From 6324826d32850a16e392e1df368a080571765311 Mon Sep 17 00:00:00 2001
From: 96941f17518054310dda274f17ef8e5e
 <96941f17518054310dda274f17ef8e5e@app-learninglab.inria.fr>
Date: Wed, 29 Sep 2021 14:20:33 +0000
Subject: [PATCH] Exercice 3

---
 module3/exo3/exercice.ipynb             | 1204 ++++++++-
 module3/exo3/weekly_in_situ_co2_mlo.csv | 3239 +++++++++++++++++++++++
 2 files changed, 4440 insertions(+), 3 deletions(-)
 create mode 100644 module3/exo3/weekly_in_situ_co2_mlo.csv

diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index 0bbbe37..dcab553 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -1,5 +1,1204 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Concentration de CO2 dans l'atmosphère depuis 1958"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from scipy import fftpack,signal\n",
+    "import numpy as np\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score\n",
+    "from statsmodels.tsa.seasonal import seasonal_decompose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Les données de l'incidence de la varicelle sont disponibles du site Web de [l'institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/mlo.html). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1958 et se termine avec une semaine récente.\n",
+    "\n",
+    "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données.\n",
+    "\n",
+    "Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data imported via the local file.\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>CO2_concentration</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Time</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>2021-02-06</th>\n",
+       "      <td>416.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-02-13</th>\n",
+       "      <td>416.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-02-20</th>\n",
+       "      <td>416.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-02-27</th>\n",
+       "      <td>416.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-03-06</th>\n",
+       "      <td>417.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-03-13</th>\n",
+       "      <td>416.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-03-20</th>\n",
+       "      <td>418.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-03-27</th>\n",
+       "      <td>416.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-04-03</th>\n",
+       "      <td>417.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-04-10</th>\n",
+       "      <td>419.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-04-17</th>\n",
+       "      <td>417.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-04-24</th>\n",
+       "      <td>418.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-05-01</th>\n",
+       "      <td>419.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-05-08</th>\n",
+       "      <td>418.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-05-15</th>\n",
+       "      <td>419.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-05-22</th>\n",
+       "      <td>417.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-05-29</th>\n",
+       "      <td>419.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-06-05</th>\n",
+       "      <td>419.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-06-12</th>\n",
+       "      <td>418.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-06-19</th>\n",
+       "      <td>418.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-06-26</th>\n",
+       "      <td>417.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-07-03</th>\n",
+       "      <td>417.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-07-10</th>\n",
+       "      <td>417.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-07-17</th>\n",
+       "      <td>416.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-07-24</th>\n",
+       "      <td>415.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-07-31</th>\n",
+       "      <td>414.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-08-07</th>\n",
+       "      <td>414.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-08-14</th>\n",
+       "      <td>414.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-08-21</th>\n",
+       "      <td>414.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-08-28</th>\n",
+       "      <td>412.68</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3238 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            CO2_concentration\n",
+       "Time                         \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",
+       "2021-02-06             416.91\n",
+       "2021-02-13             416.46\n",
+       "2021-02-20             416.16\n",
+       "2021-02-27             416.45\n",
+       "2021-03-06             417.56\n",
+       "2021-03-13             416.54\n",
+       "2021-03-20             418.00\n",
+       "2021-03-27             416.43\n",
+       "2021-04-03             417.69\n",
+       "2021-04-10             419.02\n",
+       "2021-04-17             417.66\n",
+       "2021-04-24             418.54\n",
+       "2021-05-01             419.65\n",
+       "2021-05-08             418.16\n",
+       "2021-05-15             419.02\n",
+       "2021-05-22             417.98\n",
+       "2021-05-29             419.49\n",
+       "2021-06-05             419.46\n",
+       "2021-06-12             418.90\n",
+       "2021-06-19             418.49\n",
+       "2021-06-26             417.82\n",
+       "2021-07-03             417.70\n",
+       "2021-07-10             417.08\n",
+       "2021-07-17             416.91\n",
+       "2021-07-24             415.92\n",
+       "2021-07-31             414.94\n",
+       "2021-08-07             414.56\n",
+       "2021-08-14             414.66\n",
+       "2021-08-21             414.42\n",
+       "2021-08-28             412.68\n",
+       "\n",
+       "[3238 rows x 1 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",
+    "file_name = \"weekly_in_situ_co2_mlo.csv\"\n",
+    "    \n",
+    "if os.path.isfile(file_name):\n",
+    "    raw_data = pd.read_csv(file_name)\n",
+    "    raw_data = raw_data.set_index('Time').sort_index()\n",
+    "    print(\"Data imported via the local file.\")\n",
+    "else:\n",
+    "    raw_data = pd.read_csv(data_url, skiprows=43)\n",
+    "    print(\"Data downloaded via the url.\")\n",
+    "    raw_data.index.name = 'Time'\n",
+    "    raw_data = raw_data.rename(columns={raw_data.columns[0]: 'CO2_concentration'}).sort_index()\n",
+    "    raw_data.to_csv(file_name)\n",
+    "    print(\"Data saved locally.\")\n",
+    "    \n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analyse de la courbe : tendance et saisonnalité"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On transforme l'index en Time series avec une fréquence hebdomadaire."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3310, 1)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.index = pd.to_datetime(raw_data.index)\n",
+    "data=raw_data.sort_index().asfreq('W-Sat')\n",
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Malheureusement, des valeurs NaN se trouvent dans la table: il doit d'agir de semaines où les concentrations de C02 n'ont pas pu être enregistrées pour diverses raisons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(72, 1)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[data.isnull().any(axis=1)].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Comme ces \"mauvaises semaines\" sont minoritaires (72 semaines sur 3310). Je remplace ces NaN avec des valeurs obtenues par interpolation linéaire."
+   ]
+  },
+  {
+   "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>Time</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1958-03-29</th>\n",
+       "      <td>316.190000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-05</th>\n",
+       "      <td>317.310000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-12</th>\n",
+       "      <td>317.690000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-19</th>\n",
+       "      <td>317.580000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-26</th>\n",
+       "      <td>316.480000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-05-03</th>\n",
+       "      <td>316.950000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-05-10</th>\n",
+       "      <td>317.255000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-05-17</th>\n",
+       "      <td>317.560000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-05-24</th>\n",
+       "      <td>317.990000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-05-31</th>\n",
+       "      <td>317.633333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-06-07</th>\n",
+       "      <td>317.276667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-06-14</th>\n",
+       "      <td>316.920000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-06-21</th>\n",
+       "      <td>316.563333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-06-28</th>\n",
+       "      <td>316.206667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-07-05</th>\n",
+       "      <td>315.850000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-07-12</th>\n",
+       "      <td>315.850000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-07-19</th>\n",
+       "      <td>315.460000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-07-26</th>\n",
+       "      <td>315.590000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-08-02</th>\n",
+       "      <td>315.640000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-08-09</th>\n",
+       "      <td>315.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-08-16</th>\n",
+       "      <td>315.090000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-08-23</th>\n",
+       "      <td>314.615000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-08-30</th>\n",
+       "      <td>314.140000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-09-06</th>\n",
+       "      <td>313.540000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-09-13</th>\n",
+       "      <td>313.485556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-09-20</th>\n",
+       "      <td>313.431111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-09-27</th>\n",
+       "      <td>313.376667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-10-04</th>\n",
+       "      <td>313.322222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-10-11</th>\n",
+       "      <td>313.267778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-10-18</th>\n",
+       "      <td>313.213333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-02-06</th>\n",
+       "      <td>416.910000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-02-13</th>\n",
+       "      <td>416.460000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-02-20</th>\n",
+       "      <td>416.160000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-02-27</th>\n",
+       "      <td>416.450000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-03-06</th>\n",
+       "      <td>417.560000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-03-13</th>\n",
+       "      <td>416.540000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-03-20</th>\n",
+       "      <td>418.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-03-27</th>\n",
+       "      <td>416.430000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-04-03</th>\n",
+       "      <td>417.690000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-04-10</th>\n",
+       "      <td>419.020000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-04-17</th>\n",
+       "      <td>417.660000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-04-24</th>\n",
+       "      <td>418.540000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-05-01</th>\n",
+       "      <td>419.650000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-05-08</th>\n",
+       "      <td>418.160000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-05-15</th>\n",
+       "      <td>419.020000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-05-22</th>\n",
+       "      <td>417.980000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-05-29</th>\n",
+       "      <td>419.490000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-06-05</th>\n",
+       "      <td>419.460000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-06-12</th>\n",
+       "      <td>418.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-06-19</th>\n",
+       "      <td>418.490000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-06-26</th>\n",
+       "      <td>417.820000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-07-03</th>\n",
+       "      <td>417.700000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-07-10</th>\n",
+       "      <td>417.080000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-07-17</th>\n",
+       "      <td>416.910000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-07-24</th>\n",
+       "      <td>415.920000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-07-31</th>\n",
+       "      <td>414.940000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-08-07</th>\n",
+       "      <td>414.560000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-08-14</th>\n",
+       "      <td>414.660000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-08-21</th>\n",
+       "      <td>414.420000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-08-28</th>\n",
+       "      <td>412.680000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3310 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            CO2_concentration\n",
+       "Time                         \n",
+       "1958-03-29         316.190000\n",
+       "1958-04-05         317.310000\n",
+       "1958-04-12         317.690000\n",
+       "1958-04-19         317.580000\n",
+       "1958-04-26         316.480000\n",
+       "1958-05-03         316.950000\n",
+       "1958-05-10         317.255000\n",
+       "1958-05-17         317.560000\n",
+       "1958-05-24         317.990000\n",
+       "1958-05-31         317.633333\n",
+       "1958-06-07         317.276667\n",
+       "1958-06-14         316.920000\n",
+       "1958-06-21         316.563333\n",
+       "1958-06-28         316.206667\n",
+       "1958-07-05         315.850000\n",
+       "1958-07-12         315.850000\n",
+       "1958-07-19         315.460000\n",
+       "1958-07-26         315.590000\n",
+       "1958-08-02         315.640000\n",
+       "1958-08-09         315.100000\n",
+       "1958-08-16         315.090000\n",
+       "1958-08-23         314.615000\n",
+       "1958-08-30         314.140000\n",
+       "1958-09-06         313.540000\n",
+       "1958-09-13         313.485556\n",
+       "1958-09-20         313.431111\n",
+       "1958-09-27         313.376667\n",
+       "1958-10-04         313.322222\n",
+       "1958-10-11         313.267778\n",
+       "1958-10-18         313.213333\n",
+       "...                       ...\n",
+       "2021-02-06         416.910000\n",
+       "2021-02-13         416.460000\n",
+       "2021-02-20         416.160000\n",
+       "2021-02-27         416.450000\n",
+       "2021-03-06         417.560000\n",
+       "2021-03-13         416.540000\n",
+       "2021-03-20         418.000000\n",
+       "2021-03-27         416.430000\n",
+       "2021-04-03         417.690000\n",
+       "2021-04-10         419.020000\n",
+       "2021-04-17         417.660000\n",
+       "2021-04-24         418.540000\n",
+       "2021-05-01         419.650000\n",
+       "2021-05-08         418.160000\n",
+       "2021-05-15         419.020000\n",
+       "2021-05-22         417.980000\n",
+       "2021-05-29         419.490000\n",
+       "2021-06-05         419.460000\n",
+       "2021-06-12         418.900000\n",
+       "2021-06-19         418.490000\n",
+       "2021-06-26         417.820000\n",
+       "2021-07-03         417.700000\n",
+       "2021-07-10         417.080000\n",
+       "2021-07-17         416.910000\n",
+       "2021-07-24         415.920000\n",
+       "2021-07-31         414.940000\n",
+       "2021-08-07         414.560000\n",
+       "2021-08-14         414.660000\n",
+       "2021-08-21         414.420000\n",
+       "2021-08-28         412.680000\n",
+       "\n",
+       "[3310 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data=data.interpolate(method='linear')\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous affichons l'incidence en fonction du temps. On peut voir que cette courbe à une tendance croissante."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f257712e3c8>"
+      ]
+     },
+     "execution_count": 6,
+     "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": [
+    "data['CO2_concentration'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Un zoom sur les dernières années montre une saisonalité d'environ 1 ans."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2575040358>"
+      ]
+     },
+     "execution_count": 7,
+     "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": [
+    "data['CO2_concentration'][-200:].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vérifions tous cela. On va décomposer cette courbe via la fonction [seasonal_decompose](https://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html). La tendance et la saisonalité semble s'additionner. Pour la saisonalité, on calculera la fréquence et l'amplitude de la courbe avec une [Transformation de Fourier](https://fr.wikipedia.org/wiki/Transformation_de_Fourier). Pour la tendance, on utilisera une [régression polynomiale](https://fr.wikipedia.org/wiki/R%C3%A9gression_polynomiale)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2574f02a20>"
+      ]
+     },
+     "execution_count": 8,
+     "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"
+    },
+    {
+     "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": [
+    "result = seasonal_decompose(data, model=\"additive\",extrapolate_trend=\"freq\")\n",
+    "result.trend.plot()\n",
+    "result.seasonal.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transformée de Foruier de la saisonalité"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous réalisons la tranformée de Fourier de la courbe de saisonalité."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'power')"
+      ]
+     },
+     "execution_count": 9,
+     "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": [
+    "season=result.seasonal\n",
+    "\n",
+    "# Number of samplepoints\n",
+    "N = season.shape[0]\n",
+    "# sample spacing\n",
+    "weeks_by_years=365/7\n",
+    "\n",
+    "season_fft = fftpack.fft(season[\"CO2_concentration\"])\n",
+    "amplitude_fft = 2.0/N * np.abs(season_fft)\n",
+    "peak = signal.find_peaks(amplitude_fft)\n",
+    "\n",
+    "xf = np.linspace(0.0, 1.0/(2.0*(1/weeks_by_years)), int(N/2))\n",
+    "plt.plot(xf[0:500], 2.0/N * np.abs(season_fft[0:500]))\n",
+    "plt.xlabel('Period (years)')\n",
+    "plt.ylabel('power')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "L'affichage de la transformée de Fourier montre 2 composantes principales à ces oscillations de période de 1 et 2 ans et d'amplitude 2.18 et 0.52 umol/mol respectivement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "main_frequency: 1.0088098117118676 , main_frequency_amplitude: 2.184959157879391\n",
+      "second_frequency: 2.001856970115737 , second_frequency_amplitude: 0.5192038125798919\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"main_frequency:\",xf[peak[0][0]],\", main_frequency_amplitude:\",amplitude_fft[peak[0][0]])\n",
+    "print(\"second_frequency:\",xf[peak[0][1]],\", second_frequency_amplitude:\",amplitude_fft[peak[0][1]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modèle de tendance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "La tendance semble être une fonction polynomiale d'ordre 2. Nous réaliserons une régression polynomiale d'ordre 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9994858695079444\n",
+      "[ 0.00000000e+00 -5.14361913e+01  1.33241982e-02]\n"
+     ]
+    },
+    {
+     "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": [
+    "trend=result.trend\n",
+    "\n",
+    "degree=2\n",
+    "polynomial_features = PolynomialFeatures(degree = degree)\n",
+    "\n",
+    "model_trend=LinearRegression()\n",
+    "\n",
+    "x=np.expand_dims(np.array(trend.index.year.astype(float)+trend.index.week.astype(float)/53),axis=-1)\n",
+    "y=np.array(trend[\"CO2_concentration\"])\n",
+    "\n",
+    "X_TRANSF = polynomial_features.fit_transform(x)\n",
+    "model_trend.fit(X_TRANSF,y)\n",
+    "Y_NEW = model_trend.predict(X_TRANSF)\n",
+    "print(r2_score(y, Y_NEW))\n",
+    "print(model_trend.coef_)\n",
+    "\n",
+    "X_seq = np.linspace(1950,2050,2000).reshape(-1,1)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.scatter(x,y)\n",
+    "plt.plot(X_seq,model_trend.predict(polynomial_features.fit_transform(X_seq)),color=\"black\")\n",
+    "plt.title(\"Polynomial regression with degree \"+str(degree))\n",
+    "plt.xlabel('Times (years)')\n",
+    "plt.ylabel('Concentration (umol/mol)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "La régression semble fonctionelle. Prenons ces coefficients."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "polynomial coef: [ 0.00000000e+00 -5.14361913e+01  1.33241982e-02]\n",
+      "constant coef: 49944.79903461095\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"polynomial coef:\",model_trend.coef_)\n",
+    "print(\"constant coef:\", model_trend.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On obtient la concentration en C02 en fonction du temps avec la fonction : $f(T) = 49944.8 - 51.44 * T + 0.0133 * T^2$ .\n",
+    "\n",
+    "En 2025, la concentration en CO2 devrait être de 424 umol/mol."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1.000000e+00 2.025000e+03 4.100625e+06]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([424.05170121])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_TRANSF = polynomial_features.fit_transform([[2025]])\n",
+    "model_trend.predict(X_TRANSF)"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +1215,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/exo3/weekly_in_situ_co2_mlo.csv b/module3/exo3/weekly_in_situ_co2_mlo.csv
new file mode 100644
index 0000000..3b0f915
--- /dev/null
+++ b/module3/exo3/weekly_in_situ_co2_mlo.csv
@@ -0,0 +1,3239 @@
+Time,CO2_concentration
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+2021-04-17,417.66
+2021-04-24,418.54
+2021-05-01,419.65
+2021-05-08,418.16
+2021-05-15,419.02
+2021-05-22,417.98
+2021-05-29,419.49
+2021-06-05,419.46
+2021-06-12,418.9
+2021-06-19,418.49
+2021-06-26,417.82
+2021-07-03,417.7
+2021-07-10,417.08
+2021-07-17,416.91
+2021-07-24,415.92
+2021-07-31,414.94
+2021-08-07,414.56
+2021-08-14,414.66
+2021-08-21,414.42
+2021-08-28,412.68
-- 
2.18.1