From ce6c715731b4593118b85feb041e058be3740505 Mon Sep 17 00:00:00 2001
From: 92c55694c2aeccd81224d0e8936d266a
 <92c55694c2aeccd81224d0e8936d266a@app-learninglab.inria.fr>
Date: Mon, 29 Jun 2020 14:54:07 +0000
Subject: [PATCH] =?UTF-8?q?Module=203,=20exo=203,=20sujet=201:=20Concentra?=
 =?UTF-8?q?tion=20de=20CO2=20dans=20l'atmosph=C3=A8re=20depuis=201958?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 module3/exo3/exercice.ipynb             | 1261 ++++++++-
 module3/exo3/weekly_in_situ_co2_mlo.csv | 3217 +++++++++++++++++++++++
 2 files changed, 4475 insertions(+), 3 deletions(-)
 create mode 100644 module3/exo3/weekly_in_situ_co2_mlo.csv

diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index 0bbbe37..e6cb351 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -1,5 +1,1261 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evolution de la concentration atmosphérique en CO<sub>2</sub>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import des packages Python utiles à cette analyse:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import datetime\n",
+    "import os\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import numpy as np\n",
+    "from urllib.request import urlretrieve"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideOutput": false
+   },
+   "source": [
+    "On récupère les données hebdomadaires de concentration sur le [site de l'institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/mlo.html). Ces mesures sont effectuées à l'observatoire Mauna Loa, situé à Hawaii. Date de téléchargement des données utilisées pour cette analyse: **26 juin 2020**. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url = 'https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv'\n",
+    "file_name = os.path.split(data_url)[1]\n",
+    "if not os.path.exists(file_name):\n",
+    "    urlretrieve(data_url, file_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On lit le fichier CSV téléchargé, il contient 44 lignes d'en-tête que nous ignorons. On attribue les libellés 'date' et 'CO2' aux colonnes de date (au format AAAA-MM-JJ) et concentration en CO<sub>2</sub> ([en ppm](https://fr.wikipedia.org/wiki/Partie_par_million)) des données mesurées."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>CO2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1958-03-29</td>\n",
+       "      <td>316.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1958-04-05</td>\n",
+       "      <td>317.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1958-04-12</td>\n",
+       "      <td>317.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1958-04-19</td>\n",
+       "      <td>317.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1958-04-26</td>\n",
+       "      <td>316.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1958-05-03</td>\n",
+       "      <td>316.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1958-05-17</td>\n",
+       "      <td>317.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1958-05-24</td>\n",
+       "      <td>317.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1958-07-05</td>\n",
+       "      <td>315.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1958-07-12</td>\n",
+       "      <td>315.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1958-07-19</td>\n",
+       "      <td>315.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1958-07-26</td>\n",
+       "      <td>315.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1958-08-02</td>\n",
+       "      <td>315.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1958-08-09</td>\n",
+       "      <td>315.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1958-08-16</td>\n",
+       "      <td>315.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1958-08-30</td>\n",
+       "      <td>314.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1958-09-06</td>\n",
+       "      <td>313.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1958-11-08</td>\n",
+       "      <td>313.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1958-11-15</td>\n",
+       "      <td>313.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1958-11-22</td>\n",
+       "      <td>313.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1958-11-29</td>\n",
+       "      <td>314.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1958-12-06</td>\n",
+       "      <td>314.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1958-12-13</td>\n",
+       "      <td>314.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1958-12-20</td>\n",
+       "      <td>314.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1958-12-27</td>\n",
+       "      <td>315.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1959-01-03</td>\n",
+       "      <td>315.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1959-01-10</td>\n",
+       "      <td>315.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1959-01-17</td>\n",
+       "      <td>315.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1959-01-24</td>\n",
+       "      <td>315.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1959-01-31</td>\n",
+       "      <td>315.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3143</th>\n",
+       "      <td>2019-11-02</td>\n",
+       "      <td>409.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3144</th>\n",
+       "      <td>2019-11-09</td>\n",
+       "      <td>410.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3145</th>\n",
+       "      <td>2019-11-16</td>\n",
+       "      <td>410.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3146</th>\n",
+       "      <td>2019-11-23</td>\n",
+       "      <td>410.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3147</th>\n",
+       "      <td>2019-11-30</td>\n",
+       "      <td>410.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3148</th>\n",
+       "      <td>2019-12-07</td>\n",
+       "      <td>411.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3149</th>\n",
+       "      <td>2019-12-14</td>\n",
+       "      <td>411.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3150</th>\n",
+       "      <td>2019-12-21</td>\n",
+       "      <td>412.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3151</th>\n",
+       "      <td>2019-12-28</td>\n",
+       "      <td>412.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3152</th>\n",
+       "      <td>2020-01-04</td>\n",
+       "      <td>413.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3153</th>\n",
+       "      <td>2020-01-11</td>\n",
+       "      <td>413.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3154</th>\n",
+       "      <td>2020-01-25</td>\n",
+       "      <td>413.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3155</th>\n",
+       "      <td>2020-02-01</td>\n",
+       "      <td>413.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3156</th>\n",
+       "      <td>2020-02-08</td>\n",
+       "      <td>414.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3157</th>\n",
+       "      <td>2020-02-15</td>\n",
+       "      <td>413.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3158</th>\n",
+       "      <td>2020-02-22</td>\n",
+       "      <td>414.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3159</th>\n",
+       "      <td>2020-02-29</td>\n",
+       "      <td>413.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3160</th>\n",
+       "      <td>2020-03-07</td>\n",
+       "      <td>414.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3161</th>\n",
+       "      <td>2020-03-14</td>\n",
+       "      <td>414.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3162</th>\n",
+       "      <td>2020-03-21</td>\n",
+       "      <td>414.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3163</th>\n",
+       "      <td>2020-03-28</td>\n",
+       "      <td>415.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3164</th>\n",
+       "      <td>2020-04-04</td>\n",
+       "      <td>415.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3165</th>\n",
+       "      <td>2020-04-11</td>\n",
+       "      <td>416.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3166</th>\n",
+       "      <td>2020-04-18</td>\n",
+       "      <td>416.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3167</th>\n",
+       "      <td>2020-04-25</td>\n",
+       "      <td>415.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3168</th>\n",
+       "      <td>2020-05-02</td>\n",
+       "      <td>417.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3169</th>\n",
+       "      <td>2020-05-09</td>\n",
+       "      <td>416.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3170</th>\n",
+       "      <td>2020-05-16</td>\n",
+       "      <td>416.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3171</th>\n",
+       "      <td>2020-05-23</td>\n",
+       "      <td>417.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3172</th>\n",
+       "      <td>2020-05-30</td>\n",
+       "      <td>417.19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3173 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            date     CO2\n",
+       "0     1958-03-29  316.19\n",
+       "1     1958-04-05  317.31\n",
+       "2     1958-04-12  317.69\n",
+       "3     1958-04-19  317.58\n",
+       "4     1958-04-26  316.48\n",
+       "5     1958-05-03  316.95\n",
+       "6     1958-05-17  317.56\n",
+       "7     1958-05-24  317.99\n",
+       "8     1958-07-05  315.85\n",
+       "9     1958-07-12  315.85\n",
+       "10    1958-07-19  315.46\n",
+       "11    1958-07-26  315.59\n",
+       "12    1958-08-02  315.64\n",
+       "13    1958-08-09  315.10\n",
+       "14    1958-08-16  315.09\n",
+       "15    1958-08-30  314.14\n",
+       "16    1958-09-06  313.54\n",
+       "17    1958-11-08  313.05\n",
+       "18    1958-11-15  313.26\n",
+       "19    1958-11-22  313.57\n",
+       "20    1958-11-29  314.01\n",
+       "21    1958-12-06  314.56\n",
+       "22    1958-12-13  314.41\n",
+       "23    1958-12-20  314.77\n",
+       "24    1958-12-27  315.21\n",
+       "25    1959-01-03  315.24\n",
+       "26    1959-01-10  315.50\n",
+       "27    1959-01-17  315.69\n",
+       "28    1959-01-24  315.86\n",
+       "29    1959-01-31  315.42\n",
+       "...          ...     ...\n",
+       "3143  2019-11-02  409.86\n",
+       "3144  2019-11-09  410.15\n",
+       "3145  2019-11-16  410.22\n",
+       "3146  2019-11-23  410.48\n",
+       "3147  2019-11-30  410.92\n",
+       "3148  2019-12-07  411.27\n",
+       "3149  2019-12-14  411.67\n",
+       "3150  2019-12-21  412.30\n",
+       "3151  2019-12-28  412.59\n",
+       "3152  2020-01-04  413.19\n",
+       "3153  2020-01-11  413.39\n",
+       "3154  2020-01-25  413.36\n",
+       "3155  2020-02-01  413.99\n",
+       "3156  2020-02-08  414.83\n",
+       "3157  2020-02-15  413.81\n",
+       "3158  2020-02-22  414.17\n",
+       "3159  2020-02-29  413.89\n",
+       "3160  2020-03-07  414.00\n",
+       "3161  2020-03-14  414.30\n",
+       "3162  2020-03-21  414.62\n",
+       "3163  2020-03-28  415.57\n",
+       "3164  2020-04-04  415.61\n",
+       "3165  2020-04-11  416.47\n",
+       "3166  2020-04-18  416.60\n",
+       "3167  2020-04-25  415.86\n",
+       "3168  2020-05-02  417.20\n",
+       "3169  2020-05-09  416.99\n",
+       "3170  2020-05-16  416.54\n",
+       "3171  2020-05-23  417.49\n",
+       "3172  2020-05-30  417.19\n",
+       "\n",
+       "[3173 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(file_name, skiprows=44, names=['date', 'CO2'])\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il y a 3173 relevées de concentration. On vérifie que la concentration est renseignée pour chaque date indiquée:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>CO2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [date, CO2]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analyse des concentrations observées"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Afin de les manipuler plus facilement, on convertit les dates de mesure en objet Python *datetime* associé:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.copy()\n",
+    "data['date'] = [datetime.datetime.strptime(date, '%Y-%m-%d').date() for date in data['date']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On définit à présent la date comme nouvel index de notre jeu de données. Et on trie par ordre chronologique:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CO2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1958-03-29</th>\n",
+       "      <td>316.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-05</th>\n",
+       "      <td>317.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-12</th>\n",
+       "      <td>317.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-19</th>\n",
+       "      <td>317.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-26</th>\n",
+       "      <td>316.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-05-03</th>\n",
+       "      <td>316.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-05-17</th>\n",
+       "      <td>317.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-05-24</th>\n",
+       "      <td>317.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-07-05</th>\n",
+       "      <td>315.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-07-12</th>\n",
+       "      <td>315.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-07-19</th>\n",
+       "      <td>315.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-07-26</th>\n",
+       "      <td>315.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-08-02</th>\n",
+       "      <td>315.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-08-09</th>\n",
+       "      <td>315.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-08-16</th>\n",
+       "      <td>315.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-08-30</th>\n",
+       "      <td>314.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-09-06</th>\n",
+       "      <td>313.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-11-08</th>\n",
+       "      <td>313.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-11-15</th>\n",
+       "      <td>313.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-11-22</th>\n",
+       "      <td>313.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-11-29</th>\n",
+       "      <td>314.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-12-06</th>\n",
+       "      <td>314.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-12-13</th>\n",
+       "      <td>314.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-12-20</th>\n",
+       "      <td>314.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-12-27</th>\n",
+       "      <td>315.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1959-01-03</th>\n",
+       "      <td>315.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1959-01-10</th>\n",
+       "      <td>315.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1959-01-17</th>\n",
+       "      <td>315.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1959-01-24</th>\n",
+       "      <td>315.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1959-01-31</th>\n",
+       "      <td>315.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-11-02</th>\n",
+       "      <td>409.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-11-09</th>\n",
+       "      <td>410.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-11-16</th>\n",
+       "      <td>410.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-11-23</th>\n",
+       "      <td>410.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-11-30</th>\n",
+       "      <td>410.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-07</th>\n",
+       "      <td>411.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-14</th>\n",
+       "      <td>411.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-21</th>\n",
+       "      <td>412.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-28</th>\n",
+       "      <td>412.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-04</th>\n",
+       "      <td>413.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-11</th>\n",
+       "      <td>413.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-25</th>\n",
+       "      <td>413.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-02-01</th>\n",
+       "      <td>413.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-02-08</th>\n",
+       "      <td>414.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-02-15</th>\n",
+       "      <td>413.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-02-22</th>\n",
+       "      <td>414.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-02-29</th>\n",
+       "      <td>413.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-07</th>\n",
+       "      <td>414.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-14</th>\n",
+       "      <td>414.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-21</th>\n",
+       "      <td>414.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-28</th>\n",
+       "      <td>415.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-04-04</th>\n",
+       "      <td>415.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-04-11</th>\n",
+       "      <td>416.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-04-18</th>\n",
+       "      <td>416.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-04-25</th>\n",
+       "      <td>415.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-02</th>\n",
+       "      <td>417.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-09</th>\n",
+       "      <td>416.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-16</th>\n",
+       "      <td>416.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-23</th>\n",
+       "      <td>417.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-30</th>\n",
+       "      <td>417.19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3173 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               CO2\n",
+       "date              \n",
+       "1958-03-29  316.19\n",
+       "1958-04-05  317.31\n",
+       "1958-04-12  317.69\n",
+       "1958-04-19  317.58\n",
+       "1958-04-26  316.48\n",
+       "1958-05-03  316.95\n",
+       "1958-05-17  317.56\n",
+       "1958-05-24  317.99\n",
+       "1958-07-05  315.85\n",
+       "1958-07-12  315.85\n",
+       "1958-07-19  315.46\n",
+       "1958-07-26  315.59\n",
+       "1958-08-02  315.64\n",
+       "1958-08-09  315.10\n",
+       "1958-08-16  315.09\n",
+       "1958-08-30  314.14\n",
+       "1958-09-06  313.54\n",
+       "1958-11-08  313.05\n",
+       "1958-11-15  313.26\n",
+       "1958-11-22  313.57\n",
+       "1958-11-29  314.01\n",
+       "1958-12-06  314.56\n",
+       "1958-12-13  314.41\n",
+       "1958-12-20  314.77\n",
+       "1958-12-27  315.21\n",
+       "1959-01-03  315.24\n",
+       "1959-01-10  315.50\n",
+       "1959-01-17  315.69\n",
+       "1959-01-24  315.86\n",
+       "1959-01-31  315.42\n",
+       "...            ...\n",
+       "2019-11-02  409.86\n",
+       "2019-11-09  410.15\n",
+       "2019-11-16  410.22\n",
+       "2019-11-23  410.48\n",
+       "2019-11-30  410.92\n",
+       "2019-12-07  411.27\n",
+       "2019-12-14  411.67\n",
+       "2019-12-21  412.30\n",
+       "2019-12-28  412.59\n",
+       "2020-01-04  413.19\n",
+       "2020-01-11  413.39\n",
+       "2020-01-25  413.36\n",
+       "2020-02-01  413.99\n",
+       "2020-02-08  414.83\n",
+       "2020-02-15  413.81\n",
+       "2020-02-22  414.17\n",
+       "2020-02-29  413.89\n",
+       "2020-03-07  414.00\n",
+       "2020-03-14  414.30\n",
+       "2020-03-21  414.62\n",
+       "2020-03-28  415.57\n",
+       "2020-04-04  415.61\n",
+       "2020-04-11  416.47\n",
+       "2020-04-18  416.60\n",
+       "2020-04-25  415.86\n",
+       "2020-05-02  417.20\n",
+       "2020-05-09  416.99\n",
+       "2020-05-16  416.54\n",
+       "2020-05-23  417.49\n",
+       "2020-05-30  417.19\n",
+       "\n",
+       "[3173 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_data = data.set_index('date').sort_index()\n",
+    "sorted_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Le premier relevé date du 29 mars 1958 pour une concentration en CO<sub>2</sub> égale à 316.19 ppm, le dernier du 30 mai 2020 pour une concentration de 417.19 ppm.\n",
+    "\n",
+    "Nous vérifons la régularité des mesures en affichant les périodes où il n'y a pas eu de mesures pendant plus d'une semaine:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No concentration measure are given for 2 weeks between 1958-05-03 and 1958-05-17\n",
+      "No concentration measure are given for 6 weeks between 1958-05-24 and 1958-07-05\n",
+      "No concentration measure are given for 2 weeks between 1958-08-16 and 1958-08-30\n",
+      "No concentration measure are given for 9 weeks between 1958-09-06 and 1958-11-08\n",
+      "No concentration measure are given for 2 weeks between 1959-01-31 and 1959-02-14\n",
+      "No concentration measure are given for 2 weeks between 1959-03-07 and 1959-03-21\n",
+      "No concentration measure are given for 2 weeks between 1959-05-23 and 1959-06-06\n",
+      "No concentration measure are given for 2 weeks between 1959-08-08 and 1959-08-22\n",
+      "No concentration measure are given for 4 weeks between 1962-08-18 and 1962-09-15\n",
+      "No concentration measure are given for 2 weeks between 1962-12-22 and 1963-01-05\n",
+      "No concentration measure are given for 2 weeks between 1963-02-09 and 1963-02-23\n",
+      "No concentration measure are given for 2 weeks between 1963-04-27 and 1963-05-11\n",
+      "No concentration measure are given for 2 weeks between 1963-11-16 and 1963-11-30\n",
+      "No concentration measure are given for 19 weeks between 1964-01-18 and 1964-05-30\n",
+      "No concentration measure are given for 3 weeks between 1964-06-06 and 1964-06-27\n",
+      "No concentration measure are given for 2 weeks between 1964-08-01 and 1964-08-15\n",
+      "No concentration measure are given for 4 weeks between 1966-07-09 and 1966-08-06\n",
+      "No concentration measure are given for 2 weeks between 1966-10-29 and 1966-11-12\n",
+      "No concentration measure are given for 3 weeks between 1967-01-14 and 1967-02-04\n",
+      "No concentration measure are given for 2 weeks between 1976-06-19 and 1976-07-03\n",
+      "No concentration measure are given for 5 weeks between 1984-03-24 and 1984-04-28\n",
+      "No concentration measure are given for 2 weeks between 1985-07-27 and 1985-08-10\n",
+      "No concentration measure are given for 2 weeks between 2003-06-07 and 2003-06-21\n",
+      "No concentration measure are given for 3 weeks between 2003-10-04 and 2003-10-25\n",
+      "No concentration measure are given for 5 weeks between 2005-02-19 and 2005-03-26\n",
+      "No concentration measure are given for 3 weeks between 2006-02-04 and 2006-02-25\n",
+      "No concentration measure are given for 2 weeks between 2007-01-20 and 2007-02-03\n",
+      "No concentration measure are given for 3 weeks between 2012-09-29 and 2012-10-20\n",
+      "No concentration measure are given for 2 weeks between 2020-01-11 and 2020-01-25\n"
+     ]
+    }
+   ],
+   "source": [
+    "dates = sorted_data.index\n",
+    "for d1, d2 in zip(dates[:-1], dates[1:]):\n",
+    "    delta = d2-d1\n",
+    "    if  delta > datetime.timedelta(weeks=1):\n",
+    "        print('No concentration measure are given for %d weeks between %s and %s' % (int(delta.days / 7), d1, d2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On note que juste quelques dizaines de mesures hebdomadaires n'ont pas été effectuées sur plus de 3000 mesures. L'écart maximal entre deux mesures de concentration est de 19 semaines. \n",
+    "\n",
+    "Traçons la concentration au fil du temps pour visualiser son évolution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7efbd47e9fd0>"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Sllfnq6rAGfEuP8uh5r7YPYg6B9zqHhxVbVFmwp8xuzRbz+3r0MiYV06HTZ0DoyrJyaCP0EmKJtUAAAAAAExzpmlGHCNv1Y6mPs0vy414VpDpUO9Q9Bazpt5hrbn1KUnSqtrEp5CdMbtYo16fNtb5G2G39o2oOMf69jKcWAREAAAAAABMc197eJdO/daT6gr0+UnGvLKciPtgk2qfz4x4/p3H94auz5pbkvC7q2f5m0rvaOrTy4c69crhLq2ZSaPpkxUBEQAAAAAA09jvN9Trd+vrJUnrD3cnXT8xICrIcshnSq4RT8Rzj9cnSbrrhtWWvpvndKi6IFP721z635frlee06xMXzU96fjgxCIgAAAAAAJimfD5TX3loZ+j+vo31SX/jzLmRx87nZzokSe+865WI550Dbp0+u0jrllZY/nZNYaaae4e1o6lP5y0sC30bJx8CIgAAAAAApqnwVT4zirK0ub4ntNLHiuvX1srpiDxV7PTZ/sBob6tLpjm+zazd5VZ5XmRD60RKczP06pEeNfUOa2lVXlK1OLEIiAAAAAAAmKZ6wo6jXzurSCNjPrW7Ju9DdKhjIHRdW5QV9X5GcZb+661LJCnU+LqxZ0j1XUMqz0uuyfTqsJ5DSyoJiE5mBEQAAAAAAExTwYDoujU1esvKSklSS5wj6oP2tbpC1x9605yYY8py/SuFOgf833/pQKckaWlVflLzu+GMmaHrxQREJzUCIgAAAAAApqnGHn8Y9O7TZ2pGYDXQ8/s6Jq2p6xyUJJ09r1h2W+xYoDDL3ysoGEC19o9Iki5fXpnU/Oy2NJUEjrYvzeWI+5OZfaonAAAAAABAqvP6TJmmGTewiWdDXZdyM+xaWpUnW5qhs+YW6y9bmnTLhfOUYY/sLeTzmbr6zn9qW2OfSnMz9Pt/PyPud/MDAVFvICBq6x9RSU660u3JrzN5+tPnqX9kLOk6nFisIAIAAAAAYIpd98tXNO8rj+v5/ZOv/pmof9ij4px02W1pMgxD7z59ppp6h/Xi/s6osS63R9sa+yRJtYWZk363MCtdktQV6EFU3zWkmsLofkVW5Gc5YvY6wsmFgAgAAAAAgCk06vFpc32PJOljv99iua5veEx/29asMe/4SWNnz/OfQHakazBq/IB7/MSzH7/zlEm/XZ7nVG6GXbub++X1mdrfNqA5JdmW54bph4AIAAAAAIAp9L+vHAldu8JCnERufWS3JKmpd7wpdX6mQ7lOuxq6h6LGD4z4v33nu0/VjOLJV/TY0gytnlWojXXdeulgpzoH3DpnfonluWH6ISACAAAAAGCKPLGzVbc+uifiWfBo+UQaeqJDIMMwlGG36Z5X6qP6/rgC9zkZ1toRnzmnWAfaB3Tj3RslSRcvKbdUh+mJgAgAAAAAgCnyp00NkqT/vn6V7rn5NEnSw1ubLNUOxFlt1DngliRtONwd8dwVWEGU47QWEF2/dkboOifDrjynw1IdpicCIgAAAAAApkDP4KhePdKtheW5unJVtd40v0S5GXYd6YzuHxRLa5875vOPnD9XkuR0jP/J3zc8pvf99lVJ/v5CVuRnOfS9a1ZIkhZX5lqqwfRlOSAyDMNmGMZrhmE8ErgvMgzjScMwDgT+WRg29kuGYRw0DGOfYRiXvhETBwAAAABgOvvg7zbJNeJRdob/OHrDMDSrJFt1XdFbxyYaHvWqc8CtGUVZ+ustZ0e8u3xZpSRpZMwXevbglsbQdXXB5CeYhbt2TY2+d80K3XrVcss1mJ6SWUH0SUnhGyO/KOlp0zTnS3o6cC/DMJZIul7SUkmXSbrTMAzb8ZkuAAAAAAAnn+f2tet/XqpLqmZfq0uSlGYYoWczi7MSriDy+kx9+N7NkqTPXbpQq2oLIt5nBFYOuT3e0LPsdP+2suvW1CQ1R8MwdN3aWi2sYAXRvzpLAZFhGDWSrpD067DHV0q6J3B9j6Srwp7fb5qm2zTNOkkHJZ12fKYLAAAAAMDJxTRN3fSbV/WtR3bLNM3EBQHpdv9aCqdjfE3F7JJsNfYMRYQ74QbdHv37Pa/q+f0dkqTzFpZGjXEGvusOW0EUbFj9lSuWWJ4fUovVFUQ/lvR5Sb6wZ+WmabZIUuCfZYHn1ZIawsY1Bp5FMAzjg4ZhbDIMY1NHR0fSEwcAAAAA4GSwPqwZdN/w2CQjIw2P+ptGh/cKmleWI58p1cVZRfTojhY9u8//N/S97z89ZuPo4AqikbCQqW94TGmGlGvxBDOknoQBkWEYb5HUbprmZovfNGI8i4pQTdO8yzTNNaZpriktjU48AQAAAACYDu7beDR03dgzbLluVkm2JOmdYaeFzS/zb+U62D4Qs6bD5W9M/V9vXaJz5pfEHJNhD2wxC1tB1Ds0prxMh9LSYv3JDlhbQXS2pLcZhnFE0v2SLjQM415JbYZhVEpS4J/tgfGNkmrD6mskNR+3GQMAAAAAcBLZdKRb88pyJEmNPYkbTAcVZafrlBkFumRJeehZZb7/hLH2/tgnlD2xs1WS9L6zZ8f9bkZgi9k3H9mtkTH/KqLm3mFV5ltvTo3UkzAgMk3zS6Zp1pimOUv+5tPPmKb5Hkl/k3RjYNiNkh4OXP9N0vWGYWQYhjFb0nxJG4/7zAEAAAAAmGLDo1619I3ogkAvoIZu6yuIhka9ykqPPNOpIMshh81Quys6IKrvGtSOpr6E3w2uIJKku144LMm/sqmmkIAI8SVzitlE35F0iWEYByRdEriXaZq7JP1J0m5JT0i6xTTN2N21AAAAAACYxhoCK4aW1xSoKDtdtz22RzsthDiS1D88ptyMyB5ChmGoNCdD7a6RqPGvHe2VJP339asm/W74NrInd7dpR2Of9rW5NLMoy9K8kJqSCohM03zONM23BK67TNO8yDTN+YF/doeNu800zbmmaS40TfPx4z1pAAAAAACm2qjHp5t/+6okqTg7XW8K9AT631eOJKz1+UwdaB9QcU561LvSPGeo11C44Pa1S5dWJPz+gx89S1esqFR916DeesdLkqRaAiJM4vWsIAIAAAAAIGUdbB8INaXOczr0vXeslCRlT3JSmGma+sKft2vOlx+TJHl9UWc6qSw3I2ZA9OqRHlXmO+V02KLeTXTqjEKtqM5X/4gn9Owdq2sS1iF1ERABAAAAAHAMhkbHw5e8TLvS7WmqLcpU71D8o+6//3/79MdNDaF7uy36VLHS3AztbXWprX98m5lpmtpY1611YQ2tEynPc4auv/TmRZMGVwABEQAAAAAAxyA8CMpz+nsJFWalT3qS2R9fHQ+HHDZDn1u3KGrMmXOKJUnP7m0PPRsc9Wp4zKuqAuuNpguzx7evVdOgGgkQEAEAAAAAUlpd56D+FBbcWHHHMwf0m5frQve5Tv/qnGXV+dpU36PuwdGomrb+EXUNjuqz6xZozzcv04HbLld+liNq3FtWVCo/06GNdaFWv3ogsOqoJCfD8hyLssYDojUziyzXITUREAEAAAAAUtoFP3hOn//Ldg2PWjuAe9Dt0Q/+sV//PNglSfrjB8+Q3eb/8/rChWUyTemZsNU/Qbtb+iVJp88pVmZ6/D5ChmHokiXlempPm0zT36Poz5sbJUkLK3It/155meNbyirynZOMBAiIAAAAAAApLLxJdEvfsKWa8AbSmQ6bTg9sCZPGTwr77APboura+vw9hSothDWnzihU/4gn1ATbNeLRuiXlWladb2mOkjSjKEtffPMibfjyRZZrkLoIiAAAAAAAKetrD+8MXTf1WguIgqGNJL15WeSR8/PKckLXvgknlLUGmk6X5SYOiJZW5UmStjb0qndoVEe7h3TKjEJL8wsyDEMfPm9uRLNqIB4CIgAAAABASuodGtXvNxwN3b9yqCthzZjXpxt/s1GSdNcNq3Xb1csj3tvSDH3tLUskSf0jkaeZvXywS/PKcpRuT/yneHAr2c+ePaiD7QOSpEWV1reXAckiIAIAAAAApKT+Yf8x9Z9dt0BnzyvWz58/NOkJZJJ0/8ajoW1p65ZWxOwlVBQ4PSy8UXVL37A2HunWVauqLM3N6bBpzcxCuUY8Otrtn9PMwPY14I1AQAQAAAAASEkut3+Fz/zyXN1ywTyZpnT743snrekciD6dbKLg8fI9Q+NjN9f3SJLOW1BmeX7LqvPVPzKmjXXdyrCncVQ93lAERAAAAACAlDQw4l9BlJNh11lzS/T+c2brH7taJz3NLLht7OpTquOOCR4v394/3sx6f6tLaUZyp5DlZNjlGvHo/lcbtG5phTLs8U8+A14vAiIAAAAAwLQ24PZoc3130nUb6vw1ORn+4+DPnFOsMa+p3S19cWuOdg1pTkm2fnDtyrhj5pf7+wwFVw2NjHn1k2cOymfKUv+hIFuaEbr+9CULLNcBx4KACAAAAAAwrX3hz9t1zc9fUeeAO/HgMD96cr8kKSvQRyh4AtmhjsGY44dHvXpuf4fmleVEhDcTOR02LavK0/Ymf9C0v82V1LyC2l3+3+ebVy7V7JLsY/oGYBUBEQAAAABgWgsGMMFmzlZ4w46grynMCvwzU5kOm7Y19Mas+ewD2+T1mSrJzUj4/cWVedrT0i/TNNU7NJZwfCyziv3zWladf0z1QDIIiAAAAAAA01qwKfSRztgrf2IJ9h/6zysWh04is9vSdMGiUv1jd5t8YQFS0NZAcHT92tqE319cmSfXiEfNfSOhlUBPf+Y8y/OTpH8/d44e+fg5OnVGYVJ1wLEgIAIAAAAATEsPb23S4Y4B5Wc6JEl1SQREf3j1qCQpz+mIeH7+wjJ1uNw60hX5LdM0NeD26N9Oq9WKmoKE319Q7m9GfbB9QJuOdCs73aaaJE8hs6UZrB7CCUNABAAAAACYlj55/1Zd+MPnNTTqXw3002cOqsHCNrNdzX36TuA4+1ynPeJdbWC7WWvfSMTzhu5h9Q2PaXl14nBIkirznZKkxp4hPbqjRZctq+QUMpzUCIgAAAAAAFNqf5tLu5rjnxwWi9szfhT9Pw92ha7P/d6zocAolj9vbtR//nVn6H7iqWLBYKd5QkC0vcm/vWxFjbUVPWV5/j5F3/z7brlGPDp/YamlOmCqEBABAAAAAKbEmNenv29r1rr/94Ku+MlLSdUOub1x3z25uy3uu88+sE2vHR1vQj3m9UW8ryrIVK7TrpcPdoaejXp8+th9r0ka3zqWSHC1kNvj//7qmfQRwsmNgAgAAAAAMCXWH+7Sx//wWujeyvawoAF35Cqhu25YHbr2mdENpqXIMCjNkG46a5YuWFQWMSbdnqZz5pWEGlJLUnPvsCRpVW1B1IqjyawJhEJnzytWVUFy/YeAE42ACAAAAAAwJXomHP/+8NYmy7VDo+MriL511TKtW1qhbV9bJ0nqGhiNWRPeV8hnSl9/29KYfYHmlGbraPdQKFAKnkL2mXULLM9Pkn7/gdP111vO1q/euyapOmAqEBABAAAAAKbEUGAV0D03n6aCLIda+0cSVPj5fKZ+v6E+dB9cqZOXaZc9zVBnnICosWfY0vfnlOTI4zN1NLCiqd3ln1dZrtNSfVCG3aZVtQXKSrcnHgxMMQIiAAAAAMCUCG4TW1VToOqCTMsBzt+3N+t/X/EHRD/9t1O0uDJPkmQYhvIyHXKNjMWsu/O5g6HreWU5cb8/pzRbknSofUCS1NbvX0FUHmg8DfwrIsYEAAAAAEyJ4Dax7AybqgsyVdc5mLCmoXtI33pkd+j+7HklEe9zMuxR/Ykkfx+hFw/4G0+/8LkLVJDtiPszZpf4A6JfvnBY65ZWqN01onR7mvIz49cA0x0riAAAAAAAU2LQ7VGGPU12W5pqCrPU1DssM06DaUkyTVN3PncoYgtZUXZ6xJjhMa8e3tostyfylLNg+PSh8+ZoRnGW8pzxw56CLP83N9f3yO3xqr3frbLcDBmGkfTvCEwXBEQAAAAAgBPuwS2N+uULh5WT4d/YMq8sR0OjXh0MbOuK5Y+vNugPG49O+t2OQEPpF/Z3Rjw/0uUPiG48c1ZS8+wZHNPOpj7NKY2/JQ34V0BABAAAAAA44T79p22SpJnFWZKkc+f7t4qtr+uOW/PXsFPObjxzpl743AWWf15915Ay7GmqyLPWaPoX71ktSXrwtUYdaB/QRYvKLP+F99fQAAAgAElEQVQsYDqiBxEAAAAA4IRxjYzpvg3jq4CqC/0BUU1hpvKcdu1p6Y9bG9xOtqq2QN+4clnMMXlOu/pHPOodijzJ7FD7gGYWZyktzdo2seIc/8/63hP7tLA8V9etqbVUB0xXrCACAAAAABwz0zT1b3et1//tarU0/ksP7tDtj+8N3WfY/X+WGoah2SXZaggcLR9LduC4+Hved1rcMc9+9nxJUseAO/TM6zP16pFunTqj0NIcJWl5db7euaZWly4t169vXKPMdJvlWmA6IiACAAAAABwT0zR17/p6vXK4Sx/63WaNjHkT1hydEAA5bON/lpblOdXe755YEtIzNKZFFbnKz4rfYLo4J0PzynL00oHxHkS7mvvUP+LRmXOLE84vyOmw6bvvWKFf3rBGtUVZluuA6YqACAAAAABwTF480KmvPrwrdG9lFVFwxVCQwza+5asiz6l9bS4d7Yq9iqihe0jVBZkJf8aq2gId7hgM3T+yvUUOm6Fz5pUkrAVSFQERAAAAAOCYjHl9EfctfSMJayYeFR8e2gQbVq/78fNRdX96tUH72lyaU5qd8GdUFWSqzTUSmt/Opj4trcpXcU5GwlogVREQAQAAAACOyfvv2RRx7xoZS1jjdIz38nn8k+dq3dKK0P2Vq6qjxkhSu2tEn//LdknSooq8hD+jpjBTpik19gxL8p9gNquYbWLAZAiIAAAAAABJ6xkcjX42lDggKgzrH1Q1YbtYaW6GbjprlsY8PpmmGXq++UhP6PoMC32EllT6Q6Ttjb3a29qvpt5hrawtSFgHpDICIgAAAABA0p7c0xa6vm5NjSSpz0JANDTqb2R96dJy5WbYo97PLc3W4KhXbWHNqve1uWQY0s5vXGqpB9GiilxV5Dl126N7dO/6eknS5csrE9YBqYyACAAAAABS2Hce36sfPbk/6bq9La7Q9W1XL9fqmYURR8vH0zXg1mmzi/TLG9YoLc2Ier+wYnz1T9DRriFV5jmVEyNQisVuS9NP33WK2l1u3bv+qGaXZKs8z2mpFkhVBEQAAAAAkMJ+8fwh/eTpA2ruHbZc09Y/orv/WRe6d9jSdOqMAm2s69aNd2+MW/fyoU5tOdqrkpz0uGNW1RYo3Z6mTfXj28raXW6VJRnwrJ1VpDcv8/c3ev85s5OqBVIRAREAAAAAQL94/pDlsY9sbwld//q9ayRJb1pQKkl6fn9HzJoxr0/v+tUGSdK1q2vjfjvdnqaZRVk60jl+TH1b/4jKcpM/gezn71mtpz79Jr379BlJ1wKpxtr6PAAAAADAvxTXyJi2HO2VPc2Qx2fqf1+pV3aGXV+4bFHC2u5B/1ayFz53gWYETgc7dUZh6L1pmlHH2T+2YzxUumBR2aTfn1GUpaPdQ5KkUY9PDT1DOnd+qbVfbIJ5ZbnHVAekGlYQAQAAAEAK+sQfXtONd2+Ux2eqJMe/Oufnz1lbRdQ9OKaSnIxQOCRJ2Rl2feNtSyVJHa7oXkQv7O+UJP39Y+ck/P6M4iw1dA/JNE3d/vgejYz5dMacIktzA3BsCIgAAAAAIAXtbO4PXV+yZPIVPeE8Xp+e3dsecVx9UDAwqg+s/gnXNzyqxZV5Wl6Tn/BnzCjK0uCoV12Do3rotSZJiVcdAXh9CIgAAAAAIAWNeX2h6+XVBaHr4cAx9PFc8/OX1do/ourC6OPm55XmSJL2tPRHvesbHlN+prUuJzOK/EFTXeegXCMeffzCeXLY+PMVeCPx/zAAAAAAmIZM09SPn9qvHY192nK0J3HBBKOe8YBo0O3Rd69ZLknqGox/VP2u5j5ta+yTJH3vHSui3tcUZqoq36mNdd1R71490qM8Z/Sqo1hmBlYi/dtd6+X1marI54h64I1GQAQAAAAA09DR7iH9+KkDeusdL+ntd76s/pGxpOrDVxCtmlGgomx/H6LuwdG4NRsOjwc/ZbnRoY1hGFpSlaf9ba6I548HGlT3DMX/driaQn9A5PGZkqTzF7K9DHijERABAAAAwDQ0sRH0lvrkVhGNec3Q9dpZRSrKTpckdU0SED28rTnhd+eU5qi+K7IH0T92t0mSvh5oYp2I02HTE/9xrq5YUanbrl6m6oLo7WwAji8CIgAAAACYhlr7RyLub/rNqwlr+kfG9JOnD2ho1BP1rjgQEHUPxA6IhkY92tbQqxU1+aHtaLHkZzrk9vjk9oz3Mtra0Kt1S8q1tCpxg+qgRRV5+tm7TtW7T59puQbAsSMgAgAAAIBp6Pv/ty/qmWmaMUaO+8bfdutHT+7XrY/uCT2rLfKvzinN9W8x+8wD2+TzRX9nf9uAJOnD583VO9fOiPszcp3+RtQfu+81SVLv0KjqOge1srYgbg2AqUdABAAAAADTzNCoJ2Ib14WBI+DdYY2nY2l3+Vcd3bfhaOjZVy5fLEnKzhg/YWzi6iRJenpPm+xphk6fXTTpz8h02CRJTwa2lQWbWp9CQASc1AiIAAAAAGAKba7vTni0/ERdYdvAtnz1El202B8Q9Q9P3qi6bULws+1r63TZssrQ/dtPqZYUu5n0oY4BzSjKUnFOxqQ/YyQspHJ7vNp6tFeGIS2vsb69DMCJR0AEAAAAAFPkSOegrvn5K7rtsd1J1XUO+BtUv21llYqy05UbOD4+0Ulm7S637GmGJOmUGQXKz4o8dv760/xbx2KdZNbQPayaoqyEcyvNSQ9d72t1aWtDj+aV5oTmCODkREAEAAAAAFPkx0/tlyQd7hhMqi64gujmc2ZLkvICfX++9cieuDUer099w2O6dGmFJOnohJPGJKko2x/iTAyIeodGtbe1X4sqchPO7dKlFfr8ZQslSY09w3r1SI/WzJp8WxqAqUdABAAAAABT4HMPbNNft/qPja/IdyZV29I3LEmqCtStnlkoSXp+f4fqOmOHTX3DYzJNac2sQl28uFw/vG5l1Jiawixlp9u0/nB3xPN7Xq6Xx2fq6sAWtMkYhqF3nFojSfro77dowO3ReQtKrP9yAKYEAREAAAAAnGCmaeqBzY2h+0e2tWjAHX30fCxuj1dHuoaUbk9TSaAfUK7Tob985ExJ0pGu2AHR1oZeSVJRdrp+feManb+wLGqM02HT8pp8HWhzhZ75fKYe39milTUFWlyZZ2mOE/sUnTmXgAg42REQAQAAAMAJFjxt7AuXLdJlSys06vXpt/+ss1S78D+f0P+8VKeagkylBfoJSVJFvv+4+ta+6BPIJOnR7S3KsKfp/AXRwVC4ijxnxClmu1v6tbfVpevW1FqanyTZ0gx94qL5ofv8TPoPASc7AiIAAAAAOMGCq4WyM2z62btP1cziLG2o605Q5V89FFRdmBnxriw3Q4YROyCq7xrUYztbdMWKyqjG1BOV5znV3u+Wz2dKkjYd8c/r/IWlCecX7uLAyWrpdv7sBKYD/p8KAAAAACfYkNsf9GSn22VLM3RKbYGlRtUN3cOh69LcyG1cDluaSnMyYgZED25pktvj0+cvXZTwZywoz9Wo16cD7QPy+kx9/e+7VZHnVFVBZsLacIVZ/tPMHGGrnACcvAiIAAAAAOAEG19B5D99bG5pjpp6hzU0OnkfovpAf6Hr1tTo4xfOj3pfme9US390QLS7pV+zS7ItNcMONry+9McvaP3hLknS8pr8hHUTFWb7A6Kz59F/CJgO7FM9AQAAAABINcEgKDvDJkmaW5YjSarrHNTSqvhhTH3gaPovXLYoqhG05D8NbeIpZqZp6sUDHbp4cbmluc0szgpdf/HB7ZKkm86aZak2XE6GXY98/BzNKc1OuhbAiccKIgAAAAA4wX63vl6SVJDpX2WzJHA62CuHuuLWuD1eHWh3KTfDrqLA6pyJKvKcapmwxezRHS0aGfNpUUWupbkZhqHHPnGupPEtbcca8iyrzldWOusSgOmAgAgAAAAAjtF9G45q1hcf1RM7WyzXNHQP6eGtzZKk+eX+lUOzSrJVle/U7ub+uHVv/vGL+sPGBs0ozpJhxO7rU5GfKdeIJ7SFTZK2N/ZJkt6bxCqgJVV5+tm7TpUk3XDGTFXmJ9d/CMD0Q5QLAAAAAEnqGx7TRT98Xp0DbknSh+/dom3/tc7Sce4PbGqQJN189mw5HbbQ87xMR0SwM9HhwNaxstzorWVBlYEeQ619I5oX2LZ2oM2lxZV5ynMmd9T85csr9NSnz9NctogBKYEVRAAAAACQpNsf2xMKh4L+b2drwrr/9+R+/eSZgyrNzdDX3rok4l2u0x43IAo/mcxrxv9+RVhAFLS/bUALAiuVkmEYhuaV5cRdrQTgXwsBEQAAAAAkqXdoLOI+3ZYWWuEzmf9++oAk6fTZRVHvmnqG9fKhrpjbzD507+bQ9bKqvLjfD64g+sJftqt7cFR7W/vV1Dus5dXJn0IGILUQEAEAAABAktLt439KZaXbVF2YqaPdkwdE97x8RJL0lhWVuvWqZVHvmwOrfoINrIM213drW0OvJOl716zQpy5ZEPdnVBVkKifDrqbeYT2+s0Uv7u+UJL1tVVXiXwpASiMgAgAAAJCyvL5J9mtNYnjMG7r+6y1na0llnrbU98o0Y3/P4/Xp9sf3SJI+cv5cFWRFn0JWmOXvEXSw3RXx/JbfvyZJuu8Dp+u6tbVy2OL/Geewpekfn3qTJKlrYFQbj3SrpjBTZbnOJH47AKmIgAgAAABAStrR2Ke5X35s0qPl43GNjGluabb2fusyLSjP1XkLStXaP6K9ra6Y4zsHRjUy5tOtVy3T0qrY270evuUczSvL0e7m/oigaXaJv0n0mXOKLc2tqiBThiH96Mn9enJ3m85fWJrkbwcgFSUMiAzDcBqGsdEwjG2GYewyDOMbgeerDMNYbxjGVsMwNhmGcVpYzZcMwzhoGMY+wzAufSN/AQAAAABIlmmaeusdL0mS/u1X6zUStiLIigG3RzOLs0OnkAVDmGf2tscc3+7ybx+b7ASyGcVZuumsWRoc9aolrMm02+PVOfNKkmoWnZMxfmD1qTMKLdcBSF1WVhC5JV1omuZKSaskXWYYxhmSvifpG6ZprpL0tcC9DMNYIul6SUslXSbpTsMwbDG/DAAAAABT4Ll9HRH3L+zviDMykmma+p+X6rSnxaWqgvFtW2V5Tq2sydeTu9ti1rX1u0PjJjM/eDR9+0DomWvEo1ynPV5JTHe861S9/ZRqnb+wVG9awAoiAIkl/LeM6V/bGPy3kyPwPzPwv2D7/HxJzYHrKyXdb5qmW1KdYRgHJZ0m6ZXjOG8AAAAAOGZ/39YccT80am0F0eM7W/WtR3brkiXl+tyliyLevWlBqe549qBGxryhlUWSNDLm1T0vH5E9zdCc0uxJvz8vGBC1uXReINgZcHsiVgRZcd6C0lA9AFhhqQeRYRg2wzC2SmqX9KRpmhsk/Yek7xuG0SDpB5K+FBheLakhrLwx8AwAAAAATgp7Wl06Y06R/vShMyVJ//HHrZbqDrT5/9v5z951qvIzHRHv5pfnyjSlI12Rp5k9v79DLx3s1MWLy5XnjKyZqDgnQyU5GdrT4u9lNDTqUdfgaNTPAoDjzVJAZJqmN7CVrEbSaYZhLJP0EUmfMk2zVtKnJP1PYHisjbFRrfwNw/hgoHfRpo4Oa8s5AQAAAOB4GHCPqSLPqRU1sRtGx9M16FZ+piPimPuguYHVQYc7IgOihu4hSdJ3rllu6WesqMnX5vpumaapH/5jv0Y9Pr15eWVS8wSAZCV1iplpmr2SnpO/t9CNkh4MvHpA/m1kkn/FUG1YWY3Gt5+Ff+su0zTXmKa5prSUpY8AAAAATozvPL5XDd3DynHaI7aC9Q2PJaztHHCrJCf6iHpp/LSxQ2H9gySpsWdYORl2y6uA3jS/REe6htTQPaz7NhzV20+t1uqZNJoG8MaycopZqWEYBYHrTEkXS9orf+hzXmDYhZIOBK7/Jul6wzAyDMOYLWm+pI3He+IAAAAAcCx+8fwhSVJ2oK/P3TetkSRtqe9JWHuwfUA1hVkx32Wl21VdkKlDHdEBUU1hpuVTyGYU+79/4282anjMq3VLKizVAcDrYWUFUaWkZw3D2C7pVfl7ED0i6QOSfmgYxjZJ35b0QUkyTXOXpD9J2i3pCUm3mKaZ3JmRAAAAAJDAmNenO545oNawI+GTkZPuD4jOnlciW5qhzQkCos4Bt/a3Dej0OUVxx8wpzdbhzvEtZh6vTy8d7FBNYableZXl+k86qwt855Il5ZZrAeBYWTnFbLukU2I8f0nS6jg1t0m67XXPDgAAAADiuH/jUf3gH/v1582Neu5zFyRdn5nu316WYbepMt+php6hScevP9wlSTpzTnHcMXNLc/TApgaZpinDMPRaQ69GxnyqLYq96iiWinxn6PrSpeWypVlbeQQAr0dSPYgAAAAA4GSwt7VfX314lyTpSNeQ9rb2J/2NuaU5oeuZxVmhFTuxjIx59bH7XlO6LU3Lq+M3tp5bmq3BUa/aXW5J0tEuf+h0wxkzLc+rJCdDFy8ukyT99/VR/60eAN4QBEQAAAAApp2tR3sj7rfU98YZGcnt8Xe/uOWCubpgUVno+dKqfO1tcWnU44tZ97tX6iVJH7twnuy2+H9GzQmETsFG1Y09w5KkqgLrW8wk6dc3rtWR71wR0UQbAN5IBEQAAAAApp1g8BL05Yd2hI6Tn8yBNn9ws6A8N+L5gvJcjXp9auodjlWmZ/a2a1l1nj5x0fxJvx9clfSuX2+QJG052qO5pdkEPQBOegREAAAAAKadtv7oxtS7WybfZvbPg5162x0vSZLWzopsNF1VEGwMPRBV19Q7rM31PTp9dvzeQ0HleRkR9zua+qJ+FgCcjAiIAAAAAEw7rYGA6KpVVaFn9gTNnD/xh9fkM/3XE7d8VQfub/7tJo15I7eZ/ealOv+7c2YnnJdhGHrvmTOVm2HX7zfUq3twNKLXEQCcrAiIAAAAAEwrpmlqe2OfrlxVpR9ff4ru+8DpkqT337Mpbk1916C6BkclSbFypNrC8VPGJq5OevFAp06fUxQKkRIpyHRoYNSjP77aIEm6bFmFpToAmEoERAAAAACmlSd2tqpveEzzy/wrc+aXjfcTGh71xqzZH+g9dNGiMj300bOj3qelGbp4cbkkqbUvMiBq6RvWnJJsy/PLcdplmv5+R9evrU3qiHsAmCoERAAAAACmjZ1NfXpke4sk6T2Bo+NznfbQ+8Mxegi5PV595k9bJUm3Xr1MK2sLYn77c5culCS1hAVEGw53qX/Eo8LsdMtzzMlwSJKGx7yWVx0BwFQjIAIAAAAwLZimqbf89CU9uqNFq2cWqiDLH9pk2NM0O7DCZ+LpZpL0lYd2qn/EI0kqycmIeh9Uke9vVB2+guidd62XJPUEtqdZURQWJp0+J3FjawA4GRAQAQAAAJgSbf0j+sKft+tgu8vS+GDII0WGMIZh6KGPniUpdkD0582NkqRLlpTLYYv/J1Ce066sdJua+8a/kZ/pXw101SnVluYoSectKA1dnzabE8wATA/2xEMAAAAA4Phq7RvRGbc/LUn646YGHfr25bIlOIUsfGXPxJVA+ZkO5WTY1dA9FFVXkpOhynynfvXeNZN+3zAMzS3N0YsHOkPPMh02Xbq0XKfMKEz4O4Vq0m26892nyu2J3Q8JAE5GrCACAAAAcMJtqOuKuL/hfzYkrGnq9Yc/n7lkgb5w2cKId4ZhqKYwM+YKIq/Pp5W1+ZbmdeGiMh1sH9CoxyfTNNUzNJpU/6Ggy5dX6upTapKuA4CpwgoiAAAAACfc7pb+iPuXD3XFGTnu5t/6j7G/6pTqUP+hcP6AKHIF0U2/2aieoTFlZ1j706csz78yqXtwVJkOm9wen4pi/CwA+FfDCiIAAAAAJ9zelsi+Q+V58ZtHS/7Ty8bHOmOOqSnMUlPPsEzTDD17bl+HJCk73VpAVJztn0fngFt/3dokSVpSlWepFgCmMwIiAAAAACfUmNenzfU9WlKZp8c/ea5W1Raord+tzgF3zPG7m/v1lp++JEkqyHIo3R77z5iawky53B41B3oVhQdFg25PzJqJSnP9q4V+9eJh7WnpV2GWQ+fOL01QBQDTHwERAAAAgBPq/o1HNeD26EPnzdHiyjwFe1MHV/tMdM/LRyRJFyws1davrYv73ZrCTEnS2d95RpLUPzweCsVbdTRRcAXRw1ubtbO5T4sqWD0EIDUQEAEAAABIms9n6sa7N+raX7ys/pGxpGpfPtSlmcVZunKV/+j4u29aK0mq6xyIOb65b1hOR5q+f+3KSb9bmhsZAgWPq//cpQv13jNnWppbSe74VredTf1aUJ5jqQ4ApjsCIgAAAABJe62hR8/v79CrR3r0wKbGpGpb+0dUW5gVui/IStfc0mztb4sdEDX2DOuiReVRR9tPtHRCr6CWQEB0xpxi2W3W/vTJTrdF3J+3kO1lAFIDAREAAACApB0IC3OaYhwtH8/u5n69drQ3asvXwopcHWhzRY03TVPNvcOqKki8RczpsOl9Z89STuDEsm0NfTIMaXZJtuX5GYahz1+2UNUFmfrWlUt14aJyy7UAMJ1xzD0AAACApG2s65YkVRdkhlbqJHKw3aXLf/KiJGlZdeRqn/lluXp8Z6uGR73KDFvFs7GuW26PT7NLrG31qsrP1IDbo8aeId238ajOmlusouzkjqn/6Pnz9NHz5yVVAwDTHSuIAAAAACRlZMyrB18bPwL+8Z2tEcfQx/LrFw/rzf/tD4fWzirUDWdE9gRaXJkn05S2NfZGPH+twX9/xfJKS3M7ZUaBJOmc7z6rDpdb/3HxAkt1AJDqCIgAAAAAJOXOZw+Grt8TCHp+/tyhSWtufXSPxrz+Y+d/9/7To3oCnTu/RIYhvXKoK+L5b/95RJKUn+WwNLdl1fmh66VVeVo7q8hSHQCkOraYAQAAALCsa8CtXc39kqTtX1+nPKdDb1pQqqPdQ5bq/+Pi+XI6bFHPszPsynM61Ds0Gno24PaotX9EmTHGx+N02PT5yxbKNKV3nz7Dch0ApDoCIgAAACAFHeoY0JySbBmGYbmmrX9EZ9z+tExTumhRmfKc/lU91QWZ2jXJFrNBtyd0PVlvn/xMh3qHx0L3RzoHJUnffvsyy3NM9DMAALEREAEAAAAp5s+bG/XZB7ZJkkpy0vX3j5+jyvzMhHV7W10y/bvEIho/Vxc41TU4GtVgOmhTfY8k6TfvW6t0e/wuFwVZDvWFB0Rd/oBoYXlevBIAwHFCDyIAAABgmtnR2Kcxr++Y67/92J7QdefAqM68/RlLdTfevVGStKgiV59ZtzD0vLrQHy41xznN7Ll97cqwp+nMOcWTfj8/06GeofGA6Bt/3y1JmlWSZWl+AIBjR0AEAAAATCOHOgb01jte0kfu3aIOlzvpetM0NRC25SvIkyBw2h3oOyRJd9+0VhX5ztB9TaE/wNnb4oqqc42M6cEtTTp/YWnM3kPhSnMztL2xV7975YhM0wz9flnpbHwAgDcaAREAAAAwjRxsH5AkPbWnTWfc/nTS9S63R6Mefxi0rDpPP7pupSTpcKDfTzy/W38kdB2+vUySVtYUqCQnXf/Y3RpV9+j2FvUNj+nD581NOLfqgkyZpvTVh3fp79tbJEnXralJWAcAeP0IiAAAAIBpJHyVjtdnau6XH0uq/po7X5Yk3fnuU/W3W84JHQu/qzl+k2k/fzPrrV+7JGolULo9TYsr81QXI2TaUNet0twMraotSDi32qLxrWSf+MNrkqSVFuoAAK8fAREAAAAwjeycEOR4fWbC7WHhDgRWIF2wsExpaYZmBEKZpp7Y/YOCGnuGtKImXwVZ6THfzynJVl3HoMxgF+uA/W0uLa7Ms3Ra2qVLKqKeeX1mjJEAgOONgAgAAACYRmIdJ9/SN2K5Pivdpn8/Z3botDGnwybDkP6ypSluTeeAWy8e6FRpTkbcMbNLsuVye9Q5MBp6dqhjQLua+7W4MtfS3PKzHFHPHDb+ZAGAE4F/2wIAAADTRN/wmJr7RrSgPEfF2el628oqSdK533tWLx3oTFi/taFXQ6NeleZGBj2mKdV1Dqp7cDRm3R3PHJQkHewYiPvtOaU5kqTDYWN2NPrDrLeuqEo4t3iuXU0PIgA4EQiIAAAAgCnSOZDcKWSNPUOSpE9dvECbv3qJvnvNitC7j/1hS9y6TUe6NeuLj+qXzx+SJK2dXRTxPthAek9Lf1TtwfYB3f/qUUnSD65dGfdnzC7JlqSIPkRNvf5ta3MD4ZEVP7h2paoCJ6S9Y3WN7KwgAoATgn/bAgAAAFPgz5sbtebWp/Ts3nbLNcE+QVUFmZKkzHSbCgLbskbGvHHrHt3hPxHs8Z2tSjOkU2cURry/8ayZkqQjXdFNpi/+0fMaGfPptquXae2soqj3QVUFmXI60rS3dbyJdnPvsIqy00Pb2ax4x+oafe2tSyVJC8utbU0DALx+BEQAAADACWaapl480CFJMY+Gn8jnM/Xw1qZQgBO+ReyrVyyRJGWn2+PWZ4adOrYuRiPo0pwMpRlS64ReRoNuT+j6qlXVk87RlmbojDnFem5fe6hRdXPvsKoKnJPWxXLp0nL95n1r9b6zZyVdCwA4NgREAAAAwAn246cO6OGtzZKkHTGaTk+0oa5bn7x/q7792F5JUq5zPAy65v+zd99hkhR0/sffNTnnnHbC5pzYACw5g6AiioKeAT3Dme4U06ln1jNn5QyH+jsFBQHJmYVd2JzTbJiZnZxzz/TMdNfvj+ru6Z5O1QjLLvt5PQ+P1VX17a7eP5x5vvMNqyq4bmlp0Op5f8Pj04men7x9RdD1hPg4ijJTaJmxyczbIvbuc6tJTw6fgPK6dH4Rjb0OTvY5mJhys6t5gLLs1KhxMxmGwcXzitReJiJyCkX/f3kREREREXnFNPc5+PFTR32v6ztGmHS5I27r6hwKrOyZWS1UkpUSdsD0xJSbP77URF56Eg9+9HySEkJ/zsdu+AYAACAASURBVKrqXJ4/2o1pmr6V9L961ppZdP1ye0Oml1TkWN+pc4QH97Yz4JhkaUW2rVgREXltKSUvIiIiInKKmKbJVT/aGHBuwuWmvnM4TIRl5jDruDgj4HVeRhJjky7GJoLnEHlb2Yoyk32zi0I5f3YBPSMTNPU6fOfu3dUKQEWuvSqg2kJrUHV957Bv4PVtG2ptxYqIyGtLCSIRERERkVNk2DnFqF8S55OXzSUx3vC1m4XTPewkMd4Iez0vLQmAPkdwFdH+VitR8/NbVkb8jMVlVqWPN7HjnJp+zoL05JAxM2WlJLKwNIvHDnTQOTTOmpq8iK1vIiJy+lCCSERERETkFOkZnq4EWlOTx8cvm8Oamjw21ndHjHvhWA8VuWmkhdkGlptuJYj6Z7SZdQyO88Mn68lPT4q6an5WQRoATX1WBdFJTyXRV65fFFSxFMmNqyrY2zLItsZ+qvLSbMeJiMhrSwkiEREREZFTpNeTwLlqUQk/9QyLXlaRw+GOYV9CZqb9rYMcaBsiOSGOFz93Kdu+cFnQPfmeBNGOpv6A84c6rGqgD11UF/XZslISyU1L5KQnQbS1sQ+AC+YW2vlqPsv8Zg5l2BhsLSIipwcliERERERETgHTNHnmcBcAH710NsVZ1vp3b5XNx/6yK2TcQ/vaAfjqDYvJTk0MWHHv5a0g+vIDBwLON3uSPXaHTFflpfkSVZuP91KanUJ1fmxVQP6VSu9aPyumWBERee0oQSQiIiIi8jK43WZM9z9zpItfeLaCFWRMJ3luWF4OEHILWd/oBL99oYHLFhSzpiYv7HvP8iSZEma0gm1p6KMgI5nCDHszhMpzU3nhWA/NfQ4OtQ+xrCLHt9HMrtz0JO77yHk8+e8XUhulrU1ERE4fShCJiIiIiMSovnOY2s8/zL07W2zHDDgmfce5nqHSAKlJ8Xz80jm09DuCtpAd6RhmYsrNreuqIr53Qnwc71hbRXZqYsD5HY39nDc733aS54qFJYCVWGrpH6Myz972spmWV+Ywu0jJIRGRM4kSRCIiIiIiMTjePeJbVf+HF5tsx0263L7jpITAX8MXlGbiNuHIjHX3J3pGAKIOmAZrjX2fY8KXZHp0fwcdQ+PML8my/YzXLS0lKSGOrz14kIkpNzUFSvKIiJwtlCASEREREYnBs0e68XaX9TsmME17rWaDY1YF0TvWBlcDLSi1kjjeFfNeO5sGyE9PoiI3eiXPssocTBO2eYZLb2noBeAtqypsPR9YlUiFGcm+Z71xVbntWBERObMpQSQiIiIiEoODbUMUZibzpesW0tTroHvEGTXGNE3+sccaNv2NNy4Oul6Rm0acAW0DYwHntzT0sqYmz1aL2NqaPJLi49h0rAew2tOWVWSHHGodyQ2egdbvPa+G5IT4mGJFROTMpb2TIiIiInLWMU2T3tGJgGHRdh3rGmZucQbVBdZg6Oa+MYoyUyLGPHagk32tgwAhkz3xcQaFmcl0Do37zrUOjNHSP8b7zq+x9VxpSQlU5qXS3O/ANE0OtQ9x+cJiu1/L5/ar5vMfV8wjPi624dQiInJmUwWRiIiIiJx1/r6rldVff5L9nqSNXaZpcrx7lNmFGVTlpQP4VtdHsqdlIOo9xVkp3L29BZenf21bg9UqFml72UxpSQk4Jly8cKyHfsckK6tybcf6U3JIROTsowSRiIiIiJx1nvYkdb7z6GHGJ11R7p7WMTTOiHOK2UUZ1BWmc82SEn753HGGxyfDxjT0jHLXtmYALpxbGPa+Ss+q+h5Py9qWhj4yUxJiGjKdmhTP/tYhtpywkktXLS6xHSsiImc3JYhERERE5KzS0u/gwb3WPKDnj/bw06eP2o491uXZKlaUgWEYvH1NFS636Xu/mVxuk3f/fitTLjc/f8dKfvsvq8O+95WLrGSON9m0taGXc6rzYqrmSUuKp2fEyc+eOQYQtPZeREQkHCWIREREROSscv53ngl4vb2x33asN0E0u8ha/76+Np+K3FQe3hc6QdQxNE5Tr4NPXzmPa5eWkhAf/tfvzBRrPOiBtiG6h50c7x6Nqb0MYGR8KuC1neHWIiIioASRiIiIiJxF7th43Hf81RsWER9n0DY4FiFiWtvAGD9/5jhZKQkUeoZbJ8THcV5dAQfbhkKuu/cOna7ITYv6/lmeBNHH/7Kbf+xpA6zNZLHw34L2tRsWxRQrIiJnNyWIREREROSMM+KcomNwPPqNfkzT5JsPH/a9ftf6aj5wQS1tA+OMTUSeQzQ24eLcbz9Nz4iTgozkgMqcReVZ9I5O0DEU/DxdnnN2Vs37r5T/wRP11Bams7wyJ2qcvzbPv8kHLqjl5jVVMcWKiMjZTQkiERERETnjrPjq46z71lOMOqei34w1C+gDf9wBQHFWMg9/bAMA59bl43KbPBSmRczrR0/V+47PqQ6s6llUlg3A/tahoLg7Np4AoCo/egVRdUG673jEOcUXrlkQc4vYhy+qA+BzV88nMUI7m4iIyEz6qSEiIiIiZ4yfPX2UXz57nEmX1c71vju32Ypr7R/jiYOdAPzilpUsLLM2g51bV0BqYnzUdfe9IxMAfO+mZXz1jYGtWwtKM4kzCHqPwbFJdp4cIC89iayU6MOiM5IT+MxV8wFr2PRF84psfTd/t181n8ZvX6vZQyIiErOE1/oBRERERETs+t7j9QGvX/Ksc4+mzzHhO/ZfGx8fZzCvJJND7cHVP15TLjd/29ECwFtWVQRdT0tKoK4wg60Ngc9ypGPYeuabltp6RoB5Jdbw6/yMpJi2l4mIiPyzVEEkIiIiIq973u1jv3v3atKTA/9GuqA0i0PtoYdMA+yNUl0EsKQimxdP9NLQM+o719znAKC2IMP2c55TncflC4v55S2rbMeIiIi8EpQgEhEREZEzRoZfcuf9G2oA6B+dCHe7z6f+ugeA2YWZQdcWlmYyND7lG/DszzRNfvt8A2C1l4XzlpVWZVFDz4jvXNewE7A3oNorMyWR/3nXahaXZ9uOEREReSUoQSQiIiIiZwy3p8rnsgXFvGFZGQD37mqNGONfGVSRmxp0fZ6n5exo53DQtf9+7AgP7Wvn3edWh2wv86optAZMtw1MJ5m6h52kJ8UHVSyJiIicjpQgEhEREZEzgttt4phw8dFLZvM/71rF0ooc5pdk8sTBjohx3kqer1y/iLgQc32Ks6wKH+8gaq9Jl5tfPnscgHeunxXxM4oyUzCM6bX2AO2DYxRnp0T/YiIiIqcBJYhERERE5JR6z++38h9374kpxu02+dhfdgGQmZLg29J1yfwitjX2Mzg2GTb2/t1WhdGC0qyQ13PTkwB45khXwPm2gTEA5hVnUlcYeY5QfJxBdmoi/Q7rOXpHnDyyv4PkhPhoX01EROS0oASRiIiIiJxSzxzp5p6dLfz0qaO2Y7qGnTy4tx2A1KTplq1L5hfhcps8f7Q7ZNyIc4ofPnGUNTV5rKzKCXlPpqcFzPv+Xs19VoLov65fFBQTSm5aEv2ebWnP1VvPU5CRZCtWRETktaYEkYiIiIicMv7zgL7/RD0ud+jNYTP1jDh9xzmpib7j5ZU5pCTGsaOpP2Tc/25qYGzSxWeumkdCfOhffb3VSACOiSnfcXO/tYWsMi94blEo3Z4k1qTLTVOvFfuzt6+0FSsiIvJaU4JIRERERE6ZEedUwOs+GxvIALqGp2f75KdPV+UkxMexqCybA21DIeMOtA0xKz+NVbPyIr7/Jy+ba33O0HQiqrFnlIQ4g9Jsewki73dr6BnlePcIVXlpZKclRokSERE5PShBJCIiIiKnzK6TAwGvNx/vsRX3348e8R3nzWjbqi1Ip7FnNGRc17CT8pzoCZ5zanIBaOkf8517eH87q2blEh9isHUoP755OQA9w062N/ZT59lsJiIiciZQgkhEREREYjY+6Yo5pnvYybt+t5WUxDge+LfzAPje40cC2s5CGXVOcbhjegV9UWbgZrDqgnS6hp1B1UkAnUPjFGUmR322ecWZAPxjTxsA/aMTNPeNccn8oqixvvcosd7j8YOddAyNs2FOoe1YERGR11rUBJFhGCmGYWw1DGOPYRgHDMP4it+1jxqGccRz/r/9zn/OMIxjnmtXvloPLyIiIiKn3n27Wpn/xUfZ3TwQ/WY///P8CQAuW1DM0oocvnL9Ipr7xjjSORwx7kS3VR20tiaPxz95AXnpwRVEQFAVUf/oBC39Y1E3kAHkZyRTW5jORs+w6+2emUZLyrNtfDNLQYaViPrfzY0AXLrAfnJJRETktWangsgJXGKa5jJgOXCVYRjrDMO4GLgBWGqa5iLgewCGYSwEbgYWAVcBvzAMQ/s9RURERE4jzx/tDhjIHIt9rYMA/G1Hc0xxDZ4Ezlc8W8EumGtV2OyJkGiadLn58gP7Afj6Gxcz11Pp429WvpUg8g6G9rrzxUYANsy1V8lz+cJiekcmME2TLSd6SU6IY3V15NlF/vLTk8jxmzlkp7VNRETkdBE1QWRaRjwvEz3/mcCHgG+bpun03NfluecG4C+maTpN02wAjgFrXvEnFxERETlL3berlRPdI9FvDKNraJx3/nYrt925PebY/tEJfvtCAwCbj/dGbQ/zd7LXwWULisn3VNrMyksjIzmB/a2hB0yDlcja6ZlbVJWfFvKefM9MIu+KebC2pd2/u41z6/JZXhl6vf1MBenJTLjcDDunaOwdpTo/naQE+xMZDMNgheezblpVEXZrmoiIyOnI1k8twzDiDcPYDXQBT5imuQWYC2wwDGOLYRjPGYZxjuf2csD/z0ktnnMz3/MDhmFsNwxje3d39z/3LURERETOEm63ySfu2s21P3nhZcU/fqCDL9xnVeRsPt4bc/zXHjroOz7RPWr7PUzT5GSfI2BlfFycwcKyLA60DYaNax2wtpetq80jOSF0UXq2Z+394Nik79yh9mEaeka5bmmZrecDKMi0Ek3fevgQTx7qYm5JcLVSNFcsKgEgzrA32FpEROR0YStBZJqmyzTN5UAFsMYwjMVAApALrAM+DdxtGIYBhPppGPSnJdM07zBNc7VpmqsLCzXAT0RERMSOoXErCTL2MoZEu90mH/jjDp442Ok7F0sFEMCQXxIGYNfJfltx//L7bYxNuijOChwwvbgsm4PtQ7jcoZ9jW0MfGckJ/N9t68K+d0piPEkJcQHP9tC+NuLjDK5cVGzr+QCuWGgld/681fpb5+evmW871utNK8p5y6oKPnRRXcyxIiIir6WY6l5N0xwAnsWaLdQC3OtpQdsKuIECz/lKv7AKoO0VeVoRERGRs5hpmvzoyaMvO75n1Bl0rn1w3Ha8222ywzO8eecXLyc3LZHGGXN/wtlYb1WM5/rN6AFYXJ7F+KSb4yFa5g61D/HAnjauXVJKXJRV83EG/HrjCXpGnJimycP7OlhXm+drZ7MjPTnB11L2rvWzKM2OfYZQSmI837tpGdUFWnEvIiJnFjtbzAoNw8jxHKcClwGHgfuASzzn5wJJQA/wAHCzYRjJhmHUAHOAra/O44uIiIicPfa3Dvk2ZL0cHSGSQaESM+Hc+WIj/Y5JctMSyUtPYsOcQp4+3BU1zt/M1qtFZdaWsP2twW1mH/l/OwHYMLcg6vuOT7oB+NNLTXQOOWnoGeXS+farh7xSE602thtXVsQcKyIiciazU0FUCjxjGMZeYBvWDKIHgd8BtYZh7Af+AvyLp5roAHA3cBB4FPiIaZqx10CLiIiISICZFUDuMG1Z4YRaS3+8y36CyLv6/bzZVsKmpiCdvtGJsO1hXp+9Z6/v2JiRIKorTKcgI5nHDnQExQ2NT7GmJo9rFpfafsbctCR6Rqx/p/Lc2CuA7nzvGj58UR2LyrJijhURETmT2dlittc0zRWmaS41TXOxaZpf9ZyfME3zVs+5laZpPu0X8w3TNOtM05xnmuYjr+YXEBERETlbDHi2dL3nvGoAdtic/+P1xMFOagunW5+SE+L4/uP1tmKPdQ3z0N52AP77LUuB6eHQM+cS+WvsGeUv26yZPrWF6Vy3NDDZkxAfx/q6fA61DwecH5900TPiZMPsgqjtZQD/ee0CAL78wAG2NvQBUODZbhaL5ZU53H7VfG0gExGRs45+8omIiIicIT551x4A3rKqgozkBO7YeMJ27PD4JC+d6OXyBcVs/fylvPCZi8lJS2TYOYVzKnqx97cePuw7TktKAKYTRIfaw6+pv/JHGwHISkngiU9eSEpi8CayytxU2gbGAiqR6juthJHdWT7+LWH37W4FID/d/vwhERGRs50SRCIiIiJngOHx6SqdusIMbl03iycOdtLcZ29I9PbGfiZdJhfOK6QoK4WK3DQ+dukcAPpHw1cAeXlTN7esrfKdy/IkiN7xmy0hY5p6R3FOWbOBdnzxcuLDVAJV5KYx5TZpHxzznXvsQAeGAWtr86I+G0Bq0nTiaW/LIPnpSZTlxN5iJiIicrZSgkhERETkDODfCpacEMe1S6xWrX0hhjuHsrt5AMOAZRU5vnN5aVYLVt/oRNT4toExLpxbyNduWOw7N78kM2KMd8PZ/7ttLYkRWrYq86xEzqP7p+cQbazvYX1tPkWZKVGfDax/E3/nzi7wbSQTERGR6PRTU0REROQU6hlxMuKcijnOu73sp29fgWEYzC3JICHOCLn9a6bmPgebj/dQlp1KenKC73xuupUg6ndEThA5JqY43j3C/NLMgHlAlXlpfOyS2RhG6IHZ3uom/7lHoVTnW9e//tAhAEzT5Hj3CPOiJKD8zRx+ff7sfNuxIiIiAgnRbxERERGRV8rqrz8JwP0fOY9llTlR7raM+iWUvNu1khPimV2UEXH+j9eG/34GgHPrApMmeen2Koh++exxJl0mF88rCrqWmZKIacLIxBRZKYkB1451jZCeFE9JVuQqoMq8NGA6kdTU68Ax4WJusf0EEUB5Tiob5hTwwQvrmJWfFlOsiIjI2U4JIhEREZHXwA0/30Tjt6+1dW/bwPRsHv/By1V5aTT0jNr+zJLswERNblrkCiLnlIvFX36MSZdVHbSuNrgqJzPF+nVyeDw4QVTfOczsooyg6p5Q3rCsjH0tAwDs8fzv0orsqHH+Nn32kpjuFxERkWlqMRMRERE5RZ461Bnw2j/xE0nnkBOAm8+pJDttOglTlpNK++B4xFj/6qOR8cDWtlzPe/WOhE4Qnex1+JJD4XgHVXcPO4Ou1XeOMMdmFVB2agKDY9aw7D3Ng6QkxsVcQSQiIiIvnxJEIiIiIqfI++7cDsA8T+KjsTd69Y/bbXLrb60tYf96YV3AtZLsFEacUxFnGjX3T285e+/5NQHXEuLjyE5NDNti1uKXwPr6GxeHvGd1dS5xBjw9I/nVOjBGz4gz6iBrr+zURPodk3z3scM8dbiTVbNyIw62FhERkVeWfuqKiIiI2HSy1xFTS5e/Ab82Lu98nHCVO/68VTUAZTmBLWI5nuqdobHQa+q7hsa56kfPA/D3D58bskWsrjDd19I109HOYQC2feEybl03K+Q9RZkplOWkcnDGLKRnj3QBcPH84LlFoVy3tAyAe3e20tTr4BrPljYRERE5NZQgEhEREbHp5jte5OLvPcstv3nJ1vYwr8GxSZZ/9Qnf6/+6fhEAP3qyPuT2r5mxXskJ8QHXvO1dg2ESRI/4rY2fXZQR8p5Vs3I50jEc8tq2xn5qCtIpzEwOed1raGySJw910Tsy3Wa2s2mAgowkagsibzDzWlCaxYVzC30tcxfMKbQVJyIiIq8MJYhEREREbDBNkzZP8mLTsV6++9gR27ErvzadHLr7X9f7tnod7x7lufruiLEDnuTPmuq8oGvZUSqItjb2EWdYw5szZwyQ9spNT8I55WZswhV0rb5zmIWlWRGfD+Cm1ZWA1Vbmtau5n+WVObYGVPuexTMTqTwn1bfZTERERE4NJYhEREREbOgYChwG3TsaPJQ5HJdfldCamjzi4gzetd5q2ZrZmjXTsa4RAD5z9fyga96tYffvaQu65piY4ulDXdy8porynNSw7+/dZPal+/cHnN/fOkhTr4MFpdFnCF2zpATAN8towDHBie5RVlTlRo31V1toVTldvrA4pjgRERH55ylBJCIiImKDtw3r0vlFXLGwmEPtw0y63FHjdp3sD3n+qzcsJikhjuHx8AOmxydd/Ob5EwBU5gYneao8s4y2nOgNuvbUoS7GJl28wTPbJ5yJKes7/HVHS8D5LQ19ANy4qiJiPEwnmfo9c5Z2N1szjVZU5kSN9fevF9by4EfP57MhkmEiIiLy6lKCSERERMSGes/A5u+/dRlXLS7B5TaZ84VHcE4Ft2b5+8LfpytzHvzo+QHX0pPi+b8tTQw6glvEdp3sZ/4XH+VwxzBfvG4hRVkpQfdkpybykYvraOp14JgITDQ9c6SL/PQk1tQEt6b5O2/29OBq//c43D5ETlqirx0ukvwMa0bRie5Rz7MPYBiwNMYEUXJCPIvLs0lJjI9+s4iIiLyilCASERGRs8pd205y9/bmmOM2H++lKDOZnLQkrl82XZWz6VhPxLiMlAQA/vS+tSwuzw641u+YZGh8ij+82BgU9+iB6QHTt6ytCvv+q6vzmHKb7GkOHJq9r2WQpRXZxMdFngE0uyiTH71tOQAn+xwANPc5uH93GxfOLbQ1Qyg7NZG1NXk8cbATt9vkx08dpSI3lYzkhKixIiIicnpQgkhERETOGk8d6uQz9+zj9r/tjWkLmXPKxbNHurl0gTUbJyE+jg9dVAcQsUUMYNAxyZWLijl/TkHYeyZCtKr5D56OVFFTV2DN7Wnud/jOHesa5mjXCOfb3AS2utqaFfTCUSvZtbt5gAmX2zcnye57HOsa4YlDnQDML4k+3FpEREROH0oQiYiIyFnBNE3ed+d23+uvP3TQdqx3+PLSiukKoA97EkQdg+MhY8BaP9/UN0ppdvgh0QAjzsAkU/ewkz9vtaqc/vz+dRFji7KSg55jnyf5tSFCUspfRW4aWSkJNHsqiL78wAEA5hRHH1DtVZWXxpTb5M7NjQB87YbFtmNFRETktacEkYiIiJwVhsamkzDlOakc98zLsaNn2EoQ5acn+c5lpiSSnhQftN3M3z07WhifdHPjytCDnn/4tmUADMyYQbS90RoQnRQfx/q6/KA4fymJ8eSnJ/meY2h8kk/etQeA6vz0iLH+UpPiufPFJk72OnwJMe+WNDu8g6o3H+9lXW0eJdnRZxeJiIjI6UMJIhERETkrvPmXmwArOXTrull0DzsZ8GzdiqR72MkbfvYCQNCg6OLsFDrDJIiOdAzzy+eOs6wyhyUV2SHvedOKCpaUZ9M97AyM9QzE3vTZS6I+H0BxVoqvguiZw12+80kJ9n/V6xyynuGC7z4DwKevnGc7FiDXL3n283esjClWREREXntKEImIiMhZwVsx9J/XLmB+idU6dbBtKGLMlMvN9x474nu9ZMaQ6eLMFB7e18H4ZPAms/t3t9I97OSr1y+K+BmLy7PZ3TzAlN8cov2tg9QWplOYmRz5S3mf0+3m6cNddAyOc7RzBICn/+NCW7Fe/3bx7IDXH7igNqb4ZE8yan1tvm+rmYiIiJw5lCASERGR1zWX2+TPW0/6Xq+alcvyyhzSkuK5d1dr2Lih8Ulmf+ER7vJsPPv+TcuCNoLVFVktXPtCDLzuGnZSmp3Csiir3s+bnc+Ic8r3Hr0jTp450s35s+3NDwKoKbCe4383N9I+OE5Zdgq1hRm24yFwXtEf3ruGxPjYfk1cXJbNRy6u44eejWgiIiJyZlGCSERERF7X/rq9mc/duw+Az18zn6KsFHLTk5iVn87fdrQwPD4ZMu6eHS0Br29cFTxH6MMXWVU3RzqGg651Do1TZKMCaFmFlUDyVv7sax3E5Ta5Zklp1FivH7x1OYYBXcPjNPc5Xtb8n6zU6XlDF8y1t/3MX1ycwaevnK/ZQyIiImcoJYhERETkdc2/use/Zasqz9os9tKJvpBxe1um4772xtAbuUqzU8hMTqC+MzBBNOCYYFtjn60qnvwMa3ZPr2cwtDfZtCCGNfHpyQksKc/maOcI25r6og62DiUzJQGAhBlVUiIiInJ2UIJIREREzhjOKVfYip9wXG7Td1zgNxvnP69dCBB2C1lDzyhl2Sk8f/vFvHPdrJD3GIbB7OKMoASRNZfIzS1rq6I+X1pSAqmJ8fSOWEOiG3tHyUtPIjvN/gYxsL7bvtZBTBM2zIm9AqgoM4VFZVn8+p2rYo4VERGRM58SRCIiInLGeM/vt7Hkvx7nqUOdtu4fdExy787pOUN1fhU9ZTmpxMcZdA6GThD1OyY4pyaPyry0iJ9Rlp1K11DgFrJ7drZQkZvK8ijzh7zyM5J8FUSNPQ6q8yN/Zij+K+0XldmvPvJKSojjoY9t4NIFxTHHioiIyJlPCSIRERE5Y2w+3gvA++7cjmNiKur9n7hrFxOe7WAFGUmU+s3HiY8zKM5Mpj1Egqh9cIymXgcZyQlRP6MwM5nukekEUdvAGDua+llZlUuCzUHP+RnJ9Iw4cbtNXjzRG5DssevqJSXWe6UnkZkSW/WRiIiISPTfekREREROAw09owGvD7UPsWpWXsSY/Z419nnpSTz76YsxjMD5OsXZKXQMjQXF/fjJowAc7RqJ+lwFGUkMj08xPukiJTGe3zzfAEzPFrKjID2Jpw538a1HDgEw62UkiM6pzuMHb13GrJdRfSQiIiKiCiIRERE5I/xl20ni4wz+77a1AGxt6I8a460A+sUtK0NWA5Vmp3Cyz4FpmkHXAL5y/aKon+Gda+RtEWvosZJK/3HFvKixXrnpVjLpfzzJpfecX2071t+bV1ZETZqJiIiIhKIEkYiIiJz2HBNT/Pq5E2QkJ7C+Lp9lFdn87OmjjE+6wsaYpknX0DjvPreadbWht3qtq82nuW+M+s7ASqGmXgcrqnJYUBp9lo93M9oDWgjXWAAAIABJREFUu9sAONnn4OrFJbba07z8F4ddvbiELLWIiYiIyCmmBJGIiIic9v6xx0q+DI5NYhgGbz2nktEJl69qJ5TOISejEy7qCsO3a62ozAXg3l0tvnMuzxygmgJ7bV7eCqLvPHoYx8QUzf1jVEUZbD3TrZ4taZ+8bC4/f8fKmGJFREREXglKEImIiMgpMeCY4MG9bfzm+RO2Bkz7G590B7zOT0/2vWc4v9tktWvV+m0um6kyLxWAXz93wnfunh1WsmiFzQ1kxVnTg68f3NvOxJQ75k1gSytyaPjWNXz8sjnExRnRA0REREReYRpSLSIiIqfEZ+7Zy2MHrPX0PSMTfPbq+bZjm/scAHz3LUsByEmzWrAGHZNhY/689SRAxDax7NTgVq5n67sozkr2VfVEU5Kdwi1rq/h/W05y+9/2UpCRzDnVubZi/c0coC0iIiJyKqmCSERERE6JPr92sB1NfTHFHuoYYmlFNjetrgSmE0RffuBAyPtN0wQTbl1XRV56+G1ihmHwqSvmAjA+6aJraJxnDndz0dyimBI251RPD4buGXEq2SMiIiJnHCWIRERE5FXncptsa+ynpiCd286vYdfJAQ53DNmKfeZIF5uO9bKgZLoSqNqzBv5o1widQ+NBMR1D4ww7p5hXnBn1/Ys8LWLNfQ5+9dwJxiZdvH1tla1n872HZ1A1TFc5iYiIiJxJlCASERERW7qGx1n7zSd5dH9HzLE/fKIegOHxKW7bUMuU2+Tena22Yt/z+20ALCidTvakJMb7jr/32JGgmP2tVvJpjo0E0XmzCwB4rr7bN7fI/7PsqMq3hlJX5qX6qpxEREREziRKEImIiIgtz9f30Dnk5IN/2sHu5gHbcT0jTn72zDEA/vKBtZRkpzC3OIOGntGYPj/cLKGBseA5RPftaiU/PYnlNgZNl+dYg6q//tAh3+vkhPhIIUEqctP43btX8z/vWh1TnIiIiMjpQgkiERERseVPW5p8x3dtO2k7bnh8emPZ7CKrMmdBaRY7mvqZdLnDhQGeWUIe80sCE0S3eNrANtZ3B9x397ZmHtrXzqULigIqjex6+5qXVwF0yfzioGcUEREROVMoQSQiIiJROSam2Nsy6Hs9NG5/Tb13pf2Pb17uO3f+7AL6Rido7R8LG/f80W72tVqf+f4NNWSnBW4c++oNiwFwTrn5x95233lvIus959XYfsY3rSj3HV+9pNR2nIiIiMjrhdbci4iISETOKRcLv/RYwLnjXSO248cnXQDkpE1vE/MOhu4ZcVJdkB4U43abvPO3W32vr1xUEnRPfNz0prD2gelEU+/IBG9YVhZxvf1Mt6yt4u+7Wllfm09dYYbtOBEREZHXC1UQiYiInCXcbjPkxq9omnodvuOfv2MlAIc7hukZcdqKd0xYCaK0pOl2r4IMK1kU7j16RgPPLwszS+j7Ny0DoN9hzSEaGp+kdWAs5iHTyypzePuaKr5zozaQiYiIyNlJCSIREZGzxHcePczabz5lO7Hjtc/TWvbTt6/g2qWl/PWD6wPOR+NNEKX6zQMq9lQQHQtTidQxOJ3I+tgls0mMD/0ry42rKijKTGbAMQHAkY5hABbEOAsoMT6Ob715iW8bmYiIiMjZRgkiERGRs4BjYopfbzwBwO6T9jeQAfxjbxvlOalctdhq85pTZLVg1XcO24ofC1lBlMy5dfn86aWTuNxmUExzn9Uy9tDHzuffr5gX8f2zUhMZGrcqiA63W+vt58dYQSQiIiJytlOCSERE5CzgnxR6/x+3MzEVeXuYv8Ptw6ypyfNV8eSkJVGYmcxRG3OIfvLUUT5x124A0pMDRx9evaSUjqHxkBVNR7us5FNtQfR5QKmJ8Ty8rwPTNDnUMUx2aiIlngolEREREbFHCSIREZGzwPamfgwDFpZmYZpwx8bjtuI6BsfpGBpnUVlgy9acogx2NvWHrP7xd9e2Zt9xQUZywLWybCuJ0z4YOBfpnh0t/OjJo8wpyiA1Kfqaeu+ms2fru9nW0Me8kkwMw4gSJSIiIiL+lCASERE5C2xv6mdecSb3feQ8lpRns+lYr624XSf7ATinOi/g/DVLSjnRM8ru5v6QcW63SdvAGK1+28X8t44BlHgSRG0Dgavuf/NCAwBfvG6hrWdM9ySR7tzcyNGuEd7st7JeREREROxRgkhEROR1zuU22dnUz6pZuSQlxDGvJJPDHUO22swOdwwTZ8C8ksCZPlcsKgZgV5h5Rp+8ezfnfvtpAG4+p5I/vm9N0D01BenEGdODpb0ykuNZWpHNBXMLbX2/526/GIBnj3STmZLAG5UgEhEREYmZEkQiIiKvc0c6hhlxTrG6OheAyxcW0++Y5Ln67ohxjokpnjrcSXV+OimJga1eRZkp5KQlcqJnNGTs/bvbfMfvWFvFhjnByZ60pARqCtI55Bks7dXY62Besf0h0wUZyVzpSVidW5cf9KwiIiIiEp0SRCIiImeIR/e3c/3PXgiquIlme1MfAKtnWW1iF84tJCHO8LWPhfNv/7eL/a1D1BaGHhRdnZ9OY4gE0ZRrujLpkY9vYGlFTtjPmJWfTnP/dIvZqHOK7mEn1QXpEZ9tpsVl2QAaTi0iIiLyMilBJCIicgZwTrn44J92srdlkFt/uwXnlMt27KZjPZRmp1CRmwpASmI8C8uywraHeT19uAuAgoykkNdrCtJp6nUEnf/MPfsA+OCFdSwozQq67q88J5VD7UPs9wyavndnCwALyyLHzfTmVRUsr8zhysUlMcWJiIiIiEUJIhERkTPA715oBKztYd3DThp7ghMzobjdJs8f7eHSBUUBm73Oqc5jx8l+hsYnw8Z6kzv/fvnckNdn5afRNjjG+OR0smpiys09niRPb4j19TPduKoCgOt++gJdw+P8Y287dYXpXGRz/pBXeU4q933kPM6tK4gpTkREREQsShCJiIic5qZcbv74YiNzizP47k3LANh8vMdW7ImeERwTLl8LltcFcwuZmHJzsG0oZNyOpn4OtQ9xzZISisK0bdUUpGOacLJvOlnV4Ndydu7s/KjPt7xyuv3s/l1tbG3o400ryrWmXkREROQUU4JIRETkNLe9qZ+2wXE+cdlcFpdlsaA0i//ZeCJg1k8opmlyx8YTxBn4BlR7ledY7WadQ+MhY//70cMAvH9Dbdj3r8635gT5J4WaPcmiO9+7hjcut7dN7CdvXwFYG9OAiDOLREREROTVoQSRiIjIaa5j0ErizCvJJCE+jk9cNoe2wXE2H++NGPfrjSe4e3sL1y4tY3ZR4FawkmyrKqhtIDhB9PddLWxp6OPDF9Wxoio36LpXXVEGyQlxPHWo03eusddKFi0qy7JdBXTFQmsDmbc1zZt4EhEREZFTRwkiERGRU+DKH26k+rMP8em/7ok51luVU5iZDMDSCqtdrMVv+9dMbrfJtx+xqoBWVgVX5GQkJ5CeFM//bW0KuvbkIWs49Qcvqov4XBnJCdy4qoK7t7fwKc/3OtA2RFFmMgUZydG+ls/MtfRlOdpEJiIiInKqKUEkIiLyKjNNkyOdVvvUX3e0cKzL/pr6xp5Rvv9EPQCZyQkAvuRL13Do9jCwZg8B3LSqgnetrw55T3JiPM19Y5z020RmmiZbTvTyphXlZKUkRn2+tTV5APxth1X9s791kCXl2ZFCQvrPaxf4jhPi9euJiIiIyKmm38BEREReZX2jEwGvj3WNhrkzmH+VkLdlKzE+jrz0JPa1DIaM2dM8wGU/2AjAzWsqiY8L3er12avnA9A2OP0Zn7hrNz0jE77ETzT+lUKOiSmOd4+w6GUkiN64opy6wnS+c+OSmGNFRERE5J+nBJGIiMirzH/LF8AH/7SDsQlXmLsDeVfI3/neNQHnz59dwPPHejBNMyjmhp9v8h1X5KaFfe8Vng1iXcPT6+jv390GWFvO7Di3Lp+EOIPM5AQOtg3hNnlZFUQFGck89R8X8bZzqmKOFREREZF/nhJEIiIiNpimiXPKXlJnpscOdJIYb7Dzi5dz8zmVABxoC139M9OIcwqAytzUgPMLy7KYmHIHJHcAhsYnfcdfum4hxWFW1MP0TKNuv/fITUvkzSvLKctJDRcWwDAMPnhhHY5JF7/b1EBSfBwrQsw8EhEREZHTmxJEIiIiUTinXFz7kxdY982nwq6FjxT7+IEOllXkkJeexEcvnQPAoY7oc4gGHBP88Elr/lBGSkLAtZxUaz7Qm3+xOeD8iW6rfe2Od67ivefXRHz/7NREkuLjfAmiQcck/Y5J5pdkRoybqSAjCZfb5OF9HbztnMqYBlSLiIiIyOlBCSIREZEoPvXXvRxsH6LfMcnabz7l2yoWzcSUm4VfeowTPaPcss5qnSrLTiEzJYEjHUNR45d/9QmaPAOkM5MDB0YPj1uVRa0DgZvMWvqt+2fZWBVvGAaFmcm+YdfeFfV2Yv0VZE4nhFbNyo0pVkREREROD0oQiYiIRDDinOIfe9oCzv3xpeDV8KHUdw7jcpssrcjmTSsqACspM78kkyM2Koj8pSQG/sh+w7IygKBqn94RayB2fkaSrfctyEz2VRB5E0Q1BbEliAr9KoZijRURERGR04MSRCIiIhE8c7jLd/yH966htjDdV6UTzaF2q0roB29dHnB+TnEm2xr7mZhyh401TRPP0jJ+fPNy3wYzr5LsFIqzkjncMRywJa13dALDgNw0ewmiIv8EUY/1varywg+2DmVhWZbveGlF7AOqRUREROS1pwSRiIicNTqHxgOGONvx1KFO8tOTOP7Na7hgbiFl2am0DkSfQ/TIvnY+/be9AJTlBA6KXu1pw/r1c8fDxjf3jWGa8LU3LuaG5eUh7+kcshI79+5smT43OE5uWlLY1fYzFXoSRM19Dt+8o5TEeFuxXpkpifzntQv48/vXBSWyREREROTMoASRiIi87pmmyd3bmln7zae4+LvPxhTb0OtgYVmWL+GyqDyL/a2Dvrk94dzjl7RJnZFwefPKChaWZrHpeE/IWJfb5NqfPg9Ya+TDqS202rkGHFbSyzRNnjzU6UtA2VGYkUyfY4Jnj3RFvzmC2zbUsj7Cs4qIiIjI6U0JIhERed072jXC7fdY1Ty9oxMctjEg2qt7aJyizOkKoIvmFuFym1FnCDn92sdCVdUsLs/iWNdIyNinD3f5hlDXRBgY/fcPnQeAY8JlPeuIk97RiYhJpZmKspIxTfji/QcAeOZTF9mOFREREZHXDyWIRETkdW1HUz+33bk94Ny2hj5bsd977Ahtg+MUZU0PYa4usObzNPSMRoztGIxcYVRXmEHPyETIlreDbVYCa01NHnERWsWy0xIpykzGMWElk3Y29QMwt9j+mnr/AdPFWckaMi0iIiJyllKCSEREXtdu/OVmTs5YS1/fGbpyZ6afPXMMgLKcVN+54swUKnJTeeJgZ9i4AccEx7tHqMpL454PnRvyHu8g6OYZzzY+6eLBvW3UFaZz97+uj/qM6ckJvgqiP710krLsFNbU5EWN8/LfdnbFwhLbcSIiIiLy+hI1QWQYRophGFsNw9hjGMYBwzC+MuP6pwzDMA3DKPA79znDMI4ZhnHEMIwrX40HFxERiUVlXirPfuoilpRn88eXmjBNM+L9f3ix0Xd85cJi33FcnMGyyhxa+8fCxm461ovbhB++bRmrwswDqgyTIHrsQAdHu0b43NULonwjS2piPI4JF5MuN1sb+rh2aSkJ8fb//uNtn8tNS+Rz18y3HSciIiIiry8JNu5xApeYpjliGEYi8IJhGI+YpvmSYRiVwOXASe/NhmEsBG4GFgFlwJOGYcw1TdP1Kjy/iIhIWP1+69//9z1rqC5IpyQ7hX2tg+w82c+qWeErbb7kmcnz5hXlFGUFbiHLTUvkRM8oPSNOCvxatMCqAPrVc8fJTk1kWUVO2Pf3JohmVje9dKKP7NRELplfZOs7piXF45iYYmN9NxMuN0sjfGa453jqPy6kOj/d9uYzEREREXn9ifonRtPircVP9Pzn/bPrD4Hb/V4D3AD8xTRNp2maDcAxYM0r98giInI2Ghqf5PebGmJaU1/faQ2S/sFbl1FXmAHAJy+bC0BTryNsnL+89KSgc975Qp+9Z1/QtW2NfexrHeQTl82JWMmTnZpIdmoijX7PYZomD+xupa4wPeLsIX9TbpPNx3t5353bqStM53K/aie76gozlBwSEREROcvZqkE3DCPeMIzdQBfwhGmaWwzDuB5oNU1zz4zby4Fmv9ctnnMz3/MDhmFsNwxje3d398t8fBERORsMjU+y9L8e5yv/OMg3HjxkO67esyVsXe30Vi/vavjmvvAtYjA9I+jfr5gbdM2bTGnpD04yNXqGV1+zpDTq8y0uz2L3yQHf6ycOdjI64aLWk8yyw39j2RWLSkhJjLcdKyIiIiLiZStBZJqmyzTN5UAFsMYwjKXAF4Avhbg91J8ggwY9mKZ5h2maq03TXF1YWBjLM4uIyFnmjT/b5DtuHYic2PF3sG2IjOQESrOnW8RSEuPJTEmg3zERNq6l38HJPgdvW11JWlJwN/Z/Xb8IgOIZrWdgzR/KSkmgKDM56NpM80uyaOyd3ob2wrEeAL547cKosV63XzWfJz55AR+4oJZb182yHSciIiIi4s/ODCIf0zQHDMN4FquNrAbYYxgGWImjnYZhrMGqGKr0C6sA2l6RpxURkbOOaZqc8FspPzhmv8XsuSNdnD+7AM/PKp/s1MSI7/PNh60qpSsXh27XKs1O5cpFxZzoDlx1/9ftzTx6oINb1lYFfWYoWSmJOCZcTLncJMTHcbLPweLyLLLTEqPG+ptTnMnnr7E31FpEREREJBQ7W8wKDcPI8RynApcBu0zTLDJNs9o0zWqspNBK0zQ7gAeAmw3DSDYMowaYA2x91b6BiIi8bh3vHqHmcw8HnNvXOsgdG49HjXW7TTqGxpldFNyu5Zxy8/ddrUy63EHXBscmefJQF9ctLeWS+eHn+VTkptHSPxawDe1g+xCA7WRNZor1d5oDbVbcyT6Hr7VNRERERORUstNiVgo8YxjGXmAb1gyiB8PdbJrmAeBu4CDwKPARbTATEZGX43D7cMDrt622ClQfO9AZNfbh/e24zdBDpruHnQDs8pv/4/Xo/nYmptx84ILaiO9fkZvK2KSLPr9NaW0DY8wuyiA92V6BbmqSNS/ohp9vYmtDHye6R0O2rYmIiIiIvNqi/gZrmuZeYEWUe6pnvP4G8I1/6slEROR1ZdOxHt79+6288JlLbCdB3H7VOc99+iJm5aczND7p204WzncfO8zPn7GqjCJ1eo1NBv/9YmN9D6XZKSwpz474GRW5VqVPS/8Y+Z5V9409DipyUyPG+fNvc/vOo4cByEyJrb1MREREROSVYGtItYiIyMs1Puni+aPd3PKbLUy6TB7e1247dsQ55Tuu9CRkynJSaR0Yw+0O2n/g400OARSGGBb95/evA6B3xBlw3jRNXjrRy/ra/KgzhLyJoJZ+a2h2/+gERzqHWT0rN2KcvzU1eb7jHU39AHzwwsiVSyIiIiIirwYliERE5FX1ll9t5p2/nR5FN7NtLJJtjX0AfPbq+cR5VsvPKcpgfNJta5vZNUtKuDbEuvklFVZ10H272xj3qyI62jVC7+gE62rzg2JmKvckiA60DQY861obsV4rq3L58hsCN5aF2pomIiIiIvJqU4JIREReNaZpsr91KODcXdubef5ot634e3e2AnDb+TW+c3NLMgE40hE60fTUIWs+0S1rq/jFLatCVgJlJCewpiaPjfXd3LOzxXf+xeO9AKyvi57kyfK0gv3i2eO43CZbGvpITohjaUXk1rSZSvza7QoygucliYiIiIicCkoQiYjIq+aBPW2+41/duopFZVkA3LOjJVyIj3NqurInIX76x9Ucz1ayI2HmEH3Ds6K+rjB4e5m/37/7HGC6RQzgmSNdlOekxjRHCKzh1FsaellRlUNyQnxMsUVZ0y1wv/mXc2KKFRERERF5pShBJCIir5qP/2U3AJfML+LKRcX86tZVABRFGVI9ODbJIU8r2ptWlAdcy0xJJDctke8+dgRXiDlEpgnzSzJ55/pZET8jPTmB8pxUOgfHAat66Nkj3bztnMqo84e8/vbB9QDcubmR/a1DrK8tsBXnryhz+t9ieWVOzPEiIiIiIq8EJYhERCSi8UkXH//LLhZ/+TFO9jpiil3mSXh8402LMQyDyrw0ynNSaewZDRsz5XJz/c9e4I0/3wTA+/zay7z6Hdb2r40zWtW6hsdp7B3l8oXFJMZH/xFXkZvKCc+z3LHxOCVZKVHX2/tbUZVLZV4qv3mhAYC3r6m0HevlHaK9TMkhEREREXkNKUEkIiIR7TzZz/272xhxTvGZe/bGFDs0Nsm1S0opzZ5u2VpcnsWzR7qZmHKHjOkcdtLkl4haUJoVdM9P3r4CgPoZc4ju3taMacINy8tsPd/yqhwOtA0yPD7J5uO9XLW4hJRE+y1i8XEGF8wpBKA4KzlqZVQoKYnx3POh9fzhvWtijhUREREReaUoQSQiIhH5z+jZ0dRP1/C4rbgT3SM09Iz6tn15XTyviAmXO+z7dA9Pr56//yPnER8X3O51/bIyyrJTONAWOAB7S0MfC0uzmF2UaesZq/LSmHSZPLKvA+eUm4vmFdqK8+f9fvNKghNZdq2alUd2auLLjhcRERER+WcpQSQiIhFtrO8mzoBHPr4Bt2nywyfqo8bsaOrn9r9Z1UZpSYEVOSXZVpVNx2DkBNED/3ZexLar+aVZ1M8YVH2sa4T5JfaSQwAFGVZ71+337CUpIY61NfZX1M9UlRfbYGsRERERkdOJEkQiIhLWkY5hHtzbzrvWV7OgNItrlpTy9OGuiDEjzilu/OVmtjf1A/DeGTOEZuWnA3AozJr6jkGrYqk4SrtWTloiw+NTvtftg2O0D46HbEkLx5sgAlhbk0dqUmwbyADOrbMGU791dezzh0REREREThdKEImInAU6h8Z54WhPzHEH2wcBuGVtFWDND+occtI/OhE25qhfVc+1S0rJSglsnarOT6MiN5UXj4d+nhM9oyQnxFGUmRzyuldmcgIjzukE0Z2bmwBiahPzX2f//ZuW2Y7zt7wyh4ZvXcPSCg2ZFhEREZEzlxJEIiJngVt/s4Vbf7sF55QrprhH9nUQH2dQU2BV/Xjn7Lx0ojdsTGPv9Iayt54TXFVjGAbzSzI53hW8yex49wi/39TI3OLMqKvm05MTGHVOYZrWqvvDHUMsKM1iTrH9FjP/JNTLGTDtFe1ZRUREREROd0oQiYicBY52jQAEbAeLpnfEyeMHO3G5TRI8K+MXetq3fvbMsbBxXUPTQ6YXhJkHNKc4kxM9I4xPBiasvv7gQQA+feW8qM+XnpzAlNvE6dmG1jE4TnlObHOADMPgF7es5J4PnRtTnIiIiIjI640SRCIir2N3bm7k5jte9L3+y9Zm27GHPTOC/vXCWt+5wsxkVlbl0DkUesD0+KSLbz1yGIBvvXlJ2KqcZRU5TLrMgC1kUy432xr72TCngAvmRm8Ty0hOAGDUOYXLbdLc5whoGbPrmiWlrJqVG3OciIiIiMjriRJEIiKvY19+4AAvnejzvf7dpgbbbWbeDWHvmzFk+spFJfSMTDA0PhkU87+bG33Hb19TFfa9V1RZ83qeOtTpO7e/bYgR5xQ32Rz2nJNmzTba1zrI0a5hRidcLK3IthUrIiIiIiKBlCASETkDtA6M8dft9qt/gIABzv52NPbbiq/vHCE7NZHCjMBh0d419Z0h1tR7V9dHa/Uqzkphfkkmd21r9s0Q2uwZWr2+1t6q+SsWlgDw7t9v49ueqqWVVaoEEhERERF5OZQgEhE5zf3m+ROc9+2n+fTf9nLMM0vIjsExq8LngrmFbPn8pez/ypUkxBlstLnN7GjnMHOLM4IGMJd42sY6QrSZHe6wWsb+/uHoM33eurqS3tEJekYmcLtN7tvVyrziTAqjbC/z8l9J/+yRbgBm5afZihURERERkUBKEImInMaOd4/w9YcO+V5vbeiLcHegF49bm8besaaS4qwUMpITWDUrl+ePdkeNNU2T+s7hkBvBij0Jok6/YdQAky43+1oGedf6WbY2gtUUWpvRTvaN8uKJXuo7R7h6SUnUOH+fuWq+7zgnLVHbxEREREREXiYliEREXmUut8mh9qHoN4bw6b/uCXj9+b/vw+02o8Y5Jqb4lCc2KzXRd/6CuYUcaBuid8QZLhTnlIvzvv00Q+NTnFMd3LLlazGbUUG0t2WQ0QkX62y2iHlXzHcNOXnpRC/xcQa3baiNEhVoTc308z3wkfNjihURERERkWlKEImIvIoOdwxR9/mHufrHz9PQMxpz/M6TAwDML8n0DWDe0zIQNe7mO17yHcf5VdV4t3XtbwufsHr/H3bQNjhOSmIc1y4pC7qekhhPdmoix2e0u73omSG0tiYv6vMBFGVaiaauYScb67tZXJ7t20xmV3mO1VL2hWsWUKX2MhERERGRl00JIhGRV9Evnz3uO/7Qn3bEFOs/ZPr7b13Gr25dBURO7njtbRn0HfsPbp7naRmr96ywn8k0Tbac6GVFVQ4Hv3IVSQmhf0xU56dx767WgC1kzx/tYWFpFvkZ9mYI5acnER9n8LtNDexpGeS6JaW24vyVZKew50tXcNuGmug3i4iIiIhIWEoQiYhE8eetJ6n93EP88aWmmGP3+SVqmvscMcXu9lQP3fneNSwqy6Y0O4XM5ASOdoZO7oRyy9qqgCRPbnoSRZnJHA6TIHrmSBfOKTdvWFpGXFz4eT7ff+tyAP66vQWwWtp2nuxnw5wC288WF2fgcps09Vr/Litn5diO9Zet2UMiIiIiIv80JYhERCKYcrn54n37cZvwxfv2xxS76VgPJzxtZbeuq2J0wsWRMImZULY19hFnwMoqK3FiGAaVeWm09I9FjPP316g3AAAgAElEQVT/jKsXB1flzCvJ5GCYmUi3/20vAEVZkauAZhdlcMHcQloHrGep7xxh0mX6WthejpqCjJcdKyIiIiIi/xwliEREIvj+E/VM+Q2FHnBM2IrrH53glt9s8b3OT7cSLlf+aKPtz97R1M+8kiwyU6aHTFfkptLSH7kS6WsPHgTgOzcu4fwQFT3LK3Oo7xzGMTEVcN40TcAgNy2RqxZF3yZWnpPKvtZBHtrbzjs937W2MLYkz+yi6fvz0pNiihURERERkVeOEkQiIhH8Y08bF8wt5H/fcw6A7Qog/xauB/7tPNbWTg9uHptwRY3/weNHeOFYj696yKs0O4WOwfEwUVbFU3O/g7LsFN52TlXIe1ZU5eBymwFzigBO9jnoGXHy6SvnkxAf/cdDbpqVuPrI/+1k2DMvaVaMg6Lv+sA6vnbDIn79zlUxxYmIiIiIyCtLCSIRkTDcbpOOwXEWl2UxvyQLgLfd8ZKtNfM9fmvkF5dlc25dAV+6biEAD+1rjxjbPezkJ08fA2BOUWBFTl56MkPjUzx+oCNk7G1/2E5Tr4PPXD0/7PsvLre2oR2e0WbmbYebW2yvCmjm2J/v37SMRBuJJX/5Gcm8c301V9qoWBIRERERkVePEkQiImHsax1kym1SlJlMSXaK73xjb/R19d4E0QcvrPMNe/6Xc6tJio/jQNtgpFDe8qvNACQnxPGOtbMCrnmTMh/4Y/BGtFHnFM8e6QbgmggbwfLTkzEM6HNMBpz3Dr+2uy5+Zp7sysVK8oiIiIiInKmUIBIRCeMbDx8C8K1t/9sH1wOE3QDmr6V/jOT/3959x8lV1/sff3239832lE1PNnXTSQMSJDQRpYiCIuC1ovgT20URCyiKXrw28KqoF9ArIh2khV4kkJDe62aT3STbe2/f3x/nzOzM7szsTAosu+/n45EHM+ecz9kz4cuS/eT7+Xxiorjx/GneY9FRhuyUOO55s5ia5sC9jGqaO7xTvT48d3TQMfOBPLX1KACPfGl5yJ080VGG9MRYavs8w7Pby5g5Ko2cMMfUXzSnNwl11ZJxpMTHhP2sIiIiIiIyuChBJCISQHlDG+sO1gBw3qw8ACa7DZhD9QACuOnRrfzl3weZMSqt36j4o27s/WsPBYwtqeltQH3Dqqn9zn/2jIkAZKfE0dnd43duX3kT8TFRzB878Lj4aGP429u9z9DV3cOuYw0sm5wV9sj4WaPTOd/9vYm0ObWIiIiIiAwuShCJiATw8u4KAFZNzyU+JhqA9MRYoqMM1c3tQePau7r5x7oSAD61dHzQ6xJiowMer2x07v349aczNrN/qVdyfAy/uXIeVU0dbDhU63duf2UT47OS+iWlAvEkgardUrgH15fS1tnD0klZA8b6inLv42lYLSIiIiIi709KEInIkGWt5aVd5Tyx+Qi/fWlfRLFbS+sZkRTLn69d5D0WFWXo7rE8sflo0Lgjta0AfOUDU7h8YX6/83+6xrlfS5BJZhVugig3NXiZ14qpOe4z1vXGNbTx731V3nMD+cmlswFY6+6SemLzEWaMSuOcGblhxXt887wC5o8bwdnTI4sTEREREZHBRQkiERmyth2p57P3reeGBzbzyxf29ivJCsZay9tF1RSOSe9XbjV9ZCqlta28XVQdMPZInZMgOnNqdsDz587MIzslLmCj6vqWTr772DYA8tIS+p33GJEUS3xMFFVNvT2EXt1bSVeP5WOLxob+cK4p7nS0L/99Iz09lqP1rUzLSwm7vKz3Pqk89uXTGZEUF1GciIiIiIgMLkoQiciQ9dYB/ySOp/nzQP7v7UMcrGrmknlj+p377SfmA7ClpK7fOYBi92vkBygP8zh3Zh5r9ldjbe8YsO4ey4W/fQOApLhookOUiRljyEyO82t0fai6mZgow+Sc5BCfrNfErN7rXtlTQUlNK6NHJIYVKyIiIiIiQ48SRCIyqPX0WG+fnEitPVjD+KwkLpvvJHoOVDYNGNPW2c3Pnt3NnPx0PjSn/6j4grxU8tLi+/X/8dhWWkdaQgyj04PvAJqck0Jjexf1rb1j5jccquVIXSvnzMjl1f88a8DnTI6P4eENpew82kBJTQv3rz1MfkYiMSGml/mKijJc4e42+ux96wGUIBIRERERGcaUIBKRQe2Wf+1g4W0v0tjWOfDFfewpa2Te2BHcevEsAHYdaxgwZtPhOpo7urlh1dSgjaTPnZnH6/sq/XYAHa5uoay+jSc2H2VFQU7IUq3x7u4d34RVkfv6lo/MIjc1eHLJ+/XcaWfff2I719+/kdqWTmaNSR8wztfli/x7JI1RgkhEREREZNhSgkhEBrW/vuWMYv/v5/fS02MHuLpXe1c3R+tbmZCVTGpCLHPz03ls05EB49YerMYYWDQhM+g1E7KSaevsoaGty3tsxR2vsPT2l2jv6uEjc0eH/BoLxjlj6NcddHYhdXX3cOfL+wHCSg451zlNrBNio6htcUrNrl02IaxYj7w+X2tCdnjlaSIiIiIiMvQoQSQip8zh6paAzZjD5btr6N41xd4kSjhKalqwFiZkO72Azpyaw6HqFtYcqAoaY63lmW3HmDd2BOmJwce257jJmfKGNsCZIOZrycTQo+KzUuLJSo7z7gJ6fV+lt7l1XEx435Z/f9VCAOJjoqloaOdzZ0xk8cTgSa1ActN6J6W9+I2VTFSCSERERERk2FKCSEROCWstK+54hQ/99t/86fUiv3KscN321C6/9796cW/YscVVTvJlglvOtXB8BgC/fjH4uPt71xSzt7yJjw8wCWxyjjMBbPX2MgB2HPUvXUtPCp5c8shLS/AmmDzTyH58yewB4zwK89OZP24EL++uoL2rh7OmRT5m3reEzjPVTEREREREhicliETklLjx4a3e1z95Zhd7yhsjvkd5o5NAuXbZeO8x38bOwdS3dlJc3Qz0JojOmpbDzFFprDtYE7DBtLWWu17ez6LxGXx0QX6/875mj0lnzIhE9lU4fYPWFdd4zwUbb9/XqPQE9lU00tNjKa5ynvXji0J/3b5aO7q9r6ePSo0o1uP3Vy3gma+eeVyxIiIiIiIydChBJCJB1TR38NNndtHe1T3wxX08tKHU7/0Fv34j4vtkJscB8O0PTuczp08E4F9bjoaM2XS4lrm3Ps9vXtxHemIsGe49jDEkxDrf8r7zyNZ+cQcqm6hu7uBji/LDKvOKj43iyS1H6ejq4Zltx1hZkMPa767i7qsXhfXZVs3Io6Smlb0VjTy8oZQF40YQHxO4KXYwt3zEab5dkJdClvs5I/XBwlHMHJ12XLEiIiIiIjJ0KEEkIkHd/Ng27n69iGe2HYsorjtIM+kXd1aEfY/61k7+va+KuWNHkBQXw3cvnE5OajzP7ywPGbfpcB0Aje1d5Pn02AE4b9ZIAFISYvrFbTzkxC0cH14fn6JKZ9fPfWuKOVTdwuKJmeSlJZAYF16SZ06+M3Hsr28doqKxnU8tHT9ARH9LJ2Vx8PYLWf21FSGnpomIiIiIiAxECSIRCcpTQvX1f26htLYl7DjfaWEfnjva2//n5se3hRV/sKqZubc+T0VjO51dPQDEREdx+uQsDlQ0hYz17QdU3tDud+4LZ05iSm4KgfJXm0pqSU+MZVKYjZrv+fRpgFM+B07ZWSQ8E8PuX3sYgA/NGRVRvIcxRskhERERERE5YUoQiUhAP3t2N/t9kjHH6ttCXO2vzh27DnDnJ+bzyJeWu8c7wxpV//V/bva+Tonv3e2TnhjrN9msL2stL+wsY+5YZ4z8GVP8+wFFRRlOm5BJSY1/squmuYN/rCth3tgRREWFl2w5o0+vodkRlmn5fi4g4vIyERERERGRk0kJIhEJ6A+vHfB7f9tTO8OeRHbb086uGt9dMd+/aCYADSESPJWN7fzulf1sLqkjITaKa5aN51dXzvOeT0uMpaGtq1+Cx2PtwRoa2rq4eO5onr3hTO742Jx+10zKTqamucNvNP1f3yoG4OJ5o8P6fACx0b3fPh/78nKyUuJDXB3a7z654LhjRURERERETgYliEQkLFtK6zng9t0JxTeJdOeV872vPU2Ua5o7+sV4/H3tIe5YvQeAR790Oj+6eDZjRiR6z3e45WZX3v12v9iSmhY+8SfneGF+OjNGpZEU17/X0MppOQD875vF3mNFlc0kxUVz6fwxA36+QOaPyziuuA3fO4f13zvnuMvLREREREREThYliEQkoLGZTmLmma+eyVy3ofKb+6sGjLtvTTEAXz+nwK9cyzNN7MH1pYHCAChzy9g+VDiKGQHGtlc2Oj2FjtS19jv3+9cOYC38/KOFnDYheKPpgrxUpo9MZXeZ06tozYEqntxylInZyRH38nnxGyt44vrTI4rxlZUST/YJ7DwSERERERE5WZQgEhniyurbaOuMbLz8zqMNlNS0ct3Kycwcncbj159O4Zh07nx534Cj6v/wWhEAyyZn+R33JJmKq4LvQlp3sIZZo9P43VULAiZr/sMddZ+d4j/SvafH8vTWY1xYOJIrThs34OfLTI6jub0LgL+/7TSJXjU9d8C4vqbkpnr7HYmIiIiIiLyfKUEkMoTtOFrP0ttfYvr3nwvZ3LmvnzyzE4CVBU45ljGG6z8whaqmDjYcqg0a197VTUNbJ59YPI7FE/138YxIiuPMqdkcq++/+wfgld0VFFU1MyknJej9C/PT+ewZE2lq7/Jrdl1a20p9aydnTs0J6/OlxMfwTnEtD6w7TFtnN6kJMXx11dSwYkVERERERIYiJYhEhqiGtk4+9Nt/e98/sK4krLhj9a28ub+aTy+f4LcL6PQpWcREGd7YF7jMrKm9i8/dt56Wjm4uLBwZ8JrxWUkUVTUHnGT2yMZS0hNjuePy/o2lfU3NTaGts4e9FY3eYzf8cxMAU3KDJ5d89bh9kr7z6DZe2l3B+KwkYqL17VBERERERIYv/UQkMkjtLmtg/o+e5/61h48r/p2DNX7vd7k9dway+XAdAJf0adicmhDLpJxkiiqbAsbd+dI+b/Jo+eTsgNcUjkmnsa2LF3aV+x1v6+zmld0VXFg4koTY0OPeV7i7mtYW9X6+Te4zF+T171sUyN5y/8+w/Uh4vzciIiIiIiJDlRJEIoPUg++UUtvSyXcf28bDG4I3dg7EWstn71vvfb9kYiYHQ/T+8bXtSD0xUYbpI/snW7KS46luCjyFrNWnz1F0VOBmzxfNGU18TBR3vbzfb9rZmgNVNHd0c96swDuPfHmaOje0OiVzHV09xEQZrls5mfTE2AHjAWaOSvN7/81zC8KKExERERERGaqUIBIZpMob2ryv//ZWcUSx/3ynt5xsyw/PY+boNDYdrmPDoZoQUY5tR+qZmpcacCdPRnIs64P0IPIkbGKjg08CS46P4Ywp2Ww7Us+h6hbASWbd8uROUuNjWN6nsXUgcTFRJMZG0+D2VDpY1UxXjw049SyYX3x8LqdPcb7WpJxkvnL2lLBjRUREREREhiIliEROoca2TjYeDt7UOZiOrh7WHqxh5qg0PjRnFPsrApd1BbP9aD0Au398AemJsXxk7mgAVu8oDxrT2NbJH187wKbDdRSOSQt4TU+P88++n6mru4c39lWxfHIWb9+0KuSzfXKJM2Wszk0oVTa2c7imhUsXjCE+JnR5mUdaYgwNrc4Uspsf2wbA9JGBnzmQlPgYVk3P876OdLy9iIiIiIjIUKMEkcgp0tHVQ+Etz3PZ/6yhrL5t4AAfL+8up6qpnRsvmMbU3BSaO7rpDtDYOZiiymbm5qd7dwHNH5fBpOxkDru7dgL542tF3P7sbprauygckx7wmquXjQegvM/nefNANdXNHVy7fAJZbglYMKkJThlYU5uT4NlT7jSbvmD2wOVlHmkJsTS2d9LW2e3d0VSQF16Dao+Zo9PcuPB3HomIiIiIiAxVShCJnAIlNS0UfO9Z7/sH3oms0bSn/Oq0CZmk9UmoDKS0toU1B6qZ3SfJs2B8Bm/sq6TNp1eQr+Lq3h5FnuRJX+MykwBo7PMsv3tlP2kJMZw1beAx8ynxMQA0tTs7iHYfcxJE0yJI1GQkx1He0O5NvN1x+ZyIdwEtnZTFX65dxI3nT4soTkREREREZChSgkjkFLjr5f1+7+9+vYiu7p6w42taOoiLiSIpLprUBCehcqSuNazYh9Y7Da1Xzcj1O76yIIfmju6gzaqP1LUyNTeF/zx/GvPGZgS8xpOs8vT/Aaht7mDdwRo+vXxCWCVins/zX6v38Le3irl3TTFjRiQOuPPI16zRaew4Ws+WUmd62ZgRiWHH+lo1I4/ctITjihURERERERlKlCASOclaO7p5aIPTJDo1PoZvnVdAS0c3xSHKu3xVNLTxx9eKiDJgjCHK3Rlz/f0bB4x9autRfvPSPgA+MM0/QTQpJxlwys/62nCohk2H65g3dgTXf2BK0ClkKW5yx3cH0eYSJ0mzLMho+373cHcQFVU28/0ndnCkrpXLF+aHFevx4bmjaevs4YYHNmOMsztKREREREREjp8SRCIn2b6KRnos/P6qBWy79Xw+MN1J1OwuawgZ1+juynl4o7MDqK3T2XG0ZFImgN9Y+GAe33QUgMTY6H4lV55dNmUN/fsh3fDAZgBOm5gZ8v6exNFvXtrnnbK28XAt0VGGuWMD9y3qKy3AKPpZQUraglkwLoPxWU65m7UEnLgmIiIiIiIi4VOCSOQk213m9tQZ6fTUmZLrNE/+yv2bgsYcq2+l8JbnuejON/iv5/YAcN9nFgOQn5HE+bPyKK1tpb61M+g9yhvaeHGXM6Xsnv84rd/59MRYYqMNVU3t/c55RsdfviD8nTxLfvoSbZ3dbDhUy/SRqSTFxYQVFx1lyEjyTxKtKBi4d1FfL35jJQDzx42IOFZERERERET8hfcTncgwtKWkjgnZyaQH2PESyp6yRhJioxif5ZR0xcc4fYQa27qoamonO0CvnSO1Tn+h7Ud6dxmt9EmaXLYgn9U7ynljXyUXzRndL95ay+3P7AJg8YRMlk7K6neNMYas5HiqGv0TRK0d3RRXNfOVs6cSFaS0LJhX91TyVlE1162cHFHcm985G2vhYFUz6Ymxx7UDKDY6irduOts7FU1ERERERESOn3YQyaD1+1cPcMUf3+JYfXjNmU/21774d28y99bneXFneUSxu441MDU31a+Pz91XLwJg25H6gDE1zR1+7391xVy/9ysLckiOi+beN4v7xbZ3dVN4y/M8vvkos8ek8YerFwZ9tnGZSWw/6l/qtvNYPT0WZo4Kr8zL99me31mGtXDRnFFhxXokxcWQHB/D7DHpjHUnox2PUemJ3p5GIiIiIiIicvyUIJJBp62zm8t/v4afP7ebtQdreGRD6XHdp7Gtk6v/spZbntwRUVx9Syc/f2639/3n/rqe/35+D3UtHSGiHAermgOOmJ89xkm+bC8dOEF04wXTuHS+f6lXQmw0n1o2no2Ha729ijzeOVhLU7vTNPrJ688gMzku6PN9sHAku441sK+80Xvs7aIaAE6bEF6j50vmjfG+fnTjEQCm5oY/ol5EREREREQGHyWIZNApqmxm/aFaAJLjonluR9lx3eeHT+7gjX1V3LummI2Ha8OO84xO93Xny/v52j83h4xr6+zmA794FYDPnTnR71xqQiyTspPZGmQHUYVb9rXu5lV8KUi51sqpOfRYvL83HvsqnGTPVUvGDVgidmGhs9PnxV0V3mNvF1UzLS817DHzxhh2//gC7/vlk7OIi9G3EhERERERkfcz/VQng45np86fr1nE5QvzORTmeHhfT2w+wqMbj/D5MyeSmRzHH149EHbswSpnDPwPLprpPZaWEMPreyvp6Qk+Scwz7n1sZiKTc1L6nS/MT2d7kARRUWUTo9ITyE1N6Dd9zGP+uAxiow1vF1V7j7V3dXPrv3YCcNslswf4ZJCXlsD0kam8sa8ScHoXbTtSz4LxkTV69u0ZFKghtoiIiIiIiLy/qHmHnDJri6rJTI5jal5k5Ue1LU4J1djMJLJT4mls66K9q5v4mPAaGZfUtPC9x7azcHwG375gOnUtnby8uwJrbdDki68DlU0kxUXzH6dP4DNnODuB/vxGEbc9vYvGti7SkwI3RV7rlmo98IVlAc8Xjknnic1HqWxsJye1d7dOWX0bz2wv44wp2SGfKzEumlmj09la0ptkKq7qTZ6F89kAlkzM5L63DnGoupnY6CjqWjrD7j/k62eXFdLZ3RP2vxcREREREREZvLSDSE66O1bvpuDmZ7ni7rf5yF1vhtx1E0iNu4MoIynWW/bUt4lzMF3dPd5SsF9fMY+Y6CimjUylurmDupbgI+I9/vR6EX996xAtHd1+CZeMJKevT22QPkSVje389uV9rCjIYXR6QsBrCt2+RH13ET3wzmE6u3v47oUzBny+UekJVPqMqT9U7ex2euRLgZNSgeSmOc937q9eZ6fbsHrm6MgTRFcuHsfVyyZEHCciIiIiIiKDz4AJImNMgjFmnTFmizFmhzHmVvf4HcaY3caYrcaYx4wxI3xibjLG7DfG7DHGnH8qP4CcGr9/9QAPri85rtjfvXKAju4eAFo7u1kdYQ+hsvpWoqMMGclxjHKTLYfDLDO78+X9bDhUy22XzvZOx/KMlQ+W3PFo7+rmJ+6o+L4ykp1dQ3WtgZNM24/W091j+fJZk4Pu5PEkYXaX9TaI7ujq4Z43i5k/dgRTcvuXpfWVnRJPlV+CyPl9mZITeZPojq4eXtxVTlxMFNNHRp4gEhERERERkaEjnB1E7cDZ1tq5wDzgAmPMUuAFYLa1dg6wF7gJwBgzE7gSmAVcAPyPMUY1KO8jnileNz68lb0+066OR0JsFGsP1oR9fWd3D/98p5SxGYnERkcxd6yTd9wQRpPp9cU13PnyPi6bP4aLfSZtpSc6yZ2qptAJon3lTUHPpSeG3kG0ensZcTFRzAhRqpUSH0NMlPGbQna4poX61k6uXDwu5LN5ZKfEU9fSSXtXNwCHappJT4wNWvYWSLfPjq4H3ilh2aQskjUqXkREREREZFgbMEFkHZ6fnGPdX9Za+7y1tss9/jbgmct9MfCAtbbdWnsQ2A8sPsnPLQNo6eji+R1lYe+88VXV3LtD5emtxyKKXXOgyvv6vz46h/GZydy7ppg9ZeElmh7dWEpVUzuzRjvlWJnJcUzOSWZ9cegEUX1rJzc8sJn8jCRuvXiW3zlP8uTjf3wr5D2++eAWAL5+TgFPfuV0v3MZ7j2CjbrfcbSBpZOyvMmoQIwxJMVF09ze5T3mScBNDWP3EMD4LGdX1MZDdW58ExOzk8OK9bhm2Xi/98nxyt+KiIiIiIgMd2H1IDLGRBtjNgMVwAvW2rV9LvkM8Kz7egzgW5tU6h6Td8nOow3M/MFqvvC3Day445WI46t9dtr8+Y0iWju6w4795J+cpfHGjR/g46eN5YsrJwEELd3qy1N+9V+Xz/EeO21CJhsO1YbsZfTDJ7ZT1tDGb66cR2qCf5ImxWd3THeQexRXNbOnvJFxmUl86azJzMn3n+rl6UEUrI9RWUMbo9IC9x7yFRMdxX1vHfL2IXpxVzkZSbHehNhAPGVqP35qJ//39iHWHazhtAkZYcV6jEiKIyG29z/9cHoziYiIiIiIyNAWVoLIWtttrZ2Hs0tosTHGO0/bGHMz0AX83XMo0C36HjDGfMEYs94Ys76ysjLyJ5eg7nnz4HHHdvdY706br549heaObrYFGc3el28jaU//n8sW5POJxePYEcY9unssq7eXsWDcCL+Sp9MmZFLf2skOt6FyX60d3Ty55SjXLBvP/HH9kyVTclK8fYiCNbvecMjZofTnaxcRF9P/P4u0xFiMcZpR97XxcC2Vje3kBWlO7cvz9e98eR/g7CAqzB8R8GsGUpCXSlJcNPWtnXzv8e0AfHRh/gBR/Z0zIw+Ai+aM4gcfnhlxvIiIiIiIiAwtEU0xs9bWAa/i9BbCGHMtcBFwlbXWkwQqBcb6hOUDRwPc625r7SJr7aKcnJzjeHQJ5EBlEw9tKCUzOc57zLekaSA/ebp3p8+nlo4nOS6av75VHFbsF/+2HnBKy3zlpMRR09JBl9u4Oph73jzI0fo2PrXUvwTqA9NzAXhjf+BE4q6yBnosLJ2UFfB8VJThtkucsrNACR6AQzUtGAMTsgKXa0VHGVLiY/jnO/6Nu4sqm/jF6j2Ak2wJ18TsFKy1HKxsZlKEJWIfXzSWI3Wt3vfT8iJvUP3TywpZ852zueuTC9SgWkRERERERMKaYpbjmVBmjEkEzgF2G2MuAL4NfMRa69vo5kngSmNMvDFmIjAVWHfyH10CueYvzm/1tLxUPrHYydNtKakLO/7N/b09hHLTErjitHGs3lHmNzkrkK7uHt5x+wR9eO5ov3PZqfFYC7UhSpm6eyz3rikGYGWBf8IwMzmO0ekJ7A3Sx8hTruUZIx9ITqqzg6i8sa3fuZrmDn770j5yU+ND7uRZPCGTjq7eJNf64hrO/u/XWHOgmssX5lMQQaLGYqlobKe5o5vJOZEliC6Z31uxee2y8UGnpoWSlhDL6BGJEceJiIiIiIjI0BTODqJRwCvGmK3AOzg9iJ4C7gJSgReMMZuNMX8AsNbuAB4EdgLPAddba8NvYiOUN7Sxtqj6uGI73V06c/LT+dZ50wDYE+YkstLaFu+1I9ymzBfPG01nt2VtUehJZCW1zo6WWz8yi8Q4/6bHI93ePAcqA08JO1LXyuTvPkNpbSt3fXI+WW45mK+CkansCTJlbOOhWrJT4hkVosRrSm4qMVGGdX0mqllrOeeXrwFOc+pQZo1Jp7G9i62lTsKtrKE32XTj+dNCxnr8/XNLAGhp7/b+fkzMDq9Btce8sb39kc52S8VERERERERETkQ4U8y2WmvnW2vnWGtnW2t/5B6fYq0da62d5/66zifmJ9baydbaadbaZ4PfXXkQ9GYAABWnSURBVAL50G/f4Iq73+bBd0pCNmYOJC8tgWl5qXz7gunexsq3/mun386XYF7ZXeF9/fB1ywGY7E7XKq5uDhm7v8JJdhTm99/Fs3RyFnExUfzp9aKAsXe/dgCA7JQ4zps5MuA10/JSOVDR5E2AedS3dvLSrgpWTM0OuZMmPTGWJZMyeX5Hmd/xkppWapo7WFGQM+Co+Xh3d9FH7nrT+7UBXv7mSnLDaFANcPqUbPLS4tlX0chn73VK8iZFuIMI4KeXFnLahAwWjo+sQbWIiIiIiIhIIBH1IJJTr62zmyp3itiNj2zlDZ+Sr3CUNbQxb+wIoqIMUVG9CRPf8fPBFFU5SaCN3z+XKW5iKCU+hqzkOEprW0OFese1Twkwrj0tIZazCnICJplqmjv45/oSPrYwn7dvWhW0xGv2mHQ6unvY2adR9Rv7Kmls7+KTS0IndwBWTM3hQGWzX7ncQxtKiDJw28WzQ0Q62jp7N8K9sLPcO+0tPyNpwFhf5Q3tvF1UQ6t7v5FhJpd8fXLJOB66brnfhDYRERERERGR46UE0SlgreVfW46GtWunr42Ha/3ev3Ug/FKztUXVzjSttP4lWruD9O/xaOvs5rU9lcwfN8KvwTVAelIsq3eUBfw81lpueGATd6zew8TsZNL6jJj3yEyOo7Gtf7PsJzYfoa2zh8+vmERMdPDluMgd5b6l1L+f0rPbyshIivUruwpmidvE+vFNR7zHXttbyaLxmYzLGjjJ09zemyD6/F/XU9bQRkZSbNgTyDxifBJ3n14+wS+RJyIiIiIiIvJeUILoJNtWWs/T247x//6xid+9sj/i+HvfLAackqrxWUk8v6OM7jDLzK64+20AGn2mlr35nbMBZ9pWKL96YS9FVc1c3KfBtBPbTE1zB7c/u6vfudLaVp7Y7Aypy88I3vQ4NSEmYIJo97FGslPiBmzwPDItgcTYaIqrWvyOrz1YzaoZeSGTSx7zxo5gXGYSmw47SaYnNh9ha2k9cwKUxQVy5eKxfjt27l97mLzj2P2z/dbzva9vvCC83kUiIiIiIiIip5ISRCdReUMbH77r33zl/k0A7DzWMECEv73ljTy/sxyAZ244k8+dOYmiquYBkzvgP77dd0z8mBGJLJ6YSVFl6B5Cr+ypYGVBDp8+fWLQawJNQ9vvPltSXDTf+9DMoLEp8bG0dnZT3uA/RWxPeSOTcwZu0myMYXxWkl+ZWk1zB1VNHRGNeZ+QnUxJbQuVje3c8MBmAEaGaG7tqyAvle23nu+dDgcQG0Ziqq+E2Gie+n9n8MAXlpIUpxIxERERERERee8pQXQSbepTHlZcFTop01dDa+8Y+Ogo4x1/7pv8Cea0n7wIOCVLfRMuk3NSvP2FArHWcqS2dcBmyU3t/XcAbTpchzHw1ndWMW1k8ERNSa2z8+cz977jPVbd1M6Oo/XMHxdeo+WJ2cl+CaJntx8DYPHEzLDiwUmYHa1r9TbVjo02rIpwEliqTxndLz8+N6JYj9lj0lnqlryJiIiIiIiIvNeUIDqJjtT5747ZV9FEXUvHgHHtXd08tqmU6mb/a3Pcce/H6tsChXl1+Uz2yknt339obGYiNc0dNAdI8HT3WD5733qaO7oZMyJwidiZU7MBp5zM2t5yt4NVzfz2pX0UjkknPSlw7yGPa5Y5u5p2HG2gpcN5jp8+sxuAS+ePCRnrMSE7mZKaFm/J3eObjjAtLzXsEjGAnJQ4qpo6+OLfnAliL3/zLCZmRzZFLNUtMztnRi5TI9i9JCIiIiIiIjJYKUF0Evkmg652y7w2HKoNdrnXPW8W8/V/buGLf9sAwGNfdkbMe0anP775SNDYqqZ25v3oBe/7QLtSPImfl3zG2Hu8treCl3dXMCknmUuCJGr+cu1p/Of502jp6KbCZzfTS7uccrjLF+aH/HwAc/JH8NB1ywB4ausxGts6eXRTKVcvnRBy55Gv/IxEOrstNz26lbqWDjYeruPcmXkhx9v3leUm3RrcfkjBkmKhpCU6ybD42OiIY0VEREREREQGIyWITqJanwTR5QvziYkyrA8jQdTa0Tsda/nkLGaPcXbEpCfGcs6MPDaX1Pnt3PH1j7WHvaVfr37rLBaO71+uNSnbKTn765rifud+/+oBslPieOL608lO6b/7CCAuJspbtuYZEW+t5bandxFl4JplEwb8jACLxmcwNjORl3aVs7e8CWth2eTwy6w8DaIfXF/K09uO0d1juWD2yLDjAbJSeie0Tc1NOa4JYp6JatO1e0hERERERESGCHXIPYnqWpweQlctGcfsMenMHpPO+uKaAeMa2py4zT84lxFJ/iPmV83I5cVd5RRXtwQshTrgNol+/PrTmRCkVKowP50oA6P67JbZU9bI+kO1fP2cAr++OoGkJjhLpcndeVNa2wrAmVNzBvp4XsYYxmUmUdnYzr7yRoCIGkz7fq2bH9vO1NwUZo1OCzse8EuC3X3NoohiPWaNTue5r53pTbyJiIiIiIiIvN9pB9FJlJrg7Pj5yaWFREcZFo3P4J3iWu+um0C6eywPrCthUnZyv+QQ4O2vs7W0/wSx2uYOnthylCUTM5k3dkTIZ5sxKq1fD6I/vVFEUmy0txwuFM/uHe9upb2VAHz93IIBY30lx8Ww8XAd6w/VkhgbTX5G+CVemclxfuVsHywcFVF5GUC2u4MoPiYq4t5DvqaPTCMuRv/5iIiIiIiIyNCgn3BPotsvK+TP1/buSjnDbe687PaXgk4iu+vl/bR2dgedMlaQl0p8TBRbS+v7nfv2I1uxtrcnTijJ8TF+U8istTy/o4wPFo4iI7l/Yqovzw6i0tpWHnynhO8/vh2A6WH2D/LY4ia6Ht5QypJJmRGXeH16+QTv60ANuQeSlezEtHf1DHCliIiIiIiIyPChBNEpdNa0XAA6uy3/WHc44DWPbSoNeY/Y6CimjUxlT1ljv3NvF1UDcMOqqQM+S2p8jN8OopKaVhraugL2LAokxU0Q/fDJHdz4yFYALps/hoQIGzX/5sr53tffvXBGRLHgjId/8RsrmJaXygcj7D8ETl8n8E80iYiIiIiIiAx3ShCdYvd9ZjEAT2452u/cM9uOUVzdQlxMFH91rwtkzIhEDtU0e8e7A3z3sW00tHXxnQ9O9za1DqXvDqI/vVEEwNz80KVpHml9ehSNSk/gl1fMCyvW15KJmd7XBcfZ5HlKbiqrv74iaFPtUKKiDHtuu4AfXDTzuL62iIiIiIiIyFCkBNEptrIghyUTM9lf0UR5Q5vfuae3HSMhNoptt5zHioLgzZ5HpidQUtPKz5/bDTh9i+5f6+xIWjAuvB1AuanxHKtvo63TmZi2ekcZEH6JWEJsNB+Y1vuM58zICyuuL2MMp03I4IsrJh1X/MkQHxN9XNPLRERERERERIYqJYjeBV91S8D2VzR5j9U0d/D01mOcPT2X+JjQZVofXeA0Zt5c4vTv8Uwuu/2yQhb77MgJ5cyCHDq6evjVC3tp7eimorGdb51XEFGi5FqfsqybPxR5eZjHQ9ct56bjKC8TERERERERkVNDCaJ3wZRcZxy6b4LoZ8/uApzysYHMHpNObmo86w7WsP1IPZsO1wJw2oTwkkMAK6Zm88HZI/nHusNsKnHiJ0Y4pn3WaKeU7Ycfnhlx7yERERERERERGbxi3usHGA5yU+NJjY/xSxA1tDr9gD4fZqlVVZMzBe3FXeUcrGomOyWeSRGMaTfGcM6MPJ7dXsaX/76R9MRYVk4LXtYWSE5qPDtuPZ+kOCWHRERERERERIYS7SB6FxhjmJyb4pcgOljVzKrpueSmJoR1j+e+tgKAY3Vt7ClrZG5+esR9dEalO1+rrqWTz5w+kZT4yPODyfExGKP+PSIiIiIiIiJDiRJE75IpuSnsLW+ksa2Tru4eiqqamJIXfolXQV4qZ0zJ5oVd5ewua2RsZlLEz5DlM/WrMD8t4ngRERERERERGZqUIHqXnDszj9qWDs7/1evc9cp+OrsthWGMp/e1akYuNc0dAEyMoLzMIyslzvt6bEbkCSYRERERERERGZqUIHqXnD9rJA9/aTmJcdH8+sV9AJw+OTuie1w0Z7T39aeWjo/4GbKS47iwcCTLJ2cxKSeyBtUiIiIiIiIiMnSpSfW7aMG4DJ7+6pn88bUikuOjyUiOGzjIR3ZKHFcsGsu5M/OIjrD/EDi9kP7nqoURx4mIiIiIiIjI0Gaste/1M7Bo0SK7fv369/oxRERERERERESGDGPMBmvtonCuVYmZiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwpwSRiIiIiIiIiMgwZ6y17/UzYIypBA4dZ3g2UHUSH0fkeGktymChtSiDhdaiDBZaizJYaC3KYKL1ODyMt9bmhHPhoEgQnQhjzHpr7aL3+jlEtBZlsNBalMFCa1EGC61FGSy0FmUw0XqUvlRiJiIiIiIiIiIyzClBJCIiIiIiIiIyzA2FBNHd7/UDiLi0FmWw0FqUwUJrUQYLrUUZLLQWZTDRehQ/7/seRCIiIiIiIiIicmKGwg4iERERERERERE5AUoQiYiIiIiIiIgMc4MuQWSM+V9jTIUxZrvPsbnGmLeMMduMMf8yxqS5xycYY1qNMZvdX3/wiYkzxtxtjNlrjNltjPnoe/F55P0rkrXonpvjntvhnk9wj2stygmJ8PviVT7fEzcbY3qMMfPcc1qLckIiXIuxxpj73OO7jDE3+cRoLcoJi3A9xhlj7nGPbzHGnOUTo/Uox80YM9YY84r7fW6HMeYG93imMeYFY8w+958ZPjE3GWP2G2P2GGPO9zmutSgnJNL1aIzJcq9vMsbc1edeWo/D0KBLEAH3Ahf0OfZn4DvW2kLgMeA/fc4dsNbOc39d53P8ZqDCWlsAzAReO4XPLEPTvYS5Fo0xMcD/AddZa2cBZwGdbozWopyoewlzLVpr/+75nghcDRRbaze7MVqLcqLuJfz/R38MiHePLwS+aIyZ4J7TWpST4V7CX4+fB3CPnwv8tzHG8+dgrUc5EV3AN621M4ClwPXGmJnAd4CXrLVTgZfc97jnrgRm4azf/zHGRLv30lqUExXRegTagO8D3wpwL63HYWjQJYista8DNX0OTwNed1+/AISTvfwMcLt7zx5rbdVJe0gZFiJci+cBW621W9zYamttt3tOa1FOyAl8X/wE8A+f91qLckIiXIsWSHYT6IlAB9DgntNalBMW4XqcifNDEdbaCqAOWOSe03qU42atPWat3ei+bgR2AWOAi4H73MvuAy5xX18MPGCtbbfWHgT2A4vdc1qLckIiXY/W2mZr7b9xEkV9aT0OQ4MuQRTEduAj7uuPAWN9zk00xmwyxrxmjDkTwBgzwj33Y2PMRmPMQ8aYvHfxeWXoCrYWCwBrjFntrrkbQWtRTqlQ3xc9rsBNEGktyikUbC0+DDQDx4DDwC+stTVai3KKBVuPW4CLjTExxpiJOLvaxmo9ysnk7pKcD6wF8qy1x8D5oR3IdS8bA5T4hJUCY7QW5WQLcz0Gi9V6HKbeLwmiz+Bsj9sApOL8LSQ4f+gcZ62dD3wDuN+tNY8B8oE3rbULgLeAX7z7jy1DULC1GAOcAVzl/vNSY8wqtBbl1Am2FgEwxiwBWqy1nt4cWotyqgRbi4uBbmA0MBH4pjFmElqLcmoFW4//i/OD+Hrg18AanFIMrUc5KYwxKcAjwNestQ2hLg1wzKK1KCdRBOsxGK3HYep9kSCy1u621p5nrV2I87fhB9zj7dbaavf1Bvd4AVANtODUngM8BCx41x9chpxgaxHnD52vWWurrLUtwDM4a05rUU6JEGvR40r8y8u0FuWUCLEWPwk8Z63tdEt63sQp6dFalFMmxJ8Zu6y1X3d7tF0MjAD2ofUoJ4ExJhbnh/G/W2sfdQ+XG2NGuedHARXu8VL8d/3mA0fRWpSTJML1GIzW4zD1vkgQGWNy3X9GAd8D/uC+z/E0dXP/VnIqUGSttcC/cBoFA6wCdr7Ljy1DULC1CKwG5hhjktx+GyuBnVqLcqqEWIueYx8DHvAc01qUUyXEWjwMnG0cyTjNMndrLcqpFOLPjEnuOsQYcy7QZa3V/6flhBljDPAXYJe19pc+p54ErnVfXws84XP8SmNMvFvuOBVYp7UoJ8NxrMeAtB6HL+P8ux88jDH/wFmI2UA58EMgBbjeveRR4CZrrXVH7f0IZ4twN/BDa+2/3PuMB/6G8zdElcB/WGsPv4sfRd7nIlmL7vWfAm7C2Sb8jLXW04dIa1FOyHGsxbOAn1lrl/a5j9ainJAI/x+dAtyD0xzYAPdYa+9w76O1KCcswvU4Aecvc3qAI8BnrbWH3PtoPcpxM8acAbwBbMNZXwDfxen78iAwDidh/jFrbY0bczNOOWQXTgnQs+5xrUU5Ice5HouBNCAOp4H/edbanVqPw9OgSxCJiIiIiIiIiMi7631RYiYiIiIiIiIiIqeOEkQiIiIiIiIiIsOcEkQiIiIiIiIiIsOcEkQiIiIiIiIiIsOcEkQiIiIiIiIiIsOcEkQiIiIiARhjbjHGfCvE+UuMMTPfzWcSEREROVWUIBIRERE5PpcAShCJiIjIkGCste/1M4iIiIgMCsaYm4FrgBKgEtgA1ANfAOKA/cDVwDzgKfdcPfBR9xa/A3KAFuDz1trd7+bzi4iIiBwvJYhEREREAGPMQuBeYAkQA2wE/gDcY62tdq+5DSi31t5pjLkXeMpa+7B77iXgOmvtPmPMEuB2a+3Z7/4nEREREYlczHv9ACIiIiKDxJnAY9baFgBjzJPu8dluYmgEkAKs7htojEkBlgMPGWM8h+NP+ROLiIiInCRKEImIiIj0CrS1+l7gEmvtFmPMp4GzAlwTBdRZa+edukcTEREROXXUpFpERETE8TpwqTEm0RiTCnzYPZ4KHDPGxAJX+Vzf6J7DWtsAHDTGfAzAOOa+e48uIiIicmLUg0hERETE5dOk+hBQCuwEmoEb3WPbgFRr7aeNMacDfwLagcuBHuD3wCggFnjAWvujd/1DiIiIiBwHJYhERERERERERIY5lZiJiIiIiIiIiAxzShCJiIiIiIiIiAxzShCJiIiIiIiIiAxzShCJiIiIiIiIiAxzShCJiIiIiIiIiAxzShCJiIiIiIiIiAxzShCJiIiIiIiIiAxz/x+CcqkyWyJqdgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,8))\n",
+    "sorted_data['CO2'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On observe que la concentration atmosphérique en CO<sub>2</sub> a une tendance croissante au fil du temps avec des oscillations périodique de haute fréquence. Entre 1958 et 2020, elle a augmenté de : "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31.942819190992758"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "100 * (sorted_data.iloc[-1]['CO2'] - sorted_data.iloc[0]['CO2']) / sorted_data.iloc[0]['CO2']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... 32 % !"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pour caractériser la période des oscillations de concentration, on trace uniquement les 150 dernières concentrations relevées: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7efbd47d7160>"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,8))\n",
+    "sorted_data[-150:]['CO2'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On s'apercoit que les oscillations ont une période d'environ un an. Au cours d'une année donnée, la concentration en CO<sub>2</sub> à Hawaii augmente de janvier à juillet atteignant alors son maximum. Elle décroît ensuite jusqu'en octobre avant d'augmenter à nouveau sur les deux derniers mois de l'année.\n",
+    "\n",
+    "Ces fluctuations intra-annuelles sont principalement dues aux plantes et en particulier à leurs activités de photosynthèse qui transforme le CO<sub>2</sub> et l'eau de l'atmosphère en énergie et dioxygène ([source](https://archive.epa.gov/climatechange/kids/documents/carbon-through-the-seasons.pdf)). Cette activité est d'autant plus intense les mois les plus chauds de l'année. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Estimation des concentrations à venir"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dans cette section, on cherche à construire un modèle simple permettant d'estimer la concentration en CO<sub>2</sub> pour les années à venir. D'après le tracé de la concentration entre 1958 et 2020, la concentration $c$ évolue grossièrement de manière parabolique en fonction du temps $t$. \n",
+    "\n",
+    "Nous proposons donc un modèle de la forme: $c = a * t^2  + b * t + c$, où $a$, $b$ et $c$ sont trois paramètres réels. Ces paramètres sont estimés par régression linéaire qui est implémentée dans le package *scikit-learn* ([voir la documentation ici](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)). \n",
+    "\n",
+    "Pour réaliser cette régression, il faut convertir au préalable chaque date en valeur numérique. Par souci de simplicité, on fait le choix d'utiliser l’[ordinal grégorien proleptique](https://docs.python.org/fr/3.6/library/datetime.html#datetime.date.toordinal) de chaque date. Dans cette représentation, le 1er janvier de l’an 1 a pour ordinal 1. Puis on place ces dates ainsi que leurs carrés dans une matrice désignée par la variable $X$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3173, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "t = [date.toordinal() for date in sorted_data.index]\n",
+    "def transform_input(t):\n",
+    "    X = np.reshape(t, newshape=(-1, 1))\n",
+    "    X = np.concatenate([X**2, X], axis=1)\n",
+    "    return X\n",
+    "X = transform_input(t)\n",
+    "print(X.shape)  # [nb_observations, 2]\n",
+    "c = sorted_data['CO2']\n",
+    "\n",
+    "reg = LinearRegression().fit(X, c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous obtenons comme valeurs pour les coefficients du modèle: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a = 0.0000000984, b = -0.138544, c=49090.510420\n"
+     ]
+    }
+   ],
+   "source": [
+    "a, b, c = reg.coef_[0], reg.coef_[1], reg.intercept_\n",
+    "print('a = %.10f, b = %f, c=%f' % (a, b, c))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false
+   },
+   "source": [
+    "Afin de juger la qualité du modèle obtenu, nous traçons les concentrations calculées par notre modèle (en orange) et superposons les concentrations observées (en bleu)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7efbd4e56898>]"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,8))\n",
+    "sorted_data['CO2'].plot()\n",
+    "\n",
+    "predictions = pd.Series(reg.predict(X), sorted_data.index)\n",
+    "plt.plot(predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On voit que le modèle capture bien la tendance haussière de la concentration observée ! \n",
+    "\n",
+    "Nous pouvons donc faire confiance à ce modèle pour estimer la concentration en CO<sub>2</sub> pour les années futures avec une précision relativement bonne. Par exemple, au 1er janvier 2025, la concentration devrait être proche de:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "423.89403068381216"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.predict(transform_input([datetime.datetime.strptime('2025-01-01', '%Y-%m-%d').toordinal()]))[0]"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +1272,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/exo3/weekly_in_situ_co2_mlo.csv b/module3/exo3/weekly_in_situ_co2_mlo.csv
new file mode 100644
index 0000000..f82e472
--- /dev/null
+++ b/module3/exo3/weekly_in_situ_co2_mlo.csv
@@ -0,0 +1,3217 @@
+"-------------------------------------------------------------------------------------------"
+" Atmospheric CO2 concentrations (ppm) derived from in situ air measurements                "
+" at Mauna Loa, Observatory, Hawaii: Latitude 19.5°N Longitude 155.6°W Elevation 3397m      "
+"                                                                                           "
+" Source: R. F. Keeling, S. J. Walker, S. C. Piper and A. F. Bollenbacher                   "
+" Scripps CO2 Program ( http://scrippsco2.ucsd.edu )                                        "
+" Scripps Institution of Oceanography (SIO)                                                 "
+" University of California                                                                  "
+" La Jolla, California USA 92093-0244                                                       "
+"                                                                                           "
+" Status of data and correspondence:                                                        "
+"                                                                                           "
+" These data are subject to revision based on recalibration of standard gases. Questions    "
+" about the data should be directed to Dr. Ralph Keeling (rkeeling@ucsd.edu), Stephen Walker"
+" (sjwalker@ucsd.edu) and Stephen Piper (scpiper@ucsd.edu), Scripps CO2 Program.            "
+"                                                                                           "
+" Baseline data in this file through 03-Jun-2020 from archive dated 03-Jun-2020 08:07:43    "
+"                                                                                           "
+"-------------------------------------------------------------------------------------------"
+"                                                                                           "
+" Please cite as:                                                                           "
+"                                                                                           "
+" C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and       "
+" H. A. Meijer, Exchanges of atmospheric CO2 and 13CO2 with the terrestrial biosphere and   "
+" oceans from 1978 to 2000.  I. Global aspects, SIO Reference Series, No. 01-06, Scripps    "
+" Institution of Oceanography, San Diego, 88 pages, 2001.                                   "
+"                                                                                           "
+" If it is necessary to cite a peer-reviewed article, please cite as:                       "
+"                                                                                           "
+" C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and       "
+" H. A. Meijer, Atmospheric CO2 and 13CO2 exchange with the terrestrial biosphere and       "
+" oceans from 1978 to 2000: observations and carbon cycle implications, pages 83-113,       "
+" in "A History of Atmospheric CO2 and its effects on Plants, Animals, and Ecosystems",     "
+" editors, Ehleringer, J.R., T. E. Cerling, M. D. Dearing, Springer Verlag,                 "
+" New York, 2005.                                                                           "
+"                                                                                           "
+"-------------------------------------------------------------------------------------------"
+"                                                                                           "
+"                                                                        "
+" The data file below contains 2 columns indicaing the date and CO2      "
+" concentrations in micro-mol CO2 per mole (ppm), reported on the 2008A  "
+" SIO manometric mole fraction scale.  These weekly values have been     "
+" adjusted to 12:00 hours at middle day of each weekly period as         "
+" indicated by the date in the first column.                             "
+1958-03-29, 316.19
+1958-04-05, 317.31
+1958-04-12, 317.69
+1958-04-19, 317.58
+1958-04-26, 316.48
+1958-05-03, 316.95
+1958-05-17, 317.56
+1958-05-24, 317.99
+1958-07-05, 315.85
+1958-07-12, 315.85
+1958-07-19, 315.46
+1958-07-26, 315.59
+1958-08-02, 315.64
+1958-08-09, 315.10
+1958-08-16, 315.09
+1958-08-30, 314.14
+1958-09-06, 313.54
+1958-11-08, 313.05
+1958-11-15, 313.26
+1958-11-22, 313.57
+1958-11-29, 314.01
+1958-12-06, 314.56
+1958-12-13, 314.41
+1958-12-20, 314.77
+1958-12-27, 315.21
+1959-01-03, 315.24
+1959-01-10, 315.50
+1959-01-17, 315.69
+1959-01-24, 315.86
+1959-01-31, 315.42
+1959-02-14, 316.94
+1959-02-21, 316.61
+1959-02-28, 316.62
+1959-03-07, 316.81
+1959-03-21, 316.73
+1959-03-28, 316.71
+1959-04-04, 317.72
+1959-04-11, 317.12
+1959-04-18, 317.64
+1959-04-25, 318.32
+1959-05-02, 318.27
+1959-05-09, 318.78
+1959-05-16, 318.05
+1959-05-23, 318.43
+1959-06-06, 318.53
+1959-06-13, 318.14
+1959-06-20, 317.88
+1959-06-27, 317.76
+1959-07-04, 316.86
+1959-07-11, 316.83
+1959-07-18, 316.45
+1959-07-25, 316.16
+1959-08-01, 315.62
+1959-08-08, 314.91
+1959-08-22, 315.00
+1959-08-29, 314.15
+1959-09-05, 314.45
+1959-09-12, 313.93
+1959-09-19, 313.57
+1959-09-26, 313.54
+1959-10-03, 313.04
+1959-10-10, 313.15
+1959-10-17, 313.43
+1959-10-24, 313.46
+1959-10-31, 314.12
+1959-11-07, 314.42
+1959-11-14, 314.88
+1959-11-21, 315.20
+1959-11-28, 315.11
+1959-12-05, 315.06
+1959-12-12, 315.64
+1959-12-19, 315.86
+1959-12-26, 315.77
+1960-01-02, 315.72
+1960-01-09, 316.40
+1960-01-16, 316.73
+1960-01-23, 316.57
+1960-01-30, 316.68
+1960-02-06, 316.61
+1960-02-13, 316.98
+1960-02-20, 317.42
+1960-02-27, 317.00
+1960-03-05, 316.96
+1960-03-12, 317.76
+1960-03-19, 318.07
+1960-03-26, 317.77
+1960-04-02, 318.61
+1960-04-09, 319.34
+1960-04-16, 319.01
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-- 
2.18.1