{
 "metadata": {
  "name": "",
  "signature": "sha256:139f5039e4216784ffa37cf89131cc2bd44141391b1b22b7c9d22ff517637ccc"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pickle \n",
      "\n",
      "# get the file here: https://github.com/poppy-project/pypot/releases/download/2.4.0/data.pickle",
      "with open('data.pickle') as f:\n",
      "    data = pickle.load(f)\n",
      "    \n",
      "cpu_load = data['cpu_usage']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "to_plot = (('dell', '2.7.8'),\n",
      "           ('dell', 'pypy-2.3.1'),\n",
      "\n",
      "           ('odroid', '2.7.8'),\n",
      "           ('odroid', 'pypy-2.3.1'),\n",
      "           \n",
      "           ('pi', '2.7.8'),\n",
      "           ('pi', 'pypy-2.3.1'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "indices = array([0.5, 1.0, \n",
      "                 2.0, 2.5,\n",
      "                 3.5, 4.0])\n",
      "\n",
      "width = 0.4\n",
      "\n",
      "with xkcd():\n",
      "    fig = plt.figure()\n",
      "    ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))\n",
      "    \n",
      "    x = array([mean(cpu_load[b][p]) for b, p in to_plot])\n",
      "    \n",
      "    i = arange(0, len(x), 2)\n",
      "    ax.bar(indices[i], x[i], width, color='r')\n",
      "    \n",
      "    i = arange(1, len(x), 2)\n",
      "    ax.bar(indices[i], x[i], width, color='g')\n",
      "    #for i in range(0, len(x), 2):\n",
      "    #ax.bar(indices, x, width)\n",
      "    \n",
      "    ax.spines['right'].set_color('none')\n",
      "    ax.spines['top'].set_color('none')\n",
      "    ax.xaxis.set_ticks_position('bottom')\n",
      "    ax.yaxis.set_ticks_position('left')\n",
      "\n",
      "    ax.set_xticks(indices + width/2)\n",
      "    \n",
      "    ax.set_xticklabels(['2.7.8\\n               PC',\n",
      "                        'PyPy',\n",
      "                        '2.7.8\\n             Odroid',\n",
      "                        'PyPy',\n",
      "                        '2.7.8\\n                Raspberry pi',\n",
      "                        'PyPy'])\n",
      "\n",
      "    plt.ylabel('cpu load (%)')\n",
      "    plt.title(\"BOARDS COMPARISON\")\n",
      "    savefig('cpu_usage.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rV6/m4Ycf5uWXXyYcDtPS0sLBBx/MaaedNqRcI1tDuWiHFAuJROJwOEgbZLFb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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x115376dd0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}
