{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "msd_AC = np.loadtxt('./msd-A_True-C_True.npz')\n",
      "msd_A = np.loadtxt('./msd-A_True-C_False.npz')\n",
      "msd_C = np.loadtxt('./msd-A_False-C_True.npz')\n",
      "msd_nocoop = np.loadtxt('./msd-A_False-C_False.npz')\n",
      "msd_cattivelli = np.loadtxt('./msd-catti-A_True-C_True.npz')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(6, 4))\n",
      "plt.subplot(2,2,1)\n",
      "plt.boxplot((msd_nocoop[:,0], msd_A[:,0], msd_C[:,0], msd_AC[:,0], msd_cattivelli[:,0]), \\\n",
      "            vert=False, positions=(5,4,3,2,1), widths=.8, sym='')\n",
      "plt.grid(True)\n",
      "plt.text(0.65, 1.5, r'$x_{1}$', fontsize=16, bbox=dict(facecolor='white', boxstyle='Round'))\n",
      "\n",
      "plt.subplot(2,2,2)\n",
      "plt.boxplot((msd_nocoop[:,1], msd_A[:,1], msd_C[:,1], msd_AC[:,1], msd_cattivelli[:,1]), \\\n",
      "            vert=False, positions=(5,4,3,2,1), widths=.8, sym='')\n",
      "plt.grid(True)\n",
      "plt.text(.48, 1.5, r'$x_{2}$', fontsize=16, bbox=dict(facecolor='white', boxstyle='Round'))\n",
      "\n",
      "plt.subplot(2,2,3)\n",
      "plt.boxplot((msd_nocoop[:,2], msd_A[:,2], msd_C[:,2], msd_AC[:,2], msd_cattivelli[:,2]), \\\n",
      "            vert=False, positions=(5,4,3,2,1), widths=.8, sym='')\n",
      "plt.grid(True)\n",
      "plt.text(6.5, 1.5, r'$x_{3}$', fontsize=16, bbox=dict(facecolor='white', boxstyle='Round'))\n",
      "\n",
      "plt.subplot(2,2,4)\n",
      "plt.boxplot((msd_nocoop[:,3], msd_A[:,3], msd_C[:,3], msd_AC[:,3], msd_cattivelli[:,3]), \\\n",
      "            vert=False, positions=(5,4,3,2,1), widths=.8, sym='')\n",
      "plt.grid(True)\n",
      "plt.text(9.6, 1.5, r'$x_{4}$', fontsize=16, bbox=dict(facecolor='white', boxstyle='Round'))\n",
      "\n",
      "plt.savefig('msds-boxplots.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cnv0rqdNubm5GYmIipk+frnV5CEJXyNuE2ZCUHlm1ahX279+P2tpaFBQUtBWQ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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xcae7c6c>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from prettytable import PrettyTable\n",
      "msd_tab = PrettyTable(('Strategy', 'X1', 'X2', 'X3', 'X4'))\n",
      "msd_tab.add_row(['ATC'] + msd_AC.mean(axis=0).tolist())\n",
      "msd_tab.add_row(['A'] + msd_A.mean(axis=0).tolist())\n",
      "msd_tab.add_row(['C'] + msd_C.mean(axis=0).tolist())\n",
      "msd_tab.add_row(['nocoop'] + msd_nocoop.mean(axis=0).tolist())\n",
      "msd_tab.add_row(['Cattivelli'] + msd_cattivelli.mean(axis=0).tolist())\n",
      "print(msd_tab)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+------------+-----------------+-----------------+---------------+----------------+\n",
        "|  Strategy  |        X1       |        X2       |       X3      |       X4       |\n",
        "+------------+-----------------+-----------------+---------------+----------------+\n",
        "|    ATC     | 0.0508296982314 | 0.0112835250706 | 1.25842696982 | 0.586217681968 |\n",
        "|     A      |  0.400225914985 |  0.057671827475 | 2.02205857952 |  1.2156287341  |\n",
        "|     C      |  0.222265085058 |  0.029608731681 | 2.13713621682 | 0.82904452834  |\n",
        "|   nocoop   |  7.64457985943  |  0.632263469114 | 7.11860753923 | 5.06803468276  |\n",
        "| Cattivelli |  0.501604689355 | 0.0389693359084 | 2.27996130003 | 0.696981150746 |\n",
        "+------------+-----------------+-----------------+---------------+----------------+\n"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}