{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> Analyse de l'incidence de la varicelle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On recupere les données sur le reseau [sentinelle](https://websenti.u707.jussieu.fr/sentiweb/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
       "      <th>geo_insee</th>\n",
       "      <th>geo_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>202052</td>\n",
       "      <td>7</td>\n",
       "      <td>11908</td>\n",
       "      <td>8158</td>\n",
       "      <td>15658</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202051</td>\n",
       "      <td>7</td>\n",
       "      <td>10564</td>\n",
       "      <td>7574</td>\n",
       "      <td>13554</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202050</td>\n",
       "      <td>7</td>\n",
       "      <td>7063</td>\n",
       "      <td>4744</td>\n",
       "      <td>9382</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202049</td>\n",
       "      <td>7</td>\n",
       "      <td>5026</td>\n",
       "      <td>3145</td>\n",
       "      <td>6907</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202048</td>\n",
       "      <td>7</td>\n",
       "      <td>6683</td>\n",
       "      <td>4312</td>\n",
       "      <td>9054</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     week  indicator    inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
       "0  202052          7  11908     8158   15658      18          12         24   \n",
       "1  202051          7  10564     7574   13554      16          11         21   \n",
       "2  202050          7   7063     4744    9382      11           7         15   \n",
       "3  202049          7   5026     3145    6907       8           5         11   \n",
       "4  202048          7   6683     4312    9054      10           6         14   \n",
       "\n",
       "  geo_insee geo_name  \n",
       "0        FR   France  \n",
       "1        FR   France  \n",
       "2        FR   France  \n",
       "3        FR   France  \n",
       "4        FR   France  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"\n",
    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
       "      <th>geo_insee</th>\n",
       "      <th>geo_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1564</th>\n",
       "      <td>199101</td>\n",
       "      <td>7</td>\n",
       "      <td>15565</td>\n",
       "      <td>10271</td>\n",
       "      <td>20859</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565</th>\n",
       "      <td>199052</td>\n",
       "      <td>7</td>\n",
       "      <td>19375</td>\n",
       "      <td>13295</td>\n",
       "      <td>25455</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1566</th>\n",
       "      <td>199051</td>\n",
       "      <td>7</td>\n",
       "      <td>19080</td>\n",
       "      <td>13807</td>\n",
       "      <td>24353</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>43</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1567</th>\n",
       "      <td>199050</td>\n",
       "      <td>7</td>\n",
       "      <td>11079</td>\n",
       "      <td>6660</td>\n",
       "      <td>15498</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1568</th>\n",
       "      <td>199049</td>\n",
       "      <td>7</td>\n",
       "      <td>1143</td>\n",
       "      <td>0</td>\n",
       "      <td>2610</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
       "1564  199101          7  15565    10271   20859      27          18   \n",
       "1565  199052          7  19375    13295   25455      34          23   \n",
       "1566  199051          7  19080    13807   24353      34          25   \n",
       "1567  199050          7  11079     6660   15498      20          12   \n",
       "1568  199049          7   1143        0    2610       2           0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "1564         36        FR   France  \n",
       "1565         45        FR   France  \n",
       "1566         43        FR   France  \n",
       "1567         28        FR   France  \n",
       "1568          5        FR   France  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On fait une recherche de donnée manquante dans la dataframe.On visuallise les lignes de donnée manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
       "      <th>geo_insee</th>\n",
       "      <th>geo_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n",
       "Index: []"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[raw_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
       "      <th>geo_insee</th>\n",
       "      <th>geo_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>202052</td>\n",
       "      <td>7</td>\n",
       "      <td>11908</td>\n",
       "      <td>8158</td>\n",
       "      <td>15658</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202051</td>\n",
       "      <td>7</td>\n",
       "      <td>10564</td>\n",
       "      <td>7574</td>\n",
       "      <td>13554</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202050</td>\n",
       "      <td>7</td>\n",
       "      <td>7063</td>\n",
       "      <td>4744</td>\n",
       "      <td>9382</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202049</td>\n",
       "      <td>7</td>\n",
       "      <td>5026</td>\n",
       "      <td>3145</td>\n",
       "      <td>6907</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202048</td>\n",
       "      <td>7</td>\n",
       "      <td>6683</td>\n",
       "      <td>4312</td>\n",
       "      <td>9054</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     week  indicator    inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
       "0  202052          7  11908     8158   15658      18          12         24   \n",
       "1  202051          7  10564     7574   13554      16          11         21   \n",
       "2  202050          7   7063     4744    9382      11           7         15   \n",
       "3  202049          7   5026     3145    6907       8           5         11   \n",
       "4  202048          7   6683     4312    9054      10           6         14   \n",
       "\n",
       "  geo_insee geo_name  \n",
       "0        FR   France  \n",
       "1        FR   France  \n",
       "2        FR   France  \n",
       "3        FR   France  \n",
       "4        FR   France  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = raw_data.dropna().copy()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
       "      <th>geo_insee</th>\n",
       "      <th>geo_name</th>\n",
       "      <th>period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>202052</td>\n",
       "      <td>7</td>\n",
       "      <td>11908</td>\n",
       "      <td>8158</td>\n",
       "      <td>15658</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "      <td>2020-12-21/2020-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202051</td>\n",
       "      <td>7</td>\n",
       "      <td>10564</td>\n",
       "      <td>7574</td>\n",
       "      <td>13554</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "      <td>2020-12-14/2020-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202050</td>\n",
       "      <td>7</td>\n",
       "      <td>7063</td>\n",
       "      <td>4744</td>\n",
       "      <td>9382</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "      <td>2020-12-07/2020-12-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202049</td>\n",
       "      <td>7</td>\n",
       "      <td>5026</td>\n",
       "      <td>3145</td>\n",
       "      <td>6907</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "      <td>2020-11-30/2020-12-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202048</td>\n",
       "      <td>7</td>\n",
       "      <td>6683</td>\n",
       "      <td>4312</td>\n",
       "      <td>9054</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "      <td>2020-11-23/2020-11-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     week  indicator    inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
       "0  202052          7  11908     8158   15658      18          12         24   \n",
       "1  202051          7  10564     7574   13554      16          11         21   \n",
       "2  202050          7   7063     4744    9382      11           7         15   \n",
       "3  202049          7   5026     3145    6907       8           5         11   \n",
       "4  202048          7   6683     4312    9054      10           6         14   \n",
       "\n",
       "  geo_insee geo_name                period  \n",
       "0        FR   France 2020-12-21/2020-12-27  \n",
       "1        FR   France 2020-12-14/2020-12-20  \n",
       "2        FR   France 2020-12-07/2020-12-13  \n",
       "3        FR   France 2020-11-30/2020-12-06  \n",
       "4        FR   France 2020-11-23/2020-11-29  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_week(year_and_week_int):\n",
    "    year_and_week_str = str(year_and_week_int)\n",
    "    year = int(year_and_week_str[:4])\n",
    "    week = int(year_and_week_str[4:6])\n",
    "    w = isoweek.Week(year,week)\n",
    "    return pd.Period(w.day(0),'W')\n",
    "\n",
    "data['period'] = [convert_week(yw) for yw in data['week']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
       "      <th>geo_insee</th>\n",
       "      <th>geo_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-03/1990-12-09</th>\n",
       "      <td>199049</td>\n",
       "      <td>7</td>\n",
       "      <td>1143</td>\n",
       "      <td>0</td>\n",
       "      <td>2610</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-10/1990-12-16</th>\n",
       "      <td>199050</td>\n",
       "      <td>7</td>\n",
       "      <td>11079</td>\n",
       "      <td>6660</td>\n",
       "      <td>15498</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-17/1990-12-23</th>\n",
       "      <td>199051</td>\n",
       "      <td>7</td>\n",
       "      <td>19080</td>\n",
       "      <td>13807</td>\n",
       "      <td>24353</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>43</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24/1990-12-30</th>\n",
       "      <td>199052</td>\n",
       "      <td>7</td>\n",
       "      <td>19375</td>\n",
       "      <td>13295</td>\n",
       "      <td>25455</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-31/1991-01-06</th>\n",
       "      <td>199101</td>\n",
       "      <td>7</td>\n",
       "      <td>15565</td>\n",
       "      <td>10271</td>\n",
       "      <td>20859</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         week  indicator    inc  inc_low  inc_up  inc100  \\\n",
       "period                                                                     \n",
       "1990-12-03/1990-12-09  199049          7   1143        0    2610       2   \n",
       "1990-12-10/1990-12-16  199050          7  11079     6660   15498      20   \n",
       "1990-12-17/1990-12-23  199051          7  19080    13807   24353      34   \n",
       "1990-12-24/1990-12-30  199052          7  19375    13295   25455      34   \n",
       "1990-12-31/1991-01-06  199101          7  15565    10271   20859      27   \n",
       "\n",
       "                       inc100_low  inc100_up geo_insee geo_name  \n",
       "period                                                           \n",
       "1990-12-03/1990-12-09           0          5        FR   France  \n",
       "1990-12-10/1990-12-16          12         28        FR   France  \n",
       "1990-12-17/1990-12-23          25         43        FR   France  \n",
       "1990-12-24/1990-12-30          23         45        FR   France  \n",
       "1990-12-31/1991-01-06          18         36        FR   France  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data = data.set_index('period').sort_index()\n",
    "sorted_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "periods = sorted_data.index\n",
    "i = 0\n",
    "for p1, p2 in zip(periods[0:],periods[1:]):\n",
    "#    if i < 5 :\n",
    "#        print(p1, p2)\n",
    "#        i = i + 1\n",
    "    delta = p2.to_timestamp() - p1.end_time\n",
    "    if delta > pd.Timedelta('1s'):\n",
    "        print()\n",
    "        print(p1,p2)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AL/SV1w5CoqDQR2rFX964CV/euA93bjlcdlMIcVFmkGW85wtVMjon9O1HnymyllTUVcghfv7kq4/h6w/v79g+5Gj6TJl3UugjvUI372S/I1WEQh+pFcqMjsMpqRJ9GLDTxyN7ex+gJgj9ZcLpt6sy4mj6llpmzoxzb9IrfJq+8ppBSCQU+gaEflkdb4gKBZYgJEDfdcsKCbN9d20N0W/XRZl3LhnS9LXp1EdKgHMUUkUo9A0IpkxlykZpVPgeJ1XC00CzYxYFr2w4/dbnlHknffpI3aBPH6k6FPoGhIU+CXyiNH39orkk/YHr08duWTi8xH76rc8NN5Smj9E7Sb3QF2D67bkk/QGFvgGhX6JdNlxNH0dUUh3oa1o8XOgZDIaH7IfJlHkn+w3pFX6fPvY7Uj0o9A0Iuqavzi9BoXz6+kOGJX1Gmc9WfZ/qZPT7+WWlxsN5KEOOpq/XefosS+Kz9+3C/FLbSL1k8NC7GjXMpIpQ6BsQFrQXWZ39+0QJmr46C8mkN6jFiH7rKRWK4+LCx9FPv2kUmg2zmr6k74qbHj+AD393M669Y6uResngoc8VaI1EqgiFvn4lMODomr75pfoKfU3Re98pjt2kG65wVGJfKVpAK33xg89hKGXfFtOo8+m10Dc9vwQAmFloGamXDB56T+u355L0BxT6+hylGdN9+hZqbL5SRsoGjt2kG1XI01dEP62SuVK/abRIOOo+t3J0uLufPoLHD87Z5SUsRtXXrMLDTGqJP3onxytSPYbKbgDpDbqmb67GQl8ZKRvswZsTAdKdUgSTArvmUENg0flc9nPAOVQ4/XZZ1H3OE3zsv3zuJwCAt7z6xYkXCFU+v2aDa+EkG7qgx/GKVBGObn2OGngWWp6gl2cFtWwENX2kgihRqN9e9A1N61GVYYMaPz/9plFQZ2PqPZW0GCX0qTyBhKTFbxnRX88l6Q8o9A0Ii77onSU2JCdNp8cWPdF5ZO8J9zMHb9INUQGTsCKeCd/KdcnCFh/DcOp+WYL9Vn1vmfLpSyj1ueadjfKfZVJP9K5clUUyQnQo9PUrgUmoPzl7fUcjlQS76AH11z7xY/czJ5skKf3WV6oUmKDPLq0xyr4veYlq/6KhlA1Jr0+bPn0kJ1VaJCMkDAp9A4Ju3lnnSQKTs5Mq4pp3ltmGAiarVoV8VPrNjNEYNb8swbFcfTWm6Uvt00ehj2SjSotkhIRBoW9AWNDSNNTZ7KDR6I2mT4eDN+mGkrdKTc5eiHlnObkx46hIMypD3TUKwbHcRPROf/kU+khv8EfvLK8dhERBoW9A0BOy13mSoMw7ezm5rvP1Ir2ivOTsosCImhJeMJeyn4Ky668qdZ9cRmn6TI3xSWVH+vSRvOhzhaoskhGiQ6FvQNA1fXUei8ow76zz9SK9wdP0ldsO40jPx6nsSUzfXVtD1P2yBO+rK/QZKj+5ps9+Rw5R6CNZ8QVyqfuTSfoRCn0DQt/49JVh3tm7qkhN8aaJJZp3FlCmJSUabsTcAipIRekNIAXQoelTfw3d7uRCn/2Xmj6SFfr0kapDoW9A0KN31tlcsVFGnj6O3qQL/RrwT8LT9FXlOahGK6pDVe5LVjrNO2Xo9szlJ4wHozR9FPpIVujTR6oOhb4BoV/y9JVhRlfjy0V6TJnPVhFTVSml59NXevTO4ut46Udux1s/82DxFRmk7uNTZyAX52+vNX3OfjTvJFnRF9TrvLhO+hcKfQPCQp8IfY0SoiTW+XqR3iBKDOSiKKJuCc+kesfRk1gyFEY/a1uKZvzkIu7bPp54/z/68qN448d/VGCLulO38Wmh1fYlTO8Yyw0Hckmdp6/BaRHJBpOzk6rD0W1A8Pn01XgFSmkdejn3rLv5FCmeMgO5FGlaKqW30PLmT92PD393c3GVJWhL1bjp8QN4bN9EqW3o1Xg+s9DC04emc5XRalu47H/div9f60edMp90/pohqaav1aamj+TD79NXwQGLDDwU+gaEfoneKUrx6etZVaSm9KNPn9J8jAx5r4mNu46X1RwXPo8BenQ9fv/fH8br/uFet19kQaVF+NKDe91tNz1+ALOLLfe7ur/GfPpS5ulrUOgjGdEFPWr6SBWh0DcgLLT15Oz1HY1c884e1lnfq0V6TZ216EGUKacu9I0dmsbk7FIp7emna2uSXl2VH+84ZteX4/0hQlLu/OVNm/CXN27q2NfEpLnVtvB4Qk2s8ulrclZEMkJNH6k6HN4GBJ+mr8R25MWN3tnDZTQO3qQbrk9fH3WVRUfoWzbU9G1/7zceL6M5fXVtTdKr66IENRPVBRceD07Ou5+9QC75a/qHO7bhQ99JZpKstJDsZyQzMvQjIZWBQt+A0C95+pqNEsw7e1YTqS0ZNNAHJ+dwSJvsVo0lJ/jTSED1cezkQhnN4XMYQa80oCbMLr0yAtv1qIfS/zcPmw5MJt7XotBHcqL34zpbVJH+ZajsBpDeoKds6IfpU0+Ts9f/cpGCUV5AabQTP/s3dwEAdl/zK2YaYbifLrU7ffqK4oM3PoXVy4fx3tdd1vHbYsvCpf/rFrzkgtOcLXwgy6To8VBNnE1MmkUKZ9sWnbBITpinj1QdavoGhIWW5frD1fnd5uYM6+HEj75EpBtpJpfG6y6o3DCfvqL4wv178M93bw/9TWlDH95zovB21JHg5HJusY0bHthTSbP0JE1yNX0G6ksTk0UFcqneVSN1QZ9bUdNHqgiFvgFhsWW5vjl1HotcwbWnqr7eVUXqiafpK7UZRllyffrKfU0cPVldE9gqEOxy19yyBR/49lO4c8uRYurL0ceTLKCpPcyM8Z1S3/T8EjYfmOrY7vn09dFDTHqK3nfYjUgVodA3IFhSuv5wdX6puYFcKPMRw8wutvDAzmOZju3HlA3KvLN0oW96sdT6q05wPB8/aV+vuaV22O756ytgRPSdgsGAMWGavt/53E/w+ut+2LHdoqaP5CSkGxNSKSj0DQh6kuU6j0VlJMHm4D0YvO/rT+DK6x/AgYm5zGX0kynwUkT0zl4z7gSOWTFcf0uFIohKbt4oaCUiz4JbIvPOjg/ZCbsEykxY1ySemFlEy7KM1UsGE71/07yTVBEKfQOCX9NXcmNyUEpyds4CBoKxQ7bJl54oOilqbmlZsbvVisUe+vTFcczRXJ1+ykip7agLqg8WlWM8j6VIkiNNJmePE3xV+bvHZ/DiD9+OB3Yez10fGXRkyCf7mQkzKSak11DoGxAk0BfmnYpeCn11DnxDekMZixFBTC9ORKVs6PUZKjPFoWYf2tAaINjlVB8syuQ4z/1P8u6RRs07oy+CSsa+5/isv34u8pGMRGn6vvnIM3j9dT/EHZsPl9AqQjwo9A0IlpRa5Mv6oiYEPdX09YGQTLrj3eX0s+UyA7kUNblXgS16qekLe9bajuqK5lLhBIUU9a2oiLL5Arl0L9MN5GIkZUNMW5zig7uwm5GsBLV7iq2HpwEA246c7HGLCPFDoW9AkBJ9Yd6poE8fMY6aBOaYK5ehJSiqfy6WEL1TBY/RUcInXa7C6fDpU5q+wirMcWialA1GfPpiNH0RJhzsXyQrUXn6Go3yLUEIASj0DQzSp+mr/8DDwZNUij7IgRlEmXcuG+7da2JusTPiZKvNUPppUJepqEAuud4fKQK5mLjdcX6N7YgK2M1IVvRnQ38XDDkdMWqhgZBeQaFvQLA0TV+dxx3Xyb+HATM4CSDdEChvJbco806ldRtp+qN3FnmKs0udQXRcTR+fw1B+81/ux0s/crv7vWifviLugz5Z9nz6DJh3hm1TEaAt/3dC8uLX9Hlf1AJMi4MYKRkKfQNCv+TpUxOBXmor+0EzSoqljFQiRbMUEb3zsX0T+OKDewqpM1zTR5++OI5ML7i5+QBPU1aYpi9X9M7k2jUT8+Owa6C27D0+i3u3HnUXbNy2cLwnGdF7jt5/3QV3Cn2kZCj09SuBt6iU2sS0hOaYwgvnXWQdndeO9D95brMXyKXE6J2Gq44S+gDgf37rKbOVOcyGCH3tgKavzotWvUBdJ5My39T8kvs5X/TO5PuYuM9hPn1q26/+8314+79ujKy/jiy2LHzwxqdwfGax+87EOHqf1RcPlNAXZVJMSK+g0NenBIcWKaVrV15rqc+hyFX/oEDZB5eLpCDLXFmU6NMX1FSYQpl39jKQy0Kr0257yb2ofBKToCaeJjV9f/DvD2vlZy8nyaFqsmziWcpyCercy25+8gC+cP8e/PX3tpTdlIFHf06a9OkjFYFCX5/SmbvJmwTU2UzKpJN/FK2AwyA1C6QbZfr0FWWOFqfpK4rFEKFPpWzghCkZRfTBsYPT7uc8/S1Znr7MxXcQFsilqxxY4/Fevbr4rJRDVJ6+pqDQR6pB17e5EOJ8IcTdQogtQohNQog/drZ/SAjxjBDiMef/67Vj3i+E2C6EeFoI8Tpt+0uEEE86v10nHDsLIcQyIcRXne0PCiEuNH+qg0WHiSK06J01Hnc8884CNX2BeWeNLxfpMXV+toIslZCyQaWJ8LeDgVzSUMQYqWvMCsnTl2CfLCTRgvdTIBfPt9jsw/JPd27Dtx7db7TMfsQfkMjbTk0fqQpDCfZpAfgzKeUjQohVAB4WQqhQYddKKf9O31kI8TwAVwJ4PoBzANwhhLhUStkG8EkA7wLwAIDvAbgCwC0A3gnghJTyOUKIKwF8FMBb8p/e4BIcWvTonXUedkya/kRBTd9gkuc+e+adJUTvLMi8s4w8fQtLcT59KogTicO9TkYvlNfHTJUrRHhZRjV9IV03KOR1JGc3V33P8dIymeVjt28FALzpxecZLrm/iNT0UegjFaHr21xKeVBK+YjzeRrAFgDnxhzyBgBfkVIuSCl3AdgO4GVCiLMBrJZS3i/t2dUXALxRO+bzzudvAHit0gKSbHT4pUnpJgjtByGmp5q++l8ukgB1m7MMPf0YvbPtaNiGwmbOOWm1rVBTznBNH7Oyp0GN/YVp+nKZd2plRu1j8EaHBnIJ1Nz5rjRWfc/px3GoTvhSNmjbGwzkQipCEk2fi2N2+WIADwJ4BYD3CCHeDuAh2NrAE7AFwge0w/Y725acz8HtcP7uAwApZUsIMQlgHYDxQP3vgq0pxIYNGzA2Npam+S7j4+OZj60Lhw9PAAAmJyYwNjaGpVYLi/NzAID9+5/BWHOizOZl5siREwCAycmp1Pcw6X2fmPdrG3bs3IX2iZFUdZFqkeTeLy7aEe927dyJhfHhVOVPTEwCAA4dOYyxsXSR8/KORROT9rN88NAhjI3N5ipL58j4cQDAgQPhZl16u9Oew7u+vRd7J5Zw6+8827cItXvfMxgbmfLtOzV9EgDQatvPZbvdTlxf1rE+7TFVeJ+oNszM2H1g7779GBMnjJTdbnn5E7dv34HJU1JNHVyOz3rl6KLX3Oys2341xgP5r+vUhP89NzY21rHouXvPXt/3Q4cPY2xsPle9ZXHggO17OTmV/v2YhCr08ySUNcc7cNAbu/bu3YcxaY+h40fs7cePn6jNNawjgzC3z0vikVsIcSqA/wDwJ1LKKSHEJwF8GPaCxocBfAzAf0X4Ap6M2Y4uv3kbpLwewPUAcPnll8vR0dGkzfcxNjaGrMfWhTOP7gRwDGtPW4vR0VE0Gntx6ikrgaMLOOfcczA6enbZTczE+oPbARzH8pWnpr6HSe/7kal5ALvd7xdddBEu27AqXUNJV6SUODA5j3PXrii8riT3fmTkIIAWLr74Ylx4ximpyl+7eQnAFNavPxOjo89OeNQOAMg9Fq19ahHANDZs2IDR0WflKktn3f6tAE7ggmc9C8Chjt/tdmc7h70T3nG2ydNOu871Z3Wcw7J7TgCYhxQCgESz2UxcX/qxPu35mLmH2djh+6basOLu4wDmcc6552J0dIORmoaH9wNzttB98bOfnfmZtcdWO8djoyFcbfKKFSvd9qsxHgAuu+wyPLJ3AmeuWobzT1+Zoib72px22mkA7An3UEM478JdPvXeEcv/rJ955pkYHb0o/clVgK0LBwAcwapVqwz3yTL7eXrKmuM9cXIfgKMAgHPPPQ+jo2fZ26ft7aeuXlOba1hHBmFun5dEdjtCiGHYAt8XpZTfBAAp5WEpZVtKaQG9xsZZAAAgAElEQVT4NICXObvvB3C+dvh5AA44288L2e47RggxBGAN1KhPMhEXvbMfLAwWWp2+P6YImmAwWW8xfP7Hu/GKa+7CpgOTZTclN2X69LltMFyeOpVmwZb2up9LmMnnkqWSs/vbRfwoDZbn02fQTFLrXXkSTOtHJvFFtSTw65/8MV75v+/OVJ8evbMRFsoTwHV3bfd9r3P/cvOFGiyzH9xBekaEeaf7fqBPHymZJNE7BYDPAtgipfx7bbuuKnoTAJWt9yYAVzoROS8CcAmAjVLKgwCmhRAvd8p8O4AbtWPe4Xx+M4C7JEeaXAQFFSllYU7evUR1i4WlzsmhKfrJx6PK3Lfdtt5+5sRcyS2xyXOfveTsRpqSCdNVq/Ii5srG0AXlhVa7I7G0KxTyOYwlmMTe5PzSVB+QfqkvYh9vp7yBL3SfPrV40c/RAkQBUp+KnqszPb/k+doSF33eFbYAWOd0WaQ/SKLpewWAtwF4TSA9w/920i88AeDVAP47AEgpNwH4GoDNAG4F8G4ncicA/AGAz8AO7rIDduROwBYq1wkhtgP4UwBXGTm7ASY4tkgJDDXrr+lTbV8o8IUTXI2r8/WqMvOO4L58uFlyS/xkmRQWFSo9Td3GUedS4CT55EIL/+MbT7jfP/WDnfjpD9+OPcdm3G1eygY+iHG0LL+Gz2wgF/PRO6MEyagIiFnQnw0VQbGbhrHOvUydm0nrlPkQq5oXfOg2/NGXHzVWR7/gC+QSovULkZ8J6SldffqklPch/LX/vZhjrgZwdcj2hwBcHrJ9HsBvdGsLSU5nygbZV8nZw0K7G6sjKDDXehrQG264fzc2H5zC1W98QaQZVZB55x72MiVAErI8Hl5ydsONKREJe2LeKFA18qUH9+Cmxw+435WW75kTc7hgne1r1XbNO/vo4hbAUtvC8uGmN8EsqDPmit6pHRslfOml573net9Vw1K37lxnI6MionfOB961SsN3y1Odfr6DyH+74SG8+SXn4xefd5Y/36R2E9TndjA0OCE9plqzLWKMTsHFe+nV95XmEeb7Y4rgRKPGc4Ce8YEbN+HLG/dhcm4p8TELrWpq+rLc7ir49Jmu2pISQohChb4VI+Hrjk1t4aAVSM5e50l5kajrJN3rZK5sY8nZ9ZQNEWkg/Jq+7HUB/tXqZtF2yhWgCDPzoCvFxKw9xg8NwPVMwvc3HcbvfeEhANF9V31mnj5SNhT6+hT1ElWDkCWll5y9xpMm17yzh0IfSU6aPERqBblqk7E8z0c/vdOltCeRRfpArV4eLvQNNb1XU6ufLmqBLAU0osXl6ctOkmN1ATDvJDnM6qBbd67z8O9q+gwu7QaDpk3O2dr4NSvSpbXpRzpcQfTFi5CFDLpBkrKh0FcRPnnPDvzStT8wVl5c9M46owbSIqN3BucZFAKTkyY6WZivSJkYSTpdZvROw4+3dMosUiZfvTx84ujX9HGmlISgRtSkrOyL3pmjj+sLKlHvI5+2JG8gF71cta0P3oPRmPfbn4/Q9FHoC4n0Hanpo3knqQbZMqwS43z0VjuhpGXJxD5RqZDeRKrOQoyr6SswemdQ01Pjy9Vz0mj61D2syvV1zeKyHAv/hLsMTF9HW9MnUGQkl+Fm+LqjniaCmr5keOadBWv6TJl3Ru2jfV7MKfBnCUBTZx9uT9NnjqBPnxL6VlHo69BEd/Xpq2/XIn0CNX0VI41PVBxBwcWSnjBZlUl2Hoo07+zM00eSksYcS00mqjbJyvJ86GbUvaYoxYWELFzTF3Xv9e1Boa9avaU6KPNOz6fPoNDn+2ao3ChBUvuS13e74atDBqsNpc7vxyJ8+pSmT/nwTThzlCjTbBPc8/QRfOvR/YWVb4qO8d4n6Gmb1f5cwCIlQ01fRVg+3MD8koXxkws47ZSR3OV1mnfKvkjOrpq+2LbQtmQh/mBBC4w6+0D2mjTWK/Otamn6PNI3SAb+9gNS2gJlkeZwUfdeXzygeWcyvDx95rXOjQJSNoT1ql3jM9hx1EvXkXdxL7Td3aJ35qqxXIT7jjd3FmpxbsSJsjw9bwt9py4rbvr4O5/7CQDgTS8+r7A6TBC3IKULhErYa9G8k5QMNX0VYd0pywAAR6cXjJQXHPIlgGY/RO8MJHIugo7onYXU0p+kMe9Uq/hJj9g1PoMLr7oZD+48lqFl3ckT9bBMTV9RSCkhIArW9IWjz6Vo3pkMFUq/iEAuPq1cjmL0JoW5Mbz67+7BzU8edL/n1fSFBaAZCE2fwTKVoKIWWNXiQiFuKDUjLqdvWPROynykbCj0VYTTTrHt44+YEvp00wIpISU0884av9U0ivLri7HYIF3IEm0vaX/88Y5xAMC3HzvQZc8yUP5UJTfDIK6mr0Cfvqh7rwssFPo6Cbtu/3jHNvs357vZQC4euQK5+PL0dSfvwp7fp6//+5GXp8/cuQb7kRe0ylgVtaXDp0/X7ummnmr/AeiDpNpQ6KsIK518VTOLrUT7t9q2KWgU+st1y8FpAF5whDqPO3rTi4r+2DmpqfEF6zFZJoRVu7pZ2uNq+vpIQJEoPmWDfrXOONUza4817+yfS2yU2zYfBlCQT58h805/nj6tzIj9c2v69LpD6g2jaj7GacgTjKpbme73Gl8f08QFctHfBUUEVyIkCxT6qkJK07K/vGkTXvqROzCzEC4k6uW8/rofAvDMM+o8aOvnFQwlbQo1MG9YvbyjThJPNk1fAQ3JQR7zzjJPxfRzLaXtExU1RzYiVGhFrF+13P2sJkxSyr7KfWiKuEvvmncavHA+4SmP0BdRZhR5o3eG+fR1W8So2niUBlcJZ/AcguMKn0ePuJQNuoWCeibr3LdIf0ChryK4g0LC/b+/6RCAaM1gmAVGP0Tv1F9Axfn02X9/9YVnO3WSOPTJf7ZkytW6wnmEp0yaztwPZDGqOMvO2VBofk/9Wq9ftcz93OYkKZa4y1JEIBe/b5yZgv1pICRufepgxz4mffrUdelnT7S084hkZfq/ewtcfDhjNX0hkTx5xUjZUOirCN4KnaFhIWTS5Jp3mqmhdIrS9Kl70Gw0nO+FVNM3hCWhTUOZ13dybgmzCU2q41AToCznUuWV8zjzTiOKPl3Td6on9FmcJMUS954oIqiQ7teZz7wzXNf3yN4J/P6/P9Kxf97onWFh87uad9Z4wC/CtFcGJBYKex5xPn1tn6bP3aEXzSIkEgp9FcG0+l+GfOqn5OxAZ9JYU6gBWuWNrvP16gVWxIsuKWVe3Rf+1W34T9fcZbcjxzOYZ6Jd1f4lpYQQolhNn8+8UxP6AukHfMcU1pr6EHYNlE9knii0URhLzh5RZhR5NX0+AWUAOk4vBNaKDlelEPe+a4eZdxbeIkLiodBXEdJPGrutVvr/AuirPH1AcQna1T3oh8A3RXL32BFceNXNODgx726rm6YPACZml3zfMwl9OY6trNAHxCZnN9FqvYy1K4fdz23Xpy/kmIper14SdglOCeRNM6rp8wVdyV6uL5BLgv1zC30+mS+ZeWedu1cRCqWOFEY9NL2u+rMevDbdFkArfjpkAKDQVxFMD6Rh5mZq8lancefdX3oEv/kv97vfe6Ppsytp9EHgmyL54oN7AACP7Z9wt2WJu1C1F3uW+51H01ex03dxXPpQpBeUfu91oUX59FHTF05YH221/duKCg+fr1jv4CQa5Lx+23pTk7a7F/3rwqtuxtU3bzZerqdRMmneGf+9SKqerqUjObsM/y3OcoGQXkKhryKooSD1oBCxe5izddOV+uoz8Nz8xEFs3HU89LeihD51eZqihlJyCeQN5FK1y5tN06de6umPNTURMP1YW1KiIaKTs5sQ1vUSVgw3vbpjLmSNhq/CCLsGnRoZk/WFOQxkKcf73EyQ3NukNYcbyKUi0Ts//cNdxssswrQ3GISkl3LYUs7orUWTNGVDxWVXMkBQ6KsIaSd+YS+ur/5kL47PLAIIN/PwNFf1xRe9s6BALmogbzbrf72KJMnEM2s5u8dn8N0nopKwF6TBMHJw+lLyTgiKWptQ5p1RgS+MmHdqhegTPHVNwjV91Xsiq6CtDmodTKZs0Ms2da5JhL7cC3uhwmr/5ukrIjVAx2JCD6/PUrva98IKphCN0PTlCfRFiEko9FUENXhkXfXffuQk/vw/nsSffPUxAJ2rc4Cnuap1Ammt6cWlbHCEPvr0xRK2sJBN09d5zC//4w/xni896tsmKhxsXZ1BlvPPq+krqn/a5UZr+gzV4n5aWGrjOWeeCsA2Tdx/YhYn5zsjq1Zx+Or1GBFWX7DvmbxOliWxarltfptL06d97oWmL8y8syqaviIxat4Z+O5G1u3BdTKt6funO7fhW4/uN1ZeKyD16dddH9fjFrEI6SVD3XchvSBsAp3muJNOkvaJWaXp61xZ6gdNn05xKRvsv/2QzL5I1Iq/z3k9TxQUjbmCTHeTkC16Z3bzTmmqGxufUEhb0xchbJuoTi/jl19wNv7vS9fjNR/7ASxL4uc+ejcuXLcyrFmVo9dNChuTpueXMLPQCn0u89KyJIabKoVN9nLDfMzj0DV9liXdd1iW+hRlLx0VqRUuJAl4sKwC+hcAbD4whR1HT+JXX3iOu8200Pex27cCAN704vOMlBdnUt22JJ6ZmMNSy3J/yJbHlhBzUNNXEdxALimPs6TE7vEZfOsRe/VqROUZcArSB826aK5u33wY/3zXttDfJIAh58VfeCCXmlyvsghbqMiiRY47ogyzuYm5xdRRAz1/l95r+goz75TxefqM1OH8vfVPXomzVi93F1rU5Gj3sdmQY6r3QPa6n4ZVt9SWeP5ffl/TxJhrU9uS7ribp1j93g01uk8/9MWfLEE9gn0lyTUp+k4W2VXCLHxylxm8hs5f0/LL66/7If7wy37rjmBwoqoRJZM2hP3MvOKau/Cqv7vHvVZFBVciJCnU9FWMtBNAKYFfvPYHru37yJCzGuv8ftPjnl9UQB6sLL/3hYcAAO95zSUdv0lprzhbsl1gygb7b7PPNKNFkTdPX1yftyTQ7PHS/Ns+uxG/8Nyz8Jl3vDTxMW70zhLMO4NtyMtPfej7+PWXnAcp7YWPyOTsBp4M1+TO0b+ohZa4yVEV501VapLqTyYn5W1d05ejnDDLkzh0a46WZWEk5Tp1sK9YMsEiRsEdrMjSvSTg6Y7bNT6D/Sdm8cpL1keW2alBTjtXkZhbamPlSPy0U1+oXqx4IJcO807n2gw1GqF5+mrtWkP6Amr6KkJaswz13rKk9Dk7x5nguOadVZw1JUQ6L+3lw83epWyo8fUqkrBIcaZTFgRfqkWit+OOLYczlZEtememqjow0U/v33EMU/MtfO5Hu2FJ6eTpKzBlg8qd5lShFlriJkdV9IvpuU9fzG9Wx+Q8H5/70S4cmpr3FsHyaPq0Y5P0Kn1hL5umL1i/7OobXLymr7gaPIuhdHW8+u/uwds+uzGizPDvaW/HFx/ci+d98PvYG6K91znhuKgA1df0RQVyaTZEIJCLs3+1T4cMABT6KoI3gU6v6dNxNX0hxdTFvLMbAo7QV1AgF2+1rj+uV9HoE4xMefpifuulD0SupNPO32xCb07zzkAb8vBbn37A/SwRb95p0qdPVeEKfTFlF9Ujcvmq9VjXF9dWK+OkPIq/+o6dT26oaXYRLEkpC7p5ZwYBoENgQfmBXIosPmwhLneZgRZnDUry/U2HAAC7js2E/r5yxE7ZcmJmyd1W9ZQNnYFcbIaaIhDIxf5Mnz5SNhT6KkJWB+wOoS/GBMf1UauUMVI6VMuXDTUKS9ngmnf2wfUqkrDVyyw+C3GTyKon51XkCZ6R9xSLE4LsdA3FavpsVBXqbxnmnaY0WL0gyUKJaY3SsOODl6e/ph1L9Ul/K6EA4M8pKAO/VSGQS4FlqwBuBssM3u+i0g+sXTEMADg2s+BuK1Lou2PzYbzqb+/OVUdUIJehhvAJeGquUkUrBTJYUOirCGnV/2pyFBxEhpvR2ikT5jllo09Ei5IHOs07i6mn7siQhQrTgVzaFTfvUbjPb4b5Q9V8+tzylOml2WL9dbiNtmspM61MPXqaTdy9Nm3eqTARzVhvUhKhVHddSBqt2VestBcI3/e6y5yv/jpfeP7azuMN9IRW28I3H9kf2o+LXEQsIoiPuqBuiRk1fYqo8WTtyhEAQU1fMddqodXG//z2k9h9bBbjJxe6HxCBLi9KKd17O9T0+/RNzS05+9dplCH9CIW+iuBF3Upp3hn47gVy6SwnielU2cwt+kN0B5GQEAAajeJ8I7xALk6dFb5eVcBv3pnhYsUc0stoZ7mqco7N0t7c0Tv9TTCHtJ+zyOTsBisM+vTVRcOr6PkYEVNfVp+rbqh3Sy+lY10LM7vUmbMxjIDMZ6cdURFupb8/L2t2ToFM3MvP/Wg3/vRrj+MbD5vLCZeEYqJ3+ikkLQSAU5fZAV6m5os37zw533Kjx+bxG2xrq3yWjNb0qXOipo+UDYW+ipA14lqnpi/ap08FS6uyuaJvwI9SmzjBJYoaQJUw6ZnDkjhym3fGXOG6rIx6Jk9ZzFsNtcHw82A5QS8KTdngNFlVobTr3Uz5iljwqZVPX4JnxsT4qC+8BSNDZ8Gv6eu+v0/oW0zmwx28j8L5F1bn8FBn5zZxJ4862qPjWlASr30GKgjh8NQ8/uJbTxqvI7j46i0qmD0R17Rbq68ooW9moe36qOZZYNKbZ0nvqWwGhb65lrN/Pd5npH+h0FcR2p5dRqL9vZeYf/+hmDDYdTBX9A/4IZo+Z1MvzDubjN4Zi3tZdIf1LOadMYfUReOTR7tS1dVfV0sS+Xv+dnvRO/3mnd3uexHdwpQw0wuSmHeaaJO+8LYsJkhYUvQ+k6Tf6/1gdiF94C41douIBc/hgjR9ZfDYvgn3c7GaPme74esU5rJi0rxTfze1LEvLCZpdsNQXOduaqq/ZEL7fPE1f5qoIMQKFvoqQ1SwjOPC23Rd+iHmnM6oempxP27yeobd6KSIPn4ooWNRkWa3eDTFPXyxhQQMyhVWPM++si0+fMu/MlKcvX9266ZpJVNCLqPYVEb1Tade7mVwV8eznE2Z6S1x9Jn369PughD5jPqgJ9tEn/bOLGcw7pX/hQn1XhAl9JtDr62hfLzqLwUqCRalx37imD0oI88o1ueCqv5ssKd3ARPk0fd4cRUpvoSyo6XtmYs6ul1IfKRkKfRUhq0lOcAzxyuncV2n6bnhgD/Ydj8+VUxb6oBhm3imldAO5FPXydAO59EmKi6JwFyq065MpZUHMb73M05eHPBOhvJOnovqnPYEpzowa0IQ+Z5bszMO63vdChL5cAUp6O0gkSdlgwpRMF/pMm3cmmQDr5n1zCfOy+kxIYQtgaixXuScVI2GaPhMifIxJdFGmwMHzNkVUhMq0Xb7b/mHmnSZlJL3ctqX5D+fy6dM+S+kulDWFLfStO8UOTjM933L3yctS28KDO4/lLocMJhT6KoKnoUu2f1T0TjWAhQZy0d52R6arqe3TTyfKtMNOGF2cpk9NqJquqSylvjj0+5Blohk/ge3dtTcxmS0jT59bjuF+KgMT5M76zCEC0TsXu/r0GazcQJm9XsCPqy5u4S8t+n1w0wE5F+qZiTk8svdEqvL0JiUy78zi06fVogK3eOad/vsc2r8N3suwZ7K4Ic1/3sZLd4W96DlGHsLnNCY1fV5falvSjXSeK2VDQHsoYS9KK01fsPUmFmL+/vateMv1D/jMeQlJCoW+imBlfFFHm3d27tv0+fuVna0oHH2YDDPvVL8WGchF3YM6+ECWSdiKbyahL+a3Xvr0mTDvy5ayIXu9QPHmnWeuWob3/tKlZgtXdQS+J119r5wfZK+FvgT1mVhM0CfKw4EcsK+45i782id+nKo8vU1h/f5jv/FC33d94W9mIaF5p0/jJX1vOin9vwdzUA41hPFbeXByDhNaQJeiukpei4sk5QLaWJexiqiFJM+809tWlKbPktIdaxYj3EiSoL+fpGW3t6GZdwYFShP3ZeuhaQDA0ensqSbI4EKhryK0M66eBQcRNzFvyL76YBsT76VU9EE+bAVOTURFDwK50KcvGWlX7zuOjzlEn/xXOeqsogzzToXpq6NWrYUQeM9rLun83UC7g4E2lFamW/TOqrnGVCl6pyJvv/rR9nFsPjDlfjeRssGvw+ksSEVUVPjMOxNq+nz1SQBCS9Mg/dcl+B4cbjaMm+r+7N/chZ/9m7u0NhXfV0xWEexHReWB9Mw79TQIJjV9fosUlbIhT7AYXyAX17xTuIFcgotXJjR93iJfxQbBjNz42DPYdGCy7GYMDENlN2AQuX3zYXzwxqfwg/e92n2RusJazue4FVOOrumLyr1VNkkid9k+fcUNem6ePs0PhHQSlqYgm6VM5/UVwu7Dfqf+LGX3hjzmnabcFovS9BVJWJMbQmCpa/RO850hl6a3gn0z7/zytz/zoO/7sqEmgLy+j97nsH4fDKzii96ZwacPUD59zm+Qvr4TfA+aei0GU0Qk9UfMQzFGkZ1leWOdwUrg3YuiNH26AGZJ6S4wmDbvhKbpC/ommzmfas7dsvLHX3kMALD7ml8puSWDATV9JfChmzbh4OQ8Dk95fnWW1TmBjkM99h2aPndg6yxHN2WpqqbP79MXZt5p71BkyoagT18VJ3RVwFggl5BDVPf0mc+k9HvtLWr1O/2RVV1UsP2hYn43Uon9R6+nKUR3TV8BD38uYcZgO0xVaLpfqQVKc4sUIZq+wItJmfgLAcwmNe+Ef8wQQmjvy+4+fSYum6nn5t6tR3HhVTdj1/hM1319Zq0FmHd2RGsuaNzStWdmo3f6NYjq/b5gyLzTsiQcmc8N5BK2cG1q7KrmW4NUHQp9FUENdOmTs/u/x2n6fC+7iq4W6YN8nHlnkYFc1KCsVgKrOimvCrkDuYRsEyFa1uJN+vJrMMpMvms8kAtkh8+T73cDk/9gnj7AjuDZ3acvf90dbcml6evtfU9Sm+kmmYjeqR8ddg91885mQ7gRnEeajWzpYOCkbHAjMUtfvcH+LVD8ZDrNffn2Y88AAB7e0z1gTlEmxp3RO7MvcMWhyg2mQTBFMHqn0iqb0/R5/a3REJG+gnkjeAZ9uCdmF/HDbUdzlUkGBwp9JRAWeEGNc2nHg+BkQw2YoUKf9lKoqHWn70USFsFPf4kX59Nn/2028icj7mfCdMrZond2blML/j6fPmfHn+w+XrmQ1e61KNOnz3A/7XYrTbRbFaEPR82yzDtLOjZTfSVo+rzk7PkXR4AIn76GNyVpNoSrKWk2ko/3fo2X37zT1vRp78HAsaLAVEBeo4rZN3jeRZHVlL2bUKqKC5phmiLo02cikEunT5+EgMBQQ0RqEPMuDHp91i7ndz//EN722Y2J81iSwYZCXwl4PuWdg1v6QC7+766mr6ehos2RxKcPKNqnz9H0NajpC2P7kWnfd782LstqfOcxYYl61ccdR2fwlusfSF1PkeRZ/c4dvbMgrb1t3hmj6TNRh/PXF2SqIUIj9+oU49NnRpgpmgMTc9i4+3jX/bL2q+8+cQD3PH2kY/tI00CePu1zmIJF1/QNaQmubf/qZDUH9xJCuJGYLen36QvX9BV7MwvL01dQHcHnwsox1gHR45Uqd6ndOeabIBi9U73f82j6fGVa0jWJbzYEFlrhfpx5x67gkPy0E82zl5GuSX1hIJcSCDp5A+nz9HkENX3R5eibqirI6M2aD3F+V78XmbJBrwPoTaTAqfklrBhudgQyqBrfe/Ig/r8vPoJPvfUlbofy9WNDmj71Ygv6YVQV1bIs529O02f6+shYcdKsps9v1tdtAlNEV8gnzPSub/7StffiZAL/tqz35z1fejR0u2vemeNUu/md6eOfHnis2RSJfQn1ctV9URP8thUw79SG26++6+X43S88ZManL6QtXvvyl9+NrO8s5QPp3xbcx9vXJJ7QV8yYr5fbtiSGDJh3BgVJCcenL0bTZ9ws1vmbJ8k8GRyqPcPsU7xIYs5fKVMPpCJCIPE0fZ10y5FUBfRBPkzoA6STnD35JCBrG9Sqc1EaxSNT85ieXwIA/NSHbsO7v/hIIfWYZMtBO4T71sPT3opvwGwmLWFHeOG7tQlcwX3WxGQ2W/ROU0KfkWJ85cUGpDBQn+fT521rCNER9S5I1aJ39tK+M4nAB6Qft6SUrtYgjBEj5p3xVgF6IBf9czPFIp9P4yXVJNyLkh0VvfP/unidMZ151HPTtiSmnDE/VXkJ9vEJuxnvUdhhweHJm7fkLzusHl0IM/nu9fn0aZq+PIFcgpYoyjqiKQQWlooy7/QrDdQ1yiO8ksGBQl8J6E7lQLgJW1J2Hj3p+5409UOZASfi0NutD5p65EY7T19xmh+ldS06eufL/vpO/NK197rfb9t82HgdG3cdxzcf2W+83DAtNQD8y707u0Ze7Cyr8wKrF9tXfrIP//2rdkjnOmj6spm3VhOJbkKfSU2fR0OIrhOYooev1AJTQe3IQ9pFsbufPoLX/cO9kb+bMO/UCSvHr+nzPjdSJE33+w0qczv7e0tF23Ao2rX9X+7d6X7+zuMHcM0tW/Dzf3tP4uOzXuultsQN9+9OLQiEjV9eah7/PlnH46gxxRNeilnoC0babLrmndkr6dT02dYRjYbAfJR5Z16hT1Ma7Dk2gxknfyWFPpIEmneWiHr0fSGKUw7zf/4fT/q+e8nZO8vRB+mqJvbU260PmrrWQWn6ihJc1aUZdiYdRQobByfnu++Ug9/8l/sBAL/20+cZKU9/X7uCTuA+zLcsnJrTTFXd69sdQfjat7yo+Il+nmNz+LmY6l6mL4+U8dE7TdwPtwg9ZUOj+0SsiJQN+gXspuXsOLSCw2nacevETLwGyoh5p/Y57B4GffoUaTR9wfsICE3TZ8X79Alh9N04Pe9pZf/wy+Fms0lI0hf189p7fBYfuHETFlz8wLkAACAASURBVFoWfveVFyeuJ+zMOy6H9P1JTdTlVd1h0WfembGSENoBi5RhA3n69Lmb8umDsPtupKbPUP+ypMTvfeEh93se4ZUMDtT0lYBn72//1Vdk8w5y7sAWUk5e36te4Ive2eq07Xf97RrFp2xouikbCqnGpaoCeFLUS+yK528AYOfWCjfN9bjmljH3c9jphwkbVdb0KTKZdxrz6TNSjIsl4zUhwXxomXCO0336hhqNrhOxYnz69MW3dBTZNzfuOo53/Gt8dL5XXbY+pE3p6jn9lJHY39Uk2VRy9rBL5hP0Ap+Ty3zS900I3afPf12C+WqFqKbWNglhj8zUfLqIjqGaPvXuVd9j9s2D69MX8t43QWdUUPvmbzowiRud1BhpCbPSUpo+Fcgl2MdMafpabenTXqa1sCGDCYW+MnAHAfuB9ScjzVd0vE+f97miMl9k9E5PlrVDIhcZyEXV1avonVW9F93wzJPt78OOJuBPv/YYRj9wa+yxn/rBDq+c0Oid0fVVmSwv9LynFRYN2ARSLzzsd4OaPr2akaEG5iNWyRVF+/RVybzz6w/tww+2HsW/P7Ancp/1py7D773yIn+bUp5Dt4VAzy0hVbE+wiJW6+gpG4I5+zKZTksvsAZgB4aK8ukDnOidBm5mGXlww8aeoMDRjbBzd4W9gFln1usUNaSo5resHvj0WV7Z3990GH/8lccyjd1B7aEKhDOkpRhRGnJ3v5znpPpWy5KuyTUQnuKq6tThnd5v0LyzBIKaPn+winwPgZenL2TFTvtcVa2J3qyoKF7F5+mzC24EfC+Loqpa11CEt9ofXPFVmoC7n06XKDb08oZMDKqscfX8XXpbb1gbTDDcFE7OqZj6AnVnyf0Z5tO3bKjRNeeUKRMpxa994kc4PLXgtSvl8UWOERetPwUAcGAi2hRcolM7nvYadYuY6kUzziX1hX10CQp6Xt3J+3dQmyiESvmgJube78E+G5eiJA1l5MENu39R5tnHZxbx5DOT+PlL/RriUKFPhv81PYdQz9BiQSkblgKRoIPtX2xbWN5opiozNHqnAE5d5k2tR5r+Razc5+QGObPc3JlAPaN3VnQa2tdQ01cCwciblk/oy1d2mKbvlZec4ZQdv8paBfQ2+qN4+f8WmafPXq3zJh1FCxu1EvocwkyFRzL68YXdxrCpSlX7LOBpMExFLy0LJbifceoyAPGTVxN+deoZ1ifby4ebmF2MNw82/ew/sncCz0zMaeWnO77IrhmcZEfdk6DAkvb2dOu7q5bnXyMOLjz++k+fh93X/Iq7TQ/kopt6NlJo+nyLEY5liDLVb1nxefrUMVUhTT8PE/KjNH3v+NeNeMe/buwww4+LBRD8a7LPH59ZdCND6+adRjV9AfPOYHeP8sGLLTMo9Dma5dUrht3tq5YP+44x5Y/csqRPi1jHQC5Vfqf3K11naUKI84UQdwshtgghNgkh/tjZfroQ4nYhxDbn72naMe8XQmwXQjwthHidtv0lQognnd+uE85bSgixTAjxVWf7g0KIC82fanVouCs1zkTRoDAWFr1TrQbJkP2qhs+nL0TTJ+HYzBeq6bPLV/ep6IHJtNaiSHyBXAKa6qw5BkMVfaE+fZmKj67X4HX3FiUSTkwNmnS7decrxofSiMQFcjGBanNQ0zffRegreviq4mTE06h7z5nuhxec4Kft33FpMj799pfizFXLnHLz1aOwrE4B1u/HZ5+nEOnMLoPPlu7TZ1l+sSbYu02Zd5omyWPYDpn0R2kutx+xo34H5wFhz5WM+Jv2GYnb/Vf/6T7XncOfmzVVFbHomtBg6g4AkcnU4/DP3ew6mo0GVmuC3tqVfqEvf8oGm1Y7KPRVsON2oaLT0L4mySytBeDPpJTPBfByAO8WQjwPwFUA7pRSXgLgTuc7nN+uBPB8AFcA+IQQQunMPwngXQAucf5f4Wx/J4ATUsrnALgWwEcNnFtlUTbZatDR1fJJn4Gol4AqSy9n2bB9+U1ONIvC59PX6hSG7Ze4LZAV59Mn0RDRuRBNU1UBPClhk9E0hE0aw1aoTWt3TBaX1rxTrztvP3ansQZOSNcqWV3MO33RgHPWp49ny4ebmO0SCKhqQtnO8ZnCyrYCC3m6Rv0Xn3uW+7kZeGjSXqNH905E/vYzF57m+fQF7naa4SvY74P9a7jRqemzF+Cyavr8Pn2Lbcs33jYC16zWgVzCLCYitcLh2+OSybs/5RxuwqrWteyLHQFXzKBrNYNmvvbv6TVlwRy180ttrBxpYvUKTyveIfTl9ekT4T591PSRJHSdpUkpD0opH3E+TwPYAuBcAG8A8Hlnt88DeKPz+Q0AviKlXJBS7gKwHcDLhBBnA1gtpbxf2iPLFwLHqLK+AeC1ImqJqo9whT6r04wxK60Qn75lIaG2qypo6G1cigndXKxPnydY2m0yX5HP1Lai96IbqtWeps//yCbWeoVsC9f0GRb6jJbVqbVPWrepdpgoR7+n3fz0TKZs0ANfLB9udB2f0uagS0varvaOf91YTEPQqWHRn7PhIe9zh3lnimt0++bD+Lcf7478fajZ6PBFd+tJcbGCgVyC/SvMp09ZdmT36ROuT9/sgn8xobN/J68njjImL+2QG55WUx926sGk794CbDHvLb95p7lyH9h5zP0c5tOXRdOnaw+llJhbbGPFcDOg6fNHxTX1vm9blm+htY5CH2W+3pPKSN8xu3wxgAcBnCWlPAjYgqEQ4kxnt3MBPKAdtt/ZtuR8Dm5Xx+xzymoJISYBrAMwHqj/XbA1hdiwYQPGxsaQhfHx8czHmmBh0Q4YsGvXboycXI4DU15+pInJiURtW1oKz6m0uNTC2NgYpqam3G0z0/bng4cOudv27d+PscaJTO0vkj0HZ93PR48ddz9v3boVq5Y1MTk5gaWlJcycPIm5+aVU9zHpfR8/dgxCSmzftg0AcOjwEYyNLaY4i+7ok9ov3v24+7mofmmq3PHx487fcczN2auzx0/YGoLJieO+fbeMjSWadBw8eBBjY35NSbvtD+QxNjaGo+PHOrYlb3fnvdfvwdjYGFpt/0s/TfkzM3a/XXKev27ode/duxdj1rGYveM54Vz/o+PjGBvLqTV03sJLrTZmZmdgyc7rcNFpI9h1YhE7dngRWMfGxjo0TUk4fNjOw7ht21acusy2SFiY7a4127lrFxpTy7rul3Wsf/rpp7F8OJ3muqhn9+hR+7k6ceKEXYf0JncnJycBAJOTk1je9l+3kzMzidv0o6fi3wU7t23FsTn7+Thw4CDGVp50f9sy9jRGmsnu/d5nvPFdSmBictLXxu3btrqfFxfswDUCEouLC5iathKdz/FZb+xQ74tn9u8DAGzbvde374nj3pg1NjaGdruFiYlk7+A4jh073n0ndO8zk857/MCBgxhbcTJ230OHO+/h+NGjGBvrnCtYjoC4detWrND6+dNP2+9ZXxnHvLFp85YxTE1PAwAWl9K9f2ed53rP3r04vTWOvROLWLdyCKeM+J+zmTlP63foyOFM796w537j9sPu2PXMgUOYmJzz/T62bSfaJ7qPKTonJjzt+K7dezA+MQm0LUyOe3MtLPify+0709ejMz2l5nNHsDDrXZvde/dhTFRvThfHnKZdNTF+lj23rwOJhT4hxKkA/gPAn0gpp2IUcaHR1mO2xx3j3yDl9QCuB4DLL79cjo6Odmt2KGNjY8h6rAmW33oEwCLOv+ACjD7rNAwfPQnAfhmtXr0mUdtGRg7Atrz102g0MTo6ilUPzwKwB5vT1q4FMI2zztoAwI6sePY552B09Gwj52OS8eY4gIMAgJWrVgOwXzDPfs4lOP2UEax+fB4jx9tYs3oVxhdOprqPSe/7aTs2o9mcxuhllwHYhTPWr8fo6LMznE00tqnJTgDA3913xN1uvl/uMFruGfu2AjiBdWecgeXHjgBYwOrVawBM4ewzzwTgvQQvvfQyDEWafHoCw4YNGzA6+izfr8ND+wB4L7TR0VGctmuLr/y8997OA7nTLavZ3AvAewmlKX/FD04AmEej2Ux0nL0qa9d93nnnY3T0zPgDYlizZQnAFNatW5f7Pkt1X0QDK1ashIR+Hezffvs/PRsfuXkLLrzoYjhrdbjssrh7HYZd1vr1ZwI4hksvu9RdHT/zqQVgV/wE94ILLsToeWu61pJ8rN/h+3bJpZfilGVJXo/ecUW9U9btt5+51avXYnR0FCuXHcAJZ2J85vp1wJZJrFmzBmeevhKAN+lbsWJl4jat3b8VQLSg8vznPRcHJuYA7MVZGzZgdPR8qHO/9NJLsXw4WdTDw42jUOO7BHDamrVOG+2ynjs6CmAXAGDVKSsBzEM0Gli+fDlWnrIi0fkcmZoHYKe3WL16DYaPtXHxRRcCeAZr1p0FwBtvz1i3DutXzeHo9AJGR0cxNLQfa9cmewfHsW7v09DvRRTd6ln92ByAkzj3nHMwOnpu7L6nH9iG4D3ccNaZGB3tTM7eaOwG0MYFFz8HP/3h293tl1xySYdm6vQdmwHYiwuXXXYZTnloBsAMGs2hVNdp5b0TAOZx3vnnY/SS9bjiqpvxgnPX4Dt/+HPQn6PG0AhsHYE9PmR594Y990O3HMbq5cPYdWIR6886C6fOHAPgjTNnn3c+Ri88PVU9pzr3BwDOO/9ZaDw9j9OWAc+/5CLgdrufr193OgBvEf6CCy7C6DmrU5+TYvWjdp2nrTsD6+QMsNuu/8wN52B09JzM5ZbB1PwS1PNuYvwse25fBxIJfUKIYdgC3xellN90Nh8WQpztaPnOhjeS7gdwvnb4eQAOONvPC9muH7NfCDEEYA3i3kA1x/UVU4FcAhGg8qDMy3QzmjBfjKpaFOptXAqx7ZewTXJ6EchFrWsUYXdeVfPabuhrPW4gF+XTNxQw70xYZhazLRMEfZNyla+uRcL7atKnzyR6cB6JcN9Kb8Evv0+fG5FS25ZEY1j0NavOHemM3ukz71QJ02VIEugU16ibaZh+T4KlpjLvDOwbF8hFmXo2hDq39HdFmZAq/8CTC36tV0MI3PlnP4+ZBXsB1VQgF1PDe5q2ZLHuOzHr16KFtduS/s8yZt8k6Of05DOTHb8vFpSc3ZJen7KcnHo6WXz6guad80ttrDtlBBvWLHe3B61dTJ1Ty5JYpmlp4wIxVRVZvybXniTROwWAzwLYIqX8e+2nmwC8w/n8DgA3atuvdCJyXgQ7YMtGxxR0WgjxcqfMtweOUWW9GcBdso+zNqohQI0XYakJshIWvdMTXjr3M83k3FIum3X90KWIwV/AFsiKmvi1LekKlkAxdufdcmLViaiUDYmFudCt/hflvuOzuP7enYHy85oy5jrcX5ZzFlkTSOeqW5opR6ctnUTDMd5JJupzTT20iVGSyVfRQl+1BHG7LQstCxdedTN2H/NMJHWfns6UDWmEvu77hsj6Tj2Jq4ku00EXXN3onRCpFvn03Wz/bG8sPxnw6WsIYPXyYZy9ZoXbHhO3voyIzEZ8+mICuQBeWgLnl3Rlw1u4jUMPuGLyMlpSYsjpU21Ldvi8ZvHp8wVyUT59I02cvWYFzjh1Gc44daRjMSbv3Ev1rbZl+QO5tKozZiWlSuPsoJDEHucVAN4G4DVCiMec/68HcA2AXxRCbAPwi853SCk3AfgagM0AbgXwbimlepr+AMBnYAd32QHgFmf7ZwGsE0JsB/CncCKB9itqHG7n0PRFTcbCJoC/9TO26dyrtESsRTxsx04u4IV/dRs+fvf2zGX4oneG5umzP6Rx7E+LlBLNhqbpK0BAa9XQ6Rrw97tgbjo9fLT+ezfC7mNwrvLovs7IgnlfnoVE70yq6TOgJdNLM1FOMLhQVCCXsLl/1msZpumb65KuwT4uW31JqdJcRDVFaaN0dKGvM3pn8jp07UoU0dE7U2j6EtYB6NE7kSpasy+QC+wxS2l4ZoPXMNDBBYSRPH2m3q9pZLY0efqiig1P2eDfqMaJ7Jq++AP1HJ0m371S1/SFBnLJm6cPmFtqY8WwbUB335+/Gve879Udz2XeBQGVb7DVlv5ALjXU9FHo6z1dzTullPcheox4bcQxVwO4OmT7QwAuD9k+D+A3urWlX1ADuRr8fCYCOct2NX1aSS84b40vAS5QzMN2eMoOUPO9pw7hD197SbZCtGZF5ulLOQlIi5enT01yzBMmsNQ1Xq1r3plZ0xe/uhxFy5IYSuZK1LVeKTvNfbJQSsoGQ5o+/fi2lO6zFsSk2bMqQq9nPsGKe+GThQrNRYJm1Dr6Sr8+t2yIdJrwJJH/vGjGgfalmWt2NCl60HOjdwoBpNL0+RdRhfDKmln0C31BocjUGGxKWMlr3hlMSdG1vi5jsSW9PbI+gxLx12dO0/SZXNyRQU1foOxgovok+IQ+J2XDCicwjfJz7Yyqa0bT17KkuzAC+C2j6kIfGTzVhmyJtUgkh6fm8dKP3I4tB6ci91HChKem908+8+D69HUppohFIfUSyBDEr6MMIDplgwrhXZSJqsofVWRy9jDzzlrJfJqZj3qJDQVufB5fvSTPgUlNX26Byflbhnmn14ac18P523BM3KwI807XPN3AGKL6jl5P3OTrl553lu+4ojCh7TFFUKOuo6c4aPi0ZI1UE6okQp+XXza8fUkI7hsnZA25Ql86Idb/XNtjuefTZ/ctZZUQZv5YJZ8+RaLk7CEPZNrMV2HnvhiwuLESzjEiy5bJx0mTz6ElPR9YS3b2p0yaPt06QnopG3SMm3dqFmJ6SXV0GeljL67KQqHPMDc+9gzGTy7iqz/ZF7lP0KfPl5w94TPQLblqt2KKEGQ8oS+7+OLz6dMDuQR8FUWB5p2WlGg0RKHJ2cM1fdUR++aX2jgyPd+x3dVSQ9NA5DXvDHw/Or2AYzP+AANhL4e8Lzkr8MLOg2fylG1imq9u+++JmXxpRVQ79NXwcE1fp5lf1smZ5T7P3rYzTo0OZ/6SC07zHVcURib+lsSxkwv5y3HaEmaCGeXTN9RMnswcSOfTZyo5OxC/0JU5T5/eNkvlXHU0fY5553JX6Otsj4muZer9mqaUMLm92xslOPYEm3332BF86UEvzYXu05dd0ycTmziafM4tKd0Iw2HmnVk1fUrb3rakY94ZFPqym11H1QnYgVv0xa/FGrqMdCwgSYm/v+1pbD08XU6DBgAKfYbZ4zjZX7huZdd9i0jOHhbIJa5uk6jV4jyaPv0ltBRp3ikKNe9staXPn6SI1aheaPru2zbefacI3nL9A3jZ1Xcm2teLKpjNvDO441s/82Ciw3Jr+rTPO8dnMDXf6TOVtqwsJmh5e5cq62sP7cf2I/GpDuLLsVGT7VZbhi5EeObpmatyCXuGr37TCyL3V20reoXYxNjyD3duw0s+ckfo4kkaVFPCtHFqwikhA0FQ0kU3TjJhdH05A+Wmi96ZvE1DmnmnSFGPzzdVafqafqFvxUi46Z2pxcSiNdFhZAnkErztwWt82+ZDvu8S3jiR9TpJmcYKxNx1lNLrU9ffuxN3P33U93uW6J1tS7raw7mlNiwJLB8J1/SNaAJnHlyhr22bqCohs46BXILC/9R8C9fdtR2/df0DEUeQvFDoM4wS+tasHI7eyRmIH9ptZ6XQBYC8A4IyW5BSYu3KYdzz3ldF7mcaNWjm0Vjp7fKHbrb/KnMde1JTzCDXltKdXNpR48zXYyLSWhApJV71t3fjW4/uBwC89bPJhKcwHg8JnNJRn/NXvYSCQl9yEx4/u491T84N5A9Rrfsi/dGXH81XVkDrmXR/wOzixd7jya5dHKrvty3pW4h444vsHFBhk//ME0Dnr97116yIHjuH3YlTtvqSYqL4O7fYiecPTeYU+pzWhGnjdJ8tPWDEcLORzqdPG2vf+vJnhe/kFP/0oWl8eaNf+5OVuCFPRe9siJSaPr1fAkCIT9/KkaHI+s0EcsldRGpSBXJxrVgCmr7AfgsBQUha6a0avDrhHJd8nDSbssFbzJ2c60xYnyW4WtuSGHa0xioAzfKAo7m61ko4zLtYqd57LUtCQmKoaQedq2PKhuDiiGu6SrPPwqDQZxg1mMSNH2oc/vjdOzC32HajMQGmTEvsh+ZZp6/EhWecErpPEf5wKuRxHtmlm6bPrsAeSIt6sbatoNBnvo5Q08Scqr6FloXdx2bxP77xRL6CYgh7Cavuq+cPA5L35WCRK0Y6o7OE9df8mj7v+LymovrRSSbb/v1zVe1Pz5KjE6lyXE2fZfme5X+48sV2QKiICWO2Ou0ykrZbj75XJCaKV+bOSfzl4htj/+kWYVNfbGs20vk868Ezohaf1D264YE9eP83n/Sal+JaBXeNu+8+TV9Gyw7Pp8++FzOOT58KshE8V2HIvrOclA1hQl83TV9A6Ovi5ybhmXdm9emTIaaVUZg279SjcgfJ8g6wpBdBUz2fwwE3BzWeKtPSvH1DyXZtJ8JyQwgMNURNffr831V/bFbI1aXfoNBnGDf4R8wDqPfntpTuw2rKlNCSEm1Lxg74pswmxg5N4R/v2AbA0/QV4dMX9FUsyuwS8At9ReUDbIWt2hsQ+oBOjZtJ1P2xpBftUv3NnKcvsOPK4U6hL2zCG3YN06BXm3sRxGdSlmR3cws9+vHq0ZueX0otbCgheChC0+fWEVJvVtyUDQn7/nDDjIlUN5Joe4L9dvuRabzs6jtwZMrW7Km2JvGXi0Oda7f7qY8fwyktIXTNR9T4HTU+mUzOrtP0JWfPZnZp5+kT7iTypPLpG1Y5AP2YmmuaejelKSdLROhg8cHvQaHPkt79zhe9M+G+RoU+f1/o/D19ZS1LYth5MNQ7KhjQTH0d1hLD50Fp9Jbalh1/wNFkl2FSnJcOTbP0NH3T853aWJIfCn2GUautcas5+pAgHQENUCY5CeuJ+c0ORyw7Bh8A+NRbX+LuY4Jf/8SPce0dW7HQaruO0Ll8+rTJVmj0Tuk59heq6ROepq+I+WXoCzqnqk9pWgsV+lwncm/F1+2/gRXOpFLBlzfuw/07jrnfwzR9YZHV8vZh/YWT21RU+5y2XSYXL9SE/QUfug2v/OjduOaWsdRRD9WCx1KET5+3v4kFKvtv0sm359OXu+p4Egnu/u+fvW83jkwv4LbNtlnn8JB/Mpi5KU49sXm4ZCB6Z4p3CeAX+oJ5xRRRfSFVIJfku2rvr3SaPr/ZsaM5aHpmfQ0BLHPGqrBzNbKYUYKlXaifeDdNX8ek2/97MGG57ToSvm9SpEx2L4XhhV3pCEhRaSwyafo0807lFxucd6l7MNQwY5qu1pDUPK/hCLImNX3feHg/3vf1x42VF0WwH6hzmJhdwgs+dFvh9Q8iFPoMo573uImfPhBb0ptwDjcbRlawLSnRasvQwe1nL17n1puE8ZMLODodHYFOn4yb9ukLD+QiCw/k4jfvLMYpP2yAzq3pWype06cmCfo1UX09+LJLen+ePjyN3/q057it/G10QjV9Bk0yTWoNk5y3vodJ80594n9oah6f+sEO/DBlQB/lUzcxuxjaJ3XfnLA2pEEdFxwzolbje2bemWEfV8BwLtpIwOwrb1u6laPfq6Fcmr7wfaKGpzTjY7BJcUOePgY3hMBDe07gP3/6ga6LKv48ffClbFhsWVi1fNh93pcHrAoEhBFBw7R5Z5LiZhc6o092u5fBPhL8HuxzlvSub1rfR922Icn1WT7UNOzTB1dACiPLImLL8sw71VxoKODmoMZk3Vc6D23Np8/WXtqCrEmXnfd+/XF8/eH9xsqLItjkvO9h0h0KfYaJcpD27aN9tizpdvThpjDm02dJGTq4iYbaJ1lNL/3IHfiZq++I/N1VwEkv5HEe2UV/4eovnHf+20PuZwHHp68gVV8vfPqKSNmgVmVHmvnKiZv0qGvetryXfmT0zgzlA1Gavs4Jjck8ffkFSH2imUDoSykkJq375MISbn7ioO/3R/d2D8qjt2n9KjtlwsxiG2FPszBo4Bk1cYzSNmUN5NJqW/jlf/yhG1ylG8nuYfiE2TPncgI8ZAgF76/H/qubiV6wbiW+856f8+2njx/2AmKy8i1LYkoX+iI1ffHtS4Z/57gxT8/Tp3b78Y5jmJiNT03i0/Q5x+v9adXyIXdBcVnAOkEIs2bLpkhS3rYj6cPcB9+hwVrCfPo8E//U1dllJND0jQw1Ukeg7YZuChlGFoGjbXX69CmNnkJVZ2rBSrVT+fQJIQoNbFckwTG0jsFo6gaFPsMk0fTpq/FtzbxzKMWLOo62JX2Ci04zgVCaBr0c9YLI49OnN0uf5DwzMef7vSizS8C+J/qEo5jk7CGJdHOWqTStHWaWKZmLmaSqvqpfk8g8fRHXrVsfD+Y5AsLDaec2yQw5h+xleZ8TFWVASxbGe7/+BN79pUd829ImQlZCHxA+0Q/T9GXF1fQFtkdNzNRzmfaZPDG7hC0Hp/Dn/5EsyFGS4oO7KMMEN1qf8zzMLWZPBQJoPn3aBPzsNcvxgvPW+K6bvsiXJk/fycWW715GaUKizM+LSmTvRe8UqRbE/Fp0CQG/dmf18mEsOu+WZR2aPjPPo+kFyW5tml9qY7cTOTzRcRHzlGCf6RD6JNwLXKRP37KhRqo0HUmwLM9KKIywiNpdy5ReygbPvSJgtaACuRjS9Ok+vspkdciwpq9XdGj6angOdYNCn2Fcn764zquNCZblBXIZSRFmO+4laFkSbRm+Yttw25eomq5I7QXgavpySC/6IB8WuEAqc4aCzTvVtWs0zJj7hNURJG8QAfXSCfPlTENc31VmOWqV0d5m/02q6ev2clJBFnQK0fRpn7OE6/aVlVJzl1Yz2KUwFxWowvdzSl8oPTl6WE9y9Xw+jUq2c1CT42DfjxI8XE1fWr/JzKZoMfvI4Hd7g2q7Mu+cW8yn6VMsaH00bGFNVzCk8Q+fnPUHTIiM3hkxW0jl05diX3fhDelM3zvy9Dnmb+q0Vi0fHpTZDAAAIABJREFUcp/3Tk1f3qU3Va+RYrT8n/EFHplaCB0PuzUjeEh3nz7N1SLjOYYlRg+ybKjh+PRlqyMMd+4Q0pmyRr/UzTuVpq/ZCO9Twwby9C22LOw9bgv3uk9fQ9RT6Au2OTjnq2NwmqpDoc8wUbbyUVjSW2EKrhBlpS0lLEsirLhGSvPOpFgSmHdeEHlClOvPeNQgLCDQMGz6odO29OTsBaVsCDElyW3eacinL+58fYFcAtuC/Teqj3Xre6cs6/TpCwvkktskM612LiE/9aHb8DNX34F9x2cxs9DChVfdjBvu3x1Zd96qux2ftHy137pTR9xt8Zq+/BfNDeQSqCjKxNAzkcpaY7JnLFnaDf8+akFEjbHqechv3umt7CvC7osurA2n0PRNz/sXCiLNOyOOTxW9M1hmzO3wIigL37mlGSd15Y0a01ctH3avZdCnL6yNWTD9fu1WWtiiGNC9H0dFT1R0+vTJDk1qWqTsvmC3bKhpfMFVD3oSZPlwM5PQZFmepm8x0qcPvu15hLPH90+4Vi/Kp6/hmHcWIfQ9tPs4bn3qYPcdM9IRyCUwL4oNXkUyQaHPMEk0afqQ0JbSNWMcMhjIxTbv7Ly9qn3mzU+kK3TkCVygBvmolV31yinK7BKwB1N1nYrSKP6Xf/tJx7a8gVyU0B00s0xNzOnGBXIJpmyIKqfb9WwKgRXDTVywbqW7LZgkGMjv9N0temdYAt8ogi05Or2AN3/qxzjiBEH6zH27ovcveDEzedRDR3hvNLB6uS14h2l9lJmfXm6eVf8wos07e5Oy4Z/u3N51n2AT1CMRDNwQ1PS1LYm/+NaT2D0+k6gtqhq9vuB9kfALQ0ON5O+S4GSxW0LvjvalEfoCuybL0+dvU7f69F9tTZ//fqxeMeS+czs0fQnKT4Jxoa9LeWGLYvZx8eV2Tc7e4dMX1KTGlx/apgTtGhlqGF9wtQWk8EWN4WY2TV9bapq+iOid6lk1Eb1TBdU749QRV9NnPx+ikNyQb/7U/fj9f3+k+44ZCTY5eA8Y2MU8FPoMk0STpr87Lct8ygbLUsFIOn/zfPq615FGMLSkN+hFvYCSoM5/KEJb5Zl3FufTZ1lSW60rTqMYxJSmL695Z1zfVYsZbenl6dP7r05UKd1WJC0JnH7KiE+IDFvJzh8AJb5Nv/v5TsE8sqz/w957hktylGfDd/XMnLR5tUHaIK20CkdCESQkJCwJEMEIDBhM8mvABmOibezPGGzAgfDK2P4MNgYbE2xsom1MEmAQSCBAKABKSLuKm3PePXFmut4f3VX9VHXF7j4izX1d55oz09VV1dUVnvwY+rLv2CwJ7sGs5Ws/h1eaH1hP/slYEUHVOCWZdkMN8Jxo0WEP5NKsT7INn75ta/Q9Rc5BVaI/qTF9d28/jE/cvAWv/+SPguo1Papog46dEr2zFb5v6WNp9+mz3R/WDlDWjobk6cvKRPj0aVp0cad4roVE06czfWANafoaVlD4pruV6fM8jdenT9NSpylX3nfUOsyLcu6P3tlOmo/QzWHW9F26/jiMDbUrCcL7fS7PqYO5mbQ1kIvwR65BUIjhGGol6PVTSQ/9vOTp090sBkxf8xgwfQ0j0Q58E6h0s6/49DUTgamfb6omwknsdyFSoYmIAAScc7lgZ2uZd+ZaTwvRJ3pd50D4k/+5Cx+68SHrdV3TN+eqmBw/LcnZXU8rGAxF0yc0RKVALuY6fGdTyjmSRCX8TYFc6i4V+gwmKe+d2w7Xqn+s05LjpRO33PJ/Ffjur5LfrEhebdL0ldut+gyUKKewMR6V8/Q9AktYt1IQBMsRTWMcK9sxvT/TXkHHLMY/XC8VG70zLjl7cFHFxJ52yS8coxroQqgg5s5wJyFMXzmQSxNzpTGtS14N3Z7e/Nm7cN09RRTaDbuOWBNZ27phc0PRy5uCeinrvuJjeq09EoamBa4pz+aOyJ8JZOvkE799CYbaSWVNnxAQf/u+vQDKbg5iPUnzzhpzQzDxnby/PDdZreqT+JOGLU+fwMC8s3kMmL45gjtPX/F/ZopZEOtNnBVpmvn0GU2zGAtOejphyPtjA+cFkVPHvFMMm0tbJXw8qjJ9n7h5C95x7b2OPmg+fQ3tO0enu3jb5+92BHaox/WJQDom884YkyXVbE+9T8zrPq/h0+c5nPpplm6Ezl8qSHjDVacb+1YHpqrGDKkjYu4fGWqVTP5MmPNQ26HV5+UYY4Wvk2NKNrJXcXMCeH/KhmqN215DFSV7ybwzn6KC+ermL//AhDvFgLcdw29GBWwpemdY/fpY0rn6zPNW4cMvvbD0u3J/jf3RNezCPYE52jaBPk7KUWL6WozJs2q4Yw66URecc5y4dAx/+vQzG6mPvqNP3rIFr/hYlsLowMQsnvaeG/GHnzEn0vZNAV0+q8/prqZpSTlXClVZh5z7z4DMvBNoUlojIl0unVcEqhIMWFWfuH5aFrLqe5eYu6JcHd87Sh/1iU9f8jOassEXvXOg6WseA6ZvjhBs3skLTV+71YzJYurQ9AHZoRey8RybCfdpSjmXRE4tnz6IsbCbdwJ5nr452g96fS05e0Ob6ftveBAfu2kz/uP7m50BMqrCpemrGmFPv08ckr2UEyl0zvQlgeadXnNELp3TBbr9FCcvm4e7/+KpeNz645z1h+LKv7nBed2UJN4GkxnV2FCRXFjXXCkmaDUfJNZvx1qP8JdFEeDCxVy4hAOhSLm5DYM7MoC4QC5Hpru4bXs5jL0JVZaeLZBLYd6Zrcf9dZk+w7OazIVV885wnz5R7I+eegY+99rLFLeA1z/xVDzpzJXO+5sI5PLxV1yM9734AuWaoukjfYr26UNRj/icdQZyqb/fpxxYNNrBb19+Cr72hstr12fr0UQerdc2x2L3hpRz7Dg0hXVvuhZ3bivn9+RcXXuVmD5w7/rNzDubE7gCkAzSchKdmFoWVUkB1E/Tki+7fv6WzDvraPrEWdvKtNXCpy+UpvtpQylPnyaFqBMUcAAzBkxfwxBzONy8s2AyEsYaOXBknj4LFxHqIB2j6Ut5sWDrbGqiXzaGFeBKCO+5SKeQEoa5SeaS5rhbsWAYxy8cUa7XlTELTZ8pCmxcsAU7MS8ktCmJ3llELbT7rSl1hDB9CVPqm+2lmD/cxvzhdmPRI32HZKymT5+yo52WJFrK5p31GaaiLjeCffqkQKXIlWgO5CLqrb8wbD59NhPlmEAur/34D/GWr+/EgYlZ7xhV0fDoXZApG/KJILQkBz3JxANaKv1i6i99V0OtJNjHR/T7nNWLcP7axUo91OLCNkQx00Cf6+I5Ljt1GZ5x7irlmjwDmHpm+h7LJlARbSXEFM4cyMX3FH7004IJP37RiLtwAEL3iNNWzFe+e/daw2DeeH9mpvjvN20ul+dcSzcT1C0FnKt7r+nZ2q2kcZ8+kZx9xcKC6RPVV9f0ca+mT6DdgKZP9LfTSmTaJKHp+1nkj/Sh0DXLP4smqz/tGDB9DUNsiC5tBj08hU9fK88jFDzHHTRKyvOUDZbNJ0nCDpEYKQtHEYW03qYmtEYWnz5eBHIBgA27jlZuy4YeGbvQsYoBR/aey6Gd67F9U5LpM5h3RtTDLf8DRU6+floEcknTguG56swVxb2WRm0CVVpfizEl5chsP5XMbMHw+56kHqKYPpT9g0aJpq/0brnx3zlB6DiJYpmmLzetc2ikabVX/vUNlfqWpma/wQ+95EJjeSFV95n9dPsp7tiaaSo4L/KC2VZYNU2fisKUF3kfs4l+4JhFCxP45k3rxbQ9Um1YOwm3GhHFqCZMgJqKP9LJ2ammj85DX3uKQAXFfE3IZ9/G9LHmzJaF0Eq3gKgCW5/0s/YjL7sIn33NpcV9nnr1ozpzbcj6a4penHJ1PlZKc8BVZu7rxD9RoJ2wRgWunPOcdlA1fbS9KgyG6RzXha7iUQtNX3QzRV3Cpy+PNpppsoW5588e16cz/7q2tW7+3AHKGDB9DUMs8FApq/Dp6yQMDM3kpUk5nOadoYk8YzZBzouw901IslqOnIU0hPcvv/dGHKotSVeRMczZ0qjjO6iDPlGa8pLfYl3zzt1HpvN6TJq+8Hro85bMf4i2UqBP/Edf8rh13jZtApGT3/xlHJnuoi9MVjTzzsLkt76ZTAhGozR9vORLSc07dbpPYaxrPkZsLi5fPYwx+exm885yu1VNGDm4sZFTls/HqZrWAih8sHymWKf96VdwJM8/l3L/GFfz6VMrFfseY1nC8+s3ZhqTozM9JfqsK02BsR2jpq9cTtHQtZLggBEp6TegrjsqyLAHcglqBkD5PZiqbCUMC0fahbUFNKGJp71v3LuHtMdLbdC6yoFcmrG2ESbqQJkpiIHoi20N60HThtuJKqzyzAF9jnDCxJg01Jyro1MlYiQVwgBQ/BHXLBkFALzqivXBsQfC2sw+E8awbEGZ6UsqaPr6KcfkbA8LRjrK73qqLN16qYnonUZN38+gUkxP/7FfE5Dpmr8B6iPcaWWAIIjNzJmnjxw6Kc80ZELT18QUF+adtihsrUAJWlzKBi6l73UkWYWNvbpxLsulc1IbQcawaVv2Xloktp+L5OzcwpTX1fRtOzgFwPzeYggZm3kUQAK5aOadem4yV5uueXV4siu11HQ8uj2Ozqim6Qt5mBoYNfj72JBp+tQ5O9qxB3Kh49pk6gnj9VCmL/9kDBjJiWGTAEEwLc1oROxaNlO/Rb9iiIFUIzJ1TMz0KhEXZU1f9gvnwJ9+7i7l2uRMv8RgBLdj6Jppr1DNO8OFVVTDm9VTXFM1fWbE+fT5ub57/vKpAIAv37Uz74+u6XPjr/93o9I3GRDGwNTqWpmG4rgo1g91U+gAxRzwJU8faicKE2IbK+mXq+3FKS+nIKAQESNp+VCId8+h0ggXnbwU39yQMepLxobwnT9+IoA8LVNw7W4UFhfmfb2dsOigIUemukh5ljNPr8vUtgzkUmPjFOM21E6w8/A0dh7ehZOOG8O84dacpmzoOyzH6oB2uZem+LMv/Fi5XsXPcgA3Bpq+hiE1fS7zTvJ/P+WY6aUY6bSizBlcy8/n08cCbeVjNifOC3PQpqJTCTzngtVSeplJblltBskFmtg+dKyCQLqcGnwB6mK7YPoM/a2q6dNBA7lQrXZhQkWYPks1rvqH2on0qSxp+uQ7EUzHT4+mD7wcNXWkU2j6dOZJ9emr3scQRPv0IYs8Kv7XUfhU1u6aDERg7I/hNzHGMabnfTJXTTjnz/+39Nvlpy/31qvXKd51yoFDGsFcK3ep4Tcf09dpJcHvR5+jiaLpK+a0bc+tsw5NWs/hdgvD7ZayB4fsKwCw49CU8j0zH1bvYwx46zPOwrL5w7WtImygmr4mIoLSuUVR1vS1sGCkkOX7GAFTcnZhUWGyoNHXUiUGhqs0wolLx4z9adKnTwrfEmb0eW8l8cnND+Tjs3zBMF526Tr5eylfrRRkZ+024f5C6aMtByYr+ySGYq4CqtD3a0rLNND0NY8B09cwxBQNj96ZJZ4e7uQhihsx7+ROyUwrMLxvzCYi2gRq5qHJ75XhtaWvY1EnNe8Emtf49FM1ZUPTRLmIXlbS9FlW4+4j09i0b8Jb787DmXmn6b3FPINLCyX97qh5p1XTZ4ZrWvVTjj7PCCVa10zvkffpG+3EGULoTN+8obYcL30p3r6liIxXm7Dx3B6q5ZXlGJPScBfB2gTTzbnD3NFQvZgD3Qgmysf0NUUrCaF0ynlJ6zttyHcWXK+h8+K10NdDzQjbEXn6yGsHUM73p7dZ7l9YM0DcmhV7sEgzVLRnr2TrATVaq0mokDCGlz/+ZNz2lqvMfQzvooLZXirf81xpRvS9XV8HQ+0E80jUYd+z6PVxXuSd0wUXgCGQSwVegEPVFqp+XUW5Jq1sCsEGStE2gcyyKJZpOpibtC8ZG8JTzioi3OrvXQqyRbqZhsw7ZXv5WTmXTN9cBVSha9mUymrg09c8Bkxf08jnsDt6Z4GUZ5q+4XYLDE1Jz7M/m2Q21KcvZhPhvMhL1cSmJg4eka9Nmrfk5UIlv1XQI6axTUob1aitBp8+C/F78bu+4U0vAED6DZmGv2nzTs6LOmmkOnqe2sbNNa/6aR6ESJPuK5o+6dMX9DiVMdIJ3x45ysREp10QLfpafPXHf1i3e6RtnzQ/uCIAIYFcmhv/WE2fEAK5JM86oUDNO0OVLkEMk0XTx3k5FUAdTZ9pIMqaY5UQbCfZ3Ltr22HsyoVBNuhzlM5VqvWzCQBi9nt9WF3vw+bT52pNv5YJFbR6HY2yGkK+p7332xh/61cBZBo4U77UWOiWQ/qeSjUh7dw6QhG8+QRCvPxdBJ4xEfoiN5z8HmkNJD7pGUDr0IW7TZ29hRUDM1rYtCoEchH5N5fOG1LyPdry1UpNXx2huAzkQjTw+TuvU68Pc8V8UXPOydleud25PuR/ATFg+hqGbXOm0P3RZrpZrpeYlA0uCfwNG/dkTIXFgTxJwiRosUyf2Bjq2ayLDTKbmplvl0pQMajEQtNmfqXk7HOwmaacl3IRxkqGD07M4kM3PpRHJisO47rmnXr0O4q+oY2UBHKpa94pCHRTnj4xn02BROYCcRoMjmGN0E95EWXSRWjOdUCaKj59QtPnJujr9zvWp4+xjGibdZj96AxWP3X79JnbDiijPX+xR5WD+tBALjFtZO2UYdoqKLEp9sxnvu87uPyvr/fUrzLENl9wG6LWifbd1ZLU7CdqOdd8Lpsr8tJZ6WI0mbGXYXhob2GNMdtLGzXfF2OsP/psv5hXunYZCND0cX0Ou8cn5aqW7ot37PC0UAaHOmf6lrWcMeDN7I3Up69jGKcq0S9FoJsl84YUf12bpk8GcqnxSIWmT13roYL8qtDNiOvgB5sPYt2brsW2g5PYvL/QzE8ZrCEGefqax4Dpaxhi2YUuwDTl0ryTsWrmEjrec939ismdjk7CghKoxxBKNJAL59UJcnGXYLpEQA9JUAGAxog1vdX1+umc5OkT4FwEGFDfj8nsxIV3fvlevOPae3Hj/fuUPm4/OFV6v3Hv0n5fEb2zOIBEtE1AP/DMbTp9BkkQIlXTV/hANulT5kJUbkOUiS5Fou0gpGpbd3ruD91TqDRcaKqmDcyK7iNVB1mePouvGPn/o795Ed5y9ZkAsv3LRQzoTF9mkhaHkPWiF5GEOcpzoZZPn6Evpr2dBr+iGivfXl9o+rLPWF6lag5QwKfpKzT7wZYdhnci7jTvUSqyiJGO+gPRbUjTJ0D3WorZXvHd1J7v3ZjMO11zv9dX19I1X9kQzWyI80/2waLpSxp6F7TehNl9+mIDuRzNowPPH24rmn2bT1/CcgF2AzEPFE1fLiCdS+Fh7Ni48KlbtgAAvvvAPuV3U17oJtsdIMOA6WsYYoG7BBRqSHzk5p1JdlA31I/Mp8B8bXSoFeRjoqvWj053pWZJR8o5uoS6rCp1EmMj8+SxjICh48kAHJgo/A2a3uuov1128DTTACVwsqSu9Xw+xN3bD00p471x91G8+bNq9MCYJ1CTs6vXCvNOEsiFg5jD+okz19wQzGTmp1D8PttPFe1r3kLA01RHVGQ6bmL6eJG43sX0Veqd2rbzeqRPH2M0YIpJ25Z9NsF0m8zv6DWB89Ysxit+6RQAmaTezfSpe1s/LeZ0aLqEIKbPcs+/fW9TifCbIUEKorWOht+kTx95HlX6H763FOu9vIZDUEfT50Lh02f3d/bVT82HxWO6nq+p+GCz/TRaiOeCzYKIamDMTJ+5vmINa0wfPD7XvOwfGzuf9Wi61Kzv5GXz5P9NWtnIGW7x6aviE1fMJ3Xv1902aLm6Zphij9athNpz7dPXIPMl1p/e3SOGvJADTV/zGDB9DcNlYidAL/W56tMXbIoVIMGzmZSNDrWMqnRTHRR/+cV7pGap1B+oG0PVja0wX8imZruVKIyX+Nx3bIa0XX1DOjzVxQ0b9yi/9VKq6ZsbjVLGWGpMQuRzrFw4AiAL9KLPt29uUBPexrwO5Xm1+0QIZUUbmJoDudjGzaV5SnNGKUnsefoeKU1fTP1Z8A7VvDPT9GUP69IuzLV5Z5XonYIwMh26MmVDQ/nM7HFcVKm/QKeVOKO6zXRN5p1qmbu2HcZ1hqTQRb/sfZb9M5jGAcDNDx/A//xou9onwojGE8nl38Q7eML4Cpy9eiF+90mnKYRgjIUmJUqzz1imr/o8cDHh1NqCTpLQs1V819vwjU0Tq7HbU4V673vxBfjcay+rXa+uJaKBXKim9+pzTwDgX6OmlA1O8/tcgELHsIoQg9IWX74rSzvw0ZddhL/5tfPk740mZ8+HKWF2n75opg+FBo9q+srmnXk5YbXUQMyDITK3OJ97885uDRO07YemcP/uo/K7LoQROGRi+gY+fY1jwPQ1DDFFXQuQXklTjpluH8PtBL2UY8Ouo9i83x+pMWQp2Hwzxjpto9OsDn0z33M0Y7TM0SG54uxbdY8QG2KLaHV00wXGgP2U6auxL7z24z/Eyz56q1JfmqrtNxfIRdSfS+sstv+hEPmB3nPd/fjMbVvVtnTCLaruonBZslxInAXR21MCuZDDqI55J2MKU8w58fGRKRtCnyce7STucOa8HPiFmjy7COna5p3eQC6hmr4MjBVJ0I1MX4NMd8rLybNNoIT7UMun6SszffoQPPN938ErPnabtY4Q4Ztegt5zbEbdX6cVTZ+3am9fxDJbNNrBl17/S1i/fL6q6Yvg+goNb1lwE3Z/9cIu/lJq+qBFa3Y0qK8Frp0bgHtsGJrxI9PNO59x7iqcv3ZxdD3SmoKY1evtCND39u7nnqvcb0NpLnL33O/zTIDS1vbmKBi0hS3G8ITxFUqOwSatbBSfPmP0zngNnLTcZ+rer5+9ol7GqjGXFGI86DNw8LmP3llD03fZNd/Ek//u2/K7TLmkrdXDBqZvEL2zeQyYvqYhzDud0kiiEUs5Znsphjst/GDzQQDAH//3naHNOOHU9BnC4+rQ15s4YExmJClXpTKVNX35Z+HTp4ZuFoqBk46bV7qnCu7deQSAysj20lSOXZ1objaIsbElcQ0FPeTf9nk1qan+5qv69Ol3iU1YP19kji+Leed0t483f/ZO7Ds2Y5wbp6+cD0AQ6HmePu0hpE+f7OfcHXLtiOTWoi+6pi/lRR/nMvWB9/ZgTV/eVzAMtfIk6L3yzUwrXwdZ4AiLTx+pnpEtp9Py+fRp5p08PnpnmKZP/W4iut79vHNLfYodN1NpkxCBEoIxyrpUakHUz/CxihCOaE/jaoKa2AdH79Q1fYZGfOadTewqtkAuX/7dX6pUn02YPGth+qQ2xVKfbQ9NuSqw1U0h+2kqmYzit3qaPsDMiDdpZUO1bUPtclutJIlmbKif4Pxhe3ofalbcqqm9FLfSYDRpmj3XXEbvfOp7vl1Kh1IV4lXr42Bm+gaavqYxYPoahpiiVEvwnfv3Yd2brsWWPFIRXZtFyoZEMgEhWrIQwqFl8Rkb7YSad6odEQEBTJJgzlWpTF2fPhGpsZ0khjx9DG/65XG86LFr87arbwzCb1EcniIKJiU4mmYuhAOzHl01tpmYMX77tfcYk+2aQPuhj22XjBO90jJoCeitX75rJz55y1a868v3GjVop61cAICYdzJWIgRK0TuDnsYMnxavnYQntwbMPpocXM4vp0/fHJ9rwZo+qeorBDszDk1fE/3OArl4+gOVbu94NH164JIq0TurrHnTfLn8tCzJO9U+2iIxWmEoZ/Jxo0KkKJ++/FNoU0UU2tD+xQVy0X5w9FOmaMkDYIS0p18xaZJda5GZ+lgBNp++UN9EAbHL0siwSjtkXtHnCrWGKAVygbpWdOuFzD9WnWuxlgScl+/RBaCAyJHblKYv+xTRfwV+45KTZPtVffoYy+pdv3yepVxxBtRNQyHOrY42/rGWKVXw0e9uqnW/FCoy9bvA33/j/tI9dcxKBzBjwPQ1DLGg6Qby2R9tAwDc/PB+WUZIhjKmLzPvlPcGbAoh69um6RsbamGygqZPMEamzVGYsknTr8pMX/Ypma6kSJB+ZLorzaZGOi1ccOISAHGHtL7RCEZVHJ7i2ah5Z1NEuRibWzcdzNswR/kKhWuI90+oDN5nf7gd7/ryvYH1cvK/eq2nmHcWv0vzTuYmBlKDfxVQHGKZeWchFVXLFEQg0AyzbwNjwBfu2IFP37olqD4azEaA82I+uYjwume17/bQ+qU2DCSQizHyI1PK14HQ3PtAx6/dSpSohTqM0Tsjuxqk6XOYEgqIAA8z3To+faby5VGr7tOnEmOrFo3GdC/KlD/myfVgXiF1lPws07KQwskQNxTJxRa9MzSQkA7Rd502UPP0mSxwzKMlxlOv79O3bpVRKYHyWS9MpanAsoq58u9+6kfKb6Z30qhPH2G86Ht5+7PPBpAJyGNzwunBob74+sfjxjc+oVQuJfOutnln/qmad2bn7sHJ7pyaeC4cbePyd1+PZ/7DdyrdP5HTnDHuGXP5PL+oGDB9DUNMZLrZioO/0CYVB1o/zQIPDLUTyYiFTHQOjl+9YDVOWWaWLgF234xg806LVNGUs0VE7xTPWtnUIJf8y8AgrMjT97h3fQO3bz1UhOCuVr0CXdMn+j2Xmj6BjvZ+Yve32H595rZtuPzd13vLKZo+jcQS46QTetK807Kj0IhdpvktDrEseieXkc4opKbP0M9Y+NaYuP7H/32Xs5yAHtxA/FYwfY57axqU+cYhdJ5c8dc3AMjepb5nUTSp6cuiK9rMO4sGaJGhWPNO4tMXxmAGChTyIvOGWhjttIxzSvhGqpq+ahoFvY86qkbvpL5JALBq8UhM9+LMO7Wirl4W650F52XVr3DODYFc3GNTd1r3+ilSbvYdi3SXlOMlnlmfYoqmz2DeGVq/wJfu3Im3fv5u+X1CoxOELzcVWMYKeDlUH1fATKs0efZSxsvq0xchAKVrAAAgAElEQVSpVSqECNnn2FAba5eOlcodvyhbT6sXj9aP3pnfqph35j8enuriTz4bdl5VwcKRDrYcmMRd2w9Xul9YGon1F8Jkz7UVzC8iBkxfw5ASOTKhhZmHiCpHk38X5p0tSRgEMX0cAIMz9YLtcAs179Q3c0EAmqT/mXln4ddUR9OXMCbHTEa84lweQEemM9tvFiExEtA3XMn0WTR9rMGoWDrxYYvyFYoqB+KWALt8pV6tiW6/mMNKdMV8J7GZd9IQ4aZ+i0MszRmlVlI27xSHtZQU1iDPep4DPlagIBLKq78V88vt0xfZWCRiq1c0fQ7mqolupw5NH62fjm2nlTjfnx69M4007wzV7osiSR5oytSG2McokRv7vk3z3O/TFzODVW00DaYRgjrbo6ubNIJyaJ4+U0RVcav4dObpM9QRC6F9MzEXsYpEMbbyU4/eqfj0kXZEhF3Ps5jONpcVUC8VgVzizTsFjEIMq3lnVNVW+AK5tJJ4TR81GXXh1x6zBh992UV4wUVra0fvFM+haPp4kdj801pAtyaxYMTutxiCQ5OCbsu+hwRpGWj6mseA6WsYYopev3Evbnn4AIAyAcVRHDy9NMVsP1XyvIRKQBLGMO1IvGs73MbylA2+A0Hvh+i/KVx6yjP/peF2obGpgjTXmAi/khZjJTOPrQemABQSthjiXz+g+hamTxxqD+w5hpsfPlBK69AE6vv0NdgZC2zmnXpfqWZWQA25Lxg1M5EgiGPhf5UkZfNOmrcLiDMr0+E7TOKI5kJYQcFREGk3PbgfH7rxIeO99f1W3PfH1s9Y8T5MjvSFprX+gUzzqOmwVd9pJcYAMwKl6J08TjwgmDgfqLlgNq/LZdqtzFe7TsoGU3HTmKk+feH165o+cf9jT14a2L8ITV8pkIu9o0qePqrpc/al3J4+VnNt3SmEo0bzzsgGZKoizbdPbwtQ995QbXy0oDHlpUAuTViomMIPsAr987XJiEBZbT8uOfvUbF/65vvWGmNZZFLWgHmngO5rGSLErwudXgmFGG/J9CFc0zfX6Yx+ETFg+hoGPQC/cEeWq0ls/tIUg0jKrr1zJ4DCDAgI05JlZiseTZ/VvLMNzssmFjrKOYGy7ybpv2AExXPUid7JwDCaM33DHTVPH6270B6F129jFG7ZlDHoug/Wgdw37oaNe8MbsUA/73UfjHifvrnZEGm9VvNOrvv0CfNOMzFAnbfN5p255juXJAv/B4p2SdNXHV6fvsj6TMyLEIQAWQj/d1xr9qmszfL5CLtI5pixgqjQGajseryG3YasDot5p2VkOu3EaHYqoF+LDeQSGmGvyNMFgNvX41A7UczwZCCXwP6Y+mLzgXJdt4EyrwL3v/Pp+PQrL6ncP19bIaB+1YpPn1PTV+5bjHkni+yjCeJdDxmI5Cr7SvaZfdf3TsqotJLy+/c9iuvdrV06iqectVL5zezTV38jMJt3Nqfpo3NcD7gl2g99jrd87i6c+bavSpohhpFPAvcWG0QfR7RI0S46sClUFTIfl6eW2nVkGkDBJIckXh/wfM1jwPQ1DDpJheRNqOKpaZwgYG/feggA8JSzjpf3BS0GZMSZiSgTMEXEAoDRnKDzSYfKTuN2nz5x0NU37+QK0TnWaZc2SlF3qAmLXr8J7/7qRuw9OiOfraNJaecNt0y3RUFvumzeGVffXEXrUqw7LZq+lHPloJGmUwpxVtb0pan5HYg18tC+CTy8bwItVp6/+mFdh9iIkeqGCWEMxCQPM0+Za2lmrBmskrLBmJw9QyOBXAyaGBPoWvH59JVN/IqckiEEWisJixqoavrsjKW+f0ntTfD4GbStnseIydNHA/gItBJmHKtL1x9nvT8EeknXcxTRO9VyzuTs+ncijBG3Oc07G1D1iblp9umLtyAAaKwA9Tpd20rKBnm/+9249raP/dbF+OBLLlR+K/L0xTN9xbwvXzO9E8bmQNNnaStJwhnM//h+WHAvE5Kk3jOJW2kyeACY8gjw49ow968qvbF6cRYY6qG9xwAUe5NpD3/0iWouy4Gmr3kMmL6Gofo5ZZNbD3/OUWya3T7HaKeFU1fMl/eFRe/MfIiOX2h3urdF7xQMp8+viRKsNz24X0aENC1WadJCzPSqQBDPYlMbaiclh+7CTyq/J6J+19j+YPNBTM9mzzGmbapjQ9Xs2fcdm8GF7/g67tlxpHStbp6+uTJ3V6N3mhn/bp/jCInyZtL00TvFzzsOTxk1T2JOvuVzWRCBydm+Eo0QKIhA2UaN5/fNfUoBh5ihZAnl1d+Ef6IPdc+1OtJ8Exhz+/RVWXc2pKnDpy9v4GtvuFwhoH0pG/Tn/cHmgzgwUc4BZUNoAAlRQuTytE0pvb5on75ATZ/eZnD9+WcIw/Phl16Etz/rUcb7wxpTS7tapJq+Oj59OnxjUzewkovpi+UpRU+KQC5q30zWFrQd21iJkq7z0DROGeHPFSuV6OidhvGda02fDOSSmOf5XAZso2jVjBEg7hwdUufW9Gz1PKA69O59/Q2XAwC+eOeOSvWJ8/qBPRnTJ0ZfCF7f9oyzZNlnnb/a2ZcB6mPA9DUMut4EUS9CzQttWMoLm/heP5X/i/Iha5bzbGP/zKseh7eSRUNhk/ZWyeHzon/5vvz/9i2HSmV7mh9DVfPONM0ks8K8M5Pyujf/mKa4g9a/c9shqf3UJWljQ9U0fTds3It9x2bxoRsfKpt3EqLg2eeviiY15ioZK61V1foV5orbDqoBYcRUo4wsvVcctHduO4zP3b691KYeybTbT0uaPT16Z51D2nfwUgIqpB3hh6j/FhaJtx58h/zX79mNj9+8OarO4QCmrwkiyenTl38uHlUDi7RbidGvWN6nXfrH6x/Eb3/stuA+ZaZe/nI0DLyruO4jGO3Th3KSbB/vEGfeqQrRXBgdamGdFjE6zqdPhVvTV9ZcZXWEa/podNgioMvcmnd2HT59sZq+wqcvQzmZevGdVi1NsD31u7Q3pr6KQC6KT1/FACi+tupqxdQ2xVq100SPBNNXN4m6zbyTWm3VfQz9zJqfB3C58f59leoTc3jn4cy8UyzmIuBR8U7qBrcbwI8B09cw9CTi9LcisXUxubv9QkPw7Tc+AaeumB8hZWZYvXgUTzv7eGMZg6Ax71e5rybQxU8X5n/+YBse2HNUKSsWsDjoqpoCcAhNX+G/Zduoi8M7vK1jsz3rtfff8KBkbnVJ2ryKmj7qQK6DEjXzhtvRErqmEte66t1zdEa+S0po669XBnJRmD5eug4APzIIDXRz2m6fl3wexRycK5++T7ziYvk/PXtCfQ9KgVx4uNZ+rvG3X7svuCxjjGj6yn0rzKrr94tzezAPWb92uZN4zDtrstGxBKAob1uPen1VAl/okfN85ptxefrEPWE3laPU+h/o9D/9Cv7yi/eEdwpZ7jQAeXL2UE1f+bv+VDYLmKyt+vNauFw0E70z1/Dlk0YnyL0aZJ95p+O6qa9inlfx6ROlbst94SiM5p1ojhET1dgYfhEsbq7OVIEWqxe9U3RvuGP36asrDD48pVpFmALfxEA8rjhzi5QN2Tqh6T9066e5fh+/iBgwfQ2DTtHJnMHQ0wJwzuWBMEs0fasWj+LcNYsCNX1lc7JVi0bw2iesl9/tUi1Rh7sNuuGuXjyKJ46vkN9pAlegWMDSvLOqpi+X/ItNLWF2AkayfBFNXXbNN53XReAWXdPn8gNxgWoDSsSH5njfRBS0JkCrfe4Hvod/+OYDANwmkTJPHyXO6HWlbPl+nUCa7aXotNWCLeLjAzSv6Tv9+AXyf/oc5/z517z1pYb1mPJAM+earzHk9pjZy2DWUuiVNXEguxi09cszjZJOdCSJm3Cq261Q/56CWcr+p7e8+OIT8d4Xnp9f13ySo4U7hbRdwMc8UOLWV9bk0+euW7s/wJ1otp/iI999uDSuLq2bIABdZ0AZagOmOWrLJQpkjEZ9804hADUwMrE+fUXst+yTdI1zrvqKavf6NNCAOziHiX7op5m1B92vY8+t6+4tR8I2MeIsoP+hoOcwALz6yvX4EPFXDKWJ6u55VVJDmNp3Re+sGx30pR+5Rfmuu1nEQvQnTdV3IAR3lNGrG+dgAD/qJd4YoAwySSdm1GTr4jDgMEfaAtwhrJVmuBoRUWD98sI30Mao6BpIG+jm0e1zJa3EvGF16vQ0TV8dnz4Gat5Zztcm0KRvkY5RjemrzsRmn+a8WiphFkoMTnf7GOm05ixlg/7qvnXfHvzeVac5w+SLV2TL00efzTQv9Uh3M/1UmkXL+zQzrTrnrymQi8knJhSmPH0ADwoYU5d5b5r3Z8weBAogwpYG2qJ51HR88DcuxO3bDmHx2JDye+Z/Y6+zLmGmRwv2Qeyn0nwJwJ88/UzMz/fIhKmWD9EafUDWVfTRp+kjhJSn7COh6SvaCi8r8/RBZZZM7X3vwX3Ye3SmJCBQz8ns0/mc1WR7CmYdmr5Y2WERvbOs6euldu0yILRX1TV9NqYvTblydoWe9a5HN53xTaU3AMrn8B8/bVxti9BEiaOnLrPyEAxrkXxjIV6XLpSebtC8856davyBqgJvATFHBQ0l3oEYS/ru9bQQA/PO5jHQ9DUMOkUnZsyaPpqcHVAnfSjBwWHelOkCXTw6VLoOcp+vGfWASTVGVS1bOK/nm2fFfY3zzDdKMI8JsxOFTZqZ6dA31TrRSIFcuqw9CDVrCHVav37DHoy/9atY96Zrsf3QVKU++aDPP7lJO14qNe+84vTlpXro8JmIUJOmTz8ARJEqUVt1mIiJOmdbxryUpZQhwoJH4lyLYWIZisiNv3/VadZyzZh3ciuJtWisI+cShS+8et1uBadsEISk4RSlc0nvb3Tgi9wy5H0vvkCG0Pe9Ttq+zxS0MEEP608tpk/77vbpK0z8aTlTcy/+l5vxe5+6vTS2qWF++ZjbuvN6Irfw0QWjQLhQV/ZF65MS0KzP1XNW16LC/ixiCFxMlWna9FOOPudacvCwAXOVMp0JQ616DBIFTc5uglgjvrVJ821WgZ6+JRaie7pQ+uOvKNKrVBVQH5iYxc0P7S+ZkvuERj6IMZVR1yXTZ9L0qZvpXEUo/0XGgOlrGCnneOL4CpyybJ7c/PtpwewBqk8foG5EzCPFTnPpnukwA9Tcb5ecYk6uK9qL1fSpYZrVsoKx7dQ279Q1Li6fvuyzqjmO67ASm+qfP/Ms2a8qEPf5fPpCNX3fvr/IF/j9h/ZX65QH+rMylh0ILj8qOod/54pTSvXYAg4I6CYkM71y9E4ZIbQBDa/JVDU0F5gO3WylqIPLte+8v+a5xgEs0oKd1IEYhk3XXI3fv+p0w/UwS4EQmJhlf/88TJ+nW3uOTjuvBydnl3n6yv2nvyWMoZ8C///XNuLenUcqjVvCgGecuwqrl4xa27S179X05Z/hTJ/6PcbiIObRRTuM2c3GS/VrV+n8ElV48/SFd9EI4fqwcKTM9MUKlvT0HnTu9LmaJqSU+J4FBHLxaAp19HkWnIpa/dTZv576qEyI0TLkzvPl4zQhTTnee939ODSlup+4fOuz39VyNtRlQocqPBOFDOSiMX2PPXmpDOhXVTv6vA98Dy/44PdxzupFyu91NX1SS62ZkQsrGFp/OaJ5raYHMGDA9DUMzrPNfvWSUUzOCvNO5J8F06drMQR8PgWn/MmX8dKP3pqbrah1MMZkve2EWW2xZc40n6aPbIBTs31FYlxOEqtGLNt9xE1YmdDrp/jfH+9SJJQuf45QO3wb3vDp263XRvNonVefuwpAjWTz+X0mwovOgVBNH63HZYJXB/r8u3XTQTz67V/HJ2/O8hOZ/L0UHyKhiSP10PniM3UFcp8+7fmkuZeYvzVOBJ+mL6bm1PKOUx6W7qGuD5HJv7eM8LniKymuN6Xpi4VPE+c2eQN+6a+ud9cf7dPnZvoYA6Z7ffz9Nx/Acz/wvUq+u2LOiznme99tR0Q8HTE5DE3lYvbGkqbPMdsYY2gnLBf8kToiGH4Og6bP5dPXANcnAmHMHzYIYiK3bKkl0T6BPFG6415f1Gu9Ph22lA19zaevjglmos1riuEKmr5bNx3A3113H/7uu3uV332mvaHWT3UYNqC+9rIw77SbDt+/+yj+98e7out+aN8EAGDpvCHFnNy0f9y9/XBwvWJ69KVwVNX00foH0TvnHl6mjzH2EcbYHsbY3eS3P2eMbWeM3Z7/PZ1cezNj7AHG2EbG2FPJ749hjN2VX/t7lp8cjLFhxtin899vZoyta/YRH1lkyYaZ4rxfaPqQf/KSGlsgSfwbz7fv2wuadJZCpolwON+GSLU+c+tWfPS7m+T3qW5f2Zj1e791X7bJCp+K3/n3HzifwYT/+P5m7Dk6gyvPWAFx8jLYNX3iBK26MXzudnveGSFJE5tQZfNOklNQfwo9JHnIc7js33XojFQobN34zgNZyOZhw9xSEgNLrqC47vPpM5t3lgN4AE1p+sp3q5q+8NqLHFCsxDiGJnavC5/mJ8q801M2VCoeApdPnw0JcxOafhMtN9HVCvCFAor5ZyKOS+ad0q87rRClt9g7pMDFMmgvvvhEvOqK9cp68jN95T67UApYVIPo9737VsJKPn1uTZ8K0/xya/rqB3I5kjN9upmcr20TpHUQOP7ztq34tX+6SV7rp5qmT7fQgH8fC0nZsGi0g7NXLwRQpGxQA7lUH69l84cBWPy8K5hCCj+xGc33zmvemf/uE2A0oumrxfRl/Rtul1NIiTF83j/dVIn+Epju9rFy4bD8bhqzZ/zDd4Lrk1pqzSJGnMEDTd8jixBN378CeJrh97/jnJ+f/30ZABhjZwF4IYBH5fe8nzEmZucHALwSwGn5n6jz5QAOcs5PBfB3AP6q4rP8VCBNcwKfFZNdTG66OdrymQFh/iSz/VQyYasWjeK5j16Df/6Nx8gF5Iq+J3P4ODa4N/73naXf9CAd9H4RkcvFbPqwaf8k5g218LfPPw9nnZCZGDzr/FVek4y6tOdop4X/fNXjlCShQpImxriqNNMVyEXx6QvULtB69JQGOkwHQwhs/RBM2LBDypj9LzR9BUypTChKTJ8hT58M5CKZfXM/Q2B6n1X1ptQnSmccdebSRGQ1EQUz1kTSU5vnanNtufL0Wdv3mXfW7FOweackJN2avoQxSYymvFr0TlGd8BOzmVq/6znn4E2/PO7MfaVDmqAHvtcmA7n4WmwnzODT59L0ccN3VYvjGo/s3PZ0yoMjU10kzJzbNXblUA0fFcICuaaP9LWkRWX1zDvFe77jz56CL73+lwCYE8+Hjpde7i9+5VHSosZs/ZEEpctR2rA8sescpr/75rJPYOTDULtVS1vIkb1XY4qLhs6AqW5foS0YY7VMPEvmnXlVIdE7BykbmoeXQuecfxtAObGKGc8C8CnO+Qzn/GEADwB4LGPsBAALOec38ewtfgzAs8k9/5b//18AnsSapWAecQjtlJivfY3p0wO5KPcyIIRsme6mOG/tYgAZkfK3zz8PZ69eJBeri+mrahapm3eapEnOUO8epJzL+088bgybrrkav3zOCd6UDXXxh085HRetW6psOEJjyZKib1VAk8Lqs7pdIXon5Y18q6Tqu7D1Q4yJKW+PKUS84n9CzjnT+zSad2rtSPPO/Oc6B4KPmIipmZoO0WdLeTl6p0mSXPdY49yvpYlZK4+kpi/TYsWtZJ/5pc+80wdfdFAgmz/XfGWDtU49OBf165Zh+EOJZRTmncLXeHLGnm8UUIlyb+AS6ZsY1h+9vqYiLJrQSljZpy+gud+45CQANk2f/b4mKI8j013MH24bifBYTZ94WG54jlT36Ssx1My7R7o0W0zb5lsJk1oqmo6iqutDkjC575tkxVW0YjattS9YkRRUepqrrelrwLzTNofqBlwRmJrtG4Ko1WH6sk9x5OqBXBIH0zcw72wedXz6XscYuzM3/1yS/7YawFZSZlv+2+r8f/135R7OeQ/AYQDH1ejXTxTC7JKhmLCFpk+U0bRm5H6GcGLg0vXlYZKaPofGLdSnr1Q3Y3jl5SJIB8fX7tldKlNH09dPTWHv7ZtcobGs3KRSv2qimP8WKAG0Qdx21/ZD+MHmg8o1JYKrxf/iR1vUe+jG7iO2qiZVtdUqDoJhzfQV0IIRiXpIRf6UDXogl7T03mkId73+WBg1fYo2Qb3mIp6o6ZBgYNo5Y6IHjDHNoyYOtibFZL6qxPUmaP0qmj6feWcMnvuB7xnq9xPLX7pzh9z/QoKqiDOAV9D0pbwYc6E9Ev7iNqjmnf76AQRLBkzMRyhKJoieNtutpCRMcbUm6j9+0Yjsm7hVtOWTKdedWYenulgwYg6sFDvXC5qh3Kt+yp1r0K21zM+1APNOgRbRWA81YN6ZMGAkt0YxdaNK0BNbX1xaedEX1/0C9TV9Sa06bAH8AP86d4EKQae6KdoJwzPOPUH69tVhKMUcE4IvUZUQiLaTQiA+yNM396iap+8DAN6ObH98O4C/BfBbMB8b3PE7PNcUMMZeicxEFMcffzw2bNgQ1+sc+/btq3yvD7PdLo4cOYyJ6T6mp3vYsGED9h/ICPejR4/htjt+jPv3HMPxY8Uj9ro92Z/Dhw+h2+sF9W/XloewV1skO3bnYfxTex07dhwDADz40EPgh4aNZUw4cvgQzlgzBgB4eNNmdBKgq+1fRw8VSuHYMT5w8BDStF+6b8+estPwhg0bsH37RN6Xh9E5NuKtf+/evcbf9+3dgw0bZrB3T5GfRvRBbNC7du/Bhg3dsAch2LUne/e3bjpYurZ71075/4H9+9FP09KzP+dfH1T6tH9/Mb6T07POthk3awPuufdeJ6G6ZduE8fepiex33s/GoZ0U0rvpqSnZ9y17siA+W7ZuxYY0izC6fUcxtjPT5SA/O7ZvVb4/ZtUodu3crvy2besWbJjdi6l80u3eswcbNrjHQEBf85u3ZM/ye5cux3u/l82L++67T17vaaG577l3g1XaOZET4Hv37iXEBXBsYgL7mdq/ezdsxIimgT148FCt/ejYxAT6Pbfmp98P21MAYPv27djQOmS9vnVntsfs2aMmWa7yDNPTM+j2edS9hw4eNK4VgV277X3vzqprWBfEZGVmvHvw935cRM6dnZkpXaf3dmdncfho8X527MzW/czMTNBzT05OImFZnQf3HQUA7N5/0Hnvjj3FGuP98p5KsWtXtr8+9MADODDqJwm2HFCfN2Zv3K3Nmb179jrv5WkfR48ewd49xTravHkzFkyXE3wDwPYdmZ/2vnyv5xw4duwYNmzYgF6+RrZu2YyxybLAEgAmJyYx3bPPrRDs2HcYQ8w85sdm1H3F187kVLbWDh46hBltnt13/wM4fLg4G6fIHgwA4Cn2HzhgbEPsFwcPHyldE3jgvvuUgCEMHLv3ZefP0SPFGtu0yT6eFFNTaoqhPbt3Yzrfy/ceKO+BRw4eQLfPce+99wabLm7Nz65et6vU93BOF23bVpxJSl9yGmPj/fdj8YjdLeKBneU0STFzZeLoYUzPdivPr3379wPI9ssnn7oAX3/gqOzD7l3qu4xpY+9EsT8dmZjCopEWXvfoZXjdo0/Chg0bwFBmVEPrn57J1m63l62JvTlNdGRiEgCwY9s2GTV3xzaVDti/3zx/bZhL2v7nBZWYPs65XOGMsX8B8KX86zYAa0nRNQB25L+vMfxO79nGGGsDWASLOSnn/IMAPggAZ599Nh8fHzcV82LDhg2oeq8PrfY2LFm0GLwzi0PdSYyPj2P+nTMAjmJ0bAzv/1E20Q/32rjyjOW4YeNetNpt2Z+lG3tItkw5+pcxASOdBI8668zS1amxgwB2YMHYiLWOzf1dAHbjpHXrML5qkbGMaIdi2XFLcdKJywHsxNoTT0SKHbji9GUyiAsAnLByOYBsUceO8cK7ZzHUmSndd9uhzQD2Kb+Nj49jO98NYBdOOmkdxnNTVxcyaXv5kFu96niMj5+Eu45tBbBX6XtmivEwjlu2HOPjp0Y9DwAct+sB2KyjV69eDSAjXpYvWwaww4YxK97D+Pg4lmzeCDG+nLUA2In9eaMjwNFjpd9PPe0Mp+nnTuwBUI7+NTZvHoBJLFkwhk0HZzE61MZML49UN2+e7Pvk2EEA27FmzRqMn7ECAPCDw5shxnZkZASASsCcesrJKLYE4GO/czluenC/0o+T163D+ElLMDnbA/Awli1fjvHx9dbnoNDX/EPdnQB24fR1a4Gc6Rs/4wwADwMAkiQByEF3+hlnWLXYhye7ADbhhJUr0WodRDePbjc6OoZ5C0YBHC2e87TTc+lp8V4XLlpcaz8au/EQhqcATNrzNnbanYA2sj6tXbMG4+MrraUOdvYD2IFly5eDzu24Z8ja6gwNgaU86t5lmzaAw7RWMizf/SCA/fjNy9aV/KA6Qx241gwAjI2OIpmedPbp4M23yv9HR0eAgypzT+8dHd2L4eEWgIwRu2V3Nq+Gh4eDnnv0hgNoJwnGx8exLd0NfHsP2iPznPd25x8GkAlNRoaHnGVvPrAJwD6cdtppOG6+XwjIdh0FNdwJ2xuz9718+XIABdG9YsUKjI+fYr1rZGg7Fi1ahJUrF8n71q49EeOn6FYuWf0nnHACgD1YuXIFxNxcuGABxsfH0W5vA9DHKetOxvga87k3/7uHgZlexfWY9YG3hrBkATPWcXQ62ysEfO0M/e9eALNYuHARRiePAijm2cmnnIL5980CyBidkdFRpb5WshmLlywxttHKx2L+ggUAymdE1rczlNQAnfZmjM1fCOAIVi5fhswoC1iz9kSMn2xOD0UxfN0+0H1/9QknZJq1W/djeLQ8n1ftfAC44yBOOe30YP/0HchoguEhdc4fHsr2rJNOPBHjpy4r3XdrTmOcsn49ViywC5B3J3tBzykgbt87/qF70XvgaOX9fsnD9yJJjmB8fBz/Mj6OdW+6Vrn96sgAACAASURBVPbhxxPbIM7Y2H7NbjsEYDMAoI8W5s8bU+7vtLcAXVU4E1p/u7MTQBdg2R62bGdGE7U7wwBmsO6kE5GwnUg5x8nr1oGO76LFcWfjXNL2Py+opBDOffQEngNARPb8AoAX5hE5T0YWsOUWzvlOAEcZY5fk/novAfB5cs9L8/+fB+Cb/GfYe1PY3lOlgFBr91OOh/OwuL0+x5PPKhNWLvNOOizzDYlfgcJ0xe3TV80sspUwJbCJHroZ0B284xroc240I/Dn6QuD7l8lIMwsTdEwpdlHRTuD0CFIAn36aHJYU645Cpt5p68d23Wh0RImZjRXE31FZvPO4n9T5Mz5w8WhvnLhMEY6rZJmTXyvOn8pRB9sEVD1ql1jRs07ZS5Bad7JjWXdrcWjUfPOwLqa2KVd5ko2+HzuxKXfe5I9sbyz/sQfTGuqW6xDn79LwoqoggDw3Qfi8mtyXqQZEGtvwuPTR+e1z7fXZ/qmo5ynzz1YrnPAl+xaRO+kfXM1t3HXUWMfs35kn77HrDuvZ/vcmi4pNlyBOHdMz9zTArmUG7M/Cws410y+kLOGQC6h5p16sSRhMpDLtMHkUZxfMT5wso2SCXL+s2X4W4Fnyky3ZnL2Gj59v/r+7+Kfv/WQdb/0xHVzYs+Rghmf6vbLQdSaCOQiTdyF21MeyKVVRGgfRO+ce4SkbPgkgJsAnMEY28YYezmAd+fpF+4E8AQAbwAAzvmPAXwGwD0AvgrgtZxzsUpeDeBDyIK7PAjgK/nvHwZwHGPsAQB/AOBNTT3cTwIcIjx/EQGO+nNQQr1gcIqZzRz+JJSAnGdh+qSjtStlQ/5p26xtB3EWjER1wqUO3dn36vl7aD4qtV1zeZkPLvDQseVMK5J+lxsSm13dPH0+iJxKvsh09MDoGg6P1z6h0HzZiD1f7jhbFwShOdppl+pX85Ll74XMa0pcmOYFnc8tywFQzoNX/UQQghjbOilHAbTXJZm+pAhJ0k6y0O89zSfFHL0zsNMWuJz7BWJozdBALk3I5rjN+N8BX0oFcckY4S6wMd/cMs13G1oJ8wpofH0R/RZE8pSH+KREuc+310cQ6yjl6YvYT/SSIpG5DScdN4Y1S0Y1nz57e//87YcAlPMkUrijd9aXnnQNkYcFYmlnmrKhdC0gZYMPrndn8qnuSvqCMuGBTJ/2DK0EmDeU7fsmZkqMYdcirDVBRulElkvulocPKG3X9emrnaevnWT5WyvU88MtmUmtLwJpFew9Rpi+2X4ppVgdpk8MqaChxDvqyuTsiZyseuqnQSCX5uE17+Scv8jw84cd5d8J4J2G328DcLbh92kAv+brx88KMk0fU/Lt0eidQtvU7adygtN57XK+phu02Cx1CKbAFVBFrGc7cW8mKFpJsTna2qHfeylHTNaANOXGzcW6mcVq+iwFi4T25qiUjFXX9IXu7QUhbSe+OFcdyScMwRyeff5q3L/7GL52z25FE6f2qaKmL2f6Ck1f8XJ9gVxomyamkzJ9gvBKtLkgXk+dw032QTiRaxFUBfQehuSFY0Qo0kqyw10nWEzV1D3YOPzJ2X0jFsPAmd5vVXDEm5tQAYmJSBfjWTWoVK+fep9NTcngLssYQ88goAnNBycEiQAwlu/7vkAulNHr+DR9pJ8hUNZ6AGFGr+pFj3iYvo+/4hIAwL/ftMlcobWP5XUtPv3RTOshO9stmr5ICYdk+gznQp+rgVz0frsEyIXgN/v8n9dciue8Xw1qZGT6pLCXaPoCzzi9XMIYxoYdmr78fInT9InNuMglt+maq5UIyyaEBrezWQuFQozbbL+chzYUVm1lDcbswERhNjzbT9HxCFxjIKPWE+UHQJKzk0BN9Dzu5GfoAM2iTvTOAQzgubmSSdPX51wSgbOE6aPIksOaQSMs2cw7F+aO+GeesNDaRyY3OHNLxywHcYsV+VpsTB89DGJz7KTcln/GXD7WzM+2YYsD2rZpthirrOkLJehNue1MdfkOwCRhkjE05YkCAiTzlt9FEAKTeaeelwywR+80tU+JVEEv2cw7Q0yTfBB9oPPXRZC5NUtCilz0LYveyaVGUW9XvT+421bUZYQVwZMvT1/F6L8m2LT7Loh54Gu/6pD0+ty7bk15KV1lfdp1F2hXli/IfO4uWuf2oeq0wx9ezN/Q8aLP22IsSoikM7qZj5sfocnZi/Lkf+j7iP2+hMUJQLbsnyztybO91JOSKRw0eqd+a5anz6Wp84+VeDcmgaf+CK2E1TPvLNXPpBDDpOkTtMQl//cbuH2rPTiTqQ39aXzJ2UPPlLpRg6uYrOqgj3Daivn4o6eeAaDeGaA/l+3spQhdJ3qePkkXS02f2byzlfijKA8Qj6rROwewQEhlqcaO2uULM59uj2j6yP2mQ2e2l+Ird+/E5actl7/NGzYT9OeuWYz/ePnFeKzDsdon1Zq2mHe2kkQevqbNH1A3pBizDCDbFEz7ltWnL/+sa94pffosJ0LC/D4+NoT2jZqXtCxmqxz+kNEtxnBwMpPa2RzSfaZmti7vy01ARo0+fWXJOiUGfEyfTkgC5YNGvJ8QBtmHnmT6LJo+rXLX++9LgqI4vFq5X1iIT19d5smlHRaIClPvrSv7bML0pqpPH5DNIxcxYmJeQ+iiXpp6n02Z7576QhgjFzhpb+m8IXzrj67ECYtGnfco+7JPE+fRguhQBDyJXyCmmHdGavoE6HsOmXeJyvUpcK0FW+ocEw5MzOLyv74ev37xiXjnc86Rv8/2zALdrO2wugWK3L6Ga6n2u9Zx5jGDpvWHJPtusUKgWM2nTy2XMIZ5+VliOtfo3vzVu3fh/IBgbYUAQ+07tcYwITQ5e1Xhr4DU9NVg+ujc/vofXGH8PRb6c4fk6ZvppUqgHxuE7F9YZ4h3JGhI6tNHNeTtxD9/B4jHQNPXMHjuf8FYobGTefpSjj7R9JkWaWYuo/72D9+8H7/3qdvxlbuLSIY2nz4AePxpywKTs5sXlFhoV525Ao8+sdhoTeadQy19cy3qjLVb59wSyMXyKNIkMrB+fcMWYyQ3HJsfRlJdqxQq4ReHESUOp7V8GEene7j2rp1woZUw7D+WMX0rF5mZPp85jo9RFQmiVZ8+Qz3kfzoVTPPCpDnR54L4vTBNalbTR6FrJFxj8sPNwteCaPpamZRS1y6b+hxq5mcDR31NH+2Xr6ZYYYuz3TSeEBZmv/acXHm5ikPiy3+m120LBlSUZdFWDwq4ar570nHzvMFZOgklyt3Vy8TVgd2h76vFmHdvpO9JJ+5DNX10LwiZdnS8hKlaCHMbGlALAA5MZEKwmx5SA/PM9u2avth1Wvj0obRQMvNOqkVVweAfK7EP6nPY1P2EJmdvIJBLKykEiCZNHxUqhs4Tad2pCPCKcbLtCaHWA3WsS4DizKyV78/yDDWsO0vPrWt+TfN5Rs/XZQE9J/ppcdqJuZQw1RdettmqLmwfwI4B09cwMgJMPTwUn778/xliAkIXRcYsqjN91+Es1Pc+4mxrM+8MgU/TJ/r7vMesxYsvPqm4LyHmnRZNH6VtupErNjo5O9TD3Ad6rvzHyy/G8jw8ua7pK5m11JDUh95nMok8pkXou/ZONVS0sZ6ESfv8lQvN4de9mj5PG+Lgou/e5ENjM+80McK6A7f+G/1uqj8WMnpnQs07C+h1u97jaz/xQwBlTR9Heax/Ws07U20PckFcNvmUxkIIyWLgM+v2SfV96PbdZnN63UMex+UkMZuWh753mpw9FNS80ydUEFeDzTs1E6wYflYPEnaCRTClw+Vvay5P+6gFpXA+aDihKYRyejqBrsNfK/Y9iu3DxFj10xQpd5ss+h5FVKsT9ab9pNMqEovT+RXq06fPQ8aY1BSZmKAhhekL0wjLtU9+m5zteyPUhlov1NX0DROfvqqwPUO9YCuapq/kT1+u22YRpkOx8iEMuGD6bMnZM7/4AdfXNAZMX8NI08xHhZqJCMKPmnfO9lLjQjJJ58QipyYBLk2fD74NTmzi2YGuRhvV+6IHCTjzhAXyf1N0SRf6qXlzsRFvBfEftjFQgptu3kLDJ4gBfVMNMWGyIdTEVdAI9J1k+egKLAvIodViTEb2s5l3+g7p0MiFLbJZU7qqGL+iHl/0ThEwh95flj4zWVatPR49Q/RZl99QCCFIJZYiwmQpkIth7GtrzHh4xE1rFaQLftIhK2Hz/Y0BRwVNX17etiYFcWmq1tWUSES9dN6QlyGj9ejWDjoSxur59CHe71Exvwtc71VSNoRoxuhlnfm95rnnBrVJicGQ9UIZBmEmKB7P9ZgxPn2zhqAmgNunr7Kmz+jTl/1uF4L4TVX7BuGXrZ+dFpMauSZSNrQYk8LrC9ctMbRXQdNnOBWOzfTkGrANfzGGHq11TdWTeN46e6f1GRqIsClQMu80NBqq6aNDlqZFW2L9ZHSEKngX/w94vuYxYPoahpSawqDpSwsisNtPi9ww5H5qFlr8ln1204aYPghNn4XpI6YQlFihRH7XoulbONrBP7zoAgB+jZIOzrnRyd6esiG/L7D+nsXHTGw0uiapaL/65qMH8qBYsaBg4sShQwnZsj9Y9mnT4AEZ8yXyk9mid1b16Sv6mn22SYoCs09fcY/ruQR0Xz5dm61rA+swS4VPn1nTp0+qkLaSRNVScl5mcM3mnfWQRe9sUtPnLiuu65rouu2Gwud/YzLxCsGrrzgV//Zbj8WVZywP8IUq/vdFCc2YPlP0zjDwCpo+SjyFantCofjfJnGBXHTT7oUjneg2aWv7j83IBNUU1NdI14C4iOOYvV4IPofbibI/2AKSAdV9+qa7aTl6Z56nr/BxVjuedcH8MKIusS/rrg2mfnZaiRQotpMKTJ/2PUmy93TdH1yO97zgglJ5akIautfIlA2k/0enu1IrZfNBC43eWdfccOm8IQDA/okZT0k7bFOoSZ++kJQNVTR9vVw7TTHcSeT7omuz3arnCz2AGQOmr2lwEciFavpypi/gYDARs5LRIjkH5lsCuYRANmtZTzIwhXagJ6ww7xTMqy7lbjFWKb+OaDfGvFOmbAhshkqZM41s9r/MCyckwqVob9U3H5uJ69PPOR6XnHKc/C4PnVTto9L/nHB0ScdbjOENTz4dm6652kpgVE3ZICAYPMroKeadBrNb13PJOhK13jVLxvBbl50sr9P1UocR7/ZTfDDP56UwfWS8dAIqWNNH5lKWskEz7zR0uolzzZeY10cP0D6Emls2w/TFm2Ga1gqFFLxF1ssYcMXpy/MAGO6ydI/2Mn0Jw6HJME2Fua0qzxKuGYtNzk6LtUICuZD/Y03+BSgxSJ/n/j3HjOVHCXGvvx+XQiRJwpmYGYXpU6/Zo3fGvUexfezMXTwo0txUzpaCibFw/+0Q886hNmX6iuuhr1QfV9HGqSsWSN8+Cmoie+umg0HBT0xBnI5M92SKk1Er05d9+s7GukyIsNYRfvdVYDXvrMX0qd9DkrOHBqPhnEZHLe9Hw+2WcV10WgPzzrnAgOlrGByC+Csmt9go9MPRmKcPJiYmK0elxfOHwySkJhSBEMzXRb91XzYaZckUxQvInkn8ViVlQyWfvkCZ+f6pQjKVko1IvAch3TJp+qqad9qC2Txu/TLlu0wCbzFBBYhk2UFkhtj11/VLoIFWZF460qw0u6XmnaTJbppibKiF6/+/K5V6C/Pa4rcnnbmi1C6gatJj8Ymbt+DwVEaE24gzfW2EjFlm1l30NUvZoN5Ho5cBGSFV17yTW9aN0jcPI6f6FbvbE5ebYPpMZms++IIuZPn7zNdcRLcYAl+gq6ztCKavnhI2T2tR734XYjWjenqWmEAuVZJSA/ZALrYuC1NdoHzOutZCSMRLAWHqeGS6hw27jqr9rfvSc4g5uOPQVOlaFnDIruXP0j+FMTElbahF0zedPzPVDL7m4z90tiGgzxPfnqXvzffsPOJvQzB95NZj0z1M5nuVzULKFxxKr78qCk1fdabPbqKqfo85V/SyISkbQum7lHMZWKrPyzNyuJ1g9eIsGnFL2Vua8XcfQMWA6WsYIgR5wgoJp7B9zoizomxxGKnaNH2e6yaVgD1lQwhEH2wbmGgmYWVNX8mnz8D0tSXTF7di09ScZNrlqA4gyE7q6/fsxrtu2C2/99MigESRnF1omtR7W3MQvVN/JhMjrjMM0iTREbkvREvjSzAbmqOsRcw7TcmqaTWUaeqnWZTWk5fNs9YrQJ+GEgGmdRIKmthaNUu1a0ds7/8rJJpqworxF8Ib/f33iU/D7191GhaOdPClO3fWCuHNEa9B0KFq+twQbU00pumLu8e3f1UxhwQKIUWIqRe9NBSRE68q6rTgI5wKk7iwVkS5oXYSZAXh8ukLBd0vFabP0mdqxueLrqq2E2HemR+Ud2w9hKf//Y3KNZ8gIBRijh+e6mJyRjWnE1FmpZuIQdMX+u5tkZIphlqJDF7TYgzXkXQBIdCjUfsYY/16iMBAzC/GCm1VL00x2VVzzOoIzflbV9M3NtTCSCdRkqHHwpp2QhuvmK7qRU10nY5Q+q6fcvku+ikvnaVDrQQfftmFeO8Lz8eSnCkWbQ40fc1jwPQ1DKHKpsnZRdRNGq4WMG+sjJWJGVGKLjKbv1YIfMnZxcaWJCrRylhx+EqfvrZB0ydNQGM1fea8W948fQF13/SgGlZbZ7RNn/R61c3HRuSU28g+FRNgbfhk8BEHQUFnmI0BjAm8oONll64rxon4eKoHUXl+0Y2+2+c4mjMMH3rJhfj8ay8DQH0qqdqw+Fc51AzrJATfum8vvnZPkfokVCBva+rVRMrdYkxhYDi4MXqnqCphTO4NX7nbnYqDYt+xGWzXJP9+Rs19XWFyAzV9oRH1XKiixZL7l9W8Mz7wCQAsHs2sJ3xMZXat+N9H4NclFKl5VBV417sj8I2rvnlDrSArCDq3qga0Ucw7ye+2cRkhETVtwjxjOxH7is7EUDSl6eunHIvHsnm55+h06ZpTqw3/2WgL4mOqkwasyRJqeyrXoPuA+d6HzqyHMBk0/Yg4//opMDXbB2N2uilkzQP1rWQYYzhu3rASiT26Dsvv+pyL0fTpe2mIpi/GvFMoAjKTZPV6kjCsWDCCZ52/Wv29Bt0FAL/7yR8Z/X1/0TFIzt4wBMGRJNlk7/ZTqcrv9lOFeDT69MEssRP3C+hhomPgk2pRHw9KsFAzMluevlbCJCMYK9Xtp2ZiTfdXeunjTgJAojgGNKMzoFTDIJzSC58+rX0WF5acwkbw6ZJV8d1l3ikIJleOrgUBgRF8hJfr8muuXI8v3JGljig0W6qG1nSY2zbvq85aKf8XB4PK2xVfVJ8+VIqA8tKP3KJ8tzEHetUhh4+I2pt/yaL1GvL0mXJGxaznC99xHQBg0zVXZ33lZg15DGJ8+sQjNmPeGe5DKOA374w3qbz45KX4jcetA+AXimVtFNdcQhjRnzqoG6jHT/hnn6FNCC3acx+9Bl+7Z7fXCkLR9EUG9xJQzTupYMvWR5rkOVxAmgT4cwpMGXLLFW02w/RxnvmhHUK3xPT0cwL6UasW4cSlY/jty09RrtO4AjoKhki8fPW6KdgN9fNqJUzZjzPm0/3M09p4+XzQ9OshwhNxtrGM65P3Tcz0MW+obdeSBWj3gfrRO4GM8axj2RFq3hnT1ZJPn870GRqNMu/M586hyS4+8t2Hg+4L8a12QdApA6gYaPoaRsFMZBOWSnTopnfemkUyUqUyryUjU9ZE0UXmS87rgli+Vk2f8OnTTHc4CoLLat7JmDzwupEHfMrNydkpUfjhl16Iv3jW2QDCpXOAOaCGqLXw6TNr+uqYGdiInJLfoMGnoGTeKYLnWN79/7zm0iAJs59Is1/vtBLFp08M4jAhsqQGllQTcj7I96CYXJLrik9fM6YftuHSqw6R8CasCD0+b6hlTtlAmD5KgCwYqS5/o+vSBt+siIreKVI2NBS9s6p5p+2dpA5G0tbUCy5aG5UHUvHp8+zFNmI1dP5WMYEFgBdetBZAANNpmI8uzB9u4/a3PRlvfvqZeSAXd3n6nLO9amuW8m2KgMKm6euUNX0hMFna2DDtyFPZlKYv5Vxqp0qCy9ynr91i+KvnnYtTV8xXrmfmnR7NlbDq0c8jw8DSsz7T9BVl/Ca+vL55ZwA9Ic42wvMh5RxT3Z4xWIyAeBR/IBdvF/xg9SI22/Y2/Z2FxjoQZVU/WL95Z2iuwZRzKXj5m69tDO5TEjB/B4jHgOlrGjnBka0Rjj1HMqZv2fwhuen9zhWn4JOvvKRYWGRem/yhxHJryrwzNLlxwhhOWU78rgiBZkvZ0GqRQC6R0ixbRFOa0JdKICVxFlA33aBOXTEfV525Qom0CBDiQOtCneidtn3RZEIKqCadOvEx6zHvDCU0fJo+1z471FbDKwvmf/3yguAwhRAPIaTaBqabPpGaC7AZJ+9QLVNQygbG8FfPPRfX/u7jcdz8YYCXU3aIMOuASrDSA7cK/No59/UI685gAikEVfzv/NE74yvVg5OIvtlAX6suFS+VdfgehqBKAnsgi/L7rPNX+XOPVdCMLh4bkiZ+XiES+V9Pzh4Kdb+kNZo7ToVQwoLgbc94FBaPdWQwDVs7oe9F11xRNKXp63MuhXz6ehOaPlce20B+v1SHSROlm3fSW3xbgSn5um9P0jW0IZZDhaaPKfvUxEzf6s8HmOMrmNCMoLEe12dj5srmnRF18sza5FcvyEws9XVaL5BLQWMdDPBlfOZ5qwDEad0HCMeA6WsYmXmnkBgWmr7lC0akTfuaJWMYG2o7tVrKsWbQ9J22YgGqQjRrT85eSP+uPucEnL92sbwmo3famD5WMH2x/huZeWf5d+o7pB7+YRs1oB4YH33ZRRgbapeCkEhJv3ZvjPRXh+0+3eJIDKMe8IRCJtK1BCYI8csA/IenS0LYaSWS8abz9/SVxXyU80tjYH2EkClPImXyVQ1gMwcCC9wBg1I25HmnHrVqkYwuWjbvpFEii+epQ0vU9fnK6qCavmYI1hCkFTrvZcqqMJK66TDca56uEZ9Pn124FvbSOVA5kks2D331V/OBBOLz9IUmdDa1I6AIKCzdHm6VNX1Xn3sCbn/bU5xWMjE+fU7zzsYCuRSm3+WgUNlmYg10BubX9BlMzQGzFp8KG1tMNe/0jZmJQfZq+rRzzkdP9Pop/uKL9wBAHkwvu7+fckzO9jE2ZLemCM/TV//QEWdDVdjWW0nTF9FEms+jp519PADgh1sOKtdNZ3cI0yfmn9gjj0z7U9e89wXnY+M7noaENSNYHEDFgOlrGMKfRIRsn8hNQBaOtOUiFCH3DYo+YlpUnuyCgPzC6y7DorEaKRs8GxwN48wYw6NWLZTXdPNOPXJdlrJBLROKLDl7eXOhTN9Ml0ZezO8LqJtuUJJIk00Vz5vVqz1TDYfi0A2aHlACpTx9+TPYDstQTd/krNssz7XPdlokYidp78SlY/J/k+Cin5rfLYVP01dKzl47rXk4LR1y+Kh5C7Pn1wkVm09fnbONownzTlLWU7hJnnByto87th6KuicxCEgoOOzBn2wm53T4fMnfs2vF/17zTqsZaqiqrzLPl0e5bV7TR+v3mj6TyzSYR8w8siVnt4LUHWNqGWM27mL6mkzZYGNSRfRO21wXe5ALReATf39d5p1+pq+87nxDFOvTd+/OIm0G8TxAP83MO12aPp8gPLQPIcjMbqvfH8z0RZyPae6T+fjTlmHlwmG86or1ynWTUKgbYKotuirO9iNTfpeAJGEyd98gemfzGARyaRhpboojJKwiPwz12RGbuDlSZfZpYgRnPUR/KIpkruUFdXiyi1d87La8L6rmhZP/readCcPCPAqeyIUWCltydvrTNGEkNZ7NCZXpM2v0Cp++8u9VN3urps/gNwio76SUpy9n+m0O8OFMn9vEyrXP6oyNACVMTIKL7z+030vk6b6VtC69bebpZyhCA2SEHD5lTWQ5eqfww9Hbrnu41WXEFJ++elXNOXxMWWqxGACArQfK+c5onQAN5GLvQ0xydns/nbcp91cN5MIYC0jQHR9MR6CVhOTpK/6f6aaS6I1JJm1bK9bIzuRnPdG0s50kfF95JMw7U2435y/y9JnvDbGGkD5wAd11mXf6zkfTWPnmdKxPnzovinXc55l5p8tvOkTQI+qqi5D8iS5Y00Bp0yQqZUMu+BkbauPmP7mqXLfhVYX49InxFJrvGJqwlYSbWg8QjoGmr2EIxohpmj4aVVEQCW0DoW+KHCcOZBujFQuXpu+u7YdL5aTmhkgVXYFcROjzg5NxuWj6qfkgePHFJ8r/pw051kI20FliZqeb3ki/BpQ1TeJ709E7rZo+p3mnm+kvmXda+jTh1fSFSTtpP0yMmqjl7u2H8eDeCWeIc6Dw4VAfw/Ksid90KQQ+Yuf1TzwVQBghqDIOGVFvi7iXlSHaizqPEsIUeC5XCeTyk4Lfp6+Kn2D5f3dy9uJ/X1AtWz9jzDurMvUZg+VuxxX234cskIunfrI/T/f68syIEV7SoiH+p3Q96EEpXIgxG3+kUjYMW/x9Rfh7q08f7O9e+rsJTV9Ad3VNH2XafWOmp2sA/Eyfzjj73BKU9FJkl0pTjqlZt0+fyafehCaid1bR9HEHXSCgC1GiUjY45hFQ3adP9FVEeXdpx3XEmFoPEI4B09cwRGACsbCFpm/+cFnTVwS8MNcjINai2PRqa/ocpgy06nI0uyK6po3pS5IsOfuCkTYOTcZp+myh54fbLZmmYaZf1vSF7As0qIzU9GnjL/NVaX1Ikuqbj3WD1lae0bxTa7Pref+hhJue5FeH70nF2UsP7U5CNX2qMMMlEacwRU+1PRNDPZNIAR/hcd6azJ815P3T18Jg1g6naTEuDIVAow4DmzEF7ufwTQ21eU9dP2FVYEjKBjEeoVulKZBLqKZPT1ujw2qGGvjKxZlSBSEmfnWYGVgv0QAAIABJREFUSj2tjwn08ub9kzLwTYw2TPHpCyjPUIxZXHL28LU45bCYaELTJ/phC9rWT+1nJgCERIkUmu8QQc6QlrJB9Ul2t2QaKx8do6eNiNEmMjIB+pxjsttz+vSJ89gv8HReDkIVf3T67HZNnzpecZo+d9of0zkZ5tOXfVZRVAzMO+cGA6avQUhijrHclwKYmO1jqJ0o0mC3eWf5N/GLWGT1DxS7KYPJfE8yV6R/XU8kySVjQxU0fXa/rz948hl40WNPxPMevYb0Ne9XCNNn8Ol7ZZ7X6IRFIwAKxvw5F6hJQuv49NnuK/kNSq1v8Zu+p/Y8mr7QWeHT9HkjmJk0fYQg0Jnx+YHpCARxpmgNLWVDfJXq4L9ffSle8fiTZV+q+PSZDsWUF9E7E1bMtboMbN0tISQ4RtFWUaBO6hiBZ5+/Kqq8L9CKCKYFhJsQJoZ9L9Snz6dJsmkPwjV91QOthPiopWl189GQdDb6fiKk/VGaPiWQi10wJsBY8R5jzsuYhNBzHchFzLEhSw7PPhfmnY7zIHBfCRkixbyTxaVsqOLTp783Xwoo3W2BavomZ/qelA12moiiqUAuseE7XRZAAqV5ENGEcEuywTTFdCsWW71AnOBFINP0Rd82gAcDn74GIc0EWRGhaXI2cyCm+5fQNLn810zmVkVOnWY0fb5rpgStItqhCMHcaZsrWzLWwedv34Gdh6bxmVc9LqhfKefGNgFg0VgH//dXz1H7ErF9UgJcHCbPv3Atnn/hWvn72FAbd//FUzHWUQ+HpIZPX7h5Z7m8LU9f3fc/4cmv5jvXTIFI6AGtm3eGnpMmn0pXOPLYV3LDxj2l32xj+ZiTluAxJy3Bt+7bCyAweiddO4wZw5QLQg3I5lUIg+FDiCbIxzTE+PRRYn2oVS/RMAC84KIT/YUIpK+OzbyTjEcrAUICRip8WwABGGc6ZWNOw5BpLoObUxDio8ZRXZPYCtD06e3L4A4RjJFtndqaZsjyxfb7PLqd0H1lrpOzi3G1afpSbyCXcMFYiFChFMjFkjvRBKN5p2eMdKGAb57RAGUpL+oX0TvnBZh3+tbKTyqQC+V3g807Y/L0cR9dWL4Ysu/bLMJCkFkR1FetpqmdrvxFxEDT1yDEEmNguc8RMDHTx7yhtrJoxCYupB8mHwX6m+7vVXcCu5yW1dDl2f+MbIjSvFNqHc1TaPFYlgvplk0HgvvlOsBMoGanIXUX99nbmD/cLo1vreidlttK5p2J+o4Bu3mnjaAoPZflMSc8gVxCndnpONFNXT9AQ8dOPJcesMWEzGc2qFqJl330VkM97ntC/LtkWY3xNUVJFYRaVoYFS5hdCNEE+VaVKmRyl6aBMZrQ9MVuZy0PgZb5qGVlXve45UF1miwcXK+E7id+TZe9nyHImNiqe35AMI8Qn1ALksTvB6U/pshJSQVuPqj+Y0WFLp+1jp5/NQCZMCnsvcw4mL4mTKBFP2xrrCeCQlnaEn7Fxmva95AhqhO90zRWviZjffomiNtCltsyvy/lmOr2MepM2ZB9PhKaPmEFFoOQADL6nIvhT6v49IUEcvnVD3wPgH0NPu6U46z3NpWnb2AiqmLA9DWIwrzToOkzEMiSeAEluPK6aMViQyL58+rA5bSs+PSVJEflQC7UvPO6P7hC/i9MJmOQco4YKwDjWM0BQvxWbLA5ftMN9upzTpBjTcuXNH0e885QTFbQ9C0kJprSvNNAKAPErCavKHTPFQIEtV4Lg4t6fnC++vXrsZo+m99Gllor3ydQZpCrQn+Kc1Yvwj/9n0cH368IRTxlqebEpoWIQawQKyRlg3itV61fgJOOGzOWoxhyCC1MiCEk7Ckb/PfesHEPth+aCk6ErCMb2gCmtOKWEhLIRR+rkU4LG97+NLzxqWcEt0Nli7Q61xgOVWD6QpKzc85xypuvxR3bDlvLmLT8sRD90NfYB349W9dv/9I9eGjvhGOPtGv6dAI/RNOnR++MYfpMWlHf9I+N3qlo+lA8k2AGQzR9MTknqyJGsCAQQn+Uk7PHaPrcwZyM5p0Bc/zhfRMAzFr9TddcjU++8hJnm01oVpuIuPrzhAHT1yDE1BLhgjkyrcrYcNsY3t5E7BQEBzW3UrVAdaPnmUxIi2uE4E7U8nRjEDb69CCgh9Nv5/5yMehH+pYUUUVDNH3VF34WyKXavT7zzk3XXI1//PVHG4NT6H3u9bkSihoAVhHmOnTkqmj6vvfmJ+FHb30ygMLXUI3YWeb6RC3Bmj5h9kx2Jdt0CCHOQuAbM920OrheS8X9lJN9ojnzTh3/85pL8bSzT/D2p6ijLHiyoUPeexOavtjdzBdenWsWA59+pd+8nDIGtneycddRGZBCueTTpFnNUP3v/B3X3gsA2HJg0lvWBJ8Z9GwvzYM4VNT0hZh3at/TlGOk04pi9tWgIdlnP+V443/dYSyvaPoiA7n41mKPaOttcAV5CYU474eJT98zz1uFK85Qtde2YaxiRugCFYx0WomyT1fx6fNBZ0R9ydmppu/QVB8HJrKYAkfzhOBB0Ts949VPOVYsGMZ/BrqsmJAJLOPuCYkaqjN9cZo+9x5g8o2OEUTFpE0RELmu62LA86kYMH0NQiY6ZUyGyp6c6WGs01IWzVCrrNEo11X8L5m0fI3V1vQZgobItmi5vOHL1i8DAJy/drHcWPYdm8HSeUNYTJLE001n/fL5ePo5x+O0FfOD+xVre10wo/6yPtMQF0JyUdlgIyD0d29iLvSDtNtPSxu7MKMFUHJUp8KBR61aiBvf+ARceNISr0+fCfOH21gyL2vLlGfO2G5eTjzGR152obONIlqsfw5UkZba6nH2ySCEsYGegbZpzLmanF2MVR2Bpsk0JzaQREz7uk9fXcQGKTEJxihSYtoFAMcbrA5+5Tw1eExHyTNZZipnen089T3fxms+/oPSNX/yc/X6hSctwfMvXBM05mJ8q2v67ITToclZnPm2r+LfbtpcK2VDrElclaluit65cddRbNpvZoYZGNH0xfr0uXsYIgCqwuToMJl3tpjJH9z+8pqkd6n/fqelR+9Uy/7nbVvxoRsfkt9FZM3vvemJWLNktFL7fc8ZTjV9t2wr5sWx/Lxzmnc6chcrfUgzhrfqMwAAHiHzziifPlTw6Yugqar49GV7S/RtJTShLfx5woDpaxCqH172fbqXRY2ihI1YACbmS5YzmFuJyVs1kptenzllQ1G3OGivOmsl7vzzp+DCdUuV65euP06RkOsMSUjkOIqUxyXsjTHv9EX+ciFhfhMmG+zJ2dXvLQOhWTbvtEvjXnXFeqxcaDepbScMa5eOYXSo5QxAMNPrY/eRaet12kd7AIHsU9f0heZlCknZUMUvQscXX/d471oSazTk3DDl1tRB8/QljBHBRfWnOTLVxcLRejG5KIHg6wplKH8SPn2FKZb5OoefmR/VgjX5zDvF/9dv3Gu9ZgOdO7952Tq878WPxthQO2hvFONbh+mzzd2Dk93awcGCNH365QpT3ZQewGeOViU1RFBC84D3ZgpcEguev/JhxawyKZ+zc2wNITDUKtZMp5VY/SwB4I/+606ppQYKJnjJ2JBca7F963pejO1cO5ozfSHmnUH+r0m9YGoJi9/vgzR9ujtOlKbP/UwmuUnMnlRltEK07iEY+PSpGDB9cwDGCoZnpptiuK1u1KXk7OTegmAum1vJABp1NX2s3K7elv7/wjy5PG17xYIRLdR5+TCKWW79fEMNBU0a7627hrQnYdU1fbZ2bSkb6AZVDuSSWgmYy061O0QDBfMy3G45pdC//6nb8S83Puysq0jObr4uNSV5OR7I9IkxoJYgLlPmmM3cdMiesNjvd+oyv9TrpHPEtkb7KZflGAsnNlw4ODmLpUTja2vXhS/esUP+7+sKNdWpIsHVEUtAiT3CZd7pIzNGtITXaiCicv16W6qmzw1a9oUXnYjjF+X7ZoimTzJ91SeIbZxiIrbaEMb02ccuvB1aX/bpmjYJKzR9MT7QwkLHhZCzZMSSZiEGhXknZfrKxL3V4qICcyHwnhecX/pNX/dJwvC2Z5yV9TUwh95wO6k02dqJP5KjrQ9Hp4Wmz8X0ZZ8hWt4Wq+dgwxDP8IYInfV5EMf0ced7qZqnT5atcMBVyWdoQgMBQH+uMGD6GgTVaCT5mT7Ty5g+JUBKYj+MCi0c/U01Z6qr6XNtcEr+KUM7wnQVAIY7iXLw6s8TK2mM9S0pNH3+Rmqbd1a83XZfWVortBdU06fe0+sX5q9//LRx5WD2MlT59dGhljNZ+lfu3uWsB/BHkRW/Fpq+sD4KM6wgTV+CKImCiWgOmWvyvXjWil7GtkY5p0QrCzYrsmG628fkbF+a3up49/POle268I/XP0j66C5MzeUa8emrqOmzC2LciYaBLJgIBfX7MjHi+pDQryF58Iq6i88Q5kcQ/FXTYriYS9qvqmdKKzAlhOt7WDtE05fX4MwrhoJJibHSEBY6LoQQkb9zRbxPe6kdg3nnUDsp7btOn76KbT/pzBWl3zpaIBcAWLZgOO+ru77pbj+jgxKGk5ZmgZVigkC1Eub16bOtp8Knz24NERpFORNMs1o0WBbvIe7NhAgaysnZY4gvj6avLtNXYf+qohE1YaDpUzFg+hqEJOaAzG6bZ4f1cFs379QWEGXwDP4qur9XXU0fMxA1AnSB+JKAD7US5blMfmpRUe6iA7lkCGmiqnkUUC96pz2Qi/rdpOkTxMrrn3gqgCx6mSj36ivX49kkibxp1Ggb4kAY7SROpi8EpuidFLqvZWjUWZNPn206xJoOTxi0myGmxC6fPv3dmvxwTfeIAz9pQNN3cDILVrDEoul7/oVr8azzV8VpRT3Xqba5keidkQSUKegRRZr6Gclhh3mnuNen6VuxYBh//6ILvP01pYvJ9kbvrXJ8q+5frj24bzhjYhESvVO/XE3TR5g+KUSyl2esiN4ZE0kzxKfPF0Xy1y8+sSRUqAIqRBZrzqRZd0bvrEjwmtoZNrat9tWG6W5fjsl7XngB/un/PAZrl/qj6tL++AS3aQqsXDiM9cvnKb8fnsqYvgUjdqbP5F5hAuc8T0wf0mszQgQLOkIEDaXonTH1c7egzHQthibyMezmNqunyqJokunbsOsI7t15pLH6fhIYMH0NQkytzGwr+3+618eQZt4p/h9uJ7jyjOX44EseI6+Z/NTEnWLhNKXpMx0IKrNpbkes32HNREo3zYzR9N380H4cnOxGm+IAYZtblU1HoJVU2zi2HZy0Rt3Th1YcOpS2E8zS6sWZ0/iWA5PhOfoAXLhuKU5fmQXSEfeNdNw+fSHD79PcScGFVp728Uuvf3zpPtFHSls4TZf8XZWYNDB9zLL7mfy7TIeuy+TP1u9uP1XGr270zoMTGUGzdF7HWibWJ9VHKFKJcjOBXOLK+8aMg3sjHOs+fR2F6Ssz+iVtFQcuOeU4/Mp5qwJ8+sqCtNDE2ULLU3X/ShzrJA0IPOSv32/6XgrkUuFR6Jyj56wNjJh3xmhJQ0zKXGvpqjNX4k+vPjO4PReKwG1MaqLFPP3GH14hy1kFY4F75J8986zSbyamr2MQ8IQyS9PdVJpULxrt4GlnHx/QM9JO4he8igiU+t57eNLP9Ln2eQohmK7j0xcrsATCtNX6XhzD8Puid5roixg5VBWhVVN5+ppM2fDOa+/Fmz97V2P1/SQwYPoaBPVdEkTH1Gy/ZN4pzKMYY/jX33wsrjyjMKUwmRnoPlJN+fSZDmvFvNPT0LDmt2ByMA9yeu/28YIPfl/eEw67FkbHT0LT9/i/ut56TUgfZRsGP6XCdy57zt1HZrzBUyhaCcNbrj5LqWO003KGEw85zKTG2cqAZp/ivRTroihz3PyyZooSxLIuRz9jXomJzzU967+//LFGgso0j/U5oaZZMWNytk+i/IabFZnAOcf1G/cAUKO46sgI85h6w8s2k7IhbkNzmdzi/7H33fGWHNWZX3Xfe1+aeZPzjDSj0Ywe0ighIYQAS0gEYZFMjibY2Ahjm8X2kgzYYO8K47W9NottGWzhgMPaGGNjgpGNSAoIJFB6oxlN0OQ34b15+Ybu3j+qq7u6urq7uuv0SMO+o59+b27fvlUdKpzwne+AX7/6aq/avDzxWc3pS8I743bkNmWRPePyV1dtWY5/VOjck86A+B7KsHfmFQLPE5bjLafwgFeJ9FXpVo7Kx7/PHzdXbuSRpHNKRJTyHKJC8ubSzdedlwsjLCPi/bgO0Aw3CIES2rpqERb38X6ynLPM0On6lms2p47p9n6dIahLS9DJfM+zin42HFYYYRVkJOq6LtAQwwPZjrG8dV4Wz+f7no3RhwqRPhP9o9Vw8L0PPtcY0i9LUfO68VBm/aiSXlMWKZYllOjOtuS8OFvl7L76J5nIE0fMkXbPR1/TSSwSecZU9I0GKhYTudhG+rLhUSZkFEJUaFeKyAVmm87kfGwAVWHvNBGbnD7HgJa8dJvKxesMcdGnrJBmQm4zHoY6ZvqaLto9P9M7rxtbX3n3sxOfhZc2izxGtBDBO0VkS7p2XT/iPs3gneU9maroxtqzt61KwI7yasKpyu6O9Uvi32W8p9lOL5Gbqz6rMvKF+w/hE1/dCQAYyqMjL7l5lrmUskbflx84go9/ZTRxrAx5E1BcRiNAenzd9ranJT6rCmhTF91NGH3pqG7Uh/TddReswpWKgZncF8QYNxu/4vnOV8zpYzkKpjwmqq6PDccAdqdG+ipkmsnTSXaa5MlPbh/G3R+4AU9ZN1yin+y9UUiekeuWHcw5IpeAcpVIHz/O/2bm9MFs3psih3RRfbEfFXUz1/GsyG0aLiscZ6KOsHo7fugEWpRbssHsPvwg4EgUO5uv9AwwXb9XLe6T1kfz9oOgKKcvfawcvLNipI8g1EdZsqHd81LBjrNNaFxSC8IlHFsy2QnAI2LyhMqjkNbDO5MbkaXNFy1YWkZC6d9FxqVq9KWJXMwUG8GupWsjT3TU6llSZdER4hLBDITc+qYrcO32ZIFdcd+yQiEWK5k4I+v5ZLJFeslooYC1tXu+ls1MfeX9TQcja5NK09XnrcC+W27SdwgZ3sn7luvSZfUjX2NSt8iOJpZ5JbpAr8k8yssfkzek33/NpVgtlczQNe0wHumTc5KKCo0L+as79+Fbu07g1p+Oax0+NjYT/TuvALVJLTVZSkX6SsI7b/6bH6SOlWbv1EChZdHd62Crgf6mEzHX5pdsSLeji1apMGbdefw6ZdRG3IfJcxYKRlXFJc/xJrdZFQnRarDSvyWDdxb8hjGWW8ZG24/07t2ssis5xkeZ8hBFIsM7hchGnw4ZIYu1nqBIq5Fu0Dinr+ejP4c9s0gajmNE5OJmROEW9TVyawCLr4qi1oK90y6nrzxyqMz5plFLWYIgnzld90zLtF+FfZg7K0v/LCWUDvv5hUjfgsgSs4olvWctBd6Zv/ikDZloElvWVIr7yP7OhMhFiEqGoKOSNpm0kxLUsUy+ojjTJPnahvLcJJ8gT1Yv7sPHX3Fx9Pn5F61N3afOwyw2oLxaiEKyHtuF67nB9uorNwHgRC4AMslc1LFVJvIat8H/xpG+2GMdXa9GoSrD3smhS2U8jZpIn8HOnafUeImouBLlDj8vG4whRYv6GiG8M/6NqePiQ//yEL728DHttQEacijlWsoM3zLPlQbeWU6KSjYg0I8b+X2pDivZaNZBbuV//8kdj+Hg+FyKsChLdLmepiQFts+X5/Tp+5HHRNX1sek6hUZfXu6rqSTWIc16QiUmcOs8wyDP+VJWZGeZ6FKXb5xN5KIfm2NT85WcoHkkMiYlG/otxrLrMPQKx1m4pmq6ESWnMts3dL7xOn2WOX2s/Bwoo3/kleTSyd17TuLw6fn8SJ9mrzRBBywPWaWfq2GDLRIqhBVlyYYfh0jfgtFHKDoPPoBUnb48yTNkvGizs7rM3OiCfKhspE893XRxS0b6Ck9P9VfUhVgwh/sc3PLyi/NPzujHxuj7yYvX4TVPOyf3nMi4kOGdItIne3dz8jd0sn7pAPbdclMEyRSwtiwyl1S0toJLU41Mx/MibisrB1E9T/xLjSgVRUp8P8DPfvZ7uPOxkwD0ilqZkg261+9plPn4d/zvhmUD0bGhvgZm2r2EMlfFK6teG5APK3OYPn83S8pcCU3JhmqRvjx4p67JvHxl2bGiLc4unXvLl0fD85B7HUL+8m1Xpfpl0I8pVcogH3SS53hLwDsrakYNxyk0GFNR0gr9JNg7o3YJ4RdKP3lN5+0FpJG+KKcvblPnnMh1jGme9lW/fTuOTbZLX49urov9oWgst7uWOX2uWckGxvTreh6JC5C/zssSR/rsjL6yI7euSJ/vB3jNrXfhkSOTuc43LbzToP3NKwZx5bnL8PKnbiw8N91nuZJfWbIQ6UvK2X31TzKRIxoqvNNUsdHBO8WY1SnPVSQ3py+hyOa3I4y+c1fwHCj1HvmmUyxyTl+5kg1mHi2xWbz8oqV47VX5xpdO3JJRJQD40zviumcm9xTBOzV1+kwifabPTUA6s4w+tZUqSoy66ejgndqcPietxIgxpUay8hjQ5rseHjx8Gl9/ZAw3/833AeihgCa3pjPGAeCj//owPvSFB6PP6pWI3wnmVQAYbLkKkQszziXRX5ukDObcjCDb+L3/eBRHTs8VtlsK3klSsqHs+WKu6L/PqveZhFnqI7Py9eSVbJCvQ/7mNU/blDrv8nOWSf0k+zMtBP7sbStzz8uSPMebPKYrR/oaDJ2Skb5q8E6pPV+sK+XbKewnR2Hu9HzsOT6dq0TS5vTxv4zpSzaIa80j96K0i/WRPnGtRZE+O2W5YYC2EXlpOl2rKNJn6sDxfFGnL/9686RKKY0qDJQmPzkxHRv/eTqqFt5pMAH9ABjsa1RyXpXNRc8SSvbOhUjfgiREDC3ubYqPl4r0aZQBdeDb+hLzPEG6mlJZIgb/P918DT739qenvq+S01epOHtBH8Loq+qFLWKo++NvPIbv7x9PHPufX47JKkyil5EiK+f0CXinCZFLcRcA4neWBe9UH3+VxTrKg1OUs2SkL91ulNOni/RpospZr+Rdn/sBXvLJ7ySO6d6fiSNGVz8RAP78O3vx1YeO6X7C2w6vfP1SJdLX6SXqeZoqTTpJwjvzIn0ME7Nd/OHtu/CLn7uvsN0yRBs0JRvKjTG3AN7pB8XzIW9Ya51imq7itZp/fss1m7FyUV9Bv8lotik09NNvvjL/xAzJY3CkUIZarlMIuxO9fOylF1XuJ1mcPf0vKslziH7oCw/i+v91B45PZUfJ6oj0OSy+/5Y2p0//+yqlAfJEt8ZEsMgCA2Cu66XyaMuIaxBRFoy6uuehlpdSJY/RXBZuWNqhrapE+sogNeK9Nf83n//BQXzxh4el32Wfq67RK4ZaRutH/E6qGH3F0V0ToUQEtHtnf6RvgciFUGRlTs5ZajUcY/hMNF0zoEUAXaRPNxfKbBJCEV+5qE+r7HAvc3E7ck5fFSOjMNIXKiVVgxKMsdyaNIKNMI/cpEhiFjRJpdHAVWwjff1FOX0pMp4KRp+iOOnY9nQ18nSRPtG/qnDk5andPjqWOlYV12/C5gekN5Zjk/MAgM0r4kLBgy0Xs20vEbE3bV8n8nPKyyWSzzMqVF0m0kdg9JWd8kV5VwHyDclvv/c5ePTYVE4HSLWv60mHyigSmb1T9OHkmKi+H2DJQLOydzneT4LUM6HQhZquAz8IYW8ZL1LMjVWLy5GqyJKAdyrIF0rJc4h+e/cJAMn9SpU6cvpcxqJn25TIVOKxlLEnOLTRUDWdQ+67yACYt4R3mtTKzarTBxSvU7HTNf86vCBA03GsdLCyOdZAVXhn/nnv+YcfJj7n3ZPsiP21F1yAe/aewkRYCiNP/CBIjN8ysri/gan57LlmKhbVuhISBAHmuwuRvgWRJCJyScE7nRLwTo1Bpkb6LPeVaFHQrQolFiPdJiCLYwiLnGnLkT7z/g0dWpGHsGp+TNXi7EJMFh0dZE3kD8jvuyyRiyqCLCXrmtSF3wbeKRQBuX5lVj8A0A4valFfGoqjGn38sjKUfuVwz/MxZ0F5D5R//6NHuVEhiHQAXlZhpiPl9Dl2OX2yNHNgZbKyZTJOylxJkQfdRMoqUEWkC0EQ5Eb6hloNIyiT3Lwe3hn3BxiywUaRvvC3Bef7gV1eX56Dj4LKXMzLPDIX0bfNWNHl9NUB74z235zlIq9b2xxMWYSjikmGjK5kQ9a40xGQmezHD/3mC7THtZE+Q3i6rdFnQnzESzPomTXzkBBADB82gndmGJamwreu+uCdZVjNZcm7JfmZug4zqs8JcD2DMVaJEG7pYAvzXT/TQW0qVNHunh/AD9I1Xs82Obuv/kkmUaSPJb1vfU3XeNDHdowM75S+Z9mePVPJY3cqM0GKBr8pe6ecX1aGOEQtDZAlIspaGd5psOHkiclvxaajwjtVpjDrKK/ox7BOXyUil3DjzYd3pn938BTPN9u8Mq6T1w3fnQ7eaar0veUvvoff/kY2FDNPTGE/qqxfyqMaI2sX4/dfcyledtl6DIZELj88OAGAowGivFTLjSkvwjBUkiq9zKVQ5jCZSmFOH/IVGNfNLwevy+/RPRNdXdIiERFuE5ZIIFwDLKZ8nlOBAt4pcm3z8vrE1BGOiUs3LS3dj/wMxHupUu/PtB9d22qOsk4aNdTpc1g8v3X1JLNz+tJGn4mhP9SnB4DpDNoyJRtsjH6T6FgQzhWdflSUe2xaOicuC5F/LXlSDd5Zon3xm5Lzu5MT5kyUTAkEm2px+0HA6xq6FSLgywY58+e4QUQxT6iMPmF8LkT6FiSSGN6ZXBRarmNcgDj2OEntSkuErdLP2+B/i3L6in7fcosHv8mEE7WzgJLF2cO/RV2Ixakq8saxLNlg8gx0Baf9MNIni23OSFGURG2+enSUpZSkIiK8uOf+AAAgAElEQVSXfSd53blzJUhkJ4zQpdk7zZPhBSyriuTV6cuTT7zqUnzxXc/E4v4mfuryjfiD116OJQMN7Ds5iw//y0NR26YRnyLJM/oGpaLEJm8zr/yDKhT5EmUdC0XRUR2UUZYieJYOcptL5CI5+4pEhXcWPb4sUhpTyRtfFO8uivRpIum7x6bxhk/fhdlOL7qWf/vFZyXYTE3FVZRO+S+lmJCc5fVLGukT8E4njpTIczMmcsm+FnXcUuRIyRLBO3Pa9fwAnZ5fqTj7q6/ciJc/dYMRP0BUsqFKpM8QZh+jb+yi72UNkXbPPNqlRYsZSF5u7uWKo8bUES4io1UifaLc0fhMeYhnkgW99M+1IlIjFiJ9CxJJDO9MKlfLh1qFi46QmO4+I9JnfZXZ9MQTsx28/S/vLfz9f3vudgDA8EB+Sqhj6NISkb6fv/Y8vHDHuuIfhGKaU9P17CJ9DmOlIz2yGEX6NJun56c9alUib7JkEZOo1xGdX3FzYyyGf8gsdPH36d/88g3bsHSwiZG1i6Nj4t01lcLADOUNsSpiCr9Uvx7ub+KSjcmNUnguhciFhKuML/l5yhGGP39LkvRjUI70KQ/+v0bHcMejx6PPN160FldtWV76Wmyk7JDW5b/KEgT566RbwL6ne+d5r1+uz1okKuNi0dIglKaqkhdRpMh1ieGd6fZ/60sP4zu7T+Jtt30vvBhgx4YlWDKQz6SoE3msi+dNSVIixMQhmtdvGYdJkcgIiSinT470RUQu+j7dipG+MmIC7xQGy0CF4uy/88pL8XuvvszIyMgjDdEVlpfFdJ0X7J02wlDeIDPKxRbtG96LKnlD45rzV+JVV8RlF0xrF0d1DStYGkvD/dIkd1AVL6E/L0T6ZFkgciGUmKAhqZyvHe7HgVOzRm3oDBl5zFJE+ng7aaXpocOTRr991/Xn483P3GxEg2wW6fOwZeUQ3v/Cpxj1H7dvpjjVzd5ZJEK5+vp7fiLzHDFe5IVUbGJdv1okVCdFnln5Eb388g14+0+cl9vessEmrjk/TSfvSoZynNMn95O+j+dftBbPv2ht4tjGZRzq+dNXb07dxxmw+YxhPyaSa/RVaF6ev3KE4fqRNYnzBvuyI31vFQp5KG++ZnMpLzbFfmoCi5SluGRDftSt4eTn5Ojeue4+1chPmUifKSzONqcvL6Io9y07WsqIMHJ0OX0qoRPV3lVnpC/PSBbzLQ/WVvZd5UWlxRrNGPRGXxQ11v9eh1BRI32/fMO2Uteb6oMlr1WV7+8fx5aVHLlhU5ydO17zz4lz+tLPwzjSp7mPIAjww4OncenGJRExiY0wVsXoKxHpK3k9QooiqcsXxfsXL5xe3KaIvlaK9A2Fkb7Z8pG+hPOcaKEQhjdFHvsTKQtGH6FELIWKGjM80DAeKLrQvDwZifZNLcQgj5VMFsZYocHHzzNTZnkNn/LeE/EoihQnoZBUJRo0zU3MEvH+zl+drVjpInCCEU+GTtmywxVF+mQF4vdec1lhe/d9+PmZ/Qg9MIZ3SpE+o6vlUXIdKyozgPtQiBsZGARG31ByzjQcFuV41RG1EFImp486EmAiZW0ap+CZBciPjjlOUU6fZg3OyfES35gYy5GirkF06MT3A+uaYECG0Re+63c953zcfN3WSu2LXCmd0adGRKhiYHWO0DxHojiWx8RdNqfP84PMNV2sb4I4A1CIXKJr1reti47JEL6L1g/jvz1ve6nrBYArz43rThY5xV7xx9/Fd993PQBYEbkwAwdynNOX/q7I6MuD8f/zfYfwnn/4IT75+stzWWpNpYrDUk6BKZKqjsoy57vMbK/gRnI1x9XiUMeswuApXxuVnrAQ6VuQlERji8UKwECTF2Y3XfDE1EiQCEjf00X60obMJAE9rtqHSbJ9u+dVw0lroqI6EZ7Z6pG+bPid/J5e9n++g3dcex5uVCCqJouj6hUHuIfKdVgCOmX7/uO6Sunv9p2YwaGJ4uLdJiJHeUVfpIQ0hjkF01INyEr9aCKwVWWpEumTSXqq7Eumv0nk9BU8dtPSMtE1EKjgZfNjipQao1cVdrlkoIk7fu06pX3Rjhx1TzehRpzKwDtNoemCOKKqmMAVX3LZ+kzyjiLJg3eq3n1bWJwQcStnHt7Jj+XViyt7iz0/QJYOqYN36slUMuCdmkhfMn2g/PN7+KMv0EYbE3NFaVcoy7bsnUXXK0o2VCNyidtQZffYNAC+PxahCEyEwzvLPft2CQZLk3xh3bM0HQ4BAmOeAwFPr5ID2TJgBs7sN+E8L/1zrUT8AgVQ4Se7nN1xyiepMMQTb0UYEjdNYtYpA/IiSpYnrvGcnTaM9Bl3YRghm+t4lZK8I399wQIqFNmqsAyHZcM75cP3H5jAL/3t/alzTJ6BuLQEvDNcMGVF3N7LGPajuZ/X3HqnVduyOBoiF/nx226crgHcB7AnLiiKjAoxMX6WK0ZfQyJyMVVgZWXBFLYy1CeVbCg411QR3LhsILweo9NzpXSkryAPMgiKDUnRxpKBZsoYj3Ni5DazlaTo3ZeCd4bGfsEY9gI7B0keU7MYPzZLSl7JBjXoRbV1mRCqVJV8Ihf+N4/woqxymzfvdfBOeX6KrrJ61EHw5PVQ7dvk0gdbjYTRp4uQqWvuHIXRZ1Knz69O5JLFcwAkI/nCEWsj1eCd5SN9eV3onHvlIn1mTtcgqO7sEYZ6mXsX4nnZ47yqxMRKZ7fZdHZf/ZNMZBibmHhi4JpGsnRwHHnI2pZrEOIwpFaFyTm7qIgqphC8+YqRPlNvecTeaQHvDIIsxS95rNVwUkqByTOINk/FE+s6LPIwyedVFV0/Qqpg5zP7kTyzUa6rdO2245gxOqx+nsTwzvzzTC5l/dKBZNsVcvrkfkwoswE10pf/3POiGLLc/ivXYvRjNyq5x9XeR9mxoIuKJ0Vfp+/Z2+LcU/G9Lvqvy+vSdaXWn8xzKr3x6nP4uWF/sWFZpMjWV7JBVz+zrOSVbEiVtLCc87//mksTn+uY/dHz0kZC+DFbR5L8PvOcLBG8U8qJkt+j0BWylGodBC8v0nfX+2/AV9+dnXeuE53TSm1XQBNtWA/N6vRxKLS+OHvx2DNhCBXsnTbCYIaAkqUMvBMGa4tu7yizfDdcZjQPPIv1q5WDIigSeT2yIeGTRTRp+/6faCmchYyxP2eMjTHGHpSOLWeM/QdjbFf4d5n03fsZY7sZYzsZYy+Qjl/BGHsg/O4PWbgDMMb6GGN/Hx6/mzG2mfYWz5zICf1iWIgBYurl0i2i8mSsNaePHN5ptpDY5vQV9UFB5ALke3+FtBoO5hXPlIlxIsbJVx8+Gh1r93y0Gg62r1mcOq+qRB7jHCWQQmQvs65kg624jh2jqqkU5Y+VkVWL+xKfZQPetH35PHXTXTLQxIsuSbPflon0iQhekfQ13NScNbkF3fAtOy6K8iC5dzl9/NNvvhL3f/h5vE8nW2GOlMbCSB8/9tPPOBdvePo5uXlxH33JDuz8rRtTfRQ9MgFZqyp59O1CObcz+viDfvtn78U/ff9g4jvVQWW7d9108XoA8buoB96Z/bzEsbyahGX6APLp5CN4p8OwLdwDhlppGG7Wcy0iclGXzzXD/bigJKGPjhhMjSK1CSJ9ZnX6qkf6gGwIqTwWfAL2TsepRuRiqr+YpAzoDLai+SRnQpsymtvA0/PyhYtEdpTL7NQ2EiHGKBWZJ0BMXC+3AbhROfY+ALcHQbANwO3hZzDGLgTwWgAXhb/5FGNMzPQ/BvBzALaF/4s2fwbAeBAE5wP4fQAfr3ozT7TEMADJ+xsOEHMil7AteaGRPtSZ0zfbMceNm/eRvTDMtHu4d98pzHc9DFQx+qLFLX/xiYlc7Iw+PQ5eifS5DuaU51gG3vngocnofqbbPSzub+DSTUvxmis3Ja6lqsROhfR3lDqUw5BbnN1WbBlVjfvRKDU6EQx1RSJHm4pYJHWSgFEpm+EPP/J8fPL1T039Rp5bWd21Gg5uungddmxYUup6shAJWZIXWTOVolqTfhBoGUH7Gm4E5VSdcrIUOd7ifvjfwVYDv/1TF0fEAzpxHJYgADCF9doSR8SOMd3axf/atC+U6ZMzHXzsSw8nvlOfre3sV/fGOqZ/XmRUHPMqRB5kked83ho2I9U3/MiLL8Rtb30aLlw/HH0vokV5JRvU+/Akg4wiT1lXsiEV6etR5PSZ1OkL4Dj69cTI6MtgpJRLcXkBTaSvfJ0+H30NBx972Q780esuL2ifS97z0kGUS8E7DfdfHn1NPq/XXLkJ77i2mDhK1LHtVIB3ymynf/rNPaV/rxMxdc52o68wezsIgm9qom8vBXBd+O/PAvgGgPeGx/8uCII2gL2Msd0ArmKM7QMwHATBnQDAGPtLAC8D8OXwN78RtvWPAD7JGGPBmaDmI5YEXIYlF2Rzxh/hAdbfPtV4Yyw9yamfOEO+wfPLf3c/vv7IMTgM6LOJ9BWcFxO5lO6C95MLkUp+7ms6UeK6EBOPmLww+gGH5kzNd7G4jyuSKxdzZdXe6GOZ10TpOXeZXKcv3jSpxJZR1VTyDH4hv3T9+bj8nGWZ38vyqTc8FRf/xtfCtp1Y+Te8GfkdmXpA5TGTyVsZAOesGDRqL0v4+pf/klViIqD8uCgs2WDQpphveZE++ZXoo/wWA7DAcJX7tSsEzf9qjVbffl7KdelWK5Fs9dlSkDcB8Xpfh4qQRxIUEblYLjyblg/gseMzALKJk971uR/g3350BABfJ/qbLq67YHXiHHGJWREgHQRP/kxh9OlKNqh9di2J1Hg/xeu9iIprI30Gm3+mYRkeYuCM1NaERKw8NHm+66G/6eJNV59beK4JikDrwC5hW5kQ6wBCl0k+r4+/8hLjfpquUynSVwoOayhCn6maJvRkkarsnWuCIDgCAEEQHGGMidVoA4C7pPMOhse64b/V4+I3B8K2eoyx0wBWADihdsoY+znwaCHWrl2L0dHRShd/4sSJyr/NkwMTvIjk4cOHo4Wv22ljdHQUHUnRyev7yGG+Gezduw/uJN9ET546FX3vex7JtQe+j5MnTyXaOn16Ivr3z165wrqfkyf5dT/yyCNaxeX+/fwV+wEwN3W6dH9TbW5cHTt2DKOj7czz9h3g7FtTk+X7AICTJ8YBAI+M7sSAErFVF5fA6+KhnbsTx05PThr1++bLl+Oz953Cw4+MoukynDg9g7WLGhgdHcV4OAamp5JttdvzAIB9+/ahMdWnbVeWw5Mcwnvw0GGMDkwlvlPXcJv373k9TExMYHR0FEeOngYA7NkdPxfbsTU7O4PZObO58KvPXo3f/dZY6rj82y3LWpltMQBjx09gdFS/kQRzE8b3I0N/9+/dg7njfAkeO34co6P6SPtf3RfP/4dHd0b/Pn5yPPp3Xv9yn3Nzs9pzu56PUydPln4vY2PSNezcWajYORpVZPeuXRgske8j1tJjY2MYHU1D0qenpzHf9jE6Opq51j8+xudNL1yfZdl/kq8lBw4cxKjD72/veHp9mZiotp4AwNgxPid27dqNU4PZ2/Dk1BS6nV71fsZ4P1+660FcuSFp1B86wuuy7t2zB9ND1VSBQyfj59JuJ5/l1OTpxLn79+9D30x/pX6A2OgSc2XfsWym4bx3nydHjvA18bE9e9A5mST48aJxlw0XM+nvo89Zid/9lo/7jszh0Ud345Tm2QuDDwCaU0cwOno0dc58mz/7kyeOY3Q0XcB68vRpdLrdxDXtlt9Xp2O9Dh+d4vPv0OHDGB3i++zJ2SQ3wPv+kZObHXjcbI/SyczMNGbn8ufB7OwcWM9B0EmvJaeOH8PoaAEzdRDghGYNFPrXieNj6Ha7mDqdXO/LPsPpySm028XP/u4DM7h83QBaDQdjJ8fhwDfq68BhXhN63779WDyf3vcAYGw6vW6qY0WVUydPAgCOjx3H6XkPvV7x/tvp9nB6Yrzy82ow4Nhx831JzPld4fpepc8s2X+Q6+YHHn8cQ3P653o2CHXJBt2On+X+jdCQOd8lDwbBrQBuBYAdO3YEIyMjVa4Ro6OjqPrbPGmMTQE4gA0bNoS11Y5jcHAAIyMjoQeJh5nz+j7gHwNwFOeeuxkjGznUauloDwDfoJvNBsm1t5oHMLx0aaKtxT+cB8AX7rc+91JsWGqW35Mlqw/uAjCOCy4Y0XrHGo1DALiiu27NytL3dXq2C2AfVq1eg5GRLZnn7WofBnAMK5cvq/Ts1h3fA+AUtm7blqpPON3uAdgbfV48OID1m85B6McAAAwOLTLqd83R3QBOYdv27ehvuuh84TDWreTXvPrgowDGsUx5Z31fPQ6gg82bN2PEAJo3dGoWwONYs3YtRkY2Kd8+lvhkM876WkewaPEwRkZGcPepfQBOYPv27QD2W7cNAEvunsKMN5/RTvI+Nm/aACC9SIvf7vytbXAYy4QAuc4eLFu+XOkr7uPVz74Ym5abRck4VIWPl23bzsf6Jf0A9mD5ipUYGdHXzPqb274U/Xvr1m3R7xcND0OsC3nPU+5zYHBQex8BgFUrV2Jk5AKj+xCy8ggfswCwffsFhdToXrAX6vI+csH2BNlMkXDP7x6sXLkSIyNxcWkRuRr87iR6ThcjIyOZa/3s4DiAQ9H6LEtwZBLAQazfsB4jIzxH0j/Mj8myeHi48ji+b/JxACewdev5WLsk2xAavHMSbXQq9/Pt43sAnMAXd7XxxhuS0N/vn94P4Di2bzsfa4arGWOdgxMQz6XZ6ktc5/KHuwBix9J5W7YYrVFZIvbQFeFcmew7BeCw9ty8d58nfK8Yw5YtW1J1VRnjY3d46TIAE7qfG/d30Hsc9/3TA9iydWvGPsvn5YsvXY+LLnyKto1m8yiALtavW4uRkXQEaMXOHtiBucQ1zR+I35fj2usSiyfmEO8nnKyIl/3ZH50zPsf3+PPPOy/KTSwrw/dMY7I3l3u9fV8/gcVDrTDvcSbx3Tkb12NkZGNuHw13P5YuU9d5YPmeRwBMYNXq1WDuFFaEesT/ef0SbFuzKJFzbyJL7ptDa/p07r08fnIWH7ntv/DJ11+OF42sR/99c1g04Bu9r1ONEwCOYNM552DkvBXacwZPcj1AFrdgPCzfOwpgAitXrUJzrotg11Th9TBnP1asEM+Uj+kyY66/dQBDw0uMfnP09Dxuf2wKv/CsEYw3TwI4FH1HoTML3Xzrli2Rbn42StVA5THG2DoACP8KjeogAFmT3Ai+Kh8M/60eT/yGMdYAsARCizjLJCJyQUwkINAvphCdGLIYK0YJxi4ijJyuho8My6CAkZoy1AHAuhzFJ7sD/qcI5iMgNFXhnXn06jr2zio5fUA6f0zk9MnXYPv6hfFdmLRt2Q/H/CPRFyUU3rROH1AMK+pruLk5H05O/sI7rt1qbPCp19JweP0iU5ZbIAkHM2XvbCTgndli+35MGOl09eDyS6WnJQveeeFHvoIb//c3EQTFBc1jZsTs9hMlGzT3ZoXuDP+a5PTZrPmvvIJvu1tXp3NOhZFsA7tct4QbLDpiJerUF6bA1uRn98zz9cptWckv2cAP6kgwhlrl0hOKyo4IGcxJexDXk7W+6SB4ck4fBTxelyOelfNoA4vMgl7uPzmD2TD30Q8Q1oRL/96EZp8xPexR9MuQnI83XbKutMEn+ima96J8luBZmO966DNUYHTsw6p0K5RskJ8rJ3IpvhZRdqqqtBrm8M43fuZufOJbY5hp9xI5fVQSEV+d5fDOqpf/RQBvDv/9ZgD/Ih1/bcjIuQWcsOWeEAo6xRi7OmTt/GnlN6KtVwL4z7Mxnw+INyOHsUiRKZuDJQZUFkEC1Uba1OTWyBsCBelGlE8Sfp6a7+JT39itXVg3rzAjwtC2XzBaxH1Wr9PH/2oZL5X1qOU6UV0iIa9/uhpR04vMrBkEAabbPSwKjT6q5GHTEgQ2+ReAnsiFymHB2y9j9Nmt0m4OU1lZfL+s+Ih3WiY/UZ47prlFyVIZ+rb4d+XfT9mlepOGHbR8Th//q77/+a6PR49N8zp9BW3EZVx0OX3p9vVELtW3KVP2ziCwm/tLB1tYt6Q/txizTfurFvdh3y034UWXrNOQhqhGoP38ZwzRy5C7u277av0PSkoewU5k9GmMmm+/93p8678/x7gfk1xhABjIMSaL3p/OEO8R1y/TkSpl5Sna5/Slr/faT3wDb7vte9E1OEy/js0bkNS5Dstdz1i4p1nX6UOxziIMWfG+2j3fmPcguv2cPkxI6VSRv244LPM9J9u0J4oyLdlweILDdwOka/tRmBRxnT5ib9YZFpOSDX8L4E4AFzDGDjLGfgbALQCexxjbBeB54WcEQfAQgH8A8DCArwD4hSAIxGy7GcCnAewGj/N+OTz+GQArQtKX9yBkAj0bRSasEBOv7EYnjMUshYOKAbHhpuvJyRsCRTeqx+mWL4/id76yE197KJ2fYMp+qGu/KMLQs0wkL8Pe2dd0E0Qu12xdgetH1hj1I3t/57oePD+IGAGjSJ/ym4++9CLs2DCM81cvMuvDsATB8y40u+YscZ14k44JjqyaTLVvSkTgGtRoKu4r67vqBqUrvVNTBUw29PKKRBu1pfzeds6b3ILulZVeI0NvfmbJBhRHx+LC5OnzxKE/+PquaOzmUfhXkQgFUTCGbepcCXEdfU2tuGSDXfu8jbTjwpawRyd8rvB/y+u+NbmGaD9ag9PfiX51yu6yoVapiL/YU4pqnQ3mGH3i/rP2NR3DYl6dvirCpD2rqF0b3UU3vsTcvGsPB4YJ0iPdcm9SjirL+RYjuKiKsxfX6ZsNdQgx1spE+kwcSrroWZnhIJhOC+saGqAu8qTVcIzZOyN2XT9I/YZirIs2zvY6fSbsna/L+OqGjPN/G8Bva47fC2CH5vg8gFcVXcfZIDK8UyyGDWkFetEl63DZpqX5jSjRMd5uoH5tLQ2NIiBPjLKQK52oFNsCqqArDbF6uHyCd+TQKpjPXlRfpXQXAPIhkbqSDTK5SxnFUDYup+e5p0/A4cQ+o643V25ejn/7xWcb95HHTifL77/mMuM2s/oR48nPUbArt59Br60TkqhlxvOyaVkYo3mRvi/+MJmz9NUHY4dJlaK18rxW53+V91PW8NE9xyrDQkdHL19T0SvPo98Wa/fusWn86OBpXLppqVZJs4mU5NXPU/uwnTcNh2mjU9G8JDCYdNA41TiiifTFCrP87Cz9OpGYlNLo9AgiZIYw+zzW7yIKeR0EL1Gnj7BkQ6KUTCYqovpL0jl51PUvCCN9Oqi+SZQsa52Xj/i+/TjmEcP8c2ZDkjpxj1PzPWxYapYCY5JWkwdjzZKVizix0fKhFsYm22Ef+XMvCOxKXDRdx7guprgl3w9Skb6uF8CYQD+z/R+PSB81kcv/1xIZfSzePOQFQldDSxWdISP/mwoi13DTioDsFaTy/gLx9Ue5OJrFpVkhYhIZlQXn2VJG59UFU48whgS80yTPSYhQvrwgiBa6vnADo4d3Zl/XqsV9JUqM6EWG49RSpy/HEEuda/nsykQVy4gYj4zpx8lcx8Mv/e19iWMf/be4FppQqlcMJVkG80R+Bd2eqpQbNxNJ0jlVfD5FpE/8JuuVZNXpk8XL2cDl6xHPmLpkQwx9z2/DDwJreHLDdXJRChTz0mVpaJyq/FNF+kQ3CaOPaH00KWwtp0FsW70Iu8amS/djWv8z77binL4sAqr0XpuI9BFA3nQlG7LuyeYdOYyl3okarRIOErk8w+uuOgeXblwS5bbmSVEB+ABBGOkrdenpfgxchTG8k9/j1HwXw/1m+YMmaS86h2HRWHzrM7dg+VALL7tsAz71Dc7E3fN9uE62ruDZ5vS5zDinTzgxen6Qyunr+j4GYKfT5KUEnE1ylqckPrkkLuJpkdOnAWQnirMTvbGG4+RG+ihz+mIPSfhZs7hU8TaLZ6xb3A6Oz+KBg5wyvBdF+qrdk5OzQYt7+62X7cDFG5bA84MEkUupSJ8ELVK9uDG80zJqZZBLQoF/d6VIXF11+kwNMeucvhwiF5t7Eu9Up9AAxUpZzwuwqK+BO9+vBV1oJWH0+Sq80y7SZ+Lg0I2tKtNS5NfoxA9QGIL1InInndEnn8f/5hU3ryJ5hCFqH7ZKRlb+TZQTRrLWpxVmFX5MDSOtA95pAn+XIdZ//panYedv3VihHzOjL0+KIg+uZo0kr9OniVhmPTo7o08X6VONPj4+WpJV5jDgtVedg4ZJcfaMNUV06/n8mVkXZ2f69WTP8ekoNWQugnfy8ybnuhGpm0EPAKpE+vJbdR2Glz91IxyHRe/9DX92d+5v/MBubpaBd8p1gR86PJn4TnVwVpG8lICzSRaMPkKR4Z1inJddIOLQvNSu9D1dTl9aEZAjfyRQHGXxMc1jMG4/x1v+rI//F178yW8DkCJ9FW8p2tg064YcxRS5M2LBvnjDEnzoRRca9yP2JS8IUoZqFryzrIj28hZ4ApsPjgT3UiO9FOJoSAqyhKKgPQUUSpWG9G6rtN/1fPQ33cIyCbLITgPqnCuzSF/6pCrGppwzqspMu1foGvFyoHGmkT6SnL6inBjfLicGCHP6cjz7FNPS0USVel6Ai9YPS8YeQUTRYZHBLr8TqjybLOZDOU9bNmabrlMJFZGHHpEl71vx02YGvk7sW19/+Fh0bLcUlaQgctFFRrPatXlHOiIXdf3yw/yxMuuhen25xn743m0dDA5Lv9f5rofr/9cdePff8ZqGMyG8s9Pz8btf3YnJ+V6U318kJggoXT54mfEg3uW9+8czz4nZgY2bTUmZ4uzi8qfme/jc3clyFBQ6p7ifhUjfgkQSwztj2uCyC4QuepUo2WB1hbHo8jwSOX0EI0NdfFRClDLQxzwpWqtsw/KyMaaKXI6gESok7a4HhwFffNczsaNEXSqZyEU1lOngS/xvXhSJ4q04klJOsfir4hZAcWRpugwbhs02TG1fNRVPKGUAACAASURBVME7i9g7izbhrueXhiznwzsrRPqk0WLyhKgeI4/06r87PdctvJeYxCR9nnwodvToPOPVb8Y0py8gII5ouGlUh2gboFlbHA28s+sHaLhOynFlI3IuutwflUMpKwL7m/8aw6plY6MqiEDk89pF+sK2ciJ9APCzf3lvdOzjX4mLVJNE+jT7Sda6ZWMsMU1+omoMBGGkTzaCy9xhFrxTrHGR0Wcb6UPauGyHPADfeewEAGBOwDt9H5/8Lw6lNI30xYZ49t1r1wOj1rmYrBmRDmMD7ywR6RMyPttJHTM1HPNEjHFbjoAnWhaMPkKJ4J2IN/OyAz7GY+tDfaTsnWqkj7xkQ7j4hM0KmB2VAm16iT3fB2P28E49kUt8jsiTnOt66G+6pSMYslEsFuU470v8rXQLkeRBVYVQUXnXmdOniyxkXovD8KmXbMSHpajrReuHzfti2fBOG5HfqXZsFcyTniV9eJpoo3wb8mXvPT6TfWIoVBHTPHKdyblu4Tx51raVuOLcZfjvN6aL0cvjVLx3XVdWRC7hXxP2OwoiF918F3oQ1Vqvg3c2HRZD0wn6abgshSDgjVs3nWhGfS+PHInhYrLCXFWhNc3py5MgUkL1alyRkUUxFXV7Y1a7diUb0u9En9OXJHIpM0UdJwPGHd6QMD7s2TvT16XmGAuyu3+492B0Th6Ta6L98G/evevg3mXGosnaF8EhLZ4XJ3IpN1DHZ3RGn/1gj+v0LRh9CxJKBGNz0pPYWDTwzmRxdpsrjEUb6UsYl/Z9qBAmU+ZI4/ajqGhRRCSoRBQjJK+QrjjGWJwn2e75xvTKssh1+sS7cSKjT5xl92JieGdOpI9IGVDZO6lz+kwNiIbjoK/h4CWXrcfqxX348i8/G1/6JXPGU129KyEULLeOo68PVXR7PS8oHWmQFW+VhbDKvcgtCDh1nlDN/axnBvAaTUVjbVFfA/908zU4f3WaHCFh9OUSuZhfb1YfhTl9BGyBrqMnQ4hzVKyaj9pI5Y95ARouI430uVIuuhxlplpasuju5ejRNx89Ll1PVfSImdH3ihwCkqKcvjMRkYjQPLKPOgveaUnkoj4qHbyTR/pko898kmbVAhQ5nIIRkoK9U+0lSucI254Jjb7jU+3onOm2WcFxEzIiHdy7zNI81ymOnFGkdbRKwDuFTMyly3PYljcCfnxKNiwYfYQSKbdglb0CkSGTAZuijPSpxZ1lVjISZVbZQCOYpM5TW0F0G45Oep6fKJ1RVmRjTBVdTl8vhDWVFR28s+EkF3C6SF/2ORRELo4TQ+NErgVlcfY8chXduQCwclEf7vngc/GUdeZRvriv0pdoLAx65b9IIez5vtUGpHp7KzVVcqyQwjvzjD6L9Ut+DvlELtVvpgx7p63u3nT1kb6AcF4yXc6V76MpwTsp9hTXAT539+P4/A8O1sJqHZGPKc9LVwYAqO71z2OyFn2/+7nbsGHpQGYb4pdZe9uZyD3SOUSz5rhVnT7HgMjF5+NAdriWivQx/TovDAbBqG3N3sl0TKRJA17AO2VZt4SuZIMO3llmPZvtpq9PFYo6oFXgnRMhvPPm67biT954BQAYl33Ik4VI34KkJJoyTI5ulWsjOj/hOZO/pxlwTSkhXgh1cXaVvdPJMZ6qta/3yqrS8wNLaEm2VzZmbI1z+jyvGsNXItKnLDCiZ9vXYpTTR/B6ZKOMotaYKuUifXZ9VyVaMW9fX6y3yPjmkb6yTqVYVKXpTLCSkUX6CnI6f3hwonLb8mMQCl9tRC4FuohPkNPnapiaARomwriPtCLb8/i6Gxl9hMPr1/7xR4l3QtV0TOSSPJ7lxKsM78whBxOsulmGppAiYokzMZ91e3DWHLdZh3X5dumcPg28s0zJJKa/djF3OlSRPmigqgp0VK1l/IqnbsRLL1tv1n7kUMqWL95/OHWszHI2r6m1LKTr+Zic75LUtetrOKnyC0UyMcsjfS+/fANaIXufLrJZVn5c6vQtGH2EEkVjILHDlVwgdJtOEi9PozTpGN2oSzYgupdk1MojCpuIKzQivLBwz+UxXsr5auKZVs21ko1L8S6oITqMMW1+hCwUb0et00e9ThYp/bLYRHmB+ohchGQRCBQ5RzqWRC4peGeFx1T2qVClRhYZ4lPzxZ7o7LYlCGy4kNdVnN2EvZOkOHtGyQayUgcahVmsuw1Co0/OlZfXMCr7RptTD14vTCd1kIOJfTmLlVOI+GnWGnBmIn38r4mOYjPW9Dl9yc+iZEPlnD4NGREQvw/SnD7lWNfLNvqG+xv4xCsvMY5mm6S9fOWho6ljZfY41SiV5ea//j4u+Y2v4Us/OsKvx2Jy9jddzHfLErlwo29xfzPKd6UgclF5Fs5WWTD6CEVMMjkKUdoTr4H9yFORSvnUUeEmjT77PtSoZZS8TpXTZwjv9KwjfXE7qsj5as2wCLIfBJUMDdn766leXMK8uKIadyTwTmkOcHgn7ULpOuZzwbZOXx6UkCoiXjmnr6xTSfq3oKEXTZxNkb68kg22Ij+HX/67+9HueeRELqbPOiAwzHT52wANdFSIbk3p+QGabkzkQjG+RA+Ok9wXyYw+sQarkb4sspTKkT5BapZWRsW7Klq3iiIPunHjMOCqzctLXWueaJ3U9vp1SvQlGzRELk71kg18z0ofFw4TMqMPaeOykzL6YqfVdResLrUGiGGTtzyZksJkyWw32+j7+iNjAID3ff4BAICNz7Wv6STKpZjI6TkO71zc34j0MAoil5iFfMHoW5BQIggek4hcKiplWYnRakHlqtLQ5HnIii0le2cUDYuMGiqjrxje6fkBJ3KxiPTlwVJlQ1/O6asG70TUj5o0HMM7Cd5LQT4cxeuR4Z1BQJ/87JRQ+p+sdfqS7aePF7N3+lb3NhMqFkOtRngd5dtQX0GRw+BMwTvt2k5+HptsaxUoK3hn+Lcw0kdgmOnWeoCPL6p56WjgneMzHSwZaMZICYJ+MiN9RADPLPh7M8OQqBzpy8mtFgZAUaRPvNIs41D3bpuug8vPXVriSotFdVrV4YzRE7mki7MzZlOyIX3tu8em8KODpxP92epGjjbSlzQo5Ehaf7Oc7hLXR84+Z+lA0xguqpNNywaNzxWX8YMPPQ/f//Xnluqnv+Gi3fNL7b/jM124DsNgy0XLpYv05dV2PZtkwegjlBjeaUHkojFk5HWIChrJGd0UzyxxTp+q2FBH+iLJTVj20fMtiVw0yeoAcGqmg7+6cz8AvlgLGJVXURnXwjvdbGhpVXELjBiK+omyIeP7dBGFRPuGD8UWjlGGNKaKZOWSFMOWy8OI5YirUCwir2+FSa+OlXZB0j2VocYK4J12bSefg+vocy6tIn0G3niAJu8uM6ePMNdWHcPtnoeTMx2sGe7PZT8uL7FynCRyIWgaiAqtq+QRzcy8uWr9iPevM8Z7hjl9YkxmrQHymI1zU/k7v+6CVfjD111e+rp1oq7FdRh9OoNMjV6LnL7hgbgma5lr0e0pz/29b+Lg+ByA2Bi3Z+9M77+9HHhnf7NcVE6HFlOl5wfob1SP9r3nedvRdBkuNqhDLKD2y4daWLGor1Q/4t6L9hVZTky3sWywBcZYlNajg7eXFUq24ydSzKo9LoiRBNKgiKEX5drQMS/J/1YZN6tK00nX6UsUZyeM9IlWxV+dAlJVdPh4WXpeEBEKVJUIdql09O6/vz+i73ZYaBx43GCrYvTF/cRGn1polUK54dC45DHZU0uxZ8tzQORaUIpbAFFNnGuZ0+eEOX1VIdtFkpnTV3B/VfK95LPnQsViUV8DY1Ntkkjf1HwvV0mhgA4DdcM7k597XpB4P//7tZfhrj0ncfO151fuwzSnLwjs1+JmRk4fBXRUiAqBHpvkVPNrh/sjxxVFaoLownEYSeRQFTF2ZxX2xCynYdV3I6/1qkTwzkIil/DaMt6h/Lznez4WuU6UX33bW6+qctla4QZZ/LmOaaka+UCakTEyaLevwpsuW4Yv757BO67datyHbl+UpR3BO42bzBS1mwjeqY30lTPO4jzL7HM8P0CzUX3utxoOrrtgdWQQy9J0kwGF05oSCqYiopzzXQ8DhpDUY5PzWB+y3oqor5q/XkWEM586VeVMy4LRRyjRJGPVa3pEZ2csohRhakAP+aE0xgCZoY63K/oT0UqK3hjyN5mJuS6+9MARDPdXH+riPtTnJdfQEV6lnm9h9EmRvjhpOLnDUCw3jKXvRU6WpngvMvmJKNlAKU64QQcG+YI2NRoBnpPgBwFe8SffxX2PT2DfLTdF31HcVmZR4IIX0a1A5CJLBO/sE/DOKpG+pEy3e1i1ONubS1uygaYtXduydDw/ej+ff+c1eOo5y/DSyzZY9RGjIPLP42uJVVeRM0rXNmVOXyDNx2OT8wCANUv6SREeogXV6KdSxIRiqeYR6d7T6pxxXiR5xdm7xvDO/EifHAmb63hY1NfgbLDEi7FagqAeeKdByYbQucgYwxsuW46PvfYa6z5kIWPvZEgtnHklG/pL5ygmHcU6qZqCIotgK1elv+Gi68XXb2f0hfOxBIPn5HwPFy9qAYij5SSRPv/sr9EHLMA7SSWi77eAd8bRMSnyIn1PBe9saOCduglsIxFUNexGLKi0kT499ErIXY+dBMAXgqoSwTuVRVSGaDAmFsGgcokIOedRJXKh3EZ1UZLpdvx8aOr0xX0EQfnSAkXiKmMr91wKeKcf4L7HJ8I+iZ0j0EetTFhpq8LHAa4IMhZ7U6s8ppdfnjR+1AiJKnQ5fcm2KN+Juq93PT/BzEwhseKYf90UJRsaLksgRD7wzw9g8/u+RNK2ELUg9Ilp7hBbvbgvqotZNmKhEzmHOgHvtG6Zy2B4jXMKO6HO2XrPB8vlJ8mSF+nrliRyyYKBJiJ9XQ9BEJBEjlVJ5/SRNh/2kV4jVXinrXMxC3EhhIrIRUtKI7UdBEGCKKW/JOmKyeVxZ5KlMzTkMFClT5nnooRCFYkjfeV00xVD3CHTJMzpE0RBZ7v8GNzCk0iEYiDDO8tG+qLomNRsLUQuToQjF0JNSx8RTyIZ6aMolCmkKNJnS9cPSPXz/PQmI0QQuXQ93xremSBycZLKFMWG7Wo2nZmE0WfdRSLxvg54Zxbhgk75t6/Tx5T5aNWcpn296l9kIPlB9XwPgEOIBptu9G6qkGFsW7MYH3vpRfE1FdWdI1pjVHp1yqVLHatdz4/WMLocOD1kXBUK5tuG4yTWrs/d/ThZ20LU+dgJFfK+hotPvOoSfO7tT88tNF5WXKVYN9XyIiJ9KjshRZ0vWcS6rmvXNNInRz11IivkMgNtLTVTNeko771xhKwPnUEm9Igoh81yn8liUVb7s2fvzC7Z4DBu4MiXUTb3zgQ67vkxw/iivmooKOHkVkUlnnn90zdVah+I770sg+eKMNI3EDlxaIqzL0T6FiQhYvhz+mr+79JECyI0L7crfaDafBpu2ktDb/QlFRuhEHRLJOUWSVFOn/DY/vpNT6ncRxZ7Z9LoixfBqkZfgsilALpjI0wan0JmpAgNCbxTgpBSUsMLcTIM8UMT6RwD2yijSuQi90ixB2QxUWbNx3NXxMxpA6WZ3WKZ7XgYaDWie6h8L9IPiyB8VAazWiKAcu3SRfqEMUu156vQ9yzp+UEmiYip8Pqh6TW33fNJ2TuBeE0UqJGGwzDYauCarStJ+okMHeW6qdg7+0IonVp8mtJRCUhrvS6nz8+P4AkRP83O6Yuveb7r10ZEoa5fwnAqMlrL9aGr0xcafeFn232mqJQRJbwzKz/RdVgKLVE1py9vrRU6yhff9Uzc/ivXAgAu21SO1dV1HK0+Kl/vCy5ag+tH1pRqV9dWWaNv2SA3+haFaT1T89WjjUKq6nVPNlkw+ghFrtn2/Iv4QC+b+xF7rdKeM4AOGtnQhOZry+kThB5h+13Px4v/6NuJnLjqfaQTvGURCdFXbalemyiLvVPu15Fy+qoWZ08SucRKE0AN70zfy0w7XlQpIHiOI+f00UOK1AiokEePTZH2I/qSlQH5+axe3F+6vaWDzcRnHTMdkL1py8rggAVkbrbTw2DLjRTmqsqM/KsiiDgZvFMhXaDMI0rl9PViADmVcREz7OVLt+dblZsB9Gs9wKP7lDl9QDxme0qOEpXIiIcEvJPMGGcYaLqYS0X6/Ij+nULimqzZkT5TlEoWUZVsp3Y8P5oj9ERUKvEc/1u1Xp5OdI6xD/7zg4nPgsilch8F5FCU8E41JUWeL2rh8zpKNvR87vC5ZONSrBnux+2/ci3++mefXqofwVauiny9LQuGUIDX6QPKwzvF2BtquWAsmb5SVX5cjL4FIhdCkfM+tq5alCB8MBWdMkCMJgMQQ35kIgzqSJ+a0yc2oY7n44FDp2n6QD4kQ2zeNhtQBMVRI6MKvKjlMnQ8Hz3Px2Cr/NSSazepShMpe+cZgHfKdbQElTalZME79xyfoe0I2fClSzYuwauu3Fi6vTvfd0OqJqZuDGfNR9kIKOsFlluc7XgYbLlRnkLVsSX/rgiJQEfkklSYKdcuLbyTcP7JfRQZqx3Pz6wRZyrCGaXK5FzPum0h0XwM+4mIqAijPUC8DqaIXAj7GGyljb6uF6DVcMgifjKUX5UY3mn2bkwifT0vqA3eqaZYiDFg66yQJY9kRS4Qb+Nc5H1kfy/IRKz3Mk0/3USkLzn2lgwknYSFzWsCB7IEAWcjlg2YrasWleoD4M4GLbxTMvRsHSVViFyAeOwxxrCorxGVjbARjzAH+omUhUgfoUTeYItxwTTMS3wxs7gwjQjohawMUEf6HGXxEYs2BX2ukCJ4p4BK2GxAWV5ZNadv6WALQQCMz3YreVPl2k1ZzGwUkQbG0nXnKDxhssx2PBw+PY/PfneftQdWJ06Go0Il7LlkY3EdoSJRI33i0b1wx7pKSsZAy03kUWQWZ8/YtGXYVGmjT2pzttPDAEmkT4J3FpWZIIrIqUo/baQv+bnr+ZGSRhZRCv/mXXYQBNzYIIj0ye9FjJ/J+S6ZYq4asWIvIY/0hX854QX/9x2/dh0pkqC/6aYU767nk0au8iJ9wnFiCo80yenryZG+GqD2OmQSZWRURHZ1hkxM6mUP78x1IHe86DwbYUgrLd0ceOfTz1tRrv0CeGdUA9hyIDQzEARynUTbOSMMyHZJeGdLmjuLiYw+Xm/47Df6FiJ9hBJ7g6sPDN2EDYIAfQ2ndIg7TwRzU88L0HTrKXSswgzEYkNVdkL0kbdQi8275TqoiupuZHhlZUXdYQzLhziO/PhUG1tWDpXuR4Z3ZilNJJE+J10ctoh1sawcDHPr/vaex7Fjw5JosXzrMzenmPGqSJbSNKsYr19817Os+1JzPaiVJ8b0xlCWISNvPKa1i+I2439PzHaxenFfNNYoIn3FOX0064xK7EBJPKyu3/J6RUbkkgFPlkVElWwVJ+G0EKiOvpBSfXK+i6EKiASdxDl9/LMnolXUdHdRtCqZQ0+piw203FQOUZca3sn06BHRF1DM3ikk6zx53HT9uNZkPUQu8WcxpqmiyKIP0TbPiZOxvfyPLWFYVm61kDYRvJM7qpMdCeIjh8WRvndetxXrlw6UJlqJEFYZ7vBIt7CMwruOo2WTl59Pn+UYEPDOrOLs810PP3h8HE/fkjSM5RqXi/ubmG7T5PTZGspPBlmI9BGKDO+sKmpBc9FunyU2WpWoaGW4wVBH+YA0e6dQYkmNPpavOAkDw2bxySINkTcexoBlodE33e5Z1+nzFaNP0CAPlVTytf046Rpn0217Q0wWkTg9PNBMUGl/5MUX4ZZXXGLdfhY8TmXdoxCVKZBaedIZ4XI/qshQ3LI5ffJ9TMx2o4R3gCanr2gdIYV3SvdCFUHUSUcqzk4H7+R/8yKUgrrf1thQUR1iLZya7xFG+vhfsSZSKZaqyJG+WuGdimOq5we0OWoZewoglWwwfHZZW83P/8RWPOeCVQB4pM+zdO7k9a+LulMayep8UaOY4jsbnZwxM5i4bU6ko9FZdPDOF+5YhzdefW6l9oH6I306MkAg6Yi1nTPiGrPSBj76bw/j9X92Nx4+PJk4Lq9ri/obZDl91PmwT4QsGH2EEtXpI4j0JWpQIbD2mKgiYGEibE6dzwdIWPvQxovhncQlG3K+FwuojXLTyNig1Zy+5ZICXYUVT2bA6ykL82uftgnved52vPM555duN9WPJj+C8p0AwPhMBwAw3N+MvLOUksWoOtfxSJUzIJvIheqesog2suakfLSsc0Fucny2g6WDrbhkA0WkrzCnjwjeqeRZ1rF+Cen2Ygp1uiiJ3mmh9gvYsyAKVIenGH2Tc10yhkUVbq2uX1Qi1+mTSyTVDe/s9HzSPTivTp8gxygymq4+j5OTZd37QMvFr77gAgDckJTzISklFXUP/91q0PWjRpKFETDQdOEH3NDg+0z1PkWNvMLzCOCd6ZqDftS2QN2URXHI7QPZDjYxN23XMnVfFCLvybaGfx7LLQDsHpsGABybnE8cl9e1xf0LOX2yLBh9hEIT6eN/VbhEX0kGpyKJ6peERp+OhclWVANWLBBlk3LzRFfoVJYI3mkT6cuA4sgfHcawbCjGslfxcMuRPvGsxGbXdB380g3bSAoc8xyy5L0IT+OqxX34Hz91sXUf42FB1iUDzRAWQWyIKQ4FITPtHhZXrDuUJSn4UkTfTxjpKwHvfMFFayIG0LIlXIRS0+55mO14WD7UjOYphUEz3e7h3x84ktl3ENAZAt/ZfTLy4FIWZ1el58f5UFRbfoyCyBaBwrAmcgk76ypwUT9IwqBsJAXvrDmnT2ZAdBgVpyqXlob4Ro302eYKy6RdqsTsnfnv5s/f8jTc8WvX5Z4jnJ18DPNjddRM1eX0URK5qLqEqFcsmCLFXKkT3imEBt6ZFAHvZBK8c7Ci0TfUx383PtvRfu8TOWSy2DtlQ1AlRCorMQxe/2LEPFJ1SnnsDfU1ME1h9PkLdfoWRBEKb3C0SEu7gR8E5PBOsaCIBaYOT7lK4y1uaZYQSpjFICVkrlsfkYu8EDmMJaByVRZUuQg8FQQjqx/1mfU8H4wB93zgBrz+6edY9zGydjEArmDWQXUsXucbPn1XArox1/Wi2jx0fSlELpGySdN+I6PeUZYfpuE4+OlnbAZQHtYolKaJ0ChfOtiKFOaq9yMPpfd9/kd459/8AA9q2HnFpVKMhXv3jwMAPvGVUQD1wztF69TsnbnFoHtmEZ/CvqL1i3/uI2TXi/pQ4Z2CjITY2SOPIRlyS6mLOZrIu0rk8lc/U47eXpV4rU9P8gjeWTBPBlsNnLsiP3dchsfVReSiRq6CGoy+lC4hRfoA2eiz6cMMiWBP5KKp0xfO9SAIIp2sar7t4v4mNi4bwMNHJrXfx9Br+1xhEWWVRX6GWYancR85zhFxDUC6pIM89lquEzkJbMRfiPQtiCoUsC8xWGW4Hc/pI4Z3tpKRvi5R0XdZsnL6ZghJQxqOno5cyHd2nwRgB5HKInKRDQGHJfOrqOCddbBF6TyaHS9A03HIolefe/vVAHhEqef75MaruM7Hjs/g/scnouOzHa904nuRuCkiF/6XMqdP57jIUkAaDsusHZkl12xdEZ7PPwujb5kE76wax5KvU2y+M5ociixGWhsR/anP7ynrhsn64PDO2BNPIaKZt912b3a/VEQuYV9i/ZJRI1QlFVQYludzJxJ1DkzkcJHYOxmSRC4fetGFVn1wttOkkthTWFTL0ujr+gD0ObDCYKaAqQt9ouv50VpBXTNVLXUg/k1dsoG3rUT6Qj0mKpxuMd5UmGpmdMk60pd2+LTDSJUfBJizhHcCfP0bzTD6yNg7oyhytl4k9pmqEjGaF7wLNaIor2tZ7NhlpectGH0LogiF2SQv0lG7NRh9g6L+SehVahNCLoXEkAz+VywGp+fsmZSENByWiIpmX4sF7CMjp0/+yBiD47DI8LMjcqFbmHXiOGmDouv5ZPk9ALB8qIWtq4bQ7vn1RPqk9ykrYDPtHrnRpxbtjaC3RLfkZsBk9hyfzrwet2AzVOWzb7sKF29YEt2HmINLBmJ4Z9UporM7xaGdR6ciUh9xHiVERhgw6uP7wi9cQ9YHr9PH/00H7yxuKYJ3EnjkgXROH2DOEFnYR0SsxD/3amK6iyJ9UlTGYcmyIT/zrC1WfXAnTPJYh7hkA5/DTEtqJtYCiucnlN+uVx97pyinIKQeIpfYIQokc/qA2OizuTWHJZ1oWY5w29eiY2gX7JReAMx0PDQcZjXeVgy1MslLxPiyXYfVdUWIH8SEc0OWe3GRc1Ncg1rSQR57rkODZKuj9NQTIQtGH6FQwDvFRO9IC04AWuYwIPYizUZGXx05fcmFWiiolKUnGi7DP9x7EP85eoyszVQfmYtbMtIHxM+1ipEje7XqyokB0pErgMM7KSm2AU6I0O56NcE74/bk9zDXrT/S5xF7zHVsqvNdD7/xrw9rz284DM8ZWQ0AeN6Fa4z6aLoOhvrcyBgTkTiR/wFYGDQaw1McesEffBNv/Mw9AKRIH6FzoS/KTwuU43Rw+K7nJ/LHKMSkmW6PRnlW82IS8E4iso3IwScRudSxdom37DosdrIykNJ38vme3KO6nk+STy1L02VaWLcwYCjyLZM5fWJPsW42IY6j5PSFY6BJSOTCFKeCWIP7FaPPZn6qudVZPAe20euoFrN0rB3qRL4f4MjEnFWUD8hGjxw4NYtD43PROTYSR6uTz8n3A1y6aSl+55WX4GMvvciqjyzDUv1e1V+bCaMvHw1mKl0vINeRnghZqNNHKDEEqHobLU2kzw+AFvEGOqjAO8Wi0yzIkSsjKg6/jlqAYuF52233Yt8tN5G3D+RE+qTPYlOiiPT5YU6fw+ihOEDomVWOdTx6shVRW5KxOhjj4n/L0a7ZDn1O35LBJiak6HSXgDRAFh2cTLCS6cR1GC5av6T0eHckZVZ4OAQ4bAAAIABJREFUgbmBnFZCykjRtP7hAQ6/jaM0lEYfn2915fQxFpZs8OPPJO1KVoqon6cKFZGLCr2UoU9Uc14XiaFeT2RhjEUDSiZyoVhmXA0VfZeYvRPgimlHG+kLjSYC54jIqexKOX308M6ksRSxd9YA7xQ6lliDKXP6mHIfWZE+26WGJe5FGC1cD5tq93DP3lMYWbfYqo8sRuhn/85/xedYswLr9SLBcvnqKzdZtQ8kU1601xA+TLXEinxvasmlqtL1fDR/DOCdC0YfoYhhZZfTF8IxEjl9AWj5yWIPmZgsYtG89U1XRlEEWxFXLBbqOsliAAFRpFc0ZFZNWZLsnfyvMKatiVyC+pQmlW0NEMWH6cdYu+eh6TrkUK9EpE9mC+t41pASVc5dMZjIsY3zLWnad520x3/06FTu+VVEzuUUtOBDfY3Cuk5FottQdYWB68jpiyJ90hh4+pblZO03XSeM9HGhK84e/3s2Y8xSEbmo65e8blGtl3EJAoR9+WT5ggmRHAcRkQtiQ4ZibDUcHbsxPZmaGFuyfHvXiagwPMW7Ee+g59VRdiRuL5nTJ4zWOuCd/LNYgyP2ToJIn6PAVLPSRiYt01N0DO3i+ifnuuh4Pl51xUa7PhxWWD6HLtKXdoaT5btn6F7R91GkLwfeqUE2VZGeF9SiX55pOfvv4EkkcmJ5VXEdnpSeyOkDfZ2zwZAZKo702RcxV0VdqGuw+RJSV60uN2txU9g7gRjeWQUCInu1eCHQSpdb3I/k0ZzverjzsZO1wDtFpK8OqJcaCRfS7nlRviqVnLs8yZAn+qaakzoojoDg6KSqAc2kPKjpdswQl/Q8lxfttAvS7Ym+KR0AAgInolifesNT8fc//wyy9psOQ6fnk3iKZZGVIh3pDSATudg9L5W9U34vVHm8KqV+bTl9iB0HMrmO6IlinXFZMkoSBAE6nk9eNkmFd35//zje+Jm78Qdf3wWANqevJ5cBIn4t8roCyHX64ue1enGfVR8pIhdPlGwI6w1HOX02Rh9v/z9Hj2G200uMgRddsi76tyiXU1UiIhfJMSauP9LDLPewrEifLPY5fYJpXh/poxBVh0xfA//+m4+eSBxPRvoca5TZmz5zN+7Zd6oeR9YZlgWjj1AoqNwZY2i6DtqKUksNyVDr9IlFh3JjUyEZ1IoTkISk1W305VETi9cjnmulSJ/k1aoTHiWzWf3mvz6E1/3ZXXj4yCS5kiYifXXU6TstsYJFEQw/QNcLyHNvNi0fSHwWmxxpTp8ytvJqWVbNKZE98nJOny7HpIxkGYvqdIyIXAg3TgFXjRVa+oiy5weJQuAUIjcz09G/6y4ZkQv/62kQF1Sea7UERR15vABw4XpeH48xuWZf/F4o1hl1PgrlmRre2XCS8M5ZidW64TCS9SWGd/q1RNqBdAkCXcmGb7/3ers+VH4AJadPRHpsbs1hDPtOzuJtt92LX//Cg1H07XdecQk2LR8EANx08Tpcfs6y6p1IkiRySephts4Y13EK9aH6cvroERGZ8M7wGnYeSyJjmgqRi21O37d2caOSErL8RMnZfwdPIvGJFIOW60RJ/ACAICD3zglYhErk0nLpFOas5GtKkRVOodRQF2qOjLFU5CL+txrpcysoHxG8MwhqrQkje2ZF7tjYVJsculBnpG9SKrYq3rdQoAZaLpYPtUiKzANpUhCxyZHm9Cljaz6nqG1V41yG9c60e+hrOGi4TrSx0sI708dF35Q5fSLvJoaukTUNgEcS/SColcglK9IXwTuJcvpUhRmooWRDqP91a3JaffatTwOg1OkL/xPHbaWh5LVTvQdVWo1kfU4Z4ktmjDsMDhN1+vixOnL65KhVDO+M+6Eaw197iBO2dRX2TsFJYEN+I8/t/Sdn49zKBovSbS7ZuKRy+7p+hESRvh6NoyercHriHKKcPrUbn1BfNYV3qiIbZ45mf60qPw6RvoWcPkKJjQ27gdFqJLH+7R6vc/a0zcvIvEyMMfQ3nUi5FJ4mykifChurPdLnpZUaAPjV52+36sMJIbf57J38Zgcjo69CPxKRS8/3azP6HAnjLvqc73rkRl8c6aOv0yd77sSCHkWrGw5+8KHnkfWlXrpQOOhy+tLF2fPYdKsaHjKsd1oqbSEU5qrzU1uyIUi3F0X6pAdXFfa1enEfxqbakWJTF9utE0L9qBx6QuTxm0WtTlWyQV5XgKRxT5fTx//GhmU9OX1LB1u4bNNSeH4Q77dSpI/i/cvrIxBHXOlz+pIlG2RnJeWza4TFqUX71NuKWgdNPDpKo19c869/4UHs2LAkyrcTTlahx9jsM+pPRR9yLWCK+aJCoYHYaBV6mO2zE06RLJIofg5NKRjVuKSM8heyd2bcWwLeydI5ulVlIadvQbRiqxjIm4HnB9h7YgbnrhjC/33HNfjATz6F4Aq5NCQIgFh06sjpE9NNnbgfJLgXWYGJ4EuKsvniS9db99PQQPDkbsQ7728SRPr8+uBRQLJOn0x5TFmnD+DOi/muX0tR05/7ifPwjPN4wXHxXjo9+jEMpD3jPa+GSJ8K78yL9FV8T0xSzmbavTiyIJwzlVrNJnJRD6uG2RuvPgf3fPC5lfr8z1+9DkAMtRVznroYuOtwY4maBGNWgnRm5/TxTq2JXCQEgfwXoMzpSzoO6irZAIh8vvizw0Ca06fmQ3W8etaVhpN07sqOH0rlshkSRfnEY1hIOqcviI5TiXzNnu9LRC6h0RcaSzbvX+4jCIJo/jVdhnddfz5efOl6vPpp9oyUohd5DIsxJvdpI1llpmSxRVxkMWt6QUC2DjPGUuMLAP79gSP49q4TmakC8vwxyW80lQV454IkhEoxkKmc95+cQbvn44K1dhS+OnGk6FWnBm+mmnytLkAXE0AlZIlzu5LHKTZQ1fur3ou4V/H8qngcxU+8gOf01UUPLEd85LIa1F6sluugF+aSUHv9F/U18N4XjgCI513HqweGlfIA+7RKjaPZlNo5tSwX9VUjEnCkzXOm40VR6VgJqbYxZv0sC95ZlJxvIov6Gljc14jeufDkUkJHRXueL9HdE7XbL62zsxk5fdTwTrFmJeCd1CUbwmHr1UTkwvviYytRgiDsiqJPlfhBKOLkdfoaTqIsgLwGdAnr5jZCltC6iFxU9s46WELlpgaajRR7p6j9axXpU6ZCV4r0rVzUhz963eUkNWAjBJR0TGWftK3R6EoEPpnnEOX0qSSnvh+QrsM69s13/s0P8MbP3J3ZT9NJwjvFddnKjwO8c8HoIxQqxaDlxpvBgZDFb8vKobyfVBI5Yb0O9s4YxiD+Jidd02X4+WvPw9++/erKfci5BGKBUyN9FBNVTe6fmO0kvhee7kVhsesq+jNjPP+CwzsDUsILtZ/I6yst/NRGn1BquNeffqmJjOTUGKZVzlTlpY46fercUJUAWRZXrEMoU5K3e3Gxadv70BmLfpA26joK454t2rshsR/qoKM2smywiVddsTHKB4mA+0Tv/ML1w/jIiy8EkA2rnSdak8UzEd3I74vKQaLCO7tePXMeiOupSehO0pw+lfihW1dOnwLvlPucyoj+VpFmqE+ojj4qUYuz12FcytfsB0EM72wm4Z0271+d26JN20LpqqikR0DayWfr8DWJ9NnqReI+UkQuAS3MXnUqyJLVT1NiPM7iZKgitsb4k0HO/jt4Ekm0CVnDOx10QqWvWxNkDQgZntR8KNKcvuTipi5ADcfB+1/4FDxj64rKfcjz2Nd4soGk16equEoy8KmZpNEnFsDNoXF+YHzWqp+eH5Bct05kQg953aeGd4qIdc8LUIf9qhJU1BfpU+GdtMoTr9OX3DjncyJ9lY0+Cdbb7fkRVEWXY1JGdBuy56fLHIg1ZiAy+uw24YbrpHL6qPTZ+z78fHziVZdGzp64PABN+wBw3QW8HmrWYxCQNdsIk+ockdcxqmhcml2RPo9XCI/0IVE7kTKnT2U+7NYI75QhnVl14WyFl4aor04fg1qcPQnjphB5K/T8IEXkEkf6qr8jOWoUAJityegTIq+bag63bfmk2CCrL9KXReTi+QEttDenuHqm0Sezd7rFBrCpnP1xvgWjj1QibzAJkUucGwHQJkULEfkqgMzeWUedvqQ3Pu7ffgrJa0HPD/BXd+3Ha2+9K3EORf05NdL3vX3jie/FrZy3chEAYM/x6Ur9OGHScc+rl8hFFxmhh3fytts9r6ZIn2L01TCGAYApzcXsnTTt63L68iJ9w/3V4J2yctbz/cgbqssxKSO6Ddnz0+2pnnNbx2vTYdE6GSmaNZVskKNKVKLC31WZJ8qzFtH8uGSDVXP6PjQ5fXVBoXjEWoZ30uf0yYZxXTl9zYaDe/adwsUf+SoAe1r5LGm4gowodFYRL8UOS0IV62AJTeT0BUG0BvcTRvrUn86HsOsBYlhv9FwSRp8C76wh0qc62cj6SBF21Q/vjL5T7mFkVV/i2sTvARqjr6YpekZlgb2TUKi8wTKRi1jc6thA5cnEFXNGGr6OJxv/7AcBrrtgFfaemMH+k7PWSp8qnu/jQ194MHWcpMitoph/4J8fSHwvNqVLNy3B0sEm3nnd+ZX6EUomV5rqi/TpvLHk8M6wvbmOV4vXP/I0hq+lLmr1NLyTNurjanL66oj0MUk563gBBlv8Ocl5nVVEH+kLUkqGuCdh9Nmy+TYlluO62DvdCEpID40rym1s9zz0NRxr5VmswzF7Z9yhKNliK65yL/Xm9CmGOIsVaYo+nXANvvmvv481w/146WWcCIycvTO8VgHlVBl86foJc/rkHEhCkdMFAD6+qF+9fM2eHxeaV4lc7HL64t8yxDWMB4kjfZGTDfF87KiRPuucvrBwuq+HDwP2a1nkTKqRvVP0k2WwqXl6H7h2DbZv25YYLyqRlZ2c/VbfQqSPUGjhnUllpo4NVK5f0u765BESYaj2JMXMZQxLB3ikgmISJnMJ9OfUQeSiinjng60G7v/w8/HcC9dU6sdlIbzTqw8eJW/SSaOPHt4J8M2zjvzEqOh0zeydaSIX3g9lcXbVAFLZO3dsGI7+PVSRTEDO6ev2/HheWMI7dTBNTypzIETkXEbwzkq9xdJw2Blg72SJe6HUl4tgte2uT0IeotKee36AkZAYjIogLIpa+nGkrz724ZiOHgjr9EXwTvu5L9bdLz94FLd9d1/k5CGP9Cn7UlFdtaoicl/rqJMJJNMFAFGnjbYP2bBIwDtbyTp9NvuMatTP1hTpE9NCPLL5rp9aK22d/GIMy0NKdSpY1+lTnPpCfEL2TkC/Pwq5Z9+p6N/PuWAVVgw2sHZJf+IctWSNjVAHKp4IWTD6CEV4bmyVQblOn1jc6oF3cijhN3aO4dPf3kseIRFGRDf0+o9NtbFkoCkpIfabnDwHszZNmjyPpNE33N/Am64+N/pMZQA44TupFx6VZlIE6iFyAUShZvp7UfOI2mc40kdJ5JJi71Q8v7e+6cro31Xfkxzh7Xo+WhG8M4z0VWo1A94pwe+EqDl91pE+N14n64jEifY8H+RELnJbWbmN812PxNBQI4peEGDr6kW4+wM34K3P3GLdPhDfizC+udOq3pzkRKQv/I5iCVP3i7py+tS0A6GU/+mbrsC//MIzyfrhdeZiw6KOOZLM6aPv4/DEfNy+n03kYrPPyO83kNrsp470KfuWrk4nRZ0+IKkTdRX9iKoP1RkunPtUkgfv/NHB09G//+KtV2l1vYYBk6mp/DgYfQvwTkKhyvuQSzYIw6iWSAlj8ALgLX/xvbDfeiI9Pc/HoYk5HJ9q47JzluLdz92OP75jNy7duNS6jySRi3VzmaISucx3fQz2xZsBlU0TEbnUZCgByU1aXiSp4aQtaTzV4fV3z5DRp+5fMZELTfuu4/Bi5n7sIVUjfVWje7LI773nB9H8jO6PEN7p+2mjjzqnT+QqAfE7oZ4z3Nnjk0H3ZYlz+vTfz3c9kkifmjsYBHzurBnuz/lVOVEZQr2ac/qyiVxo8rdliXL6CEnOgCRDoy9BFndsWIINSwfo+nF57quIdNRSsiFRnJ2WyANAAv7Y8wN0UyUb7HP61Pc7V1tOH/8rxq8w+mTnny3qSpfTp0b6qqYJCMky+rjRb9V0QpycSJ/R7ykjfQvwzgWRhcqT1nQZuj3hked/66jZ5oQKjRBqxVwYEV3Px65jPHfkwnXDOGfFIP7nyy8hMTKSJRviezmPuMSFHOnz/AAdz0/U2qLybIrIAs+JqctTLsE7petu1WT0AzXBkxV4SV2ECyoxU0zkQhTp03gi1UjfUMvF/33HM/C7r7q0cj9ycfZOL47EXBrWyzxnxWCldvVELuni7CLvhoq9U470RTB44jHMnTD11B5TyU9Ume/6kVJrIzp4J73iz/8mirNTa/5SX4k6fYjfD1X+tiwxgzZxTp+0PnZ9P4rEUK+VguW2DoIV3l5yDAc1RPp+/trz8JJLeW6lFwTwUpE+e/bOFLyz66HpMnIETORjCx/Z9Dw3+oYHYoIua+ilk95TugoO07bmYJReoRK5+LTwTocVO/Tfe+NI5ne2OX1J6HKlJp5UsmD0EUoM77RrR8Yw10VQAKTD5raso6oIQ7XrBZFCTk1/LM/jhMeJ+HElahr20lTOdKQefNHs+n5tnnJ5k5YX50WWnj9VErTJdbB3KrXBOjUpZ+rUoyZyUYtnA+lIX8N18LTNy/HKKzZW7idAgEMTc/j+/lMJeOcbrz4XX333T+Dq8yqWTtFshHJBcyFCMRuMiFyqdSdEEFQAiDz/dUT65KglZeuRxz8r0tcjivSl2DtplTIgNiR+eGACAFcwqZVluS9fMsQZi5VbCkMzDe8Moy/k8M64H5mchNzoc7gTOa7TR9o8J4hKIG7onQqDrQZ+9tlbovbj4uxJIhcbPUl+v0HAI30U80+VCNaNJLxzWNp/qYw+XekRIbYENWJPVyNoXh3snbLhpfS3clEfbr5ua8516iOSptKRntuPA7xzwegjFCp4J5MGuZiodTA5ckMm/kwdupbhnX5Nxqt8xUkDllZkA1kor/3SJkFWsy1893Wz3+k840sGqpUCyBJ5E62VvVMhcqk7p++hQzyPgMpjrqO+ljfoez5wA0k/N128DgDw2PGZhFLOGLMi9MjO6Usea6vwzso9chEEFe2eFyEWqKPjYt7HOX10beuKNAt5/Z/dhW/sPJ5AE1QVV+knIFbKgHgu/u7XHgXA5yL1PBTCPf9BIs+S0jmaldNHbcTKY7XrBbXl77caDrpS3UzqPdhhLKE7eDUQuYh+gBDeqRC5UOf0iTapmTuBtLMnMvqk/de2Rq94Dsk6kMl1hooVWM2Vq4O9Uzb01NzEoq5sSzbI0OL/7+GdjLF9jLEHGGP3M8buDY8tZ4z9B2NsV/h3mXT++xljuxljOxljL5COXxG2s5sx9oeMGn9whkTehGzElZTyOtk781iRKER4q7peECm0dUF+AMXoq0GpiY2+dNFkMnhnuMB1vaCW6BiQJPRw6jT6EpG++pQA8doFxTZ5nT7l0j9/3yEASaPfRiJPpKg5pzBfLh1skfTzlHXDUftdLyBTYo1LNvSStbVs156G6+De/eO44Ne/gm8+egJADQqtk4SqnqmSDd997CQAmjwyFQZdh1Iu2EBFnlCdRp/YtwIpd6xOo08ofdQ57/Lz6Xl+7Lgg7kew3NYF7xTpAhOzHWx+35dw+yNj5JFkIH4uPP9RrdMXch8QGn2zHY88nw+QiLMio68LILn/2kf6+L0kcvqISQ+EepLO6aNn75QdoqrxWvTObSN9Xbm/s9/mI4n0PScIgsuCIBD0cu8DcHsQBNsA3B5+BmPsQgCvBXARgBsBfIoxJmbUHwP4OQDbwv9vJLiuMy5Uyf6yUh4VZ68B6pdX/4RChFLZ9f3aYKoJeKdUH4j6aTmM4Y5HjyMIgsi4kI0+qv7EAuf5PrmSIUQm9JBfx5IBGuNCSPMMGX1iQ3jkyCRWDLWwdJDWeGUSUYQsFOQqgJzTJ6CKyc2Z6tnJdQ27Hh182DinTynZYLuByjmo/xwa4vXk9KWhqhSi5sHphAKqrCpnnk9f2qK/6eJVV2yM8oQ6hE4FVRgTRl+87lLuL6qDVcC7yEsaSf3I0as6cuu7nl8rvNMPAuwKaz4+fmqWvA9AitgE8bMSjrco0kdYsmG63cNgi57rUDybCN6pyemznTt69AjtGtbQGJYA31/qZO9Ujb4iB1a879lH+uoMkpwpqYO986UArgv//VkA3wDw3vD43wVB0AawlzG2G8BVjLF9AIaDILgTABhjfwngZQC+XMO11SpU8E65JlyvJsgHALis3kEcwzuD2ow+JIhc6oFhAcDDRyYBAP/+wFFsXskJL2SSBaoomVjgel59da5kQg95vaaO9DVrZu+MlObwJn7w+DguP2cZuSeb98VSieBURp+a06duzlSPLoqKBAHP6SNSYnVLiC6nr93jfdpuwkIGNAoZeXF2x4Hnx/mVlBEylhPpE0JJ5CKetx8EJKUNVBnqa0RQtU6PptyETgQ8PUCQmjsU7z9doqUeeKfcXk/K6aPvh7NC1lnWxA+SkbLxWR69+sybryRzMDhSxKbn+3AdFr1voZhbwTuVubb/5Ay2raapYymLePxi3k+3+foi5/RZF2ePnpVUsiGrkHHlPvhfdV+kJopS2Ts7yn0UDWcdqU0ZkY0+KtTNEym2WksA4GuMsQDAnwZBcCuANUEQHAGAIAiOMMZWh+duAHCX9NuD4bFu+G/1eEoYYz8HHhHE2rVrMTo6WumiT5w4Ufm3eXL0GM/12b17N473V/fQTk1NotPpYnR0FMeOnwIDsOvRnURXGUt7fh5+N54x3W6P/LkwAEfHjgOzfKjt27sHc8fpjItuL65xs//xA5ECGvS6+L2f3IDxufieKN77Y/sPoD3BJ/6Jo0fwzqevxHnLW3iU6P30uh1MnJ7EXLuN2empWsbp1ORpHJqYw+b3fQmXr4upwcePHcRo9zhZP4fH4tpKp04ex+houh6RjUyFm+WRo0cxOjqPsdNzuHhlQ/vMbN8904Sljjy+F+0T9obf8THuUNi5azdODTWi+xKycyfN2JqY5+0eOnyEw7HGT5KMr5OnTqWOHTl2DLtbU9Hn0dFRHDl2Ak0HOHr4MABgaspufPfmplLH9j32GE5Ja6/te5+bncHMnIdjY3xePProTjKo/UyHKxNHjx3D6Ghbe057dtr6He2f6AAADhw8hNHmaXR7PZyemCBfW+anJzDT7uGRRx5Bu+th6vR4LevX9NQk5tsdHD9xEkCA0dFR7D/Ao0xzM9PW6/3YseS4OnTkGABg757d0TGK+zo9Ph79e+eju3F0jPf76M5RUsfV7PQ0Zufa2P84V7Me378ffTNH6dqfncHsnIf9+/Ynjo+OjmIDAxAAo6PpNaKsHJ7khuSBQ4dxbLwDl/G1seEAU3N8/uzbuxe9U61K737syFz075nZOewfb+OKtU3yMXz0KF/vd+/ejalFTRw6yp/N/DQ/TqHvHT4yCwDYs3c/hubG0PUC3HLHscQ5tvd18DRfVw4ePITRPn7twjg7dYpmbwGAbqeN05NB1N7xmaQe4fW6uXP+yGG+Njz22F5goq90/2L9vGh1P162hdWypp1JsdVYnhkEweHQsPsPxlje09CtYkHO8fRBblTeCgA7duwIRkayaVrzZHR0FFV/myd3ntwL4AQu2L7NyiOw7ME2nLEuRkZGsGTfKBruRC3Xu+iO8fBB88Wu0WiQ99N092J46XKsXjEI4Di2bzsf65bQ1SDatuYU7t3PN8916zfgnOWTePzULD7ztmtSFPR27/0xAMCObeeG5AqHcP555+KNVRkPM2SgfwxDiwbBJjwsX7a0lve+7MEOAK5gjJ7oRMcvvXA7aX2o3uLT+H/tnXm0XFWd77+7hjvmZrhj5olIbgbCEAggkyBDFJBu0degAo6ojfJQfD7FZtli+xrXetpt67MVFaG1tdWFLlAQtXFARUEEmYNAEkhISEhCkps71bTfH+fsql3n1s29VbV/59Y59/tZKytV51btferss/fZvxnw3O7m9fWhv3+5s7YB4OBIFsBWdPf0or9/OfJ6K/p6uipes3rnfCKxZUze6GPW9jtx/3ns0DYAL2PpsuVY1NmGlwdGAWzF1WetwLFL5qB/Ze9ETUyK/UMZAFsxq7MHwF7M6+tFf/+Kutv94JwF+NWW+9CSTmLPIW/z1dXdg6XL+gBsAwD09/dDPfIXzG7P4LKzj8XWkSfxgbNWoLej9lpxC54tAH8t36D39x+JmS0lpVK94z7r/gEMFUbQ3d0NYB9W9fc7syYOjuYAbEFPTw/6+4PZ57z1Zn5PZ91rQPrlQwC2Ye68eejvXwCo59HVWX+7QRa/9CwKj+7HshVHIlvYjHm9PejvX+m0DwCY/cgIkvv3obOzC4nEQfT392NzdieAXZg9a2bxd9U69n8d3QFgd/H9nK5uAHuxdlU/gC0A4OTazdv1HABvw7946TLM3rcdqcR+rFq1qu62bTofHYF6ZR/mL1gA4CUsX74U/fNnOWu/448DGNYjmL9oMWz9vev7q33fEIAX0Nc3F6/oATSlDqG/vx+p5BYU/EilI1eswOKutprGfrB1HwBPIbVvRCNXAI4/cjH6+xc5/R2P+uv98uVHYFFnG+a88DSUegW93V0ADiCdTNR97Q407QWwEwsWLkL/im58/bebcd8LgwCAzvYmXHLCorr7aNs7BGAbeufOQ3+/l1XasyZuRl9PD/r7X1VX+4bWlt1oa28rnq93H5QUDM1NTYed89sKuwDswuIlS9G/sPr7Prv9AIBt+NDGtThmdV+tP6NhqMuGrLXe4f+/G8CPAGwAsEspNQ8A/P/N6rkdgD17FsKbYdv918HjkaMYKF2ng2ewZIOUm5+dnEQKL8NeQSyRy9cuP75Yo8WkvX7T+oU11xwTeYKzAAAgAElEQVSbiEIBFWP6XJHwM6p6xbOFYvqsWW+P/1yHxZqB8Or0Geuul5FSKg5y7DFXQf4mDqXk3ukJl/Nnt+JMRwIfUHInNCVHXLl3HtnXgcc/dR4WzikpDColctm8ZxBLu9uQTiZww0Vr6xL4gMruta7vM+NqL1GyYbxELvZ1c+FCHKwHWBB6pph4voPDWRS0ezdFg1LeOqy1Lo5HzqF7Z/AZFUzk8u5Tl9Xdh9ee7d5ZQE6ooH064dXpk3Pv9NbgTM6t+2AQOyFHLl8ovk8nEsW6pklHMX0DFTJquiJ4/XMFjXQiUdxPuIjkCdZ+PThSso79+1uPw0cPU9dushRLJlkLmHmGuU7kUpa9M+DeOVGsYqX4xmow5UAk9ntTQc23l1KqXSnVYV4DOBfA4wDuAHCF/7ErANzuv74DwCVKqWal1DJ4CVse8F1BB5RSJ/lZOy+3vhMptKOgPhOoDvgbWaEsjsmEGlMPzDXpZKIsXsH1ZmNOexPOWe1tjHN+0XSJzUZvR3Oxj2LJBgfxNkGSCW9zlhMU9m3XobzWaE4l8NQNG533VxbTJ1FyxArsN7WbpDaalTZKrlyw7FTkgBXH6/i3mPE196/ra2XfP8EMpEs/die2vHwIy7rbnfXXUaGupPOSDb5izK4J54pSbE/5ZsSOPXEh9JVSlpv+3CfzAIB23+ptPC/EsncqBa29+G1zDY0CpntG9e5bY9qvULIhnVRQSmHrjefjHy5YXXcfQPn6mMt7cdwSsfumtIm5rVwLfaZuouuYsSD25j1rKUWTSVVUZLkq2SBZH9m0WEzW5wuwpjyEC0X8mNqv1hrj6rlSTORitW1eurxuweydRsg7YalXGGB0AmVDKRa0tvuzlK09HhXu6nmi9AH4kb/xSQH4jtb6bqXUnwB8Xyn1LgAvAHgzAGitn1BKfR/AkwByAK7SWhuJ4/0AbgHQCi+BS+SSuNjUXZzdX0QB39InZL0ICn0SNr90UiGbl8ve6bVZKhKayRXKsvq54tvvPhHn/su9yBUK2HXQc2HrcbDBCJLLa/xyk2ccl9gAAOUbvnxBY3FnW1mheVdIW/rsdO0m46WYdUGkVY9gFrTSb3GclEQZoS/vty8n9OUqJHI5OJLD4k53FvgwLH1mw1FKEOWu/aCl2mBbTNodzMugRj7vOKW6ob3ZO9e//8+HAMgJfSZxiLZKT5y9qhf/9Ddr8ab1Cyf49sQE7yGpQvPBRC65gruMusF+MtYz2PXQe5Y+LW7pM/dswbf0mXUzlUg4yXxqW/okS2WZeWGul7HwGqHPxRpjrk2l5CWuPDzMumL3kS3Un1BnTD+B7J1GuVDMFJw7vOEiqPSqlpKSPx6WvpqFPq31ZgBHVzi+F0DFasJa688A+EyF4w8CWFvruTQKrmo5mZIN9z23B79++mWxzX9SqeINLYW3IAsLfWVFW2Ue0EUtY0Hjhb2D6GhOobPdfSanAcsNQ7I4e/l7kW7KNn0S9RnNvaStFN6u06qHQTELWsC90/V9bJYRoxmVqAdmKGgdDIEEgLJ4u3qpJPS5FmaMa5FdE84VweyzBi9W1cOJpS+YvbPgvjg7MPZcxYQ+3xuiYJVsUErhbSctcdK+/YxqSiWQyYUg9OV9906BxThdtPS5d70DSiWAwrL05XyvDrN+2coxl9k7AZn9SrC2oGfhVSX3TgddBmvT2SuMq3XfrCH2+mUEWZdzP1hP2giZM/xnSTCbZ6XvA7VbUIdp6SPj4SruQ/majbd87X4A7mOtDIlEyS1CinRKCZdsKPnxf+7nT2MokxfZbBjBO5vX2Lp3CEu620RKA9jXx7V7nyEo9Em5kdqaMRdFpoPYGrysUAHlMCil7g+WaXG/OQPcx/QZ7PsoX9DQFXwHXFqUOxyVzDgcSWXqZroXlCrF9N312M6ipQxwG9NnXKS8kg3u50kwxlXC4wIouRNq7b7QOFB+H3c0p8RqDtqb72y+3Hrlth8T0+e9lyrZMNHmu+5+ymL6SgKyPV4ui7PX295E/ZhYMS+UI1FMCuZinQnGidtCk6tnZKU6fUWhz+F8CdbpK1n6JucOW6vQN5rL441fvg9P7PAyk7qomdoIxEN0bRBc1YhLJlSZy4+EywfgTabhjLDQl0ggW9DFDYfrBw5Q2hzvPOCVCJB8QOcLBWx/ZQiL5sgkirE3ylKWvuAQSIwJUL4JdJX0xMaOiSpax4SsC8HHxQPXVXRmqIlkQOiTsvSZfkZDiOnL67HF2QG394GrOomHI5FQyOe1SJKNSjF9v9q0u+wzLsYoYWnktbGQSazDgXOVc+/0rpmGe+srUP68bUkn/ZqWAm5+1vXKF+TiuNMJhayleHXehT8eE8VW1Yu9TnqusN71Kw8jqP2eq3S/Slr6zDqcL3gxo61NXv8unsel2nRjx8SVYsE0U0kgc/lsUapcMWb6aE2nxvRfifHqCU7EC3uHigIfEB/3Tgp9DinlcanfvdO+kaU2/8mEwoi1UEvUaS9m7xSyXgBjF0kZS5/XRzavsXcwg54O9/F8QHlVAClhPyxLnz0OLkobBFFKFTeAGSFBaTx6HVrfg5pI46rq3r2z3NIn6d6Zz4+N6QPcWvpMDJkkxtIn4TaulIJS5dk6g/s9F13a95d5rEi4dwYtCE1JmfEx7oRay8Ta9llz2yiUJJ4p9v2ULXghEBKeCkY4MgKAhKUPISRysYtsZy1Lnz1/6nmWVbJOyQh9vqXPdxvM5T1h3wgxLoan6ArrP0skjAjFdUVXsPS5du+09sNXf/dhACjGQE5kwLOVXtXw4v7hsvdxce+Mx69oEIyVpt5Jmwj4MIu5+YVQsiGd9GP6TMkGgUU0KEhKPjhHsnnsH8qiq11I6NPywn6wWSmhz0YiUQxgx5PIxvRJKEQMxQQFVjY3QEboTyggI6SACVr6Ki0tLi19Hc3u06kHSSa9EipSscLGNc5+X9a/A628HfP8we8+5Ldbd7NjCF4fSaVVKc7SfR92rdK8ZJx4IHvnaFZGuDT9mA25RGxqIYRELsXYVD8bePB3AfWtaZX2WaIxfZZ7ZzqZKD4jXcRcFpPF+M8Seyl29Yys5DY5KiT02YLlnkNebeGZrZNTJB8uqc3h2LF/pOx9XCx9jOlziKu03mYjaxBz8xNptZxU0susVShoJJRQDEYyKPRJlFLw+vAKZwNdM9wncQECFt6QYvqk3DttJNw7gVJdQymXyEq4LGAPjM0uli0YS59MvJLJduZ6QzO2ZEM8LH0FXR5D5BKzYTYEp+LKvo76+7AS+Nz12Et+P/LKNymFYkJ54Q9ZIcuYvbnzhBmZmD57853LFzCUzaNVwCPClHwyiUNcz/uEUti6dwi33LcVAPCD951cjLF2STBhm9nMlylNHP82iTlfjOkrunfqspINLp7Hbb7VcMgP3ym39DkS+oJlIVASMl0KfUbJY1jc2YaRbB5HL5w9ue9XcEOdDI+9uL/sfVheRNLE41c0CMbNoO7i7AHtr5QlppK/t2vSCVUsPCtWZD6wSEpoZMzm4qWDnvbHRT2oSoQi7Ifk3mnTJmTpM5vyjFBGyiDXX7Aav//YWU7bDD6USklpZCxLxWvlvKZdeQr6YHF2IHoxfcmE556eFUrmoQJrvT03b3nHCZg7q343YrOJtC0iIrFjgesj5e5nBOWRbF4sucLCOZ5ix9Sfk4gVtjffuYLGSCaPVgEXMrMmfuaupwC4T0hhbtnn9w4BAI5eOBuvXtHttA/AE+iUMiUbSgJ/rUW3J9WngHKkGNPnK988AVYV10YXfRrl2g8e3IaRbL48V4Br905rATsw5GUedprIJWDpG8nmcebK3kkLlsHsxZNhKJPDDx7cXt2JRgQKfQ4pZceqr52g9lfK4pPJBSeB+8UzlfSDyK2aSq4JbmBkiqb7Qt8BI/TJW/qkhLEx7p0hWPqkhL6E2QQUhN07IZQEAWMTueQKMkIZ4N1TLmpaVWJMyYYKy4nL2M5K2fZcY9wvc36yBfftB2L6rL+50pabcbYTbUiXIAj25xIv/MFLhCEVZ/Pbj56JK05egrxg7dey4uyFAoayOZHY5+D+wfU1Cz7XJTMomxjbXMGy9AmGqEgoEVtS5SUbjKuq6cvFsmzGYNNLA7jxp5vKtnZpR88Vo6D6xu+2IO+73L7jlj8BcG/ps/VHw9k8WpuSk17D7FIfk2Uok0euoHHBunlVnWsUoNDnEHNP1es6o1R59k4pQ0wolj4T0yfkHgWM3bxKaH/NQnlg2NNkzWiRsTLY65KUD/kY984QVgGxmL5EKdEGIO+CISGIB2P6SolcJIQMVbxWku6dOd+lO4hLS5+Ei2KQZMJKES8W01fZvdNZEeUKlj4JYSm4QXbhmloJpTzrzkg2L7ZGKqU84VIwpq8skUteYyiTF3GDzwcsrq4VYyYhiUFyXiYTakydPklLn4RCtDmQyCXrl2zontGM/rkd+OzF6+ruwx6DZ3cfEqnTZzg0msPNv9tSdh+4vMeCirHhTB5tTclJC5ZmnuWq8Dwwz0gpj66phDF9DikmcqmzneBmTEqTZTI7SdKcSmLfYMaz9AkJfUFhUmJDY1xLBjNe8XQptyJ7rMXi4KYgkYuEBhvwNrRDo3nc+ehOAPJ1+iQ2NMU6asLF2b2+Spt/50KfdW1e2DeEQ6O5MZ9paYqWnjGZSCDvZ4eVyjw8XiIXV+NvxjmTL23KJNYW24Jw5enLcfSiycXcVEtSKWitMZLLi1p77cytEq7E5cXZffdOAeXYvsFM8XUqoZwrL4wiNAxMJkc7xtY8M7/ytuNE+nONuWdHfSEpX/DWlnQygbuvOd15fyPZfJnQJLGObd07WCb0uXx2Ja2Eg9m8FyrUmp68pc98rhp386zvBbeo0yvN9Z7TllVzyg0NhT6HuEvkUv+5TIZgMdVTBPzwm9MJjOYKKBTkLH3BjbiUQJZOJDA0mvf7kNls2FpLKetY8HqFkchFLJ4zofC9B7cV30vV6TOIuHeOyd7pZ9cUSuQyWozpcyz0Wed7/5Z9uH/LvjGfkXK/lSKZMDFEMtYeFUzkYv3NlYuUGeZyS5+A0JcqnX2vUEkbwLh7ad+9Uy6Zj8miLVWcfax7p4ylb68l9Elcr8EKyh0pkqqUuMsIr0ZpIiGYS7jYK6XQnEoUy2VJJYkyjOTyRe8R079rChpl5b9cunfOaE4VFQsmMU1rFZa+YpbXKowcZm/c09GMTZ/eGJvMnQDdO51SKs5e36QKWsSkEhbYmo+vXrbeiVtBkOZUAqO5vGgil0p9SpBMqKKlT6rwcD4US1/4iVykGFOjUbhkg4SAPDZ7p2wiFyn3zslsXMJwyXRJUpXcyaTcbW3vNPv6uFrHTJvftxITiLh3WhtkqfUR8OZFQXsbQFGhTykUfAFD4pkStPQZtzXXnNXfW3wt8TsGR/MTf8gRXgmV8pqGRmkisfZLhT60pJNFS5/tqirBSLYgXkMxXyiUWfpc3mcL57Rh98AoRrL5Yh9tTalJr8fmvqgmo6y5Xk1JFSuBD6DQ5xSvblD97QQ3ljOEhD7bvfPIvg6Rm7s5lcBotoCCYCKXIFKTNJVUxeBrqU2NrfWXTH5iI5nI5cyVPWJtA2N/i3RMn4R8HGr2zoSc0He4+f2e05bh9qtOcdofIJ/MxSjgfvvMHqGYvvGzys1uk0kWBchmOPZeCwp9vkVxYDQrWjA5mfA8L4Yz+WIcltv2S9frhp88idFcQcS747Wr+vC6tXMByMwX48b93tOX4zvvPtF5+zalRC66mC3YrJsiNQ6FpL7mVALDttAnGFifzRfEQ3lyBV0e0+dwLBZ1epl0d+wftix9iUkL+cWYvipyWIRZAips6N7pEK3d1L4LbsKlkobY2h+pWKjmVBLDmTwyOVkXhvI+ZSaqff5SfYSSyCUwDlKxlgDwjStOqCpVcrUEBRfpmD4RS1/QvbMg6N6pFIaE3DsP1957TluO3pn1lx8I8tD152D/cBan3PhL520DwP6hUrySxL2lAolc7DV5ptC6D8isLbaVUtKN17Q9MJIrZkKUwLiRvnRwBPNnua3NadoPIuXdYcZbxL3T93558/ELsaJXJnmPwa7Lmg6smxJCn5QXzNxZLdjpZwLPCcUL28hb+nRRIQ64FZYWzvHi6ra9MlzMmt6aTtUQ0zf5fUichb74/aIpREM7cV8KNtEhZOmzY/qkHtLNqQQGRnO47aHtosKFjZylz3JfErpeZe6dYjF95e+fePGASD+A95CWKjkCVEoXLm3pk3TvDGTvjFjJhuRhhKKZrWmnfRnam1NYMNv9htzwzO6B4msJbbxn6Su9tzdnLudNR0CAlBSWANnY2rQl9DULu3cC3rw0dftcUmkpkfLuMEpKCcHIxPR1tMjMcZtUwnPv9LLpGqHP+5tELL+U0HdEzwxsfnkQQKk4uxSZXGFM/gbX5Aq66K4KuL3POts9QW//UKZoTaymZINR1mWqcO805cwo9JHDorUb96/gxrLPQYHeStgmf6mb27aIxcXS15RKhBKbJOfeWX7urwyFl33NNcF9uNR9bGaKhBeOuZdKJRuMllHCslRS9khm77Q5YemcUGrqSTDPsvBIWfp0maVPxir+8PXnlL2XUigZJOraGcwczxe0sHtn6TcYa4NLejua8fZXLy07JqWwNJtwCSHZ3LNBxYIEpmabFwdX7t4pscbICX3teHH/MAZGssgIJYma63tWZHJy7p0PXPdaLJzTinzey6ZrcKkUN2vVaLZQTA7W1d406fVYKYWUFdYwGYoxfalw9qxhEs0ncYNS0IBy4OBpLzQJBbz71OV1t1kJu26JlGbWfsiEZemT0v4azWJzSNqfsEo2DGfDC8R3TVDQkLpmZmOeFLK+AXb2ThmhLNhmWIlcrr9gdeQSuBg+9YY1xddSJTTsUBMpjbx0ge4gsolcbDd7OeHVnh99M91nI1VK4R+t+wsAZglZxI1AJKl8kVp7bZrTCQxlcmXunYYouXf2dngC2VH/+HNsfnlQpJ9ffPh0vOHo+RjNySVy6Z3ZgpktaT+mTyZcyNxXw9k8fr3pZRy1YBbWzJ9Z1TPF1IueLHF272RMn0M03AT12fP/fWccIfYAzZRZ+qRi+krnLpkwZLw+XWLcuySC+ishVtA8ohvwSgR/i9Q1M9rk4EbDBWOzd3qZ6SQEJXsOunZXHE8gDuN+e+C614ooldqbU1jW3Y4tewbFirNrq3RyNRnm6kHcvVMykYvVtqTwat+2YWXwm9Muk7zHCMcSz8Z/+buj8cOHXgxFsbO0y5uLmVxhzL7IhXXptve/Gn/dNYCP//AxAHLeScE9hET8dkdLGgvmtGI0lxd170z5GVXtRC4u74UWq5h9Jl/A7LZ01e2nk4oxfT7x+0VTiaNELvbmRdIlsiyRi2CWKkPUSzYU3TtDWgik+gkumN0z5GpqSRPc6MtlVfX+F9n4+00WrOydUg+bhKSlb5yNSxg6ht6ZLWL3sVGISQj8weLsZk2++LiFzvuykXbvDE/oE7T02QoS4QRRhtnClj6J/cTfHrsQ33qXbNZOw3JfATOSy48V+hys/euXzMGlGxYX30vtWYLPdjHhMpVA1i8HIkUyocosfXdfc5rT9o2CajibRzZfqGlf1JSqztJnDCJxFPpo6XOIhhuttt2GhDuZ4bMXr8N1P3oMX7jkGDHXS3vShCX0SWkczflLJg+wkfodZhg2LO3EjRcfJZZkIwzCihOV7M/cV3kre6fU77KbDatkQ9Qty2YzKbHxDxZnz+Y11i+Zg8/9j6Od92UjHWMpGd9le6VIuhTa8yOszZ9UmY6wvFOkWdrdjtGcqaFWPvYiMX1Ca1dwPKT2eWbt2juYEWkfMMl1SiUb+jrc5qBIJBSa/BIXdgKf6s6xSvfO4j0Wj3ljQ6HPIYWCmzp9YWkYN66di41+/R4p8taGJowN+lVnHiHWtnnwh7EQ3PrODWJtm014KqmwvGeGWD9hIBk7VAmpMgqAnb1TztK3dc9QqV/nQt94xyMu9PljIRPTV16c3UvqIH+9pF3xJL0H7PhzSeHSvkbhCX0yCjjJezhM7JrFwbVfxgtDytJXLrBKlswCgD0DoyLtA5alz0/kImF9b00nMZotIFso1DTO6dTh3TvvfvwlXPv9v+DP15+DlnSy5N4Zw0QuFPocouHGvVMJauPDxs4aFUYil8tOWirWthkLSUHjq5etx+1/eRFnHClX1NwMQ9Q3AED4mjiRtP3+gHztt5uxcE6rqNBnx3a4VsKMlxI74ktYcSzEErkE6vTNECrRY/jxB04VbR8A5ggWlrfnvOS1svfhYQjiXj8y837bK8MAgFXzZoq0Hxb2s9e8vvPqU/H7Z/dM1SnVRHAPkSvIZNc0/Qz4ZTUkSCU8K9yB4SySCSVicW1Ne/Weg+6dN77xKCzumjiz7kSJXP75p09hMJPHjv3DWN4zI9YxfRT6HKK1Gw1qmXtnxLXk9kQL47dIWka7/CB7Sdeo89bMxXlrZK2v5h4NayMjSdiLssQ1M/P9+b1DePs3/4Q3HrsglBgi1wolO2W3TVQzdxqaBOOhgjF9mVwB6TbZezoM7bVs9k5L6BO09E2Fe6cUxyyaDQA4f928KT6T+rDHwdxja+bPwpr5s6bqlGoiuId4wC9FIN3Pics6nfdhLH0PP78faxfMElHut6Qt906r/Uus+MvD0VRl9k7G9JFJ4RVnr7+dspINEVeT2yZ1GV1WOZIupEctnIV7Nu3GkGBQdBgkikJf9Be0sN07RcooBBYNL3un/O9y/VuGM/G09BWTYAiMSTCmL5MriMfbSVirw8RWvHQ0y8Uj28qKMEITbr/qFLG2L1g3D2ev6hNP4CNNmaUvwkrL4HPro+etFOnHXkuue30/rjzdffhLKqGQzRXw1M6DuOykJc7bBzyX0RE/kUst5cU8S9/EO9Bgrdw4xvTF7xdNIdpR9k57D2gX7o0i56zuLb7OCaYNNkhszAwnL+8CAHTNkHNdCgOzf5G8VmERvqVPLnunwcveGYKlz7EFbrx6j1G39JXcO2UsffYan8mPTUXvmrCTH7lmKix9YdzD7YKuqkqpyAt8QPkcDFvh5xJbGPvqZetxrpB3T3NZ/KuMgiSZUNg3mEEmV8CC2a0ifbQ2Jf3snbqmLMqp5OSKs7/71gfx7O5D+PYfnwcQD2+oILT0OURr7eThYD9sChEX+lb0duBTb1iDT97xhJjfuo3khubE5V343pUnYe4st9mpwsbconFY0KStIkFEErkE7tlcoRCKNca1F8HoOEJfxGWMYoIdiU15Qqmy4uyj2RAsfYLz/g8fP0usbYO92ZeN6Qv3xo2jVcE1zWWWvugKsfY9LDnf7X4OjcjE9aWSCrsGRgDIKcRbUp6lL5evMZFLMjFuzLnN1r1DOPvzvym+j3pOjUpQ6HOIV7Kh/nbsmL4QjGPimEUtjA26tOXnRN/aF2WM7C1VmzFMQs/eKZHIJejemdeRFMjHs/RFPXvnYMbbLM0RyKw4xr0zFEufXPvzZslo+m1sxZ5s9k6xpisSVi3AKFMppi+KlAt9csKr3fbq+TJJfJKJRDEDcY9Q1t7WpiReHsj5z8Ya6vQlE+M+nw5H1L1UKhHdWdOAFBxZ+sqFvuhLfWaBk8zoZoijZsY1xs02DpsMIxw1JROiMTEGyTp9ANDe5KWLjqLr7YVHzwcAzArUfYz6c/PQqLdZkKihFkzkMprNi24CgejPe3vTJ6lIDPtZEvVxCYNK2TujiD3HJWsoGpfG4xbPxikrukX6sJ+JXUJCn0nkki3UFvqQnqR753QgurOmAXEV02c/a+Jwn5rJJlV4FgBue/+r8d4zlou1HyfilJnKPPgv3bAIR/sZ6iSRrNMHAG3NKeQiaul7/VHzsPXG88fEdUTd0jc0aix9AkJfAozpqxL7+khq4sMW+uLgeSGN7QIbZXfY5pDcOwd9hdXizonLGtRKskzoE3LvTCcxOJqD1rXtWyZy74x4FFVV0L3TIRputNpllr4Y3I37BrMAZNyjDOuXzMH6JXPE2o8TuWINmmhv/oBSXEebcG0zg0wiFys1fEIhWyhgRjo+S3PkhT4/W6/E+uVZ+rw1vlDQyOZ1CDF90d0sA+Epq8J27aKlb2Ji496ZDMe987WrenHphsW49twjxfqwlUhBLw9XtKaTGPBjEmuZJ83pJEYnEdNnE/HH1rjEZ2fRALiq01eWyCWE5CfSLOtuBwBsEKgRQ6onToVHzXRrS4cT1C9tJUn6bihRt8bYRP2nHPItfRKeCspy78yYNOG09B2WsJRVYSdyicN6LE1YFjJpbEWf5O9oSSfxz288Sqx9oLRfTSaU2D3ckk4W1+FaLOKt6QRGqozpi/YqOT4U+hyitXZzo9junTGw9G1cOxe/vPYMLO+ZMdWnQlCqnRh1jT9QslqGlY5cOqtmKpHw3TujOzbBFSvqwfDnr5uH79z/gogWO2ElcjGaaPGYvogLfS2+gucdpywV7SfsKRj1cQmDsCx9n75oDTbvGRRr30Yypi8MzH0rKby2WkrdWpQ+relk1Ylcou6hMh4U+hziWfpctFPaNuVjYOkDQIGvgSgVHo3+opb150dzWJY+4WumlDc+URb6gkR9L3vDG9bgo+etFNlkenX6vNejOW9TIrWZPXd1H37+5K7IJ7tKJxN48obz0CIsHLc2hbs9ivq4hEF5cXa5NfKyk5eKtR0kjAR3kiR9RWiL4DPYVurWoqxuSScxnKnS0hfT6UihzyEaGsqBrc9O2NkSYRcG0piYeolxsvSFpSWXFvpyRStsdJ84OuCdEHVLXyqZEEtCZVv6MkVLn8y8/OJbjsW+wUzkxwMA2kIQyDpD3ozHYVykiUtMHwB8+a3HYVl3e+QVfOZZJWnps9uuxdLX4sf0FQq6Yn3aSpk94zofKfQ5RGs3Wm3bpfPvz1xRf4OEWMQppi8XcnBFmmQAAA7qSURBVCZS6Qx7uRiNjYEGjPFRViKXG3+6CYDc5qk5lQyljl5cmNMul3iM1Ia94Z8hWKMxDF5/1LypPgUnGAt1WJa+Wp6N5vsjufwYhdHdj7+EnQdGxnwnro+t+OwsGoCCo0QuJnnLeWv6RCcSmZ5kY5S907h3hvVbKmkJXbLjwAh2HBiJ9Nh88sI1OKKnvfg+rhpTF3iWPu/1Tx7dCSDaqejjRGd7tN3u4oi9lsxsoVDeCJiER2HF9NXioWS+P5Ida9F7YMu+it+Ja0wfny4O0WNSGNSG2QTQx59I8LaTlqCtKYlzVvdN9anUjbGMxW2uSCeMkeTkI7pwz7WvKb6P2dA4JaEUHt2+H/dv3ls8FvXEDnGhlQpXQibEPHsl3W1t40ctuQjMXK6UzMXIdp+8cHXZ8bg+t6JtH280HCVyMe6d1JATCfrnzsSTN2yc6tNwQt/MFgDhx99I0yHkunTPtWfgud2HcNTCWSLtVyKuGlMXJJTCSLaAv7vpj8VjmVw8kndFnbCev3defSqeezmcTJGEuMbE00vG1dsKmFwNyQ1bfPfOSslc9g1msGB2K/722AX41I+fLB6P63OLQp9Dbrx4XTE+ox6Me2fYdYIIiRofe10/jl08Gycf0TXVp+IUqWy3R/TMwBEhZdJVyl1G47hS6doMjGTDPxFSkTXzZ2LtfFkFyZr5s7BGuI+4cWQfs4E3Cknf8iaZGM4urL5j/3DV3zcJEYO1+m669zn86OEXsW7hrDGlct73miNqONPGh0KfQ1yZt02ZhrialwlxRUs6iYuOWTDVp+GcZd3tE3+owbnytOX46r2bI+2qKk3QmnTmyh5cePT8KTobEuTOq0+b6lMgAR66/hy63jYQYVj67Bj389bMrfr7JpHLM7sHsHaBp2DRWuP/3OUlz+psbyrbv2/69MbY5tOg0NeAGFthkpslQqYlK2JQ1/Jjr+vHtefK1LeLC/Y+qSmZwM1vP4Fu/YQcBibYaSxMNsxKZQ9ccVZ/L770lmOxcc3cuhK5fOh7j+BD33sEP7vm9LL7qHtGc1legLgKfACFvobkgnXz8ODWffjIeSun+lQIISHzhUuOway26GemU0qhKUUB5nDYcSPLe9op8BFCIsWSrjYAwEsHx5Y9cIVSChesq90Dwi75AAC3/mErLrI8Kk5Z4YWHfP3y47F6/sya+4kCFPoakJZ0EjdevG6qT4MQ4vOza07HzgPVxxLUQpy1jKQc29K3vCf6Lr2EkOnF0i5v3dp1YHSKz2R8utqby97v2D+Mba94z/O3nrgYF/oC5dkxyGg+EfS7IYSQCVg5twOvWdkr1v5/f/iMYnICyXpHpNEoSX2fOH/1YT5HCCGNx7xZXgbtRo5D75pR7hL866dfxkd+8AgA4BPnrxJNQtNo0NJHCCFTzIreGZjd6j2YglnESHzJF7w4mI9uXIkFs1un+GwIIaQ6UskEbnv/yVjU2TbVpzIu6XGEutZ0shiTOF2YPuItIYQ0MCcu7wQAbv6nEbP9+pJtdOklhESU9Us60dvRMtWnUTWVirXHnekl4hJCSINyzdlH4s3rF2FxV+NqTIlbeju8WJPBCkWDCSGEEJc0jNCnlNoI4AsAkgC+rrW+cYpPiRBCQiOZUBT4phk9vtC3bzAzxWdCCCHx5bb3n4yDwzmcsKwTP35kB754zzM46YiuqT6t0GkIoU8plQTw/wCcA2A7gD8ppe7QWj85tWdGCCGEyHDJhsX4y7b9uPL05VN9KoQQElvWL+ksvr50w2JcumHxFJ7N1NEQQh+ADQCe1VpvBgCl1H8BuAgAhT5CCCGxZEZzCl96y3FTfRqEEEKmAY0i9C0AsM16vx3AicEPKaWuBHAlAMydOxebNm2qqbM9e/bU/F0SXTju0xeO/fSE4z594dhPXzj20xOO+8Q0itCnKhzTYw5ofROAmwBg7dq1ur+/v6bONm3ahFq/S6ILx336wrGfnnDcpy8c++kLx356wnGfmEYp2bAdwCLr/UIAO6boXAghhBBCCCEkNjSK0PcnAK9SSi1TSjUBuATAHVN8ToQQQgghhBASeRrCvVNrnVNKfQDAz+CVbLhZa/3EFJ8WIYQQQgghhESehhD6AEBrfReAu6b6PAghhBBCCCEkTjSKeychhBBCCCGEEAEo9BFCCCGEEEJIjKHQRwghhBBCCCExhkIfIYQQQgghhMQYCn2EEEIIIYQQEmMo9BFCCCGEEEJIjKHQRwghhBBCCCExhkIfIYQQQgghhMQYCn2EEEIIIYQQEmMo9BFCCCGEEEJIjKHQRwghhBBCCCExhkIfIYQQQgghhMQYpbWe6nOoCaXUAICna/z6YgAvODydSswCcCAGfYTVTxh9hDHuQHyuV1z6AOIz58PqJy59cM43Xh9h9ROXOR+nMeF633h9hNVPXOY80JjXa6XWumPCT2mtI/kPwIN1fPflEM7vpjj0EaffEsa4x+x6xaKPsMae87Eh++Ccb7A+4jT2HJPG64Nj35j9xGXON+r1mqxMNF3dO/eH0MePY9JHWP2E0UcY4w7E53rFpQ8gPnM+rH7i0gfnfOP1EVY/cZnzcRoTrveN10dY/cRlzgMRvl5Rdu98UGt9fNjfJdGF4z594dhPTzju0xeO/fSFYz89mc7jPtnfHmVL301T9F0SXTju0xeO/fSE4z594dhPXzj205PpPO6T+u2RtfQRQgghhBBCCJmYKFv6CCGEEEIIIYRMAIU+QgghhBBCCIkxsRD6lFI3K6V2K6Uet44drZT6g1LqMaXUj5VSM/3jTUqpb/rHH1FKvcb6znr/+LNKqX9TSqkp+Dlkkjgc918rpZ5WSv3F/9c7BT+HVIFSapFS6ldKqaeUUk8opf6nf7xTKfULpdQz/v9zrO983J/bTyulzrOOc95HBMfjznkfIaode6VUl//5Q0qpLwXa4pyPEI7HnvM+ItQw7ucopf7sz+0/K6XOstrinEdMhD4AtwDYGDj2dQAf01ofBeBHAP6Xf/w9AOAfPwfA55RS5jr8O4ArAbzK/xdskzQWt8DNuAPAW7XWx/j/dsueNnFADsC1WutVAE4CcJVSajWAjwG4R2v9KgD3+O/h/+0SAGvg3TNfVkol/bY476ODy3EHOO+jRFVjD2AEwPUAPlKhLc75aOFy7AHO+6hQ7bjvAXChv8+7AsC3rLY45xEToU9rfS+AfYHDKwHc67/+BYCL/der4d0k8Cf7fgDHK6XmAZiptf6D9rLb/AeAv5E+d1I7LsY9hNMkAmitd2qtH/JfDwB4CsACABcBuNX/2K0ozeGLAPyX1npUa70FwLMANnDeRwtX4x7uWRMXVDv2WutBrfXv4AkARTjno4ersSfRooZxf1hrvcM//gSAFqVUM+d8iVgIfePwOIA3+K/fDGCR//oRABcppVJKqWUA1vt/WwBgu/X97f4xEi2qHXfDN31Xj+unq9k/qiillgI4FsD9APq01jsB74EBwLjuLACwzfqamd+c9xGlznE3cN5HkEmO/XhwzkeYOsfewHkfMWoY94sBPKy1HgXnfJE4C33vhGcK/jOADgAZ//jN8Ab8QQD/CuA+eCbkShOf9SyiR7XjDniuHkcBOM3/d1moZ0xqRik1A8BtAK7RWh883EcrHNOHOU4aGAfjDnDeR5Iqxn7cJioc45yPAA7GHuC8jxzVjrtSag2AzwJ4rzlU4WPTcs7HVujTWm/SWp+rtV4P4LsAnvOP57TWH/J9uS8CMBvAM/AEgoVWEwsB7Ai2SxqbGsYdWusX/f8HAHwHdP+KBEqpNLwHwX9qrX/oH97lu3IYNy4Tr7Ed5ZZdM7857yOGo3HnvI8gVY79eHDORxBHY895HzGqHXel1EJ4+Rwu11o/5x/mnPeJrdBnMjL5yTr+AcBX/PdtSql2//U5AHJa6yd9E/GAUuok39x/OYDbp+bsSa1UO+6+u2e3fzwN4AJ4LqKkgfHn6DcAPKW1/rz1pzvgBXDD//926/glvn//MniB3A9w3kcLV+POeR89ahj7inDORw9XY895Hy2qHXel1GwAdwL4uNb69+bDnPMllBfTGG2UUt8F8BoA3QB2AfgkgBkArvI/8kN4N4H2/YJ/BqAA4EUA79JaP++3czy8jJCtAH4K4IM6DhcoprgYd18QvBdAGkASwH8D+LDWOh/eLyHVopQ6FcBvATwGb0wB4Dp4/v7fB7AYwAsA3qy13ud/5xPw3H9z8NxEfuof57yPCK7GnfM+etQ49lsBzATQBC9517m+so9zPkK4GnsAz4PzPjJUO+5KqX8A8HH4Xlw+52qtd3POe8RC6COEEEIIIYQQUpnYuncSQgghhBBCCKHQRwghhBBCCCGxhkIfIYQQQgghhMQYCn2EEEIIIYQQEmMo9BFCCCGEEEJIjKHQRwghhNSBUup9SqnLq/j8UqUU64MRQggJjdRUnwAhhBASVZRSKa31V6b6PAghhJDDQaGPEELItEYptRTA3fCK/h4L4K8ALgewCsDnAcwAsAfA27XWO5VSvwZwH4BTANyhlOoAcEhr/X+VUscA+AqANgDPAXin1voVpdR6ADcDGALwu/B+HSGEEEL3TkIIIQQAVgK4SWu9DsBBAFcB+CKAN2mtjcD2Gevzs7XWZ2itPxdo5z8A/G+/nccAfNI//k0AV2utT5b8EYQQQkglaOkjhBBCgG1a69/7r78N4DoAawH8QikFAEkAO63Pfy/YgFJqFjxh8Df+oVsB/KDC8W8BeJ37n0AIIYRUhkIfIYQQAujA+wEATxzGMjdYRduqQvuEEEJIaNC9kxBCCAEWK6WMgHcpgD8C6DHHlFJppdSawzWgtT4A4BWl1Gn+ocsA/EZrvR/AAaXUqf7xt7o/fUIIIWR8aOkjhBBCgKcAXKGU+iqAZ+DF8/0MwL/57pkpAP8K4IkJ2rkCwFeUUm0ANgN4h3/8HQBuVkoN+e0SQgghoaG0pscJIYSQ6YufvfMnWuu1U3wqhBBCiAh07ySEEEIIIYSQGENLHyGEEEIIIYTEGFr6CCGEEEIIISTGUOgjhBBCCCGEkBhDoY8QQgghhBBCYgyFPkIIIYQQQgiJMRT6CCGEEEIIISTG/H/D+muyndTvJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_data['inc'].plot(figsize=(15, 8)).grid(linestyle='-', linewidth=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
       "      <th>geo_insee</th>\n",
       "      <th>geo_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-02-17/2003-02-23</th>\n",
       "      <td>200308</td>\n",
       "      <td>7</td>\n",
       "      <td>36298</td>\n",
       "      <td>22548</td>\n",
       "      <td>50048</td>\n",
       "      <td>61</td>\n",
       "      <td>38</td>\n",
       "      <td>84</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         week  indicator    inc  inc_low  inc_up  inc100  \\\n",
       "period                                                                     \n",
       "2003-02-17/2003-02-23  200308          7  36298    22548   50048      61   \n",
       "\n",
       "                       inc100_low  inc100_up geo_insee geo_name  \n",
       "period                                                           \n",
       "2003-02-17/2003-02-23          38         84        FR   France  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mmax = sorted_data['inc'].max()\n",
    "sorted_data[sorted_data['inc'] == mmax]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2000-07-31/2000-08-06', 'W-SUN')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Period(pd.Timestamp(2000,8,1),'W')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Period('1990-07-30/1990-08-05', 'W-SUN'),\n",
       " Period('1991-07-29/1991-08-04', 'W-SUN'),\n",
       " Period('1992-07-27/1992-08-02', 'W-SUN'),\n",
       " Period('1993-07-26/1993-08-01', 'W-SUN'),\n",
       " Period('1994-08-01/1994-08-07', 'W-SUN'),\n",
       " Period('1995-07-31/1995-08-06', 'W-SUN'),\n",
       " Period('1996-07-29/1996-08-04', 'W-SUN'),\n",
       " Period('1997-07-28/1997-08-03', 'W-SUN'),\n",
       " Period('1998-07-27/1998-08-02', 'W-SUN'),\n",
       " Period('1999-07-26/1999-08-01', 'W-SUN'),\n",
       " Period('2000-07-31/2000-08-06', 'W-SUN'),\n",
       " Period('2001-07-30/2001-08-05', 'W-SUN'),\n",
       " Period('2002-07-29/2002-08-04', 'W-SUN'),\n",
       " Period('2003-07-28/2003-08-03', 'W-SUN'),\n",
       " Period('2004-07-26/2004-08-01', 'W-SUN'),\n",
       " Period('2005-08-01/2005-08-07', 'W-SUN'),\n",
       " Period('2006-07-31/2006-08-06', 'W-SUN'),\n",
       " Period('2007-07-30/2007-08-05', 'W-SUN'),\n",
       " Period('2008-07-28/2008-08-03', 'W-SUN'),\n",
       " Period('2009-07-27/2009-08-02', 'W-SUN'),\n",
       " Period('2010-07-26/2010-08-01', 'W-SUN'),\n",
       " Period('2011-08-01/2011-08-07', 'W-SUN'),\n",
       " Period('2012-07-30/2012-08-05', 'W-SUN'),\n",
       " Period('2013-07-29/2013-08-04', 'W-SUN'),\n",
       " Period('2014-07-28/2014-08-03', 'W-SUN'),\n",
       " Period('2015-07-27/2015-08-02', 'W-SUN'),\n",
       " Period('2016-08-01/2016-08-07', 'W-SUN'),\n",
       " Period('2017-07-31/2017-08-06', 'W-SUN'),\n",
       " Period('2018-07-30/2018-08-05', 'W-SUN'),\n",
       " Period('2019-07-29/2019-08-04', 'W-SUN')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_aout_week = [pd.Period(pd.Timestamp(y,8,1),'w')\n",
    "                  for y in range(sorted_data.index[0].year,\n",
    "                                sorted_data.index[-1].year)]\n",
    "\n",
    "first_aout_week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = []\n",
    "yearly_incidence = []\n",
    "for week1, week2 in zip(first_aout_week[:-1],\n",
    "                       first_aout_week[1:]):\n",
    "    one_year = sorted_data['inc'][week1:week2-1]\n",
    "    assert abs(len(one_year)-52) < 52\n",
    "    yearly_incidence.append(one_year.sum())\n",
    "    year.append(week2.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f80cf5926a0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_incidence = pd.Series(index=year,data = yearly_incidence)\n",
    "yearly_incidence.plot(style='*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2010    848236\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence[yearly_incidence==max(yearly_incidence)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2002    502271\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence[yearly_incidence==min(yearly_incidence)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1991    507329\n",
       "1992    821558\n",
       "1993    653058\n",
       "1994    682920\n",
       "1995    648598\n",
       "1996    553859\n",
       "1997    679308\n",
       "1998    660316\n",
       "1999    784963\n",
       "2000    605096\n",
       "2001    650660\n",
       "2002    502271\n",
       "2003    770211\n",
       "2004    736266\n",
       "2005    654308\n",
       "2006    657482\n",
       "2007    701566\n",
       "2008    745701\n",
       "2009    822819\n",
       "2010    848236\n",
       "2011    645042\n",
       "2012    620315\n",
       "2013    708874\n",
       "2014    673458\n",
       "2015    613286\n",
       "2016    780645\n",
       "2017    557449\n",
       "2018    543281\n",
       "2019    584926\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f80cf429588>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_incidence.hist(xrot=10)"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}