lapdata

gistfile1

{
 "metadata": {
  "name": "LAP_DATA_5-15-13-Copy0"
 },
 "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\nimport glob\nimport os\nfrom scipy import stats as st\nfrom scipy.stats import norm",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "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============================================================\nVObigframe = 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\nVObigframe.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)\nVOCbigframe.to_csv('/home/jminas/Dropbox/Language/Data/
Training_Sessions/Summaries/Vocab_Summary')\n\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']\nGRbigframe.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)\ngrambigframe.to_csv('/home/jminas/Dropbox/Language/Data/Training_Sessions/Summaries/Grammar_Summary')\n\n#===========KC_test============================================================\nKC_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)\nKC_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)\nKCTestbigframe.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)\nDSATbigframe.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",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "IOError",
       "evalue": "[Errno 2] No such file or directory: '/home/jminas/Dropbox/Language/Data/Training_Sessions/Vocab_Master'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-27-8b6ee0ce2be4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mVObigframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVObigframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcsv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'\\t'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m#Makes a CSV file called Vocab_Master\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mVObigframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/home/jminas/Dropbox/Language/Data/Training_Sessions/Vocab_Master'\
u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m#Taking the merged dataset and looking at the mean ACC for each participant\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/amyfinn/Library/Enthought/Canopy_32bit/User/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mto_csv\u001b[0;34m(self, path_or_buf, sep, na_rep, float_format, cols, header, index, index_label, mode, nanRep, encoding, quoting, line_terminator, chunksize, **kwds)\u001b[0m\n\u001b[1;32m   1391\u001b[0m                                          \u001b[0mindex_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_label\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1392\u001b[0m                                          chunksize=chunksize,engine=kwds.get(\"engine\") )\n\u001b[0;32m-> 1393\u001b[0;31m             \u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1395\u001b[
0m     def to_excel(self, excel_writer, sheet_name='sheet1', na_rep='',\n",
        "\u001b[0;32m/Users/amyfinn/Library/Enthought/Canopy_32bit/User/lib/python2.7/site-packages/pandas/core/format.pyc\u001b[0m in \u001b[0;36msave\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    936\u001b[0m             \u001b[0mclose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    937\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 938\u001b[0;31m             \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_handle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    939\u001b[0m          
   \u001b[0mclose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    940\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/amyfinn/Library/Enthought/Canopy_32bit/User/lib/python2.7/site-packages/pandas/core/common.pyc\u001b[0m in \u001b[0;36m_get_handle\u001b[0;34m(path, mode, encoding, compression)\u001b[0m\n\u001b[1;32m   1631\u001b[0m             \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'replace'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1632\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1633\u001b[0;31m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1634\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;
34m\u001b[0m\u001b[0m\n\u001b[1;32m   1635\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '/home/jminas/Dropbox/Language/Data/Training_Sessions/Vocab_Master'"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "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": 15
    },
    {
     "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\">\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> 0.966667</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      <td><strong>1007.0</strong></td>\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      <td><strong>1010.0</strong></td>\n      <td> 0.933333</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      <td><strong>1013.
0</strong></td>\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      <td><strong>1014.0</strong></td>\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      <td><strong>1015.0</strong></td>\n      <td> 0.966667</td>\n      <td> 0.933333</td>\n      <td> 0.866667</td>\n      <td> 0.933333</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1016.0</strong></td>\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      <td><strong>1019.0</strong></td>\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      <td><strong>1022.0</strong></td>\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      <td><strong>1023.0</strong></td>\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      <td><strong>1024.0</strong></td>\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      <td><strong>1028.0</strong></td>\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": 16,
       "text": "session             1         2         3         4         9\nparticipant                                                  \n1006         0.966667  1.000000  1.000000  1.000000       NaN\n1007         1.000000  1.000000  1.000000  1.000000       NaN\n1010         0.933333  1.000000  1.000000  1.000000       NaN\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       NaN\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\n1028         0.966667  1.000000  1.000000  1.000000       NaN"
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\nVOCbigframe.plot(kind='bar')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": "<matplotlib.axes.AxesSubplot at 0x3acd5d0>"
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAElCAYAAAAr/2MeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrbCwsMFPVJWGH+gSkTnw6/
EK4ejYFiqVSn8z0gymH4fh446OjhaecePUfl0P5ta2qaelCG0dHQ1/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\n29tj4sSJeP3115Gamor8/
HwcPXoUAFBRUYG0tDQIIZCVlQW1Wo0PPvgAt2/fRmlpKf7880/4+/vj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\
n3n33XbRs2RLx8fHYunUrWrRogZUrVyIrKwsODg7IyckBAISFheGNN95AaGgoPD090b17d6xYscKS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\
nPHky7O3tkZCQUOcYLoVGRFJjeZsghEB0dDRu3ryJXbt2wcbGps5xXAqNiKRmdeXt7OBgwUXQHjx/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\
nsqXOMVwGjYikZHLPW6fTYd68edBoNEhLS0NiYiLS09MNxhQVFWHu3LlITk7G77//jm+//daiE24K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\
nHY3y8nJzTJeIqE5WV97WorKyEqNGjUJkZCQKCwuxfv16TJo0CRkZGUZjuQwaEUmN5V2PCxcu4Pr1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\nyM/Px48//
gg7OztERkZaYLpERAQ0UN7BwcHIyMhAZmYmOnfujO3btyMxMdFgzOXLl/U/T5s2DRER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\
niYhkiOVNRCRDLG8iIhlieRMRyRDLm4hIhljeREQyxPImIpIhljcRkQyxvImIZIjlTUQkQyxvIiIZ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\nMGGC/
p8h2dnZ+OabbzB9+nRmWWGeUrOkzlNqltR5Ss2qZtVnm6SlpSEpKQnXrl0D8OC/YpGRkejZ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\
nn82p+u+s+ueSkhIhxIPX6e/vb9YsIaR9P9acf0VFhRg4cKAQQoiCggL936c5s8rLy4UQQhQWForh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\
n89G+fXuzZ0mdp9Qsqz1s8tNPP4nu3buLsLAwER0dLaKjo0VYWJjo1q2b0Gg0ss0yxc3NTbIsqfPc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\
npWZVs9ryHjlyJEpLS+tcCHjw4MHMssI8pWZJnafULKnzlJpVTZbHvImInnRWe6ogERHVj+VNRCRD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\
n42vu4To4OKC4uNjgcZVKhR49euD69es4ceIEAKCkpAQ6nc7oeZKSknD//n3cunULWq0W/fv3R3Fx\nMTp06AAbGxukpKTg6tWr9c69ei51zYPIHLjnTVarR48e+OSTTzB9+nT4+/tjzpw5KCwsRK9evdCp\nUycMGDDAYHzNPdyoqCjMnDkTa9euNSh5Ozs7bN++Ha+++irKysrQsmVL7Nu3DyqVSv/nVSoVAgMD\nERoaivz8fCxfvhydOnXCxIkTERERgcDAQAQHB8PPz6/O7JrbU6dOxezZs9GyZUv89ttvsLe3N/vv\niZ5MPFWQrFJmZiYiIiJw/vx5ybNXrFiB1q1b47XXXpM8m6ixeNiErFZTHivmcWqydtzzJiKSIe55\nExHJEMubiEiGWN5ERDLE8iYikiGWNxGRDLG8iYhk6P8BkJsO727xRJUAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "vocab_acc_plot = VOCbigframe.mean().plot(kind='bar', color= 'grey', xerr = voc_std_error, yerr = voc_std_error,title=\"Training_Vocab Accuracy\")\nplt.ylabel('Accuracy')\nplt.grid(None)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEfCAYAAABGcq0DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\
nPGTIELzwwgtwcnJCTEwMLly4gGHDhsHJyQmdOnVCeHg4CgsL9bfXaDSYMGECOnfuDCcnJ0RFRaGy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\
nt33ffffpL3fs2BHArdcUGjL20qVLcHBwQIcOHfT7XV1d61V/eHg49u/frz+r9/Lygr+/v/6YlpaW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\
nr8eDmheDnxrsznB9/vnnUVZWBicnJzz00EMICgqqM4DvfFGxtuPVd2xiYiJ+/vlnODo6YtmyZZg8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\ngv/i/
znjU6sBtVqB6GhAJpPeWZ+dnQIqVcvt2cbOBsWFxSIdPRrAbwB+h0zWS5R7sLa1RtH1IsMD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\nogZ/
amoqfHx84O3tjdjY2Br78/PzMWrUKDzwwAPo1asXPvvsMzHLISIiiBj8Wq0W8+fPR2pqKjIy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\
n8PCAubk5QkNDkZKSUm3M5s2bMXHiRMjlcgCAk5OTWOUQEdHfRDvjz8nJgaurq35bLpfjyJEj1cZk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\
nm5OTAzMzM1y+fFn0woiISBwG5/jHjh2LgoICLFq0CP379wcAzJo1S/TCiIhIHHcNfp1Oh2HDhsHe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\
n64KIiOqtzuDfvn07LCws8Mgjj2DOnDnYt29fg4I5LS0NXl5e8PDwgLm5OUJDQ5GSklJj3Jo1a/Dk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\
nXfXbcrkcOTk5dY5fv349Ro8eLVY5RET0N9GmemQyWb3H7t+/H59++il++umnWvcrlUr9ZYVCAYVC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\
nsUoiIiKIGPwBAQHIzMyEWq1GRUUFkpOTERwcXG1McHAwNm3aBAA4fPgw7OzsakzzEBFR0xJtqsfM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\
nmpoCaH1PdxvijTfewJo1a2BmZoalS5di6dKlmDRpEiorK/HFF18gPj6+xuqzbcm0adMgk8lqfD/G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\
nALNnz27z61QBQFVVFf744w8AQI8ePVrfWf5tGPxERBLDOX4iIolh8BMRSQyDn4hIYhj8RE3k0qVL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\
njdUspbC6JbUsfDsnEZHEcKqHiEhiGPxERBLD4CcikhgGPxGRxDD4iYgkhsFPRCQxDH4iIon5f1M7\nokobDRR2AAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": "#have to figure out how to save these figures to files!!\n\n\n#from matplotlib.backends.backend_pdf import PdfPages\n#pp = PdfPages('multipage.pdf')\n#plt.savefig('/home/jminas/Dropbox/Language/Data/Training_Sessions/Summaries/Vocab_Summary')"
    },
    {
     "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\">\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> 0.516667</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      <td><strong>1007.0</strong></td>\n      <td> 0.550000</td>\n      <td> 0.966667</td>\n      <td> 0.883333</td>\n      <td> 0.983333</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1010.0</strong></td>\n      <td> 0.483333</td>\n      <td> 0.416667</td>\n      <td> 0.666667</td>\n      <td> 0.666667</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1013.
0</strong></td>\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      <td><strong>1014.0</strong></td>\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      <td><strong>1015.0</strong></td>\n      <td> 0.583333</td>\n      <td> 0.616667</td>\n      <td> 0.750000</td>\n      <td> 0.633333</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1016.0</strong></td>\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      <td><strong>1019.0</strong></td>\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      <td><strong>1022.0</strong></td>\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      <td><strong>1023.0</strong></td>\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      <td><strong>1024.0</strong></td>\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      <td><strong>1028.0</strong></td>\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": 20,
       "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       NaN\n1010         0.483333  0.416667  0.666667  0.666667       NaN\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       NaN\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\n1028         0.566667  0.583333  0.600000  0.716667       NaN"
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\ngramm_all = grambigframe.plot(kind = 'bar')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAElCAYAAAAr/2MeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\
nksbrDBE9NI68iYgUiMWbiEiBWLyJiBSIxRvA1atXMXbs2MbuBhFRg7F4A+jcuTN27tzZ2N0gImow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\ngoODg/
GLvAfxIlNE1Fga9ZKwSr3O9+NorPdc2yVhTa9tYv5nmtrvhkhuLNULxU2bEBERizcRkSKx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\
nSm7xEhI16fV66PX6BrW1WLzrO7sQAN5991306tULer0ef/75J4YNG4YzZ86YnR7+YPEmoqat5FaJ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\
nrf36D3Hq9MSJE3HgwAHcvn0b7du3R2xsLN58881a265evRorV67EnTt3MGbMGHz66ad44oknrNVt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\
neDBKSkowf/58TJo0yey1li9fbryv1Wqh1Wofr+cSKC4uRnx8PFJSUrBu3bo626WlpWHUqFHG4we3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\
nZm25DRoRSY3Fuw7nz5/HtWvXsGDBAmg0GgwePBgDBw6sdQNiboNmWc1LmXJ6QR5qTglp7ORx2vfD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\
nTk7GwYMHcezYsTrndB8s3kREZK7mwNbSRuYWp00a8uXaggUL8P7770Oj0Rh3aCciItuyOPJ2d3dH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\
nJERERMBgMCA2NhYBAQFYu3YtACAuLg5vv/02CgsLMXv2bACAg4MDUlNTbd9zIqImzGLxBoDIyEhE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\
ns2SWp9YsqfPUmiV1nlqzjCyt825MPXr0EJcvXzZ7/NKlS6JHjx7MkmGeWrOkzlNrltR5as2qJtsL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\
nhBAXLlwQCQkJIjAwUPj5+Ynly5eLCxcuWDXLx8en1serqqpE9+7drZolhBCBgYFCCCHKy8uFq6ur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\
nGXFXKysrA4Ba/9WXk5Njtu2hrdy+fRulpaU23YXL1p/H8vJy2Nvbo1mzZsa5727duuHJJ5+0SZ6s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\
nnlqzpM5TQ5Yilwp6enoiOzubWQrKU2uW1HlqzZI6Tw1Zsj09Pjg4uM7nHpxXYpZ88tSaJXWeWrOk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\
nwN3dHQkJCSZTEa1atarzz1t6rKF92bJlC/Lz83Hy5EnY2dmha9euxvzmzZsb29rZ2Zn061EziSzh\nyJtkKysrC0eOHAEAfPXVV/j73/8OAGjXrh1KS0uxc+dOk/YPjnCdnJxQXFxs8rxGo0GPHj1w7do1\nHD9+HABQUlICg8Fg9jrJycm4d+8ebt68Cb1ej379+qG4uBgdOnSAnZ0dUlJScOXKlTr7Xt2X2vpB\nZA0ceZNs9ejRAx9//DGmTZuGoKAgzJ49G4WFhejZsyc6deqE/v37m7R/cIQbExODGTNmYM2aNSZF\n3sHBAdu3b8crr7yCsrIytGzZEvv27YNGozH+eY1Gg5CQEAwePBj5+flYtmwZOnXqhAkTJiAqKgoh\nISEICwtDQEBArdkPHk+ZMgWzZs1Cy5Yt8dtvv8HR0dHqPydqmrhUkGQpMzMTUVFROHfunOTZCQkJ\naN26NV599VXJs4kaitMmJFuNOVfMeWqSO468iYgUiCNvIiIFYvEmIlIgFm8iIgVi8SYiUiAWbyIi\nBWLxJiJSoP8DgaEeak28fSAAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "grammar_acc_plot = grambigframe.mean().plot(kind='bar', color= 'grey', 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": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEfCAYAAABGcq0DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntSe1Wi1MTU3FiRMnhJmZmbh27ZpunVarFStXrtTtY/
DgwUKj0QghhEhLSxOjRo0SHTt2FF27dhXv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\n2WHEiBEoKCiAqOMtoW7duunuW1paAgBu3rzZqLG///
47OnbsCHNzc916FxcXvbV36tQJ2dnZuuWO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\nz2/Kvu/23XffwcrKCk8//
TS6dOmiu82dOxcVFRXYs2cPXnzxRUydOhWjR4+GnZ0d5s2bh9LSUlhb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\
nDH4iIplh8BMRyQyDn4hIZhj8REQyw+AnIpIZBj8Rkcww+ImIZIbBT0QkMwx+IiKZYfATEcmMpMGf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\
n5yRqBJWq9vP163rckIoKipo/+FQAOl4AbmcDGisgzxzwTQUuugBPlgNmzberosjm/qVFdzD4iRqh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\n+vfv3/
guiIiowfRO9WzduhUFBQWIj4/HnDlzoFAoMHfuXEybNg02NjZ1b9jEBDExMRgzZgy0Wi3C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\
nYN68eZIX1qqpRwDqQOCGK5DbD7DNAlQrqk5plMPZLXf6B4DO/wV+eREwLpNP/0RtXL3BX1lZiUce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\
nMZKTk3Hu3DnEx8fXeh1/rVaLV199FcHBwRBCNK0LIiJqsDqD/5tvvoGFhQUefvhhLFiwAPv3729U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\
nsGHDYGdnBwAYMmQIMjMzpSqHiIj+IlnwZ2VlwcXFRbesVCqRlZVV5/gNGzZg3LhxUpVDRER/kWyq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\
n9UREdH8km+pxdnaGRqPRLWs0GiiVynrHZGZmwtnZWaqSiIgIEga/v78/0tPToVarUVZWhoSEBISE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\
nV+LNN980dCmSSU5OxtSpU9GrVy9cvHgRc+bMQUxMDIyNjQG0vT93G2PlypVYt24dTExMsHz5cixf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\
nstb1V69eRc+ePVu4KsO7fv06FAoFOnXqZOhSWtTp06dx8OBBzJ8/v91fpwoAKioq8NtvvwEA+vXr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\
njkVWVpbuSpXPPfccFi5cCB8fH5iYmGDjxo0wNTWFQqGocTVLOVzdkloXns5JRCQznOohIpIZBj8R\nkcww+ImIZIbBT0QkMwx+IiKZYfATEckMg5+ISGb+P7DZh7jEZA1JAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": "grambigframe2.unstack()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": "<class 'pandas.core.frame.DataFrame'>\nIndex: 12 entries, 1006.0 to 1028.0\nData columns:\n('NC', 1.0, 'f')    12  non-null values\n('NC', 1.0, 'n')    12  non-null values\n('NC', 2.0, 'f')    12  non-null values\n('NC', 2.0, 'n')    12  non-null values\n('NC', 3.0, 'f')    12  non-null values\n('NC', 3.0, 'n')    12  non-null values\n('NC', 4.0, 'f')    12  non-null values\n('NC', 4.0, 'n')    12  non-null values\n('NC', 9.0, 'f')    1  non-null values\n('NC', 9.0, 'n')    1  non-null values\n('VA', 1.0, 'f')    12  non-null values\n('VA', 1.0, 'n')    12  non-null values\n('VA', 2.0, 'f')    12  non-null values\n('VA', 2.0, 'n')    12  non-null values\n('VA', 3.0, 'f')    12  non-null values\n('VA', 3.0, 'n')    12  non-null values\n('VA', 4.0, 'f')    12  non-null values\n('VA', 4.0, 'n')    12  non-null values\n('VA', 9.0, 'f')    1  non-null values\n('VA', 9.0, 'n')    1  non-null values\ndtypes: float64(20)"
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\ngrambigframe2.mean().plot(kind = 'bar', color = 'grey')\nplt.ylabel('Accuracy'), plt.xlabel('Noun Class        |     Verb Aggreement'), plt.title('Grammar: Conditions'), plt.grid(None)\ngrambigframe2.to_csv('/home/jminas/Dropbox/Language/Data/Training_Sessions/Summaries/Grammar_Summary_conditions')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEwCAYAAACwiBrgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\
nCxbIu++++8J93LlzR958802xs7OTevXqSffu3aW4uLjCfUPGh8FPL+S7774TMzMz0Wq1zx0XEhIi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\
ns2bNgqurK/r164cWLVpgyZIlv7kPql4MfnohXbp0Qa1atbB3794q/+yIESMwaNAgpKamIisrCxMn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\nVTYXA8C/
ikXCnp+NzztorrbTOR0dHZGSkqJbTk1NrdTFLERE9GKqLfiDgoKwadMmAMCZM2dgZ2dX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\
nmZkhPDwcAQEB0Gq1GDt2LDw8PBAREQEAmDBhAj7++GOMHj0a3t7eKC4uxtKlS1GvXj2lWiIiIgAa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\
nQURElVbhVM8333yDBw8eYNu2bRg1ahQ0Gg1Gjx6N4OBgWFtbl79hMzOEh4cjICAAWq0WY8eOhYeH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\n/
Pv379dbTklJwdSpUxVriIiIlFXhm7vPcnJywpUrV5TohYiIDKDCI/4PP/xQ931xcTHOnz+vu4KX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\
nYmg0GohIuf+jeDr4iYiotGcPihcuXFju2AqD/2n16tXD+PHjMX78+ArHOjo6IiUlRbeckpICJycn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\
nRAAAJkyYgE8++QT379/HpEmTAADm5uaIi4tTqiUiIoKCwQ8A/fv3R//+/fXWTZgwQff9119/ja+/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\
nPQBPrmTfu3cv0tLSADw5uWHQoEEIDAxURX32YBz1gRoc/C1btsS1a9eq/Bh7qFn1jaWHqVOnIjEx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\
nZsyYgfr162PRokUYM2YMEhIS0LZtW6xfvx6urq6K95CQkADgyetQo9FARBAUFIR//OMfAKD4H4C0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\
niIhotVpxc3MzWB81NviJjMnx48clOTm5zMfi4uIUr798+XI5duxYqfUJCQnSp08fxeuLiKxatUqy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\
n+RgxYgSCg4PRsmVLVdRnD8ZRXxVTPQ8ePJBPP/1UXFxcZPbs2ZKRkcEeqqGH6q5vLD2USEhIEG9v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\
nt99G586dkZCQAODJ73/mzJnw8fHBv/71L2zZsgWdOnWCr68vJk6cqPtjcPjwYXTt2hV+fn4YOnSo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\
nTU3xzjvvAACOHDmC+Ph4tG/fHr6+vjh27BiSkpIQGxuLy5cvo2vXrvD19cWmTZtw69Yt3TZKprO8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\
n5OTgrbfeQkFBAURE96b07NmzkZiYCBFBnz599KYiXiUajQZDhgzBjh07dG9S9+3bF1euXEGXLl0A\nPAm0LVu2lHkGlJWVFeLi4vDZZ5/BwcEBkZGReo//85//hJOTExo1aqRb1717d1y+fBkZGRlYu3Yt\nxo0bBxMTE/Ts2RM2NjYAnrzRHhISAk9PT7i7u8PT0xO2tra6nkt4eHjgs88+Q79+/VBcXAxzc3Os\nXLkSHTt2xIYNGxAcHIxHjx4BAP785z/Dzc2t1PN/ensl31dlH5Qsjxo1ChMnTkSdOnVw+vRpWFpa\nVvr3QKXxXj1ET2nWrBmSkpKqu42XIi8vTzd9tHjxYmRkZODLL79EcXExCgsLUatWLdy4cQN9+/bF\ntWvXYGbG40C14G+aqIY6cOAAFi1ahKKiIri4uGDDhg0AnvxB6NWrFwoLCyEiWLVqFUNfZXjET/SU\n5s2b4+eff67uNogUxeAnIlIZntVDRKQyDH4iIpVh8BMRqQyDn4hIZRj8REQqw+AnIlKZ/wd+pvgF\ns8Rm6QAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "grambigframe2.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": 34,
       "text": "(<matplotlib.axes.AxesSubplot at 0x380e050>,\n <matplotlib.text.Text at 0x37631d0>,\n <matplotlib.text.Text at 0x3760910>,\n <matplotlib.text.Text at 0x374cc10>,\n None)"
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAE6CAYAAAAREzmGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\
nLZycnHDo0CGEhYWhcePGqFevHtasWaO012fdq1atQpMmTdCzZ0+dn0NISAg6d+6MuXPnol69egCA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\
nR44cERGRAQMGyLhx4yQjI0P++ecfadeunSxdulTZLjp37lxkpjVr1kinTp2U4T///FNsbGwkMzNT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\n9uxSr9/
f3x8ODg5YunSp1jF+XbmGDx+ODRs2AADWr1+PF154AQBw+fJlZGVloUGDBrC1tYWtrS3G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\
nWrUq7OzskJ6ejnfeeafE9oaQmZmJzMxM1K5dGyYmJti5cyd2795t8PVUBiz85WD69OmYP38+5syZ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\
n7q6XUO1znqWev5hlFFc7+cgGIiKVYeEnIlIZFn4iIpVh4SciUhkWfiIilWHhJyJSGRZ+IiKVYeEn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\
nRPRESb2bCoSWon1oqtGyVFTs8RMRqQwLPxEZDA+zVA481ENEBsPDLJUDe/xERCrDwk9EpDIs/ERE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\
njBkzRmfh53FIIqKy0esYv7W1NQYNGoR79+5hwYIF2LJlC+bMmYPXXnsNr732WrHz+fv7w9/fX2tc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\
nGjQI1atXV75wPScnBxkZGahRo4bRwxERkeHpdefuvXv3lOGMjAz07NnTqKGIiMh4dBb++/fva33d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\
nxaFhw4YIDw/Hhg0btNpcvHhR+XnkyJF49tlnWfSJiIzMaIXfzMwMixYtQu/evZGTk4PRo0fDxcUF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\
nCz8Rkcqw8BMRqQwLPxGRyrDwExGpDAs/EZHKsPATEakMCz8Rkcqw8BMRqQwLPxGRyrDwExGpDAs/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\n/
fdYuXIlDh48CB8fn0LT9+7dq/eyPD09cerUKWaoxBkMmaMsGaZPn47ly5cjLS0N1atX15qm0WiQ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\
nXFEM0UNkBmbQV6Ur/IbQs2dPmT17tly/fl0Zl5iYKLNmzZIePXowAzM8coZr167J0aNH5ejRo3Lt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\
n3x4rV65E165dERERgdq1a+Py5cvlkmHcuHF45513kJaWho4dO2L+/PkYOnQotm/fjgkTJpTLXdQ+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\
n7DHMmzdP9u7dW2j8iRMnpGfPnjrnL+seg4jI4sWLJSUlpdD42NhYmTx5ss75y7rH8LDjx49L165d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\
nhshQlAsXLmD9+vXYuHEj/vzzzxLbhoWF6Z2hNL/ozMAMRSrzWYJy5uXlJQ8ePCg0/sGDB+V2hyQz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\n3HPP6TX/
o/YQmYEZSqNSFv5bt27hiy++wLp16/Dyyy9jypQpsLW11Xt+Q/QQmaHiZChrDkNk2L17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\
nmzdvBgCsXbsWXl5ecHNzw9GjR4tcV3R0NDw8PDBq1Chs2LBBGX/z5k306tULrq6uGDNmDBwdHXH7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\
nJSUFKSkpsLKyUqZnZmZi586dWLBgAWrWrIn27dsjMjIS/fr1w8GDB5XtrXfv3rC1tYWI4ODBgxgw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\
n+SUAYMyYMdi3bx88PT1x+PBhrWd+P9zDK+pKj/Hjx6Nbt25o27Yt3Nzc4OvrC1NTU602Hh4e8PLy\ngrOzM1544QV07twZAJCamopnn30WHh4e6NKli3JSesaMGXB3d4ebmxs6deoEd3f3Mn4y6hQYGIjf\nf/9dOWRTp04drF69GoGBgfDw8EDHjh2VE6MP02g0qFatGtq0aYMJEyZg5cqVWtMzMjKwa9cupXcP\nADVq1EDnzp3x008/ISQkBLt374abmxs2b96M+vXrw9LSEj4+PggICIC7uzv69u0LNzc3WFtba60X\nyOu5b968GW+++SY8PT3h5eWlXFK5bt06rFy5Ep6ennB1dUVERESh+R/+OV/t2rX1+gwKXmH17LPP\nYsuWLfDy8sLBgwdL/tBVgM/qITKgbt264dtvv0Xjxo0fd5Qyy8zMhKmpKUxNTfHrr79i4sSJOHHi\nBIC8XnbNmjWRkZGBrl27Yvny5fD09HzMiUlfPMZPREW6cuUKhgwZgtzcXJibm2P58uXKtLFjx+LM\nmTO4f/8+goKCWPQrGRZ+IiqSk5OT0sN/2Lp168o5DRkSD/UQEakMT+4SEakMCz8Rkcqw8BMRqQwL\nPxGRyrDwExGpDAs/EZHK/B863HcFrifHHAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 34
    },
    {
     "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": [],
     "prompt_number": 42
    },
    {
     "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": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAElCAYAAAAr/2MeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKytr7254VFBQEEpLS9u7G+TFWLzJ6/
nie8QXXzO1jbP3CE+PJyKSIBZvIiIJYvEmIpIgFm8iIgli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\
nKioqUFtbiyVLliAiIgJz5sxBRUUFhBBYvHgxVCoVli9fjn379qFDhw4YPnw4EhMTUVxcbF9B6pln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\
n8NBZcgcWb6JWamnk3a1bN1RWVtq3KyoqAAABAQFu7Rf5JhZvolZqaeQdGRmJ48eP27dPnDiBPn36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\n/
ZgyZYrbOkrkDWpqajBr1izMmzcPERERjfZzKTTyFKcjb6VSiczMTCQkJMBmsyElJQU6nQ5ZWVkA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\nQ+Hv74/
k5GTk5OQ4tHn//ffx6KOP2leV79mzp/t6S+Qlampq0LVr10aPR0ZG4vjx4/btEydOoE+f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\non///k6/
3OYyaOQpPv2FJVFrHD9+HHv27ME333wDAE5H3lwGjTyFxZuoBf/4xz9gNpsxYMAAAHdG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\
nYsqUKTAYDCgrK8Obb76J2bNno7CwsFFbLoPWOvaphbobtRmLN1ELTp8+jcuXL+P555+HQqHAhAkT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\
nHkkmundymepyWrzj4uJQWFgIs9mM/v37Izs7G1u3bnVoc+7cOfv9J554AklJSSzcd+lePoJ7cqGJ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\
nYvEmIpIgFm8iIgli8SYikiAWbyIiCWLxbkdc8JXI9YJVvvF7xUMF2xEv7E/ker5yVixH3kREEsTi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\
nyDsvLw9hYWEIDQ2Fv78/kpOTkZOT49CmV69eiIuLg7+/v1s7SkREP3M68i4uLkZISIh9W6PR4PDh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\
nsDB07doVb7/9tkc6TkTkyxSCE9JERJLjdOTd3m7fvo28vDwUFxdDoVBArVZj9OjRrf4ilVmez5Nr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\
ncSK2b9+OQYMGISIiAm+88QaWLVuGGTNmuCxr2bJl+OqrrzBy5Ejs3LkTixcvxnPPPQfA8WfsKnJ9\nPyYmJiI6OhqVlZUoKChAVFQUpk+fjt27d+PkyZONViBzCbec+uMCERER9vsjR4502BcdHe3SrBEj\nRgghhDhz5oxYtWqVGDZsmIiIiBDp6enizJkzLs0KCwtr8vHbt2+LIUOGuDRLCCGGDRsmhBDCarWK\noKAgcfPmTSGEEDU1NSIqKsqlWUlJSeLxxx8X+fn5wmw2i/PnzwuNRmO/70p1/2Z196uqqoQQd15n\nZGSkS7OE8Oz7sX7/a2pqxNixY4UQQpSWltr/PV2ZZbVahRBClJWVicmTJ4vFixeL27dvO/yMXUWu\n78e698Dt27dFv379mtznal47bTJ+/HisWLECN27cgF6vxyeffAIA2LdvHwIDA92SGRERgRUrVuCf\n//wnPvjgA9y4cQOJiYkuzejUqRPy8vIaPZ6Xl4fOnTu7NAsA/P397X/Gx8ejY8eOAO5c7tfVF8zZ\nsWMHHn30UTz11FM4fvw4QkNDoVQqMXDgQISGhro068aNGzh27BiOHj2KmpoadOvWDcCd1+nn5+fS\nLMCz70c/Pz/7FE1xcTFu374NAAgKCnJpDnDnzMC690hgYCB27tyJyspKTJ8+HVar1eV5cn0/3r59\nG6WlpbBYLKiursb58+cB3Jneq/v3czm3/JfgArdu3RIrVqwQISEhIiQkRCgUCtG1a1eRnJwsLly4\n4NIsd4wwmnPkyBERHx8vtFqtmDRpkpg0aZLQarVi9OjRjdb+dIWEhAT7qLS+S5cuifj4eJfnCSFE\nVVWVeP7554XBYBD9+/d3S8b48eOFXq+334qLi4UQQvz0009i1KhRLs/z5Ptx27ZtYsCAAWLixIlC\no9GInTt3CiGEuHLlipg5c6ZLsx588EFhMpkaPf773/9eKBQKl2YJId/348aNG0VwcLAYMmSI+PTT\
nT8XgwYPFxIkThVqtFps2bXJLptfOeddXXl6O2tpa9OjRwy2XV6yqqkJAQIDLn9eZy5cvo7i4GMCd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\
nyTWrjteOvAcOHIg1a9bgypUr9sd++OEHZGRkYMCAAczywjy5Znk6T65Zns6Ta1Ydry3e2dnZKCkp\nwfjx4xEUFISgoCDo9XpcvXoVH3zwAbO8ME+uWZ7Ok2uWp/PkmmXnlvG8m23cuJFZEsuTa5an8+Sa\n5ek8OWRJ8lDBkJAQWCwWZkkoT65Zns6Ta5an8+SQ5bWnx0dFRTW7r/68ErO8J0+uWZ7Ok2uWp/Pk\nmlXHa4v3jz/+CKPR2OS1jO+77z5meWGeXLM8nSfXLE/nyTWrjtcW74ceegjV1dVNLgQ8fvx4Znlh\nnlyzPJ0n1yxP58k1q44k57yJiHyd1x4qSEREzWPxJiKSIBZvIiIJYvEmWcrJyUFBQYF9e+XKldiz\nZ0+z7Y8ePYrFixe7vB+bNm3C5cuXXf68RPzCkmSntrYWTz75JJKSkvDoo4+2a18mTJiAtWvXYtSo\nUe3aD5IfjrzJK5nNZmi1WsyePRvDhg3D9OnTcePGDfzhD3/A6NGjERUVhdTUVHt7vV6PJUuWID4+\nHmvWrMHOnTvx4osvYuTIkTh37hzmzZuHjz/+GADw9ddfY9y4cRgxYgTGjBmD6upqmEwmJCUlAQDS\n09MxZ84c3HfffYiIiMBbb70FAKiursakSZMwatQoREdHY8eOHfa+6nQ6PPXUUxg+fDgSEhJw8+ZN\nfPTRRzhy5AhmzZqFkSNH4ubNmx7+KZKsueWke6J7dP78eaFQKMTBgweFEELMnz9frF27VpSWltrb\nzJkzR+zcuVMIcWfdx0WLFtn3zZs3T3z88ceNtm/duiUGDx4sjhw5IoQQoqqqStTW1op9+/aJhx9+\nWAghxMqVK8WIESPEzZs3RUlJiQgJCRGXLl0StbW1orKyUgghxE8//STCwsLsfVUqleLEiRNCCCFm\nzJghtmzZYu/X0aNH3fIzIt/GkTd5rZCQEIwdOxYAMHv2bBw4cAB79+7FmDFjEB0djb179yI/P9/e\n/
rHHHnP4+6LBjKAQAmfOnEG/fv3s0xjdunWDn5+fQzuFQoEpU6agY8eO6NGjByZMmIC8vDwIIbBs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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": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEfCAYAAABGcq0DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\
n794drq6uCAoKwsmTJ7Fy5Up89tlneOutt+Ds7IwJEyY0up21a9di8uTJmDBhAtauXVtn2cmTJzF6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\
nBOGzzz4TBg0a1ODr+vbtK6Snp9d7/tdffxVkMlmdWm79THr16nXbmrZu3SoEBwcLgiAIBw4cELp2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\
nBHq9HgDwzDPP4OWXX8bZs2chCAKOHz+OK1euoGvXrlAqlUhOTobJZMKnn36Kc+fO3bZ/paWlcHR0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\
nRERo9FRr0Y7xNyewKysrcfjwYWzfvh07d+7Ea6+9huzs7HrthJvnirfFx/Lly61eA/vPvrP/7P+t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\
ndDXWrKk2z8fHf2/Rbbl0cbH2W9hhibbHHxISguzsbBgMBvTs2ROpqalISUmp06agoADdunWDTCaD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\
nBhp+g0jWUns8+nI3wGTH8eipTdDpdNA1dNZFA0QL/sZCvLbnn38eb775JmQyGQRBaPRQT+3gJ7Kq\n2uPRA0D489arhaiWW3eK4+PjG20rWvArlUoYjUbzvNFohEqlqtPm559/RlRUFADg999/x44dO2Bn\nZ4eIiAixyiIikjzRgj8kJATZ2dkwGAzo2bMnUlNTkZKSUqfNL7/8Yp5+6qmnMH78eIY+EZHIRAt+\nuVyOxMREhIWFwWQyITo6GgEBAUhKSgIAxMTEiLVpIiK6DZnQxs+h/OP4P7VNnp7A6dM1P9samUwG\nxFm7irsQh7v6ty/1/kvd7bKTY/UQEUkMg5+ISGIY/EREEsPgpxZr6BqRZl43QkRtAIOfWozBT9S+\nMfiJiCSGp3N2cC5dXFBSVGLhtX4IQHFzOgrAVgABAP4KQGuxrTi7OqP4avEdv17qpzNKvf9Sd7vs\n5I1YOriSohLL/+fXlgM9bg64t30yMOoH4FcF8LAR6Ga5zZTEWfoXFhEBDH66E/bFwH1ra6a/eQcY\nsBEo9wS6nbRuXUTULDzGTy2n1jXvOSJqkxj81HINjTvPseiJ2o0mgz89PR3VvKkqEVGH0WTwp6am\nwtfXF0uWLMHp06dboyYiIhJRk8G/YcMGHDlyBD4+Ppg9ezYeeOABrFy5EiUlPOOCiKg9atYxfldX\nV0yZMgXTpk3DhQsXsHXrVgQHB+Nf//qX2PUREZGFNRn8aWlpmDhxIjQaDSorK/HTTz9hx44dOH78\nON59993WqJGIiCyoyfP4t2zZgkWLFmH48OF1nndwcMB//vMf0QojIiJxNBn8y5cvh5eXl3m+vLwc\nBQUFUKvVCA0NFbU4IiKyvCYP9UydOhW2trb/e4GNDaZMmSJqUUREJJ4mg7+qqgqdOnUyz3fu3BmV\nlZWiFkVEROJpMvg9PT2RlpZmnk9LS4NnW7yzNhERNUuTx/g//vhjTJ8+HbGxsQAAlUqF5ORk0Qtr\ny3S6/
914pLQU6NwZsLMDNJqaBxFRW9Zk8Pv6+iIjIwMlJSWQyWRwcnJqjbratNoBP2EC8PTTNT+J\niNqDZg3L/NVXXyEzMxPXr183P/fKK6+IVhS1cYYRgEFTM93lV+DAC4C8omaETg7WRtTmNRn8MTEx\nKC8vx549ezBnzhx88cUXGDp0aGvURm2Vet//Al4Tb91aiKjFmvxy98CBA1i3bh3c3d2xfPlyHDx4\nEGfOnGmN2oiISARNBr9CUXNvVQcHB+Tl5UEul+PixYuiF0ZEROJo8lDP+PHjUVhYiMWLF2Pw4MEA\ngDlz5oheGBERieO2e/zV1dV4+OGH4ebmhsmTJ8NgMOD06dN47bXXmrVyrVYLf39/+Pn5ISEhod7y\ntLQ0DBw4EMHBwRg8eDD27NlzZ70gIqJmu23w29jYYMGCBeZ5e3t7dOnSpVkrNplMiI2NhVarRWZm\nJlJSUnDq1Kk6bUJDQ3Hs2DEcOXIEa9aswdy5c++gC0RE1BJNHuMPDQ3Fpk2bIAhCi1as1+vh6+sL\ntVoNOzs7REVF1bkCGAAcHR3N06WlpbwimIioFTQZ/B9//DGmTp2KTp06wdnZGc7OznBxcWlyxXl5\nefD29jbPq1Qq5OXl1Wu3bds2BAQEIDw8nDd2ISJqBU1+uVtaWnpHK5bJZM1qFxkZicjISHz33XeY\nOXNmg6eKxsXFmac1Gg00HBeBiKgOnU4H3R9jyTShyeDfv39/g8/femOWWymVShiNRvO80WiESqVq\ntP1DDz2EqqoqXL58GR4eHnWW1Q5+IiKq79ad4vj4xi+ubDL433rrLfPe+/Xr16HX65t1Bk5ISAiy\ns7NhMBjQs2dPpKamIiUlpU6bc+fOwcfHBzKZDIcPHwaAeqHfFul0DQ/G1tjzRERtSZPB/9VXX9WZ\nNxqNeO6555pesVyOxMREhIWFwWQyITo6GgEBAUhKSgJQMxTE5s2bsW7dOtjZ2cHJyQkbN268w260\nLgY/
EbVnzRqkrTaVSlXvtMzGhIeHIzw8vM5zMTEx5uklS5ZgyZIlLS2BiIjuQpPB/5e//MU8XV1d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\n/S/
gNY3fyZ6IqC1q8qye2tzd3TF37lzs2bNHrHqIiEhkLQp+IiJq/xj8REQSw+AnIpIYBj8RkcQw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\
n1bCzs0NUVBTS0tLqtOnatStCQkJgZ2cnVhlERHQL0fb48/Ly4O3tbZ5XqVTIyMi4o3XFxcWZpzUa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\
nCAsLg8lkQnR0NAICApCUlAQAiImJwdixY7F9+3b4+vrC0dERq1evFqscIiK6SSbwIDrdgcLCQpSU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\
n5syZkMlk9e6PsXnzZjz66KNQKBTmkXY7om3btmHGjBkYNmwYqqursX//fkydOhXnzp1DQUEBvv32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\
nuHABjz32mLXLIGoSv9wlIpIY7vGTJF27dg2PPvoo7rvvPgwYMACff/45fv75Z2g0GoSEhGDMmDG4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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\">\n  <thead>\n    <tr>\n      <th>Session</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td><strong>1006.0</strong></td>\n      <td> 0.480769</td>\n      <td> 0.625000</td>\n      <td> 0.740385</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1010.0</strong></td>\n      <td> 0.509615</td>\n      <td> 0.548077</td>\n      <td> 0.576923</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1013.0</strong></td>\n      <td>      NaN</td>\n      <td>      NaN</td>\n      <td>      NaN</td>\n      <td> 0.586538</td>\n    </tr>\n    <tr>\n      <td><strong>1014.0</strong></td>\n      <td> 0.586538</td>\n      <td> 0.682692</td>\n      <td> 0.634615</td>\n      <td>      
NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1015.0</strong></td>\n      <td> 0.509615</td>\n      <td> 0.634615</td>\n      <td> 0.596154</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1016.0</strong></td>\n      <td> 0.432692</td>\n      <td> 0.442308</td>\n      <td> 0.403846</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1019.0</strong></td>\n      <td> 0.692308</td>\n      <td> 0.788462</td>\n      <td> 0.673077</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1022.0</strong></td>\n      <td> 0.451923</td>\n      <td> 0.557692</td>\n      <td> 0.653846</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1023.0</strong></td>\n      <td> 0.509615</td>\n      <td> 0.586538</td>\n      <td> 0.586538</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n      <td><strong>1024.0</strong></td>\n      <td> 0.538462</td>\n      <td> 0.576923</td>\n      <td> 0.596154</td>\n      <td>      NaN</td>\n    </tr>\n    <tr>\n   
   <td><strong>1028.0</strong></td>\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": 45,
       "text": "Session             2         3         4         9\nparticipant                                        \n1006         0.480769  0.625000  0.740385       NaN\n1010         0.509615  0.548077  0.576923       NaN\n1013              NaN       NaN       NaN  0.586538\n1014         0.586538  0.682692  0.634615       NaN\n1015         0.509615  0.634615  0.596154       NaN\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\n1028         0.567308  0.590476  0.615385       NaN"
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\nSATbigframe.plot(kind = 'bar')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 46,
       "text": "<matplotlib.axes.AxesSubplot at 0x6828050>"
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAElCAYAAAAr/2MeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQOsHooiucpedbYa3lB3o27cvampqpC7vV0JDQ1FdXe3rbpAXMbypo/
w2vANxRB6I+xzoGN7UUe7y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\n/cd/
+ctf4q233rLXO3HiBHQ6nctAby3kiYikJLuRd0NDAx555BHU1tZCqVQiMjISa9euxfz58/Ho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\
neq+srAzx8fH27bi4OFRWVqKmRrq5e0/oGxLi8j/z+4aE+LprRNQGv7sx1ZU5Qim7dOMHDJuammA0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\
nGAyYNm0aNm7c6PQ+l0EjIm9zG942mw3Z2dkwmUwoKyvDxo0bcejQIYc2tbW1yMrKQlFREb755hu8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\
nss3q1auxcuVKXLhwAVOnTsVLL72E//u///up3SXqEF9ePELe4Xcjb3+UlZWFMWPGtDrHxWXQyN+0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\
n5TJoRORNbkfeSqUSeXl5SElJQXR0NGbMmAG9Xo/8/Hzk5+cDAN59913ExsZi1KhRWLRoETZt2uSV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\
nPoeXzZP3mc1mmM3mdrV1G97tuSpt0aJFeO6556BQKOyr1bhybXgTEZGz6we27lbkchvearUaVqvV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\
nJiKSIYY3EZEMMbyJiGSI4U1EJEMMbyIiGWJ4ExHJEMObiEiGGN5ERDLE8CYikiGGNxGRDDG8iYhk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\
nOUREnZXZbIbZbG5XW7fhrVAoPNEfAI7hTUREzq4f2C5fvrzVtm6nTdRqNaxWq33barVCo9H89B4S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\nnF4/
evSoiIqKYl2Z1mRd79QNpH0NxLp+fWMqm83m8rRDtVqN5uZm1pVpTdb1Tt1A2tdArOvX0ybz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\
nsrq++k5du09NTU1i3LhxQgghqqur7X93KWo2NjYKIYSoqakRU6ZMEQsXLhQtLS0Ov39P88X3WAjf\nfZevfm9aWlpEWFiYy/ek4NfTJhMmTMDSpUtx8eJFGAwGvPfeewCAkpIS9OnTR7K63bp1Q2lpqdPr\npaWl6N69u2R1VSqV/X+Tk5PRtWtXAFduzSvVDW7ef/993HPPPZg/fz4OHDiAiIgIKJVKDB06FBER\nEZLUBIC4Da4tAAAI0ElEQVSLFy9i//792LdvH5qamtCrVy8AV/Y9KChIsrq++k4FBQXZp2sqKirQ\n0tICAAgNDZWsps1ms3+n+vTpg6KiItTV1WHatGlobGyUrK4vvseA777LLS0tqK6uhtVqRUNDA44d\nOwbgytTg1b+zJCT7Z8EDLl++LJYuXSrCw8NFeHi4UCgUomfPniI9PV0cP35csrp79+4VycnJQqfT\nicmTJ4vJkycLnU4nxowZ47Q+pyelpKTYR6DXOnXqlEhOTpasrhBC1NfXi0WLFom0tDQxePBgSWsJ\nIcSECROEwWCwPyoqKoQQQvz4449i9OjRktX11Xdq06ZNYsiQIWLSpElCo9GIoqIiIYQQlZWVYubM\nmZLUvO2224TZbHZ6/Q9/+INQKBSS1BTCt99jIbz/XX7ttddE3759xc033yw++OADMXz4cDFp0iSh\nVqvFunXrJKvr13Pe16qtrUVzczP69esn+e0drzp9+jQqKioAXLliKiwszCt1r3f+/HmcP38eAwcO\nlLzWgQMHsHv3bjz00EOS13LFZrPh8uXL6NGjh+S1vP2dOnv2LI4ePQqtVivpiPuqixcvAoDL/1o8\nefKk05KGUjt//jwaGhq8ttKWN7/LjY2NUCqV6NKli33ue/jw4RgwYIBkNf0+vIUQ9vvkAvDa/Xlb\nc/jwYeh0uoCoG0j72pnrNjU12acyrqqqqkL//v0lq8m60tf16znvTz75BJGRkVi2bBk++ugjfPTR\
nR1i2bBm0Wi22bt3qkz7dcsstAVP31ltv9XpNwHe/4862vyUlJdBoNBg0aBBuvfVW+1yslDVZ13t1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\
nJeIBS+p0mpub8cADDyA1NRX33HOPT/syceJErFq1CqNHj/ZpP6jz4cib/JLFYoFOp8Ps2bMRHR2N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\
nWLBgAWpqajBy5EgMGjQIY8eOdWh/7Qg3PT0dDz74INasWeMQ8iqVCgUFBXjkkUdw8eJF9OjRA9u2\nbYNCobD/vEKhQFxcHCZOnIiqqiosXboUgwYNwr333ovU1FTExcUhKSkJer3eZe1rt+fOnYuHHnoI\nPXr0wK5du9CtWzeP/54oMPFUQfJLFosFqamp+Prrr71ee/ny5ejVqxd+97vfeb02UXtx2oT8li/n\nijlPTf6OI28iIhniyJuISIYY3kREMsTwJiKSIYY3EZEMMbyJiGSI4U1EJEP/D8qS5Ld/OlWYAAAA\nAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 46
    },
    {
     "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": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEfCAYAAABGcq0DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njz+W1qWlpTW6vx07diAmJsbqtb/
+9a+YMGFCg7YlJSUwGAyYPXs21Go1XF1d8eyzz2Lo0KFW7TZs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\nQggh3n//fdG5c2dRW1srhBDiRz/
6kVixYkWD9xw9elR07ty50c8v7u+DxvpBp9OJpUuXPrSmCRMm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\nbt26hZdffhnLly/
HyZMnIYTApUuXUFRUhKeffhrBwcH49a9/jaqqKlRXV+Po0aMAgKeeegpHjhyB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\
n556TvmTzIPHdOeRt9bF06VKn19BWH+w79h37rmWPh5Et+ENCQmA0GqVlo9EIjUbTaNvt27dzmoeI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\n/
BBVVVUYMmQInnnmGYSHh1u1uz/4SSE63gaiNgPf9Ac8SoAaD8CTU4HNoj388GVqlx4cFKenpzfZ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\
nyRb8JpMJoaGh0rJGo8Hx48et2qhUKhw9ehRRUVEICQnB8uXL0adPH7lKcizt4f8P+Fu96oO/9z7n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\ni1/
8Aq+99hpUKhWEEE1O9aSlpUnPdToddDqdzW0TESmJXq+HXq9vVlubwb906VIEBwdLy3fv3kVx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\
nLt1w3WKxoKqqCh4eHrIXR0RE9tesb+7evXtXWq6qqkJcXJysRRERkXxsBn91dbXV7Ra9vb1RVVUl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\
nH374oV2LIiIi52gy+CdMmIDs7GycPXsWw4YNw5tvvombN29i3rx5+OCDDxxZIxER2ZHND3e9vLww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\
nCsPgJyJSGFmDPy8vDxEREQgPD0dGRkaD9efPn8eQIUPQqVMnvPHGG3KWQkRE31HLtWGLxYLU1FQc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\
nSstGoxEajeahba5du4aQkBC5SiIiIsgY/DExMSgsLITBYIDZbEZ2djYSExOt2iQmJmLjxo0AgGPH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\nX0ZxcTH+/
e9/o2fPns4u064Y/HYWHR2NP/7xj3j++edRXFyM6dOno1OnTti5cyc6duzI4H+Inj17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\nBwaDAf379wdQ/6Wzn/
70pxgwYAAGDhwIvV4PAFi/fj0mTpyIhIQE9O7du13e2o9aOUGkQJWVleKp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\nYII=\n"
      }
     ],
     "prompt_number": 47
    },
    {
     "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": 26,
       "text": "     dprime session\n0  0.152171       2\n1  0.534095       3\n2  0.548770       4\n3  0.525321       9"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "dprime_mean.plot(kind='bar') ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-25-d22dd5af275a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdprime_mean\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/amyfinn/Library/Enthought/Canopy_32bit/User/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36mplot_frame\u001b[0;34m(frame, x, y, subplots, sharex, sharey, use_index, figsize, grid, legend, rot, ax, style, title, xlim, ylim, logx, logy, xticks, yticks, kind, sort_columns, fontsize, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m   1564\u001b[0m                      \u001b[0mlogy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort_columns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1565\u001b[0m                      secondary_y=secondary_y, **kwds)\n\u001b[0;32m-> 1566\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1567\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\
u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1568\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/amyfinn/Library/Enthought/Canopy_32bit/User/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    798\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    799\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 800\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    801\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_post_plot_logic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0mself\u001b[0m\u001b[
0;34m.\u001b[0m\u001b[0m_adorn_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/amyfinn/Library/Enthought/Canopy_32bit/User/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36m_make_plot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1418\u001b[0m                 rect = bar_f(ax, self.ax_pos + i * 0.75 / K, y, 0.75 / K,\n\u001b[1;32m   1419\u001b[0m                              \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1420\u001b[0;31m                               label=label, **kwds)\n\u001b[0m\u001b[1;32m   1421\u001b[0m             \u001b[0mrects\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrect\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1422\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/amyfinn/Library/Enthought/Canopy_32bit/User/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36mf\u001b[0;34m(ax, x, y, w, start, **kwds)\u001b[0m\n\u001b[1;32m   1357\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bar'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1358\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1359\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[
0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1360\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'barh'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1361\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[
0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/amyfinn/Library/Enthought/Canopy_32bit/User/lib/python2.7/site-packages/matplotlib/axes.pyc\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, left, height, width, bottom, **kwargs)\u001b[0m\n\u001b[1;32m   4902\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interpolation_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4903\u001b[0m             \u001b[0;31m#print r.get_label(), label, 'label' in kwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4904\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_patch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4905\u001b[0m             \u001b[0mpatches\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\
u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4906\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/amyfinn/Library/Enthought/Canopy_32bit/User/lib/python2.7/site-packages/matplotlib/axes.pyc\u001b[0m in \u001b[0;36madd_patch\u001b[0;34m(self, p)\u001b[0m\n\u001b[1;32m   1568\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1569\u001b[0m             \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1570\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_patch_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1571\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[
0m\u001b[0mpatches\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1572\u001b[0m         \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_remove_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatches\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/amyfinn/Library/Enthought/Canopy_32bit/User/lib/python2.7/site-packages/matplotlib/axes.pyc\u001b[0m in \u001b[0;36m_update_patch_limits\u001b[0;34m(self, patch)\u001b[0m\n\u001b[1;32m   1586\u001b[0m         \u001b[0mvertices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1587\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvertices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1588\u001b[0;31m             \u001b[0mxys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_patch_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvertices\u001b[
0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1589\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_data_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransData\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1590\u001b[0m                 patch_to_data = (patch.get_data_transform() -\n",
        "\u001b[0;32m/Users/amyfinn/Library/Enthought/Canopy_32bit/User/lib/python2.7/site-packages/matplotlib/patches.pyc\u001b[0m in \u001b[0;36mget_patch_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_patch_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_patch_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rect_transform\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/amyfinn/Library/Enthought/Canopy_32bit/User/lib/python2.7/site-packages/matplotlib/patches.pyc\u001b[0m in \u001b[0;36m_update_patch_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    574\u001b[0m         \u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_xunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_width\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m         \u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_height\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 576\u001b[0;31m         \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_bounds\u001b[0m\
u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    577\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rect_transform\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBboxTransformTo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/amyfinn/Library/Enthought/Canopy_32bit/User/lib/python2.7/site-packages/matplotlib/transforms.pyc\u001b[0m in \u001b[0;36mfrom_bounds\u001b[0;34m(x0, y0, width, height)\u001b[0m\n\u001b[1;32m    784\u001b[0m         \u001b[0;34m*\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m*\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m*\u001b[0m \u001b[0mmay\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mnegative\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m         \"\"\"\n\u001b[0;32m--> 786\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_extents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx0\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my0\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[
0m\u001b[0m\n\u001b[0m\u001b[1;32m    787\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD9CAYAAABHnDf0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuAOAgQh3ADAQ4Q4ABiLcAcBAhDsAGCjvI38BjM7kydO0f/9/
y3b+qqqp2rdvb9nOD2fgkb+AzQ5+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\
nkkIBAIXLGe5DQ0OSpMbGRnm9XjU1NSmZTA4b19XVpauuukrTp08vTZUAgFGpyLUzlUqppqYms+33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\
nrpgZGBhQd3e3QqFQ1pivvvoqM+G/bt06nXPOOTr++ONLVC4AoBB5p2U6OjoUjUaVTqfV0tIij8ej\nWCwmSYpGo3rttdf0/PPPq7KyUoFAQI899ljJiwYA5JZ3KaQtJ2EppOON5+VmdmMppL3G87VZsqWQ\nAIBjE+EOAAYi3AHAQIQ7ABiIcAcAAxHuAGAgwh0ADES4A4CBCHcAMBDhDgAGItwBwECEOwAYiHAH\nAAMR7gBgIMIdAAxEuAOAgQh3ADAQ4Q4ABiLcAcBAhDsAGIhwBwADEe4AYCDCHQAMRLgDgIEIdwAw\nEOEOAAYi3AHAQIQ7ABgob7gnEgn5fD5VV1erq6tr2P7Vq1errq5OdXV1uvbaa/XFF1+UpFAAQOFc\nlmVZuQYEAgF1dnbK6/WqublZGzZskMfjyez/+OOP5ff75Xa79dxzz2n9+vV64YUXsk/icinPaVBm\nLpdLUrn+jMy6PsrbS4l+2nr2svZyLNmZ85P70NCQJKmxsVFer1dNTU1KJpNZY+bPny+32y1JikQi\n6unpKaoQAIB9KnLtTKVSqqmpyWz7/X719vYqEomMOP7ZZ5/VokWLRtzX1taWeR8OhxUOh0dfLQAY\nLB6PKx6P23KsnOE+GuvXr9eqVau0cePGEfcfHu4AgOH+/cG3vb296GPlnJYJBoPq7+/PbPf19amh\noWHYuM8//1zLli3TmjVrNGXKlKKLAQDYI2e4H5pLTyQSGhgYUHd3t0KhUNaYnTt36sorr9Tq1at1\n5plnlq5SAEDB8k7LdHR0KBqNKp1Oq6WlRR6PR7FYTJIUjUb14IMPau/evVq2bJkkqbKyUps2bSpt\n1QCAnPIuhbTlJCyFdLzxvNzMbiyFtNd4vjZLthQSAHBsItwBwECEOwAYiHAHAAMR7gBgIMIdAAxE\nuAOAgQh3ADAQ4Q4ABiLcAcBAhDsAGIhwBwAD2fZlHUfb5MnTtH//f8ty7qqqqdq3b29Zzg0AhThm\
nnwo5np8UVwr00z48FdJe4/na5KmQAIAshDsAGIhwBwADEe4AYCDCHQAMRLgDgIEIdwAwEOEOAAYi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\
nyWyvXLnSuu+++7LGXHfddda7776b2Q6FQtaOHTuyxkjixYsXL15FvIpVoTGyLEsH8/v/XC7XsDEA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\
n2mlZ+53Sy3x1OqGXknT77bfr8ccf14QJI0dyMf08qg8OswpYE+8Ec+bM0a5du5RKpeT3+9Xa2lru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\n"
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}