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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Introduction"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook is a simple demonstration of how you may be able to predict the phase of the gait cycle from the states from a simple linear regression.\n",
      "\n",
      "**warning** This notebook doesn't seem to run with Pandas < 0.14.0. I get an error calling `results.rsquared`."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Imports and Setup"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "sys.path.append('..')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "from pandas import concat\n",
      "import statsmodels.formula.api as smf\n",
      "from gaitanalysis.gait import plot_steps"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/moorepants/anaconda/envs/walk/lib/python2.7/site-packages/pandas/io/excel.py:626: UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.\n",
        "  .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from src import utils\n",
      "from src.grf_landmark_settings import settings"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.pylabtools import figsize\n",
      "figsize(14, 10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Load First Data Set"
     ]
    },
    {
     "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": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is the \"training\" data set. We simple collect all of the gait cycles from the perturbed portion of one trial from subject \"A\"."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trial_number = '068'\n",
      "event_data_frame, meta_data, event_data_path = utils.write_event_data_frame_to_disk(trial_number)\n",
      "walking_data, walking_data_path = utils.write_inverse_dynamics_to_disk(event_data_frame, meta_data, event_data_path)\n",
      "params = settings[trial_number]\n",
      "perturbed_steps, walking_data = utils.section_signals_into_steps(walking_data, walking_data_path,\n",
      "                                                                 filter_frequency=params[0],\n",
      "                                                                 threshold=params[1],\n",
      "                                                                 num_samples_lower_bound=params[2],\n",
      "                                                                 num_samples_upper_bound=params[3])"
     ],
     "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",
        "Temporary data directory is set to ../data\n",
        "Loading pre-cleaned data: ../data/cleaned-data-068-longitudinal-perturbation.h5\n",
        "0.10 s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Loading pre-computed inverse dynamics from ../data/walking-data-068-longitudinal-perturbation.h5.\n",
        "0.08 s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Loading pre-computed steps from ../data/walking-data-068-longitudinal-perturbation.h5."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.077136\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now create a big data frame which has a column with the percent gait cycle `phi`. It also contains columns for all of the state values."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfs = []\n",
      "for i, df in perturbed_steps.iteritems():\n",
      "    dfs.append(df)\n",
      "bigdf = concat(dfs)\n",
      "bigdf['phi'] = bigdf.index.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Fit a model"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now specify a patsy formula for the module. We simply want to predict `phi` using all of the states as the multivariate linear regressors."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sensors, controls = utils.load_sensors_and_controls()\n",
      "model = 'phi ~ Q(\"' + '\") + Q(\"'.join(sensors) + '\")' #+ ' + Q(\"FP1.ForY\") + Q(\"FP2.ForY\")'\n",
      "model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "'phi ~ Q(\"Right.Ankle.PlantarFlexion.Angle\") + Q(\"Right.Ankle.PlantarFlexion.Rate\") + Q(\"Right.Knee.Flexion.Angle\") + Q(\"Right.Knee.Flexion.Rate\") + Q(\"Right.Hip.Flexion.Angle\") + Q(\"Right.Hip.Flexion.Rate\") + Q(\"Left.Ankle.PlantarFlexion.Angle\") + Q(\"Left.Ankle.PlantarFlexion.Rate\") + Q(\"Left.Knee.Flexion.Angle\") + Q(\"Left.Knee.Flexion.Rate\") + Q(\"Left.Hip.Flexion.Angle\") + Q(\"Left.Hip.Flexion.Rate\")'"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = smf.ols(model, data=bigdf).fit()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The results show that we have a decent model with a high $R^2$ value."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(results.summary())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                    phi   R-squared:                       0.907\n",
        "Model:                            OLS   Adj. R-squared:                  0.907\n",
        "Method:                 Least Squares   F-statistic:                     6971.\n",
        "Date:                Fri, 17 Oct 2014   Prob (F-statistic):               0.00\n",
        "Time:                        13:11:56   Log-Likelihood:                 8690.6\n",
        "No. Observations:                8580   AIC:                        -1.736e+04\n",
        "Df Residuals:                    8567   BIC:                        -1.726e+04\n",
        "Df Model:                          12                                         \n",
        "=========================================================================================================\n",
        "                                            coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "---------------------------------------------------------------------------------------------------------\n",
        "Intercept                                 1.1361      0.035     32.176      0.000         1.067     1.205\n",
        "Q(\"Right.Ankle.PlantarFlexion.Angle\")     0.4920      0.014     34.856      0.000         0.464     0.520\n",
        "Q(\"Right.Ankle.PlantarFlexion.Rate\")      0.0086      0.001     11.482      0.000         0.007     0.010\n",
        "Q(\"Right.Knee.Flexion.Angle\")            -0.0145      0.011     -1.297      0.195        -0.036     0.007\n",
        "Q(\"Right.Knee.Flexion.Rate\")             -0.1013      0.001   -110.647      0.000        -0.103    -0.100\n",
        "Q(\"Right.Hip.Flexion.Angle\")             -0.5951      0.027    -22.308      0.000        -0.647    -0.543\n",
        "Q(\"Right.Hip.Flexion.Rate\")               0.0607      0.004     14.145      0.000         0.052     0.069\n",
        "Q(\"Left.Ankle.PlantarFlexion.Angle\")      0.0269      0.014      1.962      0.050      2.55e-05     0.054\n",
        "Q(\"Left.Ankle.PlantarFlexion.Rate\")       0.0124      0.001     16.831      0.000         0.011     0.014\n",
        "Q(\"Left.Knee.Flexion.Angle\")             -0.0420      0.011     -3.801      0.000        -0.064    -0.020\n",
        "Q(\"Left.Knee.Flexion.Rate\")               0.0454      0.001     53.012      0.000         0.044     0.047\n",
        "Q(\"Left.Hip.Flexion.Angle\")               0.8355      0.026     31.640      0.000         0.784     0.887\n",
        "Q(\"Left.Hip.Flexion.Rate\")               -0.1008      0.004    -25.103      0.000        -0.109    -0.093\n",
        "==============================================================================\n",
        "Omnibus:                     1351.627   Durbin-Watson:                   2.368\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             5590.747\n",
        "Skew:                           0.729   Prob(JB):                         0.00\n",
        "Kurtosis:                       6.676   Cond. No.                         151.\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Load validation data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we load in data from the same subject but at a different speed to test the model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trial_number = '069'\n",
      "event_data_frame, meta_data, event_data_path = utils.write_event_data_frame_to_disk(trial_number)\n",
      "walking_data, walking_data_path = utils.write_inverse_dynamics_to_disk(event_data_frame, meta_data, event_data_path)\n",
      "params = settings[trial_number]\n",
      "perturbed_steps, walking_data = utils.section_signals_into_steps(walking_data, walking_data_path,\n",
      "                                                                 filter_frequency=params[0],\n",
      "                                                                 threshold=params[1],\n",
      "                                                                 num_samples_lower_bound=params[2],\n",
      "                                                                 num_samples_upper_bound=params[3])"
     ],
     "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",
        "Temporary data directory is set to ../data\n",
        "Loading pre-cleaned data: ../data/cleaned-data-069-longitudinal-perturbation.h5\n",
        "0.08 s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Loading pre-computed inverse dynamics from ../data/walking-data-069-longitudinal-perturbation.h5.\n",
        "0.08 s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Loading pre-computed steps from ../data/walking-data-069-longitudinal-perturbation.h5."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.093573\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfs = []\n",
      "for i, df in perturbed_steps.iteritems():\n",
      "    dfs.append(df)\n",
      "bigdf = concat(dfs)\n",
      "bigdf['phi'] = bigdf.index.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Test the model"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following plot shows the actual percent gait cycle and that predicted by the linear model given the states from the new trial."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(results.predict(bigdf)[:400])\n",
      "plt.plot(bigdf['phi'].values[:400])\n",
      "plt.legend(['Prediction', 'Actual'])\n",
      "plt.ylabel('Percent Gait Cycle')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.text.Text at 0x7fc95c642510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VMT0IolSqTNIjIiIiIqITw6SpjhWLQEMDEI2y2kREREREdLKYNNUxd9LEdU1ERERERCeH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a8wAAymnPNPkymwcHOluRzycfWrgRRNSzwGkn2Nltz9D54+LMNCmlMDYGfOxjwHs+YEc9E9De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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fc95c629a90>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "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": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!git --git-dir=/home/moorepants/src/GaitAnalysisToolKit/.git --work-tree=/home/moorepants/src/GaitAnalysisToolKit rev-parse HEAD"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "a3732352747bc03ca839df9ff02ddcbd889e636d\r\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "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": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext version_information"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%version_information gaitanalysis, dtk, 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>dtk</td><td>0.3.5</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>0.14.0</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 13:11:58 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\": \"0.3.5\", \"module\": \"dtk\"}, {\"version\": \"1.8.2\", \"module\": \"numpy\"}, {\"version\": \"0.14.0\", \"module\": \"scipy\"}, {\"version\": \"0.14.0\", \"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",
        "dtk & 0.3.5 \\\\ \\hline\n",
        "numpy & 1.8.2 \\\\ \\hline\n",
        "scipy & 0.14.0 \\\\ \\hline\n",
        "pandas & 0.14.0 \\\\ \\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 13:11:58 2014 EDT} \\\\ \\hline\n",
        "\\end{tabular}\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "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",
        "dtk 0.3.5\n",
        "numpy 1.8.2\n",
        "scipy 0.14.0\n",
        "pandas 0.14.0\n",
        "matplotlib 1.4.0\n",
        "tables 3.1.1\n",
        "oct2py 2.4.0\n",
        "Fri Oct 17 13:11:58 2014 EDT"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "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==0.14.0\r\n",
        "patsy==0.2.1\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",
        "sympy==0.7.5\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": 20
    }
   ],
   "metadata": {}
  }
 ]
}