{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Étude sur l'évolution de la concentration en CO2 dans l'atmosphère depuis 1954"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import isoweek\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importation des données \n",
    "On utilisera les données fournies par Scripps CO2 program, en particulier celles du Mauna Loa Observatory, Hawaii.\n",
    "\n",
    "Les données sont chargées depuis un dossier local grâce au code suivant (on note le lien utilisé en commentaire du code dans le cas où il serait nécessaire ou intéressant de mettre à jour le jeu de données):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv\"\n",
    "data_csv = \"\\\\lurs\\_comptes\\AB283490\\personnel\\documents\\MOOC\\co2.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les 43 premières lignes du fichier csv étant des commentraires, nous pouvons les exclures lors du chargement des données via pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1958-04-05', 317.31] <class 'numpy.float64'>\n",
      "0.36000000000001364\n"
     ]
    },
    {
     "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>concentration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958-04-05</td>\n",
       "      <td>317.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958-04-12</td>\n",
       "      <td>317.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958-04-19</td>\n",
       "      <td>317.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958-04-26</td>\n",
       "      <td>316.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958-05-03</td>\n",
       "      <td>316.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1958-05-17</td>\n",
       "      <td>317.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1958-05-24</td>\n",
       "      <td>317.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1958-07-05</td>\n",
       "      <td>315.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1958-07-12</td>\n",
       "      <td>315.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1958-07-19</td>\n",
       "      <td>315.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1958-07-26</td>\n",
       "      <td>315.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1958-08-02</td>\n",
       "      <td>315.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1958-08-09</td>\n",
       "      <td>315.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1958-08-16</td>\n",
       "      <td>315.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1958-08-30</td>\n",
       "      <td>314.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1958-09-06</td>\n",
       "      <td>313.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1958-11-08</td>\n",
       "      <td>313.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1958-11-15</td>\n",
       "      <td>313.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1958-11-22</td>\n",
       "      <td>313.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1958-11-29</td>\n",
       "      <td>314.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1958-12-06</td>\n",
       "      <td>314.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1958-12-13</td>\n",
       "      <td>314.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1958-12-20</td>\n",
       "      <td>314.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1958-12-27</td>\n",
       "      <td>315.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1959-01-03</td>\n",
       "      <td>315.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1959-01-10</td>\n",
       "      <td>315.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1959-01-17</td>\n",
       "      <td>315.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1959-01-24</td>\n",
       "      <td>315.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1959-01-31</td>\n",
       "      <td>315.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1959-02-14</td>\n",
       "      <td>316.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>2024-07-27</td>\n",
       "      <td>424.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3387</th>\n",
       "      <td>2024-08-03</td>\n",
       "      <td>424.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3388</th>\n",
       "      <td>2024-08-10</td>\n",
       "      <td>422.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3389</th>\n",
       "      <td>2024-08-17</td>\n",
       "      <td>422.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3390</th>\n",
       "      <td>2024-08-24</td>\n",
       "      <td>421.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3391</th>\n",
       "      <td>2024-08-31</td>\n",
       "      <td>421.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3392</th>\n",
       "      <td>2024-09-07</td>\n",
       "      <td>421.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3393</th>\n",
       "      <td>2024-09-14</td>\n",
       "      <td>421.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3394</th>\n",
       "      <td>2024-09-21</td>\n",
       "      <td>421.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3395</th>\n",
       "      <td>2024-09-28</td>\n",
       "      <td>421.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3396</th>\n",
       "      <td>2024-10-05</td>\n",
       "      <td>421.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3397</th>\n",
       "      <td>2024-10-12</td>\n",
       "      <td>422.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3398</th>\n",
       "      <td>2024-10-19</td>\n",
       "      <td>422.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3399</th>\n",
       "      <td>2024-10-26</td>\n",
       "      <td>422.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3400</th>\n",
       "      <td>2024-11-02</td>\n",
       "      <td>423.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3401</th>\n",
       "      <td>2024-11-09</td>\n",
       "      <td>423.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3402</th>\n",
       "      <td>2024-11-16</td>\n",
       "      <td>423.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3403</th>\n",
       "      <td>2024-11-23</td>\n",
       "      <td>424.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3404</th>\n",
       "      <td>2024-11-30</td>\n",
       "      <td>424.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3405</th>\n",
       "      <td>2024-12-07</td>\n",
       "      <td>424.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3406</th>\n",
       "      <td>2024-12-14</td>\n",
       "      <td>424.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3407</th>\n",
       "      <td>2024-12-21</td>\n",
       "      <td>425.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3408</th>\n",
       "      <td>2024-12-28</td>\n",
       "      <td>425.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3409</th>\n",
       "      <td>2025-01-04</td>\n",
       "      <td>425.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3410</th>\n",
       "      <td>2025-01-11</td>\n",
       "      <td>426.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3411</th>\n",
       "      <td>2025-01-18</td>\n",
       "      <td>426.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3412</th>\n",
       "      <td>2025-02-01</td>\n",
       "      <td>426.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3413</th>\n",
       "      <td>2025-02-08</td>\n",
       "      <td>427.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3414</th>\n",
       "      <td>2025-02-15</td>\n",
       "      <td>426.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3415</th>\n",
       "      <td>2025-02-22</td>\n",
       "      <td>427.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3416 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  concentration\n",
       "0     1958-04-05         317.31\n",
       "1     1958-04-12         317.69\n",
       "2     1958-04-19         317.58\n",
       "3     1958-04-26         316.48\n",
       "4     1958-05-03         316.95\n",
       "5     1958-05-17         317.56\n",
       "6     1958-05-24         317.99\n",
       "7     1958-07-05         315.85\n",
       "8     1958-07-12         315.85\n",
       "9     1958-07-19         315.46\n",
       "10    1958-07-26         315.59\n",
       "11    1958-08-02         315.64\n",
       "12    1958-08-09         315.10\n",
       "13    1958-08-16         315.09\n",
       "14    1958-08-30         314.14\n",
       "15    1958-09-06         313.54\n",
       "16    1958-11-08         313.05\n",
       "17    1958-11-15         313.26\n",
       "18    1958-11-22         313.57\n",
       "19    1958-11-29         314.01\n",
       "20    1958-12-06         314.56\n",
       "21    1958-12-13         314.41\n",
       "22    1958-12-20         314.77\n",
       "23    1958-12-27         315.21\n",
       "24    1959-01-03         315.24\n",
       "25    1959-01-10         315.50\n",
       "26    1959-01-17         315.69\n",
       "27    1959-01-24         315.86\n",
       "28    1959-01-31         315.42\n",
       "29    1959-02-14         316.94\n",
       "...          ...            ...\n",
       "3386  2024-07-27         424.72\n",
       "3387  2024-08-03         424.42\n",
       "3388  2024-08-10         422.50\n",
       "3389  2024-08-17         422.80\n",
       "3390  2024-08-24         421.45\n",
       "3391  2024-08-31         421.57\n",
       "3392  2024-09-07         421.81\n",
       "3393  2024-09-14         421.39\n",
       "3394  2024-09-21         421.77\n",
       "3395  2024-09-28         421.51\n",
       "3396  2024-10-05         421.86\n",
       "3397  2024-10-12         422.13\n",
       "3398  2024-10-19         422.16\n",
       "3399  2024-10-26         422.36\n",
       "3400  2024-11-02         423.15\n",
       "3401  2024-11-09         423.18\n",
       "3402  2024-11-16         423.51\n",
       "3403  2024-11-23         424.03\n",
       "3404  2024-11-30         424.44\n",
       "3405  2024-12-07         424.93\n",
       "3406  2024-12-14         424.79\n",
       "3407  2024-12-21         425.35\n",
       "3408  2024-12-28         425.57\n",
       "3409  2025-01-04         425.93\n",
       "3410  2025-01-11         426.35\n",
       "3411  2025-01-18         426.56\n",
       "3412  2025-02-01         426.28\n",
       "3413  2025-02-08         427.11\n",
       "3414  2025-02-15         426.94\n",
       "3415  2025-02-22         427.29\n",
       "\n",
       "[3416 rows x 2 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(data_csv, skiprows=44)\n",
    "raw_data\n",
    "list_raw = raw_data.columns.values\n",
    "list_columns = ['date','concentration']\n",
    "true_raw_data = []\n",
    "for i in range(len(raw_data)):\n",
    "    tertiary_list = []\n",
    "    for j in range(len(list_raw)):\n",
    "        tertiary_list.append(raw_data[list_raw[j]][i])\n",
    "    true_raw_data.append(tertiary_list)\n",
    "print(true_raw_data[0],type(true_raw_data[0][1]))\n",
    "print(true_raw_data[0][1]-true_raw_data[4][1])\n",
    "true_df = pd.DataFrame(true_raw_data, columns = list_columns)\n",
    "true_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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": [
    "plt.figure()\n",
    "plt.plot(true_df['concentration'][2000:2108])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ce zoom sur les premières valeurs nous permet d'estimer la période associée à l'effet oscillatoire.\n",
    "De ce fait nous pouvons considérer que l'effet oscillatoire a à peu près toujours les mêmes valeurs\n",
    "que celles observées lors des premières périodes. On a donc fait le choix de crée un second DataFrame en prenant les valeurs du premier et en soustrayant les valeurs correspondant à l'effet oscillatoire. Cette méthode devrait pouvoir nous donner une valeur correcte de l'effet progressif."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On observe une période de l'effet oscillatoire d'environ 52 semaines soit un an."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On prendra la seconde année comme valeur de référence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>concentration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958-04-05</td>\n",
       "      <td>3.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958-04-12</td>\n",
       "      <td>4.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958-04-19</td>\n",
       "      <td>4.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958-04-26</td>\n",
       "      <td>3.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958-05-03</td>\n",
       "      <td>3.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1958-05-17</td>\n",
       "      <td>3.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1958-05-24</td>\n",
       "      <td>3.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1958-07-05</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1958-07-12</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1958-07-19</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1958-07-26</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1958-08-02</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1958-08-09</td>\n",
       "      <td>-0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1958-08-16</td>\n",
       "      <td>-0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1958-08-30</td>\n",
       "      <td>-1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1958-09-06</td>\n",
       "      <td>-2.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1958-11-08</td>\n",
       "      <td>-3.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1958-11-15</td>\n",
       "      <td>-3.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1958-11-22</td>\n",
       "      <td>-3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1958-11-29</td>\n",
       "      <td>-2.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1958-12-06</td>\n",
       "      <td>-2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1958-12-13</td>\n",
       "      <td>-3.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1958-12-20</td>\n",
       "      <td>-2.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1958-12-27</td>\n",
       "      <td>-1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1959-01-03</td>\n",
       "      <td>-2.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1959-01-10</td>\n",
       "      <td>-2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1959-01-17</td>\n",
       "      <td>-2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1959-01-24</td>\n",
       "      <td>-2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1959-01-31</td>\n",
       "      <td>-3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1959-02-14</td>\n",
       "      <td>-2.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>2024-07-27</td>\n",
       "      <td>110.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3387</th>\n",
       "      <td>2024-08-03</td>\n",
       "      <td>109.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3388</th>\n",
       "      <td>2024-08-10</td>\n",
       "      <td>107.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3389</th>\n",
       "      <td>2024-08-17</td>\n",
       "      <td>107.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3390</th>\n",
       "      <td>2024-08-24</td>\n",
       "      <td>106.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3391</th>\n",
       "      <td>2024-08-31</td>\n",
       "      <td>105.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3392</th>\n",
       "      <td>2024-09-07</td>\n",
       "      <td>105.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3393</th>\n",
       "      <td>2024-09-14</td>\n",
       "      <td>105.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3394</th>\n",
       "      <td>2024-09-21</td>\n",
       "      <td>106.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3395</th>\n",
       "      <td>2024-09-28</td>\n",
       "      <td>105.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3396</th>\n",
       "      <td>2024-10-05</td>\n",
       "      <td>105.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3397</th>\n",
       "      <td>2024-10-12</td>\n",
       "      <td>105.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3398</th>\n",
       "      <td>2024-10-19</td>\n",
       "      <td>105.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3399</th>\n",
       "      <td>2024-10-26</td>\n",
       "      <td>105.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3400</th>\n",
       "      <td>2024-11-02</td>\n",
       "      <td>106.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3401</th>\n",
       "      <td>2024-11-09</td>\n",
       "      <td>105.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3402</th>\n",
       "      <td>2024-11-16</td>\n",
       "      <td>106.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3403</th>\n",
       "      <td>2024-11-23</td>\n",
       "      <td>107.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3404</th>\n",
       "      <td>2024-11-30</td>\n",
       "      <td>106.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3405</th>\n",
       "      <td>2024-12-07</td>\n",
       "      <td>106.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3406</th>\n",
       "      <td>2024-12-14</td>\n",
       "      <td>107.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3407</th>\n",
       "      <td>2024-12-21</td>\n",
       "      <td>106.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3408</th>\n",
       "      <td>2024-12-28</td>\n",
       "      <td>106.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3409</th>\n",
       "      <td>2025-01-04</td>\n",
       "      <td>106.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3410</th>\n",
       "      <td>2025-01-11</td>\n",
       "      <td>107.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3411</th>\n",
       "      <td>2025-01-18</td>\n",
       "      <td>106.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3412</th>\n",
       "      <td>2025-02-01</td>\n",
       "      <td>106.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3413</th>\n",
       "      <td>2025-02-08</td>\n",
       "      <td>107.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3414</th>\n",
       "      <td>2025-02-15</td>\n",
       "      <td>106.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3415</th>\n",
       "      <td>2025-02-22</td>\n",
       "      <td>107.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3416 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  concentration\n",
       "0     1958-04-05           3.77\n",
       "1     1958-04-12           4.65\n",
       "2     1958-04-19           4.43\n",
       "3     1958-04-26           3.05\n",
       "4     1958-05-03           3.49\n",
       "5     1958-05-17           3.44\n",
       "6     1958-05-24           3.57\n",
       "7     1958-07-05           0.97\n",
       "8     1958-07-12           0.65\n",
       "9     1958-07-19           0.35\n",
       "10    1958-07-26           0.53\n",
       "11    1958-08-02           0.00\n",
       "12    1958-08-09          -0.76\n",
       "13    1958-08-16          -0.68\n",
       "14    1958-08-30          -1.58\n",
       "15    1958-09-06          -2.86\n",
       "16    1958-11-08          -3.68\n",
       "17    1958-11-15          -3.31\n",
       "18    1958-11-22          -3.11\n",
       "19    1958-11-29          -2.60\n",
       "20    1958-12-06          -2.42\n",
       "21    1958-12-13          -3.01\n",
       "22    1958-12-20          -2.23\n",
       "23    1958-12-27          -1.75\n",
       "24    1959-01-03          -2.52\n",
       "25    1959-01-10          -2.57\n",
       "26    1959-01-17          -2.08\n",
       "27    1959-01-24          -2.75\n",
       "28    1959-01-31          -3.92\n",
       "29    1959-02-14          -2.07\n",
       "...          ...            ...\n",
       "3386  2024-07-27         110.30\n",
       "3387  2024-08-03         109.54\n",
       "3388  2024-08-10         107.30\n",
       "3389  2024-08-17         107.69\n",
       "3390  2024-08-24         106.39\n",
       "3391  2024-08-31         105.93\n",
       "3392  2024-09-07         105.95\n",
       "3393  2024-09-14         105.62\n",
       "3394  2024-09-21         106.05\n",
       "3395  2024-09-28         105.11\n",
       "3396  2024-10-05         105.13\n",
       "3397  2024-10-12         105.56\n",
       "3398  2024-10-19         105.48\n",
       "3399  2024-10-26         105.75\n",
       "3400  2024-11-02         106.17\n",
       "3401  2024-11-09         105.76\n",
       "3402  2024-11-16         106.51\n",
       "3403  2024-11-23         107.07\n",
       "3404  2024-11-30         106.68\n",
       "3405  2024-12-07         106.86\n",
       "3406  2024-12-14         107.02\n",
       "3407  2024-12-21         106.74\n",
       "3408  2024-12-28         106.23\n",
       "3409  2025-01-04         106.92\n",
       "3410  2025-01-11         107.32\n",
       "3411  2025-01-18         106.79\n",
       "3412  2025-02-01         106.32\n",
       "3413  2025-02-08         107.29\n",
       "3414  2025-02-15         106.90\n",
       "3415  2025-02-22         107.23\n",
       "\n",
       "[3416 rows x 2 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revised_data = []\n",
    "for i in range(len(true_raw_data)):\n",
    "    revised_data.append([true_raw_data[i][0],true_raw_data[i][1]-true_raw_data[i%52+58][1]])\n",
    "revised_df = pd.DataFrame(revised_data, columns = list_columns)\n",
    "revised_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut maintenant représenter sur un graphique la courbe révisée de concentration en CO2 dans l'air. Ce qu'on obtient n'est qu'une approximation\n",
    "grossière de l'allure de la concentration au cours du temps en ne tennant pas compte des effets oscillatoires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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": [
    "plt.figure()\n",
    "plt.plot(revised_df['concentration'][:500])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque que cette méthode ne donne pas de résultats concluants. On se propose donc de faire une moyenne de la concentration\n",
    "sur chaque année et d'afficher cette valeur moyenne au lieu de tenter de supprimer l'effet périodique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65\n",
      "316.29519230769233 353.10076923076934 422.42807692307684 332.3986538461538\n"
     ]
    }
   ],
   "source": [
    "mean_values = []\n",
    "for i in range(len(true_df)//52):\n",
    "    M=0\n",
    "    for j in range(52):\n",
    "        M += true_df['concentration'][i*52+j]\n",
    "    mean_values.append(M/52)\n",
    "mean_values\n",
    "print(len(mean_values))\n",
    "print(mean_values[0], mean_values[30],mean_values[64], mean_values[17])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff0d3ed6be0>]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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awKJ0rFtEMsChQ36z7ilTfPfJESOg4HcroGFDKFwYFi6EK64Iukw5Q6m6GBsakS8HygNvOeeWmNkTwEwzG4D/l8E1odNLAouTffuu0LE/fmYnoBNAmTJl/vYPICLpY80av3Ry2zYYONDfEGVr1/iQL1TIj+IvvDDoMuVvSNXFWOdcYmiKphRQy8yqAA8DTzrnSgNPAsNDp59uws6d5jOHOueinXPRxYoV+3vVi0i6GDPG7+p35AjMnw9PPgm2aaNvQpY3L8ybp5APY2e06sY5dxBYgJ97bw98FHprEn56BvwIvnSybyvF79M6IpKFxMVB585wzz1+RmbFCr/Khu++87e/mvmrshddFHSpkgapWXVTzMwKh57nAxoAG/HhfW3otPrAd6Hn04BWZpbHzMoBFYCl6V24iKTNjh0+1IcM8Te0zp0LF1yAn4evXx9OnvQHL7446FIljVIzR18cGB2ap88BTHTOTTezg8AgM8sJnCA03+6cW2dmE4H1QALQ2TmXmDHli8jf8dlnfpVkYqLvHX/bbfgrsTExvolNmTL+TtfKlYMuVdKB+UU1wYqOjnaxsbFBlyES8RIS4Lnn4KWX/GrJDz+E8uXxy2wefdR3LHv8cXjxRShYMOhyJQVmttw5F53SeWqBIJJN7NkDd9/tL7Z27Ahvvgn5csRBq/bwwQf+JqipU7V8MgKpBYJINjB/PlSv7jcHGTnStzTIlzvRz9988IEfwcfGKuQjlIJeJIIlJfkMb9DA3++0dCncey/+9tdOnfzczcCBfqPXXLmCLlcyiKZuRCLUvn1+wD5rlp+yeffd0LS7c36ZzYgR0KuXXzQvEU1BLxKBvvgCWreGAwd8wD/wQLLeY337+lH8Y4/5PV0l4mnqRiSCJCb6qZr69eGss2DJEj9D81vIv/66H8W3awdvvKHOk9mERvQiEWLPHj9VM3eu39nv7bd92AN+sr57d3jtNd/QZvhwyKFxXnahoBeJAHPm+JA/dMivqOnQIdlg/cQJP4KfNMn3Oxg0CKLUOTw70a90kTB28qTf+alRIzjnHN87vmPHZCH/889+yc2kSTBggF88r5DPdjSiFwlTO3b41TQLF/pwHzQIChRIdsL69b63wY4dfq38XXcFVqsESyN6kTA0ZYq/AWrNGhg/3k/X/BbyR49Cjx6+x8HPP/t5HYV8tqagFwkjv/4KjzwCt9/ue9SsWAGtWoXedM7fAHXppX4vwHbtYMMGqFMn0JoleAp6kTCxdq3vUPD22/5+p2++SdYmfvVqaNzY7/9XtKh/c/hw0KY+goJeJMtzzof7FVfA/v0wcyb07w+5c+P3/Wvb1s/jLFvmL7YuWwbXXJPi50r2oYuxIlnY/v3+rtapU6FJExg9Gs47D79ovk8ff9trrlx+Tr57dyhSJOiSJQtS0ItkUXPm+Gn2n3/2HQu6dIEcu3fBEwNg6FCIj/e/BXr1ghIlgi5XsjAFvUgWEx8PPXv6Ze+XXup3g6peaBs81A9GjfJ3ubZt60+qUCHociUMKOhFspCNG/3a+BUr4OGHYUB/R/4XY3zqR0X5BfPdu0O5ckGXKmFEQS+SBTgH77wDXbtC/vzw8cfQrBnQ7xV45RVo397v/6cpGvkbFPQiAfvpJz9Q//RTv0Jy5EgoXhwYM8Zv1t26te8dryZk8jfpb45IgD75BC67zF94/fe//Xx88eL4FpQdOsB11/nkV8hLGuhvj0gAjh6FBx/00zMlSsDy5X4fkBw58H0Nbr8dKlb0vQ7y5Am6XAlzCnqRTLZokb+/6b334J//9JuDVK4cenPbNrjxRr/n3+ef+41eRdJIQS+SSeLj/R7cdepAQgIsWOBb0uTJg+8Z36cPVKkCR474kC9dOuiSJUIo6EUywfr1cPXVfrvWdu18a5p69UJvTp/uh/S9esHNN/upm6pVA61XIouCXiQDJSb63ftq1oSdO+Gjj/y11UKFgM2b4ZZb/CN3bpg9228QUqZM0GVLhFHQi2SQ7dvh+ut9p8kmTXz3ydtuw/c0ePxxP4pfsMB3KFu1yu8EJZIBFPQi6cw5f6G1alWf36NG+cUz5xeO88P78uXhrbf84vktW/xvgty5gy5bIliKQW9mec1sqZmtMrN1ZvZCsvceM7NNoeOvJjseY2ZbQu81zqjiRbKaXbv8oplOnXxb4dWr/U2ttnaNXzDfrZufrF+92t8Ke/75QZcs2UBq7oyNA+o7546aWS7gazP7HMgHNAeqOufizOw8ADOrBLQCKgMlgDlmVtE5l5gxP4JI8JyD99/3HSZPnoTBg32vmhw5gIkT4b774OyzYcYMf/urSCZKcUTvvKOhl7lCDwc8DPRzzsWFztsbOqc5MME5F+ec2w5sAWqle+UiWcSePdC8Odx7rx+0r1oFnTtDDpcITz8NLVv6hfPLlyvkJRCpmqM3sygzWwnsBWY755YAFYG6ZrbEzL4wsytCp5cEfkj27btCx/74mZ3MLNbMYvft25e2n0IkAM7B2LFQqZJfMDNwoL+2Wr48cOCAXyr5yivw0EMwf36ot4FI5ktV0DvnEp1z1YFSQC0zq4Kf9ikCXAX8E5hoZgbY6T7iNJ851DkX7ZyLLqZ9LSXM7N7tR/Ft28Ill8DKlfDkk76TMEuXwuWX+3B/7z2/D6AutkqAzmjVjXPuILAAaIIfqX8UmtpZCiQBRUPHk9/SVwrYnS7VigTs1Fx85cp+FP/aa/DVV3DxxaE3X3/d3/rqHHz5Jdx/f9Ali6Rq1U0xMyscep4PaABsBKYC9UPHKwK5gf3ANKCVmeUxs3JABWBpxpQvknl++AGaNvWraCpV8nPxTz0VGsUfOOCH+E895adsVqyAK68MumQRIHWrbooDo80sCv+LYaJzbrqZ5QZGmNlaIB5o75xzwDozmwisBxKAzlpxI+Hs1Lr4bt38na6vv+47TUZFAb/+6nfu7tHDX5UdNMi/aaebwRQJRopB75xbDdQ4zfF4oO1ffE9foG+aqxMJ2LZtfvZl/nx/l+uwYfCPcs7Pw48aBePHw6FDvqXwwoUQHR10ySJ/oh2mRE4jMdFvBPLss37k/u6bcTxQ4QvsnTm+CdmGDZAvH9xxh19Xef312hxEsiwFvcgfrFnjuxMsWwZNK21lSOGelO42FeLiIFcuf7H1qaegRQt/E5RIFqegFwmJi/Mt4fv1cxTJc5zxBbrQcv1wrGpVePRR33Ssbl0oUCDoUkXOiIJeBL9EstP9iWzcHEXbXBN5/VhnijarDb1j/Zp4kTCmoJds7eBB6NHlBEPfz8uF9iOf8SA33pQHes/yTeRFIoCCXrIl52DyiEM81sXYe6wAT/Ea/7plOQWefwlq/GmRmUhYU9BLtrNz9UEevf1HPtlamRp8y/TGk7n8tbuhctegSxPJEFoPJtlGwuHjDLx5DpWq5WTu1rL0rzmepesKcvmMvr6ngUiE0oheIl9iIsue/ZhOAyqyMqEBTc9fyuD3z+bCRq2DrkwkUyjoJaId3HmYnvW+4u0dt1I8989M7rOB256ppQ4Fkq1o6kYiknMwbtA+Lrkonnd2NOHRa9eyYW9Rbu95qUJesh0FvUScTZug4RUHafNEMcq4HSwbEsu/F1Sl0NlKeMmeFPQSMY4f971pqlZJJHY5DCn2HIvWnEXNh9UuWLI3zdFL2HMOpk2DLl0cO3YY9zCWV+t8wgXThkKRIkGXJxI4jeglrG3d6jcDufVWKLh3G19Qj/e7ruaCeeMU8iIhCnoJS8ePQ+/efvn7l/MTeK1gb1bY5dSb+BgMGOC7TIoIoKkbCTPOwUcf+S7BO3fC3VXX8OramylZIT9MWQSXXhp0iSJZjkb0EjY2bIBGjeDOO6Fw0s98ce7tjF1dlZJ3Xu13fFLIi5yWgl6yvEOH/Ai+alXHsoXxvFnsXyzfdT71Ku6BL76ADz6AQoWCLlMky9LUjWRZSUkwciTExDj274cHzp1Cn/0PUqzsefDeZGjWTJtwi6SCgl6ypIULoUsXiI2FawqtZYZrT82Cv0D//nDPPX4jVxFJFU3dSJaycyfcfTfUrg271+xnLHfzdf7G1Hzrfn/L6733KuRFzpCCXrKEY8fguefgkkscUyadpJf1YXO+6tzdvya2dQs88gjkzh10mSJhSVM3EqikJBg7FmJi4McfoWWB6byS8CgXtr8e+q+AYsWCLlEk7CnoJTBffeVX08TGQvQ5W5lAe+qU2g/vjIbrrgu6PJGIoakbyXTbtvm18PXqwZ4f4vlPsadYcuhS6rzQCFatUsiLpDON6CXTHDgAffrA4MG+Q8G/bltB189uIP+5+eCL+f4KrIikuxRH9GaW18yWmtkqM1tnZi/84f1uZubMrGiyYzFmtsXMNplZ44woXMJHXBy89hpcdBEMGgTt2ybw3W3d6TWlJvmvqQ7ffquQF8lAqRnRxwH1nXNHzSwX8LWZfe6cW2xmpYGGwM5TJ5tZJaAVUBkoAcwxs4rOucQMqF+ysKQkmDgRnnkGtm+HGxvE8+rlE6ky5UXYvBl69PBD/Jz6h6VIRkpxRO+8o6GXuUIPF3r9OtA92WuA5sAE51ycc247sAWolX4lSziYNw9q1YLWraFQ1FFm39CPz74qRJVX7oHCheGTT6BfP4W8SCZI1cVYM4sys5XAXmC2c26JmTUDfnTOrfrD6SWBH5K93hU6JtnA6tVw441www2wb8cx3r+wF99uKUSDZS9Dx46wYgUsWeKbyItIpkjVcCo07VLdzAoDU8ysKtATaHSa00/XfMT96SSzTkAngDJlyqS6YMmavv/e94cfM8ZROF8cA859nc77XyDvORfC0Hf97a4FCgRdpki2dEb/bnbOHTSzBfjpmXLAKvNNpUoB35pZLfwIvnSybysF7D7NZw0FhgJER0f/6ReBhIe9e6FvX3j7bYgigW75hxJzrCdFKpWHoeOgeXO1LBAJWIpBb2bFgJOhkM8HNABecc6dl+yc74Fo59x+M5sGjDOzgfiLsRWApRlSvQTm8GEYONCvpvn1V0eHC+fRe1t7StUoCS99CPXrq7OkSBaRmhF9cWC0mUXh5/QnOuem/9XJzrl1ZjYRWA8kAJ214iZy/PorvPUWvPyyXxd/R+3/o8+mu7jkhyXQ93no3l0XWEWymBT/i3TOrQZqpHBO2T+87gv0TVNlkqXEx8Pw4X415O7d0Lh+PH3OepXoj3tBlSoweylUrx50mSJyGmqBIP9TQgKMGgWXXOIbSJYrm8SCRyYy49vzif7kOT+Cj41VyItkYQp6Oa2kJBg/HipXhvvugyJFHJ/2XMhXP13MtUNawpVX+rWUr7wCefIEXa6I/A8KevkvSUkweTJUq+ZXRObODR/130psweu5qW9tLE9u+PxzmDHD/xYQkSxPQS+AD/gpU6BGDd9ZMj4exr91gFU17+O27hWw9ev8VdhVq6BJk6DLFZEzoOUR2ZxzMG0aPP88rFwJFSrAf4adoNWOV8nZrR8kJsI//+kb1px9dtDlisjfoKDPppKSYOpUePFFH/Dly8Po0XD3ZWvI2epO33Tsrrt8P5py5YIuV0TSQFM32UxSEkya5BfJ3HGH36t11CjYsAHaJY0iZ+0r/d1Qc+fCBx8o5EUigII+m0hIgHHj4LLL/EA9Ph7GjIH166F9i+Pk7NTBL6+56irfeKx+/aBLFpF0oqCPcKdudLrkEmjTxnclGD8e1q2DNi0TyDljug/3UaOgVy+YPRsuuCDoskUkHWmOPkIdP+4Dvn9/+OEHuPxyv6qmWTPIsXkjPDMS3n8f9uzxwf7ZZ1pNIxKhFPQR5uBBvwpy0CDYt8/v0Pfee9DomqPYpIlQZxgsWuQ7St58M3ToADfd5DdxFZGIpKCPEHv2wOuv+3bBR474zT9innbUzRfrk/7O8XD0qJ/D6d8f2rbVFI1INqGgD3ObN8OAAX5pZEKCo8W1e3m62gyq75kBHZbB1q2QLx+0bAkPPABXX632wSLZjII+TC1ZAq++6ufdc+d2dCg1m67bHqH8/K0wHyhd2k/Md+vmN27VzU4i2ZaCPowkJcH06X4E/9VXfo/tZzr+xGOzm3H+zm+hR1e4/nqoWROKFQu6XBHJIhT0YeDXX+E///G7OW3eDGXK+N2d7s87hrNIrf9UAAAJ7ElEQVSeegDOOQcWLPBXXkVE/kDr6LOwn36C556DCy+EBx+EggX9Gvita47z5PoHOOuRe+Caa/wNTgp5EfkLCvosaM0av+qxTBn417986/f58yF2aRKtTv6HnJUqwrBh0LMnzJoF552X8oeKSLalqZssIjHR37M0aJBvM5M/P9x/P3TpAhUrAl9/DVc96Xdzio6GCROgTp2gyxaRMKCgD9jhwzBiBLz5JmzbBiVL+o23Oz3gOOfw9/6qa48pvtVkyZL+btY2bSCH/jEmIqmjoA/Ixo3+DtZRo/x9TNdcAy8/e4zbEiaRa8FsqP4l7NrlTy5SxDeM79YNChQIsmwRCUMK+kyUmOiXRw4eDHPm+G36WrZI5PEaXxO9eDA8/AnExUHx4lCvHtSt679WrqwRvIj8bQr6TLB3r28w9u67sGMHlCoFfXvHcf/hgZz3/gAYe8Cve3/wQd+aIDpad6+KSLpR0GcQ5+Cbb2DIEPjwQzh5Eq67Dl4b4Gh+4gNyPt0NfvzR7/7RoQM0bKjGYiKSIRT06ezQIb+hx7vv+mWShQrBQw/5R6WE1fDYY/Dll34X7okT/eS8iEgGUtCnA+dg2TIf7hMm+F7wNWr4ppGtWzkKrPwGXnjTD+2LFPEnduzoWwWLiGQwBX0aHDwIY8f6e5dWrvQLYu6+20+1R1f+1d/GWvdN/2bhwtC1Kzz9tG9ZICKSSRT0Z8g5f+/Se+/5TbZPnPCj9yFvJtKm8koKfbsA+nwFX3zhfxNUqeJH8G3aaGmkiAQixaA3s7zAl0Ce0PkfOueeM7P+wC1APLAVuM85dzD0PTFARyAReNw5NzOD6s80u3f7e5VGjvSNxQoVgnvvhQcabKfm2K7w9Cw4dsyffNFFcNtt0K4dXHutVtCISKBSM6KPA+o7546aWS7gazP7HJgNxDjnEszsFSAG6GFmlYBWQGWgBDDHzCo65xIz6GfIMPHx8Mkn/s7VGTN8m+B69SAmBlo0PEiBAS9Aq8F+pH7vvf7NOnWgRImgSxcR+U2KQe+cc8DR0MtcoYdzzs1Kdtpi4M7Q8+bABOdcHLDdzLYAtYBF6VZ1BnLOt5MZPdpPsR844DsPxMT4LC9fLtFPyld/Fn7+2e/a9OKLaiwmIllWqubozSwKWA6UB95yzi35wykdgA9Cz0vig/+UXaFjWdquXTBunA/49eshb1649VZo394vcY+KwjejqdMGFi/2o/c33vAT9CIiWViqgj407VLdzAoDU8ysinNuLYCZ9QQSgLGh0083Ie3+eMDMOgGdAMqUKfM3Sk+7w4fho4/8ph7z5/vRfO3aMHQotGjhF8r8Zvx4v5wmRw6/1KZ1a829i0hYOKNVN865g2a2AGgCrDWz9kBT4IbQFA/4EXzpZN9WCth9ms8aCgwFiI6O/tMvgowSHw8zZ/qs/vhjv2rmoovguW5HaVNjA+UvPxvKlfv9LtWjR/1NTqNG+Zubxo3zO4GIiISJ1Ky6KQacDIV8PqAB8IqZNQF6ANc6544n+5ZpwDgzG4i/GFsBWJr+padeUpLv9jtunF8S+csvcG6heO6rvIJ78kziqu3jsf7JfhdFRUHZslChAmzZ4qdseveGXr0gp1akikh4SU1qFQdGh+bpcwATnXPTQxdZ8wCzzU9hLHbOPeScW2dmE4H1+CmdzkGsuHEOli71d6pOmuTbyhTIn8StZVdxNwNo+MtEcq10cOml0OAGqF7dP9+7F7777vdHgQIwb55fJikiEobs9xmX4ERHR7vY2Ng0f45z8O23Ptg/+AC+/x5y50qiSYVttEoYQ7PN/SkQFQc33+xbEDRsCPnypf0HEBEJgJktd85Fp3Re2M9DOAexC+P5cNhBPvwsH9v2nkVOS6BB7q94nlE0P/kxhdcf8qP1V5+He+6BCy4IumwRkUwT1kG/bMwm7upQgO9PliInRbiBuTyT4yNurbCOc6PL+emY6vdAtWq+37uISDYU1kH/j2pnUfmc3fS+YinNbzXOubICVBzst24SEREgzIP+3MtKMH1PCSDFKSoRkWxLG5GKiEQ4Bb2ISIRT0IuIRDgFvYhIhFPQi4hEOAW9iEiEU9CLiEQ4Bb2ISITLEk3NzGwfsCMNH1EU2J9O5WS2cK4dwrv+cK4dwrv+cK4dsk79FzrnUuzvkiWCPq3MLDY1HdyyonCuHcK7/nCuHcK7/nCuHcKvfk3diIhEOAW9iEiEi5SgHxp0AWkQzrVDeNcfzrVDeNcfzrVDmNUfEXP0IiLy1yJlRC8iIn8hrIPezJqY2SYz22JmTwddT0rMbISZ7TWztcmOnWNms83su9DXIkHW+FfMrLSZzTezDWa2zsy6hI6HS/15zWypma0K1f9C6HhY1A9gZlFmtsLMpodeh1Pt35vZGjNbaWaxoWNhUb+ZFTazD81sY+jv/9XhUvspYRv0ZhYFvAXcCFQCWptZpWCrStEooMkfjj0NzHXOVQDmhl5nRQlAV+fcpcBVQOfQ/97hUn8cUN85Vw2oDjQxs6sIn/oBugAbkr0Op9oBrnfOVU+2LDFc6h8EzHDOXQJUw/9/EC61e865sHwAVwMzk72OAWKCrisVdZcF1iZ7vQkoHnpeHNgUdI2p/Dk+BhqGY/1AfuBb4MpwqR8ohQ+U+sD0cPu7A3wPFP3DsSxfP1AI2E7oemY41Z78EbYjeqAk8EOy17tCx8LN+c65/wMIfT0v4HpSZGZlgRrAEsKo/tDUx0pgLzDbORdO9b8BdAeSkh0Ll9oBHDDLzJabWafQsXCo/x/APmBkaNpsmJkVIDxq/004B72d5piWEGUwMysITAaecM4dDrqeM+GcS3TOVcePjmuZWZWga0oNM2sK7HXOLQ+6ljSo7ZyriZ9q7Wxm9YIuKJVyAjWBt51zNYBjZPVpmtMI56DfBZRO9roUsDugWtLiJzMrDhD6ujfgev6SmeXCh/xY59xHocNhU/8pzrmDwAL89ZJwqL820MzMvgcmAPXNbAzhUTsAzrndoa97gSlALcKj/l3ArtC//gA+xAd/ONT+m3AO+mVABTMrZ2a5gVbAtIBr+jumAe1Dz9vj576zHDMzYDiwwTk3MNlb4VJ/MTMrHHqeD2gAbCQM6nfOxTjnSjnnyuL/ns9zzrUlDGoHMLMCZnbWqedAI2AtYVC/c24P8IOZXRw6dAOwnjCo/b8EfZEgjRdKbgI2A1uBnkHXk4p6xwP/B5zEjxQ6AufiL7J9F/p6TtB1/kXtdfBTY6uBlaHHTWFUf1VgRaj+tUDv0PGwqD/Zz3Edv1+MDYva8fPcq0KPdaf+Ww2j+qsDsaG/O1OBIuFS+6mH7owVEYlw4Tx1IyIiqaCgFxGJcAp6EZEIp6AXEYlwCnoRkQinoBcRiXAKehGRCKegFxGJcP8P711CBU9w8ncAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(mean_values, 'r')\n",
    "approx = []\n",
    "diff = []\n",
    "a=0.013\n",
    "b=0.84\n",
    "c=316.3\n",
    "for i in range(len(mean_values)):\n",
    "    approx.append(0.013*i**2+0.84*i+316.3)\n",
    "    diff.append((approx[i]-mean_values[i])/mean_values[i])\n",
    "plt.plot(approx, 'b')\n",
    "#plt.plot(diff, 'g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque qu'il s'agit probablement d'une fonction parabolique. On cherche donc à estimer les valeurs de a,b et c dans la formule $f(x)\\:=\\:ax^{2}+bx+c$. On peut déjà voir que $b\\:=\\:f(0)$. Après calcul on obtient que si on prend trois valeurs $x_{1},\\:x_{2},\\:x_{3}$ on a $a\\:=\\:\\frac{\\frac{f(x_{2})-f(x_{1})}{x_{2}-x_{1}}-\\frac{f(x_{2})-f(x_{3})}{x_{2}-x_{3}}}{x_{1}-x_{3}}$. Finalement on peut déterminer b : $b\\:=\\:\\frac{f(x)-ax^{2}-c}{x}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En utilisant les données mises à notre disposition, on peut extrapoler un modèle parabolique avec les valeurs suivantes : $a\\:=\\:0.013,\\: b\\:=\\:0.84,\\: c\\:=\\: 316.3$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut comparer notre modèle théorique et les valeurs réelles avec oscillations pour voir si on a bien une représentation\n",
    "cohérente de la tendance :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff0d36a3550>]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(true_df['concentration'],'b')\n",
    "new_approx = []\n",
    "for i in range(len(true_df['concentration'])):\n",
    "    x=i*65/len(true_df['concentration'])\n",
    "    new_approx.append(a*x**2+b*x+c)\n",
    "\n",
    "plt.plot(new_approx, 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}