{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "memoire = np.array([92.43, 90.9, 88.82, 86.04, 82.66, 78.85, 73.23])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = memoire\n",
    "numerical_indices = [10, 20, 30, 40, 50, 60, 70]\n",
    "\n",
    "# Les étiquettes correspondant aux indices\n",
    "labels = [\"4Ko\", \"8Ko\", \"16Ko\", \"32Ko\", \"64Ko\", \"128Ko\", \"256Ko\"]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "plt.bar(numerical_indices, memoire, color='blue', width=4)  # Largeur des bandes ajustée à 5\n",
    "plt.xlabel('Taille de page')  # Nommer l'axe des x\n",
    "plt.ylabel('Taux de page utilisé (en %)')  # Nommer l'axe des y\n",
    "#plt.title('Histogramme de A en fonction de l\\'indice')  # Titre du graphique\n",
    "plt.xticks(numerical_indices, labels)  # Utiliser les étiquettes pour l'axe x\n",
    "\n",
    "for i in range(len(numerical_indices)):\n",
    "    plt.text(numerical_indices[i], A[i], str(A[i])+'%', ha='center', va='bottom')\n",
    "\n",
    "plt.ylim(0, 100)\n",
    "plt.yticks(range(0, 101, 10))\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('2mm-taux-de-pages.svg', format='svg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}