{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Data analyses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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 = 0 #if I want to reload data from csvs = 1. If i want to load python pickle == 0\n",
    "\n",
    "filename = 'AdditionalSingleton_TargetUncertain_Runs.csv'\n",
    "csvName = 'AdditionalSingleton_TargetUncertain_Runs'\n",
    "\n",
    "if sys.platform == 'linux2': #is this my linux laptop\n",
    "    path = '/home/dan-laptop/Dropbox/TargetUncertainty/Exp3'\n",
    "elif sys.platform == 'darwin': #is this my mac work comp    \n",
    "    path = '/Users/danvatterott/Dropbox Encore/Dropbox/TargetUncertainty/Exp3/'\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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>ConstantTar</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>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1039.0328</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3067.5919</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1230.1895</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TarType  TarLoc  TarColorCode  DistCond  TarDistDist  DistLoc  DistLine  \\\n",
       "0        1       3             2         2            1        2         1   \n",
       "1        2       3             1         1            0        0         0   \n",
       "2        2       5             2         2            2        3         2   \n",
       "\n",
       "   Resp  Block  ConstantTar  ChangeCounter  Acc         RT  Sub#  \n",
       "0     2      0            0              1    0  1039.0328     1  \n",
       "1     1      0            0              2    0  3067.5919     1  \n",
       "2     2      0            0              3    1  1230.1895     1  "
      ]
     },
     "execution_count": 8,
     "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": 9,
   "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#'] != 8)\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": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "5760\n",
      "0.001736111\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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sub#\n",
       "1     0.908854\n",
       "2     0.966146\n",
       "3     0.940104\n",
       "4     0.950521\n",
       "5     0.947917\n",
       "6     0.968750\n",
       "7     0.984375\n",
       "8     0.882812\n",
       "9     0.984375\n",
       "10    0.958333\n",
       "11    0.955729\n",
       "12    0.968750\n",
       "13    0.979167\n",
       "14    0.908854\n",
       "15    0.934896\n",
       "16    0.960938\n",
       "Name: Acc, dtype: float64"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sub#\n",
       "1     760.463287\n",
       "2     608.875852\n",
       "3     766.333202\n",
       "4     589.946120\n",
       "5     693.575033\n",
       "6     656.393220\n",
       "7     931.161534\n",
       "8     813.083693\n",
       "9     841.531879\n",
       "10    571.117581\n",
       "11    606.915263\n",
       "12    654.725679\n",
       "13    689.595730\n",
       "14    586.671355\n",
       "15    836.928029\n",
       "16    650.852772\n",
       "Name: RT, dtype: float64"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "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=['ChangeCounter'], aggfunc=np.mean)\n",
    "DTRIM = df['DistCond'] == 2\n",
    "tableRT2B = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & DTRIM].pivot_table(values='RT', index='Sub#', columns=['ChangeCounter'], aggfunc=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6kiQnnXRS/uzP/ixr167NzJkzkyR/8Ad/MHauq1at2q7zSZJ169bllFNOSaPRyJFHHpn3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1095098150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xdata = df['ChangeCounter'][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(0,5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tdjtOnDgRw8PDERExMjIS27Zti1qt9kauHwAA4Ia05jbIdrsd586dW1LUFhRv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\nlzg+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1094862ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tableRT2 = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4].pivot_table(values='RT', index='Sub#', columns=['ChangeCounter'], aggfunc=np.mean)\n",
    "\n",
    "xdata = df['ChangeCounter'][PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4].unique()\n",
    "Absent  = [np.mean(tableRT2[1]),np.mean(tableRT2[2]),np.mean(tableRT2[3]),np.mean(tableRT2[4])]\n",
    "\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "axes = fig.add_subplot(111)\n",
    "\n",
    "axes.plot(xdata, Absent); \n",
    "axes.legend()\n",
    "axes.set_ylim(600,900)\n",
    "axes.set_xlim(0,5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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\t743.49\t\t\t776.57\t\t\t\n",
      "2\t\t\t580.78\t\t\t616.86\t\t\t\n",
      "3\t\t\t776.09\t\t\t771.29\t\t\t\n",
      "4\t\t\t596.91\t\t\t567.36\t\t\t\n",
      "5\t\t\t706.48\t\t\t703.54\t\t\t\n",
      "6\t\t\t647.45\t\t\t642.59\t\t\t\n",
      "7\t\t\t911.51\t\t\t938.37\t\t\t\n",
      "8\t\t\t778.33\t\t\t867.4\t\t\t\n",
      "9\t\t\t814.52\t\t\t904.53\t\t\t\n",
      "10\t\t\t588.13\t\t\t549.34\t\t\t\n",
      "11\t\t\t571.73\t\t\t594.16\t\t\t\n",
      "12\t\t\t678.81\t\t\t647.37\t\t\t\n",
      "13\t\t\t686.13\t\t\t682.38\t\t\t\n",
      "14\t\t\t611.9\t\t\t586.84\t\t\t\n",
      "15\t\t\t880.54\t\t\t844.17\t\t\t\n",
      "16\t\t\t648.49\t\t\t659.07\t\t\t\n",
      "\u001b[1;31mmean\t\t\t701.33\t\t\t709.49\t\t\t\n",
      "STE\t\t\t26.76\t\t\t31.37\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (-0.81454974991010287, 0.42807335264220103)\n",
      "n = 16\n"
     ]
    },
    {
     "data": {
      "image/png": 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wyL/44ovavHmzjh8/rnA4rHA4LI/Ho5KSEqWnp8e3cxxHNTU1CoVCkqRwOKysrCx5PJ7B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\nABYj5AHAYv8PA9XDMLkUPxsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1095098b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConType = df['ChangeCounter'] == 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": 64,
   "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\t765.06\t\t\t729.31\t\t\t\n",
      "2\t\t\t593.84\t\t\t611.29\t\t\t\n",
      "3\t\t\t779.68\t\t\t775.66\t\t\t\n",
      "4\t\t\t574.58\t\t\t621.82\t\t\t\n",
      "5\t\t\t696.86\t\t\t682.56\t\t\t\n",
      "6\t\t\t629.09\t\t\t666.11\t\t\t\n",
      "7\t\t\t967.83\t\t\t969.28\t\t\t\n",
      "8\t\t\t797.54\t\t\t831.07\t\t\t\n",
      "9\t\t\t814.78\t\t\t835.77\t\t\t\n",
      "10\t\t\t566.26\t\t\t571.09\t\t\t\n",
      "11\t\t\t596.89\t\t\t651.37\t\t\t\n",
      "12\t\t\t627.53\t\t\t666.78\t\t\t\n",
      "13\t\t\t696.44\t\t\t670.77\t\t\t\n",
      "14\t\t\t589.99\t\t\t566.47\t\t\t\n",
      "15\t\t\t804.92\t\t\t870.75\t\t\t\n",
      "16\t\t\t640.37\t\t\t644.61\t\t\t\n",
      "\u001b[1;31mmean\t\t\t696.35\t\t\t710.29\t\t\t\n",
      "STE\t\t\t28.67\t\t\t28.83\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (-1.8279415797055887, 0.087515561985674811)\n",
      "n = 16\n"
     ]
    },
    {
     "data": {
      "image/png": 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jSEtLIxwOR+cjkQhpaWk4jtPmvIiI9LyYQ/7RRx+lvr6exMRE7rzzThISEsjNzaWkpASA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\nCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1094d900d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConType = df['ChangeCounter'] == 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": 65,
   "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\t693.03\t\t\t843.02\t\t\t\n",
      "2\t\t\t579.34\t\t\t659.73\t\t\t\n",
      "3\t\t\t746.01\t\t\t782.71\t\t\t\n",
      "4\t\t\t580.12\t\t\t596.97\t\t\t\n",
      "5\t\t\t695.53\t\t\t711.94\t\t\t\n",
      "6\t\t\t663.85\t\t\t663.88\t\t\t\n",
      "7\t\t\t962.41\t\t\t908.13\t\t\t\n",
      "8\t\t\t775.41\t\t\t824.19\t\t\t\n",
      "9\t\t\t882.88\t\t\t810.08\t\t\t\n",
      "10\t\t\t590.62\t\t\t585.32\t\t\t\n",
      "11\t\t\t611.07\t\t\t625.14\t\t\t\n",
      "12\t\t\t652.02\t\t\t645.46\t\t\t\n",
      "13\t\t\t707.38\t\t\t695.06\t\t\t\n",
      "14\t\t\t593.02\t\t\t578.77\t\t\t\n",
      "15\t\t\t793.4\t\t\t805.86\t\t\t\n",
      "16\t\t\t628.29\t\t\t675.75\t\t\t\n",
      "\u001b[1;31mmean\t\t\t697.15\t\t\t713.25\t\t\t\n",
      "STE\t\t\t27.97\t\t\t25.6\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (-1.2416328737167686, 0.23343733722119631)\n",
      "n = 16\n"
     ]
    },
    {
     "data": {
      "image/png": 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JCTfccAOO47Q7n5ycfNp+KisrSU9P7+lyRETOKtXV1WRkZLR7X9RvvLbe+ahRo0hOTqau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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1094d48850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConType = df['ChangeCounter'] == 3\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 2 Trial Prev')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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\t727.18\t\t\t782.85\t\t\t\n",
      "2\t\t\t607.75\t\t\t619.51\t\t\t\n",
      "3\t\t\t731.9\t\t\t764.23\t\t\t\n",
      "4\t\t\t584.85\t\t\t603.97\t\t\t\n",
      "5\t\t\t676.56\t\t\t675.03\t\t\t\n",
      "6\t\t\t640.28\t\t\t696.41\t\t\t\n",
      "7\t\t\t889.1\t\t\t894.72\t\t\t\n",
      "8\t\t\t818.82\t\t\t804.39\t\t\t\n",
      "9\t\t\t843.22\t\t\t814.65\t\t\t\n",
      "10\t\t\t569.48\t\t\t548.86\t\t\t\n",
      "11\t\t\t608.61\t\t\t595.75\t\t\t\n",
      "12\t\t\t650.77\t\t\t670.26\t\t\t\n",
      "13\t\t\t682.59\t\t\t692.71\t\t\t\n",
      "14\t\t\t579.16\t\t\t587.27\t\t\t\n",
      "15\t\t\t813.33\t\t\t879.68\t\t\t\n",
      "16\t\t\t641.52\t\t\t666.43\t\t\t\n",
      "\u001b[1;31mmean\t\t\t691.57\t\t\t706.04\t\t\t\n",
      "STE\t\t\t25.48\t\t\t26.57\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (-2.0761530869853169, 0.055485451117655703)\n",
      "n = 16\n"
     ]
    },
    {
     "data": {
      "image/png": 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NzcUYQ2lpKTfddBOO47Q5n5KScs55qqqqyMjI6O5yRET6lZqaGjIzM9t8LOoXXluefOzY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1094ce6410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConType = df['ChangeCounter'] == 4\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 3 Trial Prev')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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\t730.98\t\t\t788.16\t\t\t\n",
      "2\t\t\t593.86\t\t\t630.9\t\t\t\n",
      "3\t\t\t753.27\t\t\t774.96\t\t\t\n",
      "4\t\t\t579.42\t\t\t606.76\t\t\t\n",
      "5\t\t\t690.23\t\t\t689.43\t\t\t\n",
      "6\t\t\t645.53\t\t\t676.9\t\t\t\n",
      "7\t\t\t941.52\t\t\t925.65\t\t\t\n",
      "8\t\t\t796.76\t\t\t819.34\t\t\t\n",
      "9\t\t\t848.41\t\t\t820.45\t\t\t\n",
      "10\t\t\t573.95\t\t\t569.77\t\t\t\n",
      "11\t\t\t605.16\t\t\t624.74\t\t\t\n",
      "12\t\t\t643.13\t\t\t661.17\t\t\t\n",
      "13\t\t\t695.68\t\t\t686.99\t\t\t\n",
      "14\t\t\t587.39\t\t\t577.73\t\t\t\n",
      "15\t\t\t803.78\t\t\t852.09\t\t\t\n",
      "16\t\t\t636.94\t\t\t662.24\t\t\t\n",
      "\u001b[1;31mmean\t\t\t695.38\t\t\t710.45\t\t\t\n",
      "STE\t\t\t27.2\t\t\t26.55\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (-2.5161373131992133, 0.02373700601612631)\n",
      "n = 16\n"
     ]
    },
    {
     "data": {
      "image/png": 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PB7/f32Te7/e39/OgENj3JKNibepH007earVi+vTpeP3119GtWzfcuHED1dXVsNlsKCws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\nGEOeiEhiDHkiIokx5ImIJPa/YqL5zjghsOoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f109499e250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConType = (df['ChangeCounter'] == 4) | (df['ChangeCounter'] == 3) | (df['ChangeCounter'] == 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 1-3 Trial Prev')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAACAAEbBFkRYl2wemZtH5uaRuXlkbh6Zm0fm5pG5f6JgAwAAAAA/RQ8bAAAA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\nAABASRnYAAAASsrABgAAUFL/B8f+L0DTNn6PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10952bbbd0>"
      ]
     },
     "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": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ExpHalf = df['Block'] < 5\n",
    "DTRIM = df['DistCond'] == 1\n",
    "tableRT2A = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & DTRIM & ExpHalf].pivot_table(values='RT', index='Sub#', columns=['ChangeCounter'], aggfunc=np.mean)\n",
    "DTRIM = df['DistCond'] == 2\n",
    "tableRT2B = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & DTRIM & ExpHalf].pivot_table(values='RT', index='Sub#', columns=['ChangeCounter'], aggfunc=np.mean)\n",
    "ExpHalf = df['Block'] > 4\n",
    "DTRIM = df['DistCond'] == 1\n",
    "tableRT2C = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & DTRIM & ExpHalf].pivot_table(values='RT', index='Sub#', columns=['ChangeCounter'], aggfunc=np.mean)\n",
    "DTRIM = df['DistCond'] == 2\n",
    "tableRT2D = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & DTRIM & ExpHalf].pivot_table(values='RT', index='Sub#', columns=['ChangeCounter'], aggfunc=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kNR4CQkNDdfjwYa/jt912m7Zu3Wr0qeXk5Oiaa66Rw+E4p2vJycnRkCFDjO2B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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1094b18bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xdata = df['ChangeCounter'][PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4].unique()\n",
    "\n",
    "Absent1  = np.mean(tableRT2A)\n",
    "Present1 = np.mean(tableRT2B)\n",
    "Absent2  = np.mean(tableRT2C)\n",
    "Present2 = np.mean(tableRT2D)\n",
    "\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "axes = fig.add_subplot(111)\n",
    "\n",
    "axes.plot(xdata, Absent1, label='Singleton Absent:ExpHalf1'); \n",
    "axes.plot(xdata, Present1, label='Singleton Present:ExpHalf1')\n",
    "axes.plot(xdata, Absent2,'--', label='Singleton Absent:ExpHalf2'); \n",
    "axes.plot(xdata, Present2,'--', label='Singleton Present:ExpHalf2')\n",
    "axes.legend()\n",
    "axes.set_ylim(600,900)\n",
    "axes.set_xlim(0,5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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\t803.82\t\t\t809.54\t\t\t\n",
      "2\t\t\t631.92\t\t\t649.11\t\t\t\n",
      "3\t\t\t805.43\t\t\t808.94\t\t\t\n",
      "4\t\t\t609.08\t\t\t628.29\t\t\t\n",
      "5\t\t\t714.66\t\t\t684.61\t\t\t\n",
      "6\t\t\t617.44\t\t\t645.53\t\t\t\n",
      "7\t\t\t961.2\t\t\t961.67\t\t\t\n",
      "8\t\t\t870.63\t\t\t931.6\t\t\t\n",
      "9\t\t\t635.22\t\t\t568.68\t\t\t\n",
      "10\t\t\t577.34\t\t\t636.93\t\t\t\n",
      "11\t\t\t627.45\t\t\t651.8\t\t\t\n",
      "12\t\t\t712.68\t\t\t700.7\t\t\t\n",
      "13\t\t\t631.4\t\t\t630.9\t\t\t\n",
      "14\t\t\t878.2\t\t\t820.53\t\t\t\n",
      "15\t\t\t636.91\t\t\t696.82\t\t\t\n",
      "\u001b[1;31mmean\t\t\t714.23\t\t\t721.71\t\t\t\n",
      "STE\t\t\t30.96\t\t\t30.29\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (-0.75004445782591422, 0.4656467623214815)\n",
      "n = 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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dHQ2v1wufz9di3ufzhfs6KAT2PUmvWJvaUbWTN5vNmD59Ot544w306NEDdXV1qKmpgcVi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\nQ56ISGIMeSIiiTHkiYgk9n9Tau3xe6ow9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb8dd54890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ExpHalf = df['Block'] < 5\n",
    "ConType = (df['ChangeCounter'] == 1) | (df['ChangeCounter'] == 2)# | (df['ChangeCounter'] == 2)\n",
    "tableRT = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & ConType & ExpHalf].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 1-3 Trial Prev')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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\t805.02\t\t\t848.0\t\t\t\n",
      "2\t\t\t659.18\t\t\t667.56\t\t\t\n",
      "3\t\t\t754.42\t\t\t796.86\t\t\t\n",
      "4\t\t\t637.0\t\t\t644.23\t\t\t\n",
      "5\t\t\t660.9\t\t\t699.07\t\t\t\n",
      "6\t\t\t655.07\t\t\t674.55\t\t\t\n",
      "7\t\t\t924.94\t\t\t943.03\t\t\t\n",
      "8\t\t\t894.19\t\t\t857.9\t\t\t\n",
      "9\t\t\t597.93\t\t\t589.36\t\t\t\n",
      "10\t\t\t635.95\t\t\t629.24\t\t\t\n",
      "11\t\t\t647.3\t\t\t684.77\t\t\t\n",
      "12\t\t\t728.08\t\t\t751.48\t\t\t\n",
      "13\t\t\t637.3\t\t\t599.13\t\t\t\n",
      "14\t\t\t797.28\t\t\t819.1\t\t\t\n",
      "15\t\t\t659.46\t\t\t675.25\t\t\t\n",
      "\u001b[1;31mmean\t\t\t712.94\t\t\t725.3\t\t\t\n",
      "STE\t\t\t26.01\t\t\t27.22\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (-1.8674739209698312, 0.082912413517392938)\n",
      "n = 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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io6Ph9Xrh8/lazPt8vnA/DwqBfU/SK9amdlTt5M1mM6ZPn4433ngDPXr0QF1dHWpqamCx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb8dab4b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ExpHalf = df['Block'] < 5\n",
    "ConType = (df['ChangeCounter'] == 4) | (df['ChangeCounter'] == 3)# | (df['ChangeCounter'] == 2)\n",
    "tableRT = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & ConType & ExpHalf].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 1-3 Trial Prev')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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\t731.12\t\t\t708.59\t\t\t\n",
      "2\t\t\t567.07\t\t\t609.58\t\t\t\n",
      "3\t\t\t746.03\t\t\t746.01\t\t\t\n",
      "4\t\t\t589.51\t\t\t592.54\t\t\t\n",
      "5\t\t\t701.64\t\t\t734.69\t\t\t\n",
      "6\t\t\t687.88\t\t\t672.41\t\t\t\n",
      "7\t\t\t905.32\t\t\t950.01\t\t\t\n",
      "8\t\t\t772.4\t\t\t823.54\t\t\t\n",
      "9\t\t\t593.95\t\t\t595.22\t\t\t\n",
      "10\t\t\t603.26\t\t\t633.56\t\t\t\n",
      "11\t\t\t715.1\t\t\t726.41\t\t\t\n",
      "12\t\t\t682.04\t\t\t660.72\t\t\t\n",
      "13\t\t\t600.94\t\t\t570.72\t\t\t\n",
      "14\t\t\t811.43\t\t\t864.97\t\t\t\n",
      "15\t\t\t687.17\t\t\t649.24\t\t\t\n",
      "\u001b[1;31mmean\t\t\t692.99\t\t\t702.55\t\t\t\n",
      "STE\t\t\t24.23\t\t\t28.09\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (-1.1866935185492082, 0.25509615435003241)\n",
      "n = 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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h8/ng9/ubzfv9/o5+HhQC+55kVKxN/WjayVutVkyfPh1vvPEGevTogRs3bqCmpgY2mw1F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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb8da64450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ExpHalf = df['Block'] > 4\n",
    "ConType = (df['ChangeCounter'] == 1) | (df['ChangeCounter'] == 2)# | (df['ChangeCounter'] == 2)\n",
    "tableRT = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & ConType & ExpHalf].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 1-3 Trial Prev')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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\t669.81\t\t\t775.85\t\t\t\n",
      "2\t\t\t563.57\t\t\t629.07\t\t\t\n",
      "3\t\t\t722.79\t\t\t748.56\t\t\t\n",
      "4\t\t\t602.55\t\t\t586.84\t\t\t\n",
      "5\t\t\t748.15\t\t\t699.96\t\t\t\n",
      "6\t\t\t688.98\t\t\t723.86\t\t\t\n",
      "7\t\t\t919.9\t\t\t846.72\t\t\t\n",
      "8\t\t\t837.22\t\t\t784.02\t\t\t\n",
      "9\t\t\t624.76\t\t\t559.52\t\t\t\n",
      "10\t\t\t639.66\t\t\t638.35\t\t\t\n",
      "11\t\t\t681.61\t\t\t658.0\t\t\t\n",
      "12\t\t\t689.17\t\t\t662.78\t\t\t\n",
      "13\t\t\t581.96\t\t\t614.12\t\t\t\n",
      "14\t\t\t810.22\t\t\t849.1\t\t\t\n",
      "15\t\t\t634.91\t\t\t678.36\t\t\t\n",
      "\u001b[1;31mmean\t\t\t694.35\t\t\t697.01\t\t\t\n",
      "STE\t\t\t25.71\t\t\t23.07\t\t\t\u001b[0m\n",
      "----------------------------------------------------------------------------------------------------\n",
      "ttest = (-0.19755853692214057, 0.84623012370577866)\n",
      "n = 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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R0dHwer3w+Xwt5n0+X7jvB4XAvifpFWtTO6p28mazGdOmTcMbb7yBHj16oL6+HrW1tbBY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\nDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb8d9d9150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ExpHalf = df['Block'] > 4\n",
    "ConType = (df['ChangeCounter'] == 4) | (df['ChangeCounter'] == 3)# | (df['ChangeCounter'] == 2)\n",
    "tableRT = df[PartTrim & Trimmer & Trimmer2 & Trimmer3 & Trimmer4 & ConType & ExpHalf].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 1-3 Trial Prev')"
   ]
  },
  {
   "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",
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