notebooks/famousFaces_003a_filteredChoiceLog.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Celebrity Multiple Choice - Filtered Version  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "psychoPy stores data in log and in csv format. For most participants, this notebook (using .log) gets all data, but for two participants (JB and #3) this does not work and only the csv version is complete. So we first run this, and then later we run another script for data extraction and merge the two."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import fnmatch\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get logfiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getLogfile(whichfolder, whichexperiment):\n",
    "\n",
    "    loglist = []\n",
    "    for fileName in os.listdir(whichfolder):\n",
    "        if fnmatch.fnmatch(fileName, whichexperiment):\n",
    "            loglist.append(whichfolder+fileName)\n",
    "    loglist.sort()\n",
    "    return loglist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "logList = getLogfile('../famousFiltered/experiment/data/','*.log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../famousFiltered/experiment/data/10_famFaceFilter_2017_Feb_06_1253.log',\n",
       " '../famousFiltered/experiment/data/11_famFaceFilter_2017_Feb_07_1055.log',\n",
       " '../famousFiltered/experiment/data/12_famFaceFilter_2017_Feb_07_1327.log',\n",
       " '../famousFiltered/experiment/data/13_famFaceFilter_2017_Feb_08_1104.log',\n",
       " '../famousFiltered/experiment/data/14_famFaceFilter_2017_Feb_08_1322.log',\n",
       " '../famousFiltered/experiment/data/15_famFaceFilter_2017_Feb_08_1512.log',\n",
       " '../famousFiltered/experiment/data/16_famFaceFilter_2017_Feb_09_1301.log',\n",
       " '../famousFiltered/experiment/data/17_famFaceFilter_2017_Feb_09_1535.log',\n",
       " '../famousFiltered/experiment/data/18_famFaceFilter_2017_Feb_16_1655.log',\n",
       " '../famousFiltered/experiment/data/19_famFaceFilter_2017_Feb_20_1110.log',\n",
       " '../famousFiltered/experiment/data/1_famFaceFilter_2017_Jan_24_1324.log',\n",
       " '../famousFiltered/experiment/data/20_famFaceFilter_2017_Feb_20_1713.log',\n",
       " '../famousFiltered/experiment/data/21_famFaceFilter_2017_Feb_22_1111.log',\n",
       " '../famousFiltered/experiment/data/22_famFaceFilter_2017_Feb_23_1505.log',\n",
       " '../famousFiltered/experiment/data/23_famFaceFilter_2017_Feb_23_1717.log',\n",
       " '../famousFiltered/experiment/data/24_famFaceFilter_2017_Feb_24_1121.log',\n",
       " '../famousFiltered/experiment/data/25_famFaceFilter_2017_Feb_24_1505.log',\n",
       " '../famousFiltered/experiment/data/26_famFaceFilter_2017_Feb_28_1246.log',\n",
       " '../famousFiltered/experiment/data/27_famFaceFilter_2017_Mrz_01_1642.log',\n",
       " '../famousFiltered/experiment/data/2_famFaceFilter_2017_Jan_25_1055.log',\n",
       " '../famousFiltered/experiment/data/3_famFaceFilter_2017_Jan_25_1257.log',\n",
       " '../famousFiltered/experiment/data/4_famFaceFilter_2017_Jan_25_1502.log',\n",
       " '../famousFiltered/experiment/data/5_famFaceFilter_2017_Jan_26_1056.log',\n",
       " '../famousFiltered/experiment/data/6_famFaceFilter_2017_Jan_27_1049.log',\n",
       " '../famousFiltered/experiment/data/7_famFaceFilter_2017_Jan_30_1503.log',\n",
       " '../famousFiltered/experiment/data/8_famFaceFilter_2017_Feb_01_1709.log',\n",
       " '../famousFiltered/experiment/data/9_famFaceFilter_2017_Feb_02_1304.log']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logList"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get content from logfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getContent(logFile):\n",
    "    with open(logFile,'r') as f:\n",
    "        return f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logFile = logList[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../famousFiltered/experiment/data/9_famFaceFilter_2017_Feb_02_1304.log'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "thisTxt = getContent(logFile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get familiarity ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def getFamiliar(thisTxt,cond):\n",
    "    d = {}\n",
    "    # find stimulus\n",
    "    for i,entry in enumerate(thisTxt):\n",
    "        if cond in entry and '.png' in entry and 'imageBekannt: image' in entry:\n",
    "            \n",
    "            thisEntry = thisTxt[i].split('\\t')\n",
    "            stim = thisEntry[-1].split('/')[-1][:-6]\n",
    "            \n",
    "            for n in range(i,len(thisTxt)):\n",
    "                thisEntry = thisTxt[n]\n",
    "                if 'Keypress:' in thisEntry:\n",
    "                    try:\n",
    "                        resp = int(int(thisEntry.split(' ')[-1][:-1]) == 1)\n",
    "                        #resp = thisEntry.split(' ')[-1][:-1]\n",
    "                        break\n",
    "                    except:\n",
    "                        for j in range(n,n+10):\n",
    "                            try:\n",
    "                                thisEntry = thisTxt[j]\n",
    "                                resp = int(int(thisEntry.split(' ')[-1][:-1]) == 1)\n",
    "                            except:\n",
    "                                pass\n",
    "            for n in range(i,len(thisTxt)):\n",
    "                thisEntry = thisTxt[n]\n",
    "                if 'rating=' in thisEntry and 'ratingB' in thisEntry:\n",
    "                    resp = int(thisEntry.split('/')[-1].split('.')[-1][:-1] == 'ja')\n",
    "                    break\n",
    "            d[stim] = {'resp': resp}\n",
    "    df = pd.DataFrame(d).T\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "famDf = getFamiliar(thisTxt,'smoo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>resp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>smoo15_UrsulavonderLeyen4_1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_UrsulavonderLeyen5_1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith1_1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith4_1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith5_1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             resp\n",
       "smoo15_UrsulavonderLeyen4_1     1\n",
       "smoo15_UrsulavonderLeyen5_1     1\n",
       "smoo15_WillSmith1_1             1\n",
       "smoo15_WillSmith4_1             1\n",
       "smoo15_WillSmith5_1             1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "famDf.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluate context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getContext(thisTxt,cond):\n",
    "    d = {}\n",
    "    # find stimulus\n",
    "    for i,entry in enumerate(thisTxt):\n",
    "        if cond in entry and '.png' in entry and 'imageBekannt: image' in entry:\n",
    "            \n",
    "            thisEntry = thisTxt[i].split('\\t')\n",
    "            stim = thisEntry[-1].split('/')[-1][:-6]\n",
    "            \n",
    "            for n in range(i+15,len(thisTxt)):\n",
    "                thisEntry = thisTxt[n]\n",
    "                if 'Keypress:' in thisEntry:\n",
    "                    try:\n",
    "                        resp = int(thisEntry.split(' ')[-1][:-1])\n",
    "                        break\n",
    "                    except:\n",
    "                        pass\n",
    "                        \n",
    "            for n in range(i,len(thisTxt)):\n",
    "                thisEntry = thisTxt[n]\n",
    "                if 'rating=' in thisEntry and 'ratingK' in thisEntry:\n",
    "                    resp = thisEntry.split('=')[-1][:-1]\n",
    "                    if resp == 'dummy':\n",
    "                        # because there are hidden placeholder buttons next to the act button in the experiment\n",
    "                        # any dummy response will be an act response where the participant aimed the mouse cursor\n",
    "                        # a little bit to the side\n",
    "                        resp = 'act'\n",
    "                    break\n",
    "\n",
    "            d[stim] = {'resp': resp}\n",
    "    df = pd.DataFrame(d).T\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "contextDf = getContext(thisTxt,'smoo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>resp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>smoo15_UrsulavonderLeyen4_1</th>\n",
       "      <td>polit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_UrsulavonderLeyen5_1</th>\n",
       "      <td>polit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith1_1</th>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith4_1</th>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith5_1</th>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              resp\n",
       "smoo15_UrsulavonderLeyen4_1  polit\n",
       "smoo15_UrsulavonderLeyen5_1  polit\n",
       "smoo15_WillSmith1_1            act\n",
       "smoo15_WillSmith4_1            act\n",
       "smoo15_WillSmith5_1            act"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contextDf.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### recode context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is necessary in all cases where a participant did not use the mouse but the keyboard. On the keyboard, each answer could be given using a number. So we also need to look for numbers and recode them to strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def recodeThis(contextDf):\n",
    "    recodeDict = {1:'act',2:'music',3:'host',4:'polit',5:'sport'}\n",
    "\n",
    "    d = {}\n",
    "    for idx in contextDf.index:\n",
    "        thisAns = contextDf.loc[idx]['resp']\n",
    "        try:\n",
    "            int(thisAns)\n",
    "            recoded = recodeDict[thisAns]\n",
    "            d[idx] = recoded\n",
    "        except:\n",
    "            d[idx] = thisAns\n",
    "    return pd.DataFrame(d,index=['context']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>context</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>smoo15_RobertSeanLeonard(Wilson)8_1</th>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_TomCruise1_1</th>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_TomCruise4_1</th>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_TomCruise5_1</th>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_UrsulavonderLeyen1_1</th>\n",
       "      <td>polit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_UrsulavonderLeyen4_1</th>\n",
       "      <td>polit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_UrsulavonderLeyen5_1</th>\n",
       "      <td>polit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith1_1</th>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith4_1</th>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith5_1</th>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    context\n",
       "smoo15_RobertSeanLeonard(Wilson)8_1     act\n",
       "smoo15_TomCruise1_1                     act\n",
       "smoo15_TomCruise4_1                     act\n",
       "smoo15_TomCruise5_1                     act\n",
       "smoo15_UrsulavonderLeyen1_1           polit\n",
       "smoo15_UrsulavonderLeyen4_1           polit\n",
       "smoo15_UrsulavonderLeyen5_1           polit\n",
       "smoo15_WillSmith1_1                     act\n",
       "smoo15_WillSmith4_1                     act\n",
       "smoo15_WillSmith5_1                     act"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recodeThis(contextDf).tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### merge familiarity and context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makeMergeDf(thisTxt,cond):\n",
    "    famDf = getFamiliar(thisTxt,cond)\n",
    "    contextDf = getContext(thisTxt,cond)\n",
    "    recodeDf = recodeThis(contextDf)\n",
    "\n",
    "    mergeDf = pd.concat([famDf,recodeDf],axis=1)\n",
    "    return mergeDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "thisDf = makeMergeDf(thisTxt,'smoo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>resp</th>\n",
       "      <th>context</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>smoo15_UrsulavonderLeyen4_1</th>\n",
       "      <td>1</td>\n",
       "      <td>polit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_UrsulavonderLeyen5_1</th>\n",
       "      <td>1</td>\n",
       "      <td>polit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith1_1</th>\n",
       "      <td>1</td>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith4_1</th>\n",
       "      <td>1</td>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith5_1</th>\n",
       "      <td>1</td>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             resp context\n",
       "smoo15_UrsulavonderLeyen4_1     1   polit\n",
       "smoo15_UrsulavonderLeyen5_1     1   polit\n",
       "smoo15_WillSmith1_1             1     act\n",
       "smoo15_WillSmith4_1             1     act\n",
       "smoo15_WillSmith5_1             1     act"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thisDf.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluate context responses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "reference list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "contextDf = pd.read_csv('../famousCheck/contextList.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>context</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Lisa Edelstein</td>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Mila Kunis</td>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Omar Epps</td>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Sahra Wagenknecht</td>\n",
       "      <td>polit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Shakira</td>\n",
       "      <td>music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Sigmar Gabriel</td>\n",
       "      <td>polit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Taylor Swift</td>\n",
       "      <td>music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Tom Cruise</td>\n",
       "      <td>act</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Ursula vonderLeyen</td>\n",
       "      <td>polit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Usher</td>\n",
       "      <td>music</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name context\n",
       "32      Lisa Edelstein     act\n",
       "33          Mila Kunis     act\n",
       "34           Omar Epps     act\n",
       "35   Sahra Wagenknecht   polit\n",
       "36             Shakira   music\n",
       "37      Sigmar Gabriel   polit\n",
       "38        Taylor Swift   music\n",
       "39          Tom Cruise     act\n",
       "40  Ursula vonderLeyen   polit\n",
       "41               Usher   music"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contextDf.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def evalResp(df,contextDf=contextDf):\n",
    "    \n",
    "    respDict = {}\n",
    "\n",
    "    for i in df.index:\n",
    "\n",
    "        resp = df.loc[i]['context']\n",
    "        bekannt = df.loc[i]['resp']\n",
    "\n",
    "        for idx in contextDf.index:\n",
    "            correct = contextDf.loc[idx]['context']\n",
    "            thisName = contextDf.loc[idx]['name']\n",
    "            \n",
    "            for namePart in thisName.split(' '):\n",
    "                if namePart in i:\n",
    "\n",
    "                    respDict[i] = {'correct':correct,\n",
    "                                          'bekannt':bekannt,\n",
    "                                          'context':resp,\n",
    "                                          'eval': int(resp in correct)}\n",
    "                    \n",
    "    respDf = pd.DataFrame( respDict ).T\n",
    "    \n",
    "    return respDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "evalDf = evalResp(thisDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bekannt</th>\n",
       "      <th>context</th>\n",
       "      <th>correct</th>\n",
       "      <th>eval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>smoo15_HughLaurie(DrHouse)2_1</th>\n",
       "      <td>1</td>\n",
       "      <td>act</td>\n",
       "      <td>act</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_HughLaurie(DrHouse)4_1</th>\n",
       "      <td>1</td>\n",
       "      <td>act</td>\n",
       "      <td>act</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_GuentherJauch3_1</th>\n",
       "      <td>1</td>\n",
       "      <td>host</td>\n",
       "      <td>host</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_GuentherJauch2_1</th>\n",
       "      <td>1</td>\n",
       "      <td>host</td>\n",
       "      <td>host</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_MariskaHargitay(Liv)4_4</th>\n",
       "      <td>0</td>\n",
       "      <td>host</td>\n",
       "      <td>act</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_EmilyDeschanel3_1</th>\n",
       "      <td>1</td>\n",
       "      <td>music</td>\n",
       "      <td>act</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_EmilyDeschanel6_12</th>\n",
       "      <td>1</td>\n",
       "      <td>music</td>\n",
       "      <td>act</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_Ice-T(OdafinFinTutuola)5_1</th>\n",
       "      <td>0</td>\n",
       "      <td>host</td>\n",
       "      <td>act music</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_Ice-T(OdafinFinTutuola)6_1</th>\n",
       "      <td>0</td>\n",
       "      <td>host</td>\n",
       "      <td>act music</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_Ice-T(OdafinFinTutuola)7_1</th>\n",
       "      <td>0</td>\n",
       "      <td>host</td>\n",
       "      <td>act music</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_JasonSegel(Marshall)6_1</th>\n",
       "      <td>0</td>\n",
       "      <td>host</td>\n",
       "      <td>act</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_KatjaBurkard1_1</th>\n",
       "      <td>0</td>\n",
       "      <td>act</td>\n",
       "      <td>host</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_JoshRadnor(Ted)6_2</th>\n",
       "      <td>0</td>\n",
       "      <td>music</td>\n",
       "      <td>act</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_NicoleKidman5_1</th>\n",
       "      <td>1</td>\n",
       "      <td>music</td>\n",
       "      <td>act</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_GeorgeClooney11_1</th>\n",
       "      <td>0</td>\n",
       "      <td>music</td>\n",
       "      <td>act</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  bekannt context    correct eval\n",
       "smoo15_HughLaurie(DrHouse)2_1           1     act        act    1\n",
       "smoo15_HughLaurie(DrHouse)4_1           1     act        act    1\n",
       "smoo15_GuentherJauch3_1                 1    host       host    1\n",
       "smoo15_GuentherJauch2_1                 1    host       host    1\n",
       "smoo15_MariskaHargitay(Liv)4_4          0    host        act    0\n",
       "smoo15_EmilyDeschanel3_1                1   music        act    0\n",
       "smoo15_EmilyDeschanel6_12               1   music        act    0\n",
       "smoo15_Ice-T(OdafinFinTutuola)5_1       0    host  act music    0\n",
       "smoo15_Ice-T(OdafinFinTutuola)6_1       0    host  act music    0\n",
       "smoo15_Ice-T(OdafinFinTutuola)7_1       0    host  act music    0\n",
       "smoo15_JasonSegel(Marshall)6_1          0    host        act    0\n",
       "smoo15_KatjaBurkard1_1                  0     act       host    0\n",
       "smoo15_JoshRadnor(Ted)6_2               0   music        act    0\n",
       "smoo15_NicoleKidman5_1                  1   music        act    0\n",
       "smoo15_GeorgeClooney11_1                0   music        act    0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evalDf.sort_values(by='eval',ascending=False).tail(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get rid of unknown faces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkDf = pd.read_csv('../famousCheck/output/famousFamiliarity_%s.csv' % 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>img</th>\n",
       "      <th>name</th>\n",
       "      <th>bekannt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>./img/SigmarGabriel1.jpg</td>\n",
       "      <td>Sigmar Gabriel</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>./img/BritneySpears5.jpg</td>\n",
       "      <td>Britney Spears</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>./img/EmilyDeschanel3.jpg</td>\n",
       "      <td>Emily Deschanel</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>./img/AlysonHannigan(Lily)1.jpg</td>\n",
       "      <td>Alyson Hannigan</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>./img/BarackObama2.jpg</td>\n",
       "      <td>Barack Obama</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                img             name bekannt\n",
       "37         ./img/SigmarGabriel1.jpg   Sigmar Gabriel      ja\n",
       "38         ./img/BritneySpears5.jpg   Britney Spears      ja\n",
       "39        ./img/EmilyDeschanel3.jpg  Emily Deschanel      ja\n",
       "40  ./img/AlysonHannigan(Lily)1.jpg  Alyson Hannigan      ja\n",
       "41           ./img/BarackObama2.jpg     Barack Obama      ja"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkDf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cleanUp(evalDf,checkDf=checkDf):\n",
    "    copyDf = evalDf.copy()\n",
    "    for idx in evalDf.index:\n",
    "        #print idx\n",
    "        for j in checkDf.index:\n",
    "            status = (checkDf.loc[j]['bekannt'] == 'nein')\n",
    "            name = checkDf.loc[j]['name'].split(' ')\n",
    "            if status:\n",
    "                for sub in name:\n",
    "                    if sub in idx and len(sub)>3:# the >3 is a hack to prevent 'von' and 'der' in von der Leyen to be used for matching\n",
    "                        # this is a hack to prevent Robert Sean Leonard matching Leonard[o] DiCaprio\n",
    "                        if sub == 'Leonard' and 'DiCaprio' in idx:\n",
    "                            pass\n",
    "                        else:\n",
    "                            #print sub,idx\n",
    "                            copyDf.drop(idx,inplace=True)\n",
    "                            break\n",
    "    return copyDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cleanDf = cleanUp(evalDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bekannt</th>\n",
       "      <th>context</th>\n",
       "      <th>correct</th>\n",
       "      <th>eval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>smoo15_UrsulavonderLeyen4_1</th>\n",
       "      <td>1</td>\n",
       "      <td>polit</td>\n",
       "      <td>polit</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_UrsulavonderLeyen5_1</th>\n",
       "      <td>1</td>\n",
       "      <td>polit</td>\n",
       "      <td>polit</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith1_1</th>\n",
       "      <td>1</td>\n",
       "      <td>act</td>\n",
       "      <td>act music</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith4_1</th>\n",
       "      <td>1</td>\n",
       "      <td>act</td>\n",
       "      <td>act music</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoo15_WillSmith5_1</th>\n",
       "      <td>1</td>\n",
       "      <td>act</td>\n",
       "      <td>act music</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            bekannt context    correct eval\n",
       "smoo15_UrsulavonderLeyen4_1       1   polit      polit    1\n",
       "smoo15_UrsulavonderLeyen5_1       1   polit      polit    1\n",
       "smoo15_WillSmith1_1               1     act  act music    1\n",
       "smoo15_WillSmith4_1               1     act  act music    1\n",
       "smoo15_WillSmith5_1               1     act  act music    1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleanDf.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get percent correct for one participant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makePercent(evalDf,pName):\n",
    "    d = {}\n",
    "    percentBekannt= evalDf['bekannt'].mean()*100\n",
    "    percentKontext = evalDf['eval'].mean()*100\n",
    "    d[pName] = {'familiarity':percentBekannt,\n",
    "                'context':percentKontext}\n",
    "    df = pd.DataFrame(d).T\n",
    "    df.index = [pName]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>context</th>\n",
       "      <th>familiarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pName</th>\n",
       "      <td>89.393939</td>\n",
       "      <td>86.363636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         context  familiarity\n",
       "pName  89.393939    86.363636"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "makePercent(cleanDf,'pName')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make one filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makeCond(logFile,cond,contextDf=contextDf):\n",
    "    pName = logFile.split('/')[-1].split('_')[0]\n",
    "    \n",
    "    thisTxt = getContent(logFile)\n",
    "    thisDf = makeMergeDf(thisTxt,cond)\n",
    "    evalDf = evalResp(thisDf,contextDf=contextDf)\n",
    "    \n",
    "    checkDf = pd.read_csv('../famousCheck/output/famousFamiliarity_%s.csv' % pName)\n",
    "    cleanDf = cleanUp(evalDf,checkDf=checkDf)\n",
    "    percentDf = makePercent(cleanDf,cond)\n",
    "    return percentDf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "thisDf = makeCond(logList[-1],'smoo',contextDf=contextDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>context</th>\n",
       "      <th>familiarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>smoo</th>\n",
       "      <td>89.393939</td>\n",
       "      <td>86.363636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        context  familiarity\n",
       "smoo  89.393939    86.363636"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thisDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make participant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makeParticipant(logFile,contextDf=contextDf):\n",
    "    pName = logFile.split('/')[-1].split('_')[0]\n",
    "    \n",
    "    pDf = pd.DataFrame()\n",
    "    for cond in ['smoo','edges','gray']:\n",
    "        thisCond = makeCond(logFile,cond,contextDf=contextDf)\n",
    "        pDf = pd.concat([pDf,thisCond])\n",
    "    \n",
    "    pDf = pd.DataFrame(pDf.stack() ).T\n",
    "    pDf.index = [pName]\n",
    "    return pDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">smoo</th>\n",
       "      <th colspan=\"2\" halign=\"left\">edges</th>\n",
       "      <th colspan=\"2\" halign=\"left\">gray</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>context</th>\n",
       "      <th>familiarity</th>\n",
       "      <th>context</th>\n",
       "      <th>familiarity</th>\n",
       "      <th>context</th>\n",
       "      <th>familiarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>89.393939</td>\n",
       "      <td>86.363636</td>\n",
       "      <td>100.0</td>\n",
       "      <td>98.484848</td>\n",
       "      <td>96.969697</td>\n",
       "      <td>98.484848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        smoo               edges                   gray            \n",
       "     context familiarity context familiarity    context familiarity\n",
       "9  89.393939   86.363636   100.0   98.484848  96.969697   98.484848"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "makeParticipant(logList[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### do this for all participants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makeBigDf(logList,contextDf=contextDf):\n",
    "    bigDf = pd.DataFrame()\n",
    "    for logFile in logList:\n",
    "        try:\n",
    "            thisDf = makeParticipant(logFile,contextDf=contextDf)\n",
    "            bigDf = pd.concat([bigDf,thisDf])\n",
    "        except:\n",
    "            print '!!!!',logFile\n",
    "    return bigDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "!!!! ../famousFiltered/experiment/data/3_famFaceFilter_2017_Jan_25_1257.log\n"
     ]
    }
   ],
   "source": [
    "bigDf = makeBigDf(logList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bigDf.to_csv('../famousFiltered/output/filteredChoiceLog.csv')"
   ]
  }
 ],
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