notebooks/code006_representationalSimilarityAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>Mapping the emotional face. How individual face parts contribute to successful emotion recognition.</h1></center>\n",
    "\n",
    "# 6. Representational Similarity Analysis (RSA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We take the probability matrices of each picture and correlate them with each other. Then we can use the correlation coefficient as a similarity metric.  \n",
    "To plot the ratings in a space where high similartiy means close proximity and high dissimilarty is translated to high distances, we need to compute a dissimilarty metric as 1-r. Then we can use MDS to plot the dissimilarities. We do this in a simple scatterplot and by plotting the images themselves instead of points representing them. The whole approach is adapted from [Kriegeskorte et al. 2008 Neuron](http://www.researchgate.net/profile/Nikolaus_Kriegeskorte/publication/23706598_Matching_Categorical_Object_Representations_in_Inferior_Temporal_Cortex_of_Man_and_Monkey/links/02bfe5143047817531000000.pdf); see also [Edelman 1998 BBS](http://homepage.psy.utexas.edu/HomePage/Faculty/Markman/PSY394/Edelman1998.pdf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from myBasics import *\n",
    "%matplotlib inline\n",
    "\n",
    "from itertools import chain\n",
    "from sklearn import manifold\n",
    "from sklearn.metrics import euclidean_distances\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "weightDf = pd.read_csv('../outputs/weightStdDf.csv',\n",
    "                      index_col=[0,1,2])\n",
    "weightDf.index.names = ['p','ident','emo']\n",
    "weightDf = weightDf.sortlevel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>9</th>\n",
       "      <th>10</th>\n",
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       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
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       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
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       "      <th>26</th>\n",
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       "      <th>28</th>\n",
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       "      <th>30</th>\n",
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       "      <th>38</th>\n",
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       "      <th>42</th>\n",
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       "      <th>47</th>\n",
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       "      <th>p</th>\n",
       "      <th>ident</th>\n",
       "      <th>emo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">p001</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">f</th>\n",
       "      <th>ang</th>\n",
       "      <td>-0.948401</td>\n",
       "      <td>-0.006541</td>\n",
       "      <td>1.667878</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>-0.320494</td>\n",
       "      <td>-0.948401</td>\n",
       "      <td>0.412064</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>-0.948401</td>\n",
       "      <td>1.039971</td>\n",
       "      <td>1.458576</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>0.098110</td>\n",
       "      <td>0.935320</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>-0.948401</td>\n",
       "      <td>0.412064</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>0.412064</td>\n",
       "      <td>1.039971</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>-0.006541</td>\n",
       "      <td>-0.320494</td>\n",
       "      <td>-0.320494</td>\n",
       "      <td>-0.843750</td>\n",
       "      <td>2.400437</td>\n",
       "      <td>0.412064</td>\n",
       "      <td>1.353925</td>\n",
       "      <td>0.412064</td>\n",
       "      <td>1.458576</td>\n",
       "      <td>-0.006541</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>0.412064</td>\n",
       "      <td>1.039971</td>\n",
       "      <td>0.098110</td>\n",
       "      <td>-0.320494</td>\n",
       "      <td>-0.320494</td>\n",
       "      <td>0.098110</td>\n",
       "      <td>0.098110</td>\n",
       "      <td>0.098110</td>\n",
       "      <td>1.353925</td>\n",
       "      <td>-1.262355</td>\n",
       "      <td>1.039971</td>\n",
       "      <td>1.353925</td>\n",
       "      <td>1.039971</td>\n",
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       "      <th>dis</th>\n",
       "      <td>-0.245679</td>\n",
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       "      <td>-0.074772</td>\n",
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       "      <td>-1.100216</td>\n",
       "      <td>0.437950</td>\n",
       "      <td>1.548847</td>\n",
       "      <td>0.865218</td>\n",
       "      <td>-0.245679</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>1.121579</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>0.865218</td>\n",
       "      <td>0.779764</td>\n",
       "      <td>0.608857</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>0.608857</td>\n",
       "      <td>-0.587494</td>\n",
       "      <td>-0.074772</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>1.805208</td>\n",
       "      <td>1.890662</td>\n",
       "      <td>0.437950</td>\n",
       "      <td>-0.672947</td>\n",
       "      <td>0.352496</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>-0.074772</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>3.257920</td>\n",
       "      <td>-0.587494</td>\n",
       "      <td>0.523404</td>\n",
       "      <td>-0.074772</td>\n",
       "      <td>1.292486</td>\n",
       "      <td>0.352496</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>1.463394</td>\n",
       "      <td>-0.074772</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>-0.587494</td>\n",
       "      <td>-0.074772</td>\n",
       "      <td>-0.074772</td>\n",
       "      <td>-0.587494</td>\n",
       "      <td>-0.587494</td>\n",
       "      <td>-1.100216</td>\n",
       "      <td>-0.416586</td>\n",
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       "      <th>fea</th>\n",
       "      <td>0.582234</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>0.401929</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>0.401929</td>\n",
       "      <td>1.844366</td>\n",
       "      <td>0.401929</td>\n",
       "      <td>0.582234</td>\n",
       "      <td>1.844366</td>\n",
       "      <td>0.582234</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>0.582234</td>\n",
       "      <td>1.664062</td>\n",
       "      <td>0.582234</td>\n",
       "      <td>0.582234</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>0.582234</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>1.123148</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>0.401929</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>2.024671</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>0.401929</td>\n",
       "      <td>1.844366</td>\n",
       "      <td>0.582234</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>2.024671</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>-1.040508</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>0.221624</td>\n",
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       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>0.352580</td>\n",
       "      <td>1.057741</td>\n",
       "      <td>-0.151106</td>\n",
       "      <td>-0.856267</td>\n",
       "      <td>-0.352580</td>\n",
       "      <td>-1.359953</td>\n",
       "      <td>1.460690</td>\n",
       "      <td>-0.453318</td>\n",
       "      <td>-1.359953</td>\n",
       "      <td>1.460690</td>\n",
       "      <td>0.755529</td>\n",
       "      <td>-0.352580</td>\n",
       "      <td>0.554055</td>\n",
       "      <td>-0.554055</td>\n",
       "      <td>-0.856267</td>\n",
       "      <td>-0.654792</td>\n",
       "      <td>-0.050369</td>\n",
       "      <td>0.151106</td>\n",
       "      <td>-1.359953</td>\n",
       "      <td>-0.957004</td>\n",
       "      <td>0.251843</td>\n",
       "      <td>1.057741</td>\n",
       "      <td>-0.957004</td>\n",
       "      <td>-0.957004</td>\n",
       "      <td>-0.453318</td>\n",
       "      <td>0.251843</td>\n",
       "      <td>-0.755529</td>\n",
       "      <td>-0.352580</td>\n",
       "      <td>0.050369</td>\n",
       "      <td>0.251843</td>\n",
       "      <td>-0.755529</td>\n",
       "      <td>-1.259216</td>\n",
       "      <td>1.158478</td>\n",
       "      <td>0.151106</td>\n",
       "      <td>0.554055</td>\n",
       "      <td>1.762902</td>\n",
       "      <td>-1.359953</td>\n",
       "      <td>1.762902</td>\n",
       "      <td>-0.352580</td>\n",
       "      <td>1.158478</td>\n",
       "      <td>-0.352580</td>\n",
       "      <td>2.065113</td>\n",
       "      <td>-0.554055</td>\n",
       "      <td>-1.359953</td>\n",
       "      <td>1.863639</td>\n",
       "      <td>-1.359953</td>\n",
       "      <td>0.251843</td>\n",
       "      <td>1.762902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>-0.897600</td>\n",
       "      <td>-0.897600</td>\n",
       "      <td>0.157930</td>\n",
       "      <td>0.733673</td>\n",
       "      <td>0.349844</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>-0.801643</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>-0.321856</td>\n",
       "      <td>1.213459</td>\n",
       "      <td>-0.321856</td>\n",
       "      <td>0.157930</td>\n",
       "      <td>0.733673</td>\n",
       "      <td>0.157930</td>\n",
       "      <td>0.157930</td>\n",
       "      <td>-0.897600</td>\n",
       "      <td>-0.609728</td>\n",
       "      <td>0.733673</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>-0.513771</td>\n",
       "      <td>0.061972</td>\n",
       "      <td>-0.321856</td>\n",
       "      <td>0.445801</td>\n",
       "      <td>-0.033985</td>\n",
       "      <td>0.157930</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>-0.705685</td>\n",
       "      <td>0.445801</td>\n",
       "      <td>0.349844</td>\n",
       "      <td>2.077074</td>\n",
       "      <td>0.541758</td>\n",
       "      <td>1.981117</td>\n",
       "      <td>0.829630</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>-0.705685</td>\n",
       "      <td>-0.321856</td>\n",
       "      <td>0.829630</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>0.157930</td>\n",
       "      <td>-1.089514</td>\n",
       "      <td>3.708346</td>\n",
       "      <td>0.061972</td>\n",
       "      <td>-0.417814</td>\n",
       "      <td>-0.705685</td>\n",
       "      <td>1.981117</td>\n",
       "      <td>0.253887</td>\n",
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      ],
      "text/plain": [
       "                       0         1         2         3         4         5  \\\n",
       "p    ident emo                                                               \n",
       "p001 f     ang -0.948401 -0.006541  1.667878 -1.262355 -1.262355 -0.320494   \n",
       "           dis -0.245679  1.036125 -0.074772 -1.100216 -1.100216  0.437950   \n",
       "           fea  0.582234 -1.040508 -1.040508 -1.040508  0.401929 -1.040508   \n",
       "           hap  0.352580  1.057741 -0.151106 -0.856267 -0.352580 -1.359953   \n",
       "           ntr -0.897600 -0.897600  0.157930  0.733673  0.349844 -1.089514   \n",
       "\n",
       "                       6         7         8         9        10        11  \\\n",
       "p    ident emo                                                               \n",
       "p001 f     ang -0.948401  0.412064 -1.262355 -0.948401  1.039971  1.458576   \n",
       "           dis  1.548847  0.865218 -0.245679 -1.100216  1.121579 -1.100216   \n",
       "           fea  0.401929  1.844366  0.401929  0.582234  1.844366  0.582234   \n",
       "           hap  1.460690 -0.453318 -1.359953  1.460690  0.755529 -0.352580   \n",
       "           ntr -1.089514 -0.801643 -1.089514 -0.321856  1.213459 -0.321856   \n",
       "\n",
       "                      12        13        14        15        16        17  \\\n",
       "p    ident emo                                                               \n",
       "p001 f     ang -1.262355  0.098110  0.935320 -1.262355 -0.948401  0.412064   \n",
       "           dis  0.865218  0.779764  0.608857 -1.100216  0.608857 -0.587494   \n",
       "           fea  0.221624  0.582234  1.664062  0.582234  0.582234 -1.040508   \n",
       "           hap  0.554055 -0.554055 -0.856267 -0.654792 -0.050369  0.151106   \n",
       "           ntr  0.157930  0.733673  0.157930  0.157930 -0.897600 -0.609728   \n",
       "\n",
       "                      18        19        20        21        22        23  \\\n",
       "p    ident emo                                                               \n",
       "p001 f     ang -1.262355  0.412064  1.039971 -1.262355 -0.006541 -0.320494   \n",
       "           dis -0.074772 -1.100216 -1.100216  1.805208  1.890662  0.437950   \n",
       "           fea  0.221624  0.582234 -1.040508  1.123148  0.221624 -1.040508   \n",
       "           hap -1.359953 -0.957004  0.251843  1.057741 -0.957004 -0.957004   \n",
       "           ntr  0.733673 -1.089514 -1.089514 -0.513771  0.061972 -0.321856   \n",
       "\n",
       "                      24        25        26        27        28        29  \\\n",
       "p    ident emo                                                               \n",
       "p001 f     ang -0.320494 -0.843750  2.400437  0.412064  1.353925  0.412064   \n",
       "           dis -0.672947  0.352496  0.096135 -1.100216 -0.074772 -1.100216   \n",
       "           fea -1.040508 -1.040508  0.221624  0.401929  0.221624  2.024671   \n",
       "           hap -0.453318  0.251843 -0.755529 -0.352580  0.050369  0.251843   \n",
       "           ntr  0.445801 -0.033985  0.157930 -1.089514 -0.705685  0.445801   \n",
       "\n",
       "                      30        31        32        33        34        35  \\\n",
       "p    ident emo                                                               \n",
       "p001 f     ang  1.458576 -0.006541 -1.262355 -1.262355 -1.262355  0.412064   \n",
       "           dis  3.257920 -0.587494  0.523404 -0.074772  1.292486  0.352496   \n",
       "           fea -1.040508 -1.040508 -1.040508  0.401929  1.844366  0.582234   \n",
       "           hap -0.755529 -1.259216  1.158478  0.151106  0.554055  1.762902   \n",
       "           ntr  0.349844  2.077074  0.541758  1.981117  0.829630 -1.089514   \n",
       "\n",
       "                      36        37        38        39        40        41  \\\n",
       "p    ident emo                                                               \n",
       "p001 f     ang  1.039971  0.098110 -0.320494 -0.320494  0.098110  0.098110   \n",
       "           dis -1.100216 -1.100216  1.463394 -0.074772 -1.100216 -0.587494   \n",
       "           fea -1.040508 -1.040508 -1.040508 -1.040508 -1.040508  2.024671   \n",
       "           hap -1.359953  1.762902 -0.352580  1.158478 -0.352580  2.065113   \n",
       "           ntr -0.705685 -0.321856  0.829630 -1.089514  0.157930 -1.089514   \n",
       "\n",
       "                      42        43        44        45        46        47  \n",
       "p    ident emo                                                              \n",
       "p001 f     ang  0.098110  1.353925 -1.262355  1.039971  1.353925  1.039971  \n",
       "           dis -0.074772 -0.074772 -0.587494 -0.587494 -1.100216 -0.416586  \n",
       "           fea -1.040508 -1.040508  0.221624 -1.040508  0.221624  0.221624  \n",
       "           hap -0.554055 -1.359953  1.863639 -1.359953  0.251843  1.762902  \n",
       "           ntr  3.708346  0.061972 -0.417814 -0.705685  1.981117  0.253887  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weightDf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weightAvgDf = weightDf.groupby(level=[1,2]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ident</th>\n",
       "      <th>emo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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 rowspan=\"7\" valign=\"top\">f</th>\n",
       "      <th>ang</th>\n",
       "      <td>-0.107068</td>\n",
       "      <td>-0.090944</td>\n",
       "      <td>0.021346</td>\n",
       "      <td>-0.140837</td>\n",
       "      <td>-0.231097</td>\n",
       "      <td>0.120188</td>\n",
       "      <td>-0.101582</td>\n",
       "      <td>-0.155755</td>\n",
       "      <td>-0.212338</td>\n",
       "      <td>-0.097965</td>\n",
       "      <td>0.484374</td>\n",
       "      <td>0.035292</td>\n",
       "      <td>-0.089919</td>\n",
       "      <td>0.065455</td>\n",
       "      <td>-0.239384</td>\n",
       "      <td>-0.122578</td>\n",
       "      <td>-0.118693</td>\n",
       "      <td>0.146498</td>\n",
       "      <td>0.044670</td>\n",
       "      <td>0.344048</td>\n",
       "      <td>0.145883</td>\n",
       "      <td>0.255147</td>\n",
       "      <td>0.364067</td>\n",
       "      <td>-0.061439</td>\n",
       "      <td>-0.129267</td>\n",
       "      <td>-0.147019</td>\n",
       "      <td>0.547444</td>\n",
       "      <td>-0.020446</td>\n",
       "      <td>0.031519</td>\n",
       "      <td>0.969053</td>\n",
       "      <td>0.477879</td>\n",
       "      <td>-0.226840</td>\n",
       "      <td>-0.233784</td>\n",
       "      <td>-0.146768</td>\n",
       "      <td>0.651934</td>\n",
       "      <td>-0.001234</td>\n",
       "      <td>-0.250892</td>\n",
       "      <td>-0.034475</td>\n",
       "      <td>-0.133708</td>\n",
       "      <td>-0.271287</td>\n",
       "      <td>-0.215705</td>\n",
       "      <td>-0.245044</td>\n",
       "      <td>-0.127644</td>\n",
       "      <td>-0.094971</td>\n",
       "      <td>-0.183468</td>\n",
       "      <td>-0.170725</td>\n",
       "      <td>-0.187689</td>\n",
       "      <td>-0.114234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>-0.197694</td>\n",
       "      <td>0.061481</td>\n",
       "      <td>-0.234455</td>\n",
       "      <td>-0.156955</td>\n",
       "      <td>-0.116681</td>\n",
       "      <td>-0.229027</td>\n",
       "      <td>-0.159949</td>\n",
       "      <td>0.004103</td>\n",
       "      <td>-0.161257</td>\n",
       "      <td>-0.209212</td>\n",
       "      <td>-0.077736</td>\n",
       "      <td>-0.004602</td>\n",
       "      <td>0.010296</td>\n",
       "      <td>0.081108</td>\n",
       "      <td>0.119742</td>\n",
       "      <td>-0.017898</td>\n",
       "      <td>-0.184315</td>\n",
       "      <td>-0.036822</td>\n",
       "      <td>-0.108139</td>\n",
       "      <td>-0.043717</td>\n",
       "      <td>0.015189</td>\n",
       "      <td>0.921797</td>\n",
       "      <td>1.060071</td>\n",
       "      <td>-0.102794</td>\n",
       "      <td>-0.215771</td>\n",
       "      <td>-0.190616</td>\n",
       "      <td>-0.372721</td>\n",
       "      <td>-0.040695</td>\n",
       "      <td>-0.193600</td>\n",
       "      <td>1.098819</td>\n",
       "      <td>1.436805</td>\n",
       "      <td>0.007758</td>\n",
       "      <td>-0.214569</td>\n",
       "      <td>-0.222747</td>\n",
       "      <td>-0.209609</td>\n",
       "      <td>-0.005113</td>\n",
       "      <td>-0.151673</td>\n",
       "      <td>0.039613</td>\n",
       "      <td>-0.103777</td>\n",
       "      <td>0.084613</td>\n",
       "      <td>0.003531</td>\n",
       "      <td>-0.158570</td>\n",
       "      <td>-0.139650</td>\n",
       "      <td>-0.259681</td>\n",
       "      <td>-0.120911</td>\n",
       "      <td>-0.063685</td>\n",
       "      <td>-0.158616</td>\n",
       "      <td>-0.081669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea</th>\n",
       "      <td>0.077753</td>\n",
       "      <td>-0.066480</td>\n",
       "      <td>-0.141368</td>\n",
       "      <td>-0.121815</td>\n",
       "      <td>-0.012927</td>\n",
       "      <td>-0.287902</td>\n",
       "      <td>-0.147046</td>\n",
       "      <td>-0.017016</td>\n",
       "      <td>-0.061568</td>\n",
       "      <td>0.102944</td>\n",
       "      <td>0.736890</td>\n",
       "      <td>-0.114484</td>\n",
       "      <td>-0.240084</td>\n",
       "      <td>0.035541</td>\n",
       "      <td>-0.134807</td>\n",
       "      <td>-0.049666</td>\n",
       "      <td>-0.032427</td>\n",
       "      <td>-0.049715</td>\n",
       "      <td>-0.082939</td>\n",
       "      <td>-0.058181</td>\n",
       "      <td>-0.169369</td>\n",
       "      <td>0.198205</td>\n",
       "      <td>0.376390</td>\n",
       "      <td>-0.238898</td>\n",
       "      <td>-0.011844</td>\n",
       "      <td>0.122511</td>\n",
       "      <td>0.026201</td>\n",
       "      <td>-0.099165</td>\n",
       "      <td>-0.007230</td>\n",
       "      <td>0.956243</td>\n",
       "      <td>0.122989</td>\n",
       "      <td>-0.072396</td>\n",
       "      <td>-0.224578</td>\n",
       "      <td>0.057937</td>\n",
       "      <td>0.839029</td>\n",
       "      <td>0.044465</td>\n",
       "      <td>-0.080408</td>\n",
       "      <td>-0.194371</td>\n",
       "      <td>-0.158883</td>\n",
       "      <td>-0.196069</td>\n",
       "      <td>0.083162</td>\n",
       "      <td>-0.133403</td>\n",
       "      <td>-0.140680</td>\n",
       "      <td>-0.028346</td>\n",
       "      <td>-0.066560</td>\n",
       "      <td>-0.174366</td>\n",
       "      <td>0.049001</td>\n",
       "      <td>-0.214271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>-0.083151</td>\n",
       "      <td>-0.176273</td>\n",
       "      <td>-0.072354</td>\n",
       "      <td>-0.014752</td>\n",
       "      <td>-0.192790</td>\n",
       "      <td>-0.121763</td>\n",
       "      <td>-0.207403</td>\n",
       "      <td>-0.184612</td>\n",
       "      <td>-0.123583</td>\n",
       "      <td>-0.118115</td>\n",
       "      <td>0.042775</td>\n",
       "      <td>-0.155732</td>\n",
       "      <td>0.079402</td>\n",
       "      <td>0.812469</td>\n",
       "      <td>0.009954</td>\n",
       "      <td>-0.149744</td>\n",
       "      <td>-0.148304</td>\n",
       "      <td>-0.109140</td>\n",
       "      <td>-0.002345</td>\n",
       "      <td>-0.099668</td>\n",
       "      <td>-0.000368</td>\n",
       "      <td>0.759900</td>\n",
       "      <td>0.242644</td>\n",
       "      <td>-0.197336</td>\n",
       "      <td>-0.270702</td>\n",
       "      <td>-0.031605</td>\n",
       "      <td>-0.083610</td>\n",
       "      <td>-0.197457</td>\n",
       "      <td>-0.012785</td>\n",
       "      <td>0.837379</td>\n",
       "      <td>0.064816</td>\n",
       "      <td>-0.261093</td>\n",
       "      <td>0.001064</td>\n",
       "      <td>-0.101332</td>\n",
       "      <td>0.171326</td>\n",
       "      <td>0.087721</td>\n",
       "      <td>0.196049</td>\n",
       "      <td>0.715466</td>\n",
       "      <td>-0.149423</td>\n",
       "      <td>-0.221160</td>\n",
       "      <td>0.064848</td>\n",
       "      <td>0.033526</td>\n",
       "      <td>0.002885</td>\n",
       "      <td>-0.177391</td>\n",
       "      <td>-0.076576</td>\n",
       "      <td>0.016847</td>\n",
       "      <td>-0.201242</td>\n",
       "      <td>-0.197262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>-0.123786</td>\n",
       "      <td>-0.018737</td>\n",
       "      <td>0.109495</td>\n",
       "      <td>-0.083957</td>\n",
       "      <td>-0.030939</td>\n",
       "      <td>-0.008957</td>\n",
       "      <td>-0.170157</td>\n",
       "      <td>-0.192194</td>\n",
       "      <td>-0.176686</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.661145</td>\n",
       "      <td>-0.199530</td>\n",
       "      <td>-0.004440</td>\n",
       "      <td>0.459792</td>\n",
       "      <td>-0.032845</td>\n",
       "      <td>-0.071983</td>\n",
       "      <td>-0.134917</td>\n",
       "      <td>0.030365</td>\n",
       "      <td>0.063706</td>\n",
       "      <td>-0.197195</td>\n",
       "      <td>-0.052643</td>\n",
       "      <td>0.393009</td>\n",
       "      <td>0.604447</td>\n",
       "      <td>-0.353003</td>\n",
       "      <td>-0.125066</td>\n",
       "      <td>-0.119348</td>\n",
       "      <td>-0.151487</td>\n",
       "      <td>0.079267</td>\n",
       "      <td>0.216065</td>\n",
       "      <td>0.114921</td>\n",
       "      <td>0.265603</td>\n",
       "      <td>-0.147549</td>\n",
       "      <td>-0.005426</td>\n",
       "      <td>-0.022756</td>\n",
       "      <td>0.952202</td>\n",
       "      <td>-0.034270</td>\n",
       "      <td>0.063469</td>\n",
       "      <td>-0.343780</td>\n",
       "      <td>-0.107934</td>\n",
       "      <td>-0.172750</td>\n",
       "      <td>-0.253165</td>\n",
       "      <td>0.041902</td>\n",
       "      <td>0.018439</td>\n",
       "      <td>-0.266517</td>\n",
       "      <td>-0.219938</td>\n",
       "      <td>-0.091623</td>\n",
       "      <td>-0.082322</td>\n",
       "      <td>-0.078240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sad</th>\n",
       "      <td>0.184882</td>\n",
       "      <td>-0.127680</td>\n",
       "      <td>-0.060984</td>\n",
       "      <td>-0.158955</td>\n",
       "      <td>-0.057357</td>\n",
       "      <td>-0.021647</td>\n",
       "      <td>-0.145245</td>\n",
       "      <td>-0.084124</td>\n",
       "      <td>-0.059754</td>\n",
       "      <td>-0.122693</td>\n",
       "      <td>0.052222</td>\n",
       "      <td>0.053129</td>\n",
       "      <td>-0.223741</td>\n",
       "      <td>0.074674</td>\n",
       "      <td>0.087649</td>\n",
       "      <td>-0.172451</td>\n",
       "      <td>-0.018550</td>\n",
       "      <td>0.107360</td>\n",
       "      <td>0.070615</td>\n",
       "      <td>-0.133233</td>\n",
       "      <td>-0.104635</td>\n",
       "      <td>0.109380</td>\n",
       "      <td>-0.144394</td>\n",
       "      <td>0.053951</td>\n",
       "      <td>0.004425</td>\n",
       "      <td>0.706575</td>\n",
       "      <td>0.514547</td>\n",
       "      <td>-0.047641</td>\n",
       "      <td>0.051109</td>\n",
       "      <td>0.252733</td>\n",
       "      <td>-0.252668</td>\n",
       "      <td>-0.077862</td>\n",
       "      <td>-0.006967</td>\n",
       "      <td>0.136566</td>\n",
       "      <td>0.712770</td>\n",
       "      <td>0.059842</td>\n",
       "      <td>-0.249183</td>\n",
       "      <td>-0.163925</td>\n",
       "      <td>-0.086291</td>\n",
       "      <td>-0.004512</td>\n",
       "      <td>-0.093885</td>\n",
       "      <td>0.052982</td>\n",
       "      <td>0.051361</td>\n",
       "      <td>-0.106941</td>\n",
       "      <td>-0.049065</td>\n",
       "      <td>-0.166603</td>\n",
       "      <td>-0.151522</td>\n",
       "      <td>-0.244265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sup</th>\n",
       "      <td>-0.200397</td>\n",
       "      <td>0.061519</td>\n",
       "      <td>-0.204223</td>\n",
       "      <td>-0.190441</td>\n",
       "      <td>0.003277</td>\n",
       "      <td>-0.248203</td>\n",
       "      <td>-0.105989</td>\n",
       "      <td>-0.133349</td>\n",
       "      <td>0.019298</td>\n",
       "      <td>-0.028401</td>\n",
       "      <td>0.351475</td>\n",
       "      <td>0.010811</td>\n",
       "      <td>-0.186709</td>\n",
       "      <td>-0.105716</td>\n",
       "      <td>0.171615</td>\n",
       "      <td>-0.190557</td>\n",
       "      <td>-0.199700</td>\n",
       "      <td>-0.049054</td>\n",
       "      <td>-0.122689</td>\n",
       "      <td>-0.269113</td>\n",
       "      <td>-0.016272</td>\n",
       "      <td>1.022867</td>\n",
       "      <td>0.514906</td>\n",
       "      <td>-0.061519</td>\n",
       "      <td>-0.159245</td>\n",
       "      <td>-0.191216</td>\n",
       "      <td>0.214639</td>\n",
       "      <td>-0.132690</td>\n",
       "      <td>-0.186214</td>\n",
       "      <td>0.824110</td>\n",
       "      <td>0.655651</td>\n",
       "      <td>0.157623</td>\n",
       "      <td>-0.111842</td>\n",
       "      <td>-0.206953</td>\n",
       "      <td>0.576101</td>\n",
       "      <td>-0.124684</td>\n",
       "      <td>-0.047287</td>\n",
       "      <td>-0.124257</td>\n",
       "      <td>-0.100276</td>\n",
       "      <td>-0.103540</td>\n",
       "      <td>-0.165803</td>\n",
       "      <td>-0.186152</td>\n",
       "      <td>0.023175</td>\n",
       "      <td>-0.140398</td>\n",
       "      <td>-0.034845</td>\n",
       "      <td>-0.098798</td>\n",
       "      <td>-0.115898</td>\n",
       "      <td>-0.064637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">m</th>\n",
       "      <th>ang</th>\n",
       "      <td>-0.147810</td>\n",
       "      <td>-0.090815</td>\n",
       "      <td>0.079869</td>\n",
       "      <td>-0.067013</td>\n",
       "      <td>-0.003200</td>\n",
       "      <td>-0.111989</td>\n",
       "      <td>-0.116224</td>\n",
       "      <td>-0.349456</td>\n",
       "      <td>-0.063999</td>\n",
       "      <td>-0.086909</td>\n",
       "      <td>1.079562</td>\n",
       "      <td>-0.024076</td>\n",
       "      <td>-0.129015</td>\n",
       "      <td>0.049692</td>\n",
       "      <td>-0.293144</td>\n",
       "      <td>-0.141947</td>\n",
       "      <td>-0.157273</td>\n",
       "      <td>-0.137862</td>\n",
       "      <td>0.883143</td>\n",
       "      <td>0.031603</td>\n",
       "      <td>-0.135489</td>\n",
       "      <td>0.534249</td>\n",
       "      <td>0.233757</td>\n",
       "      <td>-0.115687</td>\n",
       "      <td>-0.018857</td>\n",
       "      <td>-0.053334</td>\n",
       "      <td>0.672953</td>\n",
       "      <td>-0.063929</td>\n",
       "      <td>-0.151923</td>\n",
       "      <td>0.365126</td>\n",
       "      <td>0.182592</td>\n",
       "      <td>-0.262725</td>\n",
       "      <td>-0.272455</td>\n",
       "      <td>-0.329753</td>\n",
       "      <td>0.324460</td>\n",
       "      <td>-0.104426</td>\n",
       "      <td>-0.062720</td>\n",
       "      <td>-0.171180</td>\n",
       "      <td>-0.262166</td>\n",
       "      <td>-0.004625</td>\n",
       "      <td>0.052623</td>\n",
       "      <td>-0.114931</td>\n",
       "      <td>0.007077</td>\n",
       "      <td>-0.071521</td>\n",
       "      <td>-0.010132</td>\n",
       "      <td>-0.191861</td>\n",
       "      <td>-0.054781</td>\n",
       "      <td>-0.123480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>-0.183420</td>\n",
       "      <td>-0.016911</td>\n",
       "      <td>-0.073400</td>\n",
       "      <td>0.024479</td>\n",
       "      <td>-0.234697</td>\n",
       "      <td>-0.063476</td>\n",
       "      <td>-0.084005</td>\n",
       "      <td>-0.191738</td>\n",
       "      <td>-0.090126</td>\n",
       "      <td>-0.077726</td>\n",
       "      <td>-0.187415</td>\n",
       "      <td>-0.190714</td>\n",
       "      <td>-0.026003</td>\n",
       "      <td>-0.206813</td>\n",
       "      <td>0.067961</td>\n",
       "      <td>0.172052</td>\n",
       "      <td>-0.076957</td>\n",
       "      <td>-0.304362</td>\n",
       "      <td>-0.062975</td>\n",
       "      <td>-0.059768</td>\n",
       "      <td>-0.104016</td>\n",
       "      <td>0.825245</td>\n",
       "      <td>0.957210</td>\n",
       "      <td>0.011119</td>\n",
       "      <td>-0.152729</td>\n",
       "      <td>-0.005959</td>\n",
       "      <td>-0.076461</td>\n",
       "      <td>-0.127019</td>\n",
       "      <td>-0.064348</td>\n",
       "      <td>1.033164</td>\n",
       "      <td>0.791417</td>\n",
       "      <td>-0.132160</td>\n",
       "      <td>-0.285825</td>\n",
       "      <td>-0.009192</td>\n",
       "      <td>-0.066448</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>-0.296288</td>\n",
       "      <td>0.092349</td>\n",
       "      <td>-0.133612</td>\n",
       "      <td>0.257393</td>\n",
       "      <td>-0.340888</td>\n",
       "      <td>-0.162663</td>\n",
       "      <td>-0.127511</td>\n",
       "      <td>0.004255</td>\n",
       "      <td>-0.253271</td>\n",
       "      <td>-0.051498</td>\n",
       "      <td>-0.038230</td>\n",
       "      <td>0.314981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea</th>\n",
       "      <td>-0.077231</td>\n",
       "      <td>-0.200512</td>\n",
       "      <td>-0.074860</td>\n",
       "      <td>-0.140877</td>\n",
       "      <td>-0.066320</td>\n",
       "      <td>-0.126886</td>\n",
       "      <td>0.017264</td>\n",
       "      <td>-0.107285</td>\n",
       "      <td>0.007130</td>\n",
       "      <td>-0.091135</td>\n",
       "      <td>0.777351</td>\n",
       "      <td>-0.017429</td>\n",
       "      <td>-0.177906</td>\n",
       "      <td>0.069966</td>\n",
       "      <td>0.113103</td>\n",
       "      <td>0.132541</td>\n",
       "      <td>-0.337640</td>\n",
       "      <td>0.021904</td>\n",
       "      <td>0.120930</td>\n",
       "      <td>-0.100200</td>\n",
       "      <td>-0.101916</td>\n",
       "      <td>0.377112</td>\n",
       "      <td>0.251506</td>\n",
       "      <td>-0.255817</td>\n",
       "      <td>-0.108004</td>\n",
       "      <td>-0.208893</td>\n",
       "      <td>0.525016</td>\n",
       "      <td>-0.038400</td>\n",
       "      <td>-0.129689</td>\n",
       "      <td>0.212091</td>\n",
       "      <td>0.269984</td>\n",
       "      <td>-0.190473</td>\n",
       "      <td>-0.020385</td>\n",
       "      <td>-0.178000</td>\n",
       "      <td>1.073120</td>\n",
       "      <td>0.018291</td>\n",
       "      <td>0.040373</td>\n",
       "      <td>-0.233335</td>\n",
       "      <td>0.150789</td>\n",
       "      <td>-0.113870</td>\n",
       "      <td>-0.090264</td>\n",
       "      <td>-0.290962</td>\n",
       "      <td>0.029329</td>\n",
       "      <td>-0.157190</td>\n",
       "      <td>-0.138786</td>\n",
       "      <td>-0.107487</td>\n",
       "      <td>-0.226428</td>\n",
       "      <td>-0.099618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>-0.050525</td>\n",
       "      <td>-0.290847</td>\n",
       "      <td>-0.085458</td>\n",
       "      <td>-0.092325</td>\n",
       "      <td>-0.189344</td>\n",
       "      <td>-0.080268</td>\n",
       "      <td>-0.042586</td>\n",
       "      <td>-0.213514</td>\n",
       "      <td>-0.164511</td>\n",
       "      <td>-0.152753</td>\n",
       "      <td>0.223567</td>\n",
       "      <td>0.059805</td>\n",
       "      <td>0.176611</td>\n",
       "      <td>0.617089</td>\n",
       "      <td>-0.046032</td>\n",
       "      <td>-0.169695</td>\n",
       "      <td>-0.247911</td>\n",
       "      <td>-0.235120</td>\n",
       "      <td>-0.028841</td>\n",
       "      <td>-0.054830</td>\n",
       "      <td>0.058468</td>\n",
       "      <td>0.744898</td>\n",
       "      <td>0.044865</td>\n",
       "      <td>-0.199634</td>\n",
       "      <td>-0.115767</td>\n",
       "      <td>-0.290798</td>\n",
       "      <td>-0.060222</td>\n",
       "      <td>-0.005151</td>\n",
       "      <td>-0.093014</td>\n",
       "      <td>0.867706</td>\n",
       "      <td>0.336744</td>\n",
       "      <td>-0.046914</td>\n",
       "      <td>-0.249483</td>\n",
       "      <td>-0.198457</td>\n",
       "      <td>0.007031</td>\n",
       "      <td>-0.055391</td>\n",
       "      <td>0.177247</td>\n",
       "      <td>0.942656</td>\n",
       "      <td>0.074705</td>\n",
       "      <td>-0.253048</td>\n",
       "      <td>-0.019530</td>\n",
       "      <td>-0.040397</td>\n",
       "      <td>-0.059653</td>\n",
       "      <td>0.002774</td>\n",
       "      <td>-0.095966</td>\n",
       "      <td>-0.053750</td>\n",
       "      <td>-0.206256</td>\n",
       "      <td>-0.146175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>-0.218679</td>\n",
       "      <td>-0.120495</td>\n",
       "      <td>-0.053487</td>\n",
       "      <td>-0.140802</td>\n",
       "      <td>-0.027873</td>\n",
       "      <td>0.054360</td>\n",
       "      <td>-0.109542</td>\n",
       "      <td>-0.004019</td>\n",
       "      <td>-0.036447</td>\n",
       "      <td>-0.032446</td>\n",
       "      <td>0.106896</td>\n",
       "      <td>-0.162269</td>\n",
       "      <td>-0.088113</td>\n",
       "      <td>0.503841</td>\n",
       "      <td>-0.018245</td>\n",
       "      <td>-0.156115</td>\n",
       "      <td>-0.131595</td>\n",
       "      <td>-0.010068</td>\n",
       "      <td>0.052106</td>\n",
       "      <td>-0.028120</td>\n",
       "      <td>0.320970</td>\n",
       "      <td>0.338143</td>\n",
       "      <td>0.080319</td>\n",
       "      <td>-0.077805</td>\n",
       "      <td>-0.116970</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.068886</td>\n",
       "      <td>-0.036699</td>\n",
       "      <td>0.169568</td>\n",
       "      <td>0.488858</td>\n",
       "      <td>0.257322</td>\n",
       "      <td>-0.006068</td>\n",
       "      <td>-0.177739</td>\n",
       "      <td>-0.271038</td>\n",
       "      <td>0.174192</td>\n",
       "      <td>-0.124064</td>\n",
       "      <td>-0.055240</td>\n",
       "      <td>0.169659</td>\n",
       "      <td>0.058271</td>\n",
       "      <td>-0.113255</td>\n",
       "      <td>-0.279233</td>\n",
       "      <td>-0.066863</td>\n",
       "      <td>0.044466</td>\n",
       "      <td>-0.185020</td>\n",
       "      <td>0.054816</td>\n",
       "      <td>-0.104298</td>\n",
       "      <td>-0.035720</td>\n",
       "      <td>0.055597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sad</th>\n",
       "      <td>-0.076002</td>\n",
       "      <td>-0.054205</td>\n",
       "      <td>-0.080972</td>\n",
       "      <td>-0.177725</td>\n",
       "      <td>-0.033782</td>\n",
       "      <td>-0.041417</td>\n",
       "      <td>-0.107863</td>\n",
       "      <td>-0.139478</td>\n",
       "      <td>-0.114452</td>\n",
       "      <td>-0.050734</td>\n",
       "      <td>0.111651</td>\n",
       "      <td>0.006293</td>\n",
       "      <td>0.011445</td>\n",
       "      <td>0.023983</td>\n",
       "      <td>-0.055468</td>\n",
       "      <td>-0.047892</td>\n",
       "      <td>-0.107585</td>\n",
       "      <td>0.109009</td>\n",
       "      <td>-0.007500</td>\n",
       "      <td>0.016354</td>\n",
       "      <td>0.088728</td>\n",
       "      <td>-0.057744</td>\n",
       "      <td>0.052238</td>\n",
       "      <td>-0.066769</td>\n",
       "      <td>-0.091346</td>\n",
       "      <td>0.233597</td>\n",
       "      <td>0.133486</td>\n",
       "      <td>0.253875</td>\n",
       "      <td>-0.152544</td>\n",
       "      <td>0.087752</td>\n",
       "      <td>0.261514</td>\n",
       "      <td>-0.073964</td>\n",
       "      <td>-0.216595</td>\n",
       "      <td>0.013719</td>\n",
       "      <td>1.037263</td>\n",
       "      <td>0.157944</td>\n",
       "      <td>-0.174235</td>\n",
       "      <td>-0.145792</td>\n",
       "      <td>-0.250436</td>\n",
       "      <td>0.049275</td>\n",
       "      <td>-0.065697</td>\n",
       "      <td>-0.102701</td>\n",
       "      <td>-0.016207</td>\n",
       "      <td>-0.054510</td>\n",
       "      <td>0.008708</td>\n",
       "      <td>0.003894</td>\n",
       "      <td>-0.048377</td>\n",
       "      <td>-0.048736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sup</th>\n",
       "      <td>-0.054429</td>\n",
       "      <td>-0.149024</td>\n",
       "      <td>0.035815</td>\n",
       "      <td>-0.022497</td>\n",
       "      <td>-0.062488</td>\n",
       "      <td>-0.261285</td>\n",
       "      <td>-0.173190</td>\n",
       "      <td>-0.167149</td>\n",
       "      <td>-0.030314</td>\n",
       "      <td>-0.017201</td>\n",
       "      <td>0.637808</td>\n",
       "      <td>-0.175138</td>\n",
       "      <td>-0.090473</td>\n",
       "      <td>-0.110422</td>\n",
       "      <td>-0.156938</td>\n",
       "      <td>-0.056181</td>\n",
       "      <td>0.116634</td>\n",
       "      <td>0.111349</td>\n",
       "      <td>0.192082</td>\n",
       "      <td>-0.279182</td>\n",
       "      <td>0.099153</td>\n",
       "      <td>0.550366</td>\n",
       "      <td>0.350242</td>\n",
       "      <td>-0.098910</td>\n",
       "      <td>-0.008794</td>\n",
       "      <td>0.010870</td>\n",
       "      <td>0.251621</td>\n",
       "      <td>-0.148239</td>\n",
       "      <td>-0.091247</td>\n",
       "      <td>0.832018</td>\n",
       "      <td>0.214856</td>\n",
       "      <td>-0.191427</td>\n",
       "      <td>0.170664</td>\n",
       "      <td>-0.108776</td>\n",
       "      <td>0.609113</td>\n",
       "      <td>-0.178041</td>\n",
       "      <td>-0.037244</td>\n",
       "      <td>-0.167970</td>\n",
       "      <td>0.008821</td>\n",
       "      <td>-0.227562</td>\n",
       "      <td>-0.241198</td>\n",
       "      <td>0.009626</td>\n",
       "      <td>-0.114197</td>\n",
       "      <td>-0.067991</td>\n",
       "      <td>-0.206543</td>\n",
       "      <td>-0.197860</td>\n",
       "      <td>-0.157284</td>\n",
       "      <td>-0.151846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1         2         3         4         5  \\\n",
       "ident emo                                                               \n",
       "f     ang -0.107068 -0.090944  0.021346 -0.140837 -0.231097  0.120188   \n",
       "      dis -0.197694  0.061481 -0.234455 -0.156955 -0.116681 -0.229027   \n",
       "      fea  0.077753 -0.066480 -0.141368 -0.121815 -0.012927 -0.287902   \n",
       "      hap -0.083151 -0.176273 -0.072354 -0.014752 -0.192790 -0.121763   \n",
       "      ntr -0.123786 -0.018737  0.109495 -0.083957 -0.030939 -0.008957   \n",
       "      sad  0.184882 -0.127680 -0.060984 -0.158955 -0.057357 -0.021647   \n",
       "      sup -0.200397  0.061519 -0.204223 -0.190441  0.003277 -0.248203   \n",
       "m     ang -0.147810 -0.090815  0.079869 -0.067013 -0.003200 -0.111989   \n",
       "      dis -0.183420 -0.016911 -0.073400  0.024479 -0.234697 -0.063476   \n",
       "      fea -0.077231 -0.200512 -0.074860 -0.140877 -0.066320 -0.126886   \n",
       "      hap -0.050525 -0.290847 -0.085458 -0.092325 -0.189344 -0.080268   \n",
       "      ntr -0.218679 -0.120495 -0.053487 -0.140802 -0.027873  0.054360   \n",
       "      sad -0.076002 -0.054205 -0.080972 -0.177725 -0.033782 -0.041417   \n",
       "      sup -0.054429 -0.149024  0.035815 -0.022497 -0.062488 -0.261285   \n",
       "\n",
       "                  6         7         8         9        10        11  \\\n",
       "ident emo                                                               \n",
       "f     ang -0.101582 -0.155755 -0.212338 -0.097965  0.484374  0.035292   \n",
       "      dis -0.159949  0.004103 -0.161257 -0.209212 -0.077736 -0.004602   \n",
       "      fea -0.147046 -0.017016 -0.061568  0.102944  0.736890 -0.114484   \n",
       "      hap -0.207403 -0.184612 -0.123583 -0.118115  0.042775 -0.155732   \n",
       "      ntr -0.170157 -0.192194 -0.176686  0.000314  0.661145 -0.199530   \n",
       "      sad -0.145245 -0.084124 -0.059754 -0.122693  0.052222  0.053129   \n",
       "      sup -0.105989 -0.133349  0.019298 -0.028401  0.351475  0.010811   \n",
       "m     ang -0.116224 -0.349456 -0.063999 -0.086909  1.079562 -0.024076   \n",
       "      dis -0.084005 -0.191738 -0.090126 -0.077726 -0.187415 -0.190714   \n",
       "      fea  0.017264 -0.107285  0.007130 -0.091135  0.777351 -0.017429   \n",
       "      hap -0.042586 -0.213514 -0.164511 -0.152753  0.223567  0.059805   \n",
       "      ntr -0.109542 -0.004019 -0.036447 -0.032446  0.106896 -0.162269   \n",
       "      sad -0.107863 -0.139478 -0.114452 -0.050734  0.111651  0.006293   \n",
       "      sup -0.173190 -0.167149 -0.030314 -0.017201  0.637808 -0.175138   \n",
       "\n",
       "                 12        13        14        15        16        17  \\\n",
       "ident emo                                                               \n",
       "f     ang -0.089919  0.065455 -0.239384 -0.122578 -0.118693  0.146498   \n",
       "      dis  0.010296  0.081108  0.119742 -0.017898 -0.184315 -0.036822   \n",
       "      fea -0.240084  0.035541 -0.134807 -0.049666 -0.032427 -0.049715   \n",
       "      hap  0.079402  0.812469  0.009954 -0.149744 -0.148304 -0.109140   \n",
       "      ntr -0.004440  0.459792 -0.032845 -0.071983 -0.134917  0.030365   \n",
       "      sad -0.223741  0.074674  0.087649 -0.172451 -0.018550  0.107360   \n",
       "      sup -0.186709 -0.105716  0.171615 -0.190557 -0.199700 -0.049054   \n",
       "m     ang -0.129015  0.049692 -0.293144 -0.141947 -0.157273 -0.137862   \n",
       "      dis -0.026003 -0.206813  0.067961  0.172052 -0.076957 -0.304362   \n",
       "      fea -0.177906  0.069966  0.113103  0.132541 -0.337640  0.021904   \n",
       "      hap  0.176611  0.617089 -0.046032 -0.169695 -0.247911 -0.235120   \n",
       "      ntr -0.088113  0.503841 -0.018245 -0.156115 -0.131595 -0.010068   \n",
       "      sad  0.011445  0.023983 -0.055468 -0.047892 -0.107585  0.109009   \n",
       "      sup -0.090473 -0.110422 -0.156938 -0.056181  0.116634  0.111349   \n",
       "\n",
       "                 18        19        20        21        22        23  \\\n",
       "ident emo                                                               \n",
       "f     ang  0.044670  0.344048  0.145883  0.255147  0.364067 -0.061439   \n",
       "      dis -0.108139 -0.043717  0.015189  0.921797  1.060071 -0.102794   \n",
       "      fea -0.082939 -0.058181 -0.169369  0.198205  0.376390 -0.238898   \n",
       "      hap -0.002345 -0.099668 -0.000368  0.759900  0.242644 -0.197336   \n",
       "      ntr  0.063706 -0.197195 -0.052643  0.393009  0.604447 -0.353003   \n",
       "      sad  0.070615 -0.133233 -0.104635  0.109380 -0.144394  0.053951   \n",
       "      sup -0.122689 -0.269113 -0.016272  1.022867  0.514906 -0.061519   \n",
       "m     ang  0.883143  0.031603 -0.135489  0.534249  0.233757 -0.115687   \n",
       "      dis -0.062975 -0.059768 -0.104016  0.825245  0.957210  0.011119   \n",
       "      fea  0.120930 -0.100200 -0.101916  0.377112  0.251506 -0.255817   \n",
       "      hap -0.028841 -0.054830  0.058468  0.744898  0.044865 -0.199634   \n",
       "      ntr  0.052106 -0.028120  0.320970  0.338143  0.080319 -0.077805   \n",
       "      sad -0.007500  0.016354  0.088728 -0.057744  0.052238 -0.066769   \n",
       "      sup  0.192082 -0.279182  0.099153  0.550366  0.350242 -0.098910   \n",
       "\n",
       "                 24        25        26        27        28        29  \\\n",
       "ident emo                                                               \n",
       "f     ang -0.129267 -0.147019  0.547444 -0.020446  0.031519  0.969053   \n",
       "      dis -0.215771 -0.190616 -0.372721 -0.040695 -0.193600  1.098819   \n",
       "      fea -0.011844  0.122511  0.026201 -0.099165 -0.007230  0.956243   \n",
       "      hap -0.270702 -0.031605 -0.083610 -0.197457 -0.012785  0.837379   \n",
       "      ntr -0.125066 -0.119348 -0.151487  0.079267  0.216065  0.114921   \n",
       "      sad  0.004425  0.706575  0.514547 -0.047641  0.051109  0.252733   \n",
       "      sup -0.159245 -0.191216  0.214639 -0.132690 -0.186214  0.824110   \n",
       "m     ang -0.018857 -0.053334  0.672953 -0.063929 -0.151923  0.365126   \n",
       "      dis -0.152729 -0.005959 -0.076461 -0.127019 -0.064348  1.033164   \n",
       "      fea -0.108004 -0.208893  0.525016 -0.038400 -0.129689  0.212091   \n",
       "      hap -0.115767 -0.290798 -0.060222 -0.005151 -0.093014  0.867706   \n",
       "      ntr -0.116970 -0.009940  0.068886 -0.036699  0.169568  0.488858   \n",
       "      sad -0.091346  0.233597  0.133486  0.253875 -0.152544  0.087752   \n",
       "      sup -0.008794  0.010870  0.251621 -0.148239 -0.091247  0.832018   \n",
       "\n",
       "                 30        31        32        33        34        35  \\\n",
       "ident emo                                                               \n",
       "f     ang  0.477879 -0.226840 -0.233784 -0.146768  0.651934 -0.001234   \n",
       "      dis  1.436805  0.007758 -0.214569 -0.222747 -0.209609 -0.005113   \n",
       "      fea  0.122989 -0.072396 -0.224578  0.057937  0.839029  0.044465   \n",
       "      hap  0.064816 -0.261093  0.001064 -0.101332  0.171326  0.087721   \n",
       "      ntr  0.265603 -0.147549 -0.005426 -0.022756  0.952202 -0.034270   \n",
       "      sad -0.252668 -0.077862 -0.006967  0.136566  0.712770  0.059842   \n",
       "      sup  0.655651  0.157623 -0.111842 -0.206953  0.576101 -0.124684   \n",
       "m     ang  0.182592 -0.262725 -0.272455 -0.329753  0.324460 -0.104426   \n",
       "      dis  0.791417 -0.132160 -0.285825 -0.009192 -0.066448  0.007000   \n",
       "      fea  0.269984 -0.190473 -0.020385 -0.178000  1.073120  0.018291   \n",
       "      hap  0.336744 -0.046914 -0.249483 -0.198457  0.007031 -0.055391   \n",
       "      ntr  0.257322 -0.006068 -0.177739 -0.271038  0.174192 -0.124064   \n",
       "      sad  0.261514 -0.073964 -0.216595  0.013719  1.037263  0.157944   \n",
       "      sup  0.214856 -0.191427  0.170664 -0.108776  0.609113 -0.178041   \n",
       "\n",
       "                 36        37        38        39        40        41  \\\n",
       "ident emo                                                               \n",
       "f     ang -0.250892 -0.034475 -0.133708 -0.271287 -0.215705 -0.245044   \n",
       "      dis -0.151673  0.039613 -0.103777  0.084613  0.003531 -0.158570   \n",
       "      fea -0.080408 -0.194371 -0.158883 -0.196069  0.083162 -0.133403   \n",
       "      hap  0.196049  0.715466 -0.149423 -0.221160  0.064848  0.033526   \n",
       "      ntr  0.063469 -0.343780 -0.107934 -0.172750 -0.253165  0.041902   \n",
       "      sad -0.249183 -0.163925 -0.086291 -0.004512 -0.093885  0.052982   \n",
       "      sup -0.047287 -0.124257 -0.100276 -0.103540 -0.165803 -0.186152   \n",
       "m     ang -0.062720 -0.171180 -0.262166 -0.004625  0.052623 -0.114931   \n",
       "      dis -0.296288  0.092349 -0.133612  0.257393 -0.340888 -0.162663   \n",
       "      fea  0.040373 -0.233335  0.150789 -0.113870 -0.090264 -0.290962   \n",
       "      hap  0.177247  0.942656  0.074705 -0.253048 -0.019530 -0.040397   \n",
       "      ntr -0.055240  0.169659  0.058271 -0.113255 -0.279233 -0.066863   \n",
       "      sad -0.174235 -0.145792 -0.250436  0.049275 -0.065697 -0.102701   \n",
       "      sup -0.037244 -0.167970  0.008821 -0.227562 -0.241198  0.009626   \n",
       "\n",
       "                 42        43        44        45        46        47  \n",
       "ident emo                                                              \n",
       "f     ang -0.127644 -0.094971 -0.183468 -0.170725 -0.187689 -0.114234  \n",
       "      dis -0.139650 -0.259681 -0.120911 -0.063685 -0.158616 -0.081669  \n",
       "      fea -0.140680 -0.028346 -0.066560 -0.174366  0.049001 -0.214271  \n",
       "      hap  0.002885 -0.177391 -0.076576  0.016847 -0.201242 -0.197262  \n",
       "      ntr  0.018439 -0.266517 -0.219938 -0.091623 -0.082322 -0.078240  \n",
       "      sad  0.051361 -0.106941 -0.049065 -0.166603 -0.151522 -0.244265  \n",
       "      sup  0.023175 -0.140398 -0.034845 -0.098798 -0.115898 -0.064637  \n",
       "m     ang  0.007077 -0.071521 -0.010132 -0.191861 -0.054781 -0.123480  \n",
       "      dis -0.127511  0.004255 -0.253271 -0.051498 -0.038230  0.314981  \n",
       "      fea  0.029329 -0.157190 -0.138786 -0.107487 -0.226428 -0.099618  \n",
       "      hap -0.059653  0.002774 -0.095966 -0.053750 -0.206256 -0.146175  \n",
       "      ntr  0.044466 -0.185020  0.054816 -0.104298 -0.035720  0.055597  \n",
       "      sad -0.016207 -0.054510  0.008708  0.003894 -0.048377 -0.048736  \n",
       "      sup -0.114197 -0.067991 -0.206543 -0.197860 -0.157284 -0.151846  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weightAvgDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform to dissimilarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dissWeight = 1 - weightAvgDf.T.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
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       "      <th colspan=\"7\" halign=\"left\">m</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>emo</th>\n",
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       "      <th>fea</th>\n",
       "      <th>hap</th>\n",
       "      <th>ntr</th>\n",
       "      <th>sad</th>\n",
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       "      <td>0.526918</td>\n",
       "      <td>0.426292</td>\n",
       "      <td>0.606374</td>\n",
       "      <td>0.310539</td>\n",
       "      <td>0.353957</td>\n",
       "      <td>0.463615</td>\n",
       "      <td>0.306928</td>\n",
       "      <td>0.498845</td>\n",
       "      <td>0.367775</td>\n",
       "      <td>0.433686</td>\n",
       "      <td>0.241164</td>\n",
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       "      <th>dis</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.584528</td>\n",
       "      <td>0.465495</td>\n",
       "      <td>0.636283</td>\n",
       "      <td>1.134458</td>\n",
       "      <td>0.238734</td>\n",
       "      <td>0.739679</td>\n",
       "      <td>0.134840</td>\n",
       "      <td>0.734699</td>\n",
       "      <td>0.433183</td>\n",
       "      <td>0.470010</td>\n",
       "      <td>0.856303</td>\n",
       "      <td>0.529047</td>\n",
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       "      <td>0.561444</td>\n",
       "      <td>0.330490</td>\n",
       "      <td>0.517788</td>\n",
       "      <td>0.317090</td>\n",
       "      <td>0.423827</td>\n",
       "      <td>0.583472</td>\n",
       "      <td>0.305420</td>\n",
       "      <td>0.630176</td>\n",
       "      <td>0.575863</td>\n",
       "      <td>0.423399</td>\n",
       "      <td>0.200198</td>\n",
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       "      <td>0.526918</td>\n",
       "      <td>0.465495</td>\n",
       "      <td>0.561444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.575025</td>\n",
       "      <td>0.838546</td>\n",
       "      <td>0.471021</td>\n",
       "      <td>0.681756</td>\n",
       "      <td>0.526483</td>\n",
       "      <td>0.693391</td>\n",
       "      <td>0.113149</td>\n",
       "      <td>0.304991</td>\n",
       "      <td>0.879547</td>\n",
       "      <td>0.512600</td>\n",
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       "      <th>ntr</th>\n",
       "      <td>0.426292</td>\n",
       "      <td>0.636283</td>\n",
       "      <td>0.330490</td>\n",
       "      <td>0.575025</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.696674</td>\n",
       "      <td>0.408133</td>\n",
       "      <td>0.474981</td>\n",
       "      <td>0.688317</td>\n",
       "      <td>0.251699</td>\n",
       "      <td>0.704923</td>\n",
       "      <td>0.491758</td>\n",
       "      <td>0.413829</td>\n",
       "      <td>0.325824</td>\n",
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       "      <th>sad</th>\n",
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       "      <td>1.134458</td>\n",
       "      <td>0.517788</td>\n",
       "      <td>0.838546</td>\n",
       "      <td>0.696674</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.747662</td>\n",
       "      <td>0.673415</td>\n",
       "      <td>1.024298</td>\n",
       "      <td>0.548052</td>\n",
       "      <td>1.019425</td>\n",
       "      <td>0.811255</td>\n",
       "      <td>0.397864</td>\n",
       "      <td>0.563475</td>\n",
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       "      <th>sup</th>\n",
       "      <td>0.310539</td>\n",
       "      <td>0.238734</td>\n",
       "      <td>0.317090</td>\n",
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       "      <td>0.408133</td>\n",
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       "      <td>0.288438</td>\n",
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       "      <td>0.457873</td>\n",
       "      <td>0.395880</td>\n",
       "      <td>0.588767</td>\n",
       "      <td>0.226280</td>\n",
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       "      <th rowspan=\"7\" valign=\"top\">m</th>\n",
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       "      <td>0.312079</td>\n",
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       "      <td>0.601791</td>\n",
       "      <td>0.650644</td>\n",
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       "      <td>0.526483</td>\n",
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       "      <td>1.024298</td>\n",
       "      <td>0.288438</td>\n",
       "      <td>0.723499</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.754335</td>\n",
       "      <td>0.520312</td>\n",
       "      <td>0.534927</td>\n",
       "      <td>0.854830</td>\n",
       "      <td>0.495952</td>\n",
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       "      <th>fea</th>\n",
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       "      <td>0.305420</td>\n",
       "      <td>0.693391</td>\n",
       "      <td>0.251699</td>\n",
       "      <td>0.548052</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.700505</td>\n",
       "      <td>0.585824</td>\n",
       "      <td>0.361441</td>\n",
       "      <td>0.277235</td>\n",
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       "      <td>0.433183</td>\n",
       "      <td>0.630176</td>\n",
       "      <td>0.113149</td>\n",
       "      <td>0.704923</td>\n",
       "      <td>1.019425</td>\n",
       "      <td>0.457873</td>\n",
       "      <td>0.659540</td>\n",
       "      <td>0.520312</td>\n",
       "      <td>0.700505</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.305368</td>\n",
       "      <td>0.946475</td>\n",
       "      <td>0.569456</td>\n",
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       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>0.367775</td>\n",
       "      <td>0.470010</td>\n",
       "      <td>0.575863</td>\n",
       "      <td>0.304991</td>\n",
       "      <td>0.491758</td>\n",
       "      <td>0.811255</td>\n",
       "      <td>0.395880</td>\n",
       "      <td>0.601791</td>\n",
       "      <td>0.534927</td>\n",
       "      <td>0.585824</td>\n",
       "      <td>0.305368</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.725656</td>\n",
       "      <td>0.473879</td>\n",
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       "    <tr>\n",
       "      <th>sad</th>\n",
       "      <td>0.433686</td>\n",
       "      <td>0.856303</td>\n",
       "      <td>0.423399</td>\n",
       "      <td>0.879547</td>\n",
       "      <td>0.413829</td>\n",
       "      <td>0.397864</td>\n",
       "      <td>0.588767</td>\n",
       "      <td>0.650644</td>\n",
       "      <td>0.854830</td>\n",
       "      <td>0.361441</td>\n",
       "      <td>0.946475</td>\n",
       "      <td>0.725656</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.580746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sup</th>\n",
       "      <td>0.241164</td>\n",
       "      <td>0.529047</td>\n",
       "      <td>0.200198</td>\n",
       "      <td>0.512600</td>\n",
       "      <td>0.325824</td>\n",
       "      <td>0.563475</td>\n",
       "      <td>0.226280</td>\n",
       "      <td>0.315321</td>\n",
       "      <td>0.495952</td>\n",
       "      <td>0.277235</td>\n",
       "      <td>0.569456</td>\n",
       "      <td>0.473879</td>\n",
       "      <td>0.580746</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ident             f                                                    \\\n",
       "emo             ang       dis       fea       hap       ntr       sad   \n",
       "ident emo                                                               \n",
       "f     ang  0.000000  0.476491  0.262901  0.526918  0.426292  0.606374   \n",
       "      dis  0.476491  0.000000  0.584528  0.465495  0.636283  1.134458   \n",
       "      fea  0.262901  0.584528  0.000000  0.561444  0.330490  0.517788   \n",
       "      hap  0.526918  0.465495  0.561444  0.000000  0.575025  0.838546   \n",
       "      ntr  0.426292  0.636283  0.330490  0.575025  0.000000  0.696674   \n",
       "      sad  0.606374  1.134458  0.517788  0.838546  0.696674  0.000000   \n",
       "      sup  0.310539  0.238734  0.317090  0.471021  0.408133  0.747662   \n",
       "m     ang  0.353957  0.739679  0.423827  0.681756  0.474981  0.673415   \n",
       "      dis  0.463615  0.134840  0.583472  0.526483  0.688317  1.024298   \n",
       "      fea  0.306928  0.734699  0.305420  0.693391  0.251699  0.548052   \n",
       "      hap  0.498845  0.433183  0.630176  0.113149  0.704923  1.019425   \n",
       "      ntr  0.367775  0.470010  0.575863  0.304991  0.491758  0.811255   \n",
       "      sad  0.433686  0.856303  0.423399  0.879547  0.413829  0.397864   \n",
       "      sup  0.241164  0.529047  0.200198  0.512600  0.325824  0.563475   \n",
       "\n",
       "ident                       m                                          \\\n",
       "emo             sup       ang       dis       fea       hap       ntr   \n",
       "ident emo                                                               \n",
       "f     ang  0.310539  0.353957  0.463615  0.306928  0.498845  0.367775   \n",
       "      dis  0.238734  0.739679  0.134840  0.734699  0.433183  0.470010   \n",
       "      fea  0.317090  0.423827  0.583472  0.305420  0.630176  0.575863   \n",
       "      hap  0.471021  0.681756  0.526483  0.693391  0.113149  0.304991   \n",
       "      ntr  0.408133  0.474981  0.688317  0.251699  0.704923  0.491758   \n",
       "      sad  0.747662  0.673415  1.024298  0.548052  1.019425  0.811255   \n",
       "      sup  0.000000  0.459503  0.288438  0.324296  0.457873  0.395880   \n",
       "m     ang  0.459503  0.000000  0.723499  0.312079  0.659540  0.601791   \n",
       "      dis  0.288438  0.723499  0.000000  0.754335  0.520312  0.534927   \n",
       "      fea  0.324296  0.312079  0.754335  0.000000  0.700505  0.585824   \n",
       "      hap  0.457873  0.659540  0.520312  0.700505  0.000000  0.305368   \n",
       "      ntr  0.395880  0.601791  0.534927  0.585824  0.305368  0.000000   \n",
       "      sad  0.588767  0.650644  0.854830  0.361441  0.946475  0.725656   \n",
       "      sup  0.226280  0.315321  0.495952  0.277235  0.569456  0.473879   \n",
       "\n",
       "ident                          \n",
       "emo             sad       sup  \n",
       "ident emo                      \n",
       "f     ang  0.433686  0.241164  \n",
       "      dis  0.856303  0.529047  \n",
       "      fea  0.423399  0.200198  \n",
       "      hap  0.879547  0.512600  \n",
       "      ntr  0.413829  0.325824  \n",
       "      sad  0.397864  0.563475  \n",
       "      sup  0.588767  0.226280  \n",
       "m     ang  0.650644  0.315321  \n",
       "      dis  0.854830  0.495952  \n",
       "      fea  0.361441  0.277235  \n",
       "      hap  0.946475  0.569456  \n",
       "      ntr  0.725656  0.473879  \n",
       "      sad  0.000000  0.580746  \n",
       "      sup  0.580746  0.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dissWeight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### show clustering of correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kjKkoeIiI5O9dYAszm0P5wNHp7gPqKHu/+qtVnoKHiEj+LmlUwe5+ZSPKVfAQ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\n8XAcx3GicfFwHMdxonHxcBzHcaL5Pym43NfyzLkZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f30940dcbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(dissWeight,figsize=(6, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multidimensional Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set parameters for mds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mds = manifold.MDS(n_components=2,\n",
    "                   max_iter=3000,\n",
    "                   eps=1e-9,\n",
    "                   dissimilarity=\"precomputed\",\n",
    "                   n_jobs=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply parameters to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "mdsPositions = mds.fit( dissWeight ).embedding_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get positions in a 2d-space, close proximity means large similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cave: This will give different values each time, as the distances do not  fully determine the position (so each time the space is generated, it is rotated in a different way!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.05828196  0.06669973]\n",
      " [ 0.01711763 -0.45909476]\n",
      " [-0.15731716  0.08263661]\n",
      " [ 0.3548822  -0.23839795]\n",
      " [-0.31073891  0.0184738 ]\n",
      " [-0.14997452  0.62931325]\n",
      " [-0.05205455 -0.16825512]\n",
      " [ 0.18072068  0.30324518]\n",
      " [-0.07182826 -0.46293225]\n",
      " [-0.11825031  0.24308039]\n",
      " [ 0.37666203 -0.30043075]\n",
      " [ 0.32151048 -0.07630237]\n",
      " [-0.40063541  0.33184758]\n",
      " [-0.04837586  0.03011667]]\n"
     ]
    }
   ],
   "source": [
    "print mdsPositions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "put into DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ident</th>\n",
       "      <th>emo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">f</th>\n",
       "      <th>ang</th>\n",
       "      <td>0.058282</td>\n",
       "      <td>0.066700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>0.017118</td>\n",
       "      <td>-0.459095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea</th>\n",
       "      <td>-0.157317</td>\n",
       "      <td>0.082637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>0.354882</td>\n",
       "      <td>-0.238398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>-0.310739</td>\n",
       "      <td>0.018474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sad</th>\n",
       "      <td>-0.149975</td>\n",
       "      <td>0.629313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sup</th>\n",
       "      <td>-0.052055</td>\n",
       "      <td>-0.168255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">m</th>\n",
       "      <th>ang</th>\n",
       "      <td>0.180721</td>\n",
       "      <td>0.303245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>-0.071828</td>\n",
       "      <td>-0.462932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea</th>\n",
       "      <td>-0.118250</td>\n",
       "      <td>0.243080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>0.376662</td>\n",
       "      <td>-0.300431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>0.321510</td>\n",
       "      <td>-0.076302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sad</th>\n",
       "      <td>-0.400635</td>\n",
       "      <td>0.331848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sup</th>\n",
       "      <td>-0.048376</td>\n",
       "      <td>0.030117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1\n",
       "ident emo                    \n",
       "f     ang  0.058282  0.066700\n",
       "      dis  0.017118 -0.459095\n",
       "      fea -0.157317  0.082637\n",
       "      hap  0.354882 -0.238398\n",
       "      ntr -0.310739  0.018474\n",
       "      sad -0.149975  0.629313\n",
       "      sup -0.052055 -0.168255\n",
       "m     ang  0.180721  0.303245\n",
       "      dis -0.071828 -0.462932\n",
       "      fea -0.118250  0.243080\n",
       "      hap  0.376662 -0.300431\n",
       "      ntr  0.321510 -0.076302\n",
       "      sad -0.400635  0.331848\n",
       "      sup -0.048376  0.030117"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdsDf = pd.DataFrame(mdsPositions,index=dissWeight.index)\n",
    "mdsDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a function to wrap this all together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makeRsaDf(weightDf,mds=mds):\n",
    "    # average over all participants\n",
    "    weightAvgDf = weightDf.groupby(level=[1,2]).mean()\n",
    "    # make dissimiarity matrix\n",
    "    dissWeight = 1 - weightAvgDf.T.corr().fillna(0)\n",
    "    # do the MDS\n",
    "    mdsPositions = mds.fit( dissWeight ).embedding_\n",
    "    # put into dataframe, annotate\n",
    "    mdsDf = pd.DataFrame(mdsPositions,index=dissWeight.index)\n",
    "    \n",
    "    return mdsDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ident</th>\n",
       "      <th>emo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">f</th>\n",
       "      <th>ang</th>\n",
       "      <td>0.013398</td>\n",
       "      <td>-0.004736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>-0.244885</td>\n",
       "      <td>0.387968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea</th>\n",
       "      <td>0.136745</td>\n",
       "      <td>-0.073895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>-0.428075</td>\n",
       "      <td>0.029082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>-0.000683</td>\n",
       "      <td>-0.270555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sad</th>\n",
       "      <td>0.517737</td>\n",
       "      <td>-0.394046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sup</th>\n",
       "      <td>-0.029778</td>\n",
       "      <td>0.179263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">m</th>\n",
       "      <th>ang</th>\n",
       "      <td>0.349809</td>\n",
       "      <td>0.161522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>-0.156011</td>\n",
       "      <td>0.443311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea</th>\n",
       "      <td>0.262132</td>\n",
       "      <td>-0.108953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>-0.460378</td>\n",
       "      <td>0.114683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>-0.318869</td>\n",
       "      <td>-0.093711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sad</th>\n",
       "      <td>0.230670</td>\n",
       "      <td>-0.448101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sup</th>\n",
       "      <td>0.128189</td>\n",
       "      <td>0.078168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1\n",
       "ident emo                    \n",
       "f     ang  0.013398 -0.004736\n",
       "      dis -0.244885  0.387968\n",
       "      fea  0.136745 -0.073895\n",
       "      hap -0.428075  0.029082\n",
       "      ntr -0.000683 -0.270555\n",
       "      sad  0.517737 -0.394046\n",
       "      sup -0.029778  0.179263\n",
       "m     ang  0.349809  0.161522\n",
       "      dis -0.156011  0.443311\n",
       "      fea  0.262132 -0.108953\n",
       "      hap -0.460378  0.114683\n",
       "      ntr -0.318869 -0.093711\n",
       "      sad  0.230670 -0.448101\n",
       "      sup  0.128189  0.078168"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdsDf = makeRsaDf(weightDf)\n",
    "mdsDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Annotated Scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
    "from matplotlib._png import read_png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makeScatter(mdsDf,ax,identDict=identDict,emoDict=emoDict):\n",
    "\n",
    "    # make the scatterplot\n",
    "    for ident,emo in mdsDf.index:\n",
    "        thisDf = mdsDf.ix[ident].ix[emo]\n",
    "        myAx = ax.errorbar(thisDf[0], thisDf[1],\n",
    "                           c=stackColors[emoReverse[emo]],\n",
    "                           marker='ov'[identReverse[ident]],\n",
    "                           markersize=35,\n",
    "                           markeredgewidth=.5,\n",
    "                           #label=ident+' '+emo\n",
    "                           );\n",
    "\n",
    "    sns.despine()   \n",
    "    ax.set_ylim(-0.5,0.4);ax.set_xlim(-0.6,0.8)\n",
    "    #ax.legend(loc='upper right')\n",
    "    ax.set_yticks([]); ax.set_xticks([])\n",
    "    ax.set_ylabel(''); ax.set_xlabel('')\n",
    "    \n",
    "    return myAx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makePicture(mdsDf,ax,myZoom=0.15):\n",
    "    mdsDf.plot(0,1, kind='scatter', s=50,ax=ax)\n",
    "    for label, x, y in zip(mdsDf.index, mdsDf[0], mdsDf[1]):\n",
    "        imagebox = OffsetImage(read_png(picList[identReverse[label[0]] ][emoReverse[label[1]] ]), zoom=myZoom)\n",
    "        ab = AnnotationBbox(imagebox, [x,y],pad=0)\n",
    "        ax.add_artist(ab)\n",
    "    ax.set_ylim(-0.5,0.4);ax.set_xlim(-0.6,0.8)\n",
    "    ax.set_yticks([]); ax.set_xticks([])\n",
    "    ax.set_ylabel(''); ax.set_xlabel('')\n",
    "    return ax;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LC3THXXczevO38sgLP8HDR+vP+XqPXCkZveVvcfbOuz7nawkhhBC3Kh8Uu+Oas0dHdNcq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4RlKVGaW1ONIlHxUUuWY8zljvD5mamqLIcoZBm6kaKgW2yHHDkKQc0GovMhkNiebaOHhYVeBE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\nYRiGYRiGYfwz/x/hvA18ud3pKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f305b87f150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ( ax1, ax2 ) = plt.subplots( 1,2,figsize=(16,8) );\n",
    "makeScatter(mdsDf,ax1);\n",
    "makePicture(mdsDf,ax2,myZoom=0.15);\n",
    "sns.despine()\n",
    "#plt.savefig('../figures/mdsBehavior.png',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RSA with raw pixel values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is done with greyscale images for simplicity..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def makePixel(picList=picList,identDict=identDict,emoDict=emoDict):\n",
    "    bigDf = pd.DataFrame()\n",
    "    for i,ident in enumerate(picList):\n",
    "        for e,express in enumerate(ident):\n",
    "            # open image\n",
    "            thisImg = Image.open(express)\n",
    "            # convert to greyscale\n",
    "            thisBw = thisImg.convert('L')\n",
    "            # convert to array\n",
    "            thisArray = np.array(thisBw)\n",
    "            # convert to 1d list\n",
    "            thisList = list(chain(*thisArray))\n",
    "            # standardize\n",
    "            floatList = map(float,thisList)\n",
    "            thisStd = StandardScaler(with_std=False).fit_transform(floatList)\n",
    "\n",
    "            # convert data frame\n",
    "            thisDf = pd.DataFrame(thisStd).T\n",
    "            idx = pd.MultiIndex.from_tuples( [(identDict[i],emoDict[e])] )\n",
    "            thisDf.index = idx\n",
    "\n",
    "            # append to big one\n",
    "            try:\n",
    "                bigDf = pd.concat([bigDf,thisDf])\n",
    "            except:\n",
    "                bigDf = thisDf\n",
    "                \n",
    "    bigDf = bigDf.sortlevel()\n",
    "    \n",
    "    return bigDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "bigPixelDf = makePixel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
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       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">f</th>\n",
       "      <th>ang</th>\n",
       "      <td>-67.147280</td>\n",
       "      <td>-66.147280</td>\n",
       "      <td>-71.147280</td>\n",
       "      <td>-74.147280</td>\n",
       "      <td>-76.147280</td>\n",
       "      <td>-76.147280</td>\n",
       "      <td>-71.147280</td>\n",
       "      <td>-72.147280</td>\n",
       "      <td>-73.147280</td>\n",
       "      <td>-71.147280</td>\n",
       "      <td>-67.147280</td>\n",
       "      <td>-69.147280</td>\n",
       "      <td>-51.147280</td>\n",
       "      <td>-48.147280</td>\n",
       "      <td>-67.147280</td>\n",
       "      <td>-73.147280</td>\n",
       "      <td>-68.147280</td>\n",
       "      <td>-69.147280</td>\n",
       "      <td>-63.147280</td>\n",
       "      <td>-59.147280</td>\n",
       "      <td>-50.147280</td>\n",
       "      <td>-26.147280</td>\n",
       "      <td>-23.147280</td>\n",
       "      <td>-34.147280</td>\n",
       "      <td>-37.147280</td>\n",
       "      <td>-39.147280</td>\n",
       "      <td>-41.147280</td>\n",
       "      <td>-36.147280</td>\n",
       "      <td>-22.147280</td>\n",
       "      <td>-23.147280</td>\n",
       "      <td>-27.147280</td>\n",
       "      <td>-17.147280</td>\n",
       "      <td>-4.147280</td>\n",
       "      <td>-2.147280</td>\n",
       "      <td>-5.147280</td>\n",
       "      <td>-7.147280</td>\n",
       "      <td>-7.147280</td>\n",
       "      <td>-9.147280</td>\n",
       "      <td>-10.147280</td>\n",
       "      <td>-0.147280</td>\n",
       "      <td>12.852720</td>\n",
       "      <td>15.852720</td>\n",
       "      <td>13.852720</td>\n",
       "      <td>5.852720</td>\n",
       "      <td>-3.147280</td>\n",
       "      <td>1.852720</td>\n",
       "      <td>2.852720</td>\n",
       "      <td>-0.147280</td>\n",
       "      <td>6.852720</td>\n",
       "      <td>10.852720</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>-80.495422</td>\n",
       "      <td>-78.495422</td>\n",
       "      <td>-80.495422</td>\n",
       "      <td>-82.495422</td>\n",
       "      <td>-76.495422</td>\n",
       "      <td>-70.495422</td>\n",
       "      <td>-73.495422</td>\n",
       "      <td>-79.495422</td>\n",
       "      <td>-79.495422</td>\n",
       "      <td>-81.495422</td>\n",
       "      <td>-83.495422</td>\n",
       "      <td>-82.495422</td>\n",
       "      <td>-77.495422</td>\n",
       "      <td>-75.495422</td>\n",
       "      <td>-74.495422</td>\n",
       "      <td>-78.495422</td>\n",
       "      <td>-88.495422</td>\n",
       "      <td>-91.495422</td>\n",
       "      <td>-84.495422</td>\n",
       "      <td>-75.495422</td>\n",
       "      <td>-64.495422</td>\n",
       "      <td>-58.495422</td>\n",
       "      <td>-58.495422</td>\n",
       "      <td>-52.495422</td>\n",
       "      <td>-53.495422</td>\n",
       "      <td>-55.495422</td>\n",
       "      <td>-54.495422</td>\n",
       "      <td>-49.495422</td>\n",
       "      <td>-47.495422</td>\n",
       "      <td>-47.495422</td>\n",
       "      <td>-42.495422</td>\n",
       "      <td>-38.495422</td>\n",
       "      <td>-31.495422</td>\n",
       "      <td>-24.495422</td>\n",
       "      <td>-21.495422</td>\n",
       "      <td>-20.495422</td>\n",
       "      <td>-19.495422</td>\n",
       "      <td>-16.495422</td>\n",
       "      <td>-19.495422</td>\n",
       "      <td>-25.495422</td>\n",
       "      <td>-28.495422</td>\n",
       "      <td>-27.495422</td>\n",
       "      <td>-29.495422</td>\n",
       "      <td>-29.495422</td>\n",
       "      <td>-23.495422</td>\n",
       "      <td>-14.495422</td>\n",
       "      <td>-13.495422</td>\n",
       "      <td>-16.495422</td>\n",
       "      <td>-12.495422</td>\n",
       "      <td>-2.495422</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea</th>\n",
       "      <td>-81.921512</td>\n",
       "      <td>-83.921512</td>\n",
       "      <td>-84.921512</td>\n",
       "      <td>-82.921512</td>\n",
       "      <td>-75.921512</td>\n",
       "      <td>-78.921512</td>\n",
       "      <td>-85.921512</td>\n",
       "      <td>-84.921512</td>\n",
       "      <td>-84.921512</td>\n",
       "      <td>-84.921512</td>\n",
       "      <td>-83.921512</td>\n",
       "      <td>-84.921512</td>\n",
       "      <td>-81.921512</td>\n",
       "      <td>-70.921512</td>\n",
       "      <td>-74.921512</td>\n",
       "      <td>-84.921512</td>\n",
       "      <td>-84.921512</td>\n",
       "      <td>-85.921512</td>\n",
       "      <td>-83.921512</td>\n",
       "      <td>-72.921512</td>\n",
       "      <td>-71.921512</td>\n",
       "      <td>-87.921512</td>\n",
       "      <td>-85.921512</td>\n",
       "      <td>-83.921512</td>\n",
       "      <td>-71.921512</td>\n",
       "      <td>-57.921512</td>\n",
       "      <td>-57.921512</td>\n",
       "      <td>-62.921512</td>\n",
       "      <td>-51.921512</td>\n",
       "      <td>-41.921512</td>\n",
       "      <td>-47.921512</td>\n",
       "      <td>-54.921512</td>\n",
       "      <td>-58.921512</td>\n",
       "      <td>-55.921512</td>\n",
       "      <td>-48.921512</td>\n",
       "      <td>-35.921512</td>\n",
       "      <td>-18.921512</td>\n",
       "      <td>-25.921512</td>\n",
       "      <td>-30.921512</td>\n",
       "      <td>-23.921512</td>\n",
       "      <td>-29.921512</td>\n",
       "      <td>-42.921512</td>\n",
       "      <td>-50.921512</td>\n",
       "      <td>-50.921512</td>\n",
       "      <td>-44.921512</td>\n",
       "      <td>-47.921512</td>\n",
       "      <td>-37.921512</td>\n",
       "      <td>-23.921512</td>\n",
       "      <td>-10.921512</td>\n",
       "      <td>-1.921512</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>-87.162660</td>\n",
       "      <td>-83.162660</td>\n",
       "      <td>-77.162660</td>\n",
       "      <td>-82.162660</td>\n",
       "      <td>-80.162660</td>\n",
       "      <td>-81.162660</td>\n",
       "      <td>-72.162660</td>\n",
       "      <td>-78.162660</td>\n",
       "      <td>-87.162660</td>\n",
       "      <td>-87.162660</td>\n",
       "      <td>-78.162660</td>\n",
       "      <td>-72.162660</td>\n",
       "      <td>-76.162660</td>\n",
       "      <td>-90.162660</td>\n",
       "      <td>-88.162660</td>\n",
       "      <td>-84.162660</td>\n",
       "      <td>-91.162660</td>\n",
       "      <td>-90.162660</td>\n",
       "      <td>-88.162660</td>\n",
       "      <td>-91.162660</td>\n",
       "      <td>-93.162660</td>\n",
       "      <td>-81.162660</td>\n",
       "      <td>-56.162660</td>\n",
       "      <td>-50.162660</td>\n",
       "      <td>-58.162660</td>\n",
       "      <td>-63.162660</td>\n",
       "      <td>-69.162660</td>\n",
       "      <td>-70.162660</td>\n",
       "      <td>-73.162660</td>\n",
       "      <td>-72.162660</td>\n",
       "      <td>-66.162660</td>\n",
       "      <td>-58.162660</td>\n",
       "      <td>-56.162660</td>\n",
       "      <td>-64.162660</td>\n",
       "      <td>-65.162660</td>\n",
       "      <td>-39.162660</td>\n",
       "      <td>-21.162660</td>\n",
       "      <td>-33.162660</td>\n",
       "      <td>-63.162660</td>\n",
       "      <td>-64.162660</td>\n",
       "      <td>-54.162660</td>\n",
       "      <td>-53.162660</td>\n",
       "      <td>-46.162660</td>\n",
       "      <td>-34.162660</td>\n",
       "      <td>-24.162660</td>\n",
       "      <td>-22.162660</td>\n",
       "      <td>-23.162660</td>\n",
       "      <td>-20.162660</td>\n",
       "      <td>-11.162660</td>\n",
       "      <td>-9.162660</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>-81.018184</td>\n",
       "      <td>-82.018184</td>\n",
       "      <td>-81.018184</td>\n",
       "      <td>-79.018184</td>\n",
       "      <td>-85.018184</td>\n",
       "      <td>-88.018184</td>\n",
       "      <td>-79.018184</td>\n",
       "      <td>-77.018184</td>\n",
       "      <td>-83.018184</td>\n",
       "      <td>-82.018184</td>\n",
       "      <td>-78.018184</td>\n",
       "      <td>-79.018184</td>\n",
       "      <td>-77.018184</td>\n",
       "      <td>-78.018184</td>\n",
       "      <td>-92.018184</td>\n",
       "      <td>-102.018184</td>\n",
       "      <td>-95.018184</td>\n",
       "      <td>-82.018184</td>\n",
       "      <td>-72.018184</td>\n",
       "      <td>-68.018184</td>\n",
       "      <td>-64.018184</td>\n",
       "      <td>-64.018184</td>\n",
       "      <td>-69.018184</td>\n",
       "      <td>-75.018184</td>\n",
       "      <td>-72.018184</td>\n",
       "      <td>-63.018184</td>\n",
       "      <td>-54.018184</td>\n",
       "      <td>-49.018184</td>\n",
       "      <td>-44.018184</td>\n",
       "      <td>-38.018184</td>\n",
       "      <td>-38.018184</td>\n",
       "      <td>-45.018184</td>\n",
       "      <td>-58.018184</td>\n",
       "      <td>-66.018184</td>\n",
       "      <td>-63.018184</td>\n",
       "      <td>-51.018184</td>\n",
       "      <td>-35.018184</td>\n",
       "      <td>-25.018184</td>\n",
       "      <td>-29.018184</td>\n",
       "      <td>-36.018184</td>\n",
       "      <td>-30.018184</td>\n",
       "      <td>-20.018184</td>\n",
       "      <td>-13.018184</td>\n",
       "      <td>-7.018184</td>\n",
       "      <td>0.981816</td>\n",
       "      <td>9.981816</td>\n",
       "      <td>17.981816</td>\n",
       "      <td>26.981816</td>\n",
       "      <td>28.981816</td>\n",
       "      <td>28.981816</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 121800 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0          1          2          3          4          5       \\\n",
       "f ang -67.147280 -66.147280 -71.147280 -74.147280 -76.147280 -76.147280   \n",
       "  dis -80.495422 -78.495422 -80.495422 -82.495422 -76.495422 -70.495422   \n",
       "  fea -81.921512 -83.921512 -84.921512 -82.921512 -75.921512 -78.921512   \n",
       "  hap -87.162660 -83.162660 -77.162660 -82.162660 -80.162660 -81.162660   \n",
       "  ntr -81.018184 -82.018184 -81.018184 -79.018184 -85.018184 -88.018184   \n",
       "\n",
       "          6          7          8          9          10         11      \\\n",
       "f ang -71.147280 -72.147280 -73.147280 -71.147280 -67.147280 -69.147280   \n",
       "  dis -73.495422 -79.495422 -79.495422 -81.495422 -83.495422 -82.495422   \n",
       "  fea -85.921512 -84.921512 -84.921512 -84.921512 -83.921512 -84.921512   \n",
       "  hap -72.162660 -78.162660 -87.162660 -87.162660 -78.162660 -72.162660   \n",
       "  ntr -79.018184 -77.018184 -83.018184 -82.018184 -78.018184 -79.018184   \n",
       "\n",
       "          12         13         14          15         16         17      \\\n",
       "f ang -51.147280 -48.147280 -67.147280  -73.147280 -68.147280 -69.147280   \n",
       "  dis -77.495422 -75.495422 -74.495422  -78.495422 -88.495422 -91.495422   \n",
       "  fea -81.921512 -70.921512 -74.921512  -84.921512 -84.921512 -85.921512   \n",
       "  hap -76.162660 -90.162660 -88.162660  -84.162660 -91.162660 -90.162660   \n",
       "  ntr -77.018184 -78.018184 -92.018184 -102.018184 -95.018184 -82.018184   \n",
       "\n",
       "          18         19         20         21         22         23      \\\n",
       "f ang -63.147280 -59.147280 -50.147280 -26.147280 -23.147280 -34.147280   \n",
       "  dis -84.495422 -75.495422 -64.495422 -58.495422 -58.495422 -52.495422   \n",
       "  fea -83.921512 -72.921512 -71.921512 -87.921512 -85.921512 -83.921512   \n",
       "  hap -88.162660 -91.162660 -93.162660 -81.162660 -56.162660 -50.162660   \n",
       "  ntr -72.018184 -68.018184 -64.018184 -64.018184 -69.018184 -75.018184   \n",
       "\n",
       "          24         25         26         27         28         29      \\\n",
       "f ang -37.147280 -39.147280 -41.147280 -36.147280 -22.147280 -23.147280   \n",
       "  dis -53.495422 -55.495422 -54.495422 -49.495422 -47.495422 -47.495422   \n",
       "  fea -71.921512 -57.921512 -57.921512 -62.921512 -51.921512 -41.921512   \n",
       "  hap -58.162660 -63.162660 -69.162660 -70.162660 -73.162660 -72.162660   \n",
       "  ntr -72.018184 -63.018184 -54.018184 -49.018184 -44.018184 -38.018184   \n",
       "\n",
       "          30         31         32         33         34         35      \\\n",
       "f ang -27.147280 -17.147280  -4.147280  -2.147280  -5.147280  -7.147280   \n",
       "  dis -42.495422 -38.495422 -31.495422 -24.495422 -21.495422 -20.495422   \n",
       "  fea -47.921512 -54.921512 -58.921512 -55.921512 -48.921512 -35.921512   \n",
       "  hap -66.162660 -58.162660 -56.162660 -64.162660 -65.162660 -39.162660   \n",
       "  ntr -38.018184 -45.018184 -58.018184 -66.018184 -63.018184 -51.018184   \n",
       "\n",
       "          36         37         38         39         40         41      \\\n",
       "f ang  -7.147280  -9.147280 -10.147280  -0.147280  12.852720  15.852720   \n",
       "  dis -19.495422 -16.495422 -19.495422 -25.495422 -28.495422 -27.495422   \n",
       "  fea -18.921512 -25.921512 -30.921512 -23.921512 -29.921512 -42.921512   \n",
       "  hap -21.162660 -33.162660 -63.162660 -64.162660 -54.162660 -53.162660   \n",
       "  ntr -35.018184 -25.018184 -29.018184 -36.018184 -30.018184 -20.018184   \n",
       "\n",
       "          42         43         44         45         46         47      \\\n",
       "f ang  13.852720   5.852720  -3.147280   1.852720   2.852720  -0.147280   \n",
       "  dis -29.495422 -29.495422 -23.495422 -14.495422 -13.495422 -16.495422   \n",
       "  fea -50.921512 -50.921512 -44.921512 -47.921512 -37.921512 -23.921512   \n",
       "  hap -46.162660 -34.162660 -24.162660 -22.162660 -23.162660 -20.162660   \n",
       "  ntr -13.018184  -7.018184   0.981816   9.981816  17.981816  26.981816   \n",
       "\n",
       "          48         49       ...    121750  121751  121752  121753  121754  \\\n",
       "f ang   6.852720  10.852720   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "  dis -12.495422  -2.495422   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "  fea -10.921512  -1.921512   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "  hap -11.162660  -9.162660   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "  ntr  28.981816  28.981816   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "       121755  121756  121757  121758  121759  121760  121761  121762  121763  \\\n",
       "f ang     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  dis     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  fea     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  hap     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  ntr     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "       121764  121765  121766  121767  121768  121769  121770  121771  121772  \\\n",
       "f ang     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  dis     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  fea     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  hap     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  ntr     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "       121773  121774  121775  121776  121777  121778  121779  121780  121781  \\\n",
       "f ang     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  dis     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  fea     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  hap     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  ntr     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "       121782  121783  121784  121785  121786  121787  121788  121789  121790  \\\n",
       "f ang     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  dis     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  fea     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  hap     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "  ntr     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "       121791  121792  121793  121794  121795  121796  121797  121798  121799  \n",
       "f ang     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "  dis     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "  fea     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "  hap     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "  ntr     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "\n",
       "[5 rows x 121800 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigPixelDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dissImg = 1 - bigPixelDf.T.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bLU/BQ0QkfxsB44Dfld3fBawEHAd4Fi/k7p5FWQoeIiL5WwD4v+RSrkDk9/hW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f305b82c050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(dissImg,figsize=(6, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makeRsaImgDf(weightDf,mds=mds):\n",
    "    # make dissimiarity matrix\n",
    "    dissWeight = 1 - weightDf.T.corr().fillna(0)\n",
    "    # do the MDS\n",
    "    mdsPositions = mds.fit( dissWeight ).embedding_\n",
    "    # put into dataframe, annotate\n",
    "    mdsDf = pd.DataFrame(mdsPositions,index=dissWeight.index)\n",
    "    \n",
    "    return mdsDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "mdsImgDf = makeRsaImgDf(bigPixelDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">f</th>\n",
       "      <th>ang</th>\n",
       "      <td>0.485732</td>\n",
       "      <td>0.280552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>0.444174</td>\n",
       "      <td>0.233374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fea</th>\n",
       "      <td>0.518007</td>\n",
       "      <td>-0.163537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hap</th>\n",
       "      <td>0.415278</td>\n",
       "      <td>-0.207240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ntr</th>\n",
       "      <td>0.438535</td>\n",
       "      <td>0.066314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1\n",
       "f ang  0.485732  0.280552\n",
       "  dis  0.444174  0.233374\n",
       "  fea  0.518007 -0.163537\n",
       "  hap  0.415278 -0.207240\n",
       "  ntr  0.438535  0.066314"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdsImgDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0fUZXL8jw9FmKq1v8ICO3CZSGPtTYgSMa6KIlyS3GJ8TEQFODtRiXEnNLqJYE\n5/AxQrNP3UOSBhrf07sB9A2TpGDZLYk+sHBAzO7dpa8bXr3xKjt7BxinIzNEfisqipToA9FZnE0Y\njEcUeUKSGQbTEV/53q+kDS0v37zDz3/0F/HLJVev3+Lh82fZ6AL7Wcn6dJ3VrTucf+A8B3depXEe\nYy3MG5IQmXceHw1VtSIbDdl74Rn2u4Zru7vc3dnh4umzvP2Ji3R9j0sjq0XNaLzOKwd7hBT8KuDS\nDBN61idjjLM0vsEmBXQpwdQ03rDqW5q+ovMpCx8IQBd7epMSWj2zKCJy3wsh8F3f9kG+qv0J3vO2\nms90JvE3kiWG7/uSmo9cvcxf/tGS9T/89tdkWWqyl3LhwoXXoEL5fFNWFcY5ltWc0q8zGI9YVCXj\n4QTfNdQ+Egc54+BJZhMiKav5ilHR4NNAEjZIvIU0YjxgDLQtpAOgJ6QDbL3CZBm2a2mahtzOWO1e\nZZXnRJtifQPG4PuSHo9JLDPfc315wP7+Ds5ZsmwIZs5iuTrulonIMciLjGCgyAdU3rMzX3L+xAYh\ngPNQu47oGyap4wNf/h7mB0v+6T/7WZ67cpWN8YR3fME7uXmwR07CdFmxORtgneXqq6+y3mX044Ky\nrAjW8NwrV7ixv0/nEl69u8sXv/vdfPn7niRZlcS+A+dY1BldZugWFcZlhL05eWopu0A0lugSurLC\nJAltvUdwDW0dicHRR+gCLOoS3/fs1kuIKZaG4BUWRUTuazFGvuvbvonfF/5P3nHmaHZmfP+5wCS9\nwZ/7MVj7hrd/zjOM6/k61mrDbPmPMIHYR3zMCZ1nsT8ndmdo9hYUmyN8GYn2XtiLO3c5sX6OYVbi\n0xxnLOXdHSabJ8GX+N7iYoRkAH1H9AGbG0gy2nKJS1LytKfpWtzmEFf17DUlrmzIR5t0bSRgoI+Q\nJdQxkCYJXR+YTqY0dcN8qTNDRX4rqmvPiTXHcJLTh8CNOzcJXc9wMiMA0zxjPB3y9JWXeeLxN1FM\nx3z1+7+CvvO8dPVlfvGXn+LChTNMszHL8ZzBKCdLe9bHa1x9+TpJGDMvGz7xyY+zs6hYdDWb29uc\nfPABnnrhJW6WS/7Vv/w3vO1tb+ORCw/iqxKXJzQrT1ktcIMhtq/p24YqTxkMx5Rdw3L3AJsE+nyC\naUvIDf1iSdV31CbQ5ynNakg+sNguEqI2uBERua/93e/7y3xV++NHFhR/zRedivyl7iZ/5aeHrH3N\n47/p6/QY0yN1AAAgAElEQVRNz5npydewMvl8UlYtgzzD9i0RyIqcvYMlmw8N8T6QZ56MhGGaQ2vo\n6YjJGIcjLUbkWUdb7pFlCW68gW9bElMRqhV2uklcHdA3NVmWUtYdfr6DS4cUMdI42Cgylr2hrnZp\nY0KSWVaLOb62jKzh5bIiyR3716/RdD3j8fC4WyYix8A4CwZy67DFiP3Viqt7ezzz0V+hsynPX75K\nuZwznQyZTn6BJx47z8WzD3Dn2kt80Re9my9830l2r17nqUuXWM9y1mYbhC5SL1v6IuGTz3+K2zf3\nmK9K1k5tc3J0jnnX8rM/91Fevr2g9x3jYcHHnv9p3v7OL6BaLXj83CantzeY5QmnN6bUpaNra2zT\n4QkEPNkgYVWuCH2gND1d2xGtpW88PbCsPdYZrLXcurvDzvxoV0/otrGIyBF6/tlnmH/0R3jPmfp1\nGe/95wJfdPMa5fXlb/oa819c8K3f8K2vYVXy+SR1CU1b09uE5WpFljjS2YQa6PuKInFk7t4h0dYY\nYh/o6amdBdvTNxUuL8A6aJY409NXK/yqJSxehb7CuQTT1piqJVk/QZGmrFxKxLCoe7I04EyKN579\n3TlJ4uhaTxcNJkbq+b3jMpquY5DpmUWR34r6ANZZxrMRD58+RZHmLBYlz71ykysvXeGbP/gtnH/k\nIq/uNeytakrvuL6qeNejj2M6S/nqXVa154nHHuel/Vs8/eLT7OzcoWxrcpcyGBRcfNeTvP8/+wq+\n6K1vod+7w+//4AfZPPvYveX1LuWRi2/mO/7snyfdv8G1Ky/wyvUdtkZrXHzoQU5uTJmOUqajASFx\nFG5IVxtKH+htAs4yygaEX/1+Z8H3js44kkFOSOClV65QeX+kfdTMoojIEQkh8Df/m2/mr7/5Kq/V\nM4qfif/+3RW/6yeeIv7J3/aben5x7WCDt731bUdQmXw+8MGTpxlDF5mMx6QYCt/jQsSaCM7RZQUD\n3+H7nmHq8F1CnkXa1pONhpgYIRtBMcKU+7g0vXd4dTqCvr231DUfk3QHRDpKm5A1K0gsk9ziO4PL\nHEkdSG2CCeC7mhDu7bbahkAbeoiB8Bpt+iQi95dBkTIYWjZGA2YDy8OnTrJz/SZPnD/F1omTpPPb\nnD5zipNbM8Yb64TlindurlGu9vA3b/JVX/s1vHTzDi++dIV3v+lxXJqyWFT4uiW6wLkz59gaZpw8\ne56XXniR3/WBr+Xqv/jn/JfvfTv/xVd8Ma/u3KWtG6699DG2LzzKl509w5mT64yzwDgzjIsEP8i4\nsQMmBqq+oYo9vluBTQkhI8YOX9ZUHqpoaHzLKAZWJFSlJykmHJQKi4d86K9+iBduPo8zDucc1jic\nsf/B74lLKPKC4WDIoBgwKAYMB0NGgxHDYsiwGDIajsjznDzPKYriP/q1+9W7oyIin60f/9Ef4uu3\nfulz3vX0s5Ulhu98dM5/+/NXWf+yBz+rn50/t+CPfPk3HlFl8vnAYnFJQpoNOf/wOVoLPhpSElxW\nYPIca1L6NMXZSG1zEttR2YyhTTEuwaxvgUvpb9/Axo7ozb0dBtsOBhMwFlsMcDahPZhjsoTSRKy1\n9HWHiYHhIKfB0zQJZbVisZoTu5YsSdneWGN3eUC/rBk4hUWR34ocBnzE+o4QOk6sT3j3O97O2u2b\nNI1np6t494Xz1O2c06N1zj/8MPXObdbPPkhewP78DpaOU+sTNoo11jfHrL/zUZa3XmFZ7nPp2ZcZ\nPHCOGy+/wNbaBF8vOH9ii4OyZT0f8PDpsxzUC3zbcHfnLn21YntjA5cGcueoup7Y9WTO4NKUPBtx\n4uRJ+tYzHCaUfYNNU+Iwx/iKzljqcO857a5sWa32OX3yAR79+jcdaR/vy7D49ifezr9x/4rpE5NP\n+5oYIqELBB/o2/7e16tAvxf+3Z/HLmJ7g/EWPJjegL/3Dyt67n0f++8CqDUWZx3uV7+2WC6eehPf\n85e+53X824vI/eKXPvxj/945iq+vLzsXmfz8NfgswmLoAoNPDPnG7/jg0RUm973EGYYOHnvkEYr8\nVz9GRFiUFSdSS+xT6qbC94bZ2pimqkgGCXW5Ihuts+wt41VJuXeL0akHKW9dg6plcPYsYf8utu0w\ngwFNVZIZRzIZ0awa+nRKsDm2aDAmo20DaefBGlxiSIohvm1JiZw4tclTn/okRZ6yuXnieBsmIsei\n8t2981+jgWjBd6wXA85uzFi2HbNiQJFZpqOHiMbQVbsMXY91kcJBVTXszQ/YzHOSfIBfNOy/coWm\nL9lbzDm/sUW3uMXp7ZNEn7JKKvb39hiOtuhjQzm/jguWrmvYGBaYccJoNGJoesq2oW8qGh/xBmLT\n0a728V1DsrVJ1UeMgToGuj7S+R66SOIymr4GC4u9BW/+gi9gXlVH2sf7Miz+wd//B/mRb/pheOLT\nv8ZYg8sdLneko6N4XiGy/7E93vvF7z2Ca4vI/e4TH3+Kt5hPHWsNv3O65KdenjM+P/2MXj//yJL/\n5Tt/SLugyn9SlmWMRmNOnFgDDAYwtJTzwF5veeH6LfLBmND1nDOn2W1KRhjObmzQVne5c/1VHj1/\nnvHalO76VcgiXR8pdvZY7i6J7JCtbVOXK1ZZhjeReR2pqgPoe8rakyQVuRmx30XcwFKtHJ0xVKua\nLoIPke31DV6+dYe93Z3jbpmIHIPYG/q2g9DT1itGgxGMUs7mayw7aJuKfJTjrCf0BhyMJ2NOrE+J\ndXfvaJ5VxfiBCT01q8GYvNnBm5RpknFjsUPTLunnFdvbp3ng1EnOntzG93Dl2lXGgwwXLHX0pKOU\narHP1Hja3tOXLVXs6H1PU3nKrsWZSBM8XeUZZ47KOvpoaBtPaw3WBryvCb6jrRt271xnffs07Y3b\nR9rH+zIsWmv5uvf9Pn78pf+DycPjY6khhsj4lQm/+7t+97GMLyJvbP/4h/8G3/HILq/ns4q/3rc8\n0fGP/8Vl+IZ3/IavXT634gNPfICLj118HSqT+5kPgeFsi6buMHkkyzIuPXeZ26/eYn1tws7tPR58\n0yOU85p/9dGPMVyfkWWOaZqRDVL2r97gd/z2wBNvfhzKJTZzLHf3iU3HfHWXNJ9Rzhdcu3OTJMt5\n5co1zpw/w7/96FPYtRnr587R37jBqqlZVS2PnTtJMCld3eCSlBBLQt9z9sKDvHznLm3bHHfLROQY\nuNGYrjd0yxpTpHgfcHVJPsyYJhlxOsOZgszCOM1IXEo0BkKHjx23d+cMspR61VIUY1LfMV3b5M7B\nLm4wZvOEp1sMCE1PrEvKgzlpljAYz3j00SeoF3t0bUs3yvB9y4gJbbB0vsfgaduatu8IbUXdG8aZ\nJXQ1ZVuSDqf4pqUJLd57Vl1L01iizah9z827u7z/938dTV8yXj/aHZ/vy7AI8Ee/4Y/yj77lH8HD\nxzP+wS8v+O8++J2f81lmIvL5qdt7mcHG8f7/MEgNa01N/A1et7pS8vjdN/EXvvdDr0tdcn/bXpux\ntTkjK3KqvsOXDR/92CXObp/hpSvXSAYjPv7xZ2i6jrpuSAYTyrpknAbeduEx1sYJN3cWhGde4tzE\nsTQZ7N5l+tCEu3cPGE4aFqvAJ27e5tYr17n8zAtw4hTNaoVLM/g3v8LmmW1i39OsOl545RU2pzN6\naxhOJ5Stx1jLibU1hoMx+/P9426ZiByDk2tjiD2tDYSmx1hIrCM0HltkDAPMZinRDHFpTlqMie2C\nqqy5cf1VCiKlAYoU41JmRU4IGeNiRjA9ZTpiuF6we3sPEoPteyZrZ4mhh9CSpQ4TMkxvME0NMWHR\nlNgAREMWUua1Z9UFitTx2KMP44oBbpgS+kgwns46+r7Gtx2d6+nKyLxccf3GLb7sxBmWiwWbG7Mj\n7eN9GxbTNOUr3/GVfOT6zzI88/qeoRT7yNqNNb7yy7/ydR1XRO4PIQTM8uZxlwHAhq+5G+Kn3RV1\n+dyKx+++ib/9PX9bN7/kM3KwWDEcjchTi4mO69duMnQpr1x7iSIrMCGwnM+ZbJ4hxJokdqQEGm/4\npU9+iodnE6o6MB7mvPXB02ycPsUDa1vs7O7TGsfdvRU3b+zx4qee5pNXrtFax/Rgn4899wKj6Yz1\n6TovX7/BdDLhsTc/wYtPfYxPVs+TuZS3PPkW9hdLttdnuCLj3AMPsL/cO+6WicgxGA1zVrsrWt+y\nDB256Yl9R5GNSAkM0gE9ltiUOANUkb73tNUeOZYb9T6VzRkbz5rr8aZjMlqDsmG/qtirF6Rtz7yu\nOT3cYjCd0VQLbJJg8oJhMWa5d4d6vocxLX2AxFsaPIlLCKbDBENdNfS9Jy02qPuIx1AnFh8ymmpO\nacBHT1k2lG3F8s51/sif/DZMdPS2o2q1G+qn9Wf+qz/Dz37r/wO/9/Ud9+AX5vwP3/LXXt9BReS+\ncfnyZR7J7xx3GQC8Y9LyU7dKxqdH/8Gfhy5w8JEFX/3EV/MXvvdDCoryGcuHQzrjSBPHsmw4fe4k\nWyfWuHr1Jhcfv0CMLcVgSj6e0HnPrRcv87//vz/HiemI3/bEmyh3drC5pc1z7GzE5jAhmpa26akX\nS65cv83e/oJ8c5MLbWSnLHn1xg3ecvY0V3f2ODMeUReO2XDMzqtXCTZhOl0niz0vv/Ai5y6cw2aW\nLEloQsuwOJ7HVUTkeI0GCXPTsqor1gZTEu/BOXb25mzM1ln6A4oWxgOHs47Gt6RZQoIjyQ2LOxEz\n8Ix9imkq0rRgvvsqHkhJqNtI2QRqG6nKFYNoyDdPk+YD3HBIu7cLoSHPhtQ4Wjzp0IG3LFZzHJFF\nVeO7ni468lFOWa9w+Ra+DdShZFFbltUBTdNS9T0HByVnHnsnLrWslkvMIIfuaJfa39dhcTAY8MWP\nfgm/dPcXGGwNXpcxQx/Y2t3mPV/6ntdlPBG5/9y9e5cT6eK4y/j/2bvzaM2ussD/3733mc95xzvX\nXJUaMlaaQEBmVBpBEVQQHBAQJ0SF9oeANDagiIojjt2o4IyNrS22IwuVFkiIDCFJJakMlUolNd/x\nHc+89/79UdBLux0guVVFqs5nrbvqrlW39vO8e9Va9zz77P1sAHaGmmpcwj8pFkf3jQnvjPiNt/5m\nc0ax8UWzWhPFPtYKuq0IIwSLi/PMzM3gJhFyY8jv/ulfoJXHZDIgMwFP2reHb3/bu7j3w3/G8NgR\n9mzbxmgwJE8zJkXI/O4raYsadVaymubYTpuO47HSbdPLXK5+/j5+42fewajQPPHxbZ76hKdSj0rc\n2GU6mCJdxeFDd6GrGl1Z2kmC8BTK8ZHKu9hT1mg0LoJ2y+fTZzbYuTBPUdVMJyl+4hL7DlbX4DrA\nFM0Mti5xPB+nqPDcEKkyStdja9JBuC7KOFRZjbACURaMywl+URHMJzz0cMo0zfCcgEQ4WAHpqRNI\nKTCpxlqDMJZIa0Y6w1gHg2Sqa7JsgvUkrhZ4bkg6tYwKTVBpUgF5PmWUadJJzSTNuO2223jjT72Y\n9dUBsbLkxlKZ89t1/TFdLAK86fvfxNe9/oWEz78w8UafGPOzr/nRCxOs0Wg8JpVliS/O77aQL1So\nzl0XpAvN6FNjusM+L3/WK3jFG17ZdD1tPCLtKCLB4oY+kaMIWzF6MKGSkrs+8VmYTviOZz2DXJfI\nOGJp504W+nswD97NDXt2Mu2E6BCKIsBWgla7S5z4FKXPeG0ClWHH1jmO33kfe7dvpSg8irXTfPXT\nn8E/3PxRbv7EzWQ25NCn/jdPvu5Grn78tThScP31V1IXNWujEUJJbF6h0yl5ll7sKWs0GhdBNpyi\nfI/haMRsHFC5Dm4NwlWk6ZhQtpHtBOE4OLagnBgqKaAYMM4ypDGgc6rc5faTG8RJB4qKLJ2Q5zln\na4Na9piMxhSdgOTsOvGZVYJWgqg0VV2gi/RzDWoKdA1pXVNbyPKSUVVxajxkeVTiSrg/8hhPp6R1\nhkBTVYqVbMLGaMIkLfnYLZ/mu177Wo7ee5RCG6SU1GXNdLTB4cOH/9lnv+6663DdzbkN4jFfLHa7\nXa6bv44j4/vwWud39dBUhsXJEjfecON5jdNoNB7bPM9jah3g4heMWSVxb/XZPt7F937793Lw2oMX\nO6XGY5wb+AhqrLWEnsusB862eU7c/yBPffy1lMonKsbsnJmj3ZtBqhaL21pMl0do0SLpBnTmt3Hq\nr/8GdIVMK9KVEUWeI6XDqKwpljd44rOfRmgqBoOK0khevHuRA7MtPOXgOyEHn/NVfPbWWzlzn8Ps\nlgW8wMMPQlJhCaViUpSE7TZR3DS4aTQuRxMNk9GEjVbMxmxKoCQWTVFV9KIYrWtMrSnGY7QvCTyP\nMsuYpJoiraiqKWUVcOeJ03z0pg/h6xKtNUK5uHGbpcU+3STBL0s60T66SUwrauH4EmEs0o1ptXbh\niJy6trjKo0gzynKEUYpep08UtpCOg9Q102pM0tkOUlAsn2aYr3Py9GluPXQn01gwd+UsR//o17Ct\nDstrQ1wHitKwa+ss7/rv72PHfJtpXjMuLb/+V/+bG264YVPm8TFfLAK85XU/wkvf9hK8557fYnF0\n05ifeO27zmuMRqPx2Dc7O8u9VQu4+I011m2P3/jp32Tv3r0XO5XGJWKpP4PneEgh8AJLHMcYY9i3\ney+d2TbTsqYyFu3PEc+0iZSH2+/h0KIenSWo4OaPfwLHlgTJIlpYyjIjK3J8PyD0HUZZxYn1inkf\nfD/ECvC9iN0H9nP04eNESYhYT/mar38+VBmDtQGzix2CoENYuIwHY6ZAoBTC/nv9gBuNxqWo10mo\n6pJppTFaU1YVVmh816OuBLkyuLoEU5KoiNoFRymM0ZSOQzEpWI81pa0QeUZeV+jaMMWyM7FEYUSe\nZcwvbaHT7uGHPlpWKOsTtzp4ShEGHqLURI5CCEFnfhbCKyAb4HoJ1vfBGHQ+JVE99HSE9CytpSW8\nUQtFyCRNOb26jN06j6lKxtMxAYYir5mkNYePnuaqK3dyz33H2bnYpRpPNnUeL4licXFxkb3RPs5k\nJ3HC8/ORdKHZWm3jumuuOy/jNxqNS8eePXv43WKOL4Vi8cFillfs2XOx02hcQirHw3cVRgoSrdDG\n0kpcbCBwQ4d+1MYLAxzHw1EByVwPkxaUFGhjOL5xGq0sM60OoacwpSHPhoDF4tJpJYzOrHLqxAPM\n7d6D2+0TJyHe0iJiZpHOtl1oY9kpDDYbYY2hl8Q4QYjbiiitZkQKArTRaEdd7ClrNBoXgdE1nXaH\nvC5Z38hpzTgEQQtrLZnJ8IkwWUUYJggjKcsUW0xwg4AZ4PayYHLqJLWG8bSgn0RYpyKM2iAF1hq2\n79lNuztP7IcMs5yl2R7K9fHdAGtLkIZaaoyAYjqhpVooA44bYNtdrO9jq4rQcdB1hSgKEAqT5xhT\n055ZYJ9ySDqncYMZhpMcbzTEcTJWB2O2zSecWpmwfGKZdivm+NkBM/Hmvjy7ZA6svOUH3sL45ul5\nG3/88Qlv/8HmrGKj0fj3SSmxyeLFTgMAmyw2ZxMbm2r59EMMxxmOUuSOjyMtFJZ+EOCWCscYhNZI\noen4GlFrptMx5WiVrJ5gK40vwWuFiHKMT4lbGZRQCF2TVhqhFFFtKU1NPh0SeiFqMGYubrEwM8t8\nv0/fh1BZZpOAdq9LHAj0dA3pOkg3wlqBAvR5bivfaDS+NMWtkF07Z8inKWfXJozrimlZUVqJtaCt\nJLWWvNZMywxbQ4HEOJZW7HPjgSs5dMunODsc0GsFaGFYnmrAEoYB3XafJJpDTzUaQxz5mNpQDofY\nosA1DqawUEPgRPT62xDJAhKD1RUMVlBZiphOyYoKazQqbiH7s2gnQLo+tq5oxRHdKOaqfVew/8BV\nXPfkL8d3LFWlyUYZgYIqy1B1Qavb5+G1bFPn8ZJ5gtizZw/bzDZ0pTd97Dqr2SV3s3/f/k0fu9Fo\nXJrc3k6y6uJuf8sqi9vbeVFzaFx6Ij8hCDy0FAhHIa1E+gLHcbF2RODUxKbEcT10npMu348cDvAd\niZAe1WTEXBShyxqBxQiB9hR1rdH1BKELcF20sUhHYj0oVo7hAU4gSYII5QqkUYRxhLA1SRTgByF1\nbdDpGKrJufNIAiIvuNhT1mg0LoIzxx5EVVOKqkKFAasrBWmp0bXFYlCBh+O2sFYjfReEAAtUFtdY\nZkKPV778ZSRRTBjGONqSJDGmqpibnWFmfokSgRWWLNc4WoALYatLVWqsHyBcQxBEaAu1rlHCItwI\nNbOIDBOylePU6RDludggofJDRFmgHIVVPhpLXYI2Btf12bNrF1sX5nnis59Pp90hDEMmWiCVoioq\nXJtxxY6FTZ3HS2Ib6ue96dU/zA/89vfTe3pnU8edfGzKr77pxzZ1zEajcWn7hlf9f/zRT/0Fr7jy\n4m1F/cADM7zoza+/aPEbl6a0yhnnBc7KBoVUFE6fXqAoK40VCt91yf0+bUrKssJaxb1njyE9j1PH\nT7Fy5iwCsFnNsOUymlb4kY/MaibacHpjncGkJkochncUXLPvKgZVjj57Bun2CXxLUaeQC7J8itEZ\npVbYImdcW0bplFOTCaONEYP1IZN09P90CvxCbGY3wUajceG1PEWRCaRNMVawNs3oZAGhLwjCRcqq\nxrgZDgqbauqgRKNx6oBMWHKnJEawdabDfcdqjIXltQFPe9qTmZlZoCgLPFcRd2KEUNQOpFWNkTnd\nIMLmBf5MH11mYD9XiGKp6wrGI6oyxWsvIMIQU5TIPMNOJ2jXRacTqjojTXPSEvA9Ak8wOzPDyvIa\nS/0Znve1z+PB+44ws3KWB0+fpSw0p1emzPY29yqNS6pYvP7g9cyN56h0gVCbc8F0Na3YF+xn165d\nmzJeo9G4PFx78Hp+114N3HTRcribq3jldU3308bmUrVFVzWIc3cuijIHGeB5Ckd1CS0kdkxeOLh+\nyN1338f3vOPn6HR71HVNEgRcfeUBdu9YIGm3yPsdlNB0F3yWOjM8tdUnDEHZmCjSZNoSOAF51CIY\njXFCH5uEmPEEoVxsqaGqSF0QboCfDRFGUJqcNIeqMtgiZXT0DiZr66S6Zi2roJxyZlry4P1HOHry\nLJPCcuzo/URJi4PXHuQt73j7pnUTbDQaF949D51hy+IMDy5P2b7DIAOf9fURM+2Iwub4tY/rSqy0\nVAKkrbCVonShQlDlIBC0k5jA9xDG4rsFJitYGQ7ZHieAASyutrheiDASJWE4nbDQncXWBWaSgycI\nogSBwCoHXWXU04y6NnhlhEg6TM+exot8tDy3TdYJuqhyFWElUk3Jq89d+dFp4eqKyN9Kr9vlnrsP\n0+/2eODYSYpiwPJ6sanzeMlsQ/28177idQw/M9q08aYfS/mxN7xj08ZrNBqXj8c/92XccurivJn4\nxEmXJzz32y5K7MalrShz1s+uUNeGqtLk2ZS6cjCpwPMseW4pRIQtc/y6wG+FzPYXWV1bpS5LAsfB\nk4qlpUXiKMGXkLgBkdNB1Bpta2rt48YVKQGucjEIAlMgIh8rBDadoDwfi0TaKchzZ4ZCUyLcCIHG\ncX1arRae71MqBZUm7nbO3TtaV7jKodvyafW6xH5IOlij1z53Ncd9h++82NPcaDQepZPDgjh0KUvD\naDxBJTFruWYtK7BGgrQUtaGoa1xpKI2kEA5ZpSk5dxei73k4jksQhxglCKIYFQQ4UiFx8aMQp1YI\nI6AyiDLHIvGERJcVeVmhHYuxAiNAANXqOp50Cbbtxo9jtONRj0fUpaXUNUU6xrgeeZqia4WoKmzm\nYqWPF0ZEjotvBdpw7s1mN2Jufon+TJv5boAUm1veXXLF4jOf9kxapzvYTWiVXY5Lru5ey5YtWzYh\ns0ajcbn5xm/7Lv5g9fGU9YU9u1jWlvevPYEXv+w7L2jcxuUhGKxSlDXGGKwrMTJkOs1J8zF5USE9\ngx4sE8QRUBBIzWi0hjaSsi6J+rO0OwGu56Jci6ssAkjTEdpRWBccT2MIiYsSx2vjIxC5AV1SOy74\nLZzaIGMf05lFBAlOVaONQdQTbCXQWjPOCkaTIUIpbJVBNkHYGrcq8QKXxE/YNrdAEDsEvkd/dp7R\nZMg43dwGEY1G48IbbIxxlMNiP+Dk2VVkntKKAoYbOXmVU9caR0mEcknLknFRUMiSialYmY4J/ACt\nK4osI4oSJlmN73hobUnaXVCQpymVLYlCge8IalMjjQInYmOcIoULKqSuBdYNGI6HFI5HPslw6px0\nNEWXBUU6JeiF+EGCsFDlOXWeYTCUGNKqwpUZkfJQ2sP3XVwrWV5bphe3MDrHGksvCQn8zd04eskV\niwDf+eLvZHTn+FGPk34s50df33RAbTQaj4yUkte+8328++4dFzTuu+/ewet+4n1NF9TGefH9P/hq\nukszlLVhnFeMtUabGqkl2ShjMi4o/QCRZljtIuua9fEUawyh79MJfUQY4lMSBT7toI3XSejPzOFK\nh7S0sDaESYYOBVpPqV0fENggQlbnugaWjoupNFYLbJ3iFlOkdLGVT1XnlMZFCoNvLHU5RqiAEkkU\nhrTiBM9CTQlCkCQJc9u2M81yut0Zzm0tazQaj2Wu53Dk2AkoClY31hmkFdr1WB9PWZum5FKTZ9W5\no4R4GBfyNKcoU8hKjt3/IH//wb/iU3/7MRQWjaISPhqB6zlQGQI/QAmXSrs4CsKkS1nltPwAV0nK\nvEQAndk+QiiwgulgQFVXTNcHSN9DRTHh7CwySBBJm2pSYHVNWWgcqcimGRKJQwzFuUY5tfGpPcOu\nHXvIUFRK0mlFTLOSJNzcpl6X5JPEC7/mhYRHo0c1Rj4ouH7uPzA/P79JWTUajcvRvgNX0n7SK7np\nVHhB4t10KqT9Zd/O3v0HLki8xuVn5959JJ6Lq85dTWHqnPFkzCjNMbrCTSJCY9BJlygIMNbFUQLP\nc5BCsbyyhq1qhHVASJLIRxiBNTVOXWGtZKg0WTUF38VWhlJAWZeQFshOjPQdRByAG6BClyxMwAkQ\neS/SYRAAACAASURBVE0dWTInoCwq8qyiDlyU5yJciev65LrCTXw8p0VQ1bhC4koHXVZ0uj0Wdmxn\nfvZL4+qbRqPxyFW1YG2QctXOGTxXcXZlmXIyxiDIq4oyLzC2RucW40NeCkrhsbE64ZMf/hj33n6I\nQWYZKo9UG4xUWGFp9WOsUCjPQdSSugRFRV5ofF3huT5ZOiaKE4oiA2nQWY6oCiQ5tc0wyiFY2orf\nn6McjxmeWcFKhZ1OKK1lOJogWyErGyPCwCVut/FshZQWrSrquqDf6lJqi2olRJ0+va1X0J1dQOrN\nbXBzSRaLQghe8uyXML5/8ojHKD5e8vYfevvmJdVoNC5b3/NDb+fD/ou5bXlzL8r9v3122ePDwTfy\nPa9/23mN07jMmRysoaw0utbYGrRVVEqhi4p8fYyyBjGZUFuN0AVZqREYvDCinUTYPCNM+vT8gMw6\ndDyBiwY/RqYDhHLR2qEcaBwHnDJFkkHgYYYjbGEwawNkVaDzHLfKcII2WV1R6JzA8fEDi/AAK0Em\nCAPCQmdhGwKLViWRH5LZmv5Mh+5MByRsrG1Q1M2bxUbjsS6IQobTEt+XiFqzMVxFCIXjCtbWJkzy\nmtIqSqtJcw3SpSgKjv7jZxHG8tDGgJ0veDE7n/nlIB3WJ1Oifh8rXLCSWhuGdYYbKZQf4Xkhxg3x\nfYdiOECYmqIuGZ45Q6ENFnCNj1Iu+XDE6MRxVg/dyenjR9F6hMjGZOMRSoJGUYynhEYTKge/KjHK\nJ3ADdOVgdMW09KiNS689R9juYxwX6UeM681p8vl5l2SxCPDyb34F6q5H1lgiW8u5cfsT6ff7m5xV\no9G4HAkheOsv/BYfVC/lplPn5863m06F/JnzTbz159+HEOd+URhjOHLkCLfccgsf/ehHueWWWzhy\n5AjGNA/CjUcuq/Tn/g8JsrqiNoY6GzEcrTEtCqwE64QoJahcyPKcKPCIkpjEj8gmI9wwJJ2OcVwH\nk2dUpUb4Pm4xQSoLSuL5AcqMKOqSqtIoN0BX5+5ltNMUXBerQCkHKyOMrXF9h7AOcLwaXQhMoSmK\njLwuMbbAczRetkYniEj8kMpahIHpdMpofcLqyROsnznJxvryxZ7mRqPxKJ06u8LJjQrfj4njEMft\nkJcZ1lqmacHZdEShM4SUaKspRhOmkwlB6BHPzfHCZ30l3VMPEI6WUVLiKYmuLRhDXRdMTEU2LSnT\nHKMLvMjDlRojQ/wkRrg+vXYfKx0UlmwwJastpanBV1gJCEFpK+xaxfryOrUSGMfHSknY7zE2Gr83\ng+N6uEgKR0FZUlQWkQ7xlMZWGp8A6UVI5RJH/qbO4yV1dcY/pZTiq5/01fzlw/+LeEf8Rf3b8qaK\n//KL/+U8ZdZoNC5HUkre9u7f5j0/t5uP3/I+fvDq43jOo1/9K2vLL9y9nc6XvYq3vf5t3HHoTn71\nfX/A6fUJa5OSzJ+hchKMdJCmxq0nhMUaM4nHUj/h+171rVx/8LpN+ISNy4VyXHQFJpTYogRfY4VA\nxT6O9NFSMUgNkdmgLfok/YQk8fCdgFE2pjM3R1UUZNk62oTnCkbXochTjB/gOBJ3mqN9F9ohTqFR\nfo0tI6QeI1oBIo4gG1NsKFQ3QngSkRlUGDAdTXBriSorVJHhWktdVhhrUZ6PFCVFMaUsBelghaqy\nlGlB5FmwhlprirK82NPcaDQeJSEEG6MBDzzkc9U117H9moPc9vGPEQSQlYZ0mjHtV7SFRdoap9NC\nTkYsbNnGXBAjpAILw1HJxPHJDMSmJis0WjisnhnS8x3GnR7H7nuA+V4Px3FohRFog5NPmZlboPQ9\n8iwl6HTQkxGgQAtMVpLVJYEbIlXAqbOr3POZT7Nlbp6ti4uM8xDXC1hbH9D2XNKyosoy3MgjHdd4\nvkuajUC4oAx1NcVPOjib3FPvki0WAV7zHa/hL77nz+GL6C2RLWc8bd/Tabfb5y+xRqNxWRJC8Oof\nejv33/tNvP4tr+JbZz/Dl2155GcLPnHS5f1rT+AHfvw3+egtn+F53/zdnBWzuLufguycW1n0P/f1\nTxlgBThbFbzyJ9/PImu86LnP4FXf9i1NU5zGv+srX/b9vPe//zmetSjfxWqNE7iAJcXSSSdILMmW\nGUStGZwd4suQuigIIx9tNMPhiLy2VEWNCCTGSowuyUqInICprVFKo6sRbtQFz6PWGuUqRJZhtYIw\nwXVcRJaDrbGJR6olcS/GjmtkN8aMawpdYqcTZCWYFhXWsaSFoe1B3J/DpKdJuh1OnV5nbusO1g4f\npqo398xPo9G48NqtDtO0ZKg6bJmeYZpdgRe1kI7Bdz3KomY8Somkh+/7yKogCmNWRg9TVBp/toNX\nSjzpUpia0PXwhUepBaPxhFYSEgUhg3RK2GqTWY/bP3U7JTA/32OmN8fs+ojFuQWivo8ejpFegHbV\nua6p2kEag0VQCcHf3HwLs7u28Qd/cxP7dvS45sqrWVlfw+qa7VvnWV5Lme0mZMWUuLfIZDxCehGD\n0YC8NCAjhJhg7ebuHrqknwp83+fpVz+d9OwX3gK7+oTmza9783nMqtFoXO72HbiSX/yjj/PwU36Z\nNxx+Kr99T4+s+sKWArPK8tv39HnjPU/l+FN/me99x3v5vrf8JL9y8zqDAy/G3/8spPuFbUGRro+/\n/1ls7H8Rv3LTOl/1ja/k3vvufzQfrXEZEELwC7/xW+RVTaUN1nMZOoraWqRTUWLA8dGFpUaRlyM8\nP8AKQ1mWZNMpda1BC7TV6LykNCmDXFBZSZrnuL4PsqKqNFVZwWiCWxiEsueeXKxFSIVIU6hyQCCK\nIR4aMa0ph8cpioKiAl3k+EIiFHiuoCoMXuAySg11miOVw2AwxIscRqurdNot4qRzsae50Wg8Sn7g\ns23nFfhJB10U+Eqxc/cWhNfCUZK0qBjkOaOiYlpMqdHkLqyVFdZ1cEoo64pKV9haMxinOI5AGw1I\nlpbmmZ+fZ9e2Obbs2MaePbt5zvO/iv/4vOcwGk655ZN38NlD93DXqRN89q67Obl8hvX1DWxh0ebc\nLgblKKyBylR059rM9uZ4+lc+gxKHf7zrPtanOZnRlNZh146tOG5ArzPDqRMn8cKQaVoilENRTAiE\nxRjN7OzmHqO7pN8sAvzQa97AP7z2H+Br//2fTU+lfPm1X0Ecf3HbVhuNRuOLJaXkJS//bl7y8u/m\nzjtu52fe9/NUGw/B+AxXBKvMu2N8UVNYh+WqxdFiFpss4vZ28qI3v55XXHsd7/zZX+JnfuRXEFe9\nhEA9sjPanxcsHWA4v4eXvfndvOBJ+/iRN7zu/5x9bDT+bwtLW7BSoZTCOAIpPVTooY2DDkOUqBmY\ninBaMh3mRJ7DcFigPI+iLOn1euRFwdokp+d5qMAggxDPFXSjBIGLVxuibhsjHbAu1pSYYYFqJ9is\nwNYGlEEEASpN0bJLgEDrHH92CyJNqSUoP6JKc4SsKUqLIyVploOqMYGDGhusMWycWaG25lxXVqUu\n9hQ3Go1HaX7bFQxGKUpU2MUDyPEqO/dey92jTxPhsToqmY4Kyrgi9gOKMqOocvZcfZD0/sNEQmKA\nskhZzQYo1wGpsMaQ1TXT3BCFBke5SBRFvk6RCybTCVdfeQ379lfc+tk7qB98iC39GYyrmKumJEFM\nVVqcoqJ2LKMqZzRM8YIWKydOEM/PcPDaa3GEQjqC0HNwFHhhTKvXQliH7twc47ogK0eMNjKEkCgX\nvDAg8je3jrnki8UkSbhh2w3cOThE0P23V9v1J+GN73njBcqs0Wg0zrn24PVc++7fAc41pTl69Chr\na2sURUHi++yfmeHle/b8ny2ixhi++3Vv5DP5Vrxrv4CVsC+QVC5c+wL+9IHDHH/dG/lv735Xsy21\n8S8SQiBsDdZS5TWOk2H7MUgNRpPqMX5pGKVjam1ZG2zgKIdcVyS64MzDx9k+E+N7c4z0hKTuE7UV\nWIPVgqKeMu52sJWlrRSVGKFKjdUOtl8hlAHjY6oc6gJtFFIYrNZQZdha4wQBk+E6sTdHlfhUNsdO\nckpTYPKKOsswtSVPCzpRgOe62HKIEC7dzqO7fqvRaFx83W6C63cw2TpaSk4fu5urn/EcDnADg4fv\nwQ8DVkYpWVkjpjWJbymtw9F77+DOu+/mBTc+mWc+99nc+/7/QaffI1mbAjValGjrU6Rjxg5MUguq\noMhLEIqsSllfHTCajunPtpDS4czGGNeXdMJFplkJwiKkZCo12pesjVeotSacmWM4HfGPn7yDe+85\nxPYde9lzzRX0ki5btglcByIZo5yMdFIBmqwcYRFY10E5gLO5ndcv+WIR4D+/7i189ddez1I/xyAw\nUqARGCHQQmAQFKlmSWzn3T/6eoIwwg8j/CAiiBL8KMYPEvwoIYhifN/H932CIPgXv1fNimSj0XiE\npJTs3buXvXv3/ot/b63l1f/pTdxaX4G3tPu85OAtXsVnVgJe/YM/zHve/a7mDWPjX+QIhdYaADdu\nobMCb26Wwkp8J2GysgoBTK1hNM1IQg9XeVgrkFKRqYDKVEQqIIgVbeVgTUmZpfi+j+O6FFZgpcRV\nPagmuLOz6FpgwxLIEF6IqFLwQsy0Rva7aBmBKciwhJ0lECWTMxsEcUAURwTGpTaGsbWUk+JzZ4bA\nb7XwhyMmo5QganYYNRqPddlgiN/rQ9ynmEzYdeAGPvmhP+Z53/Id/Mnth9g932GS5QzKEuVIdGkJ\nfEW4fYFdp85y/9oazp/+OZWuWT+zTBKGn+s2muBIh41pgVUBwin4zJGTnD27wdX7djEXBXRbc2xf\n2M6tn/0Uc9sW2b7Uocg1J1ZXmI/bON02jlJUGtaGE7JJSWd2lnQ8ZbCyzI2PO8iuK3axmmlEq88f\n/OWHSPOC5z7zSVyzdz8t15JVFcN0guP5WGHRwwxXCqJwc+uQy6JYnJmZYXFhH6/pfYzn7PrXD30a\ns0yhP0ORQT6GQkNRQ17/k++1ZGRdCutSGIfcOBRGURhFrhWFkRgUyHOvqv/Zn0JhpUO88wm88R3v\nvoAz0Gg0LhXv/Nlf4tP51vNWKH6eO7ebT5/J+Ymf/SXe8obXnddYjcembrfLdDxmGtSEpkIUmjIt\nUC0QekDmSOqipJDQ6/RI0yGBkgRhxPyWBbA1nnRIXI9IhSADhPFw2iFBy6XWEuoMHUWoxIdWB1OU\nyFaEtTEmG6OrGjeZQ2caui6iBilydKuHGQ3ZGKzR6s1goj7apvjWIzclgRMzkTVSlLhRCzMcsrR9\ngaNHH6TdmaE3M3uxp7fRaDxapsRa2LF9F0W6hg1DxOAkN/3V/+JF3/JSPvVXf87OrfMcP3GWyvWI\nIxetBY4wTFoh3Y1VVqoOx5bPYGYUZaVxlMICnTii1JJ7TpxibXmD1azk7nvvYbS2Bqag2+rw7Cc/\njquvOkiRD7BWEkUeRWGZ1BULjsRBoIuSSZ4RdxeodUkQxlxzxZWMs4rljTH3rgyZWT6BdBwqW3Lz\nbfdw6z3HePrBPcx22oSeRzuOmZYTdOwiUwcjmmLxEfnlX/wdvu/F1/CcXdN/9WekFIQSwn/z6I8F\nys99ffH+4sGE4MlveET/ttFoXN7uufc+/uyW+zd16+m/xVu8ig/e8ue8+L77ObB/3wWJ2Xjs+Kn/\n9l5e9/KX4kqJAKooxEqNEg7DSYF1PISRaGuoqxLpCFCS/fuuAAumrDFVSdDvUQuXs6fPkpmK1kyX\nVh5ibI1vHFy3QmwMmYvb2MAnO3saFwnCRbYjJutn0dMhvj/D2BRkkwJHDhisr3JiMkGsr2Os5NSZ\n43hSIJFsmV9gvaqwjgd6zKTQdEKPPbv3cNvhe8jz8cWe3kaj8ShNSkGrLHC8kpn+Vh685xBXXflE\nlh+6m0OfvJmp60NRMtOLyKuMslJ4SiMQ9Hft4J5P3cU1csC2uQXGHcvZyTJSGPqhx1y/x/H1IYfv\nf4iVjTWu3neAN771J/nDP/xD7rrrbgIvJYtm+KqrFjiwZRul6zEYTfG8AEfVeK5Hp91nYmtsViN8\nhV+HXHPFEqcfvJ+Ha8NTX/hSHv7AB3jS0x7P7b/5h+zce4CveeYzed/7/iu33K75xv/4JHxHESc+\nTuFQVGsIR+K4zTbUR2THjh2U/av45OlP88Sli5ODMZa/mx7k55/3gouTQKPReMwyxvAD//nHEVe9\n5ILGFVc9l+9/84/zof/xW835xcY/4zgOtTYIz0Ebg6MkQiiUqimp0ZVmWGqECvADFz0tScKIOImZ\n5lN2tbaxvDpiPMo5eeYU43FGZ75Lebtm5/atZMowHyTohx9k/9YdZHlNHEmMNIStPmK0gesLTDZl\nfWWd3qJHWmQ8dHqde+59gE6rx30njqHCDuV4RJYXVBJ0ZZnxj4EU9BcWKeW5tw/GavZes5tjDx9n\nVDT3LDYaj3W+KwjLEXPtLsbxCOOEUVXjt+fASA5ec5CbP/J37Nw2w+nlDRIhiTyH+SDA93PM9fu4\n87bDeKsPoTOf2iqiJCJJEvrdkFrDwSsW8eKraQch2XCMqUu01hjrEHdibvyar+Po//wAT3nOczkz\nTXno5AmkAb/Tw+/OEJqKViYIk5AtW7ax1Ilwlh9m2zXXM2oHeEHAr/7ae9l/zXUsLG6DwOU/fdsr\n+chH/hY9TVnYs41e0uLMxhCLQFpBrTf3osXLplgE+Jmf+U1+7LufzB8vfeFXaWymP32wwze8+q3N\n+Z9Go/FFe9/vvZ+VmSc86q6nXyypXFb6N/Bbv/+HfMfLv/WCxm586SvqihqoqpJhGhOHCmMkutRo\nQOQlZZkhrCXwPGb7s5RVQbvTZlzl3H/4COlwhBdEeBJOrY1YXlvn0N33oq2gLAva7RZf/tQbaHfb\n7N66RNdVVJXmHz/xSQ5snaOzcycPnTrF+mDCsTPr3HHsCJ+97UGmRU0QhYwnKf1WzOziLFVRMjvT\n5eT6hIePnySrbuPGJz6OuizJXEEr02zfsZXbDx+52FPbaDQeJSEVtVRUZY5OM2bnt7F68gTb913B\n8MRDnDzrcv1Tn87Juz7D4sIMyysDZltb6ChJz0votXzsaMA99x1nqx+QonCUwnNAClian6W0mn/8\n7BH2X/849MIMB298An4cYy1817e8FH34EEv9hDBQ7J7bzvEzA2I1JQw8nNlZvNE6UccH6+J22yjX\nwYRtBvffSx71+KZveD5FVbNnz15uuO5q1LG7+ftP38o1j7+SLVu20u90sI6i41tWHIsrFG68uXfF\nX1bF4sHrDrLu7eLw6t1cNXthCzZtLDeV/4Gf/4rnXNC4jUbj0vAnf/NRgv0vuiixgy1X8sd//SdN\nsdj4f3zVC76B22/6CMQxVuRIPMrKw5YS7aYUxZTRypDaGOLAoxKCuJMwGY6ZnjrL3Yfvpd9q0ZMO\nqdFUxpKWluXxiLKYMhv6lLXhg3/5Efb2+0yf+TievH8Phw8f59nPfDrRwhynjz3MQn+e5XLIep7y\nmdseYjiZMh4MSdotagtOdwfHHjyBHwRk4ylx3MKN2qgi4/Zb7+BJT3o8mdH4sYd0A5xmTbfReEyq\nqopDhw5x+PBhxlmGMZK1QUpRZ0RhwspkyNrtn2bXzgPkkwk333Y7tePSXh+TlTW3jo+ypRMTOArP\n8dm9dy+Z8AirASfGOdJqlOshaoMX1vSCmC0zCR//9K20jtzD/sVFnvGsx9PvdHBPHQcV0H/C09go\nBUfvvBdpUzrdENeLSc+cwkUT2ZLCd1k/9hCpKcHzkc4UM1yH9bN8/bW7ELrivn/4EHPdLvt2z3Pt\nFXsJHIupM1wUta4BB9fzsEWxqXN6WRWLAG9/53/lnT/8XH7/OfkFjfuBI32+6QfecUFjNhqNS8Pt\ndxziDDP825f/nF9nbZ87Dt3JweuuvYhZNL7UfPMrXsUnP/IhOu0YaT2MAaTAjWC4YtCDih1+TDtJ\nUNKl14qwFlzpQq/Nnp27ydMxhdbkRUHgO/QCSeIHrA8rHl4fsGNWMBOGzOzbw6n7T3HvOOeJT7yB\n0DpMTh8HXXPnkeMMBiPOrqzzim/7Rj578yc5cuoUZ9aGfOWNj2N2YY78im3MzC3gO5KHz54iSUJW\nl9c5cXKVY8ceZveeXaSjMVEUkLQ391LrRqNxYRw6dIhfevlzqKoa33rMdkOq29fZsXU7++eu4uu/\n4suYDMd4XsB8fyfm6gMESYwth0RRD2lBeQIpJCpKMGXNbQ+f4cN/9z8pT2xgjcarDWVVY90CX2n2\n7VlgsR1ROT7z3R7WCNLphMCWyNBjtKZI+rMom+N7ktAJEMUU4UfYGkI3oqhqpGuhM4ePJMsrHD3C\nZAULvTkqrbk22oGpBjiL24jcglbkY43HZLRGVhpcx6WoCqpqc2ucy65YfMbTnsGP6CVODI+yrXNh\nlg5rbbmVx/EtT3n6BYnXaDQuLb/6vj/A3f2Ui5qDs+cp/Mp7f59ff/dPXdQ8Gl96xnlJz1hUMUYb\nF6xBSUMQapbPjhieXMWRilJXGOESSqgxBFHAldfsw4tDOn7ANMu55ZZbue3BBximJUnS4Qn79vLW\n9/w6f/K2t7K1E/HAYJ3T6RS0QScBbjQLx4+xc64NlSa1mgc+fRutVsw73vNefuz7XsN7PvghHAuL\nvRjP9TiwcyvPeuqNWOFg+pooTnA8F3QNwiHwcpT81zunNxqNL21SKVwhWOp0WFpYYnG2T29uEel6\nTLIBYRITBz7WKISrMdYShS0qkxNHPYTRyDjCIhDWIusppQYviCkLzSTLaMUhXu3RSgKcKARiXJHj\nhgZRajQeJukS92bwQ49suMJ0MiFyJa5UGCExpx6EOKFKp+i8xNvSIbQ1SbdDmXXxHB/bm0W7glGR\n4lNRhW22LGzBlTVWO0zzFbRWVGVBWRZUVYXTdEN99F77pnfxtp/+Vt773OqCxPu9+2d52et/8oLE\najQal57T6xNk52K+VwTp+pxen1zUHBpfmt7xc7/ML/38O6iModZgpUUFEfVghJAuExeQHmUxpr80\ng+v4hG3B1sUFBoMxvjb8zu99gD1BxM5dO9nyuMczt2WWrUvzXPG4Gzn74b/kmqt30O3PEkQB040h\no2wd74zAOMG57WJxzCA9ydZ+j/2Pu4ZpYTn1px/klV/3Ap5z45O57567mJ2bYf30WWwU87d/fzMH\nDuxmy84tbF8KGA5SUuOD0UhrsJv8sNVoNC6cOA45cnbMol+x0O/jhTHdOMF3PRzpoEuLcQTSLZHW\nx1YZVQVh0kVYDTgYDSoIQCqsd65I3L5jG3k5ISszdG1QCgLPx7OazpYugyzDcXxky8eVkjjuIAVE\ntqZ0LXU6RPTnqcsam1VEu66E5RUKYTBSIYoJcRziGk3YikhdB+l6CNclHK5i4x6OB4kjqbVHVmzg\nCZ+Tw2VKBON0RFnWKDZ3seuyLBZf9MIX8f5feSdr6W3MROf37WJZWw67T+DbH3/jeY3TaDQuTcYY\n1iZfGp0Z1yYlxpimK2rjn1nauo1sZYOF+T5pXrLdd5htJWyM1tGmhsjBDzxq7dFyJFIqYi+gGzqY\nMQxPn+Frv/IZ7N+2A0XNlj37iWf7zC9uYXjyIaqFWTqJZZJJ7n3gBNfuniN3BIU0qNIwHazhmJpO\nO+LscMTcqEXQ6TO3q0sQzbJ9yzzXXb2Tk6ePUWxfIs3GXHf1HvIsJawrZlpd5roz3PXgcYrKYGoD\nYnO7CTYajQtnsDEiyyrmD8yirT63yFTWtCKF44SgJNqNwLdYDODjBy5CClAOwvXAGvASKCcgBXE7\nosokdaZwlHuu4KstZTrFjyP6vkPszlIID201rW4flY/xlaUylslgg1qG6GlGPNNFpDn52jLT6RSd\nWaQvWF9fpxUFSK1ZXFjAWst4bR3pOOiZWap0jOe6oHOKOsVThqxM6URtTk9HSO1gTEFo602dz8vy\nN74Qgl/7/b/mvUd2nPdYv3XfPK98w0+f9ziNRuPSdPToUTJ/5mKnAUDu9Tl69OjFTqPxJeTI/ffz\nvKc9mdtuO8S01mR1hVUeVV3RdlxarQ6VNqyuLpPlFZM0paozZrodwsBh69IMV129n2uuvhJ/PmZx\n926CQBKFMc5wDI5DmeYY1ePsNGf7vM/2K3ZhfRdbQj7doCpyRKFZmO8hyorVYU0sI5KkhWwpWnPz\ntJYWOXDwBvZfcyUHrrmKpaVFtm1bYLYbMxfECAntMEI5Ai8J8dTm3lPWaDQunCSOkBiCKKK0EDoB\nUkgEFqMLEt9FF+tUpcYKF4WlFmCFoK4k1XSKUC5mvIZFAYooOFdAaof/n707j5Itqwt8/917nzmG\njBzvPN+6VbdmqiiKQRqkUREElaFBARUZnJ9og91PbfvZDaJ22+3QS3HgoY362vE92m59aiuDTTEW\nVdRAzVl3zHtziMyYzrin/iPxPVEQKLLqVlHxWStWxsqbcc7v7HNXRPz28NuYukQJjxQaiSADdFEy\n045Y6ETs6WV0pCZrzyCDlLoYUFeg6xwpAC/wSYBwAlNXOGehNHitaSYFUW8WXUxQacbMriVanQ6x\nFMzOzhFTE6qQzDpsWWNUgAolTWNpmobah3T37dvR9nxSJosAu3fvZrLr6UzqR6/3sNSeh1tP48qr\nr3nUzjE1NfWVbWNjAx20L3UYAOiwQ7/fv9RhTD1OeO9575/8CYtHj3D63BnGm0OqvGRjc4PcOnq7\n95CmijDOqJuSWEEaKsIgI008ofZEwjM702HPrjYHZ+cxoxW6WUyiN7BBQT3eYmImrKyu0A7GXH/N\nSZwrCKzDUyJlhq0NIvToquSyY3uYjDfYKhvqso+bTJAOemFKEHfIAk0aRMx1FEEQoOKU3BsCXxJF\nDiUlttYE03KoU1NPWEkYMd/rooQim+liI0mchGhniBDgLaEXSCXAAV4gvNven9A3COGxTmK1B1fj\nhCIvDQuzHbaGY8rakpcFTivwjto6kriFryyx9yRBQCAlkXKoMCCRGUbXjMdjgijAC7C1xgtP9xS/\nlwAAIABJREFUIgUOw8RYVJwirCZwhrg3jwxCBALhLK3ZHs55Ahmhy4KqLhEOpPOYosFWJVVVYytD\nq5XuaHs+aZNFgNf983fw7gf2PGrHf9f9e3jDv/y5R+34U1NTX/mapsHJx8eKAScU9Q6X5J56YvLe\n829/9m2cvbjK/suOEUQxt952N4OyYjIuGU0q1jY2mVucY3Z3Rpp12XXwGJOixDuLMJYKhzGWTjum\nZQ2RcezeexQpPOWk5uF7HyL0ASvnL1BP+njh8NKgc42rDZW21HZAMpMgkSAkUZAyu7ib82fuYXll\nFVENqUZrGDshqSfEhPR6Mc4nxMLTm1mgGdY441AEeKCdJTRFcambeGpq6hGaaEun06K7uECoYkyu\nsc7hjSCQIIwjCEKED3CuxOkCpy1KhQgZ4AloyhyHQLsEOxiQ1wVZFlCVDSKMiKIQrQtsYxAqxTZj\nXFPQmAZvLUGSEQCJElibo5uaWAXMJgGRCFCtBG8FVgm8tYwm69RViTENemsT1e6CCqAuEM7QDAco\nAcVgiDMWoy1aBGxMtqitxsiA2gORpJu1drQ9Hx/fQC6RI0ePcr59I7X5b8Q73Is4qT0Xes/g+GUn\ndvS4U1NTTy5RFCHdzq4/eKSkt8TxpS20M3Xp/cuf+FdYrRFKIBQsLM5xzQ1PZfX0WW77xB009RUI\nETLbjSjLkl7SopyMmAw3aO07StqJIe0SRZrMZ4zHBeFMRtYKiOIY7wxSwq49C+SDAYeP7KIeNwxU\nzep6gfSWhVChG43wMSafUAhLOS7ZDA3Sam6+/AibWyOCLKOdzRIEITlDaEJsY2lHkKQtaj1BKMF4\npMnrnKquKa1mpte71M08NTX1CAmp0CIGuT0qFkSKQIaEscI1DSIMQYGjIZKSQEaoKMB/prhVGEeg\nLUGWIQKJyiIiBLapCWNJbQwIS9mUJHFIPdmCNERJgXAe5xxhneMtmCQlcAGbg4K5mQ5hHIB1EAhk\nKEjCCGs1ReXJZUnUSwjSEL3yMEFvCbG4F792HuUtsiyI212K0QiEwjpBFLUpyiFFZTDO4Y0hzZId\nbc8n9cgiwGve/G/5nQcXdvy4v3b/Pt70v09HFaempr48CwsLhObxUYU01GPm5x8f6yenLh2VbFcK\nnRRjokghVcjR659CO03YHI247dY7uevh06wPC4wKGGtI221sXWGsRqJoJkOGoxIlAoRQCB+C9zT5\nGO8EWadHIDwL3ZSFJMZIzXh9xMWz68ixxo8teqixVYl3jqh2hFIwWNtgOCloZ22O7j9MYiwissQd\n6IqEuJcRZwFIiMIEK8EjkUKjKo2tGwSC0aS8xK08NTX1SLXaXRyO8bgiSTOEjPECBAEui0HFqCDE\nGYOpHVoJqlKjRxO8EGiZESzuwZb19nTRIKWVxoBgNBhgy4ZaNzjtqEtDZT3Sg5SQBgJh9fb2Ha2M\nuBohAosNHL4xJHEXXEXgHGmagQwIhEJ4x+ZgjBQpdV4ikDgl8KNNhBQIFTDJtyhHOaYe4ZyjLCfY\npqZsDISKrVFOq91idf3ijrbnk3pkEeDqa6/nt9R1WPdXKLkzo4vDyjNY+ioOHT68I8ebmpp68jp6\n9Chp3d/hQtiPTNJscvTo0UsdxpOOECIErgFot9t75ubmuPPOO1lbW3vMY7nnnnt44N5llLN052cZ\nj3PEmTNsDvpEMx3UcMjFrU3+9C/fz8WrT/KUa6/Emoa23C4uZ7VmY7NPa2GOdjqDl9v7mDkkxfoq\nWXcB1XbY8Tozc/NQxigNg+FFrLUEtWMuCrDSk2YJxlqwDm0M1YalP6g4cCDlYjni0Nwic50eNSF6\nMkHGMbGuMVbinKJstgh1gK8LGi3QQYBSUG9VmGaaLE5NPVGtbm0RdpcQUUxVW1SsET7dLlBjIkws\n0C4mMhoZR5iyImq1kVGIlCFSOnCK+PAx9MWzYAxh2qKRkuULGxw7knOomaOwmqwlSAIwpsEaTV4U\nRIHAeAgzR1UMsSbCIpFZhq5HzLTm8XEEc/PQX8EJRRJIsjQhbyri0tNs1MRNjez00MWEppigVErR\nrJGXBYVukFFCYw3aWlSgcEGKFYL/+Vcf5Dmv++Eda88nfbII8NI3/Th//Ouf4BXHRztyvHfef5Dv\n/sX/sCPHmpqaenKTUjLfjli/1IEA8+1oum3GpXHNYq97a29mgaqsCC3867f+CIeO7OJZ1z+F0Dmc\nFMy2IvbPdlAyojezwIySBHGMa0kyp5AIRBrjvUI0OcQxpC3IC7wx2FpT5FvIKMEWE2R3HpCMN9cY\nGwciYFc15p9dd4AHLmyx1t/g0/c+SFFaqromjGOCrMNkMGKhnfLhj9/K+TPnOXzyGIcQbCjJZFCS\ndjo0vqbIJ7TjCJVEaK/ptecRsUJWmnarjbcGbQoqCibFhHxUcFlvlna3RxRIrJAkSYxPFK3ZEJet\n05zxDIcVH/3kg8z/k4R2MEMoSozsECGpxhX1sE9oQmzQoW4qRJxixzVSWIwBIeTjonNmamrqkVFK\noeKYQEqMa7CqjXcwQTKftCjrgjgE7TTCKZIkRfgG20hEN0LELWSrTXn2FEGa4ZxAeEMUZuzdPc/5\ntT7XH9nP3EyXqraUskG2Wpi6pjvXwegK5z2TwQhQCOEYTUaEHvq9DqEsSAzk996BtprcN9S1J/Aa\n0ZpFxhGVbfDjCa0oRucTyiYnViG2qQGL8JAXDXVl0YHkwvlVokRS5iWtrLOj7Tn91Aee8ezn8uH6\narz/8iuj9gtPvf+57N27dwcim5qamoI9c22cvrSFZZyu2TP3+KjK+mQUhxlxHKMENE3N/OwsR/bs\nRQpPYh17OiktFyK1JVYxMrDIDAQNmQPrDD4KcbqBagJZB4IUqgpm5hBRBGlC1O2AcoTdFqmtUORE\nrYxU1ogiJ5CCUCbsTlN2zfY4eflREqEwjSPrdjAWjh09weLu/bim4czKOT58y8d5yvXXs5XnjIYD\n9GRMXhpCLJM6RzQlRWHYKEoyp0iSiEZ2sEYTtCMmeYNzCoRg4gSVCOjOztOZmaOzdJCZ3fsJk4S6\nFhSpxXgD0lNaxWQ8JiRGVQXVcIPQNrRlgnUVo8Eqfe/IJw0eSVUIDAYnPVY/PvY2nZqa+tI5lYAR\nNEZgkNTNhLLRbA22OHfuHOcvrHFhY41+Y5lUmqZy1E5hlMOWA1xdY/sXacab+KZBtSJGWyMW5joc\nO7CHtc0tjGuoGk0QeKRwIBxCCPLBFiEhdTnBNyOMqSmNwVrH6dGQ5VNnuTDYop/32aor7r+wxsrK\nKuNmQKVr2rOz9I6fwDclZT5i/eIpLAZFTDkqqZynaQzaO4wTVDRYEVPVkiTLuOeO20g6O/tZPR1Z\n/IyvffUP8ZfvvY2vPfTlTT155wOH+e5f+Xc7FNXU1NSThXOO5eVlNjY2aJqGKIpYWFjg6NGjfN93\nvprveMfvEp947iWLzyzfwvf/2Gsu2fmf7IIwYvn0KQSey44dY8+JEwSxpxsohFKEUUTUDZFZjJAC\nn+dYH6NaKdI6ZLuLsxYRSkhS8AYfeESUgm0w0qNw1BbCqIUyDmSJjDLYWsUqRaMcPoxQoUaEEbO9\nAO09B/fM0VucZWV1g9FwkyCSbIwmvPafvYRbP3wbd50+w/Hrr6Z1950AVF6gG8ekgV6sGGpD1zck\nrYxaCkJhiGRBplrkoyHOatYubtLtJFwYb6IuBrSCiDiJ8XKCqxse6PdZXj+HijJanYDJeIgtRzQi\nwoQVIoxp8pwgajNuRmifQKuNXhliE8GoyKl0QaMdcRJx3bVXXOI7PjU19UiNR0Oi7iKN1RS5RQ8q\nTk/OE6I4fOgQFzfWyWvD7sU5du2aQ9bnOXhgD3GRYBJLWlXIOAUZoocXGZ9fwQdw5sGHmO+22Rzm\nrA1LZlodZBBsJ6dKbldcVRJPiDVjvIixuiYoNT2pmAtT7rjzTgyKSEVknYDBaMK5j9zGs7/5hZz7\n9L1E4wnjB+/HOIvzhrZo4QgQymClRQYhTmmc8DQGnJRsbY6IspiyKFk+t8Hemdkdbc9psvgZX/fi\nl/KW9/wMX8snHvExVicejj6fpaWlHYxsamrqK9Udd3yK337nLzFaW6EabtBzOZmvUTgskkLEDGSL\nZGaB8nR5SZPFXWKTa6+5+pKd/8nOO00rzRDC0E4zms01gu5eJtqwu5sQRCFpALHMkPkEP9fGVA6l\nBL4VIesKkYS4SYNKHL4REDloRxApgjqgsSVhEFDjCb2jkS3CylLJCNd4iBW2rACPQiClp5222XXs\nIIO7H2SYTzh07AR333U7SwsL1PuP8eI3PpXiV36VwjsOG0t65AChDGisxTSWQZnTixKM0JRG0HIO\nYduU95xBHd/HaFKwNsw5vm8X7/vAR7lw9iw3ffWzKaXn0OIi1dhTbuScHmxyfmWTuz70Zxy97Chf\n9fybCV3IcLhFd2YGPSxRPqQaj7AmQXtHkdcYAfloghIeZxQIMNoRB9OJV1NTT1RGSgIpsc4zyvsc\nWdrHsSuvYD5K8EnKiSuvxlrD7XfewZ++/6N00pAjq32uPHGMed0ikDNISrQ2tFo9pLTcfvdpnnvT\ncarKsTgXcOrcRfbt6uGkxwcO7wUN0A4D8mqLQHicKTBGYqzjhmPHGRvDvgMH+eM/+j1uePZzGW1W\n7O52ecW3vZq6nnDkqU+nzrewYRvvPHio6wqnG6xQqKiFL1dRcUYzzGkaTeNitvINwnaHSV7gnOX0\n2s4uXJkmi58hhODmF7+Bj9xyB0/f+8imn7zzwWP8wG/8zA5HNjU19ZXEOcfv/Oa7+Ov3/gEzk/Pc\nOAtRIGEetlcG/P3NdEvgLDYo+fCZO2kfvOYxj7lauZeXf/1zHvPzTv3/1jcHWO9YnOngm4rGS9Iw\npJsktDszBApCmWFtCZHD6BIbJMhMQUvitcQXNbLVRgiHdQ0ELeRkAlGMsDVSKep6C0WIzDoEo5xG\nFvjAY2uLMJCokFpo2q2EuhjTjQR5HDA316W9us7KhfPbW1IUFYkKGRHxtFe+iiMH5vi6V7+Uj58+\ni9UW7z15IGmFEj1usEqisYxrw1JY0z60i6assbakl85AL+Kf/NNnIIunctuDp3nooQtsbKyDUFSV\n5sLp8zzvec/maNzl4GWHaM0EaNkijHPKIsc5jzWOQm9vhG1HY8zEU+gc4ywGQdFMEDJEBODtpb7j\nU1NTj9R4XBBGYIyjM5MyMztHWwlkqAiFRQlL7WquuPIkS0eO87GPfoyHz5zjbH+L3b0eT7v2GN1w\nhrDVYrJyFu08W5OSoiwJhSUvPSJsU9cNk7KmFWfUuqYVRGgByjlMoMjLEuHBO6icJghCFmPFv/iB\nH2CraAiDgKVd8wwuriMSRVOXmFIQqZwobOHthKaqII4RJJTkWBFRVQ2kIbWpKRuNkAqMY7w15vjl\nJ3n4/vt2tD2nXWd/x8te/Xr+aP3kI3rt+ZEnPfn1zM3N7XBUU1NTXynuu+9eXvvi53Pm93+OF6QX\neMai3E4UvwjPO5gQfuI9OKsf5Sg/m7Oaxc1P8rrXfMtjet6pz2bd9mhXb34OEUhcMaZqGnq9Ds5N\niL3Eo3FGE8YBeW4QAuykxo9qCATCSVyV42uLCyNUGEMUIQIJIsaGIU4GBFlI4xpk0uC9Ah8SxRE6\n0HhpCaKAxjsiFRClKbGMmJ2bRakIbTRBGFF4i4wDspkW+JLJpGTfkb3YxuC1o9SWuh5iNoaUpmRS\nCrQ2CKFwZYXVHo+jKGuGWiNqiWosWLjiiv20W4pBA1u5pt1q8cxrriOwhtbeLmiNcgEqkWACxuMK\nnMezney62pA7yE1B3SjKyjAyFVooYm8ReJJ02pc+NfVEJZUkkDES0MbjnSR3FZVtyKuSUd3QOEHd\nFKim4LrLD3P0KdeSRAoTCO68f4XReERdDhBeoMclWQy3PXAGPGyMa1wgWR+O0dZTVAZtDJPK0NQK\n52qaqsJ4h9eO3DREcUrcSchaLdJOj6VdCywsLkJVkaYpyoKxkjoUTGpDXQ2xVuO9Axlh0URBiyBL\nkVmE0xYhMwa5pjvTomgcp8+f48WvfDleqR1tz+m74d+hlOKK576Ku+6/i6sXv7RuxXc+dBlv/a23\nP0qRTU1NPZF57/n5n3k7n/6LP+SF85pAhV/yMaQQvPZgxbv+5t10nvumRyHKz83f8//yn97x49Mq\nqJeYkoLZ2VkkAi9CoixBOKibnDDrUnlB6KATB9imoZV1sJGkHA1Bl7SDOTwBaE8hx7SCbLsqqoqg\nyfFCogDR6mCDDDk6R6Hj7QIPUuFMA4XDxgqpBHG7Rz0ZkymJEkMm+Zi42ybIJ1RNTRZHnNh/lJMn\nT3JPnOIH5ymLGhUpnHL4xqEbj1WWunCQNThdMxyP6O6ZxdcaoxwXSk3ZgDYTgiChCkv29WaZWVii\nGylc7ml3YuJOh/5oi14aMsxLVKVxm5u02zO4ekK3sxtLidPgnIckhuEE7RsaXZFbi3eOv61aYHag\n4N3U1NSlsTQ3S9MUtNKEqtFsjDfpmJAqBds0pK0QoVKKvCbtJGRhG0Y5rSNHGY+GqDjg9NYFFtws\nnSDFGE3dCNyoYWlulqN759jcWCNhjsuOSqzRRO02QsRETNBOMZo0GOMQCuogoRWAMA6pDMJoZFnj\n05QwjNC1hiBGNwata8Igoag1STvF2ZqgygmTGBHHNLkB6yAIyAcjmqoiaXUoJhtc/7Sno6uC1//Q\nD+xoe04//f+e17zpzfzOyuVf0mtObcHc9S+h2+0+SlFNTU09UTnneMv3vpHig7/L85csgXrkb7v7\nOhHX1ndTnrptByP8/JqL9/BNT7+My09c9picb+rz2z2/xOzMLIGKCcMAW1dYo6kLj1QADu1rhqMh\nUgSUxZCmKNFhgPWW0tRUw02KZguamrosYVKg6xHg8dYibYPQBt2/QCO6BN5jZIQe5OSlo44lQlus\ndsSiJgFGTY2xjlbWwlnBrl176HR7lLXl5MmT3HDDDZw8eZJzq6u4xmBqi6st/O22xmGI95baOLQL\n6XRb2EqivKAsJpRa44QAGeNix1w6i0rmmI8SZrIeM4sZUbdLd6ZHtz1LEEhkJMmVpjKSqvYMak2h\nJLYRGCXxCXjpabzGGEuoIrxzVNaRCgilop5Ul+xeT01NfXmiKEIqhYgl3bRFf7NPXjr64xGnNzb4\ns7/5FH/0J3/NAysXOXd2ncHaiAcePENdV+w/sJ9WK2OzP2E0njAqhpRNQagcF/pjLgyGFGXJ8umz\nGCcYb5ZIpfDWE6GpGkMjFDINKZAUwXZnm0aCE2xsrDPaWKUoB+jBFpOLa2xurFI3BdJ7fCLJXUMd\nKPK8wqkQbIO3kqao6EQthA3wOBonaXczxmVFf32TA8cOs7hrAWt3trNrOrL498RxzJ4bXszDG5/m\nyBdZTOjXTl3Oj/32Tz66gU1NTT3heO956/e9id1nbuFgd2emhXzjoYD+p36TtSgj2fuldWx9KfT6\nMk9NLvCjb/npR+0cU1+8VqdFPhnTamfokYNOhnWGNAwxZYWQMdZY0laHUkYIX6DCkLZyiDDDW4nV\nBtWO8d7jnEREDl/VGA2u26KpclwtqJqaTmOY5AVhktFkKWZSYr1j0jicK5EqwSmQjaDVbvPw+RWy\nNGQwmZCkKa3e/GfFXxtoEGAMMovxdYMDxk7RTUNkEiOkx3mHq2rKsOL8sKbwEFQVAkOcxiwstEmR\nzM8f4eK5FbpLexiOBiR4usLj2zHCO3LjGE5KZBRjrEJjCFshoiiohcMbTdJOiJqKC1ojVUQmHRNd\nIyVMxxWnpp648sbQcY7Ya+IkxBEzMAXFlubDd9/PeDjg4uaE0yvnCOKIK44e4ilXnsRayemHH+LY\n0SsQewM2B5tkrRZRGDDX6ZJrz3B9wEI74Z7NCUkaMqhHFLZHFkWkCMJQsVFVNHhcBHXZkMQK72rO\nfep+Ljx8FuE9x645ye49ezm//CAPP3iGvVccYOn4VWgjcDhKL0mlJ3JgohRTjkjSNpW3VNIyHFuM\n0wgE/UnOw2cv8HVzHcqJYXF2Z5fETZPFz+E7f/DH+Nlv/2P+zewDX/BvH9gU7L35pbRarccgsqmp\nqSeSn/+ZtzN3eucSRdguxvW6ywPe/fFfZvX67yA99JQdO/bfai7ew1OTC7zzP74DIcQXfsHUo054\nS10b2lGDbYWIUKCA1XLAnqiHlAUqCKApAEPW7iFEQK4ErVAhmwYRKRKRIkMHBiYOMiuwSiCdJs0W\nqOs+0kEZh2QzEfmwwNQ1YeiZlAUBHq8S6sYQNDVBIlgZbFfgU3gmqytsFRVZ+7Nn2vzzn/o5fux7\nXwdSIqXAIhGACKGRHo+lCi11VTMzt4CxnmG+xXA8ZFYFRGFElmT4fo6JUppoRJZKqn6fXd2Mi8vL\nmLLEEtFqt4hEw4YoMNogZMy5sxdZnJ9HBpLIKyrpMN5iUYSRQk9Kau/xXhAKQSOmE6+mpp6olIwI\ngwAp4MD8DBtFzYOnVrjzvlP0BxNe/ro3IeOM//YH/4U7T19gNX+Q02PHS64+wMndB3EqotNqgbdo\no0mSBBmm1GaTs/0t9nYiHlCK0xfXCYTnwN4GWzWE3YRGG2qraertitE4x6dv+RSTlQ2MMWRKYq3m\nlvd/CItDKkkcZgxuvZ9bb3+AgycPc/jKawilxilF4RpU5djd6lIHMU0+RheOSaXBayrjqYdbfNsb\nvx3tHAiPNztb22D6bvg5tNttssuet70VxhfwrjMnecOb/9VjENXU1NQTyX333cun/+IPOb6DieLf\nkkLwnZcHXHnvuxm//1d3rOiNsxp713/lZccFv/rzPz1dp/g4khc5rSzk/NYmRteM1zcpvSO2Euc0\nYlzSVoIgiAiiCKEkCIMMU1TpUdEMUWcOtbQPsgVEr4eyEpCgBFiHNCXZ4iK9XfPURYH3LaIsJUgz\nwlgwE0U4F9AUJUXkMNJSu+0Zpa12h0R6krlFtPMMhoN/cA1v/+V384JvfBmC7VH3MFC4SUkAFLpB\nNoogEDRNxbCeMNI11ki8VNi1kubMgNOn+ly89yyn7rqfM7cvM7p4gXs+/mnyQc64XzJaH3D2wXNs\nnB9jRoa1YovKNWjrEKLBasdQFgzLnNwIaqOpSk3tPQrAe7z3eDEdW5yaeqISwhPHATPz80Rxwv65\nBVpS0pUpb3jly/DVJv3+KnG8XZRrWBjCTocTX/MNnL7v0xyabXHi2htRKsF4AXHI7GyXrWHFHcsD\nlmY7JMpx/wMPoWvD2toW2mgaHyCVIg1DlITBsOD+D3yEzTNr9E2N2tdFnTjAvZMRZ2zDaq0Zu4Bx\nJ+ZCUzGYTDh95ynu+tBH6I+GTKoG4z0ySnCtDKdLahkwMBOcN2gZsLq5xf9434dI44A4yOi0Y+QO\nd3ZNRxY/jze+9W386nf9OT967anP+zd3ryuOPvtVJEny2AU2NTX1uOec49/88PfxwnnNo9UnJ4Tg\nmw5H3DS+m/e898ewT30t2ZexrUa1ci+Lm5/kP73jx6drFB+HnLM0lWYum0Faz+6FHnOdAImitp7W\nbI/CCnxTEBhFEktEkBJnKS6VlELh84pZbxDtNr4xBJFGpCGhyvBCIISDMCXodZhtPJOqpCgrVBjh\nmhmkGpLFJYgEV1WUVlFVBd57tgYDGpFQbj1MkmRsbn7ufb6++Vtezf/4yz8BD9p6gjjBWIGxAU3g\nKGtoxY5ah2hr0NqyVdZEw4aqskRxjAg844sDjHMUwxynBKqM0JFiY30LrzVpp40wCdY2jHsBOgpY\nKhu8TAjoYrIhYlhiohhlGkxZEScRdaUJo5Df+P338slPfvIxvstTU1M7wUuBkp521kHZivm5OQ7v\nP4iVAefOnOPKy46y9/BJbv/EJ/nqZ99EVVu+57WvRDx0Lwd3zzDeOMu+Q3sIowBXK4SXdNoRvW6L\nMxfWaaqG659yGXc92Ger0aysj9i3uyHLKpJIMpoUDLYKzn/0VlxjGZVjXvT6V5BlKd35Q1x2w43s\n332QrXxMPRyxZ+9+Pn7rB5idm+eO3/9TGI954PZ7Of7U6wiFpduOcdZipKJsNOPKMapr1gZDttb6\nfM+bv5+oO4vROaULmOn1drQ9p8ni5zE/P4/e9yyG1cPMJJ97GtZvnruCt//cjzzGkU1NTT3e/c5v\nvosr7flHVPX0S7WvE/EjV1o++MCv8fHbOkz23UT3uhciw/gLvtbpGrN8C7vEJi//+ufwute8ezqa\n+DglVMBwvEU3gXbaYvHAHopS0XQ0bRExGpQU+RCjFHkHJrkkrC0bZ06z1O7RXdzD+umHuLbbIlvK\nsI2m1gXKp4gsoNo6T5IsQhvK1VWy3jxNXmCVZ7JZ4mxBHCqCsM1k4wxBaxZfadCgyxEyt0zW11Bx\nSuIKVPC5/+9LKXHWIUOB8Y40MEgBUnZx4wKWOvQbw9jUQEzRDIkGDa5uWF45zzgJ6ApJbAVzV1zB\n+sWH6SZtpGzwNVzzjc/n/e/5PfYE4XYBn/mUYGaOfFKyOTdDrBsaa6F01FGIcg6rHV4JSmsptrbI\nsj2P7c2dmpraUU1lkCIhFIaACOlKdi20qd0iD586y8fvuJPOmYd5xU1X0U0T5ufnCc+fJpYJe7/q\nRdRVzcUzqxBIskAyGRlcU2G8BO9ZLQzp3sMcvnwfn77rY3SvuZpTFzYIY8XCTJfZmS5ivU98eB9e\nOy5Pj9KRnvCB87Q7XfbrhqXEMRe1cKEjjic85/Ib+cAH38f1V57g9JkVgvGE1TOnmTt+FIuisDX9\numS9P2QwKSgbw2gw5i/+6oM865u+mUBqyibC5Rv0xyPgmTvWnl90svgvfuJt3HdmDSkFgZIoIbaf\ny+2f6jO/T5KYLE3JkoQsjcmylCxNaaXJ9qOVEccxcRyTJMnnfK52eH+QR+r1b30H7/rh9/PDV5//\nB/9222rAVV/z7URRdAkim5qaejx733/9A76u9+gnin9LCsFz98c8l4bTg7/mP//3j3MICdkRAAAg\nAElEQVTkumfQH9dU8Tw67OCEQnpLqMckzSbz7Yg9c22+/8dew7XXXP2YxTr1yDRVTRam9OKQk9dc\nhSekmAwZRAFN2XB6+QzrwwmRCmjNtBlsDbBCkkUhs1lK0Mq46sgRVoZ9doUBbjxBectk0Cepaqyt\n8YnH5w2ra32SdIQiZDPXWNsg0x5KV0gMyewShS7w0gEa7RTZrg7N+XNgDK32DOFo8nmvpZ4U4B0q\nDnFeom1I2RS04zYYR0vA5rDBVAVmY4uq9uRlzbGvfw6dpR5X3/RMLt5zL7suPwbG4UOJs5ZP3fJR\nukcPc+gZNzG8/R5SETAcFQTDAUu75jF5jUsiXFNRE2BFw6gs8VIRSkltHN3dS0St7LG7sVNTUzuu\n0YZAeOqqwKcpea2JIsG+/XOE3hFECd4KOu0YISS2ztFNhQ0E5oJkbv9eVtdOo4uKdivEAGfPnidL\nt98blssuTzc5am6RYt9hhkXDysYm3ZmUWmtmZ7rY3hz52U1G62vMzcxw9u5lWha49R68g7W1TZzd\nXr19ui4pVtaYVZKVCxfpZhl7b76RZKYLrZBBUWK1YXPScHpjTGUsm/0B7/vgh3jes28mU4JmOKTZ\nWOf02RWyeGe/f3zRyeJ1V1/BX6ykxPs+/5cK77crjLlc44bN9nOr8WYTZzTearAa6S3Sa6QzCG8Q\nziCcBrv9U7C9p9Q/eAiBlHDVoSX+3dt+Yieu/x+1f/9++rM3UepzpOFnjy7+9oUr+ZlfePOjHsPU\n1NQTyx13fIru+Dwkl2aE7lAv5vm65vVvfRNXXXU1y8vL9Pt96romjmPm5+c5evTodATxCaaVxhRV\nze7di7S7GXXZECUd7nv4HHVe8OBDyyRxjEpiNj+9RZy2SYIM2xT0ei2edtONfOijt2JxmP05i92M\ncCamnuRMJmNoeeyw4VQ+ZPn0GnsOLHHHPQ/Tm+0yO9PG0pDoCSiJqHJk1iX2jjxS1E2Ns4bdS4sU\nZ87Q3+pz2eUnPu+13Pys5/HRWz6A9B5nLVEsGPdX6LYO44yliQRSeFyjkUHIaL3PhdGAfWELf3HI\naG2VaH6BuqnRRY2IAqx1NOOS8coa49UJczdeR/+DH6d3aBHpHGiPVQ06t+RekOuGwDuyOCT3ijqf\n4LQj6UT8Hz/9i4/hnZ2amtpxTlM7ia0NFkXgDU4J5qM2wd7dDIbrtLMZlHQ4JLacYKKEhaW9yDSl\n7K8QSklLWVq06VOyMSlJwg7HTl6LdZZRf4Nsbg8vfPE38P/8l/fQ6aT0BzkCC9bhEKzVOaP+JkpJ\nyosXSbMWg9YWQRTgnUcbw2Q8QTcaFSiMsdTGkiYxpbHYYgSNIu52mJSW8/0tBmVNOcp58P77SKTn\nyqfeyEMP3I9QAe1umz2XHWft1Lkdbc4vOll81ctfyq//3hso/5FkUQiJCOMvavrTF+IB85nH31Wf\n+gTf+8ynftnH/2J92w+9nf/8k7fwXSfX/r/ffWQl4sYXvZEgmM7inZqa+my//c5f4sYvctudR8uN\ns573/Mov8rO//BscP36c48ePX9qApr5sRmsWejPsO3QQXRuMbdjc2OSh+x7g4IH9zHTaDIuS/sUR\n++cWqT1sjvsI56h0xe0f/gRRrKjyitrk5BoGKwM6ju1e9lZCPfDkFZRFyR//7p9yYTwkCiNUt82J\nE5dRjodIa2h1WiTmLEuLe2ms3C7AoBRRK6A7P0+QNUSfZxoqwPe85S28/yV/jhta2vvaBDKks7QH\nrS3I7USxcQ5patzWkCKZ4WkHDtBbW6dKFPf/1Qfxdc2wHjEXdLHjkmy2zZ54huYTn+Kydood5ETH\nDvOxh85y8qqYpqjYsDFL3QDfeJy2lFWN8R5vGsR21R0aa8im1c2npp7Quu0OwlkEEu8MjpSWtgSx\nZX8npRUfoNINUZoQyoAkSEjCFpECbMlmOaLIxxjjaAUWoT2JEiwcOU6uEs4tL6Nbi7TThM3hBk//\n6n/K8p13MBjlRGmKVI7YeZJjR1k/t8bWcMx8r4tAQijZ3BxgG82eK4/wou/+Tn7tJ36K8aAkyjqI\nNCG44iRkISIMqLSm2BqzOs6pLWyu97lw/hz3Pfgg3/rqV3Pi2mux2lI3NVXR0B8NsDtcS+WLznak\nlLziBc/m1299kHjXpfni4b1jqXyIF339jz5m57z8ipO8K7oeY/+cQG2PLv7e2pX8++/47scshqmp\nqSeO0doKUXBpR+2iQDJav3BJY5jaWVErIWt16PZmSdKI0bkt1tbX2TXbIfCea6+9BqIA8HgpiWTI\nyvLDlBtDrHQs7p5n155ZHrj/fuZnY2a7XaQuSVpL6KaiGBo2+mt8+t5TPHDPgzTSU08qztcDqnMX\n+Mgn7+Tg4jz/5NnP4r77HmI0GnDwwIBdB/YhpaTKNYmHrcEWcZyxtbn5j16PbRxpL0YXFWSSwEZY\n7zFW4+IZfN5HBgGze/cSFyVpu0MxqpnLHXS6iCRlKZkFbxGtOZidwfX7GKNRQjEpayyCXmhpBxLv\nalxpGYZQ2wprC5RK8M7ivcFphwgk4+Hnnz47NTX1xDAzP0ujHdZDnZf00hgVxkhnccqwmGU4m0AQ\n4KUnECGZrBDdRaqN05T5gLqqiWVGoD1KeqJWigoEpnHMzfSYaNg6dTc3vfDVlLpm/cJ5JnXNbKnJ\nncFmCcY4whNHeejjt9MfDVhsd7CbkIURHjhzxwP8wof/NZXR1N7ArqPsvfwoQRoiPPTHDSqEsinR\n1rN86gKnzyxz5uFlvvWbvpHdx6+gKmtMoLbXodt6uxI2O7vl1Zf0jeaN3/EaOmu37mgAX4rm4Y/x\n1u/+tsd8369Xff9P8nsPbW9w+YGzCV/1su9/3KyrnJqaevxwzlENNy51GABUgw2cc5c6jKkdMtud\no93uoiKJ0QbrFSevOsHNNz+N7sIs11x/Fc9/7nO46qoruO22u/joh2+nKEte/uqX8ZIXfS0v+YYX\nkMUdjj3lJsajhtFkTOQCWotzOG+5/557+OgnPsUoHzK7tICbFJS6xjQlxlpCKTi/scEtH/oom+MJ\nqJSHlk/x/j/7a6KshXcN7YVZWkmb8XAT5/7xz+nde/eysT7EOouxCiMFRdNQaYMZNcjIU+Ulx+fm\n6coQ6RrwGtNOt2cxCYVSAlvXxGmALcZIJbFFjcsLEtugiwkH5xfxuaVozPb0MKDUEd5vrz3SlcZZ\ng/cOqy2+GD/q93JqaurRdezIQSpdMZyMcQg8Ac5YqD2xkIRC0p3pMdNqMdNq02rFiPndCNugG4+X\nMU57mjrfLsZlG2SgGG0OaLdbJDM9jPMcvOpGlu+5narJefpXfzXLZ86xWdWoKEQqSSgNC0sLdG+4\nns04YS3PKRtNYT3jpmRtOGQSQBXGpDfeTPfALgLncL4mt4ZOFuOxFAbuvGeZsysrXDh1mje87rUc\nvuIE3dlZwjDEliVbTjIpKgYXLhKaekfb80tKFsMw5AXPvI56c2fnwn4xvLPs0Wd5/vOe+5if+4ab\nbuaT9hqc8/zfm1fxzd/yHY95DFNTU49/y8vL9Fx+qcMAoOdylpeXL3UYUztkOM4JkhDhPF5b0iwk\ndp6o1eG6a0+SSfiFX/o1/uC9f0M9GXJm7Txnzp3nwuJVfPi2u9gzN8fznvVURhfOkKuA9a11JkGE\nyVJcJ2ZzNGLXsStYmNuNrUu+7gXP52WvehnPuvlpHN+7mxd/zbN5wfOfzTOeeQPHj+0jVJpGe+rG\ncPcn76Qw21M79x/Zhzae7AtMg/qp//BLeA9FbbaLUQQhSgrSwNMKBDJUWGFpyu0Rz+HqJh6H0h7v\nDWHgyRbnkYHEuwbyCVmWEgKT/gCdV/zV//wbds11yWOF8eADQAm8HyKEwOgGZyxSRVTGM9Yl//7/\n/L8ek/s5NTX16JFCUlmN1hqrK3RZoquSIFAoEoSSCGmQShEnHWIliaoxrhxSuxLfGAZ5TqS2Z2mI\nQBEECUopImWJg4CFpQU2RgX5hYcYrm4wGhe86FWv4a47byOvDFmgOHBwFwcXYk4cmGX3FcdYixRr\ngWK1qVmtNKe15u61CyyeOMzRXTMcnM3oJDCTJszEAUHg0Y3g7OnzrK33WT3zAN/3g9/Fwd1LqIUD\n+EgzzmtGtcGOthiOSra2xrg43dH2/JIX3f3Q972J//6tP4Cde8WOBvKF6OVb+NH/7fWP6Tn/rm94\n3Y/wg2+7jRe8+S3TwhBTU1Of08bGBpmvgZ19o34kMl/T7/en6xW/QoRBiAokUkmSJKGxFUv79jDf\nyVg9fZ733XkvN9xwHU3vAH/623ewWQrWi5IP/eF7eNvP/0du/53fYtd8QjeVNNow6Dcc3x9jJmNC\nEWC8BwT1cINXvvSF3H7bXVz2Na/kD//gvaxsbfGBWz/FTdee5Ntf/XKkCOkf7DMYbfGxD99KVQxp\nz3QIooB2kGBMg/X6H72ebq/H/v3zrA4GzGQxYXuOUblBEB5iVNUIAaX1rJ4/R2Yk2miKomY+jPDO\nYiUUa+t4Z9B1TNTq0TQVejRBJv+LvTuPtvSqC7z/3Xs/8xnvPNQ8JKkMVZVRyAABAtoiBJCpRUQU\nFBkEQQZxiv22dtvq8nVABBvRhsYXtVFbWkWZ0sgQxkCSylCpuerWne+ZnnnYu/+I7dt0kCC5lQrJ\n81mr1qo695xn/569z7p1fmfv/dsOp1bWue66Gzi72ieftUnSEhV08dIKow2ea6i0jS5zCgEVhh1T\nU8xt2frIDGitVjtnWk2L/lofPdFFo0niAd74JIXWWNkIMTI4c1vAAV3meJYkihMoDFZZEqUJSZzC\n2ASuKrEAmaVkDTBlhd+0mdkyQ7S6xMXXfy/D5TOYIsULXK562nfzlS/eRuuag4y5AVPdcWZn5rlk\nZo784BVYfoNoNMTxfMZbY1TJELfRoir7jLKSQEnCTJPkOamxGKY9PvvlL1NGI179hp/EKnM6kxOM\ndIabukRxRKFTBsOEIovxux0ce3Nrqvyrsx7f97lh/y6y4Tc+cPdcMLpiKyvccN21j1ib/7enPON7\n2f701/LM573ovMVQq9Ue3fI8R/HoWPqpTEWWbe5SlNr5oywXVVbkSY6UMD87RbfVxbMNURQyM9Zi\nt9dk3/wWGr6HJWIOTLf4ru99DmtHjhIPhhz6/L1s3TZDkReMtXwsU1GFA6L1dQSKJBywb89W7v/8\nIfbf9P04VokoUn7r3/8MyvPZ6I/4nXf9MR/9739Lo+HRbrY5cPUBtu/aTaWhLEuiQU6rM06eP/R7\nz2+32VjqoYVEKoPjuoiqZLxl07YFHddllOcEQcBXj9xHPhgyzHLyLCNOSpJRQplVlEmIZ+XkvQ3y\nomBxY0CkoGVbxEFJlQlUaVBaofyKNApB5+gqR/keaZyRx3G9vaRWe4xI84xWu0NWQVYJEi2p8oJ8\nGCKMQDVcTDWiikNEmRCFD+xVVq6F12xSZCVSVrhSIMoKU2iiPMERBpGu4QgL33aohKA/GFFkIyQC\nWznMb93KWj/ixMIKZ0dDRoMRRZji4+AoCx/NbKfDZDPAR+E2upg8hlLRCRxyoCwfOPJjedTjI7d+\nFivP+anXvIq2kmzbMc/9ayOMUcRliqMkVVKQFhlplhE0mnibUGj0//RtTZG9/U2vxT3xj5sayDdT\nHPkUv/DG81tQRgjBW37xPzzi+yVrtdp3DsdxqL69X6ubrhIK193c/zBq509eRLSmxrEbLq4j6bYC\nZjo+2ShmdmKMybkp7DRl4bN/y01XXcUvvuYVvP2n3swl2QL9w3cxtWcbF9/8fZxZHBH3V2k0m6ii\nIOz1yUzFnm3TbKysUGYa7RgOf/JvaFYZwfQsL3n9z7N13Gdu9wW86q1v5/6Txzl2zz2Mj3vs2rqV\ndqtFt+3he206XoHj+uR5/pD39Pq3/CJPetpVLCwPMKYkaAS4DijPwbJ9LAFxQ1PmOU+4eD+f+MoX\nWF5Zo5+mxEmIyCLKlXXSxVWW7zzKaGmD00trrKyvMtVok+YldrNDqiOiOEFS0Xa7WJZPa2KORuBC\nUTFMUp543VWkafoIjGStVjvnTMbEWJM4yciqkqosGOYpSiniLEcKB4SH41poo9HaYCEQxuBYbdYG\nIxzLxlMCJIgqx3d9qiJBOi2aLRvX0sxNjXP40L10tu+jd/ooQeBiKcXLXvtj/PVHb+W+U8ss5SVx\nEj+QBFYZYRqzNuyTjHos95fJ0g0GYUqc50TpA1Wae7nm5NISf/eR/8na8gI//IqXkmmNFwSslD6X\nP+EJ7Nm5DYOk0hWpLiizDNuysJVEG7Op3fltzVN2u12u2D3BF+IhdtDe1ID+b7oq2OkMuObqK89p\nO7VarfZwTU5OEotHR4IWiwfOVKw9NnQ6E4z7Fh4GS8J008c10PVdOhPTjOWGfDRg/9552q02WanY\nd+BCFo8fosxtBsM+p0+cJKoy9uzYQRiVDJwQQYkGMiMxecaR5Q2uf9qTUamgXFjilc/+Nzzn+itZ\nOBtywcw4G7d+lJuueyKrp84QT7WwxsfZPjnG2aGDQVNVkGUhxjz0F6vz27cjhWC4skiUztNojLEy\niuh2O4zNzTK+uMzqcEDYqCCMufHyq1nqraOMIfB9CiFIdIUlBAtrPU6vr7L7wMWML22QJDFqtoVO\ncwK7Td7IaLgBXmDj2x5WOiQKM4yAwcoaJilZ7w3O/UDWarVzTlk2ExNjHDl8iqIokK4LpSYvc4JG\nE61jslFM4bZxHPuBpf1FgXRcyuEGQmmKvCLKCyawCabm8RsOstEhGkWMb5mjNTmHFziMkpy19R77\nnvBkVlcXmZ7bwZnTC7z+bW/jA//5Dyjzggu2TDI1pvFwUfKBmc9BqqiyPlbkY5kC4digKvpxwdlB\nzF/93T+weHaNl77i5cT2OPM7ZmmMjzPTDAhHGQurG/iuRzgYksc5wrLwPR/LdSm+hZUd/xrf9qLW\nW97yem5+7S/DZc/ezHgepLr/Vv7dL7zunLZRq9Vqm2H37t30ZQNIznco9GWD3bt3n+8wapukv7FG\nls7BRJvA8xHCx1ElVtvHKItuy8eanaJ0bXxvDKs5xvJGn7J0CZOQKE4JowENZSh1QasSKBS+26Bq\ntRh+5R4mJ8ephOIrtx9i59QuJruSLdu30/Bsmt4GXd9iamIvY3HI7p3byJM+Y4FN6Ei6FKxvDJES\n2q0x0vSb71mEB1bsnDm6wI3ffQOnTy1ycGqOI4uLXLJzGwuLZ7ADm+54lwExA6eiOyrZOTVDd6yD\ntm0++7Wvcc13XcfUxATteETrvvtI+iHe/CS5J8iR2BhMntOWgkv2znFmZYPu1gmKYYTt2Kyf6vOs\nZ97IymqP+S3zj8BI1mq1c21pecidd92LbbfI84LCOFQC4ixFDAdYjRZClrSDFmU0pHRcLB7Yu+wE\nAVleESUpy4OIdqPD6uJpLK0Jw5julu0IU6FcTW9lyP7Lr+Hue+8mzmKmdlxAMRyxffs2FlZXefKz\nnsWpO+9hYXWDy3bPM9dt47pNJAVhGCEdC1sXFGmGW2RsxBVnN1b5wIc/xlh3nNe8/c3s3LaTielx\n/MCjzCs21vsMhz1MZRCuQ7c7zumzK/hKog2UeYYjN3cV5LedLM7OznLxjMvdeYJyzk0xB11k7Gmk\n7L/s0nNy/VqtVttMUkq8ziRw+nyHgtedrItxPYbMzM1jWQJdFkCFkBFpDEG3Q1laSASyGDEWTJNq\ng7VxGqUNYaJI4gi3YRMUFq1Om/XlFbqej62bSCTp8hr5MEbZktXeBnubHXprRwmCXTS6DWb3XcqU\nkaydPU1mJJPkjDYWadttWq5Pw3fpD3P8Fiz1T5OPhnidh151dPzoMbRyMHHKXV+5k53bthNFQ06f\nXWP3ji0cP2bQUxZxcpoiNGyMSdAFTpox6TncdPnl2BMertlgrTekMTnOcrRCu+kwLEaM2S2KQOPi\nMD7exg4mOLV0P5ftv4STfc0wG3H29FmufuIBjh8+TnuiTZzEBH5w7ge0VqudMzOz49z6+ZxGkJPk\nBVlVIjOJ49mAolIFFpAOe7Rbk0gEKIGjJCmCOOqTaYfjS0vYSrCRlDiBT0MFICVlmnH0/pM84epr\nGI76bNu2lWNHj+OvrDHemYAkxUjBFddcidEVt3/uq3z4k5+m4Xe4+pK9TE9O0mg3qdKEJNMkRc7i\n8ln+v7+7lYsOXsW/feWPcdm+3WzdsQ0lFZ7rst4bkWURSRRSSoElbXxpU7gFVVUhPQ9LCZQQlJt8\nzuLDKpdzy5tfy4ve9rtwyb/ZrHi+jj5yK7/8y284J9eu1Wq1c6E9PU++chLHOn+JWl5q2tNz5639\n2uYzRuO5HqLIsBGUGf90DAQ0HJtSG5RqIm2bIOuDMIikZCJIMP4EvSQm9yVWXqLTgsS2CeOI+akO\n/WEftCaKEtpewOR4A5wGw946WoOMh4jWNOMz02RZSDjIcW0PqyWIhzFB16LTtBmGMU3bp5QWxfCh\nj5D52Kc+Th4POHw04jnP/R4O3X0PT7rpCdz6lbv5nk6bkQWTHQ+1dytlmJGVJVprtJCsAOGwR3zv\nMrNbZqgaNtLK2T29HS0ME7qJFQjQmo1hSqvTZWl1CWNVpGnMUm+V44eP8vznfS9nlza4eP+lLC4v\n8Ad/9F/4qde8+pyPZ61WO3eWz55l9/QYRxfWOHN2nU4zwLU6GCUwjkWlFbZtk6UFeVuiyhxbCpK4\nJB/2uOHCg9x6112UWcGplWXCwhANRtiT49iOg93wcG2Pr371EJ2xgPn5LVzYvIiW1wap0VqjhKTS\ncNmBg+zeexF5pVk6eYp//OSnWLj10wghcW2HdqfL7Nw81z/lWt71Ay/h5NHTXLBvD77fQEpBFEWs\n9jYoswpLaYRlIUuFtAVGaGytcFwLpRRZaZAOSLO5hfYeVrK4e/dudrcKTpQ50nI2KyYAqjzhorGK\nCy+8YFOvW6vVaufSS3/iJ3nvGz/HtVPnL4Yv9wSvuOX15y+A2qaTWUhWZDjNMXQliJKC9oSPijVq\nTOF7FgofdIHEokj6NGyJsT1QgiLvUyFZG6YM45zxwKMwFWVm0DkkrmYQpuyYaVKhmGsGmHaD4WDE\n2PgeBtEIY9s4cYGFQUiN5zTJdMryYAmbBqUCpSSWa1MUD/3N9onFZbzxaebagrKMiYcjemdH7Lzo\nEr5w533s2jlFPy9odRoo1wVhISyDzg1aV2zdMod0HNq2wyAusOwUIUsKA3Ea4xQwyA2Bp8gzuGfp\nOHt2X8ri8gmGcUw0ilCOYHl5kZnJDllesXDmJK/56Tfz27/6Hx+BUa3VaueCLmK2z7RZ7g3oRTG9\n4ZBW0MAYG20qKBQjkzDVmkTrEeRNVMOn6K+SpDEdT/CMKw5w/9oSfneGWz/6EfzWdoq1DS7cuh2d\njphEYFngDlJ0OGDM9xB6CeE5JFFEUeRQZIgyR5WGKonoFPC0Ky+hd+EWTvdiGs0mo9GQ4/fcxQte\n+Up2XXgxuy48+HX30v0W7vfAVd/1oMf279+/Sb35MJNFgJ99w4/zo//xAzgXPW0z4vln5v5P8Cu/\n8aZNvWatVqudawcOHGTY2gIsnrcYhu0t7N9/4Ly1X9t8utIoJMmgT+QqJhourmrgB4agKAgaLQQP\n7LmJ4wEiT2h0JpFGMxoOaLXb5Kmk2DiBL21abkBLuPRX19BlgZtJ8kGEnJui5SpaqoIgoGk8+otH\ncDuTOGVJXOV4IsBvKXprp5Dtcax1TZJmkBpsx8KUFe1m55vezzv/8D1YClRV0euVjPoJN1x/BR/+\nm4/xQy+9m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80WVZnyrg988FF7hlitVntssW2bIi+49WuneNlT9nHvosvh06e5auwSosGQ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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3059b8fed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def makeScatter(mdsDf,ax,identDict=identDict,emoDict=emoDict):\n",
    "\n",
    "    # make the scatterplot\n",
    "    for ident,emo in mdsDf.index:\n",
    "        thisDf = mdsDf.ix[ident].ix[emo]\n",
    "        myAx = ax.errorbar(thisDf[0], thisDf[1],\n",
    "                           c=stackColors[emoReverse[emo]],\n",
    "                           marker='ov'[identReverse[ident]],\n",
    "                           markersize=35,\n",
    "                           markeredgewidth=.5,\n",
    "                           #label=ident+' '+emo\n",
    "                           );\n",
    "\n",
    "    sns.despine()   \n",
    "    ax.set_ylim(-0.75,0.6);ax.set_xlim(-0.6,0.6)\n",
    "    #ax.legend(loc='upper right')\n",
    "    ax.set_yticks([]); ax.set_xticks([])\n",
    "    ax.set_ylabel(''); ax.set_xlabel('')\n",
    "    \n",
    "    return myAx\n",
    "\n",
    "def makePicture(mdsDf,ax,myZoom=0.15):\n",
    "    mdsDf.plot(0,1, kind='scatter', s=50,ax=ax)\n",
    "    for label, x, y in zip(mdsDf.index, mdsDf[0], mdsDf[1]):\n",
    "        imagebox = OffsetImage(read_png(picList[identReverse[label[0]] ][emoReverse[label[1]] ]), zoom=myZoom)\n",
    "        ab = AnnotationBbox(imagebox, [x,y],pad=0)\n",
    "        ax.add_artist(ab)\n",
    "    ax.set_ylim(-0.75,0.6);ax.set_xlim(-0.6,0.6)\n",
    "    ax.set_yticks([]); ax.set_xticks([])\n",
    "    ax.set_ylabel(''); ax.set_xlabel('')\n",
    "    return ax;\n",
    "\n",
    "\n",
    "\n",
    "f, ( ax1, ax2 ) = plt.subplots( 1,2,figsize=(16,8) );\n",
    "makeScatter(mdsImgDf,ax1);\n",
    "makePicture(mdsImgDf,ax2,myZoom=.15);\n",
    "sns.despine()\n",
    "\n",
    "#plt.savefig('../figures/mdsPixel.png',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make progression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from procrustesTransformation import procrustes # http://stackoverflow.com/a/18927641; Alistair Muldal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makeProcrustesDf(weightDf):\n",
    "\n",
    "    # first we make a RSA/rsa with the averaged data, this is\n",
    "    # the template to which each participant's individual data is\n",
    "    # rotated to\n",
    "    templateDf = makeRsaDf(weightDf)\n",
    "    \n",
    "    # now do an RSA for each participant\n",
    "    myIndex = weightDf.index.levels[0]\n",
    "    for i,p in enumerate(myIndex) :\n",
    "        time.sleep(0.01) # prevent crashing...\n",
    "        thisSelection = weightDf.ix[myIndex[:i+1]]\n",
    "\n",
    "        thisDf = makeRsaDf(thisSelection.fillna(0))\n",
    "        \n",
    "        # rotate to template\n",
    "        d, Z, tform = procrustes(np.array(templateDf),np.array(thisDf),scaling=False)\n",
    "        rotatedDf = pd.DataFrame(Z,index=thisDf.index)\n",
    "        \n",
    "        # make figure\n",
    "        f, ( ax1 ) = plt.subplots( 1,1,figsize=(8,8) );\n",
    "        \n",
    "        thisSelection = rotatedDf.ix[ rotatedDf.index.levels[0][:i+1] ]\n",
    "        \n",
    "        #makeScatter(rotatedDf,ax1);\n",
    "        makePicture(rotatedDf,ax1);\n",
    "        \n",
    "        sns.despine(left=True,bottom=True)\n",
    "                \n",
    "        f.suptitle('n='+str(i+1),fontsize=30);\n",
    "        f.savefig('../moviePics/mdsPlot'+('00'+str(i+1))[-3:]+'.png');\n",
    "        \n",
    "        plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "makeProcrustesDf(weightDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mdsPics = getFile('../moviePics/','mdsHybrid*.png')"
   ]
  }
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