{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "plt.show()\n",
    "\n",
    "plt.savefig('test.svg', format='svg')"
   ]
  },
  {
   "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
}