{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Data analyses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np, sys, scipy.stats, pandas as pd, os, os.path, csv, PythonAnalyses\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "%matplotlib inline\n",
    "pd.options.display.mpl_style = 'default'\n",
    "\n",
    "Start = 1 #if I want to reload data from csvs = 1. If i want to load python pickle == 0\n",
    "\n",
    "filename = 'AdditionalSingleton_TargetUncertain_Freq.csv'\n",
    "csvName = 'AdditionalSingleton_TargetUncertain_Freq'\n",
    "\n",
    "if sys.platform == 'linux2': #is this my linux laptop\n",
    "    path = '/home/dan/Dropbox/TargetUncertainty/Exp4'\n",
    "elif sys.platform == 'darwin': #is this my mac work comp    \n",
    "    path = '/Users/danvatterott/Dropbox Encore/Dropbox/TargetUncertainty/Exp4/'\n",
    "\n",
    "os.chdir(path)\n",
    "\n",
    "if Start == 1: #load in data from csv\n",
    "    numfiles = len([name for name in os.listdir('.') if os.path.isfile(name) and name.endswith(\".csv\") and name[0].isdigit()])\n",
    "    for partnum in xrange(1,numfiles+1): #this is a loop that goes through all participants\n",
    "        filename = '%s_' % str(partnum) + csvName + '.csv'\n",
    "        if partnum == 1: \n",
    "            df = pd.read_csv(filename)\n",
    "        if partnum > 1: \n",
    "            df = df.append(pd.read_csv(filename))\n",
    "    df.columns = map(str.lstrip, df.columns)\n",
    "    df.to_pickle(csvName+'.pkl') #save this data frame all together\n",
    "elif Start == 0: #load the data from a previously saved python file. should save time if not adding csv data. \n",
    "    df = pd.read_pickle(csvName+'.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>TarType</th>\n",
       "      <th>TarLoc</th>\n",
       "      <th>TarColorCode</th>\n",
       "      <th>DistCond</th>\n",
       "      <th>TarDistDist</th>\n",
       "      <th>DistLoc</th>\n",
       "      <th>DistLine</th>\n",
       "      <th>Resp</th>\n",
       "      <th>Block</th>\n",
       "      <th>ColorFreq</th>\n",
       "      <th>ChangeCounter</th>\n",
       "      <th>Acc</th>\n",
       "      <th>RT</th>\n",
       "      <th>Sub#</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350.4923</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>616.6495</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TarType  TarLoc  TarColorCode  DistCond  TarDistDist  DistLoc  DistLine  \\\n",
       "0        2       1             4         2            2        3         2   \n",
       "1        2       2             2         1            0        0         0   \n",
       "2        2       6             4         1            0        0         0   \n",
       "\n",
       "   Resp  Block  ColorFreq  ChangeCounter  Acc         RT  Sub#  \n",
       "0     0      0          1              0    0  2000.0000     1  \n",
       "1     3      0          2              0    0   350.4923     1  \n",
       "2     1      0          1              0    0   616.6495     1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3] #take a quick look at the dataframe. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SingletonCueMatch = 1 = color singleton at cued location/object\n",
    "SingletonCueMatch = 2 = color singleton at uncued location/object\n",
    "SingletonCueMatch = 0 = color singleton absent\n",
    "BlockType = 1 = cross\n",
    "BlockType = 2 = circle\n",
    "BlockType = 3 = cardinal\n",
    "BlockType = 4 = diagonal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Trimming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data trimming. correct, slower than 300 ms and faster than 1500. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Trimmer = df['Block'] > 0 #not the practice block\n",
    "Trimmer2 = df['Acc'] == 1 #not an error trial\n",
    "Trimmer3 = df['RT'] > 300 #not a super fast response\n",
    "PartTrim = (df['Sub#'] != 66)\n",
    "\n",
    "DistCond = df[Trimmer & Trimmer2 & Trimmer3].groupby(['DistCond','Sub#']) \n",
    "RTTrim = DistCond['RT'].mean() + 2.5*DistCond['RT'].std(ddof=0)\n",
    "subList = df.drop_duplicates(subset='Sub#')\n",
    "TrimmerOld = [False]*len(df)\n",
    "for sub in subList['Sub#']: #loop through every subject. \n",
    "    TrimmerA = (df.DistCond == 1) & (df['RT'] < RTTrim[1][sub]) & (df['Sub#'] == sub)\n",
    "    TrimmerB = (df.DistCond == 2) & (df['RT'] < RTTrim[2][sub]) & (df['Sub#'] == sub)\n",
    "    Trimmer4 = [any(tup) for tup in zip(TrimmerA, TrimmerB, TrimmerOld)] #combine those 2 RT trimmers. \n",
    "    TrimmerOld = Trimmer4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34\n",
      "7680\n",
      "0.004427083\n"
     ]
    }
   ],
   "source": [
    "totalTrialNum = np.size(df[PartTrim & Trimmer],0)\n",
    "ex_counter = 0\n",
    "Total=0\n",
    "for sub in df['Sub#'].unique(): #loop through every subject. \n",
    "    TrimmerA = (df[Trimmer].DistCond == 1) & (df[Trimmer]['RT'] > RTTrim[1][sub]) & (df[Trimmer]['Sub#'] == sub) & (df[Trimmer].Acc==1)\n",
    "    TrimmerB = (df[Trimmer].DistCond == 2) & (df[Trimmer]['RT'] > RTTrim[2][sub]) & (df[Trimmer]['Sub#'] == sub) & (df[Trimmer].Acc==1)\n",
    "    TrimmerC = df[Trimmer]['RT'] < 300 & (df[Trimmer].Acc==1)\n",
    "    Total+= (sum(TrimmerA)+sum(TrimmerB)+sum(TrimmerC))\n",
    "Total = sum(TrimmerA) + sum(TrimmerB) + sum(TrimmerC)\n",
    "print Total\n",
    "print totalTrialNum\n",
    "Rt_ex = float(Total)/float(totalTrialNum)\n",
    "print '%.9f' % Rt_ex\n",
    "#measuring how many trials are excluded. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "checking acc. looks good. everyone above 90%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sub#\n",
       "1    0.959375\n",
       "2    0.878125\n",
       "3    0.950000\n",
       "4    0.981250\n",
       "5    0.917708\n",
       "6    0.918750\n",
       "7    0.981250\n",
       "8    0.922917\n",
       "Name: Acc, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tableAcc = df[Trimmer].pivot_table(values='Acc', index='Sub#', aggfunc=np.mean)\n",
    "tableAcc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sub#\n",
       "1    850.898361\n",
       "2    614.358924\n",
       "3    670.047412\n",
       "4    645.053529\n",
       "5    714.054194\n",
       "6    804.324221\n",
       "7    706.499745\n",
       "8    639.071922\n",
       "Name: RT, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tableRT = df[Trimmer & Trimmer2 & Trimmer3 & Trimmer4].pivot_table(values='RT', index='Sub#', aggfunc=np.mean)\n",
    "tableRT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RT Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "DTRIM = df['DistCond'] == 1\n",
    "tableRT2A = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & DTRIM].pivot_table(values='RT', index='Sub#', columns=['ColorFreq'], aggfunc=np.mean)\n",
    "DTRIM = df['DistCond'] == 2\n",
    "tableRT2B = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & DTRIM].pivot_table(values='RT', index='Sub#', columns=['ColorFreq'], aggfunc=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Q+Xz729/Oc889lwsvvDDJ1itXzz77bI455pg9ft93v/vdnHrqqRkYGEiSnH/++fmjP/qj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\n0u8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe16d02c4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xdata = df['ColorFreq'][PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4].unique()\n",
    "\n",
    "Absent  = np.mean(tableRT2A)\n",
    "Present = np.mean(tableRT2B)\n",
    "\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "axes = fig.add_subplot(111)\n",
    "\n",
    "axes.plot(xdata, Absent, label='Singleton Absent'); \n",
    "axes.plot(xdata, Present, label='Singleton Present')\n",
    "axes.legend()\n",
    "axes.set_ylim(600,900)\n",
    "axes.set_xlim(0,3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "\u001b[1;31mSub#\t\t\t1\t\t\t2\t\t\t\u001b[0m\n",
      "1\t\t\t862.45\t\t\t825.76\t\t\t\n",
      "2\t\t\t605.87\t\t\t621.91\t\t\t\n",
      "3\t\t\t657.69\t\t\t680.57\t\t\t\n",
      "4\t\t\t639.63\t\t\t641.01\t\t\t\n",
      "5\t\t\t712.17\t\t\t713.66\t\t\t\n",
      "6\t\t\t788.72\t\t\t805.58\t\t\t\n",
      "7\t\t\t712.63\t\t\t700.24\t\t\t\n",
      "8\t\t\t625.3\t\t\t653.67\t\t\t\n",
      "\u001b[1;31mmean\t\t\t700.56\t\t\t705.3\t\t\t\n",
      "STE\t\t\t31.18\t\t\t26.4\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (-0.62813545453384934, 0.54985118011569645)\n",
      "n = 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe16cd2b810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConType = df['ColorFreq'] == 1\n",
    "tableRT = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & ConType].pivot_table(values='RT', index='Sub#', columns=['DistCond'], aggfunc=np.mean)\n",
    "PythonAnalyses.dataTableRT(tableRT)\n",
    "\n",
    "PythonAnalyses.Test_and_Plot_PairwiseComparison(tableRT, 600, 1000, 'Singleton Condition','Changing Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "\u001b[1;31mSub#\t\t\t1\t\t\t2\t\t\t\u001b[0m\n",
      "1\t\t\t870.44\t\t\t884.31\t\t\t\n",
      "2\t\t\t598.66\t\t\t632.69\t\t\t\n",
      "3\t\t\t655.06\t\t\t690.52\t\t\t\n",
      "4\t\t\t671.03\t\t\t657.42\t\t\t\n",
      "5\t\t\t714.52\t\t\t722.99\t\t\t\n",
      "6\t\t\t869.04\t\t\t797.57\t\t\t\n",
      "7\t\t\t692.76\t\t\t721.18\t\t\t\n",
      "8\t\t\t655.33\t\t\t619.92\t\t\t\n",
      "\u001b[1;31mmean\t\t\t715.86\t\t\t715.83\t\t\t\n",
      "STE\t\t\t35.61\t\t\t31.43\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (0.0023279741346843245, 0.99820750157113358)\n",
      "n = 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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EaWlphMPh6HwkEiEtLQ3HcdqdFxGR3hdzyD/22GPU19eTmJjIXXfdRUJCArm5uZSUlACQ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\nC1rM18x7JezwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe16cd50610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConType = df['ColorFreq'] == 2\n",
    "tableRT = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & ConType].pivot_table(values='RT', index='Sub#', columns=['DistCond'], aggfunc=np.mean)\n",
    "PythonAnalyses.dataTableRT(tableRT)\n",
    "\n",
    "PythonAnalyses.Test_and_Plot_PairwiseComparison(tableRT, 600, 1000, 'Singleton Condition','Change 1 Trial Prev')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oYQMAAAAAH0bB5kdYl2wemZtH5uaRuXlkbh6Zm0fm5pG5d6JgAwAAAAAvRQ8b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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe16d214bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tableRT2 = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4].pivot_table(values='RT', index='Block', columns=['DistCond'], aggfunc=np.mean)\n",
    "\n",
    "xdata = df['Block'][PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4].unique()\n",
    "Absent  = tableRT2[1]\n",
    "Present = tableRT2[2]\n",
    "\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "axes = fig.add_subplot(111)\n",
    "\n",
    "axes.plot(xdata, Absent, 'blue', label='Singleton Absent'); \n",
    "axes.plot(xdata, Present, 'green', label='Singleton Present')\n",
    "axes.legend()\n",
    "axes.set_ylim(600,900)\n",
    "axes.set_xlim(0,9);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Col_prevTrial'] = df['ColorFreq'].shift(periods=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "\u001b[1;31mSub#\t\t\t1\t\t\t2\t\t\t\u001b[0m\n",
      "1\t\t\t861.26\t\t\t812.77\t\t\t\n",
      "2\t\t\t605.8\t\t\t625.93\t\t\t\n",
      "3\t\t\t654.16\t\t\t680.57\t\t\t\n",
      "4\t\t\t644.1\t\t\t643.74\t\t\t\n",
      "5\t\t\t705.89\t\t\t713.07\t\t\t\n",
      "6\t\t\t792.27\t\t\t806.84\t\t\t\n",
      "7\t\t\t707.5\t\t\t701.18\t\t\t\n",
      "8\t\t\t623.06\t\t\t646.59\t\t\t\n",
      "\u001b[1;31mmean\t\t\t699.25\t\t\t703.84\t\t\t\n",
      "STE\t\t\t31.23\t\t\t25.38\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (-0.53290604839900646, 0.6105908544428964)\n",
      "n = 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe16cbcca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConType = df['ColorFreq'] == 1\n",
    "ConType2 = df['Col_prevTrial'] == 1\n",
    "tableRT = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & ConType & ConType2].pivot_table(values='RT', index='Sub#', columns=['DistCond'], aggfunc=np.mean)\n",
    "PythonAnalyses.dataTableRT(tableRT)\n",
    "\n",
    "PythonAnalyses.Test_and_Plot_PairwiseComparison(tableRT, 600, 1000, 'Singleton Condition','Changing Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "\u001b[1;31mSub#\t\t\t1\t\t\t2\t\t\t\u001b[0m\n",
      "1\t\t\t881.8\t\t\t881.03\t\t\t\n",
      "2\t\t\t588.1\t\t\t625.64\t\t\t\n",
      "3\t\t\t659.28\t\t\t692.76\t\t\t\n",
      "4\t\t\t669.67\t\t\t669.84\t\t\t\n",
      "5\t\t\t701.49\t\t\t716.68\t\t\t\n",
      "6\t\t\t875.21\t\t\t800.97\t\t\t\n",
      "7\t\t\t689.99\t\t\t714.93\t\t\t\n",
      "8\t\t\t651.24\t\t\t620.46\t\t\t\n",
      "\u001b[1;31mmean\t\t\t714.6\t\t\t715.29\t\t\t\n",
      "STE\t\t\t37.71\t\t\t31.17\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (-0.052276429643544876, 0.9597689834289973)\n",
      "n = 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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/hEP+xRdf1ObNm3Xs2DGFw2GFw2F5PB6VlJQoPT09vp3jOKqtrVUoFJIkhcNhZWVlyePx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe16cac5110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConType = df['ColorFreq'] == 2\n",
    "ConType2 = df['Col_prevTrial'] == 1\n",
    "tableRT = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & ConType & ConType2].pivot_table(values='RT', index='Sub#', columns=['DistCond'], aggfunc=np.mean)\n",
    "PythonAnalyses.dataTableRT(tableRT)\n",
    "\n",
    "PythonAnalyses.Test_and_Plot_PairwiseComparison(tableRT, 600, 1000, 'Singleton Condition','Changing Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "\u001b[1;31mSub#\t\t\t1\t\t\t2\t\t\t\u001b[0m\n",
      "1\t\t\t867.22\t\t\t876.8\t\t\t\n",
      "2\t\t\t606.14\t\t\t604.79\t\t\t\n",
      "3\t\t\t672.06\t\t\t680.57\t\t\t\n",
      "4\t\t\t622.81\t\t\t630.23\t\t\t\n",
      "5\t\t\t738.14\t\t\t715.84\t\t\t\n",
      "6\t\t\t774.17\t\t\t800.98\t\t\t\n",
      "7\t\t\t734.93\t\t\t696.52\t\t\t\n",
      "8\t\t\t633.86\t\t\t680.18\t\t\t\n",
      "\u001b[1;31mmean\t\t\t706.17\t\t\t710.74\t\t\t\n",
      "STE\t\t\t31.55\t\t\t31.48\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (-0.48898503012749578, 0.63980005222491743)\n",
      "n = 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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44XCYcDiMx+OhpKSE9PT0+HaO41BTU0MoFAIgHA6TlZWFx+MZ/OqlT+p7ilupNpMn4ZCf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe16c9c6890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConType = df['ColorFreq'] == 1\n",
    "ConType2 = df['Col_prevTrial'] == 2\n",
    "tableRT = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & ConType & ConType2].pivot_table(values='RT', index='Sub#', columns=['DistCond'], aggfunc=np.mean)\n",
    "PythonAnalyses.dataTableRT(tableRT)\n",
    "\n",
    "PythonAnalyses.Test_and_Plot_PairwiseComparison(tableRT, 600, 1000, 'Singleton Condition','Changing Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "\u001b[1;31mSub#\t\t\t1\t\t\t2\t\t\t\u001b[0m\n",
      "1\t\t\t824.4\t\t\t897.84\t\t\t\n",
      "2\t\t\t652.34\t\t\t661.75\t\t\t\n",
      "3\t\t\t639.94\t\t\t679.74\t\t\t\n",
      "4\t\t\t676.27\t\t\t597.78\t\t\t\n",
      "5\t\t\t757.53\t\t\t774.19\t\t\t\n",
      "6\t\t\t802.09\t\t\t786.86\t\t\t\n",
      "7\t\t\t702.16\t\t\t739.92\t\t\t\n",
      "8\t\t\t676.66\t\t\t617.09\t\t\t\n",
      "\u001b[1;31mmean\t\t\t716.42\t\t\t719.4\t\t\t\n",
      "STE\t\t\t24.66\t\t\t35.35\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (-0.16287376179827504, 0.87522009299156978)\n",
      "n = 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe16c9d29d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConType = df['ColorFreq'] == 2\n",
    "ConType2 = df['Col_prevTrial'] == 2\n",
    "tableRT = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & ConType & ConType2].pivot_table(values='RT', index='Sub#', columns=['DistCond'], aggfunc=np.mean)\n",
    "PythonAnalyses.dataTableRT(tableRT)\n",
    "\n",
    "PythonAnalyses.Test_and_Plot_PairwiseComparison(tableRT, 600, 1000, 'Singleton Condition','Changing Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}