{
 "metadata": {
  "name": "",
  "signature": "sha256:0229f2d87717889140dca257ce7befe8b7c519b6f82e611e3d9dceadc453f760"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Introduction"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A look at unloaded and loaded belt speeds."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Imports"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "sys.path.append('..')\n",
      "from src import utils"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "import operator\n",
      "\n",
      "import numpy as np\n",
      "import pandas\n",
      "import matplotlib.pyplot as plt\n",
      "from gaitanalysis import motek"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.pylabtools import figsize"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Load and Process Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load the path to the directory with the experimental data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trials_dir = utils.trial_data_dir()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Trials data directory is set to /home/moorepants/Data/human-gait/gait-control-identification\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trial_meta_df = utils.generate_meta_data_table(trials_dir)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Make a list of all the compensation trials."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trial_meta_df[trial_meta_df['subject-id'] == 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>datetime</th>\n",
        "      <th>id</th>\n",
        "      <th>nominal-speed</th>\n",
        "      <th>notes</th>\n",
        "      <th>subject-id</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 2014-05-08</td>\n",
        "      <td> 71</td>\n",
        "      <td> 1.2</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 2014-03-28</td>\n",
        "      <td> 59</td>\n",
        "      <td> 1.2</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 2014-05-13</td>\n",
        "      <td> 79</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 2014-03-21</td>\n",
        "      <td> 44</td>\n",
        "      <td> 1.2</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 2014-02-27</td>\n",
        "      <td> 34</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 2014-03-27</td>\n",
        "      <td> 52</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 2014-02-25</td>\n",
        "      <td> 28</td>\n",
        "      <td> 1.6</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 2014-02-27</td>\n",
        "      <td> 36</td>\n",
        "      <td> 1.6</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> 2014-03-28</td>\n",
        "      <td> 58</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> 2013-10-22</td>\n",
        "      <td>  3</td>\n",
        "      <td> 1.2</td>\n",
        "      <td> This is an unloaded trial (no subject) of the ...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td> 2014-02-21</td>\n",
        "      <td> 24</td>\n",
        "      <td> 1.6</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td> 2014-02-25</td>\n",
        "      <td> 29</td>\n",
        "      <td> 1.2</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td> 2014-02-27</td>\n",
        "      <td> 35</td>\n",
        "      <td> 1.2</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td> 2013-10-22</td>\n",
        "      <td>  4</td>\n",
        "      <td> 1.2</td>\n",
        "      <td> This is an unloaded trial (no subject) of the ...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td> 2014-04-02</td>\n",
        "      <td> 64</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td> 2014-03-21</td>\n",
        "      <td> 43</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td> 2013-10-22</td>\n",
        "      <td>  5</td>\n",
        "      <td> 1.2</td>\n",
        "      <td> This is an unloaded trial (no subject) of the ...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td> 2014-05-13</td>\n",
        "      <td> 80</td>\n",
        "      <td> 1.2</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td> 2014-02-25</td>\n",
        "      <td> 30</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td> 2014-05-13</td>\n",
        "      <td> 81</td>\n",
        "      <td> 1.6</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td> 2014-05-08</td>\n",
        "      <td> 70</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
        "      <td> 2014-03-21</td>\n",
        "      <td> 45</td>\n",
        "      <td> 1.6</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55</th>\n",
        "      <td> 2014-05-08</td>\n",
        "      <td> 72</td>\n",
        "      <td> 1.6</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>58</th>\n",
        "      <td> 2014-03-27</td>\n",
        "      <td> 54</td>\n",
        "      <td> 1.6</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>61</th>\n",
        "      <td> 2014-03-28</td>\n",
        "      <td> 60</td>\n",
        "      <td> 1.6</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>63</th>\n",
        "      <td> 2014-04-02</td>\n",
        "      <td> 65</td>\n",
        "      <td> 1.2</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>66</th>\n",
        "      <td> 2014-02-21</td>\n",
        "      <td> 22</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>68</th>\n",
        "      <td> 2014-03-27</td>\n",
        "      <td> 53</td>\n",
        "      <td> 1.2</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>70</th>\n",
        "      <td> 2014-02-21</td>\n",
        "      <td> 23</td>\n",
        "      <td> 1.2</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>73</th>\n",
        "      <td> 2014-04-02</td>\n",
        "      <td> 66</td>\n",
        "      <td> 1.6</td>\n",
        "      <td> This is an unloaded trial (no subject) 10 minu...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "      datetime  id  nominal-speed  \\\n",
        "1   2014-05-08  71            1.2   \n",
        "3   2014-03-28  59            1.2   \n",
        "4   2014-05-13  79            0.8   \n",
        "10  2014-03-21  44            1.2   \n",
        "12  2014-02-27  34            0.8   \n",
        "13  2014-03-27  52            0.8   \n",
        "16  2014-02-25  28            1.6   \n",
        "18  2014-02-27  36            1.6   \n",
        "19  2014-03-28  58            0.8   \n",
        "20  2013-10-22   3            1.2   \n",
        "31  2014-02-21  24            1.6   \n",
        "32  2014-02-25  29            1.2   \n",
        "33  2014-02-27  35            1.2   \n",
        "35  2013-10-22   4            1.2   \n",
        "37  2014-04-02  64            0.8   \n",
        "42  2014-03-21  43            0.8   \n",
        "44  2013-10-22   5            1.2   \n",
        "48  2014-05-13  80            1.2   \n",
        "50  2014-02-25  30            0.8   \n",
        "51  2014-05-13  81            1.6   \n",
        "52  2014-05-08  70            0.8   \n",
        "53  2014-03-21  45            1.6   \n",
        "55  2014-05-08  72            1.6   \n",
        "58  2014-03-27  54            1.6   \n",
        "61  2014-03-28  60            1.6   \n",
        "63  2014-04-02  65            1.2   \n",
        "66  2014-02-21  22            0.8   \n",
        "68  2014-03-27  53            1.2   \n",
        "70  2014-02-21  23            1.2   \n",
        "73  2014-04-02  66            1.6   \n",
        "\n",
        "                                                notes  subject-id  \n",
        "1   This is an unloaded trial (no subject) 10 minu...           0  \n",
        "3   This is an unloaded trial (no subject) 10 minu...           0  \n",
        "4   This is an unloaded trial (no subject) 10 minu...           0  \n",
        "10  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "12  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "13  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "16  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "18  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "19  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "20  This is an unloaded trial (no subject) of the ...           0  \n",
        "31  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "32  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "33  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "35  This is an unloaded trial (no subject) of the ...           0  \n",
        "37  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "42  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "44  This is an unloaded trial (no subject) of the ...           0  \n",
        "48  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "50  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "51  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "52  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "53  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "55  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "58  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "61  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "63  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "66  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "68  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "70  This is an unloaded trial (no subject) 10 minu...           0  \n",
        "73  This is an unloaded trial (no subject) 10 minu...           0  "
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "same_speed_trials = trial_meta_df[(trial_meta_df['subject-id'] == 0) &\n",
      "                                  (trial_meta_df['nominal-speed'] == 1.2)]['id'].values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for trial_id in same_speed_trials:\n",
      "    file_paths = utils.trial_file_paths(trials_dir, '{0:0>3}'.format(trial_id))\n",
      "    dat = motek.DFlowData(*file_paths)\n",
      "    try:\n",
      "        record = dat._load_record_data()\n",
      "    except IOError:\n",
      "        print('{} skipped'.format(trial_id))\n",
      "    else:\n",
      "        plt.plot(record['RightBeltSpeed'])\n",
      "plt.xlim((50000, 51000))\n",
      "plt.ylim((0.8, 1.6))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3 skipped\n",
        "4 skipped"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "5 skipped\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "(0.8, 1.6)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RakT1yepDbnJui5T4qKpagtO5CUnSU1HxPRbLnw57m83l/fff55VXXsFkMvHB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\nocsFoujFavbRQBJoNIiikWDQGzWNrK3iDwaC7Ju6D9kl02duHxwuJz8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EWdcTq5c+wZRE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\nEomkj/H/GsLNFtWO5tcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe68f0204d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Footer"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!git rev-parse HEAD"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "7ba68f0160c23a61204291ca107ad570ac6f6e5a\r\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!git --git-dir=/home/moorepants/src/Gait-Analysis-Toolkit/.git --work-tree=/home/moorepants/src/Gait-Analysis-Toolkit rev-parse HEAD"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "fatal: Not a git repository: '/home/moorepants/src/Gait-Analysis-Toolkit/.git'\r\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%install_ext http://raw.github.com/jrjohansson/version_information/master/version_information.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Installed version_information.py. To use it, type:\n",
        "  %load_ext version_information\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext version_information"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%version_information gaitanalysis, numpy, scipy, pandas, matplotlib, tables, oct2py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>2.7.8 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]</td></tr><tr><td>IPython</td><td>2.3.0</td></tr><tr><td>OS</td><td>Linux 3.13.0 37 generic x86_64 with debian jessie sid</td></tr><tr><td>gaitanalysis</td><td>0.1.0dev</td></tr><tr><td>numpy</td><td>1.8.2</td></tr><tr><td>scipy</td><td>0.14.0</td></tr><tr><td>pandas</td><td>0.12.0</td></tr><tr><td>matplotlib</td><td>1.4.0</td></tr><tr><td>tables</td><td>3.1.1</td></tr><tr><td>oct2py</td><td>2.4.0</td></tr><tr><td colspan='2'>Fri Oct 17 11:53:23 2014 EDT</td></tr></table>"
       ],
       "json": [
        "{\"Software versions\": [{\"version\": \"2.7.8 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\", \"module\": \"Python\"}, {\"version\": \"2.3.0\", \"module\": \"IPython\"}, {\"version\": \"Linux 3.13.0 37 generic x86_64 with debian jessie sid\", \"module\": \"OS\"}, {\"version\": \"0.1.0dev\", \"module\": \"gaitanalysis\"}, {\"version\": \"1.8.2\", \"module\": \"numpy\"}, {\"version\": \"0.14.0\", \"module\": \"scipy\"}, {\"version\": \"0.12.0\", \"module\": \"pandas\"}, {\"version\": \"1.4.0\", \"module\": \"matplotlib\"}, {\"version\": \"3.1.1\", \"module\": \"tables\"}, {\"version\": \"2.4.0\", \"module\": \"oct2py\"}]}"
       ],
       "latex": [
        "\\begin{tabular}{|l|l|}\\hline\n",
        "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
        "Python & 2.7.8 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] \\\\ \\hline\n",
        "IPython & 2.3.0 \\\\ \\hline\n",
        "OS & Linux 3.13.0 37 generic x86\\_64 with debian jessie sid \\\\ \\hline\n",
        "gaitanalysis & 0.1.0dev \\\\ \\hline\n",
        "numpy & 1.8.2 \\\\ \\hline\n",
        "scipy & 0.14.0 \\\\ \\hline\n",
        "pandas & 0.12.0 \\\\ \\hline\n",
        "matplotlib & 1.4.0 \\\\ \\hline\n",
        "tables & 3.1.1 \\\\ \\hline\n",
        "oct2py & 2.4.0 \\\\ \\hline\n",
        "\\hline \\multicolumn{2}{|l|}{Fri Oct 17 11:53:23 2014 EDT} \\\\ \\hline\n",
        "\\end{tabular}\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "Software versions\n",
        "Python 2.7.8 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
        "IPython 2.3.0\n",
        "OS Linux 3.13.0 37 generic x86_64 with debian jessie sid\n",
        "gaitanalysis 0.1.0dev\n",
        "numpy 1.8.2\n",
        "scipy 0.14.0\n",
        "pandas 0.12.0\n",
        "matplotlib 1.4.0\n",
        "tables 3.1.1\n",
        "oct2py 2.4.0\n",
        "Fri Oct 17 11:53:23 2014 EDT"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!pip freeze"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DynamicistToolKit==0.3.5\r\n",
        "-e git+git@github.com:csu-hmc/GaitAnalysisToolKit.git@a3732352747bc03ca839df9ff02ddcbd889e636d#egg=GaitAnalysisToolKit-origin/HEAD\r\n",
        "Jinja2==2.7.2\r\n",
        "MarkupSafe==0.18\r\n",
        "PyYAML==3.11\r\n",
        "backports.ssl-match-hostname==3.4.0.2\r\n",
        "ipython==2.3.0\r\n",
        "matplotlib==1.4.0\r\n",
        "numexpr==2.3.1\r\n",
        "numpy==1.8.2\r\n",
        "oct2py==2.4.0\r\n",
        "pandas==0.12.0\r\n",
        "patsy==0.3.0\r\n",
        "pyparsing==2.0.1\r\n",
        "python-dateutil==1.5\r\n",
        "pytz==2014.7\r\n",
        "pyzmq==14.3.0\r\n",
        "scipy==0.14.0\r\n",
        "six==1.8.0\r\n",
        "statsmodels==0.5.0\r\n",
        "tables==3.1.1\r\n",
        "tornado==3.2.1\r\n",
        "uncertainties==2.4.6.1\r\n",
        "wsgiref==0.1.2\r\n"
       ]
      }
     ],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}