{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Sujet 1 : Concentration de CO2 dans l'atmosphère depuis 1958 \n",
    "\n",
    "## Récupération des données \n",
    "\n",
    "Les données ont été téléchargées sur le [site Web de l'institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html), dans le fichier \"`monthly_in_situ_c02_mlo.csv`\".\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "import wget\n",
    "\n",
    "# url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\"\n",
    "# wget.download(url, '.')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Formattons le fichier pour ne garder que les données, et non les commentaires (gardons les dans une variable, au cas où).\n",
    "Nous allons ici créer un fichier `raw_data.csv` dans lequel nous stockons les données.\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "with open(\"monthly_in_situ_co2_mlo.csv\", 'r') as data_file:\n",
    "    lines = [line for line in data_file]\n",
    "\n",
    "commentary = []\n",
    "with open(\"raw_data.csv\", 'w') as raw_data_file:\n",
    "    for line in lines:\n",
    "        if line.startswith('\"'):\n",
    "            commentary.append(lines)\n",
    "        else:\n",
    "            raw_data_file.write(line)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Chargeons donc les données avec pandas :"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stderr",
     "text": [
      "/home/baptiste/Documents/venvs/develop/lib/python3.6/site-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "raw_dataframe = pd.read_csv(\"raw_data.csv\", delimiter=', ', index_col=False)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "On va traiter le dataframe pour mettre en forme le header et vérifier les données.\n",
    "Le dataframe ne comporte que 4 colonnes que nous allons utiliser :\n",
    "\n",
    "* Years \n",
    "* Months\n",
    "* CO2\n",
    "* Seasonally adjusted\n",
    "\n",
    "Les deux premières seront utilisées pour générer un tableau de dates qui serront des objets datetime, initialisés au premier jour de chacun des mois du dataset.\n",
    "\n",
    "Les deux autres colonnes sont les données qui ont être utilisées pour le premier graphique. \n",
    "\n",
    "**CO2** contient les quantité brutes de CO2 relevées, et **seasonally adjusted** est le résultat d'un traitement de **CO2** pour retirer les variations saisonnières."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "co2_raw_vals = raw_dataframe.iloc[2:, 4].astype(float)\n",
    "co2_adjusted_vals = raw_dataframe.iloc[2:, 5].astype(float)\n",
    "dates = [datetime.date(year=int(year), month=int(month), day=1) for year, month in zip(raw_dataframe.iloc[2:, 0], raw_dataframe.iloc[2:, 1])]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% \n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Première analyse \n",
    "\n",
    "Pour réaliser le premier graphique montrant une oscillation périodique superposée à une évolution systématique plus lente, nous plottons tout d'abord les données brutes de la colonne **CO2** pour pouvoir distinguer les oscillations péridoques et la croissance lente systématique. \n",
    "\n",
    "Nous n'avons sélectionné que les 100 premiers points pour plus de lisibilité sur le graphique.\n",
    "\n",
    "De plus, l'axe des ordonnées a été forcé entre 300 et 340 pour ne pas afficher les données manquantes, remplacées par des valeurs à -99.  "
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, ax=plt.subplots(1)\n",
    "ax.scatter(dates[:100], co2_raw_vals[:100])\n",
    "lim = ax.set_ylim(300,340)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Séparation des phénomènes\n",
    "\n",
    "### Périodicité uniquement\n",
    " \n",
    "Nous allons maintenant séparer la périodicité. Pour ce faire, il suffit de tracer la différence entre les données ajustées et le données brutes, car la correciton a pour but de gommer la périodicité.\n",
    "\n",
    "De la même manière que précédemment, nous avons tracé que les 100 premiers points et nous avons adapté l'échelle des ordonnées pour plus de clarté visuelle."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fig, ax=plt.subplots(1)\n",
    "ax.scatter(dates[:200], co2_adjusted_vals[:200]-co2_raw_vals[:200])\n",
    "lim = ax.set_ylim(-10,10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% \n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Contribution lente uniquement \n",
    "\n",
    "Pour permettre de faire un modèle simple de la contribution lente jusqu'en 2025, il nous faut d'abord l'estimer avec une fonction. Pour ce faire, analysons le comportement de la contribution simple sur  toute la durée disponible : "
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fig, ax=plt.subplots(1)\n",
    "ax.scatter(dates, co2_adjusted_vals)\n",
    "lim = ax.set_ylim(310,420)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% \n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "On peut voir sur ce graphe que la croissance n'est pas linéaire, on pourrait donc l'esitmer avec un polynome de degré 2."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = 0.0001\n",
    "b = 0.055\n",
    "c = 315\n",
    "xs = np.arange(len(dates))\n",
    "ys = a*(xs**2)+b*xs+c\n",
    "\n",
    "fig, ax=plt.subplots(1)\n",
    "ax.scatter(dates, co2_adjusted_vals)\n",
    "ax.scatter(dates, ys)\n",
    "lim = ax.set_ylim(310,420)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "En prenant une unité mensuelle, et avec le polynome $y = 0.0001 x^2 + 0.055 y + 315$, nous arrivons a générer un modèle qui représente bien la courbe de l'augmentation de CO2 pour la durée du dataset (ces valeurs ont été trouvé par methode trial and error). \n",
    "Selon notre modèle naif, la valeur de la quantité de C02 jusqu'en 2045 peut être extrapolée. Pour ce faire, nous allons générer tous les mois jusqu'en 2045 : "
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "dates_large = dates+[datetime.date(year=year+2021, month=month+1, day=1)  for year in range(25) for month in range(12)]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Puis appliquer la fonction d'extrapolation :"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.arange(len(dates_large))\n",
    "ys = a*(xs**2)+b*xs+c\n",
    "\n",
    "fig, ax=plt.subplots(1)\n",
    "ax.scatter(dates_large, ys, color=\"tab:orange\")\n",
    "ax.scatter(dates, co2_adjusted_vals, color=\"tab:blue\")\n",
    "lim = ax.set_ylim(310,500)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% \n",
     "is_executing": false
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}