kars2.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:36e7de41e4a6e67e4f28ae14af2ef12b4ec9b849c6c73adc6362a029aedf98e7"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#%matplotlib inline  \n",
      "%pylab inline\n",
      "\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "\n",
      "\n",
      "# function to get age group\n",
      "def getAgeGrp( age ):\n",
      "    age = int(age)\n",
      "    if age <= 0:\n",
      "        return np.nan\n",
      "    elif age < 12:\n",
      "        return 'group1'\n",
      "    elif age < 18 and age >= 12:\n",
      "        return 'group2'\n",
      "    elif age < 30 and age >= 18 :\n",
      "        return 'group3'\n",
      "    else:\n",
      "        return 'Adult'\n",
      "    \n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['cond']\n",
        "`%matplotlib` prevents importing * from pylab and numpy\n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# load up labeled condition\n",
      "file = '../accByKeithLabel.txt'\n",
      "df =  pd.read_csv( file,sep=',',names=['subid', 'trial', 'block', 'acc', 'RT', 'age']).dropna()\n",
      "\n",
      "# define which columns labeled\n",
      "labInd = [ True]*12 + [True, True, False, False, True,True,False,False, True,True,False,False]*3\n",
      "df['isLabeled'] = df.trial.apply(lambda(x):labInd[x-1] )\n",
      "df['condLab'] = 1\n",
      "df[ 'pracBlock'] = df[ 'block']\n",
      "df.pracBlock.loc[ df.trial < 9] = 0   \n",
      "dfLab =df\n",
      "\n",
      "# load up besline condition\n",
      "file = '../accByKeith.txt'\n",
      "df =  pd.read_csv( file,sep=',',names=['subid', 'trial', 'block', 'acc', 'RT', 'age']).dropna()\n",
      "# define which columns labeled\n",
      "df['isLabeled'] = False #df.trial.apply(lambda(x):labInd[x-1] )\n",
      "df['condLab'] = 0\n",
      "df[ 'pracBlock'] = df[ 'block']\n",
      "df.pracBlock.loc[ df.trial < 9] = 0   \n",
      "# concatenate\n",
      "df = pd.concat( [dfLab, df], ignore_index=True)\n",
      "dfLab =df\n",
      "\n",
      "# load up supervised condition\n",
      "file = '../accByKeithSup.txt'\n",
      "df =  pd.read_csv( file,sep=',',names=['subid', 'trial', 'block', 'acc', 'RT', 'age']).dropna()\n",
      "# define which columns labeled\n",
      "df['isLabeled'] = df.trial.apply(lambda(x):labInd[x-1] )\n",
      "df['condLab'] = 2\n",
      "df[ 'pracBlock'] = df[ 'block']\n",
      "df.pracBlock.loc[ df.trial < 9] = 0   \n",
      "# concatenate\n",
      "df = pd.concat( [dfLab, df], ignore_index=True)\n",
      "dfLab =df\n",
      "\n",
      "# load up ADULT besline condition\n",
      "file = '../accByKeith_AdultBL.txt'\n",
      "df =  pd.read_csv( file,sep=',',names=['subid', 'trial', 'block', 'acc', 'RT', 'age']).dropna()\n",
      "# define which columns labeled\n",
      "df['isLabeled'] = False #df.trial.apply(lambda(x):labInd[x-1] )\n",
      "df['condLab'] = 0\n",
      "df[ 'pracBlock'] = df[ 'block']\n",
      "df.pracBlock.loc[ df.trial < 9] = 0 \n",
      "df['age'] = 24*18\n",
      "# concatenate\n",
      "df = pd.concat( [dfLab, df], ignore_index=True)\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "# clear unecessary data\n",
      "del dfLab\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# df[df.condLab == 2]\n",
      "# blockAcc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# get accuracy by block, add age groups\n",
      "blockAcc = df.groupby( ['subid','pracBlock']).mean().reset_index()\n",
      "\n",
      "blockCount = df.groupby( ['subid','pracBlock']).count() #.reset_index()\n",
      "blockCount = blockCount['acc'].reset_index()\n",
      "\n",
      "\n",
      "# blockCount = df.groupby( ['subid','pracBlock']).count().reset_index()  # nan dropped at loading\n",
      "blockAcc[ 'count'] = blockCount['acc']\n",
      "\n",
      "blockAcc['ageGrps'] = blockAcc.age.apply( lambda(x): getAgeGrp(x))\n",
      "df['ageGrps'] = df.age.apply( lambda(x): getAgeGrp(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# find first peak and max accuracy block\n",
      "\n",
      "def firstPeak( acc ):\n",
      "    ' find first peak and index of a series'\n",
      "    bPeak, aPeak = (1, acc.iloc[1])   # skip block 0 (practice)\n",
      "    # loop through and find first peak\n",
      "    for block,a in enumerate(acc):\n",
      "        if block == 0:\n",
      "            pass\n",
      "        elif a > aPeak:\n",
      "            bPeak = block\n",
      "            aPeak = a\n",
      "        elif a == aPeak:\n",
      "            pass\n",
      "        else:\n",
      "            break            \n",
      "    return bPeak, aPeak\n",
      "\n",
      "def maxPeak( acc ):    \n",
      "    'Find maximum value and index of a series'\n",
      "    bMax, aMax = (1, acc.iloc[1])    # skip block 0 (practice)\n",
      "    # loop through and find max accuracy\n",
      "    for block,a in enumerate(acc):\n",
      "        if block == 0:\n",
      "            pass\n",
      "        elif a > aMax:\n",
      "            bMax = block\n",
      "            aMax = a\n",
      "    return bMax, aMax\n",
      "        \n",
      "#---------------------------------------------\n",
      "\n",
      "\n",
      "# go through each block and find peak and max accuracy\n",
      "\n",
      "dfToPeak = pd.DataFrame()\n",
      "blockAccToPeak = pd.DataFrame()\n",
      "\n",
      "for s in blockAcc.subid.unique():\n",
      "    x = blockAcc[ blockAcc.subid == s]    \n",
      "    bPeak, aPeak = firstPeak( x.acc )\n",
      "    bMax, aMax = maxPeak( x.acc)    \n",
      "#     print s, x.acc, bPeak, aPeak, '\\n'\n",
      "#     print x.acc, bMax, aMax\n",
      "\n",
      "    # df.loc[row_indexer,column_indexer]\n",
      "    blockAcc.loc[ blockAcc.subid ==s, 'peakBlock'] = bPeak\n",
      "    blockAcc.loc[ blockAcc.subid ==s, 'maxBlock'] = bMax\n",
      "    df.loc[ df.subid ==s, 'peakBlock'] = bPeak\n",
      "    df.loc[ df.subid ==s, 'maxBlock'] = bMax\n",
      "  \n",
      "    dfToPeak = pd.concat( [ dfToPeak, df[(df.subid == s) &( df.pracBlock <= bPeak )]  ], ignore_index=True)\n",
      "    blockAccToPeak  = pd.concat( [ blockAccToPeak, blockAcc[(blockAcc.subid == s) &( blockAcc.pracBlock <= bPeak )]  ], ignore_index=True)\n",
      "# blockAcc[ blockAcc.peakBlock != 1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# show accuracy trends\n",
      "gCol = { 'group1':'r-', 'group2':'b-', 'group3':'g-', 'Adult':'c-','nan':'y-'}\n",
      "\n",
      "subplot(121)\n",
      "title( 'all participants')\n",
      "for s in blockAcc.subid.unique():\n",
      "    sub = blockAcc[ blockAcc.subid == s]\n",
      "    plot( sub.pracBlock, sub.acc, gCol[ str(sub.iloc[0].ageGrps)] )\n",
      "    \n",
      "subplot(122)\n",
      "title( 'age groups')\n",
      "for g in blockAcc.ageGrps.unique():\n",
      "    sub = blockAcc[ blockAcc.ageGrps == g].groupby('pracBlock').mean().reset_index()    \n",
      "    plot( sub.pracBlock, sub.acc, gCol[str(g)], label=g )\n",
      "legend()    \n",
      "    \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "<matplotlib.legend.Legend at 0x74b0490>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3x9DQsER6Dw8P7bW7uzux93h7LI5CocDV1VV7bWpqio2NTYn8ubm59OnThzZt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\ny0qMuUIkI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g2dFk8mWZVHooMFPbwSyTk8DRNNKFH5FSicF0miKTzs25JCMakYFPPk7uVA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4fI5DLX9/5IgFrZIzckErNCFT5xE8/d6JPhfK9YVQXNQYFx48Yw/Jo0fA1q3M+KmrM6u7dCmC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\nY4zDAjmDwWCMcf4fLxNcIinhltEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6190590>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# export to csv\n",
      "blockAcc.to_csv( 'blockAccBaselineAndLabel.txt' ,sep = ',')\n",
      "df.to_csv( 'trialAccBaselineAndLabel.txt' ,sep = ',')\n",
      "\n",
      "blockAccToPeak.to_csv( 'blockAccToPeak1.txt' ,sep = '\\t')\n",
      "dfToPeak.to_csv( 'trialAccToPeak1.txt' ,sep = '\\t')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# make tables to do some stats\n",
      "    # get trails from the 2 highest blocks, check acc vs chance (50%)\n",
      "    # ignore first block\n",
      "    \n",
      "labCond = 0 # 1-label; 0-baseline; 2 supervised  \n",
      "grp = 'group3'\n",
      "\n",
      "\n",
      "\n",
      "bAcc = blockAcc[(blockAcc.block >1) & (blockAcc.ageGrps == grp  ) & (blockAcc.condLab == labCond)] # skip first block, only ages >= 15\n",
      "dfAfter1 = df[ (df.block > 1)& (df.age >= 15 ) & (df.condLab == labCond)]\n",
      "\n",
      "bestBlocks = pd.DataFrame()  # to store trials from 2 best blocks\n",
      "worstBlocks = pd.DataFrame() # to store trials from 2 worst blocks\n",
      "\n",
      "# loop through participants, get \n",
      "for s in bAcc.subid.unique():\n",
      "    thisSub = bAcc[ bAcc.subid == s].sort( columns='acc',ascending=False)\n",
      "    \n",
      "    # get good blocks\n",
      "    bIdxGood = thisSub.block.iloc[0:2]  \n",
      "    for b in bIdxGood:\n",
      "        bestBlocks = pd.concat( [ bestBlocks,dfAfter1[ (dfAfter1.subid == s ) & ( dfAfter1.block == b) ] ], ignore_index= True)\n",
      "    \n",
      "    # get trials from remaining blocks\n",
      "    tempBad = dfAfter1[ dfAfter1.subid == s]\n",
      "    for b in bIdxGood:\n",
      "        tempBad = tempBad[ tempBad.block != b]\n",
      "    worstBlocks = pd.concat( [ worstBlocks,tempBad ], ignore_index= True)\n",
      "    \n",
      "   \n",
      " "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# look at the best/worst and see if different from 50%\n",
      "\n",
      "from scipy import stats\n",
      "\n",
      "goodAcc = bestBlocks.groupby( 'subid').mean()\n",
      "(t,prob) = stats.ttest_1samp(goodAcc.acc, .5 )\n",
      "print 'good blocks: (t,p-val)', t,prob\n",
      "\n",
      "badAcc = worstBlocks.groupby( 'subid').mean()\n",
      "(t,prob) = stats.ttest_1samp(badAcc.acc, .5 )\n",
      "print 'bad blocks: (t,p-val)', t,prob\n",
      "\n",
      "\n",
      "allAcc = dfAfter1.groupby( 'subid').mean()\n",
      "(t,prob) = stats.ttest_1samp(allAcc.acc, .5 )\n",
      "print 'all blocks: (t,p-val)', t,prob\n",
      "\n",
      "\n",
      "print 'means good, bad, all:', mean( goodAcc.acc), mean( badAcc.acc), mean( allAcc.acc)\n",
      "\n",
      "\n",
      "# print thisSub.block\n",
      "# bAcc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "good blocks: (t,p-val) 3.32683535865 0.00603334318495\n",
        "bad blocks: (t,p-val) -3.51768151889 0.00481805906791\n",
        "all blocks: (t,p-val) 2.48352474015 0.0192649143267\n",
        "means good, bad, all: 0.611943724292 0.370455586081 0.587057672975\n"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# blockAcc\n",
      "# worstBlocks\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# now get accuracy for a sliding window\n",
      "# df.condLab.value_counts()\n",
      "df[df.ageGrps == 'Adult'] "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>subid</th>\n",
        "      <th>trial</th>\n",
        "      <th>block</th>\n",
        "      <th>acc</th>\n",
        "      <th>RT</th>\n",
        "      <th>age</th>\n",
        "      <th>isLabeled</th>\n",
        "      <th>condLab</th>\n",
        "      <th>pracBlock</th>\n",
        "      <th>ageGrps</th>\n",
        "      <th>peakBlock</th>\n",
        "      <th>maxBlock</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2516</th>\n",
        "      <td> 6375</td>\n",
        "      <td>  1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2438.4</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2517</th>\n",
        "      <td> 6375</td>\n",
        "      <td>  2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2765.4</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2518</th>\n",
        "      <td> 6375</td>\n",
        "      <td>  3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2769.6</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2519</th>\n",
        "      <td> 6375</td>\n",
        "      <td>  4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2116.7</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2520</th>\n",
        "      <td> 6375</td>\n",
        "      <td>  5</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2766.4</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2521</th>\n",
        "      <td> 6375</td>\n",
        "      <td>  6</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2779.5</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2522</th>\n",
        "      <td> 6375</td>\n",
        "      <td>  7</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2760.0</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2523</th>\n",
        "      <td> 6375</td>\n",
        "      <td>  8</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2748.1</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2524</th>\n",
        "      <td> 6375</td>\n",
        "      <td>  9</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1493.2</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2525</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 10</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2787.9</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2526</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 11</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2742.7</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2527</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 12</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3030.8</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2528</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 13</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2804.9</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2529</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 14</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2763.2</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2530</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 15</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2732.9</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2531</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 16</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2738.5</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2532</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 17</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2779.3</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2533</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 18</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2791.6</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2534</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 19</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2784.1</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2535</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 20</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2777.3</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2536</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 21</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2768.1</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2537</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 22</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2780.0</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2538</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 23</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2755.1</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2539</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 24</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2767.5</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2540</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 25</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2766.7</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2541</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 26</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2764.6</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2542</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 27</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2760.2</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2543</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 28</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2776.4</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2544</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 29</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2725.3</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2545</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 30</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2439.5</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2546</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 31</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2754.5</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2547</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 32</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2729.9</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2548</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 33</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2741.8</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2549</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 34</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2749.2</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2550</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 35</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1162.0</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2551</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 36</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2731.6</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2552</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 37</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2752.2</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2553</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 38</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2770.1</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2554</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 39</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2771.7</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2555</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 40</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2753.2</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2556</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 41</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2755.7</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2557</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 42</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2764.0</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2558</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 43</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1949.5</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2559</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 44</td>\n",
        "      <td> 5</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2773.0</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2560</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 45</td>\n",
        "      <td> 5</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3037.2</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2561</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 46</td>\n",
        "      <td> 5</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2754.7</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2562</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 47</td>\n",
        "      <td> 5</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2727.5</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2563</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 48</td>\n",
        "      <td> 5</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2773.2</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2564</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 49</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3031.0</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2565</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 50</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2791.8</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2566</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 51</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2735.8</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2567</th>\n",
        "      <td> 6375</td>\n",
        "      <td> 52</td>\n",
        "      <td> 5</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2736.2</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2568</th>\n",
        "      <td> 6378</td>\n",
        "      <td>  1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3001.8</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2569</th>\n",
        "      <td> 6378</td>\n",
        "      <td>  2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3025.4</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2570</th>\n",
        "      <td> 6378</td>\n",
        "      <td>  3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2773.7</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2571</th>\n",
        "      <td> 6378</td>\n",
        "      <td>  4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2724.8</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2572</th>\n",
        "      <td> 6378</td>\n",
        "      <td>  5</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3026.5</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2573</th>\n",
        "      <td> 6378</td>\n",
        "      <td>  6</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2725.1</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2574</th>\n",
        "      <td> 6378</td>\n",
        "      <td>  7</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2767.9</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2575</th>\n",
        "      <td> 6378</td>\n",
        "      <td>  8</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1896.3</td>\n",
        "      <td> 432</td>\n",
        "      <td> False</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Adult</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>405 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "      subid  trial  block  acc      RT  age isLabeled  condLab  pracBlock  \\\n",
        "2516   6375      1      1    1  2438.4  432     False        0          0   \n",
        "2517   6375      2      1    0  2765.4  432     False        0          0   \n",
        "2518   6375      3      1    0  2769.6  432     False        0          0   \n",
        "2519   6375      4      1    0  2116.7  432     False        0          0   \n",
        "2520   6375      5      1    0  2766.4  432     False        0          0   \n",
        "2521   6375      6      1    0  2779.5  432     False        0          0   \n",
        "2522   6375      7      1    0  2760.0  432     False        0          0   \n",
        "2523   6375      8      1    0  2748.1  432     False        0          0   \n",
        "2524   6375      9      1    1  1493.2  432     False        0          1   \n",
        "2525   6375     10      1    1  2787.9  432     False        0          1   \n",
        "2526   6375     11      1    1  2742.7  432     False        0          1   \n",
        "2527   6375     12      1    1  3030.8  432     False        0          1   \n",
        "2528   6375     13      2    0  2804.9  432     False        0          2   \n",
        "2529   6375     14      2    0  2763.2  432     False        0          2   \n",
        "2530   6375     15      2    0  2732.9  432     False        0          2   \n",
        "2531   6375     16      2    1  2738.5  432     False        0          2   \n",
        "2532   6375     17      2    1  2779.3  432     False        0          2   \n",
        "2533   6375     18      2    1  2791.6  432     False        0          2   \n",
        "2534   6375     19      2    0  2784.1  432     False        0          2   \n",
        "2535   6375     20      2    0  2777.3  432     False        0          2   \n",
        "2536   6375     21      2    1  2768.1  432     False        0          2   \n",
        "2537   6375     22      2    0  2780.0  432     False        0          2   \n",
        "2538   6375     23      3    0  2755.1  432     False        0          3   \n",
        "2539   6375     24      3    1  2767.5  432     False        0          3   \n",
        "2540   6375     25      3    0  2766.7  432     False        0          3   \n",
        "2541   6375     26      3    0  2764.6  432     False        0          3   \n",
        "2542   6375     27      3    0  2760.2  432     False        0          3   \n",
        "2543   6375     28      3    1  2776.4  432     False        0          3   \n",
        "2544   6375     29      3    1  2725.3  432     False        0          3   \n",
        "2545   6375     30      3    0  2439.5  432     False        0          3   \n",
        "2546   6375     31      3    0  2754.5  432     False        0          3   \n",
        "2547   6375     32      3    1  2729.9  432     False        0          3   \n",
        "2548   6375     33      4    0  2741.8  432     False        0          4   \n",
        "2549   6375     34      4    0  2749.2  432     False        0          4   \n",
        "2550   6375     35      4    0  1162.0  432     False        0          4   \n",
        "2551   6375     36      4    1  2731.6  432     False        0          4   \n",
        "2552   6375     37      4    0  2752.2  432     False        0          4   \n",
        "2553   6375     38      4    0  2770.1  432     False        0          4   \n",
        "2554   6375     39      4    1  2771.7  432     False        0          4   \n",
        "2555   6375     40      4    0  2753.2  432     False        0          4   \n",
        "2556   6375     41      4    0  2755.7  432     False        0          4   \n",
        "2557   6375     42      4    1  2764.0  432     False        0          4   \n",
        "2558   6375     43      5    0  1949.5  432     False        0          5   \n",
        "2559   6375     44      5    1  2773.0  432     False        0          5   \n",
        "2560   6375     45      5    1  3037.2  432     False        0          5   \n",
        "2561   6375     46      5    1  2754.7  432     False        0          5   \n",
        "2562   6375     47      5    1  2727.5  432     False        0          5   \n",
        "2563   6375     48      5    1  2773.2  432     False        0          5   \n",
        "2564   6375     49      5    0  3031.0  432     False        0          5   \n",
        "2565   6375     50      5    0  2791.8  432     False        0          5   \n",
        "2566   6375     51      5    0  2735.8  432     False        0          5   \n",
        "2567   6375     52      5    1  2736.2  432     False        0          5   \n",
        "2568   6378      1      1    1  3001.8  432     False        0          0   \n",
        "2569   6378      2      1    1  3025.4  432     False        0          0   \n",
        "2570   6378      3      1    1  2773.7  432     False        0          0   \n",
        "2571   6378      4      1    0  2724.8  432     False        0          0   \n",
        "2572   6378      5      1    0  3026.5  432     False        0          0   \n",
        "2573   6378      6      1    1  2725.1  432     False        0          0   \n",
        "2574   6378      7      1    1  2767.9  432     False        0          0   \n",
        "2575   6378      8      1    1  1896.3  432     False        0          0   \n",
        "        ...    ...    ...  ...     ...  ...       ...      ...        ...   \n",
        "\n",
        "     ageGrps  peakBlock  maxBlock  \n",
        "2516   Adult          1         1  \n",
        "2517   Adult          1         1  \n",
        "2518   Adult          1         1  \n",
        "2519   Adult          1         1  \n",
        "2520   Adult          1         1  \n",
        "2521   Adult          1         1  \n",
        "2522   Adult          1         1  \n",
        "2523   Adult          1         1  \n",
        "2524   Adult          1         1  \n",
        "2525   Adult          1         1  \n",
        "2526   Adult          1         1  \n",
        "2527   Adult          1         1  \n",
        "2528   Adult          1         1  \n",
        "2529   Adult          1         1  \n",
        "2530   Adult          1         1  \n",
        "2531   Adult          1         1  \n",
        "2532   Adult          1         1  \n",
        "2533   Adult          1         1  \n",
        "2534   Adult          1         1  \n",
        "2535   Adult          1         1  \n",
        "2536   Adult          1         1  \n",
        "2537   Adult          1         1  \n",
        "2538   Adult          1         1  \n",
        "2539   Adult          1         1  \n",
        "2540   Adult          1         1  \n",
        "2541   Adult          1         1  \n",
        "2542   Adult          1         1  \n",
        "2543   Adult          1         1  \n",
        "2544   Adult          1         1  \n",
        "2545   Adult          1         1  \n",
        "2546   Adult          1         1  \n",
        "2547   Adult          1         1  \n",
        "2548   Adult          1         1  \n",
        "2549   Adult          1         1  \n",
        "2550   Adult          1         1  \n",
        "2551   Adult          1         1  \n",
        "2552   Adult          1         1  \n",
        "2553   Adult          1         1  \n",
        "2554   Adult          1         1  \n",
        "2555   Adult          1         1  \n",
        "2556   Adult          1         1  \n",
        "2557   Adult          1         1  \n",
        "2558   Adult          1         1  \n",
        "2559   Adult          1         1  \n",
        "2560   Adult          1         1  \n",
        "2561   Adult          1         1  \n",
        "2562   Adult          1         1  \n",
        "2563   Adult          1         1  \n",
        "2564   Adult          1         1  \n",
        "2565   Adult          1         1  \n",
        "2566   Adult          1         1  \n",
        "2567   Adult          1         1  \n",
        "2568   Adult          4         4  \n",
        "2569   Adult          4         4  \n",
        "2570   Adult          4         4  \n",
        "2571   Adult          4         4  \n",
        "2572   Adult          4         4  \n",
        "2573   Adult          4         4  \n",
        "2574   Adult          4         4  \n",
        "2575   Adult          4         4  \n",
        "         ...        ...       ...  \n",
        "\n",
        "[405 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def slideAcc( acc, window=6 ):\n",
      "    # return a sliding window average of accuracy \n",
      "    N = len(acc)\n",
      "    wAcc = np.zeros((N))*np.nan\n",
      "    for i1 in range(window,N+1):      \n",
      "        a = acc.values[i1-window:i1]\n",
      "        #print i1, a\n",
      "        wAcc[i1-1] = nanmean(a) # get mean over window    \n",
      "    return wAcc\n",
      "\n",
      "\n",
      "    #figure()\n",
      "    #plot( df[ df.subid ==s].trial, df[ df.subid ==s].windAcc, '.-' )\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import binom\n",
      "import matplotlib.backends.backend_pdf as pdfOut\n",
      "\n",
      "\n",
      "# go through each subject, add accuracy over sliding window\n",
      "w = 8\n",
      "df[ 'windAcc']  = np.nan\n",
      "for s in df.subid.unique():\n",
      "    acc = df[ df.subid ==s].acc\n",
      "    df.ix[df.subid ==s, 'windAcc' ] = slideAcc( acc, window=w )\n",
      "    \n",
      "\n",
      "\n",
      "# show learning curves for some condition and age group\n",
      "cond = 0\n",
      "ageGrp = 'Adult' \n",
      "pList = []\n",
      "mList = []\n",
      "#for output\n",
      "pdf = pdfOut.PdfPages( 'curves%sCond%d.pdf'%(ageGrp, cond))\n",
      "\n",
      "\n",
      "# for s in df[df.condLab == cond].subid.unique():   \n",
      "for s in df[(df.condLab == cond) & (df.ageGrps == ageGrp)].subid.unique():   \n",
      "\n",
      "    a = df[ df.subid ==s].acc\n",
      "    x, n = sum(a), len(a)\n",
      "    pVal = binom.pmf(x, n, .5)\n",
      "    \n",
      "    # Plot\n",
      "    if True:\n",
      "        fig = figure()   \n",
      "        plot( df[ df.subid ==s].trial, df[ df.subid ==s].windAcc, 'b.-', label='window (%d)'%w )\n",
      "        plot( df[ df.subid ==s].trial, df[ df.subid ==s].acc, 'g*', label ='indiv trials' )       \n",
      "        plot( np.arange(0,50),[.5]*50, 'r-', label='chance')  # chance line    \n",
      "        text(1, .03,  'Mean: %.2f,  p=%.02f' % (mean(a) ,pVal) )\n",
      "        xlabel('Trial')\n",
      "        legend()       \n",
      "        pdf.savefig( fig )\n",
      "    \n",
      "    # hypothesis testing\n",
      "    a = df[ df.subid ==s].acc\n",
      "    x, n = sum(a), len(a)\n",
      "    pVal = binom.pmf(x, n, .5)\n",
      "    \n",
      "    if len( a ) > 15:   #(at least 20 trials)    \n",
      "        pList.append(pVal)\n",
      "        mList.append( mean(a))        \n",
      "    #     print( 'Mean: %.2f,  p=%.02f' % (mean(a) ,pVal) )\n",
      "    #     print(a.values )\n",
      "    \n",
      "pdf.close()    \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sXt5sUA8JCUFpaanhfWlpKdRqtVGZH374AX/7298AAJGRkejRowd+++03JCcnN1lf46BO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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8af54d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qtRrz589HcHAw0tPTAQALFy7sWG2JiMisDJ6pm/VAPFMnIjKZqdnJO0qJiCSE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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8af5fd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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egL8/cPYskJHBq1+ISBdD3Qp5eWn/GxGhvayRiKgR+9StUEWF9gx93TrA0dHc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\nMzJa1m8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8b032d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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T9FFC+EElwkRBXaAMJUuNabIQQmAR21G6khC2PREmCuoCZegU3phgmJgInDzJ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\nIiIU1AkhREQoqBNCiIj8P0AsH+GxQc+aAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6b99e50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vw8vjgmyNQb2V2G9qO1LuguFxQbbGoN5KTJLajpSTpQzqZGsM6q3AJKltSfVM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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5cb6590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kIQx1IiIJYagTEUkIQ52ISEIY6kREEsJQJyKSEIY6EZGEMNSJiCSEoU5EJCEM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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7952bd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1AICtW7ciKioKkZGRiI+PR3l5udXqnZeXh9DQUAQHB2PNmjXtlmuvfcZuT0R2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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6bb4710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xEb1798bOnTsREBCAgQMHIicnBxEREeoyp0+fxty5c/Hzzz/j7t27SEhIwIYN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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5cedbd0>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pList = np.concatenate( (np.array(pList), np.array( mList)), axis=0)\n",
      "pList = np.array( pList)\n",
      "mList = np.array( mList)\n",
      "\n",
      "\n",
      "lrnPs = pList[ (pList <.05) & (mList > .5)]\n",
      "nLrnPs = pList[ (pList <.05) & (mList < .5)]\n",
      "print( '%d out of %d learned' % ( len(lrnPs),len(pList)))\n",
      "print( '%d out of %d anti-learned' % ( len(nLrnPs),len(pList)))\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "6 out of 8 learned\n",
        "0 out of 8 anti-learned\n"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    }
   ],
   "metadata": {}
  }
 ]
}