inter-subject_representational_similarity_ROIs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IS-RSA for ROIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/project/3013104.01'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.chdir('/project/3013104.01')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenate neutral- and threat-framed volumes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import shutil\n",
    "\n",
    "conditions = ['neutral', 'threat']\n",
    "\n",
    "for condition in conditions:\n",
    "\n",
    "    output_dir = f'analysis/aural_framing_by_movie_topic/z_roi_node/data/concatenated/{condition}'\n",
    "    working_dir = 'analysis/aural_framing_by_movie_topic/z_roi_node/data'\n",
    "\n",
    "    # Delete the output directory if it exists\n",
    "    if os.path.exists(output_dir):\n",
    "        shutil.rmtree(output_dir)\n",
    "\n",
    "    # Create the output directory\n",
    "    os.makedirs(output_dir)\n",
    "\n",
    "    # Get a list of all CSV files in the working directory\n",
    "    csv_files = [filename for filename in os.listdir(working_dir) if filename.endswith('.csv')]\n",
    "\n",
    "    # Define the order of labels and their corresponding index\n",
    "    order = {\n",
    "        f'{condition}_climate_change': 0,\n",
    "        f'{condition}_healthcare': 1,\n",
    "        f'{condition}_immigration': 2,\n",
    "    }\n",
    "\n",
    "    # Sorting function considering order and 'NA' values\n",
    "    def custom_sort_key(filename):\n",
    "        label = filename.rsplit('_', 1)[-1].split('.')[0]\n",
    "        return order.get(label, len(order)), label\n",
    "\n",
    "    # Process each subject\n",
    "    for subject_id in set([filename.split('_')[0] for filename in csv_files]):\n",
    "        subject_files = [filename for filename in csv_files if filename.startswith(subject_id + '_')]\n",
    "\n",
    "        # Filter files to include only those with '{condition}' labels\n",
    "        subject_files = [filename for filename in subject_files if condition in filename]\n",
    "\n",
    "        if subject_files:  # Check if there are files to concatenate\n",
    "            # Sort the filtered subject files using the custom sorting function\n",
    "            subject_files.sort(key=custom_sort_key)\n",
    "\n",
    "            # Initialize an empty list to store the data\n",
    "            data_frames = []\n",
    "\n",
    "            for i, filename in enumerate(subject_files):\n",
    "                data = pd.read_csv(os.path.join(working_dir, filename), header=None, index_col=None)\n",
    "\n",
    "                # Remove headers from all dataframes except the first one\n",
    "                if i != 0:\n",
    "                    data = data.iloc[1:]\n",
    "\n",
    "                # Append the data to the list\n",
    "                data_frames.append(data)\n",
    "\n",
    "            # Concatenate \n",
    "            concatenated_data = pd.concat(data_frames, ignore_index=True)\n",
    "\n",
    "            # Fill blanks with 'NA'\n",
    "            concatenated_data = concatenated_data.fillna('NA')\n",
    "\n",
    "            # Save the concatenated data to the output directory\n",
    "            output_filename = os.path.join(output_dir, f'{subject_id}_z_concat.csv')\n",
    "            concatenated_data.to_csv(output_filename, index=False, header=False)\n",
    "\n",
    "    print(\"Concatenation and saving complete.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove 'volume' column if needed"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'volume' column deleted from subject-0023_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0189_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0098_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0200_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0008_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0157_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0143_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0132_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0071_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0064_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0007_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0208_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0169_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0202_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0065_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0031_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0140_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0185_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0033_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0038_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0048_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0183_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0127_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0114_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0237_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0003_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0025_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0106_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0051_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0115_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0021_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0228_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0222_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0040_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0214_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0177_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0206_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0097_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0080_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0150_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0015_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0238_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0125_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0182_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0078_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0153_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0176_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0111_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0221_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0105_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0087_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0037_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0059_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0026_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0058_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0161_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0082_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0066_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0076_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'volume' column deleted from subject-0011_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0220_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0060_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0137_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0091_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0192_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0239_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0142_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0068_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0136_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0181_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0119_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0100_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0149_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0005_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0067_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0128_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0086_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0186_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0020_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0151_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0141_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0212_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0030_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0045_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0035_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0134_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0211_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0116_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0230_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0014_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0103_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0130_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0133_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0001_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0236_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0135_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0017_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0018_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0006_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0188_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0209_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0004_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0235_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0032_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0027_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0057_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0028_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0036_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0223_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0102_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0096_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0093_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0174_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0079_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0039_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0104_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0075_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0170_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'volume' column deleted from subject-0204_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
      "'volume' column deleted from subject-0099_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
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      "'volume' column deleted from subject-0084_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral\n",
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     "text": [
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      "'volume' column deleted from subject-0011_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
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      "'volume' column deleted from subject-0204_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
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     "output_type": "stream",
     "text": [
      "'volume' column deleted from subject-0084_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0052_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0145_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0217_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0117_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0073_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0016_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0224_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0131_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0156_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0046_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0199_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0162_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0089_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0072_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0226_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0122_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0069_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0010_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0196_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0081_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0227_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0092_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0233_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0198_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0158_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0213_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0201_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0166_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0056_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0090_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0168_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0074_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0053_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0163_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0175_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0148_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0229_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0120_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0147_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0085_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0129_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0178_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0034_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0180_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0083_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0232_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0109_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0070_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0179_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0013_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0139_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0095_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0194_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0190_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0187_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0205_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0172_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n",
      "'volume' column deleted from subject-0123_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'volume' column deleted from subject-0009_z_concat.csv in analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# Directories containing CSV files\n",
    "directory_paths = [\n",
    "    'analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/neutral',\n",
    "    'analysis/aural_framing_by_movie_topic/roi_node/z-scored/data/concatenated/threat'\n",
    "]\n",
    "\n",
    "for directory_path in directory_paths:\n",
    "    # Get a list of all CSV files in the directory\n",
    "    subject_files = [file for file in os.listdir(directory_path) if file.endswith('.csv')]\n",
    "    subject_files.sort()\n",
    "    # Iterate through each CSV file in the current directory\n",
    "    for file in subject_files:\n",
    "        filepath = os.path.join(directory_path, file)\n",
    "        \n",
    "        # Read the CSV file into a DataFrame\n",
    "        df = pd.read_csv(filepath)\n",
    "        \n",
    "        # Drop the 'volume' column if it exists\n",
    "        if 'volume' in df.columns:\n",
    "            df.drop(columns=['volume'], inplace=True)\n",
    "            \n",
    "            # Save the modified DataFrame back to the same CSV file\n",
    "            df.to_csv(filepath, index=False, na_rep='NA')  # Set na_rep to handle missing values\n",
    "            \n",
    "            print(f\"'volume' column deleted from {file} in {directory_path}\")\n",
    "        else:\n",
    "            print(f\"'volume' column not found in {file} in {directory_path}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ajwG/yfU/OAcCgUDgxvhFf187hZqo/jPgz15h25egzxj7qInrDzjn3uXXXXptv14ue89x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\n3u0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ecXu5ptnENrVmgnda/KPk8GkCZqtodGxjpTzjs+hCOfryNUoCK5N4NlVA3y3zb12P\n3BwUKrk8mm6ZX7s6MO5qhsRmas/6OL7SvVaDIqR+UdpzcbfmyWlk+3TVyGGqOUzJCVYJIQh14lhN\nTnN8tsvtUARsmje6crhxsXA4r5DKZ1dZ20xUKrGuWsvUG+YBGlTAaI+eU6duDUpApXZ6D6uTWFK2\nv23pNgO2i/0xHQxNbbFTchLd1mi7uGpiX6fvBCcEJ5nbVd1BSIldEHlQ89j0jUcfFC6lokmv8QbM\ns+T+ERhvGzikaz4KqDT43b6Cg2K/Tgl7XlATecyJi/Ps8LWR1Xdq1LDwbfwROAcmVdAFLXiTxhYx\nFWs8Td9pffyunRk8mEAwi4vMttBcgT4kB89B5++5aTAoB1Nyrl3uV9xer5CUZKZUdFLNyCWzI88h\n003YACGNKUxkVk2bkPtmbi8uWvoOyfkl1xVu7fI9Cp78d0FRUVFRUVFRUVHRy6KXeoFHS4BB5wc0\njOf8aE+rE8D4xuRyLYig03mBRybhdH3ApIWX83pHcAK1x1wbNt3AXYImm32EjL56HMu7frBa8ZVX\nLnlTzgFYdQN3dx2qtTTtxKod2aaHqicnt9yNTV5sGZ0mBOGoiQ/Y+7FC6cA4pHKwyuU2u1FDbdlf\nRbpxd3pgmMwHFouqdB6tPaa2+bXKOAZr+MrpJRBjr/e38UF9c75nv29w6/jeSnvMg56TTXx6q7Xj\nZt9i02JKZRx9AHdbUZ0NOANSzWVH4O8qlJ/B0ClSuZ2BseQHTG91rBxSC8xZDQqfHuZ8Hc8hgDpo\n1JN9LrUZbxsGJ/nhWteOMFb5wTeYgO1iTLV9MsRFmHsg2mBkAUzr2K75wdDVAQ6a8YGjutapXCq+\ndrPrEAm55E63Li7upHZV3YhNsOr9TUewgkkP127UqEnlsh9vAtVBwZsd7suHWIa1SYtqO0OoAvZo\ngfYCOeo8GI+qHAL4QYNTy8LSnSGsQe0SaLc11IolGt0LpAfX3hp0a/Pi5SvdjrfHe2VJnSPsFW/8\n3S/yK3/4OzQyoXepbNFYfBA6nRYOJS5+HqbYsafNgXetMJ4Emivh4dGWKa24DWeGprHsblLJ0qTZ\n0dA8igBg+/0jHhzHRZqrXcfp+sBujPOv0o7dUMfyLWDc1Ryf7ehv403a6pq2nkm76Z7dmwfeC1Uq\nDbNOZfD4ZnOgq6YMf/ZOIdcV1eM4D8ZRsVn3HDdxUfR3rhs26z5/ztQ4vv4wQtz/v91rPNjseOfp\naWzE3qDOfS5Xq59rxldjG88f3dGPhv0+1gRKL5he49bxYLsGSQtDYgXXkBfrQusJo1pA3oMQzFLW\npQbBeqCJZZXsNDxPA3/lYe2wab6pH7Z5LkAaa+nP40mcxyH9DRF0nEMzaD0IhHtGH3PgA+Vc4WCW\n0igbo8xz+SQK1whmH48Vr+lPUpS7j9fjruKq00EF5LKmuYwfPh3FvrHdAmqfVdeWaVujz+J3q9vP\nJbZpIWljCen44CK0XKUIeZnAr+/Nmb3GbD/lBZ7i4CkqKioqKioqKvqY+jF0gt8tEfmyiPzR9OdO\nRDafbLNenMz1R1/zatrpww96wdqc7z/ye6v04PRJS32MnXP75KO3sbr+dB7o3Jc/OkjYD59OG3/l\nD3/nI7+3ufroLJGrXfeR3/uzaLP56PdgXnT8pCX2o/fjnET3UWTeqz/ye38Wqc/+1+WnqyAf/7+i\noqKioqKioqKfa33oaoaI/DvArwPnwNeA14H/Gvgjn2zTPlzX2w61Vwwp6hYvrKoRPwM794q9TWUr\nWyEA745xh725VLRm4ja5BK6nDr3TVLfJieI0fYLWHl/HXfV1iusNJvCw3fO2jp91vtpze7OiqZey\nlicnMXb6yfqWy/1jNglie7VdobXPpSvDWGHnkqTaU9U2g57D3jBUS1zvUTvw7PkxdZfgs1bj00Pg\nNGnq2nJ7kbKKBdrNQJ3qZbQKBCecrFJJyaFGpajzEISmsbmNrx3d8Bv71xjT9XftGHfhk7PIrx1N\nakNQbXSHpN1n3afSp/SAqSxMd2mXPJWAzQ+uoQ6xtOMsvjdIfDCdHT72usmLStOhonpaYU/TbjzQ\nXi0lTahUppUcPravcgkagO0ElYwnPoGEZ0eP+Fgi5LqAGuP9nSOt1aRpVyP7dCLvBBkVrOZ+9TiV\nrvW6AgmLY2Bv8J3Dpeun8bhR8MmdEcwHS93E3ntIC9GhJYvpAWUCyljcZQ2djyWFRADvKIubYVxp\n1ED+bNaO+oexDTd3He55S/UkumVGr1ETbIdUqmIVZgfbKR7/5uGc5jpdj1fcDi3Phri+q3eK6hYu\ndut0fwPtc0GntROjPH2af0oFHm3ueDctjvTbmuCFJw9jGdZ7ckRXjXSnI28+fUB3OmYA7rodGUaD\nz6DpEAHiycE0DBUPN+l6rGHcV3kcGO24uVkzpZ+n2wYJcLjn+BlSeVCYFPWdgidL5Po4GY5TOZtq\nHZWJ/01WU1cDp3W8WFEBIz6X7qECt9crUqVbXMxIZXLDZBCJriyAUCUXToJxB61Bh1yOZ3pwswvs\nLGD2QjBpoc8n0HEaJ9UWpjO1lHftFbPhKmiFd4Jr05hrA2qSHH1OAAaFPbNIrxCn8vhzbRzXc4Wr\ntjEyfZ5DQS9tcA0wqnyseGE89RHiDLguxq831/l2Ml6keslUtqb2yblHhwqyQM9HGM+Sc22Or0/n\n3V93H1gUUweFXzum0+TatCp/joR4vfP3fXUL27Va4NWTorniU5UUB09RUVFRUVFRUdHH1E+zzfsn\ngH8BuAUIIXwHePRJNurnUVL7Dz+o6BOVGj/8mBctd/npuCtelN58+uBFN+FDlTlQn1NJ/+k4lD6O\n8uJOUVFRUVFRUVFRUVHWT1OPNIQQRkn0XxExfEZoAUoFxMEqOWtQgW8cP2VIW9LVNjpxIO5yG+Oy\nc0bvI9vjOLlWjvSI2GVXuVKObh1f02OLsvDaKroNfovXedzecHkey0m+sLrlDXnAF4/jm9/bbrIb\nZm1GjPY5Zru/bVC1I6xim/tdHR0ho4K1pd8tD/AyKNygkcSCOF/teTqe4qoED76skZPoirF3Neok\nZDCoGhT1+Y6zOpZkae0RL/wzD94C4H+9OOGVB5EpdBgrHm3uMvvn6+un/KZ+Qp3auGkHbjqfeT8j\nLBBbTXSXzDHcVVwoUdul30nMk+BT7HJ6NjO3Ku6+z+UkEiPEZ3Cva8l8G5xEhkdyHZmnFXa1sElY\nWUKtaFLcvAhMiUlD55BtjVslB9KJRT03OTLeXGuCilHxzHHq6XqUjrH1Jn3uqhu5nVR2iJwf7bm4\niw6WaYpujAy48WDuDPZxGp9DmkPJeYIV6tN4/8adibHwc7a0F7Tx2JmzUzmU9hmuW60npgSvjhwT\naJ6njz1SH+Ag3Z+uwSvUuDBpjqsePcBuSONOAnqA0zqyZoxy0Q0EWK9ojM0x6cpCtYdVu5TFiYX2\nKp7vvDlwlObmxd2a19c3XO4jxEUS+Pb1ozin3jWPWJmJb3zhPb751hNcUAw3CWLuhfGqzeYmvVfI\npsfdJfvIvcXR611HGDVTcsHd9Ab2Jv+MCqhbQziLH3ZzvWKV4Om6s4it+MJ5nMfuLL7nD5y8DcDV\n445KuewYPKl7vrKKnf43pq/TGJvBzyOw2gzsEvi6vtLZorFuRrZ9jZrnRRViWdRs3lLE+59YTrYj\nvzc4iZDzdEurbRxv7tXE+PIN5rzHJoZN9WwBJQ9fnpBbA/f4Pr6G6UE6oI3MoFB5zLOK6aHFzGWL\nlQdRTMdpPLoIgfYp+jwoydDoYITQOXRaKJIptjHzbUJk+egxvVcvvCxqT+gV1TY5eNYBX3t0gun3\nDwNiFyeRu7/eGQRZLc6s2RFj0veJfXXEzU1QAZHoOIToDDK3mumVeP/MVj79Bd/PxN+qRUVFRUVF\nRUVFL7N+mgWevyEi/yHQici/DPx7wP/yyTbr51Br++HHFH2yKqaAF65vvvXkRTfh515zIlfRp6zC\n0CkqKioqKioqKvqY+mkWeP4U8G8Dvwn8u8BfAf7bT7JRP618EJSV7MIRFVibgTXxZ69hZeKObHMN\nurI8rLZATH152O5y4tbj5iYyYNIOb1dPObLc28hCOa8j50M1jq92z/gb7/wiEGOaQxC+/d6rQEzV\nemcbY7XfvDnl9mZxCcjW4DphmF0cW4PqFfQ1/sghK0vYJyaPjXHQkrgex9UAEpO2AFzncySw1B5n\nVS66CyZGPP/S6j0Avtk+5kqO+bXN7wDwV+tv8C8+iSDdv/n+1/hDD37AxRSdKH+ge5P/Sf2BnP60\nrkaQwOun0dnw2++vMzdIA7QO9Tztkh/F6OI6sS2CBmYwcGfxenEUiI1JOlPa6Z8eTjE1KO2iy41B\nHadkoYNBT0KYE7nqCplCdg08fnTDXd/w1fMLAN7fbXh6lbgeKuDrkPkb9WbAP62oH0R+ituu7/FC\nYgy336RGjhraZSyIBMR41qsh/6xSYlOoA0GW2OlQhei2mB1KrcNbQad779aWMblwaD1Mgk7sEbeK\nSVMzEwSB9WrgNjlRvJecTma20D+EOUzOm8gYConDIsOSaKS0Z+p8jLYHlETu0GYVrQyHcROdbyn9\n6oGecjR2rR1aeb7QxXHwGwGqbYhR6+l18eSY9E2V7BFAU1m+ffkoj6nDTYtqHD/cnqTOEt68PuNo\n07O9a7narSCNC3USItMlsZlwcV6oOa1MB6bk1FPKx9jx2fEyasxW4Y5SN04p0SmVWYWd4fXX3wfg\neb3mTnd87TiOoZuxY2tr/vDRbwORR3Q5rKh17OhfO/8BX2mexs+ZFKPTdE1iUwVhe7FCpZQ01+rM\n57m4ifMsJB5RsxmYqnuLKiHyjeYypFAFzDb9+cqgRlkcczWRwzPbVSTE9L3kZAlKL+uWEtB7hT1N\nMe+tx7U6u16q1kbe1kPYfesMKo9Nt0fWFnvQ91w6gerCoGfu05nP3J9wOiFBMvtHfErTS/fTm4Bv\nArad0+WWS0diYtcHfmdC5PoAbh1j4kNyDtnVch7d2WjbmXlFsyMqzaFgZZnHvSY0HknzSXw87+yq\n8mZJ6Cv67ElE/s8Qwh/5sN8VFRUVFRUVFf286UMXeEIIHvhv0n9Fn5D8kXvRTfi5lxQM0gvX9q79\n8IOKPlHtvnX2opvw86lSovWhEpEWWAEPReSMxXd5DHzhhTWsqKioqKioqOgzop8mRetfA/5j4Mvp\neAFCCOH4E27bh2rcVdSenIQVnGDDBwGoNlkXvI5uk0sbd8/nXd/tFB8o71wLauE2jNYwpF3+eW/9\n4OKfwqTYupbb78QHoes6bnPLdfys2033Ad6E3it2sysngNpr5oIss1dUt8JgUrKSr1CJ1VLfCH2j\naS7iz//w6atUF4YxMTTUIPlY3yh8ramuZn5NSuxJMTe7sUb1iirZZ6ZJc5KsGYepYgqaqzH2Tasm\ndvsGpeOKx/P9GvZ6Ya8cdHYUiIdgFc1lvJ7pODp05vQguyZzPNzRFN0dqc3NRXS7qORUUFcmMnXG\neH1mq5h0cpo4ob4WDl1K5FKB+k7hYqATWnm8F46q6KzZNxUXM8un8dmhM78XgTa5LaZrwTWLA2Y6\nCsi8639XwXHPdB3bcXPVIINim16/frrJzpJqpzKTBEDvNGoU/Jxm5QS9U7iQrAH3WbY+Mk3mfnWt\nwimDvo3X4FeW1liudvF+6tWET3wiPUFzsSQe1dea+jYyRQB0L1Qx1I3dwWDuFD45M3pXIQ62fZpD\ntSdoRZ84MxfDUXbk9NYQJpMZV8GAqyUncHX1hFlMO1yNK46rOMa2dy27i2OSMQjlICjDOxeR117f\nCrdPo83G3Gh2FzUq9Vu/bdC9oOZxP8I4GuqrxGkJwn6M/TKOBnVQqNQ3rvNIWKC84gU1SWQeEVlV\nh5T0takHbgx8qY2DeV/X/MbV6zzWkVX1Tx+/wV97/g1Mim36p1Y/4M5HDpdYxVvPzzApGUsnJ9Fr\nr8Yopqc/eJLhxc7V0aVzHMffcdeze77BriX3a3Mh2UHSPxkxyXHVXMgH4sVnt9h0HQ9Wk+AvGsJZ\nYsnswa7SwVuD3bgcoa4mYToKrFJK2GlK2Dv6g+/z7m+9Sn00Yt9ObqOVRR8Ef5bS44yHS7M8Xm8m\nuIn9KCoQRpUZNrqPDJ7xNDGvdgr70DIdzRFcy+Jq6BXKyrLYOinMVi3XEMDshOE8sYBMYJrXBN9v\n8SdT/v5ARWD0nKwlgyIkB5K+M7gu5PEqLvKM/n/23jxmsyy/6/ucc+72bO9WW1f1Mt0zbphgZ2yH\nMWATkbDYISgRBBEgEcIBhBNCCERRCApRCJBICCVRLCtCsjCLSQIB/kgsBLGMI5MQvMwMtmMzYzzd\ns3RVd3VX1bs977Pc5Sz543fuuc/b7nFXL9VVPX2/0qN6lrv87lluvfd3vr/vt47jIl8/Bhv3McHz\nMPj3gT+BJHM+xzACl8D/9LiCGjFixIgRI0aMeFLwMCVa/yPwO4GfCyGMf4KOGDHikSE7//p2qPow\n4O7P33jcIXw0Mf7v+rYIIXwv8L1KqT8WQvi+xx3PiBEjRowYMWLEk4aHSfDcBn7+SUzuTBYN24OM\nW1OhJ7y22KdxGTou/zZXfXIDevkG7Bctt7fCulk/DZXpeNDJyrgPCp8F1rdkQfDWZM1dJySldk8R\nNBzm4kilck8XDHnUmXGlIWQBO5MmylYa3w76Gfm5xnY7jBcFoejFcmTFPsEBkbnQzQPKDavZXRe1\nVKJOhD4vCL2Wj1fgSDozKsB+taWMy9BKQZhb6sjoca1JvzVtxtYVSTPlvt3DdRofRSxCpWC6Q4Hx\nw4q7K+L7eDl2KtfUMwtMA6WQGNg+K05VJpoudQt53+t4BANu7hMryc6HmqlgAs1RQLdDu/kCQmRM\nFMbig2LZyom1Ckl3BhUGty3AOy0uQJGu5fOBuQXC+ui/UFbRdBnmvHcFU9jF0BY9GwLE0ctVHt3s\n9G0eMOvI3FAQcmEwANg9j7mI7w8carvjwKW55DgWvGLdFAPTwavkmFZfER2TrteZcdL+po7srp2c\nSTHrcM96FnNha9QuY/W85+nI3riopmyvD+5zKwounpd997Rn2+TYeMDuwLHpDKWVAXw03XA8Ew0j\nENetSdS40iaQnQ8sI59Ju/cuRq4Ud6yQB8rTf1tNAAAgAElEQVT7GlcNDk3OKpkHPe1NSd/1uixB\nB5rI4Aleizta6LVVFGarki6LaeQnFcdRMIFrE9HWWnUFfuKp4rwwymO9phWlKTa+4LSZcFhKW3m0\nMP9iX9tNTrYng7veFAQFk6zX4Alpjui1AR1wVRwXQRO0sG1A2sXnomUFkFUWFS+224P8Qn7vt0Xt\nlBcG0dYJ8frsbDiOsqLn1d8zspWiueaYFkK1qbKOylgOfvVXeOnHXqA5niSSWQjiYtez1cpJR2uq\npCtUzlvayODRmccHsLP42UYnsP7e5BToQBc5oLojOdpJfw66OSogLJv+NjcRDSW9c3/tnefMRkOn\nE+vNzbzofMXrt9cdpncu2xiUCWz7XFZkEfU6QUEP97ARTx5CCN+nlPoO4Hl2/o4JIfzgYwtqxIgR\nI0aMGDHiCcDDJHj+JPD3lVL/CEh+yCGE/+GRRTVixIiPJMr7+u03GvFI8dKPvfC4Q/hoYnTRemgo\npf4G8AngZ5BlEZA04JjgGTFixIgRI0Z8pPEwCZ7/FlgBFfBE+YoczLaUH7NsnIT11P6Snz55Ov3u\nJz5p8Lip52Q1ZT8yepojz73tgi+fHAHRbUuHpBMBsKnluKaQlfJ/vorLvQG+uj1K7ApfBcxG4eLK\nuC/DsOKcCZOkX3F2k0BxpvDVoAniyoFt4aqBTZJtFXbH2as9L8mUOHEBFEtooh6q8sNLDgSZ9txr\nZZl8VRdgAj+7+Zhsf7/kM2fyILd9MOWfzp9hHt3IPqtfwFtNiKvZxxcFZm24fX4AEF18iO+huzKw\ncvpYiqj5EjRDW6xysu3gotXuC3up37c9CKBCYlf4mU+uUv6phuzLFXYR23jfwjonO5e2ONlM6eqM\nO0sRl+mdjgC4yMnPNF3cV6mA2UIWNYZQEmevU2NmGhciI6QIdHWG6lkwSlhVdhW1cDqVGAS+CITK\nJ60VHMkBDYQtUpyoQU8EyC/63w3KK4pz+dQtNH6XmbHK2BRlKmFyrcZEplN7IKyn+qpsWp4qYe3E\ndi8uSGN1WrW4XNN20m53V3uEvZ4aA6FT2Fng/kroQLOyxc4kiM4amroYtKgKT3MNQmTPNDYjZNBF\ndslpM2EvssJcq9H5EJMKwkTpWUdu6lFe0VwNlA+EtdZrp+iNIegAOjKDioDvzKWbV72MGkJeHKb6\nOeOKgGkHPRvlL7s26U4nbanWGbyBe5FacntzyP3VnJ/cfByAz1/cYllXlFFg6p/XNzm3k3iggFpl\n1D2jzmrK1zOqXynbuokftGA8QueK7nKdNZSnA2PEVuLy1zPQXGfI4zjQrbRZz3DRjbRVz4CxCKvN\nXMhFdvOQxlt/X9HXpE/U/SnKKs5WssGmLaiKjvm3PeD8566i91qyB3J9bS5uf12MeXLU4bYqHXN/\nWnOyEUEs21z+b0VZYfH1GkP5EroDg+v1qrxCV9H1a5WL69kkamDlQdojMr30VmMng1Nb8IMzlpt5\nCLtzSgvzqx9zFxm+d/9rFeE0p4sufaH0VK9liW2XrYX99EFil0X4yM6h1G8FvhfhU/3lEMJfeNPv\nJZIk+dXAMfB7QghfefSRvWN8GvhVTyKzeMSIESNGjBgx4nFCvd3fR0qpz4YQPv0BxfOO8G3/4L8I\nJpYMGO1Tyc3d432CV2SFPOW9eO0+/+wXnhUraqCcNzTnFc88+wCA21+9yseevw/Aqz97E7tv+aYX\n7wDwhVefYvrTE2KehFvf/ipfeekpbr4g+9597RBiDNnrJfapFhWTDiEPhCxgVvJQVJwrmqMYw0lM\nOMQHN9MofDY8uKGipXP/e01KUJhGEQwpyeJzKW/qS1qKc4XP4davfRWAa5M1P31HEl/eaZ65fsrr\nPy6GI+4btugvT+gOpa1mN1fo2I4XxzPUyqTkk3KK7shS3IsPSY08iF7Eh9jZl7LUTqaVB/heqNRW\nUl7Sl2u5AtxkKCnLNvIdQLcXMLVKQtHKQvbxFc2xPGzqrcYfdOSvReHZE1h9XOIv7xspbbslZTT5\n56eETKzEQR7ammclqMXP51x8qqX6UhRuDmC2sPomadjylYL2YHgwD3lI4tmqlofHEO3UgxVba3Mk\n2Srz0hTlGcqIzI5N+5mU/Nm5XN/0VUV7MCzgV8dQX+v7Viyy+5Kc9tCTrXWyLg8Kmucb1DImXeaW\n6UtFaregh4djV4L+9rNkd//K6SFNk6Hj+L1+cMHtVyRLpBoNc0sxlbZqNznTRc36gTz1fsMn7vLy\nV2+ksQ+KZ26JvfjpP7yJnUH1rSJWfGtvyRe+ciuVDemtHsrH8picUMPDfi8UDpIQ1d2QlNHdUGLk\nZp7i2KTSSD/xqFYl0W7VSSlQKqWcDMlSVwXcJCTx5U98623ureacn8j1Pff0MXdPJFnYnZb8K9/y\nC/z4K88D0CxL9q6uubUv2bjGZnzli08NSdzpMGbK+4bim8+4eH2e2tXUgzi6mwTCrZriF2Rs+2KI\nMVvL/Ng8H+fXlzN0A9un5HfTMCTMYlleP4/VokM9KFO7BgVhYcnuydgIz23JvhjPmUkcLjr5qcIx\n/YUynbe6m8UktJyrueIpnpa6J/O5BboF9x2SHVWf2WN706f72OSuwdRDsmr1Ysf+P4vC70+L2Hl/\nvfrja9yXZ5QnUVz7WhhKt+LcyJYx4Vte/n/LbDTdkUXHssXJa5Is7C9ed8P9ES/jou8vPrmiqzNM\nvJ+YrSIXXW3aA/jFP/2ffCDUmhe+779/z8mKL/+x//RrxqqUMsAvAt8J3AE+A/w7IYTP72zzHwKf\nCiH8B0qp3wv8WyGE3/Ne43q/oZT6O8B/HEK4+7hjeRz49Kc/HT772c8+7jBGjHjf8J36337cIXxo\n8CP+7zzuEEaMGPGYoJT63MPkZR6mHuIfKqW+632I6ZHB6IF10yd3erx47f6lbct5c+nz7a9eTe/7\n5E6PL7z61KVtb337q5c+331tsBPukzs9Qn75b/XifIgpJXf6+GNyJ+FNf6KbHWeiPrnTo3/wf6vz\nAElfBIbkTg/3DdtL285urtL7PrmTQorJnSGOy+edfWm4gD6508O+ScvCvYkH1muPwJDcSee1l7fV\n28tDtnfvgiG50yP//PTStrsr8n1yJ50nJnfSsV65HORuf/bJnfSbvRyTeenyeZNOEkNyp8f01cv9\nVR0P7/vkTo8+uZOO+6ZxEuZDY/XJnR59oqnHK6eXrbCvHwwd1id30nk3lwfZN3zizc9UQyB9cqfH\nrb3lpS13+y8ld9IXvzS5c2nfbjhPn9xJu+4eh8E1azjWzvuY3OnxiW+9fWnb554eOqFP7vTokzvp\ns73MVnHTIY4+uZNiai6Pkzdfn98Zcn1yp8fsy5fPc4kx5y7rxaTkTsQvGSfPDQO9T+6kfWNyp0d1\n9/J5myvD9fXJnbTvZy4bK07uXhbMXr042FL1yZ0e+uPrS9vW13ba5k3xv1Vy59J5X9vZIVx2HcO/\n6WCfXF36aLY78+2Arzf8GuClEMKXQggt8LeA3/6mbX478Nfj+78L/Gal1AeS4HqHuAp8Xin1w0qp\nH+pfjzuoESNGjBgxYsSIx42HKdH6o8CfVEo1QMcTZJO+3FY8f+WELz+4AsCVxRp3UqYHgurpJffW\nsnJu9lvccUkTn4Sq/ZrT9RS9iWKyTYmbenQVBYxVSNT/dg/aI0cd7ZTRgW2X89RNeXib5C1f3t7k\nyjV5mD0xc7ERBrLcUauKLj4IKqeiQHBk46xFeNa0UmqTL4fyhKwWcdI+iaNCFCCNzzPKDw+B2ooo\ncYi1REHBa6f7rFt5arTbHFNZzjbypJuvwb0WBYk7KI4N/oZc8OpkijIhJTF8GcjPFeUbmSRQtCRi\nQKyFgwl08aE+X0lcfbnQ9HVwfYmIE9ZRH39bCasl26j0ezCBbB1Fpm92mONY/rMq0LMOp6IV8zaj\nW3jmt+VgxbGhOB+OtZ6U5DvtpDuJDaDZGEwDatnTQsDl8pA8vQvF7YIi5sGaj1ny2wXts/Ike3hl\nzdnxDCJjZnG04eJULjBohTE7iYYAxTlsn4p9vdXkKxGX7tGXmuhW+rJ/cJeknkqfdQeuUgQdyCOj\np993smjYXuRkkW3jTQHZThkLO21uDa0zbDcyLnxn2D9asY11VdnUwpRUgtYCB0fy8N20GcopzurI\npCodJvO0y8gKCYpuDt1+ZKkFhd6pybrxzCnWSyAn9xaoi4yDZ4QFc3YyI7Sa9rrHrDL81IkYcWqo\nYc74PIAOg0DzZEgcKReFsKNAd7bS0g5xW5/1czCk9l+1Zby+HJ17TtfSn+61KWSB17fSYc2yRG0N\nF7f32P/YGS+/cZXZNHbQxOEKdSlmOw100d6cPCQFs6B6gfAYU2Ngh9GVLxV2PvSZWZn03uciKt0L\nFBenKl0HQHahseQpJxLUZVagbuHaFWnzN16ZyNw/i2yibUbIDO1hoDiVa0klgzNh69mpBFKfVuzv\nlFt2QVHGNs6XGleG4b7lwWzAxLZRVq6x2w+pnZ47kHvn6+0c04I/iAdWgenBlu0qiuE3hZRnAsWd\nIrH85AtPaDT9jbtn6CRG1E6y1OcK7UgMp8msoc7LxPTz+eVSvg8LlFLfA3zPzlffH0L4/vj+acQ0\noccd4Ne+6RBpmxCCVUqdA1eAB48m4neN//pxBzBixIgRI0aMGPEk4m3/hA0hLN5umxHvHbvaP08q\ndtkxX4+YfgjI/vnqSVxMf/9gVk/+U/X+x87efqMPMfqk0ZOM4s4TJQf3vuD90OCJyZzv/xo/v1XH\nvvmsD7PNY0cI4R897hhGjBgxYsSIESOeRLzt05RS6je81fchhP/7/Q/nncG2GXt5QxsFVvODJfn5\nTlnRM4GTpVBL3CYnWxlCpPObI0/XGbKVfF7XhaxwR+aNDRofdTRMXD0/W8c6D6tYb0p+5TNS/nVY\nrPkyN1ltJY7pvCHPZNm4swZdOXw52GzvCg+HPBCsIl9qEfzc+VM6aFmZ70uYXJB9k0V5NZT/qFqs\npBMrIA+4dcGJje0RhE20fCCMpkUDk9cjQycTRs/5s7EdTzLsjXaw6FayzeTeoI3RMwp0J8fumUS9\nLkhfFqHboTyoOItMo/64Guyeo3wQg87lenrhXdTA2tC5x9cZ2TzaTtcGc2DJV9In5aliei+k8q/t\njaFsw2wh3+xYt9dStpFfDJbIeNjcgMn9qIMS9y2mLfmq4OC6MAw+cfCAn26eYdvKBT93cMoXo2Bx\nV2dMqpbVpLdtFnHfvo/szDN9Q3G+iJo+WUhsA19Im/XtGowwG5LQcCdjp7+GfDlcz7Ts2BaeohBm\nQzuVMdOX5qjAIAjuNJn2lJW043aVU2ZORLiBebRPPz+LDdnkzGLpYd3kqE6xjG2el5bMeLpOAlEq\nRFZZ1H0Jik1kvSkVeGH/hCJSTf6fO/uELHBQyflWZYndlviJI2QBZYKwMZCSOHa1wlTALE26/l09\nKnob8BhDcRHHb88msdKWid1hFSdRZLg5ryj3ay7O5PPsrsJOVWIIzr6U0y7g/CTq6ZxnLI+G+aXX\nZrD3tjJXw3Ec/Hsdyg+MMdWRmGptKaWFPmqEZXeNlLn1ujNrleaQKmT/flufiUBzr9kUNITc4/t8\nsQJ63agLEcGuI5swf3GJ/sm9dH+xE/Cl3EOKc2iuDGMoW4N2A8PMdjoxY0AYdlWvpdOKYHrWlzsF\n0H4opdIuaortx58rxyzvBcVk7C6uCGvMOs03Xn+Dn+lEQ6zLA4vDDfXdXmdKDffMrZF7eH/v6WRs\n7JYqJsHlWJK5N5eLz43jdrZI5aEhl3vTB4pH76J1B3h25/MzwGtfY5s7SqkM2AdOeMKglLpg6PkC\nyIH1k8AsHjFixIgRI0aMeJx4mOXy/2znfYXU8X8O+E2PJKKPKHo3lxGPD5P7b7/NiEeLVE404rGh\nL8sa8XWHzwAvKqVeAF4Ffi/w775pmx8Cvhv4ceB3Af/Xk+hU9WZmsVLqdyB/m4wYMWLEiBEjRnyk\n8TAlWv/m7mel1LPAX3xkEb0D2FXOsiuTGPCyrvB5SGUs87Ll/HZcJq682FRHvYntukBpUHF1t6sz\ncIP2CIDOBgcagkJHMedQeIzx1E6a79XNAdm5wR5IHO02x+Sy+utaQ+gMWW/3PfFka0UXBTyDBpSw\nSfokj4siouWJol0M7IPeItnsuGf1VtnKycNxr4mROYVfZriqv0BFm+cQWRH5dtCYcAXkq12ah6KY\ndtizyL5wCl+GdC7CIEaqgojH9ivjppYV+iTqq0g24tqKZXHYGXV6YgkmS22RrTW+19VwKp3HdgYz\nsZjIjHIY5rMaHRWc87WiPHeEaKVttibpaYQsXlpvEx3ZGzqyEXwhwrur52D2qlhs90wF5zQ5g5D3\nxhY4N1ACbNDYyOZRKmB0uGQFbiuSlpGyis21HTaGVZcX7cOONXaJWG/7QWMlqxH7c4S907f5tskh\nQBNZODqaUvVOWyoM/WFbw3k9YVIKzWFjAmFn35s3HnDvYgGlXK9eZiy30sbGeIIO2GiVnVeBusvT\nsMm1i65I8kWVD+q2k3nDqiu4GplDqtGg4CLq33inh32dAhNSsseVAV/sJECzQCgCoeuduDw6xqSC\nzIue+eRzmb/99RsiAy7GrDtoor4LW03YU8kFL6tFmLtdS9vkTuaejaEUp5pmP7JSSieGePXAINSW\nJPjuGpM6TDdRd6vXV2p3WCjxvG6y4xzmBo0dV0Yh514rzBl8NrBWQtKfiey0q01yQmtqgysDx/eF\n5FDOG7IW8nWv+aXYXhW9HdNAeTxoQCkvY7OI1Wn9faWPi4ud+6aVuZ6YMwHwg4i5csM9LAZNFa3n\n83UUjY7tlhnPG9t5InCF0st9eGeO9YxGwmVxbVOLc1+vp9XuC3MKwJcy73uR7NOVWMb3x7IF6Pph\nPAjeRzziNErU1PmPgB9GpsJfCSH8M6XUnwM+G0L4IeAHgL+hlHoJYe783kcb1fuDEML/rpT6U487\njhEjRowYMWLEiMeNdyN4cQf4pvc7kI86RgbP48fsTY5WIx4D3NgHjxtvdsgb8QHhA+DJhBD+PvD3\n3/Tdf7XzvgaeeL9ipdTv3PmogU/zBGoFjRgxYsSIESNGfNB4GA2e72P4w0kD3wL87KMM6mGhrCJT\nPrEc9qqa0wOLjo5A98/nmGgrra7UWOMpbstqvSsdtjH4aGscnIa5Q0cXpL28YX9P9BnOZiVMHftT\nsYmxznAw23J7KTScTV3gy4A/j7olXmF1dM1qtOiB9OSYPIi2Rb84rALtFQ9lZKbUOSGuzm+eFi0R\n3Wu87HtCHrDxIThMXGIqZKcZer/FroVt4cuA2WqSg7cOKBUIUY+jOTCpV+1UHKB6C/lmYXj+8Jyv\nPIiaQ5WFxtAcCjvB1INVezcTtkCfoGrXGlcOzjXNoehZgDCFXCFaHwD+RsNib8tm3rNwog1z/4y/\nMQPDZWsIuUvsmWA1nTWYPflsJ7C9YnD5kCCwUctHt1BfGezmlYPmYNANsvOALz0q6hWZhuQK5uoM\n5Qb9pVVd0m2ztLr/leMjQrSkDnNHpn1iCBEi26L/mAXaA5UYTX3b93ClsAxAtId8EbCznqogbJ/e\nIjoY0fgBUEGhtwY/ExZEZqWdezZXr6EEcZwDLxyIrMbJ8RyPSnpTt6ZLXjvbR1eys3kjx8Z9itwK\ne2YbD1ZZgleEqFu1VzR8dR4Ikf1zNN2wl8uYqm3OL75xnTsT+ewnQjM6fiNKZqwz2Iv0imUOnjSv\nVYjaVT2jTkdWTs/0ygMu2qSHPM61OIf8WYHd84n55AuFn/jUf64isdpAmFo9CSToyJIre5qKuDaZ\neOygoZgJFeX6/gXHFzPaSga7XWeoqxZ9JwoFtTqdMznh9SyiRmH3fGKfdDNo90Nir2kLbSxIsXMv\nmjpqcKCCgRUWsoCZWsw9iaNb5TTxJqBahW4V3u2w4ioIF/F+suPi1h7IvNFxHrdzGas9w0V3cs52\nR/Gkd4eT6wvYRdQj8hrTIPc5INto0ZzqdZAyT2UGtlcwsInOdGbaUTc5tpbrMSvDeTZHLeRYqlMD\ns3DuyM5MmkMhk/bxptfVCaioe+TKQNCK+lQmusndJVdCFUj/N3xQeD9Elj9C2GUWW+Ar/FLL9xEj\nRowYMWLEiI8cHobB89md9xb4myGE//cRxTNixIiPMpb5228z4pGiPXjcEYwY8csjhPAHHncMI0aM\nGDFixIgRTyIeRoPnr38QgbwrlKKD42ayzPz07Jz7R3O2UX/iaNpwlskKrV0VlHsN3shSd7vJ2T9c\ncx7ZCVlp6VYFoZMl3FuTM85aWX0/4xC1Mjy3EAGKEBRXJmt+4aeeB6IOziyg17JvtlWDM800Ovf0\nGhoT0XnQvXaOVVjjUNEeWltFdt5bYRG/k39D5THnJq0ye6UTy2HQFFHpOEGT2BSq8OjMk0dmhiuL\npNvhStHwOIqOMncnFTdnS17ZP0pN7XUgqN4JbMfZqxJGzyUxGQXdnixHV/cV5fHAEMjWwyp5h2hs\n9CwBV4nTTr+S7SYBE1f5nVX4xiSNE2UCzUt7TGx/3ZHpEBkF+WpgxxTnol3Tt2NWR1eq2L5BC1Mq\ntWu+s5K/zAgGui/upZhzP2ie+NWCMmqL1KWnyOxwng10eyq5SulWUZyJrguIxlJ/nmwt/TDEJBog\nfX+ajTASuh1p0b4PNssS05EoAMoL86LX4LGT4bjFtOXadMUnZqIo/cWDqyyKhlMtQbU+o6lzFgvZ\n+aKqsBthpt26dc5yNk0sINsZfKcp9oTFcqO64Gf2LHojF/XJvTcoY2O03nD8UzfYKjlPngubrdfE\nQkOtMlCB8tgQ9MDQ8aW4c+lt1LGyAd2q9LuqdRrnWHGT81nvVAbZUuOmQx/4ksReMw3YqO+j6owQ\nhv4KWpgei5lQv5ydYqeBK/vi8HShJ2nOPDs/Y9WUlNHJ7HSzIHhxUQPRrOqdvuw8kK2G+FFQvWbS\ntspDtlKXtLZ2Uc0bYVMB7culuL7Fy/cauFcNDMGpQ0fdKmwuDLBen+heJbpHXuLIlwGfaSb3hEkW\n9MC2Mx2E9k3T3EZXL2S89fo2Skd9qWwYj1k96CL11+zz2M5WU8SbgG4i+8/22kUZ/jzDxHlQHivc\nWTG4BWY7elknOT6DYh1/c8BsOKVuVGK1oWPfx/vn/mLLmZ+mORWywQ3uA8PI4HloKKWeAb4P+PVI\ny/1j4I+HEO481sBGjBgxYsSIESMeM75mgkcp9XO89Z+cCgghhE89sqhGjBjxkUR5bN5+oxGPFH2J\n04gPGGOC553grwL/K4Ne0O+L333nY4toxIgRb4nv1E+8rNeIESNGfF3hl2Pw/BsfWBTvEmbaMc9b\n0PKX8X6+ZVq1bApZdu6sSauwqtGEoERzAwiN4anFBctzoXkoBXpjEhOg1JZ1J8vT2UrjDSzyuJLv\nFTenS+5+cWAJNIeDI9L0dXG7AtHO2HtFNFFA3Ge6hac8jhogpbjq9DoSuh1YOK4KEAY2EJUjez0b\ndFnmDh21NnQHLiiqB/KTz2H7VEAVsjJeTDv2Z1vWjQRi88EOub4eCFtFGZ1sVOEotMOt47EnFpxO\njIPiXCVWQc8UyKNjVa8F0juZoWD2quy3ekaJw07voKOgbrOk1+MKhLHTawPNfPqNqYONSYwlX3iK\npaJcSn/ZTpGvPNsrPYsKmsPY91aRdQP7IKjBbUnaTuGVpjyPcT4P09dl4+JMUyyhPB2uzxVQRjch\n0wXqQ9m2fgZmecvZRfytgWyjEnMhZFEe5sGg0ZNHlo2PTl99O7YHEmh//dlW9u/bVTnRLAJhQJhG\n0TZZ7B+Jsz+WtoP+EIVlmnXsR3pPlVtx/8qlHb90doS3GtVrRhWeIrK+bk2XvOyvo6JOlT8roPKo\nOP9uVWcorzDRMU6rwNUYxJf1VWavDcyn7Q1hteRxbNdHkC8NPoe9l4WJ1e5LEM3Uk12YQf8lsrr6\ntrATsPPYUJ0mW+ueAILZCtPJpX0gGJ2cvrIVtPF6goa9aU3TU6x81LSJ19f1Yz4OJDsLXJuu0rVW\nmWVWCJ3krNlnfnXNMrp7ZadZ0qTxpYeNwc3iBZlAeZIl1lu2VeQXpLbZXh/GW1ZruisZXSt9nSth\np/Uslm7fYzY6sX9srQlxW/IgDLmb8mN+Z4qrIIv7FiuPKxX5Gs5eVJQng45TvgkoOzCS0n5x/nsz\nMMZ8Luw0Vw7ss/I8pH6rTgLNocIXcX62imultGN5HsjqgQ0Zak1xqtM8mL8asJXi4vnYdM0QY7YR\nllQ/V5sDyNYquWi5yTDn86XCbMX5DsSJbvq6YnIc+0RpzPqDTTaOGjzvCNdCCH915/NfU0r9ifdy\nQKXUAfCXEROJAPxB4J8D/xvwPKLz87tDCKdKKQV8L/DbgA3w74UQ/mk8zncD/2U87H/Ts6CVUr8a\n+GvABBG6/uMhhKCUOnqrc7yXaxkxYsSIESNGfHTxNX1gQwhf7V9ADfyL8bWN340YMWLE+4q9lx93\nBCPOXhydzEY88XiglPp9SikTX78POH6Px/xe4P8MIXwS+GbgC8CfAn40hPAi8KPxM8C/DrwYX98D\n/CWAmKz5M8CvBX4N8GeUUodxn78Ut+33+63x+691jhEjRowYMWLEiHeMr5ng6aGU+t3ATyFU6N8N\n/KRS6nc96sAeBlXVkWlPuddQ7jXMM1mZNhOLmVi09oT9jrDfkS0Nzmqao0BzFNC15mOzU5QSd6ks\nExcVvTboteG58pgHyzkPlnPyJRRLmJmGmWmou5xFVqNdQLsgOhBbYUpoG91bZvJSAbQN1FehvgoE\n0NcaghGdl5AF8gslGjKdsAIIsp3ZquTsEgxcuXpBthH2hnIw29+inGiOlCcQOk1Wi94FAdzcUc5b\nynnLi9fv86uuvEGRO4rc0dzskquVP+yorzuM9hjtmS9qbpRLVOlQpePG1SUqd7jrLe56S7cXqK95\n6muebj/QXbGix2GjdkxLuh7TiENX0I+pvQEAACAASURBVIr6phOmgZZXMenYn9Z080A3D6ggbIt8\nKS9t5fqVB5U7mHiylSZbaWETZGDagGkD2oqWiKuEXeAKWdHPNopsC/k6uispaA8GJyrRBRKWjWnB\ntOAPusHpCKhOfPpsK+nTrBamgTekc6qp5Xq1Sp9NLWyhfl9XBJQdtnflEEPSWYl97yZBThQ/o0ju\nZMpJG3sjL1U6uTarogYN5Bek8dlNoxNVgMPZhkJbNq5g4wpCgCqzHC42HC423H+wh7lf0FlDZw3M\nXZojz0+PwSkms4bJrBGHOquwjcE2hsNsjVkayhMoT+Bes2Buauam5qytojaQxFQ9kLbuJvE1l36+\neEHmS68rozzog1Z0jWL/+SyIm1Q/T2qYHm6YHm4I5Q7rCyjPRR/G1PLyBrojyy7mezXzvRpyzzde\nuZvO4yaQ39yQaU+mPXYGxY0NRnmM8viJ46jcyCvfsChrXlic8MLiBN0pbu2f8ys+fpdf8fG7uKlH\n39ygb24or29obnQU17cU17foRSfaMWeK4kwlLaZ+DtVPd8IcK6FbSEe6xuAaI2OgEre1ntnnqiDj\nTsn9JcwtYW7xlbBT9hdb9hdb0aCZBPK1J197so1jcuy4+RM2aXqZJr5aYfGkOA48pgnkK2FFTW6t\n0vzp3bjytbxMC9nGY2fyQkkZmJ86/NQRioG6km8DxcoLK1ML68fnURMng3zl8ZloI7ky0B542oNA\nexBw/XFi/2Vb2c5ORY9L9hu0rrSFfGLJJxaj5d6R7jce3MwlfbcPBKnT3sPro4M/iPw98jpwF/hd\nwLsWXlZK7QG/AfgBgBBCG0I4Q5y5eh3Cvw78jvj+twM/GAQ/ARwopW4C/xrwIyGEk8jC+RHgt8bf\n9kIIPx5CCMAPvulYb3WOESNGjBgxYsSId4yHcdH608C3hRDuASilrgH/EPi7jzKwh8Gt/XP+5cOX\n+Mq5LJBdz5dM8o6LaDn+q66+wefqZ2VjV4Lx6SEgzC0vTO9j8hcBsYBuO5XKJp7KzqjXUo9wsAaX\nw5VYE7NelZTa0i6irfFCHmhNEx/YFWxvDBa73Uxj51HI9Fzh9fCb2apUXhE0GC3lGSBJgHw1WI4X\nxmHX0BzE83Ym0folCeLJtrEMxCjMtOPanpQ+fNvRVznM1nz++AYA2dSCjiVYucOry+UIU9NCLGM5\nnGy4ny2A+HB5syGLpV92mzM72GJfF+EOuwgEo4byoG4Qag25p1hr2r0Yf52hF2EoSZt6lNO4WFq1\n+8ySVxZfOLpoi64XHe4so5vG0rYugFIc/YLj/jcbdNtbSEvbFBchWahL4mcQec2XkoA6+ReQh9va\nJFvtoOTYfWmKnffXFMt0KtjciiK9OnCzOk/izvk20O0FqvuxHE9LCYqNMbf7oCIR3x5KUiuJ4wYp\nRerL8/JVwGdSgqJCfGDtBXwLJ6VP0b7bNFL21pdDFatBhPdj81OenpylEq3MeD59+Ar/n34agOPP\nXyVbKXwshTOFQ8dB9iuquwDsT6Xe62S7j5upVLL0fPGA4kzKeCb3oXY5My2D+7ye0M3Axz5oF/IA\n3otGd/sO3cWLVzHhFcd2lg8CvCCW6W05iGLnSzBRSFhZRbYmNWRQUJ5KSRBIYkx1OrWr7mBeyYEv\nqgk3yjj5kBgmVUuVdzFGz619GdjLbQUTl0SkS2PRKnDRVWn/1hkKI3HlV2tuHklNpFGee8bz3KF0\n/p2zA9YHlYiVR9j5YFGuspDGhXJQlB2bWI5HtCSfxDG2+aaO7E6VRIKrg5rmXGJSjZaEUSzZ8pUI\nWGvbC1D74T+EIGNo+iC2vZVkYzuP+xoozzztIs5HHZjek227mdyXTLwHmCZgGk+oYqmiyggKsnkc\noEFxmK2HbbceH+8ZxUpKUl0cg91c081h8oZi/QkLhcfGcTO5nSXxZZBksYpJYojC6XFM5ecy9lwU\n1feZE4HnuG15Fi6LQn8QGEu03gn+PPDdfSlTZM78d0ji593g48B94K8qpb4Z+Bzwx4EbIYS7ACGE\nu0qp63H7p4HbO/vfid/9ct/feYvv+WXOcQlKqe9BGEA899xz7/IyR4wYMWLEiBFf73hbBg+g++RO\nxPFD7veRQ3gPkg3hw9CiHwKRiPvf/O474eZP2Lff6DHjQ9AFTO6/+32v/Oz7F8ejwnJbvf1Gjxnv\nxQHq9m8q38dIHg3Wn3jy5+o7Rc+yey+vjxA+tatTE0I4Ab71PRwvA/4l4C+FEL4VWPPLl0q9VfYv\nvIvvHxohhO8PIXw6hPDpa9euvZNdR4wYMWLEiBEfITwMg+cfKKV+GPib8fPvQQQCHzvmeUsdssQg\nOLEzmi7D2Wjj7A15XP0PK9juMF6CDhyZNbaNCYGJlOqgZN86FKhzYbhk24ByiuNOxFfdJueknSUh\n5W4O1QNYRbLQ7DXwk8jqyD2EIlkG68iKSda+xY7w5wqaa55sHdkHWn7L4rNMlXVsIIlKN8sSU0aW\nilPozCdhUxDWSx5pAM8UJ1zLLpLIsncq2bAHr9Ctpou0lc72PuYSxyunh7jWECLTZnZlQxaFaTfA\nlfmaV6d7Q8eogV3SzQexXxR00x2h66BQKiQmjdlo7MKTn0U77Oc22EboMNcWG+4dL6AvmQjgK58Y\nIdpKuVQSfzYMFuul7ND/ZmeBbrHTTmthTNz99owbP2VRXbTSRiyt7VQn+/VuEcjWijb2vS/Ax5gm\npSVXbrD3zgAVpKyGWFZS6GST3u07/ANpa1dJqV7PQAil9GW26QW3hRmTBKrDjjV2p+nmAfwgbF2s\nfWI/2WpgiS3ymoWpeRAHb+sML62vsRcFxHvGg41jwDuVLLmfy08IVpObni0jVu4hto0LiuJCWBgA\ntc14oxNm16ouCLMdm/cA2QlsbsW+P2rhtYqTb4KDL0hZTs9ca1cFsxVp3GRraG54TK1TX/dQi45g\nTPquL4/zvc7wCunfOBayGg4qYTMdT2d86/Sr/G3/7an/6janjOwgs9YstxVtPz+2htNWxmfjM9Zd\nQWPlRKpVaT4BPH10xs3ZEoBXLg6xTrPtVZuJwss6smHaOD8jimmL6ieUV2zOJmlu4om+hkMb2Nkg\n4g5wdEPOe7GuCMsZ3/zUawD85P4eYe7oplEIeqXo5hlP/ZTj1d+o4L5O9xOfKfILN5QSGmHquSIK\nJxcd3Swy1SphZ/UMsqAVyoUUczeXZMS1A7kx1DZj2g/sAHaqIYrdt/teBNb7+821kuYQspMMe2TJ\nJh0ui/ethcE0KiXLQwFu5sm2O8yweF/2uZYxEv9DCAH8ZGCYdTOV7rMjnkhopdThmxg8D/P3zNfC\nHeBOCOEn4+e/iyR43lBK3YzMmpvAvZ3tn93Z/xngtfj9v/qm738sfv/MW2zPL3OOESNGjPgleBhX\nsh/xf+cDiGTEiBFPKh6GN/I68D8jAsufAr4/hPCfP9KoPoJISZARjw03furrjxXwYcPBFz5SOiJP\nJF79jU9+H9ijr8O5Gt6H10cH/z3wT5RSf14p9eeAfwL8xXd7sBDC68BtpdSvjF/9ZuDzwA8B3x2/\n+27g/4jvfwj4/Urw64DzWGb1w8B3KaUOo7jydwE/HH+7UEr9uujA9fvfdKy3OseIESNGjBgxYsQ7\nxsOseC2APwScAH8L+UPqicCqK3i92ediLUv7nzl+jgfHC4KTvNVr6z3aHTth9aAk9KuyF7IU7aON\nsXWG6hjayFK50x5RRl2LrA4oH3hlcwSAuTD83MnNpLUSdKDdV8m22uek1V91kQl9vhs0QOxpRRmf\nT3TPsphEK2Q/WGOr2Du9DfDpdiq26v2x1iZZDwcDrhkYA9oKM2cbrd7bSA1ZPxD6iHIK06+wr3KK\npeL1U6FitOuCL2+uouPKd2cNbE2yv86ue7Z1ZAJZzWG55ZXIWlEhigabGKMeLOJVo0V7I9oph3XG\nRV0mu+v2wBPyHaZNY1Ib1DYjKx0qWnY3q5LsqS3tXK4n2waCVunYwoyKuh0zhb1QycI9GMQCOx/Y\nTwR449sybv6ERVmVdIN8LsftLdd94WkL0JHFYSeQzaQhy6KjCTkuWna7IkMftlBH6smiwxcldhLH\n4NziqnicmSccG2zcVE0tYTMwUWwVzxX7184u283rVuHqXk8ksia8nMdOZTwAvFHv8cXlNbZWGvls\nOeXz7gafvCJ1VfmFbFtv5HeVec6Phe3jEEFvHZ8kZVtF/ZR8vt1dEWv4Wj4fb2fcrmXO1JuCQkEb\n27F8oGj2B/ZNVlhsgLNPBvZelv7odXLMSZ7YStJfYFYmMURcCU0r8YaN6LD012sa6f/EKJtBvtSJ\n0WMrGVsgOjJtMCIejehqrV6dcW9PBs70AlbLihAZgtlpxp2VMJQOqi3LbUUd48jXijuvXkmsquee\nu8+qk+OcbSY0FyW3O9EOc42BhcWvJQ6zMeQXovsF0D6YMI3Xagz40zzNexFhD3SRRaWzQPb0mi5S\nzCrj8ZHK5TqNzgKffyA6XP6ou5QUCFphK8WNH4fXvwN8KeLIAPWhQTmdtg+ZaEL1rKpmWzLvSUaZ\nbJZFJpetFMEoyshWUzEZMcllAM+Khjren7q5Rnm4ck184R90++SzFltLY9iZaHWZRZxzpaWL91pb\nFASrUruhICws7kIC6w4cITKDQq7p9gM+jvNNnTEBdNsLsH/wSa6PWInVe0II4QeVUp8FfhPCYfud\nIYTPv8fD/jHgf1FKFcCXENFmDfxtpdQfAl5BzCZAWMy/DXgJIbL+gRjXiVLqzwOfidv9uVg+BvBH\nGGzS/0F8AfyFr3GOESNGjBgxYsSId4y3TfCEEP4s8GeVUp9CyrP+kVLqTgjhtzzy6D5CKM8edwQj\nPgwaPF/v2Hv5yWePfL3j9e943BF8RDEmeN4RYkLnvSZ1do/3M8Cn3+Kn3/wW2wbgj36N4/wV4K+8\nxfefBb7pLb4/fqtzjBgxYsSIESNGvBu8k5r1e0i51jHwli4PHzTureYU+hrdVlZhXzvbJ2wzyGWV\n9tU7VzBx6Tvbgtmo5GZVnmg2vkx/VDunWRwPWogvba6nFdV85ciM4qvRrStbK46Xs8TY6ebCODAi\nYyKW3VtZrS5ONT7n0oq73urEMAhGdFx69yuzVYmZ0KNnL6w3JUW02wZhnvRiqtqKN3By24n7XNTC\nGji1M65ly8TKgZ14a00WNYrkRIaVLZOGS7sqCLknX5kUh496GlnpKLRLukF9oxViGITPxSYZID8z\nYlPca9R2GtU7JgGmVqiVTrErE8iXEsTFxQSTO2yMUZ9lWJOlY3UzhbZiVw/Q7kVNpRiDrQbdFdVB\nty/XLV9At+fpJsPnJHptAu1co0Jv6RQIWaCbDzpJKjbUJGrV6MgyahcZ/iJHRaYQm4x2LzKGALxK\nTBKz0aCGfgteoXRImiBuIuOqZ415M2gzqfulXPuOdpPygWZfPisPzYFs++rFPuebKulWeatZb0rq\ngyy1VXUM26hd5a0muycNd+zmhMyziaywrIFyGei+RYK+2x4IQysyeDZe83OnN+U4rYlW5/JbthWH\nsDY6wgUvFuH1FTm/qQedKrMRVlvPzjKNzJO+7bSFzaanuwjLpP+tb8+e7eRzaZ9+/uUbWDUyR9o6\n48JPht/WUJxpukgJyVewWeap/6pjeP2eMHj0jYB1mjbGUcVYVD3Mt1fOpRPWZxP0aY7bjyy384yQ\nBcoHQ/9l9cB8MxudGEjKRvZMHELlqaJhmMudVTTbip7L11mTXNB8k+Gvd2mcnjl1aQz6QtNNFVd+\nBs6/QTS62j05Urun0E4njSjVKtqFTowz5zRNvJ6QyRzvJYjaPdjYweHKToR1VUcWWZV1A9PrUObx\nfiGdfxIdEfOJ3BTbg5xQhMRW63JDu5HGUbFNdp20ghvuiXiFai5XJeuLyHrLA90MNtd1ilkv3nQj\nHjFixIgRI0aMGDHiCcfbavAopf6IUurHgB8FrgJ/OITwqUcd2IgRIz56qI4fdwQjzr9hZFE9Fowa\nPCNGjBgxYsSIESPeIx6GwfMx4E9E+vITBaXAozBR0EbrgJrYZEgarE4uWqYRlkY0lcI08ExxjJnI\nvrNJQ7O3SKu9B/mG5qos/3dzQ32oKLR8tnueXIdBpyULaKuS01LYANE1y04C89sqMSgk0ICrenca\nWbHv5jtaOjsuzMpBG/ctChudX+Szm7uki2MniqzqcGWRjjudtMlFa25qDvQmXd9uvPio6RKdanwW\nqEyXWEfWifaKnUaWUebp6T1KBeZ5nVzCgoluPPHY2WaHTRHEQafXwmHRMStaVr0O0b5DNTqxgapJ\ny/pWXOUvLM1FydG1CwBOHlSEymEj66ab7TpmxRX56NhkakXbklzP7KEFMtw06otcMYQ8sHq6t14K\n2Fm81oOWbjbBHfWCRQpTWfw6bqsgL2QMHU42bFxOHseUnQITR+gdjyYOn5nUVqZyya3Ll6Lj1Ost\nmdLhyizF4SrAq4H5lQ2uWSELtAchHXd7zRC0Toyldp+kPaVUoCwsRXTCOge6OmOaCcWlfqYjX+VM\nZvJ5fTJN4+AT+X3M1CZGyOYGVA8UV/aEyrafbWj3Ye2kbW7MVkn/pVi02EmRdJFcEVke8XOZOboZ\nrGYwfV1+Sy5heXTf6ue1EUcy5QdWXBaZHp0O+GxwcPJZZHBF5pNuRYslW8V5M1VMoktWVjiezk9T\nu0l8orsEYCsDeSBEJ6bmUKNiux5VG3xQNFGbqt0P5AcN7bl08CTrRHkD6PYNm0aj433LTzRYRf2x\nyDa8l19y1zNbldg83RULpU8MupBlhDzg4rFRgFVJX8pZTT6JN71ak12p6XxkdmUepQO2khjrKxnd\nTDG9C+tnRIOn2RuSPfWRSu3IzNHNzaD9Yzy2dxVciG7V5kZ0pFqA8or2QOLQncyBo0pElg7KLYdR\ncKlbCAvv2kTG1Mn+lLbL8LGvfeFFn6lnZhpHF+9bQYs+TxfbxmdgJhZfxhvQDiOnOdAEE5ImD6XD\nWUW7F8fFHBaLLR8kRg2eESNGjBgxYsSIEe8VD6PB86c+iEBGjBgxYvr6445gxPqZt99mxIgRI0aM\nGDFixIgRTx7eiQbPE4fCONZdkVg6h7MNIYCLK9TNecW0ikyEp0Dtt/jontUcBa6YFbO5aD1M8o4H\nN3a0VwC/H5kYVY7PoYnOSb4MBK9on4nCHgraTUHzvBzL3S3JDkUUw+9ptqcTur24PKsUvghpJdyV\ngWAGXRZfBNxkYPMoO7BSiiCsgp55okzA70mM9dWCorCJVYSS9imiUEuuHJXqdlgRPlkY+anHNwbX\na/AAq65Mbl3KKkIWkuPR0XzDJroFOa+x3uCuxtXxiwyfDTpC3WzQEiFAfX1Ypjalo8q6dD3FUY36\nwjwxhbzXKBv1bcqOq4t1YoSo/RY6k7RJEpsqfg75oFPii0DQGruI7ZZ70emI19PNA6HwtPuxPWYu\n6Q8dzLfUs75RodhrIEAbxwZOkZnB4um0nab33SJwcLRmeSzaTeQOFLgr0jhVYWkjQyfkATv36EZO\nfLC34aTTuDKyLS4yQuGTbpA/EMcuEDJVqHxiANhZwK0V2xs9qyokRsc0b8mNS7oze5OaMzdlkcnY\nLfcb2r2ca1NhL6yXFe2BXN+B7tjb2ybXKTsLtJ3iqWpgOrT7g+vbQbHl9kroZ9f2Vty5VSbGXN2V\nFCca/5Sc1+hA/dyWi+cgf3lCe+DRXc+m8IBODCU7VYQiEGqV4lhM5TjLA41bV8SmQXdKmGML2be6\np2BusfHW1+4ZptHNqSwslWoTqwilsDOfmEM+B7PosBvZV3mSlhGIk9ZxJq5ubR6ockuXR/cnr7k5\nFfbZjcmKf1o/S1HKONhaE+3n4oF0oD3cmUN7w/hSrSY7bOjOom7QfkBPLN5ITPNFzXI7T+O3KgfW\nijls0SYwyeU7pQNZ6XBlvN9oaA4hW0N9w+FzncZjyGWcdfNh/rb7Q2x7Rcem14fKA6iBxdctpP96\nxlU3D6CgivemTPnECusZfsvoOFZmls4aXGSFsbAoFSgiay7PHNs+pMLj3cB2clPP3mLLeS1tM92r\nU+zb85yw36F2aDNh3yY9n+5aR+nftoJ5xIgRI0aMGDFixIgnCh/qBM+IESO+vpC/PHn7jUY8UtQ3\n3OMO4aOJsURrxIgRI0aMGDFixHvEhzrBMysalk3F3lRWZp3XzKsWF5evuzpLWiPbFibzhk2/+t4p\n1r7k+kJW1QvjuDsLZBvZt/EZ+1dWcpzZEbaCSc/UmDjRhOj/IO+doOpBl8Vksq2OmiA9qwFANxpX\n7qwcmzA4K+04OLkykFmVGARF7thoCFETRLUavSfsg15jpdfvcaWwNRbRjeYr9VVmuhl0HpxKbjO6\n1sICWeXxuIoH21liAahGoxrS9dY2S6yV9abkrJ0QWgm6uNCYmksOR3Zn1R81uFt1VtN5k1b221WB\n3gvpWppNnq5dq8Crd64wOxKtDv16iT2yqa18JiQIvdPOveaGfjBovwDozIv7WDyPLwPKhIG9FUgx\nbZsCpUAXMo6UCthumDZmYsnM8ED88tnV9D4YODueo/vzWC06QPE8bZ2la0XL+XVsx7bLMIWjP3Io\nAiHz+MhuUsbjJj3TJKBKB3Fs+8rTLUy6PuUU4WBgchyvZknT5NreivubnEm0OruyWPPAzHlqJvPi\nNX2QxttUKa7NVhxvhKXii0DIFE9NZNuNK7EzT76MGkqmY9vJmFoUDcqEpFmDVfgCdGTl+KCYL2r4\nlpr2c4eE0uPzwQVMDbJPwkILw7zQViU2yFmnyQK4qK3iC4PZ7uj5FKIX44pet8VQGdn34nzC6/Yg\nMdfS+dzAqPNWoaP+kp0N1/NgO+Og2qZ27dk9ITLQ1m3JPDKFpllLWbX4XYZIUPQTLFupSy5UxZlO\nrLaQB5zVEDWHfKkJrUluets6Fxe3qAXkvWa1FTaMv1/iD1vamYyTYDWd0wPzayraRdM7huYo4BYe\nV8UgtIQXooNfcApXDXNmtZoQqmE8uiKk/pF2Bl0PX+hOsYpubLXNqN3ARnMF3F0K/afpMiZlh3Ny\nIucyAoq2lWtomhwfGTqq1YTKgTKprawzhNh/XZfRbXs9HgteEWIfhACmsqkdlVU4v3MBHwBGDZ4R\nI0aMGDFixIgR7xUf6gRPCIrVtuTGvjxg+qAw2g8W0E5zbSpJmgfz6zw137C5Jw+n6MCB3vDMTPy8\nl12Fm3ncDLKVZmkrvuX6awD8+LUjVAAbHzLKeYNtM2j6JzBPvoZ8nVFf9+iOZJess4BWvY25PEDp\nWqWHb90oso1KpUXF2VCa4QuxGO9Lo7ZNLhbsts8AgYsP9YVVdF2WrIKzDWy6gsNYPvPl9VUO8zV/\n+Lf8KD/w974TwmAdrWtFvoaQDTbNb5wukr25XQRMLA3SjaJucmyMwZ0XnF+tUFGItzt0cGrSsYMR\nkWOQBzfd7FhW1yaVCoE8cMpv8vCYlQ53Ig1jlKfYa8ijIO6mDKjMk6/iNTTDA5LPY1nZqvd/lvZX\nMbFk9wymVfiYadGtwi8z2kNPfq6Z3M5SSd3iuZoVc0J82MuMJ2QOFxMxrjXpQb22GWfrCbYZEn3B\nKnzWJ2UCwQTM/Rx3rcNbTXkW973poQiomNHZ1jne6vQAqjqFauODaxEI6zyNKTd30OpLJXW6JZW3\nZWtFU0lbaBXYrgtJpiBitq+oKzxbnQDwi9V13ph5fvqzn+C7fv3PoE3A5ZIMWeicpyYXnNfCsvET\nT7uneGH6AIBSd6irDfVVKH5xwpViw7aReXA02aCWWUpYGS/94uI15QvH2QNR6S0NFA+ylKzL1pKE\nMDH5qkIsX4wH0y2pdI/akG1UEvzVLWkcg4xtvywk96BkPvYJHm08Z26aEpk+k+Rr385Bg849XK8x\nL02x05DGxdl6glYhXU+20tSbYijvCoplK2P99vKAzbJKYtyhU2RnGW4Wk8IW9EXcr4zzJs4vlyEJ\nja5XWldQa/KLmJBcFWIFHsvyqrJjs5U5FOYOljnmekxuWU12PpRlooYEcX6h8KVKQs/ZVq6/F3X3\ne5ZsNViQa+PRMYZuL5ZjhaGvlSclz4ORtn35Jz7G7BtPpNS2aFPf6hYWlSSmmy6j7QYrdFoNlcfW\nMbBAuveoAOSBPLadnSnqOqd8Q8a+W7T0HaIuMsLCQp+IP8vxmSfrkzqdxrqdthkxYsSIESNGjBgx\n4kOAD3WC51Ggd9d5N6iv+7ff6DHjB/7ed77rfXs9jkcNV737pez+gfTdID//YDQ33LXu7Tf6GugZ\nFI8a3/Xr371pXvGL777MqnylePuN3g+8h6FsXpq+/UbvA3z59tt8LYT83Y8T9QFVaM2+8eSDOdGH\nBSODZ8SIESNGjBgxYsR7xIc6wbNsKmxnkqXuy6dXuDG/GDYI8PT0DICfn3ruPjiAvnSj1Hg0sygu\nu7GF2FD31uDe8Mm5WPr84+k3kv//7L3Hr2VLeuX3C7PdMdele77eK8NikSo1u5uEBpoIAqSZhvp/\n9P+0AKknLUGjFiS1wCbBbppussgyz6e57vizTRgNInbsc6vVKKCSfPnyMRaQyHvu2Sbii4idub9Y\n31pbwRBFlktt8V7gxlIO5MMSrAFULOkx+5LKwEi6wYeXL72bGD3jLrjXDz/LXsCkhYztNXULKiah\n7NwlNo+XYG5q6tgOV8B5fWQR+7cZatZmloRqhQVXTWVhrjhhCcwdZlXBmGiZWawAL8K9FEyCzF6g\nhKOITIDhkQmlQ6MYspvKY8ylQW0Urojf9ZL1tklC0boxGOWR67jjPkhcTGi0Q8HQaupFKNFaXQz4\nXqZSMD+WkIyflYdoZ+2coLgR9JfhWuWsx+gCN4/22LsC4UKpFk5g5h4Xzz12RbAqHzf2jQplOONO\nv/KJPbLparp1hYiMF6FD6dco5lw1A66tsZENpc77VAakZgNuXabyLWdlOHdkO9UulGEBfpAIAS7G\nVfTywdzFB6Hc7lEUrl0GBhDA0jLMyAAAIABJREFUB/MVv+BJEpc9K47IwvFEh3VzHIrUd4Cq7jls\nAutt8I657pIoMYR5NIuUrNYVXJwf2BUhwWO9wJiJBSE8+FjGqI8izItY7nSYl9AquqeW2WcK20zz\nRnXxAif5U9WesN7WpLJMfFgzY6mbmU2Mn7ENjCVeBJbcZRnmlOs0u5HCEr/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3hl5i5zax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F3LxBCsCsOhD204tiVukOjRpc5I5LLHHqL7mhEPtGOKraArRzcuheplYlVRWYSb+qvf63Ev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\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Rg6DVbJFGYLYNMrLxqm0LXgQWDGCV51YRvnvraJsPvO9N7r8WjvP7inJXcH8ejvVCsb4u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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fafa65c99e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iXGYUzvIFL3+UpF8wODQhMyUPHT7PYmdKlWnnjs01LVUHSDz50VI7Pzol8qod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\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roH3SrxmsgXLhHgDeAXzsgFBGzCz6Yw1/EBg/exofXAn7IFm1Tx1I854jFcCtz50/dYlF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\nAxmpzfJ/3PYlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fafbae2cba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "for frame in ['neutral', 'threat']:\n",
    "    # Directory containing CSV files for all subjects\n",
    "    csv_directory = f'analysis/aural_framing_by_movie_topic/z_roi_node/data/concatenated/{frame}'\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",
    "        # Exclude 'volume' column while reading the CSV file\n",
    "        subject_data = pd.read_csv(os.path.join(csv_directory, csv_file), header=0, index_col=None, usecols=lambda col: col.lower() != 'volume')\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')\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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ISC calculation\n",
    "per condition, per ROI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create 3D array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(238, 185, 300)\n",
      "(238, 185, 300)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "stacked_data = {}  \n",
    "\n",
    "for frame in ['neutral', 'threat']:\n",
    "    working_dir = f'analysis/aural_framing_by_movie_topic/z_roi_node/data/concatenated/{frame}'  # 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, usecols=lambda col: col.lower() != 'volume').values\n",
    "        data_list.append(data)\n",
    "\n",
    "    stacked_data[frame] = np.stack(data_list, axis=0)\n",
    "\n",
    "    print(stacked_data[frame].shape)\n",
    "    # 238 subjects x 185 TRs (i.e., number of timepoints across all runs) x 300 nodes (i.e., ROIs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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 inter-subject neural activation dissimilarity matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating similarity matrix for node 1/300\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/5.0.0/lib/python3.6/site-packages/ipykernel_launcher.py:25: UserWarning: Warning: converting a masked element to nan.\n",
      "/opt/anaconda3/5.0.0/lib/python3.6/site-packages/ipykernel_launcher.py:26: UserWarning: Warning: converting a masked element to nan.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 1/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_1.csv\n",
      "Calculating similarity matrix for node 2/300\n",
      "Exported similarity matrix for node 2/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_2.csv\n",
      "Calculating similarity matrix for node 3/300\n",
      "Exported similarity matrix for node 3/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_3.csv\n",
      "Calculating similarity matrix for node 4/300\n",
      "Exported similarity matrix for node 4/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_4.csv\n",
      "Calculating similarity matrix for node 5/300\n",
      "Exported similarity matrix for node 5/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_5.csv\n",
      "Calculating similarity matrix for node 6/300\n",
      "Exported similarity matrix for node 6/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_6.csv\n",
      "Calculating similarity matrix for node 7/300\n",
      "Exported similarity matrix for node 7/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_7.csv\n",
      "Calculating similarity matrix for node 8/300\n",
      "Exported similarity matrix for node 8/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_8.csv\n",
      "Calculating similarity matrix for node 9/300\n",
      "Exported similarity matrix for node 9/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_9.csv\n",
      "Calculating similarity matrix for node 10/300\n",
      "Exported similarity matrix for node 10/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_10.csv\n",
      "Calculating similarity matrix for node 11/300\n",
      "Exported similarity matrix for node 11/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_11.csv\n",
      "Calculating similarity matrix for node 12/300\n",
      "Exported similarity matrix for node 12/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_12.csv\n",
      "Calculating similarity matrix for node 13/300\n",
      "Exported similarity matrix for node 13/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_13.csv\n",
      "Calculating similarity matrix for node 14/300\n",
      "Exported similarity matrix for node 14/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_14.csv\n",
      "Calculating similarity matrix for node 15/300\n",
      "Exported similarity matrix for node 15/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_15.csv\n",
      "Calculating similarity matrix for node 16/300\n",
      "Exported similarity matrix for node 16/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_16.csv\n",
      "Calculating similarity matrix for node 17/300\n",
      "Exported similarity matrix for node 17/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_17.csv\n",
      "Calculating similarity matrix for node 18/300\n",
      "Exported similarity matrix for node 18/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_18.csv\n",
      "Calculating similarity matrix for node 19/300\n",
      "Exported similarity matrix for node 19/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_19.csv\n",
      "Calculating similarity matrix for node 20/300\n",
      "Exported similarity matrix for node 20/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_20.csv\n",
      "Calculating similarity matrix for node 21/300\n",
      "Exported similarity matrix for node 21/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_21.csv\n",
      "Calculating similarity matrix for node 22/300\n",
      "Exported similarity matrix for node 22/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_22.csv\n",
      "Calculating similarity matrix for node 23/300\n",
      "Exported similarity matrix for node 23/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_23.csv\n",
      "Calculating similarity matrix for node 24/300\n",
      "Exported similarity matrix for node 24/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_24.csv\n",
      "Calculating similarity matrix for node 25/300\n",
      "Exported similarity matrix for node 25/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_25.csv\n",
      "Calculating similarity matrix for node 26/300\n",
      "Exported similarity matrix for node 26/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_26.csv\n",
      "Calculating similarity matrix for node 27/300\n",
      "Exported similarity matrix for node 27/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_27.csv\n",
      "Calculating similarity matrix for node 28/300\n",
      "Exported similarity matrix for node 28/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_28.csv\n",
      "Calculating similarity matrix for node 29/300\n",
      "Exported similarity matrix for node 29/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_29.csv\n",
      "Calculating similarity matrix for node 30/300\n",
      "Exported similarity matrix for node 30/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_30.csv\n",
      "Calculating similarity matrix for node 31/300\n",
      "Exported similarity matrix for node 31/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_31.csv\n",
      "Calculating similarity matrix for node 32/300\n",
      "Exported similarity matrix for node 32/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_32.csv\n",
      "Calculating similarity matrix for node 33/300\n",
      "Exported similarity matrix for node 33/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_33.csv\n",
      "Calculating similarity matrix for node 34/300\n",
      "Exported similarity matrix for node 34/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_34.csv\n",
      "Calculating similarity matrix for node 35/300\n",
      "Exported similarity matrix for node 35/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_35.csv\n",
      "Calculating similarity matrix for node 36/300\n",
      "Exported similarity matrix for node 36/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_36.csv\n",
      "Calculating similarity matrix for node 37/300\n",
      "Exported similarity matrix for node 37/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_37.csv\n",
      "Calculating similarity matrix for node 38/300\n",
      "Exported similarity matrix for node 38/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_38.csv\n",
      "Calculating similarity matrix for node 39/300\n",
      "Exported similarity matrix for node 39/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_39.csv\n",
      "Calculating similarity matrix for node 40/300\n",
      "Exported similarity matrix for node 40/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_40.csv\n",
      "Calculating similarity matrix for node 41/300\n",
      "Exported similarity matrix for node 41/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_41.csv\n",
      "Calculating similarity matrix for node 42/300\n",
      "Exported similarity matrix for node 42/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_42.csv\n",
      "Calculating similarity matrix for node 43/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 43/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_43.csv\n",
      "Calculating similarity matrix for node 44/300\n",
      "Exported similarity matrix for node 44/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_44.csv\n",
      "Calculating similarity matrix for node 45/300\n",
      "Exported similarity matrix for node 45/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_45.csv\n",
      "Calculating similarity matrix for node 46/300\n",
      "Exported similarity matrix for node 46/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_46.csv\n",
      "Calculating similarity matrix for node 47/300\n",
      "Exported similarity matrix for node 47/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_47.csv\n",
      "Calculating similarity matrix for node 48/300\n",
      "Exported similarity matrix for node 48/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_48.csv\n",
      "Calculating similarity matrix for node 49/300\n",
      "Exported similarity matrix for node 49/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_49.csv\n",
      "Calculating similarity matrix for node 50/300\n",
      "Exported similarity matrix for node 50/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_50.csv\n",
      "Calculating similarity matrix for node 51/300\n",
      "Exported similarity matrix for node 51/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_51.csv\n",
      "Calculating similarity matrix for node 52/300\n",
      "Exported similarity matrix for node 52/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_52.csv\n",
      "Calculating similarity matrix for node 53/300\n",
      "Exported similarity matrix for node 53/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_53.csv\n",
      "Calculating similarity matrix for node 54/300\n",
      "Exported similarity matrix for node 54/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_54.csv\n",
      "Calculating similarity matrix for node 55/300\n",
      "Exported similarity matrix for node 55/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_55.csv\n",
      "Calculating similarity matrix for node 56/300\n",
      "Exported similarity matrix for node 56/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_56.csv\n",
      "Calculating similarity matrix for node 57/300\n",
      "Exported similarity matrix for node 57/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_57.csv\n",
      "Calculating similarity matrix for node 58/300\n",
      "Exported similarity matrix for node 58/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_58.csv\n",
      "Calculating similarity matrix for node 59/300\n",
      "Exported similarity matrix for node 59/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_59.csv\n",
      "Calculating similarity matrix for node 60/300\n",
      "Exported similarity matrix for node 60/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_60.csv\n",
      "Calculating similarity matrix for node 61/300\n",
      "Exported similarity matrix for node 61/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_61.csv\n",
      "Calculating similarity matrix for node 62/300\n",
      "Exported similarity matrix for node 62/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_62.csv\n",
      "Calculating similarity matrix for node 63/300\n",
      "Exported similarity matrix for node 63/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_63.csv\n",
      "Calculating similarity matrix for node 64/300\n",
      "Exported similarity matrix for node 64/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_64.csv\n",
      "Calculating similarity matrix for node 65/300\n",
      "Exported similarity matrix for node 65/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_65.csv\n",
      "Calculating similarity matrix for node 66/300\n",
      "Exported similarity matrix for node 66/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_66.csv\n",
      "Calculating similarity matrix for node 67/300\n",
      "Exported similarity matrix for node 67/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_67.csv\n",
      "Calculating similarity matrix for node 68/300\n",
      "Exported similarity matrix for node 68/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_68.csv\n",
      "Calculating similarity matrix for node 69/300\n",
      "Exported similarity matrix for node 69/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_69.csv\n",
      "Calculating similarity matrix for node 70/300\n",
      "Exported similarity matrix for node 70/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_70.csv\n",
      "Calculating similarity matrix for node 71/300\n",
      "Exported similarity matrix for node 71/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_71.csv\n",
      "Calculating similarity matrix for node 72/300\n",
      "Exported similarity matrix for node 72/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_72.csv\n",
      "Calculating similarity matrix for node 73/300\n",
      "Exported similarity matrix for node 73/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_73.csv\n",
      "Calculating similarity matrix for node 74/300\n",
      "Exported similarity matrix for node 74/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_74.csv\n",
      "Calculating similarity matrix for node 75/300\n",
      "Exported similarity matrix for node 75/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_75.csv\n",
      "Calculating similarity matrix for node 76/300\n",
      "Exported similarity matrix for node 76/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_76.csv\n",
      "Calculating similarity matrix for node 77/300\n",
      "Exported similarity matrix for node 77/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_77.csv\n",
      "Calculating similarity matrix for node 78/300\n",
      "Exported similarity matrix for node 78/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_78.csv\n",
      "Calculating similarity matrix for node 79/300\n",
      "Exported similarity matrix for node 79/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_79.csv\n",
      "Calculating similarity matrix for node 80/300\n",
      "Exported similarity matrix for node 80/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_80.csv\n",
      "Calculating similarity matrix for node 81/300\n",
      "Exported similarity matrix for node 81/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_81.csv\n",
      "Calculating similarity matrix for node 82/300\n",
      "Exported similarity matrix for node 82/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_82.csv\n",
      "Calculating similarity matrix for node 83/300\n",
      "Exported similarity matrix for node 83/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_83.csv\n",
      "Calculating similarity matrix for node 84/300\n",
      "Exported similarity matrix for node 84/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_84.csv\n",
      "Calculating similarity matrix for node 85/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 85/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_85.csv\n",
      "Calculating similarity matrix for node 86/300\n",
      "Exported similarity matrix for node 86/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_86.csv\n",
      "Calculating similarity matrix for node 87/300\n",
      "Exported similarity matrix for node 87/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_87.csv\n",
      "Calculating similarity matrix for node 88/300\n",
      "Exported similarity matrix for node 88/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_88.csv\n",
      "Calculating similarity matrix for node 89/300\n",
      "Exported similarity matrix for node 89/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_89.csv\n",
      "Calculating similarity matrix for node 90/300\n",
      "Exported similarity matrix for node 90/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_90.csv\n",
      "Calculating similarity matrix for node 91/300\n",
      "Exported similarity matrix for node 91/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_91.csv\n",
      "Calculating similarity matrix for node 92/300\n",
      "Exported similarity matrix for node 92/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_92.csv\n",
      "Calculating similarity matrix for node 93/300\n",
      "Exported similarity matrix for node 93/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_93.csv\n",
      "Calculating similarity matrix for node 94/300\n",
      "Exported similarity matrix for node 94/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_94.csv\n",
      "Calculating similarity matrix for node 95/300\n",
      "Exported similarity matrix for node 95/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_95.csv\n",
      "Calculating similarity matrix for node 96/300\n",
      "Exported similarity matrix for node 96/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_96.csv\n",
      "Calculating similarity matrix for node 97/300\n",
      "Exported similarity matrix for node 97/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_97.csv\n",
      "Calculating similarity matrix for node 98/300\n",
      "Exported similarity matrix for node 98/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_98.csv\n",
      "Calculating similarity matrix for node 99/300\n",
      "Exported similarity matrix for node 99/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_99.csv\n",
      "Calculating similarity matrix for node 100/300\n",
      "Exported similarity matrix for node 100/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_100.csv\n",
      "Calculating similarity matrix for node 101/300\n",
      "Exported similarity matrix for node 101/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_101.csv\n",
      "Calculating similarity matrix for node 102/300\n",
      "Exported similarity matrix for node 102/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_102.csv\n",
      "Calculating similarity matrix for node 103/300\n",
      "Exported similarity matrix for node 103/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_103.csv\n",
      "Calculating similarity matrix for node 104/300\n",
      "Exported similarity matrix for node 104/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_104.csv\n",
      "Calculating similarity matrix for node 105/300\n",
      "Exported similarity matrix for node 105/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_105.csv\n",
      "Calculating similarity matrix for node 106/300\n",
      "Exported similarity matrix for node 106/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_106.csv\n",
      "Calculating similarity matrix for node 107/300\n",
      "Exported similarity matrix for node 107/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_107.csv\n",
      "Calculating similarity matrix for node 108/300\n",
      "Exported similarity matrix for node 108/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_108.csv\n",
      "Calculating similarity matrix for node 109/300\n",
      "Exported similarity matrix for node 109/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_109.csv\n",
      "Calculating similarity matrix for node 110/300\n",
      "Exported similarity matrix for node 110/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_110.csv\n",
      "Calculating similarity matrix for node 111/300\n",
      "Exported similarity matrix for node 111/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_111.csv\n",
      "Calculating similarity matrix for node 112/300\n",
      "Exported similarity matrix for node 112/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_112.csv\n",
      "Calculating similarity matrix for node 113/300\n",
      "Exported similarity matrix for node 113/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_113.csv\n",
      "Calculating similarity matrix for node 114/300\n",
      "Exported similarity matrix for node 114/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_114.csv\n",
      "Calculating similarity matrix for node 115/300\n",
      "Exported similarity matrix for node 115/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_115.csv\n",
      "Calculating similarity matrix for node 116/300\n",
      "Exported similarity matrix for node 116/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_116.csv\n",
      "Calculating similarity matrix for node 117/300\n",
      "Exported similarity matrix for node 117/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_117.csv\n",
      "Calculating similarity matrix for node 118/300\n",
      "Exported similarity matrix for node 118/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_118.csv\n",
      "Calculating similarity matrix for node 119/300\n",
      "Exported similarity matrix for node 119/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_119.csv\n",
      "Calculating similarity matrix for node 120/300\n",
      "Exported similarity matrix for node 120/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_120.csv\n",
      "Calculating similarity matrix for node 121/300\n",
      "Exported similarity matrix for node 121/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_121.csv\n",
      "Calculating similarity matrix for node 122/300\n",
      "Exported similarity matrix for node 122/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_122.csv\n",
      "Calculating similarity matrix for node 123/300\n",
      "Exported similarity matrix for node 123/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_123.csv\n",
      "Calculating similarity matrix for node 124/300\n",
      "Exported similarity matrix for node 124/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_124.csv\n",
      "Calculating similarity matrix for node 125/300\n",
      "Exported similarity matrix for node 125/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_125.csv\n",
      "Calculating similarity matrix for node 126/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 126/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_126.csv\n",
      "Calculating similarity matrix for node 127/300\n",
      "Exported similarity matrix for node 127/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_127.csv\n",
      "Calculating similarity matrix for node 128/300\n",
      "Exported similarity matrix for node 128/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_128.csv\n",
      "Calculating similarity matrix for node 129/300\n",
      "Exported similarity matrix for node 129/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_129.csv\n",
      "Calculating similarity matrix for node 130/300\n",
      "Exported similarity matrix for node 130/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_130.csv\n",
      "Calculating similarity matrix for node 131/300\n",
      "Exported similarity matrix for node 131/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_131.csv\n",
      "Calculating similarity matrix for node 132/300\n",
      "Exported similarity matrix for node 132/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_132.csv\n",
      "Calculating similarity matrix for node 133/300\n",
      "Exported similarity matrix for node 133/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_133.csv\n",
      "Calculating similarity matrix for node 134/300\n",
      "Exported similarity matrix for node 134/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_134.csv\n",
      "Calculating similarity matrix for node 135/300\n",
      "Exported similarity matrix for node 135/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_135.csv\n",
      "Calculating similarity matrix for node 136/300\n",
      "Exported similarity matrix for node 136/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_136.csv\n",
      "Calculating similarity matrix for node 137/300\n",
      "Exported similarity matrix for node 137/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_137.csv\n",
      "Calculating similarity matrix for node 138/300\n",
      "Exported similarity matrix for node 138/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_138.csv\n",
      "Calculating similarity matrix for node 139/300\n",
      "Exported similarity matrix for node 139/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_139.csv\n",
      "Calculating similarity matrix for node 140/300\n",
      "Exported similarity matrix for node 140/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_140.csv\n",
      "Calculating similarity matrix for node 141/300\n",
      "Exported similarity matrix for node 141/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_141.csv\n",
      "Calculating similarity matrix for node 142/300\n",
      "Exported similarity matrix for node 142/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_142.csv\n",
      "Calculating similarity matrix for node 143/300\n",
      "Exported similarity matrix for node 143/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_143.csv\n",
      "Calculating similarity matrix for node 144/300\n",
      "Exported similarity matrix for node 144/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_144.csv\n",
      "Calculating similarity matrix for node 145/300\n",
      "Exported similarity matrix for node 145/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_145.csv\n",
      "Calculating similarity matrix for node 146/300\n",
      "Exported similarity matrix for node 146/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_146.csv\n",
      "Calculating similarity matrix for node 147/300\n",
      "Exported similarity matrix for node 147/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_147.csv\n",
      "Calculating similarity matrix for node 148/300\n",
      "Exported similarity matrix for node 148/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_148.csv\n",
      "Calculating similarity matrix for node 149/300\n",
      "Exported similarity matrix for node 149/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_149.csv\n",
      "Calculating similarity matrix for node 150/300\n",
      "Exported similarity matrix for node 150/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_150.csv\n",
      "Calculating similarity matrix for node 151/300\n",
      "Exported similarity matrix for node 151/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_151.csv\n",
      "Calculating similarity matrix for node 152/300\n",
      "Exported similarity matrix for node 152/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_152.csv\n",
      "Calculating similarity matrix for node 153/300\n",
      "Exported similarity matrix for node 153/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_153.csv\n",
      "Calculating similarity matrix for node 154/300\n",
      "Exported similarity matrix for node 154/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_154.csv\n",
      "Calculating similarity matrix for node 155/300\n",
      "Exported similarity matrix for node 155/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_155.csv\n",
      "Calculating similarity matrix for node 156/300\n",
      "Exported similarity matrix for node 156/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_156.csv\n",
      "Calculating similarity matrix for node 157/300\n",
      "Exported similarity matrix for node 157/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_157.csv\n",
      "Calculating similarity matrix for node 158/300\n",
      "Exported similarity matrix for node 158/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_158.csv\n",
      "Calculating similarity matrix for node 159/300\n",
      "Exported similarity matrix for node 159/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_159.csv\n",
      "Calculating similarity matrix for node 160/300\n",
      "Exported similarity matrix for node 160/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_160.csv\n",
      "Calculating similarity matrix for node 161/300\n",
      "Exported similarity matrix for node 161/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_161.csv\n",
      "Calculating similarity matrix for node 162/300\n",
      "Exported similarity matrix for node 162/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_162.csv\n",
      "Calculating similarity matrix for node 163/300\n",
      "Exported similarity matrix for node 163/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_163.csv\n",
      "Calculating similarity matrix for node 164/300\n",
      "Exported similarity matrix for node 164/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_164.csv\n",
      "Calculating similarity matrix for node 165/300\n",
      "Exported similarity matrix for node 165/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_165.csv\n",
      "Calculating similarity matrix for node 166/300\n",
      "Exported similarity matrix for node 166/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_166.csv\n",
      "Calculating similarity matrix for node 167/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 167/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_167.csv\n",
      "Calculating similarity matrix for node 168/300\n",
      "Exported similarity matrix for node 168/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_168.csv\n",
      "Calculating similarity matrix for node 169/300\n",
      "Exported similarity matrix for node 169/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_169.csv\n",
      "Calculating similarity matrix for node 170/300\n",
      "Exported similarity matrix for node 170/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_170.csv\n",
      "Calculating similarity matrix for node 171/300\n",
      "Exported similarity matrix for node 171/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_171.csv\n",
      "Calculating similarity matrix for node 172/300\n",
      "Exported similarity matrix for node 172/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_172.csv\n",
      "Calculating similarity matrix for node 173/300\n",
      "Exported similarity matrix for node 173/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_173.csv\n",
      "Calculating similarity matrix for node 174/300\n",
      "Exported similarity matrix for node 174/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_174.csv\n",
      "Calculating similarity matrix for node 175/300\n",
      "Exported similarity matrix for node 175/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_175.csv\n",
      "Calculating similarity matrix for node 176/300\n",
      "Exported similarity matrix for node 176/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_176.csv\n",
      "Calculating similarity matrix for node 177/300\n",
      "Exported similarity matrix for node 177/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_177.csv\n",
      "Calculating similarity matrix for node 178/300\n",
      "Exported similarity matrix for node 178/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_178.csv\n",
      "Calculating similarity matrix for node 179/300\n",
      "Exported similarity matrix for node 179/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_179.csv\n",
      "Calculating similarity matrix for node 180/300\n",
      "Exported similarity matrix for node 180/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_180.csv\n",
      "Calculating similarity matrix for node 181/300\n",
      "Exported similarity matrix for node 181/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_181.csv\n",
      "Calculating similarity matrix for node 182/300\n",
      "Exported similarity matrix for node 182/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_182.csv\n",
      "Calculating similarity matrix for node 183/300\n",
      "Exported similarity matrix for node 183/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_183.csv\n",
      "Calculating similarity matrix for node 184/300\n",
      "Exported similarity matrix for node 184/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_184.csv\n",
      "Calculating similarity matrix for node 185/300\n",
      "Exported similarity matrix for node 185/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_185.csv\n",
      "Calculating similarity matrix for node 186/300\n",
      "Exported similarity matrix for node 186/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_186.csv\n",
      "Calculating similarity matrix for node 187/300\n",
      "Exported similarity matrix for node 187/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_187.csv\n",
      "Calculating similarity matrix for node 188/300\n",
      "Exported similarity matrix for node 188/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_188.csv\n",
      "Calculating similarity matrix for node 189/300\n",
      "Exported similarity matrix for node 189/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_189.csv\n",
      "Calculating similarity matrix for node 190/300\n",
      "Exported similarity matrix for node 190/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_190.csv\n",
      "Calculating similarity matrix for node 191/300\n",
      "Exported similarity matrix for node 191/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_191.csv\n",
      "Calculating similarity matrix for node 192/300\n",
      "Exported similarity matrix for node 192/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_192.csv\n",
      "Calculating similarity matrix for node 193/300\n",
      "Exported similarity matrix for node 193/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_193.csv\n",
      "Calculating similarity matrix for node 194/300\n",
      "Exported similarity matrix for node 194/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_194.csv\n",
      "Calculating similarity matrix for node 195/300\n",
      "Exported similarity matrix for node 195/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_195.csv\n",
      "Calculating similarity matrix for node 196/300\n",
      "Exported similarity matrix for node 196/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_196.csv\n",
      "Calculating similarity matrix for node 197/300\n",
      "Exported similarity matrix for node 197/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_197.csv\n",
      "Calculating similarity matrix for node 198/300\n",
      "Exported similarity matrix for node 198/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_198.csv\n",
      "Calculating similarity matrix for node 199/300\n",
      "Exported similarity matrix for node 199/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_199.csv\n",
      "Calculating similarity matrix for node 200/300\n",
      "Exported similarity matrix for node 200/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_200.csv\n",
      "Calculating similarity matrix for node 201/300\n",
      "Exported similarity matrix for node 201/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_201.csv\n",
      "Calculating similarity matrix for node 202/300\n",
      "Exported similarity matrix for node 202/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_202.csv\n",
      "Calculating similarity matrix for node 203/300\n",
      "Exported similarity matrix for node 203/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_203.csv\n",
      "Calculating similarity matrix for node 204/300\n",
      "Exported similarity matrix for node 204/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_204.csv\n",
      "Calculating similarity matrix for node 205/300\n",
      "Exported similarity matrix for node 205/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_205.csv\n",
      "Calculating similarity matrix for node 206/300\n",
      "Exported similarity matrix for node 206/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_206.csv\n",
      "Calculating similarity matrix for node 207/300\n",
      "Exported similarity matrix for node 207/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_207.csv\n",
      "Calculating similarity matrix for node 208/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 208/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_208.csv\n",
      "Calculating similarity matrix for node 209/300\n",
      "Exported similarity matrix for node 209/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_209.csv\n",
      "Calculating similarity matrix for node 210/300\n",
      "Exported similarity matrix for node 210/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_210.csv\n",
      "Calculating similarity matrix for node 211/300\n",
      "Exported similarity matrix for node 211/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_211.csv\n",
      "Calculating similarity matrix for node 212/300\n",
      "Exported similarity matrix for node 212/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_212.csv\n",
      "Calculating similarity matrix for node 213/300\n",
      "Exported similarity matrix for node 213/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_213.csv\n",
      "Calculating similarity matrix for node 214/300\n",
      "Exported similarity matrix for node 214/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_214.csv\n",
      "Calculating similarity matrix for node 215/300\n",
      "Exported similarity matrix for node 215/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_215.csv\n",
      "Calculating similarity matrix for node 216/300\n",
      "Exported similarity matrix for node 216/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_216.csv\n",
      "Calculating similarity matrix for node 217/300\n",
      "Exported similarity matrix for node 217/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_217.csv\n",
      "Calculating similarity matrix for node 218/300\n",
      "Exported similarity matrix for node 218/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_218.csv\n",
      "Calculating similarity matrix for node 219/300\n",
      "Exported similarity matrix for node 219/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_219.csv\n",
      "Calculating similarity matrix for node 220/300\n",
      "Exported similarity matrix for node 220/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_220.csv\n",
      "Calculating similarity matrix for node 221/300\n",
      "Exported similarity matrix for node 221/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_221.csv\n",
      "Calculating similarity matrix for node 222/300\n",
      "Exported similarity matrix for node 222/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_222.csv\n",
      "Calculating similarity matrix for node 223/300\n",
      "Exported similarity matrix for node 223/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_223.csv\n",
      "Calculating similarity matrix for node 224/300\n",
      "Exported similarity matrix for node 224/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_224.csv\n",
      "Calculating similarity matrix for node 225/300\n",
      "Exported similarity matrix for node 225/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_225.csv\n",
      "Calculating similarity matrix for node 226/300\n",
      "Exported similarity matrix for node 226/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_226.csv\n",
      "Calculating similarity matrix for node 227/300\n",
      "Exported similarity matrix for node 227/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_227.csv\n",
      "Calculating similarity matrix for node 228/300\n",
      "Exported similarity matrix for node 228/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_228.csv\n",
      "Calculating similarity matrix for node 229/300\n",
      "Exported similarity matrix for node 229/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_229.csv\n",
      "Calculating similarity matrix for node 230/300\n",
      "Exported similarity matrix for node 230/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_230.csv\n",
      "Calculating similarity matrix for node 231/300\n",
      "Exported similarity matrix for node 231/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_231.csv\n",
      "Calculating similarity matrix for node 232/300\n",
      "Exported similarity matrix for node 232/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_232.csv\n",
      "Calculating similarity matrix for node 233/300\n",
      "Exported similarity matrix for node 233/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_233.csv\n",
      "Calculating similarity matrix for node 234/300\n",
      "Exported similarity matrix for node 234/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_234.csv\n",
      "Calculating similarity matrix for node 235/300\n",
      "Exported similarity matrix for node 235/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_235.csv\n",
      "Calculating similarity matrix for node 236/300\n",
      "Exported similarity matrix for node 236/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_236.csv\n",
      "Calculating similarity matrix for node 237/300\n",
      "Exported similarity matrix for node 237/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_237.csv\n",
      "Calculating similarity matrix for node 238/300\n",
      "Exported similarity matrix for node 238/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_238.csv\n",
      "Calculating similarity matrix for node 239/300\n",
      "Exported similarity matrix for node 239/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_239.csv\n",
      "Calculating similarity matrix for node 240/300\n",
      "Exported similarity matrix for node 240/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_240.csv\n",
      "Calculating similarity matrix for node 241/300\n",
      "Exported similarity matrix for node 241/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_241.csv\n",
      "Calculating similarity matrix for node 242/300\n",
      "Exported similarity matrix for node 242/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_242.csv\n",
      "Calculating similarity matrix for node 243/300\n",
      "Exported similarity matrix for node 243/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_243.csv\n",
      "Calculating similarity matrix for node 244/300\n",
      "Exported similarity matrix for node 244/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_244.csv\n",
      "Calculating similarity matrix for node 245/300\n",
      "Exported similarity matrix for node 245/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_245.csv\n",
      "Calculating similarity matrix for node 246/300\n",
      "Exported similarity matrix for node 246/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_246.csv\n",
      "Calculating similarity matrix for node 247/300\n",
      "Exported similarity matrix for node 247/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_247.csv\n",
      "Calculating similarity matrix for node 248/300\n",
      "Exported similarity matrix for node 248/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_248.csv\n",
      "Calculating similarity matrix for node 249/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 249/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_249.csv\n",
      "Calculating similarity matrix for node 250/300\n",
      "Exported similarity matrix for node 250/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_250.csv\n",
      "Calculating similarity matrix for node 251/300\n",
      "Exported similarity matrix for node 251/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_251.csv\n",
      "Calculating similarity matrix for node 252/300\n",
      "Exported similarity matrix for node 252/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_252.csv\n",
      "Calculating similarity matrix for node 253/300\n",
      "Exported similarity matrix for node 253/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_253.csv\n",
      "Calculating similarity matrix for node 254/300\n",
      "Exported similarity matrix for node 254/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_254.csv\n",
      "Calculating similarity matrix for node 255/300\n",
      "Exported similarity matrix for node 255/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_255.csv\n",
      "Calculating similarity matrix for node 256/300\n",
      "Exported similarity matrix for node 256/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_256.csv\n",
      "Calculating similarity matrix for node 257/300\n",
      "Exported similarity matrix for node 257/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_257.csv\n",
      "Calculating similarity matrix for node 258/300\n",
      "Exported similarity matrix for node 258/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_258.csv\n",
      "Calculating similarity matrix for node 259/300\n",
      "Exported similarity matrix for node 259/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_259.csv\n",
      "Calculating similarity matrix for node 260/300\n",
      "Exported similarity matrix for node 260/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_260.csv\n",
      "Calculating similarity matrix for node 261/300\n",
      "Exported similarity matrix for node 261/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_261.csv\n",
      "Calculating similarity matrix for node 262/300\n",
      "Exported similarity matrix for node 262/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_262.csv\n",
      "Calculating similarity matrix for node 263/300\n",
      "Exported similarity matrix for node 263/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_263.csv\n",
      "Calculating similarity matrix for node 264/300\n",
      "Exported similarity matrix for node 264/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_264.csv\n",
      "Calculating similarity matrix for node 265/300\n",
      "Exported similarity matrix for node 265/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_265.csv\n",
      "Calculating similarity matrix for node 266/300\n",
      "Exported similarity matrix for node 266/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_266.csv\n",
      "Calculating similarity matrix for node 267/300\n",
      "Exported similarity matrix for node 267/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_267.csv\n",
      "Calculating similarity matrix for node 268/300\n",
      "Exported similarity matrix for node 268/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_268.csv\n",
      "Calculating similarity matrix for node 269/300\n",
      "Exported similarity matrix for node 269/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_269.csv\n",
      "Calculating similarity matrix for node 270/300\n",
      "Exported similarity matrix for node 270/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_270.csv\n",
      "Calculating similarity matrix for node 271/300\n",
      "Exported similarity matrix for node 271/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_271.csv\n",
      "Calculating similarity matrix for node 272/300\n",
      "Exported similarity matrix for node 272/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_272.csv\n",
      "Calculating similarity matrix for node 273/300\n",
      "Exported similarity matrix for node 273/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_273.csv\n",
      "Calculating similarity matrix for node 274/300\n",
      "Exported similarity matrix for node 274/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_274.csv\n",
      "Calculating similarity matrix for node 275/300\n",
      "Exported similarity matrix for node 275/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_275.csv\n",
      "Calculating similarity matrix for node 276/300\n",
      "Exported similarity matrix for node 276/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_276.csv\n",
      "Calculating similarity matrix for node 277/300\n",
      "Exported similarity matrix for node 277/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_277.csv\n",
      "Calculating similarity matrix for node 278/300\n",
      "Exported similarity matrix for node 278/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_278.csv\n",
      "Calculating similarity matrix for node 279/300\n",
      "Exported similarity matrix for node 279/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_279.csv\n",
      "Calculating similarity matrix for node 280/300\n",
      "Exported similarity matrix for node 280/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_280.csv\n",
      "Calculating similarity matrix for node 281/300\n",
      "Exported similarity matrix for node 281/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_281.csv\n",
      "Calculating similarity matrix for node 282/300\n",
      "Exported similarity matrix for node 282/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_282.csv\n",
      "Calculating similarity matrix for node 283/300\n",
      "Exported similarity matrix for node 283/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_283.csv\n",
      "Calculating similarity matrix for node 284/300\n",
      "Exported similarity matrix for node 284/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_284.csv\n",
      "Calculating similarity matrix for node 285/300\n",
      "Exported similarity matrix for node 285/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_285.csv\n",
      "Calculating similarity matrix for node 286/300\n",
      "Exported similarity matrix for node 286/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_286.csv\n",
      "Calculating similarity matrix for node 287/300\n",
      "Exported similarity matrix for node 287/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_287.csv\n",
      "Calculating similarity matrix for node 288/300\n",
      "Exported similarity matrix for node 288/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_288.csv\n",
      "Calculating similarity matrix for node 289/300\n",
      "Exported similarity matrix for node 289/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_289.csv\n",
      "Calculating similarity matrix for node 290/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 290/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_290.csv\n",
      "Calculating similarity matrix for node 291/300\n",
      "Exported similarity matrix for node 291/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_291.csv\n",
      "Calculating similarity matrix for node 292/300\n",
      "Exported similarity matrix for node 292/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_292.csv\n",
      "Calculating similarity matrix for node 293/300\n",
      "Exported similarity matrix for node 293/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_293.csv\n",
      "Calculating similarity matrix for node 294/300\n",
      "Exported similarity matrix for node 294/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_294.csv\n",
      "Calculating similarity matrix for node 295/300\n",
      "Exported similarity matrix for node 295/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_295.csv\n",
      "Calculating similarity matrix for node 296/300\n",
      "Exported similarity matrix for node 296/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_296.csv\n",
      "Calculating similarity matrix for node 297/300\n",
      "Exported similarity matrix for node 297/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_297.csv\n",
      "Calculating similarity matrix for node 298/300\n",
      "Exported similarity matrix for node 298/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_298.csv\n",
      "Calculating similarity matrix for node 299/300\n",
      "Exported similarity matrix for node 299/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_299.csv\n",
      "Calculating similarity matrix for node 300/300\n",
      "Exported similarity matrix for node 300/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral/similarity_node_300.csv\n",
      "Calculating similarity matrix for node 1/300\n",
      "Exported similarity matrix for node 1/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_1.csv\n",
      "Calculating similarity matrix for node 2/300\n",
      "Exported similarity matrix for node 2/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_2.csv\n",
      "Calculating similarity matrix for node 3/300\n",
      "Exported similarity matrix for node 3/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_3.csv\n",
      "Calculating similarity matrix for node 4/300\n",
      "Exported similarity matrix for node 4/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_4.csv\n",
      "Calculating similarity matrix for node 5/300\n",
      "Exported similarity matrix for node 5/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_5.csv\n",
      "Calculating similarity matrix for node 6/300\n",
      "Exported similarity matrix for node 6/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_6.csv\n",
      "Calculating similarity matrix for node 7/300\n",
      "Exported similarity matrix for node 7/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_7.csv\n",
      "Calculating similarity matrix for node 8/300\n",
      "Exported similarity matrix for node 8/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_8.csv\n",
      "Calculating similarity matrix for node 9/300\n",
      "Exported similarity matrix for node 9/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_9.csv\n",
      "Calculating similarity matrix for node 10/300\n",
      "Exported similarity matrix for node 10/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_10.csv\n",
      "Calculating similarity matrix for node 11/300\n",
      "Exported similarity matrix for node 11/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_11.csv\n",
      "Calculating similarity matrix for node 12/300\n",
      "Exported similarity matrix for node 12/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_12.csv\n",
      "Calculating similarity matrix for node 13/300\n",
      "Exported similarity matrix for node 13/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_13.csv\n",
      "Calculating similarity matrix for node 14/300\n",
      "Exported similarity matrix for node 14/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_14.csv\n",
      "Calculating similarity matrix for node 15/300\n",
      "Exported similarity matrix for node 15/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_15.csv\n",
      "Calculating similarity matrix for node 16/300\n",
      "Exported similarity matrix for node 16/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_16.csv\n",
      "Calculating similarity matrix for node 17/300\n",
      "Exported similarity matrix for node 17/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_17.csv\n",
      "Calculating similarity matrix for node 18/300\n",
      "Exported similarity matrix for node 18/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_18.csv\n",
      "Calculating similarity matrix for node 19/300\n",
      "Exported similarity matrix for node 19/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_19.csv\n",
      "Calculating similarity matrix for node 20/300\n",
      "Exported similarity matrix for node 20/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_20.csv\n",
      "Calculating similarity matrix for node 21/300\n",
      "Exported similarity matrix for node 21/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_21.csv\n",
      "Calculating similarity matrix for node 22/300\n",
      "Exported similarity matrix for node 22/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_22.csv\n",
      "Calculating similarity matrix for node 23/300\n",
      "Exported similarity matrix for node 23/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_23.csv\n",
      "Calculating similarity matrix for node 24/300\n",
      "Exported similarity matrix for node 24/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_24.csv\n",
      "Calculating similarity matrix for node 25/300\n",
      "Exported similarity matrix for node 25/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_25.csv\n",
      "Calculating similarity matrix for node 26/300\n",
      "Exported similarity matrix for node 26/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_26.csv\n",
      "Calculating similarity matrix for node 27/300\n",
      "Exported similarity matrix for node 27/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_27.csv\n",
      "Calculating similarity matrix for node 28/300\n",
      "Exported similarity matrix for node 28/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_28.csv\n",
      "Calculating similarity matrix for node 29/300\n",
      "Exported similarity matrix for node 29/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_29.csv\n",
      "Calculating similarity matrix for node 30/300\n",
      "Exported similarity matrix for node 30/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_30.csv\n",
      "Calculating similarity matrix for node 31/300\n",
      "Exported similarity matrix for node 31/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_31.csv\n",
      "Calculating similarity matrix for node 32/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 32/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_32.csv\n",
      "Calculating similarity matrix for node 33/300\n",
      "Exported similarity matrix for node 33/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_33.csv\n",
      "Calculating similarity matrix for node 34/300\n",
      "Exported similarity matrix for node 34/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_34.csv\n",
      "Calculating similarity matrix for node 35/300\n",
      "Exported similarity matrix for node 35/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_35.csv\n",
      "Calculating similarity matrix for node 36/300\n",
      "Exported similarity matrix for node 36/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_36.csv\n",
      "Calculating similarity matrix for node 37/300\n",
      "Exported similarity matrix for node 37/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_37.csv\n",
      "Calculating similarity matrix for node 38/300\n",
      "Exported similarity matrix for node 38/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_38.csv\n",
      "Calculating similarity matrix for node 39/300\n",
      "Exported similarity matrix for node 39/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_39.csv\n",
      "Calculating similarity matrix for node 40/300\n",
      "Exported similarity matrix for node 40/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_40.csv\n",
      "Calculating similarity matrix for node 41/300\n",
      "Exported similarity matrix for node 41/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_41.csv\n",
      "Calculating similarity matrix for node 42/300\n",
      "Exported similarity matrix for node 42/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_42.csv\n",
      "Calculating similarity matrix for node 43/300\n",
      "Exported similarity matrix for node 43/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_43.csv\n",
      "Calculating similarity matrix for node 44/300\n",
      "Exported similarity matrix for node 44/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_44.csv\n",
      "Calculating similarity matrix for node 45/300\n",
      "Exported similarity matrix for node 45/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_45.csv\n",
      "Calculating similarity matrix for node 46/300\n",
      "Exported similarity matrix for node 46/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_46.csv\n",
      "Calculating similarity matrix for node 47/300\n",
      "Exported similarity matrix for node 47/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_47.csv\n",
      "Calculating similarity matrix for node 48/300\n",
      "Exported similarity matrix for node 48/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_48.csv\n",
      "Calculating similarity matrix for node 49/300\n",
      "Exported similarity matrix for node 49/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_49.csv\n",
      "Calculating similarity matrix for node 50/300\n",
      "Exported similarity matrix for node 50/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_50.csv\n",
      "Calculating similarity matrix for node 51/300\n",
      "Exported similarity matrix for node 51/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_51.csv\n",
      "Calculating similarity matrix for node 52/300\n",
      "Exported similarity matrix for node 52/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_52.csv\n",
      "Calculating similarity matrix for node 53/300\n",
      "Exported similarity matrix for node 53/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_53.csv\n",
      "Calculating similarity matrix for node 54/300\n",
      "Exported similarity matrix for node 54/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_54.csv\n",
      "Calculating similarity matrix for node 55/300\n",
      "Exported similarity matrix for node 55/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_55.csv\n",
      "Calculating similarity matrix for node 56/300\n",
      "Exported similarity matrix for node 56/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_56.csv\n",
      "Calculating similarity matrix for node 57/300\n",
      "Exported similarity matrix for node 57/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_57.csv\n",
      "Calculating similarity matrix for node 58/300\n",
      "Exported similarity matrix for node 58/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_58.csv\n",
      "Calculating similarity matrix for node 59/300\n",
      "Exported similarity matrix for node 59/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_59.csv\n",
      "Calculating similarity matrix for node 60/300\n",
      "Exported similarity matrix for node 60/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_60.csv\n",
      "Calculating similarity matrix for node 61/300\n",
      "Exported similarity matrix for node 61/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_61.csv\n",
      "Calculating similarity matrix for node 62/300\n",
      "Exported similarity matrix for node 62/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_62.csv\n",
      "Calculating similarity matrix for node 63/300\n",
      "Exported similarity matrix for node 63/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_63.csv\n",
      "Calculating similarity matrix for node 64/300\n",
      "Exported similarity matrix for node 64/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_64.csv\n",
      "Calculating similarity matrix for node 65/300\n",
      "Exported similarity matrix for node 65/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_65.csv\n",
      "Calculating similarity matrix for node 66/300\n",
      "Exported similarity matrix for node 66/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_66.csv\n",
      "Calculating similarity matrix for node 67/300\n",
      "Exported similarity matrix for node 67/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_67.csv\n",
      "Calculating similarity matrix for node 68/300\n",
      "Exported similarity matrix for node 68/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_68.csv\n",
      "Calculating similarity matrix for node 69/300\n",
      "Exported similarity matrix for node 69/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_69.csv\n",
      "Calculating similarity matrix for node 70/300\n",
      "Exported similarity matrix for node 70/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_70.csv\n",
      "Calculating similarity matrix for node 71/300\n",
      "Exported similarity matrix for node 71/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_71.csv\n",
      "Calculating similarity matrix for node 72/300\n",
      "Exported similarity matrix for node 72/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_72.csv\n",
      "Calculating similarity matrix for node 73/300\n",
      "Exported similarity matrix for node 73/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_73.csv\n",
      "Calculating similarity matrix for node 74/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 74/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_74.csv\n",
      "Calculating similarity matrix for node 75/300\n",
      "Exported similarity matrix for node 75/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_75.csv\n",
      "Calculating similarity matrix for node 76/300\n",
      "Exported similarity matrix for node 76/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_76.csv\n",
      "Calculating similarity matrix for node 77/300\n",
      "Exported similarity matrix for node 77/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_77.csv\n",
      "Calculating similarity matrix for node 78/300\n",
      "Exported similarity matrix for node 78/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_78.csv\n",
      "Calculating similarity matrix for node 79/300\n",
      "Exported similarity matrix for node 79/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_79.csv\n",
      "Calculating similarity matrix for node 80/300\n",
      "Exported similarity matrix for node 80/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_80.csv\n",
      "Calculating similarity matrix for node 81/300\n",
      "Exported similarity matrix for node 81/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_81.csv\n",
      "Calculating similarity matrix for node 82/300\n",
      "Exported similarity matrix for node 82/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_82.csv\n",
      "Calculating similarity matrix for node 83/300\n",
      "Exported similarity matrix for node 83/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_83.csv\n",
      "Calculating similarity matrix for node 84/300\n",
      "Exported similarity matrix for node 84/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_84.csv\n",
      "Calculating similarity matrix for node 85/300\n",
      "Exported similarity matrix for node 85/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_85.csv\n",
      "Calculating similarity matrix for node 86/300\n",
      "Exported similarity matrix for node 86/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_86.csv\n",
      "Calculating similarity matrix for node 87/300\n",
      "Exported similarity matrix for node 87/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_87.csv\n",
      "Calculating similarity matrix for node 88/300\n",
      "Exported similarity matrix for node 88/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_88.csv\n",
      "Calculating similarity matrix for node 89/300\n",
      "Exported similarity matrix for node 89/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_89.csv\n",
      "Calculating similarity matrix for node 90/300\n",
      "Exported similarity matrix for node 90/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_90.csv\n",
      "Calculating similarity matrix for node 91/300\n",
      "Exported similarity matrix for node 91/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_91.csv\n",
      "Calculating similarity matrix for node 92/300\n",
      "Exported similarity matrix for node 92/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_92.csv\n",
      "Calculating similarity matrix for node 93/300\n",
      "Exported similarity matrix for node 93/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_93.csv\n",
      "Calculating similarity matrix for node 94/300\n",
      "Exported similarity matrix for node 94/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_94.csv\n",
      "Calculating similarity matrix for node 95/300\n",
      "Exported similarity matrix for node 95/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_95.csv\n",
      "Calculating similarity matrix for node 96/300\n",
      "Exported similarity matrix for node 96/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_96.csv\n",
      "Calculating similarity matrix for node 97/300\n",
      "Exported similarity matrix for node 97/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_97.csv\n",
      "Calculating similarity matrix for node 98/300\n",
      "Exported similarity matrix for node 98/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_98.csv\n",
      "Calculating similarity matrix for node 99/300\n",
      "Exported similarity matrix for node 99/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_99.csv\n",
      "Calculating similarity matrix for node 100/300\n",
      "Exported similarity matrix for node 100/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_100.csv\n",
      "Calculating similarity matrix for node 101/300\n",
      "Exported similarity matrix for node 101/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_101.csv\n",
      "Calculating similarity matrix for node 102/300\n",
      "Exported similarity matrix for node 102/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_102.csv\n",
      "Calculating similarity matrix for node 103/300\n",
      "Exported similarity matrix for node 103/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_103.csv\n",
      "Calculating similarity matrix for node 104/300\n",
      "Exported similarity matrix for node 104/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_104.csv\n",
      "Calculating similarity matrix for node 105/300\n",
      "Exported similarity matrix for node 105/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_105.csv\n",
      "Calculating similarity matrix for node 106/300\n",
      "Exported similarity matrix for node 106/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_106.csv\n",
      "Calculating similarity matrix for node 107/300\n",
      "Exported similarity matrix for node 107/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_107.csv\n",
      "Calculating similarity matrix for node 108/300\n",
      "Exported similarity matrix for node 108/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_108.csv\n",
      "Calculating similarity matrix for node 109/300\n",
      "Exported similarity matrix for node 109/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_109.csv\n",
      "Calculating similarity matrix for node 110/300\n",
      "Exported similarity matrix for node 110/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_110.csv\n",
      "Calculating similarity matrix for node 111/300\n",
      "Exported similarity matrix for node 111/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_111.csv\n",
      "Calculating similarity matrix for node 112/300\n",
      "Exported similarity matrix for node 112/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_112.csv\n",
      "Calculating similarity matrix for node 113/300\n",
      "Exported similarity matrix for node 113/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_113.csv\n",
      "Calculating similarity matrix for node 114/300\n",
      "Exported similarity matrix for node 114/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_114.csv\n",
      "Calculating similarity matrix for node 115/300\n",
      "Exported similarity matrix for node 115/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_115.csv\n",
      "Calculating similarity matrix for node 116/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 116/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_116.csv\n",
      "Calculating similarity matrix for node 117/300\n",
      "Exported similarity matrix for node 117/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_117.csv\n",
      "Calculating similarity matrix for node 118/300\n",
      "Exported similarity matrix for node 118/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_118.csv\n",
      "Calculating similarity matrix for node 119/300\n",
      "Exported similarity matrix for node 119/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_119.csv\n",
      "Calculating similarity matrix for node 120/300\n",
      "Exported similarity matrix for node 120/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_120.csv\n",
      "Calculating similarity matrix for node 121/300\n",
      "Exported similarity matrix for node 121/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_121.csv\n",
      "Calculating similarity matrix for node 122/300\n",
      "Exported similarity matrix for node 122/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_122.csv\n",
      "Calculating similarity matrix for node 123/300\n",
      "Exported similarity matrix for node 123/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_123.csv\n",
      "Calculating similarity matrix for node 124/300\n",
      "Exported similarity matrix for node 124/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_124.csv\n",
      "Calculating similarity matrix for node 125/300\n",
      "Exported similarity matrix for node 125/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_125.csv\n",
      "Calculating similarity matrix for node 126/300\n",
      "Exported similarity matrix for node 126/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_126.csv\n",
      "Calculating similarity matrix for node 127/300\n",
      "Exported similarity matrix for node 127/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_127.csv\n",
      "Calculating similarity matrix for node 128/300\n",
      "Exported similarity matrix for node 128/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_128.csv\n",
      "Calculating similarity matrix for node 129/300\n",
      "Exported similarity matrix for node 129/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_129.csv\n",
      "Calculating similarity matrix for node 130/300\n",
      "Exported similarity matrix for node 130/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_130.csv\n",
      "Calculating similarity matrix for node 131/300\n",
      "Exported similarity matrix for node 131/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_131.csv\n",
      "Calculating similarity matrix for node 132/300\n",
      "Exported similarity matrix for node 132/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_132.csv\n",
      "Calculating similarity matrix for node 133/300\n",
      "Exported similarity matrix for node 133/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_133.csv\n",
      "Calculating similarity matrix for node 134/300\n",
      "Exported similarity matrix for node 134/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_134.csv\n",
      "Calculating similarity matrix for node 135/300\n",
      "Exported similarity matrix for node 135/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_135.csv\n",
      "Calculating similarity matrix for node 136/300\n",
      "Exported similarity matrix for node 136/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_136.csv\n",
      "Calculating similarity matrix for node 137/300\n",
      "Exported similarity matrix for node 137/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_137.csv\n",
      "Calculating similarity matrix for node 138/300\n",
      "Exported similarity matrix for node 138/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_138.csv\n",
      "Calculating similarity matrix for node 139/300\n",
      "Exported similarity matrix for node 139/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_139.csv\n",
      "Calculating similarity matrix for node 140/300\n",
      "Exported similarity matrix for node 140/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_140.csv\n",
      "Calculating similarity matrix for node 141/300\n",
      "Exported similarity matrix for node 141/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_141.csv\n",
      "Calculating similarity matrix for node 142/300\n",
      "Exported similarity matrix for node 142/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_142.csv\n",
      "Calculating similarity matrix for node 143/300\n",
      "Exported similarity matrix for node 143/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_143.csv\n",
      "Calculating similarity matrix for node 144/300\n",
      "Exported similarity matrix for node 144/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_144.csv\n",
      "Calculating similarity matrix for node 145/300\n",
      "Exported similarity matrix for node 145/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_145.csv\n",
      "Calculating similarity matrix for node 146/300\n",
      "Exported similarity matrix for node 146/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_146.csv\n",
      "Calculating similarity matrix for node 147/300\n",
      "Exported similarity matrix for node 147/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_147.csv\n",
      "Calculating similarity matrix for node 148/300\n",
      "Exported similarity matrix for node 148/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_148.csv\n",
      "Calculating similarity matrix for node 149/300\n",
      "Exported similarity matrix for node 149/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_149.csv\n",
      "Calculating similarity matrix for node 150/300\n",
      "Exported similarity matrix for node 150/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_150.csv\n",
      "Calculating similarity matrix for node 151/300\n",
      "Exported similarity matrix for node 151/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_151.csv\n",
      "Calculating similarity matrix for node 152/300\n",
      "Exported similarity matrix for node 152/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_152.csv\n",
      "Calculating similarity matrix for node 153/300\n",
      "Exported similarity matrix for node 153/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_153.csv\n",
      "Calculating similarity matrix for node 154/300\n",
      "Exported similarity matrix for node 154/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_154.csv\n",
      "Calculating similarity matrix for node 155/300\n",
      "Exported similarity matrix for node 155/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_155.csv\n",
      "Calculating similarity matrix for node 156/300\n",
      "Exported similarity matrix for node 156/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_156.csv\n",
      "Calculating similarity matrix for node 157/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 157/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_157.csv\n",
      "Calculating similarity matrix for node 158/300\n",
      "Exported similarity matrix for node 158/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_158.csv\n",
      "Calculating similarity matrix for node 159/300\n",
      "Exported similarity matrix for node 159/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_159.csv\n",
      "Calculating similarity matrix for node 160/300\n",
      "Exported similarity matrix for node 160/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_160.csv\n",
      "Calculating similarity matrix for node 161/300\n",
      "Exported similarity matrix for node 161/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_161.csv\n",
      "Calculating similarity matrix for node 162/300\n",
      "Exported similarity matrix for node 162/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_162.csv\n",
      "Calculating similarity matrix for node 163/300\n",
      "Exported similarity matrix for node 163/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_163.csv\n",
      "Calculating similarity matrix for node 164/300\n",
      "Exported similarity matrix for node 164/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_164.csv\n",
      "Calculating similarity matrix for node 165/300\n",
      "Exported similarity matrix for node 165/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_165.csv\n",
      "Calculating similarity matrix for node 166/300\n",
      "Exported similarity matrix for node 166/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_166.csv\n",
      "Calculating similarity matrix for node 167/300\n",
      "Exported similarity matrix for node 167/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_167.csv\n",
      "Calculating similarity matrix for node 168/300\n",
      "Exported similarity matrix for node 168/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_168.csv\n",
      "Calculating similarity matrix for node 169/300\n",
      "Exported similarity matrix for node 169/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_169.csv\n",
      "Calculating similarity matrix for node 170/300\n",
      "Exported similarity matrix for node 170/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_170.csv\n",
      "Calculating similarity matrix for node 171/300\n",
      "Exported similarity matrix for node 171/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_171.csv\n",
      "Calculating similarity matrix for node 172/300\n",
      "Exported similarity matrix for node 172/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_172.csv\n",
      "Calculating similarity matrix for node 173/300\n",
      "Exported similarity matrix for node 173/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_173.csv\n",
      "Calculating similarity matrix for node 174/300\n",
      "Exported similarity matrix for node 174/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_174.csv\n",
      "Calculating similarity matrix for node 175/300\n",
      "Exported similarity matrix for node 175/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_175.csv\n",
      "Calculating similarity matrix for node 176/300\n",
      "Exported similarity matrix for node 176/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_176.csv\n",
      "Calculating similarity matrix for node 177/300\n",
      "Exported similarity matrix for node 177/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_177.csv\n",
      "Calculating similarity matrix for node 178/300\n",
      "Exported similarity matrix for node 178/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_178.csv\n",
      "Calculating similarity matrix for node 179/300\n",
      "Exported similarity matrix for node 179/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_179.csv\n",
      "Calculating similarity matrix for node 180/300\n",
      "Exported similarity matrix for node 180/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_180.csv\n",
      "Calculating similarity matrix for node 181/300\n",
      "Exported similarity matrix for node 181/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_181.csv\n",
      "Calculating similarity matrix for node 182/300\n",
      "Exported similarity matrix for node 182/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_182.csv\n",
      "Calculating similarity matrix for node 183/300\n",
      "Exported similarity matrix for node 183/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_183.csv\n",
      "Calculating similarity matrix for node 184/300\n",
      "Exported similarity matrix for node 184/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_184.csv\n",
      "Calculating similarity matrix for node 185/300\n",
      "Exported similarity matrix for node 185/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_185.csv\n",
      "Calculating similarity matrix for node 186/300\n",
      "Exported similarity matrix for node 186/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_186.csv\n",
      "Calculating similarity matrix for node 187/300\n",
      "Exported similarity matrix for node 187/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_187.csv\n",
      "Calculating similarity matrix for node 188/300\n",
      "Exported similarity matrix for node 188/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_188.csv\n",
      "Calculating similarity matrix for node 189/300\n",
      "Exported similarity matrix for node 189/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_189.csv\n",
      "Calculating similarity matrix for node 190/300\n",
      "Exported similarity matrix for node 190/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_190.csv\n",
      "Calculating similarity matrix for node 191/300\n",
      "Exported similarity matrix for node 191/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_191.csv\n",
      "Calculating similarity matrix for node 192/300\n",
      "Exported similarity matrix for node 192/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_192.csv\n",
      "Calculating similarity matrix for node 193/300\n",
      "Exported similarity matrix for node 193/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_193.csv\n",
      "Calculating similarity matrix for node 194/300\n",
      "Exported similarity matrix for node 194/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_194.csv\n",
      "Calculating similarity matrix for node 195/300\n",
      "Exported similarity matrix for node 195/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_195.csv\n",
      "Calculating similarity matrix for node 196/300\n",
      "Exported similarity matrix for node 196/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_196.csv\n",
      "Calculating similarity matrix for node 197/300\n",
      "Exported similarity matrix for node 197/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_197.csv\n",
      "Calculating similarity matrix for node 198/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 198/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_198.csv\n",
      "Calculating similarity matrix for node 199/300\n",
      "Exported similarity matrix for node 199/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_199.csv\n",
      "Calculating similarity matrix for node 200/300\n",
      "Exported similarity matrix for node 200/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_200.csv\n",
      "Calculating similarity matrix for node 201/300\n",
      "Exported similarity matrix for node 201/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_201.csv\n",
      "Calculating similarity matrix for node 202/300\n",
      "Exported similarity matrix for node 202/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_202.csv\n",
      "Calculating similarity matrix for node 203/300\n",
      "Exported similarity matrix for node 203/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_203.csv\n",
      "Calculating similarity matrix for node 204/300\n",
      "Exported similarity matrix for node 204/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_204.csv\n",
      "Calculating similarity matrix for node 205/300\n",
      "Exported similarity matrix for node 205/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_205.csv\n",
      "Calculating similarity matrix for node 206/300\n",
      "Exported similarity matrix for node 206/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_206.csv\n",
      "Calculating similarity matrix for node 207/300\n",
      "Exported similarity matrix for node 207/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_207.csv\n",
      "Calculating similarity matrix for node 208/300\n",
      "Exported similarity matrix for node 208/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_208.csv\n",
      "Calculating similarity matrix for node 209/300\n",
      "Exported similarity matrix for node 209/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_209.csv\n",
      "Calculating similarity matrix for node 210/300\n",
      "Exported similarity matrix for node 210/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_210.csv\n",
      "Calculating similarity matrix for node 211/300\n",
      "Exported similarity matrix for node 211/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_211.csv\n",
      "Calculating similarity matrix for node 212/300\n",
      "Exported similarity matrix for node 212/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_212.csv\n",
      "Calculating similarity matrix for node 213/300\n",
      "Exported similarity matrix for node 213/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_213.csv\n",
      "Calculating similarity matrix for node 214/300\n",
      "Exported similarity matrix for node 214/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_214.csv\n",
      "Calculating similarity matrix for node 215/300\n",
      "Exported similarity matrix for node 215/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_215.csv\n",
      "Calculating similarity matrix for node 216/300\n",
      "Exported similarity matrix for node 216/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_216.csv\n",
      "Calculating similarity matrix for node 217/300\n",
      "Exported similarity matrix for node 217/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_217.csv\n",
      "Calculating similarity matrix for node 218/300\n",
      "Exported similarity matrix for node 218/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_218.csv\n",
      "Calculating similarity matrix for node 219/300\n",
      "Exported similarity matrix for node 219/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_219.csv\n",
      "Calculating similarity matrix for node 220/300\n",
      "Exported similarity matrix for node 220/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_220.csv\n",
      "Calculating similarity matrix for node 221/300\n",
      "Exported similarity matrix for node 221/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_221.csv\n",
      "Calculating similarity matrix for node 222/300\n",
      "Exported similarity matrix for node 222/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_222.csv\n",
      "Calculating similarity matrix for node 223/300\n",
      "Exported similarity matrix for node 223/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_223.csv\n",
      "Calculating similarity matrix for node 224/300\n",
      "Exported similarity matrix for node 224/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_224.csv\n",
      "Calculating similarity matrix for node 225/300\n",
      "Exported similarity matrix for node 225/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_225.csv\n",
      "Calculating similarity matrix for node 226/300\n",
      "Exported similarity matrix for node 226/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_226.csv\n",
      "Calculating similarity matrix for node 227/300\n",
      "Exported similarity matrix for node 227/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_227.csv\n",
      "Calculating similarity matrix for node 228/300\n",
      "Exported similarity matrix for node 228/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_228.csv\n",
      "Calculating similarity matrix for node 229/300\n",
      "Exported similarity matrix for node 229/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_229.csv\n",
      "Calculating similarity matrix for node 230/300\n",
      "Exported similarity matrix for node 230/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_230.csv\n",
      "Calculating similarity matrix for node 231/300\n",
      "Exported similarity matrix for node 231/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_231.csv\n",
      "Calculating similarity matrix for node 232/300\n",
      "Exported similarity matrix for node 232/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_232.csv\n",
      "Calculating similarity matrix for node 233/300\n",
      "Exported similarity matrix for node 233/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_233.csv\n",
      "Calculating similarity matrix for node 234/300\n",
      "Exported similarity matrix for node 234/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_234.csv\n",
      "Calculating similarity matrix for node 235/300\n",
      "Exported similarity matrix for node 235/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_235.csv\n",
      "Calculating similarity matrix for node 236/300\n",
      "Exported similarity matrix for node 236/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_236.csv\n",
      "Calculating similarity matrix for node 237/300\n",
      "Exported similarity matrix for node 237/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_237.csv\n",
      "Calculating similarity matrix for node 238/300\n",
      "Exported similarity matrix for node 238/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_238.csv\n",
      "Calculating similarity matrix for node 239/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 239/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_239.csv\n",
      "Calculating similarity matrix for node 240/300\n",
      "Exported similarity matrix for node 240/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_240.csv\n",
      "Calculating similarity matrix for node 241/300\n",
      "Exported similarity matrix for node 241/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_241.csv\n",
      "Calculating similarity matrix for node 242/300\n",
      "Exported similarity matrix for node 242/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_242.csv\n",
      "Calculating similarity matrix for node 243/300\n",
      "Exported similarity matrix for node 243/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_243.csv\n",
      "Calculating similarity matrix for node 244/300\n",
      "Exported similarity matrix for node 244/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_244.csv\n",
      "Calculating similarity matrix for node 245/300\n",
      "Exported similarity matrix for node 245/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_245.csv\n",
      "Calculating similarity matrix for node 246/300\n",
      "Exported similarity matrix for node 246/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_246.csv\n",
      "Calculating similarity matrix for node 247/300\n",
      "Exported similarity matrix for node 247/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_247.csv\n",
      "Calculating similarity matrix for node 248/300\n",
      "Exported similarity matrix for node 248/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_248.csv\n",
      "Calculating similarity matrix for node 249/300\n",
      "Exported similarity matrix for node 249/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_249.csv\n",
      "Calculating similarity matrix for node 250/300\n",
      "Exported similarity matrix for node 250/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_250.csv\n",
      "Calculating similarity matrix for node 251/300\n",
      "Exported similarity matrix for node 251/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_251.csv\n",
      "Calculating similarity matrix for node 252/300\n",
      "Exported similarity matrix for node 252/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_252.csv\n",
      "Calculating similarity matrix for node 253/300\n",
      "Exported similarity matrix for node 253/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_253.csv\n",
      "Calculating similarity matrix for node 254/300\n",
      "Exported similarity matrix for node 254/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_254.csv\n",
      "Calculating similarity matrix for node 255/300\n",
      "Exported similarity matrix for node 255/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_255.csv\n",
      "Calculating similarity matrix for node 256/300\n",
      "Exported similarity matrix for node 256/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_256.csv\n",
      "Calculating similarity matrix for node 257/300\n",
      "Exported similarity matrix for node 257/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_257.csv\n",
      "Calculating similarity matrix for node 258/300\n",
      "Exported similarity matrix for node 258/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_258.csv\n",
      "Calculating similarity matrix for node 259/300\n",
      "Exported similarity matrix for node 259/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_259.csv\n",
      "Calculating similarity matrix for node 260/300\n",
      "Exported similarity matrix for node 260/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_260.csv\n",
      "Calculating similarity matrix for node 261/300\n",
      "Exported similarity matrix for node 261/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_261.csv\n",
      "Calculating similarity matrix for node 262/300\n",
      "Exported similarity matrix for node 262/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_262.csv\n",
      "Calculating similarity matrix for node 263/300\n",
      "Exported similarity matrix for node 263/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_263.csv\n",
      "Calculating similarity matrix for node 264/300\n",
      "Exported similarity matrix for node 264/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_264.csv\n",
      "Calculating similarity matrix for node 265/300\n",
      "Exported similarity matrix for node 265/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_265.csv\n",
      "Calculating similarity matrix for node 266/300\n",
      "Exported similarity matrix for node 266/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_266.csv\n",
      "Calculating similarity matrix for node 267/300\n",
      "Exported similarity matrix for node 267/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_267.csv\n",
      "Calculating similarity matrix for node 268/300\n",
      "Exported similarity matrix for node 268/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_268.csv\n",
      "Calculating similarity matrix for node 269/300\n",
      "Exported similarity matrix for node 269/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_269.csv\n",
      "Calculating similarity matrix for node 270/300\n",
      "Exported similarity matrix for node 270/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_270.csv\n",
      "Calculating similarity matrix for node 271/300\n",
      "Exported similarity matrix for node 271/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_271.csv\n",
      "Calculating similarity matrix for node 272/300\n",
      "Exported similarity matrix for node 272/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_272.csv\n",
      "Calculating similarity matrix for node 273/300\n",
      "Exported similarity matrix for node 273/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_273.csv\n",
      "Calculating similarity matrix for node 274/300\n",
      "Exported similarity matrix for node 274/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_274.csv\n",
      "Calculating similarity matrix for node 275/300\n",
      "Exported similarity matrix for node 275/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_275.csv\n",
      "Calculating similarity matrix for node 276/300\n",
      "Exported similarity matrix for node 276/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_276.csv\n",
      "Calculating similarity matrix for node 277/300\n",
      "Exported similarity matrix for node 277/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_277.csv\n",
      "Calculating similarity matrix for node 278/300\n",
      "Exported similarity matrix for node 278/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_278.csv\n",
      "Calculating similarity matrix for node 279/300\n",
      "Exported similarity matrix for node 279/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_279.csv\n",
      "Calculating similarity matrix for node 280/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported similarity matrix for node 280/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_280.csv\n",
      "Calculating similarity matrix for node 281/300\n",
      "Exported similarity matrix for node 281/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_281.csv\n",
      "Calculating similarity matrix for node 282/300\n",
      "Exported similarity matrix for node 282/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_282.csv\n",
      "Calculating similarity matrix for node 283/300\n",
      "Exported similarity matrix for node 283/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_283.csv\n",
      "Calculating similarity matrix for node 284/300\n",
      "Exported similarity matrix for node 284/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_284.csv\n",
      "Calculating similarity matrix for node 285/300\n",
      "Exported similarity matrix for node 285/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_285.csv\n",
      "Calculating similarity matrix for node 286/300\n",
      "Exported similarity matrix for node 286/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_286.csv\n",
      "Calculating similarity matrix for node 287/300\n",
      "Exported similarity matrix for node 287/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_287.csv\n",
      "Calculating similarity matrix for node 288/300\n",
      "Exported similarity matrix for node 288/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_288.csv\n",
      "Calculating similarity matrix for node 289/300\n",
      "Exported similarity matrix for node 289/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_289.csv\n",
      "Calculating similarity matrix for node 290/300\n",
      "Exported similarity matrix for node 290/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_290.csv\n",
      "Calculating similarity matrix for node 291/300\n",
      "Exported similarity matrix for node 291/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_291.csv\n",
      "Calculating similarity matrix for node 292/300\n",
      "Exported similarity matrix for node 292/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_292.csv\n",
      "Calculating similarity matrix for node 293/300\n",
      "Exported similarity matrix for node 293/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_293.csv\n",
      "Calculating similarity matrix for node 294/300\n",
      "Exported similarity matrix for node 294/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_294.csv\n",
      "Calculating similarity matrix for node 295/300\n",
      "Exported similarity matrix for node 295/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_295.csv\n",
      "Calculating similarity matrix for node 296/300\n",
      "Exported similarity matrix for node 296/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_296.csv\n",
      "Calculating similarity matrix for node 297/300\n",
      "Exported similarity matrix for node 297/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_297.csv\n",
      "Calculating similarity matrix for node 298/300\n",
      "Exported similarity matrix for node 298/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_298.csv\n",
      "Calculating similarity matrix for node 299/300\n",
      "Exported similarity matrix for node 299/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_299.csv\n",
      "Calculating similarity matrix for node 300/300\n",
      "Exported similarity matrix for node 300/300 to analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_threat/similarity_node_300.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "for frame in ['neutral', 'threat']:\n",
    "\n",
    "    n_subs, n_ts, n_nodes = stacked_data[frame].shape\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[frame][:, :, 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 \n",
    "\n",
    "                similarity_matrix[i, j] = dissimilarity\n",
    "                similarity_matrix[j, i] = dissimilarity  # Fill upper triangle symmetrically\n",
    "\n",
    "        # Set the diagonal elements to 0\n",
    "        np.fill_diagonal(similarity_matrix, 0)\n",
    "\n",
    "        # Save the similarity matrix to a CSV file\n",
    "        csv_filename = f\"analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_{frame}/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": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Use ROI "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform the IS-RSA analysis separately for the threat-framed and neutral-framed movie data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing: similarity_node_1.csv\n",
      "Complete: similarity_node_1_isrsa.csv\n",
      "Processing: similarity_node_2.csv\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import spearmanr\n",
    "from itertools import permutations\n",
    "import datetime\n",
    "\n",
    "# Function to create permuted distance matrices\n",
    "def create_permuted_distance_matrices(original_distance_matrix, n_permutations):\n",
    "    num_subjects = original_distance_matrix.shape[0]\n",
    "    permuted_distance_matrices = []\n",
    "    \n",
    "    for _ in range(n_permutations):\n",
    "        # Create a random permutation of the questionnaire scores\n",
    "        permutation_indices = np.random.permutation(num_subjects)\n",
    "\n",
    "        # Reconstruct the permuted distance matrix while preserving subject order\n",
    "        permuted_matrix = original_distance_matrix[permutation_indices][:, permutation_indices]\n",
    "        \n",
    "        permuted_distance_matrices.append(permuted_matrix)\n",
    "    \n",
    "    return permuted_distance_matrices\n",
    "\n",
    "dimensions = ['SPSQ_pos','SPSQ_neg'] # \n",
    "#start_node_number = 42  # Starting node number\n",
    "\n",
    "# Today's date\n",
    "today = datetime.date.today()\n",
    "formatted_date = today.strftime('%d-%m-%Y')\n",
    "\n",
    "for dimension in dimensions:\n",
    "    fmri_similarity_dir = 'analysis/inter-subject_matrices_fmri/roi_node/dissimilarity_matrices_mfmri_neutral' # _neutral\n",
    "    output_dir = f'analysis/inter-subject_representational_similarity_analysis/neutral_{formatted_date}_roi_node_euclidean_distance/{dimension}' #neutral_\n",
    "\n",
    "    # Delete output_dir if it exists\n",
    "    if os.path.exists(output_dir):\n",
    "        for file in os.listdir(output_dir):\n",
    "            file_path = os.path.join(output_dir, file)\n",
    "            try:\n",
    "                if os.path.isfile(file_path):\n",
    "                    os.unlink(file_path)\n",
    "            except Exception as e:\n",
    "                print(e)\n",
    "        os.rmdir(output_dir)\n",
    "\n",
    "    # Create output_dir\n",
    "    os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "    # List all brain node (dis)similarity files in fmri_similarity_dir\n",
    "    brain_matrices = [f for f in os.listdir(fmri_similarity_dir) if f.endswith('.csv')]\n",
    "    #brain_matrices = [f for f in os.listdir(fmri_similarity_dir) if f.endswith('.csv') and int(f.split('_')[2].split('.')[0]) >= start_node_number]\n",
    "\n",
    "    # Questionnaire distance/dissimilarity matrix \n",
    "    questionnaire_matrix_path = f'analysis/inter-subject_matrices_spsq/euclidean_distance_composite/euclidean_distance_matrix_{dimension}.csv'    \n",
    "    questionnaire_matrix = pd.read_csv(questionnaire_matrix_path, header=0, index_col=0).values\n",
    "    questionnaire_vector = questionnaire_matrix[np.tril_indices(questionnaire_matrix.shape[0])]\n",
    "\n",
    "    # How many permutations?\n",
    "    n_permutations = 10000\n",
    "\n",
    "    # Create a list of permuted distance matrices to use for all brain matrices\n",
    "    permuted_distance_matrices = create_permuted_distance_matrices(questionnaire_matrix, n_permutations)\n",
    "\n",
    "    # Loop through each brain node similarity file\n",
    "    for brain_matrix_file in brain_matrices:\n",
    "        print(f\"Processing: {brain_matrix_file}\")\n",
    "\n",
    "        brain_matrix = pd.read_csv(os.path.join(fmri_similarity_dir, brain_matrix_file), header=None, index_col=None).values\n",
    "        brain_vector = brain_matrix[np.tril_indices(brain_matrix.shape[0])]\n",
    "\n",
    "        # Spearman correlation between vectorized lower triangles of brain and questionnaire matrices\n",
    "        correlation, p_value = spearmanr(brain_vector, questionnaire_vector, nan_policy='omit')\n",
    "\n",
    "        # Permutations using the pre-generated list of permuted distance matrices\n",
    "        correlation_permuted = []\n",
    "        for permuted_matrix in permuted_distance_matrices:\n",
    "            permuted_questionnaire_vector = permuted_matrix[np.tril_indices(permuted_matrix.shape[0])]\n",
    "            corr, _ = spearmanr(brain_vector, permuted_questionnaire_vector, nan_policy='omit')\n",
    "            correlation_permuted.append(corr)\n",
    "\n",
    "        p_value_corrected = (np.sum(np.abs(correlation_permuted) >= np.abs(correlation)) + 1) / (n_permutations + 1)\n",
    "\n",
    "        # Dataframe to store results\n",
    "        result_df = pd.DataFrame({\n",
    "            'node_similarity_matrix': [brain_matrix_file],\n",
    "            'spearman_r': [correlation],\n",
    "            'p_uncorrected': [p_value],\n",
    "            'p_permuted': [p_value_corrected]\n",
    "        })\n",
    "\n",
    "        # Save in output_dir\n",
    "        output_file = os.path.splitext(brain_matrix_file)[0] + '_isrsa.csv'\n",
    "        result_df.to_csv(os.path.join(output_dir, output_file), index=False)\n",
    "\n",
    "        print(f\"Complete: {output_file}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenate output files across nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concatenated data for SPSQ_pos saved to analysis/inter-subject_representational_similarity_analysis/threat_03-01-2024_roi_node_euclidean_distance/SPSQ_pos_isrsa_concat.csv\n",
      "Concatenated data for SPSQ_neg saved to analysis/inter-subject_representational_similarity_analysis/threat_03-01-2024_roi_node_euclidean_distance/SPSQ_neg_isrsa_concat.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "dimensions = ['SPSQ_pos', 'SPSQ_neg'] #['SPSQ_sum', \n",
    "isrsa_dir = 'analysis/inter-subject_representational_similarity_analysis/threat_03-01-2024_roi_node_euclidean_distance'\n",
    "node_range = range(1, 301)\n",
    "\n",
    "for dim in dimensions:\n",
    "    # Initialize an empty list to store dataframes for each dimension\n",
    "    dfs = []\n",
    "\n",
    "    # Loop through each node\n",
    "    for node in node_range:\n",
    "        # Define the filename pattern\n",
    "        filename = f'similarity_node_{node}_isrsa.csv'\n",
    "        filepath = os.path.join(isrsa_dir, dim, filename)\n",
    "\n",
    "        # Check if the file exists\n",
    "        if os.path.exists(filepath):\n",
    "            df = pd.read_csv(filepath, header=0, index_col=None)\n",
    "\n",
    "            # Add 'node' column\n",
    "            df['node'] = node-1\n",
    "\n",
    "            # Append the dataframe to the list for this dimension\n",
    "            dfs.append(df)\n",
    "\n",
    "    # Concatenate dataframes in the list vertically\n",
    "    concatenated_df = pd.concat(dfs, ignore_index=True)\n",
    "\n",
    "    # Save the concatenated dataframe with only the header of the first file\n",
    "    output_filename = os.path.join(isrsa_dir, f'{dim}_isrsa_concat.csv')\n",
    "    concatenated_df.to_csv(output_filename, index=False)\n",
    "\n",
    "    print(f\"Concatenated data for {dim} saved to {output_filename}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## False discovery rate correction across nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FDR-corrected and FWE adjusted p-values added to analysis/inter-subject_representational_similarity_analysis/threat_03-01-2024_roi_node_euclidean_distance/SPSQ_neg_isrsa_concat_salience.csv\n",
      "FDR-corrected and FWE adjusted p-values added to analysis/inter-subject_representational_similarity_analysis/threat_03-01-2024_roi_node_euclidean_distance/SPSQ_neg_isrsa_concat_salience.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from statsmodels.stats.multitest import fdrcorrection\n",
    "\n",
    "dimensions = ['SPSQ_pos', 'SPSQ_neg'] #'SPSQ_sum', \n",
    "isrsa_dir = 'analysis/inter-subject_representational_similarity_analysis/threat_03-01-2024_roi_node_euclidean_distance'\n",
    "\n",
    "for dim in dimensions:\n",
    "    # Load the concatenated dataframe\n",
    "    #input_filename = os.path.join(isrsa_dir, f'{dim}_isrsa_concat.csv') \n",
    "    input_filename = os.path.join(isrsa_dir, 'SPSQ_neg_isrsa_concat_salience.csv')\n",
    "    concatenated_df = pd.read_csv(input_filename, header=0, index_col=None)\n",
    "\n",
    "    # Column names\n",
    "    p_value_column = 'p_permuted'\n",
    "    fdr_column = 'p_permuted_fdr' # FDR adjusted p-values across nodes\n",
    "    #fwe_column = 'p_permuted_bonf'  # FWE adjusted p-values across dimensions \n",
    "\n",
    "    # Get the p-values from the specified column\n",
    "    p_values = concatenated_df[p_value_column]\n",
    "\n",
    "    # Perform FDR correction\n",
    "    rejected, p_values_fdr = fdrcorrection(p_values)\n",
    "\n",
    "    # Add the FDR-corrected p-values as a new column\n",
    "    concatenated_df[fdr_column] = p_values_fdr\n",
    "\n",
    "    # Calculate and add the FWE adjusted p-values column\n",
    "    #fwe_adjusted_p_values = p_values_fdr * 6\n",
    "    #concatenated_df[fwe_column] = fwe_adjusted_p_values\n",
    "\n",
    "    # Save the updated dataframe\n",
    "    concatenated_df.to_csv(input_filename, index=False)\n",
    "\n",
    "    print(f\"FDR-corrected and FWE adjusted p-values added to {input_filename}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot bar plots of the ROI-specific IS-RSA effects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jlCK3m+TMnuPTSV6+zprdqjqR5O1J/mb/E3X35SSXk6Sqtrt760aGPo7s12rs12rs12rs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\nAACD+X8yLUt7G39CSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f072c8e79b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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P144/TUmSJEmSNJ1VKmBExO4Uq42sBv4K6KfogdHMhRSFjcPHk6AkSa2a099H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\nBXY8DaQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0701ead5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming you have the paths to your CSV files stored in 'data' variable\n",
    "networks = ['control', 'salience']\n",
    "\n",
    "# Define colors for each network\n",
    "colors = {'control': '#DA963F', 'salience': '#B345EE'}\n",
    "\n",
    "# Create subplots\n",
    "fig, axes = plt.subplots(1, len(networks), figsize=(15, 5))\n",
    "\n",
    "# Iterate over networks again to create bar plots\n",
    "for i, network in enumerate(networks):\n",
    "    data_path = f'analysis/inter-subject_representational_similarity_analysis/threat_03-01-2024_roi_node_euclidean_distance/SPSQ_neg_isrsa_concat_{network}.csv'\n",
    "    \n",
    "    # Load data using pandas\n",
    "    df = pd.read_csv(data_path)\n",
    "    \n",
    "    # Plot bar chart with specified edge color and colormap for filling\n",
    "    ax = axes[i]\n",
    "    bars = df.plot(kind='bar', y='spearman_r', ax=ax, legend=False, edgecolor='black', color=colors[network])\n",
    "    \n",
    "    # Add labels and title\n",
    "    ax.set_xlabel('ROIs', fontsize=24)\n",
    "    \n",
    "    # Remove top and right borders\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    \n",
    "    # Remove x-axis ticks and tick labels\n",
    "    ax.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
    "    \n",
    "    # Remove y-axis label and set tick label font size for the salience network plot\n",
    "    if network == 'salience':\n",
    "        ax.set_ylabel('')\n",
    "        ax.set_yticklabels([])\n",
    "        ax.set_xlabel('SN ROIs', fontsize=24)\n",
    "    elif network == 'control':\n",
    "        ax.set_ylabel('Spearman r', fontsize=24)\n",
    "        ax.tick_params(axis='y', labelsize=20)\n",
    "        ax.set_xlabel('CEN ROIs', fontsize=24)\n",
    "    \n",
    "    # Add asterisks above bars with p_permuted < 0.05\n",
    "    for index, row in df.iterrows():\n",
    "        if row['p_permuted'] < 0.05:\n",
    "            ax.text(index, row['spearman_r'] + 0.0005, '*', ha='center', va='bottom', fontsize=20)\n",
    "\n",
    "# Set the same y-axis limit for all subplots\n",
    "for ax in axes:\n",
    "    ax.set_ylim(0, 0.045)\n",
    "    ax.set_yticks([0, 0.01, 0.02, 0.03, 0.04])\n",
    "\n",
    "# Adjust layout and show the plots\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure as a PDF\n",
    "plt.savefig('analysis/plots/isrsa_roi_spearman_barplots.pdf', format='pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()\n"
   ]
  }
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