{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-82752eb5f629>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0mg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: "
     ]
    }
   ],
   "source": [
    "file = open(\"lowerbounds.txt\",\"r\")\n",
    "maxiter = file.readline()\n",
    "print(f)\n",
    "# niter = (float(f))\n",
    "# print(niter)\n",
    "g = [0]*int(maxiter)\n",
    "max = 0.0000\n",
    "for i in range (0, int(maxiter)):\n",
    "    g[i]= file.readline()\n",
    "    g[i]=float(g[i])\n",
    "    if g[i]>max:\n",
    "        max=g[i]\n",
    "file.close()\n",
    "print(g)\n",
    "\n",
    "print(max)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18\n",
      "  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36\n",
      "  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54\n",
      "  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72\n",
      "  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90\n",
      "  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108\n",
      " 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125]\n"
     ]
    }
   ],
   "source": [
    "p= np.arange(1,int(maxiter)+1)\n",
    "print(p)\n",
    "u=[92]*int(maxiter)\n",
    "b = u[0]+g[0]\n",
    "c=[max]*int(maxiter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt=1524186.09832438\n",
    "opt1=np.full((len(p),1), opt)\n",
    "np.shape(opt1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '6flights, four sectors, opt using lagrangian r.')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(p,g,'r--',p,c,'g--',p,u,'b--')\n",
    "plt.ylabel('lower bound, lr=0.5')\n",
    "plt.xlabel('iteration')\n",
    "# yticks = np.arange(1,20,1)\n",
    "# plt.yticks(yticks, yticks**2)\n",
    "# plt.title('Lagrangian Relaxation for problem problem_t_pricer_0_2.lp, with 26,000 variables')\n",
    "plt.title('6flights, four sectors, opt using lagrangian r.')\n",
    "# plt.figure(p,g,'r-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = [412, 417, 420, 423, 425,429, 430,431, 432,433, 435, 437,464]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "LpIter = [21754,22371,22826,23069,23307,23583,23732,23863,23926,24060,24245,24463,38404]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lb = [1523178, 1523444,1523561 , 1523597 ,1523648 ,1523690,1523702,1523711,1523716,1523726,1523737,1523751,1523800]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ub = [1762319 ,1762319,1762319,1762319,1762319,1762319,1762319,1762319,1762319,1762319,1762319,1762319,1541267]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Ub)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'LP-Relaxation problem_t_pricer_0_2.lp')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t, Lb, 'r--', t, Ub, 'g--')\n",
    "plt.ticklabel_format(style = 'plain')\n",
    "plt.ylabel('bound, lower and upper')\n",
    "plt.xlabel('time')\n",
    "plt.title('LP-Relaxation problem_t_pricer_0_2.lp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(LpIter, Lb, 'r--', LpIter, Ub, 'g--')\n",
    "\n",
    "plt.ylabel('bound, lower and upper')\n",
    "plt.xlabel('iteration')\n",
    "plt.title('LP-Relaxation for problem_t_pricer_0_2.lp')\n",
    "plt.show()\n",
    "# plt.figure(figsize=(25,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "38b34d0cb5915ebd706651697a9bad136b66d87bcc8c7f5d873fb1545f3d61bf"
  },
  "kernelspec": {
   "display_name": "Python 3.7.11 64-bit ('scip': conda)",
   "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": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}