inter-subject_correlation_mfMRI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Z-score, separate volumes by frame-topic, and calculate ISC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import nibabel as nib\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/project/3013104.01'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir('/project/3013104.01')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Z-score estimations within session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0001_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0003_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0004_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0005_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0006_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0007_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0008_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0009_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0010_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0011_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0012_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0013_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0014_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0015_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0017_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0018_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0019_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0020_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0021_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0022_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0023_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0024_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0025_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0026_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0028_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0029_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0030_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0032_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0033_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0034_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0035_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0036_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0037_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0038_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0039_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0040_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0042_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0043_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0044_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0045_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0046_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0047_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0048_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0049_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0050_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0051_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0052_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0053_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0054_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0055_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0056_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0063_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0057_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0058_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0059_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0061_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0062_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0064_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0065_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0066_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0067_mri_movietask_1_motion_regressed_z.csv\n",
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      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0069_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0070_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0071_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0072_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0073_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0074_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0075_mri_movietask_1_motion_regressed_z.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0076_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0077_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0078_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0079_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0080_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0081_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0082_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0083_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0084_mri_movietask_1_motion_regressed_z.csv\n",
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      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0087_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0090_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0091_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0092_mri_movietask_1_motion_regressed_z.csv\n",
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      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0096_mri_movietask_1_motion_regressed_z.csv\n",
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      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0101_mri_movietask_1_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0102_mri_movietask_1_motion_regressed_z.csv\n",
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      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0105_mri_movietask_1_motion_regressed_z.csv\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0151_mri_movietask_1_motion_regressed_z.csv\n",
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      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0078_mri_movietask_2_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0079_mri_movietask_2_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0080_mri_movietask_2_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0081_mri_movietask_2_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0082_mri_movietask_2_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0083_mri_movietask_2_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0084_mri_movietask_2_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0086_mri_movietask_2_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0087_mri_movietask_2_motion_regressed_z.csv\n",
      "Z-scored file saved to: analysis/roi_estimations/motion/z-scored_TEST/subject-0088_mri_movietask_2_motion_regressed_z.csv\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-015120ec9937>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;31m# Write the z-scored data to a new CSV file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0mz_scored_file_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf'{os.path.splitext(csv_file)[0]}_z.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mz_scored_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz_scored_file_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Z-scored file saved to: {z_scored_file_path}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/5.0.0/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mto_csv\u001b[0;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, tupleize_cols, date_format, doublequote, escapechar, decimal)\u001b[0m\n\u001b[1;32m   1401\u001b[0m                                      \u001b[0mdoublequote\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdoublequote\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1402\u001b[0m                                      escapechar=escapechar, decimal=decimal)\n\u001b[0;32m-> 1403\u001b[0;31m         \u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1405\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpath_or_buf\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/5.0.0/lib/python3.6/site-packages/pandas/io/formats/format.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1590\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mwriter_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1592\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1594\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/5.0.0/lib/python3.6/site-packages/pandas/io/formats/format.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1691\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1692\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1693\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_save_chunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_i\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1694\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1695\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_save_chunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_i\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/5.0.0/lib/python3.6/site-packages/pandas/io/formats/format.py\u001b[0m in \u001b[0;36m_save_chunk\u001b[0;34m(self, start_i, end_i)\u001b[0m\n\u001b[1;32m   1705\u001b[0m                                   \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1706\u001b[0m                                   \u001b[0mdate_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1707\u001b[0;31m                                   quoting=self.quoting)\n\u001b[0m\u001b[1;32m   1708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1709\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mcol_loc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmgr_locs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/5.0.0/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mto_native_types\u001b[0;34m(self, slicer, na_rep, float_format, decimal, quoting, **kwargs)\u001b[0m\n\u001b[1;32m   1608\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1609\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mquoting\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1610\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1611\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1612\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'object'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "data_dir = 'analysis/roi_estimations/motion' # Contains one .csv file per subject per session; each .csv file is volume rows x ROI columns\n",
    "out_dir = f'{data_dir}/z-scored'\n",
    "\n",
    "# Create the output directory if it doesn't exist\n",
    "os.makedirs(out_dir, exist_ok=True)\n",
    "\n",
    "# List all .csv files in the data directory\n",
    "csv_files = [file for file in os.listdir(data_dir) if file.endswith('.csv')]\n",
    "\n",
    "# Z-score each .csv file\n",
    "for csv_file in csv_files:\n",
    "    # Read the CSV file\n",
    "    df = pd.read_csv(os.path.join(data_dir, csv_file))\n",
    "    \n",
    "    # Z-score each column\n",
    "    z_scored_df = (df - df.mean()) / df.std()\n",
    "    \n",
    "    # Write the z-scored data to a new CSV file\n",
    "    z_scored_file_path = os.path.join(out_dir, f'{os.path.splitext(csv_file)[0]}_z.csv')\n",
    "    z_scored_df.to_csv(z_scored_file_path, index=False)\n",
    "\n",
    "    print(f'Z-scored file saved to: {z_scored_file_path}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separate fMRI volumes according to movie fMRI topic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create three new matrices per scan so that each subject has separate matrices for each unique frame-topic combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File not found for subject-0126_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0126_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0126_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0201_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0201_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0201_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0139_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0139_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0139_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0186_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0186_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0186_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0116_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0116_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0116_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0180_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0180_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0180_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0135_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0135_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0135_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0211_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0211_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0211_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0233_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0233_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0233_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0090_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0090_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0090_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0113_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0113_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0113_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0194_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0194_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0194_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0145_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0145_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0145_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0171_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0171_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0171_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0128_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0128_mri_movietask_2. Skipping...\n",
      "File not found for subject-0128_mri_movietask_3. Skipping...\n",
      "Data frames for subject-0153_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0153_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0153_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0074_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0074_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0074_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0119_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0119_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0119_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0069_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0069_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0069_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0150_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0150_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0150_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0230_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0230_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0230_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0179_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0179_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0179_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0143_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0143_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0143_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0231_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0231_mri_movietask_2 are saved in the output directory.\n",
      "File not found for subject-0231_mri_movietask_3. Skipping...\n",
      "Data frames for subject-0013_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0013_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0013_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0021_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0021_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0021_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0185_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0185_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0185_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0038_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0038_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0038_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0025_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0025_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0025_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0046_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0046_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0046_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0120_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0120_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0120_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0172_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0172_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0172_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0047_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0047_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0047_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0123_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0123_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0123_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0052_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0052_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0052_mri_movietask_3 are saved in the output directory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data frames for subject-0114_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0114_mri_movietask_2 are saved in the output directory.\n",
      "File not found for subject-0114_mri_movietask_3. Skipping...\n",
      "Data frames for subject-0205_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0205_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0205_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0032_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0032_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0032_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0103_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0103_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0103_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0031_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0031_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0031_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0162_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0162_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0162_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0007_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0007_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0007_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0011_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0011_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0011_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0183_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0183_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0183_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0053_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0053_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0053_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0072_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0072_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0072_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0029_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0029_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0029_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0198_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0198_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0198_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0159_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0159_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0159_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0181_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0181_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0181_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0202_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0202_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0202_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0068_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0068_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0068_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0043_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0043_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0043_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0086_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0086_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0086_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0187_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0187_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0187_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0105_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0105_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0105_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0189_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0189_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0189_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0095_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0095_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0095_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0178_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0178_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0178_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0208_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0208_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0208_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0039_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0039_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0039_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0058_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0058_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0058_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0220_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0220_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0220_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0223_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0223_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0223_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0104_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0104_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0104_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0212_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0212_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0212_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0037_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0037_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0037_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0018_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0018_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0018_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0112_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0112_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0112_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0213_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0213_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0213_mri_movietask_3 are saved in the output directory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data frames for subject-0118_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0118_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0118_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0165_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0165_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0165_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0022_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0022_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0022_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0049_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0049_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0049_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0218_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0218_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0218_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0102_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0102_mri_movietask_2 are saved in the output directory.\n",
      "File not found for subject-0102_mri_movietask_3. Skipping...\n",
      "Data frames for subject-0161_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0161_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0161_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0080_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0080_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0080_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0077_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0077_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0077_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0106_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0106_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0106_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0003_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0003_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0003_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0091_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0091_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0091_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0083_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0083_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0083_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0075_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0075_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0075_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0225_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0225_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0225_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0132_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0132_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0132_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0163_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0163_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0163_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0206_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0206_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0206_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0182_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0182_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0182_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0056_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0056_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0056_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0237_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0237_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0237_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0121_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0121_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0121_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0110_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0110_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0110_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0001_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0001_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0001_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0026_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0026_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0026_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0164_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0164_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0164_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0041_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0041_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0041_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0137_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0137_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0137_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0214_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0214_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0214_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0156_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0156_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0156_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0166_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0166_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0166_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0096_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0096_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0096_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0236_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0236_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0236_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0097_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0097_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0097_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0122_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0122_mri_movietask_2 are saved in the output directory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data frames for subject-0122_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0127_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0127_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0127_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0020_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0020_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0020_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0235_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0235_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0235_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0232_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0232_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0232_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0044_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0044_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0044_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0101_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0101_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0101_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0057_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0057_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0057_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0045_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0045_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0045_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0015_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0015_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0015_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0087_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0087_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0087_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0227_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0227_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0227_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0174_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0174_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0174_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0055_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0055_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0055_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0098_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0098_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0098_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0051_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0051_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0051_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0034_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0034_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0034_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0076_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0076_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0076_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0094_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0094_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0094_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0154_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0154_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0154_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0203_mri_movietask_1. Skipping...\n",
      "File not found for subject-0203_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0203_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0014_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0014_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0014_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0060_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0060_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0060_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0099_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0099_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0099_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0167_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0167_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0167_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0239_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0239_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0239_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0012_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0012_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0012_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0089_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0089_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0089_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0199_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0199_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0199_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0036_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0036_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0036_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0093_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0093_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0093_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0224_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0224_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0224_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0219_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0219_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0219_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0117_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0117_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0117_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0023_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0023_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0023_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0155_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0155_mri_movietask_2 are saved in the output directory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data frames for subject-0155_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0215_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0215_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0215_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0081_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0081_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0081_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0085_mri_movietask_1. Skipping...\n",
      "File not found for subject-0085_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0085_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0176_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0176_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0176_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0130_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0130_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0130_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0061_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0061_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0061_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0125_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0125_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0125_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0148_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0148_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0148_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0207_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0207_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0207_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0197_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0197_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0197_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0133_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0133_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0133_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0169_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0169_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0169_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0109_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0109_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0109_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0050_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0050_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0050_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0065_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0065_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0065_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0149_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0149_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0149_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0196_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0196_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0196_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0010_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0010_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0010_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0006_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0006_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0006_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0124_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0124_mri_movietask_2 are saved in the output directory.\n",
      "File not found for subject-0124_mri_movietask_3. Skipping...\n",
      "Data frames for subject-0108_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0108_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0108_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0175_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0175_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0175_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0192_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0192_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0192_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0221_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0221_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0221_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0054_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0054_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0054_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0082_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0082_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0082_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0193_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0193_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0193_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0073_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0073_mri_movietask_2 are saved in the output directory.\n",
      "File not found for subject-0073_mri_movietask_3. Skipping...\n",
      "Data frames for subject-0071_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0071_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0071_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0168_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0168_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0168_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0134_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0134_mri_movietask_2 are saved in the output directory.\n",
      "File not found for subject-0134_mri_movietask_3. Skipping...\n",
      "Data frames for subject-0070_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0070_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0070_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0048_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0048_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0048_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0078_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0078_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0078_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0107_mri_movietask_1 are saved in the output directory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data frames for subject-0107_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0107_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0005_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0005_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0005_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0030_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0030_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0030_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0144_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0144_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0144_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0064_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0064_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0064_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0092_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0092_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0092_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0028_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0028_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0028_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0146_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0146_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0146_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0063_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0063_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0063_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0024_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0024_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0024_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0177_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0177_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0177_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0140_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0140_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0140_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0042_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0042_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0042_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0234_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0234_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0234_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0100_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0100_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0100_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0170_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0170_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0170_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0035_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0035_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0035_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0152_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0152_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0152_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0228_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0228_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0228_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0195_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0195_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0195_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0158_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0158_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0158_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0009_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0009_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0009_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0191_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0191_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0191_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0136_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0136_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0136_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0088_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0088_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0088_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0151_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0151_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0151_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0141_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0141_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0141_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0019_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0019_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0019_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0222_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0222_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0222_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0115_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0115_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0115_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0067_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0067_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0067_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0226_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0226_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0226_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0210_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0210_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0210_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0111_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0111_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0111_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0059_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0059_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0059_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0079_mri_movietask_1 are saved in the output directory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data frames for subject-0079_mri_movietask_2 are saved in the output directory.\n",
      "File not found for subject-0079_mri_movietask_3. Skipping...\n",
      "File not found for subject-0160_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0160_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0160_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0066_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0066_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0066_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0131_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0131_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0131_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0238_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0238_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0238_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0157_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0157_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0157_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0190_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0190_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0190_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0217_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0217_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0217_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0062_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0062_mri_movietask_2. Skipping...\n",
      "File not found for subject-0062_mri_movietask_3. Skipping...\n",
      "Data frames for subject-0188_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0188_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0188_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0229_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0229_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0229_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0027_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0027_mri_movietask_2 are saved in the output directory.\n",
      "File not found for subject-0027_mri_movietask_3. Skipping...\n",
      "Data frames for subject-0209_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0209_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0209_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0184_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0184_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0184_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0138_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0138_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0138_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0017_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0017_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0017_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0084_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0084_mri_movietask_2 are saved in the output directory.\n",
      "File not found for subject-0084_mri_movietask_3. Skipping...\n",
      "Data frames for subject-0040_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0040_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0040_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0204_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0204_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0204_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0004_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0004_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0004_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0008_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0008_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0008_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0142_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0142_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0142_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0200_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0200_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0200_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0216_mri_movietask_1. Skipping...\n",
      "Data frames for subject-0216_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0216_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0033_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0033_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0033_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0173_mri_movietask_1 are saved in the output directory.\n",
      "Data frames for subject-0173_mri_movietask_2 are saved in the output directory.\n",
      "Data frames for subject-0173_mri_movietask_3 are saved in the output directory.\n",
      "Data frames for subject-0147_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0147_mri_movietask_2. Skipping...\n",
      "File not found for subject-0147_mri_movietask_3. Skipping...\n",
      "Data frames for subject-0129_mri_movietask_1 are saved in the output directory.\n",
      "File not found for subject-0129_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0129_mri_movietask_3 are saved in the output directory.\n",
      "File not found for subject-0016_mri_movietask_1. Skipping...\n",
      "File not found for subject-0016_mri_movietask_2. Skipping...\n",
      "Data frames for subject-0016_mri_movietask_3 are saved in the output directory.\n",
      "All data frames are processed.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import shutil\n",
    "\n",
    "mri_dir = 'analysis/smoothed/pulled-data-MRI' # for listing subjects IDs\n",
    "data_dir = 'analysis/roi_estimations/motion/z-scored'\n",
    "EVfile = 'analysis/EVfiles.csv'\n",
    "output_dir = 'analysis/aural_framing_by_movie_topic/z_roi_node'\n",
    "subjects = [subject for subject in os.listdir(mri_dir) if subject.startswith('subject-')]\n",
    "mri_movietasks = ['mri_movietask_1', 'mri_movietask_2', 'mri_movietask_3']\n",
    "\n",
    "# Check if the output_dir exists and delete it\n",
    "if os.path.exists(output_dir):\n",
    "    shutil.rmtree(output_dir)\n",
    "\n",
    "# Create the output_dir\n",
    "os.makedirs(output_dir)\n",
    "\n",
    "# Read the event files data\n",
    "ev_files_df = pd.read_csv(EVfile)\n",
    "\n",
    "for subject in subjects:\n",
    "    for task in mri_movietasks:\n",
    "        file_path = f'{data_dir}/{subject}_{task}_motion_regressed_z.csv' # Check this filename!!!\n",
    "\n",
    "        try:\n",
    "            roi_estimations = pd.read_csv(file_path)\n",
    "\n",
    "            # Insert new column 'volume' and fill it with (row number + 1)\n",
    "            roi_estimations['volume'] = roi_estimations.index + 1\n",
    "\n",
    "            # Iterate over unique combinations of aural framing' and topic\n",
    "            for _, row in ev_files_df.loc[(ev_files_df['subject'] == subject) & (ev_files_df['mri_movietask'] == task)].iterrows():\n",
    "                framing = row['framing']\n",
    "                topic_name = row['topic_name']\n",
    "                volume_number = row['volume_onset_6'] # Remove 6 seconds after the movie begins\n",
    "                volume_duration = row['volume_duration'] # Duration remains the same even though we remove the first 6 seconds after the movie begins because it's still movie-driven activity \n",
    "\n",
    "                result_df = roi_estimations[(roi_estimations['volume'] >= volume_number) &\n",
    "                                            (roi_estimations['volume'] < volume_number + volume_duration)]\n",
    "\n",
    "                # Create the output file name\n",
    "                output_file = os.path.join(output_dir, f\"{subject}_{task}_{volume_number}_{framing}_{topic_name}.csv\")\n",
    "\n",
    "                # Save the data frame to the output file\n",
    "                result_df.to_csv(output_file, index=False)\n",
    "\n",
    "            print(f\"Data frames for {subject}_{task} are saved in the output directory.\")\n",
    "        except FileNotFoundError:\n",
    "            print(f\"File not found for {subject}_{task}. Skipping...\")\n",
    "            continue\n",
    "\n",
    "print(\"All data frames are processed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optional: Add volume column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added 'volume' column to subject-0001_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0003_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0004_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0005_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0006_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0007_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0008_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0009_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0010_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0011_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0012_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0013_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0014_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0015_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0017_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0018_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0019_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0020_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0021_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0022_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0023_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0024_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0025_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0026_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0028_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0029_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0030_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0032_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0033_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0034_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0035_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0036_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0037_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0038_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0039_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0040_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0042_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0043_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0044_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0045_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0046_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0047_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0048_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0049_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0050_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0051_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0052_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0053_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0054_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0055_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0056_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0063_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0057_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0058_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0059_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0061_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0062_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0064_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0065_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0066_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0067_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0068_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0069_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0070_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0071_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0072_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0073_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0074_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0075_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0076_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0077_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0078_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0079_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0080_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0081_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0082_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0083_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0084_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0086_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0087_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0090_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0091_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0092_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0093_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0094_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0095_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0096_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0097_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0098_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0099_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0100_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0101_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0102_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0103_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0104_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0105_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0106_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0107_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0108_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0109_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0110_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0111_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0113_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0114_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0115_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0117_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0118_mri_movietask_1_motion_regressed_z.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added 'volume' column to subject-0119_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0120_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0121_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0122_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0123_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0124_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0125_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0127_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0128_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0129_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0130_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0131_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0132_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0133_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0134_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0135_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0137_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0138_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0139_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0140_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0141_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0142_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0150_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0143_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0144_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0145_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0146_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0147_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0148_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0149_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0151_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0152_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0153_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0154_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0156_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0158_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0159_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0161_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0164_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0165_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0166_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0167_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0168_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0169_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0170_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0171_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0172_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0173_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0174_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0175_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0176_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0177_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0178_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0179_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0180_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0182_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0183_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0184_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0185_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0186_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0187_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0188_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0189_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0190_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0191_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0192_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0193_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0194_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0195_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0196_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0197_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0198_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0199_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0200_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0201_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0202_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0204_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0205_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0207_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0206_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0208_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0209_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0210_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0211_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0212_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0213_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0214_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0215_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0217_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0218_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0220_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0221_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0222_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0223_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0224_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0225_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0226_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0227_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0228_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0229_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0230_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0231_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0232_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0233_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0234_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0235_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0236_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0238_mri_movietask_1_motion_regressed_z.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added 'volume' column to subject-0237_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0239_mri_movietask_1_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0001_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0003_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0004_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0005_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0006_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0007_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0009_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0010_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0011_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0012_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0013_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0014_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0015_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0017_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0018_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0019_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0020_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0021_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0022_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0024_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0025_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0026_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0027_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0028_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0029_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0030_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0031_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0032_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0034_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0035_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0036_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0037_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0038_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0039_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0041_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0042_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0043_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0044_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0045_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0046_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0048_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0049_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0050_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0051_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0052_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0053_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0054_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0055_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0056_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0063_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0057_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0059_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0060_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0061_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0064_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0065_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0067_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0068_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0069_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0070_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0072_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0073_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0074_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0075_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0076_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0077_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0078_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0079_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0080_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0081_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0082_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0083_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0084_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0086_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0087_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0088_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0089_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0090_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0091_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0092_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0093_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0094_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0095_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0096_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0097_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0098_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0099_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0100_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0101_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0102_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0103_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0104_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0105_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0106_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0107_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0108_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0109_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0110_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0111_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0112_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0113_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0114_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0115_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0116_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0117_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0118_mri_movietask_2_motion_regressed_z.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added 'volume' column to subject-0119_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0120_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0121_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0122_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0124_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0125_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0126_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0127_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0130_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0131_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0132_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0133_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0134_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0135_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0136_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0137_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0139_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0140_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0141_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0150_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0143_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0144_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0145_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0146_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0148_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0149_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0151_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0152_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0153_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0154_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0155_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0156_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0157_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0159_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0160_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0161_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0162_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0163_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0164_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0165_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0166_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0167_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0168_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0169_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0170_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0171_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0172_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0173_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0174_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0175_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0176_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0177_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0178_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0179_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0180_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0181_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0182_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0183_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0184_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0186_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0189_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0190_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0191_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0192_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0193_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0194_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0195_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0196_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0197_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0198_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0200_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0201_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0202_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0205_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0207_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0206_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0208_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0210_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0211_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0212_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0213_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0214_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0215_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0216_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0217_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0218_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0219_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0220_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0221_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0222_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0223_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0224_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0225_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0226_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0227_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0229_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0230_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0231_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0232_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0233_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0234_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0235_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0236_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0238_mri_movietask_2_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0001_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0003_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0004_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0005_mri_movietask_3_motion_regressed_z.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added 'volume' column to subject-0006_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0007_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0008_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0009_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0010_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0011_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0012_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0013_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0014_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0015_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0016_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0017_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0018_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0019_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0020_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0021_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0022_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0023_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0024_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0025_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0026_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0028_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0029_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0030_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0031_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0032_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0033_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0034_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0035_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0036_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0037_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0038_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0039_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0040_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0041_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0042_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0043_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0044_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0045_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0046_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0047_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0048_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0049_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0050_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0051_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0052_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0053_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0054_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0055_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0056_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0063_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0057_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0058_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0059_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0060_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0061_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0064_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0065_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0066_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0067_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0068_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0069_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0070_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0071_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0072_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0074_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0075_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0076_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0077_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0078_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0080_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0081_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0082_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0083_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0085_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0086_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0087_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0088_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0089_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0090_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0091_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0092_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0093_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0094_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0095_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0096_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0097_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0098_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0099_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0100_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0101_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0103_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0104_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0105_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0106_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0107_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0108_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0109_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0110_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0111_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0112_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0113_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0115_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0116_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0117_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0118_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0119_mri_movietask_3_motion_regressed_z.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added 'volume' column to subject-0120_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0121_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0122_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0123_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0125_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0126_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0127_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0129_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0130_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0131_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0132_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0133_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0135_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0136_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0137_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0138_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0139_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0140_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0141_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0142_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0150_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0143_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0144_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0145_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0146_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0148_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0149_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0151_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0152_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0153_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0154_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0155_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0156_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0157_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0158_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0159_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0160_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0161_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0162_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0163_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0164_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0165_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0166_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0167_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0168_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0169_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0170_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0171_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0172_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0173_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0174_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0175_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0176_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0177_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0178_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0179_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0180_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0181_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0182_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0183_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0184_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0185_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0186_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0187_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0188_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0189_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0190_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0191_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0192_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0193_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0194_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0195_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0196_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0197_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0198_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0199_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0200_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0201_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0202_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0203_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0204_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0205_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0207_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0206_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0208_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0209_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0210_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0211_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0212_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0213_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0214_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0215_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0216_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0217_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0218_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0219_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0220_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0221_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0222_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0223_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0224_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0225_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0226_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0227_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0228_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0229_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0230_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0232_mri_movietask_3_motion_regressed_z.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added 'volume' column to subject-0233_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0234_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0235_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0236_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0238_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0237_mri_movietask_3_motion_regressed_z.csv\n",
      "Added 'volume' column to subject-0239_mri_movietask_3_motion_regressed_z.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "def add_volume_column(csv_file):\n",
    "    # Read the CSV file\n",
    "    df = pd.read_csv(csv_file, header=0, index_col=None)\n",
    "\n",
    "    # Add a new column 'volume' with row numbers\n",
    "    df['volume'] = range(1, len(df) + 1)\n",
    "\n",
    "    # Save the updated DataFrame back to the CSV file\n",
    "    df.to_csv(csv_file, index=False)\n",
    "\n",
    "def add_volume_column_to_all_csv_files(directory):\n",
    "    # Get a list of all CSV files in the specified directory\n",
    "    csv_files = [file for file in os.listdir(directory) if file.endswith('.csv')]\n",
    "\n",
    "    # Iterate through each CSV file and add the 'volume' column\n",
    "    for csv_file in csv_files:\n",
    "        csv_path = os.path.join(directory, csv_file)\n",
    "        add_volume_column(csv_path)\n",
    "        print(f\"Added 'volume' column to {csv_file}\")\n",
    "\n",
    "csv_directory = 'analysis/roi_estimations/motion/z-scored'\n",
    "\n",
    "# Add 'volume' column to all CSV files in the specified directory\n",
    "add_volume_column_to_all_csv_files(csv_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create NaN matrices for subjects with missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of files in analysis/aural_framing_by_movie_topic/roi_node: 1914\n",
      "Missing data info saved as 'missing_subjects_table.csv'\n",
      "Frame Topic: neutral_climate_change\n",
      "Missing Subjects: {'0204', '0185', '0147', '0022', '0213', '0199', '0060', '0093', '0157', '0203', '0088', '0027', '0228', '0062', '0215', '0124', '0058', '0040', '0112', '0162', '0089', '0008', '0161'}\n",
      "Created matrix: subject-0008_neutral_climate_change.csv\n",
      "Created matrix: subject-0022_neutral_climate_change.csv\n",
      "Created matrix: subject-0027_neutral_climate_change.csv\n",
      "Created matrix: subject-0040_neutral_climate_change.csv\n",
      "Created matrix: subject-0058_neutral_climate_change.csv\n",
      "Created matrix: subject-0060_neutral_climate_change.csv\n",
      "Created matrix: subject-0062_neutral_climate_change.csv\n",
      "Created matrix: subject-0088_neutral_climate_change.csv\n",
      "Created matrix: subject-0089_neutral_climate_change.csv\n",
      "Created matrix: subject-0093_neutral_climate_change.csv\n",
      "Created matrix: subject-0112_neutral_climate_change.csv\n",
      "Created matrix: subject-0124_neutral_climate_change.csv\n",
      "Created matrix: subject-0147_neutral_climate_change.csv\n",
      "Created matrix: subject-0157_neutral_climate_change.csv\n",
      "Created matrix: subject-0161_neutral_climate_change.csv\n",
      "Created matrix: subject-0162_neutral_climate_change.csv\n",
      "Created matrix: subject-0185_neutral_climate_change.csv\n",
      "Created matrix: subject-0199_neutral_climate_change.csv\n",
      "Created matrix: subject-0203_neutral_climate_change.csv\n",
      "Created matrix: subject-0204_neutral_climate_change.csv\n",
      "Created matrix: subject-0213_neutral_climate_change.csv\n",
      "Created matrix: subject-0215_neutral_climate_change.csv\n",
      "Created matrix: subject-0228_neutral_climate_change.csv\n",
      "Frame Topic: threat_climate_change\n",
      "Missing Subjects: {'0128', '0071', '0031', '0084', '0152', '0116', '0044', '0136', '0163', '0155', '0114', '0066', '0174', '0129', '0203', '0016', '0237', '0085', '0047', '0134', '0062', '0231', '0040', '0188', '0209', '0201', '0181', '0023', '0126', '0145', '0142'}\n",
      "Created matrix: subject-0016_threat_climate_change.csv\n",
      "Created matrix: subject-0023_threat_climate_change.csv\n",
      "Created matrix: subject-0031_threat_climate_change.csv\n",
      "Created matrix: subject-0040_threat_climate_change.csv\n",
      "Created matrix: subject-0044_threat_climate_change.csv\n",
      "Created matrix: subject-0047_threat_climate_change.csv\n",
      "Created matrix: subject-0062_threat_climate_change.csv\n",
      "Created matrix: subject-0066_threat_climate_change.csv\n",
      "Created matrix: subject-0071_threat_climate_change.csv\n",
      "Created matrix: subject-0084_threat_climate_change.csv\n",
      "Created matrix: subject-0085_threat_climate_change.csv\n",
      "Created matrix: subject-0114_threat_climate_change.csv\n",
      "Created matrix: subject-0116_threat_climate_change.csv\n",
      "Created matrix: subject-0126_threat_climate_change.csv\n",
      "Created matrix: subject-0128_threat_climate_change.csv\n",
      "Created matrix: subject-0129_threat_climate_change.csv\n",
      "Created matrix: subject-0134_threat_climate_change.csv\n",
      "Created matrix: subject-0136_threat_climate_change.csv\n",
      "Created matrix: subject-0142_threat_climate_change.csv\n",
      "Created matrix: subject-0145_threat_climate_change.csv\n",
      "Created matrix: subject-0152_threat_climate_change.csv\n",
      "Created matrix: subject-0155_threat_climate_change.csv\n",
      "Created matrix: subject-0163_threat_climate_change.csv\n",
      "Created matrix: subject-0174_threat_climate_change.csv\n",
      "Created matrix: subject-0181_threat_climate_change.csv\n",
      "Created matrix: subject-0188_threat_climate_change.csv\n",
      "Created matrix: subject-0201_threat_climate_change.csv\n",
      "Created matrix: subject-0203_threat_climate_change.csv\n",
      "Created matrix: subject-0209_threat_climate_change.csv\n",
      "Created matrix: subject-0231_threat_climate_change.csv\n",
      "Created matrix: subject-0237_threat_climate_change.csv\n",
      "Frame Topic: blame_climate_change\n",
      "Missing Subjects: {'0128', '0026', '0073', '0239', '0147', '0028', '0033', '0100', '0146', '0079', '0041', '0102', '0027', '0016', '0123', '0074', '0160', '0094', '0085', '0158', '0187', '0219', '0025', '0138', '0089', '0166', '0216'}\n",
      "Created matrix: subject-0016_blame_climate_change.csv\n",
      "Created matrix: subject-0025_blame_climate_change.csv\n",
      "Created matrix: subject-0026_blame_climate_change.csv\n",
      "Created matrix: subject-0027_blame_climate_change.csv\n",
      "Created matrix: subject-0028_blame_climate_change.csv\n",
      "Created matrix: subject-0033_blame_climate_change.csv\n",
      "Created matrix: subject-0041_blame_climate_change.csv\n",
      "Created matrix: subject-0073_blame_climate_change.csv\n",
      "Created matrix: subject-0074_blame_climate_change.csv\n",
      "Created matrix: subject-0079_blame_climate_change.csv\n",
      "Created matrix: subject-0085_blame_climate_change.csv\n",
      "Created matrix: subject-0089_blame_climate_change.csv\n",
      "Created matrix: subject-0094_blame_climate_change.csv\n",
      "Created matrix: subject-0100_blame_climate_change.csv\n",
      "Created matrix: subject-0102_blame_climate_change.csv\n",
      "Created matrix: subject-0123_blame_climate_change.csv\n",
      "Created matrix: subject-0128_blame_climate_change.csv\n",
      "Created matrix: subject-0138_blame_climate_change.csv\n",
      "Created matrix: subject-0146_blame_climate_change.csv\n",
      "Created matrix: subject-0147_blame_climate_change.csv\n",
      "Created matrix: subject-0158_blame_climate_change.csv\n",
      "Created matrix: subject-0160_blame_climate_change.csv\n",
      "Created matrix: subject-0166_blame_climate_change.csv\n",
      "Created matrix: subject-0187_blame_climate_change.csv\n",
      "Created matrix: subject-0216_blame_climate_change.csv\n",
      "Created matrix: subject-0219_blame_climate_change.csv\n",
      "Created matrix: subject-0239_blame_climate_change.csv\n",
      "Frame Topic: neutral_healthcare\n",
      "Missing Subjects: {'0128', '0026', '0073', '0239', '0147', '0028', '0033', '0100', '0146', '0079', '0041', '0102', '0027', '0016', '0123', '0074', '0160', '0094', '0085', '0158', '0187', '0219', '0025', '0138', '0089', '0166', '0216'}\n",
      "Created matrix: subject-0016_neutral_healthcare.csv\n",
      "Created matrix: subject-0025_neutral_healthcare.csv\n",
      "Created matrix: subject-0026_neutral_healthcare.csv\n",
      "Created matrix: subject-0027_neutral_healthcare.csv\n",
      "Created matrix: subject-0028_neutral_healthcare.csv\n",
      "Created matrix: subject-0033_neutral_healthcare.csv\n",
      "Created matrix: subject-0041_neutral_healthcare.csv\n",
      "Created matrix: subject-0073_neutral_healthcare.csv\n",
      "Created matrix: subject-0074_neutral_healthcare.csv\n",
      "Created matrix: subject-0079_neutral_healthcare.csv\n",
      "Created matrix: subject-0085_neutral_healthcare.csv\n",
      "Created matrix: subject-0089_neutral_healthcare.csv\n",
      "Created matrix: subject-0094_neutral_healthcare.csv\n",
      "Created matrix: subject-0100_neutral_healthcare.csv\n",
      "Created matrix: subject-0102_neutral_healthcare.csv\n",
      "Created matrix: subject-0123_neutral_healthcare.csv\n",
      "Created matrix: subject-0128_neutral_healthcare.csv\n",
      "Created matrix: subject-0138_neutral_healthcare.csv\n",
      "Created matrix: subject-0146_neutral_healthcare.csv\n",
      "Created matrix: subject-0147_neutral_healthcare.csv\n",
      "Created matrix: subject-0158_neutral_healthcare.csv\n",
      "Created matrix: subject-0160_neutral_healthcare.csv\n",
      "Created matrix: subject-0166_neutral_healthcare.csv\n",
      "Created matrix: subject-0187_neutral_healthcare.csv\n",
      "Created matrix: subject-0216_neutral_healthcare.csv\n",
      "Created matrix: subject-0219_neutral_healthcare.csv\n",
      "Created matrix: subject-0239_neutral_healthcare.csv\n",
      "Frame Topic: threat_healthcare\n",
      "Missing Subjects: {'0204', '0185', '0147', '0022', '0213', '0199', '0060', '0093', '0157', '0203', '0088', '0027', '0228', '0062', '0215', '0124', '0058', '0040', '0112', '0162', '0089', '0008', '0161'}\n",
      "Created matrix: subject-0008_threat_healthcare.csv\n",
      "Created matrix: subject-0022_threat_healthcare.csv\n",
      "Created matrix: subject-0027_threat_healthcare.csv\n",
      "Created matrix: subject-0040_threat_healthcare.csv\n",
      "Created matrix: subject-0058_threat_healthcare.csv\n",
      "Created matrix: subject-0060_threat_healthcare.csv\n",
      "Created matrix: subject-0062_threat_healthcare.csv\n",
      "Created matrix: subject-0088_threat_healthcare.csv\n",
      "Created matrix: subject-0089_threat_healthcare.csv\n",
      "Created matrix: subject-0093_threat_healthcare.csv\n",
      "Created matrix: subject-0112_threat_healthcare.csv\n",
      "Created matrix: subject-0124_threat_healthcare.csv\n",
      "Created matrix: subject-0147_threat_healthcare.csv\n",
      "Created matrix: subject-0157_threat_healthcare.csv\n",
      "Created matrix: subject-0161_threat_healthcare.csv\n",
      "Created matrix: subject-0162_threat_healthcare.csv\n",
      "Created matrix: subject-0185_threat_healthcare.csv\n",
      "Created matrix: subject-0199_threat_healthcare.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created matrix: subject-0203_threat_healthcare.csv\n",
      "Created matrix: subject-0204_threat_healthcare.csv\n",
      "Created matrix: subject-0213_threat_healthcare.csv\n",
      "Created matrix: subject-0215_threat_healthcare.csv\n",
      "Created matrix: subject-0228_threat_healthcare.csv\n",
      "Frame Topic: blame_healthcare\n",
      "Missing Subjects: {'0128', '0071', '0031', '0084', '0152', '0116', '0044', '0136', '0163', '0155', '0114', '0066', '0174', '0129', '0203', '0016', '0237', '0085', '0047', '0134', '0062', '0231', '0040', '0188', '0209', '0201', '0181', '0023', '0126', '0145', '0142'}\n",
      "Created matrix: subject-0016_blame_healthcare.csv\n",
      "Created matrix: subject-0023_blame_healthcare.csv\n",
      "Created matrix: subject-0031_blame_healthcare.csv\n",
      "Created matrix: subject-0040_blame_healthcare.csv\n",
      "Created matrix: subject-0044_blame_healthcare.csv\n",
      "Created matrix: subject-0047_blame_healthcare.csv\n",
      "Created matrix: subject-0062_blame_healthcare.csv\n",
      "Created matrix: subject-0066_blame_healthcare.csv\n",
      "Created matrix: subject-0071_blame_healthcare.csv\n",
      "Created matrix: subject-0084_blame_healthcare.csv\n",
      "Created matrix: subject-0085_blame_healthcare.csv\n",
      "Created matrix: subject-0114_blame_healthcare.csv\n",
      "Created matrix: subject-0116_blame_healthcare.csv\n",
      "Created matrix: subject-0126_blame_healthcare.csv\n",
      "Created matrix: subject-0128_blame_healthcare.csv\n",
      "Created matrix: subject-0129_blame_healthcare.csv\n",
      "Created matrix: subject-0134_blame_healthcare.csv\n",
      "Created matrix: subject-0136_blame_healthcare.csv\n",
      "Created matrix: subject-0142_blame_healthcare.csv\n",
      "Created matrix: subject-0145_blame_healthcare.csv\n",
      "Created matrix: subject-0152_blame_healthcare.csv\n",
      "Created matrix: subject-0155_blame_healthcare.csv\n",
      "Created matrix: subject-0163_blame_healthcare.csv\n",
      "Created matrix: subject-0174_blame_healthcare.csv\n",
      "Created matrix: subject-0181_blame_healthcare.csv\n",
      "Created matrix: subject-0188_blame_healthcare.csv\n",
      "Created matrix: subject-0201_blame_healthcare.csv\n",
      "Created matrix: subject-0203_blame_healthcare.csv\n",
      "Created matrix: subject-0209_blame_healthcare.csv\n",
      "Created matrix: subject-0231_blame_healthcare.csv\n",
      "Created matrix: subject-0237_blame_healthcare.csv\n",
      "Frame Topic: neutral_immigration\n",
      "Missing Subjects: {'0128', '0071', '0031', '0084', '0152', '0116', '0044', '0136', '0163', '0155', '0114', '0066', '0174', '0129', '0203', '0016', '0237', '0085', '0047', '0134', '0062', '0231', '0040', '0188', '0209', '0201', '0181', '0023', '0126', '0145', '0142'}\n",
      "Created matrix: subject-0016_neutral_immigration.csv\n",
      "Created matrix: subject-0023_neutral_immigration.csv\n",
      "Created matrix: subject-0031_neutral_immigration.csv\n",
      "Created matrix: subject-0040_neutral_immigration.csv\n",
      "Created matrix: subject-0044_neutral_immigration.csv\n",
      "Created matrix: subject-0047_neutral_immigration.csv\n",
      "Created matrix: subject-0062_neutral_immigration.csv\n",
      "Created matrix: subject-0066_neutral_immigration.csv\n",
      "Created matrix: subject-0071_neutral_immigration.csv\n",
      "Created matrix: subject-0084_neutral_immigration.csv\n",
      "Created matrix: subject-0085_neutral_immigration.csv\n",
      "Created matrix: subject-0114_neutral_immigration.csv\n",
      "Created matrix: subject-0116_neutral_immigration.csv\n",
      "Created matrix: subject-0126_neutral_immigration.csv\n",
      "Created matrix: subject-0128_neutral_immigration.csv\n",
      "Created matrix: subject-0129_neutral_immigration.csv\n",
      "Created matrix: subject-0134_neutral_immigration.csv\n",
      "Created matrix: subject-0136_neutral_immigration.csv\n",
      "Created matrix: subject-0142_neutral_immigration.csv\n",
      "Created matrix: subject-0145_neutral_immigration.csv\n",
      "Created matrix: subject-0152_neutral_immigration.csv\n",
      "Created matrix: subject-0155_neutral_immigration.csv\n",
      "Created matrix: subject-0163_neutral_immigration.csv\n",
      "Created matrix: subject-0174_neutral_immigration.csv\n",
      "Created matrix: subject-0181_neutral_immigration.csv\n",
      "Created matrix: subject-0188_neutral_immigration.csv\n",
      "Created matrix: subject-0201_neutral_immigration.csv\n",
      "Created matrix: subject-0203_neutral_immigration.csv\n",
      "Created matrix: subject-0209_neutral_immigration.csv\n",
      "Created matrix: subject-0231_neutral_immigration.csv\n",
      "Created matrix: subject-0237_neutral_immigration.csv\n",
      "Frame Topic: threat_immigration\n",
      "Missing Subjects: {'0128', '0026', '0073', '0239', '0147', '0028', '0033', '0100', '0146', '0079', '0041', '0102', '0027', '0016', '0123', '0074', '0160', '0094', '0085', '0158', '0187', '0219', '0025', '0138', '0089', '0166', '0216'}\n",
      "Created matrix: subject-0016_threat_immigration.csv\n",
      "Created matrix: subject-0025_threat_immigration.csv\n",
      "Created matrix: subject-0026_threat_immigration.csv\n",
      "Created matrix: subject-0027_threat_immigration.csv\n",
      "Created matrix: subject-0028_threat_immigration.csv\n",
      "Created matrix: subject-0033_threat_immigration.csv\n",
      "Created matrix: subject-0041_threat_immigration.csv\n",
      "Created matrix: subject-0073_threat_immigration.csv\n",
      "Created matrix: subject-0074_threat_immigration.csv\n",
      "Created matrix: subject-0079_threat_immigration.csv\n",
      "Created matrix: subject-0085_threat_immigration.csv\n",
      "Created matrix: subject-0089_threat_immigration.csv\n",
      "Created matrix: subject-0094_threat_immigration.csv\n",
      "Created matrix: subject-0100_threat_immigration.csv\n",
      "Created matrix: subject-0102_threat_immigration.csv\n",
      "Created matrix: subject-0123_threat_immigration.csv\n",
      "Created matrix: subject-0128_threat_immigration.csv\n",
      "Created matrix: subject-0138_threat_immigration.csv\n",
      "Created matrix: subject-0146_threat_immigration.csv\n",
      "Created matrix: subject-0147_threat_immigration.csv\n",
      "Created matrix: subject-0158_threat_immigration.csv\n",
      "Created matrix: subject-0160_threat_immigration.csv\n",
      "Created matrix: subject-0166_threat_immigration.csv\n",
      "Created matrix: subject-0187_threat_immigration.csv\n",
      "Created matrix: subject-0216_threat_immigration.csv\n",
      "Created matrix: subject-0219_threat_immigration.csv\n",
      "Created matrix: subject-0239_threat_immigration.csv\n",
      "Frame Topic: blame_immigration\n",
      "Missing Subjects: {'0204', '0185', '0147', '0022', '0213', '0199', '0060', '0093', '0157', '0203', '0088', '0027', '0228', '0062', '0215', '0124', '0058', '0040', '0112', '0162', '0089', '0008', '0161'}\n",
      "Created matrix: subject-0008_blame_immigration.csv\n",
      "Created matrix: subject-0022_blame_immigration.csv\n",
      "Created matrix: subject-0027_blame_immigration.csv\n",
      "Created matrix: subject-0040_blame_immigration.csv\n",
      "Created matrix: subject-0058_blame_immigration.csv\n",
      "Created matrix: subject-0060_blame_immigration.csv\n",
      "Created matrix: subject-0062_blame_immigration.csv\n",
      "Created matrix: subject-0088_blame_immigration.csv\n",
      "Created matrix: subject-0089_blame_immigration.csv\n",
      "Created matrix: subject-0093_blame_immigration.csv\n",
      "Created matrix: subject-0112_blame_immigration.csv\n",
      "Created matrix: subject-0124_blame_immigration.csv\n",
      "Created matrix: subject-0147_blame_immigration.csv\n",
      "Created matrix: subject-0157_blame_immigration.csv\n",
      "Created matrix: subject-0161_blame_immigration.csv\n",
      "Created matrix: subject-0162_blame_immigration.csv\n",
      "Created matrix: subject-0185_blame_immigration.csv\n",
      "Created matrix: subject-0199_blame_immigration.csv\n",
      "Created matrix: subject-0203_blame_immigration.csv\n",
      "Created matrix: subject-0204_blame_immigration.csv\n",
      "Created matrix: subject-0213_blame_immigration.csv\n",
      "Created matrix: subject-0215_blame_immigration.csv\n",
      "Created matrix: subject-0228_blame_immigration.csv\n",
      "NA matrices creation and saving complete. 243 files were created.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "import shutil\n",
    "\n",
    "data_dir = 'analysis/aural_framing_by_movie_topic/roi_node'\n",
    "output_dir = 'analysis/missing_subjects_tables_roi'\n",
    "frame_topics = ['neutral_climate_change', 'threat_climate_change', 'blame_climate_change', \n",
    "                'neutral_healthcare', 'threat_healthcare', 'blame_healthcare', \n",
    "                'neutral_immigration', 'threat_immigration', 'blame_immigration']\n",
    "\n",
    "# Get a list of all .csv files in the data directory\n",
    "csv_files = [filename for filename in os.listdir(data_dir) if filename.endswith('.csv')]\n",
    "\n",
    "# Print the number of files in data_dir\n",
    "print(f\"Number of files in {data_dir}: {len(csv_files)}\")\n",
    "\n",
    "# Create a dictionary to store subjects with data for each frame topic\n",
    "subjects_with_data = {topic: set() for topic in frame_topics}\n",
    "\n",
    "# Populate the dictionary with subject identification numbers for each frame topic\n",
    "for csv_file in csv_files:\n",
    "    for frame_topic in frame_topics:\n",
    "        if frame_topic in csv_file:\n",
    "            subject_pattern = re.compile(r'subject-(\\d+)_')\n",
    "            match = re.search(subject_pattern, csv_file)\n",
    "            if match:\n",
    "                subject_id = match.group(1)\n",
    "                subjects_with_data[frame_topic].add(subject_id)\n",
    "\n",
    "# Get a set of all subject identification numbers, excluding files that don't match the pattern\n",
    "all_subjects = set()\n",
    "for filename in os.listdir(data_dir):\n",
    "    match = re.search(r'subject-(\\d+)_', filename)\n",
    "    if match:\n",
    "        all_subjects.add(match.group(1))\n",
    "\n",
    "# Create a list to store table rows\n",
    "table_data = []\n",
    "\n",
    "# Create a dataframe from the table data\n",
    "df = pd.DataFrame(table_data)\n",
    "\n",
    "# Save the missing subject data in a table\n",
    "df.to_csv('analysis/missing_subjects_table.csv', index=False)\n",
    "print(\"Missing data info saved as 'missing_subjects_table.csv'\")\n",
    "\n",
    "# Check if the output directory exists and delete it if it does\n",
    "if os.path.exists(output_dir):\n",
    "    for filename in os.listdir(output_dir):\n",
    "        filepath = os.path.join(output_dir, filename)\n",
    "        os.remove(filepath)\n",
    "    os.rmdir(output_dir)\n",
    "\n",
    "# Create the output directory\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "# Initialize a counter for created files\n",
    "file_count = 0\n",
    "\n",
    "# Iterate over each frame topic and find missing subjects\n",
    "for frame_topic in frame_topics:\n",
    "    missing_subjects = all_subjects - subjects_with_data[frame_topic]\n",
    "\n",
    "    # Debugging: Print missing subjects and frame topic\n",
    "    print(f\"Frame Topic: {frame_topic}\")\n",
    "    print(f\"Missing Subjects: {missing_subjects}\")\n",
    "    \n",
    "    # Add rows to the table data\n",
    "    for subject_id in sorted(missing_subjects, key=int):\n",
    "        table_data.append({'frame_topic': frame_topic, 'missing_subject': f\"subject-{subject_id}\"})\n",
    "\n",
    "        # Calculate the number of rows based on frame_topic\n",
    "        if 'healthcare' in frame_topic:\n",
    "            num_rows = 70\n",
    "        elif 'climate_change' in frame_topic:\n",
    "            num_rows = 58\n",
    "        elif 'immigration' in frame_topic:\n",
    "            num_rows = 57\n",
    "\n",
    "        # Generate the matrix filename\n",
    "        matrix_filename = f\"subject-{subject_id}_{frame_topic}.csv\"\n",
    "\n",
    "        # Create a matrix with 301 columns filled with 'NA'\n",
    "        num_columns = 300\n",
    "        new_matrix = [[\"NA\" for _ in range(num_columns)] for _ in range(num_rows)]\n",
    "\n",
    "        # Add the 'volume' column with 'NA' values to each row\n",
    "        for row in new_matrix:\n",
    "            row.append(\"NA\")\n",
    "\n",
    "        # Create the header with column names\n",
    "        header = [str(i) for i in range(num_columns)]\n",
    "        header.append(\"volume\")  # Add the 'volume' column to the header\n",
    "\n",
    "        # Save the matrix as a CSV file with the updated header\n",
    "        matrix_filepath = os.path.join(output_dir, matrix_filename)\n",
    "        pd.DataFrame(new_matrix, columns=header).to_csv(matrix_filepath, index=False)\n",
    "\n",
    "        # Increment the file_count\n",
    "        file_count += 1\n",
    "        print(f\"Created matrix: {matrix_filename}\")\n",
    "\n",
    "print(f\"NA matrices creation and saving complete. {file_count} files were created.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine actual and NA data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying files to new_data_dir...\n",
      "Number of files in analysis/aural_framing_by_movie_topic/z_roi_node/data: 2157\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "import shutil\n",
    "\n",
    "data_dir = 'analysis/aural_framing_by_movie_topic/z_roi_node' \n",
    "na_tables = 'analysis/missing_subjects_tables_roi' \n",
    "\n",
    "print(f\"Copying files to new_data_dir...\")\n",
    "\n",
    "# Create a directory for all data, both real and NA\n",
    "new_data_dir = f'{data_dir}/data'\n",
    "\n",
    "if os.path.exists(new_data_dir):\n",
    "    shutil.rmtree(new_data_dir)\n",
    "\n",
    "os.makedirs(new_data_dir, exist_ok=True)\n",
    "\n",
    "# Copy NA tables to the newer directory\n",
    "for filename in os.listdir(na_tables):\n",
    "    src = os.path.join(na_tables, filename)\n",
    "    dst = os.path.join(new_data_dir, filename)\n",
    "    shutil.copy(src, dst)\n",
    "\n",
    "# Copy data tables to the newer directory\n",
    "for filename in os.listdir(data_dir):\n",
    "    if filename.endswith(\".csv\"):  \n",
    "        src = os.path.join(data_dir, filename)\n",
    "        dst = os.path.join(new_data_dir, filename)\n",
    "        if os.path.isfile(src):\n",
    "            shutil.copy(src, dst)\n",
    "        elif os.path.isdir(src):\n",
    "            shutil.copytree(src, dst)\n",
    "\n",
    "# Print the number of files in new_data_dir\n",
    "num_files_new_data_dir = len(os.listdir(new_data_dir))\n",
    "print(f\"Number of files in {new_data_dir}: {num_files_new_data_dir}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There are 2157 output files, but only needed (238x9=) 2142 files. Which subject has more files than necessary?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subject-0022: 12 occurrences\n",
      "Filenames:\n",
      "   subject-0022_neutral_climate_change.csv\n",
      "   subject-0022_threat_healthcare.csv\n",
      "   subject-0022_blame_immigration.csv\n",
      "   subject-0022_mri_movietask_1_95_neutral_immigration.csv\n",
      "   subject-0022_mri_movietask_1_164_threat_climate_change.csv\n",
      "   subject-0022_mri_movietask_1_13_blame_healthcare.csv\n",
      "   subject-0022_mri_movietask_2_152_blame_healthcare.csv\n",
      "   subject-0022_mri_movietask_2_13_neutral_immigration.csv\n",
      "   subject-0022_mri_movietask_2_82_threat_climate_change.csv\n",
      "   subject-0022_mri_movietask_3_13_neutral_healthcare.csv\n",
      "   subject-0022_mri_movietask_3_164_blame_climate_change.csv\n",
      "   subject-0022_mri_movietask_3_95_threat_immigration.csv\n",
      "subject-0213: 12 occurrences\n",
      "Filenames:\n",
      "   subject-0213_neutral_climate_change.csv\n",
      "   subject-0213_threat_healthcare.csv\n",
      "   subject-0213_blame_immigration.csv\n",
      "   subject-0213_mri_movietask_1_152_blame_healthcare.csv\n",
      "   subject-0213_mri_movietask_1_83_neutral_immigration.csv\n",
      "   subject-0213_mri_movietask_1_13_threat_climate_change.csv\n",
      "   subject-0213_mri_movietask_2_82_blame_climate_change.csv\n",
      "   subject-0213_mri_movietask_2_152_neutral_healthcare.csv\n",
      "   subject-0213_mri_movietask_2_13_threat_immigration.csv\n",
      "   subject-0213_mri_movietask_3_13_blame_climate_change.csv\n",
      "   subject-0213_mri_movietask_3_152_neutral_healthcare.csv\n",
      "   subject-0213_mri_movietask_3_83_threat_immigration.csv\n",
      "subject-0174: 12 occurrences\n",
      "Filenames:\n",
      "   subject-0174_threat_climate_change.csv\n",
      "   subject-0174_blame_healthcare.csv\n",
      "   subject-0174_neutral_immigration.csv\n",
      "   subject-0174_mri_movietask_1_82_blame_climate_change.csv\n",
      "   subject-0174_mri_movietask_1_13_threat_immigration.csv\n",
      "   subject-0174_mri_movietask_1_152_neutral_healthcare.csv\n",
      "   subject-0174_mri_movietask_2_13_threat_immigration.csv\n",
      "   subject-0174_mri_movietask_2_152_neutral_healthcare.csv\n",
      "   subject-0174_mri_movietask_2_82_blame_climate_change.csv\n",
      "   subject-0174_mri_movietask_3_13_threat_healthcare.csv\n",
      "   subject-0174_mri_movietask_3_95_neutral_climate_change.csv\n",
      "   subject-0174_mri_movietask_3_165_blame_immigration.csv\n",
      "subject-0100: 12 occurrences\n",
      "Filenames:\n",
      "   subject-0100_blame_climate_change.csv\n",
      "   subject-0100_neutral_healthcare.csv\n",
      "   subject-0100_threat_immigration.csv\n",
      "   subject-0100_mri_movietask_1_95_neutral_climate_change.csv\n",
      "   subject-0100_mri_movietask_1_165_blame_immigration.csv\n",
      "   subject-0100_mri_movietask_1_13_threat_healthcare.csv\n",
      "   subject-0100_mri_movietask_2_83_threat_healthcare.csv\n",
      "   subject-0100_mri_movietask_2_165_blame_immigration.csv\n",
      "   subject-0100_mri_movietask_2_13_neutral_climate_change.csv\n",
      "   subject-0100_mri_movietask_3_165_neutral_immigration.csv\n",
      "   subject-0100_mri_movietask_3_13_threat_climate_change.csv\n",
      "   subject-0100_mri_movietask_3_83_blame_healthcare.csv\n",
      "subject-0166: 12 occurrences\n",
      "Filenames:\n",
      "   subject-0166_blame_climate_change.csv\n",
      "   subject-0166_neutral_healthcare.csv\n",
      "   subject-0166_threat_immigration.csv\n",
      "   subject-0166_mri_movietask_1_95_neutral_climate_change.csv\n",
      "   subject-0166_mri_movietask_1_165_blame_immigration.csv\n",
      "   subject-0166_mri_movietask_1_13_threat_healthcare.csv\n",
      "   subject-0166_mri_movietask_2_82_threat_healthcare.csv\n",
      "   subject-0166_mri_movietask_2_164_neutral_climate_change.csv\n",
      "   subject-0166_mri_movietask_2_13_blame_immigration.csv\n",
      "   subject-0166_mri_movietask_3_13_threat_climate_change.csv\n",
      "   subject-0166_mri_movietask_3_83_neutral_immigration.csv\n",
      "   subject-0166_mri_movietask_3_152_blame_healthcare.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "from collections import defaultdict\n",
    "\n",
    "directory_path = 'analysis/aural_framing_by_movie_topic/z_roi_node/data'\n",
    "\n",
    "# Get a list of all files in the directory\n",
    "file_list = [f for f in os.listdir(directory_path) if os.path.isfile(os.path.join(directory_path, f))]\n",
    "\n",
    "subject_count = defaultdict(list)  # Use defaultdict to store lists of filenames\n",
    "\n",
    "for filename in file_list:\n",
    "    subject_number = filename.split('_')[0]  # Extract the subject number\n",
    "    if subject_number.startswith('subject'):  # Check if the subject number starts with 'subject'\n",
    "        subject_count[subject_number].append(filename)  # Store filenames in a list\n",
    "\n",
    "# Filter and print the subject number counts along with filenames\n",
    "for subject_number, filenames in subject_count.items():\n",
    "    count = len(filenames)\n",
    "    if count < 9 or count > 9:  # Check if count is either < 9 or > 9\n",
    "        print(f\"{subject_number}: {count} occurrences\")\n",
    "        print(\"Filenames:\")\n",
    "        for filename in filenames:\n",
    "            print(f\"   {filename}\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exclude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subjects 0022, 0100, 0166, 0174, and 0213 had too many (n=12) matrices instead of the expected 9 because they repeated frame_topics during acquisition. These subjects saw the same frame-topics across two separate acquisition sessions. Therefore, I deleted the extra, second session data and replaced it with NaN, essentially treating the repeat data like missing data.\n",
    "\n",
    "This exclusion resulted in 5 session * 3 frame_topics = 15 missing data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "\n",
    "# Data to exclude because they are repeats\n",
    "directory = 'analysis/aural_framing_by_movie_topic/z_roi_node/data'\n",
    "file_patterns = [\n",
    "    'subject-0022_mri_movietask_2*',\n",
    "    'subject-0100_mri_movietask_2*',\n",
    "    'subject-0166_mri_movietask_2*',\n",
    "    'subject-0174_mri_movietask_2*',\n",
    "    'subject-0213_mri_movietask_3*'\n",
    "]\n",
    "\n",
    "# Iterate over the list of file patterns and remove matching files in temp_dir\n",
    "for pattern in file_patterns:\n",
    "    files_to_remove = glob.glob(os.path.join(directory, pattern))\n",
    "    for file_to_remove in files_to_remove:\n",
    "        os.remove(file_to_remove)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot one of the unique frame-topic combo matrices as a heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LoJ9HuXpuBx4ZO9T7PRjaJF7C2XMrXL89wzNPS11vv3aK6lmZG0FoU8lLvSsr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\nUdqo0ruTJShLReWbitZp2Y5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KGa90WN0bo7tVIDfWoznIYWtzt2SgSPYT5Qsh7qRIYpL3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IP0Wd1Jjbfn4vb316RG+8bz5NF9sxVpn2+8bQbloVLMGUBYlvYl5YjQ+Mu057Woc4D+VIzbMKqW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\nYtkR7E2IxWKxWCyWHeH/AQ/Qt2vMKySuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74073bddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv('analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/subject-0001_mri_movietask_1_13_threat_healthcare_z.csv', header=0, index_col=None)\n",
    "data = data.iloc[:, :-1]\n",
    "plt.figure(figsize=(10, 8))  # Set the figure size\n",
    "ax = sns.heatmap(data, cmap=\"viridis\", cbar=True)  #\n",
    "ax.set_xticks([]) \n",
    "ax.set_yticks([]) \n",
    "ax.hlines([0, data.shape[0]], 0, data.shape[1], colors='black', linewidth=3)  \n",
    "ax.vlines([0, data.shape[1]], 0, data.shape[0], colors='black', linewidth=3)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Optional: Check data means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      0.521078\n",
      "1      0.275254\n",
      "2      0.507630\n",
      "3      0.427243\n",
      "4      0.346845\n",
      "5      0.435912\n",
      "6      0.381544\n",
      "7      0.531383\n",
      "8      0.170014\n",
      "9      0.227468\n",
      "10     0.375815\n",
      "11     0.239725\n",
      "12     0.563393\n",
      "13     0.430516\n",
      "14     0.439406\n",
      "15     0.183429\n",
      "16     0.109157\n",
      "17     0.397157\n",
      "18     0.361561\n",
      "19     0.264809\n",
      "20    -0.026435\n",
      "21     0.396848\n",
      "22     0.361162\n",
      "23     0.277273\n",
      "24     0.261500\n",
      "25     0.377732\n",
      "26     0.190709\n",
      "27     0.212589\n",
      "28     0.194715\n",
      "29     0.411868\n",
      "         ...   \n",
      "270    0.435651\n",
      "271    0.425727\n",
      "272    0.448049\n",
      "273    0.135771\n",
      "274    0.178577\n",
      "275    0.490525\n",
      "276    0.486901\n",
      "277    0.553853\n",
      "278    0.473960\n",
      "279    0.453047\n",
      "280    0.528538\n",
      "281    0.432321\n",
      "282    0.329673\n",
      "283    0.461377\n",
      "284    0.339709\n",
      "285    0.498074\n",
      "286    0.066848\n",
      "287    0.138667\n",
      "288    0.371615\n",
      "289    0.053966\n",
      "290    0.345024\n",
      "291    0.137390\n",
      "292    0.001476\n",
      "293    0.093326\n",
      "294    0.303257\n",
      "295    0.124983\n",
      "296    0.415771\n",
      "297    0.008033\n",
      "298    0.386822\n",
      "299    0.284746\n",
      "Length: 300, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('analysis/aural_framing_by_movie_topic/z_roi_node/data/subject-0001_mri_movietask_1_13_threat_healthcare.csv', header=0, index_col=None)\n",
    "data = data.iloc[:, :-1]\n",
    "# Calculate the mean of each column\n",
    "column_means = data.mean()\n",
    "\n",
    "# Print or use the column means as needed\n",
    "print(column_means)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Delete the last column of each .csv "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last column, 'volume', was there to keep track of the order of concatenation across volumes but it's no longer needed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'volume' column deleted from subject-0001_z_concat.csv\n",
      "'volume' column deleted from subject-0003_z_concat.csv\n",
      "'volume' column deleted from subject-0004_z_concat.csv\n",
      "'volume' column deleted from subject-0005_z_concat.csv\n",
      "'volume' column deleted from subject-0006_z_concat.csv\n",
      "'volume' column deleted from subject-0007_z_concat.csv\n",
      "'volume' column deleted from subject-0008_z_concat.csv\n",
      "'volume' column deleted from subject-0009_z_concat.csv\n",
      "'volume' column deleted from subject-0010_z_concat.csv\n",
      "'volume' column deleted from subject-0011_z_concat.csv\n",
      "'volume' column deleted from subject-0012_z_concat.csv\n",
      "'volume' column deleted from subject-0013_z_concat.csv\n",
      "'volume' column deleted from subject-0014_z_concat.csv\n",
      "'volume' column deleted from subject-0015_z_concat.csv\n",
      "'volume' column deleted from subject-0016_z_concat.csv\n",
      "'volume' column deleted from subject-0017_z_concat.csv\n",
      "'volume' column deleted from subject-0018_z_concat.csv\n",
      "'volume' column deleted from subject-0019_z_concat.csv\n",
      "'volume' column deleted from subject-0020_z_concat.csv\n",
      "'volume' column deleted from subject-0021_z_concat.csv\n"
     ]
    },
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: 'analysis/aural_framing_by_movie_topic/z_roi_node/data/concatenated/all/subject-0022_z_concat.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-2250f9f9072a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0;31m# Save the modified DataFrame back to the same CSV file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m         \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcsv_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_rep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'NA'\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Set na_rep to handle missing values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"'volume' column deleted from {csv_file}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/5.0.0/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mto_csv\u001b[0;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, tupleize_cols, date_format, doublequote, escapechar, decimal)\u001b[0m\n\u001b[1;32m   1401\u001b[0m                                      \u001b[0mdoublequote\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdoublequote\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1402\u001b[0m                                      escapechar=escapechar, decimal=decimal)\n\u001b[0;32m-> 1403\u001b[0;31m         \u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1405\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpath_or_buf\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/5.0.0/lib/python3.6/site-packages/pandas/io/formats/format.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1575\u001b[0m             f, handles = _get_handle(self.path_or_buf, self.mode,\n\u001b[1;32m   1576\u001b[0m                                      \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1577\u001b[0;31m                                      compression=self.compression)\n\u001b[0m\u001b[1;32m   1578\u001b[0m             \u001b[0mclose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/5.0.0/lib/python3.6/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36m_get_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text)\u001b[0m\n\u001b[1;32m    383\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_text\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m             \u001b[0;31m# Python 3 and no explicit encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 385\u001b[0;31m             \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'replace'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    386\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m             \u001b[0;31m# Python 3 and binary mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'analysis/aural_framing_by_movie_topic/z_roi_node/data/concatenated/all/subject-0022_z_concat.csv'"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# Directory containing CSV files\n",
    "directory_path = 'analysis/aural_framing_by_movie_topic/z_roi_node/data/concatenated/all'\n",
    "\n",
    "# Get a list of all CSV files in the directory\n",
    "csv_files = [file for file in os.listdir(directory_path) if file.endswith('.csv')]\n",
    "csv_files.sort()\n",
    "\n",
    "# Iterate through each CSV file\n",
    "for csv_file in csv_files:\n",
    "    csv_path = os.path.join(directory_path, csv_file)\n",
    "    \n",
    "    # Read the CSV file into a DataFrame\n",
    "    df = pd.read_csv(csv_path)\n",
    "    \n",
    "    # Drop the 'volume' column if it exists\n",
    "    if 'volume' in df.columns:\n",
    "        df.drop(['volume'], axis=1, inplace=True)  # Use axis=1 or labels=['volume']\n",
    "        \n",
    "        # Save the modified DataFrame back to the same CSV file\n",
    "        df.to_csv(csv_path, index=False, na_rep='NA')  # Set na_rep to handle missing values\n",
    "        \n",
    "        print(f\"'volume' column deleted from {csv_file}\")\n",
    "    else:\n",
    "        print(f\"'volume' column not found in {csv_file}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot one of the matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gHGPD74IhoHn3ozJpvz0+a0NWQiITcNgYSC8b1hiCAMRzetHED+xExa0lDCMY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WT9e0Oc36X17PdWoCge+51Hr6K3bTfS8R3KCgB0kgxvDCyg4lGwij3y9vrqLz5hK43txgQn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\nW0hzyryE4XZEqT1UBNgT0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9DwOlJ0BEJbmEBmBKb36sQG6H3sKK1FLU8+Ayb3wZIAyQkw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amzlclC4+v2WqHN9+6y56VFquBTmOL6hk31nyRvhk3MNfuW569H/48B5CwqXstSd4NDTz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Sk2FHFvn3LFJUvzXHq1t2VHDyD29D/LoNcmFQ8AhCFxLqGSk1vn2BxZXHZnNm5vJmoBceZPWe\nZC63ufVZBJKfgLOU/PmXdk7x7NK2b/XM52Tz3uY5jhf29+ZxyIqcRgmAEqx5GrBqpSolYwuWs5D1\nTVQuMR7bjdA0FJzKXCtx4VUurY7hcYRKHRaSc6MCJY8mBJwujWnCAlIYLOk8jAQktemDqWS8kzd2\nIW9fc8Lt0ghC1OaJReng6JFtI1/dnLOSZqkkkuLa+IqYMhOA/z5OAh5x5H0Bp/J5UXWyLArJ+jzK\nCAjyF1m5N8L0XaIQrSp4BEAv+gDII6ic+ygpRmjXAJ2CxwtwDK9P4xgUJKDlidowLjtroEGbvp43\noV+yayn6MEKxab93fsfUVOlQs8ieGtbWC65fQjXtdfpnAQoa96kBeN0ltwvGZQDgNRKeOYxryjZK\nKMKo2Cqc3lmvDqVV8g5YL6hGL0Ea+8BAQV4E0LrGv21vWWXoi4PN2mCuiXqtvvBRvmkTDO9PBkjJ\nRfpwENWUcMegeEbChYXA/tJS5c9OtqC3KTkeFMzMa7QyxOTTE7bya5QgA7catVyFrIHSHywxvkNK\nxXOHVY/zpQ+f3nl9HLLukcwEs8/EhWcZQZU7tREs6mj8+rmlqWcp8rBsrIpp9tHxJqsq54XLcvuO\no9EmkbVS1Tom2gCG3i8hDY++8nEIs2bPNTx2URLLTnZzTP8XS1HONwWcqh71JLtrf3n8bI8vlISk\nlIEaLQEKMI6rgWalJNpGRiBFIcBGQ0eHQ/amkK2cX5zjx2s1GKdT2kyWZv/uwqlBfgDTVcumAZKa\nKVPsUudk7DJgKTjxUJLyohNLTjyiM2BOVH6hDC/CCpFv/16wYFIxNGg+pSh0DR0PANFnBAiUNSsn\nXTXwRyQ25gKaKlYNIFzQ7HqrrOmJGhDaYUxEdC5QkH5C0dMMOi16Bt6iBtdW9yw7bqLB1iA+igHN\n2aeAS0HeTYDOD+15JKvX6KCuRNPmDlB+DajLO3XgUCs5PvrgFgBg+6VzHB1T4jH1EW9RkhN7+OjA\n6jk8/M/3MHrffn55GNZefLng77+aNyBzgZJAy24qEBELKN7SiPbIqXVboUFgWZkJZARYDyaAJBrv\n3vYQ6ZiYJRo8U33yo5vQ6xSoC6euwFwNSZvt9LDLKHtnmF3DCrnsWGpP0H7IP3eRU3CWExfBiJ5N\n16mKP4QXEiloE/Yd+ASaLTOJ5gF5ovTtJld1bUxbQRK1FKYGS5YtDVBAbj53EL9Om0TsclckH4dM\ng06Wvm3ZA1CRxPKUujnCAHTNmeMhPSfdh34GQ5utHPsM+M3aqjZVbCouBNwzDyNlu27RngfVrUDT\nErkg4b60ppUXHQ31ua20PQ2owmf6LgBmAUEIS3+HZVRJ+r30hmFH5nyg4T0lbIIHlsxvngnMXye7\n91JAj+2abB5ILO+QMqYO2ZnVSKAknAlM7RwrZy4DymUqGaStgjo2dB678MhcdfxQQNG74F1zBA4v\nBb+Dy9sa6WELPjlJywIomrQmY+AksQlDY1ozvxqnNXDSWwJp3OLzvve2pWN9+nwTWdVV9Q26d23n\nbfHekBlxogQnvd4nDTaqW+YOx7Nv/s6n+Def2m6JO3FZYbfxok68rsZNtB/T5l8Ak7ByvpbI1wjs\nOQLrc6gQyEJ6dwIDWQiON2Zer6myaYCqm5EKBK/aQid50a6dls9CHN2lZ5U6GG7bQHd53EURUwez\nW0JSQas9w07Q5XqB7hNiKQ3B3Sx/CmRDipF7HiYP6HwGNRZH9woG/v68jy+BqX+DQ5KctTcVyPsE\niAo1mnskNrWj4dOL0D4AjmB3juZnHuIbxLhIQ6Cij/sGhjY8OXO5lQfHQOZgWq4ODLcpvblAOrRP\nq+hqRI8J8BcDMWkbabdGXzuJ3egBi9ivENdC1Y6Kwgg0TurAU1KwiE4lg73ae4JpuULZzR2wrJyK\nluikQEqulk4KNA8rpDi4whWuRkg6B97CbkrKpw5HzyLstWtZPZV9jDetwZtFFwguJZ9r9eIWPcXX\nZhwgo3uUrhm2De9+XlPUOk8lB0wj7e8DVjyp/wltbOsBJ17TP9nAb//eDwEAf/DBV/HmfRsUP/jR\nXWYifTy6hcYxSUrvKjgEUjUOuNpLXjTgZoBz6KFoWRaT9u39kblAvHWNZlmpnjfAOi4AEG9Sh+So\niXCLdoaP21AhJaexQO5SkhjWAnK6W8AlK3vtAWWbuk5zHw49EzVUDK5VoQForOQuAe8zYmWsmJ8K\nGNXYpWyAO3bepcvPTPsayy37neFl/R4Z18CZ1Ztb0QRCopPKoYQi2nC8rYFqpABg/YH90MmLASfq\nzQ8i1pVIEdaus1ryuRotWKDN/bTB4GKouiiGrN8XwOHEQShRmzOmgKBJWdlVNeth6TCVMjqVSDZI\nVXlf2q4FaMOqJoKxvWcVcFCFQE7dlsZzlxNXM5M8ahVGsP6KdoBwj9g7EbhDlmwaLiScuQMxdvm8\nHXpWbiJqpkQmEFxS17FjkNO1NU+AhADbjTPDm7PQqLuuok7sq+upftdNBIxDTJeegLuwSUbRQq0V\n5ACdZwRsvSlqZWQHXJGLqYv8z+zoDBuKEX297SniH9gYG86BnGiv2VDAeVrT2zmONhQyYl89/h8f\nQrxO55oIGKfW+qgSRJvA03l6lk4LACYN4IwrzRcwdV1mAo0XtBfcLAEh4U+t8rAR4ARD5hI+JXpl\naJMPwCY31WcgwFoskIZZkyJQENTClZceNJkT+jPJe4SRHiYvETB9VCdV2gNAI5ti7kPvkHTBQYSG\nncygbASILg2e48vjZ318oSRErRLYLvAAMq2DY7D9m1Yw5/HTGzCUdS62AEHdkmTDZetu0ShtJwUA\nlg4icm/M99pQw4LbgqpscGDUTcUbDPp5TaFUElQswIlKRsoLV6Mgpb90UyE4q22dq8BjRJ2EGNcg\nJg6/kTY7B2w1XtFvl9tgDQ/j1Hz79EYBkVZJjvgpGmNFn/OmEvmQgn+jQDYkLEYhkK5pDD6qNk+w\nUFbZVdAkMjT42LBFdjAGFru0Ca9n0F7dnqm8beK2w14gJ3+1xUFl/LrmeXe8rTlpUSsF0kqUa1hg\nsUNV+2YJj3x0CkfjX/7hNwAAa29f4JMzUqSNFMo1EgC7DBB+226QRRyyFL6+8rH6Y3sO81cUDEkk\nBzfnyA7a0DRGcToFDFUhRaj4vqJVQp+SINx2Cqdq8899ZIRfwE6O1XVbUV3sDWphJACgWfz9Gxd4\nMieVyK0l58LpOIK6Z1u8DgCQ8SDaJbrE6po/rDVGXE9hRlRkJypZDG2+3+V1UUQKmsTNujenmFSu\nsakHFRh4MwlkAuVOirhfMz9mN6myLyQEiYaZbgFJVFkdakxJ8E+GJUDqpsXXFyhJPyfsZKwHEo8j\niIAYV82c29rBm0vkJAylExeGFFNxTdpbNxRjiIpQM54q2ZCMuRBa8H3RgeZqPHklYzv1dFXaDcYz\nFrzpG4hCoGzQGqSNR8/rkWV8L+d5P1B358qWgLdCpn2nEVApqZ4HduwDwISKR3mqUQtXlU1RJ2QC\nvDlBGF7PwoCvR/m2IwkAk/uSR5bG07wu9MyBJq2WvENUVNjEs1y35zY1EYxjCwXA0k81jdeK3RzJ\nC5uVGlejQWyk5bjBVX6wneLh21a75YePb2Nt02b06v9chSbq9+z1Ap2f2AvKu8Dur9ht9Mk7N5ml\nBAD+3Tk9nxI5GQSivGYw2Qw5qQzbGfJuLdbn0nuU90pmojnNDBlpNRlHMo15sD3FdDbg5NG7Edes\nq6WPlN75di9mEPf5qF3fe1dDED5Mehp+UI3zHTjbNKa8jDipLozL9172cuDAvtutN0eYzu2f4y3J\nhbR8MMdW17IUzxstzEM7vg1vzZAIAP8AP//jy07IX39UNNupbDAtMQwLfPahtbKOtpagBgF2bl5i\nGtuHnzcK3iwa7QxLAi8iUkgmBILrlXADhZK8PeRmyg6O0IITg3LpISRTpbKUwHOiCt9PuMWdLH2o\nymtACQZsToMunHt2ETrCoKSWIJYeBwwx9dgJUhSy1oZwNUrbQYVz4SE6pfb1Si1WJOKaPiiXDicw\nRfdaNV7WmIb5SwZBL8UoqkdY4T6189v1iGS+K7iTkvXB+IWttQmOpjZrkZnkNr0qBcb/3FJr9RpQ\nrBNgb+Zw1WK6JXwCviUtB4qChXvqsz6J18vw2w9+AgD4J9//Oja+ZhObZeajE5FVetaEQ5ofb33t\nc7yzb6OikAaNlg3Uy1Li6N+X/PcQBrpfIFkEcHPAJxBZGmhI6jwIxwBUtWoDFITN6XYSxI9tVeTf\nimtVUUezceHtl0/YzXatO8fRhR0EPzsj+VvY2XK1UftrC3bjNEbAVOM5YTAljAeWDkuH57HPWhXF\nEFjKoPo4dKVaWwrGN0zHTQb+7f7SAZ78ZJs3tEYvxuIzAsWFGoaqVvgaLgE21/tzHJF0r9vLeM0I\nX8EhZ+c89vjeOVIjIX0bGSh0aYae5S77/8xGAXsq5YnLYEfMXRQVeC+XbP7mNnNeX8Vqjm/d2wcA\n/Oj5TU4K8zhi8LbjGhjatIqOQLgR8zi3219i+oLkzLfmfO+diwjqHtGJYRVYAcDbjOsxbyGRz+uu\nkKl8q1ZybqM39j1kr9jv0QBEJe4nwCDNslvWSeXE5W6MdgwLqZWRpeYCNhmpugXGq4H5qqGhVmtA\nLI/+OgX03IfxNNRmgcaeh3ylmqMCgsbO+sLj982bSaSVdpEDeDskxGUEPji2bd57t0+xf06zyX8v\nhqb333E1lkT71hsZPn1uQcqmp1i6oNFJEFPimSjB9GHjGeSkgRRuLpFSMluWEqLaKa5hNEUu2dcp\nvja60N0CORUY2UEPfiag6doaYc5u2enSR0QiY1nuIvuQHMxfWVxz0Ta1hP9VAxV2XggwxssbpGhU\nelX7A4Rfs/PryVEHmhK++TKE/zGJD24quFW3WAuc/YWNkeKtKQzJ/N8djvDoZB3/Vo4vk5C//qgQ\n2kYJlGQKVHqKs/fKQwEALmYtbFKmuf/pBiPXlZJsna0Lh/ER2tNQnmSQnz4PayKxp1EMKTCWghfJ\nYGWBCbW5tZZIL6gXGtSeAsZIrv50YLBOBknzNEBClZbbTxl0q1o5V/D9G0uM9uwGIRIXPrX+86mL\n7Bs2sLeDAhExES5GbaDahJRA9LKtWGaXzVoi/Nr9FJmEeNTC137NGke/+9FtzubRUiiramnuMejS\nufRY6+DopA+XFDpbh4ChAL44DDEjUbLg3IFHcttFGbKqpBOUSDYqP4Zan0TfzCAPbaVtnjXxf332\nLfvnjQKT79tZk364wFrbPvPpXKJ3n3Rf4jbLy7tTB4lXuesCoM1JONqq31fP3QcMZa5hO+ONqtVM\nsXTsxtNdWWD+zAaq4a0lzJl9JvFNAZzSOG53gdGZDeAj0YFPYMnRosn27a6jMYtIkjr1avbGsw6a\nd22immYeVMXWCUpmZch+DkMGWdHNOWJKFoRjGKRdrqcQVRI9d2rWxMyDTx2op6MdbL1+ipPCZrQm\nr2flspej31/Ss3IY2Hv5/Q1037QBdjZt4PbuGQBg72ANDfJz2tk4x9Hcbux54bArdAX0A4DkolGz\noyLFwm2iEPCI5ZNlDjOrjGcYmGq0hENrZDBc4IMTuymqxEVASsrh9gyzq8rOQWJ1lSrNvAfH0bh5\nwwJpCuVgMbDv5HzUxGt3bSf148UOPIoNv7i7h+8ePAQA9FoJDOmbzJOQY3WqIwxv2OfWCVLsH9qE\nPHs5t1g1EGCZ9s98peRkw7/w0HvDsklG0xXehIQwSAMCoPczqAo0uR+hIHsC31dscinaBW7dsN2/\n/dkNHtNEzRx+L0b2rgV8FC2wvg+6YBZdeM3CoOhoHoXpUEN/ZrtnqqHRJoXiZx9tcRwyWqBNXbj/\n4u53sft1ex7/9V/8PR6dBasJs0nK0uHYGz4Jkdyn80kdrO7a92s0abGWDmB1gwBbPFQaI+F6jJQS\nx7fvPccH371nz3OzqHE/2loPVGPxbMtFiwwMjZKsvr3WXCDctF2eu61L/OmhBWos4wBRyz7zjY0J\nTvds4uUO6lhdlg5rAGW3M+7sNdeXzFhc6S5w+sDGyE43wbwk35l5ABDbxytcTio7foqdlSs8xZfH\nz/r4YmJlIblRthYIXPsitLwcksLCRdLEkirkpp/jXtcuiheDARoN+2+/sn6McWaThefjPsoeZbWu\nQuCVnOgsywgbr9nK+3LaQrtpv3d82UJEL/P4rIO7t+wLvH8+5MoxCgsWolKFg1/c3QMAvBducab9\n0vAC7yVb/Pmvrh8BAJ7Nhji6sMHs5u4RrggRufnwjK/nk2jdupkC2OlMsH9lA82NtQm/zMYItvje\n3b3A+dQGFCk17u5YROgnR5soh8D7B9vodZcIVhIEm/a+DptL/g13Q6Ok6DntR3h11W5CPz7chswr\nwTGgoAUpDNB6SgAxH4jo3rvvNjB+wwaSrZsXeHFBG6FbsxXu3j/G01Pb2br75gGeHNrEo9tNMJNV\nV8DD4Sf2M623RzjfJ9tsX8MjgKdKIrgbxA46bDAwWUiDBjltLi4bMO0CWcOlZzLGom2vpxck+Cy2\niccyDuCSmdvBj7egXif7AFejIEG3PPEQ0m+vdedsDRB6NdJ9smjwprVMfZbnV536vQuDAs1X7OY0\nTwOUYZ0wVyBA31HI6Xk6t6xmDAC8tfoCJwlRurVkR9Wnx6soCqJxzwXi3OfCstdMcFkQPbiZc+tY\nOga9rt1s1r59hrOF/cy9rXM8e9fe+42HF3hlQO//fIDXVk7ptx28s3eTv/N2z24wcWuOaWq///Ss\nh2aXJOkHNZ06zyJs3r3k/x/NbFJRFo7V2oFNkKoqFV3UIFtTAzODdo7Lca23EHglFlmAhp9jmfn4\nxq4dF+xNh9ht2fNzX9V8z87TFl77RbsVHM87nOhvtOZYFHZT3Ttr4K01m8C8WPaYNZEVLnfIorUl\n8qd0Hkpg546NKaejTYynNEZYy2CogOoOF0hoE25FGebV6OvlOSKKHckygKbPGy2xd2DXiBtLtnkI\ndksU3xnAgcV66dAg+LAau3AtgGRb8fjLlAKywkH0MuQOUXS7GXfLNh6e8zhOvNPFnLor//3z32R2\nTmMYI6nAq5+1KkIYzIOCqfPatUaKAIBbS4wmxOryFMpDe57u9hLeEcWXhkH3vn1O4xc9ODSO+uRs\nnbskppSQhBvBZQCZiZp155fMRusMlsgpPo/iJs4vaBRyv2BH5sllC5JELX235DFneRnCUILl3lrg\nrRv2+f/g2S1OvAOvxJI6NDfbE7YkyAoXgqjFGysznJza+PLW9iHep6T68XgNNzuM9v+5Hl8CU/8G\nR0ybXOTl+FrfAhP3kyGeTmyruNCS7bTfXD3if1cuXQhKIlyhWbchyzwoqgg27pxjmftsIw0JbLfq\nKucqIaBV7vBG3x4ucUlGelIaeGRB7rmKv8d16j8LYbDbshvJk+kKMzRKT+HFwr6QjqyVMdfDGTTh\nLG62r7A3t5vtNI44k18UPm9gRycDvH7bLgptBCY0jur6KS4EuegWLivJVpz4djOFMQKqlMjp0YRu\nieeHNgEKooKdgJPnbcR9GwxUIVGs1Q6WOXVwHFWzSfwrYEGteW+r5vcrXQMERSm4av98fwMgCujn\nj7ewecduSKcXXax+x37/+S+XCKkyHz/vwSEl2UF3wfblWguu9sSg4FGBdDQWlw04CxcOAAUwPXQc\nN3ikMkHEwM5+d4nTJzRK6apaf6NRMGAT/Rw9qpaPLvrodmhDKl3ePKWjOTnNFgF6Q8IjTV00Nml+\nP4+w3Cc68Z0JBJ23F5Ss57KxNq0ZVYXDie2f7T/g90IpyYaPflgipWSmkBJb7SlmhX1Ak2XEwNRk\n6dfqq6mLpbDP7fN5hBVKSBpugbXX7UZqjMCff/yyvbaJh8Zb9oZ1/JRHc46j8WRk713oFZgS3sUN\nC8TknHrrxiWvL1kIeE7FgnwF+AAAIABJREFUpvCxNZjws6mSjcmohd98aMd0f/zkZWZWdFopvMqN\n1wj87ftPAACPxuv4vd0f4U8v7Lnebo/x4YUdF0wPu0gokfrwvdsIqMp/e+sFfvCcwJgANjbP+Por\n+rU/cvDjc4vxmS9DrJAh3/yqUb/bQYHwvu3IuI5CTDYRxjPQ1OXxwpIxJJ5UaBKG6OhkAFOpIZ96\nyMmDSWc1YNcowZRWS1WtOrMC8bbGxks2oV380ToWbxMtfeIzuBYGCFcJIPlXbcyJ1YNxwPc7nwYQ\n1Kl6uX+G7y1u2+e5sO7eANA4dNA+tH8++2YLTUr0l0uXR0ShozkWOpkF4QJAMyy4szObRnxt+lmL\nW7eqXWJ8QC7EoWJabW48fn/L2GGdp9wBdL+ERx3PybjJ4mKOq7kL9+DOBVJak0+u6nGp9BWWGXUt\nCw9iavce42sekT9YGWFvWsfIap2nucs4oE8uamPL7LTBI8iTkz5CMuo722oxXED3JT6cXtMx+vL4\nmR1fKAmpHvIyDzAq7INaFAFnmp1uzNXluxfb+K2djwBYu+7FgjLzdRtIAEBNfa7APamw0lji84mt\nKrypqKv/NOIXdefWBc+vzydt9NvkKCsNC9oIYbCkuflLt07wV/t20Rot8MS3L3qpJJsfKS2x/4Kw\nFYFCi4zt/uLgHpwL+6I+Gq4jJhBgmTtwaKNOCp9l6MPelLsiceKz/PPBtAfzgb1HxabCm7dshfev\nP31g7dIJdxA2c/iUSJVaWsM12IQmIWqlF0v2/DBGcLu8bAG6GpdtpwzwFMplILAbg4PzIvPReUL3\n92FZzyONwBq1Zi8fr+CYXFcbwxjj1+3vBp0M0wMCtUlwxX5x2eHKyTS1VcEEoI4b0PTx9kqC6Shk\n3E147HGLPM59xAe2am3fmjIm5uInq/BoM8hXNfqPiGny0gLHhBVwXW0rJlixrupZ3V4ZYZbZd298\n2UZA4w7EDiagzs6gwIzeT98voYhBMx23uH1dHjbRvmOT4rPLDpxqtnwaYkTjGDxu4WqTGA25hE+M\ni/xOwpWct57jk3duY/CYuocbPirMtfc0Qkb4HYSak0o999Fdt8ngLA9w8b6dWZcbOY8dta8xyWoL\nckXmiQvfw8qmPe9cOcgp8UAq4dBIZJEFmJAXEBxwFynyCxxc2Pd5rT9nWfjXbx/ig5HtIm4MZiz6\nNl/WEuZCGE7az096+CfOm/y5j+dbqKlZwHcP7thnuJFwQvP+6RbjvczNhO0DhDRYX7HrMx8q3O3Z\nEc/H+TpOTm23wH/hMxBc9IEkJlBv5uA+dU6nGDCuIfBLJKSxkxYeJ9LC0XCojZDv6HpN+bqmcboa\nHoHpi1Sy667SErIQOB/bde+1AUMbKTxdA+0FuYQDyHavgWgHZS2BHykMVm1S8efvvcpCafk9Y0XA\nAKTrHsK/ZddtkNbrKBxJHvEmVyFjk/KbCgG53M4nDfgNElK7ZiqqbqTofY/0k848zKskqpQwlEQo\nX4OgP/AnDkB+R+gruGOXadbuccD6Nqpr1a0B4MOjGyhoZCg9hWazGosBIY1d0vyas/E1k8dnl0Pe\nk7LEQ0JFpdFgr675aQuN53SuIWAmVLTeSuBQ0+b5yRAhyQSkpUDzRQVZ/zkfX3ZC/vqjqvinSYTH\npLW/zH10v29f1NkvGPifERL5DYX/Y+8NAID/gxbyh/blukybWFI7VcYO2vtEpXpbYZJGWB/OMJo3\n4WTAp394D2/81iP0oxj9CFgWPi5mVUfBwd31S3x6sFGfICUneeqiQyDaz4/W0CNPlTR3MQjJvwAC\nF1UbsHVNMhTA7MK+qJ3VJRISMeqFCQfqxtYc4yc2wMrVDEPy+Ci0gzSzC+o/e+0H+Ic/sXiKXjcm\np0oAUYlxZr9/ZThHWngI/BJZbrn6Zemj2chwuzXG7dYYf/L4ZRjqLvnDBGrWxIv3byC8N4PjKZis\n0tIAU5xN4rGHSW4EfNok57c1t2ONESgoMXA6tVIpcsnjFacEd4Wy5+0a0CIAQXiVwV+E8G7bezQY\nLjCmysdJJLesIQxTV1tBjmWlTtnUEIQPzHsWKCyGGXAZ2I7FV+Zw3m2jcVrTp83dDFcP7f93Mp/H\nK67UWGSUkDgavYYN2lWnDADklQeQWBFCxeJJ3jBFSYqhK90l8iP7XozXJCICfiZXPnc2jK5ZUAJA\nTptIoFGLcHVLeITXyQsHQTUPf9yGbmtcvgn0HkmEz+y/jc6Bq4fKJpUthdZHxHr51hza0/DJI+Z4\n2kXnod14RycdoPLm6BTwHRudJ1kEp0cJ4NzDMiH9jCiDoPMLLiWyNlXtEAyu9sYOKyAbAwY+ArY7\nBwA9P+GRyOWixcn/r935FH95aIV4mkHOVfdvvfEB/vLkDgK/hO8oBLS5rDYXOF/Y9RwnAWOx/KhA\nmvjATgoIgweb53i0T0DLUloWBYDXXjtAXNr1ttJe4pzOI12XrN1zb3iJGY34pDBYCW1c6H3rc3xO\nlXfkFTziPInbPDp1fYVhh8D4cYiS4t+D9XN8vGeTIsdTaNK4M5YGJTlCm9JhbY+zRRv5mzkaAAat\nmMcSjtQ4OrFrLWqnSNImlGcQrMVwtEAR0P3YDzFOiKJaCERr9pyWSvCaH74PXAzsvewNlnj4ti10\n3n+xxet/pbNAn3yEnlyuIM0rDrypk8e5C92tVKU1rr6i+L0AMbR6dya8FrLn9cjNmwPLHfv5tTtj\njiOym0NR0uZdetDDOgYJYeA3LA6v31vCoS53vxmzt1HDzznZMEZgTj46Ze4iqxSWuynSCrCcixpD\n2CxRtGndCsuuAwAz95nt6L4IkWwTbmziItn4t5MNfDmO+RscFfC014lZDbVUDpbfIGMnTyN/YF/y\nm51a2GHvQZuBYp+drTGwSIca0wfEw08jXE5bdQs/BPrfOMfedIjT/SG6WyRoswhY0Owk6mBz3baL\nT54PWdHT9DWmRIkUGti5aT/z4Xu3cUi0t8Uy5KQlvWjw9y8WEda3SBjnqsU01rT0uK0Zz0OAlAHV\nxMdYkpppUKI8s/flfz7/Nrq79nsafo5JNQROHayG9u2fZiGafo4XVG02GhnPvh9P1/B8b43uhYJw\nrNeJaWggUlheRTBKICDWEAQYjIZIMT1SphJ/584nAIA/WHyVpeDTFVPpcMF52oDqX6MP+pXyZMnf\nqYc5j8G0Eoge2e8Z/VIO+ekK/xbIXFDMAwbQqlJw1+l01AXtldDaVic6sLobZezZ75D2csI/r59h\nRV3Wxw2WZ55dNeGTUB4MoPYJv9AvMSUZfl1IliQXgeZZNArJWhrFKOTq8uyyA3mfAnImWWESrkGR\nVfoEPtMeRS44mVGhYY2RwnWsui8AOXWhzqnaM5YFIQsgWbM6NgF5IQYjMl4sLbZi8XoGLHxsbkzw\n+NQm/cXCR9i191JIoE24jtllE/sf27m2GGZQ155/SfLcy3vgaiu7n0ISJiBu5mxbUBaC6cRRP2Ws\nxMmzFQZHX3y0ar1QAMDRrOfwR4uHLIy2bKlrXbcAs2lUPwdPY3Vo8Smlcmq2nDDwaZzjBwWPtvYu\nh/z3+UWEcNNe8ycvNhFSBR/4JeNU5KpmO4OGm+O31j4AAPyz8zegqe3mSoUr8ghp7V7CpSRvNVri\n8COb8Ij1FDNK4LLE4wR+lDRZ/VVrwcnZNGnwyAEA9r9jx0n57RSGANvxWQsoBX7h7U8BAEf7KwiH\n5Hlz4SBbVcjOG0Cj5K5i2TLo7dj4NLlswalGR5ceDKkyX/yqQfjUft7/Ux97v2s/M+gsOaZEbsHJ\nY3LRgKGYFDZzdkUfdQLuusyXIUDvs9C1zsd8EaJF4/WkqaAoHvtXQEgqz82XM/hjB/mWtQSQgYJz\nEgDSQM99yLaNJ0XqotVJEfglRgd9GApK3bUFA7MBO+oFbKcqJNZdlnhwaf2nS587uOcv+gios5Ht\nalbW1r6GT3ifwgCT14nSGzvwWEtG/JR45ZfHz+74QklIxTJRSjLiGgLcWovCHPnHttQ8DrrW+AuA\nd+UABBosUpexENCArLyhMh9F4nELUpTABbmLeoMUiwUB9kY+t/JLJbkFK6ISklrNZXatnWYEfvKi\nDs5VRt3rxBjT5i9XMkR0rnMDLP7S7nj65YzNq8bzBjN8ELv2mmB9FypsiS4lwi27WDa6MyxpbJIU\nHmt4oJD4znNbLUphUOy3gK16I4lJi6HdTphRYyDZe0QogfbQVkKzcdOazgHWZ4TO1Tv2mdIbXgL/\n7Dtft3++qmmjZqUWEMt6hlk2xbCATxtmqUStw3ARYGCna/il/+od/KuPrWYIBADy8ijLurJXfi1P\nHy4F0v3KK7tmAYhCwFsC8Y2Cv0pQO9pzFXL6J9oH3EX1b4H2Y3t+i4c5hsR2OvtkDZrk9oUEVFzp\nbUsI0hKQqURSKaz6GpKuOTiRiG/TjD/3oCr/k1RCnNT024Keh1MCwVmlPcGXBVkIlvw2nmFZfHch\nEVpoAGYPFFrPHLZsDyZgUbb8TspgwaK+XTg5GliPHQAwQEp0StnLERNLTaQ19VtqwVLqpfRgspoh\nU60dpA43tvLcrYWhOgUEJR5F7vLaNoGomRXnAgvqKAktmNFmRtek4AMNl0ZQ00kDg8ECCZ1Hlvo8\nXtXvduE+tM8wCGpWxnIaIXthb4x2DdOjw/UYySElp6VAMiRdEhVia8tuQknWRLZjP78/G+D/xiv2\nPi46KHXd2dm9ZR/KLA0x8e17Mc3DWm9k4cFUoWrhoWja34oGuTVahDVPO3lh44hwNbxm7X2VtKxq\nqHMcIjoHloTfkCXwwamNSf6Zi3xJoFBR66EEzwKkt+x3NR/7iBckq9rTWMzsy7H6lQucH9CDCBV/\nvmx6CP/cjlHzby1wQQDM7Z1LnF0RnubSZXGvNG1i1CWsTCl4PDwYLrCkTmLZNHDJd6ZY+JjSuE9m\n12weIOHStHPvYA3tS0ASBifdvKYnVEjuvIpCIjmi8dd2CkEYr9lJm1l9UZhj/MgWOuqaVxMAaErI\n5NjHWUZ7AWzMsA/IgU/y/2VTQpEfjQkMa1eF5wLxy/TcDv1axO/nffz/LPf5YmJll2TaFdbZvigl\nIpKkNt9M2QytKBzMqT0WpILnnhAGGclwBpe1wmKc+IAgEScAOgAUVU7mqsYQ+LHglzl71mEdA1HU\n2AK3UUK8oHNtGJZzDs8lpg0yZ2pollTORIDJp7Q5tQyaJ/ac0pWa3lfOXeaYQxpE53RPQglNLWH0\nM2Q0iz1QbZbCLjoGEWkPxLdLZGScF564cA3gvm83lfaBxsXb9tFcdV14E5L97ioEF7RhjoE4psDT\nUiyTHY6AsuXRdQLtQwI4tgQWt0gPYypqKezPW/Dn1Pp8ZPVH7GdqddvmgcSSWsK6pTD6qv2tP/yj\nb0BSMtP+0OdRk/eNK5QkZx+MAEn3pfeZQRnZ65/es/L0VYJhN2d7UvFLGcJTeiaLHog0ASetxdpu\nfEdhQXoIWDrM3ghGAoSttJ2Hyjws0Cxip4NatEleeXDiGqTHNhqhQu9HNAppAcvbhDPZc6Ho+/0r\nwSDA+baAopGIdoBgTAEvdeEQ9Vh7QFxpzMQS2QA8j856YHn+zg9CVtwMroD0BmlAzF0ooji7E5e1\nLvTS4/HP2nvA2bepw5g78PYrDFbJ+JV06cOhz4fX1ID1KGBNG+MZvhcql7UKb6fW1ciGtfKobipW\n9nXj2vvGv/BQZJXCqsRl5nCnEg2FKbF9nL6Browar5w6SR4WvJ7hgEGk2qs1c4xjYKjg8DoZ2wqI\nuQuPkqEzv8MsCDH12PzOfRZhQpo5ql1i8ZH9t2VbsaBf2RDQdF+CiYRLI4W9+SbLfEPWAtDezEV5\nYt+dfK1A60xwstk8NdAOjU4VkJF0sZOBZeK9JaDOSX/jBjD4Hgn0BfX7kq9pRKt2p798tILOC4or\nmw5aB7TGXtJINumcPmzBpRh7mK5xITXcAyYvEcD/mcTiFlHr5/UzHC9dbL9fsWkETjyiwJ7UCs5O\nbu8TYGXRCSqI3o98SFWLQwIOHOoeJeum9kia1ffIOQr571UkUIAS8tCDT2vVm3m8rpZ3FFCZhJ4J\nVKImRqJWqNYOep+TLtOO5BHM5OsFgkN6Vj2g+cj+ufNcI165LqTw5fGzOr6YOL5fSSG7DA5SXQX1\ntm3fFZmHkgzchAGcSs3ubsIcczH2oUlIrLifICTwZpr4gAHynQyOr2FOQ4gpdV6aCpKqrXwo4FRc\n9BspB4By6bKAVJk6kJQXuEuJcoNonMqrpeEBBF+9on8bIqdNWKUuLn61Fswx5OvhDHIeL+SzgLEf\nulfUlaAW0JXD74WP9C5l8q0Myx7t1Aa1ZkhBbU4HiHcUjHSgqGMipIGmVqvQAjm1y7NdWRtBeZoX\ntpPXVcLCd6BJ8ts4gGpUSocSBQVeJxdY7NDmtKahqi6PAbu3Lu8oBr7KTgETULB5EfCGPL+jIIkd\nk81DuHSZ2cDwBjZ5UHdgyl6JRNTji7IpWMIZcxcxzZRlITDfpYTUB1r79iOH/3GJ8FMaNfRzqANy\n191SXDk5K7pOenMJUbkoX2/Heh7yQZWcBfU81gCTr+f8PxVeKJ23uMqPtxWSG5WeQ/1uGiVRUmva\nBBp+tVH79cbu7C6h95ooXTuGQkdgeYsiZqsESHwrXak1c37v69/H//oXv2TvWWBw62VL8T6ddiBJ\nqvtiJYCkKlI3Fb756x8DAP7y8X1O9KWjuWuVDATcwypRyaFaNKYISygaKaJRQoe0bh3N4nPF3RI3\nerYTcHC4wmuq+5VLzN8lptxmjqhDLftZiCG1+CeTBrbXr3BKFXmRNpjWWVwzZwr7KRxSHI5PWvWY\nwwDBjt1JkrMmvvKSxV3EpYdT6hAsRYScNkZXwqrAArZry5ohiqXdvVYOQRgqXxhklMxj4bLMd9qU\ncOjZhmsx412K4wYMYSiy0IE/qkdO6Vpd2p5/DRAldVjcerQhriSSqhPWBbb/1gsAwJNnG7hqEUV/\nPYF+YRfc26/t4b19ywjypwKLr5OxXyPH8hatCyUBerYqvNYVAOCck1nmL2f8vixuGsiKTl80WAwx\n6GQYv0LJYgxgQED+q9A6CQNAKVidODz0rm3+QE6vUdGyHWNQl1e6ujawmwQcY1bvjNCL7Hk8PVll\nb0I/LJH2qXPSy7mocHwN1aA16XrsWlx5i9nzlpjdIrBwACx3qMDqJVhWBKVcQkUU824ZOPMvOyE/\nj+MLJSGV5XbjRs7zVOEZlKRg5zg1JW2jOcenpLex1l5gTKZDs1LyTG9ncIVRbDeRbiNlaiAATJoR\ns2CaQY1HuHV3jIO5zRhCt8CNhg2GH15sIiU/m1ZQIO0QpYv+HwCm0x7a9+wMYtCIcTaz17AxnOKM\nRj83bl7ixYENpMJXCCioZC2JtYH9rYnUyGlGfXt1zGOXaRyiS/dIrQiMTm21I4RBo080PFcx3XZS\ndgEHkO0cEkB8y2N3TQDYatrfe2f/phVdggWnCtpUBIA0qCqVEFGP8Dh3rnB80/528riHFcK4XDWb\niGiGvpxGKCjJwyBngSoZKJhL6hxtL/DVTUu13ghn+Bf/4psAgIe/8yn+7sr7AID/4bNf5/NdLAP8\nym/Yv//jH74OVQHQSgFJ4ySvn0G1K96fgFp6SEihFaFmLyHd0tBdCjZhiYUhR+ajkBVdh70FUkLT\nb3RmaHlErZx3sda1m96D7gXeId2TxSLC9tAmnkeix27O7V+fIK86Kn7J2KeicJitYW6kKEnB019L\nkMeVJouAqTp2Bog2bZWaJR4WhC1xmwVKwgQ4AlA3al2KoJUhG9G1eRomq3+vEi77x4/fwu6rNvE4\nHPUwIa2PRphDUXL/n7z5XfxPf/Vt/r1LAj9LX0GTX0ib1h1g9WqmFbW2nWKdxOeejwbwd+yfQ7/W\nT9nuTZCSQ/YsC+HRHPXm9iUCAvkcjPss6LU6nDMW4WAeQBuByCuwvjJD08sREd6jdSfj30gczUnC\neneOyznFhp0pj0sBYLVhk5CnwuCUOiqtIENIv7coG2xIGIU55vTuaMdw0RNfA3VGYcEKwLMkRI9E\nsto7KZ7s2RaWSBzGAWkt+Pzb91I0fXrvLvrIycBtuDrHvGkTgXzpQXqa3wG3VUBVSq+bMV/bzd4V\nLsiH4vatc+w/IR2fvSY80tsYJQ006TnObrqQtFaXbQf/6dvfBwD85fld7C0tnsxpXGO+FY61vYD1\nGiq/bp9ztvTRI9GzxS1TYcgxaMU43bDPwFlKtAjXNbvhMhVXlxLdrj25iWmjt26/M3AVzk96PPJs\nrC8YwLw7HGPvwo6LTCfH2sD+G6UFYsKs7K7XrLa15gLHFa7FKzEhmrk2gvWnFtJwXFXG5WtWDYWY\nkjlnKeGQ5H+/GbN2UTyJICjxMqWDxuY1o6qf4yH++o/8O3V8oSSkot/2oxiGlG2XcYD7GzbxeHqx\ngk5gH/Kt5giPz+1C+NrKC/zZ0X0Adm6qKADf71zgtzcsaOwf7X0LUhgWBPvh7CZWOzbY9IMEt5p2\nc35vvM1UwdAtWN9jsQwZjJcnHmfL0tNYIdzApFNi0Ij5eqoN9rOrVQbOuqKWDv/anQN82rWJlEp8\nfvmLwsE9uuZFHmA8swnW7778Y/zVpaUD58pFl6o/19EseqQyyQFM9nJoJbG9bjfG5/ur2CRWx0XS\nxFc6VnPkfL2JvXKVz/vmwH7+syc3mL3iJuDuzPPRABnR3kxbMTsCAGtDmELUVLzM4ap7d32Eq469\nnmUcMA9/locwD+x9fDxaxXt//rsAAO+1KS/mYu7jk4l9MYQSMJQ4OkuX6XlaC4RRjpgo1FCoy9PY\nqZ13uxlLdavMgazyloZiQ66Lyw58okceTbrIqu5UUUvYHxyswqGEUZcCg9BuMHuLNeSUeC2dBr7x\nshW0+/B0EyVhn8rYrVkDruZuTn4esTCUs3SYSiwUkNEM3fh1y1nHIXeL/t5L7+AfffxNdCnBWMQB\nXFLSLEch3E37fqrzCAUF/bd3DiHpy87nLWy1LUjxcNbFWsuukR9Ndvl7pGOwQ/bK++EAGTn7aiPw\nYMW+t1dphEVon8Fqa4H7HSoeojmvKSEMC+YdTnpc/XeiFMcki18xXgCg30xwSkqa8yTkzdlvFhg2\nluj49vxOl23eeE/Putggz5/5cRsOdbPOpm1k1M3y2zkaVYKRBgjJnDCeRmiR3cLdzog1Q/7Dr36A\nH13axNMYgYTWc9RO+HvSxGdvq43ODBMC2kup8RtbjwAA//jTt5jS2lldYHZCrBlX8TUX2mE2jZAa\ngkYuyyTAsL1EnHuIghzzZz1QAxA6DVhKPb+MkFOHIG3PGSvjOTWw138wtYB8APsvVtGgYmO4MatF\nGd/r4qP7Fmcyjhv43a+9AwD43z58m0G0jdUlUqIrxzc0DAmaiaWDK0Hv7cLj322tjNmZFqKOL87C\ngaL2in/uIn2VmCVXLlIaiU0Jk/NTvjX0zH2pONG7+HyIExpzua06Wd+5ecmaU6OLDjY3bMw7Oe3x\n7NQJFOKylmWowPgil4jW7fpKJhED0FVHwd23sW1954j3kWi9wOiCWD6pxAqtqS+Pn+3xxXRCaKPe\nbk45CDmu5oD3TAwxCGwQ/Vf7LyMe2Qee7dY/JxzDm9YPznbxfXMLgFWh9KTC4ZIq+EmEpFJojZZY\nUqv2+f4qJFX/SdNj2lyzkWFGvhjmGtdJCPCsuDusOzLNIMcP9+1vP9w+xr6y3ZW09Kx7JKzQzeKS\nvGk6OVp0Prnn4HBiA7Ux4I7N7z96Gx4FtrXunP/eGIHdoR38Px8NEFAFnoUl0nmdIAgJ3Iho85j2\ncUmIxVd6Z+hRcvfZ5Sr2qYoQpQAqkG7DYYlpXXo1E6FZst6EGQXMt/cGKUpKKje3rpj2mJYeplf2\nmjfWplzlPhsNIR/bQDVdDRG+bJMlz1Vcyf6X3/oOfv/p2wAs/ianqsaJBYP9yrkPlbpwqtFZT8Fp\nUpJw5SMk7YJURNYDHgASF9ggsTtHoyDZfikNFP12kbkwEwJpGnCV6+ZASZ2X1d0xV/PC0zA01nJa\nBd59xybJ2tMsbe51M5SE3zGpw+Zcql0bpGlPQFISpddyyDN6nqsZVOVeWkoICrT/dP8NuJ82MKY1\nUXV+AMApBMpKZr1dd2TOkyZWiFpujMAJiXU50jDQ8sePd7mUcqISH19t8D2qEuyqUwAA/TDBWcUm\nEQZXeaM+DwKjFtrBTsu+j0dnfWgCExZ9BwPSxjk96zF4VUjD3jdaC9be6QyX2DtdwUrfBvfLqxbn\nndLVOD2pRbDUyN6/9u4UWWrPqZg3cErteLPwMG/ahGFjY4KTQ7tu//Sgj3DFbmzPFkPMSOsj8Epr\ndAYg2srZL8ichZA36oKkEnFzHI1Zaf9tt5mgRWJto2UT/9E33gUAfO/sNoPmW62Eiyp9zbbC90qc\n7A/5XTJNZc3+YMeojc/su9r/1VPc6dgC60fHO9zxPfpsDf66Pb/dwRWefnLLntObF7iktRrriMHC\n/RPggx9awLtxDf7p8k17IrHLeiCx22BQ+/IsxGDDJn/jUauOF40SPq3H42kXBYn4GS0wJEbMlRMx\nU0pFDl5esYXjoxctaHrFwk6GZBzBp6QyyzzkdG0rGws8G1+LYdUSeBFBkPfW4dM15qwax7AWFbRg\nawwlDdbIGmAah1D0npeOi4x0X9rDJZJz+34VDcVCbEN/iTd2bJH3vz97k0dTrTsT3KAO9M/9+HIc\n89cfVSX08WijdkRMfPzxJxZ93u4l+N5nVnhISMNzv3/+yUM82LE8/GXqcyC4POrxPN2PCviuz+1l\n58pFtmJP8yJp4nRJs8m5W2NFkpArZ7VoQpANvHfpwrPrC/GOZo2KqWhyhTybdRCd2Jf54xd3mMkw\naSs0DijYHnXhEIaicA1Gh6Q8eSEQE2Cx84kLp/LH0taaGgBGug3vl23i0QoyvBjTCMkvkNHGmR82\n0T6QuPrJDczvKvi9IhfIAAAgAElEQVQjB/+y8yrf7z87tBvjYhlyZwewEuIAIBMJOa1YM2A/Fu/K\nQUn6Ju7cQ5PEa2d3DbM6skWDwaWnT1YY7zBt5ByQLt5bxxu/bH1tnj/aBFmtwB85yAiB5hxLLO/Y\ne/EPnv8qV07NEaAJsNnZMxhRImCUgcmFdTdNJZr7LjsYh5tLeCSxra4cSLpPZcPAPbebRLZag3GL\noTW3s/eiThL8KwFTMXG7miuhrHRxTkwEE7uQNPrwnkTINgjXIQ2zg4pS1CJjx4GdawMIjj12B1WR\n4W6U9BUcGjRnuYPWT2xgW+5qxgAUpQM3BhS5ApdtxbRGWdgkDbC0zGjNBsO9o1WcE74CsJRvwMrK\nH5CZmXANB3OjBEISBOw1gKNTmwwUYY4fP7pl7/UgQUYMt6zj4v0Tq3uRnDcxvGmrzsgrMKkG+1c+\n3MrXZNzCVWrvoxhouOtkOvdxk2XLs14Lktg6M9OC0ygwmjahMsu4qUDnwamHnO5r7+aUu62zz/oI\nFtR5fawxu03U8qFBcEwjiK6P1ufkHNsDwi17zR9/uoO3X7OdrccXa3DofZlettC4Ya9NBxqafIHO\noxYns71Wgo8mFtWZlS7TYafnLfxRaeOcMYI3SN9RWFTy/0uv1o/pxfAva+pqNrSgcsDiNBICf8ef\nreGoTcyXxEGl/BWeS6Rte21P/80tpo22ggzTffs8876GadtrHv9Czt1MASB61yZwedeuPwCIEw/e\n2G7IXQdYLGw8GxwAMxu2LVttl8DOty+QPe7y3489Ys18LBFvEmtMAZ98YKnI/kwwYH8WtRDOBcpK\nE6iheS0dx12o9+z39o+B0Vs1yLeKc/7Yqdk7mwompuTer9ebNwqYEdN47sJUWkxdDVDyM7tooiJd\neZceA2o/HN3Av55bzxu2IAAwO+7gu8fXqGlfHj+z4wslIVWW/srwDPszu3AulIP7W+RfcTVgJUHT\nUAioPew6Cvsj+3nfVZxoCF/xd0ZBgcl5GyxeEdVtvIOjIe7ftEmMDjUc4q7nK8q28wFo34GsVDW3\ncvhUFUenEu43beCZHXcQrRPffOSzkqBqKUSH9ryThmCEd3kjQ/ddwnu0JHcRylY9Ty6b4ARGe+DW\nfNk0WD63Cy1e81mIKXdcbK5Q+xlANrDGWoDd0Kp5bBgUHACH3SXjZS5mLaBymj3weaGqAD91/RXl\n0ptKzO7RdfZLpORyKW/ECD4gs7FQMM4ifd5hxP38jQyPL8g75vYE/j2iK/9khfUwtAcGluleiXKV\nOjPjiMHLZShQtb+8qUTR05z0hJdASeCyVDYQVuyqnqWyAhZBH1BSma2Cef/QAtGAXGRnLWgCpvkH\nLtNenaVE2a7knD28vmaxFRckpAQA2Zri5ymUQEiwnKIj6nGMAm8QKpI8mtGhhqTKrMxcoJI2yCTI\nEsbSkel+xU4TYQB4D2yCUS4CuHukrbGreROLTgUm65To+apW/SwcGOpCLJYhVvu2I3Vy2Gf5cKMl\ngzSlqAHC1w0m88TjtVooB/FVRXHSmJBsdWdtxBYLTiyhaE16iWCwK0zdAch3CjSe0+gnNNDE6Hn9\n7iHi0sPTT2yiY9olHAIIO6nHQO27/RHee25HKrqpUNIoaHpHssR4eCn4vsbnLZABNdzYSoMDQHNt\nydLupZJQ9PzdRsH3QLQKbFGLX2mJiEDdy8znTm1e1O7CMlT4f9h7sxhLsvQ87Dsn9ht3v7lnVlbW\n1lW9L9M9MyQljkjR4iZRgOGBDb8YMAwDkgx4gV78YvjNgB9sGbApy6AAQ34gbFgwYBgSSXGZETnk\nzPQy091VXV17Vmblnnn3uLGf44fzxx9JwAbpBqZpYPo+VWffJeLEWf7lW37hqgnIn8yWeD9TECxW\nNp24LBJnWwpv/8IDvP/Dl1CEgMwA/XPmmZd3O3XgdjdETlm7FQs0D8w9T6/XoGj1Uo6SGES/uvYZ\nfnPTtDy/dnvXeLcAyB+20aWAbPi6YaAAxnE7vSBtkDWN6DYduJHN7ULrseAAXuSATZoZ5VXJ3i/K\nAY/XxeoaEvK4kpHFgFD7UHIS1n4iEG2A3c/DW1OEnrmfhp0jWavB8n3Scbo4arNEQ94xQH2AAPg0\nVxXAnlSZ63NSmfY1m/+hlTOGzu5mKH26n1YNtD8+7HHQllRfDMDqZFhfGuM5vvzXV2Jlf4lXvmd2\n9g9wBcUz8+/S15h1KTM5bDKtVE8tFA6B967O2VAqedBCSTzs5guB+TUzGce5heZnDmewUMCsbTZG\nd9/DbmAyPmshedNqPbJADuewF2DaJ3StvSAUMH9GwkUnEurQ/NsFWAE0fGGjpCq4e26htWv+XRx4\niDbpOx2F9qcUqKyCUfDKNnRKwAhqVZmgtgVnuJHtG4VOmArRwYHZwPqfSTiRxvSa+d6VD3I8/zvm\nnjMR8GcgNWSF5H8ccNlO5oapAQDtR5YJjgB4z13m68frGgEpjiawmTYsT0O+f5mBBcq8CwtzYmsE\nDz3kHmmdtDTsqOqtKrz0umElPP2zqxwUvvXmU3zyXVO9cadAcK5p3HMI4gZHW4B/IjljshONNllW\nTm9YnKm0Hls1ql8D4QnRTz+1Mb1Oh60LZDQnvZFATIyIrI1LNE5wJaRILdy/MJu2PbN4g5UXFuLV\nShukPuRkIVglUkvAfVG3e6pAZbZjsQ+ImjisB5Jldk3jHgtMXzU31ujHUAOB4pGZrDZ9NwD07sp6\nvmmwlsK/87Uf4rd/aEDB7rmN8yZpN8wdTD8hQbsrJbxDi+85okli+wU6D6gSgBaaB4SDuQGEu+b9\no+UGBAGHu/clxqajhiNquQJmPCsab+lrWDFt+MpgZwDAO3EQnPEnUFKl5dPFVaBV8GfkrKZrpEuK\nnVaHSS0739izmU6ZN2v9jMXmpcpRIrHyIzMvRrdsDiTLUrLJW77XRHhE1alBiLMe4UxObKQDMlEb\nNaHmFAi0cyQWCctZiuXcVWzj9/7gHfOeG3MkRA3VWiCj9qA1s7jFdxHbSP71EiQxsOxIIL9rxtOO\nAeePKQHwak2YbFBiQnLu7lSgpADI/9MmNEHCfvPDb8HbN9f06eGtmhKdAvMtavcsJZCkvYHIRuOk\n1hLyx+azs23BVFd3VmLpE/PZuC/ZC+Zku42Q/i0zIKNqUfu5hiQsSnQzh3dEY+QAzWOqNF8XsFKT\njAEGT1LhVzyr4LarMwUuWmYARC7hTAggX1zaSx0bZZ+Ao6VAfkZmkBOL5e+bewLRFUoYpg5LPZQL\nnynqVlrT24tWgYLAwaVlof+AsDzzACfP6nn41esn9/pilRDKbJNhAIseplDAi3skghAotCiEtFKN\ns3fpPZ+0UGxS5uNqtrsOTwCLyrKTl2sqWXtXYbYt0bjrI3olhSjBbITGUHDU2ntUYFqpWOY1X92/\n7zKavPABbVHWbtfiUnZsZIarv1t0+KkUWPyayVisP2vzQigA5rPbEeBeAlCn1NIOD4GsVb+HFTMT\nyRoVWovaFMoFFmt0zRfA6I6D9iMjalQGCjbR6fR2zOVrKwHrO7gTAY/8SdKekUEHjJlVFVX37gn4\nIzIkm1ssDpcMgCZpXWRNwVRf5YIXcNbRcClowVxwOXnpY+ChNsC/8ALIiMXy0dMrcIv6+6vGf9Z2\na+ExG2gcG5pi6QHBBQH0WhJaArMdoHFo3luX9oHxDfOcg3ON1R+a6z59txYiggD8Y2pNqboiZUem\n0gMAWWxjWJAAXomaNmyDtSG0BAcSWgpkxCxxx0BJe5M7rdeCf163ydyRREKHhczBAZW2gfAptQ3O\nHShXA46GvRCwYzNPvKE5bFu75veiTQFrZO75tz/4OtwzAss2FURaL9+K1dp6bGGxXlNAvV2SsH8z\nQUSaESI3vwcArYc2MooxsthBc5c27Ut0ziyxORDqHprgGwC8k1qro3QFohUCHdv1uLhTbt3DmViw\nqfXpX9TjbiXAxdc0z6tn+yuwqRWGS89QC3DAaC+suu3kaMy2zPU1TjXm1I5MAHSXicZrabjU1rFy\nYEpzVVvA5CN6WKE2OigwAVVVCWi8ECwwJkLFLZFOkCIloGjDz5BRq9C7EMhoL1C52ae8cwE7AYIL\nOvz8WvMj6RrqeuOADsBtCf+MgoFNBeybzWrlgwX2f4kE6mzFzzBZ1ui+TBL+uz0el9ZHPidnMqsx\nF/Eq+EB25kDvAak+ewKC+ppWLmDTulOjejL0P8/w7D2ziPu+4GcjozpxAICkR1isqArUTVA/26BW\nsVfgYNgFQd/MnKEEyN93ap0YCZ6fZUPxZPLOam0pJ7q0JydAuF/dm2CF5fBAwB+RTsiWRELido0H\nHmtaJcuaE7vgBFis46/m9VUl5C9+VRO4zATsaaUYqc2mClPGq7K6PBQs3GRHBvAEAGJs1X1TBTTO\nzASZvKqhLSA8NMJW7hQYvpMbnYcSQEVdFHXAsPfrgE8Hlh3Vm37RBBpndMC2BBqmW4RoE39uwURb\n5j3d+4JFopw5oP6UIvMSvODTvBbxsRNgQRuyOwEaplOExRoQHpjvnF8RplUBo/gIEokS3ZKFl9IB\n0NzTmN4QWKybrGCxWaXwAgUh//XcgU9tB1HWirHiwmZMV3AO5LTBCl0vJP8McChT1xKwIqpOHAOL\n5foAr7LO1KuVVKNtjVxV79GYvUSL+bpgobf5zZpW7ez7/Fnl1AekldSbhZUClWVF1gNkbiGnsRel\nmU+z6+bf1QFfBhopBTSza5ozcuVoDsg6jwUHg6UHoPIKm9XBQ8V0AAx4r9KYk4WA4KyrPmyLhoak\ncnKyUmf/8XI9j9KB4sBOZIIDT2jBffbgDJxdWomhQQ5+LABojF4FBoYghrwpkNImbiW17ovfSZBS\nq8U6c6CIlik6Bbfj3ImpMgGATAQ/B9fNUVJgkDUV3FkVbAiuFupS8JryxhrTStwtt5i6bSfg6po7\nBeyUMtzuJYqy1HyAFUF9DdOXSliJBeWZa3Qm9XzofWLBoTkZnTvc2nSn4MNDC8kBKUTd/kz7kg/D\neElAXao2VtgSmQkUNA8Xm5rbt0KjXp+BqnFAnRIlrbtJ4ECQholOLcaWXbzoQlApfzJq1KZzITjB\nEqlE9ykBfEMBmWukbXqfR0E6gMZJffJ4kzoRszIjagcAT77tQ1c6KVOXvU02v6NwIMwXrX0InP6a\nmQzpz5ZwPzAPN3GBjJRenWm9JkUJxMvU4rDrymHSqwNVA3qnJKdrQ9LDLV0jJgmY1pqsBNp6l6ql\nPiCURkx7jEosOK0MSgmo4lKwnoJNQpPlWkAyWy5gEb3XP6qrosrTcGaCr7tK7hYrFt9DdKVuQZYB\nUJAGSLyi0TisAj7N+0veL6Coaj+7ppnJ96W/vgpC/uJX1iVZ6F6GLKCvKAQkoakxDxh/oIWJSAFT\nbqyEjmLPZVBf2rZ4kspmCuXYrKyZdepDI29pWNVmcGBzwHCZWB1dVdAUUYtUYnKdJnNPQRZVzzqF\n88Lj66tUKLO2g/kN2sAKgeCoKokC09fMvXWW5yieGRDUYqdgrxFZuvVGuFFCUA89OAMmP0eBgxZo\nE5e+4eY4ej7gaxsHEsUy+WKsg2mDUBIrK+QXMQ8QElhiuF1yoJfcTrA0MCWZ06MuS3VPGh6Dgu3Y\nweRGVUYHkiUKgFZKZtbIkQOHBKDSuVd70PglmtdIIn4aQJJOhrLrzKG7NUHTM8/2hTdAXqkNlsL4\nwABINku4p4Q/IInnCi8wbtmQ/YpaqqAqdogGIurlIiwAqgq1diZMlRSNgkWsRn2HAz3vnDxYAORz\nyyDqYbQbKryD99IUUeUL4yikFcB5YbPWgSU15kRvzmYu1KzWVakcWMNujOjAbPiDW0NcnFDJZ6Vk\ngG/kO1DEJnBPbeh2gfO/bt4GS2N0p5JqV2zBvph5hhUEAjOTA23ZLrF+1WS/R8dd3njjFTDyv+go\nzCggW7dKVLcJv0S0ThW1FMj6VDlrJygIXOGNwQGvJRUShyidqwEHLeng0mZ+RcMhTQ49bmBB8yu6\nqnldOEsJ1EVYV6dSYLFF66eQvAf8/M9/go/PTD8qPR0g69bVGZcIC1YCVugtmyVKUvQVJSAJZ+L4\nBdbb5gOPtzykJKVb+roW1nMdpm5LAeR0UHX6EWYzagOvR2xxr5vA20Tpfz7rMb0zdDMc2GZfKDTY\nX8azSpy/3kF2naLEkQtNe5gcORysRHcK+M+onZPVSc98W0NRC6LdjzAjfx3pKJSEd9j7Oxrtu+Z5\nvvaffIzf/+A1AEDezKEr1+o7I8xjE81k/ZKrsO6w3nuFBgezRcMc0ADQWZoj7pvPxssSDiE841Wg\nvGn2s52lEZ7PyVzQVUjKujJ9/o4GiMDwq299ipjKbE+mAxxUyc3YhSQBPT13uDXV3ZxiTE7dyhNw\niLqej3yUl8QhPZLJT1UIEB7njZ0DfHzfgGW1peG+afYwkTiYLZnn1h5EyIeV9wAwJv8zaSsIfelg\n+er1E3t9sXYMLaL1pQkOjio7UomAJkK8DagZ4UAaBdImTcgwR0C01KhTMK1selNyayVsplg0fGQt\nClyaRhEPqIzUzHeF744wJRdFaSm0v2kAGaNZA23ins8WPsoV6i0qgZiyjltrZ3gemEVVKgFJh0T8\nbs4GU62NCLOOWfDJhctW6Q03x8lGBZot8fqO2ZA+xRYy6rMiKJGTME7W1QyOWtkZ4krbpIUPz5f5\nAFfdAlaQY53MmU4u2rApCClyi8Gor28coe2Ye7snFWZ0MK51prBpkafLNmugHIguVntmE35R1voi\n3iBmoyod1aJMSc9Gkpl72N48ZzXLle4Mk4UZi25ngYg2IdtSyFcp+/1XfQz/BqFGIxuSUOm2WzKo\nTy0cZFfM369tnSEpHJRELU0Li32BNgYTHF50+N97B6am3B3MsWgSpmTh4efefAgA2J93sbdv3rO8\nOmEg70mnzT4khW/BJkCpIxU7Hm+2JnhqETbnknBdszdnMSwFwXoQiZ9h9ZoJECZJgNOhef87ay/w\niWU24b975VP8cWBokkoLdvBd2o6wSyyW7vYI4yiofXXaKdKOmds7t4/5Or5+4zl+8GgHALC424fY\nqtkxR4/NPVuDFP63DD3y4rzF+gk6kxwYeFaBvF2n//F6JWl5CStTWshuk4jdSwLLJNwVpW4tUHXL\n4rXaWZpjskxYnEsCaL3Xz3A8MPuC9AuUZJew1p3hYNNFd4WM0eY+u6U2by1wShTdr7Wew6Go6ndv\ntiGoRSj7KTI6tIIwZVr+r157iN+N3zJjsRqz500rTJi6XEQOyhVisq3PMJ+biKy5HOGtNbOGPzrc\nYkBxXli4sW76cXfap5jTwTnOGrBFnSFXekhtN0W4Ycau7y1wMK9xNLs7Afrk5j2xNdYGZp0coIcG\nsZ0cu0R/26zbtcYMn5wYrY/yooHeEoHoP+jj73379wAAv/XZz2J5mzxychs3/y0TkH50usXO2Zaj\n4L9i9pvQzTBeqXpkCprG8erPHmCPDuH0IGQ5e2kpqAoHoyT0LXP9+dTF22sG1P3+eYBB21zzchDh\ngEC2YSPF2DdzfnN9hLy0cHZu5vOfHl7D26tmvBeZC03Px11b4O1N+nvhYEbCj7PMQ7Bmftt1CtwZ\nmGdy1G2xltLj8QAt0p7ZS2wMiALedFKs0RgtMod/9/msh93UrJ353Me1XzDqtEoLHBBrqNlI2Tvn\ny359BUz9S7w8yhyOzjuMSnabCetENJsJMvJgSdbqXqSauVjeMJMoqqhWAKyp4DKr6xSYLWdYtGnj\nmVto0SFZBJaRdQcwn/kMQJOHAU4peNACGFLfUCsJ7zmVCK8l8D83O8yB34G4T4Dabr2hlK2CJd+n\nqslqgFDgbHSWeAygK2MXnznm4JFDBx7hEhJp146NYckl2yh1cTAzm5OUqvbEyAWspyFGCE1bq6WQ\nVdUJu25z3D1eY+O9tfYUp2cmSCjbEodD873p1INPYkDlQQNnVQZn1TO7yGyMid7reTnmCY3RozZX\nYw6KLmudjKIGHLqOd1f20SWgzfdOr+OAjAP/0X/8T/GPX/wCAODTw0sAv+UccZWCCzAP/6DR5etp\nhQnSzGGA2JHVwVLHbDxn0yYHa6GbYXxmnpscOfje+R0ARuZ5+4o5hPcOltAmAbCq6gQA8MEBVstL\nWJTKt3L0SCL6fN5ku/NesEBGrIzdswEHc+O9PiThAwaNCMdDs5ntr3T52RxnbcTU49FaYHxsntPE\nC1lUbfTjZRRbCbMAelsxjjzyv3By1jd4Me9gZ9Pc20HQhWfV81VRxr/Rm2D/YzJCSwQfGNoHuyWH\naxlLjy8vTXGWmOemLY1VUg8GgDMK8p1dH91rZlw8p8CCDMiEpeDTupgctrlUrsMMnYZ5//Fen71s\nVFq3L+epC+fEQXTSNwywWLD+xGncgXtg7vko6+LhxATN4VMHyaukXPqDgLFWiw2HfZR+Z/gGbGrN\n2fdC9H7O9EXXwhlOSXnUDnNgn8zpZAs22R+Ul5hC8cyDR1XEjc4Uz89NcOpbBSuYHu338cpLRlci\nsHO8GJl5nDUXGJHGyKHdxmxKAfXKGEglhgcdwFGQUxuzkPa+yMaiinqUwOLI/N7JyxNeb8LWHACr\nCfBP//nfMuN9J2Jn36VWhA8f7JjvySQCYtZoAUQEiM2u1BgaOXbYX+bg6TaSa2Qc6WrW2FEAG2Fm\nhcWu4HYq2Gm4+cjGuW32nbaXoHxhksJRx2X9l/PvrxnMxUpVPV/gz/bNtd5aPsNoZIKVPLFx99Rg\nCpUSvJZmwwZ8CqSVkvj+PRPcB/0YGWn9nB92UFDV0n0Q4PyW+fHJIoD9I/Pc8jcjfPeBAcsHzRSS\ngOaqm+PJI9rDWzncqsqTOj91bZG/qtcXCkJCyqJdp+BgohfGOD41E7IsJMobVVNYAtVBmEs0SLeg\nojwCQLyd86QNhcby8hTDCUWhM4snJNuvAxj05pydp0rw550w4wNsGntIKLu03BLJgERy/AxzKnfK\nhkBBrqvW2EGxRp4tQnO509KAM6SgaL12RYUW8Kg9sghcaOLCe0OJZLWqQSt0euYwXG9NEZFwl9KC\nqyuWXyLr2cByVQu9JHbkFyyslR438MZbhkJy73QV66vm8AjsnCsb2/0Rj/HpoM0OxtZTB9F1QpYr\nQNFYCj9nvRaxs4B3nxQTX8kQE/J/Y2uIBR3g90ZrOP6UwBKbMa5tmqDy7//+v4cbN02GtHnrDGf0\n/By7hGqRl4tUSIakTugb9ctKGffBaAUeiWi1/JSznChdgUNB783OOY/F2A/h0LxYCef4bN9sJMvL\nE2y0TMAwzTzewH2rQN8z8+LRZBkpfc/utI8+CYBZLYUn++bebvfPcEpOm4N2xPLkbi9BLzDvd2Ud\nIALAK31z+J0lLaxSFcWVBY4aZl0s9ed4dWDG6LvZLbyydYzPxqZcnCvJbcc4d9ClAKrrxRinZp6/\ne2UP3398DQDQaKVY75ox0lqgJHGnwcqE1TaTzEFny2TdDTvHYMm8fxr7tWdJbKFD6rGz1K/F125F\nmJFcdlZarFppOSU7Fh/nFiStqbKQLM/uDWJkJBePsICgDT/NHBRbCf9GXkoOysq9ENm2mSefz9eQ\nUjk/ulbAIhbE5E7JODLtKiQbBExsp9AUDBWhQJfu5+moz0qyReQY/xiYAywlgHumbczzCtggWJ25\nYedoEuV2lnmshyS8EvfvGzB2d2uCTiPhZ1btQZastVrmqYe17aFx2C0k5FqM2S5VSSRYxRS5hHiJ\nQLSRh5T2BufAxe3bhl71Y7EMvDrjjy6oNXMhgM6Pzf288e/ewyebZi1sd8a4f2Tms9aCPb+UrRER\nkNfemeN618yRp0/X4C8T7XXhcJUi8HIUtDepUx/niXm281sFltbNZ/t+jGfkBeaEWf3Zd2aYPuox\n4HcRuxz4nS2a8Mk+otOo19UobnBA11maY4moOedRiKvXzFi0nBSPyA5E2JqFH2d9DYeC80ErwtGm\n+Z43149x98CMi20ptG6bfefkoItwl+Qk3luwEGVa2Pj6nUf4Z/greP2UBT9fKAipSuizScDeF8NZ\ng1XrstLijFWXAoLkr0VQssqjEJqhHJcFzaTQWOQOygpSv5xhUvVm3YJrVQqCD94yt/DuVUMV/cHj\nHZ7MN7rn+FCZDePa0gUeP9kBYCoSxSXqLgNkS9S1sNiqF60nmXHSc3PMehVeAyj3CZfQz5BVGAqp\n2V3TGlvcQjgRLT4U54mLoGMWdrJwAamhKehBowConZUIIw8PANpTeEiZ9+KwibhtNk9nvcRrA4O6\nLbTEo5FZnO/deI57p2YTSnv1zO71Ih7Tjc4EZyTclZ8GwLbZFLa6M5x/av5+4rdhOXU7Krxlgp/5\n3Mevr98FAHwcTnAcm7EYLwLkxNy4tnyBh3SwB82Un7MQwHZrjDmhBYfTBjaoTF0qibMqAAgj7B6b\nz/842OBAVJ96eONdE5B9frbCPfiVcI79qclOXauE1jUC+dnUtEImi4Apl5VXCGCs3KsqitICE6KK\nenbtV1JkFsuOj1MfgvrPgZPz/R9OOtjomHtxpc0H0rsre/jBiQk6mq0E640pPqPHMo89PpAUBJf5\nj+ZtnjNSaCOnDQCtlKXUSyUhx0Qz9UKuYAF1xfGBrttxeWIz0FQo8IE/igJWVc1HHsaV8mQhIamS\nZgnN7QjLVsgOzXO68vIxRhEJY8UOrBVzmDXDFCP6rZhUZ226Vixn7DclAGY+HMw6tYaQBqvsdq9M\naq8qJeAPqI141oB0Kq2fulIkBLjVYE1slIRB0y9C6Bb5GUUSD+kwQ2LhxSNDdT5o5wjIj2g87rMG\nirxwa7aTBh+W2/0RTmbm2iazAKqoFWOPnw0QrpkAY3HYRLBl/l2WEsWhGTNrPeZkQNr1PfTePsOP\nTww+xvmbF9xqmk0CeBTce/+ywyyQ07+/ibf+sanU/Ph0g68jn9mwiDWk2gUKChiu90d4Tlon0i+Q\nHlfIfA1B+xHdsCYAACAASURBVNkob8EhppDulsgrzZiZxdXFj19sMIassG1jkghgfNGEWE4Bank3\nggwpnQfjeYCC/n6aODiTZr95ZeMYV1p1de6cFHOXwog9xqLM5QSrcCzWsYECMvJgWvgZH+ifn65w\nUjlbOIgoUFnZGON8YfaF15bOObBZ6czw/RdX8Vfx+mlrxwit//J3LEhR7Oo/+a8BLSAyUSPCW2UN\nEA1LtD6mA1UD01cIQzCxUPaJHTO3AHKa9R/4SMgFVAxSqBGh/hUYJZ03a5poEYAZI24zg/89M3mj\nLc2iOOlqzq0i5JK1NrxdD+6bZoKnd7vIScDImUnmy6fL6pIXi7jEgNAIr01Q/tAccvFq3VKRJdD9\njISo1sF988YJMP1FAkQJIKe2kZjZNbslk8z0EFsL5OSVYUVWjRvpFBBU4pbLCVMmoQXCvgm6FjOP\ng0JhKy6top2j1TE3F3/e5dZX6QIgwGkRubDPKoGpWggu75QsOe0d2yhuxSypLTRYbEk/DRG+ShzL\n3+tjeouqX75iQKh2VO1wvFq7YEq3ZCyOFUlY2+aassjlFWmduoxWF536gEhTB4oOOa0F4yHavYiD\nh7SwOdg4nzaR7ZPKZw6odcLGlKLmktr6z/n9nB92WNBIlRI+YZ/KUrLIXpFZjJtIc5tVSKEEuy63\nO7ER4gOM6ygFZK0HDrJv0MH0PKxR+RoQrTqIEgQIlrlAQYwZbWnYxDizHwZomvMHsx0g2yLztkce\n4nXCw0wt5Jvm760feaypoJole85YD0I0joDIdHiMnk+1TWwmWF8y62eRuRhSr99vpkhIJ0NMHeh2\nXfGsXHpLTyM4JaxXWNNtFy9l6L1PwNe1mhpdBERRhWFAVcrGRQOsvZJ1ajpwEQIe6bbMX08hJg6P\nVyVgpRzNYmuyEDzPxUtzlLthPe4arIzrntkMpvWHNbMkeiVldWJRCHbRxcKCM6IAMdCwNs2Nqr0G\naxsVzZJdZ3s/cjB+j8b+3GX3WjG1oTvm35ZXwn5gDuNko4BLzyq/8FmBFIkFOac9ogBuf2MX1evz\n93fM3y9RdfONlIHV3rEDZRmGUHU/FcMlb9bMR6F5iZm9+f8Bu6nsS7T6qxkcqnbhST23lYX6mTRL\nBvh/49YzPBqZRKsoJXv8zBKPBeaSYVAf1Erw3BStnC0FrJnFopIyEdAbZCgaXwrGehkQ2fw+ocDq\nttlSAeQSz/+jfwitv1yE6tv/4L/9iYQhP/of/tP/XyJtvxgwlSa9dehBEWcelgkyAED3U+RtajvY\n4AMdArWJGC0WAFjs5GyPrUppKhCFMFTSFIjXSOlyowTIk2Jr84Izh9O/ppEekHBTt0B7KYIHY4Ql\nWrnpxdoK8lkDyiHjrQcrQNtIeevlFHkPKM49xk5IJVCspShhNvHkjpnEi9gFtb6hmyUfTmUmMd8m\ndsN2wiXoZAVokGiGY5ewCd8ydJoQhIG4cecQo7jBZfFR2MB4v2NoZ1KbIGphAhLdKkwAsrBMz1Zo\ndBsxksKGP8hRKsml45EMsbViALvrjRk+3N8CdiJYbsGqhWlhIy8sON0YWVCgpGw8uBojedyGFUsU\nvlG2zG/kaDVjTKqFfm5zH3j9nWP+3b13MkgC6f78m5/j8cRsKq5VwL9Jh7kWeEwy4t/c2cWPjjbh\n0zhttiYGmNYDBsECH3y+g6JfoNGLEZ+E0DMHhVfg9qo5iSaZj4OzHgDjjrpMJfjlIOL+dWhneDhZ\nxkpnhr3IhRy60A5wZW2InmfG/ZPdTaxSNa/lJRgSm2JlY4ySos35wmNH3fe291jmvNHJ8O6qAbh9\n99lNfOs2ydzPenh+ZDKtV5eO8afkx6OlxJ2dIzy4v4X59RJdP8P4RQcIFbSj4BLuYntpiIMKd7Cq\n0aZgcjJq8LXOEg/2H5oS/8XbJQd9lleimLlYXC0gNCCCAkU7B0oJXQpMX8shEgmrn8ECUCQOkEgU\nqwWKhsXsNZELPmuazRgd16yF/edLELQerY7iA8lZW6AT0nUuAmSE+0AqoaU50J25oU2KEgieuhi9\noliXZrFOwS+MtkjeJYVaFyxwqCyBjDDxRdvQNWVm6N7BG0N0AEySNmRsQVuGEVOtbe0ro/ApDAVU\nBgbwrpuKlTSBet+yMiAjpdbYFqxsLLRxUgaMAV1lIyEGKTKnThIUtX623zpCVtgYBCbI3p90Ed/r\nIdo0YnLFWYDS05BDh4OesLfAdBiiTGyonoJ2NEQhkC8crK2NgXaE2XdWEL2cAl4J0c7QIIfg+wdr\nrMpcDnIO+kUh2bG7uzQ3VbhOiiJysLlu9ovD0y7SyuFXAh65gheXTCQzx+G5Fq7PET83AanqFLCo\nStN2CpRKGizfTgyBWtROwWKcknRK/Mrtz8zfuwKHURtwgItZiKX2HL1wgaK0EOc2/LUcW+0JHp+b\nfeVyjJCVwgTBDmm+KKBsAv3uwoikhRniWRtWLGAtPIhbc563nUaMM99U6tfXxrggR+0v/fVTVgn5\nQkGInlKGkQle2PbEQkEtC3Hqo6DNwl4INEmOOh0AObEB3IlEQSlF46IWPUqUCwiw+mTWBvuQ6IXk\n3xvOGwhI6z85bQAkGSydkkvtspNx/zlspMiorHc2acI7o0y/rWE/p973csGVDXcskdNhU3qA94g8\nKzYKLKgXjUxCEmBLB5qrKpZbIqeI34osJHtkNGWBy/e6lBCkebI/7GG5PccplXOVElxJEYVk9oLQ\nYPVEoQRvegeij1bXZFu9cIEXJ2Z3VpmFvSemvOzcKlHQZljEDorQ4t+q+rdlLtlQLhk68C7Mb918\n9xCfPzKH7VQLxviUvuZK1d/e/BS/e1T73TjbZLM+HbDC4jx1kWYV4l7AcUqsdIxbaxx5WBCYb3ja\nYofQo0mbe+3xOOCsrywlnlyYw30xasAnmfD89RJ7p6a8fGB3/5wfRDUvdCaZKvvitIcTz4z72soE\nh3vmO4e9hA3P4CrWhgCAkAKBHzzdYf+eMszwx3tGECQ/9/Fxw5QRRictrG6YysHdszVmJWBh4cHz\nNX62ceoAleeHAFdYnh0tQVFryzl2MO5ThcUvcXaXnJ37BcRNyu5zwVlreeHBqbAS/YKfsxw7cEm7\nRxamwgcAYmJzNm8f28i8it4tUBDQuigt7E9MUBTsO4i3yTnVKtkiIJs2IG4QmDy1YF3UwmNZx/wm\nYNQ3EwIsumsLlNSCcyeCVX8hwGNf3lqgoHbU3LOgKz2QQiKzqdrWzZGRE6ps5uypVLTr6qb57WpN\nScaclA3NlTd7XldGixBcFcg2c/gvzDWkfYmMAnIZS64i6FKyuaaeOuiszZHlNs6mTSgtGF/VcHPM\nrxINWgDeuglO0shFSKyZ+cznxGvp5gXOCHeH2KoN/25nAFUJgg88pD1qqYQaBwQ0htRwKLAtFg4z\nGS8OO/gbb3wOAPjO3dsMNMfUYco8Tn0IAri6fsEJHzo5/2502IRVBXBzGxm1lLKDELpVwCH5hvzc\nr9vwCnAIE1IWFn730cv8fHy/Bg5XyebxSZeB+ndPWvC6ZCR6HvBz07KWiC89CUkA5OF+17S5AaBT\noJLkDr/fZGG+s9suz+GilPiSCyA/ta8vFITY00rOrl6cVgJouxLDEXyYl5dEr7whIFRt0uVOakGb\namOyFyYgqVog2Y0MksCM7liwL0q66SKrsCatHBaZWamtAjnJQYtSsKPmenuK59ossLKw4FByhpng\nVpBMZC3nG2o2xUr7GtkKZWbD2o7e6qbISSdFXjho7lG/1vZhUYABAc4o7YkFRRgIJ6oneKuRYCmI\neAP4xs1dfPTgtrkfuxblEQpIyatBLayax67BOgYtJ2VK8PLVIXY6JrPJlMVAWK+Ro02AuiS32Zp7\nZ/UcT7QJWnQhUdLG8+DBJkSl9BrZ3NpyRxKdp+Y9/9Mf/k1uzYnNBC5tFnHu8GLe6Y7Y/PDx+RKk\n1EhIZ2GpP0PbN9f05PE64qhmT0lCrKvIgU/08Hjso0lunrAU8IZpn6x1ZkwttiyFiBQtbbfkeRHs\n1bYAia2hqG0VOQWXY/OkwXMNcS2GpdZS7kXPFyHCJbIKjzxowvEgLDClgEp6JdNEZ7OAfVpkLvCL\nr3zOtvPDiyYsOrhsp+TfiLWAOKqXaaUGW/nsAMbkscIsyUhy0OZdyFr3oQfu2TcOBAtgIQZKCk79\nC4mUtoTSQw1eLS8lBZmNnBg34mYC58iM79RrsGx3BTAFANsr4R7V69xe1GrFygEzzdS8iSaJCc6u\nai63t3aBIYnv2Q8bEPW0gEs6Pmn3kqLvmYeU5q0zq5U3rZnkFpaMLF5T8ZWC26jRhoAd1/tZNUYy\nr1tH1jMH7qS6Aslqm+ly3cLsXJljSDox7shCNO/j9jefmbFRFhYETk8KmyuVewdLzPaD0MwCLBcO\nlt43z2TxZLkqHGB2vYQc1clgdX3zWwVXp3Q753aLNXZQVsyqhYV5aQKV4IWNz8iDBhp8CItWgV7X\nzO3xoc/ruVQCSz8y1zn85RIqp8pUKVDQOg37C2aZjccuVtYmGE7JhfyitkmAANKMgPDWJVFCD5iT\nvo/byrD/GTlBlwLKNw/UjmQVm6Gxb7HgXLJca6CIVMLuk1O5peFRWyideOzGHVkeS8RDaGZEnj3v\nM37ty379tGFCvlAQIm9Q/3ov5EWe9nWdpfZRNw5jCUnZYnY9qUuCE6fGU2QCZeeSERIhqcXMxjde\neoYfPiEGwUYt0GMJzaCrZivBYtsssO2lEUZEv5xOGkgqh9COzWZJ723v48Mjsmzv15myEro2fFtd\nYOMds0E8PljhDSLvCLavXulEGM3M4ipsG7PblJmFBV+bdeFg/Rohsc/b2FqtNp0BYxC222McRy2s\nr45xNm5imnvQ1wmrMXdR0GZoBQU04TGWb1wwg8i1S/z6lXtmLOwE+5smQ7rZO8dDAqn6dmGAoQA2\nuxMG7N1ZOeX7f3C6wuDIwdYEU9JS+MXXHuF7/9zoMKz8Gy8YjPo/3/0VTK6bcbnzxnM8OTXlUcdS\n7A+x1R3jZtPc//XgDP/i5FVz/e05fmn1AQCgZSUY0sn0dmMX//D423hz23D6j6MWsx0KJRksemPp\nHD5hPj68aHLFY/+oz8FA44UFRZ465UJCrhL+480EOeFCOstzpjS7doHomtkhdWrj5rY5FSvAIQB8\nc/05YjIYemSVrCUya3k49EyW+jPbu5jmNbPknZ5p0/z2/GtQNI96t4bY8kfY2h5hlDfwsLnCv+Fb\nBT55YSopOysXOCcA9uS8CVH51uwsYN01Y+Z8bYz4vnnmemfBVvGp8Bh31BwsOLAZui3T4gNQJBLr\nmwbLc5QswaLst9FIGQSc6AZ8spNP5i7rUOhSsIJls5VgvmP+PejPuTUHDcxeNs/m3Tu7+OhDQ7HU\ntmkrVCZk6nkD49fMtQb9mMHYw8CFS62ANHRwbYt0IsadSvQU+nkT5ZvmOWSRC5uy6G9df4R7I3OA\nHZ520SbMTjtIcPDMzNXVzTFOYMZOlAKrN8x6uJiHnOWrUsKnNur5UQce0UF9Dda2CRsppqdmTr27\nso9Z36zTD9tXsNGdYpZ7uJiH8Anj0A8WEEIjukR9Zo2izRmzNA4XfYzvmFPprW8+xs/0DBj7t/7X\nX0H5qtkj/O+HeOffNmvyu++/gs0/Mu9/8Rs1Xqy9mTLY05Yl3t/bNmP3RoKbPbM+o9TF19YNqGiY\nNvBLy6ZC8t9Pv8XJQ9PPcP66Gfcbq+esT3Q0a/M6jTIX5xe0Zto5Wl6C1nKCWerjbMdCRuPqhhlW\n2uYeRvMGirn5TLqR19iZ1ILukpjcmQuXksq8ZWN92USDF62QjUFf3T7Cvedm7SwvTTnJ6WyOsN0x\n93/Q6jCTUwwybLxm7v/5yYCB2a0rE0xGlRrml/z6Kgj5i18VxUrbGmVYI/Gr3N66cKDWzCJyjhzO\nxqStoClNU66GPa5BVA6VWfOWhhYaWNjQtsYHe1egKwyCnwAE1Ctjmw/MWW5xVrB74SHcJJGp1OKs\ncO+0z/4qT8YDqArkOLWx+bKhVp6M2sgFZcKZzRmLkBo762ai7t7b4INknrgIiKIb5Q2UVAmSxx5n\nXWWv4PKrKiycz6jk3Mh5MT44W0ankeCIKiHnUciLobBqUKCQmsWE1pozzBMylZv6+GhsWEDT1Gch\nph/MdtClNs0s8ZDTobI/7CEhpsIni000qfSbJTZahOIXQvPY/cHj27DfMxvvix9s4TcD05rpjcGA\nyodHKyhoE/VfOMiIZZP0bDwmq/Bh3mAkfuBluD83B8RpbDbvX169j8fpGlRm4fNTcyinsQNJkuxn\n0yYCwrLce3AF7TVSNL1wMI6oTB3UDrTOzBjUAQZblE7M83TbCRKat5OzJhIC+WULh2ms488GeB7W\n1zp5bg6qJ80Bnh4s8zMckoLpRnfCTI/7w1VckCLrcneO76sdADCBKVUXXKvE/3Lv61gm0bSOH+Ol\nljlgPx2t8/OPMg9xSoH30EF+zTwrkUuUAwL55TYKAti6tuIAIM19NqSzpGIdH/vMYdAlPMW9b5lJ\nDp4np00EdPgjKDkokGMDYARMdaQKQmbjAA61ky7O2vWctRRroXx6tA6hRL1nNEpuzwpP88EDgJMV\n+CUHmEVmYe8jM/fKQWHA7QDWXj3F8AeUzQ9KNIjh9P2jq0zvd7yCW1zjRcCJxNX2iCnN00uHTllK\npAQQlyMHzivmO+0wN62z6jrpPm8OzvGUxv151MOVcMzjvvc5eWoJDWzOsZh7GKIJXQq0ib4vTj24\nV81c+KXtB/jRhamQrayPcZaYefjh4218GhqaaXYtgyQ8lrKBP/t9o5IaxMDBv2mem+sVWJyZZ3vt\n9hD7JJ+73JgzE82RJbOdsszGHz+4CQDoDiL8iU3/bi8wpkpGmtqcuD07HTD+JFp4uEpV11nmcfWz\n3V3g+Vmfq5DXdk5ZEKwsrHoPG/uoCpvFzGbQtpx4XEnWFphNo3PJa68VJrgg1uWzYR+C8GhiuXaM\nPpu08bNrphr12ckqY1k21kbc7vna1X3cPaGqi9CsdPzV6yf7+mJBCPWogzOJtKyYDxqNfSprXith\nHZPM9VLBJg/lxOVSvjWTXHYqPc1BizjzYJ25tS/AskJJh0oWO4x4F6iVHuWkNmQrlUREduTOucPt\nlbINaBImy0Zh7XnTVDgk4z0V1LLi6XqOAxJMgl9i94icoPoZLLLEXlgBAzD9CwFBm3y8qWqgracw\noR61VmAcy8VZCIdK/+mwjaN2yLTes0Wf++AilVwW765OMRqbTeXByQpvkgCYujaLPRYKE42CD57Z\nfpvfm7dy2GSKJrYzbseouYMZfVYuLChiL715/QXuE022vBozXTHa9HgB3944xvOxCaJaV1LeIJ7d\n34CupO39Ag7hb8brOR5IjTlRSIXU+D/yN80FCiA+pZp9UDINMjsPkDXM9XmDmO9NraRMaZZeAfuQ\nsB92bU7oRAY7AACzRshS8spXrFp65+YhnpyY51y2SvQJgTyeN1gBdu+8z2J9ydxlvxRv6QIltVrS\nlsXtsXEU4JwCEpXaXGudxj7wIkBOipNH0zb/drFw+BCeSIWCDMyUr+BX2KRlBRC7IoscONSOzDYM\nkwgw5Ws2SVQSC8ImuYlAUeEjwpJNIe0M0NRe0Ss5EppfYmbXUvXiUs/dr/17ysxCQQeHE+TQzwgo\n3isqxW4IoeFMBVQl2OEonvPdmyMMyQ4hiVxmICUnDQiil5exjeD6nO5NIieX27VwhjPXzE+ZS0xJ\nAOvN6/vYpTmZXQQoaI2obs00++Dpdo2VGTrYI8OgYCUyNH16//FJ5UgHZocF6xECwjQoXQtsDRcN\nFqtTWpjEiRKp6LDJ7r9ZT2Hhuzyu1etPjq4zEHp43sTmLROcHjxextVt0qLxm4hI3CxvAcvvUdVu\n1EJ1dP7M9i6+p4yuzMOTFeTEOjx2euh9YK5v+E6OJ9Lsf+6hA6uy0mileDbu8z1ULTs5sbH5qrmG\ng6fLbBwKAM9G5v1J5vDY5U0LqpSM38iVhQFVP45PupiSmrB34qB4i4yrMovBvIBkwHK6lkNUgYHU\neIOUWz89XodNazK6aEAQJmocBVwhERMHf3psxsK2FAfJce7AJ12l9z+5AatXtxIrGvuX/vqqEvIX\nvyrKYbxVwqMFlS4rRHeotSE0NJVsxYXHAQK0gEsApcJ2UZIOB5SA95gst5saZUuhJB0PuxSMQSlb\ndZlONUpokqGWJ26NwFcCLqke6jAHDsxhKC7Pp6GLoltFLTXFy2rnALmUykVdBhRjB4qyN5FL6Io1\nMLN5Q47XFAdVMpZM+/JPLMTkjyGUQOBUDmElJAlmOXMBcV574aQrJQcoeaeEJo2Oyd0BFCHz08jh\ng7R3fcSS10gkl9TLiYu5JpGpQYrtFcNffLa/Ar1Jacd+AwWNo3diMwDR2onQotTks6M14InZLKwb\nERRlNUUA4Il5UJ8/bKEkkJ1nF0h3zYFnaUBOKSBzrRqwPLYxTLpM/9WWxtEJyfCntduwHNtIPdqQ\n/Jr5kT9vMg7AfiWCOjEbbKld9rOIbgLlOR3a3YwB1W4rhSIdG9FPOeN5crLEGZxoFKw9IC3FGVhx\nHCCnw1+GOTQFxcezFgeeY9FijQU0C7jkU4SlgtkX6oMOypsZ6yy0g1pxWAcCBY2xbSmUW2bTdpwS\n+gVVfBzFLcKwk6B4TvLvzzwkS9V8q7EVDTdDvmHGpchDXkc6k+isG5bNZFaDEVthitnwUjmaAlLH\nK0xPHQB0TUv1Ghm/1bIU4m5lL1xCUfM+PW5AXFIoBsBzeHjWYnB6q7uoFZUlMDmiAFpqxEMTkApX\nMcblR5/twKuWv1+rDN87XONK0yyRjDlIdb3tlROXGRpWItiQLZ76vC9Ir4Am6XlnKpkGXKwbIDhg\nsB6SPjCchvAo2fCcAqny0FonobjzEAm1jlEIiH1iS3ka6cTM1YsXIRTNMS2BY7IwaD+08Ehu8lgI\nwgFtvnyC46EZo7e3DnCnZQISX+b47uIlAEbKwJpb/J0T8vaypjbPkWylgH9ACaOSGO6ZPWVlZwiP\nwLh5U+Pw1PzdP7YQb9YnZtW+K1Krbl8/bEOHCkVV2XqlbtVJu4SmuaQlICmIvbZ+juOpuR+tTWAB\nAEEvQUZBj5q4rB+SnDawds08lNODBu/VgZchq6j/mcDZi8piBJwMj570cJXa7ps3zjCnquMrSyf4\n/uIavnr95F9fLAjZIxGnCIwgz5sSwTop/j1rQ7lmwl/9nRL7v2zeoy3NWaddAg4xK7RVA1Gb+wLR\npsUZbJIHaIzoN0ofDRP8YvxqHdCU0mW+vjO1IF6i8nrkwKEgQdt1aa17X2JiFL+hHM0skNjxUKmk\ni1LAe0rZZVvDO6bsWtb0Qe1oZgT5Z5KVVEsP7LSqbHDVwZ0KHCQm07JXY4QVmNYyIDiZm99qPLfY\nqCtdWEhWzHet/lBjdIuqHCU4e5qveWx93nwBTH+GKkSRBUEBnB0LPBuTYuDUqt1IAbjEskkHmoF2\n2cjH7KE5qLe++QIkP4E8sXH9fzeb9t7fkvgHf/t3AAD/engLT4aGWZIWNtpPzTUs1lBbsdtA+wll\n+Lc0wj0KYHvGprv/iZkPF2+XcEe1+FCZEUulpbj65cxqG/F2M8bEdKMgS2lE7WBkuCucku2WTJuU\nUiMwkBNMNwUs2pCk1MipxSekZgMz2y1rAamVBPbDSq9BQ1AQtYg9KAqqpVdCEhK/vPBQhJeqWvT+\ndElDOiXKH5sDpvmtCY4fEH5naw77yPzGoumhrKzUhxYvWO/E5mcYd1106QEt1mqwZ9nQ8OjgTXYc\nFC9MICk0GERsjSUmubkGbyhhvzD3PN/w4J/XkXumSdq/K+FWCcaDEAWxYMrCQp+8j86HrbrS5Cn2\nmgo+95G8amiaakwsuMpVeM9l/Y3peRMuVbPKpRKdu0QVtYyhGwAoRzJwMDiyagDqQrAWj9LAglRP\ng2PBMDXvvGbj5S3BdHr/AogkBZtKwD+lcZwGDEa97Kg9j1xMacMYzhtYzEwQ4e57WFC7y+pl8Hdd\n5Lsu8qaGo2p9GO3WoPMyF5D03NKtnBlo0tKwaT7Pbij83a9/CAD4v/7gPZZHmPyLdRRvmjH+8PkV\nDNfMM7zVPquf38jnCoHMBNvdT17PYU1rbY2sQ1WnscdrMNmsj4ngWKD3utmQj47W4HVJf6OwkFRC\nZ7bmNmh4CEQb9TxSuhbiUxOXK77FVoKf33oOADhctFm2fjoJeL4ttecslHm46GP/nPyJOhkmCzP2\nalAzhWxLwaaKvHVzhpJEzOTc4sBYOxo/+sQEG+5yDP3Y7Hn3XhWwDirHxy/39RUw9S/xygcklGRb\nEESlU4Mc0RGVL8KS8R7nr1sAgYnsQ5/FoYoeUBLS2VuPWEWveBqgDBS0RcGDpRmlXgYKmjYGkQsG\n2jkvDzF6YiZkvpHBrWiQhfxzwk/VxhuvgMvAItM1TXAkkfXqTE3LmpWSketr40Ai71dZhMWsBFEA\noalSYrEBlm1vPrUQv0NMlDMPctnc/9ZgjMMr5lDwz03wUW0qViqQUtCet0yAAwAXr9VCSUWzPpCX\nmjHOGmYDmF+RjANZPAiQkEiVf27VjApdo/2j7ZI3DHdYC7NpaXFA8uzFCkBZVGNjjt1fJy79qyf4\n7/7QRJgrNy4YjLr2Wz7O/kOza6cXDbhnlSOuYBQ7LI3FpmYxJiiBpKKfBgoFsYvKdlHbyPcSaMoc\nnRkQUUbZhObAI33SxvJbZpP0emP2/wj8jFsk3TDGpG0qNXeuHOOQsq7JUZtBkGVhsSR/nttsKJhO\nfCiaI0EvQb5L3iSrJYpOLdAlqh6ErSEqO/GwnlvhY4FZXyIlXMcwbnBVIT5vwK0gEUGGuKrUHQWM\nwSh9zfiqMEwRbZGFwVZ+6QDPWbDOsUuoHl2fpVh5VQwtxqmkqwXEIR34tkZO2hiiEDzX3DBjpeLJ\nLcHix+72TwAAIABJREFUUE6YczvCDXKUMdFemwIVAl1LQz8uIhtwFUQpmBWXtur5j0wiqw7xeW1l\nb88BTZgo68TD9tuHAICzZ1vIqBCobQ2HSv+2XbIv0It+7fh72YLeG9XzCLpmE1mphEuJEKbggF8U\nQEFnk1YCqy3CSo26QCVdkIIrdqoQ8N8bIvq0D1kIFKHi9mq4azFTyLtg1igyp+QDUOZAukUB41KK\nPzk2NHDtasjK5mE7w9Ov/zYA4JV/8vfwG29/AgD4P4/ewM4VE4jsn/UhyIahWM4hb5uA0ZoGUBQw\nWv2M57lvlViQrLRaeBB+FfyJWjE3Fcgqt+tSQFZr1i/gUGclGRjMX9GkOVbYUDy5FZbXDHZGQeD9\nQ5NJtIMUoWvW3qs3jnG8MGt1uznCvQtKpJo541qUFgiopfLodJOp7uNJyEF/MXdrnJIGn/TWXMIi\nwcaf236KP1oYVuJWGCG7+YWOx69e/x9fX8zAjqLfrAh4QXV6EZI9c3I23h7C3yaQ2jRkplPRUrVP\nia3ZQt61S0aNT9ZtyKkN+5qZxZ5UWJCcL/wS8ZLZVFdfOude+yxpQA7MxtPrLjCsVCmFZlCTXEi4\nt82kjedeXZpfjbnfWQwUlvvmd09PO0ipCuw0cixTH/O030K3bTa2wa2IHVKTVQdDYuKsrUwQEAe4\n+WqGY0J9T+ySdUtOpi2ka1SyfS1CedJkTvvMdWBRBUN5GgUdeu6JjdKvsigFUMl7svCZTqpczQC8\n5FoGb4/wK1sK7R2z4GePe4jXaVw6GVTVyZk7EI1aiC67be7TdUt49FvF+13gZTIq+3iV+8++k+M0\nMgPW/s/3MSOjvoWr2MwPCphSGbixPUP5UQcZnarOlTkWVygjCwo0Bma8Z6OG0a6H0Tepfm/+ei3J\nDBj9CgDQG4kBHgK41htySwkAZlTKHSGApuLKLPO4DeKe2FBEB7WcEgsy3gs7CRYzOrUsxTRzITSw\nacbCsUvEdPA4XsGlZZVIKGLliLkDEA13dquEdeqypsc89qApO5NBgewKVdikYqrkxQ0LosJmaHD2\n7zs55jRHGr0FFgT0dYIcznukPZHbsEja3g8yhANzeA5ejvD0uzvmPVcyzoRh18qiohSs7mnRd1X3\n3x7U318BsLPYASrKsFsyTTRZUfDcAg4FjFliY2Gwj2gNIkREy5ZSM+7CvjY3ujkA5gsXFu035UqG\n44mZb/HrKetqNNwMiiKGLLcR0josVjMstPnOolkCdG/OiQNFwV/qWrUejBLIm5SoKLCKKbRAm1xt\n19wcS765/6fZEoIN8/e0bPL3SEch+qTPiRt8xe7h8aaAIvn4zC0hKWtXmQW3ov5u5UZgCMDqv7Rx\n/u1KZbnGaWytn+L67/37AADXAf7Rv/pVAEBwZYaVFnnnLGzIKshLLIz3iB3Syjl4aLVirv4NWhEi\nqxbrKlbp/mOLHWuzKylcwmK8unaMj+7vmO+0tWnVwgTeci1GWQFTu0NMySFXl4I9nHynwK9duw8A\nuD9ZZb+ke+drfGYs+Qs06HkORYP1kFrtGNeWDcPrUaPAjS3DcJqlPuYVcWDYgKxENgcJvMoVO3ah\nH5n18p3kJd6fv7XyGP/b9G38lbx+yiohX0y2/Z/9V+YPRe1ZocISwTL1nAuLXUdniYfrPTNBPj9d\nYayAcjUDFle3Rowyv3i4hPYjs4mM36v7zP6ui5zK2toB2o8FT/SsU5f8lVtn+QCwuGoWSbDnIKfD\ns4hsyLh2mqwExMSpx4eC9hSLiQGovW0SyRnlZZaBPbFQ0obiH9nIuvXBO7hjmDXnFy3Ik1qSviBx\nM39lwRS44kUIuW5uIJ+73MdV3QL2Sa0LUFyPoQiM6VzYHAwWoarp0a2CBc2sRV0VEksp3IeUOfcV\nV2dUITjj8x75WPprpvd18v46cuLby1Sgd8s8z9E4xOrAPOeLaYiMGDfWrK4QOSMLOZkCykvYGhlZ\nvPkHzxykFY5hJYWgdl8+yLkKIjMBZyuC+ISExf76AZ7tkhDb0IYii3vnUcAl/min5A023BNYbNEY\ntUt4BEDO24qzJW0rlnR2JgIhtf6GrynY85oRxtTyApCUReWJDVDbRLcKyApAnZv5ClAWTa28wcYE\nk/smxbdiUfflu6rGHxUCKAUa1OYs7nWQDS79v2qKNUpsbZue+HgRIL9rDphsM2NFX/mswe2b3s+c\nIKLe99urB/jsfzS06dOfLdHYI02Kqzkau1RRCRhbDm8E5JQTNF8AkUlM4U6A+U5dLayqF94FML+d\n8zUvXzFVqrPjLl+bPvbRvV+36apX2TBzudonRCqZHSeHrim9A4YlV1U/S0D2CCw6cuu10CwYLImy\nbseoRmkSoupFz1+WXMCByAU/k8sAUi3AuhLBhw1+f9EwLD/z/QrfeOMxAOCD770EsU0S7ocNYNXM\n2TBMET3p8PuhjFIsYCpelRaTlrVirEwEt2y/9hv38MmZeRDxJ310H1JguyVYbdqZSA5a83aJ/sfm\nPxa/PEP5gPBbWT1c6UqNRZN+yYlNEGaICDTuD2Ik1OJAXg+MdjV8qqilOxnvQVqC/bi0IFsKmGo3\nNOAQZik/rDV6tMCf00mqKqmlp1GQBYh37HCl2h1JWK+agSlLyVguVdQS824j53bPYq/F+ChnKvi5\nwdKwNxZ49O3/4kuXbX/3P/hvfiJhyAe/9Z994fsQQnwbwH8J4GUAX9daf/D/8r5dADMAJYBCa/3u\nX/TdX6gSYp0RaC6qlRShBdSnJjPJr9XsiE4jwXFkJnl81kCDetRZR6CkjWDQqCsKMiOMwFtzuDAZ\npn7fLFBtAz55T8Qr4D6tzMF999LXkFX5PgJEUk10k90CAMY+GzhZM5sVU9PVvF4YscQ33zM6Fh/8\n0cu8gMNDYEatFpm5CPYpCHt9zj3HZDtnlUPRzHF6aEoN1txm0SdnCqSUgSehC51J2GPbyBpPPNhD\nGw7MxiBzAOcukmXFrZni2Gf55NLXrAAJLeANqSw6d5m6Wbg2y+qrU59LytZCoqBrdV94rPoqc2D4\nHbOxqb6GXbUpTgKmKNpOiaNHBsdgLSW8wRQdjaUN4vDrHlzStkg3cqbPQgAWmWLlIVCGpdkMpg78\nhQG9OucO8oGR1dYA8r0mQEyJFx9uQpIfhBU7KM89fs4l7Ysyk3CmFSYIjI9wxzZvZvZMco8/vqLZ\nLM1eCFy8R74dc4vfk95M4T4jEHVLQ5PfiG4qOPNqHF3EG1VgU7cQ5IHPIOv58QCWNCq9ZRto7NrI\n34pgA5D3Q57bWReQTymouFayJ4ksjO4OYA7k0zGphFqKg7AstuCR8qyY1IHE0fMBBlumKnYct5B0\nBc+FeNU8w6WNCeaHSzS+QEGCfnkoOZi5eDdnYSyhLcaipP1aoC9vCw4QGns2hvMlgwGDsW74v9l7\n01jLsus87NtnPnce3jxWvRq7qnooDk01KY6gSU1GLClxICkWnAGCEQQREiUBAkMBEiD548SIEMCB\nHTuOjECOnViyqEgmJYoSKTaHZs9DVdf8qt483PneM5+982Ovs84j4lhKR90SIh6A4KvbdzhnD2uv\n4VvfV6yH/rNlq3/7HZPKjiaCRSAnTImSVqlDZCpYBwVAFpDrNEECMLeJxXg9gv2E5kpaWLqiSxOH\nd+a/bx2KAhMRGpymd0aixKvEwOBGcRAKZluWNpCQ+Uvr2kHTzwwGllpjA68frJSfdTMEUxeYjyED\nC24rRpxakLUc7qEFTDXxVrEPranBts3tCaRE4iVjAxaVwt7pLWG0Q7ieqxPUX9Bza/+dVTQ/Rc98\n3ORW/HSngd7z+nvW6jPsLZMS+tIIR2Sr/FaE6IRAs0MTHmV5TEMyxm1ha4K9gjDSkHCJrGw29qCu\nUyt5pFl4Hep2SmYOA4fNEwvSVhCkhSQJ5G7l5fqBqbhkZaRnKPPdnJ2HeCHjgDG7EEKRXTVNCfsB\nBVubcamfFdnwKIMTZlq2AwCsA49tR9rM4b1K3vYPLgB4G8BPAfi7f4L3flYpdfon/eL35IRkzYID\nAxB0sIuhA3WNCoF9D7/yw/8EAPCN6VX8r1//YQDAv/OZr+NXx58BoKPDInL4G2tfx7ypvdefffzv\nI2sq/PUr2tFatof4b2/9FQDA2tMHzJ4nqzmkTbLw1weIH+tNaC6EiCqU4u/ZMKlME1cs/OQFTej1\nG70P4+KWRpCn0mRCm2fm9jEiEOQ0c3BA5YVf/MnfwnasD9t/9tZzuLxKBF+r4LbKlfYYoOyPa2bo\nkB5Jpgy8vkPcBpaCWtWbMdivICeq+W53ik8uP8BvvatZgK6sHGN/pJ9nOvFQIZIxPKmzM/Ts8w/w\n6p1NAMDiypB5KT59/iG+cU/nuCu1GJfa2jKezqostT7vz/D6E81DkPcd/Mh1PS6zKy6OI73xvJsZ\np013e23Yb1DZ6VqIn77wBgCgl1bx5Z0P6fmUAh++pIFlb/3BJZwKff+//LnfxD6JfJwkdfz+E00S\nd77Tx2atjzd6emwuNk85G/RHziV88imtvfLS3gY+vaEJmsKLNlyTiMWMDOc8Hf3/newzrLVirCpE\nB2S1GikSoiRPBg6vN295hpjE1jZWe0wY1TQktwOHHQvXVjXI58HJHKK2XiPXNw5wz9drYaM1wnJV\nz/lpVMXxVI/dqF+msb1ags9s6mfZunmKtyb6ee8N53F8ax6fflYTQr28sI7nlzRadvXyEE3yer7V\n28Lt1/U8O0OTlWDTjoQoMi+uxC8+/TU9nzDwK7s6HS8ygZvL+ju/Vy2zKEZo4N/cfBUA0LGm+Nu+\n/n4lgB974TUAwC/Ofw0/cv+X9PvPZL+G73SRUpZPOBJrm9rW7EdL3A6sDAV3lco0gY0vXtXP+NXK\nFXzu8l2c0Bqbpg5mtMY+Ov8Edyd6XO8lGyz69slzD/Aascpe2OqhR3o+rpVhRjw+XS/gtXo4aiBs\nEr8PwM/szQe43tF7frBaQUSdKDcu7OL2rrYpfi1GQF058YZCSMGMX4sB4hBpL44xlCSLYCu4pOSb\nTmtIqXwrHQl7jkrWjoML3b5Wqr7Rw2p9hD1b742Kk+BqS9uSwXIF95c1sPtTKw/xe9sam9CoRHCI\nlO94VMen17cBAN/8xnU8/YW7AICaHWHa0nvhew828WhXrzH3KQNXq7rs9vTVA3QcIkDcNHGPxrph\nR/jJtdf1fSsDwRqxuUobnav6/X//1sfx81e+CwDomDP8rfAvAQD+6uor+F5dgzpv9xfxc+deAgC8\nOt7EZQLI/ertj+HzW3dQMbXt+fbxORyc6OfPahKLl/X6aXohxut6TsLUxmhIDlBqcPttnhn44mUd\nGPpmgj/Y1bZkuTHGnbv6mX/+6e+iSd0S3xxcxI/e+D0AwIuji9j09Rrejdro2PrZ/lBc4i642Udy\nvLChx/fFB1sIrv4Z1UX+HJZjlFK3AUCIP/2k0Hsqxzz1n/9tAEBwNYZ/j1ojXUBd0ROrHlZLwGZa\ndjHkjRz+LgFQ/bPU7BFsklZO9yswQ6OMJFYD5kmAAtMqZ+dDlot2nAzBPjFgbowwonqntxQgLvrb\nLcVAw+RukzMECCzWFLDcDNlpgToT3FkhJhYUtfHV33YQLlJavyY53Q+FEvg3sTiF3FieIPs20cX7\nYMVPQNNYA7puKhsZnD1qfa1LTosKKRggaqSCo9mzKrQiNvj3zKnJANy0nTNIy7AkquTMzKYuc32c\nvQxT8jwke1WYpJBrGIrr8sbdKmcR5HqIxTl9CB8ctWBQl0FeyzgDhUbKLLRWJUNeKPsKHSUWtXK/\nFSF9qOdQmeDykqpnqBPr42S/joVz2pBkslSw7e20UFnQay/Yr/GciOVIt2ACULZkCXkjLmUFsnbZ\nNmu0E4gDQtkvxgzeVLbkWnF3cYzZS9rxTC6HWJnXUefRoIHsUDswZiRK7M7ZYa7kTEFv7HnIFlI0\nu9RC+rAFf1MfGBvtAQ4m+kQbj3xYFNlbQSnmaI8NJMuEKdq1Ea0UoFMFd4fIzUIgvKYPQ2vH4xJE\n7ipu0d5Y6OPJ2xSpZxprAWiSqdm9Ft960cbtd0Kt1UTjUpujEmxuIHtE87dSqjw79QRpAV7dd5Gu\nxJzz9+oxkscUbS5GwHEJxiwAuCIHFFFsy9iCOqOpVOgrLZzv4/iQDvZ7DpdCs8WUSwEAyjJXaJba\nMRI8LllVlrwnjZwVaaUvudsHAHeWBKuKAbswFK9lYSlgUtLz+0cCs8uUVfMz5AW9vys5K5AHNrqU\nPRxPPfjE+tqtzrDzmp6fjQ/t4fEbqzxehW4RphY/w9yrAic/pP/RvG1idKkYMIXGut6rUWwzV8fR\naYNtQatTNghEQamjgpkFhxhzXSdDcF+Pde4qLo+Zbo5uU3/nOPCQ0vc4boqKlzDZWT50ed7M0IB3\nYczjGhKmzrAk61yZXq6BzACcExsplWDgSiarXN06wd623pML6wOc9ApeJoEGgfRnd1rIa/qHq8tT\nzEaU8oDCU+d03fX2oxVUKFsipQH1Th13/4v/+IMvx/y771M55h+893JMcQkh/hDAf/KvKMc8AjCA\ndqX+rlLq7/1x3/neyMqKzqXY5A1vBQL5E+rn7gGyEJ0zCC0OIPAM/hsVcI3O2vFAnVc6FefozS0k\nIG7VUHTXSuvMIX7iljiKmWCSnemdNvwhoeAHdRS8SNJRCB3tMNUPgFAWff8AqC0vWrBRoe6btAqk\npn7dnhgAAavCeXC90kgFcw+kNcA80O+p7gFpnVK299uwCkyb+f0S5EVWwz42EGc2cl+hcV9AOgaI\nJBFClc6aGQERcabZBw6ni5UFmLQhcxeM8E8bQIVSsMGggvwWcSzcCCFIj6f+wEBAhI72DKy74Y4E\nEkJvZq6CSR0HQpUtZDIzMPwjIol6KmRxwdZbNkZEYY+xDb8oIfk2Vl7RhmBw1dBj8cDGdB3ITm3G\nYEw3FPxDOgyGDmaULndHBvodOgCfVNghxUKOgIyKPTKRkbExTQlVdKaYAmZQzBtK7hpRyrSrOYm8\nwIdM7e+LSExmA63DKTTBnvjYJafKGpswCjHHmYB3Qq2x8wpd3ayA44+ZGncDQC3HQG5g1Kuh/oYD\ndV4i3NaOx8M3m0iL1tfQQlFFjLoKbq9Y0GUW0oxsePtnWDyL/eIC9rberO4QLNRlBQIx9OuPe8tc\nIpS2QvVtvS8mazZcqsWn1bLGLw/rsAoNvRMTuU8HUg2waN05r/jccZKPKzDPCNYZRPRnBQK54YAE\neaGmftn5Vc+Z0CvzAes+kdW1JRQBSs1mwg7t8OV5FNl76WiNKQCo7jgwCzzlPCAHZZk2K7DuPSAm\n6hUzMnktmHG5LrLcYFyCf6pLwYDONBVrFlJAknaOFZelr3BBl238RzaSpkLeEKhsE16io2CTDIUy\ngdGIMEKRQEIH9Z7dgk+8JDunbfhUjp5VHA4wUM1QfUd/z/h8qWY+upLD6ZXlu4nUD2qPDByslODQ\nQi9pOC1l7d1ji50FIwOSlIjeUjBPUuXAQGAQsPS2i6mh92Yyrzggk3d9TEwNaE5aEs27BndEhXNA\n+g6VGucyuEfkuCWASWssbSi2n2YI5FQuR2SwVs/RcIl5Yo5Vm3F0bk9gukjZ8p6AolKm81YTIWGP\nqk8Edl8/BwDohECwROrvZtmt9EFf71eLrhDiFwD8wpmX/t5ZJ0EI8VUAS/+Sj/5NpdRv/gl/5hNK\nqX0hxAKA3xNCvKuU+sa/6gPvaZjjc3rGK40Qoa8XcxSbrFkRGCbMuARTCa7Rl8JZ2UIKUMTq9Ew+\naOP5HGYrgdihzoQ9YPjp0iAz6U1iQFGngbEcg7ppkfY9BEULZStCOCx7vQUBrWZrDgMEs5pRgtR8\nicwrmVQFeflZVAqYKRMcjVlTAwF1Afm7FtfTRzXwZjZbCdQxgVETASMrMC2KqePdYxN5N0XljoO4\nBQSXSkwJKhnMIzLcYVkTtYYmgvNUmqgnSA70IOe1HEaBa/EkvnhOp8K/6W5hdKBTsJ+6cA+vVHU7\nXLRsIyMl4LxSZgiSFqAI+Hd++RT7L+qUeLxZRrI3L+zgzVOtBXJl7QgPPVK1ndQ5Nd/ZHKAvdERt\nzUUYEm19/swU/YkLm4jl8thESjwLci5B4NChWsu4IySrKFSJJjobVOF8XGdF4tMarlA086jahTop\n9TwiIoprVCIcW6UCKRNRtRKO2n/0wh18OdIgzc5cKUJW6wS4PKdr67ePFpFfpjkwJRRFo6pTigji\nrRpmzxJexc4xpfJM8zyBBgAM9xqwhhY67ygACtMtwBnQof9UgLxooXUk4kWKsBMD8UXSv5GCO0WS\nVsk34YyA6ceJQKvnomgnyv0zAm6BCYdwQzITcK7pCHw28BGBnNOHBkY3CIBdzRCTzpHKBeMppks5\n3G29ttNuCnWGb6YAe6PvAAvElnnsQZlaYj3p6HlICEdkDSyoZaIbf+gz4ZrRiVF5qajrZ4zrUgp8\nSOYVxU7fbD2HSVmLxmNgfK7AoCnucJEHDhLq9kjaZpnNEKUgZ9qQzGPkjAUrDdsTUZLBKWC2TMDp\nDEzE54wEO4JJS2J+q4fB23MQEqgvTjHJqHuvmjNqOe9mTOqXdHO+J+VJ/NxPfBMA8I+++mlML1LG\ny1K4sE5dIL+6hulKMc8Kal2Po3fbR7RFoPuxxTpCmQRnUS9f3Mc9YptVZxlCVYnLsCcCip5f6wXp\nv4O1nPeRMs5k/ZTOcgGAvJYgjS3U6hFcAJOkySSDcVdy1tZux1AdKmE9qXImVPqSO8pqV2dIZ8TF\n4mQIpyVpXk5rTyRlN1poOyxzMWuYrNUFYbLKd5jWSi6dCnhuranxfVxKH+j1Pjkh5HD8P2YmlFKf\n/1P4jX36/2MhxG8AeB7An74TUqQaY9dmvYg4slgjAkbZrZLVyoUGVabZAbAROZtmLsSxWJNis3y7\nyATMAqR25AHEb2F1JNd4RTWFvUviYWML5mKRyjXhFlTQTRO1eZ1GcO0Mw3sUgdRTyEFBjKRQfUt/\nz/RixgBUoOQAMSOTW1qDTQWLWmyx58OimrjMDbhEZx8uKo6QrEAgIxrX+FwMRCZmF/TnW/MTjIh6\nWqUGe+Ti+gTGPh3iNckHuJSl8+A2Y+RE7a4siTa1CgWJA/OpAr9QY02FldYIj6irxRqURihr5sw3\n8uSkA0WRORKT2zLf2lvmtPaDPzrHeIV8NUH9TYoo1gQ7LfOtKY4W9IEiAxuVdsB6NqaTMwANsiyF\n+Y0IYUKst6HJra9pDQipTj+/MMLOoCwdFBk2KQWqxGkxnPrszJiBwch6eUZ2PZYWqk29Xtp+AJAc\niVQCUU5gvOYEu8e6vNauBxgTSdJqe4hxpP/uN6t8WOapiYR8n436mOvP02EHci3EgJBwys0Rd+n2\nc4HGfNmiXDiVViBgUNklDmyWtbcMMNtuVilVcSEA94jos+uq3J8oSxBGCkQROTy25LWWe9oB0v9B\nQWVl6c+iCFSGJqs6Cyl4PwsJbstVlRwujW+2GMF2cm65TBwLLpUdwqiKOpULxysWG2K/kiD+JP1G\nYENmZYeLRbiLLLLYfix0pji5pwcynCsJBJWtOAhJ5rOym0aBu9qEJZGREyYrOWRBOGgLjuxnwkKL\nWt3H99qwCxBwQ0ESxivLTP5dQHf8+XTQBRsOr22tmkzl4qHFJWgzMJDP6b3d7MxwGJe8PAfvkHxC\nPcNOT69Dc71U0a0/EhAPKahwAeMetehfiVm2wB4ZsKgT5cH+fKmca5a22UjB+7YIEAE9twU4uvg3\noB2QwmYDKLtmEgOoZZjsasfLyspMLVQpqxAGNpdXrBzlGgsMZGtE1jb2uVzsuynCI6J8XwgQU/bD\nWQyR03OKxRBZwe5L9wEAXs/EgPhNqscCnTuEublpcZAoVNnh9IPrT3YJIaoADKXUhP7+AoD/6o/7\n3HtyQriGOrUhycAIU/Emsvsl0ZURCfYoo5UMNnVEwJSAVRqCwks3AZhWjsQtat8mTKqjx1KUbJiO\n4tKMeqcOkGCaCiykBSW7K2EcknNiKUTk/VtDE0GoN8WsmaJ7Wec7ezstGGt02OYGZtf1DyzMjXHi\nEuossLjskKwmuEQ96Q/251n8S9VzqLhAjStGXAPlYaEMMDDVOnGgLMURxuikDkGcDrAU/56CpmgH\ngN5hg3/PsCQsKn+lqgIUbcaJiX/wkgYFtxYmmBKh21vHVQhyHrabfmlI1iPt3AEQlYw5A1wvQbOi\nLcfBYZuxAvl8gialo4MfnkFSpFGtx5hskbiYlTFnwlGvAUldSYYtET2po3qOWrkPaxzZ8wEBMENi\n8XpB5+1dH2JGXSMnoYXOKtXT7zYhKMM0HlSYVTceuzALyn9fwiRn1zAUt/HdG82jW6O6duRhFmmr\nutYe4sGJroNtdvsQ+0T61AwYK5NKkx0MZSnu5NTzr5/rcb+NjA7RfDWC62WIqXTkdkPEBKgVRx6C\nQjvIkpxVihczFEvJ9jJ0F/QzH7+1wORQRgzIc8TpoizE5EhZY6PsZBPfj7kocDNON0JKtZakIZi3\nxKomyIltVgmJlPZmc2GKgCTn04lTAlNdyZk8MzBhdvSeSlIHsHN2PIUAEuq0evaZbbzxQGfnDCeH\nonENA4fbwKeWizEJsolcIC8wEZGJBjn9/XHZ3hnOg52ZvJazvo6qKKanl7HLzokwJTuwwpYQ5/RB\n7bkpJkeEdxFAQOtC2gqSbJhyFFP1K+MMpsnLUX/TxuRj1KW058OgjE8eW2js0b1+fMbKxs6+jzSl\nspgt8ZVtrWD9zHOPsEdNAdcu7OHOazpCa+0oDC/ROjxDppbVym46MbQZ7yJtICUnobk6QnyXNF9W\nMnZUswrYbmdVnJG7L9tkq3u6bA3o0l/aLJ2ogtDPCA0Yfsrkg2ZUOquqlkMOy++t3SftrSXFv5HM\nZZxhl5HF9Plxw4W/T3tpTrCD0aqGOL2j96pYDhhbZ0hAUYt23CjLlNNnYoQfpj3ozQAqiaaNnAUC\nmPJ9AAAgAElEQVRPP+jrzyNjqhDiJwH8DwDmAfy2EOJ1pdQXhRArAP6+UurHoMO23yDwqgXg15RS\nX/5jv/s98YT8KvGE5AYqhQqkAAtkibkYzh3qMljTLZYAYD3xIM/TZjQkMorAmi87HAV6H+kjSU1k\nxHVQfwIMP0edJlMbc6tEuPXqHPNsyHbKxsY6cljPJe7msMhDNmP9bwBALedSELwcjq8PKvt7NYTP\nERhz1+MWMCPVmgmAjpqKrEhWU5yC9ZZnrDSKXR/2lDaOBQYp5o0cFjlhWSNnBH0a2qjedhi8piRK\nRVFPsg6DygVq9+nzVXCa3ghMruv7hwBpcMEdADElCIQCcgIO58c+iw2Gq5JbK5UBZsmUrmRjAVsx\nq2ZrYcJqmW9/Z4tr1P5nTzB+eZ6fuTBg6sw+zl0iigLgHZslN0im8T9FSj24EUEQdkD6Ep1Xy1be\n4po8remwAc11UvB4SFexc+cdWJy1UsTQqQdPoEoth7PDGrPnmpFgThMIwCDcDBYjWEW2ZFiKBgpT\nwaT35LWcyeZUzy3JvUYmqsTUGX1iivqX6TCzAEjApB7P0aWyJfTsAaAMIKLyJwITNrFkplOHVWSL\n9wGaet18mthqYxuKynRCgqNrTGw4dKhYUyDYLJz2HPYhUZgfA5OnKfXv5CUHilGq3QoFJtsSU5Nb\nw6ONlNuvlVUGGADQWhljQFow1tgsScACswQUn5EkyDspzF4hVSC4jAo3h5iWxG3F+5VR8g/5uzaP\nSzyfM4YAKMnHKvcdBMtn2G3JgRF5Sa9uxIK1poQCBNkzmZjwn1DmxDrTiXNsaqwZgOl5yUGEyASc\nocG2pLARgI66iyDOPSnL0/YUCIhY0Lk0RvRIj53sJgyWbn3LY6cy6vJXwrvZR/517WBYUcnvcjZL\ncxanFiyV+9UdlBiitFG+R5rA5Ip+/uYtC0mhiykAV1dHYcYlbnB0LYM1tFAlWYGsUq7VcEWisqf/\nkTTAkhzTc+WZ5AxL7g5lggM1kQm2efFCecbI0CrtdmjAKcoufR8+6eKIHAhXi24JlM6Zq1B/SOV4\nD4ifCfHwZ/7mBw5Mff6vvz/A1Jf+l//vwNT343pv5ZhCX6OaYUYiV14r4vqbHDlo3SvcXQve83pH\nGt/0MNIdXchTk4XwlAnOnJiGRDCoF/pCMBOFLCjQ0RZ6Baikk7Ox8bZdJCSMlXuqBHyOTXYARC4g\niFbb3vbKOntmIOkTT8iljFUa1XLMDIbm5SmqL2oHa/KsRHpNO155bMIg45zs1PgZsmYOWTgIR+Wh\npQS4DCAqGcuYG6GBcFEx1sTu2axJA0dy94Z7YvEGljbKsoMnIag0FS6AKa+NzOS6rhUITZAEwA4E\nR62ynjOIGOLMgaHA6VuzWbKODnebGBIngWED6Q/pfOpqJUCPSjbCVMjp8JOdlEmizInBBiLaSnRK\nnUpk6cThqNiwJJdFRSVD7hSYkBLkXG2FiEmBVFZySDo88nrOh0R6pRQ8tJ2cVURhlBLfMBRLk+ep\nYMPe7MwwMrTXY9sSdcoEJX2PnQ3TkkgJuV9ZmMGh9TxMTBZRjE0XGTkqvpugd5OyX1MTuS8x/3LZ\n7eUR9iGr6PQ+QJHjrMgelmy4CEw4FP0ne1UtaAcg8wViKlN5zQghdXVZI4ufTXRiONRxkzbANXfT\ny5HM62erb5dAVxmbELUCTWhCUNu7HDowKD0uY4NB6sbYKnkeFMruo1RguN1irJVaTmHSPWWpAY+E\n9JLE4oxnmlpw3tXzPLuacCt20vfgr+m1l73TRHqOcBB+yl1WkCW4UGSCcVrSl4yJSJpnkNZuzky6\nSoLLEXldwSS1bGUqWOSQyGOXD3R7CqTEtiuy8qAVmUBt28DkmVhz3WQlS660BWew7LFgfStpgTEo\nRio4M/gT59/BPx1q7qcrm4d40teZTXfk4PAT+v31+yZn4eJXOkgXS1xHkYWVjkL9kX7X6KmcQe1C\nAvFSkV4u51BaQNymsfAUZ1HjdhnYeScmg+bNGIiJz6d230Ja1zwq+vNlJl16EtGcKH4Ow2eIQfu0\nZIYONksuGmc+BB5TOboqUUhK2Y0YOdlt58RCRpgY6RsQr1MZ6HqAaF7/VmXX5MDDPxRnZAHKMzru\nKsiT0nZ/oNefw0zI+3m9N7KyQtU2F0zQlLg2y7QnTYmwS6WCFGh4elHMMn1w6/+guL7njhQqpLV0\n+qwPY2KyMF5SF5witKdgjgLI8vW0pkrRLr9kGPROgemG4NeL6Bqq1I5RzRQOkR6ldcksgSqw4PYp\nMl90YBbGzJDIKSUIKWAQADdrlKJrZmQhJoxxvpDAGBTsmYJLEzK0IAomyLGpFTy90iMvGFo52gOQ\nXwiQFgyihy4cUvxNOjmXprK6YmVaqFLnJ+2A2UCzqmJdHGNoMVodEqjd0++ZrWskOwCM2iYEbUhn\nfYbmb2tDcPohhepXiAH3ZwbotrVFzf/5PPofpi89E63njZwPJD0JYONhjSyuFedSQBbPMLU5KpQ2\n0HhEpZ2DGquImn6G4hGEqThrIXODs1xZUo6jMhXzhMArwXXCBItfSWnwvCVVA3mlSE2Ay2ByYnOX\nTRyXhzZmFrKiTBmaiOYpkhVgR7h228T4osDgKo3HkcdOZdqQJeGcC85CIROsYGvPDMZWGKlA413C\nabWBmDoWUt+C3SvwIQqCCN1kp2QitmZAUrSTVlM4x8VhW5LJ5d0UNnV+pe2ccRnWzIAiTAwqZeZM\n1sqMnxXpTBUAeIcG4jkFlRW1IIWMSmfWyEQgKdXlSGQkwW66OesoITWQkEia8nMEpFUlmpK5dJLE\n4vKXOxQAPapyyhKRMTUZFJ77CnVyZia9KgR1lijjDN7BVjxmKjVQpbUwsn1YhaPiA86wIG4D7ylr\nqssP1XdKB9gZFQ5QmXnIvfIz/jEgVPmeotvrN7/0cWBd38ejky4sau89/JRC92Xq8OuCMUj+MThT\nISR4fdkTgdkaDXXP5KyFkNCZV+hOOVHcZxuc2anuCQyvEQh6CEi7ZJ52CHetLKB5nz5bB85KDIi8\nfE6RlI6hEqUcCI5rJXZQCc4Yx0MPIHA1pOBumiz2eT7TlQRG4TzYpS6YnNowKdNmB0BAwVZaL7M0\n4VI5585QIFwtUsEf7PXnsRzzfl7vyQkpPGSRlYBIlZhIipSlFFwSsGZgMqjBDcWGXVZyJGTMJ5ul\nB5oNPJiJQDSnUNsRcEcKCy8JHL0g9eYqZNSHZW88AMRzZcrXO6GswCI4FeqdCAjKyMyGPqO3G5UI\nxxOdwxTNBDYdHrltIFooD9DkjAo0X9UMOYVaTt9EQt6/SEsnRzqSQW3mscNAztxTkHTPWStDHmve\nA3cpgLpVB2AiWU01IJCMZx6bcEmyXV6dISFOEzMwUKF0aZQJhC45WCb4UKntAINrlKY+NDhrM9uS\nsHeoVtoBBJUEjETomjoAc9dD5Zq2MEIonPv3tHDY8bvnMTmvf2tInTcAIH84RvUWOXZVICY+C3Nm\ncvpeT5AJBROoZLBHpTGXo1JsSlkSyiwjtdEFcjAPTBYYS1dTPHP+CQDglW9cQVJEPO0QDokcjno+\ncz2IRCBfLFuIi3qyMbDhnac1MnXZERKWYq0NIzIgC2xRN4J5h0jcKg5H78YZSnUjFdwpFsaloz76\nWAQVWJA+0HrTQtIQyDzAGWvq6aKub6QaYwUA6XICQVmR3JewX6Oyhg0Ei+WwFmSCIjVQpYMhbgmo\nonsnNhEuUYvinmAMRdr30H1E3+GDDy2okuZcpIIDtWwxgUWBhLlTKdd2Q6GiecG0ijJ9IFqSjAnC\nxILyKWPn5rB3LeQLlBlNDRBmG7adI6TsjIgMoKH/bndmjM3IdquYktx7a2GCSQFkzgCSCIHTiZAU\njufE4pbzfOxwqz9MCVWAEROjLNlCK+MC2kEcHFJYX8mR0z63p6Wz5R+VHXdJW0I6FMmPqLzWJeev\noQ9NQPObBCvkGDklGD1dSWCRAxitloq3/twUIT1/ZXGK7F+jQ/X1DjwK6KQFyKIjaOJwuSebz7mU\nl83nnIF1D2wOGsJFVfI7zaewiMMjWATbs3HNKDvxWgpWWDpwBcXA+hceY+crGrsi7fJ/WUVpACDN\ngwwtBp2HiylMcoxbS2MG6TcXJ5gV3TFuiog0kszFkIMH+56PuFhHtoS3R7pgVyJY1MY+upxzW3J+\nIYT3rv7OyXkgJMXy3FFw+n82mJC/aNd7ckKKrIPz3BCTnQKwabKxcQYGG2HpACMCOEKU9OIyF4x1\nMMPSezenBsxYwB3oxZrUBEZXJYxUpy4zqt+K5YjLNFbfYuyH9CR/V7oZAcQlkHsCMXFJNF9xML6s\nP3vcdGCPC7S/y46UERrwC4xCp4wE0ryUYxez0hHyj4DZCi3gSok/EBOLu4PcQZmmzX0NFgOA2o42\nWrM1hfRJDbakboahBVnNOSpWiymUScZw4HKNW5kl9iM90x6c+Qq1XSpHTBUb/fYd4ORDpYF1x5T6\nXNEpYkArZGZXqOw0dhBRpF/xE7zxh5cBAOvPH2BwT1O7z8ZOmeVwJNTHiHf87SY7Qu4AbJCjrtK4\nEwCITF2DdnWnVNpRMAqp7dxkfETlALBn+l6Pn1eo7lOmpu/jezuaPVEuJ2U6eruOmIiuzHqKnArn\nZlKWOIwcECfF/QkEPkXX9bIlwNl1kGzom/B2XQQb+nXxsAoibYUSFsKcInlLQVHbryXBxjyeOkXl\nA85DD8lWhIWvuAAUwoWSc8IKS+Bo512Fk+foRsJSYM3pm8if0Zkn452SXrqyD2QVMrB1iZD2izkz\nGFyYCrBjND1XgpqVMDDV2FB4J1yN0O2wxd9uKcDmPPSQVWl/VSQUrR27b2G6WZQcFGeL8mrObagA\ngEiT7MlCWqEAs44t7jSJJlU07xdrBkjopkaiUgKhjwWL6o2DFhxqV64cKYy3qHQwdmEQ2FsZGhRa\nDEahhYIzHT6wFAMi7bkAsa33nXtgw1rX3m8w9EvgqwtuXZZWicuCI5HRWnKGLrweECwDiaODuaIk\nDQlWUUbPgaOhbxDS4SxnvpbD3iUSL0PBps9mbzYxoy6l5Y8eQ/5jHRA4E4nZcxS0ZeAJtY5tdhJr\n+0DvRoGJAuSswAoJdoSyxOB7qPSBlBhp83rOeAqVKN4LIldcvtn5yiaqBwppVfAcwtBtz5EHBpp6\nAwMxOTF+z0BEjsTkXlu3MgOY9lp8H+GCAbMQ7TyoIKdAwpHg9QxpcgZGndELquyZvJ6tt32cfIiw\nNe+WGaLu28DsX8aY8UFcP8iE/PFXvElcDYFbMg8uR7i8oql6b28vo/ky4R0yICJdiCQRnFpENeeu\nl+p3fd4gs6dSiGOHa63SAkdPclgKUtlOhlpN12w65wI8osNQVFMkLm1UWyKjtFtczdnpmW4oCELH\nCwHILTpsI6vUp/IyxEQFjcDi9lnLzSHJkCoDHC0Pn03RWtDGafS4BUmtuyI2uO47u5xCUOQgKznc\nBvWzJ1VU9gXyBiG837XRv1l4NzliUlRFYKMyK27QYhVeERmI5sjR6QnkND+qCYxd/QwbX85Raevx\nOn2mgbRRqnEWCP+skyLJ9NilqzHsRzqiNDMAJ/qg676wA+M5/cyzX1+GTfu99sDE7BnSizAVZhSZ\n+ikQUd3YPzEx3SrAPgrGGWZcCDDttdONkBb4lZWAa/xxGwyEdYaC0/TCVnBP6UbWI8ZNzDIBn9ZY\nmljwSGAxQgWCcB0ysKBqRT3G4tfnOhP0D4kZdb1U9soq4DbxxLLgDMt2WPYwYHIdG7LMBAmUYL+8\nKuFWUpw+S2qxITjzpAwwQHYycJjFVKUCDnX1xMrDWotk5Od9xkdB2ZBUT68sThGMCZvjmtxaWdsY\nQ/yh9lqzmVG2Vi5GqBAxWNQBOzxeK0JctHFWMzSaNI7XU0hqBxYAZxSUUJBUZjP9DN0WqctmFsaD\nMyyFErAI/BUtGHzo+1tjBnkngY3xc/r1ZneKZJfkGfY9KCrHzbZSHpeF5hQHj/S8nXQFl/LcRqxF\nBgGIVsKlYOkJLq/ZXsZq3lluYkZt/0qV457EBhLC3MCSjPGCAtw5vb+iwwrscYHwzHF+7QR731pD\nVtGkcQV+TXkSiu4pWlQlb9K4xMhNlnLUHujvWpwb45Du9Vx1ihopyr6y6TNL7qHbhPpUaXvWFnUq\n7HB3mZ1+2cwxow6q8FGFWUjtXnkcGBmQF3iXRgyrGLsziUzIkjPESEsxw6IdGQCCKwmk45SO+Lws\nAb+thLug1MhD54JGtvZVB/6SNnRCKFjk9I77VXQXdDQ4HJWs3MpUTCYYzZsMUocAkgWan7HDZHhG\nCgSXqDuub8Nap84qv3QqZzMT04tn6K1/cL1v13tyQoqIIk8MGBRpul6CW/c0pbCwFKYbBIjamiIj\ng+L1BGbdwliC02/jzVJF1KqkgLCRe7SQBTifK22w45JEFhuq8PUOzEvaMDpOhpDaOl03Lev3sVmq\ndjomDItqiBOnbMvLBOw2Cb4lJqepc0MxI6Xz7AwTg1ZzJYe7o1+PF8Hp0etPP8bb97RLbU1MhNTn\nbrgZMC7prAugoLIVZmvQ7ZjQ3CgOGTQoIKF2uoXNPo6hT2p734VFjg76HtfBnYlAkBTuv0D3Lf38\ne58y0PZ0eBWOgWSNrMLEYsCafVKyhFqHDmd/wusRYyv2h03gVX0PwUdSFo6q7JrYXNah0JO3V2Au\n0LoY2ohWCWQZ60wXQCneEwsJMWBKE5xGxdTRtMwAkmMf3Fman+HD8BWL0yEwEV4iEq++D5vAi24j\nRkbzn09s5IS5gZ/zIZQfukznbE8EUrrX44MWDCo7GgObIy0jBZIzEfuMHMQiWwXoElJxyVww/4L3\nyOXDJWgopI9qzLibVTWGCdCRcsGzcfYSlkJWYKo8yaJ1yimjdiiwxEAuDWBWppQZN5QbSCnKc/vl\n67aXYbz1f+9EMM4UqVUuuHX7qdVD3DnU9KFZYPP6lbHJmUA5sXGa6/vsdKcw3RyyoPH3c9Sr1O1T\nKXVbgokL0ylb/wsSNNk2SlBrYDAhmnHswiNelYP9NhzSc0lCG6rINlZiMO5QATXijxkMqkzPX2vP\nkBLeJY4tFIYh368A1JV0/cYT3D/VTo7vphj2KHM2shAVxIiuhBLFuAs8erIAQe2rYsfgzKhKBUzi\nIlEGsPgUUQWcLMJ/Rh/IYruF4CY9z8sLRfc97ohlCEN/Z/3N8oDNeg42vqIn9MlfS7C7q0vN9RMg\nJOY6Yy6EJMc+d79fFTpeLfBCDoNDfSfjltnYKdcFhIIsuGRgMLZEWmUnm1uPoQyH1z0UAAoMhaFg\n0TxLC+g9oqjCUQiJuBCxwUBz88RGT1ApDGCArCENKHqPf2SUXXQKqFI34Wwzh69jZEw+HGHhq4St\n+rkjpnwHwNll6ZSZrQ/6+gEm5E9w1Wp6Qy43xhiQoNQ48CAoK+BUEqTkvZ4VYNq97MAkMSLDUKiQ\nIRhtlUQ1dS/FzAEyOg2TNphkLK04WGzr6O/gsAWD8msXPrXNWhujYYXTusHEY+XEmXBLrZW5kgqv\nNhegXdUOzMFpC0XHsuXkjLJGLiCv6s9cmz/CLfrsVqeH+x29gKPTCoMGH1kdjpzyiQWnq7+nXokw\noU0Xj1wsr5GseVKDsgFj4KB7+RSnoy5Wm/o5lRKIyFC/sPgI+w0dwb5ircP3i9/wWQtkfNHjsYQr\nMbihx6h7qYe5qjbUj615bKzrE+9w0EAQ6V2bLaRMSjW3MsLoLeq3P3Gx+LQ2io8fz8Mk5L45Kmme\nxfMjbO9QKO9IWEQYF3eAotVptmrBPKfvwTElwkKBNTYwuZyXdfq+ywZA2QrTdSovNXMGzubdDFFO\nllGh1KcZWkgpIq/ftRCQmq0VC2Stslw039DR+UlcQz5fqCKbsAjX4dzyEV0hlP3YRusN/VvDZzKI\noOxWKbpSzNDgcXfnQojb2shHyxkWiM9jdmeenSizE0OlHuxP6jUw69WQNsoOjMISBesSHoEuK14C\nk9Z8f1RDTnVw4UhUaZ2nHRMYEx+GFDDbJSNtQo6KCByA1su0Y6H5DoFaUQLzzFgg6VC3gp0hJsdO\njhyuWNySy7yn7GrKmYa1jR4O+3o/1msRc4m0vBBJagE+YBgSo0EVDWLJnAgXBjmDbT/E4ajo/QSy\nuv6NSZGBAAUMBX9Q08R0XIBoFFyaQ2EotkOOWQKQPTdFxdHP012dYUoZT9vMERJ5mKgoWHU9pvai\nZA6Yeyfz/JxKCs5eVDYmWGtpJ98zM9xpk3M2dYHUgPIkRGRgeF1i4Yreewe7beTVskurN9b7MJnP\nIMkhE50YzW9qp2/wXMbZ3NXlPnafaAfDngDpx7W9uPi3BLZ/Qq8997aNmNSPJxdyVnDOMxNqrexk\naxLQNui3IUaEoVhMeE8FQx/F0R+sKnai5JlDWnYTxkpNHZudq+xJDWgqpFQiNxbKTrtWI8CA8B52\nAjTe1PfXv5mz82wkBhCUZH2qoP0PS02yZCtiZya5HgB7BVmbgqRg1pqaJdA4tDA+r1+/4Ibok/5R\neFCFIgcrXMsZf/iBX/8vaDP+/3C9JydkOiVwnZOWcvRjF5fO6ULj9kkXHjkOv3z5/8R/+O2fAQBu\n5wOANLKQ2iVzoEnibXJJQDYyYFSm/xxCgSdzMY4Heju4tYSNQZRZ8MjRGQHcWdJoBdyKmZ9hdJXN\nlNOAs8jm6Cef2JxSF6aCf4tUHa9FSEg75rW9VaQH2hi+cVCHT8JphdMBANGjRtnPPhczot/YCpGc\nFH1lig1b1slgn9rIFhOcHDdh5AL9KZUz3BQTAmN9ff8iR14ITVCuBKKhdKkKQPuuwOQc9br7OQNk\ne3fnMCGVSuUBh0Mi5TmoQBT0zJWU21gHwyrkon4mb9vBMND3vbbeY+NnVTOmF8/ebKJOMJDxtRR5\nASD8qou0WmrqDOukL2ErwM9RuUuguwVVip7Nl5Fa5mbs6Nh9C0lBjJoYyFbocH7XhXQofd9QzPQZ\nLinWEckqAIpKUN9CtHRmfRWCbwkQMw27Qu1VPV6zTYkZ6YaJRHC3irTAKP7cB/NctGohxiROIlKB\nIc2laZecMerQhzUTiL+rEXyuXabpzcCAEZVtrTGxOyaRxZ0pppPjuXWtkPv6kzW0Kno1HA/r7Bjo\nG6DfO9PJ1jg/Q5/WkRGWrKIC5f1ZF6YwaH+FkQNJGAqjmWjgMHQrdZ5Q+S6yuBX7ZFTjexgcNDiy\ne9Bf5gxX8YNPjvXzO3d9JJf1M4z6NcZKuF7KfELiDJ5ESPA+NMSZ7FNgYUb4jbn2FCdESngyqCMn\ngcXE8jCtk0RA39VAAgBOO+KsLZQOAABATWx4C/qg8t0E2R6tYavsiAoDB3fGOr3kVhPEJ+QwVTI0\nl8fcijy61cExCawJU0EUekbzCfKi62hoQo0LbxUY3ChwIwLVbf2ePbfL2S/3p48wuKsDgAc/LWCe\nI26MUx8mZXbFiQeTTFS+43EZqXHbxuQ5yswsJLBIIiJrCFgFs6udI1woxClzGEXZRoE7CCPTLnFD\npgbkFvNkzMcsXZEnplYlhh7fIktoBcD4cySAOnEYmIxmAvuBtj1GCqZ8905LHa12e4b+MWUFMwMe\ndUdKWyEnPEleBUbUQt1cHmNMILp7J/OlTVZlt6N0BIxeOb8/uN6/6z05IQsd7XU7VqbprQH4doZn\n27pr4uHhPEddv977MGp1wgoIxQ6M46eQVDaRJy5yAoQ2nQQzw2fCMWkpNHziALBTdCr69/ZHTWTU\nNXEwbLJabqUeI1CU1g0dbvVzmzF8qq2PRxW+p9nUxVJL1x12d6pwC1XQzER2U29m11BIycjVqxFO\nqxSlWApBXy/g5bUBxiGBRl3J9L9yanPaeBx6ZebAydH0tNEdOFVk84BZ1KnnJTOF+naKhHgyotjm\nNHWuwPXe3aNl7vDIXZOJlRqdGcYFwt+VmKPof2fVg6BOgcr6BOm7OkyRUsCgg8Roxcgz+t2FHBFJ\na4eeA5/AcRc+u4tbb2jkuzJLLoDm/BTzNf1bhwvrTJI1RrmpjciArOSI5ugQr+YQRamhDmQLBXGM\nKpH8jZxxDdWlaVmOW7JLUGA1RxHyKksh8+kgMcvW5bySouHq+R80yqxDthVzpCk7KQLCx8hmBlm0\nhodlZiuZORrlCSolFQ03gcetgcqVSChDZsxLqELJdWqiefMUJ4865ZhQ5JktJHygq7TstOl2plwf\nT3KTMRh5auCLy7cBAF8zLmOHpOZ9L2EHW6YGMuKMyKUoVavrArNNPc+bzTEe71G3z1EFjTW9L+Zr\nU+ybtEaU4L1tWhJ/6Zm3AAB/8PgSMmJV7TZmOB1RNN4NODORpCYW6lN2PLqtKVJJOJVPDHFwqg+G\nSiPEXF0fSKeTqh4DAGimMMlhkAslaZrMDJxf0y0h2/tzcChDkivBrbvX5o/wsKl/VykB3yZMUFdg\noaLX6mlYxVJV2zYDCo/Hehy9pRSHA+20T6c+7DV9byutEXaJOl0pYG1B78cn+10YlDl6au0Q7zxc\nhUnZQLUaMUZmFjsIDGLfnVlY29DlmN2ZxYdwvTvD9LH+7a0be7j0Ef2c98bz2CWpgvHXFmHcJFxb\n34VxT4+9HwIWdd9MDJc5QM4Sxk2uZCjU2VUukNULzpgMOYnrNVcDjBU51YmA/xQRRh7WkK4WXYYx\nRnt6jYhODP82iQ5eD3UbPuHdGq2AHeYLjVPsV/Vn7lxbgiKb1F0eI6KW+jhyym4XU3EmPat4cC/r\n9TkaV3QZH7otPyV9HdvJmQPKnwsQkmN4Nit2Yf4UtwMNZrmycYj7BzqDtdSe4HyzB2oW+0Cvv2jl\nmPfEmPr0l34ZAHB9/hAHM72Yo8zGja6G9x+FdVRI+vBy7Qj/x30N7/+Fqy/iH97/IQA6ulps60V0\ntXUMg1bFo2kHD4+0i+u6KRpUsnGsDD5955I/QcvWm+40qaFKLv4kc3G7p2Hpn165j36iDTm+YLQA\nACAASURBVOrOtIWMCp+GUDhf7/O/55wpPt98BwDw4vQyOkRb2c+q+PLOUwCAK50TeCTHGWQOmg6l\nvmV5ELzTW8JyTT/PamWEF/fOA9C15bWu3rRVO8HxrMafXaxpg/d06wD/Ylv/1oeWdnFvqKOaC61T\nDGMCFkLgQ60d/f3uAHuEyuylVdwa6gjsWusQW75O9f76znN4pqudwk807+FX7n0OU0rx/pVLb+Le\nVG+2JDeR0EEw583w7Td1l0l9aYpnFvXnm3aI7xye0/Ng5gipE+Fy5xTfu6dfv7R5iF5QpfE6xpRo\npytWij1aI5PIxQvL2wCAL9+6jrVlXeK52dnDWwNtCOb9GSpWmVUKiFPitd1VNpbLnRF+evU1AMBb\n0zXszLQxvvN4CZ+48gAAkEkTTyYtDGd6/D6ysoNbfdLdkAaW63quxrHHWZ7Prd/DN/a3AAAXOz1k\n5AjePZnH86u6Bfj+aA5/df0VAMBLo/M8/5uVPh7N9Lo1hMSco9dRqgz0Yj3nm9U+XuvrlIplSDjU\nNnIS1DCJiI46tpBRO+TqWg97+/rgXFvtYRxqg1r3Yv77YyuP8eKOXmu/cPVFvDTS8/HK7joMQ+GH\nNx4CAL566yrrPHluih8/p9f8QdTE19/V3U7VZsj4j43WAL1Qz+d8ZaoNN4CKmeDdvl47xwctXN7S\ne37Bn+LNoxUaX4GFuj5oFytTPJm0eNzn6cCv2Qm6LpXEojo+1HoMAOildXx9/4Keg/YpbtT1GqyZ\nEe6Q3POtwRKutnSB/8FkDjda+h62Z10EmY37h3r/rM4NcdDXh9zlxWN+nqYXsn06CauoO9rGzLkz\nHEd6rgZhhYMEqQS+uKidvGf9x/ivH/44AOD5uSf43SdX9HyaEi5lYw2hsNXQDkWmDPxQS8/B65N1\nPJnqffvTK6/hD3pXAQCnUQUX6P3feHyBgdVr7SHmPG3nPtp6BJvqD187vYobDT0u706XcPefXWLG\naSMGGi9oR2UUePjCuTsAgDcGKzgaUQYGQH5X//2Rz9xG09bP+eU3n8YFymbPEpftWcsJsD3R63Aa\nu/jCqhbF/O2d6/ipzTf0OCZ1fPdYByQnpyVu4yNbT7AzKXWdiv0yDjysd7TTdn93gYUgV9d62Huo\n5+/pa495nl7bW8XTK3qer9YOEZPw30erD/Ffvv3jyCir7DkpVps6JXu9eYg3BnpNPtvex0+1vgcA\n+J9PPgVLlHpRNtVpXjrawJDEJg1T4sLKCb762f/+A2dMfeFn/7v3xQ359q/90p8NyOWPud6TE/Lp\nr/4SAH0wtl1KAwc1NvhLzTGenOhF+6mt+/jO7jkAwLluH/eP9AJr1wMuRzzVPcKQ6O96QRW2mWOl\nqjfAq0/WsTxXHuIFIryIUgHgzt4i5ogo63RQ40hjqTbB3ZOSv8IkIFcc20w4FEYO639cXDrBA3KA\nfC8tyzS5AUF8EM0PnyDOCu0LxT3sVzYO8fBYf7ZejTjSyqWBiN7f8CI82dHvsSop41v60wqEUNzJ\nYFg5ludH/PkqGcntoznkPUKgNVJsrGjDNZhV+F6jkwpsqrO36wGOj7UD0HzZxeQ8lRHaKYvQRYnN\n6WVrJeAU9+LlUz4YG37Ec9U7aTBvgdsHLv5lfei/8cZ5uMv6O//GU9/E7xxpNdr7ewscjQYTl8G4\nMjfKTic/hbhbZdrrpfM9LkctNCfYJaOkTMURouFlXBaoLE0RHOrD44Xn7mJv1iymHL1plZ8hTG2e\nzyZFY/1phbVMfC9hbZrl7ggn39aOUXoxRLWqn2EyrKDZ0etrtN9gYC46CVTBwjqxUF/X67fqJjg8\n1vfjVRNE5GD8tee+i3/+6BmMjynCVGAxRGEoNBpUmhhU2Hl4dnmfo/OqE3MJ8v7RPK4u6UPZMzPs\nTvXvzWIH//bF7wAAfuU7n0d3Sa8px8xxrtHnMXow1GvSNCQ6dOi9s72CSxv6O9PcxCDUa7PmJpyl\nE0Lh8I6em+bWkNeg56SYUlbQsXJ+PYmt7wOIG4biQMQ2cswIm5FJgw/0g0dz+MVP/S4AcAADAJ9d\nu4ff/PaH9T8shStXdGkqzGxMY/09/UdtgICsVzYP2al8ctrBDTrQjoMaBmS3FhsTtjuPJ20sV/T+\nHCYe7j0p+jUVlyxq1wa8XmRuoEI2ZTr2uH14eW2A49skZ9BNgKkF75D26mIOk9hnK36CjyxrR3c/\naPK99oJqubfvN7hTyjh1ULuo7eJqc4R339R946qZ4tyKDkQePV7A8iphjmKHcTMX145xPNXr7mNL\nT/CV12/oYRybwArp2owcblcWjRSqwEGlAs0NvY7SzGRnc7fXgkXZ4nivymUN89wM4laNyzOLl0+R\nF9gkoXE4gOaSKgDPneYULpXqT8Y1zoQHiYOLXf1s907nEBHWSI4dCNKIwtCBoqywygVEQe7YSLhj\ny3BzSGK6/sXP/i6+0deB12u3zqGxpOd83K/ihSsP8L+98D998E7Iz7xPTsg//vPphLynckybIgTP\nTJFRFL1RH/IhP0tcNAnUdRzW2WhJJdBt6IPKtxMYqV4I2+MODk+04VxZGH6faqlS4EilYcfoU2Zg\nyZ8goWK25eTstXt2ysbjcu0I/UgfZhU7wdFEe+iZKTklOF+bsvrpIKxgrkmZkGkF15d0VDCMPQxu\n6ns4OWmQ+iUAJXB9U0ckk9TFT1x6GwBwwTvGP3z0AgDg44vb2I/0swWZjeWVAT2XwFNtbeTvGvMI\nExvXL+p/Px63YVOUXHNidgDm2xPMyNA5doamozfnvD/D/lQ7G/tDjw8ky5A43tbOoPz8EJJEuLxG\nzGn9+cYU+1Sy8twUOQHHJpHLhuBgu4v/4JNfBQD8tnsDBzX9PFdpfAAAQuGzm/cAAP9050P4yTUd\nId17ssQgwFlagUlYoS9cuY2vbV9Gp0ag4GWHW5ybXsgO7WJlil2XyOQMxY6LHDtorFLmqTnCNr3+\nTGOX18Uw9rDU0O+pWCkM0vnZHrZ5TbarIRIy8lc7x/jenjbmNzt7+K15HfF3WzNsNLTBfzNcwUeX\ndEbq94dXAELiG6bSEqEA8tDEbKYPp4X6hFWnl1dPsJNpJ+LXbn0EeWBzTdxwM82aCs2/sL6mD8kg\ncngdTjMHn1u5q8cranI2r10PcEpRfpxZ6FIfd5Kb2COWPacRw6b9qZTgTNVJUEOFHHsJwZmQC+vH\nsOj9c94MVXpPlFscnS+4Ewyb+lDoxxXesxu1AR6N9Zy1vRCDSM+lb6c4mtbZwdhq9nBKoOhHp11u\njw1utaE29f50uiG+M9TZqZuLe7hLWcJ/8eAali/q37bNHD+yoOHiv3N0HS45c/KcYPDunDfjrOJc\nc8rZtknkIqautoXFGfpxCX4tIvjjXh3nSajy0d488mW9Bw2h0K4H/LdFB2rNizEgJzqVWr+kTev8\ncDSPcEM/5+LqEEf7+je6cwOOyB+ddPG583qea3aCmq1/77vvNrFEIOf2ZoAZ2c+7BwswCGzv+Qm2\nd/UYrW+cwqLvlEogdLQT3nIitChQ+e7hBlaoDHR40kSDaA8mCgz8bVYinPb0fEpLYamu9+nd3UU8\nCcgRGDmokXOSdGMu5eSBDZxLSjoGJ0ZM68QQiokspRTsxJmGwoiyk0oKbDT13hvGHp5q6L3QiyrY\npaBt+fwpanRG3MeCJiAEYM9FjCdy/RQBlexlYLLz963BBdyhQPXmtW3c72mH/KOXtnF3UAawP7je\nv+s9OSHPt3Wl7KXBeaz5eoHE0sLFBb1Rv3V6Hg8f6sghWzSwQJ0eD47mcGlJpwo9M0OHnJlXH67D\noZreVqOHw7DOmQ7XSzHnaqN6GNZ5AVtGjrqtN9Lnzt3FCw0dkX+1f43vc5q78Cidd7O9i9umvqeO\nG/B79oMG40wqVspGuO5GOCHDvlod40eXtJH7kvs0f3YSubhOMrIXvGPMW/rA+yfHH9NdAABadoAp\nkUMEmY0VcpamqYO6pe//59e/g/vRIh5M9aL/2MJjHEYNfs6i5GWZOadqc2lg2aeavTNhY7NydQyH\neJGv1I5wclE/w+6dRXzyIzql/OLDLdxc0VzFC+4Ev3WqnYor3RMc+NpRm/dnaND4vi0U/sc3Pg0A\n+Pj5hzj9Hd1+vPKzd/A7d3XGo/22iVtXaHy9AF/a1+P0Yzfewrqno+6XGudxs6kP8I41xZ32Ame2\nJl0XC4QjWa8O+XmebeziVVMzaC3Oj3Gwqw/Vf+uFb+F/v3cTABCmNrqEIeildfSoY2uWODhHYnuW\nkaNKB4/TyVGjZ3ONDAehfv6H4y6WmoQPCluY39T37doZHg21M7fUGfO81RoRp5crTsL4hokSWOpq\ng7xRG2BvqUXjEkJ1KUI0JB7cW2ZG37nOFAPqtKn7ZffWanvIh/vD0y5q9Awr/hAhrau2H+CEynzT\n0MVTXe2ENpwYR5Gez2YlxCLhHSxD4vUdXRZynAyri5QhMTIuf+302vjwmp6rthPwfLhmxuvrxcPz\n7PwvV8bYC/Q47gdNrNeG/AzSJTChmWFfNrDV7PHvFRH/9aVDDGNScL6u8NyCdu7vDBbw2r6+109s\nPOQMzrlGHztTPa5tN8TL403+vZBsx390+ffxj3Z19mTOmSIpDj9/hmVP33e/WcHQ1WN6vnrKe3DO\nnWGVbNuXwhtYosDGWFN4RPwxZ8uOR7M6P3MmTX5GS0h8b38dP7SwDQD4jf02tja0nVyrjvDCoral\nf3RwAQ+IuXmJskMAkEiTx3vtI3vYvqf3WH+ugiuL+nuejBzm+LD3feB5vX4O+w0OBi+3TzAK9L79\naGsbb0z0320/xAF1Ipl2zrYQ0Dg/ALjSPsbXXcIuGYozwc9s7sEzKRs37KJKwUaeG6h29Xj9N1d+\nHf/p7X8dc+QYb1YHkIRgjXMLhwQczqSBYwoSL7VO+D37swYqVG43XIVhqh2P1eoYTdILypTBdqRS\nj2G1CNeXm0gLllxoxmYAcDcm3B3XdEJ8fE3PzUlUQ83TY3e9sY+aHeFVfPDXWSbwvwjXeyrHfPx3\n/zMA318q8MwM68QRfRA28eBLuq6bVYHP/WVdQ//yN2+ifUkbkaqTYNHXC+HlVy4ysPDDH7uHB0O9\nGfunNSA2cf2KNoaOmXME8617WxxFyqHDgE+3XZJVAUB8TKWGTsQlmK12nx2M3qTK1N7TqQ/HJR4C\nVdJTxyMP1Y5e2Be7p3hnXxuCgucE0IYjpE6BJDcRxqTwOnLxmRu6LtuPKzia6Y3mWBn+jTW9xE/T\nOsbE070ftrDiD/nfwf/F3pvESpZm52Hff+ch5njze/ny5ZxZc1XX1Kwm2WpzECmSkExZNghDWgiw\nAS9oL2TZ8sayDdgLeWPAMCxqY9OmbMoUSdsU0WSTPbC7uqu75sqsysrxzVO8F3PEne/9vfjPPTd7\nJaGMLgLsjk1FvYy4cYf/P8N3vvOdzMZjcoBr9Qlv+kIK5ql8cLrOEOdCfc7IzqhfA0iqXAt1WBfU\n/U72ayhIPfXS1RNsPyRW/0LIaFZeaIiGJAs/M1joyOxGXL7Kxxa++JxCP97701ussBgvZTyx0nxx\nhK9cUFndH91/Bv/eLbUW3upvYftYGXPLyRCRFsobz93HByfr2GxT8CAK3N5WBlMzckgqR11/bp/L\nX+mZw1nxab+Bgjp2tFADyqGKiQ6fxNp0rcCLywq+f/90ndU243OXu100K2etDvPEYpE9kQssUItl\nb7+NcriYFmooSPPE6oZMxnTMFDkhFv3jBq9ZYRTQaWZRvhpDTs1KZbcTo5irazCaMW6tEv+h38WV\nrnJus9TC9r5CarxmyMhOGhk8w8cycm6V1TSJ9Y5ykgUE9o8JIYsMzoRrVoyUULfdu6u4cEtlnXmh\ncWluNnV4Ns+1xTPsj1UgIKXgssHF7gD3HqtavNsKEVM2Wq+HXL6QhQbdyNH01TM522+zCFyzHlZd\nbYXGaxIApqR6LCcmNq4rJ9wb13FlUT2TdW+M+2MVSLhGin0ib/6dq+8z7+zN/mVcqavP9+Mal7he\nWtyHTp/5dLKEgJCGaWTzXn99ZQct4lD88e5TqJPTmoQOFiiIPho10aSBh56ZKG0dAMnM4lKO3AyR\nDyy89uJDAMD371zh0p7oxhDHhIotpLAIPSwe1VBQa20xtXDjpkok7n90Ac6Z+u7GV/bw4L6690Yr\nRkak6PWtcy5H/salt3GbgpDvH1xESiTQPDTYzjXcCAHZsy+vP8QfvvsSANW51bo65HvxyVDZjlV/\nwsnS/nkbGXUTvvzUNt69rZAsrZ7gi1e2OXA/Ceq41lDP4dv7lxGQwOHaZh+nfXWsRqOySa6Z4uCe\n4nWtXD3H2UgF3qaZs01KTzzIZqV2zHIFINl/AC+89Bjv3d0CANQW55iP1Jpy6nE1kiDVYdsp7v6t\n/+pzL8f81N/50ZRjvvsv/gqVY1rEA9kftzgTeqp+jP/rvspMb62csh5CejNA21AL25hoXMeumzET\nIs1JNWvB0jPMIwvpXg0a1AyTEnbuTWr42TWFeHj1mBfn4so565WMpy480s9Ya45xb0YESbciOw4j\nl9vvCimY8JjHOlaWiGcRehg/VgZs/WaPOQode84zQlw7xZxq33mhcdkkjC2Y1FbsLM7wQl0FUW/l\nl5mYF2QmxnSTZrmNQmp451xl/L+y+CFGufq3h8EyDgxlxJ5rHuKrB4rA2vECJsg++dJFwaUcw0mR\nkfMovGqj6qFASa5wjIzbJrNUZ8XUy2tneNRXnAh7c4rwSG14w8g5W/jSjdu4M1KfsV4YYUYCQ7qQ\nSG+q+71en6JGuuu+F6NplKW8jDs0nl05xtv9y/iP3vg6P5O2pdbMOHW5O6LWjjDrOfz9coBXqoPL\nN0WqV23WY53heAgwXF53YnSIOBpGFloElZ8VHuwGaYMUgh1huhZzV0Y+NfHakiJR/snMRdJTvytW\nItQpyF1rjhk5AIBfW1alqf+u/0swD4j3sBnBuznC5LABzEwY3QgZtQrKTPuh2nSJCrb9EPsTtSaX\na1Om0VtmjpS63+NM52zWEAUTmJsLM4a+a3ZcycpPdA5gOnaItz5RDkNoktfRsjuDRqqy7yfrjGx+\nvLvGQVhncco8G0fPuLQWJwaurCr08+eXPsU/334Zs7kNoRVYa4/xbFshie+bGYKkylovEAR/PGug\nT50cX372Hj7UlIO1FnIm5yanLjxSpT2LalyOver3GBX5mdqnHGBcsns4zdSe+tnGPbzlqoTppnuM\njqH2p6Hl+MGZQlei2MR//tyfAAC+NngKX/1YcSj+0Wt/jD/tKyQwL6qBgjeXerhWV9d8GtWxc0S9\npEIiq+dYuqQSsalt479Y/1cAgF/f/w+Rn1KraM9hATG7FmOZ7rf5yhA7PZWgWd0QByO1FtwLU8RL\n6jofPFiDv6zW9ksrB3jzLYUMb9aHKOigL7k7cIS6X9/75Bn4z6ugItAsPLWoAt7H4w5+aVMhp3U9\ngiCULvfAM3s8PcUlItQOYh+9qbIRshDcxbY3bqlWfOI4BZnFpFpLy3GJJpd+2ljCAQWrpp7DoBbt\nthswynEyqwPUNly3I6xS91aUGziaqDUSuzaXkUwzR0hk+Wef2sU4cfh3X35KIVAPB13c2FJr8GDU\nwvxUfd5ZCHkq9k9eP9rXZwpCnlRJfJ8EhA4Wm4hJJGnXbXPPeB4ZeKu/BQBIWwX6U/WQzyY15gMY\nIRCsqwe+M+moGRs0/yG3gWNaYJO+jz8rFJM/zXTUifD4ZOZhmjlntrPE5pbLi60hPt5XDvMrGw/w\nB7dVx45u5dBL+DvR0KLWzUHgob6lYOpR4P4Q6lEGMFmuVVH9oME6F3475HkUxsCAfV1t+KTQ2aEc\njZrYrnf5mA0j4rrme7OLbNCOoiY7idO4gZsdlf0dBQ28f7bO9+LZFbWRJqmN4ZQ0RpwUU5e4NbHO\nA7zkQsYZeW9eY6VLeerwrIVds8Ptp3mhwT1R5xAndbTfUsbm68uLmLyq7pe17UC/oYxf8YTa58mk\ngUmTareNCb4/Up0co9iFbWa40VVGSK+nOE/Vcd89vIBby8oY7ozalaT/3IEkZOPu6TJzVkQtRYsy\n6izTWetHahY/f2gSE2oznsDjTqss0av6s1WgRoHEoFeH1yIZ7tCCQ0Gs7kcwyDmnsQGNxMB8P2LF\n3KNxRYxt+wF+5+BVAMDK0hhnVH/2ajGmO00eE2CaOfJytkUzwpTq3Zjr2Kc20Fcu7OGDE/XMHxwu\nMVIThFY1P+ZSjAeHan86XgKNnMd44imyIYDZ0hzeNgUhIdBbUOcrJXjqrj0AHjfUuR7XYt5fTS/k\nTgTTyZgTAYAD9Tg1uMvg2oVTnND+ba4GCCKL18fucRerRATtT324hEJ+Zf0Bw+7jxMGI9EFun6/y\nvjg9amHjgnKAEym4vFSvRVgnwZqH8yXujrm9fgF1jdRZpY4PJyrgf2V5G+s2OeHCxguGIod+54nk\n98rSOT4Nle3ICh1v3FDoxXdG1/DpGd1rq8q+W1aIGZW17g2XYG/biC9U/34+II0Wo8B/e6g6bYpM\nU4PYABQnLiMb84aDo4eEnl4fY5FI99PIxuWOuv7eb11C+++q/WL/Vhs7v6bu93fDy2gQanHZO8de\nqNAvXyS4R22pyUKOJSJ+zqYO26cgspCRMfjBcAsmlcvTucmB/WHY5C6wg6zFdtH3Y8xNKq0JwK1H\nPMZh12rjhNDgmhWz4GTTirBL6N/e4yVYxFnpz30YdRLom/ioE1ITZiZ3UVpWhoRmWy2tjXg/21YG\nb1Nd/73eEpdsR4nDJSXbyLkZQUqBFSrBnpy2YD/xTD/P149bi+5nCkJ0clpGI4ZPDtzQC56FMjqr\ngdB+GKcW4nUSoRpqSLpUl9UL7hrRYsA9pu6LZR+YGbCJvD9flzw8TZsYkJRUpJGB01BtNsdP+Fhp\nqnMHycxOABLrur29weqGb/W2uEsjH9oYkcKemQh8uKeMWT43GR61hhpAfjVZMRAdkBpmp5p9gJmB\n8qLnR7VK8t0r8CdnKlvan7S4bFIGbIBCDsLc4hbXgeuzsTVEwYHO7rzNMLfViFl6OntYx9snlbhR\nqVAaSAHjiJAQR+LiswpeP/6LDR4QNZ66sB6RcJMJlOBl6lflgeTchdah0lcnxfGXytk5BZpvq++u\n/foO7t5WpM7mxRGeW1RB0bc/uY6rN5SBfOv0IjwSMZvFFmYzBzuGcrD5yOIW104twKe9yriXoIKm\nFxB7pCrZzpCUpZOpjoFLAlITCxpdvzUXKEi7QhQChVfpDZRQOwYWMtIq0acGZkcqMNSuhIhp3RUT\nC0FZKunb+IGjMuQi1rkWP9ccOFeVQ41Tg+XVl+pT/q3+xOeOg3CnAdlO4NI053i/BknTSYO9Os/C\niS4lSEis7P3jdSQEnWtGAZ20KJLABGm1wbtvI6A5R9JNK1h/MWGZ6zzX4NIMorgNFhbLpQbyx+pa\nKVCNQgtBKaMNwCONnfXOiEsNkOo8ACCAo/YDgMenC0onAsD/tv8aDD1HQl0QopZyZ07yuI5wUV3P\nbX8V2+fqOaShAZsE50YjT3VVARCpxqJ5ejuGTQFMkuo4JpL2b1x6G9//VCE7D4NlXHZVwPvB9AIT\nUD+INvG7O6rL5nrnDAEN0tmZd3FKwojHeRPnc7XuLrcGOKTuoygzEVH2HscGIipfnEwa7KgLKWA+\nP8YmtY3uvrkJsUxKr6cejIu0Vh+4iK/SsMVYYH6ZJk+PDZ5aHhzX2H4WuYY9XSEh4U2BjIj9/n8c\nwvyA2r09E91lVe746uEtJib/kfcC34vWhTFO3lcllbybMZHftVNub11xp0hHZK9ygdI13+0vs8zA\nkjvDNqE0cWgy12kSOBAf1CGWK2SwFJzUOhJXSyRkuAydOlwcN0FWdgQlJjcU5EMbPgVhB0ddtuch\nLNgk1T+4u4CclE4T3+IRI8vdCY536fzWx5hTwmAZOWxCTuZjF6dnVAZqz/HF1R18hJ+8ftSvzxSE\nlC1T09DhOmucGcjJQTSvD2F+WzmX6UWBpGxpzYHVBbUZp5Fdtcy6amgTAKwtKIGYmFQFzbnAEhG1\nhlaGtkvs7UdtFOSE4r6NnASEsrkJZ0VFy3FiwjsisaaXAiSkEhlnRqUq6WUoytbNhVRh+wCsZoxi\nlzK71Wq0dMcMOMCQQmJhQW3Cs6DDx9RTwYbj0rVjLDolxFvgcEoDuJamDI8+mnTQdQJcbA5xFvo4\nmDdxo6kQj0V7hhHBiPvDFkSpJGlmzH3JWlk1PGvksMx9vuOj2KTsamhh+z5NmFqupl/eWj3F7RGN\nhc0F7HN1/c2FGeaeMjy6USCmshYKoP5YfXd2UaL5awpaPfyDLYjL6nnOPm3j4z8iItsXMuyQtOEs\nsjGy1bXU7AST7RbOz200L48AN+fssemELPyWpAZP59X0AoKcp7yYVNevG1glQunpB3XEHfWs9Eoh\nWg3NouDEdlNuxTzUF+E4yviFlgX9aeosOfdQu0+I2pWceRBJPeOWTqFLZDRES2snNG8EuLJ8jnvU\n0qkJyfVt7cCBSQNMkrbEi1f2cfs7V9X5rUcwDtW9SRdSJASSWbWEeRNX21W3w0dnq4xIuE6CeY/U\nQDerwMbQC2TUKtlamGLq0vEjA1AxHrREicsBquV2TmU0a9/Ccpc6i8wEI+pwOT9sIZDqOD0A8j7p\n3qzHWCqHi81ciF1yhNLhzC7oWqoN3c2hDUwUtob+WNmM3Kk6n8LM5NJZklnIS0FLKaoafwFuv3QX\nEg4GZC6YN/NHR89idX1Az6HAHxw9DwCYRA6eXlAB+Tf7N5hQHOUm3hurvdCyQlxcVN89HLa49PN+\nsK5E6gDc2jpi3sSzS8fYmag1P5h5iKY23fcZkjc7OEQTxheHaow97SVroOPNjxSyawtUAorrEoK6\nS8yLM8R0LP9TC/oGSRSEFiZjUoy1JdpttW4nHywgXVD37taNA+z82Za6p2s5DyR8brX07AAAIABJ\nREFUz73AxM/RfhMaqR3oUwM2cc5Oek0MSO9j5caUlZdFJrB4Sd3fMDUY9ZunFvNPIAF7RMgpfMi1\nHOaISKFGhg51SHacAA8DQttOWgC1Ac/nlfyCUU8woQDQHOk4+1h9XpdqjAMAXNg6w/mkmoVTZlKa\nmQMnZG9Wz9BZV74nyzVo79O1/VsVh2bj8hkOSEJhPndwFld6J5/r6yey7f/6V0m085wEYzLInpvA\nv6bquOOzOvAKoSVTDT2KLrGVcFaf5RoT2dJn5giIyLSQ61hvj7B7Q+PjRhQkbLUHLF5zsBrAd1I+\nlueQxLiVYkLaHYaTIrtO0zW9CPUX1Pn93Y238Ht1lf3MUgutK2rzf3yygn/3uiKLvjPcxKT1RPZH\ndclFa4rV55QBW3Tn+PhYOZunb+3xxr7gjbAfqCxl+7yLFzqKBNmPfdzqqO8+GC9ye+LPLj3EW/0t\nXKmfY9mZ4IIzREpQ6L3ZMp4lMSZDFPCWCEnILBY+M9sxYiq1aHYOpyTXXh8jvKeMhDsQML+kjOrs\n4w7kRRWoTROb6/rOcoDnXzjia/4oVUFLvF/jOQr2QqjKHFB8nfJ5ztclz+SxPnHRf4Umx5oSHk2v\nanoRO/+jeQMrN84wi2ylw2IU3AXVi2p4dlndp9Oghpic8K+s38H/kqtuB99JmKQodFnNrblWqZ5m\ngQVBrXhCU/oDgCJFl3wa0UxxfVFlYx/fb6B7jTqxUh2zq+o41kKIhJAr3c3gECcor8eIKXPMQwNX\nSLjrSv0coLjOEAX++g3VWfUt7ypmZ+qZv/70I1z1e7h/U533F9d38HZLlQgMvWBo+1JrwN0+UW5y\nN8lGY8w17knoQJK+gygElzkHMw/6DRUkd9yAW90PBy3kV0hwLxd4cVHd6+1xF69dV7Xyd5wLrBmy\nP27hafpMlJjcct3xAky+UGo4mEzM/pmtR/h6qpzranfC6rRfXryP345fw1Z3AGyptff6ouLX/N77\nL+HWJbX2klzHckOd99xJAJLoWPRm2BkqR68JCZ+6Wnr9Om5sKLTtYNTiltYL/hAPJqTRITXWD/nV\nzTt4Z6ge0DSx8XMbijgd5JXzW7Km+OWOyoMfLi3jaydKWOx0UrXr3t1Z5cAryk28saREyd43N3Ck\nVSW5pC2RLSfA0EPtDCCdQMTLGaN88WoGrURPMh0GlSPyh3Wgppxt5gEolZSdFFNCnozLM0Y5ss2I\nUdR/Z+Vd/DeL6jqff2ZHlTYBPN08Zp7WXX8Vsq4+f33jFBPqUHrh0gHunioS6HsnGxWK1s04EGje\nHMCgoMU3E1y5qtb/tcYZvrmrtDd+8eIDfPUHz1czibSCO4cmiYPfWFA6Nm+8/hC/vV/pwJxT2X4+\n8FjrJOoaGFNJVWgSNsm5v/jSIU485WO8jQSPqD3cNxM8PFbX/3p3B79z9AoA4OJKHycvKfvasiI8\ne0uV4C7XztGlNX+jfoola4Lfx+f/+nErx3ym7pibv/+PAQDhxIFHg7PyQuOa7njkwdwl0thGwsOI\npFVgaU0FAkFiYU5OpHbHUoPOAFi3xlhpTLD9oWJvO+cCtS8pJ9F0QpYqDgYu7CZB2WMbTptaLs2M\nORRSqjkuANC4PsS4HIAlwKTIeGTDpe9GJx5nYzAkNGJT534OQZmwW4sR76kIufByhh21uQ50lFE0\ndxyeHVMYQNFV96XRnnNpSdcLlh3PIgN+M+L/99wEo3MVYKysjBgilKEBn2bVpHea6L6iDG/JswFU\n6aQUK8tTDeJIPQdzJmC/TIS4h22WxRdXZ8h3KRtdSqCd05yXK2NMacPbtZhr3ku1GXbeUhs77WY8\nd0Pvmyx01GnOcP5QGQLpFAyVppHBXCHHTWAaOYJS6n5oo7OhMpVCVpNalzoTHD9QRs+caoxu5E/P\nuf3OblaQR3ziVUjYoc8dKwBgt0huPXhCPt6oHH4R6bh+RRnSw1ETIWl9uH7MWhJ5rHN9XAjJ4m5S\nl3BJrE3TJMPxDS/C8AN1/mkzB0i2vdWdIU4MZPfUsy0uBdC21bGyWiXvbtYSLFDw1HJCPDqjeqQE\nk2LtlYBLNkVooLum7uNw6DNvZH1twMqwwV6dn4MeaGg/q/bXaObxVNtZaHMNPUhMJm+HsYmcjul7\nMcbEb2h1Z6jZJACYmuifUlu5l6I4IZ2Qi1NEkVkNktQlc63SM4enq/7qcx/hW4eKLDo5qfPQwuS1\nGZIyS56aQKN6Dnw9Zo6aX+pNFIzUBonFXRDrawO2Eb1BHTkd02rG3EHkmwke9GjdmTkjM0FoQyeN\nnSsL56zCaug5eqRIKgvBehtrtQmOfvsyBl8m+3TfQbxQrUnu5AiMaq5OpHG3oCgEvvi6Ioh+761b\nAOn4FFk1MLF528D4BpH0L/dxPlDn4fox4sfqOeSdFILQXHMt4OvRv93E9Cq9DzUuZYhC8LltrvVx\n9i2FFmgxkL6i1mN67PH+Mmopz4cRqYBBM3HSqyHEYYWGaZfm/KwMPa/KfHMD9pm6nqs/u42DCUnA\nC2BEc2HsZsSokLp56j/d5QmL40UjB/5D9TzD1eo+64HGv5s1cl5rmpmzMKCuFRg8pCB3MUaeaNj9\ne//oc++O+dKv//c/kjDkO//yH/zV6Y65SXog/YbH8N04cjEJiB+wNMLAI4c/daB3yCjoBVxyZraZ\ncffB9LrGEx7j2EDHDtF5VbXqDpoNBMctuK0QnplguTHF7kkXzaUZE1D//mvfwf+7rxjrf23tAd4d\nqIxyp9fFxecVCrHiTTGhoVWXa32ck6T7ypUJZ+oHG23u2JmlNtY9ZZAuu+fYibr0/gyLz6gsLSpM\nPIpUWvNgtsTtw69+YRsfzxS3JC6qXvjjSYNFiOLCwF88VlD833ruAzyeKeey6Q0RFiawDHxn7zIK\nKbC4MMWtzilOwjq+2FWZ6rudTRaTenVxFw+nymBmCxqeaqqs9SBo4xNfZTPPLJ3AM9Rz+MbAx0Wa\ntfE3Vu/gn2lvqN9ujTFoq+f23OIxsiWSLR8uQv6hOr/DxQ6KttojP/3cPTymrGPSdjhoEQL4x3/9\nXwJQui3l9Tt6xtom396/jDGRF4UuoTcTjKcuhJBYaM7htKcQQiIvNFjLKqh49sVjJs7dO1yGRWWH\nItd4mOEvv/4B3j5VQZK1lcEh8mqv18DFhQGfR0lS/oUbd3F7oAzsYOZh/5vqu/bLA8yJaBwOLBgr\nZbBtcDfNcOoBREw1rBwhOTm3FfKcnq4TYHqD4PcngpN5YCOdm8BKAmjAUnMOvDDH+aCGCzQT6FJ9\ngIYR4dvHitdwvNPF1hUVeGZSwyEFQAv1OU4SZbS1WoorLZVp3olNnpex4M7xfFfthT+NbyKj0lHq\n6CwG1R/WWOhsNK5Eu7JcZxKw/8Qk37OTFrZI82ISO6yTsTPp8DDLNNdxcUut2UeTLhJb/W7TCVm2\nHgC8dWUXuvYM96eL+Kk19Z14xcCH62ovfWl5Fx/21Xt7PUNEa+GFhQPO/j/sr7P4HAB8aVWhE986\nugqtHfC9KJWUl7sT1Eh8bH/QZgXPk3kdixT8dZyAxQCTqYVXb6pz2520uVMqlRp2CWlqWSFu1NRz\nujtdxeQKYOw7WHjpFAM3hQXgmZUTnAYqgDs46OLazUO0SHxwELvcEXK5do6jkNqgNYmb6yRw+IOL\nPDk7XDJ4j2j/xwKKnyFuWmjg0vMKXfrNrT/HPz1QWj/Ptw5xe6zW/M6XBEBrpHlzgNED5YRb1wYc\nVO4ddRXCCBW0X1tU6+tBomODlJ3Ppz7iprrvX7i2ixnxoDwjxXZDHTOILPzm098AANwLVvHd0y24\ndgopBX7uuXv4vW8ogcd5avGaHEQeFi9VCOPXtxXCJqXg7rj+WZ271+xGjOaX1T7Ppj7z5sYTDyYh\nmLZecPfidOpidKSe7dblUzz1qgr4otzEijPB/4i/hNePGRLymYKQUlXOMVOckoPxnQS/eEk9wD98\n/0WO5I2xXg3OgoEZ8QyCyOKM1BjrnJknkYndSZtbLsVMh7U+RyEFVv0JvndbwXxogCdeOlrKmZom\nJNd467UIGz4RwmZt1kBoWBET036+8wkOEmKNGzH+dktBsI+TJfyfey8DAGrLCc+X+ePh89jX1efb\nZgCbhISWnQmGFNg8DJZ5ts1x1OTuntnUgUvaHoXUuJe+F9dxNG3gH15XbYD/9ce/gik5tCzTsNia\n4f5oEYOZx+WPw1GTIeuubXMb85o35oDE0IpKxyC1eYbF7KqDRUd994+OnuUpxf/J1p/jt09+CgBw\nMG/iZ5dUF8D3PrwOvEgOw8nhPlbn/eZ3n4JcVMf/95//AX7/saq5984a+B+KrwBQ5YTdvrpfrp2g\nRWWQll9paWhCwtAKDmLPxz5ns/PAhkaZ552TUjobcL2EkaOiEJBEBJ2mDpcLl+tTJrV5jYgJojUz\nYYLjWVxnUvNPX3iM9x3l5Cahw9N47U7Ik2xX1west9CshyzQFKUmxkTYDmc2joij0FyPEFK21+xU\ng/32TjtYWhlj/LZ6VuOag/hUrckztwbPSXCnr663RaJ+cg38nM8nNQ6Mjs+bLGEvainLtueFxlyh\n89DH7ljB8Vlk8Mh2AeD+OZUsphb2BuozOHUwo2eQJAZnzmFsMRqxuXmGLy8rnZj/9YPX8VAou+Bb\nCd9rx0jhktiUa6TwzQQvtxX8vRN2eT3XjRjXfVLDjGvYnas183J7Dwdei//+8oJqd3886+Kptvr8\nn+9cVyUeALfap/jzT9U8l3ozxF6gjtM/bvBU2KBhIiQJ83DiwC+7oE487NL5OFbKw+zWvREHIfrE\nwDuPVaDaaAWshqxBokFloJ1JBxdcKiHkBoot4vQ0z3E7XUXXm2MUO6hbMe4S0bxjh/xsLT3HizQn\nqqZHePNEBaHmWsDlaCEr6kCykcAhREn+xjn+xpK6v6bI+fofJ0sYBGp9vZ1vYucT9bs3n9vDKSHY\naaZj6WaFOpelGctLOcgvMg0ntKeEBm7jvr54hnuFSsjuny9iTryXItXh1GNcWugDdeDtySUWxEtS\nAyHtk28cXYNBvL7TSR0nUt3vL6zv86yiUXgBV5YU0fZo0mAxtZXmBLsnKhn64uYO3j9Ve3itNcY+\njQ9pNecspxBMbTy1qQKbT4Yeo7lHoya3Uzt+gneOrgL45/jJ60f7+kxBSIlA1JxK/ns09vA9TbVf\nohBorBJa0DWBc+VQrW6I4RPZbzlfwbo0A+4QCSjUMQkcNnQCYL7DhydrfA62kSF31Kb9n//054Fl\nlRV817z0hFiXDxB6XUjB6qnH8wYHKt8a3cDuVBnethPi/y5e4t8onY2rJfjP3vt1dT6axH/w1HcA\nAB19jn+28yUAytiGZcdFA3ihqQzBvekyJvvEiTEk/nxfRfKaJhn9eWtnC9YdD7/XUjXLyZkPnboL\n0rqGkanuX8ON2Ek4VordU7Xx+nMfXV9t4HeON/m9EBI9gmZPkhY+3VdOzfESuCSFPhpVE4X/4Qf/\nNvMJAOD/2VOqp2+8cA9v7WwBUC3XYcl0b6SwqKz1v3/0KgTBseimeH1lBwDwzb2rSOj5J8LBm9NS\nz0PD4uIEM9Ix6dQCTgBkUbX4pmFVwnFrMdZaKqh89HAVMMtCs2S4993jDc5yHp8swiauUDByGYJX\nk1nV+w/2Nlig7hvb1zg4kRK4cVEZqnHk4oTE1J7vHuJrDxU/4Km1E+ZlJLnOnVJba+c4HCjHefdw\nBZZHgUpi4pAMoe2mGM1cxGs08TPTYNB0XikF1/iTXMfOIZHxeibG1K7oLc+glcJyqcUcAt+LGRXI\nUx2yRPhdg52WbhXIW+TMRiaXCCEkMuq+KTop7/Ms0dWodoCOra4zSk0EpNoqdMnPcqMxZvXTKDPx\nbk8hk7aZ4XTYwHXS0Hh7f5PRqb20jU/HytkM5x4Lf3395DrvQ0MrcIeGNW74Y3z1ntLAeOPyY3z/\nQHUsHYxa/BzSTOcASwt15CRoOI0dLhHDy9GtERl53ETskPOsCexJtdcKCIR0j8RKxB1OQWgrHQyo\nwKscZ6FrBT4eK6Rtf9zC5ZUz3H+4hr+4fQNGPWGdjaXmFCZ1hHxyphBLQM0nuuir+/LVj96AQ+XG\n5NRFv6UCicKWPAvF6Fm4cVUFLQ++egV/0qaWU1Ni82m1hn935wvoDxXycjZvw6Huq5qR4B4hmJpR\nICAxOdGRGFNSkPQdGK0nUMcSaRKSnb84s6ERb2S6kkAmxF3phPCcBHd31P3YbUQ8I0u3ckjq3jo3\nXWiLVEbe8YEN9f6dP30K8UUSxKwljKRbes5iZbIQKEZVS/SEfIxvJ4x+DIc+vnJdodDf3ruMO/fV\nmtS9FILa0hNNcueXbyeIjAoN/DxfP26ckM8UhJQkveO+A61Gmf3QxvQTQi82cozP1AKxeiY0jwxq\nYAJxxQ/Rqf8/Tj2AxkwbQwOxb0IbEdtdQNV/AQRzg0mUlpGjoGaxeSvFpeU+n19phJ1awnNESsVH\nABgPfHQWaOBd5FctlHMfu2XLaCFwkzQsfv/x83h1U8Gub753A3/QUBojG7UxH7M3rbPK57v7Gzw5\nNk4NVY8EAA28QYReQC/R6D0XwcUM7x0oHowxMpjIJec6Iotk3wcuZ3PT2OBW6SzXcELSy8WdBnYv\nqY3d6c7YIMtUY7XGsGVgi9QW55HFnIhOrRoqmBcaLtC8lJOgrmZAABBWDhpACfcTG8E6cVyWQuTU\nraSfm/jaI+Wos1MXpeClyDTk5PyFBMQSWCXRMjMkNEbeXAuU2isAxBqvsWDg4hGhClYnQl5yagID\n7hHVu1dzeGSoJrnHbbYrqyOcUWtgFhgwRuRs7ZxRjo3lIXdy9fp1Zv67VgqtdBajFeY07I7a3KET\nxhaXWqKs4k0UqY4WKZj2xz6L5s3nNtYWRzigTgtZaMgHxHFZn/Fx+8cN5lGdockWKhi70M+JB7KQ\nAiTiZt8McNInJCTWAXIGI81nrZN8ajL/QDpFFZzMdIACY9QLJKCgOtQRJ9WU02Mq5fR6TbzvqDXb\naIRMGuwFNVhUpm3ZIe6eq8DBsNTYgfuE1C02Z9inoPqV9T08HCvnGQQ29mg4WasR4O9dVuTFd8Zb\neBCq7/aiGqOftp4xf8XWM8wH1K79uI6EShbWUANoaNkJWtCovdPs69hLVPCjuRJaQOjazMW0QaW/\nWWU7XD9h+zJ/3ESvRmKI3YBLOXmh4dG5upZoZmHS92HQGq75EYvJNawYJ4+Vc/Zf6FUkbyPHmHRS\nOssTpRwNALrSVgFUGOjfVdeT1YAPHymnataAYpkChsjA6USt+fDc47VgjTUQLxXv7V3AUrcapJmE\n6vonocN7B7lgG6HpBQKaQaWFGreVS6eAXCR+S67BqFcS7rPAZq5dcOYz7S6f62wDC0dWwzm9An65\nT1Yqn5EYBvamFGDlggXxJjstRjOmkc1tuWVXWvn6/pEKVOOJzUh9HhrQ6XlqRsHXHExcaI0Efymv\nH7PumM9ETL34P/0TAIBzoiMrOZES3JZaeDkb+Sf/bgQCCSkbIhX8mdwtmOBk9wwUVyuCXzKzmFCF\nTgKNpK4LW3Lng+zbkM2svCK4JGIWHtV4UFGe6CxhjlTjqD2v52ivUgvwaV0ZYgBSqzT8C0PCmKq/\n6wkQLxKpKdJgXCh7RsGkvnloc6kAUgDUeilygYw2qtWIgbs0QbUAfGpKGTyfwxroMNX+QuYB8Sbx\nDk4tpPR9s29wq7D9zIjLN8aphXyNLMzAgiSCbGtjjF/YUCO4f/edV2CSkchCk2uQTiNGuqPOqViK\n8dyW4hB8cG+Tgx9t28XSu+r6Zxs6T+Z1Lk4QHlBpbk9DtEhrwSSFVhAxszRmnRh22Rp77EPkAlbZ\n1tfNAcpapQCsM1onF0MuO5gjnREScXWGLKkEtEoNlnonqAz72GVEIglMWKRcmqwl0Ih0nIcGBAVS\niDVFNgbU2izbhLOK21U0U4CMtsgrIp/pZKyqOx+6rDFj71jIbigHlA9sOFTHz+/V4T07wJiUQbVa\nCou0C8KBCxDU7m2bCC4RghFrTORL2gXsPqm71iXyDom4zXW4RxQYPRuy05Y9m0nX1hjIv6AWW/6w\nDvuGCqxnfa+SEc8EryOjHbHuh5ybrE6rOzkERZu6UVTBrxSK+wKlBFsGUbqbQQ5s1qJxLk4g31XB\nU3g5hUYJipaAJ7LGqyn8x+pY8yspf9c6NZBeomDDTZHRGtYSgZwG4bkfuqCRP5ivKpE2dVJAdJkE\n5z61WOkZAKJVuo8SHIQ6n7iIlqgtOxaq6wWA4WQVipZXa0QMLKzeUryZy40+vvPgCq5fULyOTx+t\noX6XsvDXZlhqqedwcNhV7aVQa8n/GulkmIA5p+D0KzFq7yq7Mr8osfmMMiCnkzqK28RfaRf83LRQ\ng0amV0sFiAaHwgLiZfUPWqBD0HMrmikHuUYo1J4EAAG2O+ZYY2VXLQUyn/asDmCdxAOnFkzSj8oG\n6nxlSWYfV8mWfnHOQbymSU5WZCEYGZWZgDEgzamxQNqgFv1GAUFBnuhb7G+Qq+439WVws0RhVpIQ\n0YKEe0y+4LWpak4AIO0CWqBj5zf/wedOTP3pv/lPfiRRyLf/8D/9q0NMLTN7I1RGD1ABhnNOxqJr\ncHeI3RcoDDJIBhgetvoGq3OKTDADPq0DWWTCoUACc4M1LbKZCXtAv7EAntRoBBpkqfXhFkhKmN7L\noRM0r+kF9EcqYopWcvj7pcMzMIoVpAo352vL/AKSul20Mxs56UHoccWyhiToEEqUrE9ZrZgakOWs\nlVYMhxZ50lQTfAHV0dM6q+7pfB3IXAl9rsEaoWL7R4CYVY/J26Hs1wQc+v5k00Gd6trTXGNJbr0Q\nsE4pE/Z8/IuD19T1JAK5Q0772FLBAaB4NhTM2U6G22+rWnRzX2D+qvp7thnhYJmm+n5XQ+tTdaID\np8bZdbgiYA0p8GhIaGV8mItKhMvXEeUCxoEDE2WAQtd4pCOnpMicAvMLlLVPLZZt1ns6G0ApwPNi\npFUFOkFgoygdQqRVmX2sQS+dkCZRUL279ZGJ2QYZ7UzwGhaJQOMhnXcXLOgVrFqcURpzIKJ5QUhs\nhLRezHll/AsLVbZnSGjv1RF1JeBIjE7rMIdlUJ4hIrKgkOAAOPMVUgio9VbCttKQfC8KS3LApM81\ndqp5aHCGaAQacpdKNo7gcqcTgqF2kQleg1oqqhLHxGbNCK0b8yyfPNB578TLCVCq5sYaj65PGxL1\nxwLBCgCYkHoFPad3m8hJEM8+MJHR+WmJQEY2RmRVcOs/Nrks2Hr5DMP3iZht2tDjsrMEyEknAhJc\nRpGmhNGnNm4X7Gz1CBz85zaQtNQ16IEGg2YhiUzdewBofWIgpG6NcNWARkiDfWGGiGZWeccazscr\nSFoFjrAEa6ThEbWNao5E7Vhdz8nQwcAoYQHB91Wu5piTvE/SLtC6S4GhkJhfUN9tfyxwMlrn8xal\nTkYs4ByW9gxISXrfPQF3I5oTIK1TWWdWlUG1xIQxpwDxHEhrZPMDAfeUnlkDvI9yFzxfSZoSTVIe\n1r7tIVgzkKwngJvD3rVQCnkUVtWMmJ54MCdlIF2wzfePgPE1SlDHGlwa3J07gEa+wJzqyFydnyFN\nCYGWA7MLJPVwLOCfqOsfPCVQEMhT2xMIqWU6nVuctMojF1h5Qmjoc3z9pBzzb/AqUQSpgdUMAXCm\nEVqSP1OYFaKQm2C4U+pgp1O0UyRUJ9UjAFMDBWWtsAqYJBle2Frl0DLBRsU5B5IWGZ5MhyRhISsS\nSMnw6gcOTIWKQ9/TkRHCaU4VPAkASeMJxchzDdgmBAMADYJFZgMWQdbmFJgoPw1pSvgPqtkXhU7n\nfGqAtLpgzgCtT5/Rq5VmhOoaClPw/wu6TqkDuUvtno8lel9S/7D6dR2zNTJ6DxzMO9SBMRXIyNgY\nc8EIi9RsvgaRAZPrFDAVlZMrhnWQACLCVR3ekTqf9oMMuUWltlyJdwHA+c8keOWqKlMZv3UNvZ8i\n9ceRYOdnDQVnneZUQ7hSlWycc8HPJAr1H9p8pXGzR5UzSNo5DELCGtsSWqq+cO74sEoBuUENRpnx\nGwZEiahUDROKCE3Bg71jg0Qy4fQlw7RpDdxmWAbRAFDflUga1XOq7VcnXWbmcacSdEs9gDjKEDkw\nR7n+ycmWWeVRhUjp2w4b+tGtQgl5AWh9KjB8SvJvlQ4zrWlIm+Uz15DxCYG/K0IdkoLzwpK8voon\nLUABRpT0qc7Xo8eVs5Caxudc/67LwU+wBg5s9FOLHbWWaBXS1iOnRuswqStkEVCZaW6pf59cLThJ\nMGdAuKz+XrujI3Or32t/Qt11vUVYJXIuBAcq5lTwYEsjrJ5hbadan3r0w/+Wl9JABXgtPPlyBgAo\nYQqWwevIGupcpowPfZjk0KWugi/vgJx4BISLFADVCgxJD6l+14D5nkKCsg0gI+5PduShWFYH9vZ0\n0GQDXPgdA8dv0HF+bYD0UyojLyXQSbgOWpXxK+SDkqEW4FD1Om4D7nEVfLDAnyb4t/QYbK/ShkRO\nU5H9I8HXjBCoPyJy+JUcybs0s2gikdYF7A8pYYzBTt+cgb8/b0j+balV5aLxrRzGmIJwV8I7r/bb\ndJ2CYQk4BEg3dnOECyTid56j+Vh9/vRlA1InNDN9IoG7IrHwIb2PbA68CwPAYxc/ef3oX59NrIyM\nTZoLnpxqzIGZKg9DDwWWXlOEqCQzMP0use+tagFpGThDXFgd4TijDop9hSKUgluJabFzEhnYCOVu\nwdBhVkNl6M6BtEbEubUMNkFwmVcR6oy5ipLLY9oTmq9xqdqMuVMt1LRewaC5JVACJ15PovFYHXOW\nG+yQ+s8CLhlcMat+K6kD1qRCRcqNJgqJaEHAoyg/blab04jAhj5pCix8nzL+J2bZpHUJf1/wuZaT\nYI25gB5T9nMukJUl15HkrEPk4LqscwZEpPlln2nQSwmDBR36GxSdfaON+ppb5XBmAAAgAElEQVTy\nfrO9Bj7+V4poG36hAAhC7f7MMWuXmN9q8HUmJtB4WNZQ1HWW/6Yn1XrIXJTz9TC7AA42IYFshbzN\nXRPzZcryajlW/0Id9/RXY57ho4V6NRbbrM6vMKt1JGR1r8NFgRlll+VzAlRmVX7enIGzKHMMFGTY\nkkYVhGeRQkwAhZowSqGDn3HUEXD6ai0CZJhLHQO/gCAI2h5Uwx3nq1U5qHBzTJ+jSGKmY/2aWqy9\nUR1yqB60NtGQbNBNjXQWcSssCXOVxp0fVCRdgAYAAtDWIuRE9pN6dQ7SlEzwDJd1Pn9I8B4s9yX/\nnV65re5l+SdzWu0N/zjHdIN0L+5roI5jaJkK/ADACCWkVt6X6rl5p088k7msnE0CRKtUFjSq+5vb\nVVBqzgB7rD4TtQU/KwgwsisycPKTW8qRAirYLIM/PRR8TD3QYNJ9KUygsAtYM52PVY6kEIWOpENZ\n/kAD0V0gCqB9R+NzLVHBxfdjhDSo7uQ1k881jCx0lB4ehoXNSqJaoGH4LOkbHVdReO5ILqOKTFTI\nUQYuHRZ6tTeTOuASolJYQLhCaG6rsv9JN4dzVKrZCkSrpHjb1ND9SGK2USKjAA0Ih4Fqn+uhhvCq\n2tsyEzzxVp9pbM+KbopgsUr0yvud+UDaLJFqHQlRQXJbrwL7RCU0gAq0SiTIHgoMb9Bpm7Lq0txI\nmEP3ub9+goT8618lymFOldMDFHeh+7G6e8PrAsOvKQwxWpTQKOvwDgSSdhmNFwzfTr+5BNIdQ24D\n1rmBOU0L9QaVQWveF5yp2CONI/XmdoG4SWzvwwz9p6l754HBxqN1H4hpcTb2MowvU20xlvBOiR/h\n2nCGBCkKIFgiVb1HOSLKxlMfWP2+OmihK1l5AKjvF0hr6vPd2wLOsIRsgPlyJSOuZeoejRzB7/3j\nDKIwUDugeRFxjritjM3kooGVH5S/JzDZUr/R/jRERJoe/pFAfY/momgC/WcouzqoVrM5k8hcyigP\nUogn1CG9UyJ+Lptw+1WwZQZUc3UEwjvUlQOwWFNtW0P0urK2jTd9zDbV58++t8oZeGMgIXJ6v5ND\nj9V5jq46WLxNJNsNA92PM5hTkox+1UH9kBxDS/AzSRo6ErrHRlCgOSWZ+EuCUSR914UoheIsCZNQ\nrjzSuFZsDyoUAfhhB1PfVscJl4D1txQc03vJhaTb5Z3lKAyanWMA7Qcq8hjecDjwWPlBiv7TJIA1\nkWg+JP2Hp10sfKic/3zNwfkLGpoPJF2bgEfoh39SIKJ9ktsCo1tlaQJwzsqgx+AsN2lKHH+suivs\nvkC+qO5L2ijQoAx0viYhydkYMw3Gido8ne0CJ29UxGmH2q/tERCRoRY54ClaA+KGjpAce+eTgh3h\n2NQYOWk9lPycpF7xL7xehumGgc49td5yR4OeqGPFTR3+aUH3LMf0gkn3IuMkZLpmoHaiHlYy01A7\nUEZjuulAUJJQ248wX1b7ov0gQ7CmzsM/LeD1iESvC0wvqOvs3pkBtD7Tts2oV9Qx+NrquxHCZZvP\nLaL5LcZcIFqoUDGXsnQ9KmCE6lrOnzPh7+hI6sDCx5kK6CmO9nuANS3FwQqMyCbZoyf2bQBeCydf\ntJGQvdWuzqB9op6h/kGNA5W0m8Kk8pI0KmRv9Xsxog6VrC3BwZ85z+FtEw/oWosdoJZLmBPSgLpi\n8x6xxwVGQcWPM8hGaPc01PfUOp9s2UgaVZLj9VLUD9R1Ti5afD0iB1qPM/qOgTGVfJzTqlRv98FI\nMnomoyVaLlGSnJwBEFAg3XqY8HpJ6jqMQP3u8IYJr0eDJ32NSzOFWSVq/ac1tO+TD5PWD9mIn7x+\ndK/PGITQl0MF7QHKwVLXK9JmAZ8IcWmjqq2v/CDE3i9SrbsQvBkzD3B7ZIxaAqJQxhRQC2xedebC\n61GtPSiQeuo3wq6G1a+rTDC82GLjXDvMeIGFiwasGb1f0OEfk8Eb5ypiBtC+O2dmcu4Y0KkLoLA0\ntB4Q52LLhX1WEgrADnZ+weesoL4fI61Re6OtISLpBS0H6oSWWFOBuFnC3Qbq+wln1bmtw3+gwvb5\n6gKMQG2eQhdYIMedNi3YEwoS3MqoIJd8/fY452N68xw6Mb+lLlDfUwa8/7SDxjaNx94vML5MbP9e\nDueErvmKD494LY39HAePSTzpRoYmiQH5xy4Ko8qo7R31X3NewJiXstM6nGNVf3H7JpzjOcI1H7XD\nDHY/hj5Tx3LPbTQ/pYu4UYd7ps7PGQiML5Nzn+ZIqJbt72kQNIJ27bs5zp6jTplpVfuNFqo1ZcxU\ncACoUotGz9A7lbAmZTHDQtyx6FwlJAFBhSHYcepzQGTqd/2TnDeA1ICFjyiw9XXoQUbnLHldND88\nw3x1Gd4pcVxSgw2mkED9QH1/smnBpbKYPVElTX5x15GAWULZRlXbzx2JzifqOOGSxbyOwpTcZRYs\na9BDCs6eQG2cgeT1aU0APSL+Qa+AwWRciWCR0K+PcwyvUcv9pEDzPil7OlW9PncUzM7oFADvoYIF\nxMVWVY6TwOKbSg8i2mxycO/3cggqwRmR5OPWDmMkDQoMdY3La1Fbh3tKCdM8h9To/SSFThLz+ihA\nuKU2qH0WIFqiFlgD8I+p88PUUL+v1qN21IPbVZ8/+oVFLmvpESrU8SyBMaZWz8UWnEGOuKkjaumw\npgUKKjvVH84RrpNKriPY2drjqkzW/mQKe0nBcFoqES2of5iMaoyojL4cIqAOEr8VIu1RWaeTYfVr\nJd8jhUk8MD0G7AFNhQ5Stnkil8gtQpqGKcyBCpjFJZvtReboMAnVcfs5nHNSie7PIS11bl5PR+qr\n+9v9JIKWFGxjtQyc6ADg70cdnZsCAIUylufafERBolclpPZYMhJWmELx6ABECyY02p9mWMAhW+0s\nVfvLPY2RUOdTYWvwH6rNndS68I/K67RRO+TC5uf6+gkn5N/glTWotjzQOMvJXCCg2q2zMUVOcsFZ\nTfL49WDFRkwTJJFqSFpl7VbjTDbuqNpfibbETZWhAgqFKGiT+B9GSGoKFmnsZRi+SPMCjhPkNjHO\nGzrqu8oYaFnFA4ibGh8z83XYA2Vs4gUH9ikpY7oGvA9VG+v4jYvIyeAlvsD4Os1smT0hC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0k2UeDz4sBUn+T8vvCJx8XX/v4CcCo5f0W0QuIK4SPiYx0GjqxXd+0OJdz+jH7JCMhfkp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9RPzCxP1oDlOi0809JySEShMd+mktAmQ/ZxIhp/vW6DrfU3UjCKJLkEAkJm0lUOVZmElS57lwG3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\nX4gtnZClLW1pS1va0pb2hdj/BcvFALAY7ktvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb050102160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv('analysis/aural_framing_by_movie_topic/z_roi_node/data/concatenated/all/subject-0001_z_concat.csv', header=0, index_col=None)\n",
    "#data = data.iloc[:, :-1]\n",
    "plt.figure(figsize=(10, 8))  # Set the figure size\n",
    "ax = sns.heatmap(data, cmap=\"viridis\", cbar=True)  \n",
    "ax.set_xticks([]) \n",
    "ax.set_yticks([]) \n",
    "ax.hlines([0, data.shape[0]], 0, data.shape[1], colors='black', linewidth=3)  \n",
    "ax.vlines([0, data.shape[1]], 0, data.shape[0], colors='black', linewidth=3)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optional: Add 'roi_' prefix to column names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So that the header values are treated as strings because some packages can't handle '0'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# Specify the directory containing the CSV files\n",
    "directory = 'analysis/aural_framing_by_movie_topic/concatenated/z-scored'\n",
    "\n",
    "# List all CSV files in the directory\n",
    "csv_files = [file for file in os.listdir(directory) if file.endswith('.csv')]\n",
    "\n",
    "# Iterate through each CSV file\n",
    "for csv_file in csv_files:\n",
    "    csv_path = os.path.join(directory, csv_file)\n",
    "    \n",
    "    # Read the CSV file using pandas\n",
    "    df = pd.read_csv(csv_path)\n",
    "    \n",
    "    # Add the 'roi_' prefix to each column header\n",
    "    df.columns = ['roi_' + col for col in df.columns]\n",
    "    \n",
    "    # Write the modified DataFrame back to the CSV file\n",
    "    df.to_csv(csv_path, index=False, na_rep='NA')\n",
    "\n",
    "print(\"Prefix added to column headers in all CSV files.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot heatmaps and histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Matrix:\n",
      "0.0         0.227636\n",
      "1.0         0.164683\n",
      "10.0        0.218708\n",
      "100.0      -0.031463\n",
      "101.0      -0.034437\n",
      "102.0      -0.112277\n",
      "103.0      -0.065405\n",
      "104.0       0.082714\n",
      "105.0      -0.107303\n",
      "106.0       0.065716\n",
      "107.0      -0.067180\n",
      "108.0      -0.051520\n",
      "109.0      -0.113634\n",
      "11.0        0.086362\n",
      "110.0      -0.021844\n",
      "111.0      -0.040221\n",
      "112.0       0.131675\n",
      "113.0      -0.015384\n",
      "114.0       0.176610\n",
      "115.0       0.238697\n",
      "116.0       0.139714\n",
      "117.0       0.125417\n",
      "118.0       0.218802\n",
      "119.0       0.216178\n",
      "12.0        0.263695\n",
      "120.0       0.112363\n",
      "121.0       0.098034\n",
      "122.0       0.033693\n",
      "123.0      -0.027550\n",
      "124.0       0.017400\n",
      "             ...    \n",
      "73.0       -0.030135\n",
      "74.0       -0.030559\n",
      "75.0       -0.063564\n",
      "76.0       -0.052564\n",
      "77.0       -0.073082\n",
      "78.0       -0.000653\n",
      "79.0        0.050344\n",
      "8.0         0.045588\n",
      "80.0       -0.107172\n",
      "81.0       -0.055050\n",
      "82.0       -0.011966\n",
      "83.0        0.033592\n",
      "84.0       -0.020674\n",
      "85.0        0.007068\n",
      "86.0        0.030128\n",
      "87.0        0.001358\n",
      "88.0        0.067382\n",
      "89.0        0.027814\n",
      "9.0         0.175225\n",
      "90.0        0.146714\n",
      "91.0        0.068060\n",
      "92.0        0.125956\n",
      "93.0        0.059688\n",
      "94.0        0.075125\n",
      "95.0       -0.106241\n",
      "96.0       -0.055844\n",
      "97.0       -0.111109\n",
      "98.0       -0.051893\n",
      "99.0        0.042002\n",
      "volume    116.953810\n",
      "Length: 301, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# Directory containing CSV files for all subjects\n",
    "csv_directory = 'analysis/aural_framing_by_movie_topic/z_roi_node/data/concatenated/all/'\n",
    "\n",
    "# List all CSV files in the directory\n",
    "csv_files = [f for f in os.listdir(csv_directory) if f.endswith('.csv')]\n",
    "\n",
    "# Initialize an empty list to store subject data\n",
    "all_subject_data = []\n",
    "\n",
    "# Loop through each CSV file (subject) and read data into a list\n",
    "for csv_file in csv_files:\n",
    "    subject_data = pd.read_csv(os.path.join(csv_directory, csv_file), header=0, index_col=None)\n",
    "    all_subject_data.append(subject_data)\n",
    "\n",
    "# Stack all subject DataFrames to calculate the mean while ignoring NaN values\n",
    "stacked_data = np.stack(all_subject_data, axis=0)\n",
    "mean_matrix = np.nanmean(stacked_data, axis=0)\n",
    "\n",
    "# Get column and index information from one of the subject DataFrames\n",
    "sample_subject_data = all_subject_data[0]  # Assuming all subjects have the same structure\n",
    "\n",
    "# Create the plot\n",
    "fig, axs = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "# Plot the mean matrix as a heatmap\n",
    "sns.heatmap(mean_matrix, cmap='viridis', cbar=True, ax=axs[0])\n",
    "x_ticks = [0, mean_matrix.shape[1] - 1]\n",
    "y_ticks = [0, mean_matrix.shape[0] - 1]\n",
    "axs[0].set_xticks(x_ticks)\n",
    "axs[0].set_yticks(y_ticks)\n",
    "axs[0].set_xticklabels(sample_subject_data.columns[x_ticks], rotation='vertical')\n",
    "axs[0].set_yticklabels(sample_subject_data.index[y_ticks])\n",
    "axs[0].set_title('Mean activation across subjects')\n",
    "axs[0].set_xlabel('region-of-interest (node)')\n",
    "axs[0].set_ylabel('volume')\n",
    "\n",
    "# Flatten and clean the data before plotting the distribution\n",
    "all_data_flat = np.concatenate(all_subject_data).flatten()\n",
    "all_data_flat_cleaned = all_data_flat[~np.isnan(all_data_flat)]\n",
    "\n",
    "# Plot histogram for the distribution of all data\n",
    "axs[1].hist(all_data_flat_cleaned, bins=50, color='#440154FF', density=False)\n",
    "axs[1].set_title('Distribution of activation across subject, sessions, and nodes')\n",
    "axs[1].set_xlabel('activation')\n",
    "axs[1].set_ylabel('count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a 3d array: subjects x volumes x ROIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(238, 555, 300)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "working_dir = 'analysis/aural_framing_by_movie_topic/z-scored/concatenated/neutral_threat_blame' # Directory with concatenated ROI activation data\n",
    "subject_files = [f for f in os.listdir(working_dir) if f.endswith(\".csv\")]\n",
    "subject_files.sort() \n",
    "\n",
    "data_list = []\n",
    "\n",
    "for filename in subject_files:\n",
    "    file_path = os.path.join(working_dir, filename)\n",
    "    \n",
    "    data = pd.read_csv(file_path, header=0, index_col=None).values \n",
    "    data_list.append(data)\n",
    "\n",
    "stacked_data = np.stack(data_list, axis=0)\n",
    "\n",
    "print(stacked_data.shape)\n",
    "# 238 subjects x 555 TRs (i.e., number of timepoints across all runs) x 300 nodes (i.e., ROIs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check value ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node 1 - Min value: -4.327910316630728, Max value: 3.988791157154884\n",
      "Node 2 - Min value: -4.39795175360626, Max value: 3.6952753180327416\n",
      "Node 3 - Min value: -5.200793672836413, Max value: 4.035906937670034\n",
      "Node 4 - Min value: -4.791613026697027, Max value: 4.099968340288932\n",
      "Node 5 - Min value: -4.73564296186914, Max value: 3.71437007798046\n",
      "Node 6 - Min value: -5.158222349025814, Max value: 3.898457752090205\n",
      "Node 7 - Min value: -4.083446360760623, Max value: 4.3542110437897\n",
      "Node 8 - Min value: -5.188031021524385, Max value: 4.489039797668948\n",
      "Node 9 - Min value: -4.528591930650289, Max value: 3.98208455908513\n",
      "Node 10 - Min value: -4.714091753683615, Max value: 3.7576418549237767\n",
      "Node 11 - Min value: -5.376534949752987, Max value: 4.05394889453296\n",
      "Node 12 - Min value: -4.091542139524541, Max value: 4.023109767187574\n",
      "Node 13 - Min value: -4.609912072484199, Max value: 4.365089057462655\n",
      "Node 14 - Min value: -5.02517797249502, Max value: 4.038638537957648\n",
      "Node 15 - Min value: -4.941321438299846, Max value: 4.601581133326496\n",
      "Node 16 - Min value: -5.499494098530485, Max value: 4.246277747541384\n",
      "Node 17 - Min value: -3.667560801296719, Max value: 4.631942238865696\n",
      "Node 18 - Min value: -4.798283035505503, Max value: 4.476100515075508\n",
      "Node 19 - Min value: -4.975092671234339, Max value: 3.640865622114309\n",
      "Node 20 - Min value: -4.345305393917256, Max value: 4.239318073228795\n",
      "Node 21 - Min value: -3.626762863903781, Max value: 4.409492324148117\n",
      "Node 22 - Min value: -4.215914689694022, Max value: 3.3305821175039165\n",
      "Node 23 - Min value: -4.060207063170339, Max value: 4.449571919497866\n",
      "Node 24 - Min value: -4.561875552032547, Max value: 3.5704485827540045\n",
      "Node 25 - Min value: -4.571202619598311, Max value: 3.693404165083349\n",
      "Node 26 - Min value: -4.479294995523359, Max value: 3.8844160111069375\n",
      "Node 27 - Min value: -3.690867064809079, Max value: 4.082236770117619\n",
      "Node 28 - Min value: -4.1496448454850015, Max value: 4.203225081825089\n",
      "Node 29 - Min value: -4.073198823710943, Max value: 4.06943315972552\n",
      "Node 30 - Min value: -4.2211046960224, Max value: 4.161646064428743\n",
      "Node 31 - Min value: -4.268222563077131, Max value: 3.876906161512134\n",
      "Node 32 - Min value: -4.034728304047639, Max value: 4.38285913939056\n",
      "Node 33 - Min value: -3.900497817543531, Max value: 3.9801466290999374\n",
      "Node 34 - Min value: -4.664821234505888, Max value: 4.435353143618415\n",
      "Node 35 - Min value: -4.134897372576633, Max value: 3.932075831375932\n",
      "Node 36 - Min value: -4.419977260505507, Max value: 4.50915160104621\n",
      "Node 37 - Min value: -4.345149580290507, Max value: 4.470672029738132\n",
      "Node 38 - Min value: -3.686335280618502, Max value: 4.173227402916592\n",
      "Node 39 - Min value: -4.116770000590578, Max value: 4.345268094094668\n",
      "Node 40 - Min value: -3.9991275896598406, Max value: 4.173910652301099\n",
      "Node 41 - Min value: -3.9080521927380265, Max value: 3.804509952563376\n",
      "Node 42 - Min value: -4.161762496671133, Max value: 4.094037850253979\n",
      "Node 43 - Min value: -3.659270770253829, Max value: 3.891822203054588\n",
      "Node 44 - Min value: -4.087514801883936, Max value: 4.10981483056154\n",
      "Node 45 - Min value: -3.750247370153299, Max value: 4.367642608892576\n",
      "Node 46 - Min value: -3.6428942484791182, Max value: 3.9544097286029856\n",
      "Node 47 - Min value: -3.604550850729412, Max value: 4.097688452996522\n",
      "Node 48 - Min value: -3.6764539018459055, Max value: 4.006161305356788\n",
      "Node 49 - Min value: -3.879892232156792, Max value: 4.684023643844867\n",
      "Node 50 - Min value: -3.745621368209073, Max value: 3.841717487448335\n",
      "Node 51 - Min value: -4.027861594368143, Max value: 4.894796152583635\n",
      "Node 52 - Min value: -3.439348911200076, Max value: 4.267977212995858\n",
      "Node 53 - Min value: -3.89414631812182, Max value: 3.9633863110437\n",
      "Node 54 - Min value: -4.120932252073542, Max value: 4.440406693490262\n",
      "Node 55 - Min value: -4.622475836412101, Max value: 4.001696032977773\n",
      "Node 56 - Min value: -4.350999607748581, Max value: 3.9510029664392974\n",
      "Node 57 - Min value: -4.365057641154407, Max value: 3.967479529579461\n",
      "Node 58 - Min value: -4.127009649791871, Max value: 3.39425423734376\n",
      "Node 59 - Min value: -3.854747688739265, Max value: 4.221729329929103\n",
      "Node 60 - Min value: -4.196256178541912, Max value: 4.05473523053623\n",
      "Node 61 - Min value: -3.585534449649067, Max value: 4.090983066786662\n",
      "Node 62 - Min value: -3.698290687000663, Max value: 3.952652067697696\n",
      "Node 63 - Min value: -3.638914409545997, Max value: 4.237562499261484\n",
      "Node 64 - Min value: -3.7590505794654336, Max value: 3.684800678636336\n",
      "Node 65 - Min value: -3.9316724942002894, Max value: 4.743044453427852\n",
      "Node 66 - Min value: -3.802984290998447, Max value: 4.585799910633145\n",
      "Node 67 - Min value: -3.797181044076191, Max value: 3.916575083989633\n",
      "Node 68 - Min value: -3.946086688288077, Max value: 4.029992316897103\n",
      "Node 69 - Min value: -4.124414747008141, Max value: 4.337730642421677\n",
      "Node 70 - Min value: -4.185768704452255, Max value: 3.8170282687137314\n",
      "Node 71 - Min value: -3.7308425656471296, Max value: 4.243073646758234\n",
      "Node 72 - Min value: -3.736153695959994, Max value: 4.769064192791882\n",
      "Node 73 - Min value: -4.118476525784756, Max value: 4.1670794245301215\n",
      "Node 74 - Min value: -4.0973115142779735, Max value: 4.15725127453842\n",
      "Node 75 - Min value: -5.358409750668321, Max value: 3.937269244574487\n",
      "Node 76 - Min value: -4.383072217949411, Max value: 4.043184066087802\n",
      "Node 77 - Min value: -3.82780107828792, Max value: 4.360524274888038\n",
      "Node 78 - Min value: -4.637834240154437, Max value: 3.971176833482803\n",
      "Node 79 - Min value: -3.879432148130708, Max value: 3.9541188901137296\n",
      "Node 80 - Min value: -3.768619286258818, Max value: 3.8460752934821048\n",
      "Node 81 - Min value: -4.609035732923164, Max value: 3.8571867719639528\n",
      "Node 82 - Min value: -4.029490666451748, Max value: 4.10660972908846\n",
      "Node 83 - Min value: -3.916739674200676, Max value: 4.24602961186373\n",
      "Node 84 - Min value: -3.802453488907836, Max value: 4.247151795620857\n",
      "Node 85 - Min value: -3.923561483276599, Max value: 4.249972211997921\n",
      "Node 86 - Min value: -4.027811651691662, Max value: 4.110450276595598\n",
      "Node 87 - Min value: -3.720138415624118, Max value: 3.952703120193284\n",
      "Node 88 - Min value: -4.010987299353996, Max value: 3.8383906665657705\n",
      "Node 89 - Min value: -4.14416389218028, Max value: 3.8177680304907513\n",
      "Node 90 - Min value: -4.480957172220517, Max value: 4.198108806714135\n",
      "Node 91 - Min value: -3.954600001817597, Max value: 3.5472924833775896\n",
      "Node 92 - Min value: -3.837166916169383, Max value: 3.808335808950161\n",
      "Node 93 - Min value: -4.661804874991546, Max value: 3.857392693203417\n",
      "Node 94 - Min value: -3.880696120960687, Max value: 3.969908143435917\n",
      "Node 95 - Min value: -4.803614449266775, Max value: 4.115944717032996\n",
      "Node 96 - Min value: -3.480412912869662, Max value: 4.143294432513927\n",
      "Node 97 - Min value: -3.711461782210212, Max value: 3.859022478557852\n",
      "Node 98 - Min value: -3.5029655657769614, Max value: 4.697165348562799\n",
      "Node 99 - Min value: -3.887740469096577, Max value: 4.359481083866542\n",
      "Node 100 - Min value: -3.328896766669628, Max value: 3.927276886726937\n",
      "Node 101 - Min value: -5.472718396890038, Max value: 3.9456135806545696\n",
      "Node 102 - Min value: -4.034210143968555, Max value: 3.7723841862442544\n",
      "Node 103 - Min value: -3.725723921711311, Max value: 3.954889193001352\n",
      "Node 104 - Min value: -4.269857515259462, Max value: 4.357670480217917\n",
      "Node 105 - Min value: -4.014685117125688, Max value: 3.77027133345521\n",
      "Node 106 - Min value: -3.674153808679455, Max value: 4.19633829882472\n",
      "Node 107 - Min value: -4.500452771096118, Max value: 4.249081120888882\n",
      "Node 108 - Min value: -3.699470454327759, Max value: 4.263547030757091\n",
      "Node 109 - Min value: -3.654093824645488, Max value: 4.023373685499187\n",
      "Node 110 - Min value: -3.53178809015694, Max value: 3.766236728657965\n",
      "Node 111 - Min value: -5.3792768075094095, Max value: 4.213632385533451\n",
      "Node 112 - Min value: -4.805297222125382, Max value: 4.182947703410661\n",
      "Node 113 - Min value: -5.101283029745726, Max value: 3.6011624798963644\n",
      "Node 114 - Min value: -3.837087563321024, Max value: 4.04973997877037\n",
      "Node 115 - Min value: -3.817222756759073, Max value: 3.97310910584942\n",
      "Node 116 - Min value: -4.1543242343185725, Max value: 3.6379876875685886\n",
      "Node 117 - Min value: -3.5656837091724407, Max value: 3.539041490713258\n",
      "Node 118 - Min value: -3.688222660803661, Max value: 3.5102012533728675\n",
      "Node 119 - Min value: -4.146087359961384, Max value: 3.4442838285133064\n",
      "Node 120 - Min value: -3.73221449960426, Max value: 4.072225153607971\n",
      "Node 121 - Min value: -3.8300532013396698, Max value: 3.537154494968134\n",
      "Node 122 - Min value: -4.331311222655016, Max value: 4.13907553548567\n",
      "Node 123 - Min value: -3.914409550472742, Max value: 3.815487976638795\n",
      "Node 124 - Min value: -3.951783541079823, Max value: 5.050820008951352\n",
      "Node 125 - Min value: -3.938298116789277, Max value: 3.7455606722607606\n",
      "Node 126 - Min value: -4.429136415796299, Max value: 3.7420445657887\n",
      "Node 127 - Min value: -4.822523154225784, Max value: 3.528208241786963\n",
      "Node 128 - Min value: -4.719134753358683, Max value: 4.148787041322303\n",
      "Node 129 - Min value: -4.816884098083709, Max value: 4.290775779278729\n",
      "Node 130 - Min value: -4.414424091269573, Max value: 3.7710823125581214\n",
      "Node 131 - Min value: -3.730526724548856, Max value: 4.121920356340533\n",
      "Node 132 - Min value: -4.588026331575359, Max value: 3.916483792709214\n",
      "Node 133 - Min value: -3.942959390248245, Max value: 4.284028105505825\n",
      "Node 134 - Min value: -4.772702409130034, Max value: 3.67609762415494\n",
      "Node 135 - Min value: -3.820613604483749, Max value: 4.424074337194227\n",
      "Node 136 - Min value: -4.044637051719676, Max value: 4.043319802546909\n",
      "Node 137 - Min value: -4.0090389695717885, Max value: 4.709572584341103\n",
      "Node 138 - Min value: -3.637045957553, Max value: 3.5698692999303683\n",
      "Node 139 - Min value: -4.529524090190486, Max value: 3.5320909551689303\n",
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      "Node 141 - Min value: -3.922554126945201, Max value: 3.927068198303192\n",
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      "Node 152 - Min value: -4.380860395117195, Max value: 3.820256605736073\n",
      "Node 153 - Min value: -5.026106233087256, Max value: 3.536527869655962\n",
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      "Node 160 - Min value: -5.3132762622744405, Max value: 3.93680290411086\n",
      "Node 161 - Min value: -3.787436662497016, Max value: 3.636007045605279\n",
      "Node 162 - Min value: -4.751629821300671, Max value: 4.4314940391151705\n",
      "Node 163 - Min value: -4.871636488049812, Max value: 4.4552733769189645\n",
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      "Node 167 - Min value: -5.066323964584402, Max value: 4.43437998964873\n",
      "Node 168 - Min value: -4.680169086570007, Max value: 4.034057275632498\n",
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      "Node 180 - Min value: -3.9053228683219934, Max value: 3.828899010320864\n",
      "Node 181 - Min value: -3.944408686443278, Max value: 4.27509982629403\n",
      "Node 182 - Min value: -3.811599351467073, Max value: 4.158168825463842\n",
      "Node 183 - Min value: -4.043190121804477, Max value: 4.852448806042955\n",
      "Node 184 - Min value: -4.103172239762723, Max value: 4.1277015712186245\n",
      "Node 185 - Min value: -4.224358933571893, Max value: 4.555964914368575\n",
      "Node 186 - Min value: -4.282144451508652, Max value: 4.298405402537947\n",
      "Node 187 - Min value: -4.441204782217625, Max value: 4.221281679761424\n",
      "Node 188 - Min value: -3.8599374680786336, Max value: 4.265081959008647\n",
      "Node 189 - Min value: -3.8436689169308873, Max value: 4.272181224211061\n",
      "Node 190 - Min value: -3.942950209806405, Max value: 4.025016366271294\n",
      "Node 191 - Min value: -3.953475815302556, Max value: 3.962941028656406\n",
      "Node 192 - Min value: -3.652296085630687, Max value: 3.989421920907781\n",
      "Node 193 - Min value: -3.782815871775379, Max value: 4.572694892202594\n",
      "Node 194 - Min value: -3.904856096038227, Max value: 3.933254196751852\n",
      "Node 195 - Min value: -3.973861052938192, Max value: 4.142064927538845\n",
      "Node 196 - Min value: -3.887104866647434, Max value: 4.363751281207625\n",
      "Node 197 - Min value: -4.002338359661202, Max value: 4.006841307496294\n",
      "Node 198 - Min value: -3.594810235794054, Max value: 4.310545135817892\n",
      "Node 199 - Min value: -3.972948736594346, Max value: 4.108222450958614\n",
      "Node 200 - Min value: -3.797467059349815, Max value: 4.231543975937531\n",
      "Node 201 - Min value: -3.745193567087744, Max value: 3.9482336455644576\n",
      "Node 202 - Min value: -4.466272827165136, Max value: 4.241239281675835\n",
      "Node 203 - Min value: -3.873553103160148, Max value: 4.362815608327999\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node 204 - Min value: -4.316969074215283, Max value: 3.984656186433018\n",
      "Node 205 - Min value: -4.181638509874326, Max value: 4.059188684808862\n",
      "Node 206 - Min value: -4.34417384485544, Max value: 4.263580077623499\n",
      "Node 207 - Min value: -3.9967313348657294, Max value: 3.985894181762173\n",
      "Node 208 - Min value: -4.282878130154508, Max value: 3.9066395639205607\n",
      "Node 209 - Min value: -3.907422053119325, Max value: 4.386747364125082\n",
      "Node 210 - Min value: -3.5131831598445005, Max value: 3.9594004507948513\n",
      "Node 211 - Min value: -3.936213974644908, Max value: 3.779346855734228\n",
      "Node 212 - Min value: -3.628728403674845, Max value: 3.928613746069763\n",
      "Node 213 - Min value: -3.863417627703113, Max value: 3.662255632153984\n",
      "Node 214 - Min value: -3.855394207907672, Max value: 4.5370311069757605\n",
      "Node 215 - Min value: -4.403440929617159, Max value: 4.376837196281844\n",
      "Node 216 - Min value: -4.317748308267856, Max value: 3.8600814223208464\n",
      "Node 217 - Min value: -3.9948240810701545, Max value: 4.618619528570475\n",
      "Node 218 - Min value: -4.173119947396133, Max value: 3.8084483071007686\n",
      "Node 219 - Min value: -4.910006544988391, Max value: 3.834634432066329\n",
      "Node 220 - Min value: -4.2634167544005015, Max value: 4.282792911936832\n",
      "Node 221 - Min value: -4.287787180063048, Max value: 3.9145712772188537\n",
      "Node 222 - Min value: -3.817055101043589, Max value: 3.9779459117226574\n",
      "Node 223 - Min value: -3.7397279118194415, Max value: 4.250525205847722\n",
      "Node 224 - Min value: -3.711263941338453, Max value: 3.800428361343133\n",
      "Node 225 - Min value: -3.809220304981946, Max value: 4.104328412889959\n",
      "Node 226 - Min value: -4.555620225251142, Max value: 3.75120668738231\n",
      "Node 227 - Min value: -4.331918750446063, Max value: 3.805749074762781\n",
      "Node 228 - Min value: -4.605924336817952, Max value: 4.394286149506718\n",
      "Node 229 - Min value: -3.956077707122792, Max value: 4.465565311795248\n",
      "Node 230 - Min value: -4.190480504088057, Max value: 3.985855641799578\n",
      "Node 231 - Min value: -4.013900219356476, Max value: 3.776306493823248\n",
      "Node 232 - Min value: -4.795283699360812, Max value: 4.288484010474925\n",
      "Node 233 - Min value: -3.847781637901099, Max value: 3.9573378600323306\n",
      "Node 234 - Min value: -4.052298653234337, Max value: 4.111184147234913\n",
      "Node 235 - Min value: -3.752426077347158, Max value: 4.271188065207405\n",
      "Node 236 - Min value: -4.1901801004835, Max value: 4.028503415835978\n",
      "Node 237 - Min value: -3.617471822911039, Max value: 3.91689084702904\n",
      "Node 238 - Min value: -3.984762583969957, Max value: 4.089336339085\n",
      "Node 239 - Min value: -3.778642472150192, Max value: 4.034897225239131\n",
      "Node 240 - Min value: -3.922639324251961, Max value: 4.200444642982895\n",
      "Node 241 - Min value: -4.050833601591363, Max value: 4.586456477467766\n",
      "Node 242 - Min value: -4.470286290696328, Max value: 3.733358231023742\n",
      "Node 243 - Min value: -4.133581867685617, Max value: 4.043781717112836\n",
      "Node 244 - Min value: -4.192377368397426, Max value: 3.889134282059442\n",
      "Node 245 - Min value: -3.984536940271006, Max value: 4.109063710953166\n",
      "Node 246 - Min value: -4.206912526520079, Max value: 4.2026611233603655\n",
      "Node 247 - Min value: -4.739961540709665, Max value: 3.986981840454805\n",
      "Node 248 - Min value: -3.7133069376393615, Max value: 4.035590045675515\n",
      "Node 249 - Min value: -3.9363651358024656, Max value: 4.109553195751433\n",
      "Node 250 - Min value: -3.907454717938055, Max value: 3.932354947655532\n",
      "Node 251 - Min value: -3.946058472066244, Max value: 3.6628805009878143\n",
      "Node 252 - Min value: -4.531838348462933, Max value: 3.837285133662156\n",
      "Node 253 - Min value: -4.62659742793972, Max value: 4.139853734892792\n",
      "Node 254 - Min value: -3.974585321003216, Max value: 4.722749082718941\n",
      "Node 255 - Min value: -3.843808025037789, Max value: 3.89087316175808\n",
      "Node 256 - Min value: -3.751602333455277, Max value: 4.304331704010468\n",
      "Node 257 - Min value: -4.596610843857741, Max value: 3.7848525179097865\n",
      "Node 258 - Min value: -4.658988496984628, Max value: 4.035658297984321\n",
      "Node 259 - Min value: -3.489381026131079, Max value: 4.234366610268127\n",
      "Node 260 - Min value: -3.563759433465104, Max value: 4.364904878580794\n",
      "Node 261 - Min value: -4.064634004241916, Max value: 4.230452698258683\n",
      "Node 262 - Min value: -3.622471364992161, Max value: 3.969311296713131\n",
      "Node 263 - Min value: -3.735030961327869, Max value: 4.533515775873064\n",
      "Node 264 - Min value: -3.827474436023474, Max value: 4.895907899997294\n",
      "Node 265 - Min value: -3.651879378920463, Max value: 4.101205044127168\n",
      "Node 266 - Min value: -3.778153214673625, Max value: 4.219819581216745\n",
      "Node 267 - Min value: -3.5662196383446134, Max value: 3.896527559064201\n",
      "Node 268 - Min value: -4.868401450252862, Max value: 3.7912243737818367\n",
      "Node 269 - Min value: -3.524561077572548, Max value: 4.00497546439993\n",
      "Node 270 - Min value: -3.968766867295864, Max value: 4.307123046302329\n",
      "Node 271 - Min value: -3.986438797676719, Max value: 4.022237482558441\n",
      "Node 272 - Min value: -4.09218491556518, Max value: 3.969061075960371\n",
      "Node 273 - Min value: -3.950716809473437, Max value: 3.789222278140101\n",
      "Node 274 - Min value: -3.761789187938037, Max value: 4.051586328279285\n",
      "Node 275 - Min value: -3.421166997383098, Max value: 3.618502166051929\n",
      "Node 276 - Min value: -4.351131758234336, Max value: 4.0457349929715765\n",
      "Node 277 - Min value: -3.814394876052166, Max value: 3.5165752602392084\n",
      "Node 278 - Min value: -3.9251382665651295, Max value: 3.575080738505504\n",
      "Node 279 - Min value: -3.845847924758989, Max value: 4.09554661745815\n",
      "Node 280 - Min value: -3.789885035017224, Max value: 3.798925697426256\n",
      "Node 281 - Min value: -3.628410375435565, Max value: 3.659824460048073\n",
      "Node 282 - Min value: -4.336505209798297, Max value: 3.839310712978536\n",
      "Node 283 - Min value: -4.553971389149558, Max value: 3.56118590417191\n",
      "Node 284 - Min value: -4.182338050525436, Max value: 4.408801968324893\n",
      "Node 285 - Min value: -5.15552342059368, Max value: 3.664146314198711\n",
      "Node 286 - Min value: -4.704674140854209, Max value: 4.430602454722514\n",
      "Node 287 - Min value: -3.744887553218772, Max value: 4.166729124547639\n",
      "Node 288 - Min value: -3.870572880198996, Max value: 4.417422128873807\n",
      "Node 289 - Min value: -3.802678493206359, Max value: 4.1238804334021895\n",
      "Node 290 - Min value: -5.058744487931198, Max value: 4.208907433269308\n",
      "Node 291 - Min value: -4.319027947924513, Max value: 4.0884533968367975\n",
      "Node 292 - Min value: -4.0699364011194215, Max value: 4.486133007263366\n",
      "Node 293 - Min value: -3.69926262805162, Max value: 4.309860044524225\n",
      "Node 294 - Min value: -3.881446817059903, Max value: 4.540181211904309\n",
      "Node 295 - Min value: -3.862881089420864, Max value: 3.9242917254849\n",
      "Node 296 - Min value: -3.9394971478086647, Max value: 3.8925768934572615\n",
      "Node 297 - Min value: -4.260084133933726, Max value: 3.886967621897794\n",
      "Node 298 - Min value: -3.8651091346584368, Max value: 3.776573467043825\n",
      "Node 299 - Min value: -4.173184918070825, Max value: 3.985549238905988\n",
      "Node 300 - Min value: -4.541829448202135, Max value: 3.9719242048326993\n"
     ]
    }
   ],
   "source": [
    "# Get the dimensions of the stacked_data array\n",
    "n_subs, n_ts, n_nodes = stacked_data.shape\n",
    "\n",
    "# Iterate over nodes and check values\n",
    "for node in range(n_nodes):\n",
    "    node_values = stacked_data[:, :, node]\n",
    "    min_value = np.nanmin(node_values)\n",
    "    max_value = np.nanmax(node_values)\n",
    "    print(f\"Node {node + 1} - Min value: {min_value}, Max value: {max_value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Combine all node values into a single array\n",
    "all_node_values = stacked_data.reshape(-1, n_nodes)\n",
    "\n",
    "# Compute the minimum and maximum values for all node values\n",
    "min_value = np.nanmin(all_node_values)\n",
    "max_value = np.nanmax(all_node_values)\n",
    "\n",
    "# Plot histogram of all node values\n",
    "plt.figure()\n",
    "plt.hist(all_node_values[~np.isnan(all_node_values)], bins=20, edgecolor='black')\n",
    "plt.title(f\"Histogram: activation in all ROIs\\nMin: {min_value}, Max: {max_value}\")\n",
    "plt.xlabel(\"activation\")\n",
    "plt.ylabel(\"count (n nodes)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subject-0001_z_concat.csv\n",
      "subject-0003_z_concat.csv\n",
      "subject-0004_z_concat.csv\n",
      "subject-0005_z_concat.csv\n",
      "subject-0006_z_concat.csv\n",
      "subject-0007_z_concat.csv\n",
      "subject-0008_z_concat.csv\n",
      "subject-0009_z_concat.csv\n",
      "subject-0010_z_concat.csv\n",
      "subject-0011_z_concat.csv\n",
      "subject-0012_z_concat.csv\n",
      "subject-0013_z_concat.csv\n",
      "subject-0014_z_concat.csv\n",
      "subject-0015_z_concat.csv\n",
      "subject-0016_z_concat.csv\n",
      "subject-0017_z_concat.csv\n",
      "subject-0018_z_concat.csv\n",
      "subject-0019_z_concat.csv\n",
      "subject-0020_z_concat.csv\n",
      "subject-0021_z_concat.csv\n",
      "subject-0022_z_concat.csv\n",
      "subject-0023_z_concat.csv\n",
      "subject-0024_z_concat.csv\n",
      "subject-0025_z_concat.csv\n",
      "subject-0026_z_concat.csv\n",
      "subject-0027_z_concat.csv\n",
      "subject-0028_z_concat.csv\n",
      "subject-0029_z_concat.csv\n",
      "subject-0030_z_concat.csv\n",
      "subject-0031_z_concat.csv\n",
      "subject-0032_z_concat.csv\n",
      "subject-0033_z_concat.csv\n",
      "subject-0034_z_concat.csv\n",
      "subject-0035_z_concat.csv\n",
      "subject-0036_z_concat.csv\n",
      "subject-0037_z_concat.csv\n",
      "subject-0038_z_concat.csv\n",
      "subject-0039_z_concat.csv\n",
      "subject-0040_z_concat.csv\n",
      "subject-0041_z_concat.csv\n",
      "subject-0042_z_concat.csv\n",
      "subject-0043_z_concat.csv\n",
      "subject-0044_z_concat.csv\n",
      "subject-0045_z_concat.csv\n",
      "subject-0046_z_concat.csv\n",
      "subject-0047_z_concat.csv\n",
      "subject-0048_z_concat.csv\n",
      "subject-0049_z_concat.csv\n",
      "subject-0050_z_concat.csv\n",
      "subject-0051_z_concat.csv\n",
      "subject-0052_z_concat.csv\n",
      "subject-0053_z_concat.csv\n",
      "subject-0054_z_concat.csv\n",
      "subject-0055_z_concat.csv\n",
      "subject-0056_z_concat.csv\n",
      "subject-0057_z_concat.csv\n",
      "subject-0058_z_concat.csv\n",
      "subject-0059_z_concat.csv\n",
      "subject-0060_z_concat.csv\n",
      "subject-0061_z_concat.csv\n",
      "subject-0062_z_concat.csv\n",
      "subject-0063_z_concat.csv\n",
      "subject-0064_z_concat.csv\n",
      "subject-0065_z_concat.csv\n",
      "subject-0066_z_concat.csv\n",
      "subject-0067_z_concat.csv\n",
      "subject-0068_z_concat.csv\n",
      "subject-0069_z_concat.csv\n",
      "subject-0070_z_concat.csv\n",
      "subject-0071_z_concat.csv\n",
      "subject-0072_z_concat.csv\n",
      "subject-0073_z_concat.csv\n",
      "subject-0074_z_concat.csv\n",
      "subject-0075_z_concat.csv\n",
      "subject-0076_z_concat.csv\n",
      "subject-0077_z_concat.csv\n",
      "subject-0078_z_concat.csv\n",
      "subject-0079_z_concat.csv\n",
      "subject-0080_z_concat.csv\n",
      "subject-0081_z_concat.csv\n",
      "subject-0082_z_concat.csv\n",
      "subject-0083_z_concat.csv\n",
      "subject-0084_z_concat.csv\n",
      "subject-0085_z_concat.csv\n",
      "subject-0086_z_concat.csv\n",
      "subject-0087_z_concat.csv\n",
      "subject-0088_z_concat.csv\n",
      "subject-0089_z_concat.csv\n",
      "subject-0090_z_concat.csv\n",
      "subject-0091_z_concat.csv\n",
      "subject-0092_z_concat.csv\n",
      "subject-0093_z_concat.csv\n",
      "subject-0094_z_concat.csv\n",
      "subject-0095_z_concat.csv\n",
      "subject-0096_z_concat.csv\n",
      "subject-0097_z_concat.csv\n",
      "subject-0098_z_concat.csv\n",
      "subject-0099_z_concat.csv\n",
      "subject-0100_z_concat.csv\n",
      "subject-0101_z_concat.csv\n",
      "subject-0102_z_concat.csv\n",
      "subject-0103_z_concat.csv\n",
      "subject-0104_z_concat.csv\n",
      "subject-0105_z_concat.csv\n",
      "subject-0106_z_concat.csv\n",
      "subject-0107_z_concat.csv\n",
      "subject-0108_z_concat.csv\n",
      "subject-0109_z_concat.csv\n",
      "subject-0110_z_concat.csv\n",
      "subject-0111_z_concat.csv\n",
      "subject-0112_z_concat.csv\n",
      "subject-0113_z_concat.csv\n",
      "subject-0114_z_concat.csv\n",
      "subject-0115_z_concat.csv\n",
      "subject-0116_z_concat.csv\n",
      "subject-0117_z_concat.csv\n",
      "subject-0118_z_concat.csv\n",
      "subject-0119_z_concat.csv\n",
      "subject-0120_z_concat.csv\n",
      "subject-0121_z_concat.csv\n",
      "subject-0122_z_concat.csv\n",
      "subject-0123_z_concat.csv\n",
      "subject-0124_z_concat.csv\n",
      "subject-0125_z_concat.csv\n",
      "subject-0126_z_concat.csv\n",
      "subject-0127_z_concat.csv\n",
      "subject-0128_z_concat.csv\n",
      "subject-0129_z_concat.csv\n",
      "subject-0130_z_concat.csv\n",
      "subject-0131_z_concat.csv\n",
      "subject-0132_z_concat.csv\n",
      "subject-0133_z_concat.csv\n",
      "subject-0134_z_concat.csv\n",
      "subject-0135_z_concat.csv\n",
      "subject-0136_z_concat.csv\n",
      "subject-0137_z_concat.csv\n",
      "subject-0138_z_concat.csv\n",
      "subject-0139_z_concat.csv\n",
      "subject-0140_z_concat.csv\n",
      "subject-0141_z_concat.csv\n",
      "subject-0142_z_concat.csv\n",
      "subject-0143_z_concat.csv\n",
      "subject-0144_z_concat.csv\n",
      "subject-0145_z_concat.csv\n",
      "subject-0146_z_concat.csv\n",
      "subject-0147_z_concat.csv\n",
      "subject-0148_z_concat.csv\n",
      "subject-0149_z_concat.csv\n",
      "subject-0150_z_concat.csv\n",
      "subject-0151_z_concat.csv\n",
      "subject-0152_z_concat.csv\n",
      "subject-0153_z_concat.csv\n",
      "subject-0154_z_concat.csv\n",
      "subject-0155_z_concat.csv\n",
      "subject-0156_z_concat.csv\n",
      "subject-0157_z_concat.csv\n",
      "subject-0158_z_concat.csv\n",
      "subject-0159_z_concat.csv\n",
      "subject-0160_z_concat.csv\n",
      "subject-0161_z_concat.csv\n",
      "subject-0162_z_concat.csv\n",
      "subject-0163_z_concat.csv\n",
      "subject-0164_z_concat.csv\n",
      "subject-0165_z_concat.csv\n",
      "subject-0166_z_concat.csv\n",
      "subject-0167_z_concat.csv\n",
      "subject-0168_z_concat.csv\n",
      "subject-0169_z_concat.csv\n",
      "subject-0170_z_concat.csv\n",
      "subject-0171_z_concat.csv\n",
      "subject-0172_z_concat.csv\n",
      "subject-0173_z_concat.csv\n",
      "subject-0174_z_concat.csv\n",
      "subject-0175_z_concat.csv\n",
      "subject-0176_z_concat.csv\n",
      "subject-0177_z_concat.csv\n",
      "subject-0178_z_concat.csv\n",
      "subject-0179_z_concat.csv\n",
      "subject-0180_z_concat.csv\n",
      "subject-0181_z_concat.csv\n",
      "subject-0182_z_concat.csv\n",
      "subject-0183_z_concat.csv\n",
      "subject-0184_z_concat.csv\n",
      "subject-0185_z_concat.csv\n",
      "subject-0186_z_concat.csv\n",
      "subject-0187_z_concat.csv\n",
      "subject-0188_z_concat.csv\n",
      "subject-0189_z_concat.csv\n",
      "subject-0190_z_concat.csv\n",
      "subject-0191_z_concat.csv\n",
      "subject-0192_z_concat.csv\n",
      "subject-0193_z_concat.csv\n",
      "subject-0194_z_concat.csv\n",
      "subject-0195_z_concat.csv\n",
      "subject-0196_z_concat.csv\n",
      "subject-0197_z_concat.csv\n",
      "subject-0198_z_concat.csv\n",
      "subject-0199_z_concat.csv\n",
      "subject-0200_z_concat.csv\n",
      "subject-0201_z_concat.csv\n",
      "subject-0202_z_concat.csv\n",
      "subject-0203_z_concat.csv\n",
      "subject-0204_z_concat.csv\n",
      "subject-0205_z_concat.csv\n",
      "subject-0206_z_concat.csv\n",
      "subject-0207_z_concat.csv\n",
      "subject-0208_z_concat.csv\n",
      "subject-0209_z_concat.csv\n",
      "subject-0210_z_concat.csv\n",
      "subject-0211_z_concat.csv\n",
      "subject-0212_z_concat.csv\n",
      "subject-0213_z_concat.csv\n",
      "subject-0214_z_concat.csv\n",
      "subject-0215_z_concat.csv\n",
      "subject-0216_z_concat.csv\n",
      "subject-0217_z_concat.csv\n",
      "subject-0218_z_concat.csv\n",
      "subject-0219_z_concat.csv\n",
      "subject-0220_z_concat.csv\n",
      "subject-0221_z_concat.csv\n",
      "subject-0222_z_concat.csv\n",
      "subject-0223_z_concat.csv\n",
      "subject-0224_z_concat.csv\n",
      "subject-0225_z_concat.csv\n",
      "subject-0226_z_concat.csv\n",
      "subject-0227_z_concat.csv\n",
      "subject-0228_z_concat.csv\n",
      "subject-0229_z_concat.csv\n",
      "subject-0230_z_concat.csv\n",
      "subject-0231_z_concat.csv\n",
      "subject-0232_z_concat.csv\n",
      "subject-0233_z_concat.csv\n",
      "subject-0234_z_concat.csv\n",
      "subject-0235_z_concat.csv\n",
      "subject-0236_z_concat.csv\n",
      "subject-0237_z_concat.csv\n",
      "subject-0238_z_concat.csv\n",
      "subject-0239_z_concat.csv\n"
     ]
    }
   ],
   "source": [
    "# Check subject_files sorting. Make sure it's the same order as the questionnaire similarity matrices.\n",
    "for s in subject_files:\n",
    "    print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate m-fMRI ISC (dissimilarity and similarity) matrices per ROI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimize the computation by calculating only the lower triangle of the correlation matrix for each node, as correlations are symmetric (correlation between subject i and subject j is the same as between subject j and subject i).The inner loop iterates over j values starting from i to n_subs. This calculates only the unique correlations in the lower triangle of the matrix, and then you assign the symmetric correlation value to both the (i, j) and (j, i) positions in the similarity matrix. \n",
    "\n",
    "Although numpy.ma takes much longer than pairwise_distances (scikit-learn), it does ignore NaN values properly, without requiring imputation or zero-conversion. The np.ma.corrcoef() function from the numpy.ma module handles masked arrays that can automatically ignore masked (missing) values. Here, the np.ma.masked_invalid() function is used to create masked arrays for each subject's time series data, which automatically handles the NaN values by treating them as masked values. The np.ma.corrcoef() function then calculates the correlation while ignoring these masked values. This way, subjects with missing data will still contribute to the Pearson correlation calculations. \n",
    "\n",
    "This process takes about 2.5 hours for 300 nodes, 238 subjects, and 555 timepoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating similarity matrix for node 1/300\n",
      "Exported similarity matrix for node 1/300 to analysis/similarity_matrices_mfMRI/similarity_node_1.csv\n",
      "Calculating similarity matrix for node 2/300\n",
      "Exported similarity matrix for node 2/300 to analysis/similarity_matrices_mfMRI/similarity_node_2.csv\n",
      "Calculating similarity matrix for node 3/300\n",
      "Exported similarity matrix for node 3/300 to analysis/similarity_matrices_mfMRI/similarity_node_3.csv\n",
      "Calculating similarity matrix for node 4/300\n",
      "Exported similarity matrix for node 4/300 to analysis/similarity_matrices_mfMRI/similarity_node_4.csv\n",
      "Calculating similarity matrix for node 5/300\n",
      "Exported similarity matrix for node 5/300 to analysis/similarity_matrices_mfMRI/similarity_node_5.csv\n",
      "Calculating similarity matrix for node 6/300\n",
      "Exported similarity matrix for node 6/300 to analysis/similarity_matrices_mfMRI/similarity_node_6.csv\n",
      "Calculating similarity matrix for node 7/300\n",
      "Exported similarity matrix for node 7/300 to analysis/similarity_matrices_mfMRI/similarity_node_7.csv\n",
      "Calculating similarity matrix for node 8/300\n",
      "Exported similarity matrix for node 8/300 to analysis/similarity_matrices_mfMRI/similarity_node_8.csv\n",
      "Calculating similarity matrix for node 9/300\n",
      "Exported similarity matrix for node 9/300 to analysis/similarity_matrices_mfMRI/similarity_node_9.csv\n",
      "Calculating similarity matrix for node 10/300\n",
      "Exported similarity matrix for node 10/300 to analysis/similarity_matrices_mfMRI/similarity_node_10.csv\n",
      "Calculating similarity matrix for node 11/300\n",
      "Exported similarity matrix for node 11/300 to analysis/similarity_matrices_mfMRI/similarity_node_11.csv\n",
      "Calculating similarity matrix for node 12/300\n",
      "Exported similarity matrix for node 12/300 to analysis/similarity_matrices_mfMRI/similarity_node_12.csv\n",
      "Calculating similarity matrix for node 13/300\n",
      "Exported similarity matrix for node 13/300 to analysis/similarity_matrices_mfMRI/similarity_node_13.csv\n",
      "Calculating similarity matrix for node 14/300\n",
      "Exported similarity matrix for node 14/300 to analysis/similarity_matrices_mfMRI/similarity_node_14.csv\n",
      "Calculating similarity matrix for node 15/300\n",
      "Exported similarity matrix for node 15/300 to analysis/similarity_matrices_mfMRI/similarity_node_15.csv\n",
      "Calculating similarity matrix for node 16/300\n",
      "Exported similarity matrix for node 16/300 to analysis/similarity_matrices_mfMRI/similarity_node_16.csv\n",
      "Calculating similarity matrix for node 17/300\n",
      "Exported similarity matrix for node 17/300 to analysis/similarity_matrices_mfMRI/similarity_node_17.csv\n",
      "Calculating similarity matrix for node 18/300\n",
      "Exported similarity matrix for node 18/300 to analysis/similarity_matrices_mfMRI/similarity_node_18.csv\n",
      "Calculating similarity matrix for node 19/300\n",
      "Exported similarity matrix for node 19/300 to analysis/similarity_matrices_mfMRI/similarity_node_19.csv\n",
      "Calculating similarity matrix for node 20/300\n",
      "Exported similarity matrix for node 20/300 to analysis/similarity_matrices_mfMRI/similarity_node_20.csv\n",
      "Calculating similarity matrix for node 21/300\n",
      "Exported similarity matrix for node 21/300 to analysis/similarity_matrices_mfMRI/similarity_node_21.csv\n",
      "Calculating similarity matrix for node 22/300\n",
      "Exported similarity matrix for node 22/300 to analysis/similarity_matrices_mfMRI/similarity_node_22.csv\n",
      "Calculating similarity matrix for node 23/300\n",
      "Exported similarity matrix for node 23/300 to analysis/similarity_matrices_mfMRI/similarity_node_23.csv\n",
      "Calculating similarity matrix for node 24/300\n",
      "Exported similarity matrix for node 24/300 to analysis/similarity_matrices_mfMRI/similarity_node_24.csv\n",
      "Calculating similarity matrix for node 25/300\n",
      "Exported similarity matrix for node 25/300 to analysis/similarity_matrices_mfMRI/similarity_node_25.csv\n",
      "Calculating similarity matrix for node 26/300\n",
      "Exported similarity matrix for node 26/300 to analysis/similarity_matrices_mfMRI/similarity_node_26.csv\n",
      "Calculating similarity matrix for node 27/300\n",
      "Exported similarity matrix for node 27/300 to analysis/similarity_matrices_mfMRI/similarity_node_27.csv\n",
      "Calculating similarity matrix for node 28/300\n",
      "Exported similarity matrix for node 28/300 to analysis/similarity_matrices_mfMRI/similarity_node_28.csv\n",
      "Calculating similarity matrix for node 29/300\n",
      "Exported similarity matrix for node 29/300 to analysis/similarity_matrices_mfMRI/similarity_node_29.csv\n",
      "Calculating similarity matrix for node 30/300\n",
      "Exported similarity matrix for node 30/300 to analysis/similarity_matrices_mfMRI/similarity_node_30.csv\n",
      "Calculating similarity matrix for node 31/300\n",
      "Exported similarity matrix for node 31/300 to analysis/similarity_matrices_mfMRI/similarity_node_31.csv\n",
      "Calculating similarity matrix for node 32/300\n",
      "Exported similarity matrix for node 32/300 to analysis/similarity_matrices_mfMRI/similarity_node_32.csv\n",
      "Calculating similarity matrix for node 33/300\n",
      "Exported similarity matrix for node 33/300 to analysis/similarity_matrices_mfMRI/similarity_node_33.csv\n",
      "Calculating similarity matrix for node 34/300\n",
      "Exported similarity matrix for node 34/300 to analysis/similarity_matrices_mfMRI/similarity_node_34.csv\n",
      "Calculating similarity matrix for node 35/300\n",
      "Exported similarity matrix for node 35/300 to analysis/similarity_matrices_mfMRI/similarity_node_35.csv\n",
      "Calculating similarity matrix for node 36/300\n",
      "Exported similarity matrix for node 36/300 to analysis/similarity_matrices_mfMRI/similarity_node_36.csv\n",
      "Calculating similarity matrix for node 37/300\n",
      "Exported similarity matrix for node 37/300 to analysis/similarity_matrices_mfMRI/similarity_node_37.csv\n",
      "Calculating similarity matrix for node 38/300\n",
      "Exported similarity matrix for node 38/300 to analysis/similarity_matrices_mfMRI/similarity_node_38.csv\n",
      "Calculating similarity matrix for node 39/300\n",
      "Exported similarity matrix for node 39/300 to analysis/similarity_matrices_mfMRI/similarity_node_39.csv\n",
      "Calculating similarity matrix for node 40/300\n",
      "Exported similarity matrix for node 40/300 to analysis/similarity_matrices_mfMRI/similarity_node_40.csv\n",
      "Calculating similarity matrix for node 41/300\n",
      "Exported similarity matrix for node 41/300 to analysis/similarity_matrices_mfMRI/similarity_node_41.csv\n",
      "Calculating similarity matrix for node 42/300\n",
      "Exported similarity matrix for node 42/300 to analysis/similarity_matrices_mfMRI/similarity_node_42.csv\n",
      "Calculating similarity matrix for node 43/300\n",
      "Exported similarity matrix for node 43/300 to analysis/similarity_matrices_mfMRI/similarity_node_43.csv\n",
      "Calculating similarity matrix for node 44/300\n",
      "Exported similarity matrix for node 44/300 to analysis/similarity_matrices_mfMRI/similarity_node_44.csv\n",
      "Calculating similarity matrix for node 45/300\n",
      "Exported similarity matrix for node 45/300 to analysis/similarity_matrices_mfMRI/similarity_node_45.csv\n",
      "Calculating similarity matrix for node 46/300\n",
      "Exported similarity matrix for node 46/300 to analysis/similarity_matrices_mfMRI/similarity_node_46.csv\n",
      "Calculating similarity matrix for node 47/300\n",
      "Exported similarity matrix for node 47/300 to analysis/similarity_matrices_mfMRI/similarity_node_47.csv\n",
      "Calculating similarity matrix for node 48/300\n",
      "Exported similarity matrix for node 48/300 to analysis/similarity_matrices_mfMRI/similarity_node_48.csv\n",
      "Calculating similarity matrix for node 49/300\n",
      "Exported similarity matrix for node 49/300 to analysis/similarity_matrices_mfMRI/similarity_node_49.csv\n",
      "Calculating similarity matrix for node 50/300\n",
      "Exported similarity matrix for node 50/300 to analysis/similarity_matrices_mfMRI/similarity_node_50.csv\n",
      "Calculating similarity matrix for node 51/300\n",
      "Exported similarity matrix for node 51/300 to analysis/similarity_matrices_mfMRI/similarity_node_51.csv\n",
      "Calculating similarity matrix for node 52/300\n",
      "Exported similarity matrix for node 52/300 to analysis/similarity_matrices_mfMRI/similarity_node_52.csv\n",
      "Calculating similarity matrix for node 53/300\n",
      "Exported similarity matrix for node 53/300 to analysis/similarity_matrices_mfMRI/similarity_node_53.csv\n",
      "Calculating similarity matrix for node 54/300\n",
      "Exported similarity matrix for node 54/300 to analysis/similarity_matrices_mfMRI/similarity_node_54.csv\n",
      "Calculating similarity matrix for node 55/300\n",
      "Exported similarity matrix for node 55/300 to analysis/similarity_matrices_mfMRI/similarity_node_55.csv\n",
      "Calculating similarity matrix for node 56/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 56/300 to analysis/similarity_matrices_mfMRI/similarity_node_56.csv\n",
      "Calculating similarity matrix for node 57/300\n",
      "Exported similarity matrix for node 57/300 to analysis/similarity_matrices_mfMRI/similarity_node_57.csv\n",
      "Calculating similarity matrix for node 58/300\n",
      "Exported similarity matrix for node 58/300 to analysis/similarity_matrices_mfMRI/similarity_node_58.csv\n",
      "Calculating similarity matrix for node 59/300\n",
      "Exported similarity matrix for node 59/300 to analysis/similarity_matrices_mfMRI/similarity_node_59.csv\n",
      "Calculating similarity matrix for node 60/300\n",
      "Exported similarity matrix for node 60/300 to analysis/similarity_matrices_mfMRI/similarity_node_60.csv\n",
      "Calculating similarity matrix for node 61/300\n",
      "Exported similarity matrix for node 61/300 to analysis/similarity_matrices_mfMRI/similarity_node_61.csv\n",
      "Calculating similarity matrix for node 62/300\n",
      "Exported similarity matrix for node 62/300 to analysis/similarity_matrices_mfMRI/similarity_node_62.csv\n",
      "Calculating similarity matrix for node 63/300\n",
      "Exported similarity matrix for node 63/300 to analysis/similarity_matrices_mfMRI/similarity_node_63.csv\n",
      "Calculating similarity matrix for node 64/300\n",
      "Exported similarity matrix for node 64/300 to analysis/similarity_matrices_mfMRI/similarity_node_64.csv\n",
      "Calculating similarity matrix for node 65/300\n",
      "Exported similarity matrix for node 65/300 to analysis/similarity_matrices_mfMRI/similarity_node_65.csv\n",
      "Calculating similarity matrix for node 66/300\n",
      "Exported similarity matrix for node 66/300 to analysis/similarity_matrices_mfMRI/similarity_node_66.csv\n",
      "Calculating similarity matrix for node 67/300\n",
      "Exported similarity matrix for node 67/300 to analysis/similarity_matrices_mfMRI/similarity_node_67.csv\n",
      "Calculating similarity matrix for node 68/300\n",
      "Exported similarity matrix for node 68/300 to analysis/similarity_matrices_mfMRI/similarity_node_68.csv\n",
      "Calculating similarity matrix for node 69/300\n",
      "Exported similarity matrix for node 69/300 to analysis/similarity_matrices_mfMRI/similarity_node_69.csv\n",
      "Calculating similarity matrix for node 70/300\n",
      "Exported similarity matrix for node 70/300 to analysis/similarity_matrices_mfMRI/similarity_node_70.csv\n",
      "Calculating similarity matrix for node 71/300\n",
      "Exported similarity matrix for node 71/300 to analysis/similarity_matrices_mfMRI/similarity_node_71.csv\n",
      "Calculating similarity matrix for node 72/300\n",
      "Exported similarity matrix for node 72/300 to analysis/similarity_matrices_mfMRI/similarity_node_72.csv\n",
      "Calculating similarity matrix for node 73/300\n",
      "Exported similarity matrix for node 73/300 to analysis/similarity_matrices_mfMRI/similarity_node_73.csv\n",
      "Calculating similarity matrix for node 74/300\n",
      "Exported similarity matrix for node 74/300 to analysis/similarity_matrices_mfMRI/similarity_node_74.csv\n",
      "Calculating similarity matrix for node 75/300\n",
      "Exported similarity matrix for node 75/300 to analysis/similarity_matrices_mfMRI/similarity_node_75.csv\n",
      "Calculating similarity matrix for node 76/300\n",
      "Exported similarity matrix for node 76/300 to analysis/similarity_matrices_mfMRI/similarity_node_76.csv\n",
      "Calculating similarity matrix for node 77/300\n",
      "Exported similarity matrix for node 77/300 to analysis/similarity_matrices_mfMRI/similarity_node_77.csv\n",
      "Calculating similarity matrix for node 78/300\n",
      "Exported similarity matrix for node 78/300 to analysis/similarity_matrices_mfMRI/similarity_node_78.csv\n",
      "Calculating similarity matrix for node 79/300\n",
      "Exported similarity matrix for node 79/300 to analysis/similarity_matrices_mfMRI/similarity_node_79.csv\n",
      "Calculating similarity matrix for node 80/300\n",
      "Exported similarity matrix for node 80/300 to analysis/similarity_matrices_mfMRI/similarity_node_80.csv\n",
      "Calculating similarity matrix for node 81/300\n",
      "Exported similarity matrix for node 81/300 to analysis/similarity_matrices_mfMRI/similarity_node_81.csv\n",
      "Calculating similarity matrix for node 82/300\n",
      "Exported similarity matrix for node 82/300 to analysis/similarity_matrices_mfMRI/similarity_node_82.csv\n",
      "Calculating similarity matrix for node 83/300\n",
      "Exported similarity matrix for node 83/300 to analysis/similarity_matrices_mfMRI/similarity_node_83.csv\n",
      "Calculating similarity matrix for node 84/300\n",
      "Exported similarity matrix for node 84/300 to analysis/similarity_matrices_mfMRI/similarity_node_84.csv\n",
      "Calculating similarity matrix for node 85/300\n",
      "Exported similarity matrix for node 85/300 to analysis/similarity_matrices_mfMRI/similarity_node_85.csv\n",
      "Calculating similarity matrix for node 86/300\n",
      "Exported similarity matrix for node 86/300 to analysis/similarity_matrices_mfMRI/similarity_node_86.csv\n",
      "Calculating similarity matrix for node 87/300\n",
      "Exported similarity matrix for node 87/300 to analysis/similarity_matrices_mfMRI/similarity_node_87.csv\n",
      "Calculating similarity matrix for node 88/300\n",
      "Exported similarity matrix for node 88/300 to analysis/similarity_matrices_mfMRI/similarity_node_88.csv\n",
      "Calculating similarity matrix for node 89/300\n",
      "Exported similarity matrix for node 89/300 to analysis/similarity_matrices_mfMRI/similarity_node_89.csv\n",
      "Calculating similarity matrix for node 90/300\n",
      "Exported similarity matrix for node 90/300 to analysis/similarity_matrices_mfMRI/similarity_node_90.csv\n",
      "Calculating similarity matrix for node 91/300\n",
      "Exported similarity matrix for node 91/300 to analysis/similarity_matrices_mfMRI/similarity_node_91.csv\n",
      "Calculating similarity matrix for node 92/300\n",
      "Exported similarity matrix for node 92/300 to analysis/similarity_matrices_mfMRI/similarity_node_92.csv\n",
      "Calculating similarity matrix for node 93/300\n",
      "Exported similarity matrix for node 93/300 to analysis/similarity_matrices_mfMRI/similarity_node_93.csv\n",
      "Calculating similarity matrix for node 94/300\n",
      "Exported similarity matrix for node 94/300 to analysis/similarity_matrices_mfMRI/similarity_node_94.csv\n",
      "Calculating similarity matrix for node 95/300\n",
      "Exported similarity matrix for node 95/300 to analysis/similarity_matrices_mfMRI/similarity_node_95.csv\n",
      "Calculating similarity matrix for node 96/300\n",
      "Exported similarity matrix for node 96/300 to analysis/similarity_matrices_mfMRI/similarity_node_96.csv\n",
      "Calculating similarity matrix for node 97/300\n",
      "Exported similarity matrix for node 97/300 to analysis/similarity_matrices_mfMRI/similarity_node_97.csv\n",
      "Calculating similarity matrix for node 98/300\n",
      "Exported similarity matrix for node 98/300 to analysis/similarity_matrices_mfMRI/similarity_node_98.csv\n",
      "Calculating similarity matrix for node 99/300\n",
      "Exported similarity matrix for node 99/300 to analysis/similarity_matrices_mfMRI/similarity_node_99.csv\n",
      "Calculating similarity matrix for node 100/300\n",
      "Exported similarity matrix for node 100/300 to analysis/similarity_matrices_mfMRI/similarity_node_100.csv\n",
      "Calculating similarity matrix for node 101/300\n",
      "Exported similarity matrix for node 101/300 to analysis/similarity_matrices_mfMRI/similarity_node_101.csv\n",
      "Calculating similarity matrix for node 102/300\n",
      "Exported similarity matrix for node 102/300 to analysis/similarity_matrices_mfMRI/similarity_node_102.csv\n",
      "Calculating similarity matrix for node 103/300\n",
      "Exported similarity matrix for node 103/300 to analysis/similarity_matrices_mfMRI/similarity_node_103.csv\n",
      "Calculating similarity matrix for node 104/300\n",
      "Exported similarity matrix for node 104/300 to analysis/similarity_matrices_mfMRI/similarity_node_104.csv\n",
      "Calculating similarity matrix for node 105/300\n",
      "Exported similarity matrix for node 105/300 to analysis/similarity_matrices_mfMRI/similarity_node_105.csv\n",
      "Calculating similarity matrix for node 106/300\n",
      "Exported similarity matrix for node 106/300 to analysis/similarity_matrices_mfMRI/similarity_node_106.csv\n",
      "Calculating similarity matrix for node 107/300\n",
      "Exported similarity matrix for node 107/300 to analysis/similarity_matrices_mfMRI/similarity_node_107.csv\n",
      "Calculating similarity matrix for node 108/300\n",
      "Exported similarity matrix for node 108/300 to analysis/similarity_matrices_mfMRI/similarity_node_108.csv\n",
      "Calculating similarity matrix for node 109/300\n",
      "Exported similarity matrix for node 109/300 to analysis/similarity_matrices_mfMRI/similarity_node_109.csv\n",
      "Calculating similarity matrix for node 110/300\n",
      "Exported similarity matrix for node 110/300 to analysis/similarity_matrices_mfMRI/similarity_node_110.csv\n",
      "Calculating similarity matrix for node 111/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 111/300 to analysis/similarity_matrices_mfMRI/similarity_node_111.csv\n",
      "Calculating similarity matrix for node 112/300\n",
      "Exported similarity matrix for node 112/300 to analysis/similarity_matrices_mfMRI/similarity_node_112.csv\n",
      "Calculating similarity matrix for node 113/300\n",
      "Exported similarity matrix for node 113/300 to analysis/similarity_matrices_mfMRI/similarity_node_113.csv\n",
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      "Exported similarity matrix for node 114/300 to analysis/similarity_matrices_mfMRI/similarity_node_114.csv\n",
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      "Exported similarity matrix for node 115/300 to analysis/similarity_matrices_mfMRI/similarity_node_115.csv\n",
      "Calculating similarity matrix for node 116/300\n",
      "Exported similarity matrix for node 116/300 to analysis/similarity_matrices_mfMRI/similarity_node_116.csv\n",
      "Calculating similarity matrix for node 117/300\n",
      "Exported similarity matrix for node 117/300 to analysis/similarity_matrices_mfMRI/similarity_node_117.csv\n",
      "Calculating similarity matrix for node 118/300\n",
      "Exported similarity matrix for node 118/300 to analysis/similarity_matrices_mfMRI/similarity_node_118.csv\n",
      "Calculating similarity matrix for node 119/300\n",
      "Exported similarity matrix for node 119/300 to analysis/similarity_matrices_mfMRI/similarity_node_119.csv\n",
      "Calculating similarity matrix for node 120/300\n",
      "Exported similarity matrix for node 120/300 to analysis/similarity_matrices_mfMRI/similarity_node_120.csv\n",
      "Calculating similarity matrix for node 121/300\n",
      "Exported similarity matrix for node 121/300 to analysis/similarity_matrices_mfMRI/similarity_node_121.csv\n",
      "Calculating similarity matrix for node 122/300\n",
      "Exported similarity matrix for node 122/300 to analysis/similarity_matrices_mfMRI/similarity_node_122.csv\n",
      "Calculating similarity matrix for node 123/300\n",
      "Exported similarity matrix for node 123/300 to analysis/similarity_matrices_mfMRI/similarity_node_123.csv\n",
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      "Exported similarity matrix for node 124/300 to analysis/similarity_matrices_mfMRI/similarity_node_124.csv\n",
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      "Exported similarity matrix for node 125/300 to analysis/similarity_matrices_mfMRI/similarity_node_125.csv\n",
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      "Exported similarity matrix for node 126/300 to analysis/similarity_matrices_mfMRI/similarity_node_126.csv\n",
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      "Exported similarity matrix for node 127/300 to analysis/similarity_matrices_mfMRI/similarity_node_127.csv\n",
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      "Exported similarity matrix for node 128/300 to analysis/similarity_matrices_mfMRI/similarity_node_128.csv\n",
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      "Exported similarity matrix for node 129/300 to analysis/similarity_matrices_mfMRI/similarity_node_129.csv\n",
      "Calculating similarity matrix for node 130/300\n",
      "Exported similarity matrix for node 130/300 to analysis/similarity_matrices_mfMRI/similarity_node_130.csv\n",
      "Calculating similarity matrix for node 131/300\n",
      "Exported similarity matrix for node 131/300 to analysis/similarity_matrices_mfMRI/similarity_node_131.csv\n",
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      "Exported similarity matrix for node 132/300 to analysis/similarity_matrices_mfMRI/similarity_node_132.csv\n",
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      "Exported similarity matrix for node 133/300 to analysis/similarity_matrices_mfMRI/similarity_node_133.csv\n",
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      "Exported similarity matrix for node 134/300 to analysis/similarity_matrices_mfMRI/similarity_node_134.csv\n",
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      "Exported similarity matrix for node 135/300 to analysis/similarity_matrices_mfMRI/similarity_node_135.csv\n",
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      "Exported similarity matrix for node 136/300 to analysis/similarity_matrices_mfMRI/similarity_node_136.csv\n",
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      "Exported similarity matrix for node 137/300 to analysis/similarity_matrices_mfMRI/similarity_node_137.csv\n",
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      "Exported similarity matrix for node 138/300 to analysis/similarity_matrices_mfMRI/similarity_node_138.csv\n",
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      "Exported similarity matrix for node 139/300 to analysis/similarity_matrices_mfMRI/similarity_node_139.csv\n",
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      "Exported similarity matrix for node 140/300 to analysis/similarity_matrices_mfMRI/similarity_node_140.csv\n",
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      "Exported similarity matrix for node 142/300 to analysis/similarity_matrices_mfMRI/similarity_node_142.csv\n",
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      "Exported similarity matrix for node 143/300 to analysis/similarity_matrices_mfMRI/similarity_node_143.csv\n",
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      "Exported similarity matrix for node 144/300 to analysis/similarity_matrices_mfMRI/similarity_node_144.csv\n",
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      "Exported similarity matrix for node 145/300 to analysis/similarity_matrices_mfMRI/similarity_node_145.csv\n",
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      "Exported similarity matrix for node 146/300 to analysis/similarity_matrices_mfMRI/similarity_node_146.csv\n",
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      "Exported similarity matrix for node 147/300 to analysis/similarity_matrices_mfMRI/similarity_node_147.csv\n",
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      "Exported similarity matrix for node 151/300 to analysis/similarity_matrices_mfMRI/similarity_node_151.csv\n",
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      "Exported similarity matrix for node 152/300 to analysis/similarity_matrices_mfMRI/similarity_node_152.csv\n",
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      "Exported similarity matrix for node 154/300 to analysis/similarity_matrices_mfMRI/similarity_node_154.csv\n",
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      "Exported similarity matrix for node 161/300 to analysis/similarity_matrices_mfMRI/similarity_node_161.csv\n",
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      "Calculating similarity matrix for node 165/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 165/300 to analysis/similarity_matrices_mfMRI/similarity_node_165.csv\n",
      "Calculating similarity matrix for node 166/300\n",
      "Exported similarity matrix for node 166/300 to analysis/similarity_matrices_mfMRI/similarity_node_166.csv\n",
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      "Calculating similarity matrix for node 219/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 219/300 to analysis/similarity_matrices_mfMRI/similarity_node_219.csv\n",
      "Calculating similarity matrix for node 220/300\n",
      "Exported similarity matrix for node 220/300 to analysis/similarity_matrices_mfMRI/similarity_node_220.csv\n",
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      "Calculating similarity matrix for node 245/300\n",
      "Exported similarity matrix for node 245/300 to analysis/similarity_matrices_mfMRI/similarity_node_245.csv\n",
      "Calculating similarity matrix for node 246/300\n",
      "Exported similarity matrix for node 246/300 to analysis/similarity_matrices_mfMRI/similarity_node_246.csv\n",
      "Calculating similarity matrix for node 247/300\n",
      "Exported similarity matrix for node 247/300 to analysis/similarity_matrices_mfMRI/similarity_node_247.csv\n",
      "Calculating similarity matrix for node 248/300\n",
      "Exported similarity matrix for node 248/300 to analysis/similarity_matrices_mfMRI/similarity_node_248.csv\n",
      "Calculating similarity matrix for node 249/300\n",
      "Exported similarity matrix for node 249/300 to analysis/similarity_matrices_mfMRI/similarity_node_249.csv\n",
      "Calculating similarity matrix for node 250/300\n",
      "Exported similarity matrix for node 250/300 to analysis/similarity_matrices_mfMRI/similarity_node_250.csv\n",
      "Calculating similarity matrix for node 251/300\n",
      "Exported similarity matrix for node 251/300 to analysis/similarity_matrices_mfMRI/similarity_node_251.csv\n",
      "Calculating similarity matrix for node 252/300\n",
      "Exported similarity matrix for node 252/300 to analysis/similarity_matrices_mfMRI/similarity_node_252.csv\n",
      "Calculating similarity matrix for node 253/300\n",
      "Exported similarity matrix for node 253/300 to analysis/similarity_matrices_mfMRI/similarity_node_253.csv\n",
      "Calculating similarity matrix for node 254/300\n",
      "Exported similarity matrix for node 254/300 to analysis/similarity_matrices_mfMRI/similarity_node_254.csv\n",
      "Calculating similarity matrix for node 255/300\n",
      "Exported similarity matrix for node 255/300 to analysis/similarity_matrices_mfMRI/similarity_node_255.csv\n",
      "Calculating similarity matrix for node 256/300\n",
      "Exported similarity matrix for node 256/300 to analysis/similarity_matrices_mfMRI/similarity_node_256.csv\n",
      "Calculating similarity matrix for node 257/300\n",
      "Exported similarity matrix for node 257/300 to analysis/similarity_matrices_mfMRI/similarity_node_257.csv\n",
      "Calculating similarity matrix for node 258/300\n",
      "Exported similarity matrix for node 258/300 to analysis/similarity_matrices_mfMRI/similarity_node_258.csv\n",
      "Calculating similarity matrix for node 259/300\n",
      "Exported similarity matrix for node 259/300 to analysis/similarity_matrices_mfMRI/similarity_node_259.csv\n",
      "Calculating similarity matrix for node 260/300\n",
      "Exported similarity matrix for node 260/300 to analysis/similarity_matrices_mfMRI/similarity_node_260.csv\n",
      "Calculating similarity matrix for node 261/300\n",
      "Exported similarity matrix for node 261/300 to analysis/similarity_matrices_mfMRI/similarity_node_261.csv\n",
      "Calculating similarity matrix for node 262/300\n",
      "Exported similarity matrix for node 262/300 to analysis/similarity_matrices_mfMRI/similarity_node_262.csv\n",
      "Calculating similarity matrix for node 263/300\n",
      "Exported similarity matrix for node 263/300 to analysis/similarity_matrices_mfMRI/similarity_node_263.csv\n",
      "Calculating similarity matrix for node 264/300\n",
      "Exported similarity matrix for node 264/300 to analysis/similarity_matrices_mfMRI/similarity_node_264.csv\n",
      "Calculating similarity matrix for node 265/300\n",
      "Exported similarity matrix for node 265/300 to analysis/similarity_matrices_mfMRI/similarity_node_265.csv\n",
      "Calculating similarity matrix for node 266/300\n",
      "Exported similarity matrix for node 266/300 to analysis/similarity_matrices_mfMRI/similarity_node_266.csv\n",
      "Calculating similarity matrix for node 267/300\n",
      "Exported similarity matrix for node 267/300 to analysis/similarity_matrices_mfMRI/similarity_node_267.csv\n",
      "Calculating similarity matrix for node 268/300\n",
      "Exported similarity matrix for node 268/300 to analysis/similarity_matrices_mfMRI/similarity_node_268.csv\n",
      "Calculating similarity matrix for node 269/300\n",
      "Exported similarity matrix for node 269/300 to analysis/similarity_matrices_mfMRI/similarity_node_269.csv\n",
      "Calculating similarity matrix for node 270/300\n",
      "Exported similarity matrix for node 270/300 to analysis/similarity_matrices_mfMRI/similarity_node_270.csv\n",
      "Calculating similarity matrix for node 271/300\n",
      "Exported similarity matrix for node 271/300 to analysis/similarity_matrices_mfMRI/similarity_node_271.csv\n",
      "Calculating similarity matrix for node 272/300\n",
      "Exported similarity matrix for node 272/300 to analysis/similarity_matrices_mfMRI/similarity_node_272.csv\n",
      "Calculating similarity matrix for node 273/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 273/300 to analysis/similarity_matrices_mfMRI/similarity_node_273.csv\n",
      "Calculating similarity matrix for node 274/300\n",
      "Exported similarity matrix for node 274/300 to analysis/similarity_matrices_mfMRI/similarity_node_274.csv\n",
      "Calculating similarity matrix for node 275/300\n",
      "Exported similarity matrix for node 275/300 to analysis/similarity_matrices_mfMRI/similarity_node_275.csv\n",
      "Calculating similarity matrix for node 276/300\n",
      "Exported similarity matrix for node 276/300 to analysis/similarity_matrices_mfMRI/similarity_node_276.csv\n",
      "Calculating similarity matrix for node 277/300\n",
      "Exported similarity matrix for node 277/300 to analysis/similarity_matrices_mfMRI/similarity_node_277.csv\n",
      "Calculating similarity matrix for node 278/300\n",
      "Exported similarity matrix for node 278/300 to analysis/similarity_matrices_mfMRI/similarity_node_278.csv\n",
      "Calculating similarity matrix for node 279/300\n",
      "Exported similarity matrix for node 279/300 to analysis/similarity_matrices_mfMRI/similarity_node_279.csv\n",
      "Calculating similarity matrix for node 280/300\n",
      "Exported similarity matrix for node 280/300 to analysis/similarity_matrices_mfMRI/similarity_node_280.csv\n",
      "Calculating similarity matrix for node 281/300\n",
      "Exported similarity matrix for node 281/300 to analysis/similarity_matrices_mfMRI/similarity_node_281.csv\n",
      "Calculating similarity matrix for node 282/300\n",
      "Exported similarity matrix for node 282/300 to analysis/similarity_matrices_mfMRI/similarity_node_282.csv\n",
      "Calculating similarity matrix for node 283/300\n",
      "Exported similarity matrix for node 283/300 to analysis/similarity_matrices_mfMRI/similarity_node_283.csv\n",
      "Calculating similarity matrix for node 284/300\n",
      "Exported similarity matrix for node 284/300 to analysis/similarity_matrices_mfMRI/similarity_node_284.csv\n",
      "Calculating similarity matrix for node 285/300\n",
      "Exported similarity matrix for node 285/300 to analysis/similarity_matrices_mfMRI/similarity_node_285.csv\n",
      "Calculating similarity matrix for node 286/300\n",
      "Exported similarity matrix for node 286/300 to analysis/similarity_matrices_mfMRI/similarity_node_286.csv\n",
      "Calculating similarity matrix for node 287/300\n",
      "Exported similarity matrix for node 287/300 to analysis/similarity_matrices_mfMRI/similarity_node_287.csv\n",
      "Calculating similarity matrix for node 288/300\n",
      "Exported similarity matrix for node 288/300 to analysis/similarity_matrices_mfMRI/similarity_node_288.csv\n",
      "Calculating similarity matrix for node 289/300\n",
      "Exported similarity matrix for node 289/300 to analysis/similarity_matrices_mfMRI/similarity_node_289.csv\n",
      "Calculating similarity matrix for node 290/300\n",
      "Exported similarity matrix for node 290/300 to analysis/similarity_matrices_mfMRI/similarity_node_290.csv\n",
      "Calculating similarity matrix for node 291/300\n",
      "Exported similarity matrix for node 291/300 to analysis/similarity_matrices_mfMRI/similarity_node_291.csv\n",
      "Calculating similarity matrix for node 292/300\n",
      "Exported similarity matrix for node 292/300 to analysis/similarity_matrices_mfMRI/similarity_node_292.csv\n",
      "Calculating similarity matrix for node 293/300\n",
      "Exported similarity matrix for node 293/300 to analysis/similarity_matrices_mfMRI/similarity_node_293.csv\n",
      "Calculating similarity matrix for node 294/300\n",
      "Exported similarity matrix for node 294/300 to analysis/similarity_matrices_mfMRI/similarity_node_294.csv\n",
      "Calculating similarity matrix for node 295/300\n",
      "Exported similarity matrix for node 295/300 to analysis/similarity_matrices_mfMRI/similarity_node_295.csv\n",
      "Calculating similarity matrix for node 296/300\n",
      "Exported similarity matrix for node 296/300 to analysis/similarity_matrices_mfMRI/similarity_node_296.csv\n",
      "Calculating similarity matrix for node 297/300\n",
      "Exported similarity matrix for node 297/300 to analysis/similarity_matrices_mfMRI/similarity_node_297.csv\n",
      "Calculating similarity matrix for node 298/300\n",
      "Exported similarity matrix for node 298/300 to analysis/similarity_matrices_mfMRI/similarity_node_298.csv\n",
      "Calculating similarity matrix for node 299/300\n",
      "Exported similarity matrix for node 299/300 to analysis/similarity_matrices_mfMRI/similarity_node_299.csv\n",
      "Calculating similarity matrix for node 300/300\n",
      "Exported similarity matrix for node 300/300 to analysis/similarity_matrices_mfMRI/similarity_node_300.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os  \n",
    "\n",
    "n_subs, n_ts, n_nodes = stacked_data.shape\n",
    "\n",
    "output_directory = \"analysis/similarity_matrices_mfmri\"\n",
    "\n",
    "# Create the directory if it doesn't exist\n",
    "if not os.path.exists(output_directory):\n",
    "    os.makedirs(output_directory)\n",
    "\n",
    "for node in range(n_nodes):\n",
    "    print(f\"Calculating similarity matrix for node {node + 1}/{n_nodes}\")\n",
    "    node_data = stacked_data[:, :, node]\n",
    "\n",
    "    similarity_matrix = np.empty((n_subs, n_subs))  # Define similarity_matrix\n",
    "\n",
    "    for i in range(n_subs):\n",
    "        for j in range(i, n_subs):  # Only calculate unique edges (i.e., lower triangle)\n",
    "\n",
    "            masked_data_i = np.ma.masked_invalid(node_data[i])\n",
    "            masked_data_j = np.ma.masked_invalid(node_data[j])\n",
    "\n",
    "            # Calculate Pearson correlation coefficient between two masked arrays\n",
    "            correlation = np.ma.corrcoef(masked_data_i, masked_data_j)[0, 1]\n",
    "\n",
    "            # Convert correlation to dissimilarity (range [0, 2])\n",
    "            #dissimilarity = 1 - correlation     # Toggle this\n",
    "\n",
    "            similarity_matrix[i, j] = correlation\n",
    "            similarity_matrix[j, i] = correlation  # Fill upper triangle symmetrically\n",
    "\n",
    "    # Set diagonal elements\n",
    "    np.fill_diagonal(similarity_matrix, 1)\n",
    "\n",
    "    # Save similarity matrix to a CSV file in the specified directory\n",
    "    csv_filename = os.path.join(output_directory, f\"similarity_node_{node + 1}.csv\")\n",
    "    np.savetxt(csv_filename, similarity_matrix, delimiter=',')\n",
    "\n",
    "    print(f\"Exported similarity matrix for node {node + 1}/{n_nodes} to {csv_filename}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate m-fMRI ISC (dissimilarity and similarity) matrices per network\n",
    "Average the already-calculated similarity matrices across ROIs within the same network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Read network_info\n",
    "network_info = pd.read_csv('analysis/atlas_schaefer/label_network.csv')\n",
    "\n",
    "# Define conditions and networks\n",
    "conditions = ['neutral', 'threat']\n",
    "networks = ['control', 'default', 'salience_ventral_attention']\n",
    "\n",
    "def calculate_mean_matrix(matrix_list):\n",
    "    # Concatenate matrices along a new axis (3rd dimension) and calculate mean along that axis\n",
    "    concatenated_matrix = np.stack(matrix_list, axis=-1)\n",
    "    mean_matrix = np.nanmean(concatenated_matrix, axis=-1)\n",
    "    return pd.DataFrame(mean_matrix)\n",
    "\n",
    "for condition in conditions:\n",
    "    data_dir = f'analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_{condition}'\n",
    "    out_dir = f'analysis/inter-subject_matrices_fmri/network/dissimilarity_matrices_mfmri_network_{condition}'\n",
    "\n",
    "    for network in networks:\n",
    "        # Find nodes belonging to the current network\n",
    "        network_nodes = network_info.loc[network_info['network'] == network, 'node_1'].values\n",
    "\n",
    "        # Initialize a list to store matrices for each node\n",
    "        node_matrices = []\n",
    "\n",
    "        for node in network_nodes:\n",
    "            # Load similarity matrix for each node and append to the list\n",
    "            similarity_matrix = pd.read_csv(f'{data_dir}/similarity_node_{node}.csv', header=None, index_col=None)\n",
    "            node_matrices.append(similarity_matrix)\n",
    "\n",
    "        # Calculate the mean matrix for the current network\n",
    "        mean_matrix = calculate_mean_matrix(node_matrices)\n",
    "\n",
    "        # Save the mean matrix to a new CSV file\n",
    "        output_filename = f'{out_dir}/dissimilarity_{network}.csv'\n",
    "        mean_matrix.to_csv(output_filename, header=False, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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