python-pour-finance/04-Visualisation-Matplotlib-Pandas/04-01-Matplotlib/Matplotlib.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib est la bibliothèque mère de visualisation de données avec Python. Il a été créé par John Hunter. Il l'a créé pour essayer de répliquer les capacités de traçage de MatLab (un autre langage de programmation) en Python. Ainsi, si vous êtes familier avec matlab, matplotlib vous semblera naturel.\n",
    "\n",
    "C'est une excellente bibliothèque graphique 2D et 3D pour générer des figures scientifiques. \n",
    "\n",
    "Quelques-uns des principaux avantages de Matplotlib sont:\n",
    "\n",
    "\n",
    "* De façon générale, facile à utiliser pour des graphiques simples\n",
    "* Prise en charge des étiquettes et des textes personnalisés\n",
    "* Grande maîtrise de chaque élément d'une figure\n",
    "* Sortie de haute qualité dans de nombreux formats\n",
    "* Très personnalisable en général\n",
    "\n",
    "Matplotlib vous permet de créer des figures reproductibles par programmation. Apprenez à l'utiliser! Avant de poursuivre ce notebook, je vous encourage à explorer la page web officielle de Matplotlib:  http://matplotlib.org/\n",
    "\n",
    "## Installation \n",
    "\n",
    "Vous devrez d'abord installer matplotlib avec l'un ou l'autre :\n",
    "\n",
    "    conda install matplotlib\n",
    "ou\n",
    "    pip install matplotlib\n",
    "    \n",
    "## Importation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importer le module `matplotlib.pyplot` sous le nom `plt`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vous devrez également utiliser cette ligne pour voir les tracés dans le notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cette ligne n'est que pour les jupyter notebooks, si vous utilisez un autre éditeur, vous utiliserez: **plt.show()** à la fin de toutes vos commandes de traçage pour que la figure apparaisse dans une autre fenêtre."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Exemple de base\n",
    "\n",
    "Passons en revue un exemple très simple en utilisant deux tableaux numpy:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exemple\n",
    "\n",
    "Passons en revue un exemple très simple en utilisant deux tableaux numpy. Vous pouvez aussi utiliser des listes, mais vous passerez probablement des tableaux numpy ou des colonnes pandas (qui se comportent essentiellement comme des tableaux).\n",
    "\n",
    "**Les données que nous voulons tracer :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(0, 5, 11)\n",
    "y = x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([ 0.  ,  0.25,  1.  ,  2.25,  4.  ,  6.25,  9.  , 12.25, 16.  ,\n",
       "       20.25, 25.  ])"
      ]
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     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Commandes de base Matplotlib\n",
    "\n",
    "Nous pouvons créer un tracé de ligne très simple à l'aide de ce qui suit (je vous encourage à faire une pause et à utiliser Shift+Tab en cours de route pour vérifier la documentation docstrings pour les fonctions que nous utilisons)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(x, y, 'r') # 'r' pour la couleur rouge (red)\n",
    "plt.xlabel('X Axis Title Here')\n",
    "plt.ylabel('Y Axis Title Here')\n",
    "plt.title('String Title Here')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Création de graphiques multiples sur le même tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# plt.subplot(nrows, ncols, plot_number)\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(x, y, 'r--') # Plus d'informations sur les options de couleurs plus tard\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(y, x, 'g*-');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Matplotlib Méthode Orientée Objet\n",
    "Maintenant que nous avons vu les bases, décomposons le tout avec une introduction plus formelle de l'API orientée objet de Matplotlib. Cela signifie que nous instancions les objets figures et appelons ensuite les méthodes ou attributs à partir de cet objet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction à la Méthode Orientée Objet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'idée principale dans l'utilisation de la méthode plus formelle Orientée Objet est de créer des objets figures et d'appeler ensuite simplement des méthodes ou attributs à partir de cet objet. Cette approche est plus agréable lorsqu'il s'agit d'un tableau sur laquelle il y a plusieurs parcelles. \n",
    "\n",
    "Pour commencer, nous créons une instance de figure. Ensuite, nous pouvons ajouter des axes à cette figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Set Title')"
      ]
     },
     "metadata": {},
     "execution_count": 8
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Création d'un objet Figure (tableau vide)\n",
    "fig = plt.figure()\n",
    "\n",
    "# Ajout d'un ensemble d'axes à la figure\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # gauche, bas, largeur, hauteur (plage de 0 à 1)\n",
    "\n",
    "# Tracé sur cet ensemble d'axes\n",
    "axes.plot(x, y, 'b')\n",
    "axes.set_xlabel('Set X Label') # remarquez l'utilisation des méthodes set_ pour commencer\n",
    "axes.set_ylabel('Set y Label')\n",
    "axes.set_title('Set Title')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le code est un peu plus compliqué, mais l'avantage est que nous avons maintenant le contrôle total de l'emplacement des axes du tracé, et nous pouvons facilement ajouter plus d'un axe à la figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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hFiNHjuTo0aOoKt26daNGjRrMmzfPp/cmJydTvXp1WrduHeAqjeuOHHEGuXfsCNdcA7fd5vzUX345vP46/PIL/PYbvPMO3HmnhYS5QChuZ5odUXFFkWrcuHH07t2buXPnkpiYyEcffUSXLl1o1qxZlu8dOXIkFStW5GjqLFkTOawj2vhRqG5nmh1RFRSqCsC3335Lly5dqFq1atpj3uzatYtZs2bRv39/3nrrrUCXaYJB1WlSmjIFpk93frrBOqJNtoTydqbZEVVBUbNmTZo1a0Z8fDxDhgzh2LFjaSOhvOnTpw+vv/46x44dy/Q1o0aNYtSoUQAkJib6rWbjR+nDYepU2LHDmcfQtKkTDi1bQsmSbldpwlSob2eaHQENChEpCUwErgFSgFGqOlJErgQ+B0oDCcADqno4kLUAjB07lrVr11K2bFny5cvHwYMH+eijj7y+Z+bMmVx99dXUrFmTH374IdPXde/ene6ejWFs/+EQklk4NGsGgwZBmzZwxRVuV2kiwPDhob2daXYEujP7LPCsqlYE6gBPishNwAvAAlUtDyzw3A84EWHjxo287Zn1cvz4cU6ePOn1PUuWLGHGjBmULl2a9u3b89133/Hwww8Ho1xzqVSdJTP69XPWS6pTx5npVLmys77z/v3On3yPPGIhYfziiy/g2WehbdvQ3c40W1Q1aDfga6ApsBko7nmsOLA5q/fWrFlTs6tHjx7as2dPvfHGG1VV9dChQxobG+vz+7///ntt1apVlq/zR63mIqWkqMbFqT77rOp116mCaq5cqq1aqU6YoHr4sNsVhjT7N3vpFi5UzZ1btV491RMn3K7m0gErNZPfv0HroxCR0kB1YBlQTFX3eoJqr4hcncl7ugPdAUqVKpXtGpYtW8bq1aupXr06AIULF+b06dPZ/lzjEm/NSgMHwt132xWDCaiNG51/ZmXKwNdfR+5I6aAEhYgUAKYBfVT1qPg4XkxVRwGjAGJjY7MenpSFXLlykZycTOrxExMTferMTtWwYUMaNmyY3TJMdlg4mBCxd68zejpPHmfdxyuvdLuiwAl4UIhILpyQ+ERVp3se3icixT1XE8WB/YGuA+Dpp5/m3nvvZf/+/fTv35+pU6fy73//OxiHNtlh4WBCzLFj0KqVsyjwwoWRv3VIoEc9CTAW2KSq6ScgzAA6AUM9X78OZB2pOnToQM2aNVmwYAGqyldffUXFihWDcWhzKX7/3el8/ugjZ56DhYMJAWfOQLt2sH49fPMN1KzpdkWBF+grirpAR2CDiKz1PPZPnID4QkS6Ab8D7QJcBwAvv/wyr7zyCjfeeCPgbGjUpUuXLIfImiA6dcpp7B03zlkHQdVZcG/AALjnHgsH4ypVePxxZ6uRMWOcpqdoENCgUNXFQGYdEk0CeeyMnD17ltq1a/PRRx/xxx9/8NRTT9nqsaFi3TonHD7+2NkqtGRJZ5xhly6Rf11vwsbAgc4F7iuvhOcGRJcqqmZmDxkyhCZNmlC7dm0KFy7MokWLKFeunNtlRa/Dh2HyZCcgVq2C3Lmdq4Zu3ZyrCFs+w4SQsWOdoOjSxQmKaBJVq8cuWrSI3r17M2DAABo2bEivXr3Ys2eP22VFl5QUZ3H+Dh3gH/+AJ590Gn1HjoQ9e5xVW5s1s5AwIWX2bKfJqXlz+PDDyFjo72JE1RVFv379mDJlCjfddBMA06dPp3Hjxvz6668uVxYFfv8dxo93rtsTEpwlu7t2dW41akTfT54JG6tWOZ3XN9/sDLyLxm3Ooyooli5dSky6v1Tvu+8+bg/3HUVCWWrH9NixMH/+3x3Tr73m7A952WVuV2iMV/HxzjDYIkWcFegLFnS7IndEVVDExMQwa9Ysfvnll3PWeBowYICLVUWgdeuccPjkk787pl9+2dlh3jqmTZg4eNAZ1XT6tLM1evHiblfknqgKih49evDXX3/x/fff8+ijjzJ16lRq1arldlmRISUFvv0W3ngDFi2yjmkT1k6edKbqxMfD//4H0T7dKqo6s3/66ScmTpxI4cKFeeWVV1i6dCk7d+50u6zwdvKkc/VQuTLcdZfzk/Xmm9YxbcJWSgo8/LCz0eHHH0P9+m5X5L6ouqK4zNMmni9fPvbs2cNVV11FfOrOZubiHDoE778P//0v7NsH1ao5P1UPPBCdvX0mYjz7LEybBm+95XRimygLitatW3PkyBGee+45atSogYjw2GOPuV1WeImPd3ZoGTvW2fexeXN47jlo3NhGLpmwN3w4jBgBffrAM8+4XU3oiKqgePnllwFo27YtrVu35uTJk1x++eVpz8+fP5+mTZu6VV5oW7HCaVKaOtVpSnroIejb1xkzaEwE+OIL55/0/ffDsGFuVxNaoqqPIr08efKcExIAzz//vEvVhKiUFGcnuIYNoVYtmDPH2TUuPt6ZE2EhYSLEokXQsSPUq+dsZ3oRuw9Ehai6osiKs8mT4eRJZ2jrsGGwaZMzvHXYMHj0UShUyO3qjPGr1M2HypaN7M2HssOCIh1fN1SKWEeOwHvvOftLp3ZQf/KJ06NnHdQmAu3Z48yVyJs38jcfyg4LCuPMKHr/fRg0yBnNdOedThOTdVCbCJZ+86FFi6B0abcrCl0WFOmUjrZ/Karw5Zfw/POwbRvccQe8/jp49hQ3JlKdOeN0Wm/Y4Gw+VKOG2xWFtqgIiunTp3t9/r777vPpdRFl2TJnwPiSJXDTTc6s6jvvtCsIE/FSNx+aNy+6Nh/KjqgIim+++SbT50QkLSiiQnw8vPiiM2u6WDEYNcpZYD9nVPxTMCZqNx/Kjqj47WBbneJsEjR4sNNRHRPjLNL33HPRuxymiUrRvPlQdkTVaOF9+/bRrVs3WniuNTdu3MjYsWO9vmfnzp00atSIihUrUqlSJUaOHBmMUv3n9GlnU6By5Zwhrh06wNatTse1hYSJItG++VB2RFVQdO7cmebNm6ftanfDDTcwYsQIr+/JmTMnw4YNY9OmTcTFxfHuu++ycePGIFSbTarOgjU33eSsR1CjBqxZ42w7eu21bldnTFDZ5kPZE1VBceDAAR544AFyeKZd5syZ85yNjDJSvHhxaniGRBQsWJCKFSuye/fugNeaLatWOUte3n8/5MnjdFTPmwdVq7pdmTFBZ5sPZV9UBUX+/Pk5ePBg2sS6uLi4C5bx8CYhIYE1a9ZQu3btC54bNWoUsbGxxMbGkpiY6LeaL0pyMgwZAnXqOMNdP/zQ2USoRQu7zjZRKf3mQ7NnR/fmQ9kRFZ3Zqd566y3atGnD9u3bqVu3LomJiUydOtWn9yYlJdG2bVtGjBhBoQyWsejevTvdu3cHIDY21q91+2TnTmexmoULnaW+P/gAChcOfh3GhIgTJ6BNG2eLdtt8KHuiKihq1KjBwoUL2bx5M6pKhQoVyOVDY+WZM2do27YtHTp0CM2htFOmQPfucPass1jfI4/YFYSJasnJzt9NS5c6I8Hr1XO7ovB2yUEhIrNVNaymqpw8eZL33nuPxYsXIyLUr1+fHj16kNfLKmCqSrdu3ahYsSJ9+/YNYrU+SEqCp592BoXXquWsy1SunNtVGeO6fv1s8yF/8hoUIpLZxHYBqvm9mgB75JFHKFiwIE899RQAkydPpmPHjkyZMiXT9yxZsoRJkyZRpUoVqlWrBsDgwYNp2bJlMErO3IoVzp4Q27dD//7OoHAbymGMbT4UAFldUawAFuIEw/mu8Hs1AbZ582bWrVuXdr9Ro0ZUzWIkUL169UJr+fHkZGc9pgEDnJ65H36ABg3crsqYkGCbDwVGVkGxCXhcVbee/4SI7AxMSYFTvXp14uLiqFOnDgDLli2jbt26Lld1EazD2phMff01PPww1K1rmw/5W1ZB8S8yH0L7lH9LCZwqVaogIpw5c4aJEydSqlQpRIQdO3Zw0003uV2eb775xumktg5rYy7w9ddOX0T16s5cCdt8yL+8BoWqZjp2VFW/8ns1ATJz5ky3S8ieqVOhfXtnI6HPPrMOa2PSSR8S8+bBRUyNMj7yadSTiPQGPgKOAWOA6sALqjovgLX5zXXXXXfO/f3793Py5EmXqrlIqSFRp44zY8imlRqTxkIiOHxtxeuqqkeBZkBRoAswNGBVBciMGTMoX748ZcqU4fbbb6d06dJpCwSGJAsJYzJlIRE8vgZFamN4S+AjVV1HxiOhzn2TyDgR2S8iP6d77F8isltE1npuQRtn+vLLLxMXF8cNN9xAfHw8CxYsCN3ObAsJYzJlIRFcvgbFKhGZhxMUc0WkIJDiw/vGA3dm8PhwVa3muX3rYw3ZlitXLq666ipSUlJISUmhUaNGrF27NliH952FhDGZspAIPl9nZnfDmWD3m6r+JSJX4TQ/eaWqi0Sk9KWX519XXHEFSUlJNGjQgA4dOnD11VeTM9R2drOQMCZTFhLu8PWKQoGbgKc99/MD2RmA1ktE1nuapoI2EeDrr7/msssuY/jw4dx5551cf/31XrdJDToLCWMyZSHhHl//nH4Pp6mpMTAIZ/TTNOCWSzjm+8C/ccLn38AwoGtGLxSR7kB3gFKlSl3Coc6VP3/+tO87deqU7c/zq5kzLSSMyYSFhLt8DYraqlpDRNYAqOphEcl9KQdU1X2p34vIaCDTSQ6qOgoYBRAbG3vJ62gULFgwbQ+K8z4fEeHo0aOX+tH+cfSos/prlSoWEsacx0LCfb4GxRkRicG5CkBEiuJbZ/YFRKS4qu713L0X+Nnb6/3h2LFjPr3u8OHDFHZjSYxBg+CPP+CrrywkjEnHQiI0+NpH8TbwJXC1iLwGLAaGZPUmEZkMLAUqiMguEekGvC4iG0RkPdAICJn1HZs0aRL8g/7yi7PU5aOPOkuFG2MAC4lQ4tMVhap+IiKrgCY48yfuUdVNPrzvwQweHntxJQZP0FeJVYVevZyfgMGDg3tsY0KYhURo8XUJj38DA1X1V8/9QiLykapmOUQ2nGTUjxFQn33mLBP+wQfOzu/GGAuJEORr01NOYJmI3CwizXD2qVgVuLKiwLFj8OyzEBvrNDsZYywkQpRPQaGqLwLPA8twZlu3UtV3AliXX7Vs2ZKEhIQsXxfUpqeBA50O7HffhZiY4B3XmBBlIRG6fAoKEWkAjMSZQ/ED8I6I/COAdflV586dadasGa+99hpnzpzJ9HULFiwITkHbt1sHtjHpWEiENl+Hx74JtFPVjQAich/wHXBjoArzpwceeIBWrVoxaNAgYmNj6dixIznSbX/Vt29fAK688srgFDR/vrOl6fPPB+d4xoQwC4nQ52tQ3Kqqyal3VHW6iCwMUE0BkStXLvLnz8+pU6c4duzYOUERdKtWOVuYli3rXg3GhAALifDg6/DYZBFpBVTi3DWeBgWkKj+bM2cOffv2pU2bNqxevZp8+fK5W9CqVVCzpm1laqKahUT48HV47AdAPpwJcmOA+4HlAazLr1577TWmTJlCpUqV3C4FTp2Cn392RjwZE6UsJMKLr+0vt6nqI8BhVR0I3AqUDFxZ/vXjjz9mKyTmzJlDhQoVKFeuHEOHZnNjvw0b4MwZ54rCmChkIRF+fA2KE56vf3lGO50BygSmpNCSnJzMk08+yezZs9m4cSOTJ09m48aNl/6BK1c6Xy0oTBSykAhPvgbFTBG5AngDWA0kAJMDVFNIWb58OeXKlaNs2bLkzp2b9u3b8/XXX1/6B6Z2ZJcu7bcajQkHFhLhy9cJd/9W1SOqOg24DrhRVQekPi8iTQNVoNt2795NyZJ/t7KVKFGC3bt3X/oHWke2iUIWEuHtovcBVdVTwKnzHv4PMN8vFYWYjGZrZ7Qm1KhRoxg1ahQAiYmJmX9g585w9dX+Ks+YkGchEf78tWF0xP55XKJECXbu3Jl2f9euXfzjHxdOSu/evTvdu3cHIDY2NvMPfPrpzJ8zJsJYSEQGf806C/L63MFzyy23sHXrVuLj4zl9+jSfffYZbdq0cbssY0KehUTk8NcVRcTKmTMn77zzDs2bNyc5OZmuXbuGxnwMY0KYhURk8VdQJPjpc0JSy5Ytadmypc+vT0hI8Nr8lJiYSNGiRf1RWtixcw/Nc/dldWVfWUhEHq9B4Vn8L1OqOt3z1evros2BAwe8Ph8bG8vK1PkUUcbOPbLP/dNPnfEaFhKRJasriru8PKfAdD/WYowJU6rw2mvw8svQsCF89ZWFRCTxGhSRttWpMcb/zpyBHj1g3Dh4+GEYMwby5HG7KuNPvm5cVExExorIbM/9m0SkW2BLi1ypw2ijkZ17ZPnzT2jZ0gmJAQNg4kQLiUgkvmz/6QmIj4D+qlpVRHICa1S1SqALTBUbG6uR3r5rTDj5/XcnJDZvhtGjnb4JE75EZJWqZjgKx9d5FEVU9QsgBUBVzwLJ3t9ijIlUq1dD7dqwaxfMmWMhEel8DYrjInIVnol1IlIH+DNgVUUovy5XHuK6du3K1VdfTeXKldMeO3ToEE2bNqV8+fI0bdqUw4cPu1hh4OzcuZNGjRpRsWJFKlWqxMiRI4HIOf+ZM6FBA8idG5YsgSZN3K7IBJqvQdEXmAFcLyJLgInAUwGrKgL5fbnyENe5c2fmzJlzzmNDhw6lSZMmbN26lSZNmkRsWObMmZNhw4axadMm4uLiePfdd9m4cWNEnP9778Hdd8ONN0JcHNjc0yihqj7dcEZIVQIqA7l8fZ+/bjVr1tRw9tNPP2mzZs3S7g8ePFgHDx7sYkWBFx8fr5UqVUq7f8MNN+iePXtUVXXPnj16ww03uFVaULVp00bnzZsX1uefnKzat68qqN51l2pSktsVGX8DVmomv3993Qo1L9ATqIfT/PSjiHygqicDE1+RJ6PlypctW+ZiRcG3b98+ihcvDkDx4sXZv3+/yxUFXkJCAmvWrKF27dphe/5//QUdO8L06dCrF4wYATExbldlgsnXJTwmAseA/3ruPwhMAtoFoqhIpD4uV24iR1JSEm3btmXEiBEUKlTI7XIuyf790KYNLF8Ow4dD7962lUo08jUoKqhq1XT3vxeRdYEoKFL5ulx5JCtWrBh79+6lePHi7N27l6sjeF+OM2fO0LZtWzp06MB99zkr3ITb+f/6qzP89Y8/YNo0uPdetysybvG1M3uNZ6QTACJSG1gSmJIiky1XDm3atGHChAkATJgwgbvvvtvligJDVenWrRsVK1akb9++aY+H0/kvWgS33QZJSfD99xYSUS+zzgtPU8kGYD2wCWcORQIQ7/n+Z2/v9fct3DuzVVVnzZql5cuX17Jly+qrr77qdjkB1b59e73mmms0Z86ceu211+qYMWP0wIED2rhxYy1Xrpw2btxYDx486HaZAfHjjz8qoFWqVNGqVatq1apVddasWWFz/h9/rJo7t+qNN6pu3+52NSZY8NKZ7XVmtohcl0XI7PBLWvnAZmYbE1jpF/a7/Xb48ksoXNjtqkyweJuZndWigOcEgYhcDeT1Y23GmBBgC/sZb3xdFLCNiGzFaXZaiNMENduH940Tkf0i8nO6x64UkfkistXz1f5mMcZFtrCfyYqvndn/BuoAW1S1DNAE3zqzxwN3nvfYC8ACVS0PLPDcN8a44PffoW5d+OEH+OgjGDjQhr+aC/kaFGdU9SCQQ0RyqOr3QLWs3qSqi4BD5z18NzDB8/0E4B4fazDG+FHqwn47d9rCfsY7X+dRHBGRAsAi4BMR2Q+cvcRjFlPVvQCqutfT72GMCaKZM6F9e7jqKvjf/2zNJuOdr1cUdwMngGeAOcB2vG+T6hci0l1EVorIysTExEAfzpiokLqwX4UKtrCf8Y1PQaGqx1U1WVXPquoEVX3b0xR1KfaJSHEAz9dMF7xR1VGqGquqsUWLFr3Ew5ns2LlzJ2XKlOHQIacF8fDhw5QpU4YdOy4cGZ2QkHDOsuIZ+eGHH2jduvVF1dCwYUPcGhrdoUMHKlSoQOXKlenatStnzpxxpQ5/SEmBZ5+FJ5+EVq1g4ULwLD1ljFdeg0JEjonI0Qxux0Tk6CUecwbQyfN9J+DrS/wcEwQlS5bkiSee4IUXnDEHL7zwAt27d+e667xOsYkYHTp04Ndff2XDhg2cOHGCMWPGuF3SJfnrL2jXDt56y1nY78svoUABt6sy4cJrUKhqQVUtlMGtoKqmrXKW2RBXEZkMLAUqiMguzz7bQ4GmnuG2TT33TQh75plniIuLY8SIESxevJhnn302y/ckJCRQv359atSoQY0aNfjpp5/Snjt69Cj33nsvN910Ez169CAlJQWAefPmceutt1KjRg3atWtHUlKST/U98cQTxMbGUqlSJV555RUA/vzzTypUqMDmzZsBePDBBxk9ejQAb7zxBrfccgs333xz2uuPHz9Oq1atqFq1KpUrV+bzzz8HoGXLlogIIkKtWrXYtWuXj//VQsf+/dC4sRMOw4fD22/b6q/mImU2ZftibsBqf3yOt1skLOERzubMmaOAzps3L9PXpN9/4vjx43rixAlVVd2yZYum/v/7/vvvNU+ePLp9+3Y9e/as3nHHHTplyhRNTEzU+vXra5Jno4OhQ4fqwIEDVVX19ttv1xUrVmR63NSlMM6ePau33367rlu3TlVV582bp3Xq1NHJkydr8+bNVVV17ty5+thjj2lKSoomJydrq1atdOHChTp16lR99NFH0z7zyJEj5xzj9OnTWr16dV20aJHv/9FCwKZNqmXKqF52mer06W5XY0IZ2d2Pwgc28jrCzZ49m+LFi/Pzzz/TtGnTLF9/5swZevXqxdq1a4mJiWHLli1pz9WqVYuyZcsCzl/6ixcvJm/evGzcuJG6desCcPr0aW699Vafavviiy8YNWoUZ8+eZe/evWzcuJGbb76Zpk2bMmXKFJ588knWrXMWO543bx7z5s2jevXqgLMU+NatW6lfvz79+vXj+eefp3Xr1tSvX/+cY/Ts2ZMGDRpc8HgoW7QI7rkHcuZ0FvarXdvtiky48ldQZL5glAl7a9euZf78+cTFxVGvXj3at2+ftgFPZoYPH06xYsVYt24dKSkp5M3798ov5+/DISKoKk2bNmXy5MkXVVt8fDxvvvkmK1asoHDhwnTu3JmTJ539tFJSUti0aROXXXYZhw4dokSJEqgqL774Io8//vgFn7Vq1Sq+/fZbXnzxRZo1a8aAAQMAGDhwIImJiXz44YcXVZubPvkEunaFMmXg22/Bk8vGXBJfh8eaKKWqPPHEE4wYMYJSpUrx3HPP0a9fvyzf9+eff1K8eHFy5MjBpEmTSE5OTntu+fLlxMfHk5KSwueff069evWoU6cOS5YsYdu2bQD89ddf51yFZObo0aPkz5+fyy+/nH379jF79t8rywwfPpyKFSsyefLktBFLzZs3Z9y4cWn9H7t372b//v3s2bOHfPny8fDDD9OvXz9Wr14NwJgxY5g7dy6TJ08mR47Q/3FJTnaW4Xj4Ybj1VvjpJwsJk31eryhE5Fugp6omZPE51vQUoUaPHk2pUqXSmpt69uzJ+PHjWbhwIbfffnum7+vZsydt27ZlypQpNGrUiPz586c9d+utt/LCCy+wYcMGGjRowL333kuOHDkYP348Dz74IKdOnQLg1Vdf5YYbbvBaX9WqValevTqVKlWibNmyaU1XW7ZsYcyYMSxfvpyCBQvSoEEDXn31VQYOHMimTZvSmrUKFCjAxx9/zLZt23juuefIkSMHuXLl4v333wegR48eXHfddWmvv++++9KuNELNrl3QoYPT5NSpE3z4oa3ZZPwjq2XGHwBexVlq43VVzXAQuYhcqarnL9XhV7bMuDGZ++YbZwmOU6ecCXWPPOJ2RSbcZGeZ8S9EZBYwAFgpIpNwNi1Kff4tz9eAhoQxJmOnTsHzz8PIkVCtGnz2mTPj2hh/8qUz+wxwHMgDFCRdUJjotGHDBjp27HjOY3ny5GHZsmUBPW7t2rXTmqVSTZo0iSpVqgT0uKFqyxZnvaY1a+Cpp+D11yGv7RZjAiCrPoo7gbdwZlPXUNW/glKVCWlVqlRh7dq1QT9uoIMonEyaBE884fRBfPWVs3aTMYGS1RVFf6Cdqv4SjGKMMd4lJTlrNU2cCPXrO8NgS5Z0uyoT6bLqowif2UXGRLg1a+D//g+2b4dXXoGXXnIm0xkTaKE/MNyYKKfqrM9Upw4cPw4LFsC//mUhYYLH/qkZE8IOHoQuXZzhr61bO9uVFinidlUm2tgVhTEhatEiqFrV2aZ0xAiYMcNCwrjDgsKYEJOcDAMHQqNGcNllsHQp9O4NYusfGJdY05MxIWTXLmedpoULna/vvQcFC7pdlYl2FhTGhIiZM51lOE6ehAkTbBkOEzqs6ckYl506BX36wF13OXMiVq2ykDChxa4ojHHR1q3O3AhbhsOEMgsKY1zy8cfOMhy5c9syHCa0WdOTMUGWlOTsF9GxI1SvDmvXWkiY0GZBYUwQrVkDNWs6i/oNGADffWdrNZnQZ0FhTBCown//6yzDkZTkBMTAgbYMhwkP9s/UmAA7eBC6dnVmVrdqBePH2wxrE17sisKYAFq0yNl5bvZsGD7cWbPJQsKEGwsKYwIgORkGDXKW4cib11mGo08fW4bDhCdrejLGz7Zsge7dbRkOEznsisIYPzl+HP75T6hc2RnyOn68M7rJQsKEO7uiMCabVGH6dHjmGdi501mvaehQKFbM7cqM8Q+7ojAmGzZvhubN4f774corYfFiZ3MhCwkTSSwojLkEqc1MVarA8uXOHImVK6FuXbcrM8b/rOnJmItgzUwmGtkVhTE+smYmE60sKIzJgjUzmWjnWtOTiCQAx4Bk4KyqxrpVizEZsWYmYxxu91E0UtUDLtdgzAU2b3Y2Epo/H6pWhcmT7QrCRC9rejImHWtmMuZCbgaFAvNEZJWIdHexDmNQhWnToGJFGDIEOnRwrip69bKlwI1x80egrqruEZGrgfki8quqLkr/Ak+AdAcoVaqUGzWaKGDNTMZ459oVharu8XzdD3wJ1MrgNaNUNVZVY4sWLRrsEk2Es2YmY3zjSlCISH4RKZj6PdAM+NmNWkz0sWYmYy6OWz8WxYAvxVmcPyfwqarOcakWE0WsmcmYi+dKUKjqb0BVN45totPx4/Daa/Dmm5Avn9PM1KOHXUEY4wv7MTERzSbNGZN9No/CRCxbm8kY/7CgMBEnPt7pmLbRTMb4hzU9mYixfj385z/w+eeQIwd06gSvvmpXEMZklwWFCWuq8OOPTr/D7NlQoAD06eP0SVx7rdvVGRMZLChMWEpJgRkznCuIuDgoWtS5eujZEwoXdrs6YyKLBYUJK6dPwyefwOuvw6+/Qpky8O670KULXHaZ29UZE5ksKExYOHYMRo+Gt96C3budyXKffgrt2tlcCGMCzX7ETEhLTIS333auGg4fhoYNYcwYZ9irM7HfGBNoFhQmJMXHw7BhMHYsnDoF99wDzz8PtWu7XZkx0ceCwoSU84e4duwIzz0HN97odmXGRC8LCuM6VVi0yAkIG+JqTOixoDCusSGuxoQHCwoTdDbE1ZjwYkFhgiajIa6TJzuL9tkQV2NCl/14moDbv99ZmO+dd+DIERviaky4saAwAbN9OwwfbkNcjQl3FhTGr3bsgKlT4YsvnCW+c+WyIa7GhDsLCpNt54cDQI0aMGSIExI2xNWY8GZBYS6Jt3Bo1w6uv97d+owx/mNBYXxm4WBMdLKgMF5ZOBhjLCjMBSwcjDHpWVAYwMLBGJM5C4ooZuFgjPGFBUWUsXAwxlwsC4ooYOFgjMkOC4oIk5LiLJ2xahWsXAk//mjhYIzJHguKMHZ+KKxaBatXw9GjzvN58kD16hYOxpjssaAIE76EQtWq0KED1Kzp3CpVctZaMsaY7LCgCEEWCsaYUGJB4TILBWNMqLOgCCJfQuHmmy0UjDGhxbWgEJE7gZFADDBGVYe6VYs/pKQ4v/APH77wtnmzhYIxJny5EhQiEgO8CzQFdgErRGSGqm50o55Umf2yP3Qo4wBIf/vzT+f9GbFQMMaEM7euKGoB21T1NwAR+Qy4G/B7UBw+DAsWZP+XPTi/2AsX/vt29dVQocLf96+88tznU2/FilkoGGPCl1tBcS2wM939XcAFOymLSHegO0CpUqUu6UC//+7MIUh1qb/sCxeGfPlA5JLKMMaYsOVWUGT061YveEB1FDAKIDY29oLnfVGhAqxfb7/sjTHmUrkVFLuAkunulwD2BOJAefNClSqB+GRjjIkOOVw67gqgvIiUEZHcQHtghku1GGOM8cKVKwpVPSsivYC5OMNjx6nqL27UYowxxjvX5lGo6rfAt24d3xhjjG/canoyxhgTJiwojDHGeGVBYYwxxisLCmOMMV5ZUBhjjPFKVC9pwnPQiUgisCMbH1EEOOCncsJJtJ432LnbuUef7Jz7dapaNKMnwiYosktEVqpqrNt1BFu0njfYudu5R59Anbs1PRljjPHKgsIYY4xX0RQUo9wuwCXRet5g5x6t7Nz9LGr6KIwxxlyaaLqiMMYYcwksKIwxxngV8UEhIneKyGYR2SYiL7hdT7CIyDgR2S8iP7tdS7CJSEkR+V5ENonILyLS2+2agkVE8orIchFZ5zn3gW7XFEwiEiMia0Rkptu1BJOIJIjIBhFZKyIr/f75kdxHISIxwBagKc6ueiuAB1V1o6uFBYGINACSgImqWtnteoJJRIoDxVV1tYgUBFYB90TJ/3cB8qtqkojkAhYDvVU1zuXSgkJE+gKxQCFVbe12PcEiIglArKoGZKJhpF9R1AK2qepvqnoa+Ay42+WagkJVFwGH3K7DDaq6V1VXe74/BmwCrnW3quBQR5Lnbi7PLXL/GkxHREoArYAxbtcSaSI9KK4Fdqa7v4so+YVhHCJSGqgOLHO5lKDxNL+sBfYD81U1Ws59BPD/gBSX63CDAvNEZJWIdPf3h0d6UEgGj0XFX1cGRKQAMA3oo6pH3a4nWFQ1WVWrASWAWiIS8U2PItIa2K+qq9yuxSV1VbUG0AJ40tP07DeRHhS7gJLp7pcA9rhUiwkiT/v8NOATVZ3udj1uUNUjwA/Ane5WEhR1gTaetvrPgMYi8rG7JQWPqu7xfN0PfInT7O43kR4UK4DyIlJGRHID7YEZLtdkAszToTsW2KSqb7ldTzCJSFERucLz/WXAHcCvrhYVBKr6oqqWUNXSOD/n36nqwy6XFRQikt8zaAMRyQ80A/w62jGig0JVzwK9gLk4HZpfqOov7lYVHCIyGVgKVBCRXSLSze2agqgu0BHnr8q1nltLt4sKkuLA9yKyHucPpfmqGlVDRaNQMWCxiKwDlgOzVHWOPw8Q0cNjjTHGZF9EX1EYY4zJPgsKY4wxXllQGGOM8cqCwhhjjFcWFMYYY7yyoDDGGOOVBYWJKJ4lxuNF5ErP/cKe+9dl8NrSWS3DLiINL3bJahH5QURiL65y/xCRXp4l9VVEirhRg4k8FhQmoqjqTuB9YKjnoaHAKFXd4V5VQbUEZzZ2tJyvCQILChOJhgN1RKQPUA8YltUbPFcXP4rIas/ttnRPFxKRL0Vko4h8ICI5PO9pJiJLPa+f4lmEMEsi8r6IrEy/sZCIXO7ZYKuC5/5kEXnM8/1zIrJCRNane31+EZnl2aDoZxH5PwBVXaOqCb7+hzLGFzndLsAYf1PVMyLyHDAHaObZiyQr+4GmqnpSRMoDk3E2wAFngbWbcP5KnwPcJyI/AC8Bd6jqcRF5HugLDPLhWP1V9ZBnY60FInKzqq4XkV7AeBEZCRRW1dEi0gwo76lBgBmelUGLAntUtRU4QePDcY25JBYUJlK1APYClYH5Prw+F/COiFQDkoEb0j23XFV/g7Q1tOoBJ3HCY4mzBiG5cdbW8sUDnj0DcuKszXQTsF5V54tIO+BdoKrntc08tzWe+wVwguNH4E0R+Q8wU1V/9PHYxlw0CwoTcTy/7JsCdXAWS/tMVfdm8bZngH04v6Bz4ARBqvMXRFOcv+7nq+qDF1lbGaAfcIuqHhaR8UBez3M5gIrACeBKnGXyBRiiqh9m8Fk1gZbAEBGZp6q+XM0Yc9Gsj8JEFM8S4+/jbFb0O/AG8KYPb70c2KuqKTgrz8ake66WZ6n6HMD/4exDHQfUFZFynuPmE5Ebzv/QDBQCjgN/ikgxnCufVM/grHL8IDDOs6fGXKBrav+HiFwrIleLyD+Av1T1Y8/51fDh2MZcEgsKE2keA35X1dTmpveAG0Xk9ize9x7QSUTicJqdjqd7binO6KmfgXjgS1VNBDoDkz1LescBN2ZVnKquw2lG+gUYhzNKCU/IPAo862lGWgS8pKrzgE+BpSKyAZgKFASqAMs9W572B171fM7TIrILZ5Ou9SJi+0ebbLNlxo0xxnhlVxTGGGO8ss5sE/FEpAow6byHT6lq7QAfdxmQ57yHO6rqhkAe1xh/s6YnY4wxXlnTkzHGGK8sKIwxxnhlQWGMMcYrCwpjjDFe/X+fxb9GXRXGoQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Créer un tableau vide\n",
    "fig = plt.figure()\n",
    "\n",
    "axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # axes principaux\n",
    "axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # axes rentrants\n",
    "\n",
    "# Plus grande figure Axes 1\n",
    "axes1.plot(x, y, 'b')\n",
    "axes1.set_xlabel('X_label_axes1')\n",
    "axes1.set_ylabel('Y_label_axes1')\n",
    "axes1.set_title('Axes 1 Title')\n",
    "\n",
    "# Insérer une figure Axes 2\n",
    "axes2.plot(y, x, 'r')\n",
    "axes2.set_xlabel('X_label_axes2')\n",
    "axes2.set_ylabel('Y_label_axes2')\n",
    "axes2.set_title('Axes 2 Title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## subplots()\n",
    "\n",
    "L'objet plt.subplots() agira comme un gestionnaire d'axes plus automatique.\n",
    "\n",
    "Cas d'utilisation de base:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Utiliser comme plt.figure() sauf utiliser la syntaxe tuple pour récupérer fig et axes.\n",
    "fig, axes = plt.subplots()\n",
    "\n",
    "# Utiliser maintenant l'objet axes pour ajouter des éléments au tracé\n",
    "axes.plot(x, y, 'r')\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ensuite, vous pouvez spécifier le nombre de lignes et de colonnes lors de la création de l'objet subplots():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"252.317344pt\" version=\"1.1\" viewBox=\"0 0 380.054687 252.317344\" width=\"380.054687pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2020-11-27T18:53:50.997000</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.3.1, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Toile vide de 1 par 2 sous-graphiques\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([<AxesSubplot:>, <AxesSubplot:>], dtype=object)"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "# Axes est un tableau d'axes sur lesquels on peut tracer des axes\n",
    "axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous pouvons itérer à travers ce tableau:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ],
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\n"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "for ax in axes:\n",
    "    ax.plot(x, y, 'b')\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    ax.set_title('title')\n",
    "\n",
    "# Afficher l'objet figure   \n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un problème courant avec matplolib est le chevauchement des sous-graphiques ou des figures. Nous pouvons utiliser la méthode **fig.tight_layout()** ou **plt.tight_layout()** qui ajuste automatiquement les positions des axes sur le canevas des figures afin qu'il n'y ait pas de chevauchement de contenu:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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AEgyqPQheHo/8p2F6zi6o/wEoBaAKgHgA/33UNyqlgpVS1ZVS1X18fJwUj3Rac2QNdp3ehU9f/BTZ3LLpjmN2nCV6pJHhI5Evez7LX/3fqQWllDqnlEpVSqUBCAFg/uu9k1MopTA8fDhKeJbA21Xe1h3H9DhL9CjR8dEIPRiKD+p8AM8cnrrjZIpTC0pECt/3aQCAmEd9L9nLysMrEXkmEsNeHAZ3N3fdcUyPs0SPMjJ8JDxzeGJArQG6o2SaYSfqisg8AA0BeItIHIARABqKSBUACsBxAP9n1PbJOpRSGBk+Ev5e/uhWuZvuOKbDWaInFXkmEmGHwvD5y58jX458uuNkmmEFpZR62NU9pxu1PbKusENhiIqPwvctv+fq6SE4S/SkRoaPRH6P/Ohfq7/uKA7BK0mQVvdWT6Xzl0aXSl10xyGyrF1xu7Dy8EoMqTMEebPn1R3HIXgtPtIq9GAo9pzdg1mtZiFrFv5zJMqokREjUcCjAPrV7Kc7isNwBUXapKk0jAwfiWfzP4tOFTvpjkNkWTtO7cCaI2vwYd0PkSd7Ht1xHIZPWUmbpQeWYt+5fZgdMJurJ6JMGBkxEt45vfFezfd0R3EorqBIizSVhpERI/F8gefRsQLfLZcoo7af2o6f//gZQ+sOtdS75T4JPm0lLRb/thgx52PwY+sf4ZbFTXccIssaET4Cvrl80bdGX91RHI4rKHK6NJWGURGjUNa7LDqU76A7DpFlbTmxBeuPrsdH9T5Crmy5dMdxOK6gyOl+iv0JsRdiMb/NfK6eiDJhRPgIFMxVEO9Uf0d3FEOwoMipUtNSMSpiFMr7lEe78u10xyGyrIjjEdh0fBPGNR2HnO45dccxBAuKnGph7EIcuHgAC9suRBbhK8xEGTUifAQK5y6M/3vBda9yxYIip7m3eqroWxFtyrXRHYfIsjYd24SIExGY0GwCPNw9dMcxDAuKnGZezDwcvHQQi9ot4uqJKIPuvbHnM3meQZ8X+uiOYygWFDlFSloKRkeMRuWClRFQNkB3HCLL2nBsA7ac3IJJr01Cjqw5dMcx1D8+jRWRfo97O2miJzH317k4fPkwRjYcadvVE2eJMuve6qlo3qLoVa2X7jiGe5L/KQoB2C0iC0WkmYiI0aHItaSkpeDzzZ+jaqGqaPl8S91xdOIsUaasO7oO209txyf1P0H2rNl1xzHcPxaUUmoYgGdx9/1n3gZwWES+FJFSBmcjFzF732wcuXwEIxuOhJ3/T+YsUWbcWz355fVDj6o9dMdxiid6rUUppQCcTf+VAsALwCIR+drAbOQCrt+5jmGbhqHGMzXQ4rkWuuNox1mijFoQuwA743Zi+EvDbbF6Ap7gIAkR6Q+gO4CLAKYB+FAplSwiWQAcBjDU2IhkZV9u+RJnEs9gcfvFtl49AZwlyrikO0n4cN2HqFqoKgKrBOqO4zRPchSfN4DWSqkT99+olEoTkebGxCJX8MflP/DfHf9F10pdUbtobd1xzICzRBny1bavEHctDnNbz7XV5cH+saCUUsMf87UDjo1DrmTwz4PhnsUdYxuP1R3FFDhLlBHHrxzHf7b/B29VeAsvFn9Rdxyn4nlQZIh1f6xD6MFQjGk0Bs/keUZ3HCLLGvLzEAgEXze2325Ke56QQoZKTk3GgDUDUMqrFAbVHqQ7DpFlbTq2CYsPLMa/6v8Lfvn8dMdxOq6gyOG+2/0dDlw8gNC3Qm1ztBGRo6WkpaD/mv4o4VkCQ+oO0R1HCxYUOdSFpAsYET4CTUo14WHlRJkwNXIqYs7HYFG7RS59QdjH4Ut85FDDNg5DUnISxjcdb/vDyoky6tKNS/hs02d4ucTLaF22te442rCgyGH2xO9BSHQI+tXoh7I+ZXXHIbKs4ZuG4+rtq5jQbIKtn+ixoMghlFLov6Y/vHN6Y0TDEbrjEFnW/nP7MSVqCt6t/i4qFqyoO45W3AdFDrEgdgG2ntyK4ObB8MzhqTsOkSUppTBgzQB45vDE6JdH646jHQuKMu3eZViqFa5mm4tYEhlh8YHFCD8eju9e/w75PfLrjqMdC4oy7d5lWOa3mW+ry7AQOdLN5JsY8vMQVCpYyeXfKfdJsaAoU+5dhqVTxU6oV6ye7jhElvWf7f/BiasnEN4qnE/00hl2kISIzBCR8yISc99t+UVknYgcTv+d7y5qcUN+HoIskgVfNf5KdxSXxVlyfSevnsTYrWPRrlw7vFTiJd1xTMPIo/hmAmj2wG0fA9iglHoWwIb0z8mi7l2G5ZP6n6Bo3qK647iymeAsubSh64ZCQeE/r/5HdxRTMayglFKbAVx+4OaWAGalfzwLQCujtk/GuncZlpKeJTG47mDdcVwaZ8m1bT6xGQtiF+Cjeh+huGdx3XFMxdn7oAoqpeIBQCkVLyK+j/pGEekDoA8AFCtWzEnx6ElNiZyCmPMxWNJ+CXJkzaE7jh1xllxAaloq+q/uD7+8fhhaj+9X+SDTnqirlApWSlVXSlX38fHRHYfuc+nGJQzfNByNSjZCqzKtdMehf8BZMq+Q6BDsO7cP3zT5Bjndc+qOYzrOLqhzIlIYANJ/P+/k7ZMDfLbpM1y7fc32l2HRjLNkcQk3EzBs4zC8VPwltCvXTnccU3J2QS0H0D394+4AQp28fcqkfWf3YWrUVPSt0RflfcvrjmNnnCWLGxE+Agm3EvhE7zGMPMx8HoAdAJ4XkTgR6QlgLIBXReQwgFfTPyeLuHcZFq8cXhjVcJTuOLbBWXI9Medj8N3u79CnWh9ULlRZdxzTMuwgCaVUx0d8qZFR2yRjLfptESJOROB/b/wPXh487cZZOEuuRSmFgWsGIk/2PPj8lc91xzE1XkmCnsiN5BsYsm4IKhesjN7VeuuOQ2RZy35fhg3HNuDbZt/CO6e37jimxoKiJ/LV1q9w8upJzA6YzcuwEGXQjeQbGPzzYJT3KY93a7yrO47psaDoH+2J34Mvt36JThU7oUHxBrrjEFnWx+s/xrErx7Cp+yZkzcL/fv+Jac+DInO4lXILXZZ2gW8uX0x8baLuOESWte6PdZj4y0QMqDUADUs01B3HEljh9FjDNg7Dbxd+w5rOa/j+NEQZlHAzAYGhgSjjXQZjGo3RHccyWFD0SBHHIxC0IwjvVn8XTUs31R2HyLL6r+mPs9fPYmmHpfBw99AdxzL4Eh891LXb1/B26Nvw9/LnFZaJMmHRb4swZ/8cDGswDDWK1NAdx1K4gqKH+mDtBzh59SS2BG5Brmy5dMchsqSz18/inRXv4IXCL+DTFz/VHcdyuIKivwk7GIbpe6bjo3ofoa5fXd1xiCxJKYXeYb2RlJyE2QGz4e7mrjuS5XAFRX9xIekCeoX1QuWClTGy4UjdcYgsa8aeGVhxaAXGNx2Psj5ldcexJBYU/UkphXdWvoMrt65gXdd1yOaWTXckIks6lnAMA9cOxMslXsb7td7XHceyWFD0pzn752DJgSX4qvFXqFSwku44RJaUmpaK7su6I4tkwcxWM5FFuCclo1hQBAA4dfUU+q3uh/rF6mNwHb6FO1FGjds5DltObsHMljNRLB/fwTgzWO2ENJWGwNBApKalYlarWbzWHlEGxZyPwacbP0WrMq3QrXI33XEsjysowuRfJmPDsQ0Ibh4Mfy9/3XGILOlO6h10XdoV+bLnw9TmU/kmhA7AgrK53y/+jqHrh+L1Z19Hr2q9dMchsqzREaOx9+xeLOuwDL65fHXHcQl8ic/GUtJS0G1pN+R0z4lpLabxGR9RBu2M24kxW8cgsEogWpZpqTuOy+AKysbGbBmD3Wd2Y2HbhSicp7DuOESWlHQnCd2WdoNfXj+MbzZedxyXwoKyqagzURi9eTQ6VeyEduXb6Y5DZFlD1w3F4cuHsan7JuTNnld3HJfCl/hs6GbyTXRd2hW+uXwx6bVJuuMQWdbaI2vxXeR3GFR7EN/jyQBcQdnQpxs/xYGLB7C2y1p4eXjpjkNkSQk3E9BjeQ+U9S6Lf7/yb91xXBILymY2HduEcTvHoW/1vmhSqonuOESW1W91P5xPOo/lby3nezwZhC/x2ci993h6Nv+z+PrVr3XHIbKshbELMffXufiswWd44ZkXdMdxWVxB2YRSCu+teg9x1+Kwrcc2vscTUQadvHoS7658FzWL1MQnL36iO45L4wrKJr7Z/g3m7J+DES+NQO2itXXHIbKkxNuJaDGvBVLSUvBDqx+QNQuf4xuJf7o2sOz3Zfho/UdoX749hjUYpjsOkSWlpqWi85LOiDkfg1WdVuF57+d1R3J5LCgXt/fsXnRe0hnVn6mOmS156X+ijPp4/ccIOxSGia9NRNPSTXXHsQX+b+XC4hPj0WJeC+T3yI/Qt0J5pBFRBk2Pno5vdnyD92q8h341++mOYxtcQbmom8k30XJ+SyTcTMDWHlt5KSOiDAo/Ho53Vr6DJqWa8FJGTsaCckFpKg1vh76NyDORWNphKaoUqqI7EpElHbl8BG0WtkHp/KWxoO0CHhThZPzTdkGjwkdhYexCfN34a15ZmSiDEm4moPnc5hAIVnRcAc8cnroj2Y6WghKR4wASAaQCSFFKVdeRwxXN/XUuRm8ejcAqgRhSd4juOGQwzpIxklOT0X5RexxNOIr13dajVP5SuiPZks4V1MtKqYsat+9ydpzagR6hPdCgeANMaT6F7+9kH5wlB1JKof/q/lh/dD1mvDkDDYo30B3JtngUn4s4ceUEWi1ohSJ5i2Bx+8XI5pZNdyQiS5r4y0RMiZqCoXWHIrBqoO44tqaroBSAn0UkSkT6POwbRKSPiESKSOSFCxecHM9a7p3dfjvlNlZ0XAHvnN66I5HzcJYcaPXh1Ri0dhBaPt8SYxqP0R3H9nQVVD2lVDUArwF4T0T+toZWSgUrpaorpar7+Pg4P6FFpKalouPijvjtwm/4qd1PKOtTVnckci7OkoPEno9Fh0UdUNG3Iua0nsOT2k1Ay9+AUupM+u/nASwFUFNHDlcwdN1QrDy8Et++9i1eLfWq7jjkZJwlx7iQdAHN5zVHrmy5ENYxDLmz5dYdiaChoEQkl4jkufcxgCYAYpydwxWERIUgaGcQ3q/5PvrW6Ks7DjkZZ8kxbqfcRsCCAJy9fhbL31oOv3x+uiNROh1H8RUEsDT9CLOsAOYqpdZoyGFpm45tQt9VfdG0VFMENQ3SHYf04CxlklIKvcN6Y9upbVjQdgFqFKmhOxLdx+kFpZQ6CqCys7frSg5dOoQ2C9vguQLP8ex2G+MsZd7YrWMxe/9sjGo4Cu3Lt9cdhx7AvYAWc/nmZbSY1wJuWdwQ1jEM+XLk0x2JyJKWHFiCTzZ+go4VOuKzBp/pjkMPwafeFpJ4OxEBCwJw/MpxbOi2Af5e/rojEVnS5hOb0WVJF9QuWhszWs7gSe0mxRWURVy8cRGNfmiEbSe3YWbLmahfrL7uSESWFHYwDE3nNEVxz+JY1mEZcmTNoTsSPQILygLirsWhwfcNsP/cfizpsAQdK3bUHYnIkmbvm42ABQGo4FsBWwK3oGDugroj0WPwJT6TO3TpEF6d/SoSbiZgbZe1eKnES7ojEVnShJ0TMHDtQLxS8hUs67AMebLn0R2J/gELysSi46PRbE4zAED42+GoVria5kRE1qOUwojwEfh88+cIKBOAuW3m8mU9i2BBmVTE8Qi0mNcCXh5eWNd1HZ4r8JzuSESWk6bS0H91f0zePRk9q/bElOZTeFqGhXAflAktP7gcTec0RdG8RbGtxzaWE1EG3Em9gy5LumDy7sn4sO6HCGkRwnKyGP5tmcwP+35Aj9AeqFa4GlZ1XsUrkxNlwI3kG2i7sC1WH1mNsY3G4qP6H+mORBnAgjKR8TvHY9DaQWhUshGWdljKnbhEGZBwMwHN5zXHzridCG4ejN4v9NYdiTKIBWUCSikM3zQcX2z5Aq3Ltsbc1nORPWt23bGILCc+MR5N5zTFwUsHsaDtArQt11Z3JMoEFpRmaSoN/Vb1w/8i/4eeVXtiavOpcMvipjsWkeUcTTiKV2e/inPXz2Flp5Vo7N9YdyTKJBaURndS76D7su6YHzMfQ+sOxdjGY3nJFaIM+PXcr2gypwnupN7Bhm4bUKtoLd2RyAFYUJok3UlC25/aYs2RNfiq8VcYWm+o7khElrT91Ha8MfcN5HTPic1vb0Z53/K6I5GDsKA0uH8n7rQW09CzWk/dkYgsac2RNWi9oDWK5i2Kn7v+jBKeJXRHIgfieVBOFh0fjXoz6iHyTCR+avcTy4koA1LTUjFuxzi0mNcCz3s/jy2BW1hOLogrKCe5nXIbn2/+HGO3joVvLl+s7rwar5R8RXcsIsv5/eLv6BHaAzvidqDFcy0wO2A23xfNRbGgnOCX07+gR2gPxF6IRffK3TGu6Th4eXjpjkVkKSlpKQjaEYThm4Yjp3tOzA6Yjc4VO/PAIhfGgjLQrZRbGLFpBL7Z8Q0K5y6MlZ1W4vVnX9cdi8hyYs/HIjA0ELvP7EZAmQB898Z3KJS7kO5YZDAWlEG2n9qOHqE9cPDSQfSq2gvfNPmGL0MQPaXk1GR8ve1rjN48Gnmz58X8NvPRvnx7rppsggXlYDeSb2DYxmEYv3M8/PL5YW2XtWhSqonuWESWs//cfgSGBiI6PhrtyrXDpNcnwTeXr+5Y5EQsKAfafGIzei7viSOXj+Dd6u/iq8Zf8Xp6RE/pTuodjNkyBl9s+QL5PfJjUbtFaFOuje5YpAELygGu37mOf63/FybtnoSSniWxsdtGvFzyZd2xiCwnOj4agaGB2H9uPzpV7IQJzSbwiv42xoLKpI3HNqLX8l44duUY3q/5Pr5s9CVyZ8utOxaRpdx/GoZPLh8s67AMLcu01B2LNGNBZVDi7UQMXTcUU6KmoHT+0tj89ma8WPxF3bGILGf36d0IDA1E7IVYdKvcDeOajkN+j/y6Y5EJsKCeUty1OMzYMwNTo6YiPjEeH9T+AJ+/8jlyuufUHY3IMpRSCD8ejpDoECyIXYDCuQtjRccVeOO5N3RHIxNhQT2BlLQUrD68GsHRwVh1eBXSVBoa+zfGonaLUMevju54RJZxPuk8Zu6diWnR03D48mF45vDEgFoDMOKlETwNg/6GBfUYJ66cwPQ90zF9z3ScSTyDQrkL4aN6H6Fn1Z4olb+U7nhElpCm0rDh6AYERwcj9PdQJKclo36x+viswWdoW64tPNw9dEckk2JBPSA5NRlhh8IQEh2CtUfWAgCalW6GSa9NQvPnmsPdzV1zQiJriE+Mx/d7v8e06Gk4duUYCngUQL+a/dC7Wm+U9SmrOx5ZAAsq3R+X/8C06Gn4fu/3OJd0DkXyFMFnDT5Dj6o9UNyzuO54RJaQmpaKtX+sRUh0CMIOhiFVpeLlEi/j36/8GwFlA5Ajaw7dEclCbF1Qt1NuI/RgKIKjgrHh2AZkkSx449k30OeFPmhWuhmyZrH1Hw/REzt19RRm7JmB6Xum49S1U/DN5YvBdQajV7VeeLbAs7rjkUVp+R9YRJoBmADADcA0pdRYo7aVmpaKuGtxOJpwFEcTjuLYlWN/fvz7xd9x9fZVFMtXDKMbjkZg1UAUzVvUqChEDufMWUq6k/SX+bn/18FLB5Gm0tCkVBMENQ3Cm8+/iWxu2YyKQjbh9IISETcAkwG8CiAOwG4RWa6U+i2j93nl1pW/DMuxhGM4euXuxyeunEByWvKf3+smbiiWrxj8vfzRoXwHBJQNwKv+r8Iti1umHxuRMzl6llLTUnE68fTd+bk3T1f+/1ydTzr/l+/Pky0P/L38Uca7DNqXb49ulbvB38s/04+L6B4dK6iaAI4opY4CgIjMB9ASwFMPVcTxCAQsCEDCrYS/3F7AowD8vfzxQuEX0K5cO5T0LAl/L3/4e/nDL58fX7ojV+GwWeoT1gcz98586JO5kl4l8eZzb/45Q/5e/ijpVRIFPArwquJkKB3/UxcBcOq+z+MA1Hrwm0SkD4A+AFCsWLGH3lGxfMXQsULHvw1O3ux5DYhNZDoOm6U6Rev8+cSupNfdJ3R+ef141CpppaOgHvaUS/3tBqWCAQQDQPXq1f/2dQAo6VUSk9+Y7Nh0RNbhsFkKrBro2GREDpBFwzbjAPjd93lRAGc05CCyOs4SuTQdBbUbwLMiUlJEsgF4C8ByDTmIrI6zRC7N6S/xKaVSRKQfgLW4e2jsDKVUrLNzEFkdZ4lcnZbD2ZRSqwCs0rFtIlfCWSJXpuMlPiIion/EgiIiIlNiQRERkSmxoIiIyJREqYeet2cqInIBwIlHfNkbwEUnxjEDuz1msz7e4kopH90hngZn6S/s9ngB8z7mh86SJQrqcUQkUilVXXcOZ7LbY7bb49XFbn/Odnu8gPUeM1/iIyIiU2JBERGRKblCQQXrDqCB3R6z3R6vLnb7c7bb4wUs9pgtvw+KiIhckyusoIiIyAWxoIiIyJQsXVAi0kxEDorIERH5WHceI4mIn4hsEpEDIhIrIgN0Z3IWEXETkT0iskJ3FlfFWXJ9VpwjyxaUiLgBmAzgNQDlAHQUkXJ6UxkqBcBgpVRZALUBvOfij/d+AwAc0B3CVXGWbDNLlpsjyxYUgJoAjiiljiql7gCYD6Cl5kyGUUrFK6Wi0z9OxN1/aEX0pjKeiBQF8AaAabqzuDDOkovPklXnyMoFVQTAqfs+j4OL/yO7R0RKAKgKYJfmKM4wHsBQAGmac7gyzpLrz9J4WHCOrFxQ8pDbXP6YeRHJDWAxgIFKqWu68xhJRJoDOK+UitKdxcVxllx4lqw8R1YuqDgAfvd9XhTAGU1ZnEJE3HF3oH5USi3RnccJ6gF4U0SO4+7LTq+IyBy9kVwSZ8m1WXaOLHuirohkBXAIQCMApwHsBtBJKRWrNZhBREQAzAJwWSk1UHMcpxORhgCGKKWaa47icjhL9mG1ObLsCkoplQKgH4C1uLuTc6GrDlS6egC64u6zn73pv17XHYqsj7PEWTIry66giIjItVl2BUVERK6NBUVERKbEgiIiIlNiQRERkSmxoIiIyJRYUEREZEosKCIiMiUWlA2ISA0R2S8iOUQkV/p74FTQnYvIajhLzsUTdW1CRL4AkAOAB4A4pdQYzZGILImz5DwsKJsQkWy4e421WwDqKqVSNUcisiTOkvPwJT77yA8gN4A8uPvsj4gyhrPkJFxB2YSILMfdS+2XBFBYKdVPcyQiS+IsOU9W3QHIeCLSDUCKUmquiLgB2C4iryilNurORmQlnCXn4gqKiIhMifugiIjIlFhQRERkSiwoIiIyJRYUERGZEguKiIhMiQVFRESmxIIiIiJT+n+9zmtzHl8X1wAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "\n",
    "for ax in axes:\n",
    "    ax.plot(x, y, 'g')\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    ax.set_title('title')\n",
    "\n",
    "fig    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Taille des figures, rapport hauteur/largeur et DPI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib permet de spécifier l'aspect de ratio, de DPI et de taille de la figure lors de la création de l'objet Figure. Vous pouvez utiliser les arguments `figsize` et `dpi`. \n",
    "* `figsize` est un tuple de la largeur et de la hauteur de la figure en pouces\n",
    "* `dpi` est le nombre de points par pouce (pixel par pouce). \n",
    "\n",
    "par exemplee:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 800x400 with 0 Axes>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,4), dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les mêmes arguments peuvent également être transmis aux gestionnaires de mise en page, tels que la fonction `subplots`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 864x216 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"222.954375pt\" version=\"1.1\" viewBox=\"0 0 717.403125 222.954375\" width=\"717.403125pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2020-11-27T18:53:51.814136</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.3.1, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(12,3))\n",
    "\n",
    "axes.plot(x, y, 'r')\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sauvegarde des figures\n",
    "Matplotlib peut générer des sorties de haute qualité dans plusieurs formats, notamment PNG, JPG, EPS, SVG, PGF et PDF."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour enregistrer une figure dans un fichier, nous pouvons utiliser la méthode `savefig` de la classe `Figure`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"filename.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ici, nous pouvons aussi spécifier le DPI et choisir entre différents formats de sortie:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"filename.png\", dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "## Légendes, étiquettes et titres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maintenant que nous avons couvert les bases de la création d'un canevas de figures et de l'ajout d'instances d'axes dans le canevas, voyons comment décorer une figure avec des titres, des étiquettes d'axes et des légendes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Titres de figure**\n",
    "\n",
    "Un titre peut être ajouté à chaque instance d'axe dans une figure. Pour définir le titre, utilisez la méthode `set_title` de l'instance axes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax.set_title(\"title\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Etiquettes d'axes**\n",
    "\n",
    "De même, avec les méthodes `set_xlabel` et `set_ylabel`, nous pouvons définir les étiquettes des axes X et Y:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"y\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Légendes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vous pouvez utiliser l'argument **label=\"texte étiquette\"** lorsque des tracés ou d'autres objets sont ajoutés à la figure, et ensuite utiliser la méthode **legend** sans arguments pour ajouter la légende à la figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a9ec1f6ec8>"
      ]
     },
     "metadata": {},
     "execution_count": 21
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "ax.plot(x, x**2, label=\"x**2\")\n",
    "ax.plot(x, x**3, label=\"x**3\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notez à quel point la légende recouvre le tracé!\n",
    "\n",
    "La fonction **legend** prend un argument optionnel **loc** qui peut être utilisé pour spécifier où la légende doit être dessinée dans la figure. Les valeurs autorisées de **loc** sont des codes numériques pour les différents endroits où la légende peut être dessinée. Voir la [page de documentation](http://matplotlib.org/users/legend_guide.html#legend-location) pour plus de détails. Certaines des valeurs **loc** les plus courantes sont:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
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     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "# Beaucoup d'options....\n",
    "\n",
    "ax.legend(loc=1) # coin supérieur droit\n",
    "ax.legend(loc=2) # coin supérieur gauche\n",
    "ax.legend(loc=3) # coin inférieur gauche\n",
    "ax.legend(loc=4) # coin inférieur droit\n",
    "\n",
    "# .. beaucoup plus d'options sont disponibles\n",
    "\n",
    "# Le plus courant à choisir\n",
    "ax.legend(loc=0) # laisse matplotlib décider de l'emplacement optimal\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Réglage des couleurs, largeurs de trait, types de trait\n",
    "\n",
    "Matplotlib vous donne *beaucoup* d'options pour personnaliser les couleurs, les largeurs de trait et les types de trait. \n",
    "\n",
    "Il y a la syntaxe de base de MATLAB (que je vous suggère d'éviter d'utiliser pour des raisons plus claires:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Couleurs avec une syntaxe semblable à celle de MatLab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avec matplotlib, nous pouvons définir les couleurs des lignes et autres éléments graphiques de plusieurs façons. Tout d'abord, nous pouvons utiliser la syntaxe de type MATLAB où `'b'` pour blue (bleu), `'g'` pour green (vert), etc. L'API MATLAB pour la sélection des styles de lignes est également supportée: où, par exemple, 'b.-' pour une ligne bleue avec des points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a9ebf84a48>]"
      ]
     },
     "metadata": {},
     "execution_count": 23
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Couleur et style des lignes de style MATLAB \n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, x**2, 'b.-') # ligne bleue point tiret\n",
    "ax.plot(x, x**3, 'g--') # ligne pointillée verte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Couleurs avec le paramètre color="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous pouvons également définir les couleurs par leurs noms ou codes hexadécimaux RVB et éventuellement fournir une valeur alpha en utilisant les arguments `color` et `alpha`. Alpha indique l'opacité."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a9ebf77388>]"
      ]
     },
     "metadata": {},
     "execution_count": 24
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x, x+1, color=\"blue\", alpha=0.5) # à moitié transparent\n",
    "ax.plot(x, x+2, color=\"#8B008B\")        # RGB hex code\n",
    "ax.plot(x, x+3, color=\"#FF8C00\")        # RGB hex code "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Styles de lignes et de marqueurs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour modifier l'épaisseur de ligne, on peut utiliser l'argument `linewidth` ou `lw`. Le style de ligne peut être sélectionné à l'aide des arguments `linestyle` ou `ls`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 864x432 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"357.238125pt\" version=\"1.1\" viewBox=\"0 0 713.265625 357.238125\" width=\"713.265625pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2020-11-27T18:53:52.852164</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.3.1, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "ax.plot(x, x+1, color=\"red\", linewidth=0.25)\n",
    "ax.plot(x, x+2, color=\"red\", linewidth=0.50)\n",
    "ax.plot(x, x+3, color=\"red\", linewidth=1.00)\n",
    "ax.plot(x, x+4, color=\"red\", linewidth=2.00)\n",
    "\n",
    "# options de types de lignes possibles ‘-‘, ‘–’, ‘-.’, ‘:’, ‘steps’\n",
    "ax.plot(x, x+5, color=\"green\", lw=3, linestyle='-')\n",
    "ax.plot(x, x+6, color=\"green\", lw=3, ls='-.')\n",
    "ax.plot(x, x+7, color=\"green\", lw=3, ls=':')\n",
    "\n",
    "# tiret customisé\n",
    "line, = ax.plot(x, x+8, color=\"black\", lw=1.50)\n",
    "line.set_dashes([5, 10, 15, 10]) # format : longueur de ligne, longueur d'espace, ...\n",
    "\n",
    "# symboles de marquage possibles: marker = '+', 'o', '*', 's', ',', '.', '1', '2', '3', '4', ...\n",
    "ax.plot(x, x+ 9, color=\"blue\", lw=3, ls='-', marker='+')\n",
    "ax.plot(x, x+10, color=\"blue\", lw=3, ls='--', marker='o')\n",
    "ax.plot(x, x+11, color=\"blue\", lw=3, ls='-', marker='s')\n",
    "ax.plot(x, x+12, color=\"blue\", lw=3, ls='--', marker='1')\n",
    "\n",
    "# taille et couleur des marqueurs\n",
    "ax.plot(x, x+13, color=\"purple\", lw=1, ls='-', marker='o', markersize=2)\n",
    "ax.plot(x, x+14, color=\"purple\", lw=1, ls='-', marker='o', markersize=4)\n",
    "ax.plot(x, x+15, color=\"purple\", lw=1, ls='-', marker='o', markersize=8, markerfacecolor=\"red\")\n",
    "ax.plot(x, x+16, color=\"purple\", lw=1, ls='-', marker='s', markersize=8, \n",
    "        markerfacecolor=\"yellow\", markeredgewidth=3, markeredgecolor=\"green\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contrôle de l'apparence des axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans cette section, nous allons examiner le contrôle des propriétés de dimensionnement des axes dans une figure matplotlib."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plage de représentation graphique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous pouvons configurer les plages des axes à l'aide des méthodes `set_ylim` et `set_xlim` sur l'objet axes, ou `axis('tight')` pour obtenir automatiquement des gammes d'axes \"bien ajustés\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 864x288 with 3 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
    "\n",
    "axes[0].plot(x, x**2, x, x**3)\n",
    "axes[0].set_title(\"default axes ranges\")\n",
    "\n",
    "axes[1].plot(x, x**2, x, x**3)\n",
    "axes[1].axis('tight')\n",
    "axes[1].set_title(\"tight axes\")\n",
    "\n",
    "axes[2].plot(x, x**2, x, x**3)\n",
    "axes[2].set_ylim([0, 60])\n",
    "axes[2].set_xlim([2, 5])\n",
    "axes[2].set_title(\"custom axes range\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Type de graphique spécial\n",
    "\n",
    "Il existe de nombreux diagrammes spécialisés que nous pouvons créer, tels que des diagrammes à barres, des histogrammes, des diagrammes de dispersion, et bien plus encore. La plupart de ces types de graphiques que nous allons créer en utilisant pandas. Mais voici quelques exemples des types de graphique:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a9ecbc4b08>"
      ]
     },
     "metadata": {},
     "execution_count": 27
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(array([13., 12.,  8., 11.,  7.,  6., 12., 15.,  6., 10.]),\n",
       " array([ 10. , 108.6, 207.2, 305.8, 404.4, 503. , 601.6, 700.2, 798.8,\n",
       "        897.4, 996. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "metadata": {},
     "execution_count": 28
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from random import sample\n",
    "data = sample(range(1, 1000), 100)\n",
    "plt.hist(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
    "\n",
    "# boîte à moustache\n",
    "plt.boxplot(data,vert=True,patch_artist=True);   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lectures complémentaires"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* http://www.matplotlib.org - La page web du projet matplotlib.\n",
    "* https://github.com/matplotlib/matplotlib - Le code source de matplotlib.\n",
    "* http://matplotlib.org/gallery.html - Une grande galerie présentant les différents types de graphiques que matplotlib peut créer. Fortement recommandé! \n",
    "* http://www.loria.fr/~rougier/teaching/matplotlib - Un bon tutoriel matplotlib.\n",
    "* http://scipy-lectures.github.io/matplotlib/matplotlib.html - Une autre bonne référence matplotlib.\n"
   ]
  }
 ],
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