notebooks/code002_globalMetrics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>Mapping the emotional face. How individual face parts contribute to successful emotion recognition.</h1></center>\n",
    "\n",
    "# 2. Global Counting  \n",
    "\n",
    "Here, we look at global metrics of each face. The main metrics are %correct and %tiles revealed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from myBasics import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logList = getFile('../rawTables/','pand*.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading data of one participant for illustration purposes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testDf = pd.read_csv(logList[-1],\n",
    "            header=0,\n",
    "            index_col=[0,1,2]\n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>cumtime</th>\n",
       "      <th>e</th>\n",
       "      <th>i</th>\n",
       "      <th>button</th>\n",
       "      <th>filename</th>\n",
       "      <th>evaluation</th>\n",
       "      <th>stopRT</th>\n",
       "      <th>choiceRT</th>\n",
       "      <th>maskNum</th>\n",
       "      <th>maskList</th>\n",
       "      <th>#</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ident</th>\n",
       "      <th>express</th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>9</th>\n",
       "      <td>2016-03-10 15:08:11</td>\n",
       "      <td>198.14952</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hap</td>\n",
       "      <td>img/f_hap_cut.png</td>\n",
       "      <td>HIT</td>\n",
       "      <td>12275</td>\n",
       "      <td>16115</td>\n",
       "      <td>13</td>\n",
       "      <td>3-28-29-7-24-44-45-22-10-43-30-18-34</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2016-03-10 15:15:00</td>\n",
       "      <td>607.15364</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hap</td>\n",
       "      <td>img/f_hap_cut.png</td>\n",
       "      <td>HIT</td>\n",
       "      <td>19242</td>\n",
       "      <td>20994</td>\n",
       "      <td>20</td>\n",
       "      <td>40-15-0-37-1-27-45-46-44-23-18-43-22-3-41-29-1...</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2016-03-10 15:18:36</td>\n",
       "      <td>823.46331</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hap</td>\n",
       "      <td>img/f_hap_cut.png</td>\n",
       "      <td>HIT</td>\n",
       "      <td>16303</td>\n",
       "      <td>17847</td>\n",
       "      <td>17</td>\n",
       "      <td>9-44-23-35-39-14-1-40-46-19-28-20-26-37-17-21-30</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2016-03-10 15:20:31</td>\n",
       "      <td>937.80766</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hap</td>\n",
       "      <td>img/f_hap_cut.png</td>\n",
       "      <td>HIT</td>\n",
       "      <td>5062</td>\n",
       "      <td>7262</td>\n",
       "      <td>6</td>\n",
       "      <td>36-29-47-3-43-20</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2016-03-10 15:23:36</td>\n",
       "      <td>1123.17332</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hap</td>\n",
       "      <td>img/f_hap_cut.png</td>\n",
       "      <td>HIT</td>\n",
       "      <td>5377</td>\n",
       "      <td>6801</td>\n",
       "      <td>6</td>\n",
       "      <td>19-20-16-39-13-44</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 time     cumtime  e  i button  \\\n",
       "ident express id                                                 \n",
       "0     0       9   2016-03-10 15:08:11   198.14952  0  0    hap   \n",
       "              27  2016-03-10 15:15:00   607.15364  0  0    hap   \n",
       "              37  2016-03-10 15:18:36   823.46331  0  0    hap   \n",
       "              43  2016-03-10 15:20:31   937.80766  0  0    hap   \n",
       "              58  2016-03-10 15:23:36  1123.17332  0  0    hap   \n",
       "\n",
       "                           filename evaluation  stopRT  choiceRT  maskNum  \\\n",
       "ident express id                                                            \n",
       "0     0       9   img/f_hap_cut.png        HIT   12275     16115       13   \n",
       "              27  img/f_hap_cut.png        HIT   19242     20994       20   \n",
       "              37  img/f_hap_cut.png        HIT   16303     17847       17   \n",
       "              43  img/f_hap_cut.png        HIT    5062      7262        6   \n",
       "              58  img/f_hap_cut.png        HIT    5377      6801        6   \n",
       "\n",
       "                                                           maskList   #  \n",
       "ident express id                                                         \n",
       "0     0       9                3-28-29-7-24-44-45-22-10-43-30-18-34   9  \n",
       "              27  40-15-0-37-1-27-45-46-44-23-18-43-22-3-41-29-1...  27  \n",
       "              37   9-44-23-35-39-14-1-40-46-19-28-20-26-37-17-21-30  37  \n",
       "              43                                   36-29-47-3-43-20  43  \n",
       "              58                                  19-20-16-39-13-44  58  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testDf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get number of revealed tiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getMaskNums(df):\n",
    "    # pre-formatted dict to write to\n",
    "    d = {0:{'hap':{}, 'sad':{}, 'ang':{}, 'fea':{}, 'dis':{}, 'sup':{}, 'ntr':{} },\n",
    "         1:{'hap':{}, 'sad':{}, 'ang':{}, 'fea':{}, 'dis':{}, 'sup':{}, 'ntr':{} }\n",
    "        }\n",
    "\n",
    "    # loop trough all rows\n",
    "    for i,ident in enumerate(df.index.labels[0]):\n",
    "        # get the current emotion and trial number\n",
    "        express = df.index.labels[1][i]\n",
    "        trial =  df.index.labels[2][i]\n",
    "        # get the string emotion name corresponding to the number\n",
    "        actual = myLabels[express]\n",
    "        # get the decision\n",
    "        decision = df.ix[ident].ix[express].ix[trial][\"button\"]\n",
    "        # get the number of revealed tiles\n",
    "        maskNum = df.ix[ident].ix[express].ix[trial][\"maskNum\"]\n",
    "    \n",
    "        # put number of revealed tiles into dict, according to what face it\n",
    "        # belongs to and what answer was given\n",
    "        try:\n",
    "            d[ident][actual][decision].append(maskNum)\n",
    "        except:\n",
    "            d[ident][actual][decision] = [maskNum]\n",
    "\n",
    "    return d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example:  \n",
    "\n",
    "Get all the number of revealed tiles for each trial for the male (1) fearful (fea) face. Here we see that we not only get the number of revealed tiles for each trial, but also information about what answer was given in that trial. In the present example, most of the 16 trials of the male fearful face were (correctly) labelled as fearful and in three cases, fear was misclassified as surprised (a plausible mistake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dis': [8.0],\n",
       " 'fea': [19.0, 21.0, 18.0, 5.0, 9.0, 20.0, 9.0, 7.0, 6.0, 8.0],\n",
       " 'sad': [4.0, 6.0, 4.0],\n",
       " 'sup': [18.0, 12.0]}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getMaskNums(testDf)[1]['fea']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### average across conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def getAvgMaskNums(d,pName):\n",
    "    # i\n",
    "    newD = {0:{'hap':{}, 'sad':{}, 'ang':{}, 'fea':{}, 'dis':{}, 'sup':{}, 'ntr':{} },\n",
    "            1:{'hap':{}, 'sad':{}, 'ang':{}, 'fea':{}, 'dis':{}, 'sup':{}, 'ntr':{} }\n",
    "        }\n",
    "\n",
    "    for ident in newD:\n",
    "        # what emotion does the face have\n",
    "        for actual in newD[ident]:\n",
    "            # here, we can loop through the same keys for the type of answer\n",
    "            for decision in newD[ident]:\n",
    "                # since some keys may not be there, we have to try:\n",
    "                try:\n",
    "                    newD[ident][actual][decision] = np.array(d[ident][actual][decision]).mean()\n",
    "                except:\n",
    "                    newD[ident][actual][decision] = np.nan\n",
    "    \n",
    "    # seperately for the female and male face\n",
    "    fDf = pd.DataFrame(newD[0])\n",
    "    fDf.index = [[pName]*7,['f']*7, fDf.index]\n",
    "    mDf = pd.DataFrame(newD[1])\n",
    "    mDf.index = [[pName]*7,['m']*7,mDf.index]\n",
    "\n",
    "    rawDf = pd.concat([ fDf,mDf])\n",
    "    return rawDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example: \n",
    "\n",
    "Show the average number of revealed tiles for one participant, across all trial for the fearful face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "test  f  ang    16.800000\n",
       "         dis          NaN\n",
       "         fea    15.000000\n",
       "         hap          NaN\n",
       "         ntr          NaN\n",
       "         sad     5.500000\n",
       "         sup    11.250000\n",
       "      m  ang          NaN\n",
       "         dis     8.000000\n",
       "         fea    12.200000\n",
       "         hap          NaN\n",
       "         ntr          NaN\n",
       "         sad     4.666667\n",
       "         sup    15.000000\n",
       "Name: fea, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getAvgMaskNums(getMaskNums(testDf),'test')['fea']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do this for all participants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makeBigMask(logList):\n",
    "    \n",
    "    bigDf = pd.DataFrame()\n",
    "\n",
    "    for logFile in logList:\n",
    "        pName = \"p\"+logFile[logFile.rfind('e')+1:logFile.rfind('.')]\n",
    "\n",
    "        df = pd.read_csv(logFile,\n",
    "                header=0,\n",
    "                index_col=[0,1,2]\n",
    "               )\n",
    "\n",
    "        countDf = getMaskNums(df)\n",
    "\n",
    "        thisDf = getAvgMaskNums(countDf,pName)\n",
    "\n",
    "        bigDf = pd.concat([bigDf,thisDf])\n",
    "    \n",
    "    # output as percentage\n",
    "    return bigDf/48.*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bigMask = makeBigMask(logList)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example:  \n",
    "\n",
    "Show the average number of revealed tiles for all participants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">p096</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">m</th>\n",
       "      <th>fea</th>\n",
       "      <td>33.333333</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>25.416667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.250000</td>\n",
       "      <td>23.437500</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.354167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>4.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.916667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sad</th>\n",
       "      <td>39.583333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.722222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.513889</td>\n",
       "      <td>30.416667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sup</th>\n",
       "      <td>39.583333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>24.107143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ang        dis        fea        hap        ntr        sad  \\\n",
       "p096 m fea  33.333333  16.666667  25.416667        NaN  41.250000  23.437500   \n",
       "       hap        NaN        NaN        NaN  21.354167        NaN        NaN   \n",
       "       ntr   4.166667        NaN        NaN        NaN  37.916667        NaN   \n",
       "       sad  39.583333        NaN   9.722222        NaN  29.513889  30.416667   \n",
       "       sup  39.583333        NaN  31.250000        NaN        NaN  10.000000   \n",
       "\n",
       "                  sup  \n",
       "p096 m fea  33.333333  \n",
       "       hap        NaN  \n",
       "       ntr        NaN  \n",
       "       sad        NaN  \n",
       "       sup  24.107143  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigMask.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Restructure the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "stackMask = bigMask.stack(0).unstack(1).unstack(-1).unstack(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"49\" halign=\"left\">f</th>\n",
       "      <th colspan=\"49\" halign=\"left\">m</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">ang</th>\n",
       "      <th colspan=\"7\" halign=\"left\">dis</th>\n",
       "      <th colspan=\"7\" halign=\"left\">fea</th>\n",
       "      <th colspan=\"7\" halign=\"left\">hap</th>\n",
       "      <th colspan=\"7\" halign=\"left\">ntr</th>\n",
       "      <th colspan=\"7\" halign=\"left\">sad</th>\n",
       "      <th colspan=\"7\" halign=\"left\">sup</th>\n",
       "      <th colspan=\"7\" halign=\"left\">ang</th>\n",
       "      <th colspan=\"7\" halign=\"left\">dis</th>\n",
       "      <th colspan=\"7\" halign=\"left\">fea</th>\n",
       "      <th colspan=\"7\" halign=\"left\">hap</th>\n",
       "      <th colspan=\"7\" halign=\"left\">ntr</th>\n",
       "      <th colspan=\"7\" halign=\"left\">sad</th>\n",
       "      <th colspan=\"7\" halign=\"left\">sup</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
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       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
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       "      <th>hap</th>\n",
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       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
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       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
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       "      <th>sad</th>\n",
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       "      <th>hap</th>\n",
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       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
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       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
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       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
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       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
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       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
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       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p092</th>\n",
       "      <td>16.666667</td>\n",
       "      <td>52.083333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>20.370370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.791667</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>27.083333</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>17.708333</td>\n",
       "      <td>24.583333</td>\n",
       "      <td>36.979167</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>33.333333</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>20.833333</td>\n",
       "      <td>25</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.333333</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
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       "      <td>35.416667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>17.777778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.583333</td>\n",
       "      <td>29.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.106061</td>\n",
       "      <td>22.916667</td>\n",
       "      <td>45.833333</td>\n",
       "      <td>14.583333</td>\n",
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       "      <td>33.333333</td>\n",
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       "      <td>31.944444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.416667</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>35.416667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p093</th>\n",
       "      <td>20.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.791667</td>\n",
       "      <td>18.958333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.770833</td>\n",
       "      <td>32.407407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>22.916667</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>21.955128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.583333</td>\n",
       "      <td>28.720238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.807292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.041667</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>20.833333</td>\n",
       "      <td>29.687500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.833333</td>\n",
       "      <td>10.763889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.787879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p094</th>\n",
       "      <td>20.982143</td>\n",
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       "      <td>12.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.875000</td>\n",
       "      <td>20.138889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.166667</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>40.885417</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>29.861111</td>\n",
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       "      <td>13.194444</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.427083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.963542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.166667</td>\n",
       "      <td>22.916667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.880952</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>36.805556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.401042</td>\n",
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       "    <tr>\n",
       "      <th>p095</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>p096</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>33.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>39.583333</td>\n",
       "      <td>39.583333</td>\n",
       "      <td>21.726190</td>\n",
       "      <td>15.104167</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>25.416667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.722222</td>\n",
       "      <td>31.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.354167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.916667</td>\n",
       "      <td>29.513889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.625000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.437500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.416667</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.416667</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.107143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              f                                                                \\\n",
       "            ang                                                dis              \n",
       "            ang        dis    fea       hap ntr sad sup        ang        dis   \n",
       "p092  16.666667  52.083333    NaN       NaN NaN NaN NaN  14.583333  20.370370   \n",
       "p093  20.000000        NaN    NaN  8.333333 NaN NaN NaN  19.791667  18.958333   \n",
       "p094  20.982143   6.250000  12.50       NaN NaN NaN NaN  21.875000  20.138889   \n",
       "p095  21.614583        NaN    NaN       NaN NaN NaN NaN  12.500000  19.940476   \n",
       "p096  14.791667  12.083333   6.25       NaN NaN NaN NaN  22.500000  16.856061   \n",
       "\n",
       "                                                                      \\\n",
       "                                fea                                    \n",
       "     fea hap ntr sad sup        ang        dis        fea        hap   \n",
       "p092 NaN NaN NaN NaN NaN  19.791667  30.208333  25.000000  28.125000   \n",
       "p093 NaN NaN NaN NaN NaN        NaN  31.770833  32.407407        NaN   \n",
       "p094 NaN NaN NaN NaN NaN        NaN        NaN  44.166667  14.583333   \n",
       "p095 NaN NaN NaN NaN NaN        NaN        NaN  40.000000  22.916667   \n",
       "p096 NaN NaN NaN NaN NaN  35.000000        NaN  31.250000        NaN   \n",
       "\n",
       "                                                                             \\\n",
       "                                      hap                                     \n",
       "            ntr        sad        sup ang dis fea        hap        ntr sad   \n",
       "p092        NaN        NaN  45.833333 NaN NaN NaN  18.472222  29.166667 NaN   \n",
       "p093        NaN  25.000000  25.000000 NaN NaN NaN  12.916667  16.666667 NaN   \n",
       "p094  16.666667   6.250000  37.152778 NaN NaN NaN  17.317708        NaN NaN   \n",
       "p095        NaN        NaN  33.958333 NaN NaN NaN  12.760417        NaN NaN   \n",
       "p096        NaN  11.458333  23.437500 NaN NaN NaN  18.333333  22.916667 NaN   \n",
       "\n",
       "                                                                     \\\n",
       "         ntr                                                          \n",
       "     sup ang        dis        fea        hap        ntr        sad   \n",
       "p092 NaN NaN  40.625000  27.083333        NaN  23.674242  10.416667   \n",
       "p093 NaN NaN        NaN  25.000000        NaN  18.750000        NaN   \n",
       "p094 NaN NaN  54.166667        NaN        NaN  30.833333        NaN   \n",
       "p095 NaN NaN        NaN        NaN        NaN  37.361111        NaN   \n",
       "p096 NaN NaN        NaN        NaN  16.666667  31.666667        NaN   \n",
       "\n",
       "                                                                            \\\n",
       "                       sad                                                   \n",
       "            sup        ang        dis        fea hap        ntr        sad   \n",
       "p092        NaN  17.708333  24.583333  36.979167 NaN  28.125000  33.333333   \n",
       "p093  16.666667        NaN  31.250000  22.916667 NaN  16.666667  21.955128   \n",
       "p094        NaN  30.208333  35.416667  40.885417 NaN  23.958333  43.750000   \n",
       "p095  60.416667        NaN  62.500000  42.500000 NaN        NaN  31.875000   \n",
       "p096        NaN  18.333333        NaN  22.916667 NaN        NaN  23.437500   \n",
       "\n",
       "                                                                     m  \\\n",
       "           sup                                                     ang   \n",
       "       sup ang dis        fea        hap ntr sad        sup        ang   \n",
       "p092  37.5 NaN NaN  16.666667  20.833333  25 NaN  31.410256  26.488095   \n",
       "p093   NaN NaN NaN  40.277778  12.500000 NaN NaN  19.097222  25.000000   \n",
       "p094   NaN NaN NaN  39.583333        NaN NaN NaN  24.444444  33.750000   \n",
       "p095   NaN NaN NaN        NaN        NaN NaN NaN  18.750000  32.142857   \n",
       "p096   NaN NaN NaN  30.208333        NaN NaN NaN  21.279762  25.925926   \n",
       "\n",
       "                                                                            \\\n",
       "                                                                       dis   \n",
       "            dis        fea hap        ntr        sad        sup        ang   \n",
       "p092  23.697917        NaN NaN  16.666667        NaN        NaN  17.187500   \n",
       "p093  28.472222        NaN NaN  29.166667  32.638889        NaN        NaN   \n",
       "p094  27.083333  22.916667 NaN  29.166667  29.861111        NaN  13.194444   \n",
       "p095        NaN  39.583333 NaN        NaN  45.486111        NaN  27.083333   \n",
       "p096  36.458333  33.333333 NaN   4.166667  39.583333  39.583333  21.726190   \n",
       "\n",
       "                                                                              \\\n",
       "                                                   fea                         \n",
       "            dis        fea hap ntr sad       sup   ang        dis        fea   \n",
       "p092  18.229167        NaN NaN NaN NaN       NaN   NaN  33.333333  25.260417   \n",
       "p093  21.354167        NaN NaN NaN NaN       NaN   NaN  14.583333  28.720238   \n",
       "p094  20.512821        NaN NaN NaN NaN       NaN  12.5  22.916667  30.833333   \n",
       "p095  19.618056        NaN NaN NaN NaN  8.333333   NaN        NaN  43.269231   \n",
       "p096  15.104167  16.666667 NaN NaN NaN       NaN   NaN  16.666667  25.416667   \n",
       "\n",
       "                                                                              \\\n",
       "                                   hap                                         \n",
       "     hap ntr        sad        sup ang dis        fea        hap ntr sad sup   \n",
       "p092 NaN  25  32.291667  35.416667 NaN NaN  10.416667  17.777778 NaN NaN NaN   \n",
       "p093 NaN NaN        NaN  18.750000 NaN NaN        NaN  10.807292 NaN NaN NaN   \n",
       "p094 NaN NaN        NaN  29.427083 NaN NaN        NaN  20.963542 NaN NaN NaN   \n",
       "p095 NaN NaN        NaN  36.805556 NaN NaN        NaN  12.890625 NaN NaN NaN   \n",
       "p096 NaN NaN   9.722222  31.250000 NaN NaN        NaN  21.354167 NaN NaN NaN   \n",
       "\n",
       "                                                                     \\\n",
       "     ntr                                                              \n",
       "     ang        dis        fea hap        ntr        sad        sup   \n",
       "p092 NaN  39.583333  29.166667 NaN  23.106061  22.916667  45.833333   \n",
       "p093 NaN        NaN  25.000000 NaN  21.041667  27.500000        NaN   \n",
       "p094 NaN        NaN  16.666667 NaN  29.166667  22.916667        NaN   \n",
       "p095 NaN        NaN  50.000000 NaN  34.935897  72.916667  39.583333   \n",
       "p096 NaN        NaN  41.250000 NaN  37.916667  29.513889        NaN   \n",
       "\n",
       "                                                                            \\\n",
       "            sad                                                              \n",
       "            ang        dis        fea hap        ntr        sad        sup   \n",
       "p092  14.583333  23.750000  33.333333 NaN  31.944444        NaN  10.416667   \n",
       "p093   8.333333  20.833333  29.687500 NaN        NaN  20.833333  10.763889   \n",
       "p094        NaN        NaN  39.880952 NaN  26.388889  36.805556        NaN   \n",
       "p095  25.000000  64.583333  53.958333 NaN        NaN  20.833333        NaN   \n",
       "p096  40.625000        NaN  23.437500 NaN        NaN  30.416667  10.000000   \n",
       "\n",
       "                                                       \n",
       "     sup                                               \n",
       "     ang        dis        fea hap ntr sad        sup  \n",
       "p092 NaN  11.111111  35.416667 NaN NaN NaN  35.714286  \n",
       "p093 NaN  18.750000  25.000000 NaN NaN NaN  28.787879  \n",
       "p094 NaN        NaN        NaN NaN NaN NaN  19.401042  \n",
       "p095 NaN        NaN  33.333333 NaN NaN NaN  26.666667  \n",
       "p096 NaN  10.416667  33.333333 NaN NaN NaN  24.107143  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stackMask.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stackMask.to_csv('../outputs/stackCount.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get % of correct responses:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get response for each trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def getFullCount(df,myLabels=myLabels):\n",
    "    \n",
    "    d = {0:{},1:{}}\n",
    "\n",
    "    for i,ident in enumerate(df.index.labels[0]):\n",
    "        express = df.index.labels[1][i]\n",
    "        trial =  df.index.labels[2][i]\n",
    "\n",
    "        actual = myLabels[express]\n",
    "        decision = df.ix[ident].ix[express].ix[trial][\"button\"]\n",
    "\n",
    "        try:\n",
    "            d[ident][actual].append(decision)\n",
    "        except:\n",
    "            d[ident][actual] = [decision]\n",
    "\n",
    "    fDf = pd.DataFrame(d[0])\n",
    "    fDf.index = [['f']*16, fDf.index]\n",
    "    mDf = pd.DataFrame(d[1])\n",
    "    mDf.index = [['m']*16, mDf.index]\n",
    "    rawDf = pd.concat([ fDf,mDf])\n",
    "    \n",
    "    return rawDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example for one participant:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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></th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">f</th>\n",
       "      <th>0</th>\n",
       "      <td>ang</td>\n",
       "      <td>dis</td>\n",
       "      <td>sup</td>\n",
       "      <td>hap</td>\n",
       "      <td>ntr</td>\n",
       "      <td>sad</td>\n",
       "      <td>sup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ang</td>\n",
       "      <td>dis</td>\n",
       "      <td>ang</td>\n",
       "      <td>hap</td>\n",
       "      <td>ntr</td>\n",
       "      <td>ang</td>\n",
       "      <td>sup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ang</td>\n",
       "      <td>ang</td>\n",
       "      <td>ang</td>\n",
       "      <td>hap</td>\n",
       "      <td>ntr</td>\n",
       "      <td>ang</td>\n",
       "      <td>sup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ang</td>\n",
       "      <td>dis</td>\n",
       "      <td>ang</td>\n",
       "      <td>hap</td>\n",
       "      <td>ntr</td>\n",
       "      <td>fea</td>\n",
       "      <td>sup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ang</td>\n",
       "      <td>dis</td>\n",
       "      <td>fea</td>\n",
       "      <td>hap</td>\n",
       "      <td>ntr</td>\n",
       "      <td>ang</td>\n",
       "      <td>sup</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ang  dis  fea  hap  ntr  sad  sup\n",
       "f 0  ang  dis  sup  hap  ntr  sad  sup\n",
       "  1  ang  dis  ang  hap  ntr  ang  sup\n",
       "  2  ang  ang  ang  hap  ntr  ang  sup\n",
       "  3  ang  dis  ang  hap  ntr  fea  sup\n",
       "  4  ang  dis  fea  hap  ntr  ang  sup"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getFullCount(testDf).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get % count for one participant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def makeCountParticipant(df,pName):\n",
    "    bigDf = pd.DataFrame()\n",
    "\n",
    "    for ident in df.index.levels[0]:\n",
    "        outDf = pd.DataFrame(index =  list( df.columns ))\n",
    "        for entry in df.columns:\n",
    "            outDf[entry] = df.ix[ident][entry].value_counts()\n",
    "\n",
    "        outDf = (outDf.fillna(0)/16.)*100\n",
    "        outDf.index = [[ident]*len(outDf.index),outDf.index]\n",
    "\n",
    "        bigDf = pd.concat([bigDf,outDf],axis=0)\n",
    "\n",
    "    #return bigDf\n",
    "    stackDf = bigDf.unstack(0).stack(0).unstack(1)\n",
    "    stackDf.index = [[pName]*len(stackDf.index),stackDf.index]\n",
    "    stackDf = stackDf.sortlevel()\n",
    "    \n",
    "    return stackDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">f</th>\n",
       "      <th colspan=\"7\" halign=\"left\">m</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">test</th>\n",
       "      <th>ang</th>\n",
       "      <td>62.50</td>\n",
       "      <td>31.25</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>62.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.25</td>\n",
       "      <td>43.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>31.25</td>\n",
       "      <td>68.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>50.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea</th>\n",
       "      <td>6.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.5</td>\n",
       "      <td>12.5</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.25</td>\n",
       "      <td>62.50</td>\n",
       "      <td>0</td>\n",
       "      <td>31.25</td>\n",
       "      <td>25.00</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>93.75</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>93.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sad</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.75</td>\n",
       "      <td>0</td>\n",
       "      <td>37.50</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sup</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.5</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31.25</td>\n",
       "      <td>87.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              f                                              m                \\\n",
       "            ang    dis    fea    hap    ntr   sad   sup    ang    dis    fea   \n",
       "test ang  62.50  31.25  31.25   0.00   0.00  62.5   0.0  56.25  43.75   0.00   \n",
       "     dis  31.25  68.75   0.00   0.00   0.00   0.0   0.0  12.50  50.00   6.25   \n",
       "     fea   6.25   0.00  31.25   0.00   0.00  12.5  12.5   6.25   6.25  62.50   \n",
       "     hap   0.00   0.00   0.00  93.75   6.25   0.0   0.0   0.00   0.00   0.00   \n",
       "     ntr   0.00   0.00   0.00   6.25  93.75   0.0   0.0  12.50   0.00   0.00   \n",
       "     sad   0.00   0.00  12.50   0.00   0.00  25.0   0.0   6.25   0.00  18.75   \n",
       "     sup   0.00   0.00  25.00   0.00   0.00   0.0  87.5   6.25   0.00  12.50   \n",
       "\n",
       "                                    \n",
       "          hap    ntr    sad    sup  \n",
       "test ang    0   0.00  12.50   0.00  \n",
       "     dis    0   0.00   0.00   6.25  \n",
       "     fea    0  31.25  25.00   6.25  \n",
       "     hap  100   0.00   0.00   0.00  \n",
       "     ntr    0  31.25   0.00   0.00  \n",
       "     sad    0  37.50  31.25   0.00  \n",
       "     sup    0   0.00  31.25  87.50  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "makeCountParticipant(getFullCount(testDf),'test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do this for all participants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def makeBig(logList):\n",
    "    bigDf = pd.DataFrame()\n",
    "\n",
    "    for logFile in logList:\n",
    "\n",
    "        pName = \"p\"+logFile[logFile.rfind('e')+1:logFile.rfind('.')]\n",
    "\n",
    "        df = pd.read_csv(logFile,\n",
    "                header=0,\n",
    "                index_col=[0,1,2]\n",
    "               )\n",
    "\n",
    "        countDf = getFullCount(df)\n",
    "\n",
    "        thisDf = makeCountParticipant(countDf,pName)\n",
    "\n",
    "        bigDf = pd.concat([bigDf,thisDf])\n",
    "        \n",
    "    bigDf = bigDf.sortlevel()\n",
    "    \n",
    "    return bigDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bigDf = makeBig(logList)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of how the entry of one participant looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">f</th>\n",
       "      <th colspan=\"7\" halign=\"left\">m</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ang</th>\n",
       "      <td>62.50</td>\n",
       "      <td>31.25</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>62.5</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>31.25</td>\n",
       "      <td>68.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>fea</th>\n",
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       "      <td>31.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.5</td>\n",
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       "      <td>62.50</td>\n",
       "      <td>0</td>\n",
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       "      <td>25.00</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>93.75</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>93.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sad</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.75</td>\n",
       "      <td>0</td>\n",
       "      <td>37.50</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sup</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.5</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31.25</td>\n",
       "      <td>87.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         f                                              m                     \\\n",
       "       ang    dis    fea    hap    ntr   sad   sup    ang    dis    fea  hap   \n",
       "ang  62.50  31.25  31.25   0.00   0.00  62.5   0.0  56.25  43.75   0.00    0   \n",
       "dis  31.25  68.75   0.00   0.00   0.00   0.0   0.0  12.50  50.00   6.25    0   \n",
       "fea   6.25   0.00  31.25   0.00   0.00  12.5  12.5   6.25   6.25  62.50    0   \n",
       "hap   0.00   0.00   0.00  93.75   6.25   0.0   0.0   0.00   0.00   0.00  100   \n",
       "ntr   0.00   0.00   0.00   6.25  93.75   0.0   0.0  12.50   0.00   0.00    0   \n",
       "sad   0.00   0.00  12.50   0.00   0.00  25.0   0.0   6.25   0.00  18.75    0   \n",
       "sup   0.00   0.00  25.00   0.00   0.00   0.0  87.5   6.25   0.00  12.50    0   \n",
       "\n",
       "                          \n",
       "       ntr    sad    sup  \n",
       "ang   0.00  12.50   0.00  \n",
       "dis   0.00   0.00   6.25  \n",
       "fea  31.25  25.00   6.25  \n",
       "hap   0.00   0.00   0.00  \n",
       "ntr  31.25   0.00   0.00  \n",
       "sad  37.50  31.25   0.00  \n",
       "sup   0.00  31.25  87.50  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigDf.ix[bigDf.index.levels[0][-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Restructure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stackHits = bigDf.unstack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>56.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93.75</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>87.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>81.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.75</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>56.25</td>\n",
       "      <td>18.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>18.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>87.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0</td>\n",
       "      <td>62.50</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>18.75</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.50</td>\n",
       "      <td>37.50</td>\n",
       "      <td>0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p094</th>\n",
       "      <td>87.50</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.25</td>\n",
       "      <td>12.50</td>\n",
       "      <td>12.5</td>\n",
       "      <td>6.25</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>93.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.5</td>\n",
       "      <td>6.25</td>\n",
       "      <td>50.00</td>\n",
       "      <td>0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>18.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>93.75</td>\n",
       "      <td>31.25</td>\n",
       "      <td>6.25</td>\n",
       "      <td>18.75</td>\n",
       "      <td>0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>37.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.75</td>\n",
       "      <td>81.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>12.50</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0</td>\n",
       "      <td>87.50</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>43.75</td>\n",
       "      <td>0</td>\n",
       "      <td>37.50</td>\n",
       "      <td>18.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p095</th>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>87.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.25</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>62.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>93.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>62.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>43.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.75</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>81.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0</td>\n",
       "      <td>81.25</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.25</td>\n",
       "      <td>12.50</td>\n",
       "      <td>62.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p096</th>\n",
       "      <td>62.50</td>\n",
       "      <td>31.25</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.25</td>\n",
       "      <td>68.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93.75</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>93.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>62.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>87.50</td>\n",
       "      <td>56.25</td>\n",
       "      <td>12.50</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.25</td>\n",
       "      <td>43.75</td>\n",
       "      <td>50.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>62.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.75</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0</td>\n",
       "      <td>31.25</td>\n",
       "      <td>37.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31.25</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           f                                                                   \\\n",
       "         ang                                   dis                              \n",
       "         ang    dis   fea   hap ntr sad sup    ang    dis fea hap ntr sad sup   \n",
       "p092   87.50  12.50  0.00  0.00   0   0   0  43.75  56.25   0   0   0   0   0   \n",
       "p093   93.75   0.00  0.00  6.25   0   0   0  37.50  62.50   0   0   0   0   0   \n",
       "p094   87.50   6.25  6.25  0.00   0   0   0  25.00  75.00   0   0   0   0   0   \n",
       "p095  100.00   0.00  0.00  0.00   0   0   0  12.50  87.50   0   0   0   0   0   \n",
       "p096   62.50  31.25  6.25  0.00   0   0   0  31.25  68.75   0   0   0   0   0   \n",
       "\n",
       "                                                                              \\\n",
       "        fea                                        hap                         \n",
       "        ang   dis    fea    hap   ntr    sad   sup ang dis fea     hap   ntr   \n",
       "p092  12.50  12.5  37.50  12.50   0.0   0.00  25.0   0   0   0   93.75  6.25   \n",
       "p093   0.00  25.0  56.25   0.00   0.0   6.25  12.5   0   0   0   93.75  6.25   \n",
       "p094   0.00   0.0  31.25  12.50  12.5   6.25  37.5   0   0   0  100.00  0.00   \n",
       "p095   0.00   0.0  31.25   6.25   0.0   0.00  62.5   0   0   0  100.00  0.00   \n",
       "p096  31.25   0.0  31.25   0.00   0.0  12.50  25.0   0   0   0   93.75  6.25   \n",
       "\n",
       "                                                                             \\\n",
       "             ntr                                          sad                 \n",
       "     sad sup ang    dis    fea   hap    ntr   sad   sup   ang    dis    fea   \n",
       "p092   0   0   0  12.50  12.50  0.00  68.75  6.25  0.00  12.5  31.25  25.00   \n",
       "p093   0   0   0   0.00   6.25  0.00  87.50  0.00  6.25   0.0   6.25   6.25   \n",
       "p094   0   0   0   6.25   0.00  0.00  93.75  0.00  0.00  12.5   6.25  50.00   \n",
       "p095   0   0   0   0.00   0.00  0.00  93.75  0.00  6.25   0.0   6.25  31.25   \n",
       "p096   0   0   0   0.00   0.00  6.25  93.75  0.00  0.00  62.5   0.00  12.50   \n",
       "\n",
       "                                                                         m  \\\n",
       "                             sup                                       ang   \n",
       "     hap    ntr    sad   sup ang dis    fea   hap   ntr sad     sup    ang   \n",
       "p092   0  12.50  12.50  6.25   0   0   6.25  6.25  6.25   0   81.25  43.75   \n",
       "p093   0   6.25  81.25  0.00   0   0  18.75  6.25  0.00   0   75.00  56.25   \n",
       "p094   0  12.50  18.75  0.00   0   0   6.25  0.00  0.00   0   93.75  31.25   \n",
       "p095   0   0.00  62.50  0.00   0   0   0.00  0.00  0.00   0  100.00  43.75   \n",
       "p096   0   0.00  25.00  0.00   0   0  12.50  0.00  0.00   0   87.50  56.25   \n",
       "\n",
       "                                                                             \\\n",
       "                                              dis                             \n",
       "        dis    fea hap    ntr    sad   sup    ang     dis   fea hap ntr sad   \n",
       "p092  50.00   0.00   0   6.25   0.00  0.00  25.00   75.00  0.00   0   0   0   \n",
       "p093  18.75   0.00   0   6.25  18.75  0.00   0.00  100.00  0.00   0   0   0   \n",
       "p094   6.25  18.75   0   6.25  37.50  0.00  18.75   81.25  0.00   0   0   0   \n",
       "p095   0.00  18.75   0   0.00  37.50  0.00  18.75   75.00  0.00   0   0   0   \n",
       "p096  12.50   6.25   0  12.50   6.25  6.25  43.75   50.00  6.25   0   0   0   \n",
       "\n",
       "                                                                              \\\n",
       "             fea                                       hap                     \n",
       "       sup   ang    dis    fea hap   ntr    sad    sup ang dis   fea     hap   \n",
       "p092  0.00  0.00   6.25  50.00   0  12.5  12.50  18.75   0   0  6.25   93.75   \n",
       "p093  0.00  0.00   6.25  87.50   0   0.0   0.00   6.25   0   0  0.00  100.00   \n",
       "p094  0.00  6.25  12.50  31.25   0   0.0   0.00  50.00   0   0  0.00  100.00   \n",
       "p095  6.25  0.00   0.00  81.25   0   0.0   0.00  18.75   0   0  0.00  100.00   \n",
       "p096  0.00  0.00   6.25  62.50   0   0.0  18.75  12.50   0   0  0.00  100.00   \n",
       "\n",
       "                                                                         \\\n",
       "                 ntr                                         sad          \n",
       "     ntr sad sup ang   dis    fea hap    ntr    sad   sup    ang    dis   \n",
       "p092   0   0   0   0  6.25  12.50   0  68.75   6.25  6.25  12.50  31.25   \n",
       "p093   0   0   0   0  0.00   6.25   0  62.50  31.25  0.00   6.25  18.75   \n",
       "p094   0   0   0   0  0.00   6.25   0  87.50   6.25  0.00   0.00   0.00   \n",
       "p095   0   0   0   0  0.00   6.25   0  81.25   6.25  6.25   6.25  12.50   \n",
       "p096   0   0   0   0  0.00  31.25   0  31.25  37.50  0.00  12.50   0.00   \n",
       "\n",
       "                                                                            \n",
       "                                     sup                                    \n",
       "        fea hap    ntr    sad    sup ang    dis    fea hap ntr sad     sup  \n",
       "p092  31.25   0  18.75   0.00   6.25   0  18.75  37.50   0   0   0   43.75  \n",
       "p093  25.00   0   0.00  12.50  37.50   0   6.25  25.00   0   0   0   68.75  \n",
       "p094  43.75   0  37.50  18.75   0.00   0   0.00   0.00   0   0   0  100.00  \n",
       "p095  62.50   0   0.00  18.75   0.00   0   0.00   6.25   0   0   0   93.75  \n",
       "p096  25.00   0   0.00  31.25  31.25   0   6.25   6.25   0   0   0   87.50  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stackHits.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stackHits.to_csv('../outputs/stackHits.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting all responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAABGCAYAAADrRGIwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAl9JREFUeJzt271qU2Ecx/F/TGsXwdeContAdHSzi7O7eAeCi+BFOHsF\n4u7oHbSbVxBwVDqILwURrabHQZH40oD4e/qY8PmM51l+hxC+JxwyGoahAOBfHes9AIDVICgARAgK\nABGCAkCEoAAQsXbYwWQy2aiqa1W1W1WzI1sEwP9sXFUXqurZdDr9NH9waFDqW0y2W64CYGltVdXO\n/IVFQdmtqnr44WNtruh/Ve7cetB7QlPnLj/uPaGpG09u9p7Q1O2r93tPaGq4e6X3hKae3tvvPaGJ\nvf2hHj2fVX1vxLxFQZlVVW0OQ51f0aCMT5ztPaGp9TPrvSc0dfL4qd4Tmrp04kvvCU0dXFztV7in\nN0a9J7T226uQ1f5EATgyggJAhKAAECEoAEQICgARggJAhKAAECEoAEQICgARggJAhKAAECEoAEQI\nCgARggJAhKAAECEoAEQICgARggJAhKAAECEoAEQICgARggJAhKAAECEoAEQICgARggJAhKAAECEo\nAEQICgARggJAhKAAECEoAEQICgARggJAhKAAECEoAEQICgARggJAhKAAECEoAEQICgARggJAhKAA\nECEoAEQICgARggJAhKAAECEoAEQICgARggJAhKAAECEoAESsLTgbV1W9Go2OaMrRm71/3XtCU5/f\nfO49oam9/Xe9JzT14v2ir+fyG14e9J7Q1NtPQ+8JTezt/7iv8a9no2H4801PJpPrVbXdbhYAS2xr\nOp3uzF9Y9Aj0rKq2qmq3qmYtVwGwNMZVdaG+NeInh/5CAYC/4aU8ABGCAkCEoAAQISgARHwFNCBM\nJMaO678AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d30c25450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(stackColors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function is overly complicated, but it's supposed to make the plot look good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def makeConfPlot(confDf,ax):\n",
    "    \n",
    "    # order of face expressions is defined by hand here, instead of\n",
    "    # using the columns, so the order is as we wish it to be\n",
    "    faceExpressions = ['hap','ang','sup','ntr','dis','fea','sad']\n",
    "    faceAnswers = ['hap','ang','sup','ntr','dis','fea','sad']\n",
    "    \n",
    "    # we loop through all expressions\n",
    "    for i,emo in enumerate( faceExpressions ):\n",
    "        \n",
    "        # since we build a stacked plot, we have to initialize a value that\n",
    "        # tells us where to start. We start at the very bottom, hence 0\n",
    "        sumSoFar = 0\n",
    "        \n",
    "        # we get the values of all conditions, and their names, as defined\n",
    "        # in the index\n",
    "        thisMean = confDf[emo].mean()\n",
    "        thisIndex = thisMean.index\n",
    "        \n",
    "        # firstly, we are interested in the correct response, which will be\n",
    "        # always at the bottom of the stack plot and printed in a strong color\n",
    "        \n",
    "        # corrPos is the position of the current expression in the list of all expressions\n",
    "        corrPos = faceExpressions.index(emo)\n",
    "        corrCol = emoReverse[emo]\n",
    "        # and we also get the name of the answer at this position (cave: the order of lists\n",
    "        # must match!)\n",
    "        corrEntry = faceAnswers[corrPos]\n",
    "\n",
    "        # we get the positions of all the other answers\n",
    "        incorrAnswers = faceAnswers[:corrPos]+faceAnswers[corrPos+1:]\n",
    "        \n",
    "        # we get the value of the correct answer\n",
    "        entry = thisMean[corrEntry]\n",
    "        # we get the name of the correct answer\n",
    "        answer = faceAnswers[corrPos]\n",
    "        \n",
    "        # we take the color list and extract the color for the correct position\n",
    "        thisColor = stackColors[corrCol]\n",
    "        thisHex = rgb2hex( (thisColor[0]*255,thisColor[1]*255,thisColor[2]*255) )\n",
    "\n",
    "        # this is the first bar we generate, it starts at the bottom (sumSoFar=0) and\n",
    "        # has a strong color (alpha=1)\n",
    "        ax.bar(i,\n",
    "                entry,\n",
    "                bottom=sumSoFar,\n",
    "                color=thisHex,\n",
    "                label=answer,\n",
    "                alpha=1.)      \n",
    "        \n",
    "        # to stack the rest of the bars on top, we update the sum to be\n",
    "        # the value (% correct) of the current condition\n",
    "        sumSoFar = entry\n",
    "        \n",
    "        # we loop through the remaining (incorrect answers)\n",
    "        for j,answer in enumerate(incorrAnswers):\n",
    "            entry = thisMean[answer]\n",
    "\n",
    "            thisColor = stackColors[emoReverse[answer]]\n",
    "            thisHex = rgb2hex( (thisColor[0]*255,thisColor[1]*255,thisColor[2]*255) )\n",
    "            \n",
    "            # same thing as above, but without the legend and with transparency\n",
    "            ax.bar(i,\n",
    "                    entry,\n",
    "                    bottom=sumSoFar,\n",
    "                    color=thisHex,\n",
    "                    alpha=.6\n",
    "                        )           \n",
    "            # for each run of the loop, we update the sumSoFar so we\n",
    "            # always stack each expression at the top\n",
    "            sumSoFar+=entry\n",
    "    \n",
    "    \n",
    "    # x labels for each plot\n",
    "    ax.set_xlabel('Basic Expression',fontsize=14)\n",
    "    # y labels only for the first plot\n",
    "    ax.set_ylabel('% of responses')\n",
    "    \n",
    "    # axes annotation for all plots\n",
    "    ax.set_xticks(np.arange(0,7.1,1)+0.4,  )\n",
    "    ax.set_xticklabels(faceExpressions)\n",
    "    ax.set_yticks(np.arange(0,101,10))\n",
    "    ax.set_yticklabels([str(a)+'%' for a in  np.arange(0,101,10)] )\n",
    "    ax.set_ylim(0,100)\n",
    "\n",
    "    plt.legend(loc='best',bbox_to_anchor=[1.1,0.9])\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_style(\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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i6enJ3/72N77++msASktLgSt/0+fl5WUf4+XlRWxsLCtWrKBbt25s2bKFvLw8YmNjWbly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\nIiIiIiIiImJj1GEjIiIiIiIiImJj/g/OfvgJ36jyvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d30c25ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ( ax1,ax2 ) = plt.subplots( 1,2,figsize=(16,6) );\n",
    "makeConfPlot(bigDf['f'].unstack(1),ax1);\n",
    "makeConfPlot(bigDf['m'].unstack(1),ax2);\n",
    "sns.despine()\n",
    "plt.savefig('../figures/confusionPlot.png',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get only the correct responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def makeDiagonal(df):\n",
    "    diagDf = pd.DataFrame()\n",
    "\n",
    "    for ident in df.columns.levels[0]:\n",
    "        for real in df.columns.levels[1]:\n",
    "            for resp in df.columns.levels[2]:\n",
    "                if real == resp:\n",
    "                    thisCond = pd.DataFrame(df[ident][real][resp])\n",
    "                    thisCond.columns=pd.MultiIndex.from_tuples( [(ident,resp)] )\n",
    "                    diagDf = pd.concat([diagDf,thisCond],axis=1)\n",
    "    return  diagDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For the % of correct responses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hitCount = makeDiagonal(stackHits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">f</th>\n",
       "      <th colspan=\"7\" halign=\"left\">m</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p092</th>\n",
       "      <td>87.50</td>\n",
       "      <td>56.25</td>\n",
       "      <td>37.50</td>\n",
       "      <td>93.75</td>\n",
       "      <td>68.75</td>\n",
       "      <td>12.50</td>\n",
       "      <td>81.25</td>\n",
       "      <td>43.75</td>\n",
       "      <td>75.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>93.75</td>\n",
       "      <td>68.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>43.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p093</th>\n",
       "      <td>93.75</td>\n",
       "      <td>62.50</td>\n",
       "      <td>56.25</td>\n",
       "      <td>93.75</td>\n",
       "      <td>87.50</td>\n",
       "      <td>81.25</td>\n",
       "      <td>75.00</td>\n",
       "      <td>56.25</td>\n",
       "      <td>100.00</td>\n",
       "      <td>87.50</td>\n",
       "      <td>100.00</td>\n",
       "      <td>62.50</td>\n",
       "      <td>12.50</td>\n",
       "      <td>68.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p094</th>\n",
       "      <td>87.50</td>\n",
       "      <td>75.00</td>\n",
       "      <td>31.25</td>\n",
       "      <td>100.00</td>\n",
       "      <td>93.75</td>\n",
       "      <td>18.75</td>\n",
       "      <td>93.75</td>\n",
       "      <td>31.25</td>\n",
       "      <td>81.25</td>\n",
       "      <td>31.25</td>\n",
       "      <td>100.00</td>\n",
       "      <td>87.50</td>\n",
       "      <td>18.75</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p095</th>\n",
       "      <td>100.00</td>\n",
       "      <td>87.50</td>\n",
       "      <td>31.25</td>\n",
       "      <td>100.00</td>\n",
       "      <td>93.75</td>\n",
       "      <td>62.50</td>\n",
       "      <td>100.00</td>\n",
       "      <td>43.75</td>\n",
       "      <td>75.00</td>\n",
       "      <td>81.25</td>\n",
       "      <td>100.00</td>\n",
       "      <td>81.25</td>\n",
       "      <td>18.75</td>\n",
       "      <td>93.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p096</th>\n",
       "      <td>62.50</td>\n",
       "      <td>68.75</td>\n",
       "      <td>31.25</td>\n",
       "      <td>93.75</td>\n",
       "      <td>93.75</td>\n",
       "      <td>25.00</td>\n",
       "      <td>87.50</td>\n",
       "      <td>56.25</td>\n",
       "      <td>50.00</td>\n",
       "      <td>62.50</td>\n",
       "      <td>100.00</td>\n",
       "      <td>31.25</td>\n",
       "      <td>31.25</td>\n",
       "      <td>87.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           f                                                  m          \\\n",
       "         ang    dis    fea     hap    ntr    sad     sup    ang     dis   \n",
       "p092   87.50  56.25  37.50   93.75  68.75  12.50   81.25  43.75   75.00   \n",
       "p093   93.75  62.50  56.25   93.75  87.50  81.25   75.00  56.25  100.00   \n",
       "p094   87.50  75.00  31.25  100.00  93.75  18.75   93.75  31.25   81.25   \n",
       "p095  100.00  87.50  31.25  100.00  93.75  62.50  100.00  43.75   75.00   \n",
       "p096   62.50  68.75  31.25   93.75  93.75  25.00   87.50  56.25   50.00   \n",
       "\n",
       "                                           \n",
       "        fea     hap    ntr    sad     sup  \n",
       "p092  50.00   93.75  68.75   0.00   43.75  \n",
       "p093  87.50  100.00  62.50  12.50   68.75  \n",
       "p094  31.25  100.00  87.50  18.75  100.00  \n",
       "p095  81.25  100.00  81.25  18.75   93.75  \n",
       "p096  62.50  100.00  31.25  31.25   87.50  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hitCount.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For the number of revealed tiles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "hitMasks = makeDiagonal(stackMask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">f</th>\n",
       "      <th colspan=\"7\" halign=\"left\">m</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "      <th>ang</th>\n",
       "      <th>dis</th>\n",
       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
       "      <th>sup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p092</th>\n",
       "      <td>16.666667</td>\n",
       "      <td>20.370370</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>18.472222</td>\n",
       "      <td>23.674242</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>31.410256</td>\n",
       "      <td>26.488095</td>\n",
       "      <td>18.229167</td>\n",
       "      <td>25.260417</td>\n",
       "      <td>17.777778</td>\n",
       "      <td>23.106061</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p093</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.958333</td>\n",
       "      <td>32.407407</td>\n",
       "      <td>12.916667</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>21.955128</td>\n",
       "      <td>19.097222</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>21.354167</td>\n",
       "      <td>28.720238</td>\n",
       "      <td>10.807292</td>\n",
       "      <td>21.041667</td>\n",
       "      <td>20.833333</td>\n",
       "      <td>28.787879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p094</th>\n",
       "      <td>20.982143</td>\n",
       "      <td>20.138889</td>\n",
       "      <td>44.166667</td>\n",
       "      <td>17.317708</td>\n",
       "      <td>30.833333</td>\n",
       "      <td>43.750000</td>\n",
       "      <td>24.444444</td>\n",
       "      <td>33.750000</td>\n",
       "      <td>20.512821</td>\n",
       "      <td>30.833333</td>\n",
       "      <td>20.963542</td>\n",
       "      <td>29.166667</td>\n",
       "      <td>36.805556</td>\n",
       "      <td>19.401042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p095</th>\n",
       "      <td>21.614583</td>\n",
       "      <td>19.940476</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>12.760417</td>\n",
       "      <td>37.361111</td>\n",
       "      <td>31.875000</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>32.142857</td>\n",
       "      <td>19.618056</td>\n",
       "      <td>43.269231</td>\n",
       "      <td>12.890625</td>\n",
       "      <td>34.935897</td>\n",
       "      <td>20.833333</td>\n",
       "      <td>26.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p096</th>\n",
       "      <td>14.791667</td>\n",
       "      <td>16.856061</td>\n",
       "      <td>31.250000</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>31.666667</td>\n",
       "      <td>23.437500</td>\n",
       "      <td>21.279762</td>\n",
       "      <td>25.925926</td>\n",
       "      <td>15.104167</td>\n",
       "      <td>25.416667</td>\n",
       "      <td>21.354167</td>\n",
       "      <td>37.916667</td>\n",
       "      <td>30.416667</td>\n",
       "      <td>24.107143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              f                                                         \\\n",
       "            ang        dis        fea        hap        ntr        sad   \n",
       "p092  16.666667  20.370370  25.000000  18.472222  23.674242  33.333333   \n",
       "p093  20.000000  18.958333  32.407407  12.916667  18.750000  21.955128   \n",
       "p094  20.982143  20.138889  44.166667  17.317708  30.833333  43.750000   \n",
       "p095  21.614583  19.940476  40.000000  12.760417  37.361111  31.875000   \n",
       "p096  14.791667  16.856061  31.250000  18.333333  31.666667  23.437500   \n",
       "\n",
       "                         m                                              \\\n",
       "            sup        ang        dis        fea        hap        ntr   \n",
       "p092  31.410256  26.488095  18.229167  25.260417  17.777778  23.106061   \n",
       "p093  19.097222  25.000000  21.354167  28.720238  10.807292  21.041667   \n",
       "p094  24.444444  33.750000  20.512821  30.833333  20.963542  29.166667   \n",
       "p095  18.750000  32.142857  19.618056  43.269231  12.890625  34.935897   \n",
       "p096  21.279762  25.925926  15.104167  25.416667  21.354167  37.916667   \n",
       "\n",
       "                            \n",
       "            sad        sup  \n",
       "p092        NaN  35.714286  \n",
       "p093  20.833333  28.787879  \n",
       "p094  36.805556  19.401042  \n",
       "p095  20.833333  26.666667  \n",
       "p096  30.416667  24.107143  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hitMasks.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the relationship of hits and speed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Restructuring again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bigCount = hitMasks.stack(0).unstack(1)\n",
    "bigHits = hitCount.stack(0).unstack(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def makeMyScatter(bigCount,bigHits,stackColors=stackColors):\n",
    "    \n",
    "    # order of face expressions is defined by hand here, instead of\n",
    "    # using the columns, so the order is as we wish it to be\n",
    "    for e,emo in enumerate(['hap','ang','sup','ntr','dis','fea','sad']):\n",
    "        thisColor = stackColors[emoReverse[emo]]\n",
    "        thisHex = rgb2hex( (thisColor[0]*255,thisColor[1]*255,thisColor[2]*255) )\n",
    "        \n",
    "        for i,ident in enumerate(bigCount.mean().index.levels[1]):\n",
    "\n",
    "            assert bigCount.shape[0] == bigHits.shape[0], 'number of participants in both metric dicts not equal!'\n",
    "            n = bigCount.shape[0]\n",
    "\n",
    "            countMean = bigCount.mean()[emo][ident]\n",
    "            countCI = bigCount.std(ddof=1)[emo][ident]/np.sqrt(n)*1.96\n",
    "\n",
    "            hitMean = bigHits.mean()[emo][ident]\n",
    "            hitCI = bigHits.std(ddof=1)[emo][ident]/np.sqrt(n)*1.96\n",
    "\n",
    "            if i == 0:\n",
    "                myFmt = 'o'\n",
    "            elif i == 1:\n",
    "                myFmt = 'v'\n",
    "\n",
    "            plt.errorbar(countMean,\n",
    "                         hitMean,\n",
    "                         xerr=countCI,\n",
    "                         yerr=hitCI,\n",
    "                         fmt=myFmt,\n",
    "                         c=thisColor,\n",
    "                         ecolor='gray',\n",
    "                         label= str(ident)+'-'+str(emo),\n",
    "                         markersize=15,\n",
    "                         markeredgecolor='gray',\n",
    "                         markeredgewidth=.5\n",
    "                        )\n",
    "    sns.despine()\n",
    "    plt.xlabel('% Tiles Revealed')\n",
    "    plt.ylabel('% Correct Responses')\n",
    "\n",
    "    plt.legend(loc='right', bbox_to_anchor=(1.2,.5));\n",
    "    \n",
    "    plt.savefig('../figures/globalScatter.png',dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eh6vU91fPw/y0cwMPx8XbfV3JmDFj2LVrF4qiMHbsWBRFITExkebNm1fYPi0t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\niBX8P133qxkYDXAsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d2e3574d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,7))\n",
    "makeMyScatter(bigCount,bigHits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Violin Plots for correct responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def scriptToGetViolinPlot(df,myYscale):\n",
    "    # restructure so that the the cases move to the index and there is only one column left\n",
    "    violinDf = pd.DataFrame( df.stack(0).stack(0) )\n",
    "\n",
    "    violinDf['g'] = violinDf.index.labels[2]\n",
    "    violinDf['e'] = violinDf.index.labels[1]\n",
    "\n",
    "    # change the emoname-number mapping\n",
    "    emoList = list(violinDf['e'])\n",
    "    for i,entry in enumerate(emoList):\n",
    "        thisName = violinDf.index.levels[1][entry]\n",
    "        thisNumber = myLabels.values().index(thisName)\n",
    "        emoList[i] = thisNumber\n",
    "    violinDf['e'] = emoList\n",
    "    # change the order to be like the new mapping\n",
    "    violinDf = violinDf.sort_values(by='g')\n",
    "    violinDf = violinDf.sort_values(by='e')\n",
    "    fig = plt.figure(figsize=(14,6))\n",
    "\n",
    "    sns.violinplot(x=\"e\",\n",
    "                      y=0,\n",
    "                      hue=\"g\",\n",
    "                      data=violinDf,\n",
    "                      split=True,\n",
    "                      inner=None)\n",
    "\n",
    "    sns.stripplot(x=\"e\",\n",
    "                      y=0,\n",
    "                      hue=\"g\",\n",
    "                      data=violinDf,\n",
    "                      split=True,\n",
    "                      jitter=True,\n",
    "                      alpha=1.,\n",
    "                      linewidth=.5,\n",
    "                      edgecolor='white')\n",
    "\n",
    "    plt.xticks(range(7),myLabels.values())\n",
    "    plt.ylim(0,myYscale)\n",
    "    sns.despine()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### % Hits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The blue parts of the plot are the female face and the green part are the male face."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ALMuMHTuWSZMmMXPmTMrLy/WZKJ0Bxe128/TTT/PFF18AWqEZc/75g5PvIQharo8volck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\nOYLL5SI9PR2yJ2Iw6eh2D1wZ6HwIgoBk0RDq9KecrQEgCAIPP/wwnZ2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CPj86OUKOTeLW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\n3nv5FPXVeWxZVcLzfzjHl+9oIN/t4LM3rSUUkfjxC8fo6Qtzy1VVfPq6R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avm6xnz5H7v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\nNPD513hJsiWEEH1dffXVACxdujS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XVatWcdFFF5GQkBDWmOPZcD73Rqr3eUvgbyHwtyHC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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d30cd4350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scriptToGetViolinPlot(bigHits,105)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### % Tiles Revealed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xdhMghBChEG/lz5JACRFlfY02NTU1RSESIeJLcARKowa3yWK6QggxfPGWQEkbcyGirK/R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O30lK7WdZvRqkfPplkmlhLhG4a/2o+JUWlMDk7jmbTHmRaWTmZ9Dn/O/oxWfQcrEhaQ2ZRDcohn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8KcogDQ6n459K/fJK0T9owfYV1cSEqGCqW4fYU9BMaKCa5TOS6RkYQqtRYjB5/3fodDqRCiOT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\nfVsZ963JobC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yuKXvZtasWdxzzz397vu5FKJD9fz6vrHUNFrR65TEhLbvsQsO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\nIiIzMYEiIiIiIiIy0/8Djo4QeMKvwpQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d2c7e0f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scriptToGetViolinPlot(bigCount,myYscale=70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inferential Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make mean values (averaged over face gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def makeAverage(df,myLabels=myLabels):\n",
    "    \n",
    "    meanDf = pd.DataFrame()\n",
    "    \n",
    "    for index,entry in enumerate( df.columns.levels[0] ):\n",
    "        thisDf = pd.DataFrame( df[ entry ].T.mean() )\n",
    "        thisDf.columns = [myLabels[index]]\n",
    "        meanDf = pd.concat([meanDf, thisDf],axis=1)\n",
    "        \n",
    "    return meanDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "meanHits = makeAverage(bigHits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "meanCount = makeAverage(bigCount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def ttestTable(df):\n",
    "\n",
    "    d = {}\n",
    "\n",
    "    for entry1 in df.columns:\n",
    "        firstSample = df[entry1]\n",
    "        for entry2 in df.columns:\n",
    "            if entry1 != entry2:\n",
    "                secondSample = df[entry2]\n",
    "                thisT,thisP =  stats.ttest_rel(firstSample,secondSample)\n",
    "\n",
    "                newName = [entry1,entry2]\n",
    "                newName.sort()\n",
    "\n",
    "                d[newName[0]+'-'+newName[1]] = {'t':round(thisT,2),'p':round(thisP,5)}\n",
    "    outDf = pd.DataFrame(d).T\n",
    "    \n",
    "    return outDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p001    93.750\n",
       "p002    46.875\n",
       "p003    53.125\n",
       "p004    87.500\n",
       "p005    90.625\n",
       "Name: sad, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meanHits['sad'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>p</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ang-dis</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ang-fea</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>19.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ang-hap</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>-7.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ang-ntr</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>9.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ang-sad</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>-11.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ang-sup</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>-7.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis-fea</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>-13.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis-hap</th>\n",
       "      <td>0.06618</td>\n",
       "      <td>1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis-ntr</th>\n",
       "      <td>0.00980</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis-sad</th>\n",
       "      <td>0.95641</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis-sup</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>-16.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea-hap</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>18.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea-ntr</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>-12.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea-sad</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea-sup</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>-30.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap-ntr</th>\n",
       "      <td>0.00002</td>\n",
       "      <td>4.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap-sad</th>\n",
       "      <td>0.02760</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap-sup</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>-16.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr-sad</th>\n",
       "      <td>0.01629</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr-sup</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>18.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sad-sup</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>-18.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               p      t\n",
       "ang-dis  0.00000   8.05\n",
       "ang-fea  0.00000  19.83\n",
       "ang-hap  0.00000  -7.83\n",
       "ang-ntr  0.00000   9.59\n",
       "ang-sad  0.00000 -11.38\n",
       "ang-sup  0.00000  -7.76\n",
       "dis-fea  0.00000 -13.33\n",
       "dis-hap  0.06618   1.86\n",
       "dis-ntr  0.00980   2.64\n",
       "dis-sad  0.95641  -0.05\n",
       "dis-sup  0.00000 -16.10\n",
       "fea-hap  0.00000  18.53\n",
       "fea-ntr  0.00000 -12.47\n",
       "fea-sad  0.00000  17.00\n",
       "fea-sup  0.00000 -30.59\n",
       "hap-ntr  0.00002   4.49\n",
       "hap-sad  0.02760   2.24\n",
       "hap-sup  0.00000 -16.33\n",
       "ntr-sad  0.01629   2.45\n",
       "ntr-sup  0.00000  18.37\n",
       "sad-sup  0.00000 -18.10"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttestTable(meanHits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
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    "ttestTable(meanCount)"
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