{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# methode 1 c'est on utilison les donné extre directement du site "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chargement et traitement des données\n",
    "Chargeons les données et préparons-les pour l'analyse. Les données sont stockées dans un fichier texte avec des colonnes séparées par des espaces. Nous allons extraire les années et les concentrations de CO2.\n",
    "methode 1 c'est on utilison les donné extre directement du site "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1959., 1960., 1961., 1962., 1963., 1964., 1965., 1966., 1967.,\n",
       "       1968., 1969., 1970., 1971., 1972., 1973., 1974., 1975., 1976.,\n",
       "       1977., 1978., 1979., 1980., 1981., 1982., 1983., 1984., 1985.,\n",
       "       1986., 1987., 1988., 1989., 1990., 1991., 1992., 1993., 1994.,\n",
       "       1995., 1996., 1997., 1998., 1999., 2000., 2001., 2002., 2003.,\n",
       "       2004., 2005., 2006., 2007., 2008., 2009., 2010., 2011., 2012.,\n",
       "       2013., 2014., 2015., 2016., 2017., 2018., 2019., 2020., 2021.,\n",
       "       2022.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Charger les données depuis le fichier texte\n",
    "data = np.genfromtxt('https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_annmean_mlo.txt', skip_header=43)\n",
    "\n",
    "# Extraire les années et les concentrations de CO2\n",
    "years = data[:, 0]\n",
    "co2_concentration = data[:, 1]\n",
    "years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([315.98, 316.91, 317.64, 318.45, 318.99, 319.62, 320.04, 321.37,\n",
       "       322.18, 323.05, 324.62, 325.68, 326.32, 327.46, 329.68, 330.19,\n",
       "       331.13, 332.03, 333.84, 335.41, 336.84, 338.76, 340.12, 341.48,\n",
       "       343.15, 344.87, 346.35, 347.61, 349.31, 351.69, 353.2 , 354.45,\n",
       "       355.7 , 356.54, 357.21, 358.96, 360.97, 362.74, 363.88, 366.84,\n",
       "       368.54, 369.71, 371.32, 373.45, 375.98, 377.7 , 379.98, 382.09,\n",
       "       384.02, 385.83, 387.64, 390.1 , 391.85, 394.06, 396.74, 398.81,\n",
       "       401.01, 404.41, 406.76, 408.72, 411.65, 414.21, 416.41, 418.53])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_concentration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Methode 2: la en telechargent le document en txt dans l'espace jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  nan, 1959., 1960., 1961., 1962., 1963., 1964., 1965., 1966.,\n",
       "       1967., 1968., 1969., 1970., 1971., 1972., 1973., 1974., 1975.,\n",
       "       1976., 1977., 1978., 1979., 1980., 1981., 1982., 1983., 1984.,\n",
       "       1985., 1986., 1987., 1988., 1989., 1990., 1991., 1992., 1993.,\n",
       "       1994., 1995., 1996., 1997., 1998., 1999., 2000., 2001., 2002.,\n",
       "       2003., 2004., 2005., 2006., 2007., 2008., 2009., 2010., 2011.,\n",
       "       2012., 2013., 2014., 2015., 2016., 2017., 2018., 2019., 2020.,\n",
       "       2021., 2022.])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Charger les données depuis le fichier texte\n",
    "data = np.genfromtxt('co2_mm_mlo.txt', skip_header=43)\n",
    "\n",
    "# Extraire les années et les concentrations de CO2\n",
    "years = data[:, 0]\n",
    "co2_concentration = data[:, 1]\n",
    "years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   nan, 315.98, 316.91, 317.64, 318.45, 318.99, 319.62, 320.04,\n",
       "       321.37, 322.18, 323.05, 324.62, 325.68, 326.32, 327.46, 329.68,\n",
       "       330.19, 331.13, 332.03, 333.84, 335.41, 336.84, 338.76, 340.12,\n",
       "       341.48, 343.15, 344.87, 346.35, 347.61, 349.31, 351.69, 353.2 ,\n",
       "       354.45, 355.7 , 356.54, 357.21, 358.96, 360.97, 362.74, 363.88,\n",
       "       366.84, 368.54, 369.71, 371.32, 373.45, 375.98, 377.7 , 379.98,\n",
       "       382.09, 384.02, 385.83, 387.64, 390.1 , 391.85, 394.06, 396.74,\n",
       "       398.81, 401.01, 404.41, 406.76, 408.72, 411.65, 414.21, 416.41,\n",
       "       418.53])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_concentration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualisation des données\n",
    "Créons un graphique pour visualiser l'évolution de la concentration de CO2 dans l'atmosphère au fil des années."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Créer un graphique\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(years, co2_concentration, label='Concentration de CO2')\n",
    "plt.xlabel('Année')\n",
    "plt.ylabel('Concentration de CO2 (ppm)')\n",
    "plt.title('Évolution de la Concentration de CO2 dans l\\'Atmosphère depuis 195')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion\n",
    "L'analyse des données montre clairement l'augmentation de la concentration de CO2 dans l'atmosphère depuis 1959. Cette augmentation continue est une préoccupation majeure en matière de changement climatique. Il est essentiel de prendre des mesures pour réduire ces émissions de CO2 afin de lutter contre le réchauffement climatique.\n",
    "\n",
    "Ce document computationnel a permis de visualiser de manière informative l'évolution de la concentration de CO2 dans l'atmosphère au fil du temps. Il est crucial de continuer à surveiller ces données pour mieux comprendre l'impact du CO2 sur notre climat."
   ]
  },
  {
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