{
 "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",
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       "</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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    "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": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['date', 'CO2_concentration'], dtype='object')\n"
     ]
    },
    {
     "data": {
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       "      <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",
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       "    <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 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = data.plot(x='date',y='CO2_concentration',figsize=(10,8))\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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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = data[-110:].plot(x='date',y='CO2_concentration', figsize=(10,8))\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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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = Monthly_data.plot(x = 'date',y = 'CO2_concentration_moyenne_mensuelle',figsize=(20,10))\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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4 = Monthly_data[(Monthly_data.year == 2018) | (Monthly_data.year == 2019)].plot(x = 'date',y = 'CO2_concentration_moyenne_mensuelle',figsize=(10,8))\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) & (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": 39,
   "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": 40,
   "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],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": 41,
   "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": [
    "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": 20,
   "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": 21,
   "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": 23,
   "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": 23,
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 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=(15,8))\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": 36,
   "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",
    "def residuals_exp(parametres): # Calcul de la fonction d'erreur quadratique entre les données et la fonction modèle\n",
    "    A,B,a = parametres\n",
    "    return np.sum((np.array(C_CO2) - np.array(f_CO2_exp(t,A,B,a)))**2)\n",
    "\n",
    "## Initialisation valeurs pour optimisation\n",
    "parametres=(0.1,300,0.0001)\n",
    "#bndsMinimize = ((0, 1), (300, 350),(0, 0.1))\n",
    "bndsCurveFit = ([0, 250, 0], [ 100, 350, 0.01])\n",
    "#res = optimize.minimize(residuals, parametres, method='SLSQP', bounds=bndsMinimize, options={'ftol': 1e-9, 'disp': True})\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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig10 = plt.figure(1,figsize=(10, 8))\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": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\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": 59,
   "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 # (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])/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 $\\textbf{425.55 ppm}$.\n",
    "\n",
    "Ceci représente une augmentation de  $\\textbf{2.03 %}$ 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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 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=(15,8))\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
}