{
 "metadata": {
  "name": "",
  "signature": "sha256:09bf66c9635fdd5b044dc696fff5a395b17ddf045dfe10fdf837d88cee3fe276"
 },
 "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",
      "dt = data['controller_rw_time']"
     ],
     "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(dt[b][p]) * 1000 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",
      "    \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('time to read/write pos/speed/load (in ms)')\n",
      "    plt.title(\"BOARDS COMPARISON\")\n",
      "    savefig('controller.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2iEB1SfY8Hk+LtCeRSLSLEEIz9Z4rgbyjxDAkfOc33ngjXbt2Zdy4cZx77rls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\nNxfA76uwXoPBYEBxcXFAJ4/X661SRXO5XDhx4gQ8Hk9QF53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emjdvHikUCho+fDj16NGD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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1164dcdd0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}
