jminas/Data_5-25-13

## Data_5-25-13
{
 "metadata": {
  "name": "LAP_DATA_5-27-13"
 },
 "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\n",
     "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('/home/jminas/Dropbox/Language/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('/home/jminas/Dropbox/Language/Data/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('/home/jminas/Dropbox/Language/Data/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('/home/jminas/Dropbox/Language/Data/Training_Sessions/Summaries/Vocab_Summary')\nVOCbigframe.columns= ['S%d' % col for col in VOCbigframe.columns]\n#===========Grammar============================================================\n\"\"\"\nGRbigframe = pandas.DataFrame()\nGRcsvfiles = glob.glob('/home/jminas/Dropbox/Language/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('/home/jminas/Dropbox/Language/Data/Training_Sessions/GR_Master')\n\"\"\"\ngrambigframe = pandas.DataFrame()\ndfgram = pandas.read_csv('/home/jminas/Dropbox/Language/Data/Training_Sessions/GR_Master')\n#for i in dfgram:\n #   grambigframe = pandas.pivot_table(dfgram, values='ACC', rows=['participant'], cols=['session', 'expName'], aggfunc=np.mean)\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', 'Novel_Familiar'], cols=['Condition', 'session'], aggfunc=np.mean)\n#grambigframe.to_csv('/home/jminas/Dropbox/Language/Data/Training_Sessions/Summaries/Grammar_Summary')\n\n#===========KC_test============================================================\n\"\"\"KC_test_bigframe = pandas.DataFrame()\nKC_test_csvfiles = glob.glob('/home/jminas/Dropbox/Language/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('/home/jminas/Dropbox/Language/Data/Training_Sessions/KCtest_Master')\n\"\"\"\nKCTestbigframe = pandas.DataFrame()\ndfKCTest = pandas.read_csv('/home/jminas/Dropbox/Language/Data/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('/home/jminas/Dropbox/Language/Data/Training_Sessions/Summaries/KC_Test_Summary')\n\n#===========DailySAT============================================================\n\"\"\"\nDSATbigframe = pandas.DataFrame()\nDSATcsvfiles = glob.glob('/home/jminas/Dropbox/Language/Training_tasks/psychopy/data/*Daily_SAT*')\nfor csv in DSATcsvfiles:\n    DSATbigframe = DSATbigframe.append(pandas.read_csv(csv, sep='\\t'), ignore_index=True)\n#DSATbigframe.to_csv('/home/jminas/Dropbox/Language/Data/Training_Sessions/Daily_SAT_Master')\n\"\"\"\nSATbigframe = pandas.DataFrame()\ndfSAT = pandas.read_csv('/home/jminas/Dropbox/Language/Data/Training_Sessions/Daily_SAT_Master')\nfor i in dfSAT:\n    SATbigframe = pandas.pivot_table(dfSAT, values='ACC', rows=['participant'], cols=['Session'], aggfunc=np.mean)\n#SATbigframe.to_csv('/home/jminas/Dropbox/Language/Data/Training_Sessions/Summaries/DailySAT_Summary')\n\nSATDPbigframe = pandas.DataFrame()\ndfSATDP = pandas.read_csv('/home/jminas/Dropbox/Language/Data/Training_Sessions/Daily_SAT_Master-dPrime')\n\n#===========MRI sent============================================================\n\"\"\"\nsentbigframe = pandas.DataFrame()\nsentcsvfiles = glob.glob('/home/jminas/Dropbox/Language/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('/home/jminas/Dropbox/Language/Data/MRI/Sent_Master2')\n\"\"\"\n\nSentbigframe = pandas.DataFrame()\ndfSent = pandas.read_csv('/home/jminas/Dropbox/Language/Data/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('/home/jminas/Dropbox/Language/Data/MRI/Summaries/Sent_Summary')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "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": 3
    },
    {
     "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": 4,
       "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": 4
    },
    {
     "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 0x3b36ad0>"
      },
      {
       "output_type": "display_data",
       "png": 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M27NnT5VpL7zwQpVpXbp00Zdffinp0iFRb7zxRpUxSUlJSkpKcj3fsmXLdeW+Fg6XAgAA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\nAakc2L6F6gh4AAAAAElFTkSuQmCC\n"
      }
     ],
     "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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imWHwExHJDIOfiEhmGPxERDLD4CcikhkGPxGRzDD4iYhkhsFPRCQzDH4iIplh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\n4Ccikpn/B38K7FqfKVT5AAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "vocab_scatter_fig = plt.figure()\nvd1 = VOCbigframe.T.as_matrix()\nax = vocab_scatter_fig.add_subplot(111)\nplt.plot(range(5), vd1, '.-', ms=8, lw=2, )\nplt.figsize=(12,6)\nax.set_xticklabels(['S1','S2', 'S3', 'S4', 'S9'])\nplt.xticks(range(5))\nplt.title('Vocabulary')\nplt.savefig('vocab_scantter_fig')\n\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Qoa17JrOJTd9t4rFPH6OiueuHgZuzbmbDtRsYHzvesY9TCOFyA27CGxdkGYxl\nE54Qoj8yKA+Roih0dFT2uR7a4biwNZKgr8oI2vEt6o8/HTDCMxtx+eY/p8V5ztg8Z20Ncz+b5xzd\nupdblMsjHz/CoepDAFyUcBHPzn+W7NRsu+4vhBg+lk14O+uoy+lnE97ZNdHaDLlUSghhHxmUXczh\nuNAEgdWgrdOiDRqHNukStBfORxsz2WZc6OzNfw7FedY2z3U/P9DmuZQU62uYHdw858jWvaOnj7Lq\nk1US6gnh4STCE0IMB5cMyjk5OaxcuRKTycTPfvYzfv/73/d4fX19PcuWLaOoqIigoCDeeOMNLrzw\nQgBSU1MJDw9Ho9Hg7+9Pfn7+kA/Yo5y3Cc/42Tb0ur20xxhoT4L2RGhPUdM+LhBDmB5UjsWFtIY6\nvPlvwDgv5hbUNf0MwwNtnktNtT4MO3nz3GC37kmoJ4RnkwhPCOEOTh+UTSYTWVlZ5ObmkpSUxCWX\nXMKWLVuYMGGC5TarVq0iPDycP/7xj5w4cYJf/epX5ObmApCWlsY333xDtI1fp3vloOzAJjxnxYWq\n6mRacyNo+TACyhKhIZKQSaF9Nv9ZjfOUIEY3XkbikTRCDzbbHc9ZvV54EPHcUA1m656EekJ4Lonw\nhBDu5sjc2e+kk5+fT0ZGBqmpqQAsXryYrVu39hiUjx07xqOPPgpAVlYWJSUlnDlzhlFnfzXudYNw\nb07YhKdWBxIcPJ7g4L7R2EBxYWdnLZ2dtTQ17e/aAjfv7B+A9mBaTyWiK4vn1C8iCB3bSeekQhpH\nF6Gou/7dg0+qSPxfhfiP9fi17gZ29zwAW/FcejokJLjkYejsdbytjRsPH6bdbGZZfDxPnP087E1C\nPSE8j0R4Qghf0O9XpvLyclJSUiwvJycns3///h63mTp1Ku+//z5XXXUV+fn5lJaWcurUKUaN6noo\ntWuvvRaNRsOKFSu4//77+7yPdevWWZ7Pzs4mOzt7aB+RMwy0Ce+qq5y2CU+l8kOrTUOrTSMq6toe\nr+sRF9YdxfTDtygF30NRKX7FpwksbyOgVkfDVB2VN8OZsydbVSaI3QNJ/wuRBxRMscGYJsZjGpeG\n6oJJ+GVdjDoza8B4zp0qDAYWHDpEndHIjTExvGxtNTUS6gnhKeyK8OZEWoZjifCEEK6Wl5dHXl7e\nkN5Gv4OyPY9+8Oijj/Lwww8zffp0Jk+ezPTp09Fouh4T+PPPPycxMZEzZ84wb948xo8fz9VXX93j\n/ucPym5vQ01IAAAgAElEQVSj18OePeeGYzdswgP6xHMqnY5AnY7AwkIie8VzrWOhYhFUzQfT2UeP\n82tUEf5NIIGnoTNRj+6XoE8EU3AbUHT2z66uzYXmMWjLMtDWeMbmwvM1GI0s/O47ThoMXB4ezrsT\nJ+LX63NRQj0h3G9QEd7MCNQB8n9TCDF8ep+AXb9+/aDfRr+DclJSEmXnXYNbVlZGcnLPR0cICwvj\njTfesLyclpbGuHHjAEhMTARg1KhR3HrrreTn5/cZlN3ivAiPnTth9zBtwuu9ea73Q6v1F8/5+WHO\nSKV2QRjlV56mIa7c8qqIsKtISnmQ2NhbMWerqXq7ivLXC2hvKYTECkipIGhmLX4TT9MZWILBUIZe\nX4xeX0x9/Sd93lXfuPDcU1dvLtSbzdxy5AiHW1vJCg7mo8mTCT77gxdIqCeEO0mEJ4QYafqN+YxG\nI1lZWezatYvExEQuvfTSPjFfY2MjWq2WgIAAXn31Vb744gveeust2traMJlMhIWF0drayvz581m7\ndi3z588/986HM+ZzIMJzSPfmOWuPL2xPPGflsYUNY8Oo1Gynouo1uzfnKYpCw2cNlL9QTu3WWss3\ns5BJIcT/KoaIH7fRqS514ubCdPz944a0udCkKNz5/fe8d+YMiQEBfHnRRYwNCgIk1BPCXQwVBup2\n2hHhLYwmdJpEeEIIz+WSh4fbsWOH5eHhli9fzurVq3n55ZcBWLFiBfv27ePee+9FpVIxadIkXn/9\ndSIiIiguLubWW28Fugbun/70p6xevXrIB2y38yO8nBzYv3/QEZ5NQ9k8FxEBmZkDxnPO3JxnOGWg\n4pUKKl+ppKO6A7C9+c81mwvTCQxM7ndzoaIoPKTT9dm6J6GeEMNLIjwhhK+ShSMDRXi9NuH1G+H1\nt3mutLTn0N1b781z5z+Nju73Mg6nbc6zwtxhHtLmP6dvLjw7TAcFpfKnsoo+W/ck1BPC9STCE0KM\nFCNvUB5qhOeMzXO9h+Fx4yAsbNAfikOb84bA2Zv/wPHNhTu4nmdYhQqFv0R9yURVA09/8ym7Tn4L\nSKgnhLNJhCeEGIl8f1AebISXng5nzti+XniAeI60NOvDsJM2zw24OS/2VtRq18Ywxkajw5v/BvV+\njM3o9UV9LufIbQnh98aHMaPhfuNzVBZtZXslmIFgDSwZA0vSEogMzXRLXCiEL5AITwghfHVQHijC\nmzq165KKjIyuGK6kxLF4rndE58LNc1Y35w0Q57laf/Ff781/znL+1r2rzcf59qvf0trZjkal4rbU\nFJamaghWytwWFwrhzSTCE0KInrxzUM7Ohs8+O/eXA0V4ISGQnNw15La0dA3OA8VzvR5Fwh2b55wZ\n57naYOI/Rx1va2Pmt99SZzQSXJNH29GuxzbsHeq5Ky4sKVlPQ8NnREbOJjV17ZA/XiGcpWR9CQ2f\nNRA5O5LUtamWv5cIT7ibfN0Uns47B2U/P7j0UrjrLti6Fb74ov+zwNaMHm31YdXsiedczZVxnqsN\nNf6zpcJgYHr+l5w2qaD2SzjyRy5KmDboUG/ocWHa2cG55zBdVfUOp09vQq8vJigojfj4pfJFX3iE\nkvUlVG2sQl+sJzAtkNgbYtFmaKnbWUdDnkR4wn1KStZTVbVRvm4Kj+adg7J9N3R6POdqwx3nuZqz\n4r99lYdZcOQYzf5x0HiUpOL/y5/mrHNJqOdoXNhbaOhFXHxxPiqVZuAbC+FkiqLQeaaTdl07J5af\nsLQE1kiEJ9zlwIFsGht3W14ODZ3GjBkH3HhEQvTl3YOyStX12MYTJ8LkyT2H4dRUOLt4wpN5Qpzn\nao7Gf1UtVTyWt543jFkQOQ11Wxl/CK3iD1c86JaNerbiwvZ2HQbDyT639/OLJirqWqKjFxIdPZ/A\nwKRhP2bhuxSzgqHcQLuuHX2hnnZdO+2F7ZanpmaT1fupg9WMunWURHjCLQyGcurqdlJXt5Oamn+h\nKPoer4+NvZVx4/5EcPAFbjpCIXryzkF53DjYvr1rKHZRPOdqnhjnuZq98V/3Rr0/ffF/aM34LYy6\nhhBzO7unTebiaM88q242GygsfIT6+s9Qq4MxGmvQ64t73CYkZBLR0QuIilpAZOTVqNWe/4OccC9z\npxlDqaHPENyua0dfpMdssP3Y7H6RfmgztGgztBjKDXSe7iQiO4ILXrxAIjwxbMxmPQ0Ne6mv7xqO\nW1uP9Hi9RhOJRhNMUFAaLS0HMJvbUKn8SEz8Bamp/4G/f6ybjlyILt45KLvv3Q+JN8V5rmYt/iMc\n9v9sP3+J+wuV+krI+DUk3UqYWsUXF13M5NBQ9x70ICiKQnu7zvLNoaHhM0ymc6GUWq0lMvIaoqMX\nEB29EK02y2OvOxeuZWo3oS/qdUb47POGUkOPh2TrLSA+AG161zAclB7UNRiffdk/2n8YPwohuiiK\nQlvb8fO+9u3uEVBrNCFERs62nDTQajMsX/sMhgqKi/9IVdWbgIKfXwRjxjxGcvJDcmJBuI0MysPA\nm+M8V+uO/97f/D5/TvwzhfGFAIwavZIz4xcRqFKxc+pUron07sc+NpsNNDZ+afnm0dJysMfrAwPH\nnB2aFxAVNVce69nHGBuNfYbg7ksmDOX9PAKPCoLGBPUZgrXpXX80oXINvHA/o7GB+vpd1NXlUFf3\ncZ9L0UJDpxIV1XVSICLiStTq/vuUlpbvzv6G7hMAgoLGkpb2NHFxi0fs90rhPjIou5CvxXmucPT0\nUVZ9sooduh0AJJgSuLhmDR/dMRGVGZ78bz+WXJTs8OY/T9XRUUVd3cfU1e2kvv5jOjtrLK9TqTSE\nhV1mGZzDwmZIFOjhzo/nzh+Cu1/urOm0eV+Vv4qgtKCeQ/DZSyaCUoNQB0pcJzyLophobv7acq1x\nc/N+FOXcNfH+/rFERc0/+zVsPgEB8Q69n7q6nRQWPmK5XCMs7FLS0/9MZOTVTvk4hLCHDMpONhLi\nPGeoaqlibd5aXvv2NcyKmbCAMFZftZoLLljKncd+wASs2uLP9a90DRiu2PznKRTFTEvLAcs3naam\nL3ssTJEo0DM4Gs8BqLXqPkNw9/OBKYGoNL7z+Sx80/kRXn19LkZjneV1KpUf4eFXWn64Dw2d3u9j\nzg+GopioqnqT4uI/0tFRBUjwJ4aXDMpOMhLjPEd0h3obvthAS0cLGpWGFTNWsPaatRQaAy1b99aM\nHcvjqanDvvnPExiNTTQ0fHb2m1KORIHDyFnxXO/rhgPiA3zqhzvh+waK8IKC0iyDcWTkHPz8wl16\nPCZTC2Vlf+bkyf8jwZ8YVjIoD4HEefYzmU1s+m4Tj336GBXNXT9InL9R7/yte8vi43ktq2fcNhyb\n/zyRRIHOJ/GcEH0NJcIbThL8ieEmg7IDJM4bnNyiXB75+BEOVR8C4KKEi3ps1KswGLji2285aTBw\nY0wMH0yahJ+Nfz9Xbf7zFhIF2kfiOSEGZk+EFx29kKioBXZFeMNJgj8xXGRQHoTW1qOUl79IdfXb\nmExdK7MlzrOtd6iXEp7CU3Of6rFRr8FoZNaBAxxubeXy8HB2TZ1KsMa+YcRZm/+82UiNAiWeE2Lw\nhivCG04S/AlXk0F5AGZzJzU1/0tFxYsS59nJVqi38vKVPTbq6c1mFn73HbsbGhgfHMzn06cT4z/4\nX187uvnP1/haFCjxnBBD564IbzhJ8CdcSQZlGyTOG7z+Qr24kLgetzUpCnd+/z3vnTlDYkAAX150\nEWOHuHLc3s1/I4U3RIESzwnhXPZHeAuJjJzt8ghvOEnwJ1xBBuXzSJznmIFCvd4UReEhnY4XysuJ\n8PNj77RpTt+6N1LjP1vcGQVKPCeE63hLhDecJPgTziSDMhLnDcVAoZ41T5WW8lhxMYFqNTunTHHp\n1r2RHv/Z4uwoUOI5IYbPwBHeNMtg7GkR3nCS4E84w4gelCXOc9zR00f5Xe7v2F6wHbAe6lnzRmUl\ny0+cQAX848IL+fGoUcN0xBL/9ceeKDAi8Fq09bNQFY9Hr+uQeE6IYeKLEd5wkuBPDMWIG5Qlzhsa\ne0M9az6qreWWI0cwKQovZGbyyyT3xGQS/1nXHc+16VppPPU1De2f0Baym874A6A5L5xrDIdvL4L8\nS+HrGVAzSuI5IZxsJER4w0mCP+GoETMoGwwVVFa+KnGegwYT6lmzr7Gxx9a9J9LShuGo+zcS4z+H\n4rngVph2ENVVX8Ol/0aJKe/56oALiY5beHZDl2wKFMIR9kV43f/PfCvCG04S/InB8ulBWeK8oRts\nqGfNQFv3PIEvxX+ujOdkU6AQziERnntJ8Cfs5ZODssR5zuFIqNfbYLbueQJvif88JZ6TTYFC2E8i\nPM8jwZ8YiEsG5ZycHFauXInJZOJnP/sZv//973u8vr6+nmXLllFUVERQUBBvvPEGF154oV337e+A\nJc5zDkdDvd6GsnXPE7gz/vPWzXMjdVOgENZIhOc9JPgTtjh9UDaZTGRlZZGbm0tSUhKXXHIJW7Zs\nYcKECZbbrFq1ivDwcP74xz9y4sQJfvWrX5Gbm2vXfXsfsMR5zjOUUK83Z23d8wSuiv98ffOcr20K\nFMIeEuF5Lwn+hDVOH5T37dvH+vXrycnJAeBPf/oTAI8++qjlNjfeeCOPPvooV111FQAZGRl8+eWX\nFBYWDnhflUpFcfE6EhLulzjPSYYa6vXmiq17nmCg+M9wykDTF01Ezo4kdW0qIJvnzucNmwJ91fr1\n6/nss8+YPXs2a9eudffh+BSJ8HyPBH/ifI4Myv0+DEB5eTkpKSmWl5OTk9m/f3+P20ydOpX333+f\nq666ivz8fEpLSzl16pRd9wU4efL/UFKyHug6cInzHOOMUK83RVF4WKfjvTNniPDzI2fKFJ8YkqHr\nP0vUnCii5kT1iP9aj7RS8MsCUAEKNH/bTNXbXWcjZPPcOX5+4cTGLiI2dpHVKLC19QitrUcoK3tW\nokAnMZvNPPLII2zZsoWqqiqOHj1KcXExjz/+OMnJyajVcjZzsCTC830aTSipqetISHjAEvyVl/+V\n6uq3JfgTdul3ULbnC8Kjjz7Kww8/zPTp05k8eTLTp09Ho9HY/cXkjTe6Kvvg4Alcd93PuOaa38gX\nokFyRqhnzdMnT/JCeTmBajVbJ01y+mpqTxGYHEja42mMXTOWmg9qOLHiBKbGrkslTM2mc5dNqCBo\nrGye602lUhEcnElwcCZJSQ9ajQK7gqcc4DcSBfajs7OTkpISCgsL0el06HQ6y/PFxcUYDOfizpqa\nGjZu3MjGjRsJDAxk3LhxpKenk5GR0eNpamoq/l56qZQrSIQ3MgUGJjJ+/OskJz9sCf6Kin5HRcUL\nEvz5sLy8PPLy8ob0Nvq99OKrr75i3bp1lssnnn76adRqdZ8o73xpaWkcPnyYI0eODHhflUrFnj2R\nJCQsIyPj2SF9ICORs0I9a9y5dc/dStaXUPFqBR3lHfjF+BG9IJrU/0iVzXMOkiiwp7a2NoqKinoM\nwd1PT548iclk+3r2kJAQjEYjBoOB0NBQoqKi6OjooLq62uZ9NBoNY8aM6TFAdz8/btw4goODXfFh\negyJ8IQ1EvyNTE6/RtloNJKVlcWuXbtITEzk0ksv7RPkNTY2otVqCQgI4NVXX+WLL77grbfesuu+\n3dcop6bKdXaD4cxQzxpP2brnTiXrS2j4rKHHNcpi6EZKFNjQ0NBnCO5+WlFRYfN+KpWKMWPGWD0z\nnJ6eTmhoqNVrlFtaWigsLOzzvnQ6HWVlZf1+Y0hMTOzzvrqfRkZ659l+ifCEPST4G3lc8vBwO3bs\nsDzE2/Lly1m9ejUvv/wyACtWrGDfvn3ce++9qFQqJk2axOuvv05ERITN+w71gEcyZ4d61nji1j3h\nu7w1ClQUhdOnT9schmtra23e19/fn7S0NKuDaVpaGoGBzv1Vv8FgoLi42OoQXVJSQmen7YcnjImJ\nsTlEx8XFecyvqiXCE0Mhwd/I4ZMLR4RrQj1rvGHrnvBdnrYp0Gw2c+rUKasDZmFhIS0tLTbvq9Vq\nbQ6YKSkp+Pl5xjp1o9FIWVmZzYG/vb3d5n1DQ0NtfoyujgslwhOuIBv+fJ8Myj7IVaFeb+UGA1d6\n0dY94fuGY1PgYOK53iIjI60OihkZGcTHx3v9YKYoCpWVlTaH6IaGBpv3DQwMJC0trcf10EONCyXC\nE8NFNvz5LhmUfYgrQ73evH3rnhgZHI0ChxLPjR492moEl5GRQXR0tMs/Zk9WV1fX54eL7qfOiAsl\nwhPuJsGf75FB2Qe4OtTrzZe27omRo3cUWFHxBadOmaiogPJyqKwM4PTpCE6d6qSqyvaZT5VKRUpK\nitUzn93xnBi85uZmmz+cDBQXxsdHkpzsT1xcIwkJHSQlQWIiJCdrSEycKRGeGFYS/PkWGZS92HCE\ner356tY94XuGEs/5+UFCQgDjxiVxwQVTmDjxKjIzJ7gsnhP9Oz8uLCg4xtGjn1NQ8B1FReVUVHTQ\nzwl+r4kLhe+R4M83yKDshYYr1OtNURQe0ul4obycCD8/9k6b5rMLRYR3cGY8l5ISRlzcaSIijhEU\n9BUqVZvltrIp0H0GivAUJRi9/nIaGydw+nQcZWXNPT4HBooLrV0iMxxxoRg5JPjzbjIoe5ldRbt4\n5JNHOFjVFSm5KtSz5j9LS1lTXEygWs3OKVO4xksfL1V4l+54ztpZYVfFc8MRBQrbnBXh9RcXFhQU\n0NjYaPMYzo8Lew/TsrlQOEKCP+8kg7KXGM5Qz5qRvHVPuJ6nx3NdUeAnlsF5pG8KdDZ7IrzuwTg6\nep5TIjxFUairq7N5aY5sLhSuIsGfd5FB2cMNd6hnjWzdE84wlM1znhTPjZRNga7m6ZvwhhIX+uLm\nQuFcEvx5DxmUPZQ7Qj1rZOuesNdQN8+lpqZaHYY9PZ7z1k2Bw23gTXjjLIOxp2/C644LbV0OZDQa\nbd5X4kJxPgn+PJ8Myh7GXaGeNbJ1T/Q2EjbPDYWnbQp0J/s34XWtidZqM9x4tM7Te3Nh72Fa4kJh\njQR/nksGZQ/izlCvN9m6N3K5Ip5LT08nISHBZ4dCW0ZaFCib8PrXOy7s/cOmxIVCgj/PI4OyB3B3\nqNebbN3zfc6M587/pj3SN88NxNeiwIEjvFFER893aoTnq2zFhd3PDzYu7H4qcaF3kuDPc8ig7Eae\nEOr1Jlv3fIcz4jlr33TDwsKG8aPwXd4aBXp6hOerJC4ceST48wwyKLuBp4R6vcnWPe8ytM1zfjZ/\njevp8Zyv8tQo0JciPF+l1+v7vVxK4kLvJsGfe8mgPIw8KdTrTbbueabe8Vzvb4QDxXO2wiBfied8\nlTujQPsivDmW4dhXIjxf1R0XWhuiJS70LhL8uYcMysPEk0I9a2TrnvsMJZ6LiIggMzNT4jkf5+oo\ncHAR3kzU6oAhf0zC/brjQmtfexyJC7ufSlzoWhL8DS8ZlF3M00I9a2TrnnOsX7+ezz77jNmzZ7N2\n7doer3NFPJeenk50dLR8YRyBBhsFlpY+SUPDZ0RGziY1da1EeGJAvePC3l+7nBUX9vd1U/RPgr/h\nIYOyi3hiqGeNbN1zjvXr17Nx40aKi4uJi4tj8uTJjBkzRuI54XIDRYEqVRAqlQazuRU/v1gCAkbT\n0VHZT4S3kNDQaRLhiX5Ziwu7n7c3LmxqaqK0tJT6+nqSkpJYunQp//mf/zmMH4X3k+DP9WRQdjJP\nDfWska17zpOdnc3u3bttvt5aPNf9VOI54UwDRYHdJMITrmItLux+vr+4MD09HZ1ON8xH6xsk+HMd\nGZSdxJNDPWtk657zKIrClVdeyVdffQVAcHAw06ZN4+6775Z4TriVoijodA9TXb0Zo7EWtTqYyMhs\nMjP/IhGecIvz48K//OUv7N27l6amJvz9/bn99tv529/+5u5D9GoGQwUlJf9BZeUbSPDnHDIoO4Gn\nh3q9ydY95zGbzTz44IO89NJLqNVqJkyYwO233y7X2gmPUlKyvsc1ykJ4CrlG2TUk+HMeGZSHwBtC\nvd5k657zmEwmVqxYweuvv05gYCDvv/8+119/vbsPSwghhAAk+HMGGZQd4C2hXm+ydc95jEYjy5Yt\nY9OmTWi1WrZu3cq8efPcfVhCCCFEDxL8DY0MyoPgTaFeb7J1z3k6Ozu5++67effddwkJCeGjjz4i\nOzvb3YclhBBC2CTBn2NkULZDd6i35tM1lDeXA54d6vUmW/ecp6Ojg8WLF/PBBx8QFhbGjh07mDlz\nprsPSwghhLCLBH+DI4PyALwt1LNGtu45h16v57bbbmPbtm1ERkayc+dOLr30UncflhBCCDFoEvzZ\nx5G5c8BKLScnh/Hjx5OZmcmGDRv6vL6mpoaFCxcybdo0Jk2axFtvvWV5XWpqKlOmTGH69OluHUKO\nnj7KDZtv4NpN13Kw6iAp4SlsunUT/77/3141JL9RWcma4mJUwN8mTJAh2UFtbW0sWrSIbdu2ERMT\nw65du2RIFkII4bVCQ6cwderHTJmSQ0jIJPT6Uo4dW8K3315OQ8Nedx+eV+v3jLLJZCIrK4vc3FyS\nkpK45JJL2LJlCxMmTLDcZt26dRgMBp5++mlqamrIysqiurraspThm2++ITo62vo7d/EZZW8N9ayR\nrXvO0dLSwk033UReXh5xcXHk5uYyefJkdx+WEEII4RQS/Nnm9DPK+fn5ZGRkkJqair+/P4sXL2br\n1q09bpOQkEBTUxMATU1NxMTE9FjG4I4rO9o623hyz5Nk/jWTV755BRUqfnnJL9H9Wsfqq1d73ZC8\nr7GRO44exaQorBk7VoZkBzU1NbFw4ULy8vJISEggLy9PhmQhhBA+RaXSkJDwMy67rIDU1LWo1cHU\n1HzAv/99IQUFv6azs8bdh+hV+l0vVl5eTkpKiuXl5ORk9u/f3+M2999/P3PmzCExMZHm5mb+/ve/\nW16nUqm49tpr0Wg0rFixgvvvv7/P+1i3bp3l+ezs7CE94oC3h3rWHG9r48bDh2k3m1kWH8/jqanu\nPiSvVF9fz8KFC8nPzyc5OZlPP/2UzMxMdx+WEEII4RIaTSipqetISHjAEvyVl/+V6uq3R0zwl5eX\nR15e3pDeRr+XXrz33nvk5OTw6quvAvDOO++wf/9+/vrXv1pu8+STT1JTU8Nzzz1HYWEh8+bN49Ch\nQ4SFhVFZWUlCQgJnzpxh3rx5/PWvf+Xqq889MLYzL73whVCvN9m65xy1tbXMmzePAwcOkJqayqef\nfkpaWpq7D0sIIYQYNhL8ueDSi6SkJMrKyiwvl5WVkZyc3OM2X375JbfffjsA6enppKWlceLECaDr\nsgyAUaNGceutt5Kfnz+og7OHr4R6vTUYjVz33XecNBi4PDycdydOlCHZAadPn2b27NkcOHCA9PR0\ndu/eLUOyEEKIEUeCP8f0OyjPmDGDgoICSkpK6Ojo4N133+Xmm2/ucZvx48eTm5sLQHV1NSdOnGDc\nuHG0tbXR3NwMQGtrKx9//LFTrwetaqlixUcrmPL/prC9YDthAWE8NecpTjx4grum3OWxa6ftoTeb\nueXIEQ63tjI+OJiPJk+W1dQOqKysJDs7m8OHD5OVlcWePXsYM2aMuw9LCCGEcJvo6AXMmHGQrKxX\nCQiIp7k5n4MHZ3HkyI9oa/vB3YfncQZ8HOUdO3awcuVKTCYTy5cvZ/Xq1bz88ssArFixgpqaGu67\n7z5OnjyJ2Wxm9erVLFmyhKKiIn70ox8BXSuCf/rTn7J69eqe79yBU+DevFHPHrJ1zzlOnTrFnDlz\nKCgoYNKkSeTm5jJ69Gh3H5YQQgjhMUbahj+fXjjii6Feb7J1zzlKSkqYM2cOxcXFTJs2jU8++YTY\nWN/7Dy+EEEI4w0jZ8Oezg7IvhnrWyNa9odPpdMydO5eTJ08yY8YMdu7cafNxvIUQQghxjq8Hfz43\nKB89fZTf5f6O7QXbAUgJT+GpuU+xZPISr74G2Zo3KitZfuIEKuAfF17Ij0eNcvcheZ3jx48zd+5c\nKioquOKKK9ixYwcRERHuPiwhhBDCq9TV7aSw8BFaW48AEBZ2KenpfyYy8uoB7unZfGZQ9qWNevaQ\nrXtDd+TIEa699lqqq6uZNWsWH330EWFhYe4+LCGEEMIr+eKGP68flH091LNmX2Mjcw8dot1sZs3Y\nsTwhD102aAcPHmTevHnU1NQwd+5ctm7dSkhIiLsPSwghhPB6vhT8eeWgvC5vHWuuXuPzoZ41x9va\nmPntt9QZjSxPSODVCy7wiWuAhtPXX3/N/Pnzqa+v57rrruO9995Dq/W93zoIIYQQ7uQLwZ9XDsrx\nf44HBapau07r+2qo15ts3Ru6ffv2sXDhQpqamli0aBHvvvsugYGB7j4sIYQQwmd5c/DnlYMy67qe\n9+VQr7cGo5FZBw5wuLWVy8PD2TV1qiwUGaQ9e/Zwww030NLSwm233cbmzZvx9/d392EJIYQQI4I3\nBn9eOSir1qmYkzaHD3/yoU+Ger3pzWYWfvcduxsaGB8czOfTpxMjA96g7Nq1i5tvvpm2tjaWLFnC\nxo0b8fPzc/dhCSGEECOKtwV/Xjkor/p4Fc/Me8ZdhzCsZOve0OXk5HDrrbei1+u59957ee2119DI\n2XghhBDCbbwl+PPKQdmN735Yyda9ofvwww+57bbb6OjoYMWKFbz44ouo1b59mY4QQgjhLTw9+JNB\n2YPJ1r2hee+991i8eDFGo5Ff//rXPPfccx4fDQghhBAjkacGfzIoeyjZujc0mzdv5p577sFkMrFq\n1So2bNggQ7IQQgjh4Twt+JNB2QPJ1r2h2bhxI/fddx+KorBmzRoef/xxGZKFEEIIL+FJwZ8Myh5G\ntu4NzSuvvMLPf/5zFEXhiSeeYM2aNe4+JCGEEEI4wBOCPxmUPYhs3Rua559/noceegiAZ555hlWr\nVrn5iIQQQggxVO4M/mRQ9hCydW9onn32WR555BEAnnvuOR5++GE3H5EQQgghnMkdwZ8Myh5Atu4N\nzfGSK6EAAB9qSURBVFNPPcVjjz0GwEsvvcTPf/5zNx+REEIIIVxlOIM/GZTdTLbuOU5RFNatW2eJ\n9V577TWWLVvm7sMSQgghhIsNV/Ang7IbydY9xymKwurVq9mwYQNqtZqNGzdy1113ufuwhBBCCDGM\nXB38yaDsJrJ1z3GKovDb3/6W5557Do1Gw+bNm7njjjvcfVhCCCGEcBNXBX8yKLuJbN1zjNls5sEH\nH+Sll17C39+fv//979xyyy3uPiwhhBBCeABnB38yKLuBbN1zjMlkYsWKFbz++usEBgby/vvvc/31\n17v7sIQQQgjhYZwV/MmgPMxk655jjEYjy5YtY9OmTWi1WrZu3cq8efPcfVhCCCGE8FDOCP5kUB5G\nsnXPMZ2dndx99928++67hISE8NFHH5Gdne3uwxJCCCGEFxhK8CeD8jCRrXuO6ejoYPHixXzwwQeE\nhYWxY8cOZs6c6e7DEkIIIYSXcST4k0F5GMjWPcfo9Xpuu+02tm3bRmRkJDt37uTSSy9192EJIYQQ\nwosNJvhzZO5UD3SDnJwcxo8fT2ZmJhs2bOjz+pqaGhYuXMi0adOYNGkSb731lt339TYNRiPXffcd\nJw0GLg8P592JE2VItkNbWxuLFi1i27ZtxMTEsGvXLhmShRBCCDFkoaFTmDr1Y6ZMySEkZBJ6fSnH\nji3h228vp6Fh75Dffr9nlE0mE1lZWeTm5pKUlMQll1zCli1bmDBhguU269atw2Aw8PTTT1NTU0NW\nVhbV1dWoVKoB7+tNZ5Rl655jWlpauOmmm8jLyyMuLo7c3FwmT57s7sMSQgghhI/pL/g7fXoLaWnr\nnHtGOT8/n4yMDFJTU/H392fx4sVs3bq1x20SEhJoamoCoKmpiZiYGPz8/Oy6r7cwKQp3HTvG7oYG\nEgMCyJkyRYZkOzQ1NbFw4ULy8vJISEggLy9PhmQhhBBCuIRKpSEh4WdcdlkBqalrUauDqan5gPz8\n8ZSV/ZdDb9Ovv1eWl5eTkpJieTk5OZn9+/f3uM3999/PnDlzSExMpLm5mb///e923xe6zkh3y87O\n9rhHQFAUhYd1Ot47c4YIPz9ypkyR1dR2qK+vZ+HCheTn55OcnMynn35KZmamuw9LCCGEED5Oowkl\nNXUdBQWT+Ne/nqK5+QDQ5NDb6ndQtueRHJ566immTZtGXl4ehYWFzJs3j0OHDtl9AOcPyp7oqZMn\neaG8nEC1mq2TJslqajvU1tYyb948Dhw4QGpqKp9++ilp8vB5QgghhBhG8+bdxrx5t3HixC+prn6L\njRvbB/02+r30IikpibKyMsvLZWVlJCcn97jNl19+ye233w5Aeno6aWlpnDhxguTk5AHv6+neqKxk\nTXExKuBvEybIamo7nD59mtmzZ3PgwAHS09PZvXu3DMlCCCGEcJusrBcZM+b3Dt2330F5xowZFBQU\nUFJSQkdHB++++y4333xzj9uMHz+e3NxcAKqrqzlx4gTjxo2z676e7KPaWh744QcAns/MlNXUdqis\nrCQ7O5vDhw+TlZXFnj17GDNmjLsPSwghhBAjXGrqWofu1++lF35+fjz//PMsWLAAk8nE8uXLmTBh\nAi+//DIAK1as4A9/+AP33XcfU6dOxWw288wzzxAdHQ1g9b7eYF9jI3ccPYpJUVgzdqysprbDqVOn\nmDNnDgUFBUyaNInc3FxGjx7t7sMSQgghhHCYLBzpRbbuDV5JSQlz5syhuLiYadOm8cknnxAb2/8a\nSSGEEEKI4eSShSMjSbnBwIJDh6gzGrkxJob/J0PygHQ6Hddccw3FxcXMmDGDXbt2yZAshBBCCJ8g\ng/JZsnVv8I4fP84111zDyZMnueKKK8jNzbVcdiOEEEII4e1kUKZr694tR45wuLWV8cHBfDR5MsEa\njbsPy6MdOXKE7OxsKioqmDVrFjt37iQiIsLdhyWEEEII4TQjflCWrXuDd/DgQWbPnk11dTVz585l\n+/bthIWFufuwhBBCCCGcakQPyrJ1b/C+/vpr5syZQ01NDddddx0ffvghISEh7j4sIYQQQginG9GD\nsmzdG5x9+/Yxd+5c6uvrWbRoER988AFardbdhyWEEEII4RIjdlCWrXuDs2fPHubPn09TUxO33XYb\n//jHPwgMDHT3YQkhhBBCuMyIHJRl697g7Nq1i+uuu46WlhaWLFnCli1b8JfruIUQQgjh40bcoCxb\n9wYnJyeHG2+8kba2Nu69917efvtt/Pz6XegohBBCCOETRtSgfLytjRsPH6bdbGZ5QgKPp6a6+5A8\n2ocffsiiRYvQ6/WsWLGC119/HY08bJ4QQgghRogRMyjL1r3Bee+99/jRj35ER0cHv/71r3nppZdQ\nq0fMp4sQQgghxMgYlGXr3uBs3ryZO++8E6PRyKpVq3juuefkhwohhBBCjDg+PyjrzWYWHT4sW/fs\ntHHjRu666y5MJhNr1qxhw4YNMiQLIYQQYkTy6UG5e+vensZG2bpnh1deeYX77rsPRVF44okneOKJ\nJ2RIFkIIIcSI5bMPXyBb9wbn+eef56GHHgLgmWeeYdWqVW4+IiGEEEII9/LZM8qydc9+zz77rGVI\nfu6552RIFkIIIYTARwdl2bpnv6eeeopHHnkEgJdeeomHH37YzUckhBBCCOEZfO7SC9m6Zx9FUVi3\nbh2PP/44KpWK1157jWXLlrn7sIQQQgghPIZPDcqydc8+iqKwevVqNmzYgFqttjzShRBCCCGEOMdn\nBmXZumcfRVH47W9/y3PPPYdGo2Hz5s3c8f+3d/cxVZ+H38c/PJwSRplGUYscLEyJYJRIJkxrKw/K\ng65KVVbRlqTqvOlaH7JOlzQxmdTU3Lg2YRuVQMo6dQOpRUsBBT3wO+V2MRApOk1miitEfJhPkyI9\ns8Dx3H/0V6L1cIBW+R7g/frLw/k+fDBX4MM317muF180OhYAAIDbGRFFmV33BubevXvauHGj8vLy\nZDKZ9OGHH+qFF14wOhYAAIBbGvZFmV33BsZutyszM1OFhYXy8fHRoUOHtGTJEqNjAQAAuK1hXZTZ\ndW9genp6tG7dOu3fv1++vr4qKytTYmKi0bEAAADc2rAtyuy6NzDd3d3KyMhQSUmJ/Pz8VFFRobi4\nOKNjAQAAuL1hWZTZdW9gurq6lJ6ersOHD8vf319Hjx7V/PnzjY4FAAAwLAzLosyue/27e/eu0tLS\nVFlZqbFjx6q6uloxMTFGxwIAABg2+t2Zr6qqSuHh4QoLC1N2dvZD77/zzjuKiopSVFSUZs2aJW9v\nb7W3t0uSQkJCFBkZqaioqEdW0th1r382m02pqamqrKzU+PHjVVNTQ0kGAAAYJA+Hw+Ho60273a7p\n06fLYrEoKChI0dHRKi4uVkREhNPjKyoqlJOTI4vFIkkKDQ1VY2Ojxo0b5/zmHh5ycfuHr3/rll44\nd052h0PvhYWxoYgTnZ2dWrp0qaxWqyZOnCiLxaJZs2YZHQsAAMBQg+2dUj9PlBsaGjRt2jSFhITI\nZDIpPT1dZWVlfR5fVFSk1atXP/C1wQbqC7vu9a+jo0MpKSmyWq0KDAyU1WqlJAMAAHxPLucoX758\nWcHBwb2vzWaz6uvrnR5rs9lUXV2tPXv29H7Nw8NDixYtkpeXlzIzM7Vhw4aHztuxY0fvv+Pi4pyu\nyMCue/27ffu2UlJS1NDQILPZrNraWoWFhRkdCwAAwBBWq1VWq/UHXcNlUR7M7nbl5eV69tlnNfa+\nOcN///vfFRgYqBs3bigxMVHh4eF67rnnHjjv/qLsDLvu9e/WrVtKTExUU1OTQkJCVFtbq9DQUKNj\nAQAAGOa7D2CzsrIGfQ2XUy+CgoLU1tbW+7qtrU1ms9npsQcOHHho2kVgYKAkacKECVq+fLkaGhoG\nFY5d9/p3/fp1xcfHq6mpSVOnTtWnn35KSQYAAHgEXBblOXPmqLm5Wa2trerq6lJJSYmWLVv20HFf\nfvml6urqlJqa2vs1m82mO3fuSJK++uorHTt2bFDzZdl1r39Xr15VXFyczp49q/DwcNXV1WnKlClG\nxwIAABgRXE698Pb2Vm5urpKTk2W327V+/XpFREQoPz9fkpSZmSlJ+vjjj5WcnCxfX9/ec69du6bl\ny5dL+mYL5ZdeeklJSUkDCsWue/27dOmSEhIS1NzcrJkzZ8pisWjSpElGxwIAABgxXC4P99hv7mSZ\nDofDoU0XLui9y5c1xttb/2/2bDYU+Y7W1lYlJCSopaVFs2fP1vHjxxUQEGB0LAAAALf1yJeHMwK7\n7rl24cIFxcbGqqWlRXPmzFFNTQ0lGQAA4DFwq6LMrnuunT9/XrGxsbp48aLmzZsni8XS52YuAAAA\n+GHcpihX3Lql//P555Kk3LAwrZwwweBE7uXcuXOKi4vTlStXtGDBAlVXV2vMmDFGxwIAABix3KIo\ns+uea6dPn1Z8fLyuXbumhQsX6siRI/L39zc6FgAAwIjmctWLobCpuVlF166x614fTp06paSkJN2+\nfVuLFy9WaWnpA6uLAAAA4PEwfNULr//5H9klPT9+vA7PnMmGIvc5efKkUlJS1NHRodTUVJWUlMjH\nx8foWAAAAMPOsFz1wi7px15e7Lr3HXV1dUpKSlJHR4fS0tJ08OBBSjIAAMAQMrwomzw89KvJk9l1\n7z41NTVavHixOjs7tWbNGhUXF8vEhisAAABDyvCivC04WP936lSjY7iNqqoqPf/887LZbHrllVe0\nb98+eXsbPpUcAABg1DF8jrKBt3c75eXlSktLU1dXlzIzM7Vnzx55ehr+twwAAMCwNyznKOMbpaWl\nWrFihbq6urR582bl5eVRkgEAAAxEE3MDRUVFWrVqlXp6erRt2zbl5OTIgw82AgAAGIqibLC9e/fq\n5Zdflt1u1/bt25WdnU1JBgAAcAMUZQMVFBRo7dq1cjgc2rlzp3bu3ElJBgAAcBMsp2CQ3Nxcbdq0\nSZK0e/dubdu2zeBEAAAAuB9PlA3w7rvv9pbknJwcSjIAAIAboigPsV27dmnr1q2SpLy8PG3ZssXg\nRAAAAHCGqRdDxOFwaMeOHXrrrbfk4eGhwsJCrV271uhYAAAA6ANFeQg4HA69+eabys7OlqenZ+9K\nFwAAAHBfFOXHzOFw6I033lBOTo68vLxUVFSkF1980ehYAAAA6AdF+TG6d++eNm7cqLy8PJlMJn34\n4Yd64YUXjI4FAACAAaAoPyZ2u12ZmZkqLCyUj4+PDh06pCVLlhgdCwAAAANEUX4Menp6tG7dOu3f\nv1++vr4qKytTYmKi0bEAAAAwCBTlR6y7u1sZGRkqKSmRn5+fKioqFBcXZ3QsAAAADBJF+RHq6upS\nenq6Dh8+LH9/fx09elTz5883OhYAAAC+B4ryI3L37l2lpaWpsrJSY8eOVXV1tWJiYoyOBQAAgO+p\n3535qqqqFB4errCwMGVnZz/0/jvvvKOoqChFRUVp1qxZ8vb2Vnt7+4DOHSlsNptSU1NVWVmp8ePH\nq6amhpIMAAAwzHk4HA5HX2/a7XZNnz5dFotFQUFBio6OVnFxsSIiIpweX1FRoZycHFkslgGd6+Hh\nIRe3HxY6Ozu1dOlSWa1WTZw4URaLRbNmzTI6FgAAAO7zfXqnyyfKDQ0NmjZtmkJCQmQymZSenq6y\nsrI+jy8qKtLq1au/17nDUUdHh1JSUmS1WhUYGCir1UpJBgAAGCFczlG+fPmygoODe1+bzWbV19c7\nPdZms6m6ulp79uwZ1Lk7duzo/XdcXNywWSHi9u3bSklJUUNDg8xms2praxUWFmZ0LAAAAEiyWq2y\nWq0/6Boui7KHh8eAL1ReXq5nn31WY8eOHdS59xfl4eLWrVtKTExUU1OTQkJCVFtbq9DQUKNjAQAA\n4H999wFsVlbWoK/hsigHBQWpra2t93VbW5vMZrPTYw8cONA77WKw5w4n169f16JFi3T27FlNnTpV\ntbW1mjJlitGxAAAA8Ii5/DBfT0+Ppk+frpqaGk2ePFkxMTFOP8z35Zdf6ic/+YkuXbokX1/fAZ87\n3D7Md/XqVS1cuFD//Oc/FR4e3vu9AQAAwL19n97p8omyt7e3cnNzlZycLLvdrvXr1ysiIkL5+fmS\npMzMTEnSxx9/rOTk5N6S7Orc4erSpUtKSEhQc3OzZs6cKYvFokmTJhkdCwAAAI+JyyfKj/3mw+SJ\ncmtrqxISEtTS0qLZs2fr+PHjCggIMDoWAAAABuiRLw8H6cKFC4qNjVVLS4uio6NVU1NDSQYAABgF\nKMounD9/XrGxsbp48aKeeeYZHT9+XOPGjTM6FgAAAIYARbkP586dU1xcnK5cuaIFCxaoqqpKY8aM\nMToWAAAAhghF2YnTp08rPj5e165d08KFC3XkyBH5+/sbHQsAAABDiKL8HadOnVJCQoJu3rypxYsX\nq7y8XH5+fkbHAgAAwBCjKN/n5MmTWrhwoW7fvq3U1FQdPnz4gSXvAAAAMHpQlP9XXV2dkpKS1NHR\nobS0NB08eFA+Pj5GxwIAAIBBKMqSampqtHjxYnV2dmrNmjUqLi6WyWQyOhYAAAAMNOqLclVVlZ5/\n/nnZbDa98sor2rdvn7y9XW5YCAAAgFFgVBfl8vJypaam6u7du8rMzFRhYaG8vLyMjgUAAAA3MGqL\ncmlpqVasWKGuri5t3rxZeXl58vQctf8dAAAA+I5R2QyLioq0atUq9fT0aNu2bcrJyZGHh4fRsQAA\nAOBGRl1R3rt3r15++WXZ7XZt375d2dnZlGQAAAA8ZFQV5YKCAq1du1YOh0M7d+7Uzp07KckAAABw\natQs75Cbm6tNmzZJknbv3q1t27YZnAgAAADubFQ8UX733Xd7S3JOTg4lGQAAAP0a8UV5165d2rp1\nqyQpLy9PW7ZsMTgRAAAAhoMRO/XC4XBox44deuutt+Th4aHCwkKtXbvW6FgAAAAYJkZkUXY4HHrz\nzTeVnZ0tT0/P3pUuAAAAgIEacUXZ4XDojTfeUE5Ojry9vVVUVKRf/OIXRscCAADAMDOiivK9e/e0\nceNG5eXlyWQy6eDBg0pNTTU6FgAAAIahEVOU7Xa7MjMzVVhYKB8fHx06dEhLliwxOhYAAACGqRFR\nlHt6erRu3Trt379fvr6+KisrU2JiotGxAAAAMIwN+6Lc3d2tjIwMlZSUyM/PTxUVFYqLizM6FgAA\nAIa5YV2Uu7q6lJ6ersOHD8vf319Hjx7V/PnzjY4FAACAEWDYFuW7d+8qLS1NlZWVGjt2rKqrqxUT\nE2N0LAAAAIwQ/e7MV1VVpfDwcIWFhSk7O9vpMVarVVFRUZo5c+YD0x5CQkIUGRmpqKioR1pibTab\nUlNTVVlZqfHjx6umpoaSDAAAgEfKw+FwOPp60263a/r06bJYLAoKClJ0dLSKi4sVERHRe0x7e7vm\nz5+v6upqmc1m3bx5UwEBAZKk0NBQNTY2aty4cc5v7uEhF7d3qrOzU0uXLpXVatXEiRNlsVg0a9as\nQV0DAAAAo8v36Z0unyg3NDRo2rRpCgkJkclkUnp6usrKyh44pqioSCtXrpTZbJak3pL8rcEGcqWj\no0MpKSmyWq0KDAyU1WqlJAMAAOCxcDlH+fLlywoODu59bTabVV9f/8Axzc3N6u7uVnx8vO7cuaMt\nW7YoIyND0jfNfdGiRfLy8lJmZqY2bNjw0D127NjR+++4uLg+V6y4ffu2UlJS1NDQILPZrNraWoWF\nhQ30+wQAAMAoYrVaZbVaf9A1XBZlDw+Pfi/Q3d2tzz77TDU1NbLZbJo3b57mzp2rsLAwnThxQpMn\nT9aNGzeUmJio8PBwPffccw+cf39R7sutW7eUmJiopqYmhYSEqLa2VqGhof2eBwAAgNHpuw9gs7Ky\nBn0Nl1MvgoKC1NbW1vu6ra2td4rFt4KDg5WUlCRfX1+NHz9eCxYs0JkzZyRJkydPliRNmDBBy5cv\nV0NDw6ADXr9+XfHx8WpqatLUqVP16aefUpIBAADw2LksynPmzFFzc7NaW1vV1dWlkpISLVu27IFj\nUlNTdeLECdntdtlsNtXX12vGjBmy2Wy6c+eOJOmrr77SsWPHBj2f+OrVq4qLi9PZs2cVHh6uuro6\nTZkyZZDfIgAAADB4LqdeeHt7Kzc3V8nJybLb7Vq/fr0iIiKUn58vScrMzFR4eLhSUlIUGRkpT09P\nbdiwQTNmzNAXX3yhFStWSPpmi+mXXnpJSUlJAw526dIlJSQkqLm5WTNnzpTFYtGkSZN+wLcKAAAA\nDJzL5eEe+837WKajtbVVCQkJamlp0ezZs3X8+PGHVtMAAAAABuqRLw9nhAsXLig2NlYtLS2Kjo5W\nTU0NJRkAAABDzq2K8vnz5xUbG6uLFy/qmWee0fHjx/vcrAQAAAB4nNymKJ87d05xcXG6cuWKFixY\noKqqKo0ZM8boWAAAABil3KIonz59WvHx8bp27ZoWLlyoI0eOyN/f3+hYAAAAGMUML8qZmZlKSEjQ\nzZs3tXjxYpWXl8vPz8/oWAAAABjlDF/14ttPIKampqqkpEQ+Pj5GxQEAAMAI9X1WvTC8KEvf7Nx3\n+fJlmUwmo6IAAABgBBuWy8M9+eST+tWvfkVJBgAAgFsxvChv3bpVWVlZRscAAAAAHmD41AsDbw8A\nAIBRYlhOvQAAAADcEUUZAAAAcIKiDAAAADhBUQYAAACcoCgDAAAATlCUAQAAACcoygAAAIATFGUA\nAADACYoyAAAA4ARFGQAAAHCCogwAAAA4QVEGAAAAnKAoAwAAAE5QlAEAAAAnKMoAAACAExRloA9W\nq9XoCIBTjE24K8YmRpp+i3JVVZXCw8MVFham7Oxsp8dYrVZFRUVp5syZiouLG9S5gLviBz7cFWMT\n7oqxiZHG29WbdrtdGzdulMViUVBQkKKjo7Vs2TJFRET0HtPe3q7XX39d1dXVMpvNunnz5oDPBQAA\nANyVyyfKDQ0NmjZtmkJCQmQymZSenq6ysrIHjikqKtLKlStlNpslSQEBAQM+FwAAAHBXLp8oX758\nWcHBwb2vzWaz6uvrHzimublZ3d3dio+P1507d7RlyxZlZGQM6FxJ8vDw+KHfA/DYZGVlGR0BcIqx\nCXfF2MRI4rIoD6TEdnd367PPPlNNTY1sNpvmzZunuXPnDuhch8Mx8KQAAADAEHJZlIOCgtTW1tb7\nuq2trXeKxbeCg4MVEBAgX19f+fr6asGCBTpz5ozMZnO/5wIAAADuyuUc5Tlz5qi5uVmtra3q6upS\nSUmJli1b9sAxqampOnHihOx2u2w2m+rr6zVjxowBnQsAAAC4K5dPlL29vZWbm6vk5GTZ7XatX79e\nERERys/PlyRlZmYqPDxcKSkpioyMlKenpzZs2KAZM2ZIktNzAQAAgOHAwzGEE4XffvttFRcXy8vL\nS56ensrPz1dDQ4NycnL0xRdf6ObNmxo3btxQxQEe4Gx8/uEPf1BjY6NMJpNiYmKUn58vb2+Xf18C\nj5yzsVlQUKBTp07p3r17mjp1qv7yl79ozJgxRkfFKONsbMbExEiSNm/erA8++EB37twxOCVGK2fj\ns7OzU9u2bVNXV5d++tOfqrCwUF5eXn1eY8h+4588eVKVlZVqamqSyWTSf/7zH3399dd64okntHTp\n0gc2KgGGWl/jMyMjQ3/7298kSWvWrNH777+vV1991eC0GE36Gps5OTl68sknJUm/+c1v9Kc//Unb\nt283OC1Gk77GpiSdOnVK7e3trGwFwzgbn3fv3tXcuXNVW1uradOm6Xe/+5327t2rdevW9XmdIdvC\n+t///rcCAgJkMpkkSePGjVNgYKBmz56tp59+eqhiAE71NT5TUlJ6j4mOjtalS5eMiohRqq+x+W1J\ndjgc+u9//9u7hj0wVPoam3a7Xb/97W+1e/duVreCYZyNT5PJpCeeeELTpk2TJC1atEilpaUurzNk\nRTkpKUltbW2aPn26Xn/9ddXV1Q3VrYF+9Tc+u7u79de//lWLFy82KCFGK1djc+3atQoMDNQ//vEP\n/fKXvzQwJUajvsZmbm6uUlNT9dRTTxmcEKOZs/EZEBCgnp4eNTY2SpI++uijB1Zoc2bIirKfn58a\nGxtVUFCgCRMmaNWqVdq7d+9Q3R5wqb/x+dprryk2Nlbz5883MCVGI1dj84MPPtCVK1cUGRmpt99+\n2+CkGG2cjc1du3bpo48+0saNG3maDEM5G5/79u3TgQMH9Otf/1o/+9nP9OMf/9jl/GRpiD/Md7/S\n0lLt3btXn3zyiSQpNDRUjY2NfJgPbuH+8ZmVlaUzZ87o0KFDRscCHvrZKUl1dXXavXu3KioqDEyG\n0a60tFSrV69WQECAfHx8JEkXL17U1KlT9fnnnxucDqOds5+dx44d05///GcdOHCgz/OG7Iny559/\nrubm5t7XTU1NCgkJeeAY/vqEUfoan++//76OHTumoqIiA9NhNHM2Np9++mn961//kvTNz81PPvlE\nUVFRRkXEKOVsbL766qu6cuWKWlpa1NLSoh/96EeUZBiir9/rN27ckCR9/fXX2r17d78f0B+yVS86\nOzu1adMmtbe3y9vbW2FhYSooKNAf//hH/f73v9e1a9cUGRmpn//85yooKBiqWIAk5+MzPz9fTz31\nlEJCQjRv3jxJ0sqVK1lZAEPK2djMy8vT8uXL1dHRIembzaHee+89g5NitOnr9/r9WPUCRunr93p2\ndrYqKip07949vfbaa/2uumbY1AsAAADAnQ3Z1AsAAABgOKEoAwAAAE5QlAEAAAAnKMoAAACAExRl\nAAAAwAmKMgAAAODE/wdW5xNtTO1FiQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "VOCbigframe.boxplot()\nplt.grid(None), plt.ylim([.5, 1.1]), plt.title('Vocab')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 242,
       "text": "(None, (0.5, 1.1), <matplotlib.text.Text at 0x23d1fcd0>)"
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 242
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "vd1 = VOCbigframe[['S1']]\nvd2 = VOCbigframe[['S2']]\nvd3 = VOCbigframe[['S3']]\nvd4 = VOCbigframe[['S4']]\nvocab_4days, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize= (10,8),sharex='col', sharey='row')\nplt.ylim([.5,1])\nax1.plot(vd1 ,'o')\nax1.set_title('Vocab Day 1 (n=14)')\nax2.plot(vd2, 'o')\nax2.set_title('Vocab Day 2 (n=14)')\nax3.plot(vd3, 'o')\nax3.set_title('Vocab Day 3 (n=14)')\nax4.plot(vd4, 'o')\nax4.set_title('Vocab Day 4 (n=14)')\nax3.set_xlabel('Participants')\nax1.set_ylabel('Accuracy')\nax4.set_xlabel('Participants')\nax3.set_ylabel('Accuracy')\nplt.savefig('vocab_4days')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ewc8wDMPsIurj5+cnDy8R/yc7e4fS099XRYW/AgPtSkkZy8H7aBKrjHur9AOA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\nxpiI/gubE8BC/BmnVQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig = plt.figure()\nvd9 = VOCbigframe[['S9']]\nplt.plot(vd9,'o',)\nplt.figsize=(2,2)\nplt.ylim([.7,1])\nax.set_xticklabels(['1','2', '3', '4'])\nplt.title('Vocab Day 9 (n=4)')\n\n#plt.savefig('fig')\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 136,
       "text": "<matplotlib.text.Text at 0x17a23c10>"
      },
      {
       "output_type": "display_data",
       "png": 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8/EHsggULGt2WMTAHBwfLbre79u12u6xWq1udTp066bXXXnPth4WFqU+fPpKkoKAgSVJA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\nJnKNpUSHCgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 136
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Grammar PLOTS"
    },
    {
     "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": 28,
       "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": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\ngramm_all = grambigframe.plot(kind = 'bar')\nplt.figsize=(12,6)\nplt.legend(title= 'session', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\nplt.grid(None)\nplt.title('Grammar Overall')\nplt.savefig('gram_all')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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sxcXFio+PlyTdfvvt6tq1q0aOHKmFCxdanOzKyG4NslvDl7PDPL8p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\nQmCC\n"
      }
     ],
     "prompt_number": 30
    },
    {
     "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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2mw2JiYnYtm0b1Go1oqKiEBsb63Av/1GjRmHChAkAgJ9++gmPPPIITp06JVVL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\nExHJDIOfiEhmGPxERDLD4Ccikpn/A3yOnvD0/mrDAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": "grambigframe2.unstack()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<pre>\n&lt;class 'pandas.core.frame.DataFrame'&gt;\nInt64Index: 14 entries, 1006 to 1028\nData columns (total 20 columns):\n(NC, 1, f)    14  non-null values\n(NC, 1, n)    14  non-null values\n(NC, 2, f)    14  non-null values\n(NC, 2, n)    14  non-null values\n(NC, 3, f)    14  non-null values\n(NC, 3, n)    14  non-null values\n(NC, 4, f)    14  non-null values\n(NC, 4, n)    14  non-null values\n(NC, 9, f)    4  non-null values\n(NC, 9, n)    4  non-null values\n(VA, 1, f)    14  non-null values\n(VA, 1, n)    14  non-null values\n(VA, 2, f)    14  non-null values\n(VA, 2, n)    14  non-null values\n(VA, 3, f)    14  non-null values\n(VA, 3, n)    14  non-null values\n(VA, 4, f)    14  non-null values\n(VA, 4, n)    14  non-null values\n(VA, 9, f)    4  non-null values\n(VA, 9, n)    4  non-null values\ndtypes: float64(20)\n</pre>",
       "output_type": "pyout",
       "prompt_number": 32,
       "text": "Condition             NC                                                    \\\nsession                1                   2                   3             \nNovel_Familiar         f         n         f         n         f         n   \nparticipant                                                                  \n1006            0.562500  0.533333  0.214286  0.466667  0.533333  0.466667   \n1007            0.750000  0.533333  1.000000  0.933333  0.733333  0.866667   \n1010            0.437500  0.600000  0.714286  0.466667  0.800000  0.800000   \n1013            0.692308  0.400000  0.466667  0.533333  1.000000  0.933333   \n1014            0.750000  0.400000  0.285714  0.666667  0.666667  0.600000   \n1015            0.625000  0.400000  0.857143  0.600000  0.533333  0.466667   \n1016            0.562500  0.466667  0.500000  0.333333  0.600000  0.266667   \n1019            0.625000  0.666667  0.500000  0.333333  0.733333  0.600000   \n1022            0.375000  0.466667  0.428571  0.533333  0.600000  0.600000   \n1023            0.625000  0.533333  0.357143  0.466667  0.733333  0.733333   \n1024            0.437500  0.466667  0.428571  0.466667  0.533333  0.466667   \n1025            0.750000  0.666667  0.928571  1.000000  1.000000  1.000000   \n1027            0.500000  0.333333  0.571429  0.400000  0.866667  0.800000   \n1028            0.625000  0.600000  0.642857  0.533333  0.866667  0.733333   \n\nCondition                                                     VA            \\\nsession                4                   9                   1             \nNovel_Familiar         f         n         f         n         f         n   \nparticipant                                                                  \n1006            0.600000  0.400000       NaN       NaN  0.571429  0.400000   \n1007            0.933333  1.000000  0.928571  0.866667  0.428571  0.466667   \n1010            0.800000  0.866667  0.642857  0.866667  0.500000  0.400000   \n1013            1.000000  0.933333  0.857143  0.866667  0.533333  0.533333   \n1014            0.333333  0.600000       NaN       NaN  0.500000  0.466667   \n1015            0.266667  0.466667  0.428571  0.400000  0.642857  0.666667   \n1016            0.333333  0.466667       NaN       NaN  0.571429  0.266667   \n1019            0.533333  0.466667       NaN       NaN  0.571429  0.400000   \n1022            0.733333  0.666667       NaN       NaN  0.285714  0.800000   \n1023            0.733333  0.933333       NaN       NaN  0.571429  0.600000   \n1024            0.533333  0.466667       NaN       NaN  0.500000  0.666667   \n1025            0.866667  1.000000       NaN       NaN  1.000000  0.933333   \n1027            0.533333  0.666667       NaN       NaN  0.785714  0.733333   \n1028            0.733333  0.800000       NaN       NaN  0.428571  0.600000   \n\nCondition                                                                   \\\nsession                2                   3                   4             \nNovel_Familiar         f         n         f         n         f         n   \nparticipant                                                                  \n1006            0.500000  0.666667  1.000000  1.000000  1.000000  1.000000   \n1007            0.937500  1.000000  1.000000  0.933333  1.000000  1.000000   \n1010            0.375000  0.133333  0.600000  0.466667  0.533333  0.466667   \n1013            0.933333  1.000000  1.000000  1.000000  1.000000  0.933333   \n1014            0.437500  0.600000  0.666667  0.600000  0.733333  0.400000   \n1015            0.562500  0.466667  1.000000  1.000000  0.866667  0.933333   \n1016            0.687500  0.600000  0.600000  0.066667  0.733333  0.600000   \n1019            0.437500  0.600000  0.533333  0.666667  0.533333  0.800000   \n1022            0.625000  0.466667  0.600000  0.266667  0.666667  0.533333   \n1023            0.375000  0.600000  0.933333  0.800000  0.866667  0.800000   \n1024            0.812500  0.466667  0.533333  0.533333  0.666667  0.400000   \n1025            1.000000  0.933333  1.000000  1.000000  1.000000  1.000000   \n1027            0.625000  0.733333  1.000000  0.933333  0.933333  1.000000   \n1028            0.500000  0.666667  0.533333  0.266667  0.533333  0.800000   \n\nCondition                         \nsession              9            \nNovel_Familiar       f         n  \nparticipant                       \n1006               NaN       NaN  \n1007            0.9375  1.000000  \n1010            0.5000  0.466667  \n1013            1.0000  1.000000  \n1014               NaN       NaN  \n1015            0.4375  0.400000  \n1016               NaN       NaN  \n1019               NaN       NaN  \n1022               NaN       NaN  \n1023               NaN       NaN  \n1024               NaN       NaN  \n1025               NaN       NaN  \n1027               NaN       NaN  \n1028               NaN       NaN  "
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "nc_mean = grambigframe2[['NC']].mean()\nnc_std = grambigframe2[['NC']].std()\nnc_n = grambigframe2[['NC']].count()\nnc_std_error = nc_std/np.sqrt(nc_n)\n\nva_mean = grambigframe2[['VA']].mean()\nva_std = grambigframe2[['VA']].std()\nva_n = grambigframe2[['VA']].count()\nva_std_error = va_std/np.sqrt(va_n)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "grammar_nc_va = plt.figure()\nnc = grambigframe2[['NC']].mean()\nva = grambigframe2[['VA']].mean()\n\nplt.subplot(221)\nax = nc.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.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')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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fl5eXtV8Wog6FI16NuFasnDtn1TCI6DpTXFzc5nm/duwYBSEU+N//FPD3D8L0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\nwsKLiIiIyEL+Hzxkg9qxi7h4AAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "nc = grambigframe2[['NC']]\nva = grambigframe2[['VA']]\ngrammar_4days, ((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('NC')\nax2.plot(range(5), va.T, '.-')\nax2.set_title('VA')\nplt.xticks(range(5))\nax2.set_xticklabels(['S1','S2', 'S3', 'S4', 'S9'])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 184,
       "text": "[<matplotlib.text.Text at 0x202bfe10>,\n <matplotlib.text.Text at 0x202b81d0>,\n <matplotlib.text.Text at 0x202dad50>,\n <matplotlib.text.Text at 0x202ba410>,\n <matplotlib.text.Text at 0x202ba810>]"
      },
      {
       "output_type": "display_data",
       "png": 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RQSJraNzQE37dkuhbHI6jpxnbj4YG0UuZmCigUlmAGmy2EkymbZjN28nKimTM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4wjGlIOxpFc4KdMEZlqamVK67tMqn6BYF6QZ4Nu1rhcTCkpYcyYMej1ejo7O4dov6LR6BAbicHL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3wox/qjjx5FF6vtyTHpM73sEkRuHEyyco/bmU2B9jcY221pdYOT84UFXF+LQ03GxtiYuMpF87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q3frtPQI6QcPoXfw/nqH6Bp0eB+MB+4MYy4mtLSUqxduxbffvstxowZgwULFqBLly63Pba+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\n"
      }
     ],
     "prompt_number": 184
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "nc.boxplot()\nplt.grid(None), plt.ylim([0, 1.1]), plt.title('Noun Class')\n#ax.set_xticklabels(['S1','S2', 'S3', 'S4', 'S9'])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 233,
       "text": "(None, (0, 1.1), <matplotlib.text.Text at 0x22731390>)"
      },
      {
       "output_type": "display_data",
       "png": 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ABARqAABIQKAGAIAExtwa6jQdbJaWNakONgMAGLoxF6gdbJYrLcEeAKAcWfIBAAAJDBqo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\nAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 233
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "\n'''nc2 = nc[['2']]\nnc3 = nc[['3']]\nnc4 = nc[['4']]\n#nc9 = nc[['9']]\nnc_4days, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize= (10,8),sharex='col', sharey='row')\nplt.ylim([.6,1])\nax1.plot(nc1 ,'o')\nax1.set_title('Noun Class Day 1 (n=14)')\nax2.plot(vd2, 'o')\nax2.set_title('Noun Class Day 2 (n=14)')\nax3.plot(vd3, 'o')\nax3.set_title('Noun Class Day 3 (n=14)')\nax4.plot(vd4, 'o')\nax4.set_title('Noun Class Day 4 (n=14)')\nax3.set_xlabel('Participants')\nax1.set_ylabel('Accuracy')\nax4.set_xlabel('Participants')\nax3.set_ylabel('Accuracy')\nplt.savefig('nc_4days')'''\nnc1 = nc('NC','1', 'f')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "'DataFrame' object is not callable",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-214-152db322939c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[0max3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Accuracy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m plt.savefig('nc_4days')'''\n\u001b[1;32m---> 21\u001b[1;33m \u001b[0mnc1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'NC'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'1'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'f'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;31mTypeError\u001b[0m: 'DataFrame' object is not callable"
       ]
      }
     ],
     "prompt_number": 214
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "nc.to_csv('/home/jminas/Dropbox/Language/Data/nc')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 186
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "\n#nc_n = nc[['n']]\n\"\"\"\nnc_f_n, ((ax1), (ax2)) = plt.subplots(2, figsize= (10,8),sharex='col', sharey='row')\n#plt.ylim([.5,1])\nax1.plot(range(5),nc_f.T ,'.-')\nax1.set_title('NC Familiar')\nax2.plot(range(5), nc_n.T, '.-')\nax2.set_title('NC Novel')\nplt.xticks(range(5))\nax2.set_xticklabels(['S1','S2', 'S3', 'S4', 'S9'])\n\"\"\"\nnc_f = pandas.read_csv('/home/jminas/Dropbox/Language/Data/nc')\nnc_f[['f']]\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "'[f] not in index'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-190-bf12707c5151>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     12\u001b[0m \"\"\"\n\u001b[0;32m     13\u001b[0m \u001b[0mnc_f\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'/home/jminas/Dropbox/Language/Data/nc'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mnc_f\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'f'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas-0.12.0.dev_2bd1cf8-py2.7-linux-x86_64.egg/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1985\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1986\u001b[0m             \u001b[1;31m# either boolean or fancy integer index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1987\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1988\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1989\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas-0.12.0.dev_2bd1cf8-py2.7-linux-x86_64.egg/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_array\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2017\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2018\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2019\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2020\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2021\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas-0.12.0.dev_2bd1cf8-py2.7-linux-x86_64.egg/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[1;34m(self, obj, axis)\u001b[0m\n\u001b[0;32m    596\u001b[0m                 \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    597\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 598\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%s not in index'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mobjarr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    599\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mKeyError\u001b[0m: '[f] not in index'"
       ]
      }
     ],
     "prompt_number": 190
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "nc.unstack().mean().plot(kind = 'bar', color = 'grey'), plt.ylabel('Accuracy'),plt.xlabel('Noun Class                  |     Verb Aggreement'),plt.title('Grammar: Conditions: Novel vs Familiar'),plt.grid(None)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 204,
       "text": "(<matplotlib.axes.AxesSubplot at 0x1f5ab750>,\n <matplotlib.text.Text at 0x1f5b2850>,\n <matplotlib.text.Text at 0x1f5bc310>,\n <matplotlib.text.Text at 0x1f9325d0>,\n None)"
      },
      {
       "output_type": "display_data",
       "png": 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L9gnz6vpn8uTJLq/B1X+4BlwDrgPXgGvAdeAacA0u9etwPk4LzeWZIX722WfVuXNnHThw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\n1Enffvutq8twuvDwcG3bts2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      }
     ],
     "prompt_number": 204
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# row and column sharing\nf, ((grammar_acc_plot, gramm_all), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAsUAAAE1CAYAAAAPsZ2cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMB6hGAAAAMYjFAMAAMB4/weDX4IlpApkrAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 132
    },
    {
     "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\">\n  <thead>\n    <tr>\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      <td><strong>1006.0</strong></td>\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      <td><strong>1007.0</strong></td>\n      <td> 0.458333</td>\n      <td> 0.555556</td>\n      <td> 0.666667</td>\n      <td> 0.472222</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1010.0</strong></td>\n      <td> 0.097222</td>\n      <td> 0.180556</td>\n      <td> 0.236111</td>\n      <td> 0.388889</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1013.0</strong></td>\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      <td><strong>1014.0</strong></td>\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      <td><strong>1015.0</strong></td>\n      <td> 0.111111</td>\n      <td> 0.138889</td>\n      <td> 0.152778</td>\n      <td> 0.097222</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1016.0</strong></td>\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      <td><strong>1019.0</strong></td>\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      <td><strong>1022.0</strong></td>\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      <td><strong>1023.0</strong></td>\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      <td><strong>1024.0</strong></td>\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      <td><strong>1028.0</strong></td>\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": 20,
       "text": "session             1         2         3         4         9\nparticipant                                                  \n1006         0.305556  0.500000  0.611111  0.569444       NaN\n1007         0.458333  0.555556  0.666667  0.472222       NaN\n1010         0.097222  0.180556  0.236111  0.388889       NaN\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       NaN\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\n1028         0.430556  0.458333  0.486111  0.458333       NaN"
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\nKCTestbigframe.plot(kind = 'bar')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 43,
       "text": "<matplotlib.axes.AxesSubplot at 0x5bdd8d0>"
      },
      {
       "output_type": "display_data",
       "png": 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k5GTk5OQ4tHn//ffx6KOP2leV79mzp/t6S+Qlampq0LVr10aPR0ZG4vjx4/btEydOoE+f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\nIgli8SYikiAWbyIiCfp/cq6dowN+SSsAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 43
    },
    {
     "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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uXVAqlRgyZAgiIiIQEBBgbuPh4YEPPvgA27ZtE6sMIiK6hWh7/Hq9Hr6+vlCr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\nxPw/OzIApz1pSRoAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "DAILY SAT PLOTS"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "SATbigframe",
     "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>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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1006</th>\n      <td> 0.480769</td>\n      <td> 0.625000</td>\n      <td> 0.740385</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1007</th>\n      <td>      NaN</td>\n      <td>      NaN</td>\n      <td>      NaN</td>\n      <td> 0.817308</td>\n    </tr>\n    <tr>\n      <th>1010</th>\n      <td> 0.509615</td>\n      <td> 0.548077</td>\n      <td> 0.576923</td>\n      <td> 0.557692</td>\n    </tr>\n    <tr>\n      <th>1013</th>\n      <td>      NaN</td>\n      <td>      NaN</td>\n      <td>      NaN</td>\n      <td> 0.586538</td>\n    </tr>\n    <tr>\n      <th>1014</th>\n      <td> 0.586538</td>\n      <td> 0.682692</td>\n      <td> 0.634615</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1015</th>\n      <td> 0.509615</td>\n      <td> 0.634615</td>\n      <td> 0.596154</td>\n      <td> 0.500000</td>\n    </tr>\n    <tr>\n      <th>1016</th>\n      <td> 0.432692</td>\n      <td> 0.442308</td>\n      <td> 0.403846</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1019</th>\n      <td> 0.692308</td>\n      <td> 0.788462</td>\n      <td> 0.673077</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1022</th>\n      <td> 0.451923</td>\n      <td> 0.557692</td>\n      <td> 0.653846</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1023</th>\n      <td> 0.509615</td>\n      <td> 0.586538</td>\n      <td> 0.586538</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1024</th>\n      <td> 0.538462</td>\n      <td> 0.576923</td>\n      <td> 0.596154</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1025</th>\n      <td> 0.923077</td>\n      <td> 0.865385</td>\n      <td> 0.952941</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1027</th>\n      <td> 0.509615</td>\n      <td> 0.625000</td>\n      <td> 0.615385</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <th>1028</th>\n      <td> 0.567308</td>\n      <td> 0.590476</td>\n      <td> 0.615385</td>\n      <td>      NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 219,
       "text": "Session             2         3         4         9\nparticipant                                        \n1006         0.480769  0.625000  0.740385       NaN\n1007              NaN       NaN       NaN  0.817308\n1010         0.509615  0.548077  0.576923  0.557692\n1013              NaN       NaN       NaN  0.586538\n1014         0.586538  0.682692  0.634615       NaN\n1015         0.509615  0.634615  0.596154  0.500000\n1016         0.432692  0.442308  0.403846       NaN\n1019         0.692308  0.788462  0.673077       NaN\n1022         0.451923  0.557692  0.653846       NaN\n1023         0.509615  0.586538  0.586538       NaN\n1024         0.538462  0.576923  0.596154       NaN\n1025         0.923077  0.865385  0.952941       NaN\n1027         0.509615  0.625000  0.615385       NaN\n1028         0.567308  0.590476  0.615385       NaN"
      }
     ],
     "prompt_number": 219
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\nSATbigframe.plot(kind = 'bar')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 220,
       "text": "<matplotlib.axes.AxesSubplot at 0x1f5d8dd0>"
      },
      {
       "output_type": "display_data",
       "png": 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BAADA4Sh8w/iv//ovXXXVVTrxxBPj2uZgx56YaJHdDLKb4eTsTubkcSc7YuXkcbdjdtv1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\n"
      }
     ],
     "prompt_number": 220
    },
    {
     "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)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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1w4cP1/Dhw/Xhhx9q/PjxvutBn+700AwAAADUhzMHY3NycmqtrXV6xuLFixUe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\nmgEAAAAD/w9gHqpXYiM0PQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 221
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "dprime_mean = pandas.DataFrame({'session':['2','3','4','9'],'dprime':[0.152170823,\t0.534094632,\t0.54877046,\t0.52532131]})\ndprime_mean[['dprime','session']]",
     "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>dprime</th>\n      <th>session</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 0.152171</td>\n      <td> 2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 0.534095</td>\n      <td> 3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 0.548770</td>\n      <td> 4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 0.525321</td>\n      <td> 9</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 222,
       "text": "     dprime session\n0  0.152171       2\n1  0.534095       3\n2  0.548770       4\n3  0.525321       9"
      }
     ],
     "prompt_number": 222
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "dprime_mean.plot(kind='bar', color= 'grey') ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 224,
       "text": "<matplotlib.axes.AxesSubplot at 0x20c34fd0>"
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 224
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}