{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evolution de la concentration de CO2 dans l'atmosphère depuis 1958."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modules Python utilisés dans cette étude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.optimize as optimize # utilisé pour l'optimisation finale\n",
    "import isoweek\n",
    "import os\n",
    "import urllib.request # utile pour créer fichier directement à partir de l'URL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En 1958, Charles David Keeling  a initié une mesure de la concentration de CO2 dans l'atmosphère à l'observatoire de Mauna Loa, Hawaii, États-Unis qui continue jusqu'à aujourd'hui.\n",
    "Les données sont disponibles sur le [site Web de l'institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le format des données est explicité dans le fichier CSV :\n",
    "\n",
    "\"The data file below contains 2 columns indicaing the date and CO2       \"\n",
    "\" concentrations in micro-mol CO2 per mole (ppm), reported on the 2008A  \"\n",
    "\" SIO manometric mole fraction scale.  These weekly values have been     \"\n",
    "\" adjusted to 12:00 hours at middle day of each weekly period as         \"\n",
    "\" indicated by the date in the first column.                             \"\n",
    "\n",
    "Les 43 premières lignes sont des commentaires que nous ignorons en précisant skiprows=43."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vérifions qu'une copie locale des données existe dans le répertoire de travail sinon nous la téléchargeons.\n",
    "\n",
    "**NB:** La version de la base de données hebdomadaires utilisée dans la création de ce document computationnel a été téléchargé le 12/06/2020.\n",
    "\n",
    "Nous ajoutons un nom aux colonnes pour les identifier plus facilement par la suite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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_concentration</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_concentration\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv\"\n",
    "data_file = \"concentrationCO2.csv\"\n",
    "\n",
    "os.path.isfile(data_file)\n",
    "if os.path.isfile(data_file) == 0:\n",
    "   urllib.request.urlretrieve(data_url, data_file)\n",
    "\n",
    "raw_data = pd.read_csv(data_file, names = ['date','CO2_concentration'], skiprows=44)\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y a-t-il des lignes sans données valides dans ce jeux de données ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Empty DataFrame\n",
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   "source": [
    "raw_data[raw_data.isnull().any(axis=1)]"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "À la date de création de ce document il n'y a pas de données non valides. Cependant on introduisons une procédure pour supprimer les lignes avec de telles données manquantes susceptibles d'être introduite dans la base de données par la suite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['date', 'CO2_concentration'], dtype='object')\n"
     ]
    },
    {
     "data": {
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       "      <td>1958-03-29</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958-04-05</td>\n",
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       "    <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_concentration\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = raw_data.dropna().copy()\n",
    "print (data.columns) #verification format du dataframe\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le format de la date est conventionnelle et compris par la bibliothèque pandas.\n",
    "En analysant rapidement les données, il est facile de constater qu'il n'y a pas toujours des relevés de la concentration par semaine. Nous pouvons néanmoins utiliser les données telles quelles pour tracer un premier aperçu des données brutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tracé des données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous indexons les données de concentration de C02 avec la date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>CO2_concentration</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_concentration\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data = data.set_index('date').sort_index()\n",
    "sorted_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tracé de toute les données depuis fin mars 1958"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = data.plot(x='date',y='CO2_concentration',figsize=(10,6))\n",
    "data_size = len(data['date'])\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il existe clairement deux tendances. Une tendance à cours terme et une à long terme.\n",
    "\n",
    "Étudions d'abors la tendance oscillatoire à court terme en traçant ces données sur les dernières années. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tracé des données sur les 110 données les plus récentes (>2*52 semaines = 2 ans si pas de trou)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CCCGEGLcPTlajFLz77cuIDPYDIMDXyIqUCD4+X9/v2PZOK3VtnaTGODeiNZnJ1KEQQgghxu1oSRMLE8N7k6wea2ZHk1fRQrPpQp1WT2uHobrCTyWSaAkhhBBiXKw2O8fLmlmeHDHgubWzo9Ea9vcZ1Srubu0w0orDqUASLSGEEEKMy6nqVjq6bCxPGZhoLUuJwN/HwL5zFxKtIiealU4VkmgJIYQQYlyOlDQBsDw5csBz/j5GstIi2Xf2QqJV0tBOTIgfoQG+Exajp0iiJYQQQohxOVraRHSwH8lRg3dtXzMrmoKqVhrbLQAU1ZlInQb1WSCJlhBCCCHG6UhJI8tTIobs2r52TjRwoU6ruL6d1GlQnwWSaAkhhBBiHJpNXZytbWfZIIXwPZYkRRDoa2Tf2XrMXTYqW8wyonUxpZRRKXVEKfVW9/e3KaXylFJ2pVTWRccuUUrt637+hFIqwNWBCyGEEMLzjpZ112elDKzP6uHnY3DUaZ2rp6zRhNaQNg16aMHoRrS+DeT3+T4XuAXY2fcgpZQP8BJwn9Y6E8gGBm5yJIQQQohJ72hJE0rBkqTh9yFcOyeawuo2Dhc3AsiIVl9KqSTgBuDpnse01vla61ODHL4JOK61PtZ9XL3W2uaKYIUQQgjhXY6UNjIvLnTEFYRrZjvqtP73YCnAtKnRcnYLnseA7wOhThw7D9BKqfeBWODvWutHLj5IKXUvcC9AfHw8OTk5ToYydm1tbRNyHXGB3POJJ/d84sk9n3hyzyfeYPdca83BcyZWxvuM+POw2jUBRvikpIkgHzh6YM+QxfNTyYiJllLqRqBGa31YKZXt5DkvBVYBJmC7Uuqw1np734O01k8CTwJkZWXp7GxnTj0+OTk5TMR1xAVyzyee3POJJ/d84sk9n3iD3fPzde20v5/D9aszyF6dMuI51hQfIOdULXNnhLNhw6VuitS7ODN1uB7YrJQqAv4OXKmUemmY48uAj7TWdVprE/AOsGLckQohhBDCqxwpcdRbDVcI39fa7unD6VKfBU4kWlrrH2qtk7TWacDtwA6t9Z3DvOR9YIlSKqi7MP4K4KRLohVCCCGE1zhS0kSIvw9z40KcOr6nTittGmy902PMfbSUUjcrpcqAtcDb3TVZaK0bgd8CB4GjwCda67ddEawQQgghvMeR0kaWJIVjNDhXa7VoZjhfWJfGjUsS3RyZ93C2GB4ArXUOkNP99avAq0Mc9xKOFg9CCCGEmII6LDYKKlv56hWznX6N0aD42eZMN0blfaQzvBBCCCFGLbeiGatdD7qRtLhAEi0hhBBCjFpPIfyylKG33hGSaAkhhBBiDI6UNJEcFUhMiL+nQ/Fqo6rREkIIIcT0Vt1i5khJEweLGlg3J8bT4Xg9SbSEEEIIMayPz9Xz+yNmHti7naoWMwC+RsU1mTM8HJn3k0RLCCGEEMP67dZCTtbbuDoznmXJESxNjiAzMYwAX6OnQ/N6kmgJIYQQYkh2u+ZkRQvrEn144nPLPR3OpCPF8EIIIYQYUlF9O22dVlLDJGUYC7lrQgghhBhSbkULAGmSaI2J3DUhhBBCDCm3vBk/o4HEEEkZxkLumhBCCCGGlFvezIKEUHyc3M9Q9CeJlhBCCCEGpbUmt7yZRTPDPR3KpCWJlhBCCCEGVdrQQYvZyqJESbTGShItIYQQQgzqRHkzAItlRGvMJNESQgghxKByK5rxNSrmzQjxdCiTliRaQgghhBhUbnkz8+JD8feRDvBjJYmWEGLaqW/r5MD5Bk+HIYRX6y2El/qscZFESwgxrWw9Wc01j+3kM3/eR253/YkQYqCKZjONpi4WzQzzdCiTmiRaQohpoa3TygP/PM6//+UQsaEBBPkZeXb3eU+HJYTXOlHm+CAirR3GRxItIcSUd7i4gesf38X/Hirla9lzeO3+dXwmK5k3jlVQ1Wz2dHhCeKW8imaMBkVGgoxojYckWkKIKa28qYPPPbkfjeblr67lB9cuwN/HyJfWz8KmNX/ZV+TpEIXwSifKm0mPCyHAVwrhx0MSLSHElPbmsQosNjsvffkSVqVF9T6eEh3ENQtnsGV/CSaL1YMRCuF9egrhM6UQftwk0RJCTGlvHK1gaXIEqdHBA577ymWzaO7o4h+HyzwQmRDOa2y3TOj1qls6qWuzsFgK4cdNEi0hxJR1pqaNk5UtbF6aOOjzK1MjWZYcwbO7z2Oz6wmOTgjnHC1tYsVDWzle1jRh1+xZkSuF8OMniZYQYsp681gFSsGNSxIGfV4pxVcum0VRvYnt+dUTHJ0Qztl9uhat4VBR44Rd80R5M0ohhfAuIImWEGJK0lrz5rEKLpkVRXxYwJDHXZs5g5kRgTwtrR6El/qkxDGSlV/ZMmHXzKtoZk5sCMH+PhN2zalKEi0hxJSUV9HCubp2Ni+dOexxPkYDX1yfxoHzDRM6NSOEM7TWHClxjGTlV01copVb3sKiRBnNcgVJtIQQU9IbxyrwMSiuWzRjxGM/syqZEH8fnpFRLeFlzte102jqIjrYj8LqNqw2u9uvWdvaSVWLWeqzXEQSLSHElGO3a946VsHl82KJDPYb8fiwAF9uXZnEOycqqW3tnIAIhXBOz7ThbVnJWKx2ztW1u/2aB4sc+4BKouUakmgJIaacwyWNVDSb+dTSwYvgB3PX2lS6bJqXD5W6MTIhRueTkkZC/X1638vurtOyWO08+sEp0qKDWJES6dZrTReSaAkhppw3jlbg72Pg6oUjTxv2mBMbwvq50Wz5uHhCpmeEcMYnxY0sS4kgPS4UX6PipJsTrb/sK+JcbTs//dRC/HwkRXAFuYtCiCnFarPzzolKrsqIJ2SUK6buWpNGRbOZHQU1bopOCOe1dVoprG5leUokfj4G5saFkl/Z6rbr1bSaeWzbaTbMj+XKBfFuu850I4mWEGJK2Xu2nvp2C58aoknpcK7KiCMhPIAXPy4e4tx1/PT1XLSW5qbC/Y6VNmHXjsa6ABkJoW6dOvzNe6fotNr4yY0L3XaN6UgSLSHElGGx2nlhbxGh/j5kz48d9et9jAY+vzqFXafrOFfb1u+5knoT9714mL/sK+ZEd9dsIdzpcLGjrcOy5AgAFiaEUdvaSV2b6xdsHC1t4pXDZXxp/Sxmx4a4/PzTmSRaQogpobTBxG1/3sf2ghruy55DgK9xTOf57OpkfI2Klz4u6X3M3GXjvpcOA2BQ8EGedJEX7vdJSSPpcSGEB/oCF7q0F7h4+tBu1/zsjTxiQ/35xpVzXXpuIYmWEGIKeD+vihue2MW5mjb+eMcK7t8w9l8WcaEBXLsogVcOl2KyWNFa8+NXczlZ2cLjty9nVVoUH5yscmH0Qgxkt2uOlDT1W/nXk2i5evrwX0fKOVraxAPXLiA0wNel5xYgvfWFEJOWxWrnV+/m89yeIpYkhfPfn1tBSnTQuM9799pU3jxWwRtHK7BpzT8/KeNbG9PZsCCOc3Xt/PytkxTVtZMWE+yCP4UQA52ra6e5o4sVqRG9j0UF+xEf5u/SRKu5o4uH3y1geUoENy8ffhcFMTYyoiWEmLQe21bIc3uK+MK6NF65b61LkiyArNRIFswI5fc7zvDgGye5Yl4s396YDsCmhY7VWFtPyvShcJ9PurfdubiXVUZCmEtbPPzm/QIa2jv5r82LMBiUy84rLpBESwgxKZU2mHh693luXj6Tn23OxN9nbDVZg1FKcdfaVMqbOogL8+fx25dh7P4llBwVREZCmEwfCrc6UtJIWIAPcy4qTM9ICONsbRsW6/h7vR0ubmTL/hK+sG4Wi5OkC7y7SKIlhJiUHn6vAIOC71873y3nv3n5TO5ak8pTd2cREdR/G59NC+M5VNzoltVfQgB8UtzEspTIAaNMGQlhdNk0Z2r6r4rVWvPzt06yzcmR1i6bnR+/eoIZYQF8d9M8l8UtBnI60VJKGZVSR5RSb3V/f5tSKk8pZVdKZQ1yfIpSqk0p9T1XBiyEEIeKGnj7eCVfvXwOCeGBbrlGkJ8PP/+3Rb0FyH1tyoxHa9ieL9OHwvVazF0U1rSyIiViwHMLE0KBgQXxH5ys5pnd5/nO/x6lvKljxGs8s/s8BVWtPLg5c9SNfcXojGZE69tAfp/vc4FbgJ1DHP874N0xxiWEEIOy2x2f3OPD/PnqFbM9EsPChDBmRgRKmwfhFsdKm9B6YH0WQFp0MP4+hn6Jls2u+e0HhSRFBmLTmgf+eXzYprqlDSYe21bIpoXxbMp0fpsqMTZOJVpKqSTgBuDpnse01vla61NDHP9vwDkgzxVBCiFEj9ePlXOsrJkfXLuAID/PfBJXSrEpM55dZ+po77R6JAYxdX1S3IRSsGyQES0fo4H5M0LJr7qQaL11vIJT1a384NoF/PC6Bew6XcffDw6+ObrWmp++notRKX62OdNtfwZxgbMjWo8B3wdGrL5TSgUDPwAeHEdcQggxgMli5dfvnmJJUjj/tsyzS9E3LZyBxWpnZ2GtR+MQU88nJY3MiwslbIieVhkzwsivbEVrTZfNzu+2FpKREMYNixO445JU1s6O5hdv5w+YQtRa87cDpXx4qpbvbppPYoR7pt1FfyN+HFRK3QjUaK0PK6WynTjng8DvtNZtSg29VFQpdS9wL0B8fDw5OTlOBTwebW1tE3IdcYHc84k3le/5a2csVLV08aUM2LnzI4/GYrNrgn3hLx8e447Z1il7z73VVH2fW+2aA+dMXDLDZ8g/n097Fw3tFl57/0OO19ooqrfw7RX+vX8nbkmy80mxlXufyuF7WQEopahos/PXAgu5dTbSIwykWYrIyRl8T8+hTNV77m7OjLuvBzYrpa4HAoAwpdRLWus7hzj+EuBWpdQjQARgV0qZtdb/3fcgrfWTwJMAWVlZOjs7e6x/Bqfl5OQwEdcRF8g9n3hT9Z43d3Txte3buWFxAvfevMLT4QBwbd0xtp6sImBR8JS8595sqr7P952tp8P6MZ/bsJTsIeqnAs/VsyX/Y4KSF/LekTyWJUfw/922jr6DG+aoYn7yWi5njKnUtJp5bm8RgX5GfnrjQu5am4qvcfRNB6bqPXe3ERMtrfUPgR8CdI9ofW+YJAut9WU9Xyulfga0XZxkCSHEaO0oqKajy8aXL5vl6VB6bcqM55+flFHY6MNVng5GTAk7CqrxMxq4dG7MkMcs6F4J+6t38qlsNvPobUu5eAbpjtUpvJdbyS/eyUcp+MzKZP7j2vnEhPi7NX4x0JgrSZVSNwO/B2KBt5VSR7XW17gsMiGE6OPdE1XMCAtgWdLAAmFPuTw9lgBfAzmlXdxn19JZW4zb9oIa1syJJniYlgvhgb7MjAikqN7E2tnRrB8kKTMYFL+5dSm/33GG21clszTZe/7eTDejGjvUWudorW/s/vpVrXWS1tpfax0/WJKltf6Z1vpRVwUrhJieTBYrHxXWcu2iGV6VzAT6Gfn3y2ZzoMrGd14+6pJu3WL6Ol/XzrnadjYuiBvx2J7+bt+7ZuiGvYkRgfzqlsWSZHmYdCkTQni9nFO1dFrtXOOFPX++e/U8KkuL+cfRChpNXfzxjhXDjkYIMZSeBrhXOpFoffnSWaxKi2Rl6sBeW8K7yBY8Qgiv925uFdHBfqyeFeXpUAZQSnHjHD9+/enF7D5dy+ef3k9Du8XTYYlJaEdBDfPiQ0iOGnlz9LVzovnqFXMmICoxXpJoCSG8mrnLxo78ajZlxvdu7OyNPrsqhT/flUVBZQu3/mkvNa1mT4ckJpEWcxcHzjdw5YJ4T4ciXEwSLSGEV9tzpo52i80rpw0vdvXCeF76yiWUN3bwf1+XjTGE83YV1mG1azZmjDxtKCYXSbSEEF7tvdwqQgN8WDdn6OXu3mRVWhTf2pjOu7lVsum0cNr2gmoignxZLoXrU44kWkIIr9Vls7M1v5qrMuLx85k8/1z9+2WzSY8L4aev52GyyF6IYng2uybnVC3Z82LxGUMjUeHd5CcqhPBa+8810GTq4tpF3j9t2Jefj4Ff3rKY8qYOHtt22tPhCC93tLSJhnYLV2ZIfdZUJImWEMJrvZdXSaCvkSvmxXo6lFFblRbF51Yn88zu8+RVNHs6HOHFdhRUYzQorkiffO9zMTJJtIQQXslu17yfV82GBbHLaWgIAAAgAElEQVQE+Bo9Hc6Y/ODaBUQG+fKjV3Ox2bWnwxFeant+DVmpkYQH+Xo6FOEGkmgJIbzS4ZJGals7uXZRgqdDGbOIID9+cuNCjpU2sWV/safDER5ms2t2FFSz/1w99W2dAJQ3dVBQ1SqrDacwaV8shPBK7+VW4Wc0sGH+5J5O2bw0kX8cLuM375/i9lUpk6qoX7iO3a554J/HeeVwWe9jkUG+RAb5AUj/rClMEi0hhFf6qLCWtXOiCQ2Y3NMpSinuWpPKrtN1fFLSyJrZ0Z4OSUwwrTUPvZ3PK4fL+Fr2HC6ZFcWZmjbO1rZxtqadjIQw5sQGezpM4SaSaAkhvE5Nq5kzNW3ctjLJ06G4xJo50RgNit2n6yTRmoae2H6GZ/ec5wvr0vj+NfNRSpE9X6YKpwsZwxZCeJ2PzzUATJmkJCzA0Yhy1+laT4ciJthze87zu22FfHpFEj+9cSFKee82UsI9JNESQnidfWfrCfX3ITMxzNOhuMyl6TEcL2+mySQbTk8X/zxcxoNvnuSazHh+/enFGLx4r07hPpJoCSG8zsfn6lk9K2pKdcm+LD0WrWHPmXpPhyImQJfNzoNv5rE6LYrHb18+pd7LYnTkJy+E8CpVzWbO17Wzds7UmDbssTQpnNAAH3afkenD6WD/uQZazFb+/fLZk7YPnHANSbSEEF7l43OOEZ+pUp/Vw8doYO3saHYW1qG1NC+d6j44WUWgr5HL0ifHZujCfSTREkJ4lX1n6wkL8CEjYerUZ/W4bF4s5U0dFNWbPB2KcCOtNR/kVXP5vBgZzRKSaAkhvMu+c/VcMtvRDmGquWyuY3RDVh9ObSfKm6lqMbNp4eTaDF24hyRaQgivUd7UQUmDibVTbNqwR2p0EMlRgew6XefpUIQbfZDn2CT6ygXSK0tIoiWE8CIfn3XUZ021QvgeSikunRvLvrP1dNnsng5HuMkHJ6tYnRZFZLCfp0MRXkASLSGEy5i7bLx5rGLMxd77ztUTGeTL/PhQF0fmPS5Pj6Gt08qx0iZPhyLc4HxdO4XVbWzKlL0LhYMkWkIIl/nbgRK++bcjFFS1jun1+87Wc8ms6Cnd2HHdnBgMCnbK9OGUtPVkFQBXL5RESzhIoiWEcJmtJ6sBqGntHPVrSxtMlDd1TNlpwx7hQb4sSYpgtxTET0kf5FWzMCGMpMggT4civIQkWkIIl2g2dbH/vGOPwroxJFr7pnh9Vl+XpcdwtLSJ5o4uT4ciXKi2tZPDJY0ybSj6kURLCOESOYU12OyO2qz69tEnWh+fqyc62I/0uBBXh+Z1LkuPxa4vJJdiathRUI3WSFsH0Y8kWkIIl/jgZDUxIf74+Riobxvdxslaa/adq2fN7GiUmrr1WT2Wp0QQ7Gdke361p0MRLvRBXjVJkYFkJEzdxRxi9CTREkKMW6fVxkenarkqI47YEH9q20Y3olVcb6Ky2cyaaTBtCOBrNLB52UxeP1pBTYvZ0+EIF2jvtLLrTB2bFs6YFh8WhPMk0RJCjNv+cw20dVq5emE80SF+ox7RemFfEQDrp0miBXDfFbOx2u08teucp0MRLvBRYS0Wq13qs8QAkmgJIcZt68lqAn2NrJ8bQ0yIP3WjGNH6IK+K5/YUcc/aVGbHTv36rB6p0cFsXprIlv0lNLaPLjEV3qXLZuf3O84wIyyArNRIT4cjvIwkWkKIcdFasy2/msvSHRvoRgc7P6JV2mDie68cY9HMMH50Q4abI/U+X98wF5PFxnN7zns6FDEOT+48R35lCw/elImPUX6tiv7kHSGEGJe8ihYqm81c1d2gMTrEn/r2zhG7w1usdr75tyNoDf/z+RX4+xgnIlyvMi8+lGsy43l+bxGtZmn1MBmdrW3j8e2nuX7xDK7JlNWGYiBJtIQQ47L1ZDUGBRu7N9CNCfGjy6Zp6bAO+7rfvF/A0dImfn3rElKjgyciVK/0jQ3ptJitvPhxsadDEaNkt2se+OdxAn2N/GxzpqfDEV5KEi0hxLhsPVnNytRIokP8AYjp/n/dML20tp6s5qld57lnbSrXL06YkDi91eKkcC6fF8szu87TYbF5OhwxClsOlHCwqJEf35BBXGiAp8MRXkoSLSHEmJU1mjhZ2cJVGRdWWvUmWkN0h7fa7Pzgn8enbV3WYL6xYS717Rb+frDE06EIJ1U0dfDwO/lcOjeG21YmeToc4cUk0RJikjpf1857uVUejWF7fg3QfwPd6BA/AOqHWElX3dpJQ7uFOy5JnZZ1WYNZPSuK1WlRPLnzHBar3dPhiBForfnP13Kxa/jlzYulb5YYliRaQkxSz+05z7f+dqR32xtP2Hqymtmxwf3aMvQmWkO0eKhs6gAgMSLQ/QFOIl/fMIfKZjPv5lZ6OhQxgiOlTewoqOE7V6eTEi2bR4vhSaIlxCRV327BYrNT5aHO4i8fLGXP2TquW9R/pVVUkB9KQe0QLR7KexKtcKlp6evy9FhmhAXw5jFJtLzd9vxqjAbFZ7NSPB2KmAQk0RJikmo2OdoBlNSbJvzaf9lXxPf/eZzL0mP5xob0fs/5GA1EBvkNPaLV7EgME2REqx+DQXHjkgQ+Kqzp/dkK77Q9v4as1EjCg3w9HYqYBCTREmKSajQ5RoxKGton9LpP7TzHT1/P4+qF8Tx190oC/QbWWcUMsw1PZVMHoQE+hPj7uDvUSWfzskS6bJr38mRUy1uVN3VQUNXKxow4T4ciJgmnEy2llFEpdUQp9Vb397cppfKUUnalVFaf465WSh1WSp3o/v+V7ghciOmuqWdEq2F0I1pvHa/gh/86PmJD0YtprXli+2l+8U4+NyxJ4A93DN1kNDp46G14KprNzJTRrEEtnhlOanSQTB96sR0FjgUgVy6QPQ2Fc0YzovVtIL/P97nALcDOi46rAz6ltV4M3AO8OK4IhRCDauod0eoY1ev+9Uk5fztQytsnRvfL/Lk9Rfx2ayGfXpHEE7cvx3eYrUaiQ/yGXHVY0dRBgtRnDUopxealiew9W0dNq2dq78TwduRXkxodxJzY6dtkV4yOU4mWUioJuAF4uucxrXW+1vrUxcdqrY9orSu6v80DApRS/q4IVgjhYLHaae9ubjnaEa3TNa0A/OqdAsxdzjfIfOnjYlanRfGbW5dgNAy/nD0mxH/IPlqVzWapzxrG5qWJ2DW8c1xGtbyNyWJlz9l6rlwQJy0dhNOcHdF6DPg+MNoGL58Gjmith24RLYQYtaYOx2iRj0FRUu98jVaHxUZZYwfr5kRT3tTBM7ud28z4bG0b5+rauWFJAoYRkixw1Gi1dloHJHLmLhsN7RZZcTiM9PhQFswI5Y1jFSMfLCbU3jP1WKx2Nsq0oRiFEatRlVI3AjVa68NKqWxnT6yUygR+DWwa4vl7gXsB4uPjycnJcfbUY9bW1jYh1xEXyD13j/JWx2eemSGK4pYu3tn6IUG+jgRouHte3GJDa1ge2kZHnJHfbzvFTEsJEf7Df+Z697yjHiy4+Rw5OUUjxldf7jj+7W0fER144dxV7Y64myuLyMkpH/E8k4Wr3+eLQi3843QXr7yzg9ggWbM0GE/82/JibicBRjCXniCnfPqNaMm/52PjzLKf9cBmpdT1QAAQppR6SWt951Av6J5qfBW4W2t9drBjtNZPAk8CZGVl6ezs7NHGPmo5OTlMxHXEBXLP3ePA+QbYs4/1GUkU7y8hNXMFmYnhwPD3/LUj5cBRbtpwCbcaDWz63Ud83BbDw9csGfZ6fzi1jwUzurj1usudiq/rZDXP5R0iffEKliRF9D6+50wd7NrPlWtWsHZOtFPnmgxc/T6fs8TEPx75kLqgVG7LnuOy804lE/1vi9aa7+/ZzpULY7jqypUTdl1vIv+ej82IH5W01j/UWidprdOA24EdIyRZEcDbwA+11ntcFqkQoldPa4elSY7kqtTJOq3TNa0YDYq06GBmxQRz99o0/vdQKXkVzUNfq93C4eLGftvsjORCd/j+BfEVvV3hZepwOMlRQSxPiZDpQy+SV9FCTWunrDYUozbmMWml1M1KqTJgLfC2Uur97qe+AcwFfqKUOtr935RuOGKx2rHaZH8yMXF6Vhz2jBYVO9m09ExNG2nRQfj5OP7qf+vKdCICfXnorfwh2z3kFNZgs2s2Zjj/Cya2e2Pp2otaPPQ0K50hNVoj2rw0kfzKFs50L14QnrU9vwalIHt+rKdDEZPMqBItrXWO1vrG7q9f7R7p8tdax2utr+l+/CGtdbDWelmf/2rcEbw30FrzmT/v4zsvH/N0KGIa6emhlRIVRESQr9MrD0/XtDE37sK+hOFBvnzn6nnsO1fP1pPVg75mW34NsaH+LJkZ7nR8Q41oVTZ3EBPiJ5tJO+GGxQkYFLwhPbW8wo6CapYlRxATIovoxehIleU4bcuv4WhpE9tOVtNpdX6pvBDj0WjqwteoCPIzkhIV5FSiZbHaKa43kR4X2u/xz69OYW5cCL98Jx+L1T7gNTtP1bJxQZxTqw17BPn5EORnHLANT3mTWTaTdlJcWABrZkfz1nGZPvS0mlYzx8qa2bhgSk/OCDeRRGscejpl+xkNdHTZOHi+0dMhiWmiucNCRJAfSimSo4KcqtEqqm/HZtekx4f0e9zHaODHN2RQVG/iL/uK+j134HwDrZ3WUU0b9ogO8RvQHb5SmpWOyprZ0ZyrbR9VvzPhejkFtYB0gxdjI4nWOHx4qoYT5c386PoF+BkN5JyasjOkwss0tncREejY0DY1Koiyxo4R6wRPV7cBMCc2ZMBzG+bHccW8WB7ffpqGPh3dt+VX4+9j4NK5MaOOMSbEf0B3+MpmMwnhMqLlrOQox70qaxxd93/hWtvyq0kMDyAjIXTkg4W4iCRaI9hZWEuruWvA41prHt9+hpkRgdyxJpVLZkfxUWGtByIU01FTh4XIIEcdVEpUEFa77i00H8rpmlaUGjzRAvjPGzIwWWw8tq0QcLzHtxdUc+ncmEE3jh5JdLA/tX26w7eYu2jrtMqKw1FIiQoCoLRxdN3/hetUNHWwo6CGaxclSDd4MSaSaA2jsrmDu589wJ3PHKDlomRr5+k6jpU2cf+GufgaDVwxL5bTNW2UN8knT+F+TaYuwoMcI1q9v4xHmD48U9NGcmTQkElTenwon1+dwpb9JZyubqWwuo3Sho4xTRuCozt83xGtC60dZETLWcmRzv1shfs8ves8GvjSpWmeDkVMUpJoDaOiyTFCcKy0iXuePdA7sqW15vFthSSGB3DryiTgwpLfj07JqJZwv0aThcjuRCu5O9EaqSD+zEUrDgfznavnEeRn5Bfv5LMt37EKcWPG2AqAY0L8aWi3YLc72kZUdv99kqlD58WG+uPvY5BEy0Ma2y38/WAJNy1NJKk76RVitCTRGkZtq+MXw7c2pnOirJkvPneQtk4re8/W80lJE1/bMLe3H9Gc2BBmRgRKnZaYEE2mrt6pw8SIQHwMiuJhfhlbbXbO1baTPkKiFRXsx7c3ppNzqpZndp9nSVI48WFjm+qLDvHDZtc0dTg+oFQ0S7PS0bqw2EFGyj3hL/uKMVlsfPUK6c4vxk4SrWFUtzjqS+5ak8oTn1vOkdImvvTcQX67tZAZYQF8Jiup91ilFFfMj2XPmboBS+SFcKUOi41Oq7136tBoUCRFBg47olXa2IHFZh9xRAvg7rVppEUH0dBuGdfmuT39hnpaPFQ2mTEaFHGhkmiNRvIIP1vhHiaLlef3nueqjDjmz5AieDF2kmgNo7rF8YshOtiP6xcn8Nhnl3GouIHDxY3cd8XsAU0Xs+fF0m6xcbhY2jwI92nqcNQ99YxoASO2eDhd7egunh4/8i8MPx8DP/3UQvx8DFy/eMaY4+xpWtrTHb6iqYP4UH+Mo+jHJbp/tlIMP+H+92ApjaYuviZ7TYpxcmZT6SlBa43NPvgWI0Opae0kNsS/t1Hjp5YmohS8fbyS21enDDh+3dwYfI2KnMKaKbVhrvAuje2Oqbie9g7gKIh/+8TQHcRP1/S0dgh26hpXLogn78Fr8DWO/bPYhREtR2JY0dwhhfBjkBwZRKvZSnOfBRDCvbpsdp7edZ7VaVGsTI3ydDhikpsWI1rVLWZW/WI7eyqso35dfFj/7RZuXJLIH+9cSYDvwJVbIf4+ZKVGSUG8cKueEa2IPiNaqdFBNJm6aO4Y2IoE4GxNGwnhAYQGOP+LejxJFgwyddhsJkESrVFzdrGDcJ03j1VQ3tTBfdmzPR2KmAKmRaIVF+qPXWsKG0dXO1XT0kncKAuBs+fHUlDVStUIPY2EGKuefQ4jgvqPaMHQbQAu3uNwIkQE+mI0KOraHCsPK5vNJEpX+FHraVoq04cTw27X/Omjs8yPD2XDfNlyR4zftEi0lFKsSovkVMPotrGoaTUTFzq6DUSv6GnzUCirD4V7NJoGr9GCwUc97HbtVGsHVzMYFFHBftS3d1LfbsFitcv2O2OQ7GSfNOEa2wtqKKxu42vZc6RBqXCJaZFoAaxKi6K2Qzs90tRptdFo6hr10vb58aHMCAsgp8/0YV5FM/f/9RNu+cMeukbYJkWIkQw3ojVYolXR3EFHl23AZtITITrYj7o2C5XdrR1k6nD0wgJ8iQjylRGtCWC3a367tZCUqCBuXJLg6XDEFDFtiuFXz3IUNB4oamDz0sQRj+/ZOuTiGq2RKKW4Yl4s75yo5MD5Bv700Vl2FNTgY1BY7ZqjpU2sSpPiSjF2TSYLAb6GfnWCoQG+RAX7UVxvYsFFb6+eQviLN5OeCDEh/tS1dfY2/50pidaYJEcGUSK9tNzujWMV5Fe28Pjty/AZZ42iED2mzTtpYUIYAUY4eL7BqeN7emiNpedP9vxYWjutfObP+zhS0sj3Ns3jw+9lY1CwS/ZDFOPUt1lpX0O1eDjTvZn03CH2OHSnmBA/6vuOaMnU4ZgkRwVSJlOHbmWx2vn/t54iMzGMTy0Z+cO4EM6aNiNaPkYDcyOMHCxyLtGqaXF8Ao8b5YgWOOq0rl88gxUpkXz+khSC/By3eVlyBB+druO7m+aP+pxC9Gg0dREeOHD1YEpUEMdKm4D+dSVnatqICfEjMnhgcuZu0b0jWh34+xiI8kAMU0FyZBDbTtZgt+vedjPCtf66v5jShg5e+NJiucfCpabNiBbAvCgDBVWtNJksIx5b0zt1OPpP4EF+PvzhjpV85bLZvUkWwOXzYjle1uTU9YUYSpPJMuiIVmpUEOVNHQP6xZ2uaZ3wQvgeMSH+mCw2ztW2kxAeIMXFY5QcFYTFZqe6VVYzu0Nbp5Xf7zjD2tnRXJ4e4+lwxBQzrRKt+ZGOmpZDRSN3bq9uMeNjUEQN8gttrC5Lj0Vr2H2mzmXnFNNPU0dXv0L4HilRQdjsmgbzhURLa+2R1g49errDnyhvlmal43Bh5aHUabnD07vOUd9u4QfXLZAPA8LlplWiNSvcgJ/RwAEnpg+rWzqJDfV36RDy0qRwwgJ82Cl1WmIcmkyWfs1Ke/T8Mq4xXUi0qls6aTVbPbLiEBw1WuAYIU4Il0RrrJIju3tpSZ2Wy9W1dfLUznNct2gGy5IjPB2OmIKmTY0WgJ9RsTQ5nANOFMTXtJpH3ax0JD5GA+vnxrDrdB1aa/nkJEZNa91dDD/IiFa0I9Eqa7PzXm4V7+dVsS2/GoAFHtoUt6c7PEBihBTCj9XMyECUku7w7vDfO85gttr53jVSOyvcY1qNaIGjzUNueTMmy/Db8dS0dI66WakzLp8XS2WzmTPdS+6FGI22TitWux506nBGWAB+RgN/K7Bw30uH+fBUDddmzuC5L6zqbW8y0aL7JFoyojV2/j5GZoQFSC8tF6ts7mDL/mJuW5nEHA+syhXTw7Qa0QJH49L/+fAsR0qaWD936KLHmlYzq2ZFuvz6l3UXWu48XUd6vGdGGcTkdaFZ6cCpQ6NBcd8Vs8k9U8RXNq1k9awoj/cCiu6zyjBBRrTGJTkyiDKp0XKplz4uxmbX3L9hrqdDEVPYtBvRWpkaiUHB/mGmD3u6wo+lh9ZIkiKDmB0bPGSd1pmaVuwXrRoTokdvojVIeweA726az90L/Vk3N8bjSRZAgK+RUH/H5zlpVjo+yVFBMnXoQuYuG387UMrGjPje+kYh3MHz/xJPsNAAXxYmhg3buLSmZWxd4Z11eXos+8/XY+7qv/fiPw6XcdVvd/LWiUq3XFdMfr37HE6iflQx3VPw0qx0fJKjAqluNdNpHd2erWJwbx2vpKHdwj1r0zwdipjipl2iBY7pw09KGrFYB993sKeHlquL4XtcPi8Gc5e9X5uJwupW/vO1EwC8n1vlluuKya+pY/gRLW8UHexHqL8PoQGTJ2ZvlBwZhNZQ3ijTh+OlteaFvUXMjQth/dxoT4cjprhpmWhdMiuKTqudE+XNgz7f2xXeDcXwjutH42tU7DrtmD40Wazcv+UTQvx9uCojno8Ka4dMAsX01tPsdrAaLW+VFhPskX0Wp5reXlqSaI3bJyVNnChv5p61qbL6W7jdtEy0sro3dR6qzUN1d6I1lq7wzgj29yErNYqPuuu0/u/reZypbeOxzy7ns6uSaeu0OtWCQkw/F4rhJ8/o0IObM3nuC6s9Hcakl9KdaEmd1vi9sLeIUH8fblmR5OlQxDQwLROtmBB/5sQGD7nvYU1rp8u7wl/ssnkxFFS18secs7xyuIxvbpjLpekxXDo3Bn8fQ2//IyH6ajRZCPH3wdcLCt2dFezvQ/gkSgy9VVyoP34+BtlcepxqWsy8c6KSW7OSCPafdgvvhQdMnn+tXWz1rCgOFjUM2BcO3NMV/mKXp8cC8Ov3ClgzO4pvXzUPgEA/I5fOjWFbfjVay+pD0V+zafDtd8TUZzAokiICpZfWOG3ZX4LVrrlbiuDFBJm2idaqtChazVYKq1sHPOeOrvAXW5gQRkyIHzEhfjxx+3KMfZK6jRnxlDV2UFgtTU1Ff40miyRa05i0eBgfi9XOXw+UkD0/llkxwZ4OR0wT03bcdGWqoxnpJyWNZCSE9XuupqWT1Gj39lUxGBR/unMloQG+A5K6jRlx8Cpsy69mvoe2ThHeqdHUReQkKoQXrpUcFcjR0qZ+jzWZLLy4rxg/HwMzwgOYERbAjPAAEiMCJ9UU80R4N7eS2tZO7lmX5ulQxDQybROtlKggYkL8OFzcyB2XpPZ7rtpNXeEv1lOUf7H4sACWJIWzPb9aOhaLfpo7ukiKlMaf01VyZBDNHV00d3QRHuhLXVsndz69n4KqgSPzmYlhvH7/eq9oXOsNOq02/phzlrToIK7oLt0QYiJM20RLKcWKlEg+KW7s93in1UaTqYt4N3SFH42NC+J5bHshdW2d/TbmFdNbo8kiI1rTWG+LhwYTHSH+3PH0x1Q0mdnylUtYkhROdYuZymYzB8838MSOM2zLr+baRQkejto7PPr+KQqqWnnq7iy31t8KcbFp/VFnRWokRfUm6ts6ex+70BXew4lWRhxaw46CGo/GIbyH3a5p7ugiUmq0pq2eFg8fn6vnM3/eR3VLJy98aTXr58YQGuDL3LhQLkuP5dtXzSMpMpBn9xR5NmAv8VFhLU/tOs9da1K5emG8p8MR08y0TrQu1GldqHmoaXX00Ip10/Y7zspMDCMhPIDt0uZhWtJaD9jzssXchdYQLiNa01ZypCPReujtfJpMFl76yiWsnjWwBMFoUHxhXRoHzjeQO0Rj5umirq2T//PyMebFh/DjGzI8HY6YhqZ1orV4Zji+RsXhPtOHvSNaHp46VEqxMSOOXafrBuyJKKa+T/9xLw+9nd/vscbuZqUyojV9hQf5EhHkS1SwH3+7dw3LkiOGPPYzq5IJ9jPy3DQe1dJa8x+vHKPF3MUTn1tOgK/R0yGJaWhaJ1oBvkYyE8P71Wn1dIWP8/CIFjjaPJgsNvadq/d0KGICdVhsHClt4tUjZVhtF7ZiurD9jiRa09lTd2fx+v3ryUwMH/a4sABfbl2ZxJvHKqht7Rz22Knq+b1FfHiqlh9fn8GCGWEjv0AIN5jWiRbAipRIjpU10dX9C20iusI7a+3saIL8jDJ9OM0UVreitWME62CfjccvbL/j+fem8JxVaVG9RfEj+cL6WVhsdrbsL3ZzVN7F3GXjX5+U8at3Cti4II6716aO/CIh3GTaJ1orUyPptNo5WdECOLrCx7m5K7yzAnyNXJYew/b8GukSP42c6l6qrxS8n1fV+3hTR/eIVqCMaAnnzIoJ5soFcbz0cTGd1qldgqC15nBxAz/813FWPbSN7758jOSoQB65dYlsHC08atonWitSHTUOPXVaNa1mYj284rCv9XNjqGw2U9Fs9nQoYoIUVLUS6Gtkw/w4Psir6k2yG9t7arRkREs474vr06hrs/DWsUpPh+I2je0Wrnt8F5/+4z5eO1LB1Znx/PXfL2Hrd64gWtrjCA9zOtFSShmVUkeUUm91f3+bUipPKWVXSmVddOwPlVJnlFKnlFLXuDpoV0oID2RmRCCHS7oTrZZO4kO95y/mkiRHInj8om7QYuo6Vd3CvPgQrls0g4pmMye6V401dXShFITJiJYYhUvnxpAeF8Kze85P2ZHxn791kjM1bfzqlsUc/M+r+O1nlrFuToxXzEwIMZoRrW8DfZdB5QK3ADv7HqSUWgjcDmQC1wJ/UEp59VKP5SkRHOke0apuNXu8h1ZfGQmh+BoVx8qm9xLt6eRUVSvzZ4RyVUY8RoPqnT5sMlkIC/Dtty+mECNRSvHF9bPIq2jpV/M3VeScquFfR8r5evYcPrc6hRD/aduHW3gppxItpVQScAPwdM9jWut8rfWpQQ6/Cfi71rpTa30eOAOsdkWw7rIyNZKKZjNFde00mbqI86IRLX8fIxkJYRyTEa1poba1k7o2C/NnhBEZ7MfqtCjez3MshnDscyijWWL0bl4+k/BA3ylXFN/WaeXHr+YyNy6E+6+U7cqEd85xE4kAAB/pSURBVHJ2ROsx4PuAfaQDgZlAaZ/vy7of81o9jUvf6x458KYRLYAlSeHkljcPaGAppp6eQvgF3ZuJX7toBmdq2jhT00aTySLNSsWYBPoZuWFJAltPVmOyWD0djss8+v4pKpo7+PWnF+Pv49UTJ2IaG3GMVSl1I1CjtT6slMp24pyDzWsMyBCUUvcC9wLEx8eTk5PjxKnHp62tbdDrWO0aPwO8vLcQgKqiU+S0n3V7PM7yb+uitdPK39/5kMSQybV+Yah7Lgb3fpGj4L3+3AlyyhShZsdnmz+9uZfSahthfmrE+yn3fOJNhnueqm2YLDae+EcOaxIn//Ta8Yo2XjjezsYUH1rPHyfnvKcjmvomw/vcGznzt209sFkpdT0QAIQppV7SWt85xPFlQHKf75OAiosP0lo/CTwJkJWVpbOzs0cT95jk5OQw1HWWnd7HgfMNAFy1fjULE72nuV1idSvP5O4kIHEe2SuSPB3OqAx3z8VAb79yjJiQWjZv2tD72AtndnO6A2w+FmYnRZKdvXzYc8g9n3iT4Z5fbte8cGoHhZYwHshe5elwxqXTauNHv3qfxIgAHv/y5QRLXdaEmAzvc2804vCI1vqHWuskrXUajiL3HcMkWQBvALcrpfyVUrOAdOCAS6J1o57pQ4B4L+gK39ec2BCC/IxSpzUNFFS19k4b9tiUOYNjZc1UNZulWakYM4NB8alliewsrKWh3eLpcMbljzlnqWjX/OLmRZJkCa835nkopdTNSqkyYC3wtlLqfQCtdR7wMnASeA+4X2vt9Z3yVqY4Ei1fo/K6PkVGg2LRzHBZeTjF2eyawmrHisO+rl00A4Aum/a696aYXG5aOhOrXfPOicndU+udE5VkRhvInh/n6VCEGNGoEi2tdY7W+sbur1/tHuny11rHa62v6XPcL7TWc7TW87XW77o6aHdY0T2iFRviHV3hL7YsOYKTlS1YrM6sRxCTUXF9O51W+4BEa05sCHPjQgDZ51CMT0ZCKPPiQ3j9aLmnQxkzq81OUZ2J1DApfheTw+SqrHajqGA/ZscEE+dlKw57LEkKx2K1965KE5OT1WZne351796affX8bDMG2fz2msx4QBItMT5KKW5aNpODRY2UNZo8Hc6YlDZ2YLHZSQj2vg/EQgxGEq0+Hrwpk/+4Zr6nwxjU0u4O8cfKpE5rsuqw2LjvpcN8+YVDvLC3aMDz+VWtGBSkx4cMeO6mZTMJ8DX0jmwJMVablyYC8MaxAWuUJoUzNW0AJEyyFdhi+pJ3ah+Xpceyfm6Mp8MYVFJkIFHBfhyXRGtSajZ1cdcz+9leUENsqD9/PVAyYDuUU1UtpEUHE+A7cEpkXnwoJx+8lszE8IkKWUxRyVFBrEyN5I2jkzPROlvbnWgFy68vMTnIO3WSUEqxJCmcY6VSED/ZVDWbue3Pezle9v/au/P4Kqt73+OflZ15ngdIIEwJKDMRFUWDiPOAYx2KcvVqPa09Hntaa1+nva/Tnt5z21vbY49aqq0DXoe+alvnWTCooCgYBIQwJEICmScyj3vdP9ggyJSE7P1k7/19/5OdvZ/97F/Wi+GbZ63nt/bx8I2z+fFFkymraz/YTuSAA1vvHMtIXDso/mnxzFGUVLdSUt3idCmDtrO2jbS4CGLC9PdB/IOClh+Znp3IjtrWgOrsHOh21rZxzbI1VDZ38dT/OI1Lp2dx6bQs4iJDef7T8oPHdfT0sbux47hBS2S4XDItC1eI4WU/vKq1s7aNiWmaQhf/oaDlR2ZkJ+C2sHmv//0WGqy+++x6uvv6+cudZzDPMy0dFe7i6lmjeWNzNU2efkY7atqwFiYfZSG8yHBLiY3gnEmpvLKh0q+29rLWUlrXprWK4lcUtPzIdM+CeK3T8g+76tvZXtPG3QsmMnX04Wurbjx9DD19bv5RvP82+wNTON9sViriLYtnjWZvcydPrPafvWvqWrtp7epjQlqM06WIDJiClh9Ji4tgdGIUG9Qh3i+sLKkFYMHkI5sqTs6MZ9aYRJ73LIovqW4lKszFmORoX5cpQeqy6aO4eGomv3x9K39dV+F0OQOy07MQfmK6fiER/6Gg5WemZyewUR3i/cL722oZnxbD2JSj//Z949wx7KxtY93uJrZVt5KXEasF7+IzrhDDgzfMZP6kVO7/+0be9INu8aWe1g4T0nVFS/yHgpafmZ6dSHljx8G1PTIytXf3sbaskfOOs0XIZdOziIsI5bm15ZSc4I5DEW+ICHXx6JI5zBqTxD//pZgPttc5XdJx7axtIzYilMwR2lha5GgUtPzMjJz9a33UuHRk+2hnPT39bs47yrThAdHhoSyeNZpXv6iksb1HC+HFEdHhoTyx9DQmpsfxnf+3nvW7G0/8JoeU1rUzIS0GY3TlV/yHgpafmTY6AWNQP60R7v2SWmIjQinITT7ucTfOHUOf564vLYQXpyREhfH0bXPJTIjk7ueK6R+hdyLurG1jglo7iJ9R0PIzcZFhzMhO5Mk1X7Grvt3pcuQorLW8v62W+ZNSCQ89/l+xU0bFMyNn/92kmjoUJ6XFRfDDC/Kp2tfFJ2UNTpdzhNauXqpbupig1g7iZxS0/NCD35qJAW576jOaO7RWa6TZUtVCTUv3Ue82PJr7Lsxn6bxcUmIjvFyZyPEtnJJOXEQoL3rajowkZXX7f7FUDy3xNwpafig3NYZHlxSwp6mTu55ZT0+f2+mS5BDve9o6FOanDej4syam8u9XnOrNkkQGJDLMxcXTMnlrczWdPf1Ol3OYA5tJa+pQ/I2Clp+aOy6ZX187jU/KGvnpS5uO2KBYnLOypJbp2Qmkx+nOKPE/i2eNpq27j3e31jhdymFK69oIDTGMTVGvOfEvClp+7KpZ2fzzwkn8dd0e/riqzOlyAsY7X1Zz3gNFtHcPfk/JxvYeiiuaKTxOWweRkeyMcSlkJUTy0gibPtxZ28bYlGjCXPpvS/yL/sT6uXvPn8QVM0bx67dKKC5vcrqcgLD2q0bK6ttZUzr4BcGrttdiLcdt6yAykoWEGK6cOZpV2+toaOt2upyDdmqPQ/FTClp+zhjDf149jYjQEF7eUOl0OQGhorEDgKJttYN+78qSOlJjw5n+jb0NRfzJVbNG0++2vLZxZHSL7+13U97QoaAlfklBKwDERoRyTl4ab22uxj1C+9/4k/KDQatuUGvf+vrdrNpWy7l56dpKR/xafmYcU7LiR8zdh7sb2ulzWy2EF7+koBUgLp6aSXVLlzrGnyRrLRWNHSRGh7G3uZMdnjudBuLz8mZauvo0bSgB4apZo9hQ0cxXI6Bf34E7DnVFS/yRglaAWDg5g9AQw1ubq50uxa81tvfQ3tPP9QU5wNetGgbiH5/vIcxlmJ+X6q3yRHzmihmjMYYRcVWr1NNDS1e0xB8paAWIhOgw5k1M5c3N1Wr1cBIqmjoBmJubzOTMOIq2DWyT3V317bywfg83zR1DfGSYN0sU8YnMhEjmTUjhpeK9jv+bsrO2jayESGIiQh2tQ2QoFLQCyMVTMylv7GBrVavTpfitA+uzxqREU5ifzme7Gmnt6j3h+x58bzthLsP3zpvo7RJFfOaqWdmUN3bwebmzSxJKdceh+DEFrQCy6JQMQgy8tXlk3Cnkjw7ccZiTFM2C/DT63JbVO+uP+55t1a28/EUlt87LVZNSCSgXTc0kJtzFz17a7JPtvrr7+nnsg1LWlNYfvIpmraVUm0mLH1PQCiCpsRGclpvMm1qnNWTlDR2kxkYQFe5i9tgk4iJCTzh9+Nt3thEbHspd50zwUZUivhEbEcojN89mZ20bSx7/lH2dJ766O1RN7T0s+fOn/OcbJdz0p7Vcs2wNK0tqqNzXRXtPvzaTFr+loBVgLp6ayY7atoN36cjgVDR1MCY5CoAwVwjz81J5f1vtMdeofFHRzDtbavif88eTFBPuy1JFfKIwP51Hl8yhpLqFW574lJYBTKUPVlldG1f9YTUbKpp54LoZ/MeVp1LT0s1tT63jmj+sAWCirmiJn1LQCjAXTs0E4O0vdVVrKMobOxiT/PVeaoX56dS0dB9z3dsD72wjKTqM287O9VGFIr63YHI6y26ew5bKfdz6xKcDWrc4UGvLGrh62Rpauvp47o7TuXZONkvOzKXoR4X85trpRIe7iAwLIT8zbtg+U8SXFLQCTFZCFDNzEnlT67QGrbffTWVz5+FBKy8NgKLtR7Z5WFvWwIc76vmnwgnE6U5DCXDnn5LBQzfOZtOefSx98jMqmztP+pxvba7i24+vJTkmnBe/O4+C3OSDr4W5QriuIId3f3AuH9+/kGRdMRY/paAVgC6emsnmvS0HF3bLwFQ2d+K2kH1I0EqPj+TUUfEUlRy+Tquv380D72wjIz6CW87M9XGlIs64aGomD904i81797Hwt6t4eOUOunr7h3y+//vWNiamx/HiP53F2JSYox7jCjGalhe/pqAVgC7S9OGQHGztcEjQAliQn8768ib2dfZireWtzdVc9PsP+WxXE/cszCMyzOVEuSKOuHhaFu/94FwK89N44J3tXPBfH/DulppB99oqrWujrL6dG+fmkBCtK8ISuBS0AtDYlBimZMXr7sNBqmjcPxVyRNCanEa/2/KHop1cvWwNdz2zHre1LLt5NjfOzXGiVBFH5SRHs+zbc3jm9tOJCA3hjqfXcffzxfT1uwd8jhVbawC0ZZUEPAWtAHXFjFGs393E1qoWp0vxG+WNHYS7QsiIP7wX1sycJBKjw3h0VRlVzV386uppvPMv53DxtCyM0ebRErzOnpTKG/fM518X5fH6xip++frWAb/3va21TMmKJzsp+sQHi/gx7WcQoG6cm8NDK3fwpw/L+N31M50uxy9UNHYwOikKV8jh4ckVYvjFlVOpb+3mptPHaKpQ5BBhrhC+v3AS+zp7+fNHXzEuNYZb5+Ue9z1N7T2s29XI9xZoJwUJfLqiFaASo8O5viCHVzZUUrXv5O8OCgbljR3kJB/9t+srZozitrPHKWSJHMNPLpnC+VMy+PmrX55wM/ai7bW4LSyckuGj6kSco6AVwG4/exwWeHL1LqdL8QuHNisVkcFxhRh+f8NMJmfG8/3niympPvayhfe21pIWF8H00Qk+rFDEGQpaASwnOZpLpmXx3Npyr3RzDiT7Ontp7ug9YiG8iAxcTEQojy8tICbCxe1PraO2teuIY3r63KzaVsfCyemEhGiNowQ+Ba0A951zxtPW3cfza8udLmVEO3QzaREZuqyEKB6/9TQa2ru5/++bjmj78OlXjbR192naUIKGglaAmzo6gXkTUnhy9S56+gZ+63WwORi0dEVL5KRNHZ3Ajy6czMqSWl75ovKw197bWkNEaAhnT0x1qDoR31LQCgJ3nDOe6pYuXv3GP3jytYPNSlMUtESGw9J5uczMSeTnr26hsb0HAGst722t4eyJqUSF68YSCQ4DDlrGGJcxptgY85rn+2RjzLvGmB2er0me58OMMcuNMZuMMVuNMT/xVvEyMIV5aeRnxPGnD8sG3b05WFQ0dZAYHUa89iwUGRauEMOvr5lOa1cvv3j1SwC217Sxp6lT04YSVAZzRese4NBudPcDK6y1k4AVnu8BrgMirLXTgDnAd4wxuSdfqgyVMYY7zhlPSXUrq7bXnfgNQai8sVPrs0SGWX5mHN8tnMhLGypZWVLDe55u8AunqBu8BI8BBS1jTDZwKfDnQ56+EljuebwcWOx5bIEYY0woEAX0AGpP7rArZowiNTaCF9bvcbqUEamisUN3HIp4wXcXTGBSeiw/fXEzr2+sYnp2whG7L4gEsoFe0XoQuA84dDV1hrW2CsDz9cCvKH8D2oEqoBx4wFrbODzlylCFh4ZwzqRUPiltwO3W9OGh+t2WPU3HblYqIkMXEeri19dOp6qliy1VLSycrGlDCS4n3ILHGHMZUGutXW+MKRzAOecC/cAoIAn40BjznrW27BvnvRO4EyAjI4OioqJBlj54bW1tPvmckSqpt5eG9h6eff19cuJ8cx+EP4x5Q6eb3n5LV30FRUX+vxG3P4x5oNGYn9j5Y0J5d3cfSR3lFBXtPenzacx9T2M+NAPZ6/As4ApjzCVAJBBvjHkGqDHGZFlrq4wxWcCBPRduAt6y1vYCtcaY1UABcFjQstY+BjwGUFBQYAsLC4flBzqeoqIifPE5I9Wk5k4e37yS7sRcCueP98ln+sOYf1LWAKs+YeEZM5k/Kc3pck6aP4x5oNGYn9hZ891s2ruP2WOShuV8GnPf05gPzQkva1hrf2KtzbbW5gI3ACuttd8GXgFu9Rx2K/Cy53E5cJ7ZLwY4AygZ9spl0EYnRpGbEs3HpQ1OlzKiHGztoKlDEa8Jc4UMW8gS8ScnM3/0K2CRMWYHsMjzPcAjQCywGfgMeNJau/GkqpRhM29iKmu/aqSvX81LD6ho7CDEwKhE7XMoIiLDayBThwdZa4uAIs/jBmDhUY5pY3+LBxmB5k1I4bm15Wwcxkv4/q68sYNRiVGEudS/V0REhpf+ZwkyZ45PAWDNznqHKxk5Kho71ENLRES8QkEryKTERjAlK541Wqd1UHljp9ZniYiIVyhoBaF5E1JYt7uJrt5+p0txXEdPH/Vt3drjUEREvEJBKwidNTGFnj43n+9ucroUx22tagVgQlqsw5WIiEggUtAKQqflJuMKMawu1TqtDRXNAMwak+hwJSIiEogUtIJQXGQYM7ITtE4LKC5vYnRilPZeExERr1DQClLzJqSycc8+Wrt6nS7FUcXlzczM0dUsERHxDgWtIDVvYgr9bsunXwXvft+1rV3sbe7UtKGIiHiNglaQmj0miYjQEFbvDN7pww3lWp8lIiLepaAVpCLDXBTkJrEmiBfEF1c0ExpiOHVUgtOliIhIgFLQCmLzJqRSUt3KlsoWp0txRHF5E6eMiicyzOV0KSIiEqAUtILYhadmEhXm4pL//pAlj69lxdYa+t3W6bJ8ot9t2bhnH7O0EF5ERLxIQSuITUyP5cMfL+CHF+Sxo6aN25evY8EDRfz1swqnS/O67TWtdPT0M0sba4uIiBcpaAW51NgI7j5vEh/+eAEP3zSLlNhw7vv7Rp5du9vp0ryq2LMQXq0dRETEmxS0BIAwVwiXTR/FC985kwX5afzspc28u6XG6bK8pri8iaToMMZqj0MREfEiBS05TKgrhEduns207ES+//znrA/Q/RCLK5qZNSYJY4zTpYiISABT0JIjRIeH8sStBWTGR3L78s8orWtzuqRhta+zl521bVoILyIiXqegJUeVEhvB8tvmEhpiuOXxT6lt6XK6pGGzcY9nfZYalYqIiJcpaMkxjU2J4Ymlp9HY3sP/fmOr0+UMm+LyZoyBGbqiJSIiXqagJcc1PTuRG+bm8MamKmpbA+Oq1oaKZiamxRIfGeZ0KSIiEuAUtOSEbjkzl95+y3Nry50u5aRZaykub9L+hiIi4hMKWnJC41JjODcvjWfXltPT53a6nJOyu6GDpo5eZuaoUamIiHifgpYMyNJ5udS1dvPm5iqnSzkpxRX721XoipaIiPiCgpYMyLl5aeSmRPP0x/7dMX5DeTPR4S7yMuKcLkVERIKAgpYMSEiIYcmZuazf3cTmvfucLmdIOnr6ePvLGuaMTcIVokalIiLifQpaMmDXFWQTHe7iqTW7nC5lSJYVlVLd0sU9Cyc5XYqIiAQJBS0ZsPjIMK6ePZpXvqikoa3b6XIGpbyhg0c/KGPxzFEU5CY7XY6IiAQJBS0ZlFvPzKWnz81fPqtwupRB+eXrWwgNMdx/8RSnSxERkSCioCWDMikjjrMmpvDsJ7vp6/ePVg8f7qjjnS01fG/BRDITIp0uR0REgoiClgzakjNyqdzXxZrSBqdLOaHefjc/f3ULY1Oiuf3scU6XIyIiQUZBSwbt3Lw0wkNDeH9brdOlnNDTH+9mZ20bP7v0FCLDXE6XIyIiQUZBSwYtKtzFmeNTWLWtzulSjmtPUwcPvrudc/LSWDgl3elyREQkCCloyZAsyE+jrL6dXfXtTpdyhIrGDv7txU2c98Aqevrd/K/LTsEY9c0SERHfC3W6APFPhfnp8OoWirbVsjR1ZKx9Kqtr45H3S3lpw15CDFw7J5u7zp3A2JQYp0sTEZEgpaAlQ5KbGsO41BiKttex9Czng1ZtaxeXPfQRbmu55cyx3HnOeLISopwuS0REgpyClgxZYX4az60tp7Onn6hwZxeav7Khko6eft68Zz5TsuIdrUVEROQArdGSIVuQn053n5tPypxv8/D3z/cyIztBIUtEREYUBS0ZsrnjkokKczne5mFLZQtbq1q4ena2o3WIiIh8k4KWDFlkmIt5E1Io2laHtdaxOl4s3kNoiOHyGaMcq0FERORoFLTkpBROTqe8sYMyh9o89PW7eWlDJQsmp5McE+5IDSIiIseioCUnpTAvDYD3S5yZPvxoZz11rd1cM3u0I58vIiJyPAMOWsYYlzGm2Bjzmuf7ZGPMu8aYHZ6vSYccO90Y87Ex5ktjzCZjjHbyDVA5ydFMSo9l1XZnusS/WLyXhKgwFkxW53cRERl5BnNF6x5g6yHf3w+ssNZOAlZ4vscYEwo8A9xlrT0VKAR6h6VaGZEK89NYW9ZIe3efTz+3tauXt7+s5vIZWUSEah9DEREZeQYUtIwx2cClwJ8PefpKYLnn8XJgsefxBcBGa+0XANbaBmtt//CUKyPRgvx0evrdrCn1bZuHNzdX09Xr1t2GIiIyYg30itaDwH2A+5DnMqy1VQCerwfmbvIAa4x52xjzuTHmvmGrVkakgtxkYsJdFPm4zcM/Pt/DuNQYZuUk+vRzRUREBuqEneGNMZcBtdba9caYwgGe82zgNKADWGGMWW+tXfGN894J3AmQkZFBUVHRIEsfvLa2Np98TjCanASvFldwbnw94a6vN3D21pjXd7r5pKyTqyeFsWrVqmE/vz/Tn3Pf05j7nsbc9zTmQzOQLXjOAq4wxlwCRALxxphngBpjTJa1tsoYkwUcuJyxB1hlra0HMMa8Acxm/zqug6y1jwGPARQUFNjCwsLh+HmOq6ioCF98TjCKHNPADY99QkVELref/fXeh94a84dX7gC2c+9VZ5OTHD3s5/dn+nPuexpz39OY+57GfGhOOHVorf2JtTbbWpsL3ACstNZ+G3gFuNVz2K3Ay57HbwPTjTHRnoXx5wJbhr1yGVHOGJ/CvAkpLCsqpbPHu0vyOnr6WP7xbuZNSFHIEhGREe1k+mj9ClhkjNkBLPJ8j7W2Cfgd8BmwAfjcWvv6yRYqI9+9i/Kob+vmmU92e/Vznly9i7rWbn6wKM+rnyMiInKyBjJ1eJC1tggo8jxuABYe47hn2N/iQYLIabnJzJ+Uyh9XlXLzGWOIDh/UH68BaWrv4Y9FpZw/JZ2C3ORhP7+IiMhwUmd4GVb/cn4eDe09PP2xd65qLVtVSltPHz+6cLJXzi8iIjKcFLRkWM0Zm8S5eWk8uqqUtmFuYFrZ3MlTa3Zx9axs8jPjhvXcIiIi3qCgJcPu3kV5NHX0snzNrmMe0++2vLaxkssf+ojLH/qITXv2nfC8D763HSzcu2jSMFYrIiLiPQpaMuxm5iSycHI6j31QRmefPey13n43L6yrYNF/reLu54pp7+mjrrWbq/6wmodW7KCv333Uc+6oaeVv6/ew5MyxZCfpTkMREfEPw79aWYT9a7Uuf/gj7l7RS9yad4gJDyUuMpSmjh5qWrqZkhXPIzfN5qKpmbR19fGzlzfz23e3s3JbLb+7fibjUmMOO99v3t5GTHgo31sw0aGfSEREZPAUtMQrpmUn8NiSObyyeiMpGaNo7e6jrauPsSnRfOu0HBbkp2PM/g7yCdFh/PeNszj/lAx++uImLvn9h0zO+noNltvCFxXN/OuiPJJjwp36kURERAZNQUu85oJTMwmvK6GwcOqAjr9ixijm5ibzm7e3Udvaddhr3yrI4fb5447xThERkZFJQUtGlMyESH57/QynyxARERkWWgwvIiIi4iUKWiIiIiJeoqAlIiIi4iUKWiIiIiJeoqAlIiIi4iUKWiIiIiJeoqAlIiIi4iUKWiIiIiJeoqAlIiIi4iUKWiIiIiJeoqAlIiIi4iUKWiIiIiJeoqAlIiIi4iXGWut0DRhj6oDdPvioVKDeB58jX9OY+57G3Pc05r6nMfc9jfnXxlpr0wZy4IgIWr5ijFlnrS1wuo5gojH3PY2572nMfU9j7nsa86HR1KGIiIiIlyhoiYiIiHhJsAWtx5wuIAhpzH1PY+57GnPf05j7nsZ8CIJqjZaIiIiILwXbFS0RERERn1HQEhEREfESBS0RERERL1HQEhEREfESBS0RERERL1HQEhG/Z4z5d2PMD4/z+mJjzCm+rElEBBS0RCQ4LAYUtETE59RHS0T8kjHm34BbgAqgDlgP7APuBMKBncASYCbwmue1fcA1nlM8AqQBHcAd1toSX9YvIsFBQUtE/I4xZg7wFHA6EAp8DvwReNJa2+A55pdAjbX2IWPMU8Br1tq/eV5bAdxlrd1hjDkd+D/W2vN8/5OISKALdboAEZEhmA+8aK3tADDGvOJ5fqonYCUCscDb33yjMSYWmAe8YIw58HSE1ysWkaCkoCUi/upol+OfAhZba78wxiwFCo9yTAjQbK2d6b3SRET202J4EfFHHwBXGWOijDFxwOWe5+OAKmNMGHDzIce3el7DWtsCfGWMuQ7A7DfDd6WLSDDRGi0R8UuHLIbfDewBtgDtwH2e5zYBcdbapcaYs4A/Ad3AtYAbWAZkAWHAX6y1v/D5DyEiAU9BS0RERMRLNHUoIiIi4iUKWiIiIiJeoqAlIiIi4iUKWiIiIiJeoqAlIiIi4iUKWiIiIiJeoqAlIiIi4iUKWiIiIiJe8v8BFp2wj8fhRy0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = data[-110:].plot(x='date',y='CO2_concentration', figsize=(10,6))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ré-échantillonnage régulier des données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour avoir un échantillonnage régulier des données nous allons moyenner sur un mois à chaque fois.\n",
    "Nous allons d'abord extraire les années et mois sous forme de 2 colonnes supplémentaires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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_concentration</th>\n",
       "      <th>year</th>\n",
       "      <th>month</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",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958-04-05</td>\n",
       "      <td>317.31</td>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958-04-12</td>\n",
       "      <td>317.69</td>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958-04-19</td>\n",
       "      <td>317.58</td>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958-04-26</td>\n",
       "      <td>316.48</td>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1958-05-03</td>\n",
       "      <td>316.95</td>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1958-05-17</td>\n",
       "      <td>317.56</td>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1958-05-24</td>\n",
       "      <td>317.99</td>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1958-07-05</td>\n",
       "      <td>315.85</td>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1958-07-12</td>\n",
       "      <td>315.85</td>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1958-07-19</td>\n",
       "      <td>315.46</td>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1958-07-26</td>\n",
       "      <td>315.59</td>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1958-08-02</td>\n",
       "      <td>315.64</td>\n",
       "      <td>1958</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1958-08-09</td>\n",
       "      <td>315.10</td>\n",
       "      <td>1958</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1958-08-16</td>\n",
       "      <td>315.09</td>\n",
       "      <td>1958</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1958-08-30</td>\n",
       "      <td>314.14</td>\n",
       "      <td>1958</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1958-09-06</td>\n",
       "      <td>313.54</td>\n",
       "      <td>1958</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1958-11-08</td>\n",
       "      <td>313.05</td>\n",
       "      <td>1958</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1958-11-15</td>\n",
       "      <td>313.26</td>\n",
       "      <td>1958</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1958-11-22</td>\n",
       "      <td>313.57</td>\n",
       "      <td>1958</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1958-11-29</td>\n",
       "      <td>314.01</td>\n",
       "      <td>1958</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1958-12-06</td>\n",
       "      <td>314.56</td>\n",
       "      <td>1958</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1958-12-13</td>\n",
       "      <td>314.41</td>\n",
       "      <td>1958</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1958-12-20</td>\n",
       "      <td>314.77</td>\n",
       "      <td>1958</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1958-12-27</td>\n",
       "      <td>315.21</td>\n",
       "      <td>1958</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1959-01-03</td>\n",
       "      <td>315.24</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1959-01-10</td>\n",
       "      <td>315.50</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1959-01-17</td>\n",
       "      <td>315.69</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1959-01-24</td>\n",
       "      <td>315.86</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1959-01-31</td>\n",
       "      <td>315.42</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3144</th>\n",
       "      <td>2019-11-09</td>\n",
       "      <td>410.15</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3145</th>\n",
       "      <td>2019-11-16</td>\n",
       "      <td>410.22</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3146</th>\n",
       "      <td>2019-11-23</td>\n",
       "      <td>410.48</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3147</th>\n",
       "      <td>2019-11-30</td>\n",
       "      <td>410.92</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3148</th>\n",
       "      <td>2019-12-07</td>\n",
       "      <td>411.27</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3149</th>\n",
       "      <td>2019-12-14</td>\n",
       "      <td>411.67</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3150</th>\n",
       "      <td>2019-12-21</td>\n",
       "      <td>412.30</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3151</th>\n",
       "      <td>2019-12-28</td>\n",
       "      <td>412.59</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3152</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>413.19</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3153</th>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>413.39</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3154</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>413.36</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3155</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>413.99</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3156</th>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>414.83</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3157</th>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>413.81</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3158</th>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>414.17</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3159</th>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>413.89</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3160</th>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>414.00</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3161</th>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>414.30</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3162</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>414.62</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3163</th>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>415.57</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3164</th>\n",
       "      <td>2020-04-04</td>\n",
       "      <td>415.61</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3165</th>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>416.47</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3166</th>\n",
       "      <td>2020-04-18</td>\n",
       "      <td>416.60</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3167</th>\n",
       "      <td>2020-04-25</td>\n",
       "      <td>415.86</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3168</th>\n",
       "      <td>2020-05-02</td>\n",
       "      <td>417.20</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3169</th>\n",
       "      <td>2020-05-09</td>\n",
       "      <td>416.99</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3170</th>\n",
       "      <td>2020-05-16</td>\n",
       "      <td>416.54</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3171</th>\n",
       "      <td>2020-05-23</td>\n",
       "      <td>417.49</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3172</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>417.19</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3173 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  CO2_concentration  year  month\n",
       "0     1958-03-29             316.19  1958      3\n",
       "1     1958-04-05             317.31  1958      4\n",
       "2     1958-04-12             317.69  1958      4\n",
       "3     1958-04-19             317.58  1958      4\n",
       "4     1958-04-26             316.48  1958      4\n",
       "5     1958-05-03             316.95  1958      5\n",
       "6     1958-05-17             317.56  1958      5\n",
       "7     1958-05-24             317.99  1958      5\n",
       "8     1958-07-05             315.85  1958      7\n",
       "9     1958-07-12             315.85  1958      7\n",
       "10    1958-07-19             315.46  1958      7\n",
       "11    1958-07-26             315.59  1958      7\n",
       "12    1958-08-02             315.64  1958      8\n",
       "13    1958-08-09             315.10  1958      8\n",
       "14    1958-08-16             315.09  1958      8\n",
       "15    1958-08-30             314.14  1958      8\n",
       "16    1958-09-06             313.54  1958      9\n",
       "17    1958-11-08             313.05  1958     11\n",
       "18    1958-11-15             313.26  1958     11\n",
       "19    1958-11-22             313.57  1958     11\n",
       "20    1958-11-29             314.01  1958     11\n",
       "21    1958-12-06             314.56  1958     12\n",
       "22    1958-12-13             314.41  1958     12\n",
       "23    1958-12-20             314.77  1958     12\n",
       "24    1958-12-27             315.21  1958     12\n",
       "25    1959-01-03             315.24  1959      1\n",
       "26    1959-01-10             315.50  1959      1\n",
       "27    1959-01-17             315.69  1959      1\n",
       "28    1959-01-24             315.86  1959      1\n",
       "29    1959-01-31             315.42  1959      1\n",
       "...          ...                ...   ...    ...\n",
       "3143  2019-11-02             409.86  2019     11\n",
       "3144  2019-11-09             410.15  2019     11\n",
       "3145  2019-11-16             410.22  2019     11\n",
       "3146  2019-11-23             410.48  2019     11\n",
       "3147  2019-11-30             410.92  2019     11\n",
       "3148  2019-12-07             411.27  2019     12\n",
       "3149  2019-12-14             411.67  2019     12\n",
       "3150  2019-12-21             412.30  2019     12\n",
       "3151  2019-12-28             412.59  2019     12\n",
       "3152  2020-01-04             413.19  2020      1\n",
       "3153  2020-01-11             413.39  2020      1\n",
       "3154  2020-01-25             413.36  2020      1\n",
       "3155  2020-02-01             413.99  2020      2\n",
       "3156  2020-02-08             414.83  2020      2\n",
       "3157  2020-02-15             413.81  2020      2\n",
       "3158  2020-02-22             414.17  2020      2\n",
       "3159  2020-02-29             413.89  2020      2\n",
       "3160  2020-03-07             414.00  2020      3\n",
       "3161  2020-03-14             414.30  2020      3\n",
       "3162  2020-03-21             414.62  2020      3\n",
       "3163  2020-03-28             415.57  2020      3\n",
       "3164  2020-04-04             415.61  2020      4\n",
       "3165  2020-04-11             416.47  2020      4\n",
       "3166  2020-04-18             416.60  2020      4\n",
       "3167  2020-04-25             415.86  2020      4\n",
       "3168  2020-05-02             417.20  2020      5\n",
       "3169  2020-05-09             416.99  2020      5\n",
       "3170  2020-05-16             416.54  2020      5\n",
       "3171  2020-05-23             417.49  2020      5\n",
       "3172  2020-05-30             417.19  2020      5\n",
       "\n",
       "[3173 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['year'] = pd.DatetimeIndex(data['date']).year\n",
    "data['month'] = pd.DatetimeIndex(data['date']).month\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous pouvons maintenant sélectionner les éléments de la base de donnée qui se référent à une année. Puis au sein de ceux-ci nous sélectionnons les mesures d'un même mois et nous en prenons la moyenne. Nous recréons ensuite un dataframe avec ces nouvelles données.\n",
    "*NB : Cela revient à un léger filtrage pase-bas des fluctuations observées dans un mois.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Monthly_data_list=[]\n",
    "years=data['year'].unique()\n",
    "for a in years:\n",
    "    months_per_year = data['month'][(data.year == a)].unique()\n",
    "    for b in months_per_year:\n",
    "        date = str(a) + \"-\" + str(b)\n",
    "        Monthly_data_per_year = [date, round(data['CO2_concentration'][(data.year == a) & (data.month == b)].mean(),2),a,b]\n",
    "        Monthly_data_list.append(Monthly_data_per_year)\n",
    "\n",
    "Monthly_data=pd.DataFrame(Monthly_data_list)        \n",
    "Monthly_data.columns = ['date','CO2_concentration_moyenne_mensuelle','year','month']\n",
    "Monthly_data\n",
    "database_size = len(Monthly_data['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tracé des  données moyennées par mois depuis fin mars 1958"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = Monthly_data.plot(x = 'date',y = 'CO2_concentration_moyenne_mensuelle',figsize=(10,6))\n",
    "plt.grid()\n",
    "# Les dates ne veulent pas s'afficher en abscisse ? À résoudre ultérieurement..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tracé des  données sur les deux année 2018 et 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4 = Monthly_data[(Monthly_data.year == 2018) | \n",
    "                    (Monthly_data.year == 2019)].plot(x = 'date',y = 'CO2_concentration_moyenne_mensuelle',\n",
    "                                                      figsize=(8,6))\n",
    "plt.grid()\n",
    "\n",
    "# Les dates ne veulent pas s'afficher en abscisse ? À résoudre ultérieurement..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Date (année-mois) du minimum de 2009 à 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min de l'année 2009 : 384.45 à la date: 614    2009-10\n",
      "Name: date, dtype: object\n",
      "min de l'année 2010 : 386.9 à la date: 625    2010-9\n",
      "Name: date, dtype: object\n",
      "min de l'année 2011 : 389.02 à la date: 638    2011-10\n",
      "Name: date, dtype: object\n",
      "min de l'année 2012 : 391.2 à la date: 649    2012-9\n",
      "Name: date, dtype: object\n",
      "min de l'année 2013 : 393.38 à la date: 661    2013-9\n",
      "Name: date, dtype: object\n",
      "min de l'année 2014 : 395.5 à la date: 673    2014-9\n",
      "Name: date, dtype: object\n",
      "min de l'année 2015 : 397.56 à la date: 685    2015-9\n",
      "Name: date, dtype: object\n",
      "min de l'année 2016 : 401.12 à la date: 697    2016-9\n",
      "Name: date, dtype: object\n",
      "min de l'année 2017 : 403.32 à la date: 709    2017-9\n",
      "Name: date, dtype: object\n",
      "min de l'année 2018 : 405.69 à la date: 721    2018-9\n",
      "Name: date, dtype: object\n"
     ]
    }
   ],
   "source": [
    "annees = [2009+i for i in range(10)]\n",
    "min_annees = []\n",
    "date_min_annees = []\n",
    "j = 0\n",
    "for i in annees:\n",
    "    min_annees.append(Monthly_data['CO2_concentration_moyenne_mensuelle'][(Monthly_data.year == i)].min())\n",
    "    date_min_annees.append(Monthly_data['date'][(Monthly_data.year == i) & \n",
    "                            (Monthly_data['CO2_concentration_moyenne_mensuelle'] == min_annees[j])])\n",
    "    print('min de l\\'année',i,':', min_annees[j],'à la date:',date_min_annees[j])\n",
    "    j = j+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De la position des minima, on peut en deduire que la périodicité de la variation rapide est de l'ordre de l'année (nous préciserons cela plus loin) et que le minimum de C02 à Mauna Loa, Hawaii, États-Unis (lieu des mesures) est en septembre ou octobre."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Étude spectrale des données pour séparer et caractériser les deux phénomènes rapide puis lent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcul du spectre sur toutes les données \n",
    "\n",
    "\n",
    "**Attention les fréquences négatives de la FFT sont situées dans le seconde moitiée du spectre**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pour_fft = np.array(Monthly_data['CO2_concentration_moyenne_mensuelle'])\n",
    "data_pour_fft\n",
    "fft_data = np.fft.fft(data_pour_fft)\n",
    "freq_pos = np.fft.fftfreq(len(fft_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creation d'un tableau avec les fréquences negatives pour affichage courbe et classement du tableau des données fft  de la même façon pour obtenir spectre classique symétrique par rapport à la fréquence nulle (concentration constante de C02) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = [] \n",
    "freq.extend(freq_pos[int(len(freq_pos)/2):len(freq_pos)-1]-freq_pos[len(freq_pos)-1])\n",
    "freq.extend(freq_pos[0:int(len(freq_pos)/2)])\n",
    "\n",
    "fft_data_plot = [] \n",
    "fft_data_plot.extend(fft_data[int(len(freq_pos)/2):len(freq_pos)-1])\n",
    "fft_data_plot.extend(fft_data[0:int(len(freq_pos)/2)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tracé du spectre complet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mod_fft_data_plot=[abs(fft_data_plot[i]) for i in range(len(fft_data_plot))]\n",
    "fig5 = plt.plot(freq,mod_fft_data_plot)\n",
    "axes = plt.gca()\n",
    "#axes.set_xlim() # non utilise \n",
    "axes.set_ylim(0, 4000)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tracé des fréquences positives uniquement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig6 = plt.plot(freq[int(len(freq_pos)/2):len(freq_pos)-1],\n",
    "                mod_fft_data_plot[int(len(freq_pos)/2):len(freq_pos)-1])\n",
    "axes = plt.gca()\n",
    "#axes.set_xlim(0, 0.1)\n",
    "axes.set_ylim(0, 4000)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous avons mis en évidence le comportement oscillant de la concentration de C02 visible au travers du contenu spectral très marqué autour de 0.1. On note aussi la présence d'un peu d'harmonique 2. Le phénomène n'est pas une oscillation pure mono-fréquentielle. Nous allons filtrer ce contenu."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cherchons l'index de la fréquence du maximum proche de freq=0.01 dans le tableau de la fft de départ\n",
    "Pour cela nous traçons cette fois le spectre en fonction de l'indice du tableau de valeurs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z6urqEeua2tTB97ZtpmLOBVRfNHXkXyDGvF4vo9WZCOxQI2id0aZ1Rpdd6pyoiFYriYibYDA8Yox5wmpusoaKsL43W+31QEXI7uVAg9VeHqZ9QvQzHZRSKvoiWa0kwC+BvcaYH4XctQFYbd1eDTwd0r5KRFJFZCbBiedt1hBUh4gssx7ztpB9xi033Y0InOrUcFBKqWiJZFhpOfBZYKeI7LDavg58D3hcRG4H6oCPAxhjdovI48Aegiud7jTGDK4zvQN4EEgDnrO+JsTtdFCYmcqJMz0TfSillFKWMcPBGPMy4ecLAK4eYZ+1wNow7duBhedTYCSm5nhoPNMb7YdVSql3LdufIQ0wRcNBKaWiKinCoTQnjca2HoKnTyillJqopAiHqbkeuvoHaO/1x7sUpZRKCkkRDqU5aQA06qS0UkpFRVKEw9RcDwCNbTrvoJRS0ZAU4TDYc2jQnoNSSkVFUoRDcVYqDoETumJJKaWiIinCweV0UJzloUGHlZRSKiqSIhwASnM9OiGtlFJRkjThMDUnTU+EU0qpKEmacCjN8dCgJ8IppVRUJE845KbR5w9wWj8uVCmlJixpwmFqTvBch4Y2nXdQSqmJSppwqMhPB+BYa3ecK1FKKftLvnA4reGglFITlTThkJPmJifNTZ32HJRSasKSJhwAKvLTONaqcw5KKTVRSRUO0/LTdc5BKaWiYMxwEJEHRKRZRHaFtH1bRI6LyA7r68aQ++4WkVoRqRGR60Pal4jITuu+e0RkpI8eHbeKvHTqT/cQCOi5DkopNRGR9BweBFaEaf+xMWaR9fUsgIjMB1YBC6x97hURp7X9fcAaoNL6CveYE1KRn07/QICmDj1TWimlJmLMcDDGbAZaI3y8lcB6Y0yfMeYwUAssFZFSINsYs8UET2F+GLh5vEWPZNrQcladd1BKqYlwTWDfL4vIbcB24GvGmNNAGbA1ZJt6q81n3T63PSwRWUOwl0FRURFerzeigk50BQB4/i+v033UHenvERWdnZ0R1xkvdqgRtM5o0zqjyy51TtR4w+E+4DuAsb7/EPg8EG4ewYzSHpYxZh2wDqCqqspUV1dHVFS/P8DdLz9HRvF0qqvnRrRPtHi9XiKtM17sUCNondGmdUaXXeqcqHGtVjLGNBljBowxAeB+YKl1Vz1QEbJpOdBgtZeHaY+qFJeD0myPrlhSSqkJGlc4WHMIgz4CDK5k2gCsEpFUEZlJcOJ5mzGmEegQkWXWKqXbgKcnUPeIKvLT9UQ4pZSaoDGHlUTkUaAaKBSReuBbQLWILCI4NHQE+CKAMWa3iDwO7AH8wJ3GmAHroe4guPIpDXjO+oq6uSVZPPnmcfwDAVzOpDqNQymlJs2Y4WCMuTVM8y9H2X4tsDZM+3Zg4XlVNw7LZhXwq61H2Xn8DIun5cX66ZRSKikl3X+tl83KB+AvB1viXIlSStlX0oVDQWYq86ZksUXDQSmlxi3pwgHgvbMLeO1IK33+gbE3VkopNUxShsP7ZhfS5w/wZl1bvEtRSilbSspwWDozH4fA1kM6tKSUUuORlOGQk+ZmVlEmu46fiXcpSillS0kZDgALpmazu6E93mUopZQtJXU4NJ7ppaWzL96lKKWU7SRtOCycmgOgvQellBqHpA2H+VOzAQ0HpZQaj6QNh9z0FMpy09jdoJPSSil1vpI2HEAnpZVSarySOhwWluVw+FQXnX3+eJeilFK2ktThsKgiF4CXD5yMcyXJp6Gthxt/8mea23vjXYpSKgaSOhzeN7uA0hwPv95aF+9Skk5NUwd7Gts50NwZ71KUUjGQ1OHgcjr41NJpvFx7ikMn9U0smvp8geB3vbihUkkpqcMB4JNLK3A5hEde1d5DNPUPWOFghYRSKrkkfTgUZ3m4bkEJT715nEDAxLucpNHnC/YYerXnoFRSGjMcROQBEWkWkV0hbfki8oKIHLC+54Xcd7eI1IpIjYhcH9K+RER2WvfdIyIS/V8nvGvnl9DS1a/LWqNIew5KJbdIeg4PAivOabsL2GiMqQQ2Wj8jIvOBVcACa597RcRp7XMfsAaotL7OfcyYuaKyCICX9jdP1lMmvXfmHDQclEpGY4aDMWYz0HpO80rgIev2Q8DNIe3rjTF9xpjDQC2wVERKgWxjzBZjjAEeDtkn5gozU1lYls3m/acm6ymT3mDPodenw0pKJSPXOPcrMcY0AhhjGkWk2GovA7aGbFdvtfms2+e2hyUiawj2MigqKsLr9Y6zzHfMSO3n2cM+nn1hE+nu6I9odXZ2RqXOWIpmjTW1/QDsO3AQrzkWlcccZIdjCVpntGmdiWW84TCScO+6ZpT2sIwx64B1AFVVVaa6unrChaVNa+GZdVtxlF5A9cIpE368c3m9XqJRZyxFs8bX+vZB7UGmVkyjunpeVB5zkB2OJWid0aZ1JpbxrlZqsoaKsL4PDubXAxUh25UDDVZ7eZj2SXPx9DyyPC7+sKtxMp82aQ3OOfTqhLRSSWm84bABWG3dXg08HdK+SkRSRWQmwYnnbdYQVIeILLNWKd0Wss+kcDsd3LyojGd3naCtu38ynzopDa1W0qWsSiWlSJayPgpsAapEpF5Ebge+B1wrIgeAa62fMcbsBh4H9gB/AO40xgy+e9wB/ILgJPVB4Lko/y5junXpNPr9AZ544/hkP3XSGVqtpD0HpZLSmHMOxphbR7jr6hG2XwusDdO+HVh4XtVF2fyp2VxUkcuj2+r43PIZTOKpFklnaLWSLmVVKikl/RnS5/rU0goONHey7fC5q3PV+RgcTurTpaxKJaV3XTh8+KIyctPd/PLlw/Euxdb6/XoSnFLJ7F0XDmkpTj5z2XRe2NvE0ZaueJdjW4OhoCfBKZWc3nXhAHDbe6fjcoj2HiagT3sOSiW1d2U4FGd7+MjiMh7ecpQf/LGGAb1a63nTcFAquUX7DGnb+LeVCxGE/95US0evj39dGdeFVLYzNOegw0pKJaV3bTh43E7+45b34HE7eHjrUT68aCpLpufHuyzbGFqtpD0HpZLSu3JYKdQ/rpjH1Jw07vrtzqH/DauxvbNaSXsOSiWjd304ZKS6+OaHLuBAcycv156Mdzm28c5qJQ1UpZLRuz4cAK6cW4RDYEddW7xLsQ3tOSiV3DQcgPQUF1VTsnnzmIZDpAZDwTdgdLWXUklIw8GyeFouO461EQjom91YjDH0+QOkOIMvH+09KJV8NBwsiypy6ej1s/dEO9f/12b+5eld8S4pYfkDBmMgOy242E2vzKpU8tFwsFw8LReAbzy5i9rmTh7ecpRNNc1j7PXuNDgZne1xn/WzUip5aDhYZhVmkuVxseNYG0um51FZnMnXn9jJ0zuO09zRG+/yEsrgZHRWWjAc9PpKSiUfDQeLwyEsqgj2Hr527Vx+8PGL6O4f4Cvrd3D1D15i1/Ezca4wcQzOMWR7rGEl7TkolXQ0HEJ8Ztl0Prd8Bu+dXcBFFbm8/s/X8NSdy8lOc7P6gW0cOtkZ7xITQv+wYSXtOSiVbDQcQly/YArfumnB0CfEuZwOFlXk8qvblwLwpUfe0LOoCZlzsCak9UQ4pZLPhMJBRI6IyE4R2SEi2622fBF5QUQOWN/zQra/W0RqRaRGRK6faPGTZVZRJt+/5T3sO9HBPRsPxLucuBvqOaRpz0GpZBWNnsMHjDGLjDGXWD/fBWw0xlQCG62fEZH5wCpgAbACuFdEnFF4/klx9QUl3LKknHu9tazfVocx795zId6Zc7DCQXsOSiWdWAwrrQQesm4/BNwc0r7eGNNnjDkM1AJLY/D8MfMvN83nspkF3PXETj7x8y382+/2cPBdOA/xzlJWa1hJew5KJZ2JhoMBnheR10VkjdVWYoxpBLC+F1vtZcCxkH3rrTbbyPa4eeQLl/HND82no9fPr189yp2PvPGu60X0nTuspD0HpZLORD/PYbkxpkFEioEXRGTfKNtKmLaw76pW0KwBKCoqwuv1TrDM6JoN3LUIXjnu4v6dHdzzm43MTutNuDrP1dnZGZUa32jyA3DkwF4Adu7ZS0FH7YQfd1C06ow1rTO6tM7EMqFwMMY0WN+bReRJgsNETSJSaoxpFJFSYPA043qgImT3cqBhhMddB6wDqKqqMtXV1RMpM2aWDwT4fd0m/nwqDSmEKVNmc938EnLTU+JdWlher5doHMv2txrgzTe5fOnF/Pj1LUybOYfqy2dOvEBLtOqMNa0zurTOxDLuYSURyRCRrMHbwHXALmADsNrabDXwtHV7A7BKRFJFZCZQCWwb7/MnArfTwf+6chbbj57mR6/38Y+/eZulazfyn3/cl9RDTcPPc9BhJaWSzUR6DiXAk9Y5AS7gf4wxfxCR14DHReR2oA74OIAxZreIPA7sAfzAncYY289k3rp0Gt39A/hOHuHq913KL18+xE83HaSz1883PzQflzP5TiUZXK2UpSfBKZW0xh0OxphDwEVh2luAq0fYZy2wdrzPmYg8bid3fmAOXm89F5bn8ONPLqIoK5X7/3yYP+w+wacvm84d1bNxJ1FIDPYcPG4HKS6HngSnVBKa6IS0OoeI8PUbL+DSGfk8uq2OH72wny0HW/ibqyvp6vOTm+5mVlEm+RmJOS8RicFhpBSXg1SXQ3sOSiUhDYcYEBGuWzCF6xZM4bev13P3Ezu59f6tQ/enuhx8/5b3sHKRrVbyDhnsOaQ4HaS6nDrnoFQS0nCIsY8tKWfJ9DyOne4my+PmdHc/P/Me5Cvrd/DbN45zwZQsrqgsYtmsfNvMT/T5B3A6BJfTgcft0Et2K5WENBwmwYzCDGYUZgz9fPmcQn74/H427Wtm68EWfr75EHnpbq6dX8InL53Gkul5ozxa/PX7A6S6gkEWHFbSnoNSyUbDIQ7cTgd33TCPu26YR69vgJf2n+S5nY08u/MEj2+v58YLp3DLknKWTMsnJ90d73KH6fMHSBkKB6eeIa1UEtJwiDOP28n1C6Zw/YIpdPf7uX/zYX720kGe3XkCgLklmXx8SQVfuGLm0KXEwxkc2vG4Y38tw9Ceg8etE9JKJSMNhwSSnuLiK9dUsubKWbxV38b2I61s3n+Ktc/uZW9jO1NyPDSe6eX2y2cyrSCd53c3ceRUF/tOdPDnAydJS3Hy5Q/M4bPvnU6qK3YhoT2HxPfiniZ+9tJB1q9ZFu9SlE1pOCSgtBQny2YVsGxWAXd+YA4/fvEA92w8gNMhpLmdPLXjOKnW+QVOh1Cel8atS6dx6FQX3/39Xh78yxH+/roqPvie0ojOrzDG0HCml8Mnu5hdnEFpThr9/gAOIewkeZ9/YCh8PG4HrV39UT8GamL+VNPM9qOnOdLSFe9SlE1pOCQ4EeHvrp3LzYumUpCRCgI/f+kgHb1+PraknAvLcnA63hlu+vOBk/z7s/v46mM7+O7v97JgajanOvu4el4xC5zDL+nR1N7L3zz6Jq8ebrWeD6pKsjjS0kVmqpuffmoxl80qOGufsyeknXoSXAIa/EjbfSc6yIxzLcqeNBxsYlbRO//E/3HFvBG3u6KyiOV/XcimmmbWv3aMhrYeMlJc3POnWrJT4NdHXiXb46bPP0CfP8DuhnZ6+ge4+4Z5LJiaw/ajrWw73MqyWQVs3n+ST//iVb75ofnc9t7pQ3MeZw0r6ZxDQjp0MthjqDnRwRL7nm+p4kjDIQk5HMLVF5Rw9QUlQ22vHmrhhxteo73HR0NbD6kuJ6luBxdPy+WfVsyjsiQLgMsrC4f2ae/18XeP7eBbG3bzdv0ZvvexC3E7g0tXhyakXU56zvM8h0DA0OMbICNVX36x0NHro7mjDwj2HJZMi3NBypb0X+e7xGWzCvjSIg/V1ZdHvE+2x826z17CTzYe4CcbD1Cel8bfXjuXPn+AHOuDfipLMnls+zGeeKOej15cHvZx+v0BHnn1KM/tOkHjmR6azvTRPxDgqnnFfPDCUmqaOijN8XDLkvKhi/mp8Tt8KthryEx1UXOiA6aNvMpNqZFoOKhRORzC3147l2Ot3fz3plquuaDkrDmHv3rfDF7Y08Q3ntzFhWU5Qz0QCC6vferN4/zspYMcaelmwdRsFlfkUXqhB4cIj26r40/7mnE7Bd+A4YfP7+dvrp7D7ED4y5239/pda1yTAAAO6klEQVQ40+2jo9fPo9vq2NPYzk3vKeX9VcVkeVwUZKQgIkOXSx9t6W8yG/zo2qvmFbPhrQZ6/elxrkjZkYaDisi3blrAKwdP8Q+/eYte38DQnIPL6eCeWxfzwXv+zOcfeo0n7lhOUVYqZ7p9fPS+Vzh4sov5pdn837+6lOqqorPesO/8wBzqWrqZU5zJvhPt/OTFA/yfZ/eR7xGm73uFucVZXF5ZyNv1bby0/yT7m975vO4Up4MZhel8+3d74Hd7AMhJc1Oa4+FoSzeZHhfVc4v44vtnM6d45CnZPQ3t/HzzQbI8LmYUZLDr+BlSXU5WLa1gUUWuLQPm0MkunA7hugUlbHirgeOdumBAnT8NBxWRnHQ3375pAXc88gYAF4dc4qMk28MvVl/Kreu28vkHX+O7Ny/kxy/up661m/tvu4RrLigO+yabmepi/tRsAN5TnssvVl/CH3ef4BcvvEVqipPf72zkse3HcDuFZbMKuOk9U5mS40FEuHJuIcVZHnYdP8P+pg7O9Pg40NzJiTO9LJtVQEtXP8/tOsHv3m7gr6+qpLI4k5Odfew6fobDp7o41dmP2+mg5kQ7GakujIHOPj9FWal09/l5bPsxLp9TyN9fX4VDguegzC7KGPo9jrV289oJP8e2HGFmYSaXzswj1eWkuaOX1w6f5sk3j/Pq4RYun1PILUvKef/cohGvndXe6wv54KQBAoHgFW/rWrtp7/ENhduZHh+l1u8/mkMnu6jIS+PCshwA6juGh4NvIMDx0z1MzU0bCnqlQmk4qIitWDiFS6bnsf3o6aFhpUGLKnL56acXc8ev32DlT18BYO1HFnLt/JJwDxWWiLBiYSmeUzVUVy+jzz/AruPtVJZkDr15nmthWQ4LrTfBczW39/IPv3mb//xjzVBbbrqb2UWZVBZn4hsIcGVlIV+qnkOmx0VLVx9Fmal09Q+wflsd92w8wM3W7wIwNcdDWV4aHb1+9p3oCDbu2A2AQ8Ahgt8aEivMTOWqecW8fOAUz+06QWFmKjMK0vEFDP6BAC6ng8KMFA40d1LX2s2sogzKctN49XAr/f4AIhDuwwQ/cUk53735wlHf0A+e7GRWUSYVeemkpzjZeWqAY63dlOWmcaSli397Zg+v1J7CN2DISnVxwdRs6lq6MRim5afT1u2j1z/AyovKWD6nkIGAwRcI4B8I1n6628fxtm6m5qaxuCKPuSWZOEQ43NJFQ1sPpzr7ONnRR7bHzYqFU8hNTyEQMLxRd5pTnf1cWJ7D1AhCbpAxhtPdPk5399PvDzC9IJ30FHu+dZ3p9rH9aCtXzQv/H6ZEYs8jrOJCRPj6By/go/f+JewZ2FfNK2Hb16/hj7tP0D8Q4NOXTZ/Q86W6nBO6CGFxtocHP3cpx1p7hv53XpGfNuI/yuIsDxDs0Xzhill89OJy/rSvmWyPi1Od/bxy8BStnf0UZqby9RvLSG07wvXvX86exjO8WddGwBjy0lNYPC2X95Tn4nY68A0E8Nac5Kkdxznd1U+600GKU+jzBzje1kNlcSYfu7ic14600tTey6cvm2b1XgaYVpBOtsdNbXMHToeDkx19PPDKYWqaOvnilbO45oKSYSERCBgOn+riispCHA7h0hn5vLT/JFd8fxMpTgcBY0hLcfK55TOZWZjBjro29jV18L7ZBTgcQl1rNzMLM+jzB/ipt5b/3lQb9liFhlea24nLKXT0+odt982nd1Gc5aG738/pbt9Q+6yiDFYsmMINC0upLMnk8Kku/lTnY8NjO6g/3cOZHh8up3Cmx0dze3ABw6Asj4uPLi6jJMdDXnoKy2cX4nYJh052cehkJ2d6fBRne6gqyWL+1GyOtnTT1N7LtPx06lq72Xqohc4+PylOB5UlWeSkuen3B6jIT2N6fgYpLge+QICuPj+CkJHqHFoo4R8I0NIT4PWjrTS09ZKd5mbpjHzSUob/ezjd1c+uhjNcOiOfFKeDP+1r5htP7aSpvY9v3TSfzy2fGfFlb870+EhxOs56npbOPpwOidln1ms4qPNy8bQ8fvzJi4aGLM6Vk+7mE5dWTHJVIxMRphWMb0I2PyOFW5a8swLrU5edvSbU6z3GlBwPU3I8XDUvfA/J7XRw7fyS8+pBDTdl6Naiabl879m9fOmRN5iS7eELV8zkM8umD725bD5wkj5/gNnWeTG/WH0JjzyziZQplRxt7cIY+MIVM4eC8NalI69zrT/dzdGWblzW5dndTsHlcJCT7mZKtofjp3t489hp3qxrwzcQ4KKKXGYUZFCUlUphZgpHW7p55u1GTnb04ZDgMunyvHTerm/jxb1N/HzzIe71HjzrOYuzTjGjMIPpBen4A4Y5xZlMyQ4e4/yMFBwiPL+niUderRvqpY3GIXDuZk6HkJ4S/ByS/givKFyYmYrLITR39AYf76UtQ/eluBwsnZHP3JIsdjWcodc3QHFWKi/XnqLXFyDb4yItxUlTex+VxZnMLsrk35/dx7HWHv5n21F6fQFy091cWJZDSbaHls4+3E4Heekp5Ga4OdjcyaaakwwEDGW5acwuzqSrz8/rR08DwdfpykVTed/sQo61dnPoVCeNbb0R/V6jEROu75pAqqqqTE1NzdgbxpnX66W6ujreZYzKDjWC1jmWgYDhpf3N3L/5MFsOtTCjIJ2/vXYu/gHDPz+1i+kF6Tz2xfcOLTdO1ON5uqufF/Y20djWy4zCdHqP1/CJGz8Q0XCLfyCAP2CoP93DXw6eQkSYXZjBrKJMctPdNLX38nb9GXY1nGFWYQYVecFeQ35GCsvnFJKR6mIgYDjS0kV3X/DzSepau6g/3YNvwOB2ytDQVXuvj0MnOxkIwNRcDx1Ndbx/6UWU5nhoau9j8/6T/PnASQ6d7GLB1Gyy09zUn+7hspn5VFcV8fzuJnp8A6xYOHiBzQFu+Mlmmtr7uGHhFBaW5VB/ups369po6/ZRkJmCf8Bwuruftm4fOeluPnpxGRkpLg6e7KS2uROHCNfOLyHN7WRHfRvP7z6BbyD4Xp7tcVGWl84fvnrl68aYS8b795n0noOIrAB+AjiBXxhjvjfZNShlZ06HcNW8Eq6aV8LLB07xjad28pX1O4DgcM2vbr9sKBgSWV5GCp+45J1eprftQMTj8C6nA5cT5hRnhl2NNr0gg+kFGdx00dShtveds43TIUM9LGBoccRYvN5GqquKAZg3Bd4/twgIDuk5HMPrX7Gw9KyfPW4nj615L63d/Vw8bfRh00iXZZ/s6KOutYsZBRnkW0u65asR/TojmtRwEBEn8FPgWqAeeE1ENhhj9kxmHUoli8srC/njV69kT2M7Asybkh12/FvFXrhgGMmMwgxmkDHmdpGGZVFWKkVZqRE/fyQmu+ewFKg1xhwCEJH1wEpAw0GpcfK4nWP+D1Sp8zXZ4VAGHAv5uR647NyNRGQNsMb6sU9Edk1CbRNVCJyKdxFjsEONoHVGm9YZXXaps2oiO092OITrIw2bETfGrAPWAYjI9olMqkwWO9RphxpB64w2rTO67FTnRPaf7FMj64HQdY7lQMMk16CUUmoMkx0OrwGVIjJTRFKAVcCGSa5BKaXUGCZ1WMkY4xeRLwN/JLiU9QFjzO4xdlsX+8qiwg512qFG0DqjTeuMrndFnQl/EpxSSqnJp5djVEopNYyGg1JKqWESNhxEZIWI1IhIrYjcFe96BolIhYhsEpG9IrJbRL5itX9bRI6LyA7r68YEqPWIiOy06tluteWLyAsicsD6Htezp0SkKuSY7RCRdhH5aiIcTxF5QESaQ8+zGe34icjd1uu1RkSuj3Od/yki+0TkbRF5UkRyrfYZItITclx/FscaR/wbJ9ixfCykxiMissNqj8uxtJ57pPeh6L0+jTEJ90VwsvogMAtIAd4C5se7Lqu2UuBi63YWsB+YD3wb+Pt413dOrUeAwnPavg/cZd2+C/iPeNd5zt/9BDA9EY4ncCVwMbBrrONnvQbeAlKBmdbr1xnHOq8DXNbt/wipc0bodnE+lmH/xol2LM+5/4fAv8TzWFrPPdL7UNRen4nacxi6zIYxph8YvMxG3BljGo0xb1i3O4C9BM/8touVwEPW7YeAm+NYy7muBg4aY47GuxAAY8xmoPWc5pGO30pgvTGmzxhzGKgl+DqOS53GmOeNMYMfsLCV4DlFcTPCsRxJQh3LQRK80NEngEcno5bRjPI+FLXXZ6KGQ7jLbCTcG7CIzAAWA69aTV+2uvEPxHu4xmKA50XkdeuSJAAlxphGCL7AgOK4VTfcKs7+h5doxxNGPn6J/Jr9PPBcyM8zReRNEXlJRK6IV1GWcH/jRD2WVwBNxpgDIW1xP5bnvA9F7fWZqOEQ0WU24klEMoHfAl81xrQD9wGzgUVAI8HuZ7wtN8ZcDNwA3CkiV8a7oJFI8KTIDwP/z2pKxOM5moR8zYrINwA/8IjV1AhMM8YsBv4O+B8Riexa1dE30t84IY8lcCtn/+cl7scyzPvQiJuGaRv1mCZqOCT0ZTZExE3wD/KIMeYJAGNMkzFmwBgTAO5nkrrBozHGNFjfm4EnCdbUJCKlANb35vhVeJYbgDeMMU2QmMfTMtLxS7jXrIisBj4EfNpYA8/WsEKLdft1gmPPc+NR3yh/40Q8li7go8Bjg23xPpbh3oeI4uszUcMhYS+zYY07/hLYa4z5UUh76Cd6fASI65VkRSRDRLIGbxOcoNxF8DiutjZbDTwdnwqHOet/ZYl2PEOMdPw2AKtEJFVEZgKVwLY41AcMfajWPwEfNsZ0h7QXSfBzVRCRWQTrPBSnGkf6GyfUsbRcA+wzxtQPNsTzWI70PkQ0X5/xmGmPcDb+RoIz8AeBb8S7npC6LifYHXsb2GF93Qj8CthptW8ASuNc5yyCqxPeAnYPHkOgANgIHLC+5yfAMU0HWoCckLa4H0+CYdUI+Aj+z+v20Y4f8A3r9VoD3BDnOmsJjjEPvkZ/Zm37Mev18BbwBnBTHGsc8W+cSMfSan8Q+N/nbBuXY2k990jvQ1F7ferlM5RSSg2TqMNKSiml4kjDQSml1DAaDkoppYbRcFBKKTWMhoNSSqlhNByUUkoNo+GglFJqmP8P5cE0zVWq5FIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig7 = plt.plot(abs(fft_data[0:int(len(fft_data)/2)-1]))\n",
    "axes = plt.gca()\n",
    "axes.set_xlim(0, 200)\n",
    "axes.set_ylim(0, 4000)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion sur le phénomène rapide\n",
    "\n",
    "À partir de cette courbe nous pouvons déterminer précisément la fréquence des oscillations rapide (calcul suivant).\n",
    "Le résultat met en évidence une fréquence à $0.084 mois^{-1}= \\frac{1}{12} mois^{-1}$, soit une période d'une année.\n",
    "\n",
    "Nous retrouvons le résultat entrevue par la recherche des minima au départ. Nous pouvons préciser que le phénomène n'est pas purement sinusoïdal car nous avons clairement une composante à l'harmonique 2 soit $1.67 mois^{-1}= \\frac{2}{12} mois^{-1}$.\n",
    "\n",
    "***Hyp :*** Le phénomène n'est probablement pas directement lié à la différence d'activité humaine à Hawaï selon la période été/hiver, mais plus probablement à un stockage du C02 cyclique dans l'océan qui entoure la station de mesure sur l'île."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vmax =  (178.51619865797664+1126.1445008434132j)\n",
      "index =  62\n",
      "frequence du maximum local = 0.08355795148247978\n"
     ]
    }
   ],
   "source": [
    "vmax= fft_data[50:75].max()\n",
    "print('vmax = ',vmax)\n",
    "imax = np.where(fft_data == vmax)[0][0]\n",
    "print('index = ',imax)\n",
    "freqmax = freq_pos[imax]\n",
    "print('frequence du maximum local =', freqmax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Caractérisation du phénomène lent\n",
    "Nous pouvons maintenant filtrer grossièrement les données spectrales en *supprimant* les pics parasites, tant sur les fréquences positives que négatives, pour essayer de préciser la nature de la variation lente observée sous la variation rapide.\n",
    "\n",
    "**NB :** Il est bien sûr possible de réaliser un filtrage passe-bas plus conventionnel mais l'approche donnée ici suffit pour modéliser le phénomène lent afin d'obtenir une extrapolation de l'évolution à moyen terme (2025)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fft_data_filtree = np.copy(fft_data)\n",
    "for i in range(55,75):\n",
    "    fft_data_filtree[i]=(fft_data_filtree[55]+fft_data_filtree[75])/2\n",
    "for i in range(110,130):\n",
    "    fft_data_filtree[i] = (fft_data_filtree[110]+fft_data_filtree[130])/2\n",
    "for i in range(675,690):\n",
    "    fft_data_filtree[i]=(fft_data_filtree[675]+fft_data_filtree[690])/2\n",
    "for i in range(610,630):\n",
    "    fft_data_filtree[i]=(fft_data_filtree[610]+fft_data_filtree[630])/2\n",
    "    \n",
    "    \n",
    "mod_fft_data_filtree = np.abs(fft_data_filtree)\n",
    "fig8 = plt.plot(mod_fft_data_filtree)\n",
    "axes = plt.gca()\n",
    "#axes.set_xlim(0, 200)\n",
    "axes.set_ylim(0, 4000)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    }\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_concentration_moyenne_mensuelle</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>filtered_data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958-3</td>\n",
       "      <td>316.19</td>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>312.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958-4</td>\n",
       "      <td>317.26</td>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>315.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958-5</td>\n",
       "      <td>317.50</td>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>318.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958-7</td>\n",
       "      <td>315.69</td>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "      <td>318.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958-8</td>\n",
       "      <td>314.99</td>\n",
       "      <td>1958</td>\n",
       "      <td>8</td>\n",
       "      <td>318.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1958-9</td>\n",
       "      <td>313.54</td>\n",
       "      <td>1958</td>\n",
       "      <td>9</td>\n",
       "      <td>316.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1958-11</td>\n",
       "      <td>313.47</td>\n",
       "      <td>1958</td>\n",
       "      <td>11</td>\n",
       "      <td>314.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1958-12</td>\n",
       "      <td>314.74</td>\n",
       "      <td>1958</td>\n",
       "      <td>12</td>\n",
       "      <td>315.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1959-1</td>\n",
       "      <td>315.54</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>315.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1959-2</td>\n",
       "      <td>316.72</td>\n",
       "      <td>1959</td>\n",
       "      <td>2</td>\n",
       "      <td>316.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1959-3</td>\n",
       "      <td>316.75</td>\n",
       "      <td>1959</td>\n",
       "      <td>3</td>\n",
       "      <td>314.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1959-4</td>\n",
       "      <td>317.70</td>\n",
       "      <td>1959</td>\n",
       "      <td>4</td>\n",
       "      <td>314.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1959-5</td>\n",
       "      <td>318.38</td>\n",
       "      <td>1959</td>\n",
       "      <td>5</td>\n",
       "      <td>314.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1959-6</td>\n",
       "      <td>318.08</td>\n",
       "      <td>1959</td>\n",
       "      <td>6</td>\n",
       "      <td>315.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1959-7</td>\n",
       "      <td>316.58</td>\n",
       "      <td>1959</td>\n",
       "      <td>7</td>\n",
       "      <td>316.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1959-8</td>\n",
       "      <td>314.92</td>\n",
       "      <td>1959</td>\n",
       "      <td>8</td>\n",
       "      <td>317.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1959-9</td>\n",
       "      <td>313.87</td>\n",
       "      <td>1959</td>\n",
       "      <td>9</td>\n",
       "      <td>317.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1959-10</td>\n",
       "      <td>313.44</td>\n",
       "      <td>1959</td>\n",
       "      <td>10</td>\n",
       "      <td>316.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1959-11</td>\n",
       "      <td>314.90</td>\n",
       "      <td>1959</td>\n",
       "      <td>11</td>\n",
       "      <td>316.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1959-12</td>\n",
       "      <td>315.58</td>\n",
       "      <td>1959</td>\n",
       "      <td>12</td>\n",
       "      <td>316.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1960-1</td>\n",
       "      <td>316.42</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>316.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1960-2</td>\n",
       "      <td>317.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>2</td>\n",
       "      <td>316.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1960-3</td>\n",
       "      <td>317.64</td>\n",
       "      <td>1960</td>\n",
       "      <td>3</td>\n",
       "      <td>316.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1960-4</td>\n",
       "      <td>319.15</td>\n",
       "      <td>1960</td>\n",
       "      <td>4</td>\n",
       "      <td>316.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1960-5</td>\n",
       "      <td>319.97</td>\n",
       "      <td>1960</td>\n",
       "      <td>5</td>\n",
       "      <td>316.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1960-6</td>\n",
       "      <td>319.51</td>\n",
       "      <td>1960</td>\n",
       "      <td>6</td>\n",
       "      <td>316.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1960-7</td>\n",
       "      <td>318.09</td>\n",
       "      <td>1960</td>\n",
       "      <td>7</td>\n",
       "      <td>317.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1960-8</td>\n",
       "      <td>315.80</td>\n",
       "      <td>1960</td>\n",
       "      <td>8</td>\n",
       "      <td>317.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1960-9</td>\n",
       "      <td>314.23</td>\n",
       "      <td>1960</td>\n",
       "      <td>9</td>\n",
       "      <td>317.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1960-10</td>\n",
       "      <td>313.88</td>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>317.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>2017-12</td>\n",
       "      <td>406.76</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>407.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>2018-1</td>\n",
       "      <td>408.09</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>407.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>2018-2</td>\n",
       "      <td>408.30</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>407.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>2018-3</td>\n",
       "      <td>409.30</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>407.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>2018-4</td>\n",
       "      <td>410.36</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>407.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>2018-5</td>\n",
       "      <td>411.15</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>407.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>2018-6</td>\n",
       "      <td>410.79</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>408.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>2018-7</td>\n",
       "      <td>408.73</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>408.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2018-8</td>\n",
       "      <td>407.08</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>409.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>2018-9</td>\n",
       "      <td>405.69</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>409.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>2018-10</td>\n",
       "      <td>406.10</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>409.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>2018-11</td>\n",
       "      <td>408.02</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>410.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>2018-12</td>\n",
       "      <td>409.21</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>409.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>2019-1</td>\n",
       "      <td>410.82</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>410.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>2019-2</td>\n",
       "      <td>411.58</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>410.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>2019-3</td>\n",
       "      <td>412.06</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>410.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>2019-4</td>\n",
       "      <td>413.55</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>410.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>2019-5</td>\n",
       "      <td>414.78</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>411.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>2019-6</td>\n",
       "      <td>413.91</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>411.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>2019-7</td>\n",
       "      <td>411.77</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>411.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>2019-8</td>\n",
       "      <td>409.97</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>411.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>2019-9</td>\n",
       "      <td>408.55</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>411.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>2019-10</td>\n",
       "      <td>408.51</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>412.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>2019-11</td>\n",
       "      <td>410.33</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>412.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>2019-12</td>\n",
       "      <td>411.96</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>412.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>2020-1</td>\n",
       "      <td>413.31</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>413.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>2020-2</td>\n",
       "      <td>414.14</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>413.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>2020-3</td>\n",
       "      <td>414.62</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>413.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2020-4</td>\n",
       "      <td>416.14</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>413.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>2020-5</td>\n",
       "      <td>417.08</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>413.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>742 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  CO2_concentration_moyenne_mensuelle  year  month  filtered_data\n",
       "0     1958-3                               316.19  1958      3         312.80\n",
       "1     1958-4                               317.26  1958      4         315.57\n",
       "2     1958-5                               317.50  1958      5         318.40\n",
       "3     1958-7                               315.69  1958      7         318.69\n",
       "4     1958-8                               314.99  1958      8         318.58\n",
       "5     1958-9                               313.54  1958      9         316.32\n",
       "6    1958-11                               313.47  1958     11         314.99\n",
       "7    1958-12                               314.74  1958     12         315.39\n",
       "8     1959-1                               315.54  1959      1         315.71\n",
       "9     1959-2                               316.72  1959      2         316.19\n",
       "10    1959-3                               316.75  1959      3         314.88\n",
       "11    1959-4                               317.70  1959      4         314.34\n",
       "12    1959-5                               318.38  1959      5         314.49\n",
       "13    1959-6                               318.08  1959      6         315.38\n",
       "14    1959-7                               316.58  1959      7         316.40\n",
       "15    1959-8                               314.92  1959      8         317.24\n",
       "16    1959-9                               313.87  1959      9         317.39\n",
       "17   1959-10                               313.44  1959     10         316.61\n",
       "18   1959-11                               314.90  1959     11         316.93\n",
       "19   1959-12                               315.58  1959     12         316.65\n",
       "20    1960-1                               316.42  1960      1         316.95\n",
       "21    1960-2                               317.00  1960      2         316.89\n",
       "22    1960-3                               317.64  1960      3         316.26\n",
       "23    1960-4                               319.15  1960      4         316.15\n",
       "24    1960-5                               319.97  1960      5         316.03\n",
       "25    1960-6                               319.51  1960      6         316.25\n",
       "26    1960-7                               318.09  1960      7         317.05\n",
       "27    1960-8                               315.80  1960      8         317.38\n",
       "28    1960-9                               314.23  1960      9         317.46\n",
       "29   1960-10                               313.88  1960     10         317.22\n",
       "..       ...                                  ...   ...    ...            ...\n",
       "712  2017-12                               406.76  2017     12         407.10\n",
       "713   2018-1                               408.09  2018      1         407.51\n",
       "714   2018-2                               408.30  2018      2         407.17\n",
       "715   2018-3                               409.30  2018      3         407.43\n",
       "716   2018-4                               410.36  2018      4         407.56\n",
       "717   2018-5                               411.15  2018      5         407.95\n",
       "718   2018-6                               410.79  2018      6         408.48\n",
       "719   2018-7                               408.73  2018      7         408.60\n",
       "720   2018-8                               407.08  2018      8         409.37\n",
       "721   2018-9                               405.69  2018      9         409.36\n",
       "722  2018-10                               406.10  2018     10         409.52\n",
       "723  2018-11                               408.02  2018     11         410.09\n",
       "724  2018-12                               409.21  2018     12         409.84\n",
       "725   2019-1                               410.82  2019      1         410.53\n",
       "726   2019-2                               411.58  2019      2         410.70\n",
       "727   2019-3                               412.06  2019      3         410.37\n",
       "728   2019-4                               413.55  2019      4         410.80\n",
       "729   2019-5                               414.78  2019      5         411.39\n",
       "730   2019-6                               413.91  2019      6         411.16\n",
       "731   2019-7                               411.77  2019      7         411.05\n",
       "732   2019-8                               409.97  2019      8         411.76\n",
       "733   2019-9                               408.55  2019      9         411.99\n",
       "734  2019-10                               408.51  2019     10         412.02\n",
       "735  2019-11                               410.33  2019     11         412.70\n",
       "736  2019-12                               411.96  2019     12         412.99\n",
       "737   2020-1                               413.31  2020      1         413.47\n",
       "738   2020-2                               414.14  2020      2         413.78\n",
       "739   2020-3                               414.62  2020      3         413.46\n",
       "740   2020-4                               416.14  2020      4         413.72\n",
       "741   2020-5                               417.08  2020      5         413.58\n",
       "\n",
       "[742 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data = np.fft.ifft(fft_data_filtree)\n",
    "Monthly_data['filtered_data'] = np.around(np.abs(filtered_data[0:len(filtered_data)]),decimals=2)\n",
    "Monthly_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace=['CO2_concentration_moyenne_mensuelle','filtered_data']\n",
    "couleur = ['blue','red']\n",
    "j=0\n",
    "fig9 = plt.figure(figsize=(10,6))\n",
    "plt.grid()\n",
    "for i in trace:\n",
    "    plt.plot(Monthly_data[i],color=couleur[j]) \n",
    "    j=j+1\n",
    "    #L'indice correspond au nombre de mois depuis la date de la première mesure en 1958-8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'allure de la courbe filtrée laisse à penser que la variation est de type exponentielle mais avec un coefficient petit. Cette hypothèse est bien sûr difficile à affirmer compte tenu de la faible courbure sur les 60 et quelques années de données.\n",
    "Nous allons tenter d'obtenir les carcatéristique de l'exponentielle qui s'ajustera au mieux aux données de la concentration mensuelle de C02 filtrée.\n",
    "La fonction considérée est: $f_{CO2}=A.e^{at}+B$ ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A =  56  B =  257  a =  0.0013751\n"
     ]
    }
   ],
   "source": [
    "# Pour le temps on choisit l'indice des data filtrées.\n",
    "# L'écart entre deux données est le mois, après moyennage sur chaque mois de chaque année\n",
    "\n",
    "taille = len(Monthly_data['filtered_data']) \n",
    "t=range(taille)\n",
    "C_CO2 = Monthly_data['filtered_data']\n",
    "\n",
    "#Définition des fonctions utilisées pour l'optimisation\n",
    "def f_CO2_exp(t,A,B,a): # Concentration en CO2 calculée par un modèle exponentiel\n",
    "    D=[]\n",
    "    n=len(t)\n",
    "    for i in range(n):\n",
    "        fco2=A*np.exp(a*t[i])+B\n",
    "        D.append(fco2)\n",
    "    return D\n",
    "\n",
    "## Initialisation valeurs pour optimisation\n",
    "parametres=(0.1,300,0.0001)\n",
    "bndsCurveFit = ([0, 250, 0], [ 100, 350, 0.01])\n",
    "popt, pcov = optimize.curve_fit(f_CO2_exp, t, C_CO2, p0=parametres, bounds=bndsCurveFit, maxfev = 10000)\n",
    "Aopt,Bopt,aopt=popt\n",
    "print('A = ',int(Aopt),' B = ',int(Bopt),' a = ',round(aopt,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig10 = plt.figure(1,figsize=(10,6))\n",
    "plt.suptitle(\"Erreurs entre modèle exponentiel et données\")\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.plot(f_CO2_exp(t,Aopt,Bopt,aopt)-Monthly_data['filtered_data'],label=\"Erreur brute\")\n",
    "plt.title(\"Erreur brute\")\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(212)\n",
    "moyenne_CO2 = Monthly_data['filtered_data'].mean()\n",
    "plt.plot((f_CO2_exp(t,Aopt,Bopt,aopt)-Monthly_data['filtered_data'])/moyenne_CO2)\n",
    "plt.title(\"Erreur normalisée par rapport à la valeur moyenne sur toute les années\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les courbes sur les erreurs montre que le modèle exponentielle est assez juste."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.title('Données et courbe du modèle exponentiel')\n",
    "plt.plot(f_CO2_exp(t,Aopt,Bopt,aopt),'red')\n",
    "plt.plot(Monthly_data['filtered_data'], 'blue')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notre modèle semble relativement bien fonctionner avec la modélisation exponentielle. Il est à noter que la concentration initiale  (B) du modèle est fixée par la relative \"jeunesse\" des données.\n",
    "**Rmq :** On pourraît encore affiner la précision du modèle en supprimant les données oscillantes initiales ou en en moyennant leur fluctuation.\n",
    "Nous allons maintenant extrapoler la contration de CO2 à **janvier 2025**. Ainsi de future données permettront de valider ou non le modèle fait ce jour (juin 2020).\n",
    "Pour cela nous allons calculer le nombre de mois entre la dernière mesures et **janvier 2025**. Ce nombre de mois sera à ajouter à l'indice finale des données pour obtenir l'extrapolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contration extrapolée en micro-mol CO2 par mole (ppm) =  425.55\n",
      "Soit une variation relative par rapport à la dernière mesure de  2.03 %\n"
     ]
    }
   ],
   "source": [
    "# Dernière année des données\n",
    "last_year = Monthly_data['year'][taille-1]\n",
    "# Dernier mois des données\n",
    "last_month_last_year = Monthly_data['month'][taille-1]\n",
    "ecart_next_year = 12 - last_month_last_year #en mois\n",
    "ecart_next_year_2025 = 2025 - (last_year + 1)\n",
    "ecart_month_to_2025 = ecart_next_year + 12 *ecart_next_year_2025\n",
    "#print(last_year,last_month_last_year,ecart_next_year,ecart_next_year_2025,ecart_month_to_2025)\n",
    "indice_temps_en_mois = (taille-1) + ecart_month_to_2025 \n",
    "# (taille-1) est le dernier indice de la matrice Monthly_data \n",
    "#print(indice_temps_en_mois)\n",
    "C02_extrapolee_2015 = round(Aopt*np.exp(aopt*indice_temps_en_mois)+Bopt,2) # calcul direct de la valeur\n",
    "print('Contration extrapolée en micro-mol CO2 par mole (ppm) = ',C02_extrapolee_2015)\n",
    "var_rel = round((C02_extrapolee_2015 - Monthly_data['CO2_concentration_moyenne_mensuelle'][taille-1])\n",
    "                /Monthly_data['CO2_concentration_moyenne_mensuelle'][taille-1]*100,2)\n",
    "print('Soit une variation relative par rapport à la dernière mesure de ',var_rel,'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conlusion sur le phénomène lent\n",
    "La variation lente est apparemment de type exponentielle.\n",
    "\n",
    "Une extrapolation à **Janvier 2015** donne une concentration de CO2 à **425.55 ppm**.\n",
    "\n",
    "Ceci représente une augmentation de  **2.03 %** en un peu moins de **5 ans**.\n",
    "\n",
    "Nous donnons au final la courbe de la variation lente extrapolée jusqu'à janvier 2015, combinée avec les données mensuelles complètes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_extrapol=[i for i in range(taille+ecart_month_to_2025)]\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.title('Données et courbe du modèle exponentiel extrapolée à janvier 2025')\n",
    "plt.plot(f_CO2_exp(t_extrapol,Aopt,Bopt,aopt),'red')\n",
    "plt.plot(Monthly_data['CO2_concentration_moyenne_mensuelle'])\n",
    "plt.grid()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}