Mayara4.ipynb

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  "name": ""
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Stretching study - 4th round"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Analysis\n",
      "--------\n",
      "\n",
      "According to Mayara:\n",
      "\n",
      "- S\u00e3o tr\u00eas testes que fiz o registro da EMG. Esse arquivo \u00e9 de um teste progressivo at\u00e9 a exaust\u00e3o nos momentos pr\u00e9 (1) e p\u00f3s (2) treinamento.\n",
      "- A compara\u00e7\u00e3o que pensei em fazer \u00e9 pr\u00e9 e p\u00f3s do grupo treinamento e pr\u00e9 e p\u00f3s do grupo controle. Acho que n\u00e3o tem sentido ficar comparando mais do que isso. Nos outros testes as compara\u00e7\u00f5es s\u00e3o as mesmas, o que vai mudar \u00e9 o n\u00famero de est\u00e1gios.\n",
      "- Nesse sinal do teste progressivo (aumento da velocidade a cada 3 minutos), o registro foi feito nos 10 segundos finais de cada est\u00e1gio.\n",
      "- Siglas: E1V12 - \"E\" \u00e9 a sigla para o sujeito, \"1\" \u00e9 o momento (pr\u00e9 treinamento) e \"V12\" \u00e9 a velocidade do est\u00e1gio. Esse sujeito foi at\u00e9 o est\u00e1gio de 18km.h tanto no pr\u00e9 quanto no p\u00f3s treinamento. Por\u00e9m em alguns casos eu n\u00e3o vou ter a mesma quantidade de registros pr\u00e9 e p\u00f3s (\u00e0s vezes pela diferen\u00e7a na velocidade atingida e \u00e0s vezes por perda do sinal)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Import the necessary libraries\n",
      "from __future__ import division, print_function\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas as pd\n",
      "%matplotlib inline\n",
      "from scipy.signal import butter, filtfilt\n",
      "from threshold_detector import threshold_detector\n",
      "from linear_envelope import linear_envelope\n",
      "from tnorm import tnorm"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# file with 3 columns: time, EMG VMO, EMG BF\n",
      "# coding for file names:\n",
      "tasks = ['exhaustion', '1RM', 'limit_time']\n",
      "subjects = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'O', 'P', 'Q']\n",
      "instants = ['1', '2']\n",
      "speeds = ['V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18']\n",
      "extension = '.txt'\n",
      "path2 = r'../X/Mayara/'  # file directory"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Teste de for\u00e7a m\u00e1xima - 1RM"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "VMO_mvic = np.ones((len(subjects), 2))*np.nan\n",
      "BF_mvic  = np.ones((len(subjects), 2))*np.nan\n",
      "# load files\n",
      "for s, subject in enumerate(subjects):\n",
      "    for i, instant in enumerate(instants):\n",
      "        filename = path2 + tasks[1] + '/' + subject + instant + extension\n",
      "        try:\n",
      "            data = np.loadtxt(filename, skiprows=5, unpack=False)\n",
      "            time, VMO, BF = data[:, 0], data[:, 1], data[:, 2]\n",
      "            fs = np.mean(1/np.diff(time))\n",
      "            VMO = linear_envelope(VMO, freq=fs, Fc_bp=[20, 400], Fc_lp=1)       \n",
      "            BF = linear_envelope(BF, freq=fs, Fc_bp=[20, 400], Fc_lp=1)\n",
      "            VMO_mvic[s, i], BF_mvic[s, i] = np.max(VMO), np.max(BF)\n",
      "        except:\n",
      "            print('MVIC file ' + subject + instant + extension + ' does not exist.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "MVIC file J2.txt does not exist.\n",
        "MVIC file Q1.txt does not exist."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "MVIC file Q2.txt does not exist.\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(np.vstack((VMO_mvic, BF_mvic)), columns=['Before', 'After'] )\n",
      "df['Muscle'] = pd.Series(['VMO']*16 + ['BF']*16)\n",
      "df2 = df.groupby('Muscle')\n",
      "df2.boxplot(figsize=(12, 6), fontsize=16)\n",
      "plt.show()\n",
      "print('\\nMVIC for each subject')\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KVy0lIiKvODKgoMaRAfmbrDUDjgyIiPwo6K5NFGzaHyZJvmPs5MT9pk5QXZuIiALf/04z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\nmAyIiAjA/wNwImHpRKISjwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x94146a0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "MVIC for each subject\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Before</th>\n",
        "      <th>After</th>\n",
        "      <th>Muscle</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>  837.162858</td>\n",
        "      <td> 980.398613</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>  170.341555</td>\n",
        "      <td> 700.889965</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>   13.939687</td>\n",
        "      <td> 941.882201</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>  338.916554</td>\n",
        "      <td> 172.767572</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>  408.565214</td>\n",
        "      <td> 297.647593</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>  258.719429</td>\n",
        "      <td> 475.613136</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>  349.811912</td>\n",
        "      <td> 768.095411</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>  739.013224</td>\n",
        "      <td> 701.923188</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>  263.843033</td>\n",
        "      <td> 508.284449</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>  275.330986</td>\n",
        "      <td>        NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>  273.565915</td>\n",
        "      <td> 282.954117</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>  315.769892</td>\n",
        "      <td> 311.008966</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 1120.192178</td>\n",
        "      <td> 516.882177</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>  355.813690</td>\n",
        "      <td> 292.175668</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>   66.558611</td>\n",
        "      <td> 188.867306</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>         NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>   42.832422</td>\n",
        "      <td>  37.980506</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>   75.424037</td>\n",
        "      <td>  45.897486</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>    3.189484</td>\n",
        "      <td>  87.123319</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>   84.194863</td>\n",
        "      <td>  58.812482</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>  252.032743</td>\n",
        "      <td> 224.457884</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>   80.755674</td>\n",
        "      <td>  74.407956</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>   33.679878</td>\n",
        "      <td>  32.344115</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>   47.495952</td>\n",
        "      <td>  74.590355</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>   79.274910</td>\n",
        "      <td>  49.499978</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>  268.836568</td>\n",
        "      <td>        NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>   33.209586</td>\n",
        "      <td>  34.754662</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>  113.246956</td>\n",
        "      <td> 103.574490</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>   78.291163</td>\n",
        "      <td>  61.931095</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>   71.297768</td>\n",
        "      <td>  98.374448</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>  151.404785</td>\n",
        "      <td>  98.299776</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>         NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>32 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "         Before       After Muscle\n",
        "0    837.162858  980.398613    VMO\n",
        "1    170.341555  700.889965    VMO\n",
        "2     13.939687  941.882201    VMO\n",
        "3    338.916554  172.767572    VMO\n",
        "4    408.565214  297.647593    VMO\n",
        "5    258.719429  475.613136    VMO\n",
        "6    349.811912  768.095411    VMO\n",
        "7    739.013224  701.923188    VMO\n",
        "8    263.843033  508.284449    VMO\n",
        "9    275.330986         NaN    VMO\n",
        "10   273.565915  282.954117    VMO\n",
        "11   315.769892  311.008966    VMO\n",
        "12  1120.192178  516.882177    VMO\n",
        "13   355.813690  292.175668    VMO\n",
        "14    66.558611  188.867306    VMO\n",
        "15          NaN         NaN    VMO\n",
        "16    42.832422   37.980506     BF\n",
        "17    75.424037   45.897486     BF\n",
        "18     3.189484   87.123319     BF\n",
        "19    84.194863   58.812482     BF\n",
        "20   252.032743  224.457884     BF\n",
        "21    80.755674   74.407956     BF\n",
        "22    33.679878   32.344115     BF\n",
        "23    47.495952   74.590355     BF\n",
        "24    79.274910   49.499978     BF\n",
        "25   268.836568         NaN     BF\n",
        "26    33.209586   34.754662     BF\n",
        "27   113.246956  103.574490     BF\n",
        "28    78.291163   61.931095     BF\n",
        "29    71.297768   98.374448     BF\n",
        "30   151.404785   98.299776     BF\n",
        "31          NaN         NaN     BF\n",
        "\n",
        "[32 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Teste de economia de corrida"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Esse teste \u00e9 simplesmente a primeira velocidade do progressivo (V12 de todos os sujeitos), pois s\u00e3o todos os sujeitos correndo na mesma velocidade (a 12km/h)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "EMGs = np.load(path2 + 'EMGS2.npy')\n",
      "T = 7\n",
      "freq = 100\n",
      "time = np.linspace(1/freq, T, T*freq)\n",
      "# shape = (7s * 100 Hz, 7 speeds, 2 instants, 16 subjects, 2 muscles)\n",
      "EMGm = np.mean(EMGs, axis=0)\n",
      "# shape = (7 speeds, 2 instants, 16 subjects, 2 muscles)\n",
      "VMO_V12, BF_V12 = EMGm[0, :, :, 0].T, EMGm[0, :, :, 1].T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Primeiro sem normalizar por 1RM"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(np.vstack((VMO_V12, BF_V12)), columns=['Before', 'After'] )\n",
      "df['Muscle'] = pd.Series(['VMO']*16 + ['BF']*16)\n",
      "df2 = df.groupby('Muscle')\n",
      "df2.boxplot(figsize=(12, 6), fontsize=16)\n",
      "plt.show()\n",
      "print('\\nEMG at V12 for each subject')\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MHXv6REQEgEU/DFXvAKTEe+/ISda+tBHImjsW/SD5+XpHICfee4dIDuzpU0QYoY/ZHvb0\n5etLG4WMuWNPn4iIALDoh5C1T6c/Ve8ASAPOd+1kzR2LPhGRibCnT10i633Z2dOXry9tFDLmjj39\nLuA9ZNp3tj8dd7aH0f90HJHZsOgH4b13tJG1v2l2HDftZM0diz4RkYmwpx/ECP04I5Oxv9kaB3v6\nMo6bEciYO/b0iYgIQDeKvs/nw2233Ybhw4fDbrejsrISXq8XTqcTWVlZyMnJgc/ni2SsPUTVOwAp\nydrfNDuOm3ay5k5z0b/33ntx4403Yt++ffjss88wbNgwuN1uOJ1OHDhwANnZ2XC73ZGMNaJSU1t/\nDQt+AOHXp6bqGy8RUSRo6ukfP34co0ePxpdffhmwftiwYaioqIDFYkFdXR0cDgf2798f+IYG6G+2\nxiFfn84IZM0be/pyjpsRyJi7iPf0v/rqKwwYMADz5s3DmDFjcOedd+KHH35AfX09LBYLAMBisaC+\nvl571EREFHGain5TUxN2796Nu+++G7t370bfvn1DWjmKokBp65dIRNY+XU8RCNP7UhSo4XpiitK6\nPxkW57t2suYuXsuTrFYrrFYrfv3rXwMAbrvtNhQWFiIjIwN1dXXIyMhAbW0t0tPTwz7f5XLBZrMB\nAJKTkzFq1Cg4HA4APycy2stA+O0ej6dL+5ttWUE5ysvD5QeAwxG6v6KiXFV1GN/W/6+uroYRGHnO\nR2r/WF0GVKjqWeZ8F/aP5rLH4/GfONPRnNd8nv6kSZPw8ssvIysrC0uXLkVjYyMAIC0tDQUFBXC7\n3fD5fGF/A9C7v9kah3x9OiOQNW/s6cs5bkYgY+7am3Oai/6nn36KBQsW4MyZM7jkkkuwatUqNDc3\nIy8vD4cOHYLNZkNJSQmSk5M7HUxPknEgjUDWvLHoyzluRiBj7qJS9KMRTM/GEX5g1F+0Ijqzv9nI\nmjcWfTnHzQhkzB2vyCUiIgA80o/a/rFK1rzxSF/OcTMCGXPHI30iIgLAoh8i+HQs6hzmTU4cN+1k\nzR2LPhGRibCnH6X9Y5WseWNPX85xMwIZc8eePhERAWDRDyFrn05vzJucOG7ayZo7Fn0iIhNhTz9K\n+8cqWfPGnr6c42YEMuaOPX0iIgLAoh9C1j6d3pg3OXHcOhZrf0OCRZ+IqB0KRGu/JvhRXh52vQJj\n98XY04/S/rFK1ryxpy/nuBmBjLljT5+IiACw6Idgj1Mb5k1OHDftZM0diz4RkYmwpx+l/WOVrHlj\nT1/OcTMCGXPHnj4REQFg0Q8ha59Ob8ybnDhu2smau3i9A9BL6wUXXdn/5/8SEcmKPf0o7R+rZM0b\ne/pyjpsRKF28wDYlBfB6oxNLZ7U350x7pE9E1Bln++En6w9G9vSDyNqn0xvzJieOW3eoegegCYs+\nEZGJsKcfpf1jlax5Y09fznEzMiPniOfpExERgG4W/ebmZowePRq5ubkAAK/XC6fTiaysLOTk5MDn\n80UkyJ7EHqc2zJucOG7a5eereoegSbeK/jPPPAO73Q7l/+c0ud1uOJ1OHDhwANnZ2XC73REJkojI\naFwuvSPQRnNP/8iRI3C5XHj44YexfPlybNiwAcOGDUNFRQUsFgvq6urgcDiwf//+wDc0QH+zNQ72\nOLWQNW9m7+l3+WRzwBgDZzBKO3nUfYx/ISrn6S9atAhPPvkkTpw44V9XX18Pi8UCALBYLKivr9f6\n8kQUQQpE139YRy8caRmpsGulqb2zceNGpKenY/To0WdNgqIo7f5UNCr2OLVh3uTEcdNO1txpOtL/\n4IMPsH79emzevBmnTp3CiRMnMGfOHH9bJyMjA7W1tUhPTw/7fJfLBZvNBgBITk7GqFGj4HA4APyc\nyGgvA+G3ezyeLu1vtmVAhaqGyw+6tH/0x7f1/6urq2EERp7zkdrfbMttjBCPx+PxnzjT0Zzv9nn6\nFRUVeOqpp7BhwwY8+OCDSEtLQ0FBAdxuN3w+X8iXuYbob0LO+2kYAXv6cr33zzHIOW6kTdTvvdPW\nxnnooYeQl5eHlStXwmazoaSkJBIvHxWxdj8NIqLOMO0VuWejKCqEcOgdhmGd7Yeiqqq/aOl0vH9P\n45G+nONmZGfLnRHwilwiIgLAI/0QPMJpn6y9YR7pyzlupA2P9ImICACLfghZ76eht+DT2EgOHDft\nZM0di34QWe+nQUTUGezpU5fI2htmT1/OcSNt+DdyKaK6cmFbSkr04iCirmN7J4isfbqeIkT4B6CG\nXc+rmI2N8107WXPHok9EZCLs6QdZurT1QV1j9B4we/rs6ZtJe3OORT8IJ7s2Rs8biz6Lvpnw4qwu\nUfUOQFKq3gGQBrL2pY1A1tyx6FNE5OfrHQERdQbbO0H4a21sYnuH7R0zYXuHiIgAsOiH4L13tJG1\nv2l2HDftZM0di34Q3nuHiGIZe/pkCuzps6dvJuzpU9TxgjbjU5TOP3jPpNjFoh9E1j6d3pYtU/UO\ngdrBeyZFnqy1gkWfiMhEWPSDqKpD7xAk5dA7ANLEoXcA0nI4HHqHoAm/yA3CL7C0MXrezP5F7tkY\nfdxIG36R2yWq3gFIStU7ANJE1TsAabGnT6bGe+/IieNmPmzvBOGvu7GJ7R0yE7Z3iIgIgMaif/jw\nYUyePBmXXXYZLr/8cjz77LMAAK/XC6fTiaysLOTk5MDn80U02J7Ae+90nqIoZ32QHGTtSxuBrLnT\nVPQTEhKwYsUK7N27Fzt27MALL7yAffv2we12w+l04sCBA8jOzobb7Y50vFHxy2JVXDyZxauThBD+\nR3l5ecAyERlTRHr6U6dOxcKFC7Fw4UJUVFTAYrGgrq4ODocD+/fvD3xD9jdJB+zpk5lEtadfXV2N\nTz75BOPGjUN9fT0sFgsAwGKxoL6+vrsvT0RRxHsmmU+3iv7333+PadOm4ZlnnkG/fv0CtsnaHpG1\nT6c35k1OvGeSdrLO+XitT/zpp58wbdo0zJkzB1OnTgUAf1snIyMDtbW1SE9PD/tcl8sFm80GAEhO\nTsaoUaP8lzS3JVKvZY/Ho+v7y7rcxkjxqKqK6upqGIGR5zyXtS23MUI8Ho/Hf+JMR3NeU09fCIH8\n/HykpaVhxYoV/vUPPvgg0tLSUFBQALfbDZ/PF/JlLvubpAf29MPjdSmxqb05p6nob9++HZMmTcIV\nV1zhb+EUFhZi7NixyMvLw6FDh2Cz2VBSUoLk5OROB0MULSz64bHox6aIF/1oBWMEqqr6f22izjN6\n3lj0w1MUFUI49A5DSkae87wil4jC4r13zIdH+mQKPNInM+GRPhERAWDRDxF8OhZ1DvMmJ46bdrLm\njkWfiMhE2NMnU2BPn8yEPX0iCov33jEfFv0gsvbp9Ma8yYn33tFO1jnPok9EZCLs6ZMpsKcfHm/D\nEJvY0yciIgAs+iFk7dPpjXmTlap3ANKSdc6z6BOZGO+9Yz7s6ZMpsKdPZsKePhERAWDRDyFrn05v\nzJucOG7ayZo7Fn0iIhNhT59MgT19MhP29IkoLN57x3xY9IPI2qfTG/MmJ957RztZ5zyLPhGRibCn\nT6bAnn54vPdObGJPn4iIALDoh5C1T6c35k1Wqt4BSEvWOc+iT2RivPeO+bCnT6bAnj6ZCXv6REQE\nIApFv6ysDMOGDcPQoUPxxBNPRPrlo07WPp3emDc5cdy0kzV3ES36zc3NWLhwIcrKylBVVYW1a9di\n3759kXyLqPN4PHqHICXmTU4cN+1kzV1Ei/7OnTsxZMgQ2Gw2JCQk4Pbbb0dpaWkk3yLqfD6f3iFI\niXmTE8dNO1lzF9GiX1NTg0GDBvmXrVYrampqIvkWRBRBknYoqBsiWvQVRYnky+miurpa7xCkxLzJ\nqaKiWu8QpCXrnI+P5ItlZmbi8OHD/uXDhw/DarUG7DNy5EjD/3AoLi7WOwQpGTlvI0eO1PW9jTzn\nFcW442Z0Rp3z7c33iJ6n39TUhEsvvRRbt27FBRdcgLFjx2Lt2rUYPnx4pN6CiIi6IaJH+vHx8Xj+\n+edx3XXXobm5GfPnz2fBJyIykB6/IpeIiPQj1RW5RUVFiIuL8z/i4+NhtVrx29/+FgcOHND0mo8/\n/jguvPBCJCQkYMyYMRGOWC533nkn4uLi8Kc//Slk2/79+3HNNdfg/PPPR1xcHEpLS/H000/jrbfe\n0iFSc+B8jz5TznkhkVWrVglFUcQbb7whKisrxQcffCBWr14thg4dKqxWqzh+/HiXXq+yslIoiiIK\nCgrEhx9+KP7zn/9EKXLja2xsFOedd57o06ePsFgsoqmpKWD7DTfcIC666CLxzjvviMrKStHQ0CAG\nDx4s5syZo1PEsY/zPbrMOuelLPoHDx4MWP/ee+8JRVFEWVlZl16vqKhIKIoivvzyy4jEd+rUqYi8\njh7+8Y9/CEVRxHPPPScURREbN24M2G6z2UR+fn7AusGDB4vZs2dHNI7Tp09H9PVkxvkeXWad8zFR\n9NuOYDZs2OBf5/F4RG5urkhJSRHnnHOOmDBhgnj//ff926+++mqhKErAY9myZUIIIY4fPy7+8Ic/\niIEDB4rExERx6aWXihUrVgS8Z3l5uVAURbz55ptiwYIFon///iI5Odm//W9/+5u44oorRFJSkujf\nv7+YP3++8Hq90UhLRFx33XXCbreLlpYWkZmZKaZPny6E+PlzBj9sNlvIunnz5vlfr6P8CyFEfn6+\nsFqt4oMPPhDjx48X55xzjrj33nt79HMbGed7dJl1zktZ9D///HPx008/iVOnTomqqiqRnZ0tMjIy\nxMmTJ4UQQnz88ceiT58+YuLEieKNN94QmzdvFjfddJNITEwUH3/8sRBCiKqqKrF48WKhKIpYt26d\nqKysFDU1NaK5uVl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       "text": [
        "<matplotlib.figure.Figure at 0x8bfecc0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "EMG at V12 for each subject\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Before</th>\n",
        "      <th>After</th>\n",
        "      <th>Muscle</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 104.618331</td>\n",
        "      <td>        NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>  41.241523</td>\n",
        "      <td>  77.707323</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>  71.486534</td>\n",
        "      <td>        NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>   0.063005</td>\n",
        "      <td>  48.973382</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>  62.324798</td>\n",
        "      <td>        NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>  41.705496</td>\n",
        "      <td>        NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>  53.954216</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>  22.756144</td>\n",
        "      <td>  41.791918</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td> 101.449719</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>  75.445380</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>  41.504527</td>\n",
        "      <td>  45.137558</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>  84.274414</td>\n",
        "      <td>  46.324496</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>  14.923344</td>\n",
        "      <td>        NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>  28.973847</td>\n",
        "      <td>  45.761023</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>  89.119608</td>\n",
        "      <td>  77.073476</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>  62.247480</td>\n",
        "      <td>  64.245251</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>  45.619374</td>\n",
        "      <td>  63.299563</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>  63.691440</td>\n",
        "      <td>        NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>  72.558155</td>\n",
        "      <td>  60.380550</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td> 104.623242</td>\n",
        "      <td>  85.258892</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>  42.269803</td>\n",
        "      <td>        NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>  39.316108</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>  21.834636</td>\n",
        "      <td>  70.852438</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>  75.131907</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>  57.294630</td>\n",
        "      <td>  60.530128</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>  36.326505</td>\n",
        "      <td>  29.323464</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>  58.894914</td>\n",
        "      <td>  35.831795</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>  32.629510</td>\n",
        "      <td>        NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>  46.563826</td>\n",
        "      <td>  22.498294</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>  39.411611</td>\n",
        "      <td>  30.095354</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>32 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "        Before       After Muscle\n",
        "0   104.618331         NaN    VMO\n",
        "1          NaN         NaN    VMO\n",
        "2    41.241523   77.707323    VMO\n",
        "3    71.486534         NaN    VMO\n",
        "4     0.063005   48.973382    VMO\n",
        "5    62.324798         NaN    VMO\n",
        "6    41.705496         NaN    VMO\n",
        "7          NaN   53.954216    VMO\n",
        "8    22.756144   41.791918    VMO\n",
        "9          NaN  101.449719    VMO\n",
        "10         NaN   75.445380    VMO\n",
        "11   41.504527   45.137558    VMO\n",
        "12   84.274414   46.324496    VMO\n",
        "13   14.923344         NaN    VMO\n",
        "14   28.973847   45.761023    VMO\n",
        "15   89.119608   77.073476    VMO\n",
        "16   62.247480   64.245251     BF\n",
        "17         NaN         NaN     BF\n",
        "18   45.619374   63.299563     BF\n",
        "19   63.691440         NaN     BF\n",
        "20   72.558155   60.380550     BF\n",
        "21  104.623242   85.258892     BF\n",
        "22   42.269803         NaN     BF\n",
        "23         NaN   39.316108     BF\n",
        "24   21.834636   70.852438     BF\n",
        "25         NaN   75.131907     BF\n",
        "26   57.294630   60.530128     BF\n",
        "27   36.326505   29.323464     BF\n",
        "28   58.894914   35.831795     BF\n",
        "29   32.629510         NaN     BF\n",
        "30   46.563826   22.498294     BF\n",
        "31   39.411611   30.095354     BF\n",
        "\n",
        "[32 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Com normaliza\u00e7\u00e3o por 1RM"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "VMO_V12n = VMO_V12 / VMO_mvic\n",
      "BF_V12n  = BF_V12  / BF_mvic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(np.vstack((VMO_V12n, BF_V12n)), columns=['Before', 'After'] )\n",
      "df['Muscle'] = pd.Series(['VMO']*16 + ['BF']*16)\n",
      "df2 = df.groupby('Muscle')\n",
      "df2.boxplot(figsize=(12, 6), fontsize=16)\n",
      "plt.show()\n",
      "print('\\nEMG at V12 for each subject')\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x93e0be0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "EMG at V12 for each subject\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Before</th>\n",
        "      <th>After</th>\n",
        "      <th>Muscle</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>  0.124968</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>  2.958569</td>\n",
        "      <td> 0.082502</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>  0.210927</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>  0.000154</td>\n",
        "      <td> 0.164535</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>  0.240897</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>  0.119223</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>       NaN</td>\n",
        "      <td> 0.076866</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>  0.086249</td>\n",
        "      <td> 0.082222</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>       NaN</td>\n",
        "      <td> 0.266635</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>  0.131439</td>\n",
        "      <td> 0.145133</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>  0.075232</td>\n",
        "      <td> 0.089623</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>  0.041941</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>  0.435313</td>\n",
        "      <td> 0.242292</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>  1.453279</td>\n",
        "      <td> 1.691532</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 14.303056</td>\n",
        "      <td> 0.726551</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>  0.756477</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>  0.287892</td>\n",
        "      <td> 0.269006</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>  1.295553</td>\n",
        "      <td> 1.145830</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>  1.255046</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>       NaN</td>\n",
        "      <td> 0.527094</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>  0.275429</td>\n",
        "      <td> 1.431363</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>  1.725244</td>\n",
        "      <td> 1.741641</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>  0.320772</td>\n",
        "      <td> 0.283115</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>  0.752255</td>\n",
        "      <td> 0.578575</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>  0.457651</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>  0.307545</td>\n",
        "      <td> 0.228874</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>32 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "       Before     After Muscle\n",
        "0    0.124968       NaN    VMO\n",
        "1         NaN       NaN    VMO\n",
        "2    2.958569  0.082502    VMO\n",
        "3    0.210927       NaN    VMO\n",
        "4    0.000154  0.164535    VMO\n",
        "5    0.240897       NaN    VMO\n",
        "6    0.119223       NaN    VMO\n",
        "7         NaN  0.076866    VMO\n",
        "8    0.086249  0.082222    VMO\n",
        "9         NaN       NaN    VMO\n",
        "10        NaN  0.266635    VMO\n",
        "11   0.131439  0.145133    VMO\n",
        "12   0.075232  0.089623    VMO\n",
        "13   0.041941       NaN    VMO\n",
        "14   0.435313  0.242292    VMO\n",
        "15        NaN       NaN    VMO\n",
        "16   1.453279  1.691532     BF\n",
        "17        NaN       NaN     BF\n",
        "18  14.303056  0.726551     BF\n",
        "19   0.756477       NaN     BF\n",
        "20   0.287892  0.269006     BF\n",
        "21   1.295553  1.145830     BF\n",
        "22   1.255046       NaN     BF\n",
        "23        NaN  0.527094     BF\n",
        "24   0.275429  1.431363     BF\n",
        "25        NaN       NaN     BF\n",
        "26   1.725244  1.741641     BF\n",
        "27   0.320772  0.283115     BF\n",
        "28   0.752255  0.578575     BF\n",
        "29   0.457651       NaN     BF\n",
        "30   0.307545  0.228874     BF\n",
        "31        NaN       NaN     BF\n",
        "\n",
        "[32 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Remove outlier now just for visualization"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "VMO_V12n[VMO_V12n > 4] = np.nan\n",
      "BF_V12n[BF_V12n > 4] = np.nan"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(np.vstack((VMO_V12n, BF_V12n)), columns=['Before', 'After'] )\n",
      "df['Muscle'] = pd.Series(['VMO']*16 + ['BF']*16)\n",
      "df2 = df.groupby('Muscle')\n",
      "df2.boxplot(figsize=(12, 6), fontsize=16)\n",
      "plt.show()\n",
      "print('\\nEMG at V12 for each subject')\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Hjh2Ii4vD448/DgAYN24chg4dqupTf53Nf69evTBjxoyu/xA6xzXvXkZe89IU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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9214c88>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "EMG at V12 for each subject\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Before</th>\n",
        "      <th>After</th>\n",
        "      <th>Muscle</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 0.124968</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 2.958569</td>\n",
        "      <td> 0.082502</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 0.210927</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 0.000154</td>\n",
        "      <td> 0.164535</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 0.240897</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 0.119223</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.076866</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 0.086249</td>\n",
        "      <td> 0.082222</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.266635</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 0.131439</td>\n",
        "      <td> 0.145133</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 0.075232</td>\n",
        "      <td> 0.089623</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 0.041941</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 0.435313</td>\n",
        "      <td> 0.242292</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> VMO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 1.453279</td>\n",
        "      <td> 1.691532</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.726551</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> 0.756477</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> 0.287892</td>\n",
        "      <td> 0.269006</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td> 1.295553</td>\n",
        "      <td> 1.145830</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td> 1.255046</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.527094</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td> 0.275429</td>\n",
        "      <td> 1.431363</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td> 1.725244</td>\n",
        "      <td> 1.741641</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td> 0.320772</td>\n",
        "      <td> 0.283115</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td> 0.752255</td>\n",
        "      <td> 0.578575</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td> 0.457651</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td> 0.307545</td>\n",
        "      <td> 0.228874</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  BF</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>32 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "      Before     After Muscle\n",
        "0   0.124968       NaN    VMO\n",
        "1        NaN       NaN    VMO\n",
        "2   2.958569  0.082502    VMO\n",
        "3   0.210927       NaN    VMO\n",
        "4   0.000154  0.164535    VMO\n",
        "5   0.240897       NaN    VMO\n",
        "6   0.119223       NaN    VMO\n",
        "7        NaN  0.076866    VMO\n",
        "8   0.086249  0.082222    VMO\n",
        "9        NaN       NaN    VMO\n",
        "10       NaN  0.266635    VMO\n",
        "11  0.131439  0.145133    VMO\n",
        "12  0.075232  0.089623    VMO\n",
        "13  0.041941       NaN    VMO\n",
        "14  0.435313  0.242292    VMO\n",
        "15       NaN       NaN    VMO\n",
        "16  1.453279  1.691532     BF\n",
        "17       NaN       NaN     BF\n",
        "18       NaN  0.726551     BF\n",
        "19  0.756477       NaN     BF\n",
        "20  0.287892  0.269006     BF\n",
        "21  1.295553  1.145830     BF\n",
        "22  1.255046       NaN     BF\n",
        "23       NaN  0.527094     BF\n",
        "24  0.275429  1.431363     BF\n",
        "25       NaN       NaN     BF\n",
        "26  1.725244  1.741641     BF\n",
        "27  0.320772  0.283115     BF\n",
        "28  0.752255  0.578575     BF\n",
        "29  0.457651       NaN     BF\n",
        "30  0.307545  0.228874     BF\n",
        "31       NaN       NaN     BF\n",
        "\n",
        "[32 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 31
    }
   ],
   "metadata": {}
  }
 ]
}