{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Import des données puis affichage pour en vérifier la bonne présence"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [],
   "source": [
    "data_url = \"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [
    {
     "data": {
      "text/plain": "    Unnamed: 0  Year  Wheat  Wages\n0            1  1565   41.0   5.00\n1            2  1570   45.0   5.05\n2            3  1575   42.0   5.08\n3            4  1580   49.0   5.12\n4            5  1585   41.5   5.15\n5            6  1590   47.0   5.25\n6            7  1595   64.0   5.54\n7            8  1600   27.0   5.61\n8            9  1605   33.0   5.69\n9           10  1610   32.0   5.78\n10          11  1615   33.0   5.94\n11          12  1620   35.0   6.01\n12          13  1625   33.0   6.12\n13          14  1630   45.0   6.22\n14          15  1635   33.0   6.30\n15          16  1640   39.0   6.37\n16          17  1645   53.0   6.45\n17          18  1650   42.0   6.50\n18          19  1655   40.5   6.60\n19          20  1660   46.5   6.75\n20          21  1665   32.0   6.80\n21          22  1670   37.0   6.90\n22          23  1675   43.0   7.00\n23          24  1680   35.0   7.30\n24          25  1685   27.0   7.60\n25          26  1690   40.0   8.00\n26          27  1695   50.0   8.50\n27          28  1700   30.0   9.00\n28          29  1705   32.0  10.00\n29          30  1710   44.0  11.00\n30          31  1715   33.0  11.75\n31          32  1720   29.0  12.50\n32          33  1725   39.0  13.00\n33          34  1730   26.0  13.30\n34          35  1735   32.0  13.60\n35          36  1740   27.0  14.00\n36          37  1745   27.5  14.50\n37          38  1750   31.0  15.00\n38          39  1755   35.5  15.70\n39          40  1760   31.0  16.50\n40          41  1765   43.0  17.60\n41          42  1770   47.0  18.50\n42          43  1775   44.0  19.50\n43          44  1780   46.0  21.00\n44          45  1785   42.0  23.00\n45          46  1790   47.5  25.50\n46          47  1795   76.0  27.50\n47          48  1800   79.0  28.50\n48          49  1805   81.0  29.50\n49          50  1810   99.0  30.00\n50          51  1815   78.0    NaN\n51          52  1820   54.0    NaN\n52          53  1821   54.0    NaN",
      "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>Unnamed: 0</th>\n      <th>Year</th>\n      <th>Wheat</th>\n      <th>Wages</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1565</td>\n      <td>41.0</td>\n      <td>5.00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1570</td>\n      <td>45.0</td>\n      <td>5.05</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1575</td>\n      <td>42.0</td>\n      <td>5.08</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1580</td>\n      <td>49.0</td>\n      <td>5.12</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>1585</td>\n      <td>41.5</td>\n      <td>5.15</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>1590</td>\n      <td>47.0</td>\n      <td>5.25</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>1595</td>\n      <td>64.0</td>\n      <td>5.54</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>1600</td>\n      <td>27.0</td>\n      <td>5.61</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>1605</td>\n      <td>33.0</td>\n      <td>5.69</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>1610</td>\n      <td>32.0</td>\n      <td>5.78</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>11</td>\n      <td>1615</td>\n      <td>33.0</td>\n      <td>5.94</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>12</td>\n      <td>1620</td>\n      <td>35.0</td>\n      <td>6.01</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>13</td>\n      <td>1625</td>\n      <td>33.0</td>\n      <td>6.12</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>14</td>\n      <td>1630</td>\n      <td>45.0</td>\n      <td>6.22</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>15</td>\n      <td>1635</td>\n      <td>33.0</td>\n      <td>6.30</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>16</td>\n      <td>1640</td>\n      <td>39.0</td>\n      <td>6.37</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>17</td>\n      <td>1645</td>\n      <td>53.0</td>\n      <td>6.45</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>18</td>\n      <td>1650</td>\n      <td>42.0</td>\n      <td>6.50</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>19</td>\n      <td>1655</td>\n      <td>40.5</td>\n      <td>6.60</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>20</td>\n      <td>1660</td>\n      <td>46.5</td>\n      <td>6.75</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>21</td>\n      <td>1665</td>\n      <td>32.0</td>\n      <td>6.80</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>22</td>\n      <td>1670</td>\n      <td>37.0</td>\n      <td>6.90</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>23</td>\n      <td>1675</td>\n      <td>43.0</td>\n      <td>7.00</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>24</td>\n      <td>1680</td>\n      <td>35.0</td>\n      <td>7.30</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>25</td>\n      <td>1685</td>\n      <td>27.0</td>\n      <td>7.60</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>26</td>\n      <td>1690</td>\n      <td>40.0</td>\n      <td>8.00</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>27</td>\n      <td>1695</td>\n      <td>50.0</td>\n      <td>8.50</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>28</td>\n      <td>1700</td>\n      <td>30.0</td>\n      <td>9.00</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>29</td>\n      <td>1705</td>\n      <td>32.0</td>\n      <td>10.00</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>30</td>\n      <td>1710</td>\n      <td>44.0</td>\n      <td>11.00</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>31</td>\n      <td>1715</td>\n      <td>33.0</td>\n      <td>11.75</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>32</td>\n      <td>1720</td>\n      <td>29.0</td>\n      <td>12.50</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>33</td>\n      <td>1725</td>\n      <td>39.0</td>\n      <td>13.00</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>34</td>\n      <td>1730</td>\n      <td>26.0</td>\n      <td>13.30</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>35</td>\n      <td>1735</td>\n      <td>32.0</td>\n      <td>13.60</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>36</td>\n      <td>1740</td>\n      <td>27.0</td>\n      <td>14.00</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>37</td>\n      <td>1745</td>\n      <td>27.5</td>\n      <td>14.50</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>38</td>\n      <td>1750</td>\n      <td>31.0</td>\n      <td>15.00</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>39</td>\n      <td>1755</td>\n      <td>35.5</td>\n      <td>15.70</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>40</td>\n      <td>1760</td>\n      <td>31.0</td>\n      <td>16.50</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>41</td>\n      <td>1765</td>\n      <td>43.0</td>\n      <td>17.60</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>42</td>\n      <td>1770</td>\n      <td>47.0</td>\n      <td>18.50</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>43</td>\n      <td>1775</td>\n      <td>44.0</td>\n      <td>19.50</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>44</td>\n      <td>1780</td>\n      <td>46.0</td>\n      <td>21.00</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>45</td>\n      <td>1785</td>\n      <td>42.0</td>\n      <td>23.00</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>46</td>\n      <td>1790</td>\n      <td>47.5</td>\n      <td>25.50</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>47</td>\n      <td>1795</td>\n      <td>76.0</td>\n      <td>27.50</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>48</td>\n      <td>1800</td>\n      <td>79.0</td>\n      <td>28.50</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>49</td>\n      <td>1805</td>\n      <td>81.0</td>\n      <td>29.50</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>50</td>\n      <td>1810</td>\n      <td>99.0</td>\n      <td>30.00</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>51</td>\n      <td>1815</td>\n      <td>78.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>52</td>\n      <td>1820</td>\n      <td>54.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>53</td>\n      <td>1821</td>\n      <td>54.0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(data_url)\n",
    "raw_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "On se débarasse des années qui manquent des données, afin de ne pas les traiter"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [
    {
     "data": {
      "text/plain": "    Unnamed: 0  Year  Wheat  Wages\n50          51  1815   78.0    NaN\n51          52  1820   54.0    NaN\n52          53  1821   54.0    NaN",
      "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>Unnamed: 0</th>\n      <th>Year</th>\n      <th>Wheat</th>\n      <th>Wages</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>50</th>\n      <td>51</td>\n      <td>1815</td>\n      <td>78.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>52</td>\n      <td>1820</td>\n      <td>54.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>53</td>\n      <td>1821</td>\n      <td>54.0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[raw_data.isnull().any(axis=1)]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "On enlève ces données, puis on se débarasse de la colonne 'Unnamed 0' qui ne sert qu'à donner l'indice de la donnée, mais qui nous est sans utilitée"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [
    {
     "data": {
      "text/plain": "    Year  Wheat  Wages\n0   1565   41.0   5.00\n1   1570   45.0   5.05\n2   1575   42.0   5.08\n3   1580   49.0   5.12\n4   1585   41.5   5.15\n5   1590   47.0   5.25\n6   1595   64.0   5.54\n7   1600   27.0   5.61\n8   1605   33.0   5.69\n9   1610   32.0   5.78\n10  1615   33.0   5.94\n11  1620   35.0   6.01\n12  1625   33.0   6.12\n13  1630   45.0   6.22\n14  1635   33.0   6.30\n15  1640   39.0   6.37\n16  1645   53.0   6.45\n17  1650   42.0   6.50\n18  1655   40.5   6.60\n19  1660   46.5   6.75\n20  1665   32.0   6.80\n21  1670   37.0   6.90\n22  1675   43.0   7.00\n23  1680   35.0   7.30\n24  1685   27.0   7.60\n25  1690   40.0   8.00\n26  1695   50.0   8.50\n27  1700   30.0   9.00\n28  1705   32.0  10.00\n29  1710   44.0  11.00\n30  1715   33.0  11.75\n31  1720   29.0  12.50\n32  1725   39.0  13.00\n33  1730   26.0  13.30\n34  1735   32.0  13.60\n35  1740   27.0  14.00\n36  1745   27.5  14.50\n37  1750   31.0  15.00\n38  1755   35.5  15.70\n39  1760   31.0  16.50\n40  1765   43.0  17.60\n41  1770   47.0  18.50\n42  1775   44.0  19.50\n43  1780   46.0  21.00\n44  1785   42.0  23.00\n45  1790   47.5  25.50\n46  1795   76.0  27.50\n47  1800   79.0  28.50\n48  1805   81.0  29.50\n49  1810   99.0  30.00",
      "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>Year</th>\n      <th>Wheat</th>\n      <th>Wages</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1565</td>\n      <td>41.0</td>\n      <td>5.00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1570</td>\n      <td>45.0</td>\n      <td>5.05</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1575</td>\n      <td>42.0</td>\n      <td>5.08</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1580</td>\n      <td>49.0</td>\n      <td>5.12</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1585</td>\n      <td>41.5</td>\n      <td>5.15</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1590</td>\n      <td>47.0</td>\n      <td>5.25</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1595</td>\n      <td>64.0</td>\n      <td>5.54</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1600</td>\n      <td>27.0</td>\n      <td>5.61</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1605</td>\n      <td>33.0</td>\n      <td>5.69</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1610</td>\n      <td>32.0</td>\n      <td>5.78</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>1615</td>\n      <td>33.0</td>\n      <td>5.94</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>1620</td>\n      <td>35.0</td>\n      <td>6.01</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1625</td>\n      <td>33.0</td>\n      <td>6.12</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>1630</td>\n      <td>45.0</td>\n      <td>6.22</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>1635</td>\n      <td>33.0</td>\n      <td>6.30</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>1640</td>\n      <td>39.0</td>\n      <td>6.37</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>1645</td>\n      <td>53.0</td>\n      <td>6.45</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>1650</td>\n      <td>42.0</td>\n      <td>6.50</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>1655</td>\n      <td>40.5</td>\n      <td>6.60</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>1660</td>\n      <td>46.5</td>\n      <td>6.75</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>1665</td>\n      <td>32.0</td>\n      <td>6.80</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>1670</td>\n      <td>37.0</td>\n      <td>6.90</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>1675</td>\n      <td>43.0</td>\n      <td>7.00</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>1680</td>\n      <td>35.0</td>\n      <td>7.30</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>1685</td>\n      <td>27.0</td>\n      <td>7.60</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>1690</td>\n      <td>40.0</td>\n      <td>8.00</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>1695</td>\n      <td>50.0</td>\n      <td>8.50</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>1700</td>\n      <td>30.0</td>\n      <td>9.00</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>1705</td>\n      <td>32.0</td>\n      <td>10.00</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>1710</td>\n      <td>44.0</td>\n      <td>11.00</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>1715</td>\n      <td>33.0</td>\n      <td>11.75</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>1720</td>\n      <td>29.0</td>\n      <td>12.50</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>1725</td>\n      <td>39.0</td>\n      <td>13.00</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>1730</td>\n      <td>26.0</td>\n      <td>13.30</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>1735</td>\n      <td>32.0</td>\n      <td>13.60</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>1740</td>\n      <td>27.0</td>\n      <td>14.00</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>1745</td>\n      <td>27.5</td>\n      <td>14.50</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>1750</td>\n      <td>31.0</td>\n      <td>15.00</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>1755</td>\n      <td>35.5</td>\n      <td>15.70</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>1760</td>\n      <td>31.0</td>\n      <td>16.50</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>1765</td>\n      <td>43.0</td>\n      <td>17.60</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>1770</td>\n      <td>47.0</td>\n      <td>18.50</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>1775</td>\n      <td>44.0</td>\n      <td>19.50</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>1780</td>\n      <td>46.0</td>\n      <td>21.00</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>1785</td>\n      <td>42.0</td>\n      <td>23.00</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>1790</td>\n      <td>47.5</td>\n      <td>25.50</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>1795</td>\n      <td>76.0</td>\n      <td>27.50</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>1800</td>\n      <td>79.0</td>\n      <td>28.50</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>1805</td>\n      <td>81.0</td>\n      <td>29.50</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>1810</td>\n      <td>99.0</td>\n      <td>30.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = raw_data.dropna().copy()\n",
    "data = data.drop(columns=\"Unnamed: 0\")\n",
    "data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "A priori les données sont déjà triés, mais par précaution on s'assure que les années sont bien dans l'ordre croissant"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [
    {
     "data": {
      "text/plain": "      Wheat  Wages\nYear              \n1565   41.0   5.00\n1570   45.0   5.05\n1575   42.0   5.08\n1580   49.0   5.12\n1585   41.5   5.15\n1590   47.0   5.25\n1595   64.0   5.54\n1600   27.0   5.61\n1605   33.0   5.69\n1610   32.0   5.78\n1615   33.0   5.94\n1620   35.0   6.01\n1625   33.0   6.12\n1630   45.0   6.22\n1635   33.0   6.30\n1640   39.0   6.37\n1645   53.0   6.45\n1650   42.0   6.50\n1655   40.5   6.60\n1660   46.5   6.75\n1665   32.0   6.80\n1670   37.0   6.90\n1675   43.0   7.00\n1680   35.0   7.30\n1685   27.0   7.60\n1690   40.0   8.00\n1695   50.0   8.50\n1700   30.0   9.00\n1705   32.0  10.00\n1710   44.0  11.00\n1715   33.0  11.75\n1720   29.0  12.50\n1725   39.0  13.00\n1730   26.0  13.30\n1735   32.0  13.60\n1740   27.0  14.00\n1745   27.5  14.50\n1750   31.0  15.00\n1755   35.5  15.70\n1760   31.0  16.50\n1765   43.0  17.60\n1770   47.0  18.50\n1775   44.0  19.50\n1780   46.0  21.00\n1785   42.0  23.00\n1790   47.5  25.50\n1795   76.0  27.50\n1800   79.0  28.50\n1805   81.0  29.50\n1810   99.0  30.00",
      "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>Wheat</th>\n      <th>Wages</th>\n    </tr>\n    <tr>\n      <th>Year</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1565</th>\n      <td>41.0</td>\n      <td>5.00</td>\n    </tr>\n    <tr>\n      <th>1570</th>\n      <td>45.0</td>\n      <td>5.05</td>\n    </tr>\n    <tr>\n      <th>1575</th>\n      <td>42.0</td>\n      <td>5.08</td>\n    </tr>\n    <tr>\n      <th>1580</th>\n      <td>49.0</td>\n      <td>5.12</td>\n    </tr>\n    <tr>\n      <th>1585</th>\n      <td>41.5</td>\n      <td>5.15</td>\n    </tr>\n    <tr>\n      <th>1590</th>\n      <td>47.0</td>\n      <td>5.25</td>\n    </tr>\n    <tr>\n      <th>1595</th>\n      <td>64.0</td>\n      <td>5.54</td>\n    </tr>\n    <tr>\n      <th>1600</th>\n      <td>27.0</td>\n      <td>5.61</td>\n    </tr>\n    <tr>\n      <th>1605</th>\n      <td>33.0</td>\n      <td>5.69</td>\n    </tr>\n    <tr>\n      <th>1610</th>\n      <td>32.0</td>\n      <td>5.78</td>\n    </tr>\n    <tr>\n      <th>1615</th>\n      <td>33.0</td>\n      <td>5.94</td>\n    </tr>\n    <tr>\n      <th>1620</th>\n      <td>35.0</td>\n      <td>6.01</td>\n    </tr>\n    <tr>\n      <th>1625</th>\n      <td>33.0</td>\n      <td>6.12</td>\n    </tr>\n    <tr>\n      <th>1630</th>\n      <td>45.0</td>\n      <td>6.22</td>\n    </tr>\n    <tr>\n      <th>1635</th>\n      <td>33.0</td>\n      <td>6.30</td>\n    </tr>\n    <tr>\n      <th>1640</th>\n      <td>39.0</td>\n      <td>6.37</td>\n    </tr>\n    <tr>\n      <th>1645</th>\n      <td>53.0</td>\n      <td>6.45</td>\n    </tr>\n    <tr>\n      <th>1650</th>\n      <td>42.0</td>\n      <td>6.50</td>\n    </tr>\n    <tr>\n      <th>1655</th>\n      <td>40.5</td>\n      <td>6.60</td>\n    </tr>\n    <tr>\n      <th>1660</th>\n      <td>46.5</td>\n      <td>6.75</td>\n    </tr>\n    <tr>\n      <th>1665</th>\n      <td>32.0</td>\n      <td>6.80</td>\n    </tr>\n    <tr>\n      <th>1670</th>\n      <td>37.0</td>\n      <td>6.90</td>\n    </tr>\n    <tr>\n      <th>1675</th>\n      <td>43.0</td>\n      <td>7.00</td>\n    </tr>\n    <tr>\n      <th>1680</th>\n      <td>35.0</td>\n      <td>7.30</td>\n    </tr>\n    <tr>\n      <th>1685</th>\n      <td>27.0</td>\n      <td>7.60</td>\n    </tr>\n    <tr>\n      <th>1690</th>\n      <td>40.0</td>\n      <td>8.00</td>\n    </tr>\n    <tr>\n      <th>1695</th>\n      <td>50.0</td>\n      <td>8.50</td>\n    </tr>\n    <tr>\n      <th>1700</th>\n      <td>30.0</td>\n      <td>9.00</td>\n    </tr>\n    <tr>\n      <th>1705</th>\n      <td>32.0</td>\n      <td>10.00</td>\n    </tr>\n    <tr>\n      <th>1710</th>\n      <td>44.0</td>\n      <td>11.00</td>\n    </tr>\n    <tr>\n      <th>1715</th>\n      <td>33.0</td>\n      <td>11.75</td>\n    </tr>\n    <tr>\n      <th>1720</th>\n      <td>29.0</td>\n      <td>12.50</td>\n    </tr>\n    <tr>\n      <th>1725</th>\n      <td>39.0</td>\n      <td>13.00</td>\n    </tr>\n    <tr>\n      <th>1730</th>\n      <td>26.0</td>\n      <td>13.30</td>\n    </tr>\n    <tr>\n      <th>1735</th>\n      <td>32.0</td>\n      <td>13.60</td>\n    </tr>\n    <tr>\n      <th>1740</th>\n      <td>27.0</td>\n      <td>14.00</td>\n    </tr>\n    <tr>\n      <th>1745</th>\n      <td>27.5</td>\n      <td>14.50</td>\n    </tr>\n    <tr>\n      <th>1750</th>\n      <td>31.0</td>\n      <td>15.00</td>\n    </tr>\n    <tr>\n      <th>1755</th>\n      <td>35.5</td>\n      <td>15.70</td>\n    </tr>\n    <tr>\n      <th>1760</th>\n      <td>31.0</td>\n      <td>16.50</td>\n    </tr>\n    <tr>\n      <th>1765</th>\n      <td>43.0</td>\n      <td>17.60</td>\n    </tr>\n    <tr>\n      <th>1770</th>\n      <td>47.0</td>\n      <td>18.50</td>\n    </tr>\n    <tr>\n      <th>1775</th>\n      <td>44.0</td>\n      <td>19.50</td>\n    </tr>\n    <tr>\n      <th>1780</th>\n      <td>46.0</td>\n      <td>21.00</td>\n    </tr>\n    <tr>\n      <th>1785</th>\n      <td>42.0</td>\n      <td>23.00</td>\n    </tr>\n    <tr>\n      <th>1790</th>\n      <td>47.5</td>\n      <td>25.50</td>\n    </tr>\n    <tr>\n      <th>1795</th>\n      <td>76.0</td>\n      <td>27.50</td>\n    </tr>\n    <tr>\n      <th>1800</th>\n      <td>79.0</td>\n      <td>28.50</td>\n    </tr>\n    <tr>\n      <th>1805</th>\n      <td>81.0</td>\n      <td>29.50</td>\n    </tr>\n    <tr>\n      <th>1810</th>\n      <td>99.0</td>\n      <td>30.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data = data.set_index('Year').sort_index()\n",
    "sorted_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Affichage des données dans une format similaire à PlayFair"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sorted_data.Wheat)\n",
    "plt.plot(sorted_data[\"Wages\"], color=\"red\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Affichage des données d'une manière plus correcte, en séparant les axes des ordonnées des deux données"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmsAAAGdCAYAAABNbzR9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAACaM0lEQVR4nOzdd3hU1dbA4d+kJ6QH0iCB0EFAEBGDICpdRdonKqioKNcrKEVAuaIgKigWEEUQRVCviOIFBFQ0dMQA0qS30CEFAumkznx/nJzJpE/NTJL1Ps88zJyZnLMZQmZl773W0uh0Oh1CCCGEEMIhOdl7AEIIIYQQonwSrAkhhBBCODAJ1oQQQgghHJgEa0IIIYQQDkyCNSGEEEIIBybBmhBCCCGEA5NgTQghhBDCgUmwJoQQQgjhwFzsPQBHkJ+fz/79+wkJCcHJSeJXIYQQojrQarUkJibSoUMHXFxqbkhTc/9mJti/fz933HGHvYchhBBCCDPs3r2bTp062XsYNiPBGhASEgIo/9hhYWF2Ho0QQgghjBEfH88dd9yh/xyvqSRYA/3SZ1hYGA0aNLDzaIQQQghhipq+halm/+2EEEIIIao5CdaEEEIIIRyYBGtCCCGEEA5MgjUhhBBCCAcmwZoQQgghhAOza7C2bds2+vfvT3h4OBqNhtWrVxd7XqfT8cYbbxAWFoanpyc9e/bk1KlTxV5z/fp1hg8fjq+vL/7+/owcOZKMjIwq/FsIIYQQQtiOXYO1zMxMbr31VubPn1/m87Nnz2bevHksXLiQXbt2UadOHfr06UN2drb+NcOHD+fIkSPExMSwbt06tm3bxqhRo6rqryCEEEIIYVManU6ns/cgADQaDatWrWLgwIGAMqsWHh7Oyy+/zMSJEwFITU0lJCSEpUuX8uijj3Ls2DFat27N33//ze233w7A+vXruf/++7l06RLh4eFGXfvSpUtERERw8eJFqbMmhBBCVBO15fPbYfesnT17loSEBHr27Kk/5ufnR+fOnYmNjQUgNjYWf39/faAG0LNnT5ycnNi1a1e5587JySEtLU1/S09Pt91fRAghhBDCAg4brCUkJACUaiEREhKify4hIYHg4OBiz7u4uBAYGKh/TVlmzZqFn5+f/ta6dWsrj14IIYQQjmTBggW0a9cOX19ffH19iY6O5rffftM/n52dzejRowkKCsLb25shQ4aQmJhoxxEXcdhgzZamTJlCamqq/nb06FF7D0kIIYQQNtSgQQPeffdd9u7dy549e7jvvvsYMGAAR44cAWD8+PGsXbuWFStWsHXrVq5cucLgwYPtPGqFw/YGDQ0NBSAxMbFYc/XExETat2+vf01SUlKxr8vPz+f69ev6ry+Lu7s77u7u+sdpaWlWHLkQQgghHE3//v2LPX7nnXdYsGABO3fupEGDBixevJhly5Zx3333AbBkyRJatWrFzp07ufPOO+0xZD2HnVmLiooiNDSUjRs36o+lpaWxa9cuoqOjAYiOjiYlJYW9e/fqX7Np0ya0Wi2dO3eu8jELIYQQwoBOx7oJszjc4yHiU7Jsdpn09PRie9FzcnIqfH1BQQHLly8nMzOT6Oho9u7dS15eXrF98i1btiQyMlK/T96e7DqzlpGRwenTp/WPz549y4EDBwgMDCQyMpJx48bx9ttv06xZM6Kionj99dcJDw/XZ4y2atWKvn378txzz7Fw4ULy8vIYM2YMjz76qNGZoEIIIYSwgbQ0eOEFHvzuOwASVq6CZ4bb5FIl955PmzaN6dOnl3rdoUOHiI6OJjs7G29vb1atWkXr1q05cOAAbm5u+Pv7F3u94T55e7JrsLZnzx7uvfde/eMJEyYAMGLECJYuXcrkyZPJzMxk1KhRpKSk0LVrV9avX4+Hh4f+a7777jvGjBlDjx49cHJyYsiQIcybN6/K/y5CCCGEKPT33/DYYxAXR77GiY+6Pc6Yx/7PZpc7evQo9evX1z823OpkqEWLFhw4cIDU1FR++uknRowYwdatW202LmtxmDpr9lRb6rQIIYQQNqXVwkcfwZQpkJ9PXv0IHrnnJeKateOfab2tfjlLP7979uxJkyZNeOSRR+jRowc3btwoNrvWsGFDxo0bx/jx4604atM57J41IYQQQlQjiYnwwAMwaRLk58OQIexetZF9DVoR5udR+dfbgVarJScnh44dO+Lq6lpsn/yJEye4cOGCfp+8PTlsNqgQQgghqomYGHjiCSVg8/CAjz+G557j4t8XARwiWJsyZQr9+vUjMjKS9PR0li1bxpYtW/j999/x8/Nj5MiRTJgwgcDAQHx9fXnxxReJjo62eyYoSLAmhBBCCHPl5cHUqTB7tvL4llvghx+UP4H4VKWXd6ifp71GqJeUlMSTTz5JfHw8fn5+tGvXjt9//51evXoBMGfOHP3e95ycHPr06cNnn31m51ErJFgTQgghhOmysqBXL/jrL+Xx888r+9U8iwKz+NSbAIQ7wMza4sWLK3zew8OD+fPnM3/+/CoakfEkWBNCCCGE6aZMUQI1Pz9YvBiGDCn1kqKZNfsHa9WZBGtCCCGEMM2mTaCWyVq+HPr2LfNlarAW7m//ZdDqTLJBhRBCCGG81FR46inl/vPPlxuoASTIzJpVSLAmhBBCCOONGwcXL0LjxvD+++W+LD07j4ycfMAxskGrMwnWhBBCCGGcNWtg6VLQaODrr8Hbu9yXqkugfp6ueLnJritLSLAmhBBCiMpdvQrPPafcnzQJunat8OVqsCazapaTYE0IIYQQFdPplP1pSUnQpg3MmFHpl8SnKGU7JFiznARrQgghhKjYd9/BypXg4gLffAPlNEo35EgFcas7CdaEEEIIUb5Ll2DMGOX+tGnQoYNRX+ZIBXGrOwnWhBBCCFE2nQ6eeUYp13HHHfDqq0Z/qRTEtR4J1oQQQghRtgULlCbtnp7K8qeL8VmdCfoEA1kGtZQEa0IIIYQo7fRpJesT4N13oUULk75cnw3qLzNrlpJgTQghhBDFFRTAk08qzdrvu69oz5qRpCCudUmwJoQQQoji3n8fYmPB1xeWLAEn08IFKYhrXRKsCSGEEKLIwYPwxhvK/Y8/hshIk08hBXGtS4I1IYQQQihycuCJJyAvDx56CEaMMOs0UhDXuiRYE0IIIYTizTeVmbW6dWHRIqUHqBmkIK51SbAmhBBCCGWP2nvvKfc//xxCQsw+lVoQV2bWrEOCNSGEEKK2y8xUljy1Wnj8cRg82KLTyZ4165JgTQghhKjtXnkFTp2C+vXhk08sPp0UxLUuCdaEEEKI2mzDBpg/X7m/ZAn4+1t8SimIa10SrAkhhBC1VUoKPP20cv+FF6BXL4tPKQVxrU+CNSGEEKK2eukluHQJmjaF2bOtckopiGt9EqwJIYQQtdGqVfDtt0p3gq+/hjp1rHJaSS6wPgnWhBBCiNomKQn+9S/l/qRJ0KWL1U6tFsQNlWDNaiRYE0IIIWoTnQ5GjYKrV6FtW6UQrhXFSyao1UmwJoQQQtQmP/4IP/8Mrq7KMqi7u1VPnyDLoFYnwZoQQghRmyxcqPz5yitw661WP/0V6V5gdRKsCSGEELXFlSuwdaty/9lnbXIJKYhrfRKsCSGEELXFTz8pe9aio6FhQ5tcQgriWp8Ea0IIIURtsXy58uejj9rk9IYFcUN9JVizFocP1tLT0xk3bhwNGzbE09OTLl268Pfff+uf1+l0vPHGG4SFheHp6UnPnj05deqUHUcshBBCOKDz5yE2FjQaePhhm1xCnVXz9XChjrsUxLUWhw/Wnn32WWJiYvj22285dOgQvXv3pmfPnly+fBmA2bNnM2/ePBYuXMiuXbuoU6cOffr0ITs7284jF0IIIRzIjz8qf3bvDmFhNrmEGqyF+9fe/Wr5+fls2LCBzz//nPT0dACuXLlCRkaG2ed06GDt5s2b/O9//2P27NncfffdNG3alOnTp9O0aVMWLFiATqdj7ty5TJ06lQEDBtCuXTu++eYbrly5wurVq+09fCGEEMJx2HgJFGpfQdysrKxij8+fP0/btm0ZMGAAo0eP5urVqwC89957TJw40ezrOHSwlp+fT0FBAR4exf/RPT09+fPPPzl79iwJCQn07NlT/5yfnx+dO3cmNja23PPm5OSQlpamv6mRrxBCCFEjnToF+/aBszMMGWKzy9S2grhz5sxh0aJF+sdjx47l9ttv58aNG3h6Fr0HgwYNYuPGjWZfx6GDNR8fH6Kjo3nrrbe4cuUKBQUF/Pe//yU2Npb4+HgSEhIACAkJKfZ1ISEh+ufKMmvWLPz8/PS31q1b2/TvIYQQQtjVDz8of/bsCXXr2uwyta0g7uOPP86XX37JG2+8AcD27duZOnUqbm5uxV7XqFEj/fYtczh0sAbw7bffotPpqF+/Pu7u7sybN4/HHnsMJyfzhz5lyhRSU1P1t6NHj1pxxEIIIYSDqYIlUKh9BXEbNmzI9u3bSU5OBkCr1VJQUFDqdZcuXcLHx8fs6zh8sNakSRO2bt1KRkYGFy9eZPfu3eTl5dG4cWNCQ0MBSExMLPY1iYmJ+ufK4u7ujq+vr/5myRsohBBCOLTDh+HIEXBzg4EDbXqp2lgQ193dnfnz5wPQu3dv5s6dq39Oo9GQkZHBtGnTuP/++82+hsMHa6o6deoQFhbGjRs3+P333xkwYABRUVGEhoYWWwdOS0tj165dREdH23G0QgghhINQl0D79gV/f5teqrYXxP3www/ZsWMHrVu3Jjs7m2HDhumXQN977z2zz+vwRVB+//13dDodLVq04PTp00yaNImWLVvy9NNPo9FoGDduHG+//TbNmjUjKiqK119/nfDwcAba+LcHIYQQwuHpdFW2BCoFcaFBgwb8888/LF++nIMHD5KRkcHIkSMZPnx4sYQDUzl8sJaamsqUKVO4dOkSgYGBDBkyhHfeeQdXV1cAJk+eTGZmJqNGjSIlJYWuXbuyfv36UhmkQgghRK2zbx+cPg2entC/v00vJQVxFS4uLjz++OPWPadVz2YDQ4cOZejQoeU+r9FomDFjBjNmzKjCUQkhhBDVgLoE+uCD4O1t00vV1oK4a9asMfq1Dz30kFnXcPhgTQghhBBm0GqLgjUbL4ECJKTWroK4KmO3XWk0mjIzRY0hwZoQQghRE+3cCRcugI8P9Otn88tdSal9maCglOuwtWqTDSqEEEIIE6izagMGKHvWbKy2FcStShKsCSGEEDVNQUFR4/YqWAKF2lcQtzwbN27kwQcfpEmTJjRp0oQHH3yQDRs2WHROCdaEEEKImmbbNkhIgIAA6NWrSi5ZGwvilvTZZ5/Rt29ffHx8GDt2LGPHjsXX15f7779fXzjXHLJnTQghhKhp1CXQwYOVzgVVQM0GrW0JBoZmzpzJnDlzGDNmjP7YSy+9xF133cXMmTMZPXq0WeeVmTUhhBCiJsnLg59+Uu5X0RKoYUHc2rwMmpKSQt++fUsd7927N6mpqWafV4I1IYQQoibZuBGSkyE4GO65p0oumSAFcQGljtqqVatKHf/555958MEHzT5v7X1HhRBCiJpIXQL9v/8Dl6r5mL9SSwviAsybN09/v3Xr1rzzzjts2bJF36N8586d7Nixg5dfftnsa0iwJoQQQtQUOTmwcqVyv4qWQKH2FsQFmDNnTrHHAQEBHD16lKNHj+qP+fv789VXXzF16lSzriHBmhBCCFFTrF8PaWlQvz7cdVeVXba2FsQFOHv2rM2vIXvWhBBCiJpCra02dCg4Vd1HvBTEtS0J1oQQQoiaoKBAmVkDGDKkSi99pRYvg1YFCdaEEEKImmDfPrh+HXx9oXPnKr20OrMWXguXQauCBGtCCCFETfDHH8qfPXpUWRaoSgri2pYEa0IIIURNoAZrvXtX6WWrS0HcWbNm0alTJ3x8fAgODmbgwIGcOHGi2GvuueceNBpNsdvzzz9vpxEXkWBNCCGEqO7S0+Gvv5T7VRysVZeCuFu3bmX06NHs3LmTmJgY8vLy6N27N5mZmcVe99xzzxEfH6+/zZ4926TrbN++nccff5zo6GguX74MwLfffsuff/5p9tgd910VQgghhHG2bIH8fGjSBBo3rtJLV5eCuOvV5ItCS5cuJTg4mL1793L33Xfrj3t5eREaGmrWNf73v//xxBNPMHz4cPbv309OTg4AqampzJw5k19//dWs88rMmhBCCFHd/f678mcVz6qBYxTETU9PJy0tTX9Tg6SKqL06AwMDix3/7rvvqFu3Lm3atGHKlClkZWUZPY63336bhQsX8sUXX+Dq6qo/ftddd7Fv3z6jz1OSzKwJIYQQ1Z26X61Pnyq/dFFBXPsFa61bty72eNq0aUyfPr3c12u1WsaNG8ddd91FmzZt9MeHDRtGw4YNCQ8P5+DBg7zyyiucOHGClWpXiEqcOHGi2Cydys/Pj5SUFKPOURYJ1oQQQojq7OxZOHUKnJ3h3nur/PJFBXHttwx69OhR6tevr3/s7u5e4etHjx7N4cOHS+0jGzVqlP5+27ZtCQsLo0ePHsTFxdGkSZNKxxEaGsrp06dp1KhRseN//vknjS1YnpZlUCGEEKI6i4lR/oyOVmqsVTFHKIjr4+ODr6+v/lZRsDZmzBjWrVvH5s2badCgQYXn7VxYr+706dNGjeO5555j7Nix7Nq1C41Gw5UrV/juu++YOHEi//73v43/C5UgM2tCCCFEdWankh2q6lIQV6fT8eKLL7Jq1Sq2bNlCVFRUpV9z4MABAMLCwoy6xquvvopWq6VHjx5kZWVx99134+7uzsSJE3nxxRfNHrsEa0IIIUR1lZ8PGzcq9+0crDl6QdzRo0ezbNkyfv75Z3x8fEhISACU/WSenp7ExcWxbNky7r//foKCgjh48CDjx4/n7rvvpl27dkZdQ6PR8NprrzFp0iROnz5NRkYGrVu3xtvb26KxS7AmhBBCVFd79kBKCvj7w+23V/nl07PzSK8GBXEBFixYACiFbw0tWbKEp556Cjc3NzZs2MDcuXPJzMwkIiKCIUOGMHXqVJOv5ebmVirpwRISrAkhhBDVlboE2rOnkmBQxapLQVxQlkErEhERwdatW00+7+DBg41+rbFZpSVJgoEQQghRXdl5v9oVB8gEtTc/Pz/9zdfXl40bN7Jnzx7983v37mXjxo34+fmZfQ3HDoOFEEIIUbbUVNi5U7nfq5ddhqAWxA3zd+wlUFtasmSJ/v4rr7zC0KFDWbhwIc6FM50FBQW88MIL+FqQqSsza0IIIUR1tHkzFBRA8+ZQoq5XVXGEgriO5KuvvmLixIn6QA3A2dmZCRMm8NVXX5l9XgnWhBBCiOrIji2mVI5QENeR5Ofnc/z48VLHjx8/jlarNfu8sgwqhBBCVEd2bDGlcoSCuI7k6aefZuTIkcTFxXHHHXcAsGvXLt59912efvpps88rwZoQQghR3cTFwZkz4OoKJUpRVKXqUhC3qnzwwQeEhoby4YcfEh8fDygFdSdNmsTLL79s9nklWBNCCCGqG3VWrUsXsLDgqiWqS0HcquLk5MTkyZOZPHkyaWlpABYlFqgkWBNCCCGqGzuX7ABIvVl9CuLagzWCNJUkGAghhBDVSV4ebNqk3LdTsJadV8BL3+8HoL6/p8MXxK3uHDpYKygo4PXXXycqKgpPT0+aNGnCW2+9VawKsU6n44033iAsLAxPT0969uzJqVOn7DhqIYQQwoZ274a0NAgKgg4dqvzy2XkF/OvbvWw9eRVPV2c+HHprlY+htnHoYO29995jwYIFfPrppxw7doz33nuP2bNn88knn+hfM3v2bObNm8fChQvZtWsXderUoU+fPmRnZ9tx5EIIIYSN2LHFVMlA7aunOnFn46AqHUNt5NDzln/99RcDBgzggQceAKBRo0Z8//337N69G1Bm1ebOncvUqVMZMGAAAN988w0hISGsXr2aRx991G5jF0IIIWzCTvvVygrUoptIoFYVHDpY69KlC4sWLeLkyZM0b96cf/75hz///JOPPvoIgLNnz5KQkEDPnj31X+Pn50fnzp2JjY0tN1jLyckhJydH/zg9Pd22fxEhhBDCGm7cUJZBoUpbTEmgZpwZM2ZU+Pwbb7xh1nkdOlh79dVXSUtLo2XLljg7O1NQUMA777zD8OHDAUhISAAgJCSk2NeFhITonyvLrFmzePPNN203cCGEEMIWNm4ErRZatYKIiCq5pARqxlu1alWxx3l5eZw9exYXFxeaNGlSM4O1H3/8ke+++45ly5Zxyy23cODAAcaNG0d4eDgjRoww+7xTpkxhwoQJ+seXL1+mdevW1hiyEEIIYTtV3LVAAjXT7N+/v9SxtLQ0nnrqKQYNGmT2eR06WJs0aRKvvvqqfjmzbdu2nD9/nlmzZjFixAhCQ0MBSExMJCwsTP91iYmJtG/fvtzzuru74+7urn+sFq4TQgghHJZOV6X71SRQsw5fX1/efPNN+vfvzxNPPGHWORw6GzQrKwsnp+JDdHZ21jdDjYqKIjQ0lI0bN+qfT0tLY9euXURHR1fpWIUQQgibOnUKzp8HNze4+26bXionXwI1a0pNTSU1NdXsr3fombX+/fvzzjvvEBkZyS233ML+/fv56KOPeOaZZwDQaDSMGzeOt99+m2bNmhEVFcXrr79OeHg4AwcOtO/ghRBCCGtSZ9W6doU6dWx6qTkxpyRQM8O8efOKPdbpdMTHx/Ptt9/Sr18/s8/r0MHaJ598wuuvv84LL7xAUlIS4eHh/Otf/yq2QW/y5MlkZmYyatQoUlJS6Nq1K+vXr8fDQ1pfCCGEqEGqaAl0/4UbLNoWB8CcR9pLoGaCOXPmFHvs5OREvXr1GDFiBFOmTDH7vBqdYTuAWurSpUtERERw8eJFGjRoYO/hCCGEEMVlZUFwMGRmwt69cNttNrlMdl4BD37yJ6eTMhjYPpy5j1Z9hwRT1JbPb4fesyaEEEII4NdflUCtYUObtpiau+EUp5MyqOvtzrT+t9jsOsI0Zi2DfvvttyxcuJCzZ88SGxtLw4YNmTt3LlFRUfpOAkIIIYSwkuXLlT8ffRQ0GptcwnD5c+agNgTUcbPJdWq6PXv28OOPP3LhwgVyc3OLPbdy5UqzzmnyzNqCBQuYMGEC999/PykpKRQUFADg7+/P3LlzzRqEEEIIIcqRng6//KLcf+QRm1wiO6+AST8dRKuDge3D6X1LqE2uU9MtX76cLl26cOzYMVatWkVeXh5Hjhxh06ZN+Pn5mX1ek4O1Tz75hC+++ILXXnsNZ4MGsrfffjuHDh0yeyBCCCGEKMOaNZCdDc2bQwU1RC0hy5/WMXPmTObMmcPatWtxc3Pj448/5vjx4wwdOpTIyEizz2tysHb27Fk6lLFe7u7uTmZmptkDEUIIIUQZbLwEeuBiiix/WklcXBwPPPAAAG5ubmRmZqLRaBg/fjyLFi0y+7wmB2tRUVEcOHCg1PH169fTqlUrswcihBBCiBJu3IDff1fu22AJNDuvgIkr/kGrgwGy/GmxgIAA0tPTAahfvz6HDx8GICUlhaysLLPPa3KCwYQJExg9ejTZ2dnodDp2797N999/z6xZs/jyyy/NHogQQgghSli1CvLyoG1bsEEPa8Plz+my/Gmxu+++m5iYGNq2bcvDDz/M2LFj2bRpEzExMfTo0cPs85ocrD377LN4enoydepUsrKyGDZsGOHh4Xz88cf6Hp5CCCGEsALDJVArk+VP6/v000/Jzs4G4LXXXsPV1ZW//vqLIUOGMHXqVLPPa1FR3KysLDIyMggODjZ7AI6gthTVE0IIUY0kJUF4OBQUwOnT0KSJ1U5tWPx2QPtwPnbw4rflqS2f3xYVxfXy8qr2gZoQQgjhkP73PyVQu/12qwZqIMufthQXF8fUqVN57LHHSEpKAuC3337jyJEjZp/T5GAtMTGRJ554gvDwcFxcXHB2di52E0IIIYQV2GgJ9OAlWf60la1bt9K2bVt27drFypUrycjIAOCff/5h2rRpZp/X5D1rTz31FBcuXOD1118nLCwMjY0qKQshhBC11uXLsH27cn/oUKuees2BK2h10PeWUMn+tLJXX32Vt99+mwkTJuDj46M/ft999/Hpp5+afV6Tg7U///yT7du3095GhfmEEEKIWm/FCtDp4K67ICLCqqe+nqW0QGof6W/V8wo4dOgQy5YtK3U8ODiYa9eumX1ek5dBIyIisCAnQQghhBCVsWEWaGpWHgD+nq5WP3dt5+/vT3x8fKnj+/fvp379+maf1+Rgbe7cubz66qucO3fO7IsKIYQQohxnz8KuXeDkBP/3f1Y/fcrNwmDNS4I1a3v00Ud55ZVXSEhIQKPRoNVq2bFjBxMnTuTJJ580+7wmL4M+8sgjZGVl0aRJE7y8vHB1Lf6Pff36dbMHI4QQQtR6P/6o/HnPPRBq/T1lqYXBmp+nJBZY28yZMxk9ejQREREUFBTQunVrCgoKGDZsmEV11kwO1ubOnWv2xYQQQghRCRsugQKkZMnMmq24ubnxxRdf8MYbb3Do0CEyMjLo0KEDzZo1s+i8JgdrI0aMsOiCQgghhCjHiRNw4AC4uMDgwVY/vU6nI/WmkmAgwZrtRERE6GfXDh06xI0bNwgICDD7fEbtWUtLSyt2v6KbEEIIIcz0ww/Kn716QVCQ1U+flVtAXoGSJOgvy6BWN27cOBYvXgxAQUEB3bt357bbbiMiIoItW7aYfV6jZtYCAgKIj48nODgYf3//Mmur6XQ6NBoNBQUFZg9GCCGEqLV0OtsvgRbuV3NzccLD1aImRqIMP/30E48//jgAa9eu5cyZMxw/fpxvv/2W1157jR07dph1XqOCtU2bNhEYGAjA5s2bzbqQEEIIISpw+DAcOwZubjBggE0ukVJYY83f01WK2tvAtWvXCC1MCvn1118ZOnQozZs355lnnuHjjz82+7xGBWvdu3cv874QQgghrESdVbv/fvDzs8klUiW5wKZCQkI4evQoYWFhrF+/ngULFgCQlZVlUUtOkxMMVFlZWVy4cIHc3Nxix9u1a2f2YIQQQohaSacr2q9moyVQMKixJvvVbOLpp59m6NCh+nacPXv2BGDXrl20bNnS7POaHKxdvXqVp59+mt9++63M52XPmhBCCGGivXshLg68vODBB212GbVsh5/MrNnE9OnTadOmDRcvXuThhx/G3d0dAGdnZ1599VWzz2tysDZu3DhSUlLYtWsX99xzD6tWrSIxMZG3336bDz/80OyBCCGEELWWugTavz/UqWOzy6TcLNqzJmzj/0p0nUhJSbG47JnJqSCbNm3io48+4vbbb8fJyYmGDRvy+OOPM3v2bGbNmmXRYIQQQohaR6st6lpgwyVQkD1rtvbee+/xg7qcDQwdOpSgoCAaNGjAwYMHzT6vycFaZmYmwcHBgFLS4+rVqwC0bduWffv2mT0QIYQQolb64w+4eBF8faFvX5teqqh7gexZs4WFCxcSEREBQExMDDExMfz222/07duXiRMnmn1ek5dBW7RowYkTJ2jUqBG33norn3/+OY0aNWLhwoWEhYWZPRAhhBCiVnr/feXPZ58FDw+bXkpdBvWVZVCbSEhI0Adr69atY+jQofTu3ZtGjRrRuXNns89r8sza2LFjiY+PB2DatGn89ttvREZGMm/ePGbOnGn2QIQQQohaZ98+2LQJnJ1h7FibX04/sybBmk0EBARw8eJFANavX6/PBtXpdBYlYJo8s6ZW5gXo2LEj58+f5/jx40RGRlK3bl2zByKEEELUOuqs2qOPQmSkzS+XelP2rNnS4MGDGTZsGM2aNSM5OZl+/foBsH//fpo2bWr2ec2us6by8vLitttus/Q0QgghRO1y7hysWKHcnzSpSi5ZNLMme9ZsYc6cOTRq1IiLFy8ye/ZsvL29AYiPj+eFF14w+7wmB2s6nY6ffvqJzZs3k5SUhFarLfb8ypUrzR6MEEIIUWvMmQMFBUrT9ltvrZJL6kt3yMyaTbi6upaZSDB+/HiLzmtWnbXPP/+ce++9l5CQEOktJoQQQpjq+nX48kvlfhXNqmXnFZCdp0ywSFFc61mzZg39+vXD1dWVNWvWVPjahx56yKxrmBysffvtt6xcuZL777/frAsKIYQQtd6CBZCVpcyoFW5CtzV1v5qzkwYfd4t3QYlCAwcOJCEhgeDgYAYOHFju6zQajdlJBib/a/n5+dG4cWOzLiaEEELUetnZ8Mknyv2JE6GKVqj0raY8XWVVzIoMt4OV3BpmLSaX7pg+fTpvvvkmN2/etMV4SmnUqBEajabUbfTo0QBkZ2czevRogoKC8Pb2ZsiQISQmJlbJ2IQQQgiT/fe/kJgIERHwyCNVdtmULGk1VV2ZHKwNHTqUGzduEBwcTNu2bbntttuK3azt77//Jj4+Xn+LiYkB4OGHHwaUTXtr165lxYoVbN26lStXrjB48GCrj0MIIYSwmFYLH3yg3B83DlyrLnBKuVm7m7jPmjWLTp064ePjo1+yPHHiRLHXWGMCaOvWrfTv35+mTZvStGlTHnroIbZv327R2E1eBh0xYgR79+7l8ccfr5IEg3r16hV7/O6779KkSRO6d+9OamoqixcvZtmyZdx3330ALFmyhFatWrFz507uvPNOm45NCCGEMMm6dXDiBPj5wXPPVemlU2t5QdytW7cyevRoOnXqRH5+Pv/5z3/o3bs3R48epU6dOoAyAfTLL7+wYsUK/Pz8GDNmDIMHD2bHjh1GXeO///0vTz/9NIMHD+all14CYMeOHfTo0YOlS5cybNgw8wavM5GXl5du+/btpn6ZVeTk5OiCgoJ077zzjk6n0+k2btyoA3Q3btwo9rrIyEjdRx99VO55srOzdampqfrb0aNHdYDu4sWLthy+EEKI2q5rV50OdLpXXqnyS3++9bSu4SvrdOOW76/ya9vKxYsXzf78TkpK0gG6rVu36nQ6nS4lJUXn6uqqW7Fihf41x44d0wG62NhYo87ZsmXLMuOPDz/8UNeyZUuTx6gyeRk0IiICX19f8yJDC61evZqUlBSeeuopQOnB5ebmhr+/f7HXhYSEkJCQUO55Zs2ahZ+fn/7WunVrG45aCCGEAHbuhD//VJY+C2ddqpKaDepXA2fW0tPTSUtL099ycnIq/ZrU1FQAAgMDAdi7dy95eXn6FlEALVu2JDIyktjYWKPGcebMGfr371/q+EMPPcTZs2eNOkdZTA7WPvzwQyZPnsy5c+fMvqi5Fi9eTL9+/QgPD7foPFOmTCE1NVV/O3r0qJVGKIQQQpRDbS01fDhY+DlmDn33ghq4Z61169bFJmFmzZpV4eu1Wi3jxo3jrrvuok2bNoD5E0CGIiIi2LhxY6njGzZs0Dd4N4dZvUGzsrJo0qQJXl5euJbYHHn9+nWzB1OR8+fPs2HDhmIdEkJDQ8nNzSUlJaXYm5uYmEhoaGi553J3d8fd3V3/OC0tzSZjFkIIIQA4fRpWrVLul1HhviqoCQY1cc/a0aNHqV+/vv6x4Wd8WUaPHs3hw4f5888/rTqOl19+mZdeeokDBw7QpUsXQNmztnTpUj7++GOzz2tysDZ37lyzL2aJJUuWEBwczAMPPKA/1rFjR1xdXdm4cSNDhgwB4MSJE1y4cIHo6Gi7jFMIIYQo5aOPQKeD+++HW26xyxD0CQZeNa8vqI+Pj9FbtMaMGcO6devYtm0bDRo00B83dwLI0L///W9CQ0P58MMP+fHHHwFo1aoVP/zwAwMGDDD+L1SCWdmgVU2r1bJkyRJGjBiBi0vRkP38/Bg5ciQTJkwgMDAQX19fXnzxRaKjoyUTVAghhGO4ehWWLFHuV1FrqbKofUFra+kOnU7Hiy++yKpVq9iyZQtRUVHFnrfWBNCgQYMYNGiQVcduVr+JuLg4lixZQlxcHB9//DHBwcH89ttvREZGcosNfmPYsGEDFy5c4Jlnnin13Jw5c3BycmLIkCHk5OTQp08fPvvsM6uPQQghhDDL/PlK14KOHaF7d7sNI6WWl+4YPXo0y5Yt4+eff8bHx0e/D83Pzw9PT0+rTgDt2bOHY8eOAcp+uo4dO1o2eFPTR7ds2aLz9PTU9ezZU+fm5qaLi4vT6XQ63axZs3RDhgwxOy3VnixJ/RVCCCHKlZmp0wUFKeU6li+361DavLFe1/CVdbozVzPsOg5rMuXzGyjztmTJEv1rbt68qXvhhRd0AQEBOi8vL92gQYN08fHxJo2na9euOo1GowsICNAFBAToNBqN7q677rIoxjA5G/TVV1/l7bffJiYmBje3onXv++67j507d1oWOQohhBA1ybvvQnIyNGoEhUtr9pBXoCU9Jx+ovTNrOp2uzJtaDgzAw8OD+fPnc/36dTIzM1m5cqXR+9UAnn32WfLy8jh27BjXr1/n+vXrHDt2DK1Wy7PPPmv22E1eBj106BDLli0rdTw4OJhr166ZPRAhhBCiRtm9G2bOVO6/9x64mLXzyCrSCjNBAXxrabBWFbZu3cpff/1FixYt9MdatGjBJ598Qrdu3cw+r8kza/7+/sTHx5c6vn///mJps0IIIUStdfMmPPkkFBTAo4/C0KF2HY5atsPXwwVnJ9u2iazNIiIiyMvLK3W8oKDAohqxJgdrjz76KK+88goJCQloNBq0Wi07duxg4sSJPPnkk2YPRAghhKgxpkxReoCGhSkJBnaWUoPLdjiS999/nxdffJE9e/boj+3Zs4exY8fywQcfmH1ek+dkZ86cyejRo4mIiKCgoIDWrVtTUFDAsGHDmDp1qtkDEUIIIWqEzZtBLYC6eDEUtjOyp9TCsh01sXuBvQUEBKDRFM1WZmZm0rlzZ32psfz8fFxcXHjmmWcYOHCgWdcwOVhzc3Pjiy++4I033uDQoUNkZGTQoUMHmjVrZtYAhBBCiBojLQ3UDeujRkG/fnYdjkqdWauJfUHtrSqaBZi92zEiIkI/u3bo0CFu3LhBQECANccmhBBCVC/jxsGFCxAVBRYse1mbBGu2UxXNAkzeszZu3DgWL14MKBvmunfvzm233UZERARbtmyx9viEEEKI6mHtWqVTgUYDX38NPj72HpGevi+oLINWSyYHaz/99BO33norAGvXruXMmTMcP36c8ePH89prr1l9gEIIIYTDu3YNnntOuf/yy2BBmQZbSM0q3LPmKQkG1ZHJwdq1a9f0BeJ+/fVXhg4dSvPmzXnmmWc4dOiQ1QcohBBCODSdDv79b0hMVJq0v/WWvUdUisysVW8mB2shISEcPXqUgoIC1q9fT69evQDIysrC2dnZ6gMUQgghHNr338NPPylFb7/5Bjw87D2iUmTPWvVmcrD29NNPM3ToUNq0aYNGo6Fnz54A7Nq1i5YtW1p9gEIIIYTDunwZRo9W7r/+Otx2m33HU46imTVZBq0Kp0+f5vfff+fmzZuA0urKEiZng06fPp02bdpw8eJFHn74Ydzd3QFwdnbm1VdftWgwQgghRLWh08HIkZCSAp06KYVwHZR+z5osg9pUcnIyjzzyCJs2bUKj0XDq1CkaN27MyJEjCQgI4MMPPzTrvGaV7vi///u/UseqInVVCCGEcBiffQa//64se37zDbg6biCkn1mTZVCbGj9+PC4uLly4cIFWrVrpjz/yyCNMmDChaoM1IYQQolbbswcmTFDuv/suOPA2IK1WR2phsOYnM2s29ccff/D777/ToEGDYsebNWvG+fPnzT6vyXvWhBBCiFrtxg14+GHIzYWBA+Gll+w9ogqlZ+ejbpmSBAPbyszMxMvLq9Tx69ev67eNmUOCNSGEEMJYOp3STurcOWjcuKgIrgNTZ9W83Jxxd5GqDbbUrVs3vvnmG/1jjUaDVqtl9uzZ3HvvvWaf16Rl0Pz8fJYtW0afPn0ICQkx+6JCCCFEtfTBB7BmDbi5wYoV4O9v7xFVKkVt4i6zajY3e/ZsevTowZ49e8jNzWXy5MkcOXKE69evs2PHDrPPa9LMmouLC88//zzZ2dlmX1AIIYSolrZvL8r4/Phjhy3TUZK+xpqU7bC5Nm3acPLkSbp27cqAAQPIzMxk8ODB7N+/nyZNmph9XpMTDO644w4OHDhAw4YNzb6oqP6uZeSw5cRVHmgbhqebTKsLIWq4pCR49FEoKIBhw+Bf/7L3iIwmmaBVy8/Pz+rtN00O1l544QUmTJjAxYsX6dixI3Xq1Cn2fLt27aw2OOG45m44yX93XiA3X8uwzpH2Ho4QQthOQQEMHw5XrkCrVvD55w6/T82Q1FizrYMHDxr9WnNjJJODtUcffRSAlwyyXzQaDTqdDo1GQ0FBgVkDEdXL6aQMABLSZElcCFHDvfUWbNgAXl5KWylvb3uPyCTqMqgEa7bRvn37YnGQSu1aYHjM3BjJ5GDt7NmzZl1I1CxXUpQgLSM7384jEUIIG/rjD5gxQ7n/+efQurV9x2MGdRnUz1P2rNmCYVy0f/9+Jk6cyKRJk4iOjgYgNjaWDz/8kNmzZ5t9DZODNdmrJrRaHfGpSr+zjJw8O49GCCFs5NIlZflTp4NRo+Dxx+09IrPIzJptGcZFDz/8MPPmzeP+++/XH2vXrh0RERG8/vrrDBw40KxrmN3B4OjRo1y4cIHc3Nxixx966CFzTymqiWsZOeQVKNO7GTkysyaEqIHy8pSEgmvXoH17JfuzmkqV0h1V5tChQ0RFRZU6HhUVxdGjR80+r8nB2pkzZxg0aBCHDh3Sr9FC0Zqs7Fmr+S6l3NTfT5dlUCFETfTGG7BjB/j6KvvUPDzsPSKzycxa1WnVqhWzZs3iyy+/xM1NWXbOzc1l1qxZxXqFmsrkYG3s2LFERUWxceNGoqKi2L17N8nJybz88st88MEHZg9EVB9XDIK1TJlZE0LUNAcOwPvvK/e/+gosqI/lCGTPWtVZuHAh/fv3p0GDBvrMz4MHD6LRaFi7dq3Z5zU5WIuNjWXTpk3UrVsXJycnnJyc6Nq1K7NmzeKll15i//79Zg9GVA+GwZosgwohapSCAqWGWkGB0v9zyBB7j8hiMrNWde644w7OnDnDd999x/HjxwF45JFHGDZsWKlSZ6YwOVgrKCjAx8cHgLp163LlyhVatGhBw4YNOXHihNkDEdWHmgkKkg0qhKhhFi6E3buV5c+5c+09GovpdLqiPWsSrFWJOnXqMGrUKKue0+RgrU2bNvzzzz9ERUXRuXNnZs+ejZubG4sWLaJx48ZWHZxwTJcN96zJzJoQoqa4cgX+8x/l/syZEB5u3/FYQVZugT4hzF+WQastk4O1qVOnkpmZCcCMGTN48MEH6datG0FBQfzwww9WH6BwPCWXQUsWAhRCiGpp/HhIS4NOneD55+09GqtQ96u5uTjh4WpSO3DhQEwO1vr06aO/37RpU44fP87169cJCAiQD+xawjBY0+mU39zquJtdBUYIIezvt9/gxx/B2RkWLVL+rAFSCltN+Xm6ymd0NWbRJ+zFixcBiIiIsMpghOPLys3nRlbxQriZOfkSrAkhqq+sLHjhBeX+2LFKXbUaIjVLmrjXBCbPiebn5/P666/j5+dHo0aNaNSoEX5+fkydOpW8PKlmX9Ops2o+Hi74eigBmuxbE0JUa2+9BefOQUQEvPmmvUdjVeoyqCQXVI3GjRuTnJxc6nhKSopF+/pNng558cUXWblyJbNnzy7W92r69OkkJyezYMECswcjHN/lwkzQ+v6epN3MIy07XzJChRDV1+HDoNYI/fTTatekvTJq2Q6psVY1zp07V2ZzgJycHC5fvmz2eU0O1pYtW8by5cvp16+f/pja9+qxxx6zerB2+fJlXnnlFX777TeysrJo2rQpS5Ys4fbbbweUtORp06bxxRdfkJKSwl133cWCBQto1qyZVcchFOrMWri/J1qdDlKl1poQoprSapWaavn5MHAg1MB2iSlStqNKrFmzRn//999/x8/PT/+4oKCAjRs30qhRI7PPb3Kw5u7uXuYFo6Ki9K0VrOXGjRvcdddd3Hvvvfz222/Uq1ePU6dOERAQoH/N7NmzmTdvHl9//TVRUVG8/vrr9OnTh6NHj+JRjduDOKqiYM1Dv3FVWk4JIaqlxYvhr7+U2bR58+w9GpuQPWtVw7BB+4gRI4o95+rqSqNGjfjwww/NPr/JwdqYMWN46623WLJkCe7u7oAyvffOO+8wZswYswdSlvfee4+IiAiWLFmiP2bYIFWn0zF37lymTp3KgAEDAPjmm28ICQlh9erVPProo1YdjyiqsRbu78mF68p9mVkTQlQ7iYkwebJy/623lP1qNZB0L6gaWq0WUGKUPXv2EBQUZNXzm5xgsH//ftatW0eDBg3o2bMnPXv2pEGDBqxdu5Z//vmHwYMH62+WWrNmDbfffjsPP/wwwcHBdOjQgS+++EL//NmzZ0lISKBnz576Y35+fnTu3JnY2Nhyz5uTk0NaWpr+lp6ebvFYawt1Zq2+vyc+hRmg0h9UCFHtvPwypKRAhw5g5YkGR6Iug/p5yZ41W8vLy6Nx48Zcv37d6uc2eWbN39+fISV6pdmqdMeZM2dYsGABEyZM4D//+Q9///03L730Em5ubowYMYKEhAQAQkJCin1dSEiI/rmyzJo1izdrWMZPVVFbTYX7e+JdGKzJzJoQolrZsAG++w40Gvj8c3CpuaWHUmQZtMq4urpy8OBBm5zb5O9QwyVJW9Nqtdx+++3MnDkTgA4dOnD48GEWLlxYak3YFFOmTGHChAn6x5cvX6Z169YWj7em02p1xKcWLYOqtdVkz5oQotq4dg3Uvo1jxijdCmqwVCndUaUef/xxFi9ezLvvvmvV8zr0rxNhYWGlgqhWrVrxv//9D4DQ0FAAEhMTCQsL078mMTGR9hUUNXR3d9fvtwNIS0uz4qhrrmsZOeQV6HB20hDi4463hzqzJvX1hBDVQE4ODBoEZ89Co0bw9tv2HpHN6YM1Kd1RJfLz8/nqq6/YsGEDHTt2pE6dOsWe/+ijj8w6r0MHa3fddRcnTpwoduzkyZM0bNgQUDbyhYaGsnHjRn1wlpaWxq5du/j3v/9d1cOt8S4V7lcL9fXAxdlJv2dN6qwJIRyeTgfPPAN//gl+fvDLL+Dra+9R2ZwkGFStw4cPc9tttwFKvGLIknZfDh2sjR8/ni5dujBz5kyGDh3K7t27WbRoEYsWLQKUv/i4ceN4++23adasmb50R3h4eLE0WmEdhmU7AIOZNQnWhBAO7s03YdkyZX/aTz9BLdj6kp1XwM08pUCrnwRrVWLz5s02Oa9DB2udOnVi1apVTJkyhRkzZhAVFcXcuXMZPny4/jWTJ08mMzOTUaNGkZKSQteuXVm/fr3UWLMBw4K4gCQYCCGqh//+t6iN1IIFYFBBoCZLK1wCdXbS6FdCRPVklX+9lJQU/P39rXGqUh588EEefPDBcp/XaDTMmDGDGTNm2OT6oohhJijIzJoQohrYvh1GjlTuT54Mzz5r3/FUIbUvqJ+nq0VLcMI0e/bs4ccff+TChQvk5uYWe27lypVmndPkOmvvvfceP/zwg/7x0KFDCQoKon79+vzzzz9mDUJUD5fLm1mTPWtCCEd06pTSRio3F4YMgVmz7D2iKiVlO6re8uXL6dKlC8eOHWPVqlXk5eVx5MgRNm3aVKwFlalMDtYWLlyor6sWExNDTEwMv/32G/369WPSpElmD0Q4vqKCuIV71mQZVAjhqJKT4f774fp1uOMO+OYbcDL5I69aU1sCyn61qjNz5kzmzJnD2rVrcXNz4+OPP+b48eMMHTqUyMhIs89r8nduQkKCPlhbt24dQ4cOpXfv3kyePJm///7b7IEIx1fenjWpsyaEcChqiY7TpyEyEn7+Gby87D2qKpdyU2bWqlpcXBwPPPAAAG5ubmRmZqLRaBg/frw+OdIcJgdrAQEBXLx4EYD169frWz3pdDoKCgrMHohwbFm5+dwonFKvXxis+RTuWcvJ15JXoLXb2IQQQk+nU/albd+ulOb45RcorMlZ2+ibuEurqSoTEBCgb2FZv359Dh8+DCh7+7Oyssw+r8kJBoMHD2bYsGE0a9aM5ORk+vXrByg9Q5s2bWr2QIRjU2fVfDxc8PFQfkurY5BdlJmTLz8QhBD299ZbSvanszOsWAFt2th7RHaj7wsqM2tV5u677yYmJoa2bdvy8MMPM3bsWDZt2kRMTAw9evQw+7wmB2tz5syhUaNGXLx4kdmzZ+Pt7Q1AfHw8L7zwgtkDEY7tcmEmqDqrBuDq7ISHqxPZeVrSsyVYE0LY2fz5MG1a0f3eve07HjuTgrhV79NPPyU7W/m8fO2113B1deWvv/5iyJAhTJ061ezzmhysubq6MnHixFLHx48fb/YghOMruV9N5e3uQnZeriQZCCHsa9EipdcnwJQp8K9/2Xc8DkD2rFW9wMBA/X0nJydeffVVq5zX5GBtzZo1ZR7XaDR4eHjQtGlToqKiLB6YcCwluxeovN1duJYhwZoQwo6WLCkKziZMgHfese94HITsWat6Fy5cqPB5czNCTQ7WBg4ciEajQafTFTuuHtNoNHTt2pXVq1cTEBBg1qCE4ylZY02lL4wrGaFCCHv473+Lit6++CJ88AFIAVjAYM+aLINWmUaNGlVYgNjcREyTs0FjYmLo1KkTMTExpKamkpqaSkxMDJ07d2bdunVs27aN5OTkMpdKRfVVVGOt9DIoSK01IYQd/PADjBihZIA+/zx8/LEEagakKG7V279/P/v27dPfdu3axcKFC2nevDkrVqww+7wmz6yNHTuWRYsW0aVLF/2xHj164OHhwahRozhy5Ahz587lmWeeMXtQwvGUbDWl8nZXfghIsCaEqFIrV8Lw4aDVKjNr8+dLoFaCugwq2aBV59Zbby117Pbbbyc8PJz333+fwYMHm3Vek2fW4uLi8PX1LXXc19eXM2fOANCsWTOuXbtm1oCE49FqdcSnlr0M6iPLoEKIqrZmDTzyCBQUwJNPKskFtaw7QWXyCrSkF/4SLXvWimzbto3+/fsTHh6ORqNh9erVxZ5/6qmn0Gg0xW59+/a1+LotWrSwqHGAyd/dHTt2ZNKkSVy9elV/7OrVq0yePJlOnToBcOrUKX2XA1H9XcvIIa9Ah7OThhAf92LP1XF3BtD/UBBCCJv69Vf4v/+D/Hx47DH46isJ1MqQVpgJCuDrYfIiWo2VmZnJrbfeyvz588t9Td++fYmPj9ffvv/+e6PPn5aWVuyWmprK8ePHmTp1Ks2aNTN73Cb/Cy5evJgBAwbQoEEDfUB28eJFGjduzM8//wxARkaGRfVEhGO5VLhfLdTXAxfn4j8U9cugMrMmhLC1mBgYPBjy8pSA7ZtvlOK3ohS1bIePh0upn9u1Wb9+/fTF/Mvj7u5OqJldL/z9/UslGOh0OiIiIli+fLlZ5wQzgrUWLVpw9OhR/vjjD06ePKk/1qtXL5wKf7sZOHCg2QMSjqe8sh1gsAyak1fqOSGEsJply5S9aTk5MHCg8thFZozKU9sK4qanp5OWlqZ/7O7ujru7ewVfUb4tW7YQHBxMQEAA9913H2+//TZBQUFGfe3mzZuLPXZycqJevXo0bdoUFwu+X836SicnJ/r27WuVdVzh+MoriAtF2aCZOdIXVghhAxkZSrHbr79WHj/4oJIF6lo7ghBzpRaW7fD3rB371Vq3bl3s8bRp05g+fbrJ5+nbty+DBw8mKiqKuLg4/vOf/9CvXz9iY2NxNmIWt3v37iZf0xhmBWsbN25k48aNJCUlodUWb+D91VdfWWVgwnGUlwkKRcGa7FkTQljd/v3w6KNw8qSyL+2NN+C112RGzQi1bWbt6NGj1K9fX//Y3Fm1Rx99VH+/bdu2tGvXjiZNmrBlyxajenuW1zigLA899JDRrzX5O/7NN99kxowZ3H777YSFhVVY/E3UDOUVxAXDoriyDCqEsBKdDubNg8mTITcX6tdXlj3vvtveI6s2Um/WrrIdPj4+ZVaqsFTjxo2pW7cup0+fNipYq6xxgOFjUwrkmhysLVy4kKVLl/LEE0+Y+qWimioqiFt6z5oUxRVCWNW1a/D007BunfJ4wABYvBiM3DMkFLVtZs1WLl26RHJyMmFhYUa9/o8//uCVV15h5syZREdHAxAbG8vUqVOZOXMmvXr1MmscJgdrubm5xQriiprPmD1rkg0qhLDY5s3w+ONw5Qq4u8OHH8ILL0ixWzOk6pu41449a8bKyMjg9OnT+sdnz57lwIEDBAYGEhgYyJtvvsmQIUMIDQ0lLi6OyZMn07RpU/r06WPU+ceNG8fChQvp2rWr/lifPn3w8vJi1KhRHDt2zKxxm5zP++yzz7Js2TKzLiaqn6zcfG4U/oZW0TKo7FkTQpgtPx9efx169FACtZYtYdcuGD1aAjUzpWQVJhjIzFoxe/bsoUOHDnTo0AGACRMm0KFDB9544w2cnZ05ePAgDz30EM2bN2fkyJF07NiR7du3G70HLi4uDn9//1LH/fz8OHfunNnjNnlmLTs7m0WLFrFhwwbatWuHa4mMnI8++sjswQjHo86q+Xi44OtR+j+9jz4bNB+dTid7GIUQpjl/HoYNg7/+Uh6PHKn0+KxTx77jquZSatmeNWPdc889pfaTGfr9998tOn+nTp2YMGEC3377LSEhIQAkJiYyadIk7rjjDrPPa3KwdvDgQdq3bw/A4cOHiz0nH9Q1z+XCTNCSDdxV6syaVgc38wrwcpMsLSGEkf73P3j2WUhJAV9f+PxzJftTWKxoz5osg1alr776ikGDBhEZGVmscUCzZs1KtbYyhcmfrCULvomaraL9agCers44aZRgLSM7X4I1IUTlbt6E8eOV4Azgjjvg+++hcWP7jqsG0e9Zk2XQKtW0aVMOHjxITEwMx48fB6BVq1b07NnTogkt+WQVFaqoewEos6l13F1Iz84nPSef4KocXC3yV9w15m08xVsD2tAsxMfewxHCfEeOKE3YjxxRHr/yCrz1lhS5tTL9njVZBq1yGo2G3r1707t3b6ud06hgbfDgwSxduhRfX18GDx5c4WtXrlxplYEJx1BRjTWVT2GwJhmhtvPdzgvsPHOdH/dc5LUHWlf+BUI4Gp0OFi2CceMgOxtCQuDbb8HMUgaifFqtrqjOmsys1QhGBWt+fn766Ts/Pz+bDkg4lqIaa+UHa94eLpAqtdZs6ey1TACOJ6TbeSRCmOHGDXjuOWWPGkCfPkr7qMIN2MK60nPy0RbuoZcEg5rBqGBtyZIlZd4XNV9FraZUUhjXtnQ6HeeTlWDtWLwEa6Ka2bIFRoyACxeUNlGzZsGECUr7KGETqYXJBV5uzri7VN7PUjg++d8iyqXV6ohPrXwZ1LuwpIcsg9rG1YwcMnOVtiTXMnK4mp5j5xEJYYSLF5XMznvvVQK1xo2V8hwTJ0qgZmMpN2W/mj3k5+fzzTffkJiYaPVzGzWz1qFDB6OzGPbt22fRgITjuJaRQ16BDmcnDSE+5RcE9JGZNZs6n5xV7PGx+DTq+dSz02iEqMTNm/DBB8oM2s2bSlHbUaNg9mylPIewObVsh5+U7ahSLi4uPP/882Z3Kajw3Ma8aODAgVa/sHB8lwr3q4X6euDiXP5vwnXclWl2CdZsQ92vpjqekMbdzSVYEw5Gp4PVq5UlTrVSe7duSkP2wtqcomqk6FtNycxaVbvjjjs4cOAADRs2tOp5jQrWpk2bZtWLiuqhsrIdKm935QdCuiyD2oS6X02tZyf71oTDOXIExo6FjRuVx/XrK7Nrjzwi7aLsIFVaTdnNCy+8wIQJE7h48SIdO3akTolOHO3atTPrvFJnTZSrsoK4KrWLQUZOns3HVBudu6Ysg3aOCiL2TDLH4tPsPCIhCqWkwPTp8OmnUFCgNF+fNAlefVXaRdlRUfcCCdaq2qOFHTheeukl/TGNRqNvx1hQUGDWeY0K1gICAozes3b9+nWzBiIcjzGZoGDYH9S8b0JRsXOFM2v92oYSeyaZuKsZ5OZrcXORTdrCTs6ehU8+gcWLIa3wl4dBg+DDDyEqyr5jEwZ9QWXPWlU7e/asTc5rVLA2d+5cm1xcODZjCuJC0cyaLINan06n41zhnrXoxkH4eriQlp1P3NUMWoXJZm1RhXQ62L4d5s6Fn38GrVY53qYNzJkDPXvadXiiiMys2Y+196qpjArWRowYYZOLV2b69Om8+eabxY61aNFC328rOzubl19+meXLl5OTk0OfPn347LPP9J3uhWWKCuJWtmdNlkFtRS3bodFAZJAXLcN82X32Osfi0yRYE1UjJwd++EEJ0vbvLzrep4/SjaB3bynF4WBSC0t3SEFc+zl69CgXLlwgNze32PGHHnrIrPMZFaylpaXhW5hynZZW8X4ZXyunZt9yyy1s2LBB/9jFpWjI48eP55dffmHFihX4+fkxZswYBg8ezI4dO6w6htrK6D1rUrrDZtSyHeF+nri7ONMq1IfdZ69LJwNhe0lJSqP1zz6DhATlmKcnPPkkvPQStJa2Z45KP7MmwVqVO3PmDIMGDeLQoUP6vWqAfiuZzfesxcfHExwcjL+/f5n71yzdPFfuAF1cCA0NLXU8NTWVxYsXs2zZMu677z5A6a7QqlUrdu7cyZ133mnVcdQ2Wbn53Cj8D290goEsg1qdWrYjqq6yWVudTZMkA9tTf6bVOgcPwscfw3ffKbNqAOHhMGaMUi8tKMi+4xOVSpG+oHYzduxYoqKi2LhxI1FRUezevZvk5GRefvllPvjgA7PPa1SwtmnTJgIDAwHYvHmz2Rczx6lTpwgPD8fDw4Po6GhmzZpFZGQke/fuJS8vj54G+yRatmxJZGQksbGxFQZrOTk55OQUVYFPT5dZipLUWTUfDxd8PSr+Dy8za7ajlu1oVNcLgJb6YE2+Z23p5wOXeX31YeYPv41uzWpBTTutFn79Vdl7tmlT0fFOnWD8ePi//wNX+eCvLopm1iTBoKrFxsayadMm6tati5OTE05OTnTt2pVZs2bx0ksvsd9wK4EJjArWunfvXuZ9W+vcuTNLly6lRYsWxMfH8+abb9KtWzcOHz5MQkICbm5u+Pv7F/uakJAQEtQp+3LMmjWr1F44UdzlwkzQihq4qyRYsx21bEejIGVmrUWIDxpNUdupehV0lhDmW73/MmnZ+Ww9cbVmB2sZGbB0qTKTdvq0cszZGQYPVoK0O++UOmnVjE6n0+9ZkwSDqldQUICPjw8AdevW5cqVK7Ro0YKGDRty4sQJs89rVp21lJQUdu/eTVJSElo1I6jQk08+afZgSurXr5/+frt27ejcuTMNGzbkxx9/xNOz8iCiPFOmTGHChAn6x5cvX6a17L8oxtj9aqDMvgFk52nJK9DiWkG3A2EatWyHGqx5ujkTFVSHM9cype2Ujeh0Og5dVpaZk2pqH9Zz55TaaF9+CampyjF/f3juOWW5MzLSnqMTFsjKLSCvQNknJcFa1WvTpg3//PMPUVFRdO7cmdmzZ+Pm5saiRYto3Lix2ec1OVhbu3Ytw4cPJyMjA19f32J7OjQajVWDtZL8/f1p3rw5p0+fplevXuTm5pKSklJsdi0xMbHMPW6G3N3dcXcvmpGoLGmiNjK2ewFAHfeib6PMnHz8pR+dVRiW7VCXQUHZt3bmWqa0nbKRhLRsrmUoQVpiWradR2NlBQXw7rswbZpyH6B5c6X7wJNPgre3fccnLJZauF/NzdkJT1dnO4+m9pk6dSqZmcrP7RkzZvDggw/SrVs3goKC+OGHH8w+r8lTIC+//DLPPPMMGRkZpKSkcOPGDf3N1gVxMzIyiIuLIywsjI4dO+Lq6spGtb0JcOLECS5cuEB0dLRNx+Go/jp9jYvXsyp/oRGMrbEG4OrshHthgVaptWY91zJy9WU7IgKLgrWWocoUu+xbs41Dl1L192vUzNrly9CrF0ydqgRq990H69bBsWPwwgsSqNUQRU3cXWtngoyd9enTh8GDBwPQtGlTjh8/zrVr10hKStInQ5rD5Jm1y5cv89JLL+Hl5VX5iy00ceJE+vfvT8OGDbly5QrTpk3D2dmZxx57DD8/P0aOHMmECRMIDAzE19eXF198kejo6FqZCbr3/A2GfbmLlqE+rB93t8XnK6qxZtxys4+HCzkZubJvzYrUJVC1bIdKMkJt6/Blg2CtpsysrVsHTz0FyclKG6j585WZNPkwr3FS1P1qUrbD7i5evAhARESExecyeWatT58+7Nmzx+ILG+PSpUs89thjtGjRgqFDhxIUFMTOnTupV09Z+pkzZw4PPvggQ4YM4e677yY0NJSVK1dWydgczR9HlaSK4wnpXLph+eyasa2mVJJkYH0ly3aoWoYpM2tq2ylhXYcMgrXM3ILq/T2dk6MUru3fXwnUOnSAfftgxAgJ1GqoVOleYFf5+fm8/vrr+Pn50ahRIxo1aoSfnx9Tp04lL8/8wvFGzaytWbNGf/+BBx5g0qRJHD16lLZt2+JaIp3b3Oq8ZVm+fHmFz3t4eDB//nzmz59vtWtWV1tPXNXf/+t0MkM7mT/zqdXqiE81fhkUDJu5V+MPNgdTsmyHqr6/Jz4eLqRL2ymrM0wuUCWlZeNdrxouEZ48CY8+WtR1YOxYeO89pdm6qLGkL6h9vfjii6xcuZLZs2frt2TFxsYyffp0kpOTWbBggVnnNSpYGzhwYKljM2bMKHXMFkVxReXiU28Wq2i//fQ1hnYyf9r1WkYOeQU6nJ00hBhZGkI/syZ71qymZNkOlUajoVWoL7vPSdspa1OTC5w0EOrrwZXUbBLTcmhcnYI1nQ6++QZGj4bMTKWI7dKl8OCD9h6ZqALSF9S+li1bxvLly0tVs4iIiOCxxx4zO1gzahlUq9UadZNAzT7UWTWfwoDpr9PX0Gp1Zp/vUuF+tVBfD1yMLMPh7a78YJCZNespWbbDUKvCpVBpO2VdanJBs2AfIoOUGc2k9Gq0by09HZ54QtmflpkJ99wD//wjgVotInvW7Mvd3Z1GjRqVOh4VFYWbm/mznVIQqwbYUhisPXVXI7zcnEnOzLXoQ9yUsh0qb3dlA7zMrFlHeWU7VC0lycAm1OSCtg38CPFVvv+T0qpJRmh2thKUffedUtj27bdhwwaoX9/eIxNVSPas2deYMWN46623inVJysnJ4Z133mHMmDFmn9foYC02NpZ169YVO/bNN98QFRVFcHAwo0aNKjY4UTXyCrTsOH0NgJ6tQugcpbQFU4+Zw5SCuCp1z1q6zKxZRXllO1StpO2UTajJBW3rFwVr1aLWWn4+PPYYbNsGvr6weTO89poStIlapah0h+xZs4f9+/ezbt06GjRoQM+ePenZsycNGjRg7dq1/PPPPwwePFh/M4XRpTtmzJjBPffcw4OF0+mHDh1i5MiRPPXUU7Rq1Yr333+f8PBwpk+fbtIAhGX2nr9Bek4+QXXcaFvfj7ua1mXziatsP32N5+42r1qyqZmgYLAMKjNrVlFe2Q5V8xBvaTtlZYbJBW3q+5FXoGTaOnytNZ0Onn8eVq9Wkgd+/hm6dbP3qISdqD87AiVYswt/f3+GDBlS7Jg1SncYHawdOHCAt956S/94+fLldO7cmS+++EI/mGnTpkmwVsU2n0gC4O7m9XBy0tC1WV0Adp9NJie/oMwP+sqYUhBXpbacypSZNas4V07ZDpWXm4u0nbKyxLQcfXJB6zBf/f8Dh59ZmzoVFi8GJyf4/ntln5qoEjqdjnPJWTQK8nKIArQnEtI5npCOq7OGLk2C7D2cWmnJkiU2Oa/Ry6A3btwgJCRE/3jr1q3Fsh06deqkLwAnqo6aXHBPC+XDukWID3W93cnO07LvfIpZ5zyvn9UxZc+alO6wpnPllO0w1FKfZCD71qzh4KUUQEku8HRzJrhwttKhZ9bmzoWZM5X7n38OgwbZdTi1zfzNp7n3gy3MiTlp76EAsHL/JQDuaRFMQB2ZWatJjA7WQkJCOHv2LAC5ubns27evWKeA9PT0UjXXhG2pJTucNHB3MyVY02g0dG2q/EZlzr61uKsZnEzMwNlJw60R/kZ/nRqsyZ416ziXXHbZDkOtQmXfmjUZJhcABgkGDjqz9t13MH68cv+dd+DZZ+07nlrmRmYuC7eeAeDTzac5cDHFruMp0Or4ef8VAAZ3kKSSmsboYO3+++/n1VdfZfv27UyZMgUvLy+6GeyLOHjwIE2aNLHJIEXZ1Fm1WyP8i/0WdVdTZSl0uxnB2qp9lwG4u1ld6nobvw+qjr7OmvkVmkURfSZoBcGarTJCdTodk3/6hzHL9llUAqa6MUwuAPQzaw7ZxeC335TyHKAUu50yxa7DqY0W/3lW/32h1cGkFf+QnWe/8lU7zySTkJaNr4cL97UKtts4hG0YHay99dZbuLi40L17d7744gu++OKLYjVDvvrqK3r37m2TQYqyqSU77mle/D+mum/t0KUUfRq3MbRaHav2K8HaoNsamDQWH+lgYDWVle1QtbJR26mj8Wn8uOcS6w7Gc/665a3LqoOSyQWg/AKizhg71Ozazp3wf/+nZIAOGwYffSSto6rYjcxclv51DoD3hrSlrrc7p5Iy+HjjKbuNaWXhL9oPtAs3a6+ycGxGB2t169Zl27Zt3Lhxgxs3bjCoxN6IFStWMG3aNKsPUJTNsGSHul9NFebnSZN6ddDqIPZMstHn/PvcdS6n3MTb3YXerUMq/wID0sHAeior26FS207lFeiIu5phtev/eihef1/dv1jTlUwuUAX7uuufdwhHj8IDD0BWFvTpA0uWKIkFokqps2qtw3wZensE7wxqA8DnW+PMWg7NL9CycGscvxn83zPFzdwC1h9WvnbIbbIE6mhSUlIsPofJ/8v9/PxwLqN2T2BgoEXVeYVpSpbsKKlr4VKoKfvW1Fm1fm1C8XA17Tcz6Q1qPZWV7VCpbafAekuhOp2OXw4aBmu1Y2atZHKBqijJwAFm1i5cUAK069ehc2f43/9AfuZWOcNZtbE9m6HRaOhzSygP3Rpu1nJofoGWl1f8w7u/Heel5fu5dMP0/3N/HE0gM7eAyEAvOjYMMPnrhfW89957/PDDD/rHQ4cOJSgoiPr16/PPP/+YfV75layaKlmyoyR139qfRgZr2XkF/FL4W91gE5dAoajVVUZOPjpd7dnnZAuVle0w1NLKbaeOxqfpkxug9gRranJBmxK/+DhMF4PkZCVQu3QJWrWCX36BOpV/fwjrM5xVM1yBePOhW6jr7WbScqgaqP18QEkMyCvQ8dmWOJPHpC6BDuxQ3yFKiNRmCxcu1NdVi4mJISYmht9++41+/foxadIks88rwVo1VbJkR0l3NgnC2UnD2WuZRv2mtvFYEunZ+dT399R3QTCFOrOm1cFNO26yrQmMKduhamXlJAN1CdTVWfmBf+F67VgGVZML2jUoO1iza621jAxl6fP4cWjQAH7/XWnOLqpcWbNqqoA6brw9sC1g3HKoYaDm4qThhXuUBL0Vey6aNLuWlJ7N9lPK58EgyQK1u4SEBH2wtm7dOoYOHUrv3r2ZPHkyf//9t9nnlWCtGlJLdmg00K1Z2cGar4crtxZ+8Px1uvJ9a6sK6/MMaB9e5kxdZTxdnVG/rLbsW7PVDKIxZTtULUOVmTVrlO8wXAId2L5+sbHUZGUlF6jsXmstN1dJJti1CwIDlUDNCtXQhXnKm1VT9W1j3HJoyUDt02G3MblvS+5qGmTy7NqaA1fQ6qBDpL9Rs/HCtgICAvQ1Z9evX0/Pnj0B5edMQYH5ExkSrNnZN7HnaD/jD7advGr01+hLdjTwJ7CCwoddjVwKTc7I0WeWDjZzc6pGo9GX76gNtdZSs/LoNnszU1Yesvq5jSnboWoR6lOs7ZQl1CVQdxcnnr4rCoAL17NqfPmO8pILAILtObOm1SrlOX7/Hby8lKXP1q2rfhwCqHhWzVBly6FlBWp924Qq5+3RHDBtdk3dayy11RzD4MGDGTZsGL169SI5OVnfPGD//v00bdrU7PNKsGZHqVl5vL/+BClZeUxc8Y/RZTa2VLIEqrrLIMmgog/cdQfjydfqaFvfj6bBPkaOvjSfWpQRuutsMpdu3GT1/stWDWZ0Op1+n5gxy6Bebi76oM7SpVB1CfSeFvVoHuKNi5OG3HwtiY6wud6G1CXQkskFUDSzZmkgbDKdTil4+/334OKiJBMYFCEXVa+yWTVVRcuhFQVqAHdEBZo0u3YyMZ0jV9JwddbwYLtw8/9ywmrmzJnDmDFjaN26NTExMXh7ewMQHx/PCy+8YPZ5JVizo8U7zupnoZLSc3hz7ZFKv8awZMe9LSoufNghMgAvN2eSM3Mr3IC+Uq2tZuFvZt61qD/oqSSlVMbNvAJ9D0lruJaRS0ZOfqVlOwy1skLbKZ1Ox6+HEgClTpOLsxP1A5TesOeu1eyl0EOFmaAll0DBjnvWZs2CefOU+0uXQt++VXt9UYyxs2qqspZDKwvUVKbMrqmJBdJeynG4uroyceJEPv74Yzp06KA/Pn78eJ61oMuI0Y3chXWlZuWx5E+lfde/7m7MF9vPsHL/Ze5vG0bPCn5rq6xkhyE3Fyc6RwWy+cRVdpy+Rutw31KvibuawT8XU3B20vBQe8t+M6tNLadOJqYXu29sYFUZY8t2GGoZ6suvhxIs2rd2ND6Ns9cycXdxokdL5ZeAyEAvzidnceF6JtE1uCl0eckFULqLgfo9blNffAGvvabcnzsXhg+3/TVFhYydVTM0/aFb+CvuGqeSMpgTc5KEtOxKAzUoml3bcTqZz7bEMXNQ2zJfp9Xq+PmALIE6mjVr1pR5XKPR4OHhQdOmTYmKijL5vBKs2Yk6q9YixIdX+rYE4PNtZ/jPqkN0ahSIn1fZfVbVJdDySnaUdFfTumw+cZU/T1/jubsbl3p+9X7z2kuVxdtDGXNtWAY9lVhUhPZUUgY9WplWRLg8ppTtUFkjI9RwCVTde9gwyIvtp2p2+Y6KkgugqItBRk4+SWnZeNfztsk4TidlkJ1XQJtdG+H555WDU6YoraSEXZk6q6YKLFwOff6/e/l8m9JDtLJATTW2R3N2nI5lxZ6LvHBPExoElP5lcOeZZOJTpb2Uoxk4cCAajaZUApp6TKPR0LVrV1avXk1AgPE18WQZ1A4MZ9XG9myGk5OG8b2a06RenUqXQ7cU1lerbL+aSm09tetsMjn5xTNRLGkvVRZvd2UmqKYXxi3QFu8YYDjLZilTynao1IxQc9tOGS6B3t82TH9c3QtXk1tOVZRcoLJ1F4MCrY5HF+1k9qsL0Q0bpiQWjBypNGcXdqfOqrUyYVZNpS6HgvGBGhi3d03dviLtpRxLTEwMnTp1IiYmhtTUVFJTU4mJiaFz586sW7eObdu2kZyczMSJE006rwRrdmA4q9b3FuU/roerM+8/fCtOGuU/4YajiaW+zpiSHSW1CPGhrrc72Xla9p1PKfbcnvM3uHTDvPZSZfF2rx1dDC5czyLHICgynGWzlCllO1QNAixrO3UsPr1oCdRghjCycGm3Jrecqii5QGXrLgYXr2cREneU+T++iSYnBwYOhIULpd+nAyg2q9bD+Fk1Q28NbMPz3ZvwzTN3GBWoqSrau3Yzt0DfmsrcDH5hG2PHjuWjjz6iR48e+Pj44OPjQ48ePXj//feZNGkSd911F3PnziUmJsak80qwVsXKmlVT3RYZwHPdlKXK/6w6VCo71NiSHYY0Gg1dmyr7jUq2nlJrq5nTXqos3u7KMmh6DV8GPVU4k+ZbmFBxOinDahmhppTtUFnaduqXQ0r19Hta1Cu2J6uhOrOWnFVju1JUlFygsnUXg7MnzrP4pzfxyb3JlVs7FWWACruzZFZN5efpyqv9WtKlMDvfWHdEBdKlSdmza2p7qYhAT26X9lIOJS4uDl/f0rP0vr6+nDmjLIc3a9aMa9eMbwUJEqxVubJm1QwVWw5dV3w51NiSHSWV1XoqO6+AdYUFUAdZ6Tez2pINqmaCdm8RjJuzk9UyQk0t22HI3LZT5S2BQtHMWnp2PilGlpWpbtSZtbb1y14ChaKZNVtlhDZ4fTKhGdeJC2zAJy9+AB4eNrmOME3JWTVzioVbamyPZkDp2TX99pX20l7K0XTs2JFJkyZx9WpR7dSrV68yefJkOnXqBMCpU6f0XQ6MJcFaFapoVk1VbDl0X9FyqGHJjnsqKdlRkhqsHbyUQupN5UNXbS8V7ufBnVHWyfTzqSXLoOoetZahPjSup8w+nUqyfN+aOWU7VOYmGZS3BArg6eZMSOF+rXM2XApNSsvm2a/3sNWEwtDWYJhc0LaBf7mv08+s2aLW2vLlNNv8C/kaJ8Y/OIF91ukaJqzgqx2Wz6pZqnPjoFKza0p7KeWzwBp7jYV1LV68mLNnz9KgQQOaNm1K06ZNadCgAefOnePLL78EICMjg6lTp5p0Xplrr0KVzaqp1OVQw+zQYwlppOfkE1jHjXaVlOwoKdzfk8b16nDmaiaxccn0bRNa1F6qQ32r/caozqzV/GVQZWateYgPzUJ8OJ6QzsnEDO5radkP9PNmlO1Qmdt2yjALtKyyFA0D65CYlsOF61l0iLTNcsuXf55lw7FErmbk0L25abPGljAmuQBs2MXgyhUoLJI5P/oRDoY1x+VqBjn5BbJh3AHEFP6i/Hz3xnaZVVON7dGMv+KS9Zmh6w8nUKDV0T5C2ks5ohYtWnD06FH++OMPTp48qT/Wq1cvnJyU+bGBAweafF4J1qpI6s08luyoeFbN0PhezYk5lsiZq5m8ue4IwT7KB8bdzeqa9YOjW9O6nLmayY7T1+jUKKCovZQV6/PU0c+s1cwlMyieCdo8xJvmwUopB2tkhJ41o2yHqmTbqXo+lZdh0el0/FIYrJVcAlU1DPJi97nrNivfYdiP9PDlVNKz8/DxKLtsjbUZk1wANupioNPBs8/CjRscDm3KJ10ewUkD+Vodp5MyuCXctF/IhHXlF2g5U/j/sUOEffeEqbNrf8Upddf+KeyIMEQSCxyWk5MTffv2pa8Vi1lLsFZFvvrzLOnZlc+qqTxcnfng4Vv5vwV/sXLfZf1mdlOXQFV3Na3L17Hn2XH6Gk2DvcnX6mhT35dmIea3lyqpNiyDqpmg7i5ONAjwolmIEqydTrI8I9Scsh0qte3U2WuZHItPo55P5TNU6hKoWxlLoKqGQV7FxmZtBy+l6vf7FWh17Dl/o9LOHNZiTHIB2KiLwZdfwm+/oXN3Z/z943F2d6NDAz/+PneD4/HpEqzZ2YXrWeTma/F0daZBYScPe1Jn1374+yIFWp20l3JwGzduZOPGjSQlJaHVFi+n9NVXX5l1TtmzVgVSb+bxlQmzaqrbIgN4tjA7NC1b2ct0t5nLRHc2CcJJA2euZbK4cN/c4A7W3e+gLoPW5KK4aiZo02BvnJ00+mD3VKLlGaHmlO0wZGrbKXUJ9N5ylkABIgvHcsFGM2vqzJ5q55lkm1ynLMYkF0DpLgYWO3NG6fsJnHzxVU7Va0jTYG99gGZpj1dhuZOFWx2aBnvbdQlUpc6uFRT+jJH2Uo7rzTffpHfv3mzcuJFr165x48aNYjdzycxaFTB1Vs3QhF7N2VC4HGpKyY6SfD1cuTXCn/0XUrhwPcsq7aVKKqqzVlDJK6svNRO0eWGQ1jDQq1hGqCVtp8wp22FIbTu1Ys8l+twSqi+9URZjlkBB+fuBbQrjGi6B9modQszRRHaeuW7165R37aLkgopnsazaxaCgAJ56CjIzoVs3NvZ5DDacpnmIj0Gwbb0iy8I8pwsThpoF26ZjhTnU2TWQ9lKObOHChSxdupQnnnjCqueVmTUbM3dWTeXh6sy8RzvQsWEAo+9tatFYuhnU+bFGe6mSvGvBnrWTBjNrAC7OTlbJCLWkbIdqYPv6+Hu5ciopgwfn/akvmlkWY5ZAoShwvJqeQ1audWdM1SVQLzdnXu2ntFxT963ZWvHkgsqXHIvKd1i4b23uXNi+Hby9YelSTl5T/s2bBnvT0qBWXk2ta1ddqDNr1twmYqnOjYMY2TWK+9uGSnspB5abm0uXLl2sfl4J1mzMklk1VZv6fvzv313oZWH6+F0GwZotUr59CpdBs/O05BWY3vaoOjDMBFWpP9BPWtDJwJKyHarIIC9+fakbHRsGkJ6Tz7+/28f0NUdKtRkDgyzQ5uUvgQL4ebni56ls+Ld2koE6s3dfy2Ca1PMmMtBLv2/N1oxNLlCpLacs6mJw5Aj85z/K/Y8+gsaN9d8zzUN8aB7ig5MGkjNzuZphmwK8wjjqL2XNQxxnZg3g9Qdb89nwjpIt7MCeffZZli1bZvXzSrBmQ5bOqllbh8gAGgV5ERHoSS8rNR43VMfgQ78mFsYtmQmqamaFjFBLynYYCvf3ZPmoO/lXd2Wv49K/zvF/C2KL7TlTCuEqgdID7cpfAlWpSQbWDNYMl0AfKFyGvbNxIFA1+9bUYK2y5AKVxV0M8vLgySchNxf69YNnny31/eTp5kyjwkxgU0uwCOsxzARtFuw4M2vCOrZt20b//v0JDw9Ho9GwevXqYs/rdDreeOMNwsLC8PT0pGfPnpw6dcro82dnZ/PRRx/RvXt3XnzxRSZMmFDsZi4J1mzIGrNq1uTm4sRvY+/m93F3GzWbYCpXZyfcXZRvqZpYa61kJqiquRUyQi0p21GSq7MTU/q1YslTnfD3cuXQ5VQemLddvyx6LD6dM0YsgarUvW8XrlsvI9RwCVTNcL6zsVKcuSr2ramZoJUlF6gs7mLw9tuwbx8EBsLixaDRcLGM7ye1uPFxSTKwG0fLBBXWlZmZya233sr8+fPLfH727NnMmzePhQsXsmvXLurUqUOfPn3Izjbu//7Bgwdp3749Tk5OHD58mP379+tvBw4cMHvckmBgIxk5+Q41q6ayRZBmyMfDhZzCJb2apmQmqKpkRqg5/9aW7lcry70tg/n1pW68+P1+9p6/wb+/28dTXRrpA+rKlkBVapLBOSvOrBkugarfk50LgzVb11szJblAZVEXg7//hnfeUe5/9hmEKTOJJ8v4fmoV6sMvB+MlI9SOHC0TVFhXv3796NevX5nP6XQ65s6dy9SpUxkwYAAA33zzDSEhIaxevZpHH3200vNv3rzZquNVVauZtXfffReNRsO4ceP0x7Kzsxk9ejRBQUF4e3szZMgQEhMT7TfIQnXcnPn8iY4Mvb2BQ8yqVRX1w78mLoOWzARVlcwINcfZZMsyQctT1rLo59uUZsLGLIGCshcOrFe+o6wlUID6/p5Vsm/N1OQCQF9k2OSZtZs3leXPggJ45BHlVqis7yf9zJpkhNqNI2aCisqlp6eTlpamv+XkmP6L1dmzZ0lISKBnz576Y35+fnTu3JnY2FhrDtdk1WZm7e+//+bzzz+nXbt2xY6PHz+eX375hRUrVuDn58eYMWMYPHgwO3bssNNIFRqNhi5N6tKlSd3KX1yD6FtO1cBgrWQmqErNCD2ekM6ppHSzEgTO2yhYg6Jl0Tujghj/4wFSsvKMXgI1HNN5Ky2DlrUEqrqzcSAXrmex80yyzYrjmppcAEUzayZ3MfjPf+D4cWU2rcSyy6kyvp9aFgZrp5Ok7ZS9OGImqKhc69atiz2eNm0a06dPN+kcCQkJAISEFP/ZGBISon+uLIMHD2bp0qX4+voyePDgCq+xcuVKk8akqhbBWkZGBsOHD+eLL77g7bff1h9PTU1l8eLFLFu2jPvuuw+AJUuW0KpVK3bu3Mmdd95pryHXWvryHTVwz1pZmaAqS3qE6nQ6zl2z/jJoSeqy6KzfjtOpUYBRS6BQlGBw+cZNcvO1uLlYNiFf1hKo6s7GQfy455JN962ZmlwAZnYx2LxZKdUBSseCoKBiT58s4/sp3M8DXw8X0rLziUvKpHW4cXvqhPWov5TJzFr1cvToUerXL6o/5+5u3dJUFfHz80Oj0ejv20K1CNZGjx7NAw88QM+ePYsFa3v37iUvL6/YlGXLli2JjIwkNja23GAtJyen2BRperosOViLdw1tOVVeJqjKkoxQa5TtMFa4vyefPNbBpK8J9nHHw9WJ7Dwtl1NuWpQEUd4SqKoq9q0dNrJzgaGSXQwqDXTT0uDpp5X7zz0H999f7GnD7yfDoECj0dAyzJfdZ69zLD5NgrUqZpgJWtYvZcJx+fj44Otr2f+X0FBly1JiYiJhYUU/nxITE2nfvn25X7dkyZIy71uTw+9ZW758Ofv27WPWrFmlnktISMDNzQ1/f/9ixyubspw1axZ+fn76W8npU2G+mjqzVl4mqMqSjFBrle2wFY1GQ8PAwqVQC3uEVrQEClWzb+1E4X4wdX+YMdQuBgBJxsyujR8P589DVBR8+GGppw0zQUsG6K3DiorjiqqlZoJ6uDpJJmgtFBUVRWhoKBs3btQfS0tLY9euXURHR9txZA4+s3bx4kXGjh1LTEwMHh4eVjvvlClTitU7uXz5sgRsVlJT96yVlwmqahpsfkaoNct22EpkkBcnEtO5YGHbqYqWQFW23Ld2M7eAK6lKEkgTE5e5gn3cycjJJzEth8YVtZxauxa++go0Gvj6a/ApPUNTViaoqmWotJ2yFzXpQzJBa66MjAxOnz6tf3z27FkOHDhAYGAgkZGRjBs3jrfffptmzZoRFRXF66+/Tnh4OAMHDiz3nB06dNAvg1Zm3759Zo3boYO1vXv3kpSUxG233aY/VlBQwLZt2/j000/5/fffyc3NJSUlpdjsWmJion46syzu7u7F1rPT0uQ3WGvxdleWrWpaNqj6Q7y8fSyNgszvEWqLsh3Wpi/fcc38YK2yJVCVLfetnb2WiU4Hfp6uBJnYZzfY150z1zIr7mJw7Zqy7AkwYQJ061bmyyr6fmoVVrztlLEfAsJy6i9lzaUYbo21Z88e7r33Xv1jdeJmxIgRLF26lMmTJ5OZmcmoUaNISUmha9eurF+/vsIJo4oCOWtx6GCtR48eHDp0qNixp59+mpYtW/LKK68QERGBq6srGzduZMiQIQCcOHGCCxcu2H3KsrZSW07VtGVQ9Yd4eRlilmSE2qpshzWpSQaWFMatbAlUZct9a2euKUFS43p1TA6Cgn0q6WKg08G//w2JidC6tVIItxwVfT+VbDulXtdadDodO04nc0u4LwEmBqw1nWSC1nz33HNPhb13NRoNM2bMYMaMGUafc9q0adYYWoUcOljz8fGhTZs2xY7VqVOHoKAg/fGRI0cyYcIEAgMD8fX15cUXXyQ6OloyQe2kpiYYlJW5V1LTYG+zMkLVPVSOHaype9bMn1kzZgkUivatXbiexZ7zN6y6FHrmqhJsNqloGbMcIb6V1Fr7/nv46SdwcYFvvoEKfhOv6PtJbTt15momx+LTrR6s/XE0kX99u5dBHeoz55H2Vj13dVfZDLoQ9uLQwZox5syZg5OTE0OGDCEnJ4c+ffrw2Wef2XtYtZbaH7Qm7VkrL3OvJOWDN15f4sMYp5MyOJ2Ugauzhk6NAi0dqs3o+4NezzKrS4OxS6Aqdd/arjPXrRqsqf+OjeuZHhhX2MXg8mUYPVq5//rr0LFjuecx5vupVagvZ65mcjw+je7N65k81orExim9V/dfsF3h4eqoeMa3zKwJ4wUEBBg9U3/9unnbO6pdsLZly5Zijz08PJg/f365fb5E1SrKBs2z80isp6LMPUNqRuipJOM3hqsN1e9qWhc/L9u0V7KGcH9PnJ005OZrSUzPJszPtEw5Y5dAVUX71qzb1F2dWWtc1/SZk3K7GOh0MHIkpKTA7bfDlCkVnseY76dWYT78csg2bafU0iUXb9yUwrsGzidnSiaoMMtctZ6iDVW7YE04Nv2etRo0s1ZR5p4hczJC1WDNmNkme3J1Vj7AzidncT45y+RgzdglUJW6b+3Q5VTj6poZQafTceaqmu1n/sxaqS4Gn38Ov/8O7u7K8qdrxUG3Md9Ptmo7VaDVceRKmv7+heQs2Z9VSDJBhblGjBhh82s4fJ01Ub0U9QYtsPNIrMfYfSwlM0Irczopg+MJ6bg6a+jd2vH7x0YGmtcj1NQlUChRb+2cdbJCE9NyyMwtwNlJQ2Sg6cFacFkza6dPw8svK/fffRdatar0PMZ8P5VsO2UtcVczuJlXYPDYOi3EagLJBBXmMqwoYdiftKybuSRYE1alr7NWg5ZBK8sEVakZoWDcUmh1WQJVqfvWzplYGNfUJVDVnY2VPXzWKuGhzqpFBHia1TIruHBmTe1iQEEBPPUUZGXBPffASy8ZdR5jvp/UtlP5Wh1xSdYLqA5dSi32WM2OFZIJKswXEBBAUlISAP7+/gQEBJS6qcfNJcugwqp8DLJBa0qNKGMyQVWmZISqwdr9Dr4Eqipq6G7azJqpS6Aqa+9bUzePm5MJCsqssbe7Cxk5+SSfOo/3hH/Djh1K0dslS8DJuADQmO8nW7WdUvuiujpryCuwbiBY3UkmqDDXpk2bCAxUfrncvHmzTa4hwZqwKjUbVKuDm3kFeLlV728xYzNBVcZmhBougfapBkugYN4yqDlLoCpr71tTl/zMyQRVBfu4c9uxXYTd8zQkXwVPT1i6FBo1MurrTfl+al0YrB1PsF6SgZpc0L15MBuOJcrMWiHJBBWW6N69e5n3ral6f5IKh+Pl5oxGoyTIZWTnV/tgzdhMUJX6AVzZMmh1WwKFolpr55IzjZ41NXcJFErUWzt33eSvL0lt0G3uzBq5uYz/4wv6xyxTHrdtC8uXKwVwjWTK95PadupYvHWSDAyTCwZ2CFeCtavG/1vWZJIJKqwpJSWF3bt3k5SUhFarLfbck08+adY5q/cnqXA4Go0Gb3cX0rPzSc/Jx7qdHauesZmgKnW/S2UZodVtCRSKZtbSs/NJycozqvr92n+uAKYvgaqK+oRaHqzFJak11swI1uLi4LHH6P/33wAcGfg4tyxbpMysmcCU76eijFDrzKypyQVebs7c1zIYjQZSb+aRnJlLXW/3yk9Qg0kmqLCWtWvXMnz4cDIyMvD19S32i5BGozE7WJMEA2F1PvqM0OpfvsPUfSyNgrxwddZUmBFaHZdAQamsr1bxN2bfWk5+ASv3XwZgQPv6Zl3zzsKlUEv3rWXnFTVwN3kZ9PvvoUMH+Ptvbnr7MmrQa6x+dorJgRqY9v2ktp26lpFbcT9SI6nJBbeE++Ll5kJ9f2X8ZyQjVDJBhdW8/PLLPPPMM2RkZJCSksKNGzf0N3ML4oIEa8IGvGtQf1BjM0FVLs5O+oKr5S2FVsclUFXDQLXtVOUf8H8cSeR6Zi6hvh7c28K8Kvwl962Zy6wG7hkZ8MwzMGwYpKdDt26sXvILfzSPJrG8/qCVMOX7SW07BXDcCkuhanJB2/r+QNEMo7pXqzZTkz6ahkhygbDM5cuXeemll/DyMr4/tDEkWBNW512DWk6Zkgmqalb4A/9kOUkG1XEJVBWptp0yIslg2a4LAAztFIGLs3k/aqxVb82wzZRR+7MOHFC6EahZntOmwaZNeDWNAiroD1oJU7+fWoUqS6HW6GSgJhe0baCcs0nhDOMZCdb0M54ysyYs1adPH/bs2WP188qeNWF1ddxrxsyaqZmgqooyQtUlUBcnDb1bG9/s3VE0MjJYO3M1g9gzyThp4JFOERZd0xr71oxuM6XTwaefwsSJkJsL9evDd99BYYZXuV0MjGDO95O12k4ZJhe0re8HFM2s1fZlUMkEFZZas2aN/v4DDzzApEmTOHr0KG3btsW1REeThx56yKxrSLAmrK6mtJwyNRNUVVFGqDqr1rVZXfy9jFyOcyCRhRmhF65X/AH/w98XAbinRbB+b5S5rFFvTV9jraI2U9euKcuea9cqjx96CL76CoKC9C8ps4uBkcz5fmoZap22U4bJBVGFAWuTwiXW2r4MKpmgwlIDBw4sdWzGjBmljmk0GgoKzOtIIsugwuq83WtGsGZqJqhK3Y90OknJCDVUnZdAARoGql0Myp9Zy8kvYMXeSwA8dkekxde0xr61SmfWtmyB9u2VQM3NDT75BFavLhaoQRldDEygfj81qWf891OrcOu0nTJMLlCv3aTwl4qLN26Sm68t92urm/PJmVzLMH7mUzJBhaW0Wq1RN3MDNZBgTdiAt7sy7VvdgzVzK5qrGaFZucUzQqv7EigUdTG4mp5DVm7Z/77WSCwwZOm+tQobuOfnwxtvwH33weXL0KIF7N4NY8ZAGXvbvN1dqFNYgiTJxNk1/b4oEzaxW6vtlJpc0KZwCRSUWcI6bs5KQ/dKZkqri7irGfSas40hC/4yOgCVTFBRHUiwJqyupmSDmpoJqiovI9QwC7Q6LoEC+Hm54uepBOMXyinf8f1uyxMLSrKkT2i5DdwvXIB774W33lL2qj3zDOzdC7feWuH51H1rpmaEmvP9pLadAsuSDNTkgnYNioI1jUaj37d2uoa0nVr851ly87WcT87S/3+rjGSCCmuIjY1l3bp1xY598803REVFERwczKhRo8jJMS+LHCRYEzbg48DLoJ9tOU30rI38fOBypa81JxNUpWaEGiYZqB8eD7SrnkugKn1D92ulg7Wz1zL5Ky4ZjRUSCwyp9dZizdi3VmYD91WrlGXPP/9UensuWwaLF0OdymuwBRfWmjO19pm+UbiJM7WtCjsZmFsct6zkApU+I7QGtJ26npnL/wqX3wG+/PMMOp2ugq9QSCaosIYZM2Zw5MgR/eNDhw4xcuRIevbsyauvvsratWuZNWuW2eeXYE1YnZoNmu5gM2tzYk4ye/0J4lOzGf/DAVbvLz9gMzcTVNWs8Ae/+gFdE5ZAVfoeoWUsnS0vnFW7p3k9ixMLDKnB2mEz9q3FGbaZSkxUZtAGD4YbN+COO5QyHY89ZvT5gn2UmbUkE2bWLMk4bKWfWTMvyeBMGckFqpqUEbps13ly8rU0C/bG3cWJw5fT2H224plYyQQV1nLgwAF69Oihf7x8+XI6d+7MF198wYQJE5g3bx4//vij2eeXYE1YnX4ZNCfPziMpMifmJB9vPAVA+wh/tDqY8GP5AZu5maAqdV+SugxaE5ZAVeq+tZLlOwwTC4Z1bmjVa4ZbsG8tLikDl4J8Ht3xEzRvrtROA5g8GbZvh8aNTTpfiBkza5Z8P1nadupgGckFqiY1pDBuTn4BX8eeB2D0vU0Z0rEBAF/+ebbCr7twPUsyQYVV3Lhxg5CQol/Et27dSr9+/fSPO3XqxMWLF80+vwRrwuocbRnUMFD7z/0tWfnvLjzaKaLCgM3cTFBVyYzQmrIECkWFcUvuWVMTC0J83a2SWFCSufvWvLZsZP1XY+j15WxIS4OOHWHHDnjvPSXz00TqzJope9bMyQRVWdp2qqzkAlVjfWHcTKOWDB3V2n/iuZqeQ6ivB/e3DeOZu5TixRuOJXL2Wvmzhob/zyUTVFgiJCSEs2eVXw5yc3PZt28fd955p/759PT0UjXXTCHBmrA6dWYtM8f8NGVrKRmojbq7CU5OGmYOalthwGZuJqiqoUFG6PbT12rMEigYlu8o/iGoJhY8crv1EgsMmdwnNC4OBgxg8pyxNL1+ibyguvDFF7BrF3TpYvY41D1rptRaMycTVGVp26mykgtUUXXrFGvoXh3pdDq+3H4GgCe7NMTNxYmmwd7c26IeOh0s2VH+7Jpkggpruf/++3n11VfZvn07U6ZMwcvLi27duumfP3jwIE2aNDH7/BKsCavzdpA9a2UFaqrKAjZzM0FVrgYZoXM3nARqxhIooA8crqRkk1eglEcollhghdpqZTG63lpGBrz2GrRuDWvWkOfkzOLbB5B24DA8+yw4O1s0DnO6GFj6/WRu26mKkgsAPFydq31D99i4ZI4npOPp6swwg++9Z7spy9sr9lwiNavsLRn6GmuSCSos9NZbb+Hi4kL37t354osv+OKLL3AzmLn/6quv6N27t9nnl2BNWF1RUVz77VmrKFBTVRSwWZIJqlIzQvdfSAHggWpaCLekYB93PFydKNDquHxDqSNnq8QCQ5XWW8vNhaVLlVppM2dCbi4Zd99Lv6c/Yd6DLxBY37xWVSWZ08XA3ExQVaswNSPUtJm1ipILVEVJBtVz35q6L+3h2xsU+2WoS5MgWob6cDOvgGWF358l6f+fy8yasFDdunXZtm0bN27c4MaNGwwaNKjY8ytWrGDatGlmn1+CNWF1arCWnafVz7xUJWMCNVVZAdvKfZcsygRVNTP4AHBx0tD7luq/BApKfa5Ig6VQa3csqEiZ+9auXlVqpTVsCE8/DVeuQFQUrF7NlnnfcrpupPEN3I1gahcDa2QctjRzZk3dr1ZWcoGqcTVuO3U6KYNNx5PQaODpwn1qKo1Go59d+/qvc6V+FkkmqLAFPz8/nMuYvQ8MDCw202YqCdaE1amlOwAyqzjJwJRATVU6YPvHokxQleH+pJqyBKpqqO8RmkXM0aLEgvtaWmf2qjzF9q0dOqQsa0ZEKF0IEhIgPFxJHDh6FAYM4ExhLbhKG7ibwNQuBpZmFkPxtlOmtIZSM0HLSi5QqW2nquMyqLofrUfLEKLqlq6R1//WMOp6u5OQll2qSK5kgorqRII1YXVuLk64FxYfrWzfWnZeAa/+7yDv/HLU5PY9hq6m5/DaqkMmB2oqw4BNZW4mqMpwf1JNWQJVqUkG55OzWLbLtokFhjo3CuC+07uZ9MFoaNdOKWSbkwOdOimFbc+dU0pyeCizX2eMaeBuBlO6GFiSCaoybDt1Osn4GTA1uaCs/WoqtaH7mQqyJh3R9cxc/rdPmdF9tltUma9xd3FmRLRSRuaL7cWL5EomqKhOXCp/iRCm8/FwIScjl8xy+keqfj0Uz/K/ldoz38SeZ1jnSP7dvYl+qakyV9NzWLQtjm93nic7T5lxMDVQU6kBG8Dyvy9ye8MAk89hqGGQF6G+HtzMK6gxS6AqtYvB9lNXOZmYgUajtJeymYwMWLqU+vPm8dUpJSDXOTmhGTIExo+HO+8ss5dnXGUN3M1Uz8edM9cyjSqlYUkmqEqj0XBrhD/bT11j1f5LtA5vXenXGCYXlJUJqlJn1tSZJn2XBwe3bJfyf75NfV86RwWW+7rhdzbk082n9UVy1UQVyQQV1YkEa8ImvN1duJaRW2l/0F8OKksTQXXcSM7MZcmOcyzbdaHSoK2sIO3WCH8m9GpO9+bm1/hyctIwa3BbHr+zIU0t2K8GSkboz2PuokCrq1FLoACRhcug6gbte5rXo0GA+UvG5Tp/Hj75BL78ElKVWaKsOj58c0svtP9+gRee6lHulxo2cFfbKlmLOrNmTBcDSzNBVSO7RrH91DW+3XmeUXc3oV5hokN5jEkugKKG7pm5BVy4nknTahC8GBbBfbZr4wr3IwbWcWNIxwYs23WBL/88WxSsSSaoqEaqx69QotpRa62lV7BnLS07j+2nrgHw/ag7+e/IztzeMICcfC1Ldpyj2+zNvLn2SLHl0avpObzzy1G6zd7EF9vPkp2n5dYIf5Y83YnVL3SxKFBTaTQa2tT3w8PVshIPoHyoh9soO9KeGgUVD8ysmlig0ylFax9+WOku8OGHSqDWvDl8+ikx6//m3Xuf4Y/Mimdfk9INGrgHWTeQNKWLgaWZoKruzevRPsKf7Dwti7bFVfp6Y5ILoHo2dF9XWAQ3xNed+43YYmBYJPdc4XKvZIKK6kRm1oRN1HErLN9RwczahqOJ5BZoaRrsTfMQH5qH+HBX0yB2nE5mzoaT7D1/gyU7zvHdrgsMuyMSV2dNqZm0cT2bcU/zelbL9BPGCff3xNlJQ4FWZ73EgtxcWLEC5s6FPXuKjvfsCePGQb9+4OTE7Sk3Yd1pfb01b/eyf4zFJRU1cHd3sTzwNmRsFwNrZhxqNBrG9WzGU0v+Nmp2raLOBSU1qVeHQ5dTTW7ofjO3gHytFh8P8yuzm0qn0+nLdYzo0sioZVu1SO7mE1dZsuMsb/S/pSjjW2bWRDUgwZqwCR99f9DygzV1CdRw871Go6Frs7qlgralf53Tv0aCNPtzdXaivr8nF65nmZ9YkJenJAScPKkEZ4sWKWU3ANzd4YknYOxYaNOm2Jep9dYuXM9iz7nr3NOi7ECxWAN3KzO2i8HGY4lWySxWqbNrBy6msGhbHK89UP7etUOXKk8uUJnT0F2r1THosx1cy8jl93HdCPKueFnWWmLjkjkWn1aqCG5lnu3WmM0nrvLjnksMuq2BPhM0whbL90JYmQRrwib0hXHLmVkzXAItq19myaBtUWEm1zNdoyRIcxDPdovi10PxPNmlUfkvKiiAixfh1CnldvJk0Z9nzyrPGwoLg9GjYdQoqFf+kvadjQO5cD2LnWfKD9bU/WqNrbxfDYpm1irqYrD5RBJjlu0HYGD7+hZlFquMnV0zNrlAZU5D9z3nb+iL9P5v3yWzknrMsbhwVu3/OjYwaS+oWiT3eEI6b607CkgmqKg+JFgTNuFdycxaySXQ8qhBW9dmdW0yTmG+J6Mb8WR0I2WP2ZUrZQdkcXFKaY3yeHpCs2bKfrSHHoJHHjGqufqdjYP4cc+lCvuE6jNBbTCzFlLJzNrmE0n865u95BZo6XNLCG8PalPm68xhzOyasckFqpIN3Y35ZeiXg1f097/ffZHnulW80d8a4q5msFFfBLeRSV+r0WgY2TWKST8dZO/5G0DxwtVCODIJ1oRNeLsre1jKC9bKWgIVDiQ3VwnArl+H5GTlVtZ9NUjLrGD5zNUVmjQpCsoM/wwPByfTl1BL9gkta99aUSaoLZZBi3cxMLx+yUDt02G34WrF+nPGzK4Zm1ygMmzofj0zt9IlTa1Wx2+HE/SPz17LZOeZ60Q3CTLjb2S8r/4sKoJrThD+UPtw3lt/gmsZyi8Qsl9NVBcSrAmb0O9ZK2MZtLIlUFHFdDq4dAliY2HnTuW2b1/FM2IlOTkpLZ6aNSsejDVrBpGR4GLdHzWV7VvLzivgcorSt9QWy6BqF4PM3AKS0rLxLgwcbB2oqSqbXTMluQCKGrpfunGTuKuZlQZre87fICk9Bx8PF/reEsqKvZdYtvuCTYO1G0YUwa2MWiT3w5iTgGSCiupDgjVhE2o7nrJm1oxdAhU2kpmpBGNqYLZzZ9HGfkPu7hAUBIGByp+GN/VYcLASkEVFGbV8aU0V7Vs7ey0TnQ58PVwIqmObcYX4enDmWiaJaTk0ruddZYEaKLNrY3s24+lyZteM6VxQUuN63ly6cZMzVzO4o4Iis1C0BNqrdQgjujRixd5L/H44geSMHJslGizYGkd2npZbwisugluZ4Xc2ZP6W0+Tma/VtvIRwdA4drC1YsIAFCxZw7tw5AG655RbeeOMN+vXrB0B2djYvv/wyy5cvJycnhz59+vDZZ58RElKzqsVXR96Fqfxl1VlTe/TJEqgN5ebCmTOl95CdOqXMopXk7Azt2yudANRbkyZldgVwFBXtW1OzGpsEe9tsH5VhF4OqDNRU9zSvx60R/vxTYnatQKvj8GUlucCkYK1uHbadvFppkoHhEuiD7cJoU9+PtvX9OHQ5lZX7LvPc3Y3N/BuV79y1TH0f0Im9W1j0bxpYx43vnr2TtJt51K+BNRBFzeTQwVqDBg149913adasGTqdjq+//poBAwawf/9+brnlFsaPH88vv/zCihUr8PPzY8yYMQwePJgdO3bYe+i1XlE2aF6x42nZeWw7KUugJtPpIC0Nrl0rvnes5F6ya9eUTf3nzoG2gobfYWEQHa0EZdHRcNtt4FW9ShhUtG9NDTis3WbKkNrF4OcDV/jz1LUqDdSgaO9aydk1w+QCU/Z1GdvQ3XAJtGtTJWN3WOdIpqw8xPe7L/BstyirB8gzfz1GXoGO7s3rca8Vavp1tLCVnBBVzaGDtf79+xd7/M4777BgwQJ27txJgwYNWLx4McuWLeO+++4DYMmSJbRq1YqdO3dy55132mPIopC6Zy0zp3hpBlkCLUdurjLjdeFC+beKNvGXpU6d4nvHDDf3B9l2I3hVqGjfmq0auBtSM0I3HU8CqNJATVXW7JqpyQUqYxu6Gy6BqgVp+98aztvrjnLGBokGf52+xh9HE3F20jD1gVZWO68Q1YlDB2uGCgoKWLFiBZmZmURHR7N3717y8vLo2bOn/jUtW7YkMjKS2NjYCoO1nJwccgw2T6enp9t07LWRfmatxDJorVsC1WohKUnZE1bRLSlJmT2rjJdX6X1jJe83aqQEZKGhDr2MaQ3l7VtTAw5bzqyptdbAPoEalD27ZmpygcqYhu4ll0BV3u4uPNS+Pt/vvsD3Vkw0KNDqmFFYE+3xzpEW91cVorpy+GDt0KFDREdHk52djbe3N6tWraJ169YcOHAANzc3/P39i70+JCSEhISEsk9WaNasWbz55ps2HLXQ9wY1WAatkUug6emlZ8AuXiy6f+mSUqnfGB4eSuZkebf69avdUqWtlbVvTafT6VtNWbuBu6E7ogJxc3ai9y0hzHmkfZUHaqqSs2vmJBeAcQ3dy1oCVQ3vHMn3uy+w/nAC1zNzCbRCYscPf1/keEI6fp6ujOvZ3OLzCVFdOXyw1qJFCw4cOEBqaio//fQTI0aMYOvWrRadc8qUKUyYMEH/+PLly7RuXX7bFmE6w5k1tchmtV0CTUsrKvhquFH/1Cllv1hlNBoICVFqioWHK0GXet/wWN26NX4mzNrK2rdmywbuhm6N8Ofg9N54uFq376ipSs6uqUwN1tSG7ocupxJ3texgrawlUJVhosH/9l6yONEgLTuPD/84AcC4ns0IsFFWrxDVgcMHa25ubjRt2hSAjh078vfff/Pxxx/zyCOPkJubS0pKSrHZtcTEREJDQys8p7u7O+7uRenlaWlpNhl7baYGa1odhZudXRx7CVSrVTblHzum3I4fV4KykychMbHir/X3L3smLCJC+TMsTCkMK6yuvr8nEYGeXLx+U79vTU0usEUD95LsHaipDGfXAJOTC1RqQ/eyMkLLWwI1ZM1Eg083nSY5M5fG9erw+J0NzT6PEDWBwwdrJWm1WnJycujYsSOurq5s3LiRIUOGAHDixAkuXLhAdHS0nUcpvNyc0WiUbVgZ2fnka3WOsQSqLluqQdmxY3D0KJw4AdkVNOVW64kZbthv1gwaNwafajRLWAPdGRXExeuX9PvWbNlmylEZzq6B6ckFqooaule0BKqyVqKBYamO1x9obbclZiEchUMHa1OmTKFfv35ERkaSnp7OsmXL2LJlC7///jt+fn6MHDmSCRMmEBgYiK+vLy+++CLR0dGSCeoANBoN3u4upGfnk5GTz5+nr9l+CfTGDThypPTm/cuXi+5nVFBDyt1dCcZatVJuLVoUBWV+pi0piapzZ+MgVuwt2rdW1GbKdvvVHJHh7Frb+v5mnaOoR2jp/yfqzHhZS6AqayUaqKU67m5ej3talB0YClGbOHSwlpSUxJNPPkl8fDx+fn60a9eO33//nV69egEwZ84cnJycGDJkSLGiuMIx+BgEa1ZfAtXp4Px5+PPPotuRI8Z9rZ+fEoipQVnr1sqfUVFKcVhRrXRurFSzV/et1caZNVB+Qfrw4XYs2naGkWa2Y1L7qMaVaOiu1er0/4fLWwJVWZpoYFiq4/UHWtm8ObwQ1YFDB2uLFy+u8HkPDw/mz5/P/Pnzq2hEwhTeHi6QCldSsi1fAs3Ph0OHYMeOouDs8uXSr2vYUNkrVt4m/rAw8K5dH+I1XYMAr2L71mzZwN3RNQ32Yfb/3Wr215fX0N2YJVCVJYkGUqpDiLI5dLAmqrc6hUkGq/dfrnwJVKdTljHPnlXaJJ05U/z++fNKwGbIxQU6doSuXeGuu5RbsOXVzUX1o+5b23Liqk0buNd05TV0N2YJ1NBjd0RyaJXpiQZqqQ5fDxcp1SGEAQnWhM2oGaEbjiXipC3gkVAN/PVX2fXIzp6F1NSKT+jrC126FAVnd9whdccEULRvbdX+yzZv4F7TlWzobsoSqOqh9uG884tpiQbFS3U0l1IdQhiQYE1YLjOz9Eb+K1cYG3uYFy9dJjztGqHp13DRVdCrUhUaqmRYNm6s7CEzvF+/PjhJVpgoTd23lnpTKUBsywbuNZ3a0F3tAmHKEqjKnESD+QalOp6IllIdQhiSYE2Y7vx5WLgQ1qxRKvSXU6fu9pIHXFyUgKusemRRUUqbJJkpE2Yw3LcGtm0zVdOpbafULhCmLoGqht1hXKJBdl4BsXHJfCWlOoQolwRrwjg6HWzeDJ98ogRp2hKzZHXqlNrU//sNJ9ZehSu+9eh7f2dGDe0i2ZbCZtR9ayD71Sxh2NDdnCVQVdsGZScaZOcVsO/8DXaeSWbnmescuJhCboHy80RKdQhRNgnWRMUyMuDbb+HTT5Xisar77oPnn4e2bZXgzMenVKukIzEnWbfxFADv3tdeAjVhU+q+NaidmaDWYtjQPfZMsslLoIbURIP/7jpPenZeqeBMFeLrTtem9XilXwtZvhaiDBKsibKdPAnz58PSpUXLnHXqwIgRMHq0UpusEj6FCQbVrheoqJbUfWtQ+wriWpNhQ/cFW+IA05dAVWqiwfnkLOZtOq0/HuLrTnTjIO4svDUM8pIgTYgKSLBWm+l0SjKAYXNy9c9jx4pe16wZjBmjBGomVPLvc0soP/9zmRfva2aDwQtRXIMALx6/M5KUrDyZWbOAYUP3P08r9RFNXQJVebu7MKF3C77bdZ529f0kOBN2NX36dN58881ix1q0aMHx48ftNCLjSbBW3el0SjZmaqpyy8hQbpmZRbeSjy9fVgKyU6cgK6vs82o0cP/98OKL0KuXWVmYkUFerHuxm4V/QSGM9/bAtvYeQo2gNnQHzF4CVY3sGsXIruZ1VBDC2m655RY2bNigf+ziUj3CoOoxypooOxuuX4fkZKUYbGUBlmFAZnhLS4OCAvPH4eysZGKqPTDVRuVt2ih70YQQtY5hqy5zl0CFcEQuLi6Ehobaexgmk2DNlt59VylzkZxcdFMDtPJmtMzl7KwUjfXxUfaWeXsrfxre1GPBwUWBWVQUuLpadyxCiGrNMJvW3CVQIapSeno6aQZlpNzd3XF3dy/1ulOnThEeHo6HhwfR0dHMmjWLyMjIqhyqWSRYs6UlS5Q9YOVxcoLAQOWmBlIlgyzDx35+ys3Xt+i+evPyKpWNKYQQ5mgV5guAv5crdzWta+fRCFG51iWS3qZNm8b06dOLHevcuTNLly6lRYsWxMfH8+abb9KtWzcOHz6Mj49jJ8FJsGZLo0ZBeroSjAUFFd3Ux76+UpFfCOFwmtTzZuHjHQnz88DdRUruCMd39OhR6tevr39c1qxav3799PfbtWtH586dadiwIT/++CMjR46sknGaS4I1W3r5ZXuPQAghzNK3TfXb1yNqLx8fH3x9fU36Gn9/f5o3b87p06crf7GdybSOEEIIIWqdjIwM4uLiCAtz/H2ZEqwJIYQQosabOHEiW7du5dy5c/z1118MGjQIZ2dnHnvsMXsPrVKyDCqEEEKIGu/SpUs89thjJCcnU69ePbp27crOnTupV8/x+9FKsCaEEEKIGm/58uX2HoLZZBlUCCGEEMKBSbAmhBBCCOHAJFgTQgghhHBgEqwJIYQQQjgwCdaEEEIIIRyYBGtCCCGEEA5MgjUhhBBCCAcmwZoQQgghhAOTYE0IIYQQwoFJBwNAq9UCEB8fb+eRCCGEEMJY6ue2+jleU0mwBiQmJgJwxx132HkkQgghhDBVYmIikZGR9h6GzWh0Op3O3oOwt/z8fPbv309ISAhOTrIybE3p6em0bt2ao0eP4uPjY+/h1Hjyflcteb+rlrzfVas6vN9arZbExEQ6dOiAi0vNnX+SYE3YVFpaGn5+fqSmpuLr62vv4dR48n5XLXm/q5a831VL3m/HIdNIQgghhBAOTII1IYQQQggHJsGasCl3d3emTZuGu7u7vYdSK8j7XbXk/a5a8n5XLXm/HYfsWRNCCCGEcGAysyaEEEII4cAkWBNCCCGEcGASrAkhhBBCODAJ1oQQQgghHJgEa6JS27Zto3///oSHh6PRaFi9enWp1xw7doyHHnoIPz8/6tSpQ6dOnbhw4YL++ezsbEaPHk1QUBDe3t4MGTJE3+ZLdeHCBR544AG8vLwIDg5m0qRJ5Ofn2/qv53Cs8X7fc889aDSaYrfnn3++2Dnk/VZU9n6XfB/V2/vvv69/zfXr1xk+fDi+vr74+/szcuRIMjIyip3n4MGDdOvWDQ8PDyIiIpg9e3ZV/PUcjjXe70aNGpV6/t133y12Hnm/FZW93xkZGYwZM4YGDRrg6elJ69atWbhwYbHXyM9v+5NgTVQqMzOTW2+9lfnz55f5fFxcHF27dqVly5Zs2bKFgwcP8vrrr+Ph4aF/zfjx41m7di0rVqxg69atXLlyhcGDB+ufLygo4IEHHiA3N5e//vqLr7/+mqVLl/LGG2/Y/O/naKzxfgM899xzxMfH62+GH1byfhep7P02fA/j4+P56quv0Gg0DBkyRP+a4cOHc+TIEWJiYli3bh3btm1j1KhR+ufT0tLo3bs3DRs2ZO/evbz//vtMnz6dRYsW2fzv52is8X4DzJgxo9jrXnzxRf1z8n4Xqez9njBhAuvXr+e///0vx44dY9y4cYwZM4Y1a9boXyM/vx2ATggTALpVq1YVO/bII4/oHn/88XK/JiUlRefq6qpbsWKF/tixY8d0gC42Nlan0+l0v/76q87JyUmXkJCgf82CBQt0vr6+upycHOv+JaoRc95vnU6n6969u27s2LHlPi/vd9nKer9LGjBggO6+++7TPz569KgO0P3999/6Y7/99ptOo9HoLl++rNPpdLrPPvtMFxAQUOy9feWVV3QtWrSw7l+gmjHn/dbpdLqGDRvq5syZU+7XyPtdtrLe71tuuUU3Y8aMYsduu+023WuvvabT6eTnt6OQmTVhEa1Wyy+//ELz5s3p06cPwcHBdO7cudhU+969e8nLy6Nnz576Yy1btiQyMpLY2FgAYmNjadu2LSEhIfrX9OnTh7S0NI4cOVJlfx9HZ8z7rfruu++oW7cubdq0YcqUKWRlZemfk/fbPImJifzyyy+MHDlSfyw2NhZ/f39uv/12/bGePXvi5OTErl279K+5++67cXNz07+mT58+nDhxghs3blTdX6CaKev9Vr377rsEBQXRoUMH3n///WJLbvJ+G69Lly6sWbOGy5cvo9Pp2Lx5MydPnqR3796A/Px2FBKsCYskJSWRkZHBu+++S9++ffnjjz8YNGgQgwcPZuvWrQAkJCTg5uaGv79/sa8NCQkhISFB/xrD/+jq8+pzQmHM+w0wbNgw/vvf/7J582amTJnCt99+y+OPP65/Xt5v83z99df4+PgUWwJKSEggODi42OtcXFwIDAyU728LlfV+A7z00kssX76czZs3869//YuZM2cyefJk/fPyfhvvk08+oXXr1jRo0AA3Nzf69u3L/PnzufvuuwH5+e0oXOw9AFG9abVaAAYMGMD48eMBaN++PX/99RcLFy6ke/fu9hxejWPs+224X6pt27aEhYXRo0cP4uLiaNKkSdUPvIb46quvGD58eKn9gcI2ynu/J0yYoL/frl073Nzc+Ne//sWsWbOkNZKJPvnkE3bu3MmaNWto2LAh27ZtY/To0YSHhxebTRP2JTNrwiJ169bFxcWF1q1bFzveqlUrfXZiaGgoubm5pKSkFHtNYmIioaGh+teUzC5SH6uvEca932Xp3LkzAKdPnwbk/TbH9u3bOXHiBM8++2yx46GhoSQlJRU7lp+fz/Xr1+X72wLlvd9l6dy5M/n5+Zw7dw6Q99tYN2/e5D//+Q8fffQR/fv3p127dowZM4ZHHnmEDz74AJCf345CgjVhETc3Nzp16sSJEyeKHT958iQNGzYEoGPHjri6urJx40b98ydOnODChQtER0cDEB0dzaFDh4p96MXExODr61sqMKnNjHm/y3LgwAEAwsLCAHm/zbF48WI6duzIrbfeWux4dHQ0KSkp7N27V39s06ZNaLVafZAcHR3Ntm3byMvL078mJiaGFi1aEBAQUDV/gWqmvPe7LAcOHMDJyUm/HC3vt3Hy8vLIy8vDyal4KODs7KyfxZef3w7C3hkOwvGlp6fr9u/fr9u/f78O0H300Ue6/fv3686fP6/T6XS6lStX6lxdXXWLFi3SnTp1SvfJ/7drxyDJxHEYx3tp8DyiSBKhwCGMiIJwEiFsixobHbTaojUaWhvEJRoCxUFaGhpta+oGJQgMDBrVxMUpUBoKkp6G4ELewPdFsv/w/cBN9+N3/J/heDju9FSjo6MqFovujt3dXQWDQV1fX6tcLisajSoajbr3u92ulpaWtLa2pkqloqurK/n9fh0eHg79vL9t0Lyr1aqOjo5ULpf1+Pioy8tLzc7OKhaLuc8g7y/98pakTqcj27aVzWa/3bG+vq5wOKzb21uVSiXNzc0pHo+799vttgKBgBKJhB4eHnRxcSHbtpXL5X78fKYZNO+bmxudnJyoUqmoVqvp/Pxcfr9fyWTSnSHvL/3yXl1d1eLiohzHUb1e19nZmSzLUiaTcXfw/v59lDX05TiORkZG/rq2trbcmXw+r1AoJMuytLy8rEKh0LPj5eVFe3t7mpyclG3b2tzcVKvV6plpNBra2NiQ1+vV1NSU9vf39fb2NowjGmXQvJvNpmKxmHw+nzwej0KhkA4ODtTpdHqeQ96f/iXvXC4nr9erdrv97Y6npyfF43GNjY1pfHxcOzs7en5+7pm5v7/XysqKPB6PZmZmlE6nf/JYxho077u7O0UiEU1MTMiyLC0sLCiVSun19bVnjrw/9cu71Wppe3tb09PTsixL8/PzOj4+1vv7u7uD9/fv+yNJw/iCBwAAgP/HP2sAAAAGo6wBAAAYjLIGAABgMMoaAACAwShrAAAABqOsAQAAGIyyBgAAYDDKGgAAgMEoawAAAAajrAEAABiMsgYAAGAwyhoAAIDBPgDA+AOKIv4zlAAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"Year\"], data[\"Wheat\"])\n",
    "plt.ylabel(\"Shillings par semaine\")\n",
    "plt.twinx()\n",
    "plt.plot(data[\"Year\"], data[\"Wages\"], color=\"red\")\n",
    "plt.ylabel(\"Shillings par quart de boisseau de blé\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Graphe montrant pour chaque année la quantité de blé pouvant être acheté, exprimé sous forme de fraction représentant la portion que pouvait se permettre un ouvrier, une augmentation graduelle est constasté (Suivi d'une chute mais le manque d'une suite de données ne permet pas de conclure)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x22347aef940>]"
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pouvoir_achat = []\n",
    "\n",
    "for value in sorted_data.values:\n",
    "    pouvoir_achat.append(value[1] / value[0])\n",
    "\n",
    "plt.plot(pouvoir_achat)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}