{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "memoire = np.array([75.08, 70.15, 64.41, 57.89, 51.33, 44.45, 36.69])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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')  # 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, 90)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('test.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
}