SpasticityDetection-Mac-Poster.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook with poster Results\n",
    "Must be run using Python 3.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112871910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "from collections import defaultdict\n",
    "\n",
    "import math\n",
    "import nolds\n",
    "\n",
    "#XGBoost now needs path to runtime compilers\n",
    "import os\n",
    "# mingw_path = 'C:\\\\Program Files\\\\mingw-w64\\\\x86_64-7.1.0-posix-seh-rt_v5-rev0\\\\mingw64\\\\bin'\n",
    "# os.environ['PATH'] = mingw_path + ';' + os.environ['PATH']\n",
    "# import xgboost as xgb\n",
    "# from xgboost.sklearn import XGBClassifier #this is the SKlearn wrapper\n",
    "\n",
    "from sklearn.model_selection import LeaveOneGroupOut\n",
    "from sklearn import preprocessing\n",
    "from sklearn import neighbors, linear_model\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import KFold, StratifiedKFold, cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import ElasticNetCV, LogisticRegression\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "\n",
    "\n",
    "path = '../Datasets/Spasticity/LabeledData/'\n",
    "savepath = '../Datasets/Spasticity/'\n",
    "sns.set_context('talk',font_scale=1)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "current_palette = sns.color_palette()\n",
    "\n",
    "def plot_roc(tpr_all,fpr,roc_auc,ax=None,plotname=None,col=None):\n",
    "    #plot mean ROC across subjects (need to add shaded conf interval)\n",
    "    tprmu = np.mean(np.asarray(tpr_all),axis=0)\n",
    "    tpr=np.asarray(tpr_all)\n",
    "    fpr=np.reshape(fpr,(1,-1))\n",
    "    tprmu=np.reshape(tprmu,(1,-1))\n",
    "    label=pd.Series(data = ['%s - AUC = %0.3f' % (plotname,roc_auc)]*len(fpr))\n",
    "    if plotname=='Threshold':\n",
    "        ls = '--'\n",
    "    else:\n",
    "        ls='-'\n",
    "    if ax == None:\n",
    "        ax = sns.tsplot(data=tpr,time=fpr,ci=95,condition=label,legend=True,color=col,lw=3,linestyle=ls)\n",
    "    else:\n",
    "        sns.tsplot(data=tpr,time=fpr,ci=95,condition=label, legend=True,ax=ax,color=col,lw=3,linestyle=ls)\n",
    "             \n",
    "    lw = 3\n",
    "    ax.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    ax.set_xlim([-0.05, 1.0])\n",
    "    ax.set_ylim([0.0, 1.05])\n",
    "    ax.set_xlabel('False Positive Rate')\n",
    "    ax.set_ylabel('True Positive Rate')\n",
    "    ax.legend(loc='lower right')\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def getFeatures(EMGSignal):\n",
    "    \n",
    "    EPS = .0001\n",
    "    \n",
    "    Feat = {}\n",
    "    Feat['MeanAbsValue'] = np.mean(np.abs(EMGSignal))\n",
    "    \n",
    "    PosInds = EMGSignal>0\n",
    "    Feat['ZeroCrossings'] = sum((PosInds[1:len(PosInds)] != PosInds[0:(len(PosInds)-1)]) & (np.abs(np.diff(EMGSignal))>EPS))\n",
    "    \n",
    "    DiffEMGSignal = np.diff(EMGSignal)\n",
    "    PosInds = DiffEMGSignal>0\n",
    "    Feat['SlopeZeroCrossings'] = sum((PosInds[1:len(PosInds)] != PosInds[0:(len(PosInds)-1)]) & \n",
    "                                     (np.abs(np.diff(DiffEMGSignal))>EPS))\n",
    "    \n",
    "    Feat['WaveformLength'] = np.mean(np.abs(EMGSignal[1:len(EMGSignal)-1]-EMGSignal[2:len(EMGSignal)]))\n",
    "    \n",
    "    Feat['WillisonAmplitude'] = sum(np.abs(EMGSignal[1:len(EMGSignal)-1]-EMGSignal[2:len(EMGSignal)])>EPS)\n",
    "    \n",
    "    Feat['RMS'] = math.sqrt(np.mean(np.square(EMGSignal)))\n",
    "    \n",
    "    Feat['Variance'] = np.var(EMGSignal)\n",
    "    \n",
    "    \n",
    "    FFTPow = np.square(np.abs(np.fft.rfft(EMGSignal)))\n",
    "    for i, key in enumerate(['FFT:0-20Hz', 'FFT:20-40Hz', 'FFT:40-60Hz', 'FFT:60-80Hz', 'FFT:80-100Hz', 'FFT:100-120Hz']):\n",
    "        Feat[key] = np.sum(FFTPow[1+int(len(FFTPow)/6 * i):int(len(FFTPow)/6 * (i+1))])\n",
    "    # TODO: Sample Entropy and FFT\n",
    "    \n",
    "    Feat['SampEn'] = nolds.sampen(EMGSignal)\n",
    "    Feat['FFT:SampEn'] = nolds.sampen(FFTPow)\n",
    "    \n",
    "    return Feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def getClips(EMGSignal, winsize, overlap):\n",
    "    Clips = []\n",
    "    Features = defaultdict(list)\n",
    "    for indStart in range(0,int(len(EMGSignal)-winsize),int(winsize*(1-overlap))):\n",
    "        Clip = np.asarray(EMGSignal[indStart:indStart+125])\n",
    "        Feat = getFeatures(Clip)\n",
    "        Clips.append(Clip)\n",
    "        for key in Feat:\n",
    "            Features[key].append(Feat[key])\n",
    "        \n",
    "    return Clips, Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 'Lab Day1', 'Gastrocnemius', 'MAS')\n",
      "False\n",
      "False\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "invalid index to scalar variable.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-a8e104e8620d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     49\u001b[0m                     \u001b[0mFeatures\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mnewFeatures\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m                 \u001b[0;32mprint\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m                 \u001b[0mSubjID\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewClips\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m                 \u001b[0mClipLabels\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlab_new\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewClips\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mLocation\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewClips\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: invalid index to scalar variable."
     ]
    }
   ],
   "source": [
    "winsize = 1 * 250 # Test different values later (*250 to convert sec to samples)\n",
    "overlap = .5 # 0-1; use value such that winsize*overlap is integer for now\n",
    "\n",
    "RawDataClips = []\n",
    "Features = defaultdict(list)\n",
    "ClipLabels = []\n",
    "SubjID = []\n",
    "Location = []\n",
    "\n",
    "for x in itertools.product(range(1,31),['Lab Day1','Lab Day2'],['Gastrocnemius', 'Hamstring'],['MAS', 'MVC', 'VCM']):\n",
    "    if x[2]=='Hamstring':\n",
    "        Act = 'KF'\n",
    "    else:\n",
    "        Act = 'PF'\n",
    "    \n",
    "    oldLen = len(RawDataClips)\n",
    "    \n",
    "    try:\n",
    "        Data = pd.read_csv(path + 'CS' + str(x[0]).zfill(3) + '/' + x[1] + '/' + x[2] + '_' + x[3] + ' ' + Act + '_labeled.csv',\n",
    "                          header = None)\n",
    "        if x[2]=='Hamstring':\n",
    "            Labind = 8\n",
    "        else:\n",
    "            Labind = 5\n",
    "            \n",
    "        EMGData = Data[4]\n",
    "        Label = Data[Labind]\n",
    "        \n",
    "        for l in zip(['Spastic Activity', 'Non-Spastic Activity', 'Inactive'],['SA','HA','IA']):\n",
    "            lab_old = l[0]\n",
    "            lab_new = l[1]\n",
    "            \n",
    "            lab_inds = Label==lab_old\n",
    "            lab_inds_diff = lab_inds[1:len(lab_inds)].values != lab_inds[0:len(lab_inds)-1].values\n",
    "            \n",
    "            # Get starts/ends of continuous labels\n",
    "            print(x)\n",
    "            cont_label_startend = [i+1 for i,x in enumerate(lab_inds_diff) if x]\n",
    "            print(x)\n",
    "            # Note: this assumes that the data stream begins and ends with unlabeled data\n",
    "            # Even indices are starts, odds are ends\n",
    "            for nLabel in range(0,len(cont_label_startend),2): \n",
    "                LabeledData = EMGData[cont_label_startend[nLabel]:cont_label_startend[nLabel+1]]\n",
    "                \n",
    "                newClips, newFeatures = getClips(LabeledData, winsize, overlap)\n",
    "                \n",
    "                RawDataClips += newClips\n",
    "                for key in newFeatures:\n",
    "                    Features[key] += newFeatures[key]\n",
    "                print x\n",
    "                SubjID += [x[0]] * len(newClips)\n",
    "                ClipLabels += [lab_new]*len(newClips)\n",
    "                Location += [x[2]]*len(newClips)\n",
    "            \n",
    "    except(OSError):\n",
    "        print(path + 'CS' + str(x[0]).zfill(3) + '/' + x[1] + '/' + x[2] + '_' + x[3] + ' ' + Act + '_labeled.csv')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "FullData = pd.DataFrame(Features).assign(RawData=pd.Series(RawDataClips), SubjID=pd.Series(SubjID), Label=pd.Series(ClipLabels), Location=pd.Series(Location))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#save Clips Data\n",
    "FullData.to_csv(path_or_buf=savepath+'Clips_ws'+str(winsize/250)+'_overlap'+str(overlap)+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1557, 20)\n"
     ]
    }
   ],
   "source": [
    "#Load Clips Data\n",
    "# FullData = pd.read_csv(savepath+'Clips_ws1.0_overlap0.8.csv')\n",
    "# FullData = pd.read_csv(savepath+'Clips_ws0.5_overlap0.8.csv')\n",
    "FullData = pd.read_csv(savepath+'Clips_ws1.0_overlap0.5.csv')\n",
    "\n",
    "print (FullData.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FFT:0-20Hz</th>\n",
       "      <th>FFT:100-120Hz</th>\n",
       "      <th>FFT:20-40Hz</th>\n",
       "      <th>FFT:40-60Hz</th>\n",
       "      <th>FFT:60-80Hz</th>\n",
       "      <th>FFT:80-100Hz</th>\n",
       "      <th>FFT:SampEn</th>\n",
       "      <th>MeanAbsValue</th>\n",
       "      <th>RMS</th>\n",
       "      <th>SampEn</th>\n",
       "      <th>SlopeZeroCrossings</th>\n",
       "      <th>Variance</th>\n",
       "      <th>WaveformLength</th>\n",
       "      <th>WillisonAmplitude</th>\n",
       "      <th>ZeroCrossings</th>\n",
       "      <th>Label</th>\n",
       "      <th>Location</th>\n",
       "      <th>RawData</th>\n",
       "      <th>SubjID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.225662e-05</td>\n",
       "      <td>4.522854e-07</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>3.042505e-06</td>\n",
       "      <td>0.556572</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.277610</td>\n",
       "      <td>15</td>\n",
       "      <td>8.436106e-09</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>SA</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>[-1.25376354234e-05, 0.000256419017388, 0.0004...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.266326e-06</td>\n",
       "      <td>8.328662e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>2.119288e-06</td>\n",
       "      <td>0.680244</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.298195</td>\n",
       "      <td>22</td>\n",
       "      <td>3.759755e-09</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>SA</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>[-1.54166153598e-05, -2.58201167855e-05, -2.52...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.509514e-06</td>\n",
       "      <td>1.404923e-06</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>3.215455e-06</td>\n",
       "      <td>1.634131</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.364294</td>\n",
       "      <td>17</td>\n",
       "      <td>2.623056e-09</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>SA</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>[1.65414212235e-06, 1.16601537833e-05, 9.49498...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.827563e-07</td>\n",
       "      <td>3.273109e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>4.519934e-07</td>\n",
       "      <td>0.821528</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>1.905419</td>\n",
       "      <td>15</td>\n",
       "      <td>9.858629e-10</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>HA</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>[1.23416107599e-05, -1.96147267426e-05, -9.001...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.930556e-07</td>\n",
       "      <td>2.265097e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>7.133348e-07</td>\n",
       "      <td>0.910212</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>2.410799</td>\n",
       "      <td>12</td>\n",
       "      <td>1.040651e-09</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HA</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>[-2.24706359465e-05, 1.10109352551e-05, -2.817...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     FFT:0-20Hz  FFT:100-120Hz  FFT:20-40Hz  FFT:40-60Hz  FFT:60-80Hz  \\\n",
       "0  1.225662e-05   4.522854e-07     0.000021     0.000019     0.000004   \n",
       "1  6.266326e-06   8.328662e-07     0.000002     0.000006     0.000010   \n",
       "2  4.509514e-06   1.404923e-06     0.000002     0.000004     0.000004   \n",
       "3  4.827563e-07   3.273109e-07     0.000002     0.000002     0.000002   \n",
       "4  8.930556e-07   2.265097e-07     0.000002     0.000002     0.000002   \n",
       "\n",
       "   FFT:80-100Hz  FFT:SampEn  MeanAbsValue       RMS    SampEn  \\\n",
       "0  3.042505e-06    0.556572      0.000043  0.000092  0.277610   \n",
       "1  2.119288e-06    0.680244      0.000035  0.000061  0.298195   \n",
       "2  3.215455e-06    1.634131      0.000029  0.000051  0.364294   \n",
       "3  4.519934e-07    0.821528      0.000025  0.000031  1.905419   \n",
       "4  7.133348e-07    0.910212      0.000027  0.000032  2.410799   \n",
       "\n",
       "   SlopeZeroCrossings      Variance  WaveformLength  WillisonAmplitude  \\\n",
       "0                  15  8.436106e-09        0.000043                 16   \n",
       "1                  22  3.759755e-09        0.000041                 18   \n",
       "2                  17  2.623056e-09        0.000035                 11   \n",
       "3                  15  9.858629e-10        0.000030                  1   \n",
       "4                  12  1.040651e-09        0.000034                  0   \n",
       "\n",
       "   ZeroCrossings Label       Location  \\\n",
       "0              9    SA  Gastrocnemius   \n",
       "1             12    SA  Gastrocnemius   \n",
       "2              6    SA  Gastrocnemius   \n",
       "3              1    HA  Gastrocnemius   \n",
       "4              0    HA  Gastrocnemius   \n",
       "\n",
       "                                             RawData  SubjID  \n",
       "0  [-1.25376354234e-05, 0.000256419017388, 0.0004...       1  \n",
       "1  [-1.54166153598e-05, -2.58201167855e-05, -2.52...       1  \n",
       "2  [1.65414212235e-06, 1.16601537833e-05, 9.49498...       1  \n",
       "3  [1.23416107599e-05, -1.96147267426e-05, -9.001...       1  \n",
       "4  [-2.24706359465e-05, 1.10109352551e-05, -2.817...       1  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FullData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -1.25376354e-05,   2.56419017e-04,   4.93338747e-04,\n",
       "         3.67581616e-04,  -2.29709275e-05,  -4.05165351e-04,\n",
       "        -3.04702587e-04,  -3.24991016e-05,   1.77747853e-05,\n",
       "        -6.06080741e-05,  -9.86944257e-05,  -6.94144339e-05,\n",
       "         1.48036758e-05,   5.09978675e-05,   1.87972303e-05,\n",
       "         1.69205166e-05,   2.15678819e-05,   2.48073147e-05,\n",
       "         1.14261645e-05,  -7.03906696e-07,  -8.79860180e-07,\n",
       "         1.74623428e-06,  -2.92009839e-07,  -2.60239581e-06,\n",
       "         8.08939505e-07,  -2.33183856e-06,  -4.60215511e-07,\n",
       "         3.98879731e-06,  -8.42855126e-06,  -4.50573511e-06,\n",
       "        -9.65539137e-07,  -1.26658962e-05,  -1.27908682e-05,\n",
       "        -1.25600618e-05,  -7.23294257e-06,  -6.77223503e-06,\n",
       "        -8.92702602e-06,   5.87283297e-06,   2.20837828e-05,\n",
       "        -5.34315793e-06,  -2.89079327e-05,  -2.78316594e-05,\n",
       "        -2.80458440e-05,  -7.93467015e-06,   1.70907648e-05,\n",
       "         1.69045512e-05,  -8.92629749e-06,  -1.00302099e-05,\n",
       "        -2.22935410e-05,  -2.42760420e-05,   7.07943245e-05,\n",
       "        -1.63477875e-05,  -2.31778774e-05,   7.93679671e-05,\n",
       "         1.32877171e-04,   2.28462318e-04,  -5.68103106e-05,\n",
       "        -1.39889319e-04,  -1.11881658e-05,   1.22971811e-05,\n",
       "        -1.02810299e-04,  -2.82977728e-05,  -3.30500724e-05,\n",
       "        -3.58683963e-05,  -9.38628275e-06,  -2.94193797e-06,\n",
       "        -1.16411415e-05,  -1.32404177e-05,  -2.06292556e-06,\n",
       "        -5.44871600e-06,  -1.05289559e-06,   1.60213906e-05,\n",
       "         1.93068892e-05,   1.69887587e-05,   6.47013515e-06,\n",
       "         5.52336326e-07,   8.22163927e-07,   1.10541227e-06,\n",
       "        -4.77315081e-06,  -1.07274028e-05,  -1.06732470e-05,\n",
       "        -4.61011778e-06,  -4.79861032e-06,  -1.12426064e-05,\n",
       "        -1.16863843e-05,  -3.08527182e-05,  -3.81521957e-05,\n",
       "        -2.75202074e-05,  -4.20419690e-05,  -1.39795177e-05,\n",
       "         3.24013913e-05,  -6.90068003e-05,   1.18905796e-04,\n",
       "         1.85127852e-04,   4.06697164e-05,  -1.28311523e-05,\n",
       "        -1.73487218e-04,   1.71276140e-04,   2.52627060e-04,\n",
       "        -9.83179135e-05,  -1.99524080e-05,  -1.57546048e-05,\n",
       "        -5.34450241e-05,  -2.74853864e-05,  -2.20620725e-05,\n",
       "        -2.14619538e-05,  -1.52670488e-05,  -6.18711279e-06,\n",
       "        -9.18443405e-06,  -1.02120924e-05,  -8.56352068e-06,\n",
       "        -9.03104217e-06,  -1.85710985e-06,  -8.14108236e-06,\n",
       "        -1.30075353e-05,  -1.53999714e-05,   2.00942789e-06,\n",
       "         2.83680295e-06,  -1.86154772e-05,  -1.94182622e-05,\n",
       "        -1.63350170e-05,  -1.41610687e-05,  -1.22638015e-05,\n",
       "        -9.70582047e-06,  -1.14442182e-05])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FullData.RawData[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[13, 73, 38, 78, 100, 16, 17, 136, 81, 40, 15, 120, 28, 28, 3]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[sum(FullData.SubjID[FullData.Location=='Hamstring']==s) for s in FullData.SubjID[FullData.Location=='Hamstring'].unique()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SA clips = 117\n",
      "HA clips = 632\n"
     ]
    }
   ],
   "source": [
    "print('SA clips = %d'%sum(FullData[FullData.Location=='Hamstring'].Label=='SA'))\n",
    "print('HA clips = %d'%sum(FullData[FullData.Location=='Hamstring'].Label=='HA'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[19,\n",
       " 24,\n",
       " 60,\n",
       " 33,\n",
       " 67,\n",
       " 71,\n",
       " 31,\n",
       " 76,\n",
       " 47,\n",
       " 49,\n",
       " 5,\n",
       " 67,\n",
       " 1,\n",
       " 3,\n",
       " 13,\n",
       " 49,\n",
       " 46,\n",
       " 23,\n",
       " 24,\n",
       " 52,\n",
       " 11]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[sum(FullData.SubjID[FullData.Location=='Gastrocnemius']==s) for s in FullData.SubjID[FullData.Location=='Gastrocnemius'].unique()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SA clips = 32\n",
      "HA clips = 686\n"
     ]
    }
   ],
   "source": [
    "print('SA clips = %d'%sum(FullData[FullData.Location=='Gastrocnemius'].Label=='SA'))\n",
    "print('HA clips = %d'%sum(FullData[FullData.Location=='Gastrocnemius'].Label=='HA'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1557, 20)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FullData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#fit base-level and meta-level classifier \n",
    "def fit_stacking(X_train,y_train,groups): \n",
    "    \n",
    "    base_classifiers = [];     meta_classifier = []\n",
    "    subj = LeaveOneGroupOut()\n",
    "\n",
    "    Xtrain_meta = np.zeros((len(y_train),len(models))) #stores the meta-level classifier features (posterior of each base-level classifier)\n",
    "    ytrain_meta = np.array([]) #stores the labels to train meta-classifier\n",
    "    i = 0 #counter for current classifier trained\n",
    "\n",
    "    #CV to train base level clf and obtain train features for meta-level\n",
    "    for clf,name in zip(models,model_name):\n",
    "\n",
    "        print(name)\n",
    "        yscoreCV = np.array([])  #stores the posterior prob of each base-level clf\n",
    "\n",
    "        #CV to obtain posteriors from each clf (meta-features)\n",
    "        for train, test in subj.split(X_train, y_train, groups):\n",
    "            clf.fit(X_train[train,:],y_train[train])\n",
    "            yscore = clf.predict_proba(X_train[test,:]) \n",
    "            yscore = yscore[:,1]\n",
    "            yscoreCV = np.append(yscoreCV,yscore) #concatenate scores for each fold\n",
    "            if i == 0:\n",
    "                #store labels to train metaclassifier ()\n",
    "                ytrain_meta = np.append(ytrain_meta,y_train[test])\n",
    "\n",
    "        Xtrain_meta[:,i] = yscoreCV #store the posterior of current clf\n",
    "        i +=1\n",
    "\n",
    "        #Train base level classifiers on all training data    \n",
    "        clf.fit(X_train,y_train)\n",
    "        base_classifiers.append(clf)\n",
    "        \n",
    "    print('base-level classifiers trained')\n",
    "        \n",
    "    #add extra meta-features \n",
    "    metastd = np.std(Xtrain_meta,axis=1)\n",
    "    metastd = np.expand_dims(metastd,axis=0)\n",
    "    Xtrain_meta = np.concatenate((Xtrain_meta,metastd.T),axis=1)\n",
    "    \n",
    "    #train meta-level classifier on posteriors (this needs another CV to optimize parameters)\n",
    "    meta_scaler = preprocessing.StandardScaler().fit(Xtrain_meta)\n",
    "    Xtrain_meta = meta_scaler.transform(Xtrain_meta)\n",
    "    print('training meta-classifier')\n",
    "    metaclf = LogisticRegression(C=1)\n",
    "#     metaclf = GradientBoostingClassifier(n_estimators=100,max_depth=2,random_state=3)\n",
    "    metaclf.fit(np.concatenate((Xtrain_meta,X_train),axis=1),ytrain_meta)\n",
    "    ypred_meta = metaclf.predict_proba(np.concatenate((Xtrain_meta,X_train),axis=1))\n",
    "#     ind = np.where(np.array(ypred_meta) != np.array(ytrain_meta))\n",
    "#     print Xtrain_meta[:10,:]\n",
    "#     plt.figure(figsize=(12,8))\n",
    "#     sns.heatmap(Xtrain_meta[ind,:])\n",
    "\n",
    "    meta_classifier.append(metaclf)\n",
    "\n",
    "    return base_classifiers, meta_classifier[0], meta_scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict_stacking(X_test,y_test,base_classifiers,meta_classifier,meta_scaler):\n",
    "    \n",
    "    Xtest_meta = np.zeros((len(y_test),len(base_classifiers))) #stores the base clf predictions for current subj\n",
    "    auc_base = np.empty( (len(models),1) ) #store auc for each base classifier\n",
    "    i = 0 #clf index\n",
    "\n",
    "    #1. base-classifiers predictions\n",
    "    for clf,name in zip(base_classifiers,model_name):\n",
    "        Xtest_meta[:,i] = clf.predict_proba(X_test)[:,1]\n",
    "        auc_base[i] = roc_auc_score(y_test, Xtest_meta[:,i])\n",
    "        i+=1\n",
    "    print('best base-clf %s, auc = %.3f, worst base-clf %s, minauc = %.3f' % (model_name[np.argmax(auc_base)],\n",
    "                                                                       np.max(auc_base),\n",
    "                                                                       model_name[np.argmin(auc_base)],\n",
    "                                                                       np.min(auc_base)))\n",
    "\n",
    "    #add extra meta features\n",
    "    metastd = np.std(Xtest_meta,axis=1)\n",
    "    metastd = np.expand_dims(metastd,axis=0)\n",
    "    Xtest_meta = np.concatenate((Xtest_meta,metastd.T),axis=1)\n",
    "\n",
    "    #2. meta-level clf predictions (final prediction)\n",
    "    Xtest_meta = meta_scaler.transform(Xtest_meta)\n",
    "    yscore_meta = meta_classifier.predict_proba(np.concatenate((Xtest_meta,X_test),axis=1))[:,1]\n",
    "    ypred_meta = meta_classifier.predict(np.concatenate((Xtest_meta,X_test),axis=1))\n",
    "    auc_meta = roc_auc_score(y_test, yscore_meta)\n",
    "    print('auc meta = %.3f\\n' % (auc_meta))\n",
    "\n",
    "    return ypred_meta, yscore_meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def LOSOCV(X,y,data,groups,models,stacking=1):\n",
    "\n",
    "    TAUC_all=[]; Tfpr_all=[]; Ttpr_all=[]; TSens_all=[]; TSpec_all=[] #store results for each subject\n",
    "    AUC_all=[]; fpr_all=[]; tpr_all=[]; Sens_all=[]; Spec_all=[]\n",
    "    \n",
    "    subj = LeaveOneGroupOut()\n",
    "    subj.get_n_splits(X,y,groups)\n",
    "    for train_index, test_index in subj.split(X, y, groups):\n",
    "        \n",
    "        Dtr = data.iloc[train_index]\n",
    "        Dte = data.iloc[test_index] #the left out subject to test the classifier on\n",
    "        \n",
    "        #ML classifier\n",
    "        if stacking == 0:\n",
    "            Xtr, Xte = X[train_index], X[test_index]\n",
    "            ytr, yte = y[train_index], y[test_index]\n",
    "            models.fit(Xtr,ytr)\n",
    "            ypred = models.predict(Xte)\n",
    "            yscore = models.predict_proba(Xte)\n",
    "            yscore = yscore[:,1]\n",
    "            \n",
    "        else: #stacking - need a second round of CV on the train data set\n",
    "            groups2 = Dtr.SubjID.as_matrix()\n",
    "            Xtr = Dtr.iloc[:,0:14].as_matrix(); ytr = Dtr.Label\n",
    "            Xte = Dte.iloc[:,0:14].as_matrix(); yte = Dte.Label\n",
    "            Xtr = np.asarray(Xtr); ytr = np.asarray(ytr) #it took me 4hours to figure this line was needed!\n",
    "            Xte = np.asarray(Xte); yte = np.asarray(yte) #it took me 4hours to figure this line was needed!\n",
    "#             Xtr = scaler.transform(Xtr)\n",
    "#             Xte = scaler.transform(Xte)\n",
    "            base_classifiers, meta_classifier, meta_scaler = fit_stacking(Xtr,ytr,groups2) #train base and meta clf\n",
    "            ypred, yscore = predict_stacking(Xte,yte,base_classifiers,meta_classifier,meta_scaler)\n",
    "\n",
    "        #compute ROC at fixed fpr (to plot error bars)\n",
    "        fpr=np.linspace(0,1,101); tpr=[]\n",
    "        nscores = np.sort(np.column_stack((yscore[yte==0],yte[yte==0])),axis=0)\n",
    "        neg_counts = sum(yte==0)\n",
    "        for f in fpr:\n",
    "            ind = neg_counts-int(neg_counts*f)-1\n",
    "            t = (nscores[ind])[0]\n",
    "            if f==1:\n",
    "                t = 0\n",
    "            tpr_t = sum(yscore[yte==1]>t) / sum(yte==1)\n",
    "            tpr.append(tpr_t) \n",
    "\n",
    "        fpr = np.asarray(fpr); tpr = np.asarray(tpr)\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        \n",
    "        #compute Sens and Spec\n",
    "        #randomforest\n",
    "        ind = np.argmax(tpr-fpr)\n",
    "        Sens = tpr[ind]\n",
    "        Spec = 1-fpr[ind]\n",
    "        \n",
    "        AUC_all.append(roc_auc)\n",
    "        tpr_all.append(tpr); fpr_all=fpr;\n",
    "        Sens_all.append(Sens); Spec_all.append(Spec)\n",
    "\n",
    "        print('AUC = %.4f, Sens = %.4f, Spec = %.4f\\n'%(roc_auc,Sens,Spec)) #random forest\n",
    "        \n",
    "\n",
    "    #summary results\n",
    "    print(('mean AUC = %.3f (+/- %0.3f)')%(np.mean(AUC_all),2*np.std(AUC_all)/np.sqrt(len(AUC_all))))\n",
    "    print(('mean Sens = %.3f (+/- %0.3f)')%(np.mean(Sens_all),2*np.std(Sens_all)/np.sqrt(len(AUC_all))))\n",
    "    print(('mean Spec = %.3f (+/- %0.3f)')%(np.mean(Spec_all),2*np.std(Spec_all)/np.sqrt(len(AUC_all))))\n",
    "\n",
    "    results = {'AUC':AUC_all, 'TPR':tpr_all, 'FPR':fpr_all, 'Sens':Sens_all, 'Spec':Spec_all}\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "models = [RandomForestClassifier(n_estimators=100,random_state=2),\n",
    "          LogisticRegression(C=.001),\n",
    "          SVC(kernel='linear',C=.001,cache_size=1600,probability=True,random_state=3),\n",
    "          GaussianNB(),\n",
    "          LinearDiscriminantAnalysis(),\n",
    "         ]\n",
    "model_name = ['Random Forest','Logistic Regression','SVM','NB','LDA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "AUC = 0.2717, Sens = 0.3333, Spec = 0.8000\n",
      "\n",
      "AUC = 0.4050, Sens = 1.0000, Spec = 0.0500\n",
      "\n",
      "AUC = 0.4683, Sens = 1.0000, Spec = 0.2000\n",
      "\n",
      "AUC = 0.7175, Sens = 1.0000, Spec = 0.4700\n",
      "\n",
      "AUC = 0.7556, Sens = 0.9444, Spec = 0.4700\n",
      "\n",
      "AUC = 0.6950, Sens = 0.5000, Spec = 1.0000\n",
      "\n",
      "AUC = 0.8450, Sens = 1.0000, Spec = 0.6100\n",
      "\n",
      "AUC = 0.7591, Sens = 0.7273, Spec = 0.7800\n",
      "\n",
      "AUC = 0.7275, Sens = 1.0000, Spec = 0.4500\n",
      "\n",
      "AUC = 0.8635, Sens = 1.0000, Spec = 0.6600\n",
      "\n",
      "AUC = 0.9150, Sens = 1.0000, Spec = 0.9100\n",
      "\n",
      "AUC = 0.7130, Sens = 0.4000, Spec = 1.0000\n",
      "\n",
      "AUC = 0.9029, Sens = 1.0000, Spec = 0.7500\n",
      "\n",
      "mean AUC = 0.695 (+/- 0.105)\n",
      "mean Sens = 0.839 (+/- 0.137)\n",
      "mean Spec = 0.627 (+/- 0.156)\n",
      "Logistic Regression\n",
      "AUC = 0.5383, Sens = 0.6667, Spec = 0.7000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/luca/anaconda/envs/py35/lib/python3.5/site-packages/seaborn/timeseries.py:183: UserWarning: The tsplot function is deprecated and will be removed or replaced (in a substantially altered version) in a future release.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC = 0.4515, Sens = 0.9459, Spec = 0.1600\n",
      "\n",
      "AUC = 0.6250, Sens = 0.6667, Spec = 0.5800\n",
      "\n",
      "AUC = 0.4575, Sens = 0.5000, Spec = 0.8500\n",
      "\n",
      "AUC = 0.6039, Sens = 0.9444, Spec = 0.3800\n",
      "\n",
      "AUC = 0.4738, Sens = 0.2500, Spec = 1.0000\n",
      "\n",
      "AUC = 0.7288, Sens = 0.7500, Spec = 0.7600\n",
      "\n",
      "AUC = 0.8200, Sens = 1.0000, Spec = 0.5400\n",
      "\n",
      "AUC = 0.7913, Sens = 0.7500, Spec = 0.8100\n",
      "\n",
      "AUC = 0.9605, Sens = 1.0000, Spec = 0.9300\n",
      "\n",
      "AUC = 0.9950, Sens = 1.0000, Spec = 0.9900\n",
      "\n",
      "AUC = 0.9640, Sens = 1.0000, Spec = 0.8600\n",
      "\n",
      "AUC = 1.0000, Sens = 1.0000, Spec = 1.0000\n",
      "\n",
      "mean AUC = 0.724 (+/- 0.113)\n",
      "mean Sens = 0.806 (+/- 0.126)\n",
      "mean Spec = 0.735 (+/- 0.137)\n",
      "SVM\n",
      "AUC = 0.8367, Sens = 1.0000, Spec = 0.7000\n",
      "\n",
      "AUC = 0.4218, Sens = 0.8108, Spec = 0.2500\n",
      "\n",
      "AUC = 0.4472, Sens = 0.4444, Spec = 0.6500\n",
      "\n",
      "AUC = 0.2987, Sens = 0.2500, Spec = 1.0000\n",
      "\n",
      "AUC = 0.6017, Sens = 0.6111, Spec = 0.6200\n",
      "\n",
      "AUC = 0.5012, Sens = 0.2500, Spec = 1.0000\n",
      "\n",
      "AUC = 0.8463, Sens = 1.0000, Spec = 0.6100\n",
      "\n",
      "AUC = 0.7723, Sens = 0.9091, Spec = 0.5400\n",
      "\n",
      "AUC = 0.8150, Sens = 0.7500, Spec = 0.8300\n",
      "\n",
      "AUC = 0.9360, Sens = 1.0000, Spec = 0.8000\n",
      "\n",
      "AUC = 0.7850, Sens = 1.0000, Spec = 0.7800\n",
      "\n",
      "AUC = 0.9320, Sens = 1.0000, Spec = 0.9100\n",
      "\n",
      "AUC = 0.8750, Sens = 0.8571, Spec = 0.8500\n",
      "\n",
      "mean AUC = 0.698 (+/- 0.115)\n",
      "mean Sens = 0.760 (+/- 0.151)\n",
      "mean Spec = 0.734 (+/- 0.110)\n",
      "NB\n",
      "AUC = 0.5700, Sens = 0.6667, Spec = 0.7000\n",
      "\n",
      "AUC = 0.4426, Sens = 0.1081, Spec = 0.9700\n",
      "\n",
      "AUC = 0.6050, Sens = 0.7778, Spec = 0.6500\n",
      "\n",
      "AUC = 0.0412, Sens = 0.0000, Spec = 1.0000\n",
      "\n",
      "AUC = 0.6422, Sens = 0.5556, Spec = 0.7300\n",
      "\n",
      "AUC = 0.5012, Sens = 0.2500, Spec = 1.0000\n",
      "\n",
      "AUC = 0.7113, Sens = 1.0000, Spec = 0.3800\n",
      "\n",
      "AUC = 0.7695, Sens = 0.7273, Spec = 0.6800\n",
      "\n",
      "AUC = 0.7475, Sens = 0.7500, Spec = 0.7500\n",
      "\n",
      "AUC = 0.9465, Sens = 0.9000, Spec = 0.9000\n",
      "\n",
      "AUC = 0.7750, Sens = 1.0000, Spec = 0.7700\n",
      "\n",
      "AUC = 0.9640, Sens = 1.0000, Spec = 0.9500\n",
      "\n",
      "AUC = 0.9021, Sens = 1.0000, Spec = 0.7000\n",
      "\n",
      "mean AUC = 0.663 (+/- 0.132)\n",
      "mean Sens = 0.672 (+/- 0.186)\n",
      "mean Spec = 0.783 (+/- 0.095)\n",
      "LDA\n",
      "AUC = 0.7383, Sens = 1.0000, Spec = 0.5000\n",
      "\n",
      "AUC = 0.4050, Sens = 0.9730, Spec = 0.1900\n",
      "\n",
      "AUC = 0.4828, Sens = 0.5556, Spec = 0.5800\n",
      "\n",
      "AUC = 0.8200, Sens = 0.7500, Spec = 0.7700\n",
      "\n",
      "AUC = 0.6400, Sens = 0.9444, Spec = 0.3400\n",
      "\n",
      "AUC = 0.5837, Sens = 0.2500, Spec = 1.0000\n",
      "\n",
      "AUC = 0.8463, Sens = 1.0000, Spec = 0.6900\n",
      "\n",
      "AUC = 0.8200, Sens = 0.8182, Spec = 0.7800\n",
      "\n",
      "AUC = 0.6825, Sens = 1.0000, Spec = 0.4000\n",
      "\n",
      "AUC = 0.8895, Sens = 1.0000, Spec = 0.7000\n",
      "\n",
      "AUC = 1.0000, Sens = 1.0000, Spec = 1.0000\n",
      "\n",
      "AUC = 0.7800, Sens = 0.8000, Spec = 0.7300\n",
      "\n",
      "AUC = 0.5614, Sens = 0.2857, Spec = 0.9500\n",
      "\n",
      "mean AUC = 0.712 (+/- 0.091)\n",
      "mean Sens = 0.798 (+/- 0.144)\n",
      "mean Spec = 0.664 (+/- 0.135)\n"
     ]
    },
    {
     "data": {
      "image/png": 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CiCVVLBYZHRilNFbCLJrEVIyx/gDN/NMZSoUC/oH9tBlRDoQBtt2BaRqYTcSo+UKWkdwA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\nT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9Ci6YgJudCOdHDsmUdoNd3gSI+JS4Txafqxd/JwJo1J9SkUhsdNpf/08NADfHYn/MUO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\n5/Msi5beCfJ6vfj9b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Iq5pK5kUCnlhY2eiGL+sG2bnlQPoda5teUwLRM5jcmLaRjk+3vw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wKkeDWjdxd7NS89RRAhMvy44db9O3WEcOiyWHgABX3N3zSmZLoojWRWOrKugMJElC9BIrJIDT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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114da1898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l = 'Hamstring'\n",
    "c = 0\n",
    "Labels = FullData.Label\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "FiltData = FullData.loc[(Labels!='IA') & (FullData.Location==l)]\n",
    "for s in FiltData.SubjID.unique():\n",
    "    if len(FiltData[FiltData.SubjID==s].Label.unique())<2:\n",
    "        FiltData = FiltData[FiltData.SubjID!=s]\n",
    "\n",
    "Features = FiltData.iloc[:,0:14]\n",
    "Features = Features.as_matrix()\n",
    "SubjID = FiltData.SubjID\n",
    "Label = FiltData.Label=='SA'\n",
    "Location = FiltData.Location\n",
    "        \n",
    "#standardize data\n",
    "scaler = preprocessing.StandardScaler().fit(Features) #define scaler on Healthy data\n",
    "Features = scaler.transform(Features)\n",
    "FiltData = pd.concat([pd.DataFrame(Features),\n",
    "                      pd.DataFrame({'SubjID':SubjID.as_matrix(),'Label':Label.as_matrix(),'Location':Location.as_matrix()})],axis=1)\n",
    "        \n",
    "for clf,name in zip(models,model_name):\n",
    "    print(name)\n",
    "    results = LOSOCV(Features,Label.as_matrix(),FiltData,SubjID.as_matrix(),clf,0)\n",
    "\n",
    "    AX = plot_roc(results['TPR'],results['FPR'],np.mean(results['AUC']),ax=None,plotname=name,col=current_palette[c])\n",
    "    AX.set_title(l)\n",
    "    c+=1 #switch color\n",
    "\n",
    "sns.despine()\n",
    "plt.savefig('Hamstring.jpg',dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use Stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "Logistic Regression\n",
      "SVM\n",
      "NB\n",
      "LDA\n",
      "base-level classifiers trained\n",
      "training meta-classifier\n",
      "best base-clf SVM, auc = 0.603, worst base-clf Random Forest, minauc = 0.333\n",
      "auc meta = 0.510\n",
      "\n",
      "AUC = 0.5112, Sens = 0.9524, Spec = 0.1900\n",
      "\n",
      "Random Forest\n",
      "Logistic Regression\n",
      "SVM\n",
      "NB\n",
      "LDA\n",
      "base-level classifiers trained\n",
      "training meta-classifier\n",
      "best base-clf SVM, auc = 0.494, worst base-clf Random Forest, minauc = 0.358\n",
      "auc meta = 0.406\n",
      "\n",
      "AUC = 0.4058, Sens = 0.8679, Spec = 0.2000\n",
      "\n",
      "Random Forest\n",
      "Logistic Regression\n",
      "SVM\n",
      "NB\n",
      "LDA\n",
      "base-level classifiers trained\n",
      "training meta-classifier\n",
      "best base-clf NB, auc = 0.489, worst base-clf SVM, minauc = 0.271\n",
      "auc meta = 0.425\n",
      "\n",
      "AUC = 0.4257, Sens = 0.2857, Spec = 0.7400\n",
      "\n",
      "Random Forest\n",
      "Logistic Regression\n",
      "SVM\n",
      "NB\n",
      "LDA\n",
      "base-level classifiers trained\n",
      "training meta-classifier\n",
      "best base-clf LDA, auc = 0.896, worst base-clf Random Forest, minauc = 0.183\n",
      "auc meta = 0.839\n",
      "\n",
      "AUC = 0.8396, Sens = 0.7200, Spec = 0.8000\n",
      "\n",
      "Random Forest\n",
      "Logistic Regression\n",
      "SVM\n",
      "NB\n",
      "LDA\n",
      "base-level classifiers trained\n",
      "training meta-classifier\n",
      "best base-clf LDA, auc = 0.691, worst base-clf Random Forest, minauc = 0.630\n",
      "auc meta = 0.694\n",
      "\n",
      "AUC = 0.6947, Sens = 0.8300, Spec = 0.4800\n",
      "\n",
      "Random Forest\n",
      "Logistic Regression\n",
      "SVM\n",
      "NB\n",
      "LDA\n",
      "base-level classifiers trained\n",
      "training meta-classifier\n",
      "best base-clf Random Forest, auc = 0.664, worst base-clf Logistic Regression, minauc = 0.507\n",
      "auc meta = 0.527\n",
      "\n",
      "AUC = 0.5277, Sens = 0.3077, Spec = 0.8600\n",
      "\n",
      "Random Forest\n",
      "Logistic Regression\n",
      "SVM\n",
      "NB\n",
      "LDA\n",
      "base-level classifiers trained\n",
      "training meta-classifier\n",
      "best base-clf LDA, auc = 0.836, worst base-clf NB, minauc = 0.626\n",
      "auc meta = 0.742\n",
      "\n",
      "AUC = 0.7420, Sens = 0.8182, Spec = 0.6700\n",
      "\n",
      "Random Forest\n",
      "Logistic Regression\n",
      "SVM\n",
      "NB\n",
      "LDA\n",
      "base-level classifiers trained\n",
      "training meta-classifier\n",
      "best base-clf LDA, auc = 0.877, worst base-clf SVM, minauc = 0.761\n",
      "auc meta = 0.833\n",
      "\n",
      "AUC = 0.8333, Sens = 0.8750, Spec = 0.6500\n",
      "\n",
      "Random Forest\n",
      "Logistic Regression\n",
      "SVM\n",
      "NB\n",
      "LDA\n",
      "base-level classifiers trained\n",
      "training meta-classifier\n",
      "best base-clf NB, auc = 0.890, worst base-clf Random Forest, minauc = 0.792\n",
      "auc meta = 0.815\n",
      "\n",
      "AUC = 0.8153, Sens = 0.9032, Spec = 0.6100\n",
      "\n",
      "Random Forest\n",
      "Logistic Regression\n",
      "SVM\n",
      "NB\n",
      "LDA\n",
      "base-level classifiers trained\n",
      "training meta-classifier\n",
      "best base-clf NB, auc = 0.968, worst base-clf SVM, minauc = 0.775\n",
      "auc meta = 0.921\n",
      "\n",
      "AUC = 0.9207, Sens = 0.9667, Spec = 0.7600\n",
      "\n",
      "Random Forest\n",
      "Logistic Regression\n",
      "SVM\n",
      "NB\n",
      "LDA\n",
      "base-level classifiers trained\n",
      "training meta-classifier\n",
      "best base-clf LDA, auc = 0.938, worst base-clf SVM, minauc = 0.747\n",
      "auc meta = 0.939\n",
      "\n",
      "AUC = 0.9383, Sens = 1.0000, Spec = 0.8700\n",
      "\n",
      "Random Forest\n",
      "Logistic Regression\n",
      "SVM\n",
      "NB\n",
      "LDA\n",
      "base-level classifiers trained\n",
      "training meta-classifier\n",
      "best base-clf Logistic Regression, auc = 0.962, worst base-clf SVM, minauc = 0.299\n",
      "auc meta = 0.869\n",
      "\n",
      "AUC = 0.8700, Sens = 0.7931, Spec = 0.8500\n",
      "\n",
      "Random Forest\n",
      "Logistic Regression\n",
      "SVM\n",
      "NB\n",
      "LDA\n",
      "base-level classifiers trained\n",
      "training meta-classifier\n",
      "best base-clf Logistic Regression, auc = 0.978, worst base-clf SVM, minauc = 0.214\n",
      "auc meta = 0.975\n",
      "\n",
      "AUC = 0.9756, Sens = 1.0000, Spec = 0.9000\n",
      "\n",
      "mean AUC = 0.731 (+/- 0.383)\n",
      "mean Sens = 0.794 (+/- 0.453)\n",
      "mean Spec = 0.660 (+/- 0.458)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11ef597f0>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IIYQoU47jkE6nSSbTZPIGhmlhWA7gQlFdKKqC2+VGdXlR1Y/mSyqc3rlLwsD52baD3hfj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f0382b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l = 'Hamstring'\n",
    "c = 0\n",
    "Labels = FullData.Label\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "FiltData = FullData.loc[(Labels!='IA') & (FullData.Location==l)]\n",
    "for s in FiltData.SubjID.unique():\n",
    "    if len(FiltData[FiltData.SubjID==s].Label.unique())<2:\n",
    "        FiltData = FiltData[FiltData.SubjID!=s]\n",
    "\n",
    "Features = FiltData.iloc[:,0:14]\n",
    "Features = Features.as_matrix()\n",
    "SubjID = FiltData.SubjID\n",
    "Label = FiltData.Label=='SA'\n",
    "Location = FiltData.Location\n",
    "        \n",
    "#standardize data\n",
    "scaler = preprocessing.StandardScaler().fit(Features) #define scaler on Healthy data\n",
    "Features = scaler.transform(Features)\n",
    "FiltData = pd.concat([pd.DataFrame(Features),\n",
    "                      pd.DataFrame({'SubjID':SubjID.as_matrix(),'Label':Label.as_matrix(),'Location':Location.as_matrix()})],axis=1)\n",
    "        \n",
    "results = LOSOCV(Features,Label.as_matrix(),FiltData,SubjID.as_matrix(),models,1)\n",
    "\n",
    "AX = plot_roc(results['TPR'],results['FPR'],np.mean(results['AUC']),ax=None,plotname=name,col=current_palette[c])\n",
    "AX.set_title(l)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gastrocnemius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1557, 20)\n"
     ]
    }
   ],
   "source": [
    "#Load Clips Data\n",
    "# FullData = pd.read_csv(savepath+'Clips_ws1.0_overlap0.8.csv')\n",
    "# FullData = pd.read_csv(savepath+'Clips_ws0.5_overlap0.8.csv')\n",
    "FullData = pd.read_csv(savepath+'Clips_ws1.0_overlap0.5.csv')\n",
    "\n",
    "print (FullData.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models = [RandomForestClassifier(n_estimators=200,random_state=2),\n",
    "          LogisticRegression(C=.001),\n",
    "          SVC(kernel='linear',C=.001,cache_size=1600,probability=True,random_state=3),\n",
    "          GaussianNB(),\n",
    "          LinearDiscriminantAnalysis()\n",
    "         ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "AUC = 1.0000, Sens = 1.0000, Spec = 1.0000\n",
      "\n",
      "AUC = 0.8650, Sens = 1.0000, Spec = 0.8600\n",
      "\n",
      "AUC = 0.6874, Sens = 0.6000, Spec = 0.8300\n",
      "\n",
      "AUC = 0.1050, Sens = 1.0000, Spec = 0.0600\n",
      "\n",
      "mean AUC = 0.664 (+/- 0.341)\n",
      "mean Sens = 0.900 (+/- 0.173)\n",
      "mean Spec = 0.688 (+/- 0.368)\n",
      "Logistic Regression\n",
      "AUC = 1.0000, Sens = 1.0000, Spec = 1.0000\n",
      "\n",
      "AUC = 0.0700, Sens = 1.0000, Spec = 0.0400\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/luca/anaconda/envs/py35/lib/python3.5/site-packages/seaborn/timeseries.py:183: UserWarning: The tsplot function is deprecated and will be removed or replaced (in a substantially altered version) in a future release.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC = 0.9768, Sens = 0.8400, Spec = 1.0000\n",
      "\n",
      "AUC = 0.1500, Sens = 1.0000, Spec = 0.0300\n",
      "\n",
      "mean AUC = 0.549 (+/- 0.440)\n",
      "mean Sens = 0.960 (+/- 0.069)\n",
      "mean Spec = 0.518 (+/- 0.483)\n",
      "SVM\n",
      "AUC = 1.0000, Sens = 1.0000, Spec = 1.0000\n",
      "\n",
      "AUC = 0.3400, Sens = 1.0000, Spec = 0.2700\n",
      "\n",
      "AUC = 0.9784, Sens = 0.8400, Spec = 1.0000\n",
      "\n",
      "AUC = 0.2450, Sens = 1.0000, Spec = 0.1400\n",
      "\n",
      "mean AUC = 0.641 (+/- 0.350)\n",
      "mean Sens = 0.960 (+/- 0.069)\n",
      "mean Spec = 0.603 (+/- 0.400)\n",
      "NB\n",
      "AUC = 1.0000, Sens = 1.0000, Spec = 1.0000\n",
      "\n",
      "AUC = 0.0500, Sens = 0.0000, Spec = 1.0000\n",
      "\n",
      "AUC = 0.9086, Sens = 0.8000, Spec = 1.0000\n",
      "\n",
      "AUC = 0.4450, Sens = 1.0000, Spec = 0.3600\n",
      "\n",
      "mean AUC = 0.601 (+/- 0.381)\n",
      "mean Sens = 0.700 (+/- 0.412)\n",
      "mean Spec = 0.840 (+/- 0.277)\n",
      "LDA\n",
      "AUC = 1.0000, Sens = 1.0000, Spec = 1.0000\n",
      "\n",
      "AUC = 0.9100, Sens = 1.0000, Spec = 0.8600\n",
      "\n",
      "AUC = 0.9284, Sens = 0.8400, Spec = 1.0000\n",
      "\n",
      "AUC = 0.9150, Sens = 1.0000, Spec = 0.8600\n",
      "\n",
      "mean AUC = 0.938 (+/- 0.036)\n",
      "mean Sens = 0.960 (+/- 0.069)\n",
      "mean Spec = 0.930 (+/- 0.070)\n"
     ]
    },
    {
     "data": {
      "image/png": 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VxlWXzKBDf8h+RRpjmsPb3i4xMYkLbQmiHcfh4pdeTTqZ5ahnrOFVF56w7H1i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\ncROr8SBaNxU2z41ZjmLbeHLa8j2okIA/QEFe9vSIhjiDaBHp2dRtlVn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hzTGOCaBBdKbK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vz4cVq2bEnfvn0xMjLizz//JDg4mBEjRrzXe/P3338DqI1Op+nTpw8//fQTXbp0YcCA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\nkJNoMzMzM7MK/X+VaGNL701kJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c55be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l = 'Gastrocnemius'\n",
    "c = 0\n",
    "Labels = FullData.Label\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "FiltData = FullData.loc[(Labels!='IA') & (FullData.Location==l)]\n",
    "for s in FiltData.SubjID.unique():\n",
    "    if len(FiltData[FiltData.SubjID==s].Label.unique())<2:\n",
    "        FiltData = FiltData[FiltData.SubjID!=s]\n",
    "\n",
    "Features = FiltData.iloc[:,0:14]\n",
    "Features = Features.as_matrix()\n",
    "SubjID = FiltData.SubjID\n",
    "Label = FiltData.Label=='SA'\n",
    "Location = FiltData.Location\n",
    "        \n",
    "#standardize data\n",
    "scaler = preprocessing.StandardScaler().fit(Features) #define scaler on Healthy data\n",
    "Features = scaler.transform(Features)\n",
    "FiltData = pd.concat([pd.DataFrame(Features),\n",
    "                      pd.DataFrame({'SubjID':SubjID.as_matrix(),'Label':Label.as_matrix(),'Location':Location.as_matrix()})],axis=1)\n",
    "        \n",
    "for clf,name in zip(models,model_name):\n",
    "    print(name)\n",
    "    results = LOSOCV(Features,Label.as_matrix(),FiltData,SubjID.as_matrix(),clf,0)\n",
    "\n",
    "    AX = plot_roc(results['TPR'],results['FPR'],np.mean(results['AUC']),ax=None,plotname=name,col=current_palette[c])\n",
    "    AX.set_title(l)\n",
    "    c+=1 #switch color\n",
    "    \n",
    "sns.despine()\n",
    "plt.savefig('Gastroc.jpg',dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC = 1.0000, Sens = 1.0000, Spec = 1.0000\n",
      "\n",
      "AUC = 0.9100, Sens = 1.0000, Spec = 0.8600\n",
      "\n",
      "AUC = 0.9284, Sens = 0.8400, Spec = 1.0000\n",
      "\n",
      "AUC = 0.9150, Sens = 1.0000, Spec = 0.8600\n",
      "\n",
      "mean AUC = 0.938 (+/- 0.036)\n",
      "mean Sens = 0.960 (+/- 0.069)\n",
      "mean Spec = 0.930 (+/- 0.070)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11f4fb2e8>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rkey6446p7NnzFtOnn8qyZSrB8BslzOIriSUZJcVBQiENLhERkewKhYJs3DhX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NAAAD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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123d40358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l = 'Gastrocnemius'\n",
    "c = 0\n",
    "Labels = FullData.Label\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "FiltData = FullData.loc[(Labels!='IA') & (FullData.Location==l)]\n",
    "for s in FiltData.SubjID.unique():\n",
    "    if len(FiltData[FiltData.SubjID==s].Label.unique())<2:\n",
    "        FiltData = FiltData[FiltData.SubjID!=s]\n",
    "\n",
    "Features = FiltData.iloc[:,0:14]\n",
    "Features = Features.as_matrix()\n",
    "SubjID = FiltData.SubjID\n",
    "Label = FiltData.Label=='SA'\n",
    "Location = FiltData.Location\n",
    "        \n",
    "#standardize data\n",
    "scaler = preprocessing.StandardScaler().fit(Features) #define scaler on Healthy data\n",
    "Features = scaler.transform(Features)\n",
    "FiltData = pd.concat([pd.DataFrame(Features),\n",
    "                      pd.DataFrame({'SubjID':SubjID.as_matrix(),'Label':Label.as_matrix(),'Location':Location.as_matrix()})],axis=1)\n",
    "        \n",
    "results = LOSOCV(Features,Label.as_matrix(),FiltData,SubjID.as_matrix(),models[-1],0)\n",
    "\n",
    "AX = plot_roc(results['TPR'],results['FPR'],np.mean(results['AUC']),ax=None,plotname=model_name[-1],col=current_palette[c])\n",
    "AX.set_title(l)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AUC': [1.0, 0.91000000000000003, 0.9284, 0.91500000000000004],\n",
       " 'FPR': array([ 0.  ,  0.01,  0.02,  0.03,  0.04,  0.05,  0.06,  0.07,  0.08,\n",
       "         0.09,  0.1 ,  0.11,  0.12,  0.13,  0.14,  0.15,  0.16,  0.17,\n",
       "         0.18,  0.19,  0.2 ,  0.21,  0.22,  0.23,  0.24,  0.25,  0.26,\n",
       "         0.27,  0.28,  0.29,  0.3 ,  0.31,  0.32,  0.33,  0.34,  0.35,\n",
       "         0.36,  0.37,  0.38,  0.39,  0.4 ,  0.41,  0.42,  0.43,  0.44,\n",
       "         0.45,  0.46,  0.47,  0.48,  0.49,  0.5 ,  0.51,  0.52,  0.53,\n",
       "         0.54,  0.55,  0.56,  0.57,  0.58,  0.59,  0.6 ,  0.61,  0.62,\n",
       "         0.63,  0.64,  0.65,  0.66,  0.67,  0.68,  0.69,  0.7 ,  0.71,\n",
       "         0.72,  0.73,  0.74,  0.75,  0.76,  0.77,  0.78,  0.79,  0.8 ,\n",
       "         0.81,  0.82,  0.83,  0.84,  0.85,  0.86,  0.87,  0.88,  0.89,\n",
       "         0.9 ,  0.91,  0.92,  0.93,  0.94,  0.95,  0.96,  0.97,  0.98,\n",
       "         0.99,  1.  ]),\n",
       " 'Sens': [1.0, 1.0, 0.83999999999999997, 1.0],\n",
       " 'Spec': [1.0, 0.85999999999999999, 1.0, 0.85999999999999999],\n",
       " 'TPR': [array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "          1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "          1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "          1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "          1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "          1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "          1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "          1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.]),\n",
       "  array([ 0. ,  0. ,  0. ,  0. ,  0. ,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,\n",
       "          0.5,  0.5,  0.5,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ]),\n",
       "  array([ 0.84,  0.84,  0.84,  0.84,  0.84,  0.84,  0.84,  0.84,  0.84,\n",
       "          0.84,  0.84,  0.84,  0.84,  0.88,  0.88,  0.88,  0.88,  0.88,\n",
       "          0.88,  0.88,  0.88,  0.88,  0.88,  0.88,  0.88,  0.88,  0.88,\n",
       "          0.88,  0.88,  0.88,  0.88,  0.88,  0.88,  0.88,  0.88,  0.88,\n",
       "          0.88,  0.88,  0.92,  0.92,  0.92,  0.92,  0.92,  0.92,  0.92,\n",
       "          0.92,  0.92,  0.92,  0.92,  0.92,  0.92,  0.92,  0.92,  0.92,\n",
       "          0.92,  0.92,  0.92,  0.92,  0.92,  0.96,  0.96,  0.96,  0.96,\n",
       "          0.96,  0.96,  0.96,  0.96,  0.96,  0.96,  0.96,  0.96,  1.  ,\n",
       "          1.  ,  1.  ,  1.  ,  1.  ,  1.  ,  1.  ,  1.  ,  1.  ,  1.  ,\n",
       "          1.  ,  1.  ,  1.  ,  1.  ,  1.  ,  1.  ,  1.  ,  1.  ,  1.  ,\n",
       "          1.  ,  1.  ,  1.  ,  1.  ,  1.  ,  1.  ,  1.  ,  1.  ,  1.  ,\n",
       "          1.  ,  1.  ]),\n",
       "  array([ 0. ,  0. ,  0. ,  0. ,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,\n",
       "          0.5,  0.5,  0.5,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "          1. ,  1. ])]}"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "FiltData = pd.concat([pd.DataFrame(Features),\n",
    "                      pd.DataFrame({'SubjID':SubjID.as_matrix(),'Label':Label.as_matrix(),'Location':Location.as_matrix()})],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>Label</th>\n",
       "      <th>Location</th>\n",
       "      <th>SubjID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.281852</td>\n",
       "      <td>-0.047540</td>\n",
       "      <td>6.548987</td>\n",
       "      <td>4.988246</td>\n",
       "      <td>0.694025</td>\n",
       "      <td>1.466070</td>\n",
       "      <td>-0.829564</td>\n",
       "      <td>2.079150</td>\n",
       "      <td>3.589152</td>\n",
       "      <td>-2.831884</td>\n",
       "      <td>0.298303</td>\n",
       "      <td>4.827728</td>\n",
       "      <td>0.993655</td>\n",
       "      <td>1.501371</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.098564</td>\n",
       "      <td>0.244480</td>\n",
       "      <td>0.824080</td>\n",
       "      <td>1.724432</td>\n",
       "      <td>1.100442</td>\n",
       "      <td>1.372337</td>\n",
       "      <td>0.322928</td>\n",
       "      <td>1.183330</td>\n",
       "      <td>1.782220</td>\n",
       "      <td>-2.476570</td>\n",
       "      <td>0.543958</td>\n",
       "      <td>1.613981</td>\n",
       "      <td>0.658532</td>\n",
       "      <td>1.238401</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.261596</td>\n",
       "      <td>0.124163</td>\n",
       "      <td>0.812885</td>\n",
       "      <td>1.649533</td>\n",
       "      <td>1.403355</td>\n",
       "      <td>1.500909</td>\n",
       "      <td>0.453803</td>\n",
       "      <td>1.307462</td>\n",
       "      <td>1.822397</td>\n",
       "      <td>-2.528166</td>\n",
       "      <td>0.625843</td>\n",
       "      <td>1.669634</td>\n",
       "      <td>0.728841</td>\n",
       "      <td>1.369886</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.906466</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.602164</td>\n",
       "      <td>1.398007</td>\n",
       "      <td>1.505170</td>\n",
       "      <td>2.148697</td>\n",
       "      <td>2.292207</td>\n",
       "      <td>1.387761</td>\n",
       "      <td>1.995183</td>\n",
       "      <td>-2.624278</td>\n",
       "      <td>0.707728</td>\n",
       "      <td>1.921902</td>\n",
       "      <td>0.917468</td>\n",
       "      <td>1.632857</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.708509</td>\n",
       "      <td>0.784601</td>\n",
       "      <td>0.930508</td>\n",
       "      <td>1.422989</td>\n",
       "      <td>3.506940</td>\n",
       "      <td>1.985700</td>\n",
       "      <td>1.444883</td>\n",
       "      <td>1.717158</td>\n",
       "      <td>2.298045</td>\n",
       "      <td>-2.747458</td>\n",
       "      <td>0.871499</td>\n",
       "      <td>2.391242</td>\n",
       "      <td>1.049668</td>\n",
       "      <td>1.764342</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.084682</td>\n",
       "      <td>0.496656</td>\n",
       "      <td>0.206128</td>\n",
       "      <td>1.235235</td>\n",
       "      <td>3.017840</td>\n",
       "      <td>0.812137</td>\n",
       "      <td>-0.582209</td>\n",
       "      <td>1.327594</td>\n",
       "      <td>1.882637</td>\n",
       "      <td>-2.772164</td>\n",
       "      <td>0.871499</td>\n",
       "      <td>1.757003</td>\n",
       "      <td>0.837542</td>\n",
       "      <td>1.764342</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.453954</td>\n",
       "      <td>0.256396</td>\n",
       "      <td>0.133891</td>\n",
       "      <td>0.902410</td>\n",
       "      <td>2.317555</td>\n",
       "      <td>0.829720</td>\n",
       "      <td>-0.259576</td>\n",
       "      <td>1.196381</td>\n",
       "      <td>1.684864</td>\n",
       "      <td>-2.779747</td>\n",
       "      <td>0.789614</td>\n",
       "      <td>1.480387</td>\n",
       "      <td>0.711590</td>\n",
       "      <td>1.632857</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.316492</td>\n",
       "      <td>0.248869</td>\n",
       "      <td>0.188638</td>\n",
       "      <td>0.892958</td>\n",
       "      <td>2.324661</td>\n",
       "      <td>0.917845</td>\n",
       "      <td>-0.463637</td>\n",
       "      <td>1.184202</td>\n",
       "      <td>1.684167</td>\n",
       "      <td>-2.770330</td>\n",
       "      <td>0.789614</td>\n",
       "      <td>1.479293</td>\n",
       "      <td>0.743371</td>\n",
       "      <td>1.632857</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.992381</td>\n",
       "      <td>1.234388</td>\n",
       "      <td>0.226473</td>\n",
       "      <td>1.204904</td>\n",
       "      <td>2.319366</td>\n",
       "      <td>1.120025</td>\n",
       "      <td>1.000205</td>\n",
       "      <td>1.248874</td>\n",
       "      <td>1.765668</td>\n",
       "      <td>-2.746335</td>\n",
       "      <td>0.707728</td>\n",
       "      <td>1.591454</td>\n",
       "      <td>0.836860</td>\n",
       "      <td>1.632857</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.500279</td>\n",
       "      <td>1.110888</td>\n",
       "      <td>0.501287</td>\n",
       "      <td>0.864296</td>\n",
       "      <td>1.295929</td>\n",
       "      <td>1.231113</td>\n",
       "      <td>1.440026</td>\n",
       "      <td>1.032204</td>\n",
       "      <td>1.626936</td>\n",
       "      <td>-2.836115</td>\n",
       "      <td>0.462073</td>\n",
       "      <td>1.402410</td>\n",
       "      <td>0.421205</td>\n",
       "      <td>1.106916</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.607531</td>\n",
       "      <td>1.307779</td>\n",
       "      <td>0.259581</td>\n",
       "      <td>0.391142</td>\n",
       "      <td>0.695549</td>\n",
       "      <td>1.595557</td>\n",
       "      <td>0.997318</td>\n",
       "      <td>0.817735</td>\n",
       "      <td>1.322388</td>\n",
       "      <td>-2.552435</td>\n",
       "      <td>0.462073</td>\n",
       "      <td>1.015750</td>\n",
       "      <td>0.469876</td>\n",
       "      <td>0.843946</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.917574</td>\n",
       "      <td>2.136057</td>\n",
       "      <td>0.138887</td>\n",
       "      <td>0.257412</td>\n",
       "      <td>0.561786</td>\n",
       "      <td>2.018466</td>\n",
       "      <td>3.490456</td>\n",
       "      <td>0.659375</td>\n",
       "      <td>1.247816</td>\n",
       "      <td>-2.595857</td>\n",
       "      <td>0.298303</td>\n",
       "      <td>0.925317</td>\n",
       "      <td>0.307040</td>\n",
       "      <td>0.843946</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.102780</td>\n",
       "      <td>2.314464</td>\n",
       "      <td>0.176808</td>\n",
       "      <td>0.547338</td>\n",
       "      <td>0.708589</td>\n",
       "      <td>2.440352</td>\n",
       "      <td>3.991472</td>\n",
       "      <td>0.893552</td>\n",
       "      <td>1.452164</td>\n",
       "      <td>-2.729821</td>\n",
       "      <td>0.625843</td>\n",
       "      <td>1.175599</td>\n",
       "      <td>0.638832</td>\n",
       "      <td>1.106916</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.965926</td>\n",
       "      <td>0.561273</td>\n",
       "      <td>-0.005751</td>\n",
       "      <td>0.096306</td>\n",
       "      <td>0.473924</td>\n",
       "      <td>1.403003</td>\n",
       "      <td>3.262664</td>\n",
       "      <td>0.441380</td>\n",
       "      <td>0.913215</td>\n",
       "      <td>-2.556182</td>\n",
       "      <td>0.298303</td>\n",
       "      <td>0.557985</td>\n",
       "      <td>0.092896</td>\n",
       "      <td>0.580976</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.974271</td>\n",
       "      <td>0.523750</td>\n",
       "      <td>0.011591</td>\n",
       "      <td>0.509258</td>\n",
       "      <td>0.398956</td>\n",
       "      <td>1.431206</td>\n",
       "      <td>1.082963</td>\n",
       "      <td>0.628028</td>\n",
       "      <td>1.028071</td>\n",
       "      <td>-2.670472</td>\n",
       "      <td>0.462073</td>\n",
       "      <td>0.678757</td>\n",
       "      <td>0.303319</td>\n",
       "      <td>0.843946</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.935244</td>\n",
       "      <td>1.263208</td>\n",
       "      <td>0.311706</td>\n",
       "      <td>0.470500</td>\n",
       "      <td>0.637478</td>\n",
       "      <td>1.244601</td>\n",
       "      <td>3.384715</td>\n",
       "      <td>0.616242</td>\n",
       "      <td>1.209693</td>\n",
       "      <td>-2.697262</td>\n",
       "      <td>0.216417</td>\n",
       "      <td>0.874217</td>\n",
       "      <td>0.144398</td>\n",
       "      <td>1.106916</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.272854</td>\n",
       "      <td>1.132937</td>\n",
       "      <td>0.993208</td>\n",
       "      <td>0.823052</td>\n",
       "      <td>1.107362</td>\n",
       "      <td>1.159070</td>\n",
       "      <td>5.232080</td>\n",
       "      <td>0.853410</td>\n",
       "      <td>1.429617</td>\n",
       "      <td>-2.789661</td>\n",
       "      <td>0.380188</td>\n",
       "      <td>1.147366</td>\n",
       "      <td>0.379513</td>\n",
       "      <td>1.369886</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.578901</td>\n",
       "      <td>0.193264</td>\n",
       "      <td>0.942222</td>\n",
       "      <td>0.568785</td>\n",
       "      <td>0.806032</td>\n",
       "      <td>-0.049434</td>\n",
       "      <td>0.840461</td>\n",
       "      <td>0.519020</td>\n",
       "      <td>1.116752</td>\n",
       "      <td>-2.694846</td>\n",
       "      <td>-0.111123</td>\n",
       "      <td>0.776411</td>\n",
       "      <td>0.018761</td>\n",
       "      <td>0.843946</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.682883</td>\n",
       "      <td>0.093634</td>\n",
       "      <td>1.017236</td>\n",
       "      <td>0.769005</td>\n",
       "      <td>0.556100</td>\n",
       "      <td>-0.304349</td>\n",
       "      <td>-0.872161</td>\n",
       "      <td>0.569711</td>\n",
       "      <td>1.094486</td>\n",
       "      <td>-2.752516</td>\n",
       "      <td>-0.274894</td>\n",
       "      <td>0.751889</td>\n",
       "      <td>-0.215540</td>\n",
       "      <td>0.580976</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.697308</td>\n",
       "      <td>-0.057689</td>\n",
       "      <td>0.841856</td>\n",
       "      <td>-0.100141</td>\n",
       "      <td>0.101079</td>\n",
       "      <td>-0.377579</td>\n",
       "      <td>-1.364567</td>\n",
       "      <td>0.111612</td>\n",
       "      <td>0.743012</td>\n",
       "      <td>-2.483173</td>\n",
       "      <td>-0.520549</td>\n",
       "      <td>0.387472</td>\n",
       "      <td>-0.448558</td>\n",
       "      <td>0.186520</td>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.075015</td>\n",
       "      <td>-0.224686</td>\n",
       "      <td>0.246476</td>\n",
       "      <td>-0.137154</td>\n",
       "      <td>-0.067799</td>\n",
       "      <td>-0.416693</td>\n",
       "      <td>-0.037391</td>\n",
       "      <td>0.471424</td>\n",
       "      <td>0.211284</td>\n",
       "      <td>0.281829</td>\n",
       "      <td>0.298303</td>\n",
       "      <td>-0.063520</td>\n",
       "      <td>0.189684</td>\n",
       "      <td>-0.470905</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.257678</td>\n",
       "      <td>-0.210485</td>\n",
       "      <td>0.226562</td>\n",
       "      <td>-0.196538</td>\n",
       "      <td>-0.030496</td>\n",
       "      <td>-0.302106</td>\n",
       "      <td>0.612491</td>\n",
       "      <td>0.483773</td>\n",
       "      <td>0.195979</td>\n",
       "      <td>0.378701</td>\n",
       "      <td>0.216417</td>\n",
       "      <td>-0.074711</td>\n",
       "      <td>0.245392</td>\n",
       "      <td>-0.470905</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.130064</td>\n",
       "      <td>-0.158961</td>\n",
       "      <td>0.243463</td>\n",
       "      <td>-0.045788</td>\n",
       "      <td>0.076589</td>\n",
       "      <td>-0.291254</td>\n",
       "      <td>1.622272</td>\n",
       "      <td>0.644746</td>\n",
       "      <td>0.293219</td>\n",
       "      <td>0.502811</td>\n",
       "      <td>0.462073</td>\n",
       "      <td>-0.001885</td>\n",
       "      <td>0.422567</td>\n",
       "      <td>-0.470905</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.420877</td>\n",
       "      <td>-0.070835</td>\n",
       "      <td>0.173334</td>\n",
       "      <td>0.104410</td>\n",
       "      <td>-0.105083</td>\n",
       "      <td>-0.403821</td>\n",
       "      <td>0.012841</td>\n",
       "      <td>0.647999</td>\n",
       "      <td>0.295074</td>\n",
       "      <td>0.993110</td>\n",
       "      <td>0.134532</td>\n",
       "      <td>-0.000230</td>\n",
       "      <td>0.351957</td>\n",
       "      <td>-0.470905</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.519684</td>\n",
       "      <td>-0.205990</td>\n",
       "      <td>0.065469</td>\n",
       "      <td>0.000481</td>\n",
       "      <td>-0.210042</td>\n",
       "      <td>-0.322735</td>\n",
       "      <td>2.164962</td>\n",
       "      <td>0.546925</td>\n",
       "      <td>0.206580</td>\n",
       "      <td>0.932204</td>\n",
       "      <td>-0.193008</td>\n",
       "      <td>-0.068748</td>\n",
       "      <td>0.238630</td>\n",
       "      <td>-0.602391</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.441386</td>\n",
       "      <td>-0.379722</td>\n",
       "      <td>0.115202</td>\n",
       "      <td>-0.071168</td>\n",
       "      <td>-0.128073</td>\n",
       "      <td>-0.242685</td>\n",
       "      <td>0.686125</td>\n",
       "      <td>0.605248</td>\n",
       "      <td>0.248523</td>\n",
       "      <td>0.792179</td>\n",
       "      <td>-0.029238</td>\n",
       "      <td>-0.035745</td>\n",
       "      <td>0.358343</td>\n",
       "      <td>-0.602391</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.647019</td>\n",
       "      <td>-0.344958</td>\n",
       "      <td>-0.020211</td>\n",
       "      <td>0.037492</td>\n",
       "      <td>0.041480</td>\n",
       "      <td>-0.311101</td>\n",
       "      <td>-0.440211</td>\n",
       "      <td>0.626718</td>\n",
       "      <td>0.275911</td>\n",
       "      <td>2.111985</td>\n",
       "      <td>0.052647</td>\n",
       "      <td>-0.015330</td>\n",
       "      <td>0.393081</td>\n",
       "      <td>-0.602391</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.553071</td>\n",
       "      <td>-0.431241</td>\n",
       "      <td>0.055999</td>\n",
       "      <td>0.203407</td>\n",
       "      <td>-0.017307</td>\n",
       "      <td>-0.159626</td>\n",
       "      <td>-0.710545</td>\n",
       "      <td>0.689254</td>\n",
       "      <td>0.340170</td>\n",
       "      <td>1.245464</td>\n",
       "      <td>0.052647</td>\n",
       "      <td>0.033823</td>\n",
       "      <td>0.397282</td>\n",
       "      <td>-0.470905</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.613927</td>\n",
       "      <td>-0.369299</td>\n",
       "      <td>0.249191</td>\n",
       "      <td>0.103032</td>\n",
       "      <td>0.151684</td>\n",
       "      <td>-0.143658</td>\n",
       "      <td>-0.421228</td>\n",
       "      <td>0.787782</td>\n",
       "      <td>0.461782</td>\n",
       "      <td>1.287973</td>\n",
       "      <td>0.052647</td>\n",
       "      <td>0.134393</td>\n",
       "      <td>0.553364</td>\n",
       "      <td>-0.339420</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.668984</td>\n",
       "      <td>-0.344569</td>\n",
       "      <td>0.303104</td>\n",
       "      <td>0.144315</td>\n",
       "      <td>0.260172</td>\n",
       "      <td>-0.263433</td>\n",
       "      <td>-0.478178</td>\n",
       "      <td>0.819002</td>\n",
       "      <td>0.504785</td>\n",
       "      <td>0.902723</td>\n",
       "      <td>0.052647</td>\n",
       "      <td>0.171145</td>\n",
       "      <td>0.579204</td>\n",
       "      <td>-0.339420</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>-0.316072</td>\n",
       "      <td>0.636778</td>\n",
       "      <td>-0.100475</td>\n",
       "      <td>-0.019096</td>\n",
       "      <td>-0.140858</td>\n",
       "      <td>-0.047612</td>\n",
       "      <td>0.065825</td>\n",
       "      <td>0.387311</td>\n",
       "      <td>0.256187</td>\n",
       "      <td>-0.394565</td>\n",
       "      <td>0.789614</td>\n",
       "      <td>-0.029904</td>\n",
       "      <td>0.555365</td>\n",
       "      <td>0.449490</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>-0.308662</td>\n",
       "      <td>0.695916</td>\n",
       "      <td>-0.023702</td>\n",
       "      <td>-0.020962</td>\n",
       "      <td>-0.391996</td>\n",
       "      <td>-0.144435</td>\n",
       "      <td>0.088913</td>\n",
       "      <td>0.240462</td>\n",
       "      <td>0.131084</td>\n",
       "      <td>-0.523746</td>\n",
       "      <td>0.462073</td>\n",
       "      <td>-0.121334</td>\n",
       "      <td>0.283897</td>\n",
       "      <td>0.055035</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>-0.319082</td>\n",
       "      <td>1.033541</td>\n",
       "      <td>0.010405</td>\n",
       "      <td>-0.145132</td>\n",
       "      <td>-0.315097</td>\n",
       "      <td>-0.036305</td>\n",
       "      <td>-0.642797</td>\n",
       "      <td>0.363465</td>\n",
       "      <td>0.196454</td>\n",
       "      <td>0.109428</td>\n",
       "      <td>0.707728</td>\n",
       "      <td>-0.074367</td>\n",
       "      <td>0.539541</td>\n",
       "      <td>0.186520</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1729</th>\n",
       "      <td>-0.309124</td>\n",
       "      <td>0.381540</td>\n",
       "      <td>0.185795</td>\n",
       "      <td>-0.136836</td>\n",
       "      <td>-0.229282</td>\n",
       "      <td>0.246630</td>\n",
       "      <td>0.245932</td>\n",
       "      <td>0.351970</td>\n",
       "      <td>0.246553</td>\n",
       "      <td>-0.036280</td>\n",
       "      <td>0.707728</td>\n",
       "      <td>-0.037175</td>\n",
       "      <td>0.534968</td>\n",
       "      <td>0.186520</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>-0.314700</td>\n",
       "      <td>0.270321</td>\n",
       "      <td>-0.085213</td>\n",
       "      <td>-0.264606</td>\n",
       "      <td>0.021274</td>\n",
       "      <td>0.430418</td>\n",
       "      <td>0.333232</td>\n",
       "      <td>0.320084</td>\n",
       "      <td>0.152594</td>\n",
       "      <td>0.511282</td>\n",
       "      <td>0.789614</td>\n",
       "      <td>-0.106041</td>\n",
       "      <td>0.546927</td>\n",
       "      <td>0.055035</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>-0.230072</td>\n",
       "      <td>0.345132</td>\n",
       "      <td>-0.100981</td>\n",
       "      <td>-0.085412</td>\n",
       "      <td>0.059443</td>\n",
       "      <td>0.844287</td>\n",
       "      <td>-0.014216</td>\n",
       "      <td>0.497575</td>\n",
       "      <td>0.352900</td>\n",
       "      <td>0.230856</td>\n",
       "      <td>1.362810</td>\n",
       "      <td>0.044854</td>\n",
       "      <td>0.761334</td>\n",
       "      <td>0.186520</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1732</th>\n",
       "      <td>-0.192112</td>\n",
       "      <td>0.012177</td>\n",
       "      <td>-0.118078</td>\n",
       "      <td>0.469601</td>\n",
       "      <td>0.204176</td>\n",
       "      <td>1.144327</td>\n",
       "      <td>-0.953774</td>\n",
       "      <td>0.831409</td>\n",
       "      <td>0.633167</td>\n",
       "      <td>-0.223601</td>\n",
       "      <td>1.608465</td>\n",
       "      <td>0.284588</td>\n",
       "      <td>1.066914</td>\n",
       "      <td>0.580976</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>-0.294890</td>\n",
       "      <td>0.169950</td>\n",
       "      <td>-0.149268</td>\n",
       "      <td>0.573602</td>\n",
       "      <td>0.846926</td>\n",
       "      <td>1.235066</td>\n",
       "      <td>-0.209094</td>\n",
       "      <td>1.115804</td>\n",
       "      <td>0.825331</td>\n",
       "      <td>-0.228478</td>\n",
       "      <td>2.017891</td>\n",
       "      <td>0.468851</td>\n",
       "      <td>1.357720</td>\n",
       "      <td>0.843946</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734</th>\n",
       "      <td>-0.302908</td>\n",
       "      <td>0.056808</td>\n",
       "      <td>-0.225175</td>\n",
       "      <td>0.459068</td>\n",
       "      <td>0.809985</td>\n",
       "      <td>1.280794</td>\n",
       "      <td>-0.368773</td>\n",
       "      <td>1.069083</td>\n",
       "      <td>0.752544</td>\n",
       "      <td>0.416959</td>\n",
       "      <td>2.017891</td>\n",
       "      <td>0.397502</td>\n",
       "      <td>1.306128</td>\n",
       "      <td>0.712461</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1735</th>\n",
       "      <td>-0.296456</td>\n",
       "      <td>0.005686</td>\n",
       "      <td>-0.233821</td>\n",
       "      <td>0.494179</td>\n",
       "      <td>0.630335</td>\n",
       "      <td>1.043744</td>\n",
       "      <td>-0.174953</td>\n",
       "      <td>0.901219</td>\n",
       "      <td>0.688551</td>\n",
       "      <td>0.249947</td>\n",
       "      <td>1.608465</td>\n",
       "      <td>0.336539</td>\n",
       "      <td>1.071828</td>\n",
       "      <td>0.712461</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1736</th>\n",
       "      <td>-0.309928</td>\n",
       "      <td>-0.157811</td>\n",
       "      <td>-0.266298</td>\n",
       "      <td>0.365621</td>\n",
       "      <td>0.430755</td>\n",
       "      <td>0.797209</td>\n",
       "      <td>0.400510</td>\n",
       "      <td>0.665285</td>\n",
       "      <td>0.577582</td>\n",
       "      <td>0.235102</td>\n",
       "      <td>1.117154</td>\n",
       "      <td>0.234977</td>\n",
       "      <td>0.831933</td>\n",
       "      <td>0.449490</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>-0.316743</td>\n",
       "      <td>-0.292318</td>\n",
       "      <td>-0.285890</td>\n",
       "      <td>0.086411</td>\n",
       "      <td>0.302937</td>\n",
       "      <td>0.559476</td>\n",
       "      <td>-0.739773</td>\n",
       "      <td>0.407087</td>\n",
       "      <td>0.300483</td>\n",
       "      <td>0.121522</td>\n",
       "      <td>0.871499</td>\n",
       "      <td>0.004024</td>\n",
       "      <td>0.544068</td>\n",
       "      <td>0.055035</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738</th>\n",
       "      <td>-0.172676</td>\n",
       "      <td>-0.061067</td>\n",
       "      <td>0.201382</td>\n",
       "      <td>-0.049787</td>\n",
       "      <td>0.726568</td>\n",
       "      <td>0.388665</td>\n",
       "      <td>-0.766200</td>\n",
       "      <td>0.850063</td>\n",
       "      <td>0.699232</td>\n",
       "      <td>0.313187</td>\n",
       "      <td>1.117154</td>\n",
       "      <td>0.346558</td>\n",
       "      <td>0.861530</td>\n",
       "      <td>0.318005</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1739</th>\n",
       "      <td>-0.091096</td>\n",
       "      <td>-0.008370</td>\n",
       "      <td>-0.003529</td>\n",
       "      <td>0.004981</td>\n",
       "      <td>0.984788</td>\n",
       "      <td>0.785602</td>\n",
       "      <td>-0.363544</td>\n",
       "      <td>1.119903</td>\n",
       "      <td>0.853353</td>\n",
       "      <td>0.915767</td>\n",
       "      <td>1.444695</td>\n",
       "      <td>0.496552</td>\n",
       "      <td>1.247634</td>\n",
       "      <td>0.843946</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740</th>\n",
       "      <td>-0.247984</td>\n",
       "      <td>0.028532</td>\n",
       "      <td>0.206156</td>\n",
       "      <td>0.389367</td>\n",
       "      <td>1.979913</td>\n",
       "      <td>1.814447</td>\n",
       "      <td>-0.850065</td>\n",
       "      <td>1.485542</td>\n",
       "      <td>1.263019</td>\n",
       "      <td>0.389405</td>\n",
       "      <td>2.263546</td>\n",
       "      <td>0.944916</td>\n",
       "      <td>1.842474</td>\n",
       "      <td>1.895827</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1741</th>\n",
       "      <td>-0.296175</td>\n",
       "      <td>0.193086</td>\n",
       "      <td>0.191667</td>\n",
       "      <td>0.570785</td>\n",
       "      <td>2.132219</td>\n",
       "      <td>2.693313</td>\n",
       "      <td>-0.331972</td>\n",
       "      <td>1.660971</td>\n",
       "      <td>1.363645</td>\n",
       "      <td>0.178407</td>\n",
       "      <td>2.263546</td>\n",
       "      <td>1.065927</td>\n",
       "      <td>1.949342</td>\n",
       "      <td>2.158797</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1742</th>\n",
       "      <td>-0.294771</td>\n",
       "      <td>1.932717</td>\n",
       "      <td>0.287904</td>\n",
       "      <td>0.615563</td>\n",
       "      <td>2.277461</td>\n",
       "      <td>2.651015</td>\n",
       "      <td>0.301818</td>\n",
       "      <td>1.833493</td>\n",
       "      <td>1.539349</td>\n",
       "      <td>-0.109595</td>\n",
       "      <td>2.427317</td>\n",
       "      <td>1.287308</td>\n",
       "      <td>2.307114</td>\n",
       "      <td>2.553252</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1743</th>\n",
       "      <td>-0.263133</td>\n",
       "      <td>2.459865</td>\n",
       "      <td>0.296774</td>\n",
       "      <td>1.371233</td>\n",
       "      <td>1.871376</td>\n",
       "      <td>2.583518</td>\n",
       "      <td>-0.750111</td>\n",
       "      <td>2.003169</td>\n",
       "      <td>1.638598</td>\n",
       "      <td>0.391163</td>\n",
       "      <td>2.918628</td>\n",
       "      <td>1.418041</td>\n",
       "      <td>2.538006</td>\n",
       "      <td>2.684738</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744</th>\n",
       "      <td>-0.236570</td>\n",
       "      <td>2.516355</td>\n",
       "      <td>0.497827</td>\n",
       "      <td>0.587013</td>\n",
       "      <td>1.569599</td>\n",
       "      <td>2.388857</td>\n",
       "      <td>-0.214951</td>\n",
       "      <td>1.816466</td>\n",
       "      <td>1.458238</td>\n",
       "      <td>0.322495</td>\n",
       "      <td>3.246168</td>\n",
       "      <td>1.183324</td>\n",
       "      <td>2.439494</td>\n",
       "      <td>2.684738</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745</th>\n",
       "      <td>-0.217183</td>\n",
       "      <td>2.182988</td>\n",
       "      <td>0.221914</td>\n",
       "      <td>0.574396</td>\n",
       "      <td>1.044008</td>\n",
       "      <td>1.498086</td>\n",
       "      <td>-0.200914</td>\n",
       "      <td>1.628966</td>\n",
       "      <td>1.203128</td>\n",
       "      <td>0.932204</td>\n",
       "      <td>2.918628</td>\n",
       "      <td>0.875220</td>\n",
       "      <td>2.066069</td>\n",
       "      <td>2.027312</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746</th>\n",
       "      <td>-0.202140</td>\n",
       "      <td>2.040176</td>\n",
       "      <td>0.135311</td>\n",
       "      <td>0.325190</td>\n",
       "      <td>1.032722</td>\n",
       "      <td>1.203356</td>\n",
       "      <td>-0.027261</td>\n",
       "      <td>1.437648</td>\n",
       "      <td>1.070867</td>\n",
       "      <td>0.161537</td>\n",
       "      <td>2.672972</td>\n",
       "      <td>0.726044</td>\n",
       "      <td>1.863745</td>\n",
       "      <td>1.895827</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1747</th>\n",
       "      <td>-0.230783</td>\n",
       "      <td>0.696499</td>\n",
       "      <td>0.036276</td>\n",
       "      <td>0.360347</td>\n",
       "      <td>0.962032</td>\n",
       "      <td>0.957352</td>\n",
       "      <td>0.750909</td>\n",
       "      <td>1.416986</td>\n",
       "      <td>1.004493</td>\n",
       "      <td>-0.119488</td>\n",
       "      <td>2.672972</td>\n",
       "      <td>0.654102</td>\n",
       "      <td>1.702473</td>\n",
       "      <td>1.501371</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1748</th>\n",
       "      <td>-0.162426</td>\n",
       "      <td>0.338473</td>\n",
       "      <td>0.057790</td>\n",
       "      <td>0.113560</td>\n",
       "      <td>0.904457</td>\n",
       "      <td>0.733281</td>\n",
       "      <td>0.170236</td>\n",
       "      <td>1.126950</td>\n",
       "      <td>0.828243</td>\n",
       "      <td>0.249947</td>\n",
       "      <td>1.854121</td>\n",
       "      <td>0.471526</td>\n",
       "      <td>1.304735</td>\n",
       "      <td>1.369886</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749</th>\n",
       "      <td>-0.187027</td>\n",
       "      <td>0.007978</td>\n",
       "      <td>-0.103198</td>\n",
       "      <td>0.293588</td>\n",
       "      <td>0.854698</td>\n",
       "      <td>0.546244</td>\n",
       "      <td>0.495842</td>\n",
       "      <td>0.898991</td>\n",
       "      <td>0.682320</td>\n",
       "      <td>-0.137118</td>\n",
       "      <td>1.280924</td>\n",
       "      <td>0.330735</td>\n",
       "      <td>0.985583</td>\n",
       "      <td>0.712461</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1750</th>\n",
       "      <td>-0.328447</td>\n",
       "      <td>1.037383</td>\n",
       "      <td>-0.019135</td>\n",
       "      <td>0.254101</td>\n",
       "      <td>0.789321</td>\n",
       "      <td>0.478192</td>\n",
       "      <td>0.449863</td>\n",
       "      <td>0.904545</td>\n",
       "      <td>0.668679</td>\n",
       "      <td>-0.088689</td>\n",
       "      <td>1.199039</td>\n",
       "      <td>0.318017</td>\n",
       "      <td>0.990454</td>\n",
       "      <td>0.975431</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751</th>\n",
       "      <td>-0.285127</td>\n",
       "      <td>1.248970</td>\n",
       "      <td>-0.090739</td>\n",
       "      <td>0.504426</td>\n",
       "      <td>0.366124</td>\n",
       "      <td>0.762015</td>\n",
       "      <td>-0.054668</td>\n",
       "      <td>0.953170</td>\n",
       "      <td>0.722308</td>\n",
       "      <td>-0.684781</td>\n",
       "      <td>1.444695</td>\n",
       "      <td>0.368412</td>\n",
       "      <td>1.131568</td>\n",
       "      <td>1.106916</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>0.096841</td>\n",
       "      <td>1.201086</td>\n",
       "      <td>0.096875</td>\n",
       "      <td>0.391029</td>\n",
       "      <td>-0.005374</td>\n",
       "      <td>0.786889</td>\n",
       "      <td>0.660461</td>\n",
       "      <td>0.968912</td>\n",
       "      <td>0.713178</td>\n",
       "      <td>-0.449570</td>\n",
       "      <td>1.444695</td>\n",
       "      <td>0.358028</td>\n",
       "      <td>1.087393</td>\n",
       "      <td>1.106916</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>-0.280901</td>\n",
       "      <td>1.079399</td>\n",
       "      <td>-0.185697</td>\n",
       "      <td>0.918809</td>\n",
       "      <td>0.270442</td>\n",
       "      <td>0.756200</td>\n",
       "      <td>-0.669832</td>\n",
       "      <td>1.146386</td>\n",
       "      <td>0.790497</td>\n",
       "      <td>0.260320</td>\n",
       "      <td>1.690350</td>\n",
       "      <td>0.434449</td>\n",
       "      <td>1.296819</td>\n",
       "      <td>1.632857</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>-0.278832</td>\n",
       "      <td>1.638613</td>\n",
       "      <td>-0.171157</td>\n",
       "      <td>0.702245</td>\n",
       "      <td>0.227868</td>\n",
       "      <td>0.567873</td>\n",
       "      <td>-0.001584</td>\n",
       "      <td>1.076386</td>\n",
       "      <td>0.736871</td>\n",
       "      <td>0.840002</td>\n",
       "      <td>1.772235</td>\n",
       "      <td>0.382402</td>\n",
       "      <td>1.269851</td>\n",
       "      <td>1.764342</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>-0.272282</td>\n",
       "      <td>0.873851</td>\n",
       "      <td>-0.087899</td>\n",
       "      <td>0.763484</td>\n",
       "      <td>0.008923</td>\n",
       "      <td>0.511721</td>\n",
       "      <td>-0.489962</td>\n",
       "      <td>0.854255</td>\n",
       "      <td>0.625068</td>\n",
       "      <td>-0.740370</td>\n",
       "      <td>1.280924</td>\n",
       "      <td>0.277798</td>\n",
       "      <td>0.846061</td>\n",
       "      <td>1.238401</td>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1756 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1         2         3         4         5         6  \\\n",
       "0     10.281852 -0.047540  6.548987  4.988246  0.694025  1.466070 -0.829564   \n",
       "1      4.098564  0.244480  0.824080  1.724432  1.100442  1.372337  0.322928   \n",
       "2      4.261596  0.124163  0.812885  1.649533  1.403355  1.500909  0.453803   \n",
       "3      4.906466  0.643078  0.602164  1.398007  1.505170  2.148697  2.292207   \n",
       "4      4.708509  0.784601  0.930508  1.422989  3.506940  1.985700  1.444883   \n",
       "5      5.084682  0.496656  0.206128  1.235235  3.017840  0.812137 -0.582209   \n",
       "6      4.453954  0.256396  0.133891  0.902410  2.317555  0.829720 -0.259576   \n",
       "7      4.316492  0.248869  0.188638  0.892958  2.324661  0.917845 -0.463637   \n",
       "8      3.992381  1.234388  0.226473  1.204904  2.319366  1.120025  1.000205   \n",
       "9      4.500279  1.110888  0.501287  0.864296  1.295929  1.231113  1.440026   \n",
       "10     3.607531  1.307779  0.259581  0.391142  0.695549  1.595557  0.997318   \n",
       "11     2.917574  2.136057  0.138887  0.257412  0.561786  2.018466  3.490456   \n",
       "12     3.102780  2.314464  0.176808  0.547338  0.708589  2.440352  3.991472   \n",
       "13     1.965926  0.561273 -0.005751  0.096306  0.473924  1.403003  3.262664   \n",
       "14     1.974271  0.523750  0.011591  0.509258  0.398956  1.431206  1.082963   \n",
       "15     2.935244  1.263208  0.311706  0.470500  0.637478  1.244601  3.384715   \n",
       "16     2.272854  1.132937  0.993208  0.823052  1.107362  1.159070  5.232080   \n",
       "17     1.578901  0.193264  0.942222  0.568785  0.806032 -0.049434  0.840461   \n",
       "18     1.682883  0.093634  1.017236  0.769005  0.556100 -0.304349 -0.872161   \n",
       "19     1.697308 -0.057689  0.841856 -0.100141  0.101079 -0.377579 -1.364567   \n",
       "20     0.075015 -0.224686  0.246476 -0.137154 -0.067799 -0.416693 -0.037391   \n",
       "21     0.257678 -0.210485  0.226562 -0.196538 -0.030496 -0.302106  0.612491   \n",
       "22     0.130064 -0.158961  0.243463 -0.045788  0.076589 -0.291254  1.622272   \n",
       "23     0.420877 -0.070835  0.173334  0.104410 -0.105083 -0.403821  0.012841   \n",
       "24     0.519684 -0.205990  0.065469  0.000481 -0.210042 -0.322735  2.164962   \n",
       "25     0.441386 -0.379722  0.115202 -0.071168 -0.128073 -0.242685  0.686125   \n",
       "26     0.647019 -0.344958 -0.020211  0.037492  0.041480 -0.311101 -0.440211   \n",
       "27     0.553071 -0.431241  0.055999  0.203407 -0.017307 -0.159626 -0.710545   \n",
       "28     0.613927 -0.369299  0.249191  0.103032  0.151684 -0.143658 -0.421228   \n",
       "29     0.668984 -0.344569  0.303104  0.144315  0.260172 -0.263433 -0.478178   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "1726  -0.316072  0.636778 -0.100475 -0.019096 -0.140858 -0.047612  0.065825   \n",
       "1727  -0.308662  0.695916 -0.023702 -0.020962 -0.391996 -0.144435  0.088913   \n",
       "1728  -0.319082  1.033541  0.010405 -0.145132 -0.315097 -0.036305 -0.642797   \n",
       "1729  -0.309124  0.381540  0.185795 -0.136836 -0.229282  0.246630  0.245932   \n",
       "1730  -0.314700  0.270321 -0.085213 -0.264606  0.021274  0.430418  0.333232   \n",
       "1731  -0.230072  0.345132 -0.100981 -0.085412  0.059443  0.844287 -0.014216   \n",
       "1732  -0.192112  0.012177 -0.118078  0.469601  0.204176  1.144327 -0.953774   \n",
       "1733  -0.294890  0.169950 -0.149268  0.573602  0.846926  1.235066 -0.209094   \n",
       "1734  -0.302908  0.056808 -0.225175  0.459068  0.809985  1.280794 -0.368773   \n",
       "1735  -0.296456  0.005686 -0.233821  0.494179  0.630335  1.043744 -0.174953   \n",
       "1736  -0.309928 -0.157811 -0.266298  0.365621  0.430755  0.797209  0.400510   \n",
       "1737  -0.316743 -0.292318 -0.285890  0.086411  0.302937  0.559476 -0.739773   \n",
       "1738  -0.172676 -0.061067  0.201382 -0.049787  0.726568  0.388665 -0.766200   \n",
       "1739  -0.091096 -0.008370 -0.003529  0.004981  0.984788  0.785602 -0.363544   \n",
       "1740  -0.247984  0.028532  0.206156  0.389367  1.979913  1.814447 -0.850065   \n",
       "1741  -0.296175  0.193086  0.191667  0.570785  2.132219  2.693313 -0.331972   \n",
       "1742  -0.294771  1.932717  0.287904  0.615563  2.277461  2.651015  0.301818   \n",
       "1743  -0.263133  2.459865  0.296774  1.371233  1.871376  2.583518 -0.750111   \n",
       "1744  -0.236570  2.516355  0.497827  0.587013  1.569599  2.388857 -0.214951   \n",
       "1745  -0.217183  2.182988  0.221914  0.574396  1.044008  1.498086 -0.200914   \n",
       "1746  -0.202140  2.040176  0.135311  0.325190  1.032722  1.203356 -0.027261   \n",
       "1747  -0.230783  0.696499  0.036276  0.360347  0.962032  0.957352  0.750909   \n",
       "1748  -0.162426  0.338473  0.057790  0.113560  0.904457  0.733281  0.170236   \n",
       "1749  -0.187027  0.007978 -0.103198  0.293588  0.854698  0.546244  0.495842   \n",
       "1750  -0.328447  1.037383 -0.019135  0.254101  0.789321  0.478192  0.449863   \n",
       "1751  -0.285127  1.248970 -0.090739  0.504426  0.366124  0.762015 -0.054668   \n",
       "1752   0.096841  1.201086  0.096875  0.391029 -0.005374  0.786889  0.660461   \n",
       "1753  -0.280901  1.079399 -0.185697  0.918809  0.270442  0.756200 -0.669832   \n",
       "1754  -0.278832  1.638613 -0.171157  0.702245  0.227868  0.567873 -0.001584   \n",
       "1755  -0.272282  0.873851 -0.087899  0.763484  0.008923  0.511721 -0.489962   \n",
       "\n",
       "             7         8         9        10        11        12        13  \\\n",
       "0     2.079150  3.589152 -2.831884  0.298303  4.827728  0.993655  1.501371   \n",
       "1     1.183330  1.782220 -2.476570  0.543958  1.613981  0.658532  1.238401   \n",
       "2     1.307462  1.822397 -2.528166  0.625843  1.669634  0.728841  1.369886   \n",
       "3     1.387761  1.995183 -2.624278  0.707728  1.921902  0.917468  1.632857   \n",
       "4     1.717158  2.298045 -2.747458  0.871499  2.391242  1.049668  1.764342   \n",
       "5     1.327594  1.882637 -2.772164  0.871499  1.757003  0.837542  1.764342   \n",
       "6     1.196381  1.684864 -2.779747  0.789614  1.480387  0.711590  1.632857   \n",
       "7     1.184202  1.684167 -2.770330  0.789614  1.479293  0.743371  1.632857   \n",
       "8     1.248874  1.765668 -2.746335  0.707728  1.591454  0.836860  1.632857   \n",
       "9     1.032204  1.626936 -2.836115  0.462073  1.402410  0.421205  1.106916   \n",
       "10    0.817735  1.322388 -2.552435  0.462073  1.015750  0.469876  0.843946   \n",
       "11    0.659375  1.247816 -2.595857  0.298303  0.925317  0.307040  0.843946   \n",
       "12    0.893552  1.452164 -2.729821  0.625843  1.175599  0.638832  1.106916   \n",
       "13    0.441380  0.913215 -2.556182  0.298303  0.557985  0.092896  0.580976   \n",
       "14    0.628028  1.028071 -2.670472  0.462073  0.678757  0.303319  0.843946   \n",
       "15    0.616242  1.209693 -2.697262  0.216417  0.874217  0.144398  1.106916   \n",
       "16    0.853410  1.429617 -2.789661  0.380188  1.147366  0.379513  1.369886   \n",
       "17    0.519020  1.116752 -2.694846 -0.111123  0.776411  0.018761  0.843946   \n",
       "18    0.569711  1.094486 -2.752516 -0.274894  0.751889 -0.215540  0.580976   \n",
       "19    0.111612  0.743012 -2.483173 -0.520549  0.387472 -0.448558  0.186520   \n",
       "20    0.471424  0.211284  0.281829  0.298303 -0.063520  0.189684 -0.470905   \n",
       "21    0.483773  0.195979  0.378701  0.216417 -0.074711  0.245392 -0.470905   \n",
       "22    0.644746  0.293219  0.502811  0.462073 -0.001885  0.422567 -0.470905   \n",
       "23    0.647999  0.295074  0.993110  0.134532 -0.000230  0.351957 -0.470905   \n",
       "24    0.546925  0.206580  0.932204 -0.193008 -0.068748  0.238630 -0.602391   \n",
       "25    0.605248  0.248523  0.792179 -0.029238 -0.035745  0.358343 -0.602391   \n",
       "26    0.626718  0.275911  2.111985  0.052647 -0.015330  0.393081 -0.602391   \n",
       "27    0.689254  0.340170  1.245464  0.052647  0.033823  0.397282 -0.470905   \n",
       "28    0.787782  0.461782  1.287973  0.052647  0.134393  0.553364 -0.339420   \n",
       "29    0.819002  0.504785  0.902723  0.052647  0.171145  0.579204 -0.339420   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "1726  0.387311  0.256187 -0.394565  0.789614 -0.029904  0.555365  0.449490   \n",
       "1727  0.240462  0.131084 -0.523746  0.462073 -0.121334  0.283897  0.055035   \n",
       "1728  0.363465  0.196454  0.109428  0.707728 -0.074367  0.539541  0.186520   \n",
       "1729  0.351970  0.246553 -0.036280  0.707728 -0.037175  0.534968  0.186520   \n",
       "1730  0.320084  0.152594  0.511282  0.789614 -0.106041  0.546927  0.055035   \n",
       "1731  0.497575  0.352900  0.230856  1.362810  0.044854  0.761334  0.186520   \n",
       "1732  0.831409  0.633167 -0.223601  1.608465  0.284588  1.066914  0.580976   \n",
       "1733  1.115804  0.825331 -0.228478  2.017891  0.468851  1.357720  0.843946   \n",
       "1734  1.069083  0.752544  0.416959  2.017891  0.397502  1.306128  0.712461   \n",
       "1735  0.901219  0.688551  0.249947  1.608465  0.336539  1.071828  0.712461   \n",
       "1736  0.665285  0.577582  0.235102  1.117154  0.234977  0.831933  0.449490   \n",
       "1737  0.407087  0.300483  0.121522  0.871499  0.004024  0.544068  0.055035   \n",
       "1738  0.850063  0.699232  0.313187  1.117154  0.346558  0.861530  0.318005   \n",
       "1739  1.119903  0.853353  0.915767  1.444695  0.496552  1.247634  0.843946   \n",
       "1740  1.485542  1.263019  0.389405  2.263546  0.944916  1.842474  1.895827   \n",
       "1741  1.660971  1.363645  0.178407  2.263546  1.065927  1.949342  2.158797   \n",
       "1742  1.833493  1.539349 -0.109595  2.427317  1.287308  2.307114  2.553252   \n",
       "1743  2.003169  1.638598  0.391163  2.918628  1.418041  2.538006  2.684738   \n",
       "1744  1.816466  1.458238  0.322495  3.246168  1.183324  2.439494  2.684738   \n",
       "1745  1.628966  1.203128  0.932204  2.918628  0.875220  2.066069  2.027312   \n",
       "1746  1.437648  1.070867  0.161537  2.672972  0.726044  1.863745  1.895827   \n",
       "1747  1.416986  1.004493 -0.119488  2.672972  0.654102  1.702473  1.501371   \n",
       "1748  1.126950  0.828243  0.249947  1.854121  0.471526  1.304735  1.369886   \n",
       "1749  0.898991  0.682320 -0.137118  1.280924  0.330735  0.985583  0.712461   \n",
       "1750  0.904545  0.668679 -0.088689  1.199039  0.318017  0.990454  0.975431   \n",
       "1751  0.953170  0.722308 -0.684781  1.444695  0.368412  1.131568  1.106916   \n",
       "1752  0.968912  0.713178 -0.449570  1.444695  0.358028  1.087393  1.106916   \n",
       "1753  1.146386  0.790497  0.260320  1.690350  0.434449  1.296819  1.632857   \n",
       "1754  1.076386  0.736871  0.840002  1.772235  0.382402  1.269851  1.764342   \n",
       "1755  0.854255  0.625068 -0.740370  1.280924  0.277798  0.846061  1.238401   \n",
       "\n",
       "      Label       Location  SubjID  \n",
       "0      True  Gastrocnemius       1  \n",
       "1      True  Gastrocnemius       1  \n",
       "2      True  Gastrocnemius       1  \n",
       "3      True  Gastrocnemius       1  \n",
       "4      True  Gastrocnemius       1  \n",
       "5      True  Gastrocnemius       1  \n",
       "6      True  Gastrocnemius       1  \n",
       "7      True  Gastrocnemius       1  \n",
       "8      True  Gastrocnemius       1  \n",
       "9      True  Gastrocnemius       1  \n",
       "10     True  Gastrocnemius       1  \n",
       "11     True  Gastrocnemius       1  \n",
       "12     True  Gastrocnemius       1  \n",
       "13     True  Gastrocnemius       1  \n",
       "14     True  Gastrocnemius       1  \n",
       "15     True  Gastrocnemius       1  \n",
       "16     True  Gastrocnemius       1  \n",
       "17     True  Gastrocnemius       1  \n",
       "18     True  Gastrocnemius       1  \n",
       "19     True  Gastrocnemius       1  \n",
       "20    False  Gastrocnemius       1  \n",
       "21    False  Gastrocnemius       1  \n",
       "22    False  Gastrocnemius       1  \n",
       "23    False  Gastrocnemius       1  \n",
       "24    False  Gastrocnemius       1  \n",
       "25    False  Gastrocnemius       1  \n",
       "26    False  Gastrocnemius       1  \n",
       "27    False  Gastrocnemius       1  \n",
       "28    False  Gastrocnemius       1  \n",
       "29    False  Gastrocnemius       1  \n",
       "...     ...            ...     ...  \n",
       "1726  False  Gastrocnemius      12  \n",
       "1727  False  Gastrocnemius      12  \n",
       "1728  False  Gastrocnemius      12  \n",
       "1729  False  Gastrocnemius      12  \n",
       "1730  False  Gastrocnemius      12  \n",
       "1731  False  Gastrocnemius      12  \n",
       "1732  False  Gastrocnemius      12  \n",
       "1733  False  Gastrocnemius      12  \n",
       "1734  False  Gastrocnemius      12  \n",
       "1735  False  Gastrocnemius      12  \n",
       "1736  False  Gastrocnemius      12  \n",
       "1737  False  Gastrocnemius      12  \n",
       "1738  False  Gastrocnemius      12  \n",
       "1739  False  Gastrocnemius      12  \n",
       "1740  False  Gastrocnemius      12  \n",
       "1741  False  Gastrocnemius      12  \n",
       "1742  False  Gastrocnemius      12  \n",
       "1743  False  Gastrocnemius      12  \n",
       "1744  False  Gastrocnemius      12  \n",
       "1745  False  Gastrocnemius      12  \n",
       "1746  False  Gastrocnemius      12  \n",
       "1747  False  Gastrocnemius      12  \n",
       "1748  False  Gastrocnemius      12  \n",
       "1749  False  Gastrocnemius      12  \n",
       "1750  False  Gastrocnemius      12  \n",
       "1751  False  Gastrocnemius      12  \n",
       "1752  False  Gastrocnemius      12  \n",
       "1753  False  Gastrocnemius      12  \n",
       "1754  False  Gastrocnemius      12  \n",
       "1755  False  Gastrocnemius      12  \n",
       "\n",
       "[1756 rows x 17 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FiltData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "      <th>8</th>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>True</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>1729</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>1732</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1735</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1736</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1739</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1741</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1742</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1743</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1747</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1748</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1750</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>False</td>\n",
       "      <td>Gastrocnemius</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1756 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Label       Location  SubjID\n",
       "0      True  Gastrocnemius       1\n",
       "1      True  Gastrocnemius       1\n",
       "2      True  Gastrocnemius       1\n",
       "3      True  Gastrocnemius       1\n",
       "4      True  Gastrocnemius       1\n",
       "5      True  Gastrocnemius       1\n",
       "6      True  Gastrocnemius       1\n",
       "7      True  Gastrocnemius       1\n",
       "8      True  Gastrocnemius       1\n",
       "9      True  Gastrocnemius       1\n",
       "10     True  Gastrocnemius       1\n",
       "11     True  Gastrocnemius       1\n",
       "12     True  Gastrocnemius       1\n",
       "13     True  Gastrocnemius       1\n",
       "14     True  Gastrocnemius       1\n",
       "15     True  Gastrocnemius       1\n",
       "16     True  Gastrocnemius       1\n",
       "17     True  Gastrocnemius       1\n",
       "18     True  Gastrocnemius       1\n",
       "19     True  Gastrocnemius       1\n",
       "20    False  Gastrocnemius       1\n",
       "21    False  Gastrocnemius       1\n",
       "22    False  Gastrocnemius       1\n",
       "23    False  Gastrocnemius       1\n",
       "24    False  Gastrocnemius       1\n",
       "25    False  Gastrocnemius       1\n",
       "26    False  Gastrocnemius       1\n",
       "27    False  Gastrocnemius       1\n",
       "28    False  Gastrocnemius       1\n",
       "29    False  Gastrocnemius       1\n",
       "...     ...            ...     ...\n",
       "1726  False  Gastrocnemius      12\n",
       "1727  False  Gastrocnemius      12\n",
       "1728  False  Gastrocnemius      12\n",
       "1729  False  Gastrocnemius      12\n",
       "1730  False  Gastrocnemius      12\n",
       "1731  False  Gastrocnemius      12\n",
       "1732  False  Gastrocnemius      12\n",
       "1733  False  Gastrocnemius      12\n",
       "1734  False  Gastrocnemius      12\n",
       "1735  False  Gastrocnemius      12\n",
       "1736  False  Gastrocnemius      12\n",
       "1737  False  Gastrocnemius      12\n",
       "1738  False  Gastrocnemius      12\n",
       "1739  False  Gastrocnemius      12\n",
       "1740  False  Gastrocnemius      12\n",
       "1741  False  Gastrocnemius      12\n",
       "1742  False  Gastrocnemius      12\n",
       "1743  False  Gastrocnemius      12\n",
       "1744  False  Gastrocnemius      12\n",
       "1745  False  Gastrocnemius      12\n",
       "1746  False  Gastrocnemius      12\n",
       "1747  False  Gastrocnemius      12\n",
       "1748  False  Gastrocnemius      12\n",
       "1749  False  Gastrocnemius      12\n",
       "1750  False  Gastrocnemius      12\n",
       "1751  False  Gastrocnemius      12\n",
       "1752  False  Gastrocnemius      12\n",
       "1753  False  Gastrocnemius      12\n",
       "1754  False  Gastrocnemius      12\n",
       "1755  False  Gastrocnemius      12\n",
       "\n",
       "[1756 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'SubjID':SubjID.as_matrix(),'Label':Label.as_matrix(),'Location':Location.as_matrix()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.281852</td>\n",
       "      <td>-0.047540</td>\n",
       "      <td>6.548987</td>\n",
       "      <td>4.988246</td>\n",
       "      <td>0.694025</td>\n",
       "      <td>1.466070</td>\n",
       "      <td>-0.829564</td>\n",
       "      <td>2.079150</td>\n",
       "      <td>3.589152</td>\n",
       "      <td>-2.831884</td>\n",
       "      <td>0.298303</td>\n",
       "      <td>4.827728</td>\n",
       "      <td>0.993655</td>\n",
       "      <td>1.501371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.098564</td>\n",
       "      <td>0.244480</td>\n",
       "      <td>0.824080</td>\n",
       "      <td>1.724432</td>\n",
       "      <td>1.100442</td>\n",
       "      <td>1.372337</td>\n",
       "      <td>0.322928</td>\n",
       "      <td>1.183330</td>\n",
       "      <td>1.782220</td>\n",
       "      <td>-2.476570</td>\n",
       "      <td>0.543958</td>\n",
       "      <td>1.613981</td>\n",
       "      <td>0.658532</td>\n",
       "      <td>1.238401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.261596</td>\n",
       "      <td>0.124163</td>\n",
       "      <td>0.812885</td>\n",
       "      <td>1.649533</td>\n",
       "      <td>1.403355</td>\n",
       "      <td>1.500909</td>\n",
       "      <td>0.453803</td>\n",
       "      <td>1.307462</td>\n",
       "      <td>1.822397</td>\n",
       "      <td>-2.528166</td>\n",
       "      <td>0.625843</td>\n",
       "      <td>1.669634</td>\n",
       "      <td>0.728841</td>\n",
       "      <td>1.369886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.906466</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.602164</td>\n",
       "      <td>1.398007</td>\n",
       "      <td>1.505170</td>\n",
       "      <td>2.148697</td>\n",
       "      <td>2.292207</td>\n",
       "      <td>1.387761</td>\n",
       "      <td>1.995183</td>\n",
       "      <td>-2.624278</td>\n",
       "      <td>0.707728</td>\n",
       "      <td>1.921902</td>\n",
       "      <td>0.917468</td>\n",
       "      <td>1.632857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.708509</td>\n",
       "      <td>0.784601</td>\n",
       "      <td>0.930508</td>\n",
       "      <td>1.422989</td>\n",
       "      <td>3.506940</td>\n",
       "      <td>1.985700</td>\n",
       "      <td>1.444883</td>\n",
       "      <td>1.717158</td>\n",
       "      <td>2.298045</td>\n",
       "      <td>-2.747458</td>\n",
       "      <td>0.871499</td>\n",
       "      <td>2.391242</td>\n",
       "      <td>1.049668</td>\n",
       "      <td>1.764342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.084682</td>\n",
       "      <td>0.496656</td>\n",
       "      <td>0.206128</td>\n",
       "      <td>1.235235</td>\n",
       "      <td>3.017840</td>\n",
       "      <td>0.812137</td>\n",
       "      <td>-0.582209</td>\n",
       "      <td>1.327594</td>\n",
       "      <td>1.882637</td>\n",
       "      <td>-2.772164</td>\n",
       "      <td>0.871499</td>\n",
       "      <td>1.757003</td>\n",
       "      <td>0.837542</td>\n",
       "      <td>1.764342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.453954</td>\n",
       "      <td>0.256396</td>\n",
       "      <td>0.133891</td>\n",
       "      <td>0.902410</td>\n",
       "      <td>2.317555</td>\n",
       "      <td>0.829720</td>\n",
       "      <td>-0.259576</td>\n",
       "      <td>1.196381</td>\n",
       "      <td>1.684864</td>\n",
       "      <td>-2.779747</td>\n",
       "      <td>0.789614</td>\n",
       "      <td>1.480387</td>\n",
       "      <td>0.711590</td>\n",
       "      <td>1.632857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.316492</td>\n",
       "      <td>0.248869</td>\n",
       "      <td>0.188638</td>\n",
       "      <td>0.892958</td>\n",
       "      <td>2.324661</td>\n",
       "      <td>0.917845</td>\n",
       "      <td>-0.463637</td>\n",
       "      <td>1.184202</td>\n",
       "      <td>1.684167</td>\n",
       "      <td>-2.770330</td>\n",
       "      <td>0.789614</td>\n",
       "      <td>1.479293</td>\n",
       "      <td>0.743371</td>\n",
       "      <td>1.632857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.992381</td>\n",
       "      <td>1.234388</td>\n",
       "      <td>0.226473</td>\n",
       "      <td>1.204904</td>\n",
       "      <td>2.319366</td>\n",
       "      <td>1.120025</td>\n",
       "      <td>1.000205</td>\n",
       "      <td>1.248874</td>\n",
       "      <td>1.765668</td>\n",
       "      <td>-2.746335</td>\n",
       "      <td>0.707728</td>\n",
       "      <td>1.591454</td>\n",
       "      <td>0.836860</td>\n",
       "      <td>1.632857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.500279</td>\n",
       "      <td>1.110888</td>\n",
       "      <td>0.501287</td>\n",
       "      <td>0.864296</td>\n",
       "      <td>1.295929</td>\n",
       "      <td>1.231113</td>\n",
       "      <td>1.440026</td>\n",
       "      <td>1.032204</td>\n",
       "      <td>1.626936</td>\n",
       "      <td>-2.836115</td>\n",
       "      <td>0.462073</td>\n",
       "      <td>1.402410</td>\n",
       "      <td>0.421205</td>\n",
       "      <td>1.106916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.607531</td>\n",
       "      <td>1.307779</td>\n",
       "      <td>0.259581</td>\n",
       "      <td>0.391142</td>\n",
       "      <td>0.695549</td>\n",
       "      <td>1.595557</td>\n",
       "      <td>0.997318</td>\n",
       "      <td>0.817735</td>\n",
       "      <td>1.322388</td>\n",
       "      <td>-2.552435</td>\n",
       "      <td>0.462073</td>\n",
       "      <td>1.015750</td>\n",
       "      <td>0.469876</td>\n",
       "      <td>0.843946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.917574</td>\n",
       "      <td>2.136057</td>\n",
       "      <td>0.138887</td>\n",
       "      <td>0.257412</td>\n",
       "      <td>0.561786</td>\n",
       "      <td>2.018466</td>\n",
       "      <td>3.490456</td>\n",
       "      <td>0.659375</td>\n",
       "      <td>1.247816</td>\n",
       "      <td>-2.595857</td>\n",
       "      <td>0.298303</td>\n",
       "      <td>0.925317</td>\n",
       "      <td>0.307040</td>\n",
       "      <td>0.843946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.102780</td>\n",
       "      <td>2.314464</td>\n",
       "      <td>0.176808</td>\n",
       "      <td>0.547338</td>\n",
       "      <td>0.708589</td>\n",
       "      <td>2.440352</td>\n",
       "      <td>3.991472</td>\n",
       "      <td>0.893552</td>\n",
       "      <td>1.452164</td>\n",
       "      <td>-2.729821</td>\n",
       "      <td>0.625843</td>\n",
       "      <td>1.175599</td>\n",
       "      <td>0.638832</td>\n",
       "      <td>1.106916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.965926</td>\n",
       "      <td>0.561273</td>\n",
       "      <td>-0.005751</td>\n",
       "      <td>0.096306</td>\n",
       "      <td>0.473924</td>\n",
       "      <td>1.403003</td>\n",
       "      <td>3.262664</td>\n",
       "      <td>0.441380</td>\n",
       "      <td>0.913215</td>\n",
       "      <td>-2.556182</td>\n",
       "      <td>0.298303</td>\n",
       "      <td>0.557985</td>\n",
       "      <td>0.092896</td>\n",
       "      <td>0.580976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.974271</td>\n",
       "      <td>0.523750</td>\n",
       "      <td>0.011591</td>\n",
       "      <td>0.509258</td>\n",
       "      <td>0.398956</td>\n",
       "      <td>1.431206</td>\n",
       "      <td>1.082963</td>\n",
       "      <td>0.628028</td>\n",
       "      <td>1.028071</td>\n",
       "      <td>-2.670472</td>\n",
       "      <td>0.462073</td>\n",
       "      <td>0.678757</td>\n",
       "      <td>0.303319</td>\n",
       "      <td>0.843946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.935244</td>\n",
       "      <td>1.263208</td>\n",
       "      <td>0.311706</td>\n",
       "      <td>0.470500</td>\n",
       "      <td>0.637478</td>\n",
       "      <td>1.244601</td>\n",
       "      <td>3.384715</td>\n",
       "      <td>0.616242</td>\n",
       "      <td>1.209693</td>\n",
       "      <td>-2.697262</td>\n",
       "      <td>0.216417</td>\n",
       "      <td>0.874217</td>\n",
       "      <td>0.144398</td>\n",
       "      <td>1.106916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.272854</td>\n",
       "      <td>1.132937</td>\n",
       "      <td>0.993208</td>\n",
       "      <td>0.823052</td>\n",
       "      <td>1.107362</td>\n",
       "      <td>1.159070</td>\n",
       "      <td>5.232080</td>\n",
       "      <td>0.853410</td>\n",
       "      <td>1.429617</td>\n",
       "      <td>-2.789661</td>\n",
       "      <td>0.380188</td>\n",
       "      <td>1.147366</td>\n",
       "      <td>0.379513</td>\n",
       "      <td>1.369886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.578901</td>\n",
       "      <td>0.193264</td>\n",
       "      <td>0.942222</td>\n",
       "      <td>0.568785</td>\n",
       "      <td>0.806032</td>\n",
       "      <td>-0.049434</td>\n",
       "      <td>0.840461</td>\n",
       "      <td>0.519020</td>\n",
       "      <td>1.116752</td>\n",
       "      <td>-2.694846</td>\n",
       "      <td>-0.111123</td>\n",
       "      <td>0.776411</td>\n",
       "      <td>0.018761</td>\n",
       "      <td>0.843946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.682883</td>\n",
       "      <td>0.093634</td>\n",
       "      <td>1.017236</td>\n",
       "      <td>0.769005</td>\n",
       "      <td>0.556100</td>\n",
       "      <td>-0.304349</td>\n",
       "      <td>-0.872161</td>\n",
       "      <td>0.569711</td>\n",
       "      <td>1.094486</td>\n",
       "      <td>-2.752516</td>\n",
       "      <td>-0.274894</td>\n",
       "      <td>0.751889</td>\n",
       "      <td>-0.215540</td>\n",
       "      <td>0.580976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.697308</td>\n",
       "      <td>-0.057689</td>\n",
       "      <td>0.841856</td>\n",
       "      <td>-0.100141</td>\n",
       "      <td>0.101079</td>\n",
       "      <td>-0.377579</td>\n",
       "      <td>-1.364567</td>\n",
       "      <td>0.111612</td>\n",
       "      <td>0.743012</td>\n",
       "      <td>-2.483173</td>\n",
       "      <td>-0.520549</td>\n",
       "      <td>0.387472</td>\n",
       "      <td>-0.448558</td>\n",
       "      <td>0.186520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.075015</td>\n",
       "      <td>-0.224686</td>\n",
       "      <td>0.246476</td>\n",
       "      <td>-0.137154</td>\n",
       "      <td>-0.067799</td>\n",
       "      <td>-0.416693</td>\n",
       "      <td>-0.037391</td>\n",
       "      <td>0.471424</td>\n",
       "      <td>0.211284</td>\n",
       "      <td>0.281829</td>\n",
       "      <td>0.298303</td>\n",
       "      <td>-0.063520</td>\n",
       "      <td>0.189684</td>\n",
       "      <td>-0.470905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.257678</td>\n",
       "      <td>-0.210485</td>\n",
       "      <td>0.226562</td>\n",
       "      <td>-0.196538</td>\n",
       "      <td>-0.030496</td>\n",
       "      <td>-0.302106</td>\n",
       "      <td>0.612491</td>\n",
       "      <td>0.483773</td>\n",
       "      <td>0.195979</td>\n",
       "      <td>0.378701</td>\n",
       "      <td>0.216417</td>\n",
       "      <td>-0.074711</td>\n",
       "      <td>0.245392</td>\n",
       "      <td>-0.470905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.130064</td>\n",
       "      <td>-0.158961</td>\n",
       "      <td>0.243463</td>\n",
       "      <td>-0.045788</td>\n",
       "      <td>0.076589</td>\n",
       "      <td>-0.291254</td>\n",
       "      <td>1.622272</td>\n",
       "      <td>0.644746</td>\n",
       "      <td>0.293219</td>\n",
       "      <td>0.502811</td>\n",
       "      <td>0.462073</td>\n",
       "      <td>-0.001885</td>\n",
       "      <td>0.422567</td>\n",
       "      <td>-0.470905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.420877</td>\n",
       "      <td>-0.070835</td>\n",
       "      <td>0.173334</td>\n",
       "      <td>0.104410</td>\n",
       "      <td>-0.105083</td>\n",
       "      <td>-0.403821</td>\n",
       "      <td>0.012841</td>\n",
       "      <td>0.647999</td>\n",
       "      <td>0.295074</td>\n",
       "      <td>0.993110</td>\n",
       "      <td>0.134532</td>\n",
       "      <td>-0.000230</td>\n",
       "      <td>0.351957</td>\n",
       "      <td>-0.470905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.519684</td>\n",
       "      <td>-0.205990</td>\n",
       "      <td>0.065469</td>\n",
       "      <td>0.000481</td>\n",
       "      <td>-0.210042</td>\n",
       "      <td>-0.322735</td>\n",
       "      <td>2.164962</td>\n",
       "      <td>0.546925</td>\n",
       "      <td>0.206580</td>\n",
       "      <td>0.932204</td>\n",
       "      <td>-0.193008</td>\n",
       "      <td>-0.068748</td>\n",
       "      <td>0.238630</td>\n",
       "      <td>-0.602391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.441386</td>\n",
       "      <td>-0.379722</td>\n",
       "      <td>0.115202</td>\n",
       "      <td>-0.071168</td>\n",
       "      <td>-0.128073</td>\n",
       "      <td>-0.242685</td>\n",
       "      <td>0.686125</td>\n",
       "      <td>0.605248</td>\n",
       "      <td>0.248523</td>\n",
       "      <td>0.792179</td>\n",
       "      <td>-0.029238</td>\n",
       "      <td>-0.035745</td>\n",
       "      <td>0.358343</td>\n",
       "      <td>-0.602391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.647019</td>\n",
       "      <td>-0.344958</td>\n",
       "      <td>-0.020211</td>\n",
       "      <td>0.037492</td>\n",
       "      <td>0.041480</td>\n",
       "      <td>-0.311101</td>\n",
       "      <td>-0.440211</td>\n",
       "      <td>0.626718</td>\n",
       "      <td>0.275911</td>\n",
       "      <td>2.111985</td>\n",
       "      <td>0.052647</td>\n",
       "      <td>-0.015330</td>\n",
       "      <td>0.393081</td>\n",
       "      <td>-0.602391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.553071</td>\n",
       "      <td>-0.431241</td>\n",
       "      <td>0.055999</td>\n",
       "      <td>0.203407</td>\n",
       "      <td>-0.017307</td>\n",
       "      <td>-0.159626</td>\n",
       "      <td>-0.710545</td>\n",
       "      <td>0.689254</td>\n",
       "      <td>0.340170</td>\n",
       "      <td>1.245464</td>\n",
       "      <td>0.052647</td>\n",
       "      <td>0.033823</td>\n",
       "      <td>0.397282</td>\n",
       "      <td>-0.470905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.613927</td>\n",
       "      <td>-0.369299</td>\n",
       "      <td>0.249191</td>\n",
       "      <td>0.103032</td>\n",
       "      <td>0.151684</td>\n",
       "      <td>-0.143658</td>\n",
       "      <td>-0.421228</td>\n",
       "      <td>0.787782</td>\n",
       "      <td>0.461782</td>\n",
       "      <td>1.287973</td>\n",
       "      <td>0.052647</td>\n",
       "      <td>0.134393</td>\n",
       "      <td>0.553364</td>\n",
       "      <td>-0.339420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.668984</td>\n",
       "      <td>-0.344569</td>\n",
       "      <td>0.303104</td>\n",
       "      <td>0.144315</td>\n",
       "      <td>0.260172</td>\n",
       "      <td>-0.263433</td>\n",
       "      <td>-0.478178</td>\n",
       "      <td>0.819002</td>\n",
       "      <td>0.504785</td>\n",
       "      <td>0.902723</td>\n",
       "      <td>0.052647</td>\n",
       "      <td>0.171145</td>\n",
       "      <td>0.579204</td>\n",
       "      <td>-0.339420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>-0.316072</td>\n",
       "      <td>0.636778</td>\n",
       "      <td>-0.100475</td>\n",
       "      <td>-0.019096</td>\n",
       "      <td>-0.140858</td>\n",
       "      <td>-0.047612</td>\n",
       "      <td>0.065825</td>\n",
       "      <td>0.387311</td>\n",
       "      <td>0.256187</td>\n",
       "      <td>-0.394565</td>\n",
       "      <td>0.789614</td>\n",
       "      <td>-0.029904</td>\n",
       "      <td>0.555365</td>\n",
       "      <td>0.449490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>-0.308662</td>\n",
       "      <td>0.695916</td>\n",
       "      <td>-0.023702</td>\n",
       "      <td>-0.020962</td>\n",
       "      <td>-0.391996</td>\n",
       "      <td>-0.144435</td>\n",
       "      <td>0.088913</td>\n",
       "      <td>0.240462</td>\n",
       "      <td>0.131084</td>\n",
       "      <td>-0.523746</td>\n",
       "      <td>0.462073</td>\n",
       "      <td>-0.121334</td>\n",
       "      <td>0.283897</td>\n",
       "      <td>0.055035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>-0.319082</td>\n",
       "      <td>1.033541</td>\n",
       "      <td>0.010405</td>\n",
       "      <td>-0.145132</td>\n",
       "      <td>-0.315097</td>\n",
       "      <td>-0.036305</td>\n",
       "      <td>-0.642797</td>\n",
       "      <td>0.363465</td>\n",
       "      <td>0.196454</td>\n",
       "      <td>0.109428</td>\n",
       "      <td>0.707728</td>\n",
       "      <td>-0.074367</td>\n",
       "      <td>0.539541</td>\n",
       "      <td>0.186520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1729</th>\n",
       "      <td>-0.309124</td>\n",
       "      <td>0.381540</td>\n",
       "      <td>0.185795</td>\n",
       "      <td>-0.136836</td>\n",
       "      <td>-0.229282</td>\n",
       "      <td>0.246630</td>\n",
       "      <td>0.245932</td>\n",
       "      <td>0.351970</td>\n",
       "      <td>0.246553</td>\n",
       "      <td>-0.036280</td>\n",
       "      <td>0.707728</td>\n",
       "      <td>-0.037175</td>\n",
       "      <td>0.534968</td>\n",
       "      <td>0.186520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>-0.314700</td>\n",
       "      <td>0.270321</td>\n",
       "      <td>-0.085213</td>\n",
       "      <td>-0.264606</td>\n",
       "      <td>0.021274</td>\n",
       "      <td>0.430418</td>\n",
       "      <td>0.333232</td>\n",
       "      <td>0.320084</td>\n",
       "      <td>0.152594</td>\n",
       "      <td>0.511282</td>\n",
       "      <td>0.789614</td>\n",
       "      <td>-0.106041</td>\n",
       "      <td>0.546927</td>\n",
       "      <td>0.055035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>-0.230072</td>\n",
       "      <td>0.345132</td>\n",
       "      <td>-0.100981</td>\n",
       "      <td>-0.085412</td>\n",
       "      <td>0.059443</td>\n",
       "      <td>0.844287</td>\n",
       "      <td>-0.014216</td>\n",
       "      <td>0.497575</td>\n",
       "      <td>0.352900</td>\n",
       "      <td>0.230856</td>\n",
       "      <td>1.362810</td>\n",
       "      <td>0.044854</td>\n",
       "      <td>0.761334</td>\n",
       "      <td>0.186520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1732</th>\n",
       "      <td>-0.192112</td>\n",
       "      <td>0.012177</td>\n",
       "      <td>-0.118078</td>\n",
       "      <td>0.469601</td>\n",
       "      <td>0.204176</td>\n",
       "      <td>1.144327</td>\n",
       "      <td>-0.953774</td>\n",
       "      <td>0.831409</td>\n",
       "      <td>0.633167</td>\n",
       "      <td>-0.223601</td>\n",
       "      <td>1.608465</td>\n",
       "      <td>0.284588</td>\n",
       "      <td>1.066914</td>\n",
       "      <td>0.580976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>-0.294890</td>\n",
       "      <td>0.169950</td>\n",
       "      <td>-0.149268</td>\n",
       "      <td>0.573602</td>\n",
       "      <td>0.846926</td>\n",
       "      <td>1.235066</td>\n",
       "      <td>-0.209094</td>\n",
       "      <td>1.115804</td>\n",
       "      <td>0.825331</td>\n",
       "      <td>-0.228478</td>\n",
       "      <td>2.017891</td>\n",
       "      <td>0.468851</td>\n",
       "      <td>1.357720</td>\n",
       "      <td>0.843946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734</th>\n",
       "      <td>-0.302908</td>\n",
       "      <td>0.056808</td>\n",
       "      <td>-0.225175</td>\n",
       "      <td>0.459068</td>\n",
       "      <td>0.809985</td>\n",
       "      <td>1.280794</td>\n",
       "      <td>-0.368773</td>\n",
       "      <td>1.069083</td>\n",
       "      <td>0.752544</td>\n",
       "      <td>0.416959</td>\n",
       "      <td>2.017891</td>\n",
       "      <td>0.397502</td>\n",
       "      <td>1.306128</td>\n",
       "      <td>0.712461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1735</th>\n",
       "      <td>-0.296456</td>\n",
       "      <td>0.005686</td>\n",
       "      <td>-0.233821</td>\n",
       "      <td>0.494179</td>\n",
       "      <td>0.630335</td>\n",
       "      <td>1.043744</td>\n",
       "      <td>-0.174953</td>\n",
       "      <td>0.901219</td>\n",
       "      <td>0.688551</td>\n",
       "      <td>0.249947</td>\n",
       "      <td>1.608465</td>\n",
       "      <td>0.336539</td>\n",
       "      <td>1.071828</td>\n",
       "      <td>0.712461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1736</th>\n",
       "      <td>-0.309928</td>\n",
       "      <td>-0.157811</td>\n",
       "      <td>-0.266298</td>\n",
       "      <td>0.365621</td>\n",
       "      <td>0.430755</td>\n",
       "      <td>0.797209</td>\n",
       "      <td>0.400510</td>\n",
       "      <td>0.665285</td>\n",
       "      <td>0.577582</td>\n",
       "      <td>0.235102</td>\n",
       "      <td>1.117154</td>\n",
       "      <td>0.234977</td>\n",
       "      <td>0.831933</td>\n",
       "      <td>0.449490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>-0.316743</td>\n",
       "      <td>-0.292318</td>\n",
       "      <td>-0.285890</td>\n",
       "      <td>0.086411</td>\n",
       "      <td>0.302937</td>\n",
       "      <td>0.559476</td>\n",
       "      <td>-0.739773</td>\n",
       "      <td>0.407087</td>\n",
       "      <td>0.300483</td>\n",
       "      <td>0.121522</td>\n",
       "      <td>0.871499</td>\n",
       "      <td>0.004024</td>\n",
       "      <td>0.544068</td>\n",
       "      <td>0.055035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738</th>\n",
       "      <td>-0.172676</td>\n",
       "      <td>-0.061067</td>\n",
       "      <td>0.201382</td>\n",
       "      <td>-0.049787</td>\n",
       "      <td>0.726568</td>\n",
       "      <td>0.388665</td>\n",
       "      <td>-0.766200</td>\n",
       "      <td>0.850063</td>\n",
       "      <td>0.699232</td>\n",
       "      <td>0.313187</td>\n",
       "      <td>1.117154</td>\n",
       "      <td>0.346558</td>\n",
       "      <td>0.861530</td>\n",
       "      <td>0.318005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1739</th>\n",
       "      <td>-0.091096</td>\n",
       "      <td>-0.008370</td>\n",
       "      <td>-0.003529</td>\n",
       "      <td>0.004981</td>\n",
       "      <td>0.984788</td>\n",
       "      <td>0.785602</td>\n",
       "      <td>-0.363544</td>\n",
       "      <td>1.119903</td>\n",
       "      <td>0.853353</td>\n",
       "      <td>0.915767</td>\n",
       "      <td>1.444695</td>\n",
       "      <td>0.496552</td>\n",
       "      <td>1.247634</td>\n",
       "      <td>0.843946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740</th>\n",
       "      <td>-0.247984</td>\n",
       "      <td>0.028532</td>\n",
       "      <td>0.206156</td>\n",
       "      <td>0.389367</td>\n",
       "      <td>1.979913</td>\n",
       "      <td>1.814447</td>\n",
       "      <td>-0.850065</td>\n",
       "      <td>1.485542</td>\n",
       "      <td>1.263019</td>\n",
       "      <td>0.389405</td>\n",
       "      <td>2.263546</td>\n",
       "      <td>0.944916</td>\n",
       "      <td>1.842474</td>\n",
       "      <td>1.895827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1741</th>\n",
       "      <td>-0.296175</td>\n",
       "      <td>0.193086</td>\n",
       "      <td>0.191667</td>\n",
       "      <td>0.570785</td>\n",
       "      <td>2.132219</td>\n",
       "      <td>2.693313</td>\n",
       "      <td>-0.331972</td>\n",
       "      <td>1.660971</td>\n",
       "      <td>1.363645</td>\n",
       "      <td>0.178407</td>\n",
       "      <td>2.263546</td>\n",
       "      <td>1.065927</td>\n",
       "      <td>1.949342</td>\n",
       "      <td>2.158797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1742</th>\n",
       "      <td>-0.294771</td>\n",
       "      <td>1.932717</td>\n",
       "      <td>0.287904</td>\n",
       "      <td>0.615563</td>\n",
       "      <td>2.277461</td>\n",
       "      <td>2.651015</td>\n",
       "      <td>0.301818</td>\n",
       "      <td>1.833493</td>\n",
       "      <td>1.539349</td>\n",
       "      <td>-0.109595</td>\n",
       "      <td>2.427317</td>\n",
       "      <td>1.287308</td>\n",
       "      <td>2.307114</td>\n",
       "      <td>2.553252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1743</th>\n",
       "      <td>-0.263133</td>\n",
       "      <td>2.459865</td>\n",
       "      <td>0.296774</td>\n",
       "      <td>1.371233</td>\n",
       "      <td>1.871376</td>\n",
       "      <td>2.583518</td>\n",
       "      <td>-0.750111</td>\n",
       "      <td>2.003169</td>\n",
       "      <td>1.638598</td>\n",
       "      <td>0.391163</td>\n",
       "      <td>2.918628</td>\n",
       "      <td>1.418041</td>\n",
       "      <td>2.538006</td>\n",
       "      <td>2.684738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744</th>\n",
       "      <td>-0.236570</td>\n",
       "      <td>2.516355</td>\n",
       "      <td>0.497827</td>\n",
       "      <td>0.587013</td>\n",
       "      <td>1.569599</td>\n",
       "      <td>2.388857</td>\n",
       "      <td>-0.214951</td>\n",
       "      <td>1.816466</td>\n",
       "      <td>1.458238</td>\n",
       "      <td>0.322495</td>\n",
       "      <td>3.246168</td>\n",
       "      <td>1.183324</td>\n",
       "      <td>2.439494</td>\n",
       "      <td>2.684738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745</th>\n",
       "      <td>-0.217183</td>\n",
       "      <td>2.182988</td>\n",
       "      <td>0.221914</td>\n",
       "      <td>0.574396</td>\n",
       "      <td>1.044008</td>\n",
       "      <td>1.498086</td>\n",
       "      <td>-0.200914</td>\n",
       "      <td>1.628966</td>\n",
       "      <td>1.203128</td>\n",
       "      <td>0.932204</td>\n",
       "      <td>2.918628</td>\n",
       "      <td>0.875220</td>\n",
       "      <td>2.066069</td>\n",
       "      <td>2.027312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746</th>\n",
       "      <td>-0.202140</td>\n",
       "      <td>2.040176</td>\n",
       "      <td>0.135311</td>\n",
       "      <td>0.325190</td>\n",
       "      <td>1.032722</td>\n",
       "      <td>1.203356</td>\n",
       "      <td>-0.027261</td>\n",
       "      <td>1.437648</td>\n",
       "      <td>1.070867</td>\n",
       "      <td>0.161537</td>\n",
       "      <td>2.672972</td>\n",
       "      <td>0.726044</td>\n",
       "      <td>1.863745</td>\n",
       "      <td>1.895827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1747</th>\n",
       "      <td>-0.230783</td>\n",
       "      <td>0.696499</td>\n",
       "      <td>0.036276</td>\n",
       "      <td>0.360347</td>\n",
       "      <td>0.962032</td>\n",
       "      <td>0.957352</td>\n",
       "      <td>0.750909</td>\n",
       "      <td>1.416986</td>\n",
       "      <td>1.004493</td>\n",
       "      <td>-0.119488</td>\n",
       "      <td>2.672972</td>\n",
       "      <td>0.654102</td>\n",
       "      <td>1.702473</td>\n",
       "      <td>1.501371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1748</th>\n",
       "      <td>-0.162426</td>\n",
       "      <td>0.338473</td>\n",
       "      <td>0.057790</td>\n",
       "      <td>0.113560</td>\n",
       "      <td>0.904457</td>\n",
       "      <td>0.733281</td>\n",
       "      <td>0.170236</td>\n",
       "      <td>1.126950</td>\n",
       "      <td>0.828243</td>\n",
       "      <td>0.249947</td>\n",
       "      <td>1.854121</td>\n",
       "      <td>0.471526</td>\n",
       "      <td>1.304735</td>\n",
       "      <td>1.369886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749</th>\n",
       "      <td>-0.187027</td>\n",
       "      <td>0.007978</td>\n",
       "      <td>-0.103198</td>\n",
       "      <td>0.293588</td>\n",
       "      <td>0.854698</td>\n",
       "      <td>0.546244</td>\n",
       "      <td>0.495842</td>\n",
       "      <td>0.898991</td>\n",
       "      <td>0.682320</td>\n",
       "      <td>-0.137118</td>\n",
       "      <td>1.280924</td>\n",
       "      <td>0.330735</td>\n",
       "      <td>0.985583</td>\n",
       "      <td>0.712461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1750</th>\n",
       "      <td>-0.328447</td>\n",
       "      <td>1.037383</td>\n",
       "      <td>-0.019135</td>\n",
       "      <td>0.254101</td>\n",
       "      <td>0.789321</td>\n",
       "      <td>0.478192</td>\n",
       "      <td>0.449863</td>\n",
       "      <td>0.904545</td>\n",
       "      <td>0.668679</td>\n",
       "      <td>-0.088689</td>\n",
       "      <td>1.199039</td>\n",
       "      <td>0.318017</td>\n",
       "      <td>0.990454</td>\n",
       "      <td>0.975431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751</th>\n",
       "      <td>-0.285127</td>\n",
       "      <td>1.248970</td>\n",
       "      <td>-0.090739</td>\n",
       "      <td>0.504426</td>\n",
       "      <td>0.366124</td>\n",
       "      <td>0.762015</td>\n",
       "      <td>-0.054668</td>\n",
       "      <td>0.953170</td>\n",
       "      <td>0.722308</td>\n",
       "      <td>-0.684781</td>\n",
       "      <td>1.444695</td>\n",
       "      <td>0.368412</td>\n",
       "      <td>1.131568</td>\n",
       "      <td>1.106916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>0.096841</td>\n",
       "      <td>1.201086</td>\n",
       "      <td>0.096875</td>\n",
       "      <td>0.391029</td>\n",
       "      <td>-0.005374</td>\n",
       "      <td>0.786889</td>\n",
       "      <td>0.660461</td>\n",
       "      <td>0.968912</td>\n",
       "      <td>0.713178</td>\n",
       "      <td>-0.449570</td>\n",
       "      <td>1.444695</td>\n",
       "      <td>0.358028</td>\n",
       "      <td>1.087393</td>\n",
       "      <td>1.106916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>-0.280901</td>\n",
       "      <td>1.079399</td>\n",
       "      <td>-0.185697</td>\n",
       "      <td>0.918809</td>\n",
       "      <td>0.270442</td>\n",
       "      <td>0.756200</td>\n",
       "      <td>-0.669832</td>\n",
       "      <td>1.146386</td>\n",
       "      <td>0.790497</td>\n",
       "      <td>0.260320</td>\n",
       "      <td>1.690350</td>\n",
       "      <td>0.434449</td>\n",
       "      <td>1.296819</td>\n",
       "      <td>1.632857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>-0.278832</td>\n",
       "      <td>1.638613</td>\n",
       "      <td>-0.171157</td>\n",
       "      <td>0.702245</td>\n",
       "      <td>0.227868</td>\n",
       "      <td>0.567873</td>\n",
       "      <td>-0.001584</td>\n",
       "      <td>1.076386</td>\n",
       "      <td>0.736871</td>\n",
       "      <td>0.840002</td>\n",
       "      <td>1.772235</td>\n",
       "      <td>0.382402</td>\n",
       "      <td>1.269851</td>\n",
       "      <td>1.764342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>-0.272282</td>\n",
       "      <td>0.873851</td>\n",
       "      <td>-0.087899</td>\n",
       "      <td>0.763484</td>\n",
       "      <td>0.008923</td>\n",
       "      <td>0.511721</td>\n",
       "      <td>-0.489962</td>\n",
       "      <td>0.854255</td>\n",
       "      <td>0.625068</td>\n",
       "      <td>-0.740370</td>\n",
       "      <td>1.280924</td>\n",
       "      <td>0.277798</td>\n",
       "      <td>0.846061</td>\n",
       "      <td>1.238401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1756 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2         3         4         5         6   \\\n",
       "0     10.281852 -0.047540  6.548987  4.988246  0.694025  1.466070 -0.829564   \n",
       "1      4.098564  0.244480  0.824080  1.724432  1.100442  1.372337  0.322928   \n",
       "2      4.261596  0.124163  0.812885  1.649533  1.403355  1.500909  0.453803   \n",
       "3      4.906466  0.643078  0.602164  1.398007  1.505170  2.148697  2.292207   \n",
       "4      4.708509  0.784601  0.930508  1.422989  3.506940  1.985700  1.444883   \n",
       "5      5.084682  0.496656  0.206128  1.235235  3.017840  0.812137 -0.582209   \n",
       "6      4.453954  0.256396  0.133891  0.902410  2.317555  0.829720 -0.259576   \n",
       "7      4.316492  0.248869  0.188638  0.892958  2.324661  0.917845 -0.463637   \n",
       "8      3.992381  1.234388  0.226473  1.204904  2.319366  1.120025  1.000205   \n",
       "9      4.500279  1.110888  0.501287  0.864296  1.295929  1.231113  1.440026   \n",
       "10     3.607531  1.307779  0.259581  0.391142  0.695549  1.595557  0.997318   \n",
       "11     2.917574  2.136057  0.138887  0.257412  0.561786  2.018466  3.490456   \n",
       "12     3.102780  2.314464  0.176808  0.547338  0.708589  2.440352  3.991472   \n",
       "13     1.965926  0.561273 -0.005751  0.096306  0.473924  1.403003  3.262664   \n",
       "14     1.974271  0.523750  0.011591  0.509258  0.398956  1.431206  1.082963   \n",
       "15     2.935244  1.263208  0.311706  0.470500  0.637478  1.244601  3.384715   \n",
       "16     2.272854  1.132937  0.993208  0.823052  1.107362  1.159070  5.232080   \n",
       "17     1.578901  0.193264  0.942222  0.568785  0.806032 -0.049434  0.840461   \n",
       "18     1.682883  0.093634  1.017236  0.769005  0.556100 -0.304349 -0.872161   \n",
       "19     1.697308 -0.057689  0.841856 -0.100141  0.101079 -0.377579 -1.364567   \n",
       "20     0.075015 -0.224686  0.246476 -0.137154 -0.067799 -0.416693 -0.037391   \n",
       "21     0.257678 -0.210485  0.226562 -0.196538 -0.030496 -0.302106  0.612491   \n",
       "22     0.130064 -0.158961  0.243463 -0.045788  0.076589 -0.291254  1.622272   \n",
       "23     0.420877 -0.070835  0.173334  0.104410 -0.105083 -0.403821  0.012841   \n",
       "24     0.519684 -0.205990  0.065469  0.000481 -0.210042 -0.322735  2.164962   \n",
       "25     0.441386 -0.379722  0.115202 -0.071168 -0.128073 -0.242685  0.686125   \n",
       "26     0.647019 -0.344958 -0.020211  0.037492  0.041480 -0.311101 -0.440211   \n",
       "27     0.553071 -0.431241  0.055999  0.203407 -0.017307 -0.159626 -0.710545   \n",
       "28     0.613927 -0.369299  0.249191  0.103032  0.151684 -0.143658 -0.421228   \n",
       "29     0.668984 -0.344569  0.303104  0.144315  0.260172 -0.263433 -0.478178   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "1726  -0.316072  0.636778 -0.100475 -0.019096 -0.140858 -0.047612  0.065825   \n",
       "1727  -0.308662  0.695916 -0.023702 -0.020962 -0.391996 -0.144435  0.088913   \n",
       "1728  -0.319082  1.033541  0.010405 -0.145132 -0.315097 -0.036305 -0.642797   \n",
       "1729  -0.309124  0.381540  0.185795 -0.136836 -0.229282  0.246630  0.245932   \n",
       "1730  -0.314700  0.270321 -0.085213 -0.264606  0.021274  0.430418  0.333232   \n",
       "1731  -0.230072  0.345132 -0.100981 -0.085412  0.059443  0.844287 -0.014216   \n",
       "1732  -0.192112  0.012177 -0.118078  0.469601  0.204176  1.144327 -0.953774   \n",
       "1733  -0.294890  0.169950 -0.149268  0.573602  0.846926  1.235066 -0.209094   \n",
       "1734  -0.302908  0.056808 -0.225175  0.459068  0.809985  1.280794 -0.368773   \n",
       "1735  -0.296456  0.005686 -0.233821  0.494179  0.630335  1.043744 -0.174953   \n",
       "1736  -0.309928 -0.157811 -0.266298  0.365621  0.430755  0.797209  0.400510   \n",
       "1737  -0.316743 -0.292318 -0.285890  0.086411  0.302937  0.559476 -0.739773   \n",
       "1738  -0.172676 -0.061067  0.201382 -0.049787  0.726568  0.388665 -0.766200   \n",
       "1739  -0.091096 -0.008370 -0.003529  0.004981  0.984788  0.785602 -0.363544   \n",
       "1740  -0.247984  0.028532  0.206156  0.389367  1.979913  1.814447 -0.850065   \n",
       "1741  -0.296175  0.193086  0.191667  0.570785  2.132219  2.693313 -0.331972   \n",
       "1742  -0.294771  1.932717  0.287904  0.615563  2.277461  2.651015  0.301818   \n",
       "1743  -0.263133  2.459865  0.296774  1.371233  1.871376  2.583518 -0.750111   \n",
       "1744  -0.236570  2.516355  0.497827  0.587013  1.569599  2.388857 -0.214951   \n",
       "1745  -0.217183  2.182988  0.221914  0.574396  1.044008  1.498086 -0.200914   \n",
       "1746  -0.202140  2.040176  0.135311  0.325190  1.032722  1.203356 -0.027261   \n",
       "1747  -0.230783  0.696499  0.036276  0.360347  0.962032  0.957352  0.750909   \n",
       "1748  -0.162426  0.338473  0.057790  0.113560  0.904457  0.733281  0.170236   \n",
       "1749  -0.187027  0.007978 -0.103198  0.293588  0.854698  0.546244  0.495842   \n",
       "1750  -0.328447  1.037383 -0.019135  0.254101  0.789321  0.478192  0.449863   \n",
       "1751  -0.285127  1.248970 -0.090739  0.504426  0.366124  0.762015 -0.054668   \n",
       "1752   0.096841  1.201086  0.096875  0.391029 -0.005374  0.786889  0.660461   \n",
       "1753  -0.280901  1.079399 -0.185697  0.918809  0.270442  0.756200 -0.669832   \n",
       "1754  -0.278832  1.638613 -0.171157  0.702245  0.227868  0.567873 -0.001584   \n",
       "1755  -0.272282  0.873851 -0.087899  0.763484  0.008923  0.511721 -0.489962   \n",
       "\n",
       "            7         8         9         10        11        12        13  \n",
       "0     2.079150  3.589152 -2.831884  0.298303  4.827728  0.993655  1.501371  \n",
       "1     1.183330  1.782220 -2.476570  0.543958  1.613981  0.658532  1.238401  \n",
       "2     1.307462  1.822397 -2.528166  0.625843  1.669634  0.728841  1.369886  \n",
       "3     1.387761  1.995183 -2.624278  0.707728  1.921902  0.917468  1.632857  \n",
       "4     1.717158  2.298045 -2.747458  0.871499  2.391242  1.049668  1.764342  \n",
       "5     1.327594  1.882637 -2.772164  0.871499  1.757003  0.837542  1.764342  \n",
       "6     1.196381  1.684864 -2.779747  0.789614  1.480387  0.711590  1.632857  \n",
       "7     1.184202  1.684167 -2.770330  0.789614  1.479293  0.743371  1.632857  \n",
       "8     1.248874  1.765668 -2.746335  0.707728  1.591454  0.836860  1.632857  \n",
       "9     1.032204  1.626936 -2.836115  0.462073  1.402410  0.421205  1.106916  \n",
       "10    0.817735  1.322388 -2.552435  0.462073  1.015750  0.469876  0.843946  \n",
       "11    0.659375  1.247816 -2.595857  0.298303  0.925317  0.307040  0.843946  \n",
       "12    0.893552  1.452164 -2.729821  0.625843  1.175599  0.638832  1.106916  \n",
       "13    0.441380  0.913215 -2.556182  0.298303  0.557985  0.092896  0.580976  \n",
       "14    0.628028  1.028071 -2.670472  0.462073  0.678757  0.303319  0.843946  \n",
       "15    0.616242  1.209693 -2.697262  0.216417  0.874217  0.144398  1.106916  \n",
       "16    0.853410  1.429617 -2.789661  0.380188  1.147366  0.379513  1.369886  \n",
       "17    0.519020  1.116752 -2.694846 -0.111123  0.776411  0.018761  0.843946  \n",
       "18    0.569711  1.094486 -2.752516 -0.274894  0.751889 -0.215540  0.580976  \n",
       "19    0.111612  0.743012 -2.483173 -0.520549  0.387472 -0.448558  0.186520  \n",
       "20    0.471424  0.211284  0.281829  0.298303 -0.063520  0.189684 -0.470905  \n",
       "21    0.483773  0.195979  0.378701  0.216417 -0.074711  0.245392 -0.470905  \n",
       "22    0.644746  0.293219  0.502811  0.462073 -0.001885  0.422567 -0.470905  \n",
       "23    0.647999  0.295074  0.993110  0.134532 -0.000230  0.351957 -0.470905  \n",
       "24    0.546925  0.206580  0.932204 -0.193008 -0.068748  0.238630 -0.602391  \n",
       "25    0.605248  0.248523  0.792179 -0.029238 -0.035745  0.358343 -0.602391  \n",
       "26    0.626718  0.275911  2.111985  0.052647 -0.015330  0.393081 -0.602391  \n",
       "27    0.689254  0.340170  1.245464  0.052647  0.033823  0.397282 -0.470905  \n",
       "28    0.787782  0.461782  1.287973  0.052647  0.134393  0.553364 -0.339420  \n",
       "29    0.819002  0.504785  0.902723  0.052647  0.171145  0.579204 -0.339420  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "1726  0.387311  0.256187 -0.394565  0.789614 -0.029904  0.555365  0.449490  \n",
       "1727  0.240462  0.131084 -0.523746  0.462073 -0.121334  0.283897  0.055035  \n",
       "1728  0.363465  0.196454  0.109428  0.707728 -0.074367  0.539541  0.186520  \n",
       "1729  0.351970  0.246553 -0.036280  0.707728 -0.037175  0.534968  0.186520  \n",
       "1730  0.320084  0.152594  0.511282  0.789614 -0.106041  0.546927  0.055035  \n",
       "1731  0.497575  0.352900  0.230856  1.362810  0.044854  0.761334  0.186520  \n",
       "1732  0.831409  0.633167 -0.223601  1.608465  0.284588  1.066914  0.580976  \n",
       "1733  1.115804  0.825331 -0.228478  2.017891  0.468851  1.357720  0.843946  \n",
       "1734  1.069083  0.752544  0.416959  2.017891  0.397502  1.306128  0.712461  \n",
       "1735  0.901219  0.688551  0.249947  1.608465  0.336539  1.071828  0.712461  \n",
       "1736  0.665285  0.577582  0.235102  1.117154  0.234977  0.831933  0.449490  \n",
       "1737  0.407087  0.300483  0.121522  0.871499  0.004024  0.544068  0.055035  \n",
       "1738  0.850063  0.699232  0.313187  1.117154  0.346558  0.861530  0.318005  \n",
       "1739  1.119903  0.853353  0.915767  1.444695  0.496552  1.247634  0.843946  \n",
       "1740  1.485542  1.263019  0.389405  2.263546  0.944916  1.842474  1.895827  \n",
       "1741  1.660971  1.363645  0.178407  2.263546  1.065927  1.949342  2.158797  \n",
       "1742  1.833493  1.539349 -0.109595  2.427317  1.287308  2.307114  2.553252  \n",
       "1743  2.003169  1.638598  0.391163  2.918628  1.418041  2.538006  2.684738  \n",
       "1744  1.816466  1.458238  0.322495  3.246168  1.183324  2.439494  2.684738  \n",
       "1745  1.628966  1.203128  0.932204  2.918628  0.875220  2.066069  2.027312  \n",
       "1746  1.437648  1.070867  0.161537  2.672972  0.726044  1.863745  1.895827  \n",
       "1747  1.416986  1.004493 -0.119488  2.672972  0.654102  1.702473  1.501371  \n",
       "1748  1.126950  0.828243  0.249947  1.854121  0.471526  1.304735  1.369886  \n",
       "1749  0.898991  0.682320 -0.137118  1.280924  0.330735  0.985583  0.712461  \n",
       "1750  0.904545  0.668679 -0.088689  1.199039  0.318017  0.990454  0.975431  \n",
       "1751  0.953170  0.722308 -0.684781  1.444695  0.368412  1.131568  1.106916  \n",
       "1752  0.968912  0.713178 -0.449570  1.444695  0.358028  1.087393  1.106916  \n",
       "1753  1.146386  0.790497  0.260320  1.690350  0.434449  1.296819  1.632857  \n",
       "1754  1.076386  0.736871  0.840002  1.772235  0.382402  1.269851  1.764342  \n",
       "1755  0.854255  0.625068 -0.740370  1.280924  0.277798  0.846061  1.238401  \n",
       "\n",
       "[1756 rows x 14 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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