5_28

LAPDATA

{
 "metadata": {
  "name": "LAP_DATA_5-28-13"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "LAP DATA"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas\n#import pandas.tools.rplot as rplot\nimport glob\nimport os\nfrom scipy import stats as st\nfrom scipy.stats import norm",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Merge All Data"
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": "Merging all individual VOCAB raw datasets"
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": "#===========VOCAB============================================================\n\"\"\"VObigframe = pandas.DataFrame()\nVOcsvfiles = glob.glob('../Training_tasks/psychopy/data/*vocab_day*.csv')\nfor csv in VOcsvfiles:\n    VObigframe = VObigframe.append(pandas.read_csv(csv, sep='\\t'), ignore_index=True)\n#Makes a CSV file called Vocab_Master\n#VObigframe.to_csv('./Training_Sessions/Vocab_Master')\n\"\"\"\n#Taking the merged dataset and looking at the mean ACC for each participant\nVOCbigframe = pandas.DataFrame()\ndfvocab = pandas.read_csv('./Training_Sessions/Vocab_Master')\n#for i in dfvocab:\n #   VOCbigframe = pandas.pivot_table(dfvocab, values='ACC', rows=['participant'], cols=['session', 'expName'], aggfunc=np.mean)\nfor i in dfvocab:\n    VOCbigframe = pandas.pivot_table(dfvocab, values='ACC', rows=['participant'], cols=['session'], aggfunc=np.mean)\n#VOCbigframe.to_csv('./Training_Sessions/Summaries/Vocab_Summary')\nVOCbigframe.columns= ['S%d' % col for col in VOCbigframe.columns]\n#===========Grammar============================================================\n\"\"\"\nGRbigframe = pandas.DataFrame()\nGRcsvfiles = glob.glob('../Training_tasks/psychopy/data/*grammar*.csv')\nfor csv in GRcsvfiles:\n    GRbigframe = GRbigframe.append(pandas.read_csv(csv, sep='\\t'), ignore_index=True)\ndel GRbigframe['Correct_Sent']\ndel GRbigframe['Incorrect_Sent']\n#del GRbigframe['Movie']\ndel GRbigframe['aud1']\ndel GRbigframe['aud2']\n#GRbigframe.to_csv('./Training_Sessions/GR_Master')\n\"\"\"\ngrambigframe = pandas.DataFrame()\ngrambigframe2 = pandas.DataFrame()\ndfgram = pandas.read_csv('./Training_Sessions/GR_Master')\nfor i in dfgram:\n    grambigframe = pandas.pivot_table(dfgram, values='ACC', rows=['participant'], cols=['session'], aggfunc=np.mean)\n    grambigframe2= pandas.pivot_table(dfgram, values='ACC', rows=['participant', 'Condition', 'Novel_Familiar'], cols=['session'], aggfunc=np.mean)\n    #nc= pandas.pivot_table(dfgram, values='ACC', rows=['participant', 'Novel_Familiar'], cols=['Condition','session'], aggfunc=np.mean)\n#grambigframe2.to_csv('./Training_Sessions/Summaries/Grammar_Summary2')\n\n\n#===========KC_test============================================================\n\"\"\"KC_test_bigframe = pandas.DataFrame()\nKC_test_csvfiles = glob.glob('../Training_tasks/psychopy/data/*KC_test*.csv')\nfor csv in KC_test_csvfiles:\n    KC_test_bigframe = KC_test_bigframe.append(pandas.read_csv(csv, sep='\\t'), ignore_index=True)\n#KC_test_bigframe.to_csv('./Training_Sessions/KCtest_Master')\n\"\"\"\nKCTestbigframe = pandas.DataFrame()\ndfKCTest = pandas.read_csv('./Training_Sessions/KCtest_Master')\nfor i in dfKCTest:\n    KCTestbigframe = pandas.pivot_table(dfKCTest, values='ACC', rows=['participant'], cols=['session'], aggfunc=np.mean)\n#KCTestbigframe.to_csv('./Training_Sessions/Summaries/KC_Test_Summary')\n\n#===========DailySAT============================================================\n\nDSATbigframe = pandas.DataFrame()\nDSATcsvfiles = glob.glob('../Training_tasks/psychopy/data/*Daily_SAT*')\nfor csv in DSATcsvfiles:\n    DSATbigframe = DSATbigframe.append(pandas.read_csv(csv, sep='\\t'), ignore_index=True)\nDSATbigframe.to_csv('./Training_Sessions/Daily_SAT_Master')\n\nSATbigframe = pandas.DataFrame()\ndfSAT = pandas.read_csv('./Training_Sessions/Daily_SAT_Master')\nfor i in dfSAT:\n    SATbigframe = pandas.pivot_table(dfSAT, values='ACC', rows=['participant', 'Condition'], cols=['Session'], aggfunc=np.mean)\n#===========MRI sent============================================================\n\"\"\"\nsentbigframe = pandas.DataFrame()\nsentcsvfiles = glob.glob('fMRI_tasks/Sent/data/*sent_run*.csv')\nfor csv in sentcsvfiles:\n    sentbigframe = sentbigframe.append(pandas.read_csv(csv, sep='\\t'), ignore_index=True)\nsentbigframe.to_csv('Data/MRI/Sent_Master2')\n\"\"\"\n\nSentbigframe = pandas.DataFrame()\ndfSent = pandas.read_csv('./MRI/Sent_Master2')\nfor i in dfSent:\n    Sentbigframe = pandas.pivot_table(dfSent, values='ACC', rows=['participant'], cols=['run', 'Block'], aggfunc=np.mean)\n#Sentbigframe.to_csv('Data/MRI/Summaries/Sent_Summary')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "pandas.__version__",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": "'0.11.0'"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "What do the accuracies look like?"
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": "voc_mean = VOCbigframe.mean()\nvoc_std = VOCbigframe.std()\nvoc_n= VOCbigframe.count()\nvoc_std_error = voc_std/np.sqrt(voc_n)\n\ngram_mean = grambigframe.mean()\ngram_std = grambigframe.std()\ngram_n = grambigframe.count()\ngram_std_error = gram_std/np.sqrt(gram_n)\n\nkctest_mean = KCTestbigframe.mean()\nkctest_std = KCTestbigframe.std()\nkctest_n = KCTestbigframe.count()\nkctest_std_error = kctest_std/np.sqrt(kctest_n)\n\nSAT_mean = SATbigframe.mean()\nSAT_std = SATbigframe.std()\nSAT_n = SATbigframe.count()\nSAT_std_error = SAT_std/np.sqrt(SAT_n)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Vocab PLOTS"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "VOCbigframe",
     "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>S1</th>\n      <th>S2</th>\n      <th>S3</th>\n      <th>S4</th>\n      <th>S9</th>\n    </tr>\n    <tr>\n      <th>participant</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1006</th>\n      <td> 0.966667</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1007</th>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n    </tr>\n    <tr>\n      <th>1010</th>\n      <td> 0.933333</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td> 0.966667</td>\n    </tr>\n    <tr>\n      <th>1013</th>\n      <td> 0.966667</td>\n      <td> 1.000000</td>\n      <td> 0.966667</td>\n      <td> 1.000000</td>\n      <td> 0.966667</td>\n    </tr>\n    <tr>\n      <th>1014</th>\n      <td> 1.000000</td>\n      <td> 0.900000</td>\n      <td> 0.933333</td>\n      <td> 1.000000</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1015</th>\n      <td> 0.966667</td>\n      <td> 0.933333</td>\n      <td> 0.866667</td>\n      <td> 0.933333</td>\n      <td> 0.800000</td>\n    </tr>\n    <tr>\n      <th>1016</th>\n      <td> 0.633333</td>\n      <td> 0.866667</td>\n      <td> 0.900000</td>\n      <td> 0.866667</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1019</th>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1022</th>\n      <td> 0.833333</td>\n      <td> 0.966667</td>\n      <td> 0.966667</td>\n      <td> 0.966667</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1023</th>\n      <td> 0.933333</td>\n      <td> 0.966667</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1024</th>\n      <td> 0.733333</td>\n      <td> 1.000000</td>\n      <td> 0.966667</td>\n      <td> 1.000000</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1025</th>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1027</th>\n      <td> 0.900000</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1028</th>\n      <td> 0.966667</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td> 1.000000</td>\n      <td>      NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 5,
       "text": "                   S1        S2        S3        S4        S9\nparticipant                                                  \n1006         0.966667  1.000000  1.000000  1.000000       NaN\n1007         1.000000  1.000000  1.000000  1.000000  1.000000\n1010         0.933333  1.000000  1.000000  1.000000  0.966667\n1013         0.966667  1.000000  0.966667  1.000000  0.966667\n1014         1.000000  0.900000  0.933333  1.000000       NaN\n1015         0.966667  0.933333  0.866667  0.933333  0.800000\n1016         0.633333  0.866667  0.900000  0.866667       NaN\n1019         1.000000  1.000000  1.000000  1.000000       NaN\n1022         0.833333  0.966667  0.966667  0.966667       NaN\n1023         0.933333  0.966667  1.000000  1.000000       NaN\n1024         0.733333  1.000000  0.966667  1.000000       NaN\n1025         1.000000  1.000000  1.000000  1.000000       NaN\n1027         0.900000  1.000000  1.000000  1.000000       NaN\n1028         0.966667  1.000000  1.000000  1.000000       NaN"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "vocab_bar_fig = plt.figure()\nVOCbigframe.plot(kind='bar', figsize= (12,6))\n#plt.figsize(12,6)\nplt.legend(title= 'session', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\nplt.grid(None)\nplt.ylim([.5,1])\nplt.title('Vocabulary Overall')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": "<matplotlib.text.Text at 0xb20b2d0>"
      },
      {
       "output_type": "display_data",
       "text": "<matplotlib.figure.Figure at 0xaf06630>"
      },
      {
       "output_type": "display_data",
       "png": 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48nuCy5cv680339SxY8fUqVMnvfbaa3rllVd0/vx5dejQQfXr17c6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\nAAAApqJkAAAAADAVJQMAAACAqf4fFNchtvddDJQAAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0xaf06330>"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "vocab_acc_plot = VOCbigframe.mean().plot(kind='bar', color= 'grey', figsize=(6,4), xerr = voc_std_error, yerr = voc_std_error,title=\"Training_Vocab Accuracy\")\nplt.ylabel('Accuracy')\nplt.grid(None)\nplt.ylim([.5,1])\nplt.savefig('vocab_acc_plot')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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kMwx+IiKZYfATEckMg5+ISGYY/EREMsPgJyKSGQY/EZHMMPiJiGSGwU9EJDMM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\nREQyw+AnIpKZ/w9sVSTljUFE6wAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0xaf06650>"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "vocab_line_fig = plt.figure()\nvd1 = VOCbigframe.T.as_matrix()\nax = vocab_scatter_fig.add_subplot(111)\nplt.plot(range(5), vd1, '.-', ms=8, lw=2), ax.set_xticklabels(['S1','S2', 'S3', 'S4', 'S9'])\nplt.xticks(range(5)), plt.title('Vocabulary'), gcf().set_figwidth(9), \nplt.savefig('vocab_line_fig')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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yMkFBQQQHB+Pg4EBQUBALFixg1qxZDU/elR2QTs7mqEUyGtGlXUB39RjG6+eQ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\n3759O6mpqQ80ty+4evUq8fHxVFZWEhISQmFhocqHiIhID2j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       "text": "<matplotlib.figure.Figure at 0x10c282b0>"
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "VOCbigframe.boxplot()\nplt.grid(None), plt.ylim([.5, 1.1]), plt.title('Vocab')\nplt.savefig('vocab_boxcar')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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g8Xjk4colALhpPp9PviEuxxuytW7q6up0zz33KDExsd/tpSwwAwAhc/0Bc9m1KzZeJ+jU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\nAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0xfbb50b0>"
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "vd1 = VOCbigframe[['S1']]\nvd2 = VOCbigframe[['S2']]\nvd3 = VOCbigframe[['S3']]\nvd4 = VOCbigframe[['S4']]\nvd9 = VOCbigframe[['S9']]\nvocab_4days_scatter, ((ax1, ax2, ax3, ax4, ax9)) = plt.subplots(1,5, figsize= (25,5),sharex='col', sharey='row')\nplt.xlim([0.1, 14])\nax1.plot(vd1 ,'o'), ax1.set_title('Vocab Day 1 (n=14)'), ax1.set_ylabel('Accuracy'), ax1.set_ylabel('Accuracy')\nax2.plot(vd2, 'o'), ax2.set_title('Vocab Day 2 (n=14)'), ax2.set_xlabel('Participants'), \nax3.plot(vd3, 'o'), ax3.set_title('Vocab Day 3 (n=14)'), ax3.set_xlabel('Participants'), \nax4.plot(vd4, 'o'), ax4.set_title('Vocab Day 4 (n=14)'), ax4.set_xlabel('Participants'), \nax9.plot(vd9, 'o'), ax9.set_title('Vocab Day 9 (n=4)'), \nplt.savefig('vocab_5days_scatter')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x10691cb0>"
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Grammar Accuracy Overall"
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": "grambigframe",
     "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>session</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>9</th>\n    </tr>\n    <tr>\n      <th>participant</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1006</th>\n      <td> 0.516667</td>\n      <td> 0.466667</td>\n      <td> 0.750000</td>\n      <td> 0.750000</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1007</th>\n      <td> 0.550000</td>\n      <td> 0.966667</td>\n      <td> 0.883333</td>\n      <td> 0.983333</td>\n      <td> 0.933333</td>\n    </tr>\n    <tr>\n      <th>1010</th>\n      <td> 0.483333</td>\n      <td> 0.416667</td>\n      <td> 0.666667</td>\n      <td> 0.666667</td>\n      <td> 0.616667</td>\n    </tr>\n    <tr>\n      <th>1013</th>\n      <td> 0.534483</td>\n      <td> 0.733333</td>\n      <td> 0.983333</td>\n      <td> 0.966667</td>\n      <td> 0.933333</td>\n    </tr>\n    <tr>\n      <th>1014</th>\n      <td> 0.533333</td>\n      <td> 0.500000</td>\n      <td> 0.633333</td>\n      <td> 0.516667</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1015</th>\n      <td> 0.583333</td>\n      <td> 0.616667</td>\n      <td> 0.750000</td>\n      <td> 0.633333</td>\n      <td> 0.416667</td>\n    </tr>\n    <tr>\n      <th>1016</th>\n      <td> 0.466667</td>\n      <td> 0.533333</td>\n      <td> 0.383333</td>\n      <td> 0.533333</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1019</th>\n      <td> 0.566667</td>\n      <td> 0.466667</td>\n      <td> 0.633333</td>\n      <td> 0.583333</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1022</th>\n      <td> 0.483333</td>\n      <td> 0.516667</td>\n      <td> 0.516667</td>\n      <td> 0.650000</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1023</th>\n      <td> 0.583333</td>\n      <td> 0.450000</td>\n      <td> 0.800000</td>\n      <td> 0.833333</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1024</th>\n      <td> 0.516667</td>\n      <td> 0.550000</td>\n      <td> 0.516667</td>\n      <td> 0.516667</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1025</th>\n      <td> 0.833333</td>\n      <td> 0.966667</td>\n      <td> 1.000000</td>\n      <td> 0.957447</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1027</th>\n      <td> 0.583333</td>\n      <td> 0.583333</td>\n      <td> 0.900000</td>\n      <td> 0.783333</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1028</th>\n      <td> 0.566667</td>\n      <td> 0.583333</td>\n      <td> 0.600000</td>\n      <td> 0.716667</td>\n      <td>      NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 11,
       "text": "session             1         2         3         4         9\nparticipant                                                  \n1006         0.516667  0.466667  0.750000  0.750000       NaN\n1007         0.550000  0.966667  0.883333  0.983333  0.933333\n1010         0.483333  0.416667  0.666667  0.666667  0.616667\n1013         0.534483  0.733333  0.983333  0.966667  0.933333\n1014         0.533333  0.500000  0.633333  0.516667       NaN\n1015         0.583333  0.616667  0.750000  0.633333  0.416667\n1016         0.466667  0.533333  0.383333  0.533333       NaN\n1019         0.566667  0.466667  0.633333  0.583333       NaN\n1022         0.483333  0.516667  0.516667  0.650000       NaN\n1023         0.583333  0.450000  0.800000  0.833333       NaN\n1024         0.516667  0.550000  0.516667  0.516667       NaN\n1025         0.833333  0.966667  1.000000  0.957447       NaN\n1027         0.583333  0.583333  0.900000  0.783333       NaN\n1028         0.566667  0.583333  0.600000  0.716667       NaN"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\ngramm_all = grambigframe.plot(kind = 'bar', figsize = (12,6)), plt.legend(title= 'session', bbox_to_anchor=(1.02, 1), loc=2, borderaxespad=0.), plt.grid(None), plt.title('Grammar Overall')\nplt.savefig('gram_all')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": "<matplotlib.figure.Figure at 0xdcb9cf0>"
      },
      {
       "output_type": "display_data",
       "png": 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HAAAALM73TuStJmZ5AOAxuz343yeVWgz7SABAEXyv9BdISnazzN3jAHAVy84i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\n",
       "text": "<matplotlib.figure.Figure at 0xf66ab90>"
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "grammar_acc_plot = grambigframe.mean().plot(kind='bar', color= 'grey', figsize= (6,4), xerr = gram_std_error, yerr = gram_std_error,title=\"Training_Grammar Accuracy\")\nplt.ylabel('Accuracy')\nplt.grid(None)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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8REQyI2nw6/V6+Pr6wtvbG4mJibW27969G/7+/hg0aBCioqJgMBikLIeIiCBx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\n",
       "text": "<matplotlib.figure.Figure at 0xaf01870>"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "nc = grambigframe2.xs('NC', level='Condition')\nnc_f = nc.xs('f', level='Novel_Familiar')\nnc_no = nc.xs('n', level='Novel_Familiar')\nva = grambigframe2.xs('VA', level='Condition')\nva_f = va.xs('f', level='Novel_Familiar')\nva_no = va.xs('n', level='Novel_Familiar')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Noun Class ALL\nnc_mean = nc.mean()\nnc_std = nc.std()\nnc_n = nc.count()\nnc_std_error = nc_std/np.sqrt(nc_n)\n#Verb Agreement ALL\nva_mean = va.mean()\nva_std = va.std()\nva_n = va.count()\nva_std_error = va_std/np.sqrt(va_n)\n#Familiar\nnc_f_mean = nc_f.mean()\nnc_f_std = nc_f.std()\nnc_f_n = nc_f.count()\nnc_f_std_error = nc_f_std/np.sqrt(nc_f_n)\n\nva_f_mean = va_f.mean()\nva_f_std = va_f.std()\nva_f_n = va_f.count()\nva_f_std_error = va_f_std/np.sqrt(va_f_n)\n#Novel\nnc_no_mean = nc_no.mean()\nnc_no_std = nc_no.std()\nnc_no_n = nc_no.count()\nnc_no_std_error = nc_no_std/np.sqrt(nc_no_n)\n\nva_no_mean = va_no.mean()\nva_no_std = va_no.std()\nva_no_n = va_no.count()\nva_no_std_error = va_no_std/np.sqrt(va_no_n)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Grammar Conditions: Noun Class and Verb Agreement"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "grammar_nc_va = plt.figure()\n\n\nplt.subplot(221)\nax = nc_mean.plot(kind = 'bar', color = 'grey', figsize=(7,7), xerr = nc_std_error, yerr = nc_std_error)\nplt.ylabel('Accuracy'), plt.xlabel('Session'), plt.title('Grammar: Noun Class'), plt.grid(None),  ax.set_xticklabels(['S1','S2', 'S3', 'S4', 'S9'])\n\n\nplt.subplot(222)\nax1 = va_mean.plot(kind = 'bar', color = 'grey', figsize=(10,8), xerr = va_std_error, yerr = va_std_error)\nplt.xlabel('Session'), plt.title('Grammar: Verb Agreement'), plt.grid(None)\nax.set_xticklabels(['S1','S2', 'S3', 'S4', 'S9']), ax1.set_xticklabels(['S1','S2', 'S3', 'S4', 'S9'])\n\nplt.savefig('grammar_nc_va_bar')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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aLFq0DwpF5xb15+LiYu6PhahN4R6vRtwoVi5eNGsYRHSbKS0tbfG4X/v2hUEI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\nREREZCIsvIiIiIhMhIUXERERkYn8f6I2SkEOXq4yAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0xdd2dd30>"
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "grammar_scatter, ((ax1), (ax2)) = plt.subplots(2, figsize= (10,8),sharex='col', sharey='row')\n#plt.ylim([.5,1])\nax1.plot(range(5),nc.T ,'.-')\nax1.set_title('Noun Class')\nax2.plot(range(5), va.T, '.-')\nax2.set_title('Verb Agreement')\nplt.xticks(range(5))\nax2.set_xticklabels(['S1','S2', 'S3', 'S4', 'S9'])\nplt.savefig('grammar_scatter')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ZUBIi18rCinb0w7Tv/3qML6q70Rk+rpycnHoRDY6OjkyePJnJkycTNmYMp52c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\n3TyVq0ISeKTrjTgdWZzJ3Ulq/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iGB9HquebkoQYD09AvmKSlJzDEnJl6AaDaWAVX/6wj8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M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\n",
       "text": "<matplotlib.figure.Figure at 0xdd572f0>"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "grammar_box = plt.figure()\n\nplt.subplot(221)\nax1 = nc.boxplot(), plt.title('Noun Class'), plt.grid(None), plt.ylabel('Accuracy'), plt.xlabel('Session'), plt.ylim([0,1.2])\ngcf().set_figwidth(9)\n\nplt.subplot(222)\nax2 = va.boxplot(), plt.title('Verb Agreement'),plt.grid(None), plt.xlabel('Session'), plt.ylim([0,1.2])\ngcf().set_figwidth(9)\n\nplt.savefig('grammar_box')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0xdd575d0>"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Noun Class: Novel vs Familiar Trials"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig = plt.figure()\nax = fig.add_subplot(111)\n\n## the data\nN = 5\n\n## necessary variables\nind = np.arange(N)                # the x locations for the groups\nwidth = 0.35                      # the width of the bars\n\n## the bars\nrects1 = ax.bar(ind, nc_f_mean, width,color='grey',yerr=nc_f_std_error, xerr=nc_f_std_error)\nrects2 = ax.bar(ind+width, nc_no_mean, width,color='lightgrey',yerr=nc_no_std_error, xerr=nc_no_std_error)\nplt.legend((['familiar', 'novel']),bbox_to_anchor=(1, 1), loc=2, borderaxespad=0.)\nplt.xticks(ind+width)\nax.set_xticklabels(['S1','S2', 'S3', 'S4', 'S9'])\nplt.ylim([0,1])\nplt.title('Noun Class: Familiar and Novel items')\nplt.savefig('nc_nf_bar')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Sk9fQ2m+Vv/tulJD09vZGZmamuJyTk4NevXrVZ5dERETNRr1DcufOnfjrr78QFxcHV1fX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\nOxZto3QAAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0xe5417f0>"
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "nc_no1 = nc_no[[1]]\nnc_f1 = nc_f[[1]]\nnc_no2 = nc_no[[2]]\nnc_f2 = nc_f[[2]]\nnc_no3 = nc_no[[3]]\nnc_f3 = nc_f[[3]]\nnc_no4 = nc_no[[4]]\nnc_f4 = nc_f[[4]]\nnc_no9 = nc_no[[9]]\nnc_f9 = nc_f[[9]]\nscatter, ((ax1, ax2, ax3, ax4, ax9)) = plt.subplots(1,5, figsize= (25,5),sharex='col', sharey='row')\nplt.ylim([0.2,1.01])\nax1.plot(nc_f1 ,'ro'), ax1.plot(nc_no1,'bo'), ax1.set_title('Noun Class Day 1 (n=14)'), ax1.set_ylabel('Accuracy')\nax2.plot(nc_f2 ,'ro'), ax2.plot(nc_no2,'bo'), ax2.set_title('NC Day 2 (n=14)'),ax2.set_xlabel('Participants')\nax3.plot(nc_f3 ,'ro'), ax3.plot(nc_no3,'bo'), ax3.set_title('NC Day 3 (n=14)'), ax3.set_xlabel('Participants'), \nax4.plot(nc_f4 ,'ro'), ax4.plot(nc_no4,'bo'), ax4.set_title('NC Day 4 (n=14)'), ax4.set_xlabel('Participants')\nax9.set_title('NC Day 9 (n=4)'), ax9.plot(nc_f9 ,'ro'),ax9.plot(nc_no9,'bo'), ax4.set_xlabel('Participants')\nplt.legend((['familiar', 'novel']),bbox_to_anchor=(-5.25, 1), loc=2, borderaxespad=0.)\nplt.savefig('NC_scatter_4days')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0xe6b5210>"
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Verb Agreement: Novel vs Familiar"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig = plt.figure()\nax = fig.add_subplot(111)\n\n## the data\nN = 5\n\n## necessary variables\nind = np.arange(N)                # the x locations for the groups\nwidth = 0.35                      # the width of the bars\n\n## the bars\nrects1 = ax.bar(ind, va_f_mean, width,color='grey',yerr=va_f_std_error, xerr=va_f_std_error)\nrects2 = ax.bar(ind+width, va_no_mean, width,color='lightgrey',yerr=va_no_std_error, xerr=va_no_std_error)\nplt.legend((['familiar', 'novel']),bbox_to_anchor=(1, 1), loc=2, borderaxespad=0.)\nplt.xticks(ind+width)\nax.set_xticklabels(['S1','S2', 'S3', 'S4', 'S9'])\nplt.ylim([0,1])\nplt.title('Verb Agrement: Familiar and Novel items')\nplt.savefig('va_nf_bar')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Z1t8cw5CoPvAUEGpRanvCN08QJ2rZuCfZCGk0f1whJDUVcHUF2rQBfH0fPoiI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\nsWPHjqYsyyQM9UXr1q0xatQ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       "text": "<matplotlib.figure.Figure at 0xe5413b0>"
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "va_no1 = va_no[[1]]\nva_f1 = va_f[[1]]\nva_no2 = va_no[[2]]\nva_f2 = va_f[[2]]\nva_no3 = va_no[[3]]\nva_f3 = va_f[[3]]\nva_no4 = va_no[[4]]\nva_f4 = va_f[[4]]\nva_no9 = va_no[[9]]\nva_f9 = va_f[[9]]\nscatter, ((ax1, ax2, ax3, ax4, ax9)) = plt.subplots(1,5, figsize= (25,5),sharex='col', sharey='row')\nplt.ylim([0.2,1.01])\nax1.plot(va_f1 ,'ro'), ax1.plot(va_no1,'bo'), ax1.set_title('VA Day 1 (n=14)'), ax1.set_ylabel('Accuracy')\nax2.plot(va_f2 ,'ro'), ax2.plot(va_no2,'bo'), ax2.set_title('VA Day 2 (n=14)'),ax2.set_xlabel('Participants')\nax3.plot(va_f3 ,'ro'), ax3.plot(va_no3,'bo'), ax3.set_title('VA Day 3 (n=14)'), ax3.set_xlabel('Participants'), \nax4.plot(va_f4 ,'ro'), ax4.plot(va_no4,'bo'), ax4.set_title('VA Day 4 (n=14)'), ax4.set_xlabel('Participants')\nax9.set_title('VA Day 9 (n=4)'), ax9.plot(va_f9 ,'ro'),ax9.plot(va_no9,'bo'), ax4.set_xlabel('Participants')\nplt.legend((['familiar', 'novel']),bbox_to_anchor=(-5.25, 1), loc=2, borderaxespad=0.)\nplt.savefig('va_scatter_5days')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x11e75430>"
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "KC_Test Plots"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "KCTestbigframe",
     "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>session</th>\n      <th>1.0</th>\n      <th>2.0</th>\n      <th>3.0</th>\n      <th>4.0</th>\n      <th>9.0</th>\n    </tr>\n    <tr>\n      <th>participant</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>999 </th>\n      <td> 0.000000</td>\n      <td>      NaN</td>\n      <td>      NaN</td>\n      <td>      NaN</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1006</th>\n      <td> 0.305556</td>\n      <td> 0.500000</td>\n      <td> 0.611111</td>\n      <td> 0.569444</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1007</th>\n      <td> 0.458333</td>\n      <td> 0.555556</td>\n      <td> 0.666667</td>\n      <td> 0.472222</td>\n      <td> 0.444444</td>\n    </tr>\n    <tr>\n      <th>1010</th>\n      <td> 0.097222</td>\n      <td> 0.180556</td>\n      <td> 0.236111</td>\n      <td> 0.388889</td>\n      <td> 0.194444</td>\n    </tr>\n    <tr>\n      <th>1013</th>\n      <td> 0.388889</td>\n      <td> 0.430556</td>\n      <td> 0.583333</td>\n      <td> 0.833333</td>\n      <td> 0.263889</td>\n    </tr>\n    <tr>\n      <th>1014</th>\n      <td> 0.388889</td>\n      <td> 0.388889</td>\n      <td> 0.638889</td>\n      <td> 0.625000</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1015</th>\n      <td> 0.111111</td>\n      <td> 0.138889</td>\n      <td> 0.152778</td>\n      <td> 0.097222</td>\n      <td> 0.013889</td>\n    </tr>\n    <tr>\n      <th>1016</th>\n      <td> 0.111111</td>\n      <td> 0.277778</td>\n      <td> 0.305556</td>\n      <td> 0.402778</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1019</th>\n      <td> 0.527778</td>\n      <td> 0.625000</td>\n      <td> 0.625000</td>\n      <td> 0.694444</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1022</th>\n      <td> 0.138889</td>\n      <td> 0.222222</td>\n      <td> 0.319444</td>\n      <td> 0.444444</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1023</th>\n      <td> 0.263889</td>\n      <td> 0.416667</td>\n      <td> 0.472222</td>\n      <td> 0.583333</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1024</th>\n      <td> 0.194444</td>\n      <td> 0.236111</td>\n      <td> 0.333333</td>\n      <td> 0.388889</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1025</th>\n      <td> 0.208333</td>\n      <td> 0.555556</td>\n      <td> 0.708333</td>\n      <td> 1.000000</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1027</th>\n      <td> 0.250000</td>\n      <td> 0.319444</td>\n      <td> 0.375000</td>\n      <td> 0.347222</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1028</th>\n      <td> 0.430556</td>\n      <td> 0.458333</td>\n      <td> 0.486111</td>\n      <td> 0.458333</td>\n      <td>      NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 35,
       "text": "session             1         2         3         4         9\nparticipant                                                  \n999          0.000000       NaN       NaN       NaN       NaN\n1006         0.305556  0.500000  0.611111  0.569444       NaN\n1007         0.458333  0.555556  0.666667  0.472222  0.444444\n1010         0.097222  0.180556  0.236111  0.388889  0.194444\n1013         0.388889  0.430556  0.583333  0.833333  0.263889\n1014         0.388889  0.388889  0.638889  0.625000       NaN\n1015         0.111111  0.138889  0.152778  0.097222  0.013889\n1016         0.111111  0.277778  0.305556  0.402778       NaN\n1019         0.527778  0.625000  0.625000  0.694444       NaN\n1022         0.138889  0.222222  0.319444  0.444444       NaN\n1023         0.263889  0.416667  0.472222  0.583333       NaN\n1024         0.194444  0.236111  0.333333  0.388889       NaN\n1025         0.208333  0.555556  0.708333  1.000000       NaN\n1027         0.250000  0.319444  0.375000  0.347222       NaN\n1028         0.430556  0.458333  0.486111  0.458333       NaN"
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\nKCTestbigframe.plot(kind = 'bar', figsize = (12,6))\nplt.figsize=(12,6)\nplt.legend(title= 'session', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\nplt.grid(None)\nplt.title('Korean Consonants Overall')\nplt.savefig('KC_Test_all')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": "<matplotlib.figure.Figure at 0x10cd6410>"
      },
      {
       "output_type": "display_data",
       "png": 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r1y4VFhZWebuM6QAAVDvXwYNSqudzl11xJ3/eMQCgep177rm69NJL9fjjj2vS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\nQgcAAAAATxE6AAAAAHiK0AEAAADAUz8A2E8OXyAdB1EAAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0xb869490>"
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "KCTest_acc_plot = KCTestbigframe.mean().plot(kind='bar', color= 'grey', xerr = kctest_std_error, yerr = kctest_std_error,title=\"Training: KC_Test Accuracy\")\nplt.ylabel('Accuracy')\nplt.grid(None)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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29fVFfHw8QkJCpHkqlQpxcXHo2rUr+vXrhxkzZuDvf/97iXUVP61Lp9NBV9ePohERVZLR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\n",
       "text": "<matplotlib.figure.Figure at 0xb86f1d0>"
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "DAILY SAT PLOTS"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "SATbigframe",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nMultiIndex: 112 entries, (1006, semb_verb) to (1028, syngva)\nData columns (total 4 columns):\n2    96  non-null values\n3    96  non-null values\n4    96  non-null values\n9    32  non-null values\ndtypes: float64(4)\n</pre>",
       "output_type": "pyout",
       "prompt_number": 93,
       "text": "<class 'pandas.core.frame.DataFrame'>\nMultiIndex: 112 entries, (1006, semb_verb) to (1028, syngva)\nData columns (total 4 columns):\n2    96  non-null values\n3    96  non-null values\n4    96  non-null values\n9    32  non-null values\ndtypes: float64(4)"
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\nSATbigframe.plot(kind = 'bar',figsize = (12,6))\nplt.legend(title= 'session', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\nplt.grid(None)\nplt.title('Daily SAT Overall')\nplt.savefig('daily_SAT_overall')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": "<matplotlib.figure.Figure at 0x11dee710>"
      },
      {
       "output_type": "display_data",
       "png": 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PHnXq1MnuGOVCdnuQ3R7+kv3CnZPzcnNz/WKaBNntUd2yHz58WE2b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\nAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0xb8979d0>"
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "SAT_acc_plot = SATbigframe.mean().plot(kind='bar', color= 'grey', xerr = SAT_std_error, yerr = SAT_std_error,title=\"Training: Daily SAT Accuracy\")\nplt.ylabel('Accuracy')\nplt.grid(None)\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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IstBvaZJ+c1en08HT0xPu7u6Ii4trcEx6ejr8/Pzg6ekJVXv55ISI2oTmtBDbU7tR0jP+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\nn4hIZhj8REQyw+AnIpKZ/wNLrbKt7cOJBgAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0xddc49f0>"
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "dfSAT = pandas.read_csv('./Training_Sessions/Daily_SAT_Master')\ndf = pandas.pivot_table(dfSAT, values='ACC', rows=['participant', 'Session'], cols=['Condition'], aggfunc=np.mean)\ndf_sem_verb = df[['semb_verb', 'semg_verb']]\ndf_sem_wo = df[['semb_wo', 'semg_wo']]\ndf_syn_nc = df[['synbnc', 'syngnc']]\ndf_syn_va = df[['synbva', 'syngva']]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "semv_mean = df_sem_verb.unstack().mean()\nsemv_std = df_sem_verb.unstack().std()\nsemv_n= df_sem_verb.unstack().count()\nsemv_std_error = semv_std/np.sqrt(semv_n)\n\nsemgv = semv_mean['semg_verb']\nsembv = semv_mean['semb_verb']\nsemg_err = semv_std_error['semg_verb']\nsemb_err = semv_std_error['semb_verb']\n\nsemw_mean = df_sem_wo.unstack().mean()\nsemw_std = df_sem_wo.unstack().std()\nsemw_n= df_sem_wo.unstack().count()\nsemw_std_error = semw_std/np.sqrt(semw_n)\n\nsemgw = semw_mean['semg_wo']\nsembw = semw_mean['semb_wo']\nsemgw_err = semw_std_error['semg_wo']\nsembw_err = semw_std_error['semb_wo']\n\nsynnc_mean = df_syn_nc.unstack().mean()\nsynnc_std = df_syn_nc.unstack().std()\nsynnc_n= df_syn_nc.unstack().count()\nsynnc_std_error = synnc_std/np.sqrt(synnc_n)\n\nsyngnc = synnc_mean['syngnc']\nsynbnc = synnc_mean['synbnc']\nsyngnc_err = synnc_std_error['syngnc']\nsynbnc_err = synnc_std_error['synbnc']\n\nsynva_mean = df_syn_va.unstack().mean()\nsynva_std = df_syn_va.unstack().std()\nsynva_n= df_syn_va.unstack().count()\nsynva_std_error = synva_std/np.sqrt(synva_n)\n\nsyngva = synva_mean['syngva']\nsynbva = synva_mean['synbva']\nsyngva_err = synva_std_error['syngva']\nsynbva_err = synva_std_error['synbva']",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig = plt.figure()\nN = 4\nind = np.arange(N)  # the x locations for the groups\nwidth = 0.35      # the width of the bars\nheight = 3.0\n\n# row and column sharing\nf, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize= (10,5), sharex='col', sharey='row')\nplt.ylim(0,1)\n#plt.set_title('Daily_SAT Accuracies')\nax1.bar(ind, sembv, width, color='grey', yerr=semb_err)\nax1.bar(ind+width, semgv, width, color='y', yerr=semg_err)\nax1.set_ylabel('Acc')\nax1.set_title('Semantic Verb Accuracy')\nax1.set_xticks(ind+width)\nplt.ylim(0,1)\n\nax2.bar(ind, sembw, width, color='grey', yerr=sembw_err)\nax2.bar(ind+width, semgw, width, color='y', yerr=semgw_err)\nax2.set_title('Semantic WO Accuracy')\nax2.set_xticks(ind+width)\n#ax2.set_xticklabels( ('T2', 'T3', 'T4') )\nax2.set_ylim(0,1)\n\nax3.bar(ind, synbva, width, color='grey', yerr=synbva_err)\nax3.bar(ind+width, syngva, width, color='y', yerr=syngva_err)\nax3.set_ylabel('Acc')\nax3.set_title('Syntantic Verb Accuracy')\nax3.set_xticks(ind+width)\nax3.set_xticklabels( ('S2', 'S3', 'S4', 'S9') )\nplt.ylim(0,1)\n\nax4.bar(ind, synbnc, width, color='grey', yerr=synbnc_err)\nax4.bar(ind+width, syngnc, width, color='y', yerr=syngnc_err)\nax4.set_ylabel('Acc')\nax4.set_title('Syntantic NC Accuracy')\nax4.set_xticks(ind+width)\nax4.set_xticklabels( ('S2', 'S3', 'S4', 'S9') )\nplt.legend((['bad trials', 'good trials']),bbox_to_anchor=(1.5, 2.2), loc=1, borderaxespad=0.)\nplt.ylim(0,1)\nplt.savefig('Daily_SAT_bar_withoutT1')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": "<matplotlib.figure.Figure at 0x151cfb50>"
      },
      {
       "output_type": "display_data",
       "png": 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ePKm8v3r1KszNzdG2bVv07NlTpywAnDp1Cr169ULr1q1hZmaGpKQknWXlDdcw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\nAAAAwAQ02gAAAIAJ/hd6tPaUht9uZwAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x17d02770>"
      }
     ],
     "prompt_number": 156
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "D-Prime Daily SAT (under construction)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "SATbigframe = pandas.DataFrame()\ndfSAT = pandas.read_csv('./Training_Sessions/Daily_SAT_Master')\n#dfSAT['condition2'] = dfSAT.Condition.apply(lambda x: ('sem_wo' if x == 'semg_wo' else 'sem_wo' if x== 'semb_wo' else 'sem_verb' if x == 'semg_verb' else 'sem_verb' if x == 'semb_verb' else 'syn_verb' if x == 'synbva' else 'syn_verb' if x == 'syngva' else 'syn_nc' if x == 'syngnc' else 'syn_nc' if x == 'synbnc' else 2))\ndfSAT['datatype'] = dfSAT.Condition.apply(\n               lambda x: (1 if x == 'semg_wo' else 1 if x== 'syngnc' else 1 if x == 'semg_verb' else 1 if x == 'syngva' else 2))\n\nhit = lambda s: 1 if ((s['datatype'] == 1) & (s['response'] == 1)) else None\nmiss = lambda s: 1 if ((s['datatype'] == 1) & (s['response'] == 2)) else None\ncr = lambda s: 1 if ((s['datatype'] == 2) & (s['response'] == 2)) else None\nfa = lambda s: 1 if ((s['datatype'] == 2) & (s['response'] == 1)) else None\n\n                                    \ndfSAT['hit'] = dfSAT.apply(hit, axis=1)\ndfSAT['miss'] = dfSAT.apply(miss, axis=1)\ndfSAT['cr'] = dfSAT.apply(cr, axis=1)\ndfSAT['fa'] = dfSAT.apply(fa, axis=1)\n#dfSAT.to_csv('./Training_Sessions/Daily_SAT_Master-dPrime')\n\ndfSAT['condition2'] = dfSAT.apply(lambda row: ('sem_wo' if row['Condition']=='semg_wo' else 'sem_wo' if row['Condition']=='semb_wo'\n                                               else 'sem_verb' if row['Condition']=='semg_verb' else 'sem_verb' if row['Condition']=='semb_verb' \n                                               else 'syn_verb' if row['Condition']=='syngva' else 'syn_verb' if row['Condition']=='synbva' \n                                               else 'syn_nc' if row['Condition']=='syngnc' else 'syn_nc' if row['Condition']=='synbnc' else None ), axis=1)\n#dfSAT[''] = dfSAT.apply(hit, axis=1)\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 155
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "dprimedf = pandas.DataFrame()\ndfSAT = pandas.read_csv('./Training_Sessions/Daily_SAT_Master-dPrime')\nfor i in dfSAT:\n    #dprimedf = pandas.pivot_table(dfSAT, values=['hit', 'miss', 'fa', 'cr'], rows=['participant', 'Condition'], cols=['Session'], aggfunc=np.sum)\n    dprimedf = pandas.pivot_table(dfSAT, values=['hit', 'miss', 'fa', 'cr'], rows=['participant', 'Session'], aggfunc=np.sum)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 164
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "dprimedf['HminusM'] = dprimedf.apply(lambda row: (row['hit']-row['miss']), axis = 1)\ndprimedf['HR'] = dprimedf.apply(lambda row: (row['hit']/ (row['hit']+row['miss'])), axis = 1)\ndprimedf['FAR'] = dprimedf.apply(lambda row: (row['fa']/ (row['fa']+row['cr'])),axis = 1)\ndprimedf['zHR'] = dprimedf.apply(lambda row: norm.ppf(row['HR']),axis = 1) \ndprimedf['zFAR'] = dprimedf.apply(lambda row: norm.ppf(row['FAR']),axis = 1)    \ndprimedf['dPrime'] = dprimedf.apply(lambda row: (row['zHR']-row['zFAR']), axis = 1)   \ndprimedf.to_csv('./Training_Sessions/dPrime_all')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 163
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "dprimedf.dPrime",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "AttributeError",
       "evalue": "'DataFrame' object has no attribute 'dPrime'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-166-7c946ce2145f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdprimedf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdPrime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/gablabmcgovern/Library/Enthought/Canopy_32bit/User/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   2005\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2006\u001b[0m         raise AttributeError(\"'%s' object has no attribute '%s'\" %\n\u001b[0;32m-> 2007\u001b[0;31m                              (type(self).__name__, name))\n\u001b[0m\u001b[1;32m   2008\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2009\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'dPrime'"
       ]
      }
     ],
     "prompt_number": 166
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}