{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse d'incidence de la varicelle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek\n",
    "import requests\n",
    "import csv\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lecture des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.get(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_data = r\"C:\\Users\\renau\\mooc-rr\\module3\\exo3\\incidence_varicelles.csv\" \n",
    "if (Path(file_data).is_file() == False): \n",
    "    file_data = open(file_data,\"w\",encoding = \"ANSI\")\n",
    "    file_data.write(r.text)\n",
    "    file_data.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>202106</td>\n",
       "      <td>7</td>\n",
       "      <td>14719</td>\n",
       "      <td>10462</td>\n",
       "      <td>18976</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202105</td>\n",
       "      <td>7</td>\n",
       "      <td>12379</td>\n",
       "      <td>9107</td>\n",
       "      <td>15651</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202104</td>\n",
       "      <td>7</td>\n",
       "      <td>12026</td>\n",
       "      <td>8826</td>\n",
       "      <td>15226</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202103</td>\n",
       "      <td>7</td>\n",
       "      <td>8913</td>\n",
       "      <td>6375</td>\n",
       "      <td>11451</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202102</td>\n",
       "      <td>7</td>\n",
       "      <td>7795</td>\n",
       "      <td>5430</td>\n",
       "      <td>10160</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1571</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>1572</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>1573</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>1574</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>1575</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",
       "<p>1576 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
       "0     202106          7  14719    10462   18976      22          16   \n",
       "1     202105          7  12379     9107   15651      19          14   \n",
       "2     202104          7  12026     8826   15226      18          13   \n",
       "3     202103          7   8913     6375   11451      13           9   \n",
       "4     202102          7   7795     5430   10160      12           8   \n",
       "...      ...        ...    ...      ...     ...     ...         ...   \n",
       "1571  199101          7  15565    10271   20859      27          18   \n",
       "1572  199052          7  19375    13295   25455      34          23   \n",
       "1573  199051          7  19080    13807   24353      34          25   \n",
       "1574  199050          7  11079     6660   15498      20          12   \n",
       "1575  199049          7   1143        0    2610       2           0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0            28        FR   France  \n",
       "1            24        FR   France  \n",
       "2            23        FR   France  \n",
       "3            17        FR   France  \n",
       "4            16        FR   France  \n",
       "...         ...       ...      ...  \n",
       "1571         36        FR   France  \n",
       "1572         45        FR   France  \n",
       "1573         43        FR   France  \n",
       "1574         28        FR   France  \n",
       "1575          5        FR   France  \n",
       "\n",
       "[1576 rows x 10 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw = pd.read_csv(file_data, sep =',', encoding = 'ANSI', skiprows = 1)\n",
    "data_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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",
       "  </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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw[data_raw.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Je ne constate pas la présence de données nulles. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "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:])\n",
    "    w = isoweek.Week(year, week)\n",
    "    return pd.Period (w.day(0), 'W')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['period'] = [convert_week(yw) for yw in data_raw['week']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_data = data.set_index('period').sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11/2021-01-17</th>\n",
       "      <td>202102</td>\n",
       "      <td>7</td>\n",
       "      <td>7795</td>\n",
       "      <td>5430</td>\n",
       "      <td>10160</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-18/2021-01-24</th>\n",
       "      <td>202103</td>\n",
       "      <td>7</td>\n",
       "      <td>8913</td>\n",
       "      <td>6375</td>\n",
       "      <td>11451</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-25/2021-01-31</th>\n",
       "      <td>202104</td>\n",
       "      <td>7</td>\n",
       "      <td>12026</td>\n",
       "      <td>8826</td>\n",
       "      <td>15226</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-01/2021-02-07</th>\n",
       "      <td>202105</td>\n",
       "      <td>7</td>\n",
       "      <td>12379</td>\n",
       "      <td>9107</td>\n",
       "      <td>15651</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08/2021-02-14</th>\n",
       "      <td>202106</td>\n",
       "      <td>7</td>\n",
       "      <td>14719</td>\n",
       "      <td>10462</td>\n",
       "      <td>18976</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1576 rows × 10 columns</p>\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",
       "2021-01-11/2021-01-17  202102          7   7795     5430   10160      12   \n",
       "2021-01-18/2021-01-24  202103          7   8913     6375   11451      13   \n",
       "2021-01-25/2021-01-31  202104          7  12026     8826   15226      18   \n",
       "2021-02-01/2021-02-07  202105          7  12379     9107   15651      19   \n",
       "2021-02-08/2021-02-14  202106          7  14719    10462   18976      22   \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  \n",
       "...                           ...        ...       ...      ...  \n",
       "2021-01-11/2021-01-17           8         16        FR   France  \n",
       "2021-01-18/2021-01-24           9         17        FR   France  \n",
       "2021-01-25/2021-01-31          13         23        FR   France  \n",
       "2021-02-01/2021-02-07          14         24        FR   France  \n",
       "2021-02-08/2021-02-14          16         28        FR   France  \n",
       "\n",
       "[1576 rows x 10 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "periods = sorted_data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['1990-12-03/1990-12-09', '1990-12-10/1990-12-16',\n",
       "             '1990-12-17/1990-12-23', '1990-12-24/1990-12-30',\n",
       "             '1990-12-31/1991-01-06', '1991-01-07/1991-01-13',\n",
       "             '1991-01-14/1991-01-20', '1991-01-21/1991-01-27',\n",
       "             '1991-01-28/1991-02-03', '1991-02-04/1991-02-10',\n",
       "             ...\n",
       "             '2020-12-07/2020-12-13', '2020-12-14/2020-12-20',\n",
       "             '2020-12-21/2020-12-27', '2020-12-28/2021-01-03',\n",
       "             '2021-01-04/2021-01-10', '2021-01-11/2021-01-17',\n",
       "             '2021-01-18/2021-01-24', '2021-01-25/2021-01-31',\n",
       "             '2021-02-01/2021-02-07', '2021-02-08/2021-02-14'],\n",
       "            dtype='period[W-SUN]', name='period', length=1576, freq='W-SUN')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
    "    delta = p2.to_timestamp() - p1.end_time\n",
    "    if delta > pd.Timedelta('1s'):\n",
    "        print(p1, p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='period'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ObATcVWT00srNeOaNd+0V2wkm8Y5tZsjn+b8dQWTkgu/duwhL3tmWhAdpT3hx/F0AXkrlWJRzGIA5cdGXiOglIrqBiAbGZaMArBAuWxmXjYqP5fLMNYyxFgBbAAxW3P88IppHRPPWr1/v03Utqv2ZOYfQ6mD+2JHHWGuJ4drH3sCOJru3rwmuy0m16L8rkf3Ir57Gl/78YuX3q7gFNYwEwXKt/C55Szc8tQxzl6njH+nQHjrlPS3R3OkIOS04txU4BAlE1AfAnQC+whjbikj8sy+AKQDWAPg5r6q4nBnKTddkCxi7jjE2jTE2bejQoa5dlyBNlip/aD6Im1rsBKEjD7H7XlqNKx5Ygp/969U2uZ8z4bDULPed+kYorfU6aRqWNtqpW8S37m7Bx3+rjn+kQ3tYGXHT4cWrt+LJ16uz8SsXnDB3NIu0WsGJIBBRPSJi8CfG2F0AwBhbyxhrZYyVAPwOwPS4+koAY4TLRwNYHZePVpRnriGiOgD9AfhtZcpEtQl/Q5xRZI8LQegAu47dza34zB/m4oGX12TKt+6KzOxcnkOFcp/NdpndLLW8+8oRStv02yhW+Eos0GRFcAcYZl7gY+7i2xfg09fPbdN7z1+xGUcKSm/OGQSRUYxYln89gFcYY1cK5SOFah8F8HJ8fA+As2LLofGIlMdzGWNrAGwjohlxm2cDuFu45pz4+AwAj7AyZ+SjS9Zhu4eYo9qfubNxCO9s2Y3HXl2Py/+xJFPeVCW23VUUlCaF19mdurVTrXfapouo4mam+9vWJvlVdTaP+D3taON51YOvYc2WNNc5JwTdRWTk4odwFIBPA1hIRPPjsm8A+AQRTUE0B5cDOB8AGGOLiOh2AIsRWShdyBjj5gIXALgRQE8A/4j/gIjg3EJESxFxBmeV8zBrtuzCZ298DjMPGIbrP/Mep2uqziHUuXMIHWGe8i6s39aUKeeTsq3kuEmGrwrfSbW+Z4kxFBwEQ7XiJEwcgu89O9vu1sd0mOP1tdswdnAvNNYV7ZUNkCVkXFTUXfwQrASBMfYU1CLT+w3XXAbgMkX5PAAHKcp3AzjT1hcb+DxZsHKz+zVVXpW5yMiFQ+gIjmm8D3JPmpqj/ssT7LnlGzGkTyPGD+nt1L7r4lU1pXJVWrG3U2sbONMabnulssjoptlvVaFHbQdfMeWmHXtw0lVP4GOHjcKV/zalonvL45BbMXeEudoW6FKeyvyTbdppcjOvrWNaKjKy21B3hDGmW7A5h9AocQhn/iZSSi6//IPmdsvuT6Xnq/NS2/vbmJ7DtjhV05msPd6DrwJ3x55IRPzsm2b/DBfIHEIiMupkXFa5aH+7ripC9/GO+NFDmPnzx9qkDz4io5ED2t99n78qeeInpn/FyoaI6zTqaBFz2lvublp/fHUIlaC934MLuCVUdeT82ZfH/VoCh9AJoaPia7c2YS2alOeqLQOu9xAZ9WqIxDH7DHUTv9QCqchIzTmVs7jcPX8VNsdcmvPrTZTKlmqW/lTrc9rb4cH4agPTQmzrWzWJa3usg75jLs0IV/17J/5E3YMedDGCEI9eH9PpanGCG3fswcJVWxIRiwuHwBVV7bX5WLFxJ2b94smq9+GiMkIFuyZxsfWzrXZyaeL62rRv8mu07drbI4fBwpVb0FIq4bCxA+2Va4RqKM/ltSNwCJ0YfEDoJsSG7U3Y3pSV7VeLJT7nhrlYuGoL3jdxCABXHQLL/LY1Hn8tdfqReyC/l7c37MSYQT297+H7fnW1XZe4aloZtSeM97cqlavXD9fX8OFfPQXArluqBRLnsSp8M9nxkcfG6iYqhK6lQ7BxCFN/+BD+868LMmXVmvevr4virtTFN3fhEHQWPm0Fl/sSEWa/sQHH/PRR3PnCKvsF5dykiqgWgW/vDaHp/vsO6wMAGKkJIV1NDqGjr4OlEsMb63Ykxyb8c9E7OP3aZ4wbsIK0Im6JHTQ7gy6lGuhaBMHCIahQjc98xQNLsDs20ywWfMxO4z50hLFm6MPSmNjNX7HJu9kXV2x2qpd8sgrexaLVW7C4gvzIIly7UatPZ+IQ+MKnM/31IQcdwVseiBZeH4dSjptmL8e/Xx+FVrM9ypf+/AKef2uTcW7Ka8ctz0Ymu4FD6ITgclcflrka8+Hax94Q2osadNMhdBz5pHEHVIHS7vxbnneql9KD8t5FU0srPnj1U9i6229R2X94X0yMd9wibAtlzWMZGc79/MHXAOjNS6ub7rltxuah3/8XDv+fB53qnvLLJ3FSHGrklTXpBsAmMuoR+9SYCI/21bX/FG0TdCmCwAeELQBaFtX90nxxdwpdkegQqtoFdwg3NgZTc6hTKSpdxB54+Z2yr1UtrO09/9tOOV5+sMBNO/ZUNWeBa8iKxWu24vU4GGG/HvVJue2dNdZHBGHaDx/CHc+vVNbR56Nu7xHRNuhaBKHkb2VU7e/s49nYkdhQU1cK1ZDnuPajzFuU64xFpI75X0k2tWrAZWeu46aqOqYNbR32gwfx6evn6CvITbl6rXts6Pr0SO1ibBHne9Sny93X71igrKO7cweaqjVFlyIIpUSp7KFDqPKXLnmIgVoTDqF9hpt4V1Mfam1iCbgvArou1FVgWqO61Fl0VbNYRm1z60rH3nPL3fVKT75e/YREYkY3m8hIdLLUvV9dC4FD6IRIHNM81oa/zFthr+QBPnDcJnT7mrT5jvG2EBlpzU4t37TcuP1EpLzW6vxVY1t/ncUMk8R8G7Y35Rb16jII1Wtt8y5TSJnyIH4G2xdxeRIdgTSNh3sWrG73vA3VQpciCKVEZNR+gRBKHrv+1Amy/XcfZpERr1O7flbKhdQVyyQIUC/utm7UmqvTbRI27tiTHC9esxVTf/gQbpc2NT59s+oQHJp64W03LsE90KFTtbguCcfmurXa5X/51hdrkreBMeaduKlSdAmCsGrzLkz4xv1YHFsctEfaP46Sh/dxKjKqYYcMkHebOnBxTjU4mUpzJutQFAzIDx0zwPk6IkBFS+z9rC1072HqDx9KjrmN/NNLs0HdfIhZNZ7zY9c8oyx/4OU12CQQMFeUOx+s2fRcuPbybl0T3PnCKpx45eMZB9Jao0sQhAcXvYOWEsMfY5vhjsAhuCmVY4JQ0x6Vh0z3q6hD0LdR2TcTZck++gSiyqyMavXtarlJaGkDGeW6bbvxhT++gPP/6GZ2XC58prrTZqOCV/Oj+1/B6s27ym9AwsurtgDwT+9aCboEQeBsY0vimNZ+fUlS7nkoBduDQ3h7w07c7BAnn6D2Efj7S2uU9csVpVQqMhL1AL4cosrKyEbQqxdEj+Gn/8znrVbd/64XdKaScpvme/qITsp9Tp5PY9WmdIF0bctLZCRuJKoQ+LASseh1T7yJi2+fX/b1MtpjHesiBCH6LcdTuVLICyAnBF4cgkPdVZt34Xv3LMK4S/6OR5asNdZtbi0l3sU6fOJ3z+LNd3dY7wuk73P2G6loYmG8e5FhexSt0thy3oZ6Qe7j8/0JpCYg1o5UhyKs396kdKaTNxQ797Tg4ttdTSXdiZndD6F6uxWxrR1leCWr4KVUbgPLLR77qJpoSyvErkEQ4t+WMvwQKoX8rbhi20mHwOs63OczN8zFjc8sBwDcu0C9O+e47O+v4MQrn8AqA/u6eWdetitatryxPmVT+esUc83qUHNlrOb6QpkcApHaQsn+HO730OGN9dvx9oadynPyhuLPc952btdKlJl43D4Cy4/EwfAqRSVTXWXJVenrqOZe1M/BtjqwEgQiGkNEjxLRK0S0iIguissHEdGDRPR6/DtQuOZSIlpKRK8S0clC+VQiWhifu5ri7QkRNRLRX+LyOUQ0rpyHaW0HKyN5/Pjs+lORkb3u1t2pyZ7t6eYu2wgA3gq9A7/zQHIs2ozLAb8A/cCvWEmpud5ncvh8f0JWIW3pRoJHlqxzqmfCSVc+jjPiDHT5DmT/7dNYvcDE1RAZlUNIxEveWB9xp82tpdw4LXf22nNlZPv80CtmTrs7woVDaAHwNcbYgQBmALiQiCYBuATAw4yxiQAejv9HfO4sAJMBzAJwDRHxxLzXAjgPwMT4b1Zcfi6ATYyxCQCuAnCFz0MkOoTY9b0t6ao8uVKRkfu1LlOr6MMblwlVuA0i9WJcrkenbh2x+SHYILbr5ZNApMwKZxOVXCPEryoX5qxo2ZO9DARBFvvY3qEXQdCVO3IhYtdU13z9jpdw2A/cYhipIC7qVisj6f8l7+TFqpWKyDp7qk0rQWCMrWGMvRAfbwPwCoBRAE4FcFNc7SYAp8XHpwK4jTHWxBhbBmApgOlENBJAP8bYbBaR6pula3hbdwCYSfIoN6B9dQjZ/708lXldh0FUFGXkngNfBS85u6Kq7vJybb3TBDllXQ7xqX05hMb6/DSodF5XmqxFvlzU38iQn9auVPboiKaxcr6z6oq755cRUl2Aj6d0syTfVz1CpSIj+R6dDV46hFiUcxiAOQCGM8bWABHRADAsrjYKgOgpszIuGxUfy+WZaxhjLQC2ABisuP95RDSPiOatX5/a5vLFJNEhtKFmRJ4YPr4FSfhrh/uIHEJ7WlElfdAQJbtSWSMSqvCZxPv66hBWbsrrWiqVrc9fubmi6+Vxdetcgw5BtjKyKpUrX7TKoXfV1lf89J9LMv/bxtC727NpdFWhLirtoUuUY1d0aCsjIuoD4E4AX2GMmYLO62w2TLYcTnYejLHrGGPTGGPThg4dmrugnFhG1YaPH0IyQRxGoag0rcbT+bRRzXlsU2iWu2iIV/l+/wWKnA2u3aiV97aXt7HniPBZzHVVrWa54Ny6+7188etHs2I731u1KqLhVTrWXTIldmQ4EQQiqkdEDP7EGLsrLl4bi4EQ/66Ly1cCGCNcPhrA6rh8tKI8cw0R1QHoD2Cj60PwQZf4Ibhe6Ijb5r6NcZf8XW2ZI+sQfKyMPHQIorOVbZJV+/lVk1+vVHZvN2P+6NknE1R+BTrUar2qtN1KFia7fN/e+J8+f4SxLVeRkUisVFe4iC5diaNvPCtbtO0hfRq92gOqKzJKTLHbUArlYmVEAK4H8Apj7Erh1D0AzomPzwFwt1B+Vmw5NB6R8nhuLFbaRkQz4jbPlq7hbZ0B4BFmGQWvrNmKB16OzC/lF+e7Q7QNOG7uuXpz3uwyp0NIlMr2r+hTt1pcTzk78J178rsevVLZJq5Ij8Xn5guDVSnt0K6fyMhf9OXyDiv9Xj67+JxjWhXatscFMp9XviLHZ9ohjTfXd1GvMA4wQT3v0rLPHjUODXV+bQ7u0+BV3wT+DbbsasZyR5+hSuHytEcB+DSAE4hofvx3CoDLAZxERK8DOCn+H4yxRQBuB7AYwAMALmSM8S98AYDfI1I0vwHgH3H59QAGE9FSABcjtlgyoaXE8JPYw1MevL5zsRyLCY68lREXGdnvy1lW3zVaJyJoaS3hocVrk5hOMp5/axPGX3o/nlvuzHwBAL71fy8rOlGeDiFT1+N6frv/fWSp5r4CcfHYnxOAI8YPUvRN/yCuC+oXbnke4y75u3Nfsvcw38RkdGY1/fUgaLq6zhyC51wUzat97+VLEGwWQY2exAAADh09wPsaG3716FIc97PHqt6uClbjZsbYU9BvCGdqrrkMwGWK8nkADlKU7wZwpq0vMob3VScZd9md3XnBkTj92sgGvMQYCppHfHvDTmyKRUWqZuUhlYqM7IOYezW6yKGz3qXqOlc//Dqu1iyYAPBwbKI3580NzjINXTVduatsWVfXhQNQtyv0jYBTDh6B+xfas6gRARedOBGf/F020YtprXAJCvjEa+vxwKLys7j5hM7wXXRdCFrBwrHZEgip9972G+9WcKOuBME34q1y/AlFjXUFby1ziy1LTwdHp/ZU5jJDme13mSAj+/dMjsUJwhhDsyBcPOanj2Ldtsg6QfWt5YnhIwZq8dA3ZMUr6jq2UBTb4hAJfYW0gzZ8797FznUBPz+Eche1cZf8HV/9y3xtu0TAlR+f4tQWgbI+Hkl7lXEIP/vXa07310G8fbVt200e7Bx8s12uDqFc82OVL4xrUyp/EhNUpsGi5VFdsWB/TqkNGz049ddPa9N32vDduxWcepXRqQkC/1jydHbhEIoFwtdn7Z9pBwC+c/ciTPzmP5TXNCu0UPKu552tu+NyO7gjnUvd7MDUiYzMLfHk4tXwetXJVn128uodmroB+ZP+7cVV2Z260LLXAkpuoSs27tiDg7/3T7z49iZvy6KyvHqFe+yy5C2WRWS2251+rTpctYjGuqLxvJ2Dyc/NLQ4JclRWOq6vz5dDUEV9FcdOsUDW55RNV21Z2xas2Iz//OsCp/7JG92bHIJRVoquQRByHIJ9YBSIEsIhfvRb4hDaqkmsIgi6tcdlTWrxEC+57KZldlWuxxOi96g3T3YXaC06PNa+Bxevxb0LVmPr7uay4rZkXptwvHlnszPHQVCPF/mTzHlzA7btbsFvH39Ty+XooNr12iB+yp2WQHC1MO3k8nPd49nGt+r82xvVcZtE7G5WzTG3QaUKQWKCql2xrK5A1uG8TQpM6OqQ2FE9mjs1QdCNExcrk2IhFRWovo2q7T0qDkHTCS8dgsPYEMVBusdzNXmLwlG4o3dDnoDoBrRq96xLyHLRbfPx/259Ef/11wVlLWq6hWLLLj8CoyZuTPufeFudAl/E7uZW/PcdL+EPTy9z7pP4bDaCkrcyqnyx4RxCueNbdXrXHkVoFOn/3QpuyJUgNHhyCKoxLHMItluLQSABM4cgEotrHtXr+nToofCorzY6N0GIf+Vh4LK4FCitZ9spcKgWXD2HkD+xcOUWTPvhQ0kwL76jtw33ZZJuwJVDqBYO33tgrky3E1IVi4um6ioximq5Vkri8Z7WkjuHQFDqEHTflchfPr6ruRV/mbcC3/fQx+jMc32vLReqcB4i7BxCVGF7UwuO+cmjuH/hGtypyecgQiUec91M+/ifAGqRkTiF6hw4DpmomHb+IrFw2Ujk1rU2iNLWqQkCXwXk+eyiQygUUpGRymJC9V1Vbum63Zhqbf71o0vx7vYmzH4zikvjKjI6/mePGc9zuMRq4fCJZaTaoerG/cOKCJKisk/VpwLZh7rqfCnDeaTlPuEDCKQMdSK298Rr63Hbc2k0Ft/1dpfCcsYG3bNxZLk2Px2CC2wml65K5Xe378HbG3fii396wem+qtzErjoYXymMTans4uimMztXQSQWA3r5+yvUKie0iE5NEFIdQrbcZakrUpoYxZVDUFF/3Tea/eaG3MIk95OLjHwHsm75VOk4RJQ7npYodjM6Qnj9U3mxSEZBqSQIYrRTnShKUSbK8oUaza0lJ1EEEN1XtDjj4N+/qaUVZ98wF08IeW3L4RB8kbF8i3/HD+kNAPjyzImZ8ybafv+X32e8z44mdd/4Yqh7VJsMvJprl/P8MNxURVRsSmU5FauSK5aKTO9FHDf1LuItqUogCBboXpDLaysWKGExXV+0zW5Zxt8XrlaW82vKFfHoFgDX/Ae+jKdLRq9KUCBKPZV9REZMfdzcynIc0EbNuyECBvXO79b4xJbj5QDAxu1+eSZ0xMgEkcDxxYz3qa5AmZ1szjFNOC4UgMs+mnP9SfCNvy1Ulqee4zodgrZJp/M+cJ+f+nOqJlTt7tyTjvWitGi7BMMz7clEYlHO4t4WeuhOTRD4+5FFRC7vmigd9KoP7co1mD5sz/qseae8CxbNRP2CmUW4/qlleH1tGtOd+0vYUOm4IvLzYM2IPxR3LxDhztg2W9c3tdpXbDdFc0ueQ9ARURu3JUfIBICvSD4QNqiUqTaIxJ0vBHxBKRbIuBOVv82njtgbB43q53zvL8+caNw0bNyxB7c+Z87gVs3drEtTMw8YZrynK8cvmsbKHIKLhMDov1IS62mrJZBD5bSFZVKnJgg+Cl0ZGZGRow5BOSAM9xjYK+sAJi8+IsvqM384IfvBfYvxof99Ctt2N+PLt76ojDmkQyWhiAukt76w7cRU54mADZ7Z3aJ2xfum/zTZopZJ91aBfxtZTECk5zZ0KEdk9LN/vYaVm7iZZtQH/ox1hexIMsUy4jV9YitdfNJ+aVuK7/WlP7+Aay0JgqoZ2uJVRSKbfDtmiyDX+bx5Z0oQZB2C2rM+W2ayMmrNbIzM+NyNz+HeBWoJQy3RqQlC4vxSBodQLFAS+0Qle//ePYtyYZGVu18D1dY5ymzf3YLdza2JY5qubR0a6grJsze1lHD3/NW4RzF49LviyriEgsHSRs0hpMeqq8TFyodOlTQTrNnDykiH5lj/I8uZt+1uwQYF12BCOQQBSK2v+GPyrojiTsBsfcLfg2+wPVMGu/UOnGg1N7Nn3zDHWsdm/aXiTFUEQTSgkK2MVPXlIqOVkcMGsKmlFbv2tCYpWtsanZwgqMsXr9maWWxVICL0jB20VDLeO55fibOuezZT5ivy1/XvkrsW4pRfPpnlEDzarS9SZiCqZODl9q1UYnjh7U3GawmknfA2rY6KMxF3Yj429CYupdyseUdPGAIAaNZkvnvy9Xdz0ThtUMXnkfGFY/fFKQePyJSljpPR/60Ch2AMh654L54Wmc4mjj/+2MHKch0HykNK7z+8r3NfbMTl/GP2sT6fqjuyFbncZ7lNZegaDysj8dzzb6kDTM76xZOZ3OZtjU5NEEqMYfplD+Hy+1/JnVvvsIvrkRAE9UovDwhXXQNH7ozQ3pvv7kBLiaU7MQ+KUFfIxljp20MdiuJ3T7wp9Ycl/dLtZG54ehk+do05tAGRYeFWFK/dmn4L1W1teXflOmldC+vhAJlwcK6ObygqTYUJuHEIh48dgP943z6ZskKyS+cio/ScyneCQ/w2vFa54bht43LUgJ74zb9PVfRBj0kj+2HMoJ5x/yq3rT9u/2HxJsVXh5Cd93KVnI+Bk1LZjUN4be12ZfpQ2eeordGpCQJjkSJ19ZZ8noImzSIvIuEQNFmOcjJED7NT2zkgGiD1BR4iwH3hKZA9xgoA/N/81Xjg5XzETcb0A/e1tXZ5rVGHoCj71O9Tll+1c8yKjDQ8hqJYLPuvO15SdyiGbuGRS/lCy306VKaJvnAhCAWi3HiTTT/794wI/5ZdLcYMeuJ7qVxkZH7+uiKhX8/8hkRHSFtLpdgowas7RkQiND8ro+njBuXmAJ9TF82ciOvPmYbp4wdhWN80SY5yzkhFz765UWtVJl9/0W3zscIhnEdbolMTBJO333Yh/ovO5ZuX6xyH8kqlfB0jhyCdU01JHg5j4cot2nZU99SZXMq48Zll+X6BaZVfLjs2IpOnsnmmqy57XLDx9yE04r1U1kAukNdJ/s25SXA1rGVcHNMKhfyiLcfaOmxs5DE+ckAPSWTk4ojp3N0MlIRYOG4oFpRjxrQ4Rxxm9VAgrlR25xBUllr/Nz/Sw9UVCDMPHA4iwj1fOhofOGiEsg1d2Zvr1bt8Vd1y4lzVEp2aIJggJtrQjZMeBh0CkCcIvkm55XOmifugwsOXMYaHFivKoVeo5tsQ7o9UJq3jEFwWjgLpg35VGhbZp91qLCryF9l3WB8AfnGmbLA5DEb9IC1x4n04adJw3H3hUThz6mhMHtVf25ZqG1Iuh2BDfbGglN9rOT1Ez1qJlZuMQmx15WNlFEUyTf/fsL0piUK6bEO6oI/o3wPH7hflb3d1TN2mSPKju766pLFydFmCkNHoa+rwCadbHN+VHJB8k8L7jPkeinDD9720Bp+/eZ7ynmKXXfUYqSza1DFHDsFDtCOiXIKh4kjunl+5WZ5MpD85fSyAdBGvRupS1wxr8r3kDQkBOHTMABARfv3Jw5TtrNu2O2OhUq7IyARx/NQVSbnRMY4wkUOoQreKceRi42uWTspjWJQoyP4HBcM6oeZc1V1oC0/jStHlCEL/npHtP8uuhErYskLJ8AldEbWbPWmyK1aFpH5HoRsBuMhIIHjmTuRw0W3ztdVd1g3TbkxXzm3qbXNCd17Fnf38X6+aGysD8iahGuuoy26YhHDsSVlyffQrnheTHIkLzad+NwcPKrhK1XOYEuVwbtLW9wYNh2DaMNh2874oFiLuyjVsBBC9b7G6eK2cipPrldTz302MBAC3OOQzkIlRW6PLEQRuzqbaGcswxTJSwcUOOQOPQd/TI7RtiWXv+7kb81xEuXAZj4WCP8s/P/bpcE2sIkOVsEXX1MRY7CPC5JMBAA9+9Rj89QtH5kw9fXbWct9H9OuBXg1FvKTRD8mJinJmjoz/momT+E7f2pBVUpqsjI66/BF1g4Z7yWioK2hyjavrs8gmuKqCEiK99/amHXuwbuvu5B1964MHYskPZkXvWnhvJoLALc/UVob5/qjG+I6mFqcEN7XIbeED6ypERDcQ0Toielko+x4RrSKi+fHfKcK5S4loKRG9SkQnC+VTiWhhfO5qivlMImokor/E5XOIaFwlD8QVzXxyHnX5I9o8AWn46+jXJut9aum7uTKTVY7LoD9u/0g+OUIRYE0bSVXiEEzIxsTR1/v80eNRX8zvUlUokN4PQbfgi/oLE3SnVVneVO/n+nOm4W8XHmW+idiv+HEnDu+L94wblOhQ+HNUwub371mPnXtak+i2qvMcJcZy4ZtFM2GxrzLEd5oTMxHXIXh0XOyD5fF7NdRBJfcxxRmL1uLqkYRigdBQLChjgx32gwcx/UcPJ++wsa6AHvXF3BjOxIaSHifZJDjqAC69a2Gurs6ze8GK7GahLUJcm+CyLb0RwCxF+VWMsSnx3/0AQESTAJwFYHJ8zTVExLd21wI4D8DE+I+3eS6ATYyxCQCuAnBFmc8CQLTdjsDZ4gNG5B1h0oBqUe2rHjTnwX3s1fW5Mt1kj9q19RaYMLRPpg8u1zPmJpf2AQ/k5TIcCX6eyoDwXcrUISinoqJwRP8enilC1ZY9vJ8+61YlFiPNLSXsM6Q3zjsm9UXg65vOI59DfKe6KJouoZxFJOIqS70+jXVqYmO40Lbn+LdpYyx3zaJIUdQBU4IoObui7G1vSj+biBEd5+jKTbuwYlOWU9OFZvmanE6zo3MIjLEnAKjd6vI4FcBtjLEmxtgyAEsBTCeikQD6McZms2j03gzgNOGam+LjOwDMJN3Id0AiW5U+1AXH7YvZl56QKZNltDpzMRNaWksY2b8H/nFRPsywi28Bz03sYOKcoFRiWPKOPcEGoLZJV6EY+xa4vHoysPzD+/XQXBP9WjkEjwVYVdV3h6XdDcaN+wQUu+i2F41tyxAX8l3NrSAifOOUA9Pz4FxK3J6mHXFjLIs7+DW+U8o1+myP+oKybS1n5aBD+K8417krCgVCXZGScCPK2/J3SPw3yyGY/E34mPCJZdZTyjK41SGfdEdAJTqELxHRS7FIaWBcNgrACqHOyrhsVHwsl2euYYy1ANgCYLDqhkR0HhHNIyKr0FwekEP6NGZYdCBv5y1/RBe0lqJ2DhyZjybpsrhxguC6+wCiQXjezc/7dNMKV2e3qK5+pz95L3VUTZkb08Eoe84Vqu6ju15dLleX9UqD+7iHBfnnorwy1wSxSyrT57TPZg5B/G55kVH06x+6wrEeqUmw2Q+BEmKnutZ3N1igSGSkSnHLISvm5TFsSqrE36kqdMXcZZq9svT8ohn8l2dO1N6r1gzCLbOXG8+XSxCuBbAvgCkA1gD4eVyuYx5NTKUzw8kYu44xNo0xNs3WQXkB6NVQzE2WgrQLKif5PGNMa7vvsrzyHZ3SC9qgQxDjthv7Jx5b2HgGtxwNkWOa+pxuR83fvF2H4L4jV9XVEgRNu3J9kjiEPpqwINWA+D1U4VP4+WR3q2lHfKcyh8BxyOgB/h2E+r3JJSq9k12HoL9nOSay9cWCcZd/XRzGpZAQyOwGSNQfyn3jr1S1abvxmeXK+8k1RQ5h36G9tf2stVLZFqW2LILAGFvLGGtljJUA/A7A9PjUSgCiAHA0gNVx+WhFeeYaIqoD0B/uIqocxCiNW4RQtr0a6pLohdwKhaTdYDlJrEuMaQewi+Is2X20sw6BT1IXEUlB2OHJ0E1KmRvTwUtkpOIQNMuma7s5XUcF79kmphHfxUBFgEJZqewyzuQIu1yvccGx++KHp+kT5cjQxdi6e/6qnGjVGmdKgin0CT/vg9YSQ10xsjK65jF18vobnl4W9VVw1KuGyEgHeZyLBF9HtMX+1Qq2UCxlEYRYJ8DxUQDcAukeAGfFlkPjESmP5zLG1gDYRkQzYv3A2QDuFq45Jz4+A8AjrAomCIwxHPo//0r+5xzCjZ99D249b0b8HHHduE45gcxKzDBRHa6vK1Q+2ExwfpXxM5iUaxxGKyMdhxC/opdXqU0wObxiGRnu41IXyE9AmXDV0pmIATjrPWNww2em4cOHjMyfj2/N36mL2alsx84drgoFwkEG72YZaca0LFQ+LGqCoG6XxY4IpvdKnqtSa4klz/2TB8y+KakOIdsH09w3bdo47vrie/HXLxyZ/J/nMkh5rOtfrWBbZ6z8MBHdCuA4AEOIaCWA7wI4joimIBovywGcDwCMsUVEdDuAxQBaAFzIGOPC0QsQWSz1BPCP+A8ArgdwCxEtRcQZnOX6cOr+Rr/yY/eK9QPH7T8sKZMtSsrZdbcypv+IDu3Zcteq4EUQHOvxRxjcp9FYz+a7oduB8HdkC0LnxyEoREbul0f1LUrlSuiBrS9ciX/CAcOV5/k7NslWo3rpsRzDf/zgVDwhrkPrtqqdHkWQQVeUqecbywjmcen7DXvUF3Imu9q2Ex1ClksxzSm+ozfpGQb0rDeGkxGJgMn5rNY6BBuHYCUIjLFPKIqvN9S/DMBlivJ5AHI8K2NsN4Azbf1wRephmS2P7KWlupLliynbEQAM75dfLBljxlDENphM2vQB5Nzbd+V6+CPIWd5kFCiKLKma0Ywx3PfSGk37bu9In2fBLss2Qbew5XUI0S/fSdnu0bMMvZPQK+OOkN87tZCxK5XlKqIoSuRkz/ztbGvvbKId3T3lPolgcX1mUFX5iIxu+tx07D24t/M1jbERh2x2ato5c2OULQZLIVm5Lo83kSCYiFcFBpZOsOWJ6XKeyimyH0SlH5CVyrbdkFJ5VjKJjNL2bp2rzkFr1CFo+uEjUTPZZovgw1lHFD9/9HgA0WDWWSSZUku6Tlgu6xWhCz6o1CF4WxllL0g5Nma8Lmm3AiVDLD0xnhf74vJspvcsnpI9mlXI2+rr8oZ4KJVZ9M5N782HIPDAc65XcEmBPIZN+6aBvSKiKqbXlEEwZ/6rJYfwzpbd+NeifJh7FWqiQ+jI0CnDVJQ3tXyJRUYWAxvV4loyiIzEPtyksUaoS0zafPQC+nPcjJXDxWoIELgloR9H7jM4IaR8QBeIO6bl2zj6iked7uWDpeu24YBvP+ARyM5zStlERoaF65DR/a1hnk1gljoJUVJ3NYG4sJmi1XpHPJUy4+kc73ya/bf3jMnkQ1Bd6/JOyu0D5+iIKDPfTSIjbmmmi2LK7180EQThnI9E4egJQ/DefZVW+AlOv/YZnHeLmxm6TYfQ9QhC/OsWYTKrOLPJ5uUMS/waeaL99IxDonaF5nRhMYrxDPYJnGfq53WfzmavEpXELnJbcQdx8uThGBfLoAsJQdBHljQlgilXObto9dbMrx36nakK8tSUdSSmbh81YUhFIRgYY0r5+8cOi1x0bCKjR//zOPSXZNemxcaFIAwQRIaRKXLato5Tcw1uN2XMAHznQ5Os+RBM3aw0fwD3NZL1I6bxKUc/UNfJhi+XNxKNgoTCqO9QnDrYYgzAozHYxiJjrPtwCCMkD1nxg+jMSWUTw2fe2JDIGFVQcwh5q4F+POKqUKYbyAmHoJLJa/0QtF1Eg2TS5jqB+GAWzeMyMXLiCsV44Psu8PJg/fSMvb2ud7+PX31Z5yH7IZhQIPP9yuUQTp8aWWgnVkbxgbyOjB/SG3sN6Jnpq0kGbevPQxcfg6f+O/Xml3UIeg5BLUqV0buxGOcuMAdHNBEuH4Lw5vrtubKeicgo+41NO2dZtKytJ+YGN0gofEVG/3ny/pi8Vz8M7Ws2+LCNWacxba/SOcAXZZXLvc4zMJn88ZtatXmXccDt2NOSE+1EHEK2HucGxEFvS8Lj44dgXIylvrg6sPF38ZvHU8eVSBwWcwbJ+409TT1XXkfJVcXQ6l08ZP2iF6tt4arELFWnQ5BFmYnISFG5QFkxn2nzaTPEGdKnMRMHiqS2tWPYUYfAF1Ybh2AmCOo+qDgtVfygXvV1yT1cAz9yGE1lKft+5bqMMRwwoi+e++aJ3krl+mIBh44ZYJ1zNnGQS6KmLkMQ6qXgbOKr0bHRIivossAxlk/bWWLpR/zvWQcASJVQYouyJyoPtsdj//gplfV9lCfGDof0jYDelpwXJxxCgaw7YxVyE6SqAZDt8OmvuNDbxGxm80rzCswEgptrGCpjh3xdmSiZlcrm/qg8+U1jWHcd4KCMN5w3ES6XXOmciKn0cqnISJ8PQYaLyJ+IsjoE6TxjQO/GOgzt22jmEKRTfJ64zDm7yNs+CWrnl9/GkL3/mMMkEUMyuy4YH/rfp7D88g8m/5dKKYfwhWP3weeOHoel67bHfUiv47urjx42Cl88bl+MH9IbC1ZuwaQ4BpKSeGs65bMrNdlOi1DbkqcKc/56uQ5B7sP6beqcxoeNHYAX397s3Od9hujd+l2gu43u7t/50KRcmeh4Z94Vcm5Us7BbsHV3i3KSpombskRJtY4UpFSQpt2nTYcg+zDIosEFKzer21URBMUbTzkEcz4E07t0UWy3MoYC1PfoJYiMdDqEfC50UpZn15hsv+VhI0oSdN9h0449OUumQ+OQIy7cqG2K2fQHQBfiEOpM7uCa8cU9IqOUlOaX9cuzpijLS4xlxFWNdUVhcU3b5B+DEMXerysWMHXvgbn4+yLkkhMPHB7XNXY1A1mnoIPqHYlOdwVJJCf3QZfkvl8cfVauX5Y4zAG+16vCRYuiEsaAwYqwErweEO28VKHTTevvU69HuTVuefat/HXxL3+Ue2ILK9VCKZuGimvznG/MVLarg2yhRMh+p0vvWqi8Ti0yyteb/caGtB/clNbDKmzBis1GXwAOTmRVIi5uZSR729vEQUB+zIr/Eyjz7vPEI31WObwIhxwyGwAuPmk/ZX9VqAaH0GUIQjKxFR9PR5FFZZH4rj52+CiMHdQrU/eoCUMA5GO1i3J2Dt0AUoFPJhez0+s+PRX7DO1tFG/JeR8mCdFHxdSK15+TjQ/In2C8sEMvlVIrGN7PAqkdlnTy5TpBRyI+o+4JKo3RpOUQtGZGZjEMi6u8Z9xAZT0AePbNjfjlw6/nm5b+Fw0fbE5OUZ+j///24iple7wPurGuC0Wug8whuGbGU5m6qhYnHo3UpkNQ4Z0tu3Hqr5/GxbfPt9ZtMRCE1FpOCl1hFP1FkKvISXVE0ZlcV+S2deuRbDLeWFdINrouhhy28y4huLsMQeALj6yQAwwcglBXZHGv/PgUfPaocZm6BSIM69uYG/xRLCOpXUE3YUMSOMtBqUyaxViEHCSNT4o3FBYXctsAMlZWLSVRZBQd1BcLKBTyC6zO5DRVmrvt3vPKuHwd7jnKxUs6YpRpR1OukrAUBTFMdH9S7mQTEYDDLBrYqz5jeihPflW7sthFtZDkOQR3+bTuvmJbLgTalUPg4hqR87DtWvk33t4ULWYuDnW8TZMONfJDSO9t1iFkCbTqGgKMIiO+seB1VTBx8y5e46ZXuXrzLlz45xfMDaALEYT94lzKPFaRF4cA+24+MbcUBtmuPa2Yu2wjdkmKLl34DGUfDGantz2X9W6OErFHC8AkIf8CV2arwBdLmQORJ0DiqSyUf/SwUckxf1f1RYodlrLX6xZlHhupVGIZoqcVGTmsQB+MA8FNHz8IQFY2qlNWaxkExfTM7sZYbFmluDYu1IWvEIkzSTJgXWYzfn8gPyZ0fXDZ/Lggx+nCTMRvOTcKcqwM1qa4TnQK49/JlMMAAA4c2Q99FRnwLj5pPzz8tWOVfedj2Oxb4B7LKOUQDJsVyi7o+bqpv5JORyKXZ0VS9g2ViZu7Z8FqJ1+eLkMQJg7vixe+fRLOOXIcgOyOUMshJBOP5cavHPWTCnlTtZvjZBML4gTycruuljSy6SDHu9vzZnNcnCEu3L0bswsSd4wD0p27/A4mS84uiTxceBF7D+6dTAY+5+uLhZwNN6DejX3nQ5Pw+feNB8BFRuk5XUx4sd3tTS2Yo0lAMrJ/j+QdiI+mnxPpiTufT3M1KRW1wmLBLa2+GstyRZDwTlS46uNT0rrIbibMuh2+obDnzi5ImxQfCzQRnLhm6luUv++bGIWNUCmVxe+4f7xZ40WybsKExrqCciGceeAw7Bunn5XBvfNtzma6aKfyVToRsDhXCkTo2VDEuXGIF9nMmrG0HZ3e32SJFxkPlK8jcM3Y1mUIQn2RMKh3g/DxxF2TjiLzuvmP0Sx90SJRzsxQZ9crKwVFNCqc5IoFsgbWGxTvNrm5nBiSQuaA9hYiXKamgtk6owb0xEMXHwsZuW4kIq3o3/piIbdQLFy5BZt35onXp2aMTRY+WWTEdTIyxDpf+vMLyhhQjGXFGb7yaDGPrWpoiIsFn8gz9smHD7DFyc9yCNlz9U4io+x4U/W1rpBNLm8KVWLiHm4//8hcmSkzngibH4J8mih6ry5tN9QVlNxzY112EyTewkl5KllnmUJ+6bL9ZZ4x/n3PuIiwiov5hu1NmPfWJjS3RGWu+VPkb1+JyMgVXYYgyGyrxM0pIYa/lgmCzCFErulZNtNKaISyI+Id2CWzDszVF5WYm3bswVsbduDVd7Zl6hwzcUhcN+qvOOjlZ58+fhD++ZVjcP4x+ySiHLEKP54wrI9QFhXKCwq/jIftqK8rZBaKBSs248O/ekoZ1ppAGS7sy7e+mJ7T7pLS41fW6Fhchq27m3HnCyuxdutuyWRQc4XHZMkqldXhJQDh3Tg17u7EJi5A4ndWLST1Rcp40JeT9U/fj3Sn+09D8DSVyMi8OEUbirc32vUBDRoOQY4+kDE71YiMRP2YrKS1ESeVIlzkKvg3U3ET594UZfydu3xjrq+Z9pj8f/bb24aP6RlcRYldhiDUF1JtPIDM19P7IUQosfzH5nLpf58xFr/99FT0bCjm4p/ood5RTB8/CP0V4aULgoJr5pWP49ifPoY/zXkrU4fPeYrFNaLcXLVD239EX/SoL6KppRT7SqR1VBOYF7XKorK4nC86DUXKyK1P/fXTubbEa5NgcSWGh5esS855WwNJ2LY78sD+7eNvZtrS6hCcWo0gOi2JrL4M8dlk8Cicx+8fR+PUiCiu+dTh+fvzPjPzThuIzK1FTnVwb3N4Ax+IItLzDcHTVPNLxaHzMr6hcInE21hXUHPadXrClxAEiVkSRXvyAmvjKlRiLlmpzNsV8fKqLZgviZR165HcB/F+9XHOaJOOzfQIrhuiLkMQZDt5cWGwTWjGoI3PPqBnA06ePCKp70QOFPdrLTGtIlG0auEhpGUnEr5DjwaytHPUCCVFk08RqgHJiYR8X+4owy1E6gqcQ1DeMncf0cpIhE7c5rLbznJp2cXHl0PQWQ+JSZO0GwpORFUesYICVdUH/pzcqz17f2Fcis+q6Gt9kZJvtnHHHixavUXZV7G/rnC2MrJ4Kstni7E8XLXz//UnswSyvqjmEGTRq/iNdE6FMpfsanYKZBXhHLIOAchbOa6OA8/p202PTRxkvyTiqj4UTTWy+3UZgkDSb9bKSHONIM64e8GqzLmpew8EAAzuk5UDq3ZsojWOrg8tJZZENlX1Q15U5J3A0NhaJ9UhiCIjZbMZCyax330UVht84ZL7wUVd3Ia+vq6Qs2zRgZB9xyIOHtUfv/7k4Xjh2ydlyl2sjI6Jd98cP/tXmjZxjOQ/YoPOozZNkGNiw7l+JV+HP7c4LsVH45eoFlPxnVk5hEIhyVNwyi+fxJJY1Pi3L75X228fuHxnW7RTedjz96tK1zpA4qB1mzA5COXHp41JxEhy2HAO8V2LlnKtJZYxm1Y9sopDyHA4/HtLIiOVwcGI/ql/iKhLMb1qlyQ9pm/lmqK3yxCENCJkfkdqM/NiAH50/yuZc8fuNxR3XnBkJiqnzGbysBA/O/NQTbtp5ZZSSRvDhO+YRPDwF0DkJX3pKQfGfUDOysi2gy0xhp1CTKPbhdyvn5g+Fn/+/BGCDiHbj++fOhmzLz0Bxx8wDMftPxTf/fAkawwf8f5yjuL0HOGDh4xMlOUcLpucUw5O8w8TgD8+myqe5faSdj2D2yXPZxQZRb8qPS5R/lcVKsEUt4chS2xkZ0kg8nrlC9M7QlrMw8YOVPTJj0XQZcZzaTcz/5DOs6hdym1SDhrVDyL2Gx7pt/h4l8ebbKXVs6GIn5xxaObexqxlgk7gK3+Zj6sVjoUiVDoEVVgYWX+o8kqWiYSLmSyPoLzVkJPBNHdco8RaCQIR3UBE64joZaFsEBE9SESvx78DhXOXEtFSInqViE4WyqcS0cL43NUUjyIiaiSiv8Tlc4honFPPJfCXmX6QvHxPBS4eUAXumrr3oExIDEL2o+1qbkVjXSG3y1NyCK1Mba8NdZySeW9tSo5PnTIqURZyomRSKovt8n6ce9NzSblorvfjjx2M904YknAZ8k6isa6Ikf17okd9ETd+djr2HdonatdxoTDJ2VXwZXtd1zgfUZJo4sc09xCJqJJDgDwmsjtd/jrU4SjyoszvfGiSUmFcL1kZmVBvC3cqQeVvYkMqatNzNsWYIxY3H9yvhnOvfz3/vfjnV45JDDnEcdkYc6ky5HD2eT8Oyhzz3fm9C+zJl6KQ3dkykSDw8S3rS2TvbxVSEZe+DueITAt7W3EINwKYJZVdAuBhxthEAA/H/4OIJgE4C8Dk+JpriIiP4msBnAdgYvzH2zwXwCbG2AQAVwG4wqnnEsRYQUB2AbJFgHQe9NI6uG5rUxI9Mdtm9CsrrXQ6hIiFduvC829twlNL35UUWrp2o98SY1i7VR1rKO0z5xAcQuQW3BfuJDSH4yv2NZ0z7XqfuSSN7e9rZcTri0lsxMxVYwb10orDAOD9k6O4U1+fdQAOHd0fR00YIsms9RwCCQubjZNoKUXf9u+aXNYihsViP5XIUAWVv4kNqvrD+mZDaHCCK47h73/kIFz7qcNx6JgBAID+veqx/4i+StNeXc4SMVhl9CtxCMJYcc1xIDSe4zLFUNzJhjT5P77MgQaXFERUBueITCGsRamCDDdLOAeCwBh7AoDsHXQqgJvi45sAnCaU38YYa2KMLQOwFMB0IhoJoB9jbDaLSOfN0jW8rTsAzCTTLNcgIQDJJE3PmVrzCeUs7ozve2k1/vbiKmWeVZlFBqIBrdMhFBWhIGwQF26rFYyTHFitQ1DBZ+fIAwia6p944LDkWBy4KiI2+9ITcmUcXzxu38z/ew3omRz7WB9l/BCQvt8//8cMjIrbJEqJkcz9vPrDWTh1SqRX2m94X9z9paPRt0ddph4/Vm1WeNGOPa0pQdBQBB6O/aqH8sH1VPjIoXthSB+1WE2GqxGFiJZSCVt2NeOn/4z0Oj894xB88fjou/An4FZ14ljr2VDEBwRRYNoH3m5aVzdG5ZAf8pBTmV67jmN5ndjR1IKtuyIF79EThiQe+enSFS/yDvMptYrK1r33S0cnxzzUiSl68Rf+qLcEc+XQy9UhDGeMrQGA+JfP6FEAVgj1VsZlo+JjuTxzDWOsBcAWAMokokR0HhHNI6J58rnUazW/CNo4BHERUgUxS+oK7fJoleo282XNBh2CqMS04ewj9wagNnnL98N9d85FRi4hcm0So32G9k7MLlVE6fxj9snUf39sxQXYCePI/j0z/4vPPnqgn0JZB25d89aGHXho8drMPfhz8LwQQP6dqUwi5XfGL1GJ+7iY4aZnlidjU7dHemvDDgBq/YIKjtK+uLL7gnn42AEAIn3Ka2tTH5ozp41Jdrd8LBTjOefGjcacq7Az1uX4SAl0VL9F2k2LYTJMIWM+cNCIXJmc5W3yd/+Jf79+DgDg/50wQagXgVd1mU+tCYeQLT94dP/kuF7BIeza04qv3b4g+d90q/ZSKqtGLTOUm67JFzJ2HWNsGmNsmnxOtNMH3DmEukI2yNW0cXkXfg4u6//u3S/jeUHGr4M4gFpbmZEguLLmfXvUoa5AbrHNJZmqua47C23r70mThuOmz03P9EGs/8XjJyiuytdzgmLXp4L4XNxiQy5PmowXwpN/8QR27GnNLMapaCANeOcSXE+WQcs6LxE8rMfE4X1SayTNIP7d2dFU0CnT8/0ojyOWIRP1DxwU7e5bWX6ci5nSAEGp7JFFz2e8lxjDhG/+A9+7d3Hm/Pam1GRTJ+5bfvkH8V6FFz0ZJAlioEIxPhrgthBzPZFRZKTgEP76/Arc+YI5DAuHq8io3AQ5a4loJGNsTSwO4h5HKwGI8aFHA1gdl49WlIvXrCSiOgD9kRdRWcGVUiodgkkCVZQWV9N7i8xDgZtmv+XUJ7GplhLTxkE3yeTlwGkFyhMDIuDnZx6aWCKIdYHsIjtxmDr+i4+UTraYkaHS34jPZ8oYxevJ8aF0ELvh+giT9+qHZ+LY/DqzU9HQQGWvXiBgW2zxccmd6jwB2TZlfwmTyIgwsFc9+jTWWXUIR00YgtEDe3p5QfuISXR1udVbUjfuYGsry1nRcElpko6VojHixCF4GCXYNjViTmJfHQJBz1mJyv6E0MT99eMQ9HX5OxW5HPmdmGJV1VpkdA+Ac+LjcwDcLZSfFVsOjUekPJ4bi5W2EdGMWD9wtnQNb+sMAI8wD4H6CQcMw9xvzMSRscJPZflhWifqCllxjc3uvNVhEItixM079+D79y7Crj2tWmugoiAy4ko1jlEDsyISnfjr9KmjcdKk4VLd6DcTH+gE9e5ctwNVweZGL4491cTTvQdejzGm9IA+dcpeuTLx25Xjqam0MiLK7F5Fk91kIS9QQjB0ob8zbUqxc1LCohkThQJaSsxaj59jzPxeOQjui4NOV/TvM8bmyjiRV+1EZaetYoGwYccepR9C/trot9lRlAnoF9bbzkvNrX11CNwqadm7O5KglhyZPNTxb8ohqNeLB796DA6LxWwPLo7Cgpi60qjgEPJWVPrrHV41AAcOgYhuBXAcgCFEtBLAdwFcDuB2IjoXwNsAzgQAxtgiIrodwGIALQAuZIzx2XIBIoulngD+Ef8BwPUAbiGipYg4g7Pcuh6hQKn1RNTf6NdVh8AnXgITh4A8xVc1LfohXPHAq0mANp0JmmjmyB3QOH5/dlY65jLp5X6U61xkqmtqM/vuo18Xv4n0enX5J6fnFyJRTmxSuLmaZgIxJ8giM+HWEsNOQdRQL8jDdRyfsk1k3wt/H7r3XlcgtLYywZxR3zb/HkdPGILHX1tv7IfMERv7HItJ5ry5IVP+/Y8clO9D4uleyrWf5I2IH+L+hZFF1OdvzqkC8+0mxg7u3ITu6UYJRgY+BhcA5xAYTvv10znnsIzVViKmRdxvdXsTh/dNrrvtuRX4t/eMdeIQRGc4ubZpXrk+p5UgMMY+oTk1U1XIGLsMwGWK8nkAciOJMbYbMUEpB/JLUOUiMC12fOIl/THdq5CXCap21qJiSRzIZh0Ci69J2z94VH+Mk3IMq5vQt8v7waEbF36GXWYdwgWCtU8auoLlynTQDV7VdVlxn75Tl961EA985RgA2cVVdUWxEImMGusK2LmnNaPE/OPnj8C9C1ZjYK96r1AQcoRYm/UQX7hFJbYOXKfjQvSKBcI6Tf5rXbvPv71JKle0Kyh05cVbDvymCuuuA7/GJe6RjUNQ9clZZxUTR5WncG+BIIgBMwHzN+E+BXyxN+kbUh1CnlvlmDhcLQ4G3LnCcnUIHQa5iaLYkZombl6HYBAZIb+7MoUeYMgSrKIulpEgohBbVwahM9wvV1cxQXwWWx10YZEXfOf9aKgrZHwzVBPPditdH1XPnhX36bFEih5rAl8IVcR+36F98JUTo9wIfr4Nsg4hvZcKabwfcz0gVYK7iGD+JaRRtYHriuolzla1eRBFRnzx/sNn3pPpu40zVMHHHFpejM114VwXMIudRaWyLDJ66JV1ufoc3GKosa6AzTv3GL2l+T1EwijPEx5uRwVXK6NOTxByHIJiETTtfuuKlKHipvFRIAWHoFqgBS5FvLeOQ+AiCrnf6qikfrL+qM20TPd8fiIjtQ5BFcmVt+2Sn4JD10fVAm2SqTpBKfOOvoPtVfv4jkSLdvq/TVlcJ3EIZpFRxNG4iIJ48EQXcD8EF9GYqFTmhInvnPkzlkEPkmtczaEBt3GgmhvG+orwMgBwxekHS33IcuWvvqPPUsYJQn2xgEvvWogX3t6srcudWjNKZak7pkXf2ZDAqVYHhrxr5P9lzE4N1+c4BNPNFBY+SpFRwiGwzITX6RCKsenrll3NeOzVVAasatskotL1wyW5vQ+h4TtSV/j4WQAGDkHRx2xQMrd72KrxAII6cY5rOyL4os1hVypHBgwmBzax7VLJHPisHBQKhObWktPYEDkEvsGSCUk5BKEsHYLDd+F9eWnlZqd+kKZdMRlV1IfolxunHDZGv2vn2dUOGd0/E8X0B6dOxlP/fXymLvflyG6Ash0yidW6DYcgb15U2Y1M7yKnQzDUJeRlgkoRjtCWOJlWaULhcvHAbx5/I1cuQy0y8tEhuItjdPD1YG0pMVzz2Bv2ijG0nqgKerpT46TkCtWdOEdjeyPytUTAXxWZx6JzkpWRRVlcLBBaWhnui0NSmER6nECvd9QNuKJHXQFNLSWnhTzhEEqlhEPgREIWj3FlNRApZOd8Q6mOjK+Jfl3EYTodwj5DeuMbkpksnzNyIip92/nw10A+jIbMpZjs/4/dL/LnHdCzPtPOXgN65pwsiQj1RcpwCGLTg3s35BzxRLhaGXV+DiGnVI4gLiom6lgnWxkZ75UfmEoxUEaHkBb/S5N1KvKWVsReUYqMnLoa1VWEjdBzCO7t+nIIvtB9DpHD4v3dIVgAqbr0kUPzpqo2cCW/r2jrtv+YoXVsTDcJWdGgiUPYsqsZv4zlyqbvw3UePBKmKj9yOehRX0RTc2vGzl6OSpr0N9nJp5sm/r3kOD+iTqJXQzGjlJWRxthy1yHIY/OfXz0GJ2pMsj10ysrxlfdKz25ITWtP8s5YNr+D6C8hoqFYQLMioB4Qief2GAhCrf0QOgwOlxQpKj8Ek6a/rpj1LbD7IWTPqzmERGOVWVR+KoXJ5ihyGbt0a1Xbap2FGsoJ4iGf10GMFFkNjIzjww+LJ4KuXTFtIn820ftU9e1OOGBYrixjZaS4FRfB2IiefD8TAZG5Nb7A6eTzsn+Mse1CtDNnDPjKiROV+ZHTfmhP5dCjvhD5WgiPOVwKVMeRJlgq5Z6NX56ErhA6YRNH6cKym+quFcKA6+7hq+AmUk8dOVEPSYSGf8O6AuHLkg9QsmErsQxhOWT0AGUf6usKWh1Cr4aikVvu8iIjAvD4fx2Xi+FC6VqcwMRu+ngqK5XKRh1CduCJdtDZdqPJn4/OqK6ru1++PK840y1yPks7F6mIi7ErxgzKv4P3TRyK286bgcWrt+J/7lus5RDEnWoh0vxmZK+qR7PlQVARH84B2SaRfKl5Fx/9lhhDAZQqFA16JTFujcm7W/ReN9UD4jHhSMh71Bexu6UVzZlAinqOBuBxhGSRUXw/4m2IfTf3IRUZuTuEPrM06zehukdBsU5YWlfWHSL5DRUkitDKGPYZ2huPfO243LWcg2opMWXUZBkNxUKiQ2gtsUxuhD6NdRluWQaf92dOHY2fGe7RqTmEvQf31g7QkrPIiJzkk4CH2Wn8G+kQ0nJV9iQgWtxaS/kkICoWUCQIfMesyy+rMq3TPelWQSE5bnAv/OijB2tqpiIKMTb7jz+mry/imIlDleUz9hmcWFLoiFYP4Tk5Id4mTArVVbYJr9YhxCafVoIgcwj6urJsmY+5ek0o57pCAXuEcVmnS4uHaJHmC4Uuom5S13g2ix51Rexubs3MD5NVFMDNTrP29bIO4T/el8ZBsonl+Gk3s9PoV47rpLqHKomVvR/Zup88YmwmLhYgGrXEosGSIYaZINLdq7+a8xJRXywkiuPv3vMyrn9qWXKulwNBOHKfwVopRdInay86GVQspondbKwrZuKaG/0QFByCav6Jim3KEATN7ioWwcgL4XZF/lRxXbjstIPx7Q9NwlETlMFhlaZ1ulhGmwWCMPPA4fjkEXmvYA4C8PbGndjZlL431+BqtsizgF7e2Vif1yFst+gQxKZekBysdFi0egueW77JKqZQ8BbauvICxIm9abEQnZBMyW0KlJowGuhG3A/zeRGN9ZFSWeRUdClKRaUynyN8szRmUC8M6t2ASz9wAADgqyftl3ovW/vrLjLidcWNlO55vUVGyI+vz7x3XK4eF5PxzVJLien1RIneJR//SYWCECb/rheyKX/7NBaNHHsUft/+zF2OIPAJJuoNTCZriZw0hmnYEeX1EUrT0GS3lB14eg4h2lnJA04V5ldsb0Cvepx79HiDlVH0W2JRcp4PH7qXVul5tBDh0SZ2eCwOj/CbJ1LLoQbNTleGaR6mXs3q86IlBtcd2cRhYtnHrnkGu5tbrZ7bW2NCbEs7KF9rerbl70Zhql+Mbc0TDkEzJmRRps1TuSVZhG0cQr4dneKdizLFfnzt/fsp6yYcQimNO8QXxx71Rbzw7ZMw88DhQv1sSGwdynFMEwmYrn1fkZEqq6EceBJIzVCXx2HJS4aFOFknFNIBXR+4blRed+qLZuOYVuZmSdh5CYKO8hcIxQJl7HWNHEIsJ+Uw6xDclMp8cvzgvsWZ87pFMxFRSDdXyU0zns8u8mLEXqwlhnGD9THzxwzqlSROsbXLnZs2CU5OjQ47HMBmTx/96kz1Mgr6OH+uLnsWh8zxHfDtB5zzy3LsrXlv8vcyPRuPMfR/L0Y7u8i+35z+tMVRZFSg1D7dRsxV8+YHp+VjEwGpf4xo2aIXT6YbsVaLfgRIiYWdIES/Tman8W9zi13EVZZSWeqCKqVpn8Y6FAuEHU0tWLFxJx5esk77jaMUs8joDw8e1V/bBzHsvLzu8LhbOrQa8rFk7mGt0UFhCvVaV6CMk4ZpMPWoK6JJ4BAuNMTqjziEbFuqlywOgF3CLl/LIVA08eQFRhWsTWzblq+VD/odTdGu2FafO7+4hrEQg3qZOITeDfnwwCrwBX/D9rw9/RP/dXzmf66EExd3lbhPRVsy0Uv13Ul0NLodtHyt6a3JVkbNpZJRTBDt+tNn04kbgei98V2xbReoOq0jqsVCtCMVI43qmudjpqmlhP+5L8pDoAvVIta3rcsiobFBxSHouGde7OrMR8h/b52YlHN3/xEH71uzZbeyHq/bKuir/vbF92rrFigVp8pztKjYrHKUSgwvr9J7TGfu4VSrk0HUxgNmDqFHfQG7mltRXyR88bh9McKg3ClQ1omNl8kQiYQozzSxjq1MpVRWW8DY2kv7Fv1+/Lezo35ZQhBwBafr7klsz0QQxHdqIuT8vu9KBKFXQxFjpV266tFtOoSkzNEEb9zg3uhZX0xiF+Xbdlcqy9xPc4tZbiz7vJhFRqmXqm0XuPeg3rky47gssQynarMy+teid5J3buIQ6otuY42fFuewbqzxuqL1ja51ft/rnnjTeP+07ayVUUNdwRjOvlRiyWbFtHPnm0E+Lqwe6cn4yRLIKASP+j5/i7nSR5bo4yol97DW6ISoK1ImpMEBI/pq6/aojywpmluZkS3ncFFuiQNF3LX2qNdxCJG5nrzDVSXUznAIlgVeHlymXabYtgtrCSDhrA4Y0ReT99KzuqKYwcU0U+aMXO3IVV9GpVcQRVL7DMkvkBx7WkuYvFc//WIs6xAclMpp4pSS8XvIOgQbN5Eqlc3f7upPHJa/l0HpKesQTP2V728an7yeVcLFOQRhLlxw7L7KunxMiAHlTBGGZRytyJQmQrRIMvU78iFJ723yZ+H+JjpRkAge0WBHUwu2SQpkLt5TYbNHSJMuShAKiRLvRx89GMcrnJOSuoK9t4sNt0ztVfbDmZSLwtpmkr+WGMuIlwANQfDSIWT/t4mM+PPbxA7XfXoqgDSO0BWnH2JOeiMc2wY8kFfmmiLKZu6jmA9KIiF8Q9XYuGjmRAARYTLt2HIiI8Nr2ydOiynmxrWZkrY4+iGIPgs2B8MBigCEum/Ck/qoxqEM3j9R0Wrqc31CEBx1CBlfIfXCpxre2meTis+YOhpXflxvkkmSzMi4ky9Ezq78W5s40kJM+Esl5kQcW0tIcjmLMEVc8HEg7ZIEoaFYwDuxt6IpJCyAOIBX1ipCW5fyssxff/Jw4zUPL7GHG+aUf70kKlF5HrpET+Xw5RB4fVu7k2PFFycIsremCaaW+bOJOh1Abc2hmpCqnZhqMtiUyuOGROKpPS0lo1mknFfYtLb96hPROOHis93NJS3HCEQLuyi7NxHzSIfgNobdeL+0D4D9fQHp9xAjdhpT1xbJWkds1yXMuYlDy9WVqn7zlAMzibZU9cX7mghvXSwCVkUolZGGObebhRZjs9MXFVFRfQNI6tAlCUJdkZLE57YJUvRcYMV3PmvyCOyl8T7mcEkGwj/m05KH5ZH75P0LynH757CJxPi7sg7MuF3O0eg4HxXM4R2i3yZpAqmsjpQcgqJN1eZI9DtRgS++eyyRPgf3acRPzjgk7ZNhQeofJ9ThBGp3c2vG0U5GoeDmnQvIfgh2LtcVfDz8ec7bznWff8vN16M+MTs111NzCOa6LpC/q8piSAQhG63WqC+KRUaqLGcyuFiu1SF2lmh2KuL8Y/aJQ/DoOARjs9l7uFftWDC9uvpiIVm4rYu8xwIrt+Vqe29DoZB3i5/7zZm4IU4wIkJc0104GhG2d8EXeldlNSe6PqafZsVrvKOSdqRNipzFysXX4ofAIYvmZPD3ZBMZAdkNhW2tJaRK7t1S0DgZ8uZjeH91wDNe11VkVMmiWa26gKhDcCNgojGHTiYvy9UBaNkJ2UPcNoYL5CH6pEhkxOenMcBdzCEw5vLt8pkK9+rfA5eecqDV7NQVFa1oRLSciBYS0XwimheXDSKiB4no9fh3oFD/UiJaSkSvEtHJQvnUuJ2lRHQ1+eVzzEFc+FwXN8BOScVBdPrho/G9j0wuq3+qPsiUf1jfHlb9hFPcGgE2b0iVYtDULhcZ+RBGF6WyvIP/7FHjndoZ0T/PranmyC4FgRHBOak9LSUHNt6PY+OKSZvISGzrmk8dbuXC+C7UbnbqPrUcXUviutl2TeaTQPqO7RxCVKE5E4BSjdEKbl1XV/SbaagrWN+brD80Ld5FiUMwgYc5b3XQIYhmpxw/Pv2QpD8601zX8BxAdTiE4xljUxhjPBv8JQAeZoxNBPBw/D+IaBKAswBMBjALwDVExEf5tQDOAzAx/ptlvavh5RU9CIJPlE8xfMB3PjzJOVyDDSYbYlXd5Njqh5D9v5clgJbrro3X2+XIIYhwMTsVdQhzvzETXzlxorYPADDzgGG4/pxpOOs9Y3L1VDoE26tOvd31cWg4xFflYlNfYsCyd3dg9psbsGqTOj8GIBMac7uiOWGDZRGq1a5ffk+uBgx2MUn0+/c4LwSg5xCG9euBDx4yMlOmU6iKm7seDuOXkNXpGfUjklLZBC4GKjFmJUqcmxBx7H5DhXPuvjja/rhXdcapAG6Kj28CcJpQfhtjrIkxtgzAUgDTiWgkgH6MsdksepqbhWvKgo83r09iGPED++QgtmH99iasNCwOIgoeHIL8bH17qFNccrianfLT67c1oa5ASqWvDis27TS0m1fCDevXQxuc7JDRkXK7rkiYeeBw5bcsJ0K3KIrbbdE3iOPAOiIoWszuW7AaALDa4LCUJTTmlmdNHpEc2xYh8pjxrubHgNpz1th2olQ2t8vPi4rtXvX6IM2uGzyRcLpEGgVl42ZZzU6ZYLVn2byWEisjPy5FvifgHuZah0oJAgPwLyJ6nojOi8uGM8bWAED8y+36RgFYIVy7Mi4bFR/L5TkQ0XlENI+I5jGD56L4AWw7FZ+FXdxV+HAWAHDy5OHac0++/m7m/88fnReRcIiP4yMOA4C+PczRzpP4Mo4io93NJYwb0tvJf0O+hwr8lGxlpG+LE7Dq7mtE2b6s6Jfho0MoUBwB12HMZThBS8PH7Z9GkHW1JHOBz9zIcQiWfqSLpZ/Y88snTMD5x+6jqZ3vs255FMWcIwzWRUk/kNU92cxOW1pL+Nfitbl7yeDEo8TsnCAPgqm7J6D2k+JFslWcsh1rDTOOYowdDuADAC4komMMdVWPywzl+ULGrmOMTWOMTSsYF5bydAg2iCIjnzXo52ceit9+epq9IoBvffBAfOtDk7Tnfayi5Mk03DLwC8kC6/7OfLgDwDw50miV0cT75VlTLP2w6zw+NUMdtXXCsD6490tHK8/ZRGuZPogcgosOgdl3grxueg9zXXGHqwunnbZrvbVw3/IJgpVDcLYyylb44vETrMp4F4jj0DYvgGx4EMAsl68rEJau2578f8cXDOEoYvFSq4PZKQ+CqbsnoBan8X5/fdYBxvaBCgkCY2x1/LsOwN8ATAewNhYDIf7lAs6VAEQh72gAq+Py0YrysuElVhHq2pQvGZGRZeDddt6M5JiLNnQ4bUoaK6dfT7NYx4/YpecPGNFXm5qPg78rH0WqbScIZMU2LlZGnEN4jyYyq1zf9I17NdTh1Cn5WEQXzZyIgzXfpXeDe96oDIdgqct1CC4MlQ+h6SX011eHoHJU4xCf7cCR/fDMJSdo68pcom3e1Tn7Icj/28Zm9n/djlp8T07KXyJttjIZsk/AQYaAdVx/WCq5mZ2WGMPx++dzihQNHAI3jqhp+Gsi6k1EffkxgPcDeBnAPQDOiaudA+Du+PgeAGcRUSMRjUekPJ4bi5W2EdGM2LrobOGasiA+t8/iZkOdhw5BnBC2AXfSpFQGbFu0vfQjwmmbg55Y34fQmOLVqDBmoD7iquwIZXtv/NZWqyhFmUl85sMhFD0WbkK0g/vR/Uus7WbGsJUgCByCTYcgNPXzMw/F/O+836nuoN71Rp8bmRC5zg9fDsF3Pus2eaIhhAsn1FhfkERG5j40OwTj4/duLUVWYi7EvKQJJZKE0pZ8Ht5Yvx2/enRpm6TQHA7gb/EkqAPwZ8bYA0T0HIDbiehcAG8DOBMAGGOLiOh2AIsBtAC4kDHG3/AFAG4E0BPAP+K/slGJN68JDYIizDb5xYFpY+PFukP7mAmCjwWVD/EA0l2PzcRRfHQXDkHEeQY5pmx26uxZbfPOVXwrk9ihlyHpe77t9NjFIslVye1jzprdfLiPCdvQF/3irFZD0n3tC7dfcDsOXwKyW6OPasjoA81tAhHB25FRKusv4qakLqiLLYeaW0tW821uSSTrHHk7QJ5DeMHRUTBpx6u2AMbYmwBywT8YYxsAzNRccxmAyxTl8wCog7KXAR8O4Y31243nRZSrPLVRfnFC2zkE9T1UEMesC+ETE4KbkNFj+BirwxK1s5DlEGxtu+gQADWHYCIIPnqRDOG3cjRZb1eTKDGjQ7B8OvE9uYaCkI9V6N0och7uRAmwj3kufrESBOnr+WzETGgoh0NodlMqN9QVjCGvRfBYRs2t9nwFBdIH1+QEVtYhrNiot+pToRIOoV1hjCwpnLMNIFuSdhFyjlgTMqGhbQ5hQl2bb4OPstFn8gPpYHKxp+do8OAQrjjdnHdZ9lS27nZdrYwUzRjjCHmIEb2CDSIrezbFwcoQcw/xpC3chdiSzX/kREWGM2270qCxmXI+EScMWrfNvHCKj64zAsjWdyQIwpx89Z1t1vqNdcVMCArTu5u7bKNTHwBgwYrNznULlIbkkcEfRyYYC1dtcW4fqI0fQtvA8N29PPN8HNM8Fr8MG19XPX1DdjF2r+uykecEwSeKqo+d/2iD/iC6b/Sb6BAcHe/sC7FCZOQRf8nYB09xjTg2TXoMkdDYFm5x8yHnjZAhfjubiKJ3Yx3OmDo6dw8XuL5fG6ERh5dtHgHuxFyst2j1Vmt9ca5N3qsffneOm9VgNVEgyoitRHAOQdYhmOIoKe9RXtc6Nrw884Txc8rBI/UV4WaNwOEjSvDZkYosoa+FhrXtEr/OXSb/sEPSjfQ623kuMmpFsUDOYRh8vIk5+lh8Mlzho9Ph4Ys5TONCjGFv0+mIi6pd/yMSGvuizQmTbfMhw9Vk1fbORDGNS3/l5kQuR4RvdBwxou+3PzQJ+w7t43V9NdC3Rx3WbctnEwQExzQ562JrCcP7NeK5b57odI9uTxDEcTFmkHl35UMQxElqNcHzsNQxJduQIQ76TTvtSTK++5FJOHKfwTh87EDne7iA74pdA8U1NTvmfy1ThzCkTwOGWJT3gx3DkoiZq+zcXVakY7JmeuiVNGy6Lby47+7dtV0gFUv29lC0u+Dyj5nFhxyiZY8pJziHTIh8N0U6iKa91YxS4IORBiuvxMpIsm5qaS1hv+F2k3OOLkkQbHJJEa7WAEA+taMJol6gWoowwB6HR4TY7B3Pr9RXjHHAiH649bwZbq78MXzCG9hqJn4ILa5xYNz6IL/+q886zNr2vG+57aj2OCax4f0Q65vGxQ+FxPe20My+pr8cLjGouPjOlFq2HLguUDx+0EcPG+W0q5dNdF1Ewn0diN2YQeliXE1n14NG9XOu29/go6TzQ3CJxyWi0xIE0yMu3xBp1l1c0ps9VljX+PSA30LpU9eHQ/ANSVwObIsVkBIxq9VQokNo9dLX2IL8yTqE91pSJQLRYj12UC/82LKT5cHFon7YRVxyaG8djts/zeTmY5TgAxcRzPbY6MLkwMbxpeMnON/bNUIuzzR3zH72bwbk43W50Mpejfb3MKhXyjHa5usDXzEFbMjiWx/URyWQ4WIIIfsb7GkxZ+aT0WkJggumjbOLPo4YH3nDvnfffDKaSuBlqeJDEDwImEgQxEQu1YQTQXA0ZxU5BJdBvCWWs9uqujoJyXji68fjE9PVoS84xF2ri8kn33HvbRF/iATRNj7qyxRhuHAIfAPiwoX4hEF31UkcPnYg5n5jJj562Gh7ZWR38oD5mzz8tWMBuImYfQJbjvTgpsQ58YNTzeH0TQRcDm63ctNOvPj2JrSUmJ8xjHPNTggXFvNTR4zFiQcOd2KJfbgJLw7B44NxOeIJhjzRHPzxG+oK+Pi0fGjoaqBng/ui4uplunlnM/Zy+B5zYvO++SvMpnWuwfLaAtzp7guaRPEc4gI8wBLOpFyZtsvOOOXuqsttclFQv572JciU2lJGH0n8c8pBekMRTpRcpnXGkc3yvr3ygwhtjR9iVlSLBHxk/x4ZEbYc3O7oKx4FEOldvHSfzjU7IVzmCRE5y0d9REZ80LhQZ5t3sohRA3rixW+f5MTC80HiG5nVBy5OXNzywZ7hTTTVdR/EsiJNhi1lZluhUEh9LLwy+TkSUnkxtMElZlPirOixqPR20EFNHTcQR00YjB+cWjV/VADpIk8EvP7DDxj7nS6UdoogLqq29ywS8x+cZn4+cRzsO6y3sa7IjT908bGZuZcEt5Oo2/INOzF1b3NMMBGdV2TksMZVW4Z+wXHuMlIfqjy4TyOOmjDYGPZaxMDeDU7cj6vStRK47Ib4eu0TRdWnzzYdgkuS+LaAKDKqVvpVILIAGtKnAZd91G9xdSHmqcjI/j246OVzDuO4X496/OnzM7BPlc03RQJgI2KckLqIjPjuvH/Peqs/jUjAz5xqFnWJa9TwvuaNqcgh1BcLykCXKk/mpevsjnccnZZDaA/DL58MafxDu+7a/vT5GfZKnuB98Alj7ItPTt/bWocvKj65KfxMfM3Pd9Co/sr4L20NguiFXT2CUF8sYN63TvK+zmVcJN/Oo7/tY5QZwc8YId5VO1AE/r1cDFVE+DgV2r6HyCHIz8mlAKogdu9u32PtZ9IH55qdEO05MBvqCvjmKQfir4ZY6LUG33zU0mz6k0eYla6Auw4hIzLycQK0LAJfO2k/57ZqCZFDqCZBqCVSkVF7ziZ3+LxX/kQ+OgQfocP7Jw23m5x7NCgSF7ldTrCbFWJtk7mqjE7LIbigsmRyanzw4JGYMMyNzf0PhwxFNUX8AmwilVqDj1GrDkEgGD4LkK2mbwC+WoEoVXD77GTbE3yxLNfXoa3hQxC4v81HDs3ny5DhO4Re++EHnJT9PgYBJkdCTixU4lGfcO5dmiD4Ktlc8OtP6QOSdTS0Jjvz9u2HO4eQHvssQD6+GbXA4/91nBPRKRDhna2R06SL2eX7Jg7BUQ4+E7548uvHOxNc3k9dpi4RPjHEagVOaF2erkd9EQu+836nMCZcb/BFR18LVx2RD0EwxYfi4iRV8DsfrqZLE4RLPmBPGdeVkYazbl+K4KxDEEbujj3qIF7K9h10xiceODwTDqKa2Huw2TpERCLjdZikt5x7RJk9MsMWokUE313u1ARV62jgHIKLfwwA9Hew1gOiNJvLfnyKdwwkG3wMX1w5hC27smFqfPrcOfhABUzhrzmqHX+ls2HUgJ449+jxuOlz72nXfvBF0CYvFQeuSwRKnnrURTR3TQfg7F4X8uxucYgt1RHA59DOPXbTXe61fez+dh+ZWoFvPgb2cjcAcUW1iQHgJxp14RCamlvx2tqsVZGPDrHTrph7DahubJWuCCLCtz/k7hrvg7svPMpZVMOrVZtR+cVZh+EXDnGJgOqaeVYDrnqo9salpxyAEmP4wMEjrHUPGzsQyy//YBv0So8R/XvggBF9azbuqw0vpbKJQ4jPffvuRblzP7T4QojotATBtPt/38QhHcLMsCvj0DEDnOsO79eI7etbrPLSFkH28/VZ+5fbNS2e++aJ2gQjbYkhfRowcXjf9u6GE4b17YFfOhLdjoDGuqJXLKH2hhx7yYReDXW46t8OxeS98pn2dNzDF4/bFxOGuY+1TksQTLjxs9Mzi0tA++JPn5+BOcs2ZEIIqyD6eYhB46oF1wibtYaPg2NA14ZPZGEA2phOKg64HJ1Hh+GjiWgWEb1KREuJ6JJK2ioWyCmSY0DbYET/Hjh1yihrvca6Ij4cmwAOqIEMuKPgXEeP9IDugdvOm4FH//O4qrZ58mS7D4QKHYJDIKIigF8DOAnASgDPEdE9jLHF7duzgLbGFacfjE8dMRajDMlAAgK6EmbsU91Iy0CU26QcdAiCAGA6gKWMsTcBgIhuA3AqgEAQuhl6NdTVZIJ0BNz1xfc6WU8FBJQDrtB/be02jB/ibgotoqMQhFEAVgj/rwSQM8ImovMAnAcAY8faQyYEBHQkHD52YNXTkwYEyNivAoOFjqJDUAm7cjaNjLHrGGPTGGPThg6tvtIxICAgoDujoxCElQDEDC6jAaxup74EBAQEdEt0FILwHICJRDSeiBoAnAXgnnbuU0BAQEC3QofQITDGWojoSwD+CaAI4AbGWN7lLiAgICCgZugQBAEAGGP3A7i/vfsREBAQ0F3RUURGAQEBAQHtjEAQAgICAgIABIIQEBAQEBCDWDtnmyoXRLQNwDsAtjhe0r8D1B0CwDUMa636UMu2O8LzhWcrr35ne77wbCl8n28EY0ztvcYY65R/AOYBuM6jfkeoO6+9+9DVny88W/d4vvBs5T+fqX5nFxnd28nq+qCWfejKzxeerfz6tWi3I9T1QUfob62ezdp2ZxYZzWOMTWvvfvigM/bZB135+bryswFd+/m68rMB/s9nqt+ZOYTr2rsDZaAz9tkHXfn5uvKzAV37+bryswH+z6et32k5hICAgICA6qIzcwgBAQEBAVVEIAgBAQEBAQACQagYRHQDEa0jopeFskOJaDYRLSSie4moX1zeQER/iMsXENFxwjWPxTml58d/w9r+abIgojFE9CgRvUJEi4joorh8EBE9SESvx78DhWsujfNiv0pEJwvlU+PnXkpEV1M5CV+riCo/W6f/dkQ0OK6/nYh+JbXVqb+d5dm6wrc7iYiej7/R80R0gtCW37fzsXcNf0q73mMAHA7gZaHsOQDHxsefA/CD+PhCAH+Ij4cBeB5AIf7/MQDT2vt5pGcbCeDw+LgvgNcATALwEwCXxOWXALgiPp4EYAGARgDjAbwBoBifmwvgSETJkP4B4ANd6Nm6wrfrDeBoAF8A8Cuprc7+7UzP1hW+3WEA9oqPDwKwqtxvFziECsEYewLARql4fwBPxMcPAjg9Pp4E4OH4unUANgPosOZwjLE1jLEX4uNtAF5BlO70VAA3xdVuAnBafHwqgNsYY02MsWUAlgKYTkQjAfRjjM1m0Si9WbimXVCtZ2vTTnvA9/kYYzsYY08B2C220xW+ne7ZOirKeL4XGWM8odgiAD2IqLGcbxcIQm3wMoCPxMdnIs0GtwDAqURUR0TjAUxFNlPcH2K29dvtzZbLIKJxiHYicwAMZ4ytAaLBi4jbAdS5sUfFfysV5R0CFT4bR2f/djp0hW9nQ1f6dqcDeJEx1oQyvl0gCLXB5wBcSETPI2L59sTlNyD6KPMA/ALAMwBa4nOfYowdDOB98d+n27LDJhBRHwB3AvgKY2yrqaqijBnK2x1VeDaga3w7bROKss727UzoMt+OiCYDuALA+bxIUc347QJBqAEYY0sYY+9njE0FcCsieTMYYy2Msa8yxqYwxk4FMADA6/G5VfHvNgB/RgcRRxBRPaJB+SfG2F1x8dqYHeUihXVxuS439sr4WC5vV1Tp2brKt9OhK3w7LbrKtyOi0QD+BuBsxtgbcbH3twsEoQbglgpEVADwLQC/if/vRUS94+OTALQwxhbHIqQhcXk9gA8hEju1K2L2+XoArzDGrhRO3QPgnPj4HAB3C+VnxfLL8QAmApgbs7fbiGhG3ObZwjXtgmo9Wxf6dkp0kW+na6dLfDsiGgDg7wAuZYw9zSuX9e3aQ4velf4QcQBrADQjosjnArgIkWXAawAuR+oRPg7Aq4iURA8B2Dsu743I4uglREqhXyK2YGnnZzsaEYv5EoD58d8pAAYjUo6/Hv8OEq75JiKO6FUIFg2IlOcvx+d+xd9JZ3+2LvbtliMykNgej+VJXejb5Z6tq3w7RJvOHULd+QCGlfPtQuiKgICAgAAAQWQUEBAQEBAjEISAgICAAACBIAQEBAQExAgEISAgICAAQCAIAQEBAQExAkEICKgBiOgLRHS2R/1xJETMDQhoD9S1dwcCAroaiKiOMfab9u5HQIAvAkEICFAgDir2AKKgYochcjI8G8CBAK4E0AfAuwA+wxhbQ0SPIYpNdRSAe4ioL4DtjLGfEdEURN7qvRA5CH2OMbaJiKYiim+1E8BTbfd0AQFqBJFRQIAe+wO4jjF2CICtiPJZ/C+AM1gUp+oGAJcJ9Qcwxo5ljP1caudmAP8dt7MQwHfj8j8A+DJj7MhaPkRAgCsChxAQoMcKlsaG+SOAbyBKQPJgHCW5iChsCcdf5AaIqD8iQvF4XHQTgL8qym8B8IHqP0JAgDsCQQgI0EOO67INwCLDjn6HR9ukaD8goF0RREYBAXqMJSK++H8CwLMAhvIyIqqPY9BrwRjbAmATEb0vLvo0gMcZY5sBbCGio+PyT1W99wEBnggcQkCAHq8AOIeIfosowuT/AvgngKtjkU8dokRHiyztnAPgN0TUC8CbAD4bl38WwA1EtDNuNyCgXRGinQYEKBBbGd3HGDuovfsSENBWCCKjgICAgAAAgUMICAgICIgROISAgICAAACBIAQEBAQExAgEISAgICAAQCAIAQEBAQExAkEICAgICAAA/H/l0iHQWBehlQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_data['inc'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='period'>"
      ]
     },
     "execution_count": 31,
     "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": [
    "sorted_data['inc'][-200:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Etude de l'incidence annuelle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Etant donné que le gros de l'épidémie va de janvier à juillet, la période de référence entre deux minima de l'incidence, est fixée du 1er septembre de l'année $N$ au 1er septembre de l'année $N+1$. On comence en 1991."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
    "                     for y in range(1991,\n",
    "                                    sorted_data.index[-1].year)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = []\n",
    "yearly_incidence = []\n",
    "for week1, week2 in zip(first_september_week[:-1],\n",
    "                        first_september_week[1:]):\n",
    "    one_year = sorted_data['inc'][week1:week2-1]\n",
    "    assert abs(len(one_year)-52) < 2\n",
    "    yearly_incidence.append(one_year.sum())\n",
    "    year.append(week2.year)\n",
    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 36,
     "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.plot(style='*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020    221186\n",
       "2002    516689\n",
       "2018    542312\n",
       "2017    551041\n",
       "1996    564901\n",
       "2019    584066\n",
       "2015    604382\n",
       "2000    617597\n",
       "2001    619041\n",
       "2012    624573\n",
       "2005    628464\n",
       "2006    632833\n",
       "2011    642368\n",
       "1993    643387\n",
       "1995    652478\n",
       "1994    661409\n",
       "1998    677775\n",
       "1997    683434\n",
       "2014    685769\n",
       "2013    698332\n",
       "2007    717352\n",
       "2008    749478\n",
       "1999    756456\n",
       "2003    758363\n",
       "2004    777388\n",
       "2016    782114\n",
       "2010    829911\n",
       "1992    832939\n",
       "2009    842373\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aKD9g2uXzaRvR51bpoXvMS4A/7KZfBrymey6VeU3P92+QMexfAt/oDiaH9hbj2My8gLandyhAZt4MXB0Rx2bme4EfAh+l/R7kFZl5w9QCM3PRf6E4M2+LiAMj4oO0IZHVtBV2VESM9rmHaf/3+wD/TttLPT4zLwGO7Hv9s5wPnBkR50TEZES8Cfg67SfoDulq63sftwK/FxEbgK0RcQFtr+63u3c+ve4hM3+d7YO4qTH4oH2Z0SW09VDiNQ38C3BqRHwceDPwWOC7tB4O7urqew/zM4+t2dSexAXA6d30h4C3d9MPBs6j22LRxpceDTxwqbdKs/p4Je2t32bamPZXgTfRnrS97gH4OG3vaBXteNI30J6gbyy2Dj5LO0HhCNqL53Tga4XWwyO6ev+etrf2EuCdtON0X0/bsPa6h1n9fAs4uZu+sNJrunsNn0/bAXszcCZwbff8KrUeBvkb+CiRzMyIOBw4lLZlo/sPIiIupY0RL8tui5WZd2fmdzPzl4M+xmLIzHOzfUp8Lm3c8R9pb48+RY97iIgR2p7d+4DP0D7hPhZ4MXBQRFxCj+uf5XmZ+dbM/DHwVtohV++hwHro3EA7FXl5tnHSS7t5l9I+VK3QAxEx9fq/gvaBHcBZ7aoyz6ejgclse8cfoO3QfIQ6z6X5mefW7Nm0L0HZl7ZlewbtP+glwOOXeuszz16OAD4HPKS7/FLgmKWuaxf1PpD27uY82odF48Dnpl3f6/p30dfDaB8IPbhSH7RTk7d20w+ivVM4plIPXa370zaWJ8+av6HvPXTZ82fAed3l1bSdySOrrYdB/+b1AwYR8RXgN4HraccuviUzvzPwApZY9+n+CbQNzNG0YZH3Zubdu7xjD3UH9p8MTGTmT5a6nvmIiAfQNvanAL9DOyTr3MzcsaSFzVNEnEV7PRxD2/ifmW2Pu5SIuBZ4U2Z+dOo8i6WuaVARcRTtdXwXbV38M/A32c5M3OsMHNjdIVlnAtcBF2b3oUUlEbGc9n0Cv6L1UO5tUUQsA+6p9KKaS0S8gnZI5YcqrocpEfFI4D8q9jDtJLjH0T7I3lHxedXtvDwC+Gpm3rnU9QxTb38iTJI005L/4owkaTAGtiQVYWBLUhEGtiQVYWBLUhEGtiQVYWBLUhEGtiQV8X9Ln2DtibXzVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_incidence.hist(xrot=20)"
   ]
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}