7txI74FBVTFxpI1cCD6vn2xBdlnonaPHj0U0Vnri6hUQ4A7/iE624mi+PhlnnPFmU6bTeqLmz4ddDpp7cmcOdC27SWeUFICCxZIJxk6HTRrJjUQP/igLDXctUUUBfT6BZw58xyiaKZp09eIj38KD4/LTUJyL9z1xPD4cejSBRISpIynunHoE7sip6/YbBXk5X2GXp9Kael+VCpvmjQZiEaTQlhYT5crJ6wrRVVFdFvSjXPF5/hh9A+0j28vt0n/iTPFFqPVSPcl3fkl7xf2jtvLjTGuMy1Zyqa9TXb2PGy2ciIi7kGrnUpIyKU+kF0PZ/KVK8FiKSYr630yM9/FajUQHt4PrfYlQkM7yG1avTBZTdz+6e0czjrMjrE76BDvHL+Hu/hLo8VikUpvP/gA9u6FwEAYPVrKfl7dsHu8lUxnXS7i4PJaUYTNm6WJtL/+CrfcIu3avP32Szzh9GmpXnvxYigvl0pnJ02Cu+92+oWJlZUnSUubQEnJTtTqHrRu/QkBAY0ju/lX3Dl4b9smDU677Tapx9MJW4hdCmfxlYqK38jOTiU391Os1iL8/Jqj0YwnJuZBfH01cptnN/Rleros7kJRVRE7xu5w+hUfzuIvoigycsNIVv6yknVD13Hf1ffJbVKtMBozych4C70+FUEwEhk5FK32RYKCnPvvXh+cxVcaCqu1lKyseWRkzMVqLSQs7Ha02umo1V3kNq3OFFQW0H5Be8rN5RxMPkhTdVO5TXI7f2nUHDkiic+VK6XS2969/yy9bQAdoYjOulxEpfJCqi/rBWQhDRJ6QBTF3y7znHqJzj17pCm0e/dKe14v7Nr8V7m1KMKOHVK/5ubN0n6UYcOkqUK3OH/ZlyBYyMiYy/nzr+Dh4UfLlnOJiRnnsr0wV4q7B+8VK2DkSMlFV6xwqvWvLoez+YrNZqSgYD16fSoGww7Ak4iIu4iNTSE8vK/LTO6sC+cN5+m8qDNWwcqecXtoGe68B2XO4i+zd89m6rapTtmbdjGqqs6Snv5G9XoOkejokSQmTiEgwHVLNf8LZ/GVhsZqLSc7+yMyMt6q7r/tRtOm01Gre7jUPccfBX/QYUEH4kPi2TtuL6F+8lawuau/NGry8iA1VZp6m5UFzZvDY4/BuHFXVKqmiM66Xkil6ge8i7QyZZEoirP+4/F1Ep2//CJlNjdvBo0GXn5Z+ht7/3Otl9EIq1ZJK0+OH4cmTeCRR6QvjWtkF8rKjpCWlkx5+c80aXIfrVr9n1tnRmpDYwjeb7whHag89xy8+abc1rguzuwrlZWn0OsXkJOzBIslD1/feGJixqHRjHe7Re4n8k/QdUlXAr0D2TNuD/Eh8XKbdFGcwV82nNjAfZ/dxwNtHmD5wOVOfaNfUXGC9PQ55OauRKXyRKMZT0LCZPz9m8ptmt1xBl+xJzZbJXp9Kunpb2I2ZxMS0gmt9iXCw+9wap/8Kz+c/YG+K/rSq1kvNj+wGS8Z25Dc3V8aNRYLbNggZT/37JF2MV4ovb3mmjq/nCI67UxtRef581LP5vLlEBIi3ZQ/8cRFdm3m5konD/PnSycR110nldA+8AD4+9vld2hobLYqzp9/hYyMufj4RNKq1TwiI12jxMreNIbgLYpSvJo3T6oGf/ySHdEKl8MVfEUQzBQWbiI7O5Xi4m8BCA+/A40mhYiIAXh4/PM0zTU5oj9Cj6U9iA2OZdfYXUQGRspt0r+Q21+O5RzjtkW3cW3UtewYswN/b+f8vCorO0p6+izy89fh4eFPbOzDJCQ8g69vrNymOQy5fcVR2GxGcnIWkZ7+OiZTBsHBbdFqXyIi4i6XEJ+pP6UyYfMEHr31Uf6v3//JZnNj8ZdGz88//1l6azJJA2aeeAL69//P0tvc3BWcPTuVsWN1pKWJzv/m+g9cVnTm5Umls/PnS3+zJ56Qdm2G/3OQ39GjUlZz1SqpzvquuySx2bOnU648uRTFxTs4eTKFqqrTxMSMp0WLtxrlzr9L0ViCt80GgwfDl1/C55/DfcqZQ51xNV+pqjpPTs4i9PpFmM1ZeHtHExMzFo0m2S36t3frdtNneR+ubnI128dsl73k7Z/I6S+55bm0TW2LiMih5ENogp2voqWkZD863SyKirbg6RlCXNzjxMc/iY+P8x0g2BtXiy1XiiCYyclZSnr6HIzGcwQF3YRWO40mTe51+qFoz337HP/b/z/e6/seT7R/QhYbGpu/NHry86VhpR9+CJmZ0rDSC6W3Yf++n8/NXUFa2gQEoZKHHkIRnfbkUqKzrOzPXZtVVdLfavp0iP9rZZbNJtXZvvuu1LcZECBNoH3iCWjd2mG/Q0NgsRg4e3Yyen0qfn7NSUpKJSysp9xmOR2NKXhXVUkHZT//DN9/Lw0YUqg9ruorgmClqOhr9PpUCgu3ADbU6h5oNCk0aTLQpXfxbj21lXtW30P7+PZ8M/IbArz/WaoiH3L5i9FqpOfSnhzNOcqecXu4WXOzw224FKIoYjDsQKebicGwDS+vCBISniI29jG8vRvviG1XjS1XiiBYyMtbiU43i6qqUwQGXodWO43IyMFO25NuE2wM+mwQm05uYuOwjfRv3d/hNjRWf2n0WK3wxRdSydru3ZJGGTVKKl+79tqah+3fr8VkSgdQRKe9+afoNJng449h5kzpsGDQIOn7q676y5PKymDRIukPefastGfi8cchOfmipwjOTn7+F5w69Shmcy4JCU/TtOmreHo6z82YM9HYgndBAXTqBIWF0tCsv70PFC6LO/iKyZRNTs5i9PoFGI3n8fIKJyZmNBpNCoGBde8XcQbW/raWYeuG0adFH74c9iU+nj5ymwTI4y+iKDLmizF8evxT1g5Zy+BrBjv0+pdCFEWKirai082ktHQ/Pj4xJCQ8i0bzEF5e9tlP50q4Q2y5EkTRRl7eGnS6mVRWnsDfPwmtdipRUcOdcoVbhbmCLou7cKroFHvH7XX4JO3G7i8KSNWY1aW3oslIxf3tMIxuQ3F8PoWFG2sepohOO6NSqUStVmTGDOnf06dL/Zs9e0rrT/62a/PcOemPtnAhlJZKd+OTJsHAgdJUWhfDZMrh9OnHyc//nMDA60lKWkhIiMv3D9uVxhi8z56Fjh2lQ7L9+yEmRm6LXAN38hVRFCgu/gG9PpWCgi8QRQshIZ3QaFKIihrqcodUC48sJHlTMkOuGcKqQavw9JA/SyKHv7yx5w2m/DCFV7u/yvRu0x167YshigIFBRvQ6WZRXv4zvr6JJCY+T0zMOJfOsDc07hRbrgRRFMjPX4dON5OKiuP4+bVAq32R6OhRTtePnlWaRbsF7fBUeXIw+aBDS9gVf2nciKJIZWUaBsN2DLlbMRRuw+JdAYBfnhfmMBC8rYAiOu2OSqUSQUSlkgao3Hzzn7s2VSqkH+7ZI5XQfvGFtENiyBBJbLZrJ7f59UIURXJylnDmzDPYbJU0bfoyCQnPOl2QdkYaa/D+8Ufo3l2qGt+5E4KD5bbI+XFXXzGb88jJWYZen0pV1Uk8PUOIjh6BRpNCcPBNcptXa97e/zbPfPsM428aT+qAVNkHkzjaXzambeTe1fcy9NqhrBq0StbfXxCs5OWtJj19DpWVv+Pv35LExBeJjh6Bh4dzZKKdCXeNLfVFOqzYiE43g/LyI/j5NSUxcQoxMWPx8HCehdM/63+m8+LOXBN5DTvH7nRYeb/iL40LURQxGs9SXLxdEpqG7ZjNegB8feNRq3ugDulG2CELfu+uItd7F2nPguCniE67c0F0grTVJDe3ejeh2Qxr1khi88gRqWz2oYekZtx45xy5Xxuqqs6SljYBg+EHQkO7kJSU6tb7zBqaxhy8t26FAQOkA5lNmy6yJkjhb7i7r4iiSEnJbvT6VPLzP0cQjAQF3UJsbApRUcPx8gqR28T/ZPr26czYNYOnOjzF3D5zZRVejvSX47nH6bSwE1dHXs2usbtkm1QrCCZycpaRnv46RuNZAgOvIzFxKlFRQ5y2R88ZcPfYUl8ulGWfP/8aZWUH8fWNJyFhMhpNMp6ezjGN+cJhz31X38dnQz7DwwGDkBR/cX+MxgwMhm01QvNCj6a3dzRhYT0koanuib9/i39/znl4kNtT5GwyjH1ZEZ125a+iU6UCITdfauqcNw9ycqQmtkmTpObbf+1HcR1E0UZm5nucOzcNlcqL5s3fJDZ2gtNPfnM2GnvwXrhQal0eO1Zqa3ahwcwOpzH5isVSTG7ucvT6VCoqfsHDI5CoqPuJjZ1AcHA72bOIl0IURSZ9PYn3D73Pa91f46VuL8lmi6P8Ja8ij3ap7bAIFg6nHCY22PGrRmy2KvT6BWRkvInJlElw8K1otdOIiBigfCbVgsYUW+qDKIoUF3+PTvcaJSV7qnuCnyM29iE8PQPlNq+mymLKbVOYc/scu19P8Rf3w2TKqcliFhdvw2g8A4CXVzhqdY8aoRkQcPV/f/42bQo6HQC3Aj+Kiui0GyqVSjyHlvk8zE1BpxlmWS5NE7rjDkls9ulTnfp0XcrLj5OWlkxZ2WEiIgbQqtWH+Pm5brZWTpTgDa+8Aq++KvU/v/qq3NY4L43RV0RRpKzsENnZqeTlrUYQKggMbINGk0J09EinXL8kiALjvhzH0mNL3X6tgclqoteyXhzRH2HXg7u4NdaxPfxWaxnZ2fPJyJiLxZJHaGhntNpphIX1cdqDCWekMcaW+iBNP96JTjcDg2Eb3t6RJCQ8Q2zso3h5ydcjIooiD29+mE+OfMKiuxfx4E0P2vV6ir+4PmZzAQbDjhqhWVl5AgBPzxDU6m6o1T0JC+tBYGCbuh/crVgBEyZAZaUiOu2NSqWqsczm6Y1n8jhp5ck1rjmZ8a/YbEZ0uplkZLyBl1cYrVp9QGTkUOXD/QpQgrfU5pycLGU6P/kEUlLktsg5aey+YrWWkpe3Gr0+lbKyH1GpfImMHExsbAqhoV2dKg5ZBStD1w5lwx8bWHzPYsbeONbhNtjbX0RRZNzGcSw5uoQ1g9cw9NqhdrvWP7FYisjK+oDMzPewWosJC+uNVjsNtbqrw2xwJxp7bKkPJSV7OX9+BsXF3+DlFU58/FPExz+Ol5c8+3otNgv9VvZj5/mdfDvqW7o37W63ayn+4npYLAZKSnZRXLwNg2E7FRXHAfDwCESt7lJdLtuDoKCbGmZi84oVMHUqt+p0iui0J38VncTHQ0aGnOY0GAbDHk6eTKGy8g+io0fTsuXbeHtHyG2Wy6MEbwmLBe6+G777Dr78Evo7fvWY06P4yp+UlR1Fr08lN3c5Nlsp/v6t0WiSiYkZg49PlNzmAVIWcMCqAfxw7gfWDlnLfVff59Dr29tf/rfvfzz33XO83O1lXun+it2u81fM5jwyMt4mO3seNls5ERF3o9VOJSTENYfwOQtKbKk/paWH0OlmUFi4GU/PUOLjnyQ+/km8vcMdbovBaKDTwk7klOdwIPkArSPss99d8Rfnx2otp6Rkd00ms6zsCCDg4eFHSEgnwsJ6olb3IDi4rV2HfqpUqp9EUXT5NRauITpVKhAEOc25YqzWUs6efYHs7A/x9dWSlPQx4eF3yG2W26AE7z8pL4du3eCPP2DHjn+sF1JQfOUi2GyV5OevJTs7ldLSvahU3jRpci8aTQphYb1k7+erMFfQZ3kfDmcdZvMDm+nToo/Drm1Pf9l8cjN3r7qbQdcMYs3gNXYfXmI0ZpKR8RZ6fSqCYCQyciha7YsEBTl2P6G7osSWK6es7Gd0upkUFKzH0zOYuLiJxMc/hY9PpEPtOFt8lvYL2hPmF8b+8fuJCGj45IDiL86HzVZFaem+mkxmWdlhRNGKSuVNSEiHmkxmSEgHh66LUkSnnfmb6NRqpSWdLkph4RZOnnwYkymLuLgnaNZsprJIu4FRgvffycmRdnhWVEg7PFu0kNsi50HxlctTUfE7ev0CcnKWYbUW4ufXDI1mPDExD+Lr6/jhNhcwGA10X9KdU0Wn+G7Ud3RK6OSQ69rLX37N+5WOCzvSOqI1ux/cbdc1DVVVZ0lPf52cnCWIokBMzCgSE6coE9IbGCW2NBzl5b+g080iP/8zPDz8iY19hISEZ/H1ddxC6r3pe+m5rCcd4zvy7ahv8fFs2DVBir/IjyCYKC09WDP4p7T0AKJoBjwJDr61JpMZGtpJ1mFXiui0MzWiMyBAalAbMUJuk+qM2ZzP6dNPkpe3ioCAa0lKWkBoaAe5zXJLlOD9b9LS4LbbpK1C+/ZBpGMPip0WxVdqhyCYyM/fgF6fisGwDfAkIqI/Gk0K4eF9G6ZfpY7klufSdUlXcstz2TF2BzfG3Gj3a9rDX/Ir8mm3oB0mq4lDKYeID7HPALmKihOkp88hN3clKpUnGs14EhIm4+/f1C7Xa+wosaXhkXx4Nrm5K/Hw8EGjmUBi4mR8feMccv2Vv6xkxPoRjLlhDIvvWdygPe+KvzgeQbBSVvYjBoOUySwp2YsgVAEqgoJuqp4w25PQ0M5OtV5MEZ12RqVSiaJWC7NmuZzgFEWR3NzlnD79FDZbKVrtVBITX1CWadsRJXhfnH37oFcvuOEG2LbNpbcLNRiKr9SdysrT5OQsRK9fjMWSi49PHBrNODSa8fj5aR1qS3pJOp0XdcZoNbJn3B679VtdoKH9xWwz0/vT3hzKOsTOsTtpF9fwfZRlZUdJT59Ffv666izRQ9VZIvky1Y0BJbbYj8rK09Xi81PAA41mPImJzzsk/ryy4xVe3fkqs3vO5oUuLzTY6yr+Yn9E0UZ5+dHqPZnbKCnZjc1WDkBg4HWo1T2rS2a7ytI/XFsU0WlnVCqV6Ky2XQ6jUcfJkw9TVPQ1ISEdSEpaQGDgtXKb5fYowfvSbNgAgwbBgAGwfj14NvLd7oqv1B9BsFBYuBm9PpWioq8BCAvrQ2xsChERd9t1kMJfOVl4ks6LOuPn5ceecXtIDE2027Ua0l9EUSRlUwoLf17IyvtWMrzN8AZ53QuUlOxHp5tFUdEWPD1DqvvhJjm8H66xosQW+1NVda66VHwxIBIdPQat9gX8/e3XQyKKIiPWj2DVr6tYO2Qtg68Z3CCvq/hLwyOKAhUVv2EwbKO4eDslJTuxWg0A+PsnVe/J7Ila3c1phuXVBkV02hlXE52iaCMrax5nz74IQPPms4mLewyVqpHf4TsIJXhfnnnzYOJEeOQR6Xsn2orhcBRfaRiMxnT0+kXk5CzEZMrE2zuKmJixaDTJBAS0svv1j+YcpfuS7kQFRrH7wd1EB0Xb5ToN6S/v7H+Hp799mmldpjGj54wGeU1p5+EOdLqZGAzb8PKKID5+EnFxE/H2VjfINRRqhxJbHIfRmEFGxptkZ6ciilaio0eg1b5otz5lo9VYs0u3oSoUFH+5ckRRpLIyrXq67DYMhh1YLAUA+Pk1q9mTqVb3cOlKD0V02hlXEp0VFb+Tljae0tIDhIf3pXXrjxxectbYUYL3f/P88/DmmzBnDkyZIrc18qH4SsMiijaKir4mOzuVwsLNgA21ujsaTQpNmtxn1wl/+zL20fvT3rQKb8X2MdsJ8w9r8Gs0lL9sPbWVu1bdxT1J9/D50M+veFKtKIoUFW1Fp5tJael+fHxiSEh4Fo3mIWVQnUwoscXxmEx6MjLeIjv7IwTBRFTU/Wi1U+1SYZZXkUf7Be2pslRxKOXQFVdYKP5Sd0RRxGg8W10uK32ZzXoAfH3jq0tlJaHpTvfhiui0M64gOgXBTHr6HHS6WXh6htCy5btER49wquXqjQUleP83ggCjRsHKlbBsmfR9Y0TxFfthMunJyVmCXr8Ao/EsXl7hREePIjY2xW5tBt+d+Y67Vt3FLZpb+G7UdwT6NOyEwYbwl9/zf6fjwo40D2vOngf3XJGNoihQULABnW4W5eU/4+ubSGLi88TEjHPoCH+Ff6PEFvmQds/OJStrHoJQSWTkILTaaQQF3dCg17nwXtaGatkzbg8hvvUfNqP4S+0wGjNqpssaDNsxmdIB8PaOrsliqtU98fdv4bb334rotDPOLjpLSg6QlpZMZeVvREUNp2XLd12qPtzdUIJ37TCZ4M47Yfdu2LoVbr9dboscj+Ir9kcUBQyG7WRnp1JQsB5RtBAS0hGNJoWoqKENPnp+/Yn1DFk7hF7NerFp+CZ8vXwb7LWv1F8KKgtov6A9FeYKDqccJiE0oV6vIwhW8vJWk54+h8rK3/H3b0li4otER49QhtQ5CUpskR+zuYDMzHfJyvoAm62UiIi70WpfIiSk4e7XvzvzHXeuuJM+LfqwcfhGvOo5yVvxl4tjMuXUZDGLi7dhNJ4BwMsrHLW6e80ak4CAq91WZP4TRXTaGWcVnVZrOefOTSMr6318feNo1Wo+TZrcJbdZjR4leNeekhLo0kVafbt7tzTZtjGh+IpjMZvzyc1dRnZ2KlVVaXh6hhAd/QAaTQrBwTc32HWWHF3Cg18+yMCrBvLZkM/qfSP4T67EX8w2M30+7cOBzAPsGLuDDvF1X5klCCZycpaRnv46RuNZAgOvIzHxRaKihiozA5wMJbY4DxaLgays98nMfBertZjw8DvRal8iNLRjg7z+xz9+zMNbHubxdo/z/p3v1+s1FH+RMJsLMBh21AjNysoTAHh6hqBWd6tZYxIY2AbVFbYluCqK6LQzzig6i4q+JS1tAiaTjtjYR2nefI5T7fFpzCjBu25kZkLHjlLJ7f79kGi/4Z9Oh+Ir8iCKIiUle9DrU8nPX4sgGAkKuhmNJoXo6AcaJJa+f/B9nvz6ScbcMIZF9yy64r5JqL+/iKLIw5sf5pMjn7B84HJGXF+31V82WxV6/QIyMt7EZMokKOgWtNppNGlyd6O98XJ2lNjifFitpWRlzSMjYy5WayFhYbej1b6EWt31il/76W+e5p0D7/DBnR8wsd3EOj+/sfqLxWKgpGRXTSazouI4AB4egajVXarLZXsQFHSTLPugnRFFdNoZZxKdFkshp08/TW7uMvz9k0hKWoBa3VlusxT+QmMN3lfCr79C584QFwd79kBYw89gcUoUX5Efi6WY3NwV6PWpVFQcx8MjgKio+9FoUggJ6XBFJVMzds5g+o7pPN7ucd7r+94Vl1/V118uCOAXOr/A7F6za/08q7WM7Oz5ZGTMxWLJIzS0M1rtNMLC+jSaUjJXRYktzovVWk529kdkZPwPiyWX0NBuNG36Emp1z3q/r2yCjYFrBrLl1BY2D9/Mna3urNPzG4u/WK3llJTsrslklpUdAQQ8PPwICelUUy4bHNzWYWu3XA1FdNoZZxCdoiiSn/8Zp049jtVaTELC82i105RhDU5IYwneDc2OHXDHHdChA3zzDfg1AtdWfMV5EEWRsrLD6PWp5OauQhAqCAy8Do0mmejoUfVa1i2KIs9++yxvH3ib6V2n82qPV6/Ixvr4yzenv6Hfyn4MaD2A9fevr1XG1WIpIivrAzIz38NqLSYsrDda7bQGycgoOAYltjg/UgVBKunpb2A2ZxMS0hGtdjrh4XfUS3yWm8vpsrgLZ4rOsHfcXtpEt6n1c93VX2y2KkpL99UM/ikrO4woWlGpvAkJ6VCTyQwJ6aDcT9cSRXTaGblFp9GYyalTj1JYuIng4FtJSlpIUND1stmjcHncNXg7gtWrYfhwGDoUVq0CDzev3FN8xTmxWsvIy1uNXp9KWdlhVCpfIiMHodGkoFZ3q9MNoSiKpGxKYeHPC5nbZy5Pd3y63nbV1V/+KPiDDgs6oFVr2TtuL0E+l19fIk3dfJvs7HnYbOXVg0+mEhJy5XsAFRyLEltcB5vNSE7OYtLTX8dkSic4+Fa02ulERNxVZ/GZWZpJu9R2eHt6czD5IDFBMbV6nrv4iyCYKC09WF0uu53S0v2IohnwJDj41ppMZmhopwYfItdYUESnnZFLdIqiQHb2J5w9OxlRtNKs2Uzi4p5Q6sqdHHcJ3nLxv//Bc8/B00/D3LlyW2NfFF9xfsrLj5GdnUpu7nJsthL8/Vuh0SQTEzO21lPCbYKN4euGs/b3taQOSCX55uR62VIXfymqKqL9gvaUmko5lHwIrfrSe+KMxkwyMt5Cr09FEIxERg5Fq31ROdx0YZTY4noIgrl6UNdsjMZzBAXdWN07PbBOvdM/Zf9E1yVdaRPVhu1jtuPv7f8pqDrXAAAgAElEQVSfz3FVfxEEK2VlP2IwSJnMkpK9CEIVoCIo6KaawT+hoZ2VuScNhCI67YwcorOyMo20tBRKSnajVvciKekT/P2bO9QGhfrhqsHbWRBFePJJ+OADeOcdmDRJbovsh+IrroPNVkl+/ufo9amUlOxBpfIiIuIeYmMnEBZ2+3/eFJptZu5ZfQ/fnP6G1YNXM/TaoXW2obb+YrFZ6LuiL3vS97B9zHY6JXS66OOqqs6Snv46OTlLEEWBmJhRJCZOISAgqc62KTgXSmxxXQTBQl7eSnS6WVRVnSIg4Fq02mlERQ2p9ZToL/74gvvW3MfgawazevDq/yyrdxV/EUUb5eVHKS7eXi0yd2GzlQMQGHgdanXP6pLZrvVqiVD4bxTRaWccKToFwUJGxlucP/8anp7+tGjxNjExY5WhDS6EqwRvZ8Zmk0psN2yAzz6DwYPltsg+KL7imlRUnECvX0BOzlKs1kL8/JoSEzMejeZBfH3jLvm8Sksldyy/g4OZB/ly2Jd2G/bx6JZHmf/jfJbeu5TRN4y+qP3p6XPIzV2JSuWJRjOehITJ+Ps3rZM9Cs6LEltcH1G0kZf3GTrdzOp9uElotVOJihpeq4q3t/a+xeTvJzO1y1Rm9px52cc6q7+IokBFxW8YDNsoLt5OSclOrFYDAP7+SYSF9agWmt2U/fQOQhGddsZRorO09EfS0pKpqDhGZORgWrb8AF/f2tXjKzgPzhq8XY2qKujdG378Eb77Ttrn6W4ovuLaCIKJgoIvyM5OxWD4AfAgIqI/Gk0y4eH9LnpjWGIsoeeynpzIP8E3I7+hi7b2jl0bf5l3aB4Tt05kcqfJvNH7jb/9t7Kyo6SnzyI/fx0eHv7Exj5EQsKz+PrG1toGBddAiS3ugygK5OevR6ebQUXFcfz8WqDVvkh09KjLTlgVRZEJmyaw4OcFlzyAuoCz+IsoilRWplVPl92GwbADi6UAAD+/ZqjVPauFZg8lbsmEIjrtjL1Fp81WyfnzL5OR8TY+PtG0avUhkZH32u16CvbFWYK3O1BYCLfdBrm5sHcvXHON3BY1LIqvuA9VVWfQ6xeSk7MYszkHH59YYmIeRKMZj79/s789Nr8in65LupJVmsX2Mdu5JfaWWl3jv/zluzPfceeKO+nXqh8b7t+Ap4dUildSsh+dbhZFRVvw9AwhLm4i8fGT8PGJrP8vrODUKLHF/RBFgcLCTZw/P4Py8p/w9dWSmDgFjeZBPDx8L/qcC6X2u3W7+X7093TVXnwCtVz+IooiRuO5mumyBsN2zGY9AL6+8dWlspLQ9PO7dF+6guNQRKedsafoLC7eRlraBIzGM2g0KTRv/ibe3mq7XEvBMSgf9g3L+fPQsSP4+MD+/RDrRoebiq+4H4JgobBwC3p9KkVFXwMiYWG90WhSaNLkbjw8fABpymTnRZ2psFSwa+wuro68+j9f+3L+crLwJO0XtCc+JJ594/YR5BOEwbADnW4mBsM2vLzCiY9/iri4icpnTCNAiS3uiyiKFBVtRaebQWnpAXx84khMfB6NJhlPz38PDSquKqbjwo7kV+ZzMPkgLcNb/usxjvQXozGjerqsJDRNpnQAvL2jaqbLqtU98fdvobSWOSGK6LQz9hCdFksxZ848R07OQvz9W9K69SeEhfVo0GsoyIPyYd/wHDkC3bpBixawaxeEuMkQOsVX3BujMYOcnEXo9QsxmTLw9o4kJmYsGk0yAQGtOV10ms6LOuPl4cWecXtoqm562de7lL8UVxXTYWEHiqqKOJR8iBDhBDrdTEpL9+PjE0NCwrNoNA/h5XX5lSkK7oMSW9wfURQpLv4Bne41Skp2V7/XnyM29qF/rQM5U3SG9gvaExEQwf7x+wn3//uQHXv6i8mUU5PFLC7ehtF4BgAvr3DU6u41QjMg4GpFZLoAiui0Mw0tOvPz13Pq1GOYzfkkJDxD06avXPR0SsE1UT7s7cM330D//tCzJ2zeLGU+XR3FVxoHomijqOhb9PpUCgs3IYpWQkO7ERubQq6QRPdlfQj3D2f3g7vRBGsu+ToX8xeLzUK/lf3YdX4H3w16mYDK9ZSX/4yvbyKJic8TEzNOWXreCFFiS+PCYNjJ+fOvYTBsw9s7koSEZ4iNfRQvr+Cax+zW7eb2T2/ntoTb+Hrk1/h4/vkh2pD+YjYXUFKysyaTWVl5AgBPzxDU6m41a0wCA9vUaRWMgnOgiE4701Ci02TSc+rURAoK1hMUdCNJSQsJDr65ASxUcCaUD3v7sWQJPPggjB4tfe/qh6KKrzQ+TKYccnKWoNcvwGg8g5dXGGJgbx7euQkP3xbsHLvzX1mIC1zMX5746jFO6D7kuWtj8RGy8fdvSWLiC0RHj6wp5VVofCixpXFSUrIPnW4GRUVfV5fUTyIu7vGakvrlx5czasMoxt04jgV3L6jJLF6Jv1gsBkpKdtVkMisqjgPg4RGIWt2luly2B0FBNyl75t0ARXTamSsVnaIootcv5MyZZxFFE02bvkJ8/NOXnTqm4LooH/b2ZcYMmD4dpk6FmZefAu/0KL7SeBFFAYNhB3p9Kvn56xFFMydKVfxa1ZRZA/ahDvj35PK/+osgmFhzcDyCYQVx/tKOusTEF4mMHKLc2CkosaWRU1p6CJ1uJoWFm/D0DCU+/gni4yfh7R3OS9teYubumbxx+xtMvm0yUDd/sVrLKSnZXVMyW1Z2BBDw8PAjJKRTTSYzOLitcp/rhiii085cieisrDzNyZMTMBi2ExrajaSkVAICWjWwhQrOhPJhb19EER56CFJT4aOPpO9dFcVXFEAqR8vN/ZST59/G05aJSfAkIfZBEuIeJjj4FtavX4EgTGXIEB1r1ybg79+dgJCvUdnyyTGH0uPGRURF3quUqinUoMQWBYCysp/R6WZSULAeT88g4uImEhs3ibGbnmTNb2t4sv2TfPHHF+ie0qF9R8usXrMY0WbE317DZquitHQfxcXSGpOyssOIohWVypuQkA41mcyQkA5KKX8jQBGddqY+olMQrGRmvsP58y+jUnnTosVbaDTJyk1BI0D5sLc/Vivcey9s3QpffAEDBshtUf1QfEXhr4iiyNojL3Pk1Ax6RXvirbJhtWoRRT3e3mZ69IDt26XHZlSq2JifyIL7jxHqFyqv4QpOhxJbFP5Kefkv6HSzyM//DA8Pf6JiUnjg+y8IR0dycxh+J6zaCkvOeTPqtk/on9i8ulx2O6Wl+xFFM+BJcPCtNYN/QkM7/WtgkYL7o4hOO1NX0VlWdpS0tGTKy38iIuIeWreeh69vnB0tVHAmlA97x1BRAd27w2+/STfi7dvLbVHdUXxF4WJ8ePhDJn/zGC/c1Jb2vkfx8rIA/E105lR50K7DKZqHNZfRUgVnRYktChejouIP0tNnk5u7AosgoAK8PP6MLTYRBBG8PQBUePldTUhoVyIj+hAZ3gsvLzcZHa9QbxTRaWdqKzptNiM63Wukp7+Jt3cErVr9H5GRg5UR0I0M5cPeceTlSTs8S0ulHZ4t/71+zKlRfEXhUrzw1RxeP/wiP3QFj+qPkL+KTkFU0bKFQGKifDYqOC9KbFG4HJWVp9mxrxUB1e3ff40tFVaY8wccL4Ey65/P8fHwI8hLTYh3mPTloybUN4xQXzVhfmGE+YcR5q8mIjCMcH81TYLCaBIURmSQmhC/YDyUSj+3wF1Ep0tPPjAYdpGWlkJV1UliYsbSosVcvL0vPoFQQUGhYYiKgq+/loRn376wb5/0MwUFV8RohI0bpcnMX38zBXoayGv/JjEXaZPKy02kV0+4/nqpvPyuu6BdO/BQ7usUFBQuQWUlbNsGW7a0ZMjQiz/G3xP2vn4A/IvBrxj8DOBfjNnPQJFfMUX+1T/zywG/P6ofZwDVZQ45BA8whaIyheFhCsPDrMbTEoaXVY23NUz6EtT42MLwFdX4imH4E4YfagI8wvDx9Mbbm4t+eXnV7ef1/W9//bmXl+tPz68rK1ZIAxzhllvktqUhcMlMp9Vaytmzz5Od/RF+fk1p3foTwsN7O9hCBWdCOWF2PAcOSPs7r7tOOq0NdJE2E8VXFEQRDh+WhOaqVWAwQHw8jBkDo0eLzNp1HcOb/Y6f55/ZCKMN9p55BHXZh2zaBHv2gM0mHbj07y+J0N69IShI7t9OQS6U2KJwgXPnYMsW6Wv7djCZpM/IhcuaEB1eCPw905lniODm6wqwWPjXl9X6759ZLGC2CJSZyyg1F1NqNlBmKabMWky51UCFUEylYKBSKKZSLMYoGjCqijGpDJhUxZg8ihE8TJf9HVTWADzNkmBVmdRgDAOjGrEqDKEyDLFSja0iDKqq/1uV9N8xhoE5EGh4hfhPEeoIUWxPIe3peWkhvWIFTJggHVrArYjijy4vuV0u01lQsImTJx/BbNYTH/8UzZrNUJqqFRRkoEMHWL0aBg6EYcNgwwYpmCooOCvZ2bB8uSQ2T5wAPz8YNAjGjpVuAD09AVTs+KqcrDRIrm7dzDHCgrNw2vwV55+BZ56BoiIp4795s+T7ixeDj490EHPXXZIIVcpwFRQaBxaLdBD11VeS0DxxQvp5y5bw8MPSwVTXrrBly3sYzePw8zHXPNdo9sFLeK8erSoeQGj1V90xWo0UVxVjMBooNv5/e3ceJ0V17n/8+7Dvi6K4D+pV3JeohLjhghtoYtQkJuRGTH4/fjGL0cRsck3MNWjUa0xyk7jEa0iUKDFqDGAUEREFXEAQcEUNuODClUWRnTm/P85puxmmu6tn5vRS/Xm/XvOa7urqququp0/VU+epqvB/7YotHmde88Pe0Iq187Vy3UqtWr+q4LQ7WAf16tRHvTr1VY8OfdSzY191b99HPdr3Vff2fdXV+qib+V7VLuqrLi70tjb2VYfNvdW4qUPRpLvQa7nD1671pwOVMq1yypeQvv22X6Y0qZmezg0b3tWiRRdq2bK/qnv3AzRw4P+oV69BFVxCVBOOMFfODTdI3/iGPyJ3443VX/5CrNSX3PLZBx+UGhulo47yiebnPif1bmZ/rd3P2skpxMjl4U+SydT408atxt+4UZoxQ5owwf8tWuSHZ8pwzzhDOuIIynDTjralvrz7rr+a+6RJ0uTJPrHp2FEaMsQnmcOHS3s1c7e+LW/H1KB27cborLNGbD1iFdvcuFmr1q/aOjltmsQ2N2ztCm1sLJzZ9ezUU3279lWfLv7c1T5d+vjnnf3/LYZ1yZ7f2qdLH3Xt0LVV13VxzlextFXS29L3jB2bu1Tp6Oms+qTTOad3371Nr7xysTZvXq2Ghsu0224/ULt2nSq9iKgibOwr69JLpauukn7+88z5B9WLWEm/QuWz553X/I5grgG/GqAlq5b4J5fr46SzoXeDFl+0uOj8X3rJ94BShltfaFvSrbFRmjMnWzY7e7YfvtNO0rBh/vd94olSz57Jplev8eKc05qNa0roYd1y2OoNqwtOv1P7Tlsloh8nqc0Ny0lie3fpXTUXXxowQFqyJPOMpDMqM3MzZuysDh36as2aherV60gNHHiLunfft9KLhipUr413tXDO78zfdpvf0T/vvEovUX7ESnolK58tbtyCcRo1YZTWbFzzcdLZrWM33XzGzVvdxL2YTBnuhAm+V2TVKspw04q2JX1WrfK9mJMm+d/ve+/5ap7Bg7OJ5iGHtKzCh3hpmY2bN2rV+lWFe1hzk9gmietmtznvtE2mXp17bd2bmqCHtW+XvurcoXObfc5x46Tzrx+njceMlsYvkVvqSDpjMTOXOcG6f//ztM8+t8qq5OgDqg+Nd+Vt2OA3wNOm+Q30ySdXeomaR6ykS0vKZ5MYt2CcRj88WksuXqKG6xs05sQxJSecTVGGm260LbXPOX/AKnNu5uOP+7LHvn2lU07x27hTT5X69Wv9vIiX8nPOafWG1S3qYV2xdoXWblpbcPpdOnRpUQ9r36591bNTzy3KgsctGKev3jtKG9wa6SaRdMaUm3R27tygT31qcUWXB9WNxrs6fPCBdMwx0muvSdOnS4ceWukl2hqxUvtaWz5bipjx8tJLPvmcOJEy3DSgbalNa9dmD5ZOmiQtXuyHH3hg9tzMwYPb/kJ5xEvtWb9pvVauW1lyD+uKdSu0at2q7LUCmtHO2m2RlD637Dmt27TOv0jSGVdu0imZjjtu64s3ABk03tVj6VK/gd640d9WpaGh0ku0JWKldrVV+WwpyhUvzZXhdu7sP1fmnqCU4VY32pba8frr2SRz6lSfeHbr5s/JHD5cOu20+L834qW+NLpGfbD+g8I9rGtXaOV6P+yfr/wz+2aSzrjo6UQpaLyry3PPSUcfLe2wgy8n3GabSi9RFrFSW2KVzyZViXihDLc20bZUr02bpJkzfZJ5//3SwoV++B57+CRz2DDpuOP8gaxyIV5QyBYXtCPpjCuTdLZr100DB96s/v1r63LSKC8a7+rz6KP+vM5Bg6SHHirvxrwQYqX6lbN8tphqiJdMGe6ECT4ZpQy3OlVDrCBr2TJfNXD//f6A1cqVvkT2mGOyZbMDB1buNl/ECwrZ4oJ2JJ1xmZmbObNBe+wxhoQTRdF4V6e//lX6whekc86Rxo+vjp4ZYqV6NS2f7dpVOuusuOWzxVRbvFCGW72qLVbqTWOjNG9etmz2qaf8Aaz+/bNXmj3pJKlXr0ovqUe8oJiPL2h3DVevjSpzn04gCRrv6nX99dJ3vyt95zv+caWOKmcQK9Wl0uWzxVRzvGzc6C9AlOkFfeUVP5wy3Mqo5lhJqw8/9JU0mbLZd97x25gjjsj2Zh56aHX+BogXJGVmc5xzh1d6OVqLpBOpQONd3S6+WPrVr6TrrvMJaCURK5VXTeWzxdRSvDRXhtu/v9/xPv10ynBjq6VYqVXOSS+/nO3NfOwxf/Cld+8tb2my/faVXtLiiBckRdIZGUknSkHjXd0aG6Vzz5Xuuku6805fclspxErlVGP5bDG1Gi+U4ZZfrcZKtVu3zl8jIHPvzFdf9cP33z9bNnvkkVLHjpVdzlIRL0iKpDMykk6Ugsa7+q1b5y8s9OST0uTJ0pAhlVkOYqW8qr18tpg0xAtluOWRhlipFm++mU0yp0yR1qzxF6M74YTs1WYHDKj0UrYO8YKkSDojI+lEKWi8a8Py5f5WKm+/7XeC99+//MtArMRXS+WzxaQxXgqV4Z5xhjR0KGW4LZHGWCmXzZv9fZ0zZbPz5/vhDQ3ZczOPP95XR6QF8YKkSDojI+lEKWi8a8eSJdKnPuUvXT9rlrTzzuWdP7ESTy2WzxaT9nhZvtyX306cSBlua6U9Vtra++/7EvD77/f/ly/3bcTRR2d7M/fbr/IXn4uFeEFSJJ2RkXSiFDTetWXePOnYY6Xdd5emTy9viSWx0rZqvXy2mHqKl3xluAcf7JNPynALq6dYaQnnpGefzZbNPvGEby+2284nmMOG+VMw+vSp9JKWB/GCpEg6IyPpRClovGvPQw/5nYwhQ/xOSKdO5ZkvsdJ6aSqfLaZe4yVzlVDKcJOr11gpZPVq6eGHs7c0eestP/yww7Jls4cfXp8HMogXJEXSGRlJJ0pB412b/vxnn6SMGCHddlt5yqiIlZZbutSvp7FjpRdfTEf5bDHEi0cZbnHEivfKK9lzMx99VNqwQerZ0/diDh8unXaatMMOlV7KyiNekBRJZ2QknSgFjXftuvJKafRo6Uc/kq66Kv78iJXSrFsn3XefTzQnT05f+WwxxMvWCpXhZhLQeizDrddY2bDBnyaR6c18+WU/fJ99sr2ZRx1VvmqWWlGv8YLSkXRGRtKJUtB41y7npAsukG66Sfrd76RvfCPu/IiV4uqpfLYY4qUw5/zVcCdO9Ano44/7AxO5ZbgnnSR1717pJY2vnmJl6VKfYN5/vz9VYvXqbM935iJAe+xR6aWsbvUUL2gdks7ISDpRChrv2rZpky/TnDRJuuce6TOfiTcvYiW/eiyfLYZ4KU2mDHfCBH9F0noqw01zrGze7A9EZcpm5871w3fZJdubecIJ9XFwoa2kOV7Qtkg6IyPpRClovGvfRx/5nZb586WpU/1tVWIgVrZU7+WzxRAvLVdvZbhpi5UVK/wVqSdN8gcQ/vd//bo68shsonnAAem9pUlsaYsXxEPSmXQGZp+TdLmkfSUNcs7NTvg+kk4kRuOdDsuW+R2aFSukmTOlvfdu+3kQK74k8qmnfKJ5552+fHbXXX3p7Fe+Ul/ls8UQL22jHspwaz1WnJMWLsyemzlzpu/h3HZbf/Gf4cP9xYC22abSS5oOtR4vKB+SzqQzMNtXUqOkmyRdQtKJGGi80+PVV30vZ48e0qxZfqe0LdVzrOQrnz3/fF/+mJYep7ZUz/ESU74y3BNOyN4TdNddK72UpanFWFmzxt/SJHPvzDfe8MMPPTR7buagQfVZWh9bLcYLKoOks9QZmU0TSSciofFOl6ee8knQvvtK06a17b0A6y1WKJ9tnXqLl0ooVoZ7xhm1cS/HWomVf/0re27mI49I69f7Nvakk3ySOWyYtNNOlV7K9KuVeEHlkXSWOiOSTkRE450+Eyf6CwqdeqpPmjp0aJvp1kOsUD7bduohXqpJpgx3wgTfBtRSGW61xkomqc8kmi++6IfvtVf23MxjjvE9zSifao0XVJ+0JJ1tshtnZlMkNXer39HOuftKmM4oSaMyz6dNm9b6hUPdIF7SpUcP6aKLdtQvfzlQZ565VN/73sttdsGKtMbKsmWdNGVKfz3wwA56/fXu6tx5s449dplOOeUdHXroSrVrJ731lv9DcmmNl2p2xBH+74MPOujJJ7fRrFn9NH78Nrr11g7q2LFRn/jECg0e/L6OPPJ9bb/9+kov7seqJVaWL++kJ5/cRk88sa3mzOmrjz7y39vBB6/UN7/5vgYPXq5ddln78fizZlVwYetYtcQLUA70dCIVOGKYXpddJv3859LPfib95Cetn17aYqW58tmjj86Wz/bqVeklrG1pi5datnGj9Nhj2YsRVVsZbiVjpbFRmj07e27m7LCntdNO2d7ME09s21MV0Dq0LUgqLT2dJJ1IBRrv9HLOX+jmT3+Sbr3VP26NNMQK5bPlk4Z4SaNqLMMtd6ysWuUPNE2a5C/K9N57PuEePDh7EaCDD+aWJtWKtgVJkXQmnYHZZyX9t6TtJK2UNM85d0qC95F0IjEa73TbuNHvRE2d6ncwTz215dOq5Vh56y3p9tu3vPrs2Wf7Xk2uPhtHLcdLPSl0NdzMPUFjXw03dqw4J73wQvbczBkzpE2bpL59fZs4fLj/v+220RYBbYi2BUmRdEZG0olS0Hin34cfSsceKy1aJE2fLn3iEy2bTq3FCuWzlVVr8YLKleHGiJW1a/0VZjP3zly82A8/6KBs2ewnP9l2F1pD+dC2ICmSzshIOlEKGu/68Pbb/h6e69b5C1/svnvp06iFWKF8tnrUQrwgv9wy3AkTfO9grDLctoqVJUuySebUqT7x7NZNGjrUL/Npp9XePUyxNdoWJEXSGRlJJ0pB410/XnjB32dy++39DmSppWTVHCuUz1afao4XlO799335bYwy3JbGyqZN0syZ2bLZ557zw/fcM3tu5pAhUpcuLVsuVCfaFiRF0hkZSSdKQeNdXx5/3B/1P+wwacoUn5wlVW2xQvlsdau2eEHbyZThZnpBX33VD29pGW4psbJsmT8HddIk6cEHffLbsaM/hWDYMJ9s7r03FwFKM9oWJEXSGRlJJ0pB411/7r7bJ2VnninddZfUvn2y91VDrFA+WzuqIV4QX74y3B128Ang6acXL8MtFCuNjdLcudnezKef9vPcYYdskjl0KAeZ6gltC5Ii6YyMpBOloPGuT7/+tXTRRdK3viX95jfJegUqGSuUz9Ye2pb6VEoZ7rhx0ujR0pIlpoYGpzFjpBEjpA8+8JUYmfMz33nHt1GDBmUvAnTIIfzu6xVtC5Ii6YyMpBOloPGuX5dcIl13nXTNNdL3v198/HLHCuWztY22BYXKcHff3Sel69ZJkkly6tjRVyssWuTf26ePdMop2VuabLddBT8MqgZtC5Ii6YyMpBOloPGuX42N0pe+JI0fL/3lL9IXv1h4/HLECuWz6UHbglxNy3Afeyz3VZ90Sv78zIsv9onmkUdySxNsjbYFSZF0RkbSiVLQeNe39et9T8LMmf6iHMcfn3/cmLFC+Wz60LagkHbtfCLqZZNOM39ADMiHtgVJkXRGRtKJUtB4Y+VKX7b6xhv+6rYHHtj8eG0dK5TPphttCwoZMMDfV9PLJp0NDdLixZVZJtQG2hYklZakk+PuAFKhTx9/C4IePfzN0998M968nJOefFK64AJpxx2lc8/199a79FLp5Zd9yd3XvkbCCaTdmDFSt25bDuvWzQ8HAGTR04lU4IghMubP9z2NDQ0++evTZ8vXWxMrlM/WH9oWFJPv6rVAIbQtSCotPZ0knUgFGm/kevhh39t51FH+ypKdO2dfKzVWKJ+tb7QtSIpYQSmIFyRF0hkZSSdKQeONpsaNk778ZX8129tvz/ZCJomV3KvP3nGHv0cfV5+tT7QtSIpYQSmIFySVlqSTi3gDSKURI/xFhX78Y58wXn118fdQPgsAAND2SDoBpNYPf+gTz2uukd55R3r0UT98wAB9fN5VvvLZW26hfBYAAKAtUF6LVKBMBfls3iwNGiQ980xmiL+tQZcu/qbtc+ZQPov8aFuQFLGCUhAvSIryWgCoAe3bS8uWbT183TrpkUd8byflswAAAPHQ04lU4IghCmnXzl8cyMvewN3Ml9MC+dC2ICliBaUgXpBUWno6Oa4PIPV226204QAAAGg7JJ0AUm/MGKlbty2HdevmhwMAACAukk4AqTdihHTzzVJDg3/e0OCfjxhR2eUCAACoB5zTiVTg3AgkRaygFMQLkiJWUAriBUlxTicAAAAAAEWQdAIAAAAAoiHpBKvZbpkAABTRSURBVAAAAABEQ9IJAAAAAIiGpBMAAAAAEA1JJwAAAAAgGpJOAAAAAEA0JJ0AAAAAgGhIOgEAAAAA0ZB0AgAAAACiIekEAAAAAERD0gkAAAAAiIakEwAAAAAQDUknAAAAACAakk4AAAAAQDQknQAAAACAaEg6AQAAAADRkHQCAAAAAKIh6QQAAAAAREPSCQAAAACIhqQTAAAAABANSScAAAAAIBqSTgAAAABANCSdAAAAAIBoSDoBAAAAANGQdAIAAAAAoiHpBAAAAABEQ9IJAAAAAIiGpBMAAAAAEA1JJwAAAAAgGpJOAAAAAEA0JJ0AAAAAgGhIOgEAAAAA0ZB0AgAAAACiIekEAAAAAERD0gkAAAAAiIakEwAAAAAQDUknAAAAACAakk4AAAAAQDQknQAAAACAaEg6AQAAAADRkHQCAAAAAKKJnnSa2bVm9qKZzTeze82sT+x5AgAAAACqQzl6Oh+SdIBz7iBJL0v6cRnmCQAAAACoAtGTTufcZOfcpvD0CUm7xJ4nAAAAAKA6dCjz/L4qaXy+F81slKRRmefTpk0rwyIhLYgXJEWsoBTEC5IiVlAK4gX1xJxzrZ+I2RRJOzTz0mjn3H1hnNGSDpd0lkswUzNLMhogSTIzES9IglhBKYgXJEWsoBTEC5IysznOucMrvRyt1SY9nc65oYVeN7PzJJ0u6UQySQAAAACoH9HLa83sVEk/lDTEObcm9vwAAAAAANWjHFev/a2knpIeMrN5ZnZjGeYJAAAAAKgC0Xs6nXP/FnseAAAAAIDqVI6eTgAAAABAnSLpBAAAAABEQ9IJAAAAAIiGpBMAAAAAEA1JJwAAAAAgGpJOAAAAAEA0JJ0AAAAAgGhIOgEAAAAA0ZB0AgAAAACiIekEAAAAAERD0gkAAAAAiIakEwAAAAAQDUknAAAAACAakk4AAAAAQDQknQAAAACAaEg6AQAAAADRkHQCAAAAAKIh6QQAAAAAREPSCQAAAACIhqQTAAAAABANSScAAAAAIBqSTgAAAABANCSdAAAAAIBoSDoBAAAAANGQdAIAAAAAoiHpBAAAAABEQ9IJAAAAAIiGpBMAAAAAEA1JJwAAAAAgGpJOAAAAAEA0JJ0AAAAAgGhIOgEAAAAA0ZB0AgAAAACiIekEAAAAAERD0gkAAAAAiIakEwAAAAAQDUknAAAAACAakk4AAAAAQDQknQAAAACAaEg6AQAAAADRkHQCAAAAAKIh6QQAAAAAREPSCQAAAACIhqQTAAAAABANSScAAAAAIBqSTgAAAABANCSdAAAAAIBoSDoBAAAAANGQdAIAAAAAoiHpBAAAAABEQ9IJAAAAAIiGpBMAAAAAEA1JJwAAAAAgGpJOAAAAAEA0JJ0AAAAAgGhIOgEAAAAA0ZB0AgAAAACiIekEAAAAAERD0gkAAAAAiIakEwAAAAAQDUknAAAAACAakk4AAAAAQDQknQAAAACAaMqSdJrZFWY238zmmdlkM9upHPMFAAAAAFRWuXo6r3XOHeScO0TSREk/KdN8AQAAAAAVVJak0zn3Qc7T7pJcOeYLAAAAAKisDuWakZmNkfQVSaskHV+u+QIAAAAAKseca5tORzObImmHZl4a7Zy7L2e8H0vq4pz7aTPTGCVpVHh6gKSFbbJwqAf9JP1vpRcCNYFYQSmIFyRFrKAUxAuSGuic61nphWitNks6E8/QrEHSJOfcAUXGm+2cO7xMi4UaR7wgKWIFpSBekBSxglIQL0gqLbFSrqvX7pXz9NOSXizHfAEAAAAAlVWuczp/YWYDJTVKWiLp62WaLwAAAACggsqSdDrnzm7B225u8wVBmhEvSIpYQSmIFyRFrKAUxAuSSkWslP2cTgAAAABA/SjLOZ0AAAAAgPpE0gkAAAAAiKbFSaeZ3W9mfdpyYVC/So0nM7s05vKgepnZ183sKyW+Z5qZJbrcuJmNNLPftnReBaZ7aZPnM9tiumiZ2HEUm5mNNbNzKr0caWNmh5vZb0p8z+VmdknCcQeY2cKWzqvAdEea2U45z28xs/3aYtpomdixFFvutrCeJWn3zewiM+uW87zNcqTcNqOtmdl/mtnQZoYfZ2YTw+NPm9mPwuMzW9OutPhCQs65Yc0spMmfJ9rYkmmaWQfn3KaWLlO1zgvFtSCeLpV0ZfQFQ9Vxzt1Y6Xm1sP3YImadc0e2ZtnQOuWMI9QO59xsSbMrOa8Wti8jJS2UtDRM+/+0egHRKuWMJbROa/MXSRdJul3SGqn5fdpq5Jz7SYJx/iHpH+HpmZImSnq+JfNL1NNpZn83szlm9pyZjQrDFptZv5CBv2Bmv5f0jKRd80xjtZldZ2bPmNnDZrZdGD7NzK40s0clfcfMtjOzu83s6fB3VBhviJnNC39zzaynme1oZtPDsIVmdkxmXjnzPcfMxobHY83sl2b2iKSrzay7md0a5jPXzD7Tki8RpWltPJnZLyR1Det9XBj2ZTN7Kgy7yczah+GrzezqML8pZjYoxNxrZvbpMM5IM7vPzB4ws5fM7Kdl+zJqWNP1aGYXmNk1Oa+PNLP/Do8vM7MXzewhM7sj35FcM9vezOaExwebmTOz3cLzV82sW+6R4LAurw7r/uWcNqCrmd1pZvPNbLykrkU+y/nh/Y9KOipneNN5JWmrepjZH81sQZj/2XlidnX4b2Z2bWjDFpjZF8Lw48I8/xa+u3FmZqWvqeqWljgys/bmtzGZ9XhxzrR/ZWYzw2uDwvBmtz9hOteG4fPN7P+F4WZmvzWz581skqTtW/vdVxPzbf+L5nvoFoZ4H2pmM8xskfm2e1D4HueG/wPDe79rZreGxweG93fLM58FZtYnfJ/vW+jtNrPbwvxyj/BfHtZRZptxYc50RpvfXkyRNLDIZzvMzJ41s1mSvpkzvOm8bjazyZL+nC8Owrg/CJ/jWTP7hfke78MljTPfxnS1nN4ZM/tiGH+hmV2dM53VZjYmTOcJM+tf0kqrUimPpQtDGzDfzO7MmfZtZjY1fL7/mzP+93Ni6Gc5w/PtM51vzWwL08i23t/8dzObZT5PucvMejTznhvMbLb57dXPwrALJe0k6RHz+UXuPu3VZvaNnPdfbmbfC4+bXTd5tDezP4T5TjazrmEaub/zfma2ODweaX7bOsHM/mVm3wqxPTf81rcJ440N7YfM7NTwu3lc0lk5yzzS/LbnSEmflnRtiJs9zeyZnPH2srDdzcs5V/RP0jbhf1f5I2nbSlosqZ+kAfL33xxcZBpO0ojw+CeSfhseT5P0+5zx/iLp6PB4N0kvhMcTJB0VHveQ76X9nqTRYVh7ST3D49U50ztH0tjweKx8ht4+PL9S0pfD4z6SXpbUPcl3wl/L/9oonnLX8b4hPjqG57+X9JWcuDstPL5X0mRJHSUdLGleGD5S0tthOTLLdHilv6dq/2tmPfaX9ErO6/+UdLT8ztC8MF5PSYskXVJgus9J6iXpW5KeljRCUoOkWeH1yzPvD+3HdeHxMElTwuPvSro1PD5I0qZ861TSjpJel7SdpE6SZuS0T03nlaStulrSr3LG69s0ZnOfSzpb0kPybVj/sCw7SjpO0ipJu8gfIJyVmV+a/lIUR4dJeijneZ+caf8hPD5W0sLwuNntj6RRkv4jDO8s31Oyu/xOQCZOdpK0UtI5lV5/bRgHA8L3e2CI9zmSbpVkkj4j6e9hfXYI4w+VdHd43E7SdEmfDd/XUQXmc6Ok4ZIOCHGRWTeL5PctjpM0MSdGZob10E/S+/Lbj8MkLZDULSzTK0Vicb6kIeHxtTkx0HRecyR1Dc/zxcFpYZm6Nfn9TMuNzczzECuZ9q2DpKmSzgzjOElnhMfXZOZX638pj6WlkjqHx31ypv2sfNvYT9IbYb2fLH+rDQufa6J8G9TsPpMKbAvT+Kec/c3wvU1XyAEk/VDST5r+tnJ+b+3D8IPC88WS+uVMe3GY5qGSHs0Z/rz8/kKz66ZIPB8Snv9V2W1H7rL1k7Q4PB4ZYqlnWJ+rJH09vHa9pIvC47HyeVKXEDd7hWX6a07sjlR2n2iscrY7kh7JWa4rJX270HeetLz2QjP7bHi8a1ioXEucc08UmUajpPHh8e2S7sl5bXzO46GS9rPsAf1eZtZTPvh/ab6X4B7n3Jtm9rSkW82so6S/O+fmJfgsdznnNofHJ0v6tGWPlndR2HlMMB20XFvEU64T5Rvup0PcdJX0Xnhtg6QHwuMFktY75zaa2QL5H3LGQ8659yXJzO6R38mlLKawputxd0mvmdlg+Y3uQPnf7Xck3eecWytJZjahyHRnyh9hPVa+ETtVvhF8LM/4mbZkjrLr9FhJv5Ek59x8M5tfYH6flDTNObcsLN94SXvnGTdJWzVU0rmZgc65FQXmLflYuyO0S++GI8xHSPpA0lPOuTfDcs0Ln+/xItOrNWmJo9ck7WG+V3aS/AGujDvCNKabWS/z5/rk2/6cLOkgy56v2Vu+jTxW2ThZamZTC3/8mvQv59wCSTKz5yQ97JxzOe11b0l/MrO95BOmjpLknGs0s5Hyyd1NzrkZBebxmPx3uUTSDZJGmdnOkpY751bb1sUEk5xz6yWtN7P35A+KHCPpXufcmrCs/2j6pgwz6y2fHDwaBt0mnzg25x+Z+Fb+OBgq6Y+ZeTvnlhf4rJJvS3Lbt3Hh8/9dfvs4MYw3R9JJRaZVS1IXS8F8+R7tv8uvw4xM27g29LYNkt+2nCxpbhinh3wMHaTm95lK2RamxRLn3BNmdrqk/STNCN9JJ/kDvU193nyFXgf5JH0/+XXSLOfcXPOVNzvJJ38rnHOvh97R5tbN9DyT+ldOjpO7jSrkEefch5I+NLNV8gcaJL8vfFCTcfcJ81gkSWZ2u/yBr2JukXS+mX1X0hfk4y6vokmnmR0n38h9yjm3xsymyW8cc32UYMGayr1BaO7724V5rW0y/i/MlxQNk/SEmQ0NG/Bj5Y803WZm1zrn/txk2oWW1SSd7Zx7qQXLjxaIFE8m6U/OuR8389pGFw7ByB/4WC99vGHJjf+mN6zlBrYFFFiP4yV9XtKL8htSZ81seYt4TH5D3CDpPvkjjk7ZnaOm1of/m7Vlm1bKOkw6btG2KnzeUuZd6PtZn/O46eereWmKI+fcCjM7WNIp8iWUn5f01TzTcMqz/Qmf89vOuQebDB+WdFlqWG68N+Y8b5RfJ1fI70h91swGyB/lz9hL0mr5Hp5Cpsuvn90kjZbv0TpH+Q9G5PsNJl0XpbQHTfdPmouDU0uYXmY6+eRuH9PWvqQxliS/v3usfJnjZWa2f55pZNqYq5xzN+W+YGbfVjP7TGZ2ZonLkgaZ35zJdz58Md+IZra7pEskHRHa+7Haev+1OX+Tj4sdJN2ZM7+t1k0BTWMnc6rHJmVPlWy6LMV+A021ZN3fLemn8hUUczKdN/kkOaezt3xmvsbM9pHvhm6JdvJfuiR9SfmP1k+WL4eSJJnZIeH/ns65Bc65q+V7oPYxswZJ7znn/iDpfyR9IrztXTPb18zayTcC+Two6duZnRkzO7RlHw0laKt42hh6uCXpYUnnmNn2kmRm24TYKMVJ4X1d5U+ULnR0E/nX4z3y398Xle0VfFzSGWbWxfw5EsOLTHu6pC9LWuT8Sf3L5Q82lbJOpsuXU8rMDtDWR/VyPSnpODPbNsTU5xLOo9m2qpnhfcPD3JhtuqxfMH8O13byOxRPJVyGWpeaODKzfpLaOefulnSZstsjyR8BlpkdLWmVc26V8m9/HpR0QSZWzGxvM+seluXcECc7Sjq+hM+RFr0lvRUej8wMDL2Jv5b/7WxrBa7q65x7Q74MbS/n3GvycXWJ8icKzZku6bPmz53sKemMAvNbKWlVWPdSiKcE8sXBZElftXCeoYVzsyR9KF9K19STkoaYP9+rvfxv6tFmxqs3NRdLYZ92V+fcI5J+IF+Wnznv8DOhbdxWvqz3afkY+mpoL2VmO4f9pHz7TC3dFqbBE5KOMrN/kyTz5/037eXtJZ+krjJ//nNuxUK+35/kE81z5XOgv4Vh+dZNqRbL91pL2RyrJV6UtLuZ7Rme50u+t/iczrl18p/lBkl/LDaTJEnnA5I6mC8rukJ+xbTER5L2N3+S6QmS/jPPeBdKOtz8ibXPS/p6GH6R+RO6n5W0Vv48n+MkzTOzufLnRf06jPsj+aPZU+XP1cvnCvmSivnmL0d8RQs/G5Jrq3i6WX69jXPOPS/pPyRNDtN9SL7soRSPy5c9zZM/t4PS2sKaXY+hlPR5SQ3OuafCsKflr3z2rHwyMVv+/IJmOecWh4eZMpPHJa1MUKaa6wZJPcLy/UAFkjjn3Nvy58TMkjRF/oICSeRrq34uqW9Oe5VJDj6O2SbTuVe+POdZ+TbrB865dxIuQ61LTRxJ2lnSNPNl0GMl5fYirDB/i5wbJX0tDMu3/blF/rM/E4bfJH9U+l75cuMFYbnqMXG4RtJVZjZD/pyqjOvlz7d+Wf77/UWRHbgn5c+hlXyCsLNKKFt3zj0jfzBknvyR/mJJxvmSfmf+QkJNq7jyaTYOnHMPyP8OZodYy5Rnj5V0o4ULCeUs69vysfiI/G/nGefcfQmXIc1qMZbaS7rdfInwXEnXh4Makm+bJsm3oVc455Y65ybLX3tgVnjP3+SvfdLsPlMrtoU1L5QUj5R0R/hOnpAvOc0d51n57/05+XOEcw9g3izpnxYuJNTkfc/JJ2pvhe9Y+dZNCxb9v+QPTs2UPwDSIiF5HCVpkvkLCS3JM+qdkr5v/oJEmQR1nHwv6eQ87/mYZSsr4jKz1c65ra4EBVSa+fM3DnfOfavYuGgZM+sRznHpJp8EjAobWyCxWowj8yXDl3AgC0AMZna5/IXp/qvSy4L6Y/66BL2dc5cVGzdN9fsAqtfN5m8o3EX+XJKqThRQtYgjAACqgJndK2lP+QrW4uO3dU+nmT0pf0noXP+euYoYUAriKb3M7Hfa+j5gv3bOFT0voBXzJJ5ShjhCc8zsfPkrHuea4Zz7ZnPjt9E8yx6LiI9YQjmE83EfbualE4tdoKdWlK28FgAAAABQf5JcSAgAAAAAgBYh6QQAAAAAREPSCQAAAACIhqQTAAAAABANSScAAAAAIJr/D0eks08CqfIXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parallel_plot(P[P['air_temp'] > 0.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# journées fraiches "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PC\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: 'pandas.tools.plotting.parallel_coordinates' is deprecated, import 'pandas.plotting.parallel_coordinates' instead.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parallel_plot(P[(P['relative_humidity'] > 0.5) & (P['air_temp'] < 0.5)])\n"
   ]
  },
  {
   "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": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}