{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ee178c7c-e8a4-4481-9b95-0368f9fde565",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-14T11:00:43.071629Z",
     "start_time": "2024-10-14T11:00:43.069275Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   "source": [
    "Fieldtrip: KIT Attention Task Analysis\n",
    "======================================\n",
    "\n",
    "Lead authors: Hadi Zaatiti <hadi.zaatiti@nyu.edu>, Karima Raafat <karima.raafat@nyu.edu>\n",
    "\n",
    "The `Attention Task` code experiment in `PsychToolBox` can be found here:\n",
    "\n",
    "[Attention Task](https://meg-pipeline.readthedocs.io/en/latest/3-experimentdesign/experiments/7-attention-experiment.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a2ee750-18df-45e2-9864-8a6e855abaca",
   "metadata": {},
   "source": [
    "Importing data \n",
    "--------------\n",
    "\n",
    "The data used in this notebook is hosted on `NYU BOX`. Permissions are given upon request.\n",
    "\n",
    "- Install the BOX app from [here](https://www.box.com/resources/downloads)\n",
    "- Set an environment variable with name `MEG_DATA` to the path of the Data folder e.g.,\n",
    "    - `C:\\Users\\user_name\\Box\\MEG\\Data`\n",
    "    - or `C:\\Users\\user_name\\Box\\Data\n",
    "\n",
    "MATLAB setup\n",
    "------------\n",
    "\n",
    "Make sure that:\n",
    "- Fieldtrip is installed in MATLAB\n",
    "- Add to MATLAB path the custom-made functions for NYUAD MEG lab found [here](https://github.com/Hzaatiti/meg-pipeline/tree/main/pipeline/field_trip_pipelines/matlab_functions)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab810bb2-6028-49a9-9deb-2127a1549560",
   "metadata": {},
   "source": [
    "Each experiment run using the KIT system generates a `.con` file and two or more .mrk files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "443da161-1e0d-4bb1-ad33-b95b1f7abb07",
   "metadata": {},
   "outputs": [],
   "source": [
    "%% Attention task pipeline for external noise reduced data\n",
    "\n",
    "MEG_DATA_FOLDER = getenv('MEG_DATA');\n",
    "\n",
    "% Set path to KIT .con file of sub-03\n",
    "DATASET_PATH = [MEG_DATA_FOLDER,'attention-task\\'];\n",
    "\n",
    "%THis needs fixing to sav eproperly\n",
    "SAVE_PATH = [MEG_DATA_FOLDER, 'attention-task\\'];\n",
    "\n",
    "SUB_ID = 'sub-01\\';\n",
    "\n",
    "ATTEND_RIGHT_CON = [DATASET_PATH, SUB_ID, 'attention_attend_right_01.con'];\n",
    "\n",
    "ATTEND_LEFT_CON = [DATASET_PATH, SUB_ID, 'attention_attend_left_01.con'];\n",
    "\n",
    "\n",
    "\n",
    "% Pipeline Configuration\n",
    "\n",
    "\n",
    "APPLY_FILTERS = false;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de056121-1af8-42e5-87f1-7f5a80527ddf",
   "metadata": {},
   "source": [
    "The `Attention Task` experiment was coded with the following trigger channels\n",
    "\n",
    "Trigger information: KIT channel indexing starts with 0 while MATLAB\n",
    "indexing starts with 1, so ch224 on KIT is ch225 in MATLAB\n",
    "\n",
    "There is four trial types coded on KIT trigger channels (in KIT indexing):\n",
    "\n",
    "- Attend left and target appearing right triggered on channel 224\n",
    "- Attend left and target appearing left triggered on channel 225 \n",
    "- Attend right and target appearing right triggered on channel 226\n",
    "- Attend right and target appearing left triggered on channel 227"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b8b082-6fbd-48cc-a4cc-8c06cda0fb5d",
   "metadata": {},
   "source": [
    "We can now perform the preprocessing of the data. Two `.con` files have been acquired, the first one is when the participant was attending the right side and a second one when attention was drawn to the left side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67b81dec-a6d8-465e-b82b-6f7fac629b8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------------------\n",
      "FieldTrip is developed by members and collaborators of the Donders Institute for Brain,\n",
      "Cognition and Behaviour at Radboud University, Nijmegen, the Netherlands.\n",
      "\n",
      "                          --------------------------\n",
      "                        /                            \\\n",
      "                     ------------------------------------\n",
      "                    /                                    \\\n",
      "          -------------------------------------------------\n",
      "         /                            /\\/\\/\\/\\/\\ \n",
      "         ---------------------------------------------------\n",
      "                  |        F  i  e  l  d  T  r  i  p       |\n",
      "                  ------------------------------------------\n",
      "                   \\                                      /\n",
      "                     ------------------------------------\n",
      "                          \\            /\n",
      "                            ----------\n",
      "\n",
      "Please cite the FieldTrip reference paper when you have used FieldTrip in your study.\n",
      "Robert Oostenveld, Pascal Fries, Eric Maris, and Jan-Mathijs Schoffelen. FieldTrip: Open\n",
      "Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data.\n",
      "Computational Intelligence and Neuroscience, vol. 2011, Article ID 156869, 9 pages, 2011.\n",
      "doi:10.1155/2011/156869.\n",
      "-------------------------------------------------------------------------------------------\n",
      "processing channel { 'AG001' 'AG002' 'AG003' 'AG004' 'AG005' 'AG006' 'AG007' 'AG008' 'AG009' 'AG010' 'AG011' 'AG012' 'AG013' 'AG014' 'AG015' 'AG016' 'AG017' 'AG018' 'AG019' 'AG020' 'AG021' 'AG022' 'AG023' 'AG024' 'AG025' 'AG026' 'AG027' 'AG028' 'AG029' 'AG030' 'AG031' 'AG032' 'AG033' 'AG034' 'AG035' 'AG036' 'AG037' 'AG038' 'AG039' 'AG040' 'AG041' 'AG042' 'AG043' 'AG044' 'AG045' 'AG046' 'AG047' 'AG048' 'AG049' 'AG050' 'AG051' 'AG052' 'AG053' 'AG054' 'AG055' 'AG056' 'AG057' 'AG058' 'AG059' 'AG060' 'AG061' 'AG062' 'AG063' 'AG064' 'AG065' 'AG066' 'AG067' 'AG068' 'AG069' 'AG070' 'AG071' 'AG072' 'AG073' 'AG074' 'AG075' 'AG076' 'AG077' 'AG078' 'AG079' 'AG080' 'AG081' 'AG082' 'AG083' 'AG084' 'AG085' 'AG086' 'AG087' 'AG088' 'AG089' 'AG090' 'AG091' 'RM092' 'AG093' 'AG094' 'AG095' 'AG096' 'AG097' 'AG098' 'AG099' 'AG100' 'AG101' 'AG102' 'AG103' 'AG104' 'AG105' 'AG106' 'AG107' 'AG108' 'AG109' 'AG110' 'AG111' 'AG112' 'AG113' 'AG114' 'AG115' 'AG116' 'AG117' 'AG118' 'AG119' 'AG120' 'AG121' 'AG122' 'AG123' 'AG124' 'AG125' 'AG126' 'AG127' 'AG128' 'AG129' 'AG130' 'AG131' 'AG132' 'AG133' 'AG134' 'AG135' 'AG136' 'AG137' 'AG138' 'AG139' 'AG140' 'AG141' 'AG142' 'AG143' 'AG144' 'AG145' 'AG146' 'AG147' 'AG148' 'AG149' 'AG150' 'AG151' 'AG152' 'AG153' 'AG154' 'AG155' 'AG156' 'AG157' 'AG158' 'AG159' 'AG160' 'AG161' 'AG162' 'AG163' 'AG164' 'AG165' 'AG166' 'AG167' 'AG168' 'AG169' 'AG170' 'AG171' 'AG172' 'AG173' 'AG174' 'AG175' 'AG176' 'AG177' 'AG178' 'AG179' 'AG180' 'AG181' 'AG182' 'AG183' 'AG184' 'AG185' 'AG186' 'AG187' 'AG188' 'AG189' 'AG190' 'AG191' 'AG192' 'AG193' 'AG194' 'AG195' 'AG196' 'AG197' 'AG198' 'AG199' 'AG200' 'AG201' 'AG202' 'AG203' 'AG204' 'AG205' 'AG206' 'AG207' 'AG208' 'RM209' 'RM210' 'RM211' 'RM212' 'RM213' 'RM214' 'RM215' 'RM216' 'RM217' 'RM218' 'RM219' 'RM220' 'RM221' 'RM222' 'RM223' 'RM224' '225' '226' '227' '228' '229' '230' '231' '232' '233' '234' '235' '236' '237' '238' '239' '240' '241' '242' '243' '244' '245' '246' '247' '248' '249' '250' '251' '252' '253' '254' '255' '256' }\n",
      "reading and preprocessing\n",
      "reading and preprocessing trial 1 from 1\n",
      "the call to \"ft_preprocessing\" took 37 seconds\n",
      "processing channel { 'AG001' 'AG002' 'AG003' 'AG004' 'AG005' 'AG006' 'AG007' 'AG008' 'AG009' 'AG010' 'AG011' 'AG012' 'AG013' 'AG014' 'AG015' 'AG016' 'AG017' 'AG018' 'AG019' 'AG020' 'AG021' 'AG022' 'AG023' 'AG024' 'AG025' 'AG026' 'AG027' 'AG028' 'AG029' 'AG030' 'AG031' 'AG032' 'AG033' 'AG034' 'AG035' 'AG036' 'AG037' 'AG038' 'AG039' 'AG040' 'AG041' 'AG042' 'AG043' 'AG044' 'AG045' 'AG046' 'AG047' 'AG048' 'AG049' 'AG050' 'AG051' 'AG052' 'AG053' 'AG054' 'AG055' 'AG056' 'AG057' 'AG058' 'AG059' 'AG060' 'AG061' 'AG062' 'AG063' 'AG064' 'AG065' 'AG066' 'AG067' 'AG068' 'AG069' 'AG070' 'AG071' 'AG072' 'AG073' 'AG074' 'AG075' 'AG076' 'AG077' 'AG078' 'AG079' 'AG080' 'AG081' 'AG082' 'AG083' 'AG084' 'AG085' 'AG086' 'AG087' 'AG088' 'AG089' 'AG090' 'AG091' 'RM092' 'AG093' 'AG094' 'AG095' 'AG096' 'AG097' 'AG098' 'AG099' 'AG100' 'AG101' 'AG102' 'AG103' 'AG104' 'AG105' 'AG106' 'AG107' 'AG108' 'AG109' 'AG110' 'AG111' 'AG112' 'AG113' 'AG114' 'AG115' 'AG116' 'AG117' 'AG118' 'AG119' 'AG120' 'AG121' 'AG122' 'AG123' 'AG124' 'AG125' 'AG126' 'AG127' 'AG128' 'AG129' 'AG130' 'AG131' 'AG132' 'AG133' 'AG134' 'AG135' 'AG136' 'AG137' 'AG138' 'AG139' 'AG140' 'AG141' 'AG142' 'AG143' 'AG144' 'AG145' 'AG146' 'AG147' 'AG148' 'AG149' 'AG150' 'AG151' 'AG152' 'AG153' 'AG154' 'AG155' 'AG156' 'AG157' 'AG158' 'AG159' 'AG160' 'AG161' 'AG162' 'AG163' 'AG164' 'AG165' 'AG166' 'AG167' 'AG168' 'AG169' 'AG170' 'AG171' 'AG172' 'AG173' 'AG174' 'AG175' 'AG176' 'AG177' 'AG178' 'AG179' 'AG180' 'AG181' 'AG182' 'AG183' 'AG184' 'AG185' 'AG186' 'AG187' 'AG188' 'AG189' 'AG190' 'AG191' 'AG192' 'AG193' 'AG194' 'AG195' 'AG196' 'AG197' 'AG198' 'AG199' 'AG200' 'AG201' 'AG202' 'AG203' 'AG204' 'AG205' 'AG206' 'AG207' 'AG208' 'RM209' 'RM210' 'RM211' 'RM212' 'RM213' 'RM214' 'RM215' 'RM216' 'RM217' 'RM218' 'RM219' 'RM220' 'RM221' 'RM222' 'RM223' 'RM224' '225' '226' '227' '228' '229' '230' '231' '232' '233' '234' '235' '236' '237' '238' '239' '240' '241' '242' '243' '244' '245' '246' '247' '248' '249' '250' '251' '252' '253' '254' '255' '256' }\n",
      "reading and preprocessing\n",
      "reading and preprocessing trial 1 from 1\n",
      "the call to \"ft_preprocessing\" took 16 seconds\n"
     ]
    }
   ],
   "source": [
    "    % Preprocess the MEG data\n",
    "    cfg = [];\n",
    "    cfg.dataset = ATTEND_RIGHT_CON;\n",
    "    cfg.coilaccuracy = 0;\n",
    "    data_MEG_RIGHT = ft_preprocessing(cfg);\n",
    "\n",
    "    % Preprocess the MEG data\n",
    "    cfg = [];\n",
    "    cfg.dataset = ATTEND_LEFT_CON;\n",
    "    cfg.coilaccuracy = 0;\n",
    "    data_MEG_LEFT = ft_preprocessing(cfg);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f277a04-99c6-483b-894d-b8617791d301",
   "metadata": {},
   "source": [
    "Optional filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b8c364c3-fdbf-4474-932a-b0f9b15e8d6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "    %% Filtering data\n",
    "    \n",
    "    if APPLY_FILTERS\n",
    "\n",
    "        % Notch filter the data at 50 Hz\n",
    "        cfg = [];\n",
    "        cfg.bsfilter = 'yes';\n",
    "        cfg.bsfreq = [49 51]; % Notch filter range\n",
    "        data_MEG_RIGHT = ft_preprocessing(cfg, data_MEG_RIGHT);\n",
    "    \n",
    "        % Band-pass filter the data\n",
    "        cfg = [];\n",
    "        cfg.bpfilter = 'yes';\n",
    "        cfg.bpfreq = [4 40]; % Band-pass filter range\n",
    "        cfg.bpfiltord = 4;    % Filter order\n",
    "        data_MEG_RIGHT = ft_preprocessing(cfg, data_MEG_RIGHT);\n",
    "    \n",
    "    \n",
    "        % Notch filter the data at 50 Hz\n",
    "        cfg = [];\n",
    "        cfg.bsfilter = 'yes';\n",
    "        cfg.bsfreq = [49 51]; % Notch filter range\n",
    "        data_MEG_LEFT = ft_preprocessing(cfg, data_MEG_LEFT);\n",
    "    \n",
    "        % Band-pass filter the data\n",
    "        cfg = [];\n",
    "        cfg.bpfilter = 'yes';\n",
    "        cfg.bpfreq = [4 40]; % Band-pass filter range\n",
    "        cfg.bpfiltord = 4;    % Filter order\n",
    "        data_MEG_LEFT = ft_preprocessing(cfg, data_MEG_LEFT);\n",
    "\n",
    "    end"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6246635c-974f-4ccc-8eab-b28a1f94a2e8",
   "metadata": {},
   "source": [
    "We are now ready to define trials for the `Attend Left` block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5b7cb28a-f577-47f0-94ec-5b15a05bff33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating trial function 'ft_trialfun_general'\n",
      "reading the header from 'C:\\Users\\hz3752\\Box\\MEG\\Data\\attention-task\\sub-01\\attention_attend_left_01.con'\n",
      "reading the events from 'C:\\Users\\hz3752\\Box\\MEG\\Data\\attention-task\\sub-01\\attention_attend_left_01.con'\n",
      "found 231 events\n",
      "created 231 trials\n",
      "the call to \"ft_definetrial\" took 28 seconds\n",
      "processing channel { 'AG001' 'AG002' 'AG003' 'AG004' 'AG005' 'AG006' 'AG007' 'AG008' 'AG009' 'AG010' 'AG011' 'AG012' 'AG013' 'AG014' 'AG015' 'AG016' 'AG017' 'AG018' 'AG019' 'AG020' 'AG021' 'AG022' 'AG023' 'AG024' 'AG025' 'AG026' 'AG027' 'AG028' 'AG029' 'AG030' 'AG031' 'AG032' 'AG033' 'AG034' 'AG035' 'AG036' 'AG037' 'AG038' 'AG039' 'AG040' 'AG041' 'AG042' 'AG043' 'AG044' 'AG045' 'AG046' 'AG047' 'AG048' 'AG049' 'AG050' 'AG051' 'AG052' 'AG053' 'AG054' 'AG055' 'AG056' 'AG057' 'AG058' 'AG059' 'AG060' 'AG061' 'AG062' 'AG063' 'AG064' 'AG065' 'AG066' 'AG067' 'AG068' 'AG069' 'AG070' 'AG071' 'AG072' 'AG073' 'AG074' 'AG075' 'AG076' 'AG077' 'AG078' 'AG079' 'AG080' 'AG081' 'AG082' 'AG083' 'AG084' 'AG085' 'AG086' 'AG087' 'AG088' 'AG089' 'AG090' 'AG091' 'RM092' 'AG093' 'AG094' 'AG095' 'AG096' 'AG097' 'AG098' 'AG099' 'AG100' 'AG101' 'AG102' 'AG103' 'AG104' 'AG105' 'AG106' 'AG107' 'AG108' 'AG109' 'AG110' 'AG111' 'AG112' 'AG113' 'AG114' 'AG115' 'AG116' 'AG117' 'AG118' 'AG119' 'AG120' 'AG121' 'AG122' 'AG123' 'AG124' 'AG125' 'AG126' 'AG127' 'AG128' 'AG129' 'AG130' 'AG131' 'AG132' 'AG133' 'AG134' 'AG135' 'AG136' 'AG137' 'AG138' 'AG139' 'AG140' 'AG141' 'AG142' 'AG143' 'AG144' 'AG145' 'AG146' 'AG147' 'AG148' 'AG149' 'AG150' 'AG151' 'AG152' 'AG153' 'AG154' 'AG155' 'AG156' 'AG157' 'AG158' 'AG159' 'AG160' 'AG161' 'AG162' 'AG163' 'AG164' 'AG165' 'AG166' 'AG167' 'AG168' 'AG169' 'AG170' 'AG171' 'AG172' 'AG173' 'AG174' 'AG175' 'AG176' 'AG177' 'AG178' 'AG179' 'AG180' 'AG181' 'AG182' 'AG183' 'AG184' 'AG185' 'AG186' 'AG187' 'AG188' 'AG189' 'AG190' 'AG191' 'AG192' 'AG193' 'AG194' 'AG195' 'AG196' 'AG197' 'AG198' 'AG199' 'AG200' 'AG201' 'AG202' 'AG203' 'AG204' 'AG205' 'AG206' 'AG207' 'AG208' 'RM209' 'RM210' 'RM211' 'RM212' 'RM213' 'RM214' 'RM215' 'RM216' 'RM217' 'RM218' 'RM219' 'RM220' 'RM221' 'RM222' 'RM223' 'RM224' '225' '226' '227' '228' '229' '230' '231' '232' '233' '234' '235' '236' '237' '238' '239' '240' '241' '242' '243' '244' '245' '246' '247' '248' '249' '250' '251' '252' '253' '254' '255' '256' }\n",
      "reading and preprocessing\n",
      "reading and preprocessing trial 3 from 2reading and preprocessing trial 5 from 2reading and preprocessing trial 7 from 2reading and preprocessing trial 9 from 2reading and preprocessing trial 11 from 23reading and preprocessing trial 13 from 23reading and preprocessing trial 15 from 23reading and preprocessing trial 17 from 23reading and preprocessing trial 19 from 23reading and preprocessing trial 21 from 23reading and preprocessing trial 23 from 23reading and preprocessing trial 24 from 23reading and preprocessing trial 26 from 23reading and preprocessing trial 27 from 23reading and preprocessing trial 28 from 23reading and preprocessing trial 29 from 23reading and preprocessing trial 31 from 23reading and preprocessing trial 33 from 23reading and preprocessing trial 35 from 23reading and preprocessing trial 37 from 23reading and preprocessing trial 39 from 23reading and preprocessing trial 41 from 23reading and preprocessing trial 43 from 23reading and preprocessing trial 44 from 23reading and preprocessing trial 45 from 23reading and preprocessing trial 46 from 23reading and preprocessing trial 47 from 23reading and preprocessing trial 48 from 23reading and preprocessing trial 50 from 23reading and preprocessing trial 51 from 23reading and preprocessing trial 52 from 23reading and preprocessing trial 53 from 23reading and preprocessing trial 55 from 23reading and preprocessing trial 56 from 23reading and preprocessing trial 57 from 23reading and preprocessing trial 58 from 23reading and preprocessing trial 59 from 23reading and preprocessing trial 60 from 23reading and preprocessing trial 61 from 23reading and preprocessing trial 62 from 23reading and preprocessing trial 64 from 23reading and preprocessing trial 66 from 23reading and preprocessing trial 67 from 23reading and preprocessing trial 69 from 23reading and preprocessing trial 70 from 23reading and preprocessing trial 72 from 23reading and preprocessing trial 74 from 23reading and preprocessing trial 76 from 23reading and preprocessing trial 78 from 23reading and preprocessing trial 80 from 23reading and preprocessing trial 82 from 23reading and preprocessing trial 83 from 23reading and preprocessing trial 85 from 23reading and preprocessing trial 87 from 23reading and preprocessing trial 89 from 23reading and preprocessing trial 91 from 23reading and preprocessing trial 93 from 23reading and preprocessing trial 95 from 23reading and preprocessing trial 97 from 23reading and preprocessing trial 98 from 23reading and preprocessing trial 100 from 2reading and preprocessing trial 102 from 2reading and preprocessing trial 104 from 2reading and preprocessing trial 106 from 2reading and preprocessing trial 108 from 2reading and preprocessing trial 109 from 2reading and preprocessing trial 111 from 2reading and preprocessing trial 112 from 2reading and preprocessing trial 114 from 2reading and preprocessing trial 116 from 2reading and preprocessing trial 118 from 2reading and preprocessing trial 120 from 2reading and preprocessing trial 121 from 2reading and preprocessing trial 123 from 2reading and preprocessing trial 124 from 2reading and preprocessing trial 125 from 2reading and preprocessing trial 127 from 2reading and preprocessing trial 129 from 2reading and preprocessing trial 131 from 2reading and preprocessing trial 132 from 2reading and preprocessing trial 134 from 2reading and preprocessing trial 136 from 2reading and preprocessing trial 138 from 2reading and preprocessing trial 140 from 2reading and preprocessing trial 142 from 2reading and preprocessing trial 144 from 2reading and preprocessing trial 146 from 2reading and preprocessing trial 147 from 2reading and preprocessing trial 148 from 2reading and preprocessing trial 150 from 2reading and preprocessing trial 152 from 2reading and preprocessing trial 154 from 2reading and preprocessing trial 156 from 2reading and preprocessing trial 158 from 2reading and preprocessing trial 160 from 2reading and preprocessing trial 162 from 2reading and preprocessing trial 164 from 2reading and preprocessing trial 165 from 2reading and preprocessing trial 167 from 2reading and preprocessing trial 168 from 2reading and preprocessing trial 170 from 2reading and preprocessing trial 172 from 2reading and preprocessing trial 173 from 2reading and preprocessing trial 174 from 2reading and preprocessing trial 176 from 2reading and preprocessing trial 177 from 2reading and preprocessing trial 179 from 2reading and preprocessing trial 181 from 2reading and preprocessing trial 183 from 2reading and preprocessing trial 185 from 2reading and preprocessing trial 187 from 2reading and preprocessing trial 189 from 2reading and preprocessing trial 191 from 2reading and preprocessing trial 193 from 2reading and preprocessing trial 195 from 2reading and preprocessing trial 197 from 2reading and preprocessing trial 199 from 2reading and preprocessing trial 201 from 2reading and preprocessing trial 203 from 2reading and preprocessing trial 204 from 2reading and preprocessing trial 206 from 2reading and preprocessing trial 208 from 2reading and preprocessing trial 210 from 2reading and preprocessing trial 212 from 2reading and preprocessing trial 214 from 2reading and preprocessing trial 216 from 2reading and preprocessing trial 217 from 2reading and preprocessing trial 219 from 2reading and preprocessing trial 221 from 2reading and preprocessing trial 223 from 2reading and preprocessing trial 225 from 2reading and preprocessing trial 227 from 2reading and preprocessing trial 229 from 2reading and preprocessing trial 230 from 2reading and preprocessing trial 231 from 231\n",
      "the call to \"ft_preprocessing\" took 23 seconds\n"
     ]
    }
   ],
   "source": [
    "    %% Define trials and segment the data: Attend Left Target Right\n",
    "\n",
    "    previewTrigger = data_MEG_LEFT.trial{1}(225, :);\n",
    "\n",
    "    threshold = (max(previewTrigger) + min(previewTrigger)) / 2;\n",
    "\n",
    "    cfg = [];\n",
    "    cfg.dataset  = ATTEND_LEFT_CON;\n",
    "    cfg.trialdef.eventvalue = 1; % placeholder for the conditions\n",
    "    cfg.trialdef.prestim    = 0.5; % 1s before stimulus onset\n",
    "    cfg.trialdef.poststim   = 1.2; % 1s after stimulus onset\n",
    "    cfg.trialfun = 'ft_trialfun_general';\n",
    "    cfg.trialdef.chanindx = 225;\n",
    "    cfg.trialdef.threshold = threshold;\n",
    "    cfg.trialdef.eventtype = 'combined_binary_trigger'; % this will be the type of the event if combinebinary = true\n",
    "    cfg.trialdef.combinebinary = 1;\n",
    "    cfg.preproc.baselinewindow = [-0.2 0];\n",
    "    cfg.preproc.demean     = 'yes';\n",
    "\n",
    "    TRIALS_AL_TR = ft_definetrial(cfg);\n",
    "    \n",
    "    SG_AL_TR = ft_preprocessing(TRIALS_AL_TR);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9c286e3-2967-4741-a573-a42133acd229",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating trial function 'ft_trialfun_general'\n",
      "reading the header from 'C:\\Users\\hz3752\\Box\\MEG\\Data\\attention-task\\sub-01\\attention_attend_left_01.con'\n",
      "reading the events from 'C:\\Users\\hz3752\\Box\\MEG\\Data\\attention-task\\sub-01\\attention_attend_left_01.con'\n",
      "found 232 events\n",
      "created 232 trials\n",
      "the call to \"ft_definetrial\" took 13 seconds\n",
      "processing channel { 'AG001' 'AG002' 'AG003' 'AG004' 'AG005' 'AG006' 'AG007' 'AG008' 'AG009' 'AG010' 'AG011' 'AG012' 'AG013' 'AG014' 'AG015' 'AG016' 'AG017' 'AG018' 'AG019' 'AG020' 'AG021' 'AG022' 'AG023' 'AG024' 'AG025' 'AG026' 'AG027' 'AG028' 'AG029' 'AG030' 'AG031' 'AG032' 'AG033' 'AG034' 'AG035' 'AG036' 'AG037' 'AG038' 'AG039' 'AG040' 'AG041' 'AG042' 'AG043' 'AG044' 'AG045' 'AG046' 'AG047' 'AG048' 'AG049' 'AG050' 'AG051' 'AG052' 'AG053' 'AG054' 'AG055' 'AG056' 'AG057' 'AG058' 'AG059' 'AG060' 'AG061' 'AG062' 'AG063' 'AG064' 'AG065' 'AG066' 'AG067' 'AG068' 'AG069' 'AG070' 'AG071' 'AG072' 'AG073' 'AG074' 'AG075' 'AG076' 'AG077' 'AG078' 'AG079' 'AG080' 'AG081' 'AG082' 'AG083' 'AG084' 'AG085' 'AG086' 'AG087' 'AG088' 'AG089' 'AG090' 'AG091' 'RM092' 'AG093' 'AG094' 'AG095' 'AG096' 'AG097' 'AG098' 'AG099' 'AG100' 'AG101' 'AG102' 'AG103' 'AG104' 'AG105' 'AG106' 'AG107' 'AG108' 'AG109' 'AG110' 'AG111' 'AG112' 'AG113' 'AG114' 'AG115' 'AG116' 'AG117' 'AG118' 'AG119' 'AG120' 'AG121' 'AG122' 'AG123' 'AG124' 'AG125' 'AG126' 'AG127' 'AG128' 'AG129' 'AG130' 'AG131' 'AG132' 'AG133' 'AG134' 'AG135' 'AG136' 'AG137' 'AG138' 'AG139' 'AG140' 'AG141' 'AG142' 'AG143' 'AG144' 'AG145' 'AG146' 'AG147' 'AG148' 'AG149' 'AG150' 'AG151' 'AG152' 'AG153' 'AG154' 'AG155' 'AG156' 'AG157' 'AG158' 'AG159' 'AG160' 'AG161' 'AG162' 'AG163' 'AG164' 'AG165' 'AG166' 'AG167' 'AG168' 'AG169' 'AG170' 'AG171' 'AG172' 'AG173' 'AG174' 'AG175' 'AG176' 'AG177' 'AG178' 'AG179' 'AG180' 'AG181' 'AG182' 'AG183' 'AG184' 'AG185' 'AG186' 'AG187' 'AG188' 'AG189' 'AG190' 'AG191' 'AG192' 'AG193' 'AG194' 'AG195' 'AG196' 'AG197' 'AG198' 'AG199' 'AG200' 'AG201' 'AG202' 'AG203' 'AG204' 'AG205' 'AG206' 'AG207' 'AG208' 'RM209' 'RM210' 'RM211' 'RM212' 'RM213' 'RM214' 'RM215' 'RM216' 'RM217' 'RM218' 'RM219' 'RM220' 'RM221' 'RM222' 'RM223' 'RM224' '225' '226' '227' '228' '229' '230' '231' '232' '233' '234' '235' '236' '237' '238' '239' '240' '241' '242' '243' '244' '245' '246' '247' '248' '249' '250' '251' '252' '253' '254' '255' '256' }\n",
      "reading and preprocessing\n",
      "reading and preprocessing trial 3 from 2reading and preprocessing trial 5 from 2reading and preprocessing trial 7 from 2reading and preprocessing trial 9 from 2reading and preprocessing trial 11 from 23reading and preprocessing trial 13 from 23reading and preprocessing trial 14 from 23reading and preprocessing trial 16 from 23reading and preprocessing trial 18 from 23reading and preprocessing trial 19 from 23reading and preprocessing trial 21 from 23reading and preprocessing trial 23 from 23reading and preprocessing trial 25 from 23reading and preprocessing trial 27 from 23reading and preprocessing trial 28 from 23reading and preprocessing trial 29 from 23reading and preprocessing trial 31 from 23reading and preprocessing trial 33 from 23reading and preprocessing trial 35 from 23reading and preprocessing trial 36 from 23reading and preprocessing trial 37 from 23reading and preprocessing trial 39 from 23reading and preprocessing trial 40 from 23reading and preprocessing trial 42 from 23reading and preprocessing trial 44 from 23reading and preprocessing trial 46 from 23reading and preprocessing trial 48 from 23reading and preprocessing trial 50 from 23reading and preprocessing trial 52 from 23reading and preprocessing trial 54 from 23reading and preprocessing trial 56 from 23reading and preprocessing trial 58 from 23reading and preprocessing trial 60 from 23reading and preprocessing trial 62 from 23reading and preprocessing trial 64 from 23reading and preprocessing trial 66 from 23reading and preprocessing trial 68 from 23reading and preprocessing trial 70 from 23reading and preprocessing trial 72 from 23reading and preprocessing trial 74 from 23reading and preprocessing trial 76 from 23reading and preprocessing trial 78 from 23reading and preprocessing trial 80 from 23reading and preprocessing trial 82 from 23reading and preprocessing trial 84 from 23reading and preprocessing trial 86 from 23reading and preprocessing trial 88 from 23reading and preprocessing trial 90 from 23reading and preprocessing trial 92 from 23reading and preprocessing trial 94 from 23reading and preprocessing trial 96 from 23reading and preprocessing trial 98 from 23reading and preprocessing trial 100 from 2reading and preprocessing trial 102 from 2reading and preprocessing trial 104 from 2reading and preprocessing trial 106 from 2reading and preprocessing trial 108 from 2reading and preprocessing trial 110 from 2reading and preprocessing trial 112 from 2reading and preprocessing trial 114 from 2reading and preprocessing trial 115 from 2reading and preprocessing trial 117 from 2reading and preprocessing trial 118 from 2reading and preprocessing trial 119 from 2reading and preprocessing trial 121 from 2reading and preprocessing trial 123 from 2reading and preprocessing trial 125 from 2reading and preprocessing trial 127 from 2reading and preprocessing trial 129 from 2reading and preprocessing trial 131 from 2reading and preprocessing trial 132 from 2reading and preprocessing trial 134 from 2reading and preprocessing trial 136 from 2reading and preprocessing trial 138 from 2reading and preprocessing trial 140 from 2reading and preprocessing trial 141 from 2reading and preprocessing trial 142 from 2reading and preprocessing trial 144 from 2reading and preprocessing trial 145 from 2reading and preprocessing trial 147 from 2reading and preprocessing trial 148 from 2reading and preprocessing trial 149 from 2reading and preprocessing trial 150 from 2reading and preprocessing trial 152 from 2reading and preprocessing trial 153 from 2reading and preprocessing trial 154 from 2reading and preprocessing trial 155 from 2reading and preprocessing trial 156 from 2reading and preprocessing trial 157 from 2reading and preprocessing trial 158 from 2reading and preprocessing trial 159 from 2reading and preprocessing trial 160 from 2reading and preprocessing trial 161 from 2reading and preprocessing trial 163 from 2reading and preprocessing trial 164 from 2reading and preprocessing trial 165 from 2reading and preprocessing trial 166 from 2reading and preprocessing trial 167 from 2reading and preprocessing trial 168 from 2reading and preprocessing trial 169 from 2reading and preprocessing trial 170 from 2reading and preprocessing trial 171 from 2reading and preprocessing trial 172 from 2reading and preprocessing trial 173 from 2reading and preprocessing trial 174 from 2reading and preprocessing trial 175 from 2reading and preprocessing trial 176 from 2reading and preprocessing trial 177 from 2reading and preprocessing trial 178 from 2reading and preprocessing trial 179 from 2reading and preprocessing trial 180 from 2reading and preprocessing trial 181 from 2reading and preprocessing trial 182 from 2reading and preprocessing trial 183 from 2reading and preprocessing trial 184 from 2reading and preprocessing trial 185 from 2reading and preprocessing trial 186 from 2reading and preprocessing trial 187 from 2reading and preprocessing trial 188 from 2reading and preprocessing trial 189 from 2reading and preprocessing trial 190 from 2reading and preprocessing trial 191 from 2reading and preprocessing trial 192 from 2reading and preprocessing trial 193 from 2reading and preprocessing trial 194 from 2reading and preprocessing trial 195 from 2reading and preprocessing trial 196 from 2reading and preprocessing trial 197 from 2reading and preprocessing trial 198 from 2reading and preprocessing trial 199 from 2reading and preprocessing trial 200 from 2reading and preprocessing trial 201 from 2reading and preprocessing trial 202 from 2reading and preprocessing trial 203 from 2reading and preprocessing trial 204 from 2reading and preprocessing trial 205 from 2reading and preprocessing trial 206 from 2reading and preprocessing trial 207 from 2reading and preprocessing trial 208 from 2reading and preprocessing trial 210 from 2reading and preprocessing trial 211 from 2reading and preprocessing trial 212 from 2reading and preprocessing trial 213 from 2reading and preprocessing trial 214 from 2reading and preprocessing trial 215 from 2reading and preprocessing trial 217 from 2reading and preprocessing trial 218 from 2reading and preprocessing trial 219 from 2reading and preprocessing trial 220 from 2reading and preprocessing trial 221 from 2reading and preprocessing trial 222 from 2reading and preprocessing trial 223 from 2reading and preprocessing trial 225 from 2reading and preprocessing trial 226 from 2reading and preprocessing trial 227 from 2reading and preprocessing trial 228 from 2reading and preprocessing trial 229 from 2reading and preprocessing trial 230 from 2reading and preprocessing trial 232 from 232\n",
      "the call to \"ft_preprocessing\" took 31 seconds\n"
     ]
    }
   ],
   "source": [
    "    %% Define trials and segment the data: Attend Left Target Left\n",
    "\n",
    "    previewTrigger = data_MEG_LEFT.trial{1}(226, :);\n",
    "\n",
    "    threshold = (max(previewTrigger) + min(previewTrigger)) / 2;\n",
    "\n",
    "    cfg = [];\n",
    "    cfg.dataset  = ATTEND_LEFT_CON;\n",
    "    cfg.trialdef.eventvalue = 1; % placeholder for the conditions\n",
    "    cfg.trialdef.prestim    = 0.5; % 1s before stimulus onset\n",
    "    cfg.trialdef.poststim   = 1.2; % 1s after stimulus onset\n",
    "    cfg.trialfun = 'ft_trialfun_general';\n",
    "    cfg.trialdef.chanindx = 226;\n",
    "    cfg.trialdef.threshold = threshold;\n",
    "    cfg.trialdef.eventtype = 'combined_binary_trigger'; % this will be the type of the event if combinebinary = true\n",
    "    cfg.trialdef.combinebinary = 1;\n",
    "    cfg.preproc.baselinewindow = [-0.2 0];\n",
    "    cfg.preproc.demean     = 'yes';\n",
    "\n",
    "    TRIALS_AL_TL = ft_definetrial(cfg);\n",
    "    \n",
    "    SG_AL_TL = ft_preprocessing(TRIALS_AL_TL);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42c0d0cf-f549-4b8f-bef2-a81096e0b6b4",
   "metadata": {},
   "source": [
    "Same for the `Attend Right` block."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c6eb214d-1c79-43fa-8db0-0091eac88a63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating trial function 'ft_trialfun_general'\n",
      "reading the header from 'C:\\Users\\hz3752\\Box\\MEG\\Data\\attention-task\\sub-01\\attention_attend_right_01.con'\n",
      "reading the events from 'C:\\Users\\hz3752\\Box\\MEG\\Data\\attention-task\\sub-01\\attention_attend_right_01.con'\n",
      "found 231 events\n",
      "created 231 trials\n",
      "the call to \"ft_definetrial\" took 42 seconds\n",
      "processing channel { 'AG001' 'AG002' 'AG003' 'AG004' 'AG005' 'AG006' 'AG007' 'AG008' 'AG009' 'AG010' 'AG011' 'AG012' 'AG013' 'AG014' 'AG015' 'AG016' 'AG017' 'AG018' 'AG019' 'AG020' 'AG021' 'AG022' 'AG023' 'AG024' 'AG025' 'AG026' 'AG027' 'AG028' 'AG029' 'AG030' 'AG031' 'AG032' 'AG033' 'AG034' 'AG035' 'AG036' 'AG037' 'AG038' 'AG039' 'AG040' 'AG041' 'AG042' 'AG043' 'AG044' 'AG045' 'AG046' 'AG047' 'AG048' 'AG049' 'AG050' 'AG051' 'AG052' 'AG053' 'AG054' 'AG055' 'AG056' 'AG057' 'AG058' 'AG059' 'AG060' 'AG061' 'AG062' 'AG063' 'AG064' 'AG065' 'AG066' 'AG067' 'AG068' 'AG069' 'AG070' 'AG071' 'AG072' 'AG073' 'AG074' 'AG075' 'AG076' 'AG077' 'AG078' 'AG079' 'AG080' 'AG081' 'AG082' 'AG083' 'AG084' 'AG085' 'AG086' 'AG087' 'AG088' 'AG089' 'AG090' 'AG091' 'RM092' 'AG093' 'AG094' 'AG095' 'AG096' 'AG097' 'AG098' 'AG099' 'AG100' 'AG101' 'AG102' 'AG103' 'AG104' 'AG105' 'AG106' 'AG107' 'AG108' 'AG109' 'AG110' 'AG111' 'AG112' 'AG113' 'AG114' 'AG115' 'AG116' 'AG117' 'AG118' 'AG119' 'AG120' 'AG121' 'AG122' 'AG123' 'AG124' 'AG125' 'AG126' 'AG127' 'AG128' 'AG129' 'AG130' 'AG131' 'AG132' 'AG133' 'AG134' 'AG135' 'AG136' 'AG137' 'AG138' 'AG139' 'AG140' 'AG141' 'AG142' 'AG143' 'AG144' 'AG145' 'AG146' 'AG147' 'AG148' 'AG149' 'AG150' 'AG151' 'AG152' 'AG153' 'AG154' 'AG155' 'AG156' 'AG157' 'AG158' 'AG159' 'AG160' 'AG161' 'AG162' 'AG163' 'AG164' 'AG165' 'AG166' 'AG167' 'AG168' 'AG169' 'AG170' 'AG171' 'AG172' 'AG173' 'AG174' 'AG175' 'AG176' 'AG177' 'AG178' 'AG179' 'AG180' 'AG181' 'AG182' 'AG183' 'AG184' 'AG185' 'AG186' 'AG187' 'AG188' 'AG189' 'AG190' 'AG191' 'AG192' 'AG193' 'AG194' 'AG195' 'AG196' 'AG197' 'AG198' 'AG199' 'AG200' 'AG201' 'AG202' 'AG203' 'AG204' 'AG205' 'AG206' 'AG207' 'AG208' 'RM209' 'RM210' 'RM211' 'RM212' 'RM213' 'RM214' 'RM215' 'RM216' 'RM217' 'RM218' 'RM219' 'RM220' 'RM221' 'RM222' 'RM223' 'RM224' '225' '226' '227' '228' '229' '230' '231' '232' '233' '234' '235' '236' '237' '238' '239' '240' '241' '242' '243' '244' '245' '246' '247' '248' '249' '250' '251' '252' '253' '254' '255' '256' }\n",
      "reading and preprocessing\n",
      "reading and preprocessing trial 3 from 2reading and preprocessing trial 5 from 2reading and preprocessing trial 6 from 2reading and preprocessing trial 7 from 2reading and preprocessing trial 9 from 2reading and preprocessing trial 10 from 23reading and preprocessing trial 12 from 23reading and preprocessing trial 13 from 23reading and preprocessing trial 14 from 23reading and preprocessing trial 16 from 23reading and preprocessing trial 18 from 23reading and preprocessing trial 20 from 23reading and preprocessing trial 22 from 23reading and preprocessing trial 24 from 23reading and preprocessing trial 26 from 23reading and preprocessing trial 28 from 23reading and preprocessing trial 30 from 23reading and preprocessing trial 32 from 23reading and preprocessing trial 34 from 23reading and preprocessing trial 36 from 23reading and preprocessing trial 38 from 23reading and preprocessing trial 40 from 23reading and preprocessing trial 41 from 23reading and preprocessing trial 42 from 23reading and preprocessing trial 43 from 23reading and preprocessing trial 44 from 23reading and preprocessing trial 45 from 23reading and preprocessing trial 46 from 23reading and preprocessing trial 48 from 23reading and preprocessing trial 49 from 23reading and preprocessing trial 50 from 23reading and preprocessing trial 51 from 23reading and preprocessing trial 52 from 23reading and preprocessing trial 53 from 23reading and preprocessing trial 54 from 23reading and preprocessing trial 55 from 23reading and preprocessing trial 56 from 23reading and preprocessing trial 57 from 23reading and preprocessing trial 58 from 23reading and preprocessing trial 60 from 23reading and preprocessing trial 61 from 23reading and preprocessing trial 62 from 23reading and preprocessing trial 63 from 23reading and preprocessing trial 64 from 23reading and preprocessing trial 65 from 23reading and preprocessing trial 66 from 23reading and preprocessing trial 67 from 23reading and preprocessing trial 68 from 23reading and preprocessing trial 69 from 23reading and preprocessing trial 70 from 23reading and preprocessing trial 71 from 23reading and preprocessing trial 73 from 23reading and preprocessing trial 75 from 23reading and preprocessing trial 77 from 23reading and preprocessing trial 78 from 23reading and preprocessing trial 79 from 23reading and preprocessing trial 81 from 23reading and preprocessing trial 82 from 23reading and preprocessing trial 84 from 23reading and preprocessing trial 85 from 23reading and preprocessing trial 87 from 23reading and preprocessing trial 89 from 23reading and preprocessing trial 91 from 23reading and preprocessing trial 92 from 23reading and preprocessing trial 94 from 23reading and preprocessing trial 96 from 23reading and preprocessing trial 97 from 23reading and preprocessing trial 98 from 23reading and preprocessing trial 99 from 23reading and preprocessing trial 100 from 2reading and preprocessing trial 101 from 2reading and preprocessing trial 102 from 2reading and preprocessing trial 103 from 2reading and preprocessing trial 104 from 2reading and preprocessing trial 105 from 2reading and preprocessing trial 106 from 2reading and preprocessing trial 107 from 2reading and preprocessing trial 108 from 2reading and preprocessing trial 109 from 2reading and preprocessing trial 110 from 2reading and preprocessing trial 111 from 2reading and preprocessing trial 112 from 2reading and preprocessing trial 113 from 2reading and preprocessing trial 114 from 2reading and preprocessing trial 115 from 2reading and preprocessing trial 116 from 2reading and preprocessing trial 117 from 2reading and preprocessing trial 118 from 2reading and preprocessing trial 119 from 2reading and preprocessing trial 121 from 2reading and preprocessing trial 122 from 2reading and preprocessing trial 123 from 2reading and preprocessing trial 124 from 2reading and preprocessing trial 125 from 2reading and preprocessing trial 126 from 2reading and preprocessing trial 127 from 2reading and preprocessing trial 128 from 2reading and preprocessing trial 129 from 2reading and preprocessing trial 130 from 2reading and preprocessing trial 132 from 2reading and preprocessing trial 133 from 2reading and preprocessing trial 134 from 2reading and preprocessing trial 135 from 2reading and preprocessing trial 136 from 2reading and preprocessing trial 137 from 2reading and preprocessing trial 138 from 2reading and preprocessing trial 139 from 2reading and preprocessing trial 141 from 2reading and preprocessing trial 142 from 2reading and preprocessing trial 144 from 2reading and preprocessing trial 145 from 2reading and preprocessing trial 146 from 2reading and preprocessing trial 147 from 2reading and preprocessing trial 148 from 2reading and preprocessing trial 149 from 2reading and preprocessing trial 151 from 2reading and preprocessing trial 152 from 2reading and preprocessing trial 153 from 2reading and preprocessing trial 154 from 2reading and preprocessing trial 155 from 2reading and preprocessing trial 156 from 2reading and preprocessing trial 157 from 2reading and preprocessing trial 158 from 2reading and preprocessing trial 159 from 2reading and preprocessing trial 160 from 2reading and preprocessing trial 161 from 2reading and preprocessing trial 162 from 2reading and preprocessing trial 163 from 2reading and preprocessing trial 164 from 2reading and preprocessing trial 165 from 2reading and preprocessing trial 166 from 2reading and preprocessing trial 167 from 2reading and preprocessing trial 168 from 2reading and preprocessing trial 169 from 2reading and preprocessing trial 170 from 2reading and preprocessing trial 171 from 2reading and preprocessing trial 172 from 2reading and preprocessing trial 173 from 2reading and preprocessing trial 174 from 2reading and preprocessing trial 175 from 2reading and preprocessing trial 176 from 2reading and preprocessing trial 177 from 2reading and preprocessing trial 178 from 2reading and preprocessing trial 180 from 2reading and preprocessing trial 181 from 2reading and preprocessing trial 182 from 2reading and preprocessing trial 184 from 2reading and preprocessing trial 185 from 2reading and preprocessing trial 186 from 2reading and preprocessing trial 187 from 2reading and preprocessing trial 188 from 2reading and preprocessing trial 189 from 2reading and preprocessing trial 190 from 2reading and preprocessing trial 191 from 2reading and preprocessing trial 192 from 2reading and preprocessing trial 194 from 2reading and preprocessing trial 196 from 2reading and preprocessing trial 198 from 2reading and preprocessing trial 199 from 2reading and preprocessing trial 200 from 2reading and preprocessing trial 201 from 2reading and preprocessing trial 202 from 2reading and preprocessing trial 204 from 2reading and preprocessing trial 206 from 2reading and preprocessing trial 208 from 2reading and preprocessing trial 209 from 2reading and preprocessing trial 210 from 2reading and preprocessing trial 212 from 2reading and preprocessing trial 214 from 2reading and preprocessing trial 215 from 2reading and preprocessing trial 216 from 2reading and preprocessing trial 218 from 2reading and preprocessing trial 219 from 2reading and preprocessing trial 220 from 2reading and preprocessing trial 221 from 2reading and preprocessing trial 223 from 2reading and preprocessing trial 224 from 2reading and preprocessing trial 225 from 2reading and preprocessing trial 226 from 2reading and preprocessing trial 227 from 2reading and preprocessing trial 229 from 2reading and preprocessing trial 230 from 2reading and preprocessing trial 231 from 231\n",
      "the call to \"ft_preprocessing\" took 38 seconds\n"
     ]
    }
   ],
   "source": [
    "    %% Define trials and segment the data: Attend Right Target Right\n",
    "\n",
    "    previewTrigger = data_MEG_RIGHT.trial{1}(227, :);\n",
    "\n",
    "    threshold = (max(previewTrigger) + min(previewTrigger)) / 2;\n",
    "\n",
    "    cfg = [];\n",
    "    cfg.dataset  = ATTEND_RIGHT_CON;\n",
    "    cfg.trialdef.eventvalue = 1; % placeholder for the conditions\n",
    "    cfg.trialdef.prestim    = 0.5; % 1s before stimulus onset\n",
    "    cfg.trialdef.poststim   = 1.2; % 1s after stimulus onset\n",
    "    cfg.trialfun = 'ft_trialfun_general';\n",
    "    cfg.trialdef.chanindx = 227;\n",
    "    cfg.trialdef.threshold = threshold;\n",
    "    cfg.trialdef.eventtype = 'combined_binary_trigger'; % this will be the type of the event if combinebinary = true\n",
    "    cfg.trialdef.combinebinary = 1;\n",
    "    cfg.preproc.baselinewindow = [-0.2 0];\n",
    "    cfg.preproc.demean     = 'yes';\n",
    "\n",
    "    TRIALS_AR_TR = ft_definetrial(cfg);\n",
    "    \n",
    "    SG_AR_TR = ft_preprocessing(TRIALS_AR_TR);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "339dfd2b-5ae7-462f-92db-dfb736c3b896",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating trial function 'ft_trialfun_general'\n",
      "reading the header from 'C:\\Users\\hz3752\\Box\\MEG\\Data\\attention-task\\sub-01\\attention_attend_right_01.con'\n",
      "reading the events from 'C:\\Users\\hz3752\\Box\\MEG\\Data\\attention-task\\sub-01\\attention_attend_right_01.con'\n",
      "found 231 events\n",
      "created 231 trials\n",
      "the call to \"ft_definetrial\" took 41 seconds\n",
      "processing channel { 'AG001' 'AG002' 'AG003' 'AG004' 'AG005' 'AG006' 'AG007' 'AG008' 'AG009' 'AG010' 'AG011' 'AG012' 'AG013' 'AG014' 'AG015' 'AG016' 'AG017' 'AG018' 'AG019' 'AG020' 'AG021' 'AG022' 'AG023' 'AG024' 'AG025' 'AG026' 'AG027' 'AG028' 'AG029' 'AG030' 'AG031' 'AG032' 'AG033' 'AG034' 'AG035' 'AG036' 'AG037' 'AG038' 'AG039' 'AG040' 'AG041' 'AG042' 'AG043' 'AG044' 'AG045' 'AG046' 'AG047' 'AG048' 'AG049' 'AG050' 'AG051' 'AG052' 'AG053' 'AG054' 'AG055' 'AG056' 'AG057' 'AG058' 'AG059' 'AG060' 'AG061' 'AG062' 'AG063' 'AG064' 'AG065' 'AG066' 'AG067' 'AG068' 'AG069' 'AG070' 'AG071' 'AG072' 'AG073' 'AG074' 'AG075' 'AG076' 'AG077' 'AG078' 'AG079' 'AG080' 'AG081' 'AG082' 'AG083' 'AG084' 'AG085' 'AG086' 'AG087' 'AG088' 'AG089' 'AG090' 'AG091' 'RM092' 'AG093' 'AG094' 'AG095' 'AG096' 'AG097' 'AG098' 'AG099' 'AG100' 'AG101' 'AG102' 'AG103' 'AG104' 'AG105' 'AG106' 'AG107' 'AG108' 'AG109' 'AG110' 'AG111' 'AG112' 'AG113' 'AG114' 'AG115' 'AG116' 'AG117' 'AG118' 'AG119' 'AG120' 'AG121' 'AG122' 'AG123' 'AG124' 'AG125' 'AG126' 'AG127' 'AG128' 'AG129' 'AG130' 'AG131' 'AG132' 'AG133' 'AG134' 'AG135' 'AG136' 'AG137' 'AG138' 'AG139' 'AG140' 'AG141' 'AG142' 'AG143' 'AG144' 'AG145' 'AG146' 'AG147' 'AG148' 'AG149' 'AG150' 'AG151' 'AG152' 'AG153' 'AG154' 'AG155' 'AG156' 'AG157' 'AG158' 'AG159' 'AG160' 'AG161' 'AG162' 'AG163' 'AG164' 'AG165' 'AG166' 'AG167' 'AG168' 'AG169' 'AG170' 'AG171' 'AG172' 'AG173' 'AG174' 'AG175' 'AG176' 'AG177' 'AG178' 'AG179' 'AG180' 'AG181' 'AG182' 'AG183' 'AG184' 'AG185' 'AG186' 'AG187' 'AG188' 'AG189' 'AG190' 'AG191' 'AG192' 'AG193' 'AG194' 'AG195' 'AG196' 'AG197' 'AG198' 'AG199' 'AG200' 'AG201' 'AG202' 'AG203' 'AG204' 'AG205' 'AG206' 'AG207' 'AG208' 'RM209' 'RM210' 'RM211' 'RM212' 'RM213' 'RM214' 'RM215' 'RM216' 'RM217' 'RM218' 'RM219' 'RM220' 'RM221' 'RM222' 'RM223' 'RM224' '225' '226' '227' '228' '229' '230' '231' '232' '233' '234' '235' '236' '237' '238' '239' '240' '241' '242' '243' '244' '245' '246' '247' '248' '249' '250' '251' '252' '253' '254' '255' '256' }\n",
      "reading and preprocessing\n",
      "reading and preprocessing trial 2 from 2reading and preprocessing trial 3 from 2reading and preprocessing trial 4 from 2reading and preprocessing trial 5 from 2reading and preprocessing trial 6 from 2reading and preprocessing trial 7 from 2reading and preprocessing trial 8 from 2reading and preprocessing trial 9 from 2reading and preprocessing trial 10 from 23reading and preprocessing trial 11 from 23reading and preprocessing trial 12 from 23reading and preprocessing trial 13 from 23reading and preprocessing trial 14 from 23reading and preprocessing trial 15 from 23reading and preprocessing trial 16 from 23reading and preprocessing trial 17 from 23reading and preprocessing trial 19 from 23reading and preprocessing trial 21 from 23reading and preprocessing trial 23 from 23reading and preprocessing trial 24 from 23reading and preprocessing trial 25 from 23reading and preprocessing trial 26 from 23reading and preprocessing trial 28 from 23reading and preprocessing trial 30 from 23reading and preprocessing trial 32 from 23reading and preprocessing trial 33 from 23reading and preprocessing trial 34 from 23reading and preprocessing trial 35 from 23reading and preprocessing trial 37 from 23reading and preprocessing trial 38 from 23reading and preprocessing trial 39 from 23reading and preprocessing trial 41 from 23reading and preprocessing trial 43 from 23reading and preprocessing trial 44 from 23reading and preprocessing trial 46 from 23reading and preprocessing trial 48 from 23reading and preprocessing trial 49 from 23reading and preprocessing trial 50 from 23reading and preprocessing trial 52 from 23reading and preprocessing trial 54 from 23reading and preprocessing trial 56 from 23reading and preprocessing trial 57 from 23reading and preprocessing trial 59 from 23reading and preprocessing trial 60 from 23reading and preprocessing trial 61 from 23reading and preprocessing trial 62 from 23reading and preprocessing trial 63 from 23reading and preprocessing trial 64 from 23reading and preprocessing trial 65 from 23reading and preprocessing trial 67 from 23reading and preprocessing trial 69 from 23reading and preprocessing trial 71 from 23reading and preprocessing trial 73 from 23reading and preprocessing trial 75 from 23reading and preprocessing trial 77 from 23reading and preprocessing trial 79 from 23reading and preprocessing trial 81 from 23reading and preprocessing trial 82 from 23reading and preprocessing trial 84 from 23reading and preprocessing trial 85 from 23reading and preprocessing trial 87 from 23reading and preprocessing trial 88 from 23reading and preprocessing trial 89 from 23reading and preprocessing trial 90 from 23reading and preprocessing trial 92 from 23reading and preprocessing trial 94 from 23reading and preprocessing trial 95 from 23reading and preprocessing trial 97 from 23reading and preprocessing trial 98 from 23reading and preprocessing trial 100 from 2reading and preprocessing trial 102 from 2reading and preprocessing trial 104 from 2reading and preprocessing trial 105 from 2reading and preprocessing trial 106 from 2reading and preprocessing trial 108 from 2reading and preprocessing trial 109 from 2reading and preprocessing trial 110 from 2reading and preprocessing trial 111 from 2reading and preprocessing trial 113 from 2reading and preprocessing trial 114 from 2reading and preprocessing trial 116 from 2reading and preprocessing trial 118 from 2reading and preprocessing trial 120 from 2reading and preprocessing trial 122 from 2reading and preprocessing trial 124 from 2reading and preprocessing trial 126 from 2reading and preprocessing trial 128 from 2reading and preprocessing trial 130 from 2reading and preprocessing trial 132 from 2reading and preprocessing trial 134 from 2reading and preprocessing trial 136 from 2reading and preprocessing trial 138 from 2reading and preprocessing trial 140 from 2reading and preprocessing trial 142 from 2reading and preprocessing trial 144 from 2reading and preprocessing trial 146 from 2reading and preprocessing trial 148 from 2reading and preprocessing trial 150 from 2reading and preprocessing trial 152 from 2reading and preprocessing trial 154 from 2reading and preprocessing trial 156 from 2reading and preprocessing trial 158 from 2reading and preprocessing trial 159 from 2reading and preprocessing trial 160 from 2reading and preprocessing trial 161 from 2reading and preprocessing trial 163 from 2reading and preprocessing trial 164 from 2reading and preprocessing trial 165 from 2reading and preprocessing trial 166 from 2reading and preprocessing trial 167 from 2reading and preprocessing trial 168 from 2reading and preprocessing trial 169 from 2reading and preprocessing trial 170 from 2reading and preprocessing trial 171 from 2reading and preprocessing trial 172 from 2reading and preprocessing trial 173 from 2reading and preprocessing trial 174 from 2reading and preprocessing trial 175 from 2reading and preprocessing trial 176 from 2reading and preprocessing trial 177 from 2reading and preprocessing trial 178 from 2reading and preprocessing trial 179 from 2reading and preprocessing trial 180 from 2reading and preprocessing trial 181 from 2reading and preprocessing trial 183 from 2reading and preprocessing trial 185 from 2reading and preprocessing trial 186 from 2reading and preprocessing trial 188 from 2reading and preprocessing trial 190 from 2reading and preprocessing trial 192 from 2reading and preprocessing trial 194 from 2reading and preprocessing trial 196 from 2reading and preprocessing trial 198 from 2reading and preprocessing trial 200 from 2reading and preprocessing trial 201 from 2reading and preprocessing trial 203 from 2reading and preprocessing trial 205 from 2reading and preprocessing trial 207 from 2reading and preprocessing trial 209 from 2reading and preprocessing trial 211 from 2reading and preprocessing trial 213 from 2reading and preprocessing trial 214 from 2reading and preprocessing trial 215 from 2reading and preprocessing trial 217 from 2reading and preprocessing trial 219 from 2reading and preprocessing trial 220 from 2reading and preprocessing trial 222 from 2reading and preprocessing trial 224 from 2reading and preprocessing trial 226 from 2reading and preprocessing trial 227 from 2reading and preprocessing trial 228 from 2reading and preprocessing trial 229 from 2reading and preprocessing trial 230 from 2reading and preprocessing trial 231 from 231\n",
      "the call to \"ft_preprocessing\" took 27 seconds\n"
     ]
    }
   ],
   "source": [
    "    %% Define trials and segment the data: Attend Right Target Left\n",
    "\n",
    "    previewTrigger = data_MEG_RIGHT.trial{1}(228, :);\n",
    "\n",
    "    threshold = (max(previewTrigger) + min(previewTrigger)) / 2;\n",
    "\n",
    "    cfg = [];\n",
    "    cfg.dataset  = ATTEND_RIGHT_CON;\n",
    "    cfg.trialdef.eventvalue = 1; % placeholder for the conditions\n",
    "    cfg.trialdef.prestim    = 0.5; % 1s before stimulus onset\n",
    "    cfg.trialdef.poststim   = 1.2; % 1s after stimulus onset\n",
    "    cfg.trialfun = 'ft_trialfun_general';\n",
    "    cfg.trialdef.chanindx = 228;\n",
    "    cfg.trialdef.threshold = threshold;\n",
    "    cfg.trialdef.eventtype = 'combined_binary_trigger'; % this will be the type of the event if combinebinary = true\n",
    "    cfg.trialdef.combinebinary = 1;\n",
    "    cfg.preproc.baselinewindow = [-0.2 0];\n",
    "    cfg.preproc.demean     = 'yes';\n",
    "\n",
    "    TRIALS_AR_TL = ft_definetrial(cfg);\n",
    "    \n",
    "    SG_AR_TL = ft_preprocessing(TRIALS_AR_TL);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80e5cfc-8335-4703-99af-ac498bd7f0e6",
   "metadata": {},
   "source": [
    "We can now visually inspect the trials and eliminate ones considered bad using several metrics offered  by Fieldtrip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b3b1569-0e0d-422c-a101-0aeecc587f9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the input is raw data with 256 channels and 232 trials\n",
      "before GUI interaction: 232 trials marked to INCLUDE, 0 trials marked to EXCLUDE\n",
      "before GUI interaction: 207 channels marked to INCLUDE, 49 channels marked to EXCLUDE\n",
      "showing a summary of the data for all channels and trials\n",
      "computing var [-------|                                                   computing var [-------------------/                                       computing var [---------------------------------                          computing var [---------------------------------------------\\             computing var [---------------------------------------------------------| computing var [-----------------------------------------------------------/]\n",
      "after GUI interaction: 225 trials marked to INCLUDE, 7 trials marked to EXCLUDE\n",
      "after GUI interaction: 206 channels marked to INCLUDE, 50 channels marked to EXCLUDE\n",
      "the following channels were removed: RM092, AG094, RM209, RM210, RM211, RM212, RM213, RM214, RM215, RM216, RM217, RM218, RM219, RM220, RM221, RM222, RM223, RM224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256\n",
      "the following trials were removed: 21, 22, 23, 36, 43, 100, 108\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the call to \"ft_rejectvisual\" took 28 seconds\n",
      "the input is raw data with 256 channels and 231 trials\n",
      "before GUI interaction: 231 trials marked to INCLUDE, 0 trials marked to EXCLUDE\n",
      "before GUI interaction: 207 channels marked to INCLUDE, 49 channels marked to EXCLUDE\n",
      "showing a summary of the data for all channels and trials\n",
      "computing var [--------------|                                            computing var [------------------------------/                            computing var [-------------------------------------------                computing var [---------------------------------------------------------\\ computing var [-----------------------------------------------------------|]\n",
      "after GUI interaction: 227 trials marked to INCLUDE, 4 trials marked to EXCLUDE\n",
      "after GUI interaction: 204 channels marked to INCLUDE, 52 channels marked to EXCLUDE\n",
      "the following channels were removed: AG078, AG087, RM092, AG094, RM209, RM210, RM211, RM212, RM213, RM214, RM215, RM216, RM217, RM218, RM219, RM220, RM221, RM222, RM223, RM224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256\n",
      "the following trials were removed: 98, 99, 100, 109\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the call to \"ft_rejectvisual\" took 21 seconds\n",
      "the input is raw data with 256 channels and 231 trials\n",
      "before GUI interaction: 231 trials marked to INCLUDE, 0 trials marked to EXCLUDE\n",
      "before GUI interaction: 207 channels marked to INCLUDE, 49 channels marked to EXCLUDE\n",
      "showing a summary of the data for all channels and trials\n",
      "computing var [----------------|                                          computing var [-------------------------------/                           computing var [------------------------------------------------           computing var [-----------------------------------------------------------\\]\n",
      "after GUI interaction: 228 trials marked to INCLUDE, 3 trials marked to EXCLUDE\n",
      "after GUI interaction: 206 channels marked to INCLUDE, 50 channels marked to EXCLUDE\n",
      "the following channels were removed: RM092, AG094, RM209, RM210, RM211, RM212, RM213, RM214, RM215, RM216, RM217, RM218, RM219, RM220, RM221, RM222, RM223, RM224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256\n",
      "the following trials were removed: 185, 202, 203\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the call to \"ft_rejectvisual\" took 12 seconds\n",
      "the input is raw data with 256 channels and 231 trials\n",
      "before GUI interaction: 231 trials marked to INCLUDE, 0 trials marked to EXCLUDE\n",
      "before GUI interaction: 207 channels marked to INCLUDE, 49 channels marked to EXCLUDE\n",
      "showing a summary of the data for all channels and trials\n",
      "computing var [---------------|                                           computing var [-------------------------------/                           computing var [-----------------------------------------------            computing var [-----------------------------------------------------------\\]\n",
      "after GUI interaction: 229 trials marked to INCLUDE, 2 trials marked to EXCLUDE\n",
      "after GUI interaction: 206 channels marked to INCLUDE, 50 channels marked to EXCLUDE\n",
      "the following channels were removed: RM092, AG094, RM209, RM210, RM211, RM212, RM213, RM214, RM215, RM216, RM217, RM218, RM219, RM220, RM221, RM222, RM223, RM224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256\n",
      "the following trials were removed: 173, 174\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the call to \"ft_rejectvisual\" took 12 seconds\n"
     ]
    }
   ],
   "source": [
    "    %% Visual Inspection ALTL\n",
    "    \n",
    "    cfg = [];\n",
    "    cfg.method='summary';\n",
    "    cfg.channel = {'AG*'};    \n",
    "    dataALTL_rej = ft_rejectvisual(cfg, SG_AL_TL);\n",
    "\n",
    " %% Visual Inspection ALTR\n",
    "    cfg = [];\n",
    "    cfg.method='summary';\n",
    "    cfg.channel = {'AG*'};\n",
    "    \n",
    "    dataALTR_rej = ft_rejectvisual(cfg, SG_AL_TR);\n",
    "\n",
    "\n",
    " %% Visual Inspection ARTR\n",
    "    cfg = [];\n",
    "    cfg.method='summary';\n",
    "    cfg.channel = {'AG*'};\n",
    "    \n",
    "    dataARTR_rej = ft_rejectvisual(cfg, SG_AR_TR);\n",
    "\n",
    "\n",
    "    %% Visual Inspection ARTL\n",
    "    cfg = [];\n",
    "    cfg.method='summary';\n",
    "    cfg.channel = {'AG*'};\n",
    "    \n",
    "    dataARTL_rej = ft_rejectvisual(cfg, SG_AR_TL);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d3140a5-ac0b-41f9-93e4-58b0e5b1b6c4",
   "metadata": {},
   "source": [
    "Averaging the trials of same types over repetition times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "081fa977-1948-4bc7-bdd6-f85f06bf76bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the input is raw data with 206 channels and 225 trials\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the call to \"ft_timelockanalysis\" took 5 seconds\n",
      "the input is raw data with 204 channels and 227 trials\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the call to \"ft_timelockanalysis\" took 2 seconds\n",
      "the input is raw data with 206 channels and 228 trials\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the call to \"ft_timelockanalysis\" took 2 seconds\n",
      "the input is raw data with 206 channels and 229 trials\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the call to \"ft_timelockanalysis\" took 2 seconds\n"
     ]
    }
   ],
   "source": [
    "    %% Averaging trials together\n",
    "\n",
    "    cfg = [];\n",
    "    \n",
    "    avgALTL = ft_timelockanalysis(cfg, dataALTL_rej);\n",
    "    avgALTR = ft_timelockanalysis(cfg, dataALTR_rej);\n",
    "    avgARTR = ft_timelockanalysis(cfg, dataARTR_rej);\n",
    "    avgARTL = ft_timelockanalysis(cfg, dataARTL_rej);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0054b05-5cd9-4eed-9e5f-3bc6573da3a4",
   "metadata": {},
   "source": [
    "To visualise the data from a hand-picked sensor, we will use a custom-made function that creates the sensor layout of the KIT system at NYUAD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fa7739cb-8c45-47ac-9b79-1a3640998643",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating layout from cfg.grad\n",
      "creating layout for yokogawa208 system\n",
      "the call to \"ft_prepare_layout\" took 0 seconds\n",
      "reading layout from file CTF151_helmet.mat\n",
      "reading 'layout' from file 'CTF151_helmet.mat'\n",
      "the call to \"ft_prepare_layout\" took 0 seconds\n"
     ]
    },
    {
     "data": {
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"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%% Prepare KIT layout\n",
    "\n",
    "kit_layout = create_kit_layout(ATTEND_LEFT_CON);\n",
    "\n",
    "figure('Position', [100, 100, 1000, 800]); % Adjust the width and height (1000 and 800) as needed\n",
    "ft_plot_layout(kit_layout, 'box', 1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "212cf15e-1290-4924-bdb5-dd08ad8925ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the call to \"ft_prepare_layout\" took 0 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the call to \"ft_topoplotER\" took 3 seconds\n",
      "the call to \"ft_prepare_layout\" took 0 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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IeNHqdvHkz3oO+PtFs4B+QSvudAvOW2HARTLu2LOhKss+0svtJACeqcEL3EKKJLyqkVvzSJ90yFo+0tMxF47JXzgm/4XyhUSN/EGiRn4eLTwZWrjWV3/SPe9ePvFMd+X7K59plPtwI2lQZQpDgpwuPdwRBfWvNgC3kCIS5a4jGTUiVhEZnfMY9K2YtqBGhOD7k2SgdpKvE2ZDAjeSuIUkT5tQ3daB10AGT94jtywck7/ywHh/fEUsbUeNCH0LNoZP6B21k2SS3amAjNdpdTRqJ3FnEkcGLkiRgcqwOg/m0aAJlVpJkYQHyn4XiMOGJ+GjSRStNElbNZLQpjUpABVjWxN8yC4CUPIPHGW62nB6sPU8sybp0Mldg07fsD8+r+mQBACLkz4GgFfLH/tEHMa+vRbrnCQiQIIgoACwQ2MXkEZy8buXLWAUULByjK4RL6KtUTq5AOUGG4oXkEQxNFjj2cVGK9rahGMNJEjOG6VvYBdNApoZqtehz2vUO7d22v/hobHpfQo20TU1Uagb7Uz2QAyryQIgLU4hrY5jtaGGiDeLkioYzqsi96GG7Z+hiA+bHl0zZhOOYohCH78FL1pNVvoXACwm9PGb9ZIYB7QYj+NBjDrr8cw5g/euB7AO3rv+eOYcR991ycXvlox5GtoafMhOFi5I4Q6rRsrNo9h+x0/uGiRZuTjp41fLH9OsZwCA1UjbI2vI6ayZ7GK/PV8GuQN3f3jh1IJed394weuertkZFDLpCbNW5tHpn/Tu98/z3vfzneTid0tTnwnEkSMGLkiycEGKGGw2m7mlq9tNxHtklb2HBFSNwhBWhCQKdDpr5uC967U6UZ+CTeeyp7NGUqigmrRjtWncf6gsTXri590CpEYEqkmlqc+Are1ZSBxZuCCFNSpKFgFAbL/jDacHXzjjfMuACf8OqBqFlXl0LGMuyJpB/fZ8eTxzJgBoJUvho0mU3X8VVGjSiZ93u+dvVywQpGi9thh01zZcQarhghS+KFQjSXAdUSN2hwET/n1y16B/ejvdwbFPKunV8H2rJWvCR42IFA0pXAcATbJ3YyJFDveGBrQCTSJqpG0f3H7DbXrgjg/ZycIFKUxhXUeeRuoAq5F4siPbWNZ5OACsSPrbs+U/XwawtzGDfeN5c2cAqJuQS9cY8gpoW8C+91jGU30kPZfd1KdgU30LCgGQBAiIepR8QRTQohVw+ADOmNBkQ5kaJLkYblk6sIuVmQ8l7NoOANVmAIBbOKhB8om6G+pJ549nTu9TsCnacIvd2tFwnV3srLvBLkpCLWjIQ/fi98tTp7NfIwBYouw/St+Pz556rNewt26zW22SKFfyo9OfXhLMItlZj5ZtBoEmHMr70P5/vfuvQvoLIgBYY1BggiUGuakssWa2ISkw0WhCv9FtA9qqwxEusTitQx2AQXDuTwNe0kreLkp5HtqekSRyQZKFh32HOz7Fecfl7r+1fSxdJGrkds+6Cbl1E3ITdm0nL8ltVAZy06ecy56uvHuBg6pRpDNpnnnHGn9jE+Tz4LklEIEM1eOmDdu3xtPWtJK3SaNNR4FzMNxCCkeUzDqiaYQ8zYf1pEZHMx4FF11RDmtUkRGq45nTNYwR8JXKzIcAYODezy7LBZAHiaTi98tTs5Wre+BIf0Hct0wAgH3L7EaSDIELq1NI27GT2sanVA+3kMIOJa4jKkKsFEnMI1eOZjx6NOPRoYVrhxau9b+f1F/Czi8JCQP3fqb6vcT3QxcPjV1waOyC7aN/pfqAhrwCS0626rerRiZBOFEmTxx/rkuo1KiNiBCLaFP/agtwQQovZIYvokxX6YvqEGsnsWoUl7tfYh5pIkXUPJJ470OlSZWZD7lVo7oJuZacbHXCMHL/h/52ywN9Pz5b8bL3CtYqRu22r4t2VSOvhhEAHH+uy+B3PJZz9IfqcdMSd2/xuhvVpDYycCeCTfUr1H0PBnzILkxpqO/deKtPbLzzZtHU2N3aZE/qbLkNAGA9cCdZ1JvKrQeS3tO1J4vPpmxbUTJ5cf1U+t7qcdPabf72TJ3z+DFWI3u69vhyb8CO6zpDEwDA5DF0zcWW9rTdzSAAQOLuLcczpyXu3oIiDQB0OkllB1SewCibqaEF14xosqAkCJWZD5l27j7j6KmxsTO7tfsXJy7PuqdzfSJZlOQXqMUV6oxM1AYJZ79T55zE2l9EE1otOhSXEW9EQRy3oxJg7/rjmdlkGPNqbE/JhxKjnecym1CWbmOU8wFRjBIE/PF1VvQb2UzoadIahW7ot2NFABj5a8uh/zEAwLevRg/9X+fPT4qUn/pp77v/cd4MTbfj0Ue4HoUiL1r0eKIu/gVjcGKOWGaxety0aizwBpzzYu5BZIm2nYE7zamtrX3nnXdee+21UHfEX7iFFEbQh8SG+t6k0dTYnb4UHoSoEV2sHjdN4bOqcjz5SBJ3b6keN03DE8lwNONR087dMjtcnnWPisMmF7+bXPzu0+X/oXD/V0Zv+CjpTx8l/UnFuWSYuNCyc6XSh0X5PN8jf23XgKO/SNCgZ8ogNZAUFp54uPyVAHcnjBBBVP2SOexvf/vbL78Mdv6RQMAFKVxQPWQhUwpWS3mg5tG2AzJ7Je7eElp/EqHd5m+7f3Gi+xcngnCux8tfdF3paQzz3r/UfvdfiurBT1xo2fGxXDo+gk/pglhNIuaR8veqgJbm8wrVpFY/cBcIQfryyy+jo8O0arCv8CG7sIOaR17RjykHAFaNJOYRaGW1MIN1XiH34oDG3RF/2Ak1Kd+0R3PziDLpsWaqSa4J7ogUKamBRAfuKEFQI2IexRtveN2T8HD5K58mLYHWPnAnb+h4YvRztZ42nTt37p///OfKlSt37tzpR7/CBS5IYYHrYJ1X2nX8XmIYuaoRaGskgRfzqHXzUvJXriuXlM0+prvL7f5EmLt+WSlZT4yke//i8RbDQnXI1VryqRzf0P+tI+bR0V8ksM6kQFCVNdvPKrGtW5NUUPZOR/AgSy+99NLixYtjYsK3ZLBPcEEKMU07MmJy99HFqBJ0d9MZceb/Jj3duXF7+p4W6ePnF7ps2q4eN43qR3UzJDah+C69BWdMMCBPtd7m3FozcyBpRG85DBYAgCYcAXETT86PFlpIsjiSTc4komvMIqBgCivOVaDHDnMJDWIM2xaa0NhXj6ud2EXT7WhgosiIG59yKhrdlOP09eziveIZdnFR8lYAyCscbu+zztnnyaM3LCt9gN35YrQzEP84QG0cqirUvr09LqOpfQMACJ2RzIg6NPDS7pZz/Gr8z604JQJY8P9uYzs02EWTLxDMsU4D6+gvEnpsOHkbnGVBrsShjEE9jejL0eOwlAZcmiRWQCeqypo9tHAtgP10Rkk1CkCXiiQxx6Plv1yb9AfS/n8HfwoAfxz1D2hdqLOQPLFixYrMzMyhQ4c2NDR43zsS4IIURjRuT9dPLPS6j9v14zMqJpa+pXmXqBqFhMKUF0gjo2QZXXk6a6aS+hHHn+sCAIPfuUoaqkeoFiVvpVLkyrZ9Iycnb5ZoEmVi6Vs7k93Mk1VejUJD7nn38olnlIbGhIqXyucvTVoFAEuTVr1UPj/EvQkAVpB75PKVTZs2Xbly5YMPPgCA27dv/+hHPyopKTEajV7fGLZwQQoxrMUDANadKOOcq4Xk9iDjMyryC4e9GZgeEqK3HPZp/357vlSoHPJklCyjsqSQUz/tDQB0eg1pHP+pGllalLx1eekUaJZTjmWlD7zgWZPIPFm3cYmnFvQCgGFLgvdsSzXp0uwBPTac9OdQ18bbB4djmRlp57KnazLnmrA0adUfbdxCkmPbtm2k0dDQkJ6e/q9//UvDg4cELkihJMxjitBgXYhQoUZ21UFzaexS5JMz365GCpDXpPBBuYWkxPXYOX/btfGT2TwXfQo2AdySeYsSqJHEaYNwQQoLPA3EKYGYR5KVO5NfVh6AcO6RO0kj8bNTqrsRCNiROk24+x/nAxRgJqNJMkYSAFS8EhtWRpJtUno1ADtxza0P6dr4ycRICkTFDapJrS+6QZRWrdeG2NjYVmAeARekEELNo6WlM95xrOwk3mT3iRfRkJ3ZhCyqeakVALCo/CkS/nXN3A2fAf0niwL6T9Dp0aESPztV/dDd7BpqHnX9srLBpj7C2orTDdikFRd8OpTANjqYY9mtMbVx9hCyWgCA2Mvx7NaG7vXsnroeznF2G0BXAdWbuKfp4uT0Q9v2jQTzRQC40yz38U2270kjv3DY+OTNr5eh4JH6+PYAAAdWlIzJTjmwoqSX47zbiwDgjmu9AaDi0VgAuGPtWUs8em9jLfoIxkZcyANb19ZodKmIOIijxRHj0MIEO1yaPUD3TSkADI21f4SK9HnD9q3paroIcNx5ZDxbkVT9uHP/B/atNvTV6XEuBskip41kAFINnxgbFryU/JXbqGIZ5qUWLip/alH5U5p0QEaNNDl+GyG/cNjro9e53ZRyYEXJmGfdbrpj7dk71p794dG+gewanHmiz12rzwEA+esKUaOA9kE5NKIhzIe1fcWqE1W/Qt33YMAtpNDAmkf0r1tNWr8/lV2cl2oPw1tTnFGqUWd6rT9t00X8g5vCGTZkRg6xDNzyadKSTzXtGCXlwIqSMWnENnLljrVnjz9qjwwMzPmd6L4ptd2fDAC2+5PJVxFWaiSh9Q3ccTzBBSkEsAUm/lQ2k65fWjrDZciucc7YYnbNmmIUhhcIqLXkj3mkSYid/5T+wT7M5dNs0G37Rk5OP6TujK+XzX199LrXy+a637y9CHI9apI9IPA57WWJmkcRRKuMbuBDdvJwQQp3WAvJrIvg34sUBgSA5OJ3g3ne5F9aqSyBw5D6DuTS3KlWI4IiTQLwKkv3vRHA5EiskVQBEJ7mkc1mI09vrcZIsnJBkiWCb3ARCjWPhu1bc1/RxzYbSsEZbUBRs92Ml9nFBB3aes2GSj1Y8BR6A64aUG9qZBdjLe4Td1LvUaevjtFJfGYBzeYTcGqG9rgAgVFncZQpsgB2aw/ft/pI+hNsQQpJBJdk6r4Ru8SjHUkBSOO2IJ1jKOqdbzeb7G2iRiKTikI0WG2eU0I8XP7Kk41bczKO6G32I0SLKJogxoa+OqEZfbFpcBwA8gqH54xel1c43BCNPlFCpzoAgLI3AWD76DQDNiJ/iHd68tpt/vbbB6KAmTtlbEDn1dnknCts6o0Tz3S/Y+1ZK/Pd6vRNgINeupuckYfpOhTlfxvnj/jBhApq1IgoQUazzYdkr1YXH7YF0Ff9f2VT2fZ/jv5a+cHDE2vkj40HFB7UwAkeR9KfCNzBh7x9/djznSQrR7zWQl6BO68n8gqH52Qckdkht+zN01kzT2fN9LTD3f84T+LUyVRfzRF27A3EYbWFTgUjCZw4rRsuSEGFNY9C2xO3sOaR2x1apo5smTpSnJQpcxBPVVyH71s9fN/qlAMr/O9npJBXOPzV0XJZz/vt+ZKktJDZh07p/fd/dP33f3T1tQ8nnul+z7uXPW2lmrQz+WVfjxw0qCa1gog7Uaf+1RbgQ3bBg/13qkif52m3pOL3g9IdKWyot0zGLePXh1qmjvS0tXrcNLdqpAmVmQ/RRuDOoi2Ly+a8Onr94jK5GlFUk9pt/tbTPsRUGvTXGhlNksRBqM5cRyPXPbrBOGrhPiR5uCCFANY8kkwUjTbcKk99CkInS16RV6PE3VsAGj3t4D8D935GZckVMmo35O3rnnY4tqjzkOXXAtM1jxBNIg1P+5CIxNNZ93nSJDpqN+ivNaTh6kMi4XkUGcPILTuTX6b5eUlchk9vDyjLS6eQIbtIj26wtg1DRzVckIIENY+itxw+yUQgx+HggqvRN0jKn/LUMQAgsQM6mdAtRsCeeQFHBHTSo4Q0V4zoRJJ5dk3TRpBG/KaKJjA36VE0QYvBcajtRQDQ2VgP4Dx4B30DADiK8jWYBJQjwIDLE5h0KAJCj00xyaKr05uqkV5nrTWiDxjf0Z697tjzne5afa4lFp3o5M/97/H5AAAgAElEQVR6AkCfT76vh3oAiDM6w0NuATTbUILkq4Y4+hcAbgvI8oiyop1jRbSY2II+fpLuJABs2zcSACaPXv9eCSpps68d0s72RasOp90HACOKVgFAZUwfsj5m5y4AMDcnnIQf6M4dW3CiisaYPp98TxcNLfoWcH7zEle6yPZ5WzFMtkdyio6YAqJG7WxNANDBhtICWgRcuAQv1oioJohZRFddM+BFmzQvdbMOxUQ0gw8hEpxWABekcGXbAQCozHQjS0oYvm/1kfQp3fKD5AcOdIlYwpBC+zP7sQyPQ0lkts2ZJ/oAwIC/22sRnfxZT/ZmHSq27Rs5OSUPAN4ryfG0D5Giw2nzSSPI5Ce/NL50KZmmHW4sLZ1BZo5HtJGkZfGJ1ggXpGDAzoSN+Vou8koCkaJAu0xY8yhwZwkmd60+Z2g2EKsIAAb8/WITuJ/Tc2tCTtyuPABpGapPitMeSS36pDhN244RKXo6Jc+xIu/x8hdddxtRtOpw2nxw2EacVkMbiU1QDY+y4/hLcMwjFQz4+0XyCnVH3CNjJwHAiKJVMTt3NU6c0DhxQhA6M750KWnkJ78UhNOpgxpGkRtuZ9XpVL9C3fdgwC2kgMOaRzK7XZ0x2N5yKRtBPPkBMpL8NI+ujZ8cnmrkPwEykiiMneQRYiGxmqT/+mCA+sPhhBwuSMEjbksFAHSrR15fY7PdJ3zVEdrb85YzVuqU3u4hN+QVVGZmD9+3Gr1Xj/znEvoWbDybPaVvwUb7EXDiBrMo9SezgQxWHC5hwKkZOuidcXTXACRRDBJEbIVLFq14Zn4jXpS4xNk+m0WjVY/CJW60r2cXBUBPlNdNyDPfy/ERbgF01DfetKFCD98Z7ySNV8vvfCTp441FSezWvmZ0Ij1+zLhuQP9TDQL6npt1RrbuX5XQG8CZvbB/VBW7c6LJkX2u7A0AuCG2B4CilFFkXWf8gFLZmMgudm1AEXemFmc3LszpZ++5Q9usADcsHUft/+Dg2CcB4KXkr54u/w+6f0+bXFyiJA5FxFGjZhFleTADWjS6FKdI0KHvVpIk4p8l41YdyJ4/pgAi1pNkhTZh6KiGC1JgoeaRzBQTipIJ+TTZgUSc1CFOsd9qjV+ryd7myKBa733XyCE/+aV8AAB4tfyxEHfFHWklb5NGUcpDoCrgBQD0Xx+0Th1FF4kUjXKUOApb5o8pWHUgO6I1qY2MvKmG+5DCBZInpufnVZ52MOQVEDXSRIr8R5N83ttH/4q8lL+lKmt2/z0b/DyvPKwUvVr+2INp5VodWcP8NwP3fkbGcmUmZnmCVSMWqknvJf3Vr86pIj/5pVVJy1YleSwTTKUocrHqBNWvUPc9GHALKYD4ZB4pgSQn1SojHDWPhK3lKuaPS3LeqJbJ3LI3fRIkX6HOOWGrUl1ZnPQxu7ixKIlokmTsTjmzxtrLL9HBOq2gcZiecoe70mv96Uvtar3vFwrml78gI0iUVqBMHLdwQYo8wsRCAocmEZlUfZCAqhGhy1fHnTEj3hhfunSscFiykkgRNZUO77anWRoxzl4yqiJ/oOuhsjK/I40v9ieTxnnXnbRg4N7PKjM9llnyCepJei/pr6wnKTgoUSOWiBu1s3AfkixckAIFNY8SNn4HnqsPi9gnLojY529Bs/GvWxL6FGw6kj69T8EmAIi3oVQF0iODjv4FF28zNY/0Xx8EG5hwhQUzHh6w2NB1csuKYg2IGtVb7XEBVnwiieNakiqJekRuufuKGq1x7OJNa8yV8VO65W+96W56YYMBxTi0MHEZRI1sBmfwBe1k4u4t57KndS5axb73gHEYu3g+qhtpzC/PAIA7bRdHMGl1lpTNfmX0hi3xQwHgldFoLPGV8icuQmcA2EQPJaL8cjdbEtjFGD2KvBguoAKJej362Mdi0M7GwrVHM9KGFq4liydwxIfFjKI2BCOqY0IKl0jKlwDAZVtncCkRIkGSbaFZjGEXGyzt2cUWG7rMJBEuI/d/2NN4AQCuOP4n2uvQZ+ymq1tRMlnuog972sjIm2q4IEUefQo2ncu2a5InZHIZaEu3/K1x+mZ27O5s9oO0TWP8QgiJJRONWmbYo1lHXx+9jogQ+bukbPZNXTsNT+QTQwvXkiqIVJbUMaPsN1+N/j0AfDX69zPKfqNN57RmRcnkZ1O2QaQZSa7ZsDgsXJACAjv3qOMm96Uc/IFoEjDZdNSh1aQW1p/EihArTv5HIhDzyM+DaMvrZXOjIODFlpYn2TPtvlC+UH5PIkVHMx417dwd6F5xOJrDBSlS6VOwKV7fcCxjrqsmeTWP5AsaqYNG3FmZp9W+BRvpkF0QouPUkbh7y+G0aSHJHaecReVPUVnyytDCtUczxinXpCPpTwTHMVmVNZu2VV8Mz6ZsW1EyGSKzwDkfspOHC5L2KEzNoAlDCte5yg+RqHprrLt3hIz+ezaQ+5G6W//htPmBM49o7rhgypJMTbys0j9L1ihXI4Jp527zRB80SQI7avds+c/VHYTleOYcwCJUlTV75P4PVRyKDNYRTYo4uCDJwwUpgMTn7wSARgOqqx0VjZzAeitO5o8d1wacT+GCGfnA2+ujACBh13bJeavNAAC3cJoDcMRH0HCGhI3fgSPgwCiiKyEG+7EbcchDjRV5v6/gtA467Li2sVkethf1ibp2OM2jA0yPIyAarcgfc9WCYhyiLch/Ho2rQugF9J/fYEWdvATI2d4gGsnXeDgtFwAk3TsJKEJPEqYRLeCCGngrjfUgnG7sQWs9wLbiro3o2mgw2A+1J/k+AKhx2L5DCscDwN3RlVXME04MoAQZHUw17OI9AFC49mjGODKId8MgSWPhPNDZ7Acl5lGtrQO7eA1w5AU+ryTXhlUS/yJGAwB1eV5liq0k7Np+aGyuZGJvrYDymNQabrCLbOKG27roTftH04j8SDGSLNyHJAsXJI2h5lHcLu+ZytomSoIyJJBUfiflUhRpA5Glc9m5dI1P/ZTH7mbbVux1TzJx7VjGfX76CGmYg4pPkVX65z3J/w0AnyYtebj8FX+6QSBeT2AeoQKapJETiXBBalsg8yh0qNCkYMJ2jN5GPeE1pI2Ge/Tb8+Xpxh7Ku0HHY/2RJUeYQ8i+bfaHTti1vW5CLrtVnSaR2khkclhkGUl8yE4eLkhaws0j5RBNkkwAcktoH6L7FGwScB4LyZDd0YzZ4AI7WdifBEtEityGrviEpyeAs9kP9i3YCHDD7bs0MZLYU0vUqA3Ch+zk4YLUFhG2lgc+Vtk7fQo2HU6bHubhbV6RWEjEh0Tj1iQ+JLfUzJRmeZDkmiKm0jGAB8p+p7qfdKoASzAnivUp2FSPp1RrRQQZSVad3vtOvnD58uVPP/20urp6+PDhDz/8sCBEtuBxQdKefnu+BHDOhL8YhS6RKwLyrsdaUIJ9wYYyi+jxoiRxww0rfi8OPYjFoQexlqjLs+4h7a5N7Q0W9NMLVrnr2GhE8QIxVmnpCrSzxb714o/7k0aXr47Tred10vsFG7kQLaDpq7WWdmTuUa0FAMBkRpEICc1yU1CNAorLkHyxDfi7uom/2Ns4LiPBjLaypRwA4LoJubauR98AANhWfOQWAICAw1L61aAhu46VPQ6+aRj1K7vJZXJkyCh+QACA5g8v0D3v/vDC5b7HNo/Oocb3sHYoj0M7ASdfMNaxizqdCAAJ+9bYtzqTMjSDS8YESSwJ4Zpoj3RIEHAUA36vDofDGB0BL6Shx79+k2gCgL4FGysziaEGTSL6qutt+HfB1SiadXLXYVugvr5+1qxZCxcuzM3N/fzzzw8dOvTHP/4x1J3yi8iW07AicqtYBg6Z5OUE4kIITmfCjdM/6c2qEUvqi2Lqi+KpBb1OLejFro/blXdrQs6tCXKlZrUlufhd0nAbpP550uKto1/bOvq1oPXHE+v3p3rfKQywgqD65Xq0AwcOJCcnL1iwYNy4cUuWLNm8eXPwP5G2cAuprUDNo+5fnAjaSamRJAPr1pZUe9IwNUP9dHt6OnWFcTXn9E969/vn+Y6VcjEOd394AQBOLeh1N2MqEQvp1oQcOPBOoDuphCllb4REkF4vmztn9DpXHQrzUTuLqiG7Pzx+zu36nJycnBz708mpU6e6dOnidrcIgltI2uB2Miyb4a0N0vPzqp6fV7HjdUoIXMqAMJEiX7n7wwsSOwkA4nblFY95LjgdmFj6lsxWJWokqVTC8ZVfftTnlx/1kdmhpqbmxRdffPllj1OtIwUuSAGE/x+qQKtqT65QI0kTLszpR14aHjPMcR21+3H5q1PK3phS9obX98r/L/Qt2MimPVQHfRYM58FzK+hVvzwd88yZMw8//PCCBQumTp0azM8SCPiQnQakly4njbSSt9NLl3cwOgOrRu7/8NDYCQAweO96suaKgOLbrutR9gHAkQiSTA0xeGuUFf18JhxrYGhxbj0/9y7S6LvmB2gAANBhJ79NEmuAFyU762xyzzGSChqmFvyJWpDDvN4YAwCJu7dUZk5L3L2FBje3OM5vZuILaC6GugfvBYBLAL3Wn/b0ESSLnb5ypLgVAQCiZf3hkqiNmEbkSzc12rf2X1ldtTAxtgkljLAHNVAMqFoC2yubztaxBn2xO//u/OpyH6hrMdgH9JJetpYv0Cd+dord+Y7CdcVjnOHgpiiUMELAJU8k+SOsOnTlSApPNNhQ8AibfKHW0qVRjz5vlA7VuTDixXi9M0PE8H2rj6RPZoPgXapR6FpwnM4tAwpqaBakBTLYaT1Fe+9l22mZoZxp5wmL1vOQysvLX3jhhd///vfjxo3T9sghgVtIGlCU8rzM1sF71w/eu57k8opEzj7Wt/qhu6sfujugZ0ncvaV63DR2zbGMua5ZkSihndhLqFqYGLRzJb1sDfRPIAMtbU5q96km0GMG+/YMIY30LO1T7GuCthbShQsXnnvuueXLl7cONQIuSEEjHDSp75of1L1R8mweIBJ3b1FexokYSSGk/8pq8tLqgDv/7rzj5D5Qt31zgmSHxM9OSTTJPj8pY+6xjLlkBmv4w8exNWT16tW1tbVz584d6CDUPfIXLkj+QgesafFTT4RKk+h4nWqC82xePW4am49gSOE6TxP7EzZ+l7DxO3a8LuK4a/W5M08gN/XEn1kBIPeButwH6jy8ySN+5nEIB9zO2219WHR61S/Xo73yyiuVmOB/Im3hghRUBu9df2385GvjIylzft+PzyZ+dio4RlIbhxpG2zcnKJQlmltI+VkOp80nRpXrpuOZc45nzjma8Sh5sZvoqJ38ALUSPBlJfQo2SU4qzyfFaY+kFklW0lG78AxtsIBe9SvUfQ8GPKhBG3LL3gS4TdoSf7LEzeu4g0wmDT3e+SqgGAfJnMlo2SgGU7MkfABdwXd/eAGYjogGdF4rXjRHoT7fMCHPfJMB5SYQcOmKTriGd5QZZYuRfAQ9nthvEtCR++/ZUJWVS4voVEXdBM9IKmjIY8Xnbdajz9uIFy145xgT+kQ3o9CX0y/mErvYM/oHegdPK3n7X0xIS8zOXTsn6u/9Sy1d0zWqQ/Zz4vZ3hOznxGYAG042EVMfTf8CQFXH7qRBSh8N37e6jum1IKBrRxK2QGpTmR0hMw1iFHHgJe7eAgDNjiiGoxnOJyeyiVBr7k7bHU2X2SO316PfyKBDITw0PmJo4dqjGTNdJ5n1Ldh4NONBmtAoXkT/Dk0CuuBJpgaar+GWPiB5ibSFlzCXhwuSX6h+CiOj/0EYaTn9k96BPkVAIbfO8Cw165XTWTNPM2O5RSnPx+zcxe5w719qv/uvjqwmAUD2c0iHWMicJHaerDrYyq0AUD1uGqs3lM752wAgSmch+/h50qCxbd/IyemHQt0Ljhq4XIcMt8VeOa2G01kz++35kvUsppW83ThxQiDOZdq5W/n8raGFa/vv2UA0viprdlXWbLdqJCFx9xa6mybX7dDCtVfGT3Fdr8mcJEIYjtpp60NqfXBBUg+93HPL3pTfszT1GfKSrB9SuE7yPylOyiQvDfsJAP3+eV7bA6rjwpx+9dOHuZ2gKlMeidY+15CmaSPIS9vDUogasWuKUp4nY3cSTSJGkv9nZAte+IQ665M/S6kjEBNjWxNckIIETVLpFWHHXq1OGobjdW7z94SkWF/0lsNBPiOxlmJ27lKtSZqM1/kDNZICat8rN5JWHcieP6bAdf22fSM17pNGWEBQ/Qp134MB9yFpgB5QRQlJATdRaAYAYh4ZhWZ3fvsp9Cn1CgAxjzoaGgAA+RZciiZEYTe+EWcfYD3xOhevhMWI+lwfe5tdrMPhA51NKPtAoh4ViWjC6SSq9ejeGi+gaf/EPKo32U93p+EmAJwDSDDcBIBOxhp2Z5pfYNT+Dw6OnU1cGpRafN52FuTTjrVIXNzSB0xiHrXorADQDn/AznoUp9CIT3SxpQO7KOjQN9nBWHto7IKR+z8kv94A/fdkfZEjPi25/WEo/fOe5AlZpX/eO9Ce1EP3Tel393cEgP4fOCc2mRrQR7DpbfQvAJhx4RJSeImWX4q2ocQNVmlOBAEcZpwI0IA/YB3+gEZ3RYyIGlU1dx9hQNGAvfUoGtOqR+c9apAWtqgX0cGvW52pGS60dEgwoN/lJn57jZAAAEtLZ8xP/mpp6QyDHv0QBJ1Ot6XoRwAwdexB162ccIMLkkrYbKpTyxfL7+x1ihJFcs+VwTp11FUAwNWG3HL3PwI1XkeDd5U4IQAgflMFlSJKRfq8YY46PTKM2v/BwbGTlX8/8ui/Vnp7oo/qfuYdn1/+AgCcEe02KynGqvumlO6g+6b07gu9q55MZDXJJ0g4tT8Fan2CRPeBYtPfJwx5BZacbHDJtLsyyZ6ma2H5IrqSVDRfU5wRiJ5oSxuJ3lZNmzADwxzVPhJ5KTr108CO1xE1CtrtL4T4JEUO88g7WaV/tt2fLFnZ/4PqqicTq550k5RI2wFY1dJl2rmbbbt6RrXCkFfg1ivGSpEMxDAKN7gPSR5uIYUF6oKbr84YHKD+yEOl6HTWTNL2FDccZDQsesT6MEjgiYbFmeQhFpJbTQJHISX/z+K/OWWeOI4Vp6BBjSSWpaUz5iV/BQCudtK0tH+Fjzi1kWA51XBB8gsNS4ERTVI+JKX/+mDHllivu9HxOlpTxx+vOMkxQe9ipEF8SP5rUkX6PNIYX7pU9UHiN1X4X2bibLZzbiYwPiR7SOR2aXYA1ei+KbXdn8wO3FH6f1At8SEdW9S53z/Py9hJvgpMkIf4VEBCB9kSWTLmEZGieamF4E6WOBEBFyQ1UAfSQwdfAwArIGFosaFvtUWUm0BuwtUoOuB4AUkehzoBuW2v4cUOTOgCzT3a1M45fb/X+tMX5vRrimsGgJvRyJfTGHONXexnQuEU8fp6MnhCbg1GPQo9IFkAuhWtOpw2DQDYWzkA/IDLQBh0bip2UzJKlhWmvCD5AlmsTCEMSTGODg1x9Q4jqUNDHFt9AwAsRnReWxTqVR+j/fOSZIPjS5cCOOvq0tn1g0v/DAB7ktNIfVvC5RYUtVGHc3N0BBQe0lFAWaibOsbAgRUlY5JTDqwAgDI9Cnhp39gZMESNzna5DAB9otGvcDpr5rB9a2i6EKtNkhFDEndjv+pIxgTJk0QLftDpcgOleW1oF0tP3KE+sVfRqtLU+SOKVpE1w0VUtD7Khi7v8/ru7KJp5+5GbGbZzPHsDteNCQBw3WLvQJXYl91qwNd/s84EAIvL5gDAvNHrV5RMXlEy+dmUbQDwg6EbhAdtJFhONVyQwoghheuOZQTwiVV1NTnJU6onyF3pcNqDEk1STmHKC+reSOi97ow/bz+eOcdRtuqszG6k5jppSDbVTchVHt9PSTmwomTMs+At4l95IEag6fplZc1MjRNLmyfaCyhIPiZJuurrrIDFZXOeHb2eLj6bsu3n4VHWvI24glTDBUk9Py5/VfNjBm4URV1ubOIi8qmsOJlEokKThu1bk4DDvsMWIkWVmQ+5apI6iIVUMiZT3Sy0c9nTlUQqumVo4dqjGSqHW2tmDuxVtErdeV2xh+1pxIqSyQBALKTwgUfZycPtR58JdD4S/wvG0PE6Pws0EGkMgo+Beo9CCGMeKYWYSqRGRt2EXE/FMpQj7Njra5KOc9nT/Z9WTKojBi5b3dMpeU+n5HndTUM1ohBZ4kQK3EIKR1hNCk5klwQ/Q7p9MpLIo73X2UgHxz4pU0DWle8ft1cbuvOjc8rf5SsD9352uaU9kaKEXdv91yTlkOpBWmW4IBaS8sgUdtRuRNGqw2lONxJhUbLzun2vJMerIBFPkrW5vesmdaN2EnQ6nYYhSKrhQ3bycEFSyZSyNxocl3eDGMNuahTRfHJJ5n/JooRowT7BniQCF0A8muFMdid5hG8wIIf5NZM06K46wTnF0oazPMQYUDKCu03Xabsy8yHHzcU+T94goHIMeklgAjazox072xs49CDOjPI4VNAcAduLKtLTAN9hBbB/xUTefmhE772zFi3GXo8DAJqDZ+j/2jMIHH08AQAkpV118cjMTTBdoX8BIEd3gN3azoYiTfbGohjiDi29ofjd0tRniBr1NyI/1nALylwgcfI3G/G1EWeC/R8cHJsJAKP2f3DV1JXd2pdJW3Ak/YkZZb8BAIBysiZWh1IzXBDtbnxaSTajZBndGi/cYneOtdhPVA3QO/r85Yae7Nbom+jyNpjtFxIZXT2cNp9cls3WWAAwMHVMSEzBF/uTwXoDALpab/TGtSruMl2FvZ9VZo6jI5+nbOh3aWZykTSLhhstKMrjlBF9mfUCqlXRXX8Vwg8uSPJwQfKN4OcPHlq4lraPZtgLzioZXNLt2KfidA6/iDSfQjDYXtQn6po/z8KolIPjxkiU6ejCBA3LjbvCxDJ4DKxgs659UpzmaTdSCu/g2CfBJWSRThQdvm81QJXre91CskIo2TO5+N3S1GfcRk9UPmsXLeXTBsLNfxMO8HpI8nBBiiSoDrGl0CX3rFsTclQfnwSPMRaSeoh/RYXP320pa6+jf6QcuKSwEEv/ldVVCxM9aVLxmOdSD7zjY099gEjRqgPZ1EJ6JLVIRpOAWEjmrnTk82z2g2d9jC6hKFQjrwxcUVP5bFcyoc2rLDnNIwAA+GJ/8qyxpfPL3Xxk1ZeKQl4oX7gsaWWADs7RFi5IEQlRpgYxCpicAnG7vPuNZaChzKTh/6FUQy0kOmQnz5kn+ty1+hwZspNBXpPkIR6R5aVu6vd4Zf6YglUHsr3utiLpb6RBi4VTiCxJMo0qJKv0zwAg2pQ+mCcXv1uaOkpJiDmpFqiiS64EWpMI4eBGstj4kJ0cXJDUMKXsjVB3wQm1Hs5m+2UbkZuC/2pE0ETYlEDUSMUbrVNHkYanm+8ro52ZnIgULUre6qsmeVKjT4rTHkkterb8PnblqP0fkJG68ISO2klg7XWWFSWTZzmMpFljS7/YnywzFEsuGENegf/9DGe4D0keLkg+QB1It20ofEASxdBkQY/qTThTg4gHkSW1KmL0yHkThesgmLDjWnKorsXvA0B56lNk0TYpnfU/SSIvWE5nzRxSuA7ASkuqW204r4EVnagZn7fRio5MEsXaR/8aet9VI+ch79wOVTq41gklBbjhSA1Ok5D2/NzpOIm7nFDxUvywpfVwGQCgQzX6FW7HoiQXzXH2JAg6qwAAVkMTAMD2IshNI+1ehkt056RG+1nyCofnZBwBgEXJW4v3DC7eMzg1eetynOjnjPEiuzjU4vQhLRyTX5E/EBqcO1w3oP+4ZkB1H4gaNVjskWZnsx/slr+1UQQAaBSNvY1oQPIBK/IRxoso8uKUEaXCuy6gDAhRgMIBLuLpXyd2ba+bMIrGNDZ16AIAfT+2TxZuMVkAoBFQUglwhLpU66XZJW4KsbQhyVvRCcc4pB54p3hMNmsnNTERQDFCi1lEFTdqLV3YxWY9+kdrEJyLM8p+89Xo30MYwH1I8vBvp1VB1Sip+P2hhWuPZjxKXkHrQFXWbNhW7Hx54PhzXY4/18XTVre4TfjmL7lp5K/rHOecjCN5hcNJu3iPM4lt8Z7BbECzDAvH5K88MF55XxaWLxq8d73bcJVu+VvdVvt2Zc5Yj9+5PCuTlpOXurd7wlMBPU8E2rAOw6LmHBZuIQUVSZmJQM85pRYSq0mSkzoSQyDbSwXko/Xfs6Gq0eM+JPQAAAa/cxUAiCYpLNfkWqnBbh6phuZIzZULLgCA1CxU5mN56RSvY3e+qpErxzPnKJyCtnBMPm2v359KNGlJmc8FTRaWL/JVkBI/O1X90N3y+/ijSapzUIUtfMhOHi5IPmOz2bLL/tfXd9H7NTvORuafBtqXCwBDC9eSITvXHBB0Ei4dr/MV+tHkdyPOHnbIbvA7V83tmkjdJnlZIuZRj9ud1PXQH/IKh8eJZslKJZqkGuKSGbx3/TU83aso5XlPlR5XHhhPh+zW708FgDmjN/iqSawakUm+Ps1E1hD6H1GZ+aDqTFThCR+yk4cLklJUG/sy9+t+e7406ixBiC9iz+iaK6/fni+jhWZSlFq5LNF8P0rKOMmEHhApOvXT3j5Vtr344/5+mUeU3DTYXgTwDbuOHa8LMp4yGHXL3zqg3UlXTSLm0cIx+USHKEvKZr/iiyYprHoXZDRUI5vNFg7jdVYeZScLFyRFTC//Pds2i8hz22JFvlY2fOB01kzymHnV8bQryT/fUd/Yp2BTZaZ9NmisAd1kE/R17GJnHQr81QNKv2/WG8ms/YfLXwE4c0mPnDQ6sz3z/7B9ayrSZ0r+yesscSRnzLEMpQnNaJT56dsoY0LfWhSLFXeuS8WvY4b9TyOcAwBIuII8z3U97c/1w966XfHTdhLRiopDPn9TE05yYUZXb9RtFNF7sxP6chrbN56fe1fvdWcaoREABsbYg78rAQbGVPcBFJgAuPCHFdCNzGAT2UY0DhCQ0Cig9x6JttQ5ke0AACAASURBVBdQIAbWX26j7G2ns2b2/fhsg+M7aN+EPn7JHbGGvIKilGwSipbZ0R4c+Er5E0uSpPOTOok36V8AsAroPnhFh4JHztt6sIvx+mYAqHM0znZGX06c0f1zQLeoCwDQziYNdmDpbEPv7a6/ItnBbED/SoKl/eC96+l4suRu3mhF0UOSihsWPfr2JpcvYRvbkl6R6ScnVHBBCiDEFrkuVwDIjibZuj5NWqJwz2H71lSku3/wVJLK7KZshScJdjVS0qu3blc8IdWkVkNy9r8BAODfngb6iBp5PY4hr8CSk82GRxM1mjO2WGIkkQxy75WonwzQKtk++le5ZW+G6ux8yE4e/u34xgNlv1O4Z5jX4iSDIYE+y9nH+krUqPBtgbzc7n/X6nM08EEFu97Xk5fC/YM5WAoApQWDSMPV/6T6anml/Any0qB/vnNrQo4/5X3bIFbQq36Fuu/BgFtIitg8+rc+7R/magQuVboDcorH+vb9+Cx8L12f8bzoSZD8Z8JTVreCRMbr2DUBVaOVB8YT7w4VIXAYSV7V6OxjqC4qgU0wQYwkwCJE4+tYVJtHSuz1WxNy4nblARwmi/nJL7kVJzI12KdAu1aMVXHKDIXU19d/9NFHP/zww9SpUzMyIr5wOxekcIGM2nXZ/6Gfx3m4/BVghu9I5piwQhM16rvmh4p5bgYDXdXo29eiwJdissuSVuYXDpPf59mUbV4L7ZCw72QmJhvcJR9yVSM6cGdkfEhVCxMBgI7UGfIKliRlv+KiSbRdq/eSSEkG/0ePCSTVBUlUQTSJ1BcPFbllb24f/asQdiAQ/OxnP0tOTs7JyVm6dGltbe306dI8kJEFFyTfMIMRXNynLTbkbD+dNbNb/tZbDp/6TZzIQBSRr1WCpDiFJGwhBtD914C3EkwOH7skxzObT6Eqa7Yhr+AC44y34LgMeYxW5EPq1oTufcZGo9s2YdSv7C61BhI1oEf5FEjxAVqCoMGAZkc1tEeLd6w9W/FoDGkAQGMPY/8UuxlxFi7Xd7wFAJdn3dP9ixMAoOtUwb63MvOhGWW/AbCvHGT9weOnBajDnvZz+m70LwCUiUPYrUf1aF7Oj8vHsot/bUDBe6ezZvb/oBpOOhafTJz0hBkcsda6WmeejrsfqKtON1TkxA37g71yRHX+tiVJkzvn2zNqT2y/hz1yZ0DhMNcARTF8b0HjotVNvZ2fLnu6bse+80yguySKYUjsKQAoTX0mufhdgFMpQgWV3BSh4s4We8ILkj1WbxNBdP5qkkIeCYI0PuKaHq2RpGaQ5ARpwv8sZvyfZfNsi2wf/SuwhSaoQdTUS3Lo0KHbt2//4he/AIDOnTv/5je/4YLECRJbR79GGm4jdN9L+qtkjaccz1VZs/vv2fC9dGpNREKk6IdH+wIe1CL2BAAQNZJwLnu6o5iQFHsUgPmS262EJUmrNfHZVGY+1P8Dps9EjVzYvjkh9wG7ugz7w62KX8aRBgB0zt92bbxTk/yH2EbnvV0bDjXyiNtc5uv3p85J3rys9AE/O0lhnaCRMlFJnSvo8rwVbtefPXt20CD7mPB999138uRJURQFIYIjA7gg+YCS4JxjGXNV13iN25VXkZ4jUzhV+Vz6x8tf/EHs6X2/gFH1ZCJ7tw0oRJaqHnVmb+u/sppYSK44xqPKg9M3TxAPltUhl/ava699ccdq57N/7gN1bjWJoKEmKRypc1UjSdy518oafkLTnbAiRMRJyZS40KI87TpL14+eA4Cax6UVUhobG6Oi7MMVgiAIgmC1WrkgtXLCYT4dwf9UY8Q80qQz8vT/oFqFJn3/eB/VFce91pXwWvNbSZD0tLR/+Wke0TIfdI3ki9qx2kRMJTpkRzRpWLpdYokmUREimgTl6seglFdDvzUhR8Y2WpK0Wl6KlpU+8IIWRhK5hluYiWd9CzYadRavl3fqgXeKxzzn59nDB6JAdDHSzSPgghQyJjv8z4qLGE0pe6Onrsb7fp4JmhoRiCYpnIQEABUvxatWI3kuz7oHvN1w/ZmyszP5ZdKYWPqW/J6uoX2e1EhC7gN123/pdCAN+8OtivFxmhhGJWOe1SSEgRBo80ie/ns2BPki9xVtfUjt27dvaLA/tdTW1hqNRr0+sqPDuSBpj2Tum4inlwuiEQBEAGFruTglqQEHCAzc+1lFuvOeVWtFSRD0OARA7xLUML506QXHLnVmlDHhh5ZO9C8AxDWgrUYRXQlWHTqyqENJEAx42EEQ0WJLtHMmMImFY2+4zS3oRKIJTRu+HYfLbcShsqR3RGMbCKcLFwB9OYONF8ARr/9A2e8G6c4AfEG3DrKgKIaFY/LzCoeD2Z44IM6Kki80CMhbfgNQnYurLT0AYETRqsNp86+29LhpQQU1Lra0p+3GiRP6fPJ9A/OZzj2ZmPqiCP+2L9oqKwHAVnmKLDbdOL7337NIO3PQFzkLLHm/NOQssH9jFQDRV+8h7V7rT3+a1I9N+9TBeJXtxi0LCmr4ruEOABAnZQKAsGOv5SqqVdEb/0bnO6OUTokG9Cvc13KKXcwvHAYWZ42JegFd3uSKpddtFEilNwoXWDEIaAfRav/myW29EUcxtOj0AJC4e0tV1rTE3Vv0OnQ9GHBCwlAV65MJtVBBcnLyr3/965qamq5du3755ZeTJ3uJ/Ax/uCCFDHFKUjBPZ544zrRzdzDPSFA4dkdqud4A7+npDo1d4GkTqbVKqgqRvDpKJjLb1cg/DqfN97SpceIE0ojZuQtkTdy8ksE5KcclKzMHfUFliaXX+tMX5vTrtf40WRxSuO5YxlzlqQjFSZnCjr3e94tAEndvqR43LZhTnpUjajq/lUTWzZkz56677rp48eLq1Woq3IcVXJCUojCiAQCUOJmFrdSp3iC3nzK8Fh87kv5ESNSIIK9JJCJOSWVxIkUjPUzVEkAkUkRkKdF4we1uEux1IlquKdnZEyOKVrmupInVY3bucvuuc4/cmfqi8ym++E+CqxoBAKtGOQsseR86jSQJyjUpctUo/KecB5kZM2ZMnz69ubk5JsZjBc4IggtS2EHqwYTn851qiCa536RAigDg2vjJnqSIQqRIISSTgp9VizxB75vskJ0KMgd94X0nH6lInxehagQe0tW7krh7S2VmOBpJ2g7ZEQRBaB1qBFyQvBI+IXaqOZL+xPB9q4+7y8Jc9+C9pNH1y0o/z0ILtfVd436SKbGQBFkfkiccRqfPceQ059Og8vl05dKkVRAwKQJlT/HnHrmzzyff2wcWAQAg9UUxz4ORRJExjxRSkT5v2L4136k1y3U79n01Ot3TLC55HkwLcag9YdT+D4glHRK0DWpofXBBUoqJqTKg16GbAl0kU4gq0ufFMs7Yej3ypkrcuAbseq21tAOAbvlbKzOndMvfqtchx32TGMsuStz4AFBjds49umzuBABXxk/plr/1ohks5nh2z2iLfaZLjw0nL80eYLSgK0HQIxm2Cm5SQjg/kdH+mbp8dfzqjME34pEfqCkBBQjEWtDsepMV+aXrTOhOOcR0BRyCCnBlqgmNfXUTa9nFKBs6UQ9zLQBsBthYlPRgWvn9jQfHZ9jzMuQXDosRLdDsrKoQI0q/SXRkEX38djhfhuRiICYRNYyibqPKDtG3oti2VS/1qwt3OBPZxYgon8L2Dw0jXmupAftbrHor/QsATY4U7KQhtKBihufNnQGA1Nw71wzd6tEctY7VztiZY4tQaZVhf7jVGIM+71WARvCY12N8RsUX+521fWv0KMTj1fLHFid9fEWwn64ZcD0RBhq4yFq9RsdX7Wj4kHU+TJQgEBZSa4ILUpjSLX/rlfFTuijwB9DHPdcZtUSN5N9+afYAdT105eqMwVodSkPIg/n4jAqvGeq8kpNxBACWJq16ibG3IgWvFWCJFA1Zfk3faL/RszNw3bJ99K9cjzhrbCmrSYSnU/IAYHHSx8o7PLH0LSpLMlwZb08PqKRyCkuoAu04MnBBimxkBh+UqFGPDY40anIWgiK6fGUfa7L4fywGh3lkh97RXi1/zNNbyL2PsLHIHsoYLzZ72N038gqHE1nyROPECZ6iGCgnf9ZzwN+lJQEBIPehhu2fxeY+5GZAbfuGuBGvIRPw0uwBzp/PgScXixI1GrJcGtlBZuDSX9YtnxSnPZJaJLMD5b2SHJoAUAme1MjVyUqe3pQfOYRwC0keLkiKUF4GCTSqtgcA3fK3HsuYojyQl6UifZ7qDEZhDhn2cV1PdYjMb+1hqXXdx3/k1UgTtn8W67oyd/aty97eSOP6JBzNeFRejVyhttGwP9yqmIE0ieTMpkGnbtVo1thSyRpfJx2TKca1li6edqjMfIhe4a5qFMZh31yQ5OCCFNaQQF7ScN3qyTwijuvLrSJ9qisSNVqUbL8rBaE0KjGP/B+vG/D3izJGknPBs2fr8BtGV/NIWyRJ89ySW/bmfMvXpO2qTF/sT5b4kLSCiA3xszIPXvavq3rctMTdWwCUpggJJtxCkocLkhyqQ+y0MpLAIUVEloCZheN2fmhF+jxw50yKXIbvW82O2rkdqbMXGcJBDWEO0aTkX8pFi7jl8BtGABjxWssl19KHmiKjRhIjiaB87E4ryEgdFaTqcdNIw6szaeT+D2WmV3NCCBckRUTp0M3OJDTjRRRSHSeYAGDw3vXHM6cP3rveqkfPRDdxUJYZl1aqF1HgkLXFnvGFzLSN0lkOjZ3mqZN0mI7YRtetbkZ+KE0GZEAJNiS9bK4gGhoevwmVFHIeSo8+kRWXz+lhvMkuXrWivDsd6tHkpE7NaGudIw0PadzGwV09LLVzxhav358KlloAaC+iX8Fkw5GQNjnPlhk/eZh16Cer1aNvUhIbptP54DMzxzgvpL5rfiidp7/nXedQ3I2+6LyWaPQbnVrQCxy+uvNQZYlH4fXd9fUA4FDueknJrma8GIN/bmkdKlEYtsRhqIlw1+pzZ2b0uWu1Pc3gofYj7I36EQBgjWP7XAQAj5b/cm3SHwBg1tjSR8t/yZ5HklWr3ia9RFsARV1KIhijBWT0dDbYOu9dfzzTPl43eO96x5YGcPmvlCyGChEifhpJQOGCFEAG711/PHOOr8E/8pCj0YdBYoqRduCcRvGbKuqn+xuiBky3Q5g2Iqy4593LJ57pzmqSBCJCBPngAkn0R6CRWCchhNEhnwl+oB0fspOHC1JgGbx3/fHMadpqEr2tBw1N1Ihg2rnbPHGcT2/pW7DxbPaD4VCBjRhk+zQ9JtEkT1vv/tCZAOkG3PS0W92E3GCqEYFo0k6XHOdrk/5AjSTSDnLHwhke1CAPF6SAQ1I9aqtJBA2rBsiQsPE7qy+jUvL4qkZtAWoh6US5ITtX6ibkkkbCru0AHs2soYVrj2Z4CUYf8vb1Y893GvL2dbdb71p97swTzlE7Srf8rffFfbsz+WXXuhtckzgq4IIUDIgmge9z91zBnlunTvQp2NSs5fyfgJC4e4vEh+Qn6/en2t1IYQbN8B218TvND04nICsP5o7ZuUvJBCl1KJzBygG1FWPbDlyQvGOz2Z44+BK7pp0OTV0U9XIXGXHMdipcCwBHM6ZJJi3esMbgReS3v9biMUdLtbmjjgk9OG/upJOdkSrqUSBGnSCJSUO+Vp204hHKmW/EFZ4kxZMMsnmGLPhQphbkxI6pR5+3qp1jGsr2orPZaefKX2W3DtDbh7Ou6jsAgFmH3fg6dGRJYqF4Eaf/wV+d5FCXDB0BYFHy1uWlUy4B3AQvmhortDQ6CrneMN1mN0kSKTV1QZ72fjEoELxn9A8AUJTyvOT440uXOprf0pWSeIFGLPy9xKgqgF5Gex3007EoGtvUxQgAfT8+e+yxTgBwz9+usFsNTUa2EXV5CLt1T2NPADB+nQsALVNHkpXENiIqSBZJ0bw4Awp4idJLpwCbcD2kKEEuS1M0uK9Sb+8tDkSK0YVFFLg/Q3ZtIRyCC1JQGVq49mjGTABQmUI/11GLc3tQ42uDDClIAaDoY64omfxsyrYVJYEtTUbUSMme9oLiAOSvOsgUV1LpKK3kbbIySufXzDIl1VT7fnwWAE481k2iSQAw4P1LJ5/qMeD9S0rORUwxaiZyKKJNvaxEdi1YZXBB8kiA8nwTKeJlXeTpv7LaKUveIJpE2muKMwBgXmqh2z3JVl9RrkYUWhDruu8Zi+i1QSwkDVFY4fuev1058XOPmgQAfT75XsnpYnbu6mWsq8qaDQDkr9vCURwOhQtSaFBY1gXhzjyyTUrXtF/hgnI1IqwomdxevA0OKVpTnOF2yI4KFc1x55VZY0t9VSPVEMMooE8qRJOY+pDuIZoELsN3A96/1BTfdO6RO0GxLLGnPpwWLrJks9lCUlnG6seQHbeQOAGEaJLX2rJtEFqyrwrO+PpeeRuIbn0wtRB8kSWfIKN2Pv+yk8cEx2juv2dDVVaSck1yhUgRkSWC8etDsqezG0lEig6nzQ8HTQoJVh7UIAsXJEXEA3JNW3X4YQVfY0bsem3Wo/carc66RCP3f3ho7GT2NlRrQXPXjcwicdG22/wtLanUJLTQ+AGhJcZqwFPTcR2mWJ3EPyw3H9CKR7pv4sgLM+6kDu/cIiLT5KYO5Z6w4S/LbESeZ7EDCi5IMDi91nW4JBW4hC18b0A1fq7oEtjF9vgXJEbPg8lbSbsdzvLQrDMBABkGXFEyuVroyG6VVAOSVKXqqL8NANccDUnJHskXOyCqhjRI+sGM6B3s1inNJdPS/kXaW4p+9IMBycNR/V3o8wKqY1SPowOMktwiheuOZSSBIzHVvw0ouCBRb6/D1HvdmRNzu0mygEdfs5d6SiDZHAxWAKiYiqpt9TprjwO83rmOXV/V1AsA4nblHU7LAXc5rkzGGnYxXkBhC5ILwCA7G0EPKLhG8t5Q4Y+F1BbgghRiFI7d0filtomjekK+T+9anvQ+aSwqf8r9DqVTgEnPSmrIktLmABDoQAmCwvSDRJn87xKN2aM5EuXTyfded+bYXDeVKSQMW2oPn6t4KV5+T0LcrjwAqEjPaU15Fzn+wwUp9KjxJ3mibYThKWdR+VNUljxBLSQiRSsPjCcWkj/QcSqZgbu6Cbl1ijPhEgtJk3jCtJK3WVkieXuFHXs97T9k+TW31ZJkIC5AOvTa6atj12cMAYBbE3KIFLVZrH5E2bUFuCBFANQ8arf5W/k9AQC2FzllKVhYp44iDf3XB4N8anm8qhELsZA8QctePFz+ipKjkWC2qqzJ4E6WaJIFMlgnc5wtRT8iDa2i2yUTm4YUrvt3Y6I4KVNGk3yFSFHVwsROXx2T2W3YvjVeP34rg/uQ5OGCFBYExEjyETJvRnWQhf7rg1SWtEVS7ZRMugSA0aUPyL/R00idajzVBpSBfJ+uc5ISdm2vm5BL7sgyb99S9KNpaf8ifzVRIzKrqdYszZ4n7NgrTsoEWVPJV/qvrK6aYdckaiRxODJwQVJEB0lQA/ZM6nGqAjPO42DF4Zr1AjqUKDp/gnh9vUFArvgoqwHA6Y014jQHTTjbgqCzgvNuYo3HfuwOehTU4FpddKg9l4T0rjekcF0nEX2ii/i8jdZocBhJVkNTDD5ve7wYhb+rmlicIQKHWgyNqgGAOoA+UTUA0AXsRWDdDsQ14RiHZlzIQJJewYC7cVuPojZu61DYwnWwu/GJGjXb0JEl8QIdjKhSLclN0KdoFUi5eNjhQEpwePIN2A9/2tiDNKal/Wt56RRJbEWdo1cEC3707qy7wS721qMJreYYlJWuo6kPAEDJ20Upz6d2OFSEM3EMre4IAILFvrLHCfTxdWa0M6lQ0umEXfAG/P3iyRn2UoQkTd6tCTnUjLYYawHAIjoHSKNwLEZPHYpx6ITTy0bJxilYwzJM2sKDGmThghQukEp0MnUZ4jdVgBaZ8okUSVzZNFSMyBKb4IT4GLxYbwFzWbkd0vFpIE4rni3/OWlUa1SvYETRqniD9zrrQZsFBdS95OOvuePjKACY9Jj7OcAy5XHBUaLFnxISkQUfspOHC1Lbgg4MHsuwW0jyQVZkhyYxKghzNhXiHIgT3d/jOP6QVvJ2UUqaEk3K+4fdVCJStOPjKHlN0rCTkYulTWSkUw+X6zBi+L7VrtUZaC0i/4sSSdxUXqWIpd+eL4mjy3WsL3C0NY93uKAgLibvH8acn7bk/LSFitCkx5qJqcThqIZbSG0F16AJMhbnE45EfBPAkUBTIa5FBb1GT9gmpXM1iizkNYnbScCH7LzBBUkR7W0oEsGik7uqYkGutEEHHXLMNhiRO71PwaZz2eNo5b3zRuSKv2nEKfSZCfY24+2OeL49G8VwOmvmkMJ1AM4RFeIrAgBSVEma2F+PMhegxAwA0fY5lRPAMaDHbmUzF1RlzW6Uns7O0YzJQwvXxuGIAJEZ0DjnUp5AEqfQhGcLGWwoIiBWhz6CJF7AgH8U+QiIemlABHpvVwFN0PnKEVT94/JXASAGUDfM+ER1NjSN9Kg4gF08AgPRey3oe26xoY8fg4NlEvQoMWt/2wV2sZOILsKBAtr5RmxXKFx7NCON/Go2YQgA2AS760y4dHP7N51z778Gl24CgK3RTVmH9lftO9f3cX5eQ7O9LTgGZgyCWdLoJiCn2l02FIvR03qVXZSUFLHg/CmSRB6SxVAR0HpIly9f/vTTT6urq4cPH/7www8LQuSJHxek0LAyaTlpZJQsU/gW3Tel6oIaHLaR73mnZaFT/cHFt0TmhAJA/z0bogXpeY9mPMo23NYmP5c9vU/BJgBFxQ784dXRdnf662U+24ue+HH5q58nLdbqaEooT7X71ZRfTp54dfR6gPVgL5XyKNGke/9S+91/dbz3L95DMHxF3agszaYhw3slOap6FFjEgPmQ6uvrZ82atXDhwtzc3M8///zQoUN//OMfA3SuwMEFKWQsLF9EZSlAaBKJQG92ScVuYtvYkAeK1wIH9GZXb213NvtBt5oUNBaXzaGypAlBViNCRsmywpQXNDkU+XW87rb9G3v2vEnZ5yWbdq40TFyIbO7B71w9/lwX8BuaTQO8WUjzU5wpIcJTnLTlwIEDycnJCxYsAIBRo0alpKRwQeL4gCc16lOwyWEfAADovim13Z8MALb7k3XeMjRTro2ffE27oLik4vepLLmFPZEgW7iWwN7v+hZsPJv9oGQH+vGDgLZq9GNc1jZoqFCjp1PywN3NWokaEXLvv0ZliUWiRgDgvxqRn0k+mwYL+7nIJ2UD6ENSewIAbAEbssvJycnJsX/kU6dOdemigfwHHy5IoWFh+SLSOOGu3rdEk3yCJlyQzIT1B3k18hVXfxK1kOiAxrns6QBwzoNZpiGLy+ZA5E9XTCp+X+JD8srTKXnklv10Sh75EgiLy+asa5oh/16qQ27VKBC8Onr94rI5Eh+Sct4ryWnWGVUUWtQcm6or7Z7n5ivfuaam5sUXX3z55ZdVnCjkcEHyCFvCq4OI/tubBeQglYwVSKaIS9LgS7I8xApINjoZ7On6O+1bfSR9euLuLQAAu7eQKDVxShIAdMu3Z6eO0zefdrzxzqhrsUIjHfQHuK7Dvnd2PjwA1IsxAECHy/R45/bM4oiiVY7gAvtjb7zxOvoINnQVyT8Divjjt/dQQeCcI9FZO309XamXtb0kcQodbD7coCXZVCUxDpJIBEmcQl8dmg7V34LCB7pYb4IjV/eWoh/R5AuEbbpUdvFk42B28QdzR0tOtky32aHO7lGoG3o9uuokF/DTKXmfFKeBIwFHOxu6CNlYkih9w414+9bm+EYAELt1JYs5P2kBAOGWG99kQwf7r9wSbQaAqidRuUWL0d6ZivR5qQfeAXCWqOgL6FPcbbnwSGoRAHxSnAaWC10suBqFDdlhZh26DhvwP2m9ELOmOAOs9RBaVCVXPfF//wSAe/7zJ5L1n3766cGDBwFg1KhRDz/8MACcOXNm4cKFCxcunDp1qt99DQFckDRmWdJK0vA/kdrwfauPpE+zaxLDlfH2pzy2lifx4rgaHzIQNWKHy8Jh3itFPslb+DN/TAFtkwSpRJaUPKRTn5whz3kQI0531M1wi/x2fnrgPilOeyR58zJviQF9Yv//amlxflIc7GTB/7+9M49vqkr//5ObNF0plAqyKCpT9rKVtKV7qSyVRVAchuELiKLjDOPogDIOPxEVUWZEh68LiAtOBRTBLyCCDAiUQvc2yNIKsuiAbLKUlqVbmtz8/jjJ6Tknzc1NcrO15/3Kq9ybe+69J2nJJ885z/k8gUKvXr2Cg4MBoEePHgCg1+vnzp27ePHiESPY5YyBAhck5ZFT8kA+F0eM77ZvO5Il29U8mJ77tzDBljQ4NiIjJP+xY0CZVx21l33dEVdAUpRTkgnWCAmBIqRnrFUB7Z2OV4z9Yoiy1waBfnfOZoXMSdzpwkd8zOqLp2d3wxUlRj3atPszSwgyZrIlcNm1KULqCp9c/KnHzxINbPl9UmFrUyNF55Di4uLi4ixm8BcuXHj66ac/+OCDoUMDuHYaFyTlUVCNGB1Cg3U4QnKZ4+lTWvwIs657fQgA2myRaTeZNTwPSZE9cFVApeYzUJh7Z/Eqme1Xlmb/ntCk3ycVuhYe4SE7rENjJt9uUZNiPrlo+6R5dGpSyfv2Lr5Ut8aFLvk/Ko/VQ1qzZk11dfXUqc2rF06cOOGhe3kOLkiOUalU8p3/5+pnK24zjEftLo6wfIQhZYpQN7pg5IO8LOvsT8f03L8lUnPjcMosrklOgbzApdXIIYpVIZFkfXEKCj7QT3urvYYVfXIw+QkcFaEg6b5HWY9tHCFhkudZ/ryu9P+VmUBq46g8lj6zYMGCBQtkVeryZ7ggySLSOic8PSl/XXGa9KpvpniBUVKftLSFfpiGWkLfmZ7z71yYY928AgBaoQGPgNyhvRxMD9kZzNTC/l8buoN1eKdOhLMNnemOUF/cOmuDO+fuOJwyFinfIZfrNgAAIABJREFU3SHUN9wwNTUzLDD3pY0bTHTKA9C7WqFBYreLQGVVdTNTngjMRD2yz8D1yJlv/TapJVK/FJtKFlTKQ1eguhFnOAUADyeXbS5KAICBtdTHe7SRihg6hJxpPrH+zO6wRPLoz/Vd8E8A6FJLZe7e0lLv8xXK4oM1bjDQfWb+JkGwxEkAUC20uyhQyXJMOsy1O5rXw3bY+sPuiUFk9Vh1bXM/KhZYPD2qYiwNTj/R7Y6vj9eApRbGfcGWchJId7tr2HKOXUU2j45MZOhopP48mF9hvSCV82KSdFfh+An8l+Qc64rTpifl+7oXXqJz7g73hwe9j/dTe7EaSdBxjL7jGH1qhqWCasH+/ni7ZbKTfp3c69fJvaTaWAnds/dQ8uPyOtvMytJsF4r+oYrmclqemNPpjq+PO3t9zLtlY3fnD5ZuM2TECfRw7RZms0KlRGQjmAWXH17uqk9oEy+yFZMge+bANQJRk3CQpDg5uuU5uuXM9IYcNUJc36Vz0CI7CXYW470um04538cWWKt7a37C1/MTvlbkajI5MadTn5VXWzzkcFjSqQm2w/v6OG7kNwhmlcsPX/fdG/AhO6dZV5w2Xek0WY7LzElsdg1/t2ws/iBracGxAszSz83RNfvFLdWtkalGANBxjFyjDYTM8EgOy8omuSZIuuKP9EkDOmz9gXySDJIGLq2zPcsdNXIWl8Mjn9BGAh2X4YLkCsvLJswNQE3yuWuc4sxJ3IkHnYwqQU5s5OaAHqNGC/QzwfCjnBNReHQspIWP72Z2FjcHSTuLmTkkd1A8PMLTSBXPhNketadGcmjF4RHHIVyQpMBmDR3Y+XN1TknmrIRtlpQqyS89N1RU8QKjpI+DlvbGFui1kIxNXDuheb73Ds3Vdirqw44pbfBf8V4AqBct0/Vdb3cij2qaqL+Ec1HE2PrO4oPJSWTGnfSCJyO9YJ6ZbGecGphXpFFRKR6MJwJTNMGynkZszrCQWLOCDTKm24hWTklmJFC/3450iRCj1YshrmwsAHQxVoPh1JTk4o1FSWA41avhBtk42ki97Rra5UFNG7YzNTJCNXX1AKHWMiIGNfVumOhcA9Hmz44046gyt8fbY8tfu1v1KwCcbu4GdakaiCR3meyYe/K2ns0cgL/KHO9F17kggqeaiQPQxql7z6KN7mFUOszPGQ+llS4HOIN27wPWlbWj6Rb+CTZeDAwmdpca1GoU7FajQO4PPkHdNkbeXIYLkuvklGQ6XHTiNXbEv/Q7fcAnfcoBj9G5tmRyXXEa0G5P2FJBpnHnlORiANhYlOSwJYfkXOaD7lfHCHTUfNpeEi5Ishif8j1yf2FAmuS3Y3e74v8f2hhT/oZve6IsK0uzyakjN8kpyUTxk70qO3gECQ0JtkEpQmtv3RnvtZoF/yTdDC2rcPku/k8byU1wGS5I7oLG7vxWk8aUv4FlyZ85mPwE2pDzJVpBNSLBERLjtTrHOspnUSaj8nXqMKF79taPvN+p8vDeAdseqv5TJt1SoIukION2b9YT4QQuXJBaOQGhRgjkC+CwGc5i+L1Lnmwu4POaBX6C1TovAWTIEgCIY3XnnJGiZbqc1h0eAY+QHMEFSS7RxuaZXqaExOqSrNkJ2/D3ayM9m8oskmc8AmxqVTAT19RukIqa42VcHphzTWZhZNk/rNvMqwG1sfm+56fehzbuW3OObYfvRXgoMLkGt81UnlUT7dTQZKJePjMVryZeEVIj0mCCycu4qKbWY64uyfr98Fz8tpM2pmD7btD3Zdbth4l0MoVooHep6fNwkXo3O5iotAWRTj24QedH3Bao2CuaqLwAAL8JvgwAldaNH83U34ZIZwxH0fkvVXThjyoT9V7VCtTvSJRMPtbQf2b3hp0md7uUrASA0uGWZHfqBaCLj7Ust9IVfxStuQZQiQ/1UZ0hW95nYk2L2olUPgXza7qhod495ndqW2+C3K0VaK8KX6yKBZ727QguSMqwuiRrNvHhGHDct+bcf2fe7cMOoCw+shJPwNF9lOWT99p3LUw3ygfXd1eiU54isWQl2jCIlg96bBXhWk1FSwa/0cUSfHLANQl9CI+QpOFyrRhIk7x/30nli9y/iEw1Kkv6Y1nSH7+OX+z+HRXBV++5PS7sjlXkOkiTFLlUQECuJ/ME05PyyQq5nrsRx024IDnAJ3G9h7DnA3TXl/+9b805ifE6Ek+bFTmLX2kSDpLcJzb/C+lysSRVWdl+HlFJ4FE1mp6UjzL3fB4bIVRmlcsPX/fdG3BBkktShmObSN9+OH6h+6dHrpudRIpQWdIfPXIXN/ATTbqwO/bC7lg3x+tINLvz5GtSgOIhNZqYosdS5FeJEtxcVRo+h+QEUUbLfLUJqKkOcup1c1HCwwlfk//HNCDlim+gV/I30r4GRnOQxK7t14lb5hZ8XBB3aq8DwJ0F6ypSxw4sWFfZ8QJ5VGWkpnyjtc3uA2StBaRMEepbN4m4sVakrCjqTVTBBaYahVolZTLXQBdrOG++i7qUhn6viN35+lkTdTnkIuVIkfodMQYZGnqZP5PF0K2JOhphon4pjfScfw3tp9BAm00w8/CXNbQnAlD3ba+lHHd6mTVwYOPx9Mx+BzYCQCP928cZLifSf9vnwFdAZ7jUGakb3TJ3JHdNdLrEifTfoo3++V8CQPsgaiLnPg0VOpP+IEC8sYcAAGCO/k/WTQCAzmYqRb6riarc8VTi7s1FCWC6jp9hUlFuCcH0rlSeQp0qZG7CNrS9vGxCrSoUsCeETUkRX9FGAh2X4YLEaSX4lXGGQ1ARk+W61QAwVz9buvHx9CnQUml5JCR9Dnzlfn/65395LG2qw2Y4Cn9K/2d3bocmcuT70sq5GtgUwZKgNQ3Ftya4ICkP8hHw6CStOwwsWFeROl31XYGvO+IpsBWQUp93nmOufjbSJAlQeAQAx9PZ6sBuShGpZ3LUCPGU/s8f6lYwT67WvSv/vs3ZbqYbjtrahSxLhq5Wp2ITu21BVX19CI+QpOGC5Bhsseq3ZJUty02YL7/9wIJ1FampcjSpKis7OncnMfLh16Agyde9cAD6JEWVHh2qEQkTITGL0mRCCg+SIjxeJxNbNXIKN3OvsQ6tK06zXVrk/3BBkoYLkhP0vf/Ej3tl2d37KkjaFv/yhPJX5bRUfVdgHp2Kt20bVGVlA0B0rgImPfgjT5HBJWlITZJfOk+arqOPoo1L3w1y+SJZaRVoA8+xrytOOxjU282+yQdJEZolIueQ+hz4Ck/sHUubihrYY5r+hXZwW6KBNO6rkV9lKLgAFyRpuCA5R3tTMACYVNQieQNt13/TamoQZm4AgGAzNXFtUlHzyU30THWDiUoQwKsOW8TQ0jfEGtEym91BoDwC7g2mfC0jg6qg9B20XZiYigbUBxesOZI6EwCqAAYXoLqoV8CmhMT1Jqp0RaOJyqRg5t5RgYmY/ZtOZ0w2mYVQNVW8QE1/zWcSIq43diB3mYl6QzA9TW39W56vnwUA3cQqAHjYOsttjxZtgdrbDCWd2tu31/0/3lYbAOCyhkomOaPtTO6e01h2F8RvIp9foJ8JAA0QdJB4spqu+xAh0FVOtFRyAVldAgBMZo3E0VriD+nnjIcAAPnj/bcRAKCBzjQJEiwJEdo9+46ljcCDhIhf6T/vUHq3M+HVsEA/s7NIVZToZLIcRZHN9sI4MDbnNQSLbIbLLTXVsV/VllwM5F64UP8/ZO2pm0D9Z6kFKUsUTkDABclTBNAce0rpO7ebogDgSOpMqw65C/oQxKNMpzMmu3wpd9w57c1yozJUzyTsQAbeDgOpXvfLqsIHhA4tLZ8MADfpalheAK+oRW8+qtB6qSlS8qRmtHv2HU9nNclNmiMbuq6YHJAUocGGi44ay2SWfq5CV3IeHiFJwgXJX8CTQMOKPvFVHxRRI1KK0HbM/k0OzrEDKUVoW3pAyQXeLRvrsM7sqb195VwKBQFIhxSkdPgctCH/DwMbPbhWL7zfgY0or0+mLC3V2f2zwRNmzvYBoey4N1nt11eoeHKfJG1isZX7uDN5IJ+ssmUun+tDGUMcT5+CHj33b8Efgi58GjLgwOjuvG9kpiY7BVKjh5PLHk52bF9tD0+vwcTGcTJxR40Q/Q5sxLLkJoE+66Ms3KlBGh4hOUfX0UflixMatXulXO5nqFOZcvbIT5zr5bqczLfpRg98B0ThEViXyygVJ+EJpLuM18ClJIjUjGPg+c9cHCTJAXkIVaZNc//bANKkrm58T3KHh5PL/HbtBMdDcEGSRZdRRwBUeFvcM5A82qCi0hai6KXs4WaqWANjVdBknZpGKlLT1MlArFWvsU7j409khhZnVm42RQNAdDC1SH4InCR3G9XUHPjP6u7k7nlTF3IXzTARvWqPNlDFtoi9uwHgnNUoIIywKriSNXZwwRqA5pmDO4KoigNa2tfgElDdAIA661x9dO7Oqqzsy00donN3HkvLjs7dGaymsgl+DKIyTa4LjCdCE71L/cpuBkUAwJLyex+O3wjWdS0YjZn6lZ1TdwaAV+K/BIBXyqdWqmK+Jo5eMFJvHUM4/bcRSmfHMPVEotVVADC2/DW0azBTOQ63ROoFMqklAHC+qTklRGigfoOdDVRjk0C9wOMhRHbAzuLchCRy7E4IoYtxWFN4ntH/odY6OUe9KHNz3optFgNDtWDJakHe+QfV95FHz5mp97aW9iVhqlEwpg9745/PgeXg6yWxbSTQcRkuSJ5la6FuYsLXy8omuXMRa/nn5k9nRJhg8GE5TlzTusrBh4wyoDR0BBKnHkWfKn6XJeVTAOCplmQJgU3z5Ae+3ud4+hTtnn1KXQ3FSfbmk97V2S028UzCDtfKvXuikoufLCVUSfmIcbggycYny2Md6g054Y+TtqVBX+3BmojsGliNvIMiy6HkI1GnYHVJ1jk6MvM3kHj8ZHDcUj7SmsSRD4+QpOGC5DQqlUqpsjdykBP9oDZHUi3Deg6zt18pn4plyQW8pkYoEvKyGmFwhMQM2bVNXNCkd8vGTpERJI1KO4I2ducPBs+ER5xAgWfZeZxlZZPmJ3yNHk6dWJE63amxOPlJ2+6oEUcOxcOfRg9fd8Sz7En4O9p4Rv8Hd66DpAg8r0Y+91RVmV1/yKS6uvq1117z5IvwIDxCcoV2Yjtyt8FUQ+7eNlFL2SM1tQDwYemopxJ3R5prmel0BqZYw2UjdaOmJmqVZTVdIqU6ODRi7+7b948CgJ8au4ZpKKeGRs0ZtIFG6jqKN0FsbsDYglWp2tM3upPp5xWiTkS9MQwAINv6RXgvNdKlEajX21WgZubbAbVS8paacmrorInofGDj8fRspv4CSmgWxVtk4xp6kt8kSE1xM5yjcyu0dEUJZrb8qkiVcqhqpN6c60ZLKsHded+cy3zwuoE6atBQ77OooRJPOgqUQ0QE/eYwHh/1ZqoWw20xBKzTjbdFMDVS70bn29QvNLiOmoxUidQ4Umg4deXjeN5jV+Hx9JSQlqburoElaaKzivq/sLoka4p9jamzVpdAQdKotCNz9bPJEocVJspaCWXrYBivChU9P0P+4eHy6r7H80N2L7/8ckFBwUsvveTpG3kCHiE5Af56FTmm2NlznS2cbC1y4yJIljzBsbSpyISmBXY697as1b21UvfBSt0HDluiFU5OXdzn2EuMDGx2FSr+4Z5TkokMTQLC1sRNPB0hbdmyJSQk8DxnMVyQvAGajfCTIsoeJNvplKo5+j85bIMWafY7sPHnjIeQ9YOfg8IUNNx6Iv23ztpp+4TTT3Q7/UQ3OS2HFn3KaNI0/QsS7eXU8w0Uky0/59y5c5999tkLL0j9OvwcPmTX2sCjdt6mOTxywg1aTniEIc3ZnOiY77g775swodFrgoSXB8jnp8ctC79iPrkIAEiT7l5/Rs658kMlrElMggP2ZfeOGvl8AgnAxSE74R+ZcprNnz9/yZIloaGhjpv6K1yQnMP/ayNhioc/nVTyvrLXRC4J/2103BIRsXf3weRR9myNZuifZ+aQ5ODnauSCKviKnx7v/ptPLwAxh2SRpd93k6lJ8kHTSFPoUEmmDuUnWrxQXfNa9KMJJJvpOpmY/7YfAFRvZjDPb9iw4eDBgwAwbNiwqqqq9PT02NjYuro69/vpK7gguUjkmGIxNwVtG4zUV5JbJupDNlKsA4D1xSm/T9y9vjglkq6/IF1mrUmkKiy0N1BJDaFNVAbEbSO1CwA1huYVM+c11OqZcBXlH8GkWjAGCmYie8JsFpjiBQLdSYOZ/aMiKyPUmKk0DWaiXqDnpYPV1H8tDX1lJl2COddAV8EQ6WIE9WIofZQauzbT2SLM5DlTE6TexH4hFaD5m7jJLOCftpdqNGsLE59F2yml7xjp+zKmHgx1JuqdPJf5IOwqPGf9rtC1ns5iqKfeDW1d8PGn7+j3/jWoAwBQN1JHe33066nfd+n1kcVTIzyCTuExRgDxKT+2/LUaIvC4oqIqhjAlIBjn2R9U1JUB4IT5XnK3xlroBJU5vtrUEQAujhgPdiBXI4QIii7FUgpFkxp69eoVHBwMAD169Pj3v/995cqVTz75BABqa2vj4uJKS0uDgoIcXcO/4ILUCmn3TcWtBweCdY2kr7vjv+C0bGfdS+1xOmOyCyu0UkrfwbLkHSxqZJ9eH/166g/NmsSglO+qfCpSp6ONiyPGd9u3vcU2QSoT8rICWpkQfjFepzRxcXFxcXFoe+dOy3K9urq61NTU77//3nf9ch0uSE4TQKN2wDXJEUkl73t0tZCcQlBOqRGuoTCUSL8+lPw47CqUeYWfHrtLWo0Q0prkTQYWrEMbFal21QiBdehs5kTXCrR7Gl5+QhouSK4jZBXiUTs5rC9O+X1S4Xx9Lw/1B0VFANDumwoP3aL1oawaxezfdDpjIvOM9Ckp1rq98pmln0uW9jmU/PjQok8P3ZI4wwkqn2se67OnRjg86ndgI8AJZW7siIrU6dJqRHJP3tYT6RPdWTjhKTy/DiksLCxAwyPggtTKwIN1eMPnIIc9pQrRKgtO+lDQ8dJmsEhqjtA1bNXIqdN/8+/zxx+TGrJDsZHr/fMPSDXyH2Vyx8uuLQRXXJBcAY/aqUYUAEDkbir7JdpIZaHdaWw2RMjNH5ilyyHNv5nF+VVCs8tDiFCvplMeTI68glsUoSazGgAMIDW9ydTIYOogiKAit80i9WcTTO820jkODWIo/gkAV0ydyKM1dP0Fg5nNyyBhshhC6PSQcBWVAcFYM9RLXpnJU2gy0btmqbcuiE49CBGoV8T8BjWSM+036YyPajOVmHCjKRoAhhTmoN1DN/tAdhLsLD50EwDgrhpKQrR0noLtF3NjcPOv2KSm3iukRvetOWeEJgCo0zD1UyyvKDb/CwCjic5buAlU3o2RTlphPC+qgCqKAQDVJuoZI21vzyR9GMzUxbUq6uiZjAfx0PqPaY8AJxDgC2PdwnOTSTKtu0nafVOBHmjX5cLh/sbhlFno4euO+BnZSc5aY2B6fXzp1JNdWzzU+4MrAHDfmnP2zkXlaDkuYla5/mgDcEFq/ciZV/cofQ585ebcMg4LOIjDKbNcViPMqSe7ogf55Mk/dZZQI1/hTm6OX+XXqUSVyw9f990b8CE7F3E51w6Zf7tZsq+twcMjksMps4YU5hx2L4uh18eX8PbJJ5vXqPX+4EoT2F35bH7AUuIdFUr3GijRvHPuDvmn+GcqLK+HJA0XJHdRqVSG79KdOkWmJg0uWHMkNVOzO8/1zlk5nTF5mH6B+9fxCUR4xMttWtRI2WuiYToOx+dwQVIATSj1jbJ9HeX8f2fTdXL3HvVly4bxMgBc1FKO+peCqDl/AOge1Jz1cNZEzczXaqj7arTUd+bTGZN1xR/pkyyFau6EKvJoJ5EqfMBMTbejLRJI6wEBzGoTNWEeJFLnNtDfAevEYAC4O++bE+kP3p33jdZIvTmNTNYGbWTA7GoFanZdoPUpjM6PUINJYpcZq2YNFCCM3G0ya4Dw0AumbSxCNJR3H9NJBqbPIj0tf1OkshhuNVEz/GjYU9ihP3oDACDmEuWFqr0dAgDHnu0IAP3fuW6IoLpRF0mVRDEEUbkVIp3AdSOIatwp2JKPdyVrLNpIKF5VlmSZSepe/gbZ+Dad1FAPVHoI82dWI1LVRsBRRYlggU5ZpL+faIjG2M3Br8brgEdIjuBzSK6D/9aFtFJnz91YlDQl2fEcwD15W/Hic9fQFX+ENt7VfeTOdWwxjRuGHspe1p+R4zVeOnwOeih76xPpvxV26IUdensNjj3b8dizHfu/c73/O9ePPdvx9GxZ1t3ukFC8ytO3aH3wOSRpuCC1TgwjR2Ap8hzqbw/Kb4wK1nmuMwDwf7ol6KH4lVF41HP/FjmapJQREUa6ONbp2d1Oz+6GpAg90/+d6zGrLyqrSWR4hDbKkv6o4PUd0j//S2f/fvwtPAIAlVnl8sPXffcGfMjOZ6Agaa6+v3QzNkhyPrcKD9y9q/uIKTWNygaShZrmJCJHrJ0z9M87vLIfhkeP6Bd6QpCwFPXcvwXoRVq2KBseOVSjmNUXAZwq+qEARHh0XaqdL5BwX+X4OVyQ3ALn2qFROzE/0anTNxYlTdGtnqufLd0MaZL84TvDyBHaPfsATkk3eypxN5KipxJ3ryzNxs+vLM22ypIUzeGRlCe1t/GEGiFklr1QPDySAw6GcJCkLDg86py7A+AXT9xCDijIDpTqHi3TNgIdl+GCpBjGQ4OFoaXGQ4MjRGpK/M5Gapq31lBNnwd9Tc3/w6+pqXnsBq3l3LMASI3uCWlOiKqnPRE6B9UAQGXatNj8LwAuddU05/VOKH91W/zLAPCu7qPlZRMAoJPpBv65uSjh4cSdqGINNAdJdjGpqRChgR74VUn6RzKVHYxmaik+U9iiyaaSBUkjXUKiRhUxsuwflm0RImjjBg09PR4BdV/HL0bbk8oXNampd7KJ7pUAIl4NGpv/RZCayhcIU1O5JBrJeiIMRvoFGk3UfS83RdbcP6bD3l2XmwAA7qyjKjtob4egSSMUG8X+q6ZyXgfBZHl769s14J8AUB11hjy3e1CNRK/upL0nSGPv7iG/Bgv1Nmc0UyfpiMHA1AQBmywGJkMkwiz0O7DxePqDANDvwMYgVQsOGnhM1Q/H64AnNTiCC5K74CBJM/SI8dBgzdAj4n4nKnl/WDoKRyoSoBp3B5OfkG4mfxX9w8llm4sS8C6u6YlkKU8TJ/M6HkWimoAiTCpfhGXJIbH5X/izSUHlPEquLkzp2X3jz25ek6kx4axpnodAK2SPp0/x81KNLdM2chNchguSwhgPDRaGFntCk4AYu2vxqDU2apnlZRPmJmwDAPSTVCME1iSfg60lyGoCntAk+WoEvrDMQeGRnJb937mOYyP5OLTwwB/9zl7ZC/Q7sPF4uiUYslWmgNQqDhckRUCBkXfuhWeSmA/oK1ljHS6ex5rkE6yj/44n32P2bzqdMTlm/6Ym66ALetX9879UsD+TyhfJb+xlYwI3qZzXQTo8QlJkz+2QtIvFauQn4REJVh05qY/+Ah+yk4QLkpKgUTtxf5KQ4akgCaxShIezOufuuJI1tnPuDgCpiQFOG6FyXofYf9VU20l+qx95P8g23vVnNSIJJGXigiQJFyQFUA85DNYCDeohh8WDcSb9UEFXbNIPBYCoJmo5evcmapF8vXARbew/MCAjcffScsrIoCqIWrovEukDHYs+RVUSKtNQbFQTprlJNu6goibbo0zU7sPJZVsLdXj3phA+N2Hb8rIJ6BI1dB0EVMCCiFqo6WKRXjHPFFwIExqhOXe5US05568mpqnVKrGJnpY20b4GjSYqeaSGzq1oFKjZ9TB6Kp6pr8GUrggPonaZwh1qSQcjpsIC401gonM6gP50sn1ztESw0qSh+twYWfebf58/9ljH3/z7PACYNGYAODPjbgC43enWhcjLZOPEiBNA5KP3Djsm0UlcbuO49ZloTXMqjZrOO2AsMMhL7Yr/f2hjjNXNgXnbQ1Ssq4VJcGLgUUOnhOAy5/6ZzsCRA18Yqwz4/wC2dDTph6p1h7xwa5dHkyam2F3273OQQfjZzIluGlW0BX567K6fHrvrzIy7z8y4+9615+5d27JXN1Ij+VnpuQnz0QbOXXSBMbSxEAdEleuPNgAXJA/irCbtPzBgQbzHixjllGTaPonCI0/f2k3crGERcKDBWOk2v/n3efSQkCIAKB0+B0mRzEW7WI3cBAdJniZQwiPu1CANH7JTDJz/rdYdQoN1TpGR/gPaWBC/aWm5BysYzRqeh7cnpui3Fur8OVQCvAArb2uIZLlVP2RH/Etow08CBSI8qpJqR5BVtszllc9+8qr9i7ahKy7DBcmzmPRD1bp80540OY33HxiAZclD5JRkkoIEVk36Oajl+qEKguKbwQVrZLbvc+CrBrqCdSAypvwNr0UJSoHDo6yyZb7tiUwCJTziOIQLkpKQQVL9iQHoSVOJTj0831RiySDoeINawHiH8SreRmq0rjhtevymdcVpANA16CrZuFqgfBxEkfr1Mf4CV8wdyd0Tmh72un0y6K7TQnfqXCNVBaPaFF6VlR2du7PaBAAQZKJqCqjpL30Gc8vjwGhaSENHOcxusJnKJoiQrEbB1oygcxwYRHp0OlRF5Th0EKhk9Gig3DQ60emLjXSSw3Wg8lBu0LtIjZpn/unvx6JZypggSlMbdeCrE+ljkZfdyWAqLUXdnk7xoO0zOmubX8KVrLFppcuBKLsXrbpBNiYTE3J0y9HGNP0LANcAwEC/XqOd368VqhuMQQaT1GCymTJgGtwUqNM1dOGPJtEJVwh/gZf0koQLkr/wQ24fADgU2tlhSzdZoJ8JAB3FmwAwP+Fr9PMp/Z89fd+2NgMEAI/oFzplpeNzsBoFENh0PDDCIz5kJwlPalAY/L8itE/z4JupRKePp09HAAAgAElEQVQe7sQ8zbritOlJ+Qr3TJIPdSs8d3GU840enrtLK6N4+NMn0n+L3jrva/k0/QtevmMbgSc1SMMFyUs4q0negSyjLqFJaLzO5bt45yO1InU6enjo+kt0n6OHh65vi5f1mx6sCwxweMSrBbYOuCApD549IoMkv4XUpEBnYME6j15/of5/PHp9BkVU3Ori4QCsRrP0c92/qUw26Jaih9fu6HtENx5tAD6HpDwhvSvx5HVI78qmysFo21AxSD1QL+5LJht3MFIzxh3EWnI7EmrJo1qhkdy9QWcx3DJQ6RINRsohokZLHW0nNNs6jCwbvifh7wDwoW5FSuk7AHCrqTkh4ljaVOG7A9WEh0CkUSr5jUmNu2UKBYBu+7afSB/fbd/2UDX1iiIEana9k0BlEzSaqTenRqTyBZjyE2DNtkKpAYLk/2AR1GQuGWM3EAZUckG42bKLwqP2dOZFo4p6vfVA9aoOqDkkxqmBmdUnMy8SS1beNNwBAMgXQwAzqKkEkBtauhv00buE5l9YsGA00u8kk6ewQfcW3laDSQvUpdjUA8Zdgn7rGBsL5lLBwCbuz9H/aaXug2hr2kidisqXYXIimAm5HYmWrPrAmD0CAF5+whE8QvIItsYNzrK9MG58yvcunOhamXCkQwBQmPis/LNqJg5ADxfuqDjDij5BD/mnOJXWvLR8Mno43zW/hgxQZuuf8fLdV+o+8PIdfYtKVLn88HXfvQEXJK9iqBgkjCjy0MXPZT6IHLXRhrNVA1rUpGNpU6U9tjtslTss2W3fdn+rLa2UH0HgwgyXrda9u1r3rtfuPkf/J/Rw4Vy86DiAwiOOQ/iQnafAa5K0A48aKgZ556aoujP6GSE0YE1ChW3k41CHMH4SHrlAoKz69A6z9c+s1r2Lfvq6L60ZyXLK7mIymdavX//DDz/07dt3xowZgjNOtX5C4PU4gMDf3bQDj+InxX3JngiSjqVNRTpE0u/ARvSQGS3hIAld0GH7Dlt/QA+Zney2b/vhlFkOm/2fbgl6yLwsxzVweIRH6gJFjQI3PPLokN0zzzzz448/ZmdnHzx4cMmSgPzvwyMkL2EMtSwyV4caAMAcapndDb9B+Qt0MDZbBhTs75+qW72kvFlLgpiF7nQ5hvMGyprBTPo47CwpHT4cAHAF0q5BVDaByjp73D//SyxFSORu2TiZ1WqoqWnGqUFFz9tfM4Uzp9cTz4Sq6Xp91lMf0S/8P92SYHplPlNCIlhFpXgwiHRJBYYgFWMZQDW+CXSfJT8KGOMG6fITTazrAfUf0Ejng5A5HY3mIBWdL9Ckon4xZnqXzJ4wmQWDSCUL1JjbkbUhbkAdADyiX4ieYUooMtYMGrb8BLUbCtTvKJxOD7FNamDQsCkS1K5WFQoAX+j+iXYDTo0AQPBYl48dO3bmzJkVK1YAQP/+/T/88ENP3cmT8AjJs+D/M2ExuLiM8muSrNWGpOiwd1eHvbtq7h9Tc/8Y6ZbkYJ1rKRISyFyTxMMjzM8ZD0kU5DaNG4Ye9hq0OHVnW6mI48/c2NTvxqZ+0m2OHz8+dOjQoqKil156qaCgYOHChd7pm7LwCKltgSKkmvvHoDRbOXNL5zIfFL474OF+UeCv6q0GLAAZZW8rfnH1twclBMmWg8lPoI1AVCMcHgXQ6l0S15LlOjz0IwDUbOkr0ebYsWP79+83Go0pKSlr166tqKhYtGiRi730HTxC8jh1py1fbcggSSZLyqcsjHcuH0EOHfbucihF5IyUODpdwbv7xAvH53ju01++Gh1MfiKg1agVIJhdf9iyYcOGv/3tb3/72982bNigUqm6d+/+j3/8Y8KECR988MH69etNJhfrhvgQHiF5lbCY48j2G43aYQtwj5A93LKxs6TF4yjZwVaZcH4EyiBHT4qj070cJ7UyPFSEQv3tQbRhcjQ9g6UIAlaNAj08AlcjJHv06tUrODgYAHr06BEcHHzliqXSfHR0tCAIBoMhNJRdPO7ncEHyOKG/OWY2N6+QFUObp9PF0KaIW5T7QJSJyjXoarqGfwJAZw1lZHCVmOQPEerVKiN51Agg7NCLY3WCqAGAmybqTzNMaAKAbvu2H08f323f9jDaA0IAy/exe/K24iLi4uh0NOJ3m57WbqRn5pm5d5FOeWCMG4KaqESMM7T3BOO2EER3sj1hNgEtlDZQ07tSgwGMJcQNFfVLYcwXbLwJpL6HoqNjy19Du7X091zGQIFJamiyJq2gDYF+2420NYMgUC+fgVSjwQVrbovUH5JJRb05avpGjLtEBNAlQlRUN5gsBjapwexANZk0FjIJYpkuB29/Psz1quqtibi4uLi4OLTdpUuXxYsXX716tVOnTvn5+ffee2/AqRHwITuvgbMbggYfQRtNRwbjbQlWl2TNHp4r3WZwwZojqTNtnxfHOo7A0KT36Qy7BgT35G3F23JyImSi4DrZHfEv7Yh/6ev4xYpcza9AK50VvKD8Aol+SyAm12GUHbIj6dat22uvvTZlypQ5c+a88sorS5cGpEMgj5B8QNDgI01HBnv6LsIOuYl8KEI6nTE5Zv8m9LOJ/uvHqRBo1zgqU7M7z9n+GEdlAsBFgG77tjt7rkPGlr+G16Z4FDxqNEP/vBdu5z6k6geuGuHwKKDVCABUnvRIHTdu3AMPPNDY2BiIsRGCR0jew/b/UtORwZpRjuseyQyS0Ce+a+AICWnS2cyJ6EG2wWuYwKouzsLIWLd92yvTprlwHVu8o0aIQJEiUFqN9iT8fU/C370fiZKDdYGO5yIky/UFIXDVCHiE5GWwn5AngiTN7jwXYhf0mRWzfxN+Jmb/pibrtBDSJCxFaCUT2nbhXrYyFpv/RWXatNj8L5y6DgOaoWHmkDzHWsIb229hhkOVio1Glv0DGcP7hEAPjzgO4YLkMzSDDgOAWJQgJOeLRfHoya632pFtYgyX0cb+AwMyhucuLY8ij94KplweAAAK1hxJzUSfPjVB7ckjzEx1mFCPohMkBmoVVRUC10FAFYYqUseAdcFsN8LKAQkMKWa2NJdU2L8FLDPkzVkAN0wR+CcAXGmiamQwmQhRdOmKJu1VcvcODbUbRvs4GNm6D1TKA1OOoV6kyhxoiZn2seWvoSvjfJIIpkQIrYvMbgOdpmFmemWW+v8YRHsxiHQWQzCxWz/yfryNUiWZK7OVO+iBEg1Q2TGkPwhSI9KsgSnVwRRMwZU7LFc2O0hENtK/l9d063A2UOtQI48O2bUCuCB5GxwkqVQqp/6P7T8wICN+k8PyBzjBgUxGsMWpuARJEdYhpEB4lA9NO8m8lC1kcrk0UW7cJVDAY5iuRY2kFAG9mMx9Rpb9AwA6CLcdtlQKl6u3+C2qViGrnoMLkg8gNUksiheL4oXkchwkuQ+KkI6kToSWZOnnjIfApc87bCl0LI0VRYkkPQCQcL5B4M/NRpH6g2QipNMZ2WjDHf3zf9AwJjifYudRNfItrSM8Ah4hOYILko9BUiRTjZaWT14gI0hCICliEhOgWR7qbc6QC6kHjBTZSoUIApLAFrvn2n3djMn8HBcSPcTR6eSvM3TP3js0tXZbBwgL4i2/4lajRhyHcEHyDThIwsgMkhhN2hb/MtqYUP5qi+3x577aM9/NbIfvwEaWyAgJr7I8mznRBU1q9bgQuTLGTqF79irXHZ+B1aiV4dF6SK0ALkg+A2uSkFxuqBxoqBwoxJaL+5LJNjGN1JywSXUSAHLzB2bFb8rNHxgSbNgGsLxswtyEbVlm/ZGQGrLxFTNlgmCgTQGk9anBTM3qN9Bz4MxUvEolRpesLB0+Bz+DZCmtdDmQSQ0AAHCzKRpvR2puXaZLZlxpohIxQpqoMhBVQdZv/bsKT2ekMKZHoXTKQzsVZSigpjMC6ujKHU30m9MkmVzApEswTg1MhQWTirrRbTOVh8K8OaLNMgzSJYFNS1E33r5/FPlMdO5O1H0gMiCQWbiKTlphfoOMYQRjkKGlsydC6SyG9pJZDCG0NYO0qwUABNt8Xrey8IgP2UnD1yH5BdrYCqfa5+YPzEqznDI3YZsHeuQ0iSUrE0tWks/kJ86VaD+wYF1F6nTX77er0Nka7a2J2/ePsqNGrYFnEnagjVamRgAAZrPrjzYAj5B8CTlwp42tMFQOFGKLmCDJHrn5A7MSti0vm+DJDjpNYslKraoRSxHaIAvRkgwsWFeROrZz7g6Hl20YP8SytasQP2nPHLZ1w+gQAETs3Q0A4Cj4CBSwGr1bNta3PeF4Hx4h+RhD5UBy16kC52iwzk8iJBI0WIcpTHy2MPFZN68Zsv2wm1cIdGyjooi9u61q1DLSxf38EKxGrRWV6PqjLcAjJN9jqByIhuy0sRXivmSkSTLjJBQhIU2apZcaIvMySJPIUTukSWilrQs0B0n+zYe6FWjjKf2flbqmrQuttA55maU6iw3EAn0LDr/yIdWotYZHPKlBGi5IPiZowFEAALAM3AkjipClkKljLQB0vU7VQQiqpybq2xtPoo2D+/oAwDDd8s1FCfjor2oqX+CGQCUINNLeBLWqEHL3qoqyhLik6kTuMikPanoOvKPQXEHjEf1Co1kg3c/QvNGwok+gRehcA7V17j1821EAqA2qBeL/swgq/NO2G8zcOzOdzuQaMFUhbHINqMYOmaP/00rdB0x/WryUjVND89ET6b9t0RDdOldkYl8vY9ygarKOZzaBTZ4CA9MNUSXlatFi9Y2F+v9ZovtcDSbGi4FprDGLEkeJ/rTej22x9b40JeCC5BeQk0nI5s4Fs7vNRQkPJ5ehDeW76AaTyhcBAClLZHked0DFZ/sc+MpegxydZfBwtv4ZRe4oh5W6DxS5DqNG/py2sET3uZtXeCrREvO1ZjXiOIILkr/QoibJHLjDIClCsgQAK0uznTod2yorNfr3f7olaGNS+SJbWUJcyZKV1+Ays/RzsSzJB0962cvIsMcc/Z+cvRdJi8XdUcGORmc+qL2Z7rFQ/z9uXgGrUauHD9lJwwXJj2BWyzYdGSwMljuZRIJk6Vd1xzmJ1HdqORYP8/WzlHX7n1S+iBQhPFhHBklXsqwTBnaqrdtDOjxCuKBGiJTSd9zPxZBPi1Gja7WjzmZODKDkQ1KNWn941OpfoHtwQfIvcBlZFCQ5leBgCxkh3RDCmdXvr5RPtT1F8doz9srnIGViP4Wzh1s2nFQmexChntNZ0d5RoxZ1CA1FuqxG9+RtBWh03NQPINXow1I2o70V0jaS5VyGC5LfQWoSTroDAJdlCUNGSI0Q9Eq8xSwVK9N8/Sy0cRWUASU1SLdB43XNQRIiezgA1FrTGbyPsyN1TpGbMN/eIYcBnzSBa8jUJtSI4wguSP4FKpJEJt0ZKgYZKgYBgDCwSMxPJBu3q4sgd7sYKeugGvUNcrdWoKzzbqiDd+dbkiZGxX8JADklmfjoD/TfRRVQZYpqRGrXKGrpG1HpfAyNJraGE7L0RjP2VVnspFfthEF4m8l1PpH+28EFa7BLbDjtFcQU5mGz7OjMMSOdV3YbqE6S9ZAcXqrOfqUlPKNmy9CiTwGgwRgB1jzCfgc2Hk+3JDUg3+4wFWXDw5RHQg62/Q5sRLGRRqAaq+hlLGwaoZn6DTLf4pvo+xpU9K8bqPzMEPq9YpLuNPSl22AiAy8/IQ0XJH+EcnAYeBQJkqFikDCwlNEkRdidP/hCUNSs4XlAy5KnqUybxmaOEUYMMCaFaU8uC/WrhTj2cDh9hXRIAjnFoggpksXhlFky7+5R8ARn21EjAD5k5wAuSH5Ki5ok5icKaaVoQ/E7IimaNTzPm5okxa5CQMN92Um2B5E4oVKEoFyJbjeRk0CRUfY23r5plGgIACCtRkiKrF4MTlRwH1r06aHkx+W3V5w2qkbAkxocwAXJf7GnSQCAZKl+V6oPu+cmqGTtJTmfojuL0b8RQbdtndwQR1JnHrF58hn9H9zooGM26JbKb/yIfiEAXBWjHLbEMOGRrTi5bAvkJ2rE4TBwQfJrDBWDtAMts/ragUdxYGSVpQLFNSmnJHPW8Dyc3aA4bpboxiN19pSJ5F3dR/KvbG+ZlMugBL/bIDWj5hA0e4SkiKkAy8whyWdIYY7Kd85opBq1ufAIeITkAC5Ifk1Q7BGzGXCcJKSV1p8YgI+a9EPVugK0AQBd66nPvjsbqMnnuiZqeOi6QKU8AFSTO+3NVIIAU0q8tolKajhviK65f4yMVwNA2A2g2KhOpCvx0B+yGjU1nR6tbu7VbYCE4lXk0Zig/36h+6fMbtjivhQhBTKA5W1HXW+kSw2JdM4h4+jDZCLglBU0P6RiKzwZJXYZyWELINF5KMxRs0DtNgpUBnkdbRxVR5tOhasox6Zguh7S3MRmI+C2qEbArYMcwAUpALAtL4tBUqTWHQLlJpZ25w8eFb9xSbncakM194/psHcX3jXS6hXs6hd5CazJzZeY56fpX8DbPeE83nbf2IZkhv55ctdh0TkOYn7C13i7jaoRALTV1y0TLkiBAdak0D4/kEESAsmSoPNUvoMElWnTSDXyT2y9bW7SI2nMbj2dymxjk8oVyGm4GnHkwAUpYJDWJKDzHRzK0r2jjqGNM7v7u9wllJhw3uC4Jactw9UIo+JDdpLwAn2BBNah0D4/2Gsj5iei7HDNqHzpq0lI0ZLyKQvjA8YMjePPcDWi4CXMJeERUiAR0rsS5ziE9vmh9mwf8qi6rjmHuuF4bFBtkFqXD9YBvYi6YPpiliCpo9geADoar5OHws31+CfYVA+6ZQoH60TOLRPUNnSmOmmi/qhq6MQEE72rEujyOfTMvJadtxfJbcZugMm8wPNGLVaNCwaqG5H00TBoAKKKhLSBN1NZiumGgT7KwL5eoUFil0l50NCeCEx+hJFOpjCI1CAkk9TQRDdWmaUuxYxnNoKW3qUaczVi4W+CJFyQAg88dhd+zwkAYGSJhEp5ODCcPHRzVwurTd2kadxQAGgCaPdNheIXx8gvy71APxMXM3UNVGrPnSu0ZUijXq5GXqC6unr9+vW//PJLRkbGAw884OvuuAIfsgtIyP/eSJYkMOmHmvRDhXRl/LOlCfr2kKdv0XP/FuRQ4BA31QiUK7XXBuFq1DKi2fWHI2bOnGk0GseMGfPZZ599+qkvfaFchkdIgQqZCx5+zwmJOAkhHhgupJcwcZLioCDJH2hxpM4p3Cy1F1jgwoBu2o0juBrZxWPvxtWrV8+dO/fMM88AgNFo/Oqrrx5/3Jd+HK7BI6QAhhQhh3ESWDVJ5sWXl02Ym7DNcTuCoG8PBX17yKPjdRzPoYgUAVcjaVxKZ/j5uO7n4zrpC0dHR0dHR+/atauurm7Xrl0DBrSQiOv/8AgpgAnr8SPp4xDSr/L2xV74qFlDTU2jqWeTfqigKzHph4Y1UqUrwumVNpFiLbnRTkN5BIRZl+6jjSY1tZKfSWoIob0YqGl6ABOdXABqKbfRBuuluu3b/nPG+JD8L8mjZ1R3k7u31FQJiQ6q29Qu3CR329O1KsLNTDfpbqioV3SdTom4TTc20SYITCIGk9QQRHsiBNN1HyJVt8jddrRxA8MtM/Xyq1SUhx7TDbSwCgVJQaompleCpEM1U32Dq5En6Nm3HAB+/jFeoo0gCDNnzlywYEHPnj0vXbr07LPeK3asIFyQAh5y7C6i2ylSkzxKvwMbj6dPCaBS2a0A7G+EnPcUxDVrQYa1urfwNlejllF0HdKGDRsOHjwIAMOGDbvnnnvWrVuXl5cXGRm5f//+Rx99NDc3V8F7eQc+ZNcaIP/zR3Q7Jd3YpB+K8u4Cmm77th9La6EEe6tHcSlShLW6t7Aamc1mrkZ2MYuuP2zo1atXcnJycnJyr169vv/+e51OFxkZCQAZGRnXrl2rq5OKof0THiG1EjwRJ60uyZo9PHd1SZa9BihIgp3eyN+zpX/+l0iT+tNjd60bBf3IlYIHRk6gaIQUFxcXFxeHtmtra7/99tvGxsbg4OCjR4+GhYWFhIRIn+6HcEFqPdy+2AuHR2ij/pTrtkABAZIiJEuJJSt93R2P44fhEalGjO0sx5ukpaWVl5dnZ2f369evsrLyf//3fwUh8AbAVPwbTeuDtAa/fb432tDWUE4N2tgKcX/z2tgLBmqSvzzcYjb6cHLZ5qKEPdph5NGDxoHkbunwOd32bce7FxujyaNqE7WSn8Gkpu0GaDMCga6hIKiov9Uwes4flcDAPemgodIL2gddI3djNGfI3T7mX8jdriaqMWOuWi20I3d/ESijiotA7V4VO5K7jWapdyNCoHIrOqso+4wuQPWqs1gjcanrApVqcZ7u1SXxDnK3VqQyXBhChXpyl8ytIMM1/knikD59+vTsWeTy6T//nHzihINkWlEUGxsbQ0NDXb6Lb+ERUiuEGr676yTWpNYNkqKLI8aj3Q5KjOPNHs5OC/tLfXdfw4wccjWSidnD5qqCIASuGgEXpNaKUpq0uSjh4eSyOfphEm267dt+ccR4MkhyGXF0OtoQvjvg2hVwN46ljQf3ppfw/BkZIc0anre8bILL12wdcDVynZZyEziYwBtk5MiEFKGIu05qY6n1quL+JCGjWKl7IU1S5FIuSxFD//wvcdaDC9jL5sgpyXR2vbCnmZ/wNX544XakGk0qX8TViKMgPEJqzSBNirjrJNrVxlYYKgdKnuEcpcPnoI1u+7YrFSfhIEkRkCa11jQ8pEDLyiYxz4ASzkktkpswH2/7YYZFAMD1WxIuSK2W8O6W+U/SzUEbW4G+0pqLEgEANJbqD6GNVMpDtLF5dn3/gQEZug+Wlk/Gz1xU/4q3J5Uv+jp+cWftNQC4CNBZe63RTP1RVTVRKQAqkal0QMXoZmsGuSgCAIiSBZ8NKipBgJniv2ay5GVo9+w7ljZiYME68mi9hqkJS1XBmD08d3NRAoiWtAitmXIu+LB01FMJ2z4sHYV2NfS5jHNBk1mTnzgXbaeVLmdKOTAlJEJpI4s7oJrc7WukMi9mD8/dWTAUAMBwFgCMKhUAbC+0JAGP160BYsYrmK76EUbnKdTQGRCNdCe36F4FOlMG+DCdy4h8yE4KPmTXJiA/PlQqFfPh4g7kAM6QwpzDKbOUurKCaPfsq0idruAFPywd9VTibvnt00qXK3j32cNzm9XIDjklmTklmbOG5ylyR/IPhq975XgOLkhtBeZzREgpEwsTZJ67tHzygvhNts9PKl80qXzRkMIc/MyQwpyqrGy3OqochpEj0MPXHQEcJLkPmtySWK1M4r4mfR2/mFEjd67G4RVjpeFDdm0LMvtOSCkDAPmyJJPo3J1VWdnRuTuVvaxraPfsc0eQHk4uQxt4KMwFlA2PGLJTLS5Q9nqINemVcqfzO3g2nfLwITtJeITU5mA+VpAstVbcD482Fyks2IojPXYHADklma+UT30l3onMjq/jFzOLXrkaKYOiXnatDx4htUXQhwsTKtXvSsUN2psoy4CupqqcksxZ8ZvQJHk3zVXyaJWGKm1whyn8jgNfnUjPRvV16ugshno6i8HGx4HKCBAFKl/ArKJ2VbRxg4r2U2gnNEJzlNZYRdd6YLIYJEBBkon46obTFtBGI1AvgckIYLIYjCLVWBCoPmtoZ4pwOsehnVg/Jbl4Y1ESiPUA0N5kOYqCpM5NlJNmsMi+wJySzFnxX6LfYLhAXfm6QBW2WBpPVdrlUsTxGlyQ2i7k8B0AhI4pIDWJA9bwSG3z5XRO4s6VpX4xVVa8v59lS4ZsoOE7CbMJpug7lyLF4W+pNHzIrk3DDMWEjikIHVNgr7FTM+R9DnyFq2L7nKqsbDLzoi0j8UvkauQNRNH1RxuACxKH/egJHVMwZITjgugO8RNNUjzDwn/CI9fIKclkPB2W6taQasRnjDwIn0OShA/ZcQCsE0hkeDRkxInD+/r4rkfK4JoaIQc//09ncBls7sAok4f8HTgcmXBB4gAAhIzOB+s0BJ5YQnGS2Ww+X2QxV91eGDd+eN4r5V3IczsIVOmK25rmGfKhRZ8eSr7/7rxv8DOXzFTagonYNT9gEQBhh97yDJ3FoNVQU/EdmLIIamr3dMZkABhSmAPwKwC0D6oijzKeCIy9QrUQgX8yzB6eu7R88g3imUag0hYM9K6J9q0Q6TEJUXKIQmOmXr4lo8EOajqmiRApa4YuRrLL8GjKPuDmCz7BLDebpm3Ch+w4LMwHk0qlGp/yvctXuzvvm3OZD8psjKXIHU5nTD6dMXlIYY4780aoWi7zpHT93ACCcevgY3Rew2wWXX74uu/egEdInBZg8sIBAGnS9sK47YVxCoiGHcSxOndOR1FRzH5kKnFLurEcGE1SUI0OJT+ONrxc6BYvpEVwHfI2bUNXXIYLEscu9mTplXLnqiuhIIkcuGsR1X/KGK9V+VzJGnulWYqUwdPB0NCiT7EsyWFjUZL0qJ00jBQBVyOO/8EFieMAs9n8bdEwctQOrfl3yopGpia5wJWssQDQOXcHM4fk/zilRu6QlHGceYZLkc+wWbDMIeGCxHHMuOSDTL4DWGXJbDY/cfBZsnGNmqpl0KS2FHo4B9BefdtEx0DXaLcF0UxNs0cKjeRuezWd1KC5cSR15uAClK98LSKIKtbQnk616KC6TV0ZKCsKhut0OQajikp5uKkKJ3cZpwYG0h4CiCwGXfFHABAq1AI0v0Zp/wiTSsA/AcCgot5JLW1UMTz9GNikLQCXIl/TRqaCXIYLEscJbAfx0PZs/TMOz43N/6IybZoilc4B4GzmxLMAVjWiKEy0COTY8tcUuVcgwqXIT+FZdpJwQeI4ja0srda9CzJkKTb/i8o0BarKns2ceE/e1g6aG/YapJS+g2VJEXD1DbJQoX8yzGZRM5ciTqDABYnjIvZkSbragpuVzs9mTgSAe/K2SjdTVo0Q9opC+Qm2OgRcivwPM4+QJOGCxHELNIeEpAhBVuxu8RSkSWhD/o1q7iRenHoAABKcSURBVB9TI0OKACCl9B2wmUNyH79Vo4z0H2yf5FLkp/A5JEm4IHHc5ZNh73xifgds5i2QMiWVvE8+qVKJABBdsA4AKlLHAwCqUoFgUh6CrE4Nx9On9DuwMURzG6DZcCFCTQ3Z3ammvBjaAyVITBYDU9mBgcliqFWFLNT/j2UboIHxYqBdHrTQRO3SeRlaoHZDVNQu4x+hofMUUAkMXAiD61BAwiMkSbggcRQDfxqSylQ8/Gm0wSgTAAwsWGcQg7EBK6lMYF3liuh3YKPivQ1cpifl2z7JpYjTCuCCxFEe2+klIJRpYME68nmsQ4w1eMz+TUEq/nWyGaRDtmrEpSiA4HNI0nBB4ngK9EGZXLoCSxGiInU62rBVJpOrTg2tmIXxLUeHXIcCEi5IknBB4ngcNFjHyBIQysQM1nEAIEfXckrIuuI0L/eEoyB8Yaw0XJA4nqUo8c+WLbNlw3bNJjlYl1C8Cm8zNgehdL2JjgKV1BANlFNDR9pcNdxM5QswnghMoQcmqUEaLVCFHsJUUukSDO1UdeSuPRECHg9xZFNaWgoAiYmJaPfWrVtr16795Zdfxo0bl5bm799muCBxvE2LuQ+YsqQ/4m3bPIjWxxe6f0oc5TrU2vDwkF1lZeVf//rX5557DgvSk08+mZCQMGrUqGXLllVXVz/4oNxaMD6BCxLHZ5Cfti2KEznK15rEaVv8y9INuA61Vjya1PD555+vXr367rvvxs8cOnSotrZ23rx5ABAdHb1o0SIuSByOY9BHcGLJh2SERMJMQWWVLfNGtxTCoQIBwDT9C58P+4cXOsPxJS4J0kXjPDnNYmJivv3229dffx0/c/bs2b59+6LtQYMGnTp1ShRFQfDf1CFvCJLRaBw5cuR3332n1WoB4I033igsLESHXnzxxeTkZNyyoaFhwYIFOTk5RqOxZ8+er7/++vjx41u85ooVK/785z8zT7733nuffPJJQ0PD7373u8WLF2/fvv3FF180GAyvvvrqlClTbBvgExcvXlxbW/vPf0oNnnC8AzOHZJsKgchNmM8885Se/XvwFRt0S2W2nKZ/waM94bQOumn+BTJkCQ/TYerr64ODg9G2IAiCIJhMpjYtSBcuXJgxY8b+/fvxM2vXrt2zZ09ISAgAtGvXjmw8efLke++99/Lly1qt9tixY2PHjhUEYezYscw1DQbDyy+/zAhSQUFBTk5OaWmpVqsdOXLkqlWr3n777bKyMgBITk7W6XQXL14kG2zcuBGpVFFR0fLly6dNm+ahd4Ajn9LhT7FPmZt/yy0O62E+1K2wd8gTI2DSnZGAD8e1ZZTNstuwYcPBgwcBYNiwYb/73e9sGyAFwrt+Hh4BgMc79+abbz7//PPR0dFo12g01tbWhoSE/PTTT9HR0ShmQpSVlZ08eXLFihXoyf79+69cufKll15CRzdv3jxhwoQJEyZs3rx5yZIlt27devxxqr5Zly5d3nvvvZCQEEEQsrKydu7cmZGRERUVFRUVNXny5G3btjENjh49CgC3b99+/vnnFy1a5On3geM+ZhtknqjyAJ7uM6d1Yja5/rChV69eycnJycnJvXr1avFukZGRdXWWTM7q6uqgoCC12okMUu/j8QjpnXfeIXfLyspu3bo1ffr0pqamy5cv5+bmdurUCR368ccfmZBz9OjREyZMAIDDhw+/+OKLeXl5Go0mOTl58+bNK1eu/PTTT8nGMTExMTExAHD16tVVq1aNGjUqPj4eHerdu/eBAweeffZZssGePXsA4Omnn160aNGVK1dOnz7tqbeA4zFsP99dDlzch4sNxyHKJjXExcXFxcVJNEhISHjxxRevXr3aqVOnLVu2ZGdnK3h3T+DtpIZ77rln69at6enpADB//vy33npr8uTJFy9eBICqqiqmsSAIoiiKovj1118/8cQTd955JwAcP37caLQsTykrK0PnJiYmdu3aFQAuXLgwcuTIN998s7CwsMXvArhB3759N2/eHB4enp2dvWZNC3XeOIGIQ1XgQ22ctgPKrJsyZcp999136dIl//+g87Yg3XnnnUhXAGDYsGHffPNNeXk5WsmVmppaUVFBNi4qKoqNjRUE4erVqz169LC9Gj73rrvu6tq1q16vnzJlyvvvvz927Ni6ujp8tcuXL997770AQDYAgHnz5vXs2XPcuHHnz5+/ceNG7969n31W+To6HL+C6wrHh/xq/n+evsWSJUvI3UmTJj344IONjY2hoaGevrUC2I5xe4Lo6OjGxkaz2bxq1arx48ejJx999NH333+fbJaSkvL666+j7WvXrsXFxa1du9ZsNm/ZsgWfNWLEiF27dkVHRzO3+OWXX+65555Dhw6h3SNHjsTFxeHLlpaWMg3MZvMtK6tWrXryySfr6+uVfdUcDofDkY+3My5mz56NcgpSU1MNBgOTKbd169aDBw/26tVr3LhxAwcOfOKJJ6ZPnw4AkyZNio6OTk1NTU1NjYmJGT16dFRU1NChQ8lz33zzzQsXLqSlpbVr165du3afffbZAw88kJCQkJycHB8fn5CQwDR47rnnIqyEhoYGBQWhxD8Oh8Ph+ASV2RcjGAaDAQDIFDsSURQbGhrCwsKkz5KTwih9Iw6Hw+H4D74RJA6Hw+FwGPx6kRSHw+Fw2g5ckDgcDofjF3BB4nA4nLbOmTNnmGewxYM34YLE4XA4bZr//Oc/y5ZR9vkXL17MyMjwviYpL0grVqzYs2fPzJkz3b/Unj17unTpMmzYMADYvn374MGD+/Xrt3HjRqaZ7aE33nhjnJWioiKmvdFozMzMRAl4mPfee2/w4MF9+vRBvnazZs3q0qXLjh073H8VHA6H47csWbJky5YtzJOvv/46WVfJeyi7rKmxsTE6OvratWtHjhxx/2rffvvtpEmTzGbz+fPnY2Jirl+/fv369b59+/7000+4TYuH+vbte/78+WvXrl27dg0tyCXbjxgxAgDI5/Pz8+Pi4urr600m04gRIzZs2GA2m2fMmLFlyxb3XwWHw+H4Ld9///3169fnzJmDn/n444+3b9/+5JNP1tbWerkzClsHIR/uxx57LDk5edCgQXq9fufOnWfOnDl37txvf/tbjUazfv367t27r1y5MiQkZMeOHR988AEAPPTQQ48//rher//3v/+NL9W7d29sYbtv3z5k3Q0AyLobe/zYHvrzn/+MDcUTEhKYHiL3cWT1jcFG4ACAjMBRZQoOh8Np3QwdOrS6uhrvnjp16uTJk0888cTWrVu93xmFBWnhwoUrV6784x//+PHHHwPAlStX/vnPfx46dKhdu3Y9evR4+eWX//Of/0yYMOGbb77p3bv3c889l5eXFx4ePn78+B49evTp0yctLQ1fqkuXLngE89ChQ4x1N25me0jCUBxs3McRjFM4MgLncDicQAQ5fOLiCbdu3Vq7du0vv/wybtw49Bn78ssv5+bmAkB+fj5z7nPPPde7d+/FixefPn166dKlr7zyijcrVnjcXHXkyJHosz4sLGzevHmCIGRmZt68eXP9+vWxsbG7du0CgH79+n3xxRcfffQRWR9Wo9GgtwwAGhoa7L0ptodsDcVlloIljcBdeq0cDofjYyorK//6178+99xzWJCefPLJhISEUaNGLVu2rLq6+sEHH3z11VdfffXVFk//+9//3tTUBABHjx5NTU31cj0XjwsSLqCrVquR0w/yBKqqqrrjjjuQqU9GRkaPHj2KiopI5Rg6dCiubj506FBb6257h2wNxeX0kzEC53A4nIDj888/X716NZmPcOjQodra2nnz5oG1GsWDDz4ocQX8qfv555+npaV5ucKsz9K+R48efebMmalTp06dOvXChQvnz59PT0//loA0UU9ISCgoKEDbW7duHT16NFh96mwPrV69+qGHHkLP7Ny5MyUlRaIb6CLnzp175JFHNm/ezNWIw+H4M1evXv3ss8/w7uHDh3fv3o13Y2Jivv322969e+Nnzp49i4d8Bg0adOrUKVFky6hHRUWtWLGCefKjjz6yNRT1NAoLklarjYqK+stf/uKw5ZQpU+68886EhIQHHnhg69ato0aNkmg8aNAgxrobAIKDgw0Gg+0haUNxBnQRWyNwZ184h8PheIFOnToNHjwYadLRo0ePHDlCfngmJiYydY/q6+vxMJUgCIIgmExKVq1VFo+Yq8rx4UZIu3Hv2LHj448/xjnyEo1tD7nv8z1z5syHH3540qRJLl+Bw+FwPMHhw4f37NnTqVOnRx991PbowoULhwwZ8sgjjwDAhg0bDh8+vHTpUnSoX79+lZWV3sxTcAqPDNnJH3bUarXSmnHw4EEc4kg0tj3k8MrSvP3223gkkMPhcPwNo9Eop1lkZCROV66urg4KCvJbNQI/tw5KSkpat27djBkzvH/rMWPG5OTkkGnoHA6H4w8cPnz4yJEjf//73/HYnQQJCQn5+flXr14FgC1btmRnZ3uljy7i8Sw7d4iKikLZ294nNjbWJ/flcDgcCa5evVpZWYlG6oYMGQIAe/fuvf/+++21R5l1U6ZMue+++y5durRmzRrv9dV5eIE+DofDaeWIotjY2MjkO/ghXJA4HA6H4xcoNodEOnMztOiuDS25dK9YsWLAgAGDBw/evHmznJsyV7Z3+nfffTeO4O233yaP2jtr8eLFL7zwggvdYE7nxuEcDocjC6VcWrEzN0OL7trmlly6i4uLhwwZ0tjYeP369bvvvvv8+fPSd2SuLHF6Y2Mjcv7+5Zdf+vbtW15ejg/ZO6uwsLBDhw6kA67MbrR4OjcO53A4HIdYkhqWL1+em5trNBpjY2OXLVv2xhtvjB07Fs2YvfLKK1OnTi0qKtqyZUt4eHjfvn1HjhwZEhJiz5mboUV3bWjJpXvmzJmfffYZSteOioqqrq7u3r074wguceU+ffrYno4OabXa6OhoAHjjjTceffRRnU6HL9LiWbdv337++ecXLVp0+vRp1Ex+NwDA9nQOh8PhOEQDAHv27Nm0aVNeXp4oiqmpqTt37oyOjl69evV7771XXV396aefjhw5ctWqVbm5uVVVVUlJSX379k1JSbHnzM3Qors2tOTSHRUVFRUVtW7dulWrVsXHx8fGxh4+fJhxBB85cqS9K9ueztzx5MmTmzZt+vnnnx2e9fTTTy9atOjKlStIUZzqhu3pHA6Hw5GDBgBGjhyZmpq6Z8+eX3/9tbq6uqGhYcaMGb17937nnXe+/PLL6dOnf/75588880xERERERARye+vatas9Z+6ysrKLFy8CQGJiYteuXe3d2J6Bd1xc3MKFC//yl78UFBRs27aNcQSPjIyUvjh5empqKnloxYoVf/nLX1pctEuedeXKlfDw8OzsbJwfaWtMLtGNzZs3M6dzOBwORw4aACgpKZk2bdqECRMSExN79uwJAGFhYaNHj965c+fatWtzcnLeeOMNjcYyuBcUFAQAEs7c5eXlqBrHXXfdJSFIti7dBoOhtra2f//+/fv3nzdv3ieffKLRaBhHcImL257OCNKGDRt++OEHphu2Z+Xl5fXs2XPcuHHnz5+/ceNG7969bY3JJboxb9485nRcS5DD4XA4UpjN5scee+xf//qX2WxuamqKjY1FBbzz8/PT0tKSkpLMZvOGDRvGjx9vNpvr6+tjY2PXr19vOxllL6kBER0djef80caRI0fi4uLQMykpKaWlpWvXrn344YfRM48++uibb765YcOG7Oxs9Mxbb72FOmbvyrank80qKiqGDBlCPmPvrFtWVq1a9eSTT9bX1zvVDdvTzTypgcPhcGSgAYA//OEPU6dOPXjwYHV1dUxMzIULFwAgNTX1/PnzKHF5ypQphYWFAwYMiIqKcscgDhEcHNzY2IhdujUaTWJiYkJCgk6n27p1a1ZWFgBERUXNnTtXo9Hs2LEjISEhOjq6trZWuqTutGnTmNPJoydPnuzTp49tN2zPwrFgaGhoUFBQSEjIlClT5HcjIiKCOd3pN4jD4XDaJJaFsaIoGgwGe5+eR48ebWhoQEUfJk6c+Kc//cnWEIlx5paJHJdup3y7XTP5lnOWO/bh3Dicw+FwHGKZ4RcEQeK7fEhIyIwZMxYuXDhz5szq6mpUH88W0plbJnJcup3y7XbN5FvOWS7bh3PjcA6Hw5GDXOugmzdvFhUVRUZG4uQFhurq6oqKCq1WO3z4cEV7GPBUVlZev359wIABaC0Uh8PhcFqEe9lxOBwOxy/w63pIHA6Hw2k7cEHicDgcjl/ABYnD4XA4fgEXJA6Hw+H4BVyQOBwOh+MXcEHicDgcjl/ABYnD4XA4fgEXJA6Hw+H4BVyQOBwOh+MXcEHicDgcjl/ABYnD4XA4fgEXJA6Hw+H4Bf8fn5Hv7+PWrbkAAAAASUVORK5CYII="
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the call to \"ft_topoplotER\" took 2 seconds\n"
     ]
    }
   ],
   "source": [
    "%% Plotting in space\n",
    "\n",
    "% for a single trial type, for each channel, average over time the trial\n",
    "% and plot the average value on the helmet\n",
    "\n",
    "% You can still see the time behavior when clicking on one sensor\n",
    "\n",
    "cfg = [];\n",
    "cfg.xlim = [0.05 1.2];\n",
    "cfg.colorbar = 'yes';\n",
    "cfg.layout = kit_layout;\n",
    "ft_topoplotER(cfg, avgALTL);\n",
    "\n",
    "cfg = [];\n",
    "cfg.xlim = [0.05 1.2];\n",
    "cfg.colorbar = 'yes';\n",
    "cfg.layout = kit_layout;\n",
    "ft_topoplotER(cfg, avgARTL);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16afc180-5b1d-46ba-bbb2-ad6174be1360",
   "metadata": {},
   "outputs": [],
   "source": [
    "Plotting ERP in sensor space for ALTL and ARTL for the `AG147` sensor (right head side), reexecute this cell for the `AG183` sensor (left head side)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9e2ddf07-cdb1-460b-a00c-61c22b6325a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FC0pKSubPn2/324yMjFmzZmVmZhJCtm7d+v33369atYp+9fbbb/v7+9P77NRUUVFx6tSpiIgIca8Wmc3mq1evhoaGSl/itisgIKCiooLebFFQUNC4cWPLb+/cuVNSUiKzEd/SzZs3S0tLmzRpIh7R3A/VzSlIFKBrk6WXT6R/X1FRcf36ddtycHmmxHHZSnC50Mxm8/nz5319fVu1amU7uzt37ty6dSssLEx+JJ7bMOS7fPlySUlJmzZt5IdNCLl+/fr169fbtWtn9Ve///777du3xcwEyrLToKw/JpOpurq6ZcuWJ0+etLwYa1ejRo1Onz4tvi0qKho0aJCHA7TD39//scces/sVvXHPzen7+vpaZSNCSK1atWz7X8kREhJC+0dYcT9Ul6cgUYCuTVZOnvb39xd7DCsyU+K4bCW4XGhGo7Fdu3aOvnVh8/DchiGfxBqREBYWZnsllRDSpEkT1SL3Qjrv1EAIMZlMKSkps2bNmjBhwsGDB0+ePCn9+27duhFCMjIyCCE5OTlZWVl07Bl98Pf3V+GcFADABTwlJNqkJrp48eKuXbvEGxXtMpvNmzdvnjVrFn2blJR08ODB4uJiiT8xGo2LFy9+5513xowZk5CQsHDhQrsnSpy6fft2eXk5chIAMIiba0grV67ctGmTmJO2bt26cOHCHj16/Pjjj88+++zUqVMVn2NpaWmtWrWcancGAACXcZCQbt68uXDhwvT09MDAQJqQqquru3btmpqa2rZt24KCggEDBmzZsoWOQQIAAJzi4PRfkXtXAQCAcRz0spszZ47RaKS9DKiioiLLvkCBgYF2ryRFRkaqER8AACekL7prjoOEZHsVp7q62mAwiG99fHwcNTwyVfqRkZGIRwLiqRFrISEeaQzGo3UINeCgyc6Wv7+/5Yhh1dXVro13CQAA7OAyIdneu0rHfwQAAH5xmZA4vXc1LS1N6xDug3iksRYPYS8kxCONtXjYx8E1JFv03tVp06a1adPm9OnTOrt3FQDAO3GTkPr27Ws5UkP37t3R1RsAQE+4bLIDAAD9QUICAAAmICEBAAATkJAAAIAJ3HRqAACQj/1RCTyBqYEhXICEBAD6xPvR2Vk6yMFosgMAACYgIQEAABOQkAAAgAlISAAAwAR0agAAUMm7777bokWLl19+mb69c+fO5MmT165da/mbFStWnDhxwuoPly5d+re//c3ql/qDGhIAgBp+++23TZs2zZgxQ3ycW1VV1bp166x+9tBDD/Xp06dPnz6pqan169enr81ms+0v9Qc1JADQPwMx1PwjpQnkvidZr1u3LjY29uTJk19++eWoUaMc/VW/fv3oizfeeGPAgAGDBw8mhPz3v//1ZKSsQA0JAEANn3766ciRIxMTEz/++GOtY2EUEhIAgMft3r27fv36nTt3HjVq1IkTJ86dO6d1RCxCkx0A6J9V65n61q9fTwh57bXXCCFhYWGrV6/+5z//qW1IDEJCAgDwrJs3b27evHnDhg1Go5EQ0qZNm7lz5y5cuFDruJiDhAQA4Fmff/55v379LDsyrF279vPPP09ISNAwKgbhGhIAgGetX78+MTHR8pNx48atWbOGvjZY2LFjh8R05P+SU6ghAQB41k8//WT1ybRp06ZNm0YIEQSHF7du3Lghvg4KCpL4pW6ghgQAAExAQgIAACYgIQEAABOQkAAAgAlISAA8MRCDJsOyAagACQkAAJiAbt8AACqxeh5SVVXVpEmTxG8DAgIGDRo0ZMgQ4q1PRUINCQBADbbPQ6JPOerXr19MTExMTEyHDh0mTpy4bNky4q1PRUINCQBADY6eh/TCCy/4+/vT14GBgZs3b546dap3PhUJNSQA8AIGgwb/7ifneUhHjhwJCQnxcFmwCzUkAACPE5+H1LFjx9dee+3cuXPt2rWjXz377LN0FPAff/wxIiIiNTVV00i1hIQEAOBxEs9Dmj59utFoPHHiRF5eXkpKSpMmTbQMVFMcJySTyfTrr782a9asQ4cOWscCAGzTdGRS6ech9erVy9/fv1+/fjdv3nziiSd+/vnnWrVqaRithni9hrR+/frRo0enp6e/8cYbs2bN0jocAACHxOchJSQkJCQkvP76682bN//888+tfjZ37lxfX993331XkyBZwGVCMpvNS5Ys+fTTTxcvXvzVV19t2bLl7NmzWgcFAGCf9POQREajccOGDQsXLjx9+rTE1HT8VCRem+zMZjOt1dauXdtgMFRUVNj9WWRkJH2RlpamXnAOXLp0SesQ7oN4pLEWT6vwViT87muTyaRpLHexVkSsxSOS/zyk7t27V1dXW37i1FOR7G4YAwcOdDZgrXCZkIxG49y5c5OSkmJiYrKysuLj4zt37mz3l9nZ2SrHJi08PLzmH6kI8UhjLR4RO4GxEwnFWjwqs7v44mFQPEFnFpdNdoSQY8eO1a5du2HDhsHBwRcuXCgtLdU6IgD1YIhV0CUuE9KePXuOHz++adOmxMRE2g6r7+E0AAC8AZcJqaioKDIy0sfHh75t2bLlxYsXtQ0JAADcxOU1pPbt28+bN+/ChQutW7cuLi4+duzYuHHjtA4KANjC/iUTsMJlQurQocPMmTNfeOGFjh07nj59Oi4u7rnnntM6KABgCAsdmkwmk5d3snAWlwmJEBIfHx8fH691FAAAoBguryEBAID+ICEBAAATkJAAAIAJSEgAAMAEXjs1AIAtcfgGgWj5tAUA16CGBAAATEBCAuAVhrMDnUFCAuBYjTmJDsOK1AVcQEIC0CFkIOAREhIAADABCQkAAJiAhATAAYkmOLTOgW7gPiQAPUBaAh1ADQnAKyBjAftQQwLQJ2Qg4A5qSAAAwAQkJAAAYAISEgDr0PgGXgIJCYB7MjMWEhswDgkJAACYgIQEAABMQEICAAAmICEBAAATkJAAAIAJSEgAAMAEJCQAAGACEhIAADABCQkAAJiAhATgRTBYA7AMCQkAAJiAhAQAAExAQgIAACZw/MTYgoKCkydPBgYGdu/eXetYAADAXbwmpIyMjBkzZvTs2TMvLy8gIGDjxo1GI2p7AAAc4zIhVVdXz5gxY+nSpdHR0YSQZ555ZufOnYMGDdI6LgAAcB2XCWn//v3NmjWj2YgQsn37dke/jIyMpC/S0tLUiEzSpUuXtA7hPohHGkPxhCs5MZPJpNSkGCoiQgjicWDgwIFahyAXlwmpqKjogQcemD179nfffefr65uUlDR+/Hi7v8zOzlY5Nmnh4YoeWtyGeKSxFo8iWoW3Eoig1NRYKyLEY0s8DIon6Mzi8rpLTk5Oenp6x44dT506tWnTptWrV2dmZmodFIDyDMSAW1nBe3CZkFq2bNmiRYv4+HhCSGRkZExMzI4dO7QOCgAA3MJlk12DBg0s3/r4+GgVCQAAKIXLGtITTzxx8+bNffv2EUIKCgoOHDgwZMgQrYMCAAC3cFlD8vPzW758+fTp01evXp2Tk/PKK6/g3lgAAN5xmZAIIY899hitIQEAgD5w2WQHAAD6g4QEAABMQEICAAAm8HoNCUDfcD8seCHUkAAAgAlISABeByMSAZuQkAAAgAlISAAAwAQkJAAAYAISEgAAMAEJCQAAmICEBAAATEBCAgAAJiAhAQAAE5CQAACACUhIAADABAyuCgByiQMOCUTQNhLQJdSQAACACUhIAADABDTZAYDa0PQHdqGGBOCl8AQKYA0SEgAAMAEJCcB7oZIETEFCAgAAJiAhATAHFRfwTkhIAADABCQkAK8mURszEAP9p2Y84M2QkAC4kWvKxY07oGO4MRaAMzQneajiQidrlfbcnBdugwWZUEMC4JJHD+5oqQNNoIYEwCsxJyF5gD6ghgQeh8Ol/qAKBZ7AfUI6efLk9evXtY4CaoDjl/s8fQEG6wg0x3dCysnJGT169MmTJ7UOBAAA3MVxQqqsrJw2bVpYWJjWgQDwzeWKUavwVspGAl6O404NS5YsiYmJOX36tMRvIiMj6Yu0tDRVgpJy6dIlrUO4j3rxhN/932QyiYewXFOuZvHIo2U84XY+M5lMDkOy93unmEwm1yZiIAbbVWnNYgNw+IkSsAnZNXDgQK1DkIvXhHTkyJEffvjh22+/nThxosTPsrOzVQtJjvBwt48cilI5HsvZ2Z21l5ePNBqM3ZAEIrh5+cedJZX/t7a/VLCEDcRA8xxTdzuxsAmJh0HxBJ1ZXCak4uLiOXPmrFq1SutAwDkevZeTMHYk4gu6MwALuExIixYtat++fX5+fn5+fmFh4dmzZ1u0aMF+8gdQgfu1JQCtcJmQwsLCrl27lpKSQgi5fPlyRkZG3bp1kZDAmyEPgQ5wmZCmTp0qvp44ceLzzz//5JNPahgPAAvQYgm847jbN4CCcFsogOa4rCFZWrNmjdYhAPeQitRhdyhxABFqSKAN1EjUh0zAKdd2Fh53MSQk0BU85NQRmo2Qk4Bl3DfZATuYuh/IMiexEI+abB9LwXIJcBGkXfxGziwNakhms/nDDz8cNGjQiRMnsrOzjx8/rn4MAMxC9Q68ltoJqbS0tH379qmpqbm5uYSQS5cujRw58qOPPlI5DABQgd3mU2RcEVqYraidkEaMGDF+/PisrCx6H+uAAQPWrl27cuVKlcMANmH/BBUwuJmxFo9W1E5If/zxx/jx4y0/6d27t4+PT3FxscqRAIAicDAFpaidkHx8fMrKyiw/MZvNlZWVvr7oXqEABk/9PMTukvK+4BLrTq+XzWWuMu/ZsG151VKrnZCGDBkydOjQgoIC+ra0tHT06NHNmjWrU6eOypGABG/e/wFAK2rXS+bNm5eXl9ejRw9CyNixY8vKymrXrn306FGVwwDG4ZZ+fqEztPu8tgw1aCj77LPPrly5cuPGDbPZHBwc/OCDD6ofA5sUPAobiEHNTRl1KTd5qAC97XCmIa9NIcpSKSFdvnzZ6pPQ0FDLr5o1a6ZOJN7DtZykcmpxPwcjFwKbFH+CrTc0G6iUkF566SWrx8v7+fkZjcby8nJCSEBAwKlTp9SJRK88fVz2nhNAcUlzSa62kYh0X+YAlEoJKT09nb7Yvn37vHnz0tLSwsLCCCFlZWXTpk2rV6+eOmGAanhJYKhggUy8bNKE561apV52PvckJydv27aNZiNCSO3atVeuXPndd9/RqhKAF+L38MEd9B1lnNrdvisrK/38/Gw/R0ICFuBoBaAhtRNSx44dX3rpJXFchsrKyilTpjRo0ACtdnbhfiBtieWPtcARrCx+qd3t+4svvnjyySe7du0aGBhICCkpKalXr97BgwdVDgMAgDXe0I9Omgb3Ie3atevy5cvXr18XBKFhw4YPPPCA+jGwjKNrpypAaQB4D7UTknhDktivwQvvQ8J5EIBXwXmVTGonpJEjR169etXqw3r16nnh6EFISzXS9kpAq/BWGs6dwubhiOUhXsdXjFxYNK5LQ+2EtG/fPsu3xcXF//u///voo4+qHIY3Y/DGTwAdczZDcJ1R3KTB4ycshYSErFy5ctmyZSqHoT4DMbBwxq0+BfcuZXu7oS8WKAUbklLUTki26G1JN2/e1DoQb4QOzZpD4euMOjuUXndbtZvs8vLyrD75xz/+4efnFxISonIkLvDEVR+Vh+XWB9faQFDO3sadrgTSf+tOMkAHBwlqJ6SXXnrJqlNDUFDQmjVrVA6DC4ycATESBgDonsadGnx8fFQOAOzSNuvQuaOThZdDRRbUvoYUGxt79epVsVMDIaS4uLh9+/bMjmWnbFuto6kx2yKsZlTSnT7YLB/gGrP7nYL4WkaVakhHjhyhXel+/fXXN9980/Kr0tJSs9ns66vBmBHgPo62dYLmew9jdmNgp+7FbBExQqU0EB0dfefOnaKiIkLIH3/8YfmVj4/PW2+9xXjbnS43I891Q2ezuNiMCsAp0psx7xu5evWSb7/9lhCSmJi4ePHipk2bqjZfAK9jMBBCiKBqhcD2UOiJg6NtXUeRuThVdeb9oM8ytRvKUlJSVJ4jAABwQaU5IrrLAAAgAElEQVSEdOHCBUJI69at6QtbrVu3dnaaOTk5eXl5oaGhUVFR7sbnHXBmB+xj53qPntBSjSARWgdSA5US0qRJk8xm8549e8aPH291DYkQYjQaz5w549QEk5OT9+3b16VLl+zs7KCgoA0bNgQEBCgXr34ofhlfH8cLRnIzI2EAMEKlhLRr1y76wuo+JNecPXs2NTU1MzOTju8wdOjQbdu2xcXFuT9lAN3T9/DYwDWVEpLtiEFWHnzwQflTCw4OXrNmjTjaUHh4+JUrV+z+MjIykr5IS0uTP/37hFt/YDKZFJnOfRN0/K3cmdqbguWU5cxFvj/jUW6a7HNx1UuQLL1cU66J3DfHS5cuyZ+qVLTMrzXr4MNtvpWzy8j7K+vd5P45upa8XYvQlblYYX7NSjMIqnTFGTBggMS+ZDQaf/nlF9emnJeXN2TIkK+++qpDhw5WX0VGRmZnZ7s2WZHt5uhaa5Wb56Q1ztTu9C3PhZU9Lxbj8apzbcUbKqVLz3Z2JpMpPFzGIaemXnbsrzWrZbcKWM7GbPsbR39ltZvYnSObbLcQ6bAjIiPcPyR6lEo1pPT0dE9M9urVq2PHjk1KSrLNRgDgzbjIKMrSwSKrlJAs73stLy9fs2ZNWlqa2Wzu0aPH1KlT69Wr58I0T5069T//8z8TJkwYN26ccpECABMwrIYXUnssu4sXL3bq1CklJcXHx8fPzy89Pb1r166HDx92djoHDx585ZVX5s6di2zE11hVIBOOwiAXfaiZLmjw+InJkyf/9a9/FT/ZsmXLxIkTT506JX8iFy9enDx58pIlS3r16lVZWUkIMRqNmg8+pI/+0ADAi3vHnD9f807tGlJBQcH48eMtPxk+fDhx8omxKSkpJSUlkyZN6njPe++9p3CgbMOTXsEd7J82abVtY7fSlto1pNDQ0J9++ql79+7iJ2VlZeXl5U49MXbGjBkzZszwQHTAJeHeAcTA+mFWXQaDysPZ6QZyklbUriGtWLFizJgxK1euvHr16tWrVw8ePBgdHZ2UlHThHpXjcYHdQSTZ3ILZjApYwH4lCbyQSvchifr06WP1CHNL7tyQZMtD9yERyZskHO3nyt6H5MLdRZ64P5+Re/5VqyGpeR/S3Xndf0eRc/chEalbkWznzsjalMO1UOXch8Qjuv3L2fhxH5K1AwcOqDxHAAAJXGcjndHgOa2nTp1auHChZc3MYDBw+lgKbMrACi2egQSgLLUT0tSpU9PS0ho3bmz5zHKDXjrRE0IMxOCh1nm79wl6SUZ0p1EOXR4AeKF2QsrIyFizZk3fvn1Vnq8mcGcSqE1H53YSvOQ8zAup3cvOx8dH9+POWe0tjPTBYyEGb8bIZgDAMrUT0uzZs6dPn15dXa3yfJWFI4s3Q2oBTp3PPq91CDVQu9t3eXl5p06dCCG1a9cWPzQajcePH1d8Xp7r9k3uNcS59q0LPPcgCXeoFon0dSB3vnUuDIvV6mxLrN17A5Ts9m23sc7Jbt9yo+LznIydHUcpgsXSyNy8Gb+CoPY1pIEDB4aEhCxbtqxu3boqz5prOtuR3KFtJwVvWBH6O3DrkqDHVaR2QiooKEhLS2vatKnK8wUAAMapfQ2pYcOG+fn5Ks9UZzg9e2W8rUB9iq1HV58+IBDB5ZWi4NrEhuECXVaPiPo1pC+++KJ///5Tp059+umnLT9v3bq1ypEAAABTmBjLTtkh7ER66tSgA+5cmbC8bmR7DUnNTg0uP8bUtlODdGk40alBonokY+92tEQ1juKo1OaNS1YusK0hoVODKzCWHSjFdkxJq0/02qzhBDyBArii9jUkW1u2bOnZs6fWUeiEYLj7DwAYh13VlgaDq1LXr19PTk7euXMnIaR58+ZaheEhaIJQClN7LHOr1TsGCgIFeW6wTUVokJAyMzP//ve/X7lyhRDy8ssvT5o0yanHxYJdTB241cTmglvePMtcGpPHQ8G7cx0OdE+9Jrvi4uJ33333oYceGj9+fFBQ0Pvvv9+8efO3334b2chDGGy+wwFIMa529QZGMLVjskOlGtLLL7986NChRo0azZ8/f8iQIX5+fqdOnVJn1gAS8HAKmXAy4T5sbDVSqYZ0+fLl2rVr9+vXLzo62s/PT52ZehSn7TCa0/C4xlp9USW0LoXqFPBApYS0a9eu1atXZ2Vl9e/fv3v37p988klZWZk6swanMNjQBwBeQr1ODdHR0Xv37i0uLl67du2qVatKSkoIIfv27evfv79qMYAK5LdLeK4FQ4WEqtnTF9Wt69A+CGivY4qOzxfVHqlBlJeXN2XKFDqSwkMPPfTtt98qPguPjtRAGLvD3GobtTuigVPTcTlJOBpVwfbJBXLmJbFcjkj/xmpettN3iqMjdY297GrceO4bqYHcHXPBZDKFt2rlXIh/TlEqVOJ8cnVt4xcX3BODPjBOesAR+39ibzuxu3nL33RZPr3Q7MbYBx98cOvWradPn37vvfdsBxMC+bhoYROIgIsZlrzkEAwi9ndSFmg8UoOfn19cXFxWVpa2YbgGxxTwIP0mb3dGGecUspFMmo3UAKxRfJ9hfCdkPDwAL6T9WHYAYMmDbbD6rXURQgQi5Jpy9Vf3krlE+jjBQkJyCxfXb1iDQtOGflMR6Aaa7ADAU2y7VvJLzXEW/jxjU7eLrOZQQ9InyyqIazUSvdZjVF4uHRyIAVTDcQ3p4sWL586da9GiRWRkpNaxaEOXCUNbtg/9cwGSkC39XdrRir73el5rSFu3bk1ISEhPT3/11VeXLVumdThqwKA+3s5gcP2u2PunY/uZCwlDuhOBF/btdhkKSsRlDam6unru3Lmpqalt27YtKCgYMGBAbGxseHi41nGBY7iiziocDYEdXNaQDhw4EBwc3LZtW0JIaGhonz59OL21VmdwaAMAd3BZQyoqKmrXrp34NjAw0NGYdeLlpbS0NBdnxkC9i5dmOpPJpEhxSSxvrimXECWarWQEIOdKkslksvNpuMW3Cm0/JpNJwS3RftjOu3TpEiH29xGnZpFLcluFu7taTSaTRDzMEgxE3NZsNzlajLkklxDifhGxj8uEVF1dbbBoAvLx8XE0RKz7g6uCfO60mtIxpWuehSIXUZQjvcgKNiMr2yKtQmDOzsKdoYrvVs3DXZkv4+5u8BoNga0+Lpvs/P39zWaz+La6utrHx0fDePTNsjOFgo1yVh3TueCoUwkdNVaZWaDZk09OrTiXN3he9hSXcZmQGjVqdPr0afFtUVFRly5dNIyH961EZ533VF0WXQ/Go29s5n4v753IZULq1q0bISQjI4MQkpOTk5WV9fjjj2sdFHgLbfI30h6rVEohBoOezhod4fIaktFoXLx48bRp09q0aXP69OmFCxeGhYVpHRQQYnB3yBJv2OXAS+hgIB/1cZmQCCHdu3dHV2+teOee5kKy9M4hG/htcdL8GdC0x52yXRgkHlvMIF4TEoD+3D0eeSu+Dp3yod4vHxKSV8OuAt6J32qcviEheR2XG9zutSdITdM73V18V8rTu5o9QWVi3tW8NVImLnvZgQswNquzck25KDQdsK0Meah6pFh3Oy/uUYmEBK7DwRq0gjY3XUKTnYs8fSCWbs9BGnATCpBlvHTjVC8pek2dCTUkDeDkDkBLdHwNg0HfeyKPS4eEpAXJm64tNyPNT+Q1D4BlKBzVuHVsNRgsB2Pk8TDtPZCQNKP5joHjKXCNZhf5+5FgqLntS87U2L90ynh4EpCQwDme29b53YsscdG51rPuNYhpHYcsmp8XgiV0atCSuDPgKAbACJm37AhE+DPpes3zijwNNSQ11HgWhtM0BrH2MEAA+Thtb0ANyeMkBjjQB043facIRLAaZU7Zpb53RqKvoqQVCKdqDzYDxqtwrubozlmxnuQNWzgjUENi0X0d7XScyoCHK+TgAqxT1yAhAXgffjodiDjqsY1s5DIkpBq40LVU3BxdOPnlZZcDN+npmOW5VCGnozYhTvbr4y0TexUkJFZwdALojeQfFgGYJBAhIjKC8YMMOjXIJfbyVGx8LYOBCOg5CoQQnLZ7hFtdRcTuFYJ1fxbxB45nirXpIiQkxchphNFTQw0AgLKQkABAdUo1DKBmqS+4hgQAAExADUkGbc/CcA4IPJD7kGxsz+AYEpIycHEI5NPr1qLP8SZARWiyc4ULBxS9HoOALfSuOf1x5k4j5+4d5GpscueIi8bPMqKGBKAXSnUQ4Dal3R0PV9n4Zd6Cpvh8vRJqSE5Tsq5jua3zcP4CwAFOagNgCzUklsg7RUXrH9jBwuk50gC4BwkJABggO5ndOyEzeCoHI61qBwkJALSDoz9YwDUkT7Ht5JNrytUkEgC+8ZK0cO3KbUhI4PXUvfrC4iVAHEmBDUhITMLRQadYzEYu4Oe+Fifob4k4xPE1pJycnLy8vNDQ0KioKK1jsSYYCNuPHQG1sZiKDB7rF+AxLBYjKIfXhJScnLxv374uXbpkZ2cHBQVt2LAhICBA66Duh7Mt8CjLh2mphqmtmqlgQAlcJqSzZ8+mpqZmZmaGhIQQQoYOHbpt27a4uDit42KMJgcsr0dP4RV7iqOLQQiEyB5iQKKSVNONcR4ZGUFb2GU0xWVCCg4OXrNmDc1GhJDw8PArV65oGxIAMAfZhTdcJqSmTZs2bdqUvs7Ly9u7d++kSZPs/jIyMpK+SEtLc21ed88BlXDp0qVwpaYlg8lkUnN2/OK0oKzCNplMVj+QuVB2Ft9gIISYcnPDLX4jMU2Z3zoVlfvkrFZPrHqVNydTbq78Y1T2+fMeDcZ9BoHn6vbVq1cTEhKef/75pKQk228jIyOzs7PdnYdyJ1lObToKYKHJTqkYapyOOzPyQEHZNtkpfzXeKmzbHdnNhbKcvjhxu9OU+a0iUbFP5f3O2dmxfcDnptt3cnJyVFRUVFRU79696SenTp0aNmzYiy++aDcbAXPY3hPAdegwbQlF4QZumuxGjRo1YMAAQoiPjw8h5ODBg1OnTp0/f/7TTz+tdWhyqVo90h/LfObl+7xtakeyB13gssnu4sWLzz777JIlS3r16kU/MRqNNFFZYq3JTm0aNtnZTR5utqpZsZqU/H5ljqasaFl5tsnONma7e7FqTXa2v7f6XMGowIq+muy4qSFZSklJKSkpsezIkJiY+Pe//13DkOBP6mzxTmUgl9OV5mwPN/KLV+UzEh6L1wuxfTc0lzUkmVBDuvtC5UVwdEasbA3JduLE8ZJKfMt4DUk6IdX4AC13FsrZGpLEdJQKCWy5sFsxfMznplMDAFhj+MgC4AIkJJ1S8FDl5qQ0P2hqHoBHCYLUAkp/qzJ0xvMQdlax27i8hgSc0dEO4xouhwRF8mCcHncrJCTgH3t7JhMj2ikL+Ykp7G3zikCTHWjBqa5iTv1YN5hqbQNQBWpI/GNhiCAXKHKjKw7ZXOBx+wQtoIakCzibBuACdlVJSEh6hC2+RigiUB+2upogIXkB7AZWxAJRpWS072KHDQA4gYQE93jbYctqeb1t8QHYg04NAACc08tY+KghAQAAE5CQdMdu0xNf7VHuREt7MfG1vPrG8wk7qAxNdvqi7YGYnQH0AFgm3jvI6U2EHoMako6wfxB3VH0RBCIIptzcu6+1ovSsBQMDXewUgYOm56BCbwEJCZzh6T0HeyZ4CeQhe5CQwIKzA8dhj1LX3UokeDn97ne4hgSOyb9Th4s9hIsgAbwYakggAypDqpHoJYhV4A28ey0jIQEA6AjPKQ1NdpygG5ninZ3s9jrleYP+k2tLIf4V+pUBg/SxbzqGhMQ5zTdQzQMA0CvvOz1Ckx2A85CGwVnYZmRAQgJwCTp6eBs3R7QCGdBkB07CrsUjDFGjFewvzkANiSvYuFVDhzKqscCVXSMeXb+o0vFLwSGD2d4GUEPiFtsblm6h2LmGmiLbkJAA3OPpYxxSIHgNNNkBeBM03IlQDuxBQlICdnIABWFv8lZosuOBghcz0YDuLFx1AKJujvTifIwaknK8eDMC4IY77Rm8tIVwEaQ93CekkydPXr9+XesoAIAr3B6y9Y3vhJSTkzN69OiTJ09qGQS2bAA1YY/TL44TUmVl5bRp08LCwrQORF0K3iIH4Am63zhlL6ATT/jFfk0I4TohLVmyJCYmpm3btloH4knYRpniaF1gHQEogddedkeOHPnhhx++/fbbiRMnSvwsMjKSvkhLS3NtRuGOv7p7+mMyyfmxa0wWE1eEZYSZBw40N5noJ6bcXKL0vJx16dIlbQOwIsYjFprE6lBk1de4upUqIsU3VEumexuVh6bgzvStSvjPNZubG96qlQt/KE3DTdqjq9hzuExIxcXFc+bMWbVqVY2/zM7O9lwY4eEeX+kenUXz5s3F6auwLHIwEobobjz3KkCeDU4Q5EyftSKy5X6E0lNwZ/qO/rbGabo2U8tdDOTgJiElJydv2bKFEBIYGNi3b9/27dvn5+fn5+cXFhaePXu2RYsWYmUIZBGEu1UiNDcB6A+f989xk5BGjRo1YMAAQoiPj8/hw4evXbuWkpJCCLl8+XJGRkbdunV5Skh8bisAAB7FTUJq3bp169at6evo6Gjx84kTJz7//PNPPvmkqtGgVgGgPux3esdxLzsuOeo1hz3NO2G9O+JU/1IUozyRERFah1ADbmpIjqxZs0brEBRiO9acJ3YztBaCR+kyN+hyoZiEGlJN9HfDms4WB9ghZ7tiattjKhhAQgIAJjiVG1w4qcJ5GA+QkGTD1gzALMV3T+zvWuD+GpKqsI2CI85enNPBtqTC9Ujph3hZlSGuj/IPNSQA74P2K5GOi4LD5UJCAtAUI0cNRsLgjo7zmRaQkABUZ9vWhIOayI2iMOXmMlSSLETCQgzOQEJSlNWRRewyLghEEO57OAqOQd6JzfVOQ9I8MG0Lx2rPBS2gUwNjcGFWx3CYs8tDHeRc3o+4u5tKR1BD8hiXN1ls67rE4Gp1+6Zvh01kGCKLHVyVORKSB7DZLANQI5mbrpds4S4nbDbLh8GQbKDJDgBUwf4B0f0I2VxGNqOyBzUkAHDMQyP88nOIBDWhhsQk7K6ge9jIwQZqSAzAnglswpYJ6kINSVPY4UEHsBmDQpCQAIBtSHheA012AArBtXqmYHVwCDUkAEVhrA2mICdxBQkJAMA9SHsKQZMdAAAwAQkJAACYgCY7AE9CYw6AbKghAQAAE5CQAACACUhIACAJT/YCtSAhAXgMjsgAzkBCAoCaYNQDUAV62QGAM5zKTEhj4AwkJACQB9kFPAxNdgAAwAQkJAAAYALHCamgoGDPnj2HDx/WOhC5Bg4cqHUI90E80liLh7AXEuKRxlo87OP1GlJGRsaMGTN69uyZl5cXEBCwceNGo5Hj5Aq6gmstAC7hMiFVV1fPmDFj6dKl0dHRhJBnnnlm586dgwYN0jouAABwnUHg8Gxuz549q1at+uabb6R/FhkZqU48AABcyM7O1joEKVwmpM2bN//nP/8JCgr67rvvfH19k5KSxo8fr3VQAADgFi6vu+Tk5KSnp3fs2PHUqVObNm1avXp1Zmam1kEBAIBbuElIycnJUVFRUVFRvXv3btmyZYsWLeLj4wkhkZGRMTExO3bs0DpAAABwCzedGkaNGjVgwABCiI+PT1FRkeVXPj4+GgUFAACK4aaG1Lp16x49evTo0SM6OvqJJ564efPmvn37CCEFBQUHDhwYMmSI1gECAIBbuOzUQAg5duzY9OnTGzdunJOT88orryQlJWkdEQAAuIXXhAQAADrDTZMdAADoGxISAAAwwWfu3Llax6CYixcv/vDDD1VVVQ0bNrT7g4KCgkOHDl27dq158+YsxEOdPHnSx8cnMDBQ83hycnKOHz9+69atJk2aeDoYZ2PTPACVC4e7jUflnUtOSCaT6dixY+Xl5WFhYeqEZCszM7Nly5ZazV06AA33d0f0k5C2bt36+uuvV1RUfPLJJ0VFRd27d7f6QUZGxrhx48rLy3fs2PHdd98NHz7cYDBoGA+Vk5MTHx/fuXPn1q1bey4YOfEkJyd/9NFHZWVlmzdv3rZt25AhQ3x9VborQGZZaRiAyoXD3caj8s4lJ6T169fPnj27oqJi48aN586de+KJJzwaj10rV65ctmzZuHHj1J91jQFouL9LEXShqqrq0UcfPX/+vCAIN27ceOSRR3Jzc61+0L1798OHD9O3gwcP3rFjh4bxUBUVFUOHDu3Xr196errngpETz5kzZx566KHCwkL6dsiQIV9//bVHQ5Ifm+YBqFw43G08Ku9cckKqrq7u0KED/cGtW7c6dOhw5swZj4ZkpbCw8K233nr00Ud79eql5nxlBqDh/i5NJ9eQDhw4EBwc3LZtW0JIaGhonz59srKyLH+wf//+Zs2a0dHBCSHbt2/36OjgNcZDLVmyJCYmhv7Mo2qMJzg4eM2aNSEhIfRteHj4lStXPB2VzNg0D0DlwuFu41F555ITEiHEbDbXqlWLEFK7dm2DwVBRUeHRkKwsXbo0NDT0vffeU3Om8gPQcH+XppOEVFRU1K5dO/FtYGCg1aC2RUVFDzzwwOzZszt16hQVFbV27Vpt4yGEHDly5IcffpgyZYpHI5EZT9OmTXv06EFf5+Xl7d27NyYmRoXA5MSmeQAqFw53G4/KO5eckIxG49y5c5OSkpYtW5aYmEgbNj0dlaU5c+ZMnz69Tp06as5UfgAa7u/SdJKQqqurLdusfXx8hPvvr1J5PNYa4ykuLp4zZ86SJUs8F4NT8YiuXr06duzYpKSkDh06sBab5gGoUzjcbTzqD3YsZ5UdO3asdu3aDRs2DA4OvnDhQmlpqUdDsqL580JlBqD+/i6N44RkOdyqv7+/2WwWv6qurrYa4E6F8VidimfRokXt27fPz8/PyMgoLCw8e/as4tUCp+KhTp06NWzYsBdffFHNkS9kxqZ5AKoVDgsbj1PxqD/YcY0h7dmz5/jx45s2bUpMTFyzZg0hZN26dR4NiUea7O/SGOhW4SrL4VYFQTh9+rT4VVFRkVUrdoMGDSzfeuKQ51Q8YWFh165dS0lJIYRcvnw5IyOjbt26yj5R0Kl4CCEHDx6cOnXq/Pnzn376aQXDqFGjRo1qjE3zANQsnBrjUWHjcSoeFXYuZ0MqKiqKjIwUI2nZsuXFixc9HRVftNrfa6BRZwqFVVdX9+rVa//+/YIgnD9//uGHH7527ZogCCdOnLh8+bIgCBUVFdHR0Xv37hUE4caNG7179z506JCG8ViaMGGCpztK1RhPfn7+o48+unfv3op7qqqqPBpSjbGphrXC4W7jUXnnkhPSmTNnHn744V9//VUQhFu3bg0ePPibb77xaEh27d+/X6tednYDYGF/l6aThCQIwqFDh3r06PHSSy9FRUWJvU7HjBkjdmc8evRov3794uPjo6KiVqxYoXk8IhWOKTXGs2DBgoj7zZs3z9MhScemJtYKh6+NR1B955IT0pdffhkVFUV/8I9//EOFkGyxlpAY2d8l6G1w1dLS0lq1aklc0CsrK/P391ftKkWN8aiMtXgsaR6b5gFY4S4elXeuGkMym8137twJCAjAI9N4obeEBAAAnGLl5AsAALwcEhIAADABCQkAAJiAhAQAAExAQgIAACYgIQEAABOQkAAAgAkcj2UH4CGfffbZDz/8YPt5YGDgokWLXnvttQkTJnjicQZjxoyZNWuWo0ccJSQkvPfee55+OCyAhlBDArBWv379xo0bN27cuEGDBrt3766oqKBvGzVqRAiprKz0xO3kn332WZMmTSQeuDdlypRp06YpPl8AdmCkBgCHSktLH3300eXLlz/55JMenVFZWVn//v1TUlKkK0DDhg2bMGHCM88849FgALSCGhKAcyZOnHjixAn6YteuXaNHj46KikpISMjLy9u8efOAAQO6du26YMEC+uPy8vLFixf36dOnS5cuSUlJeXl5dqe5devWv/zlL2I22rNnT1xcXFRU1JNPPrly5UrxZ7GxsRs3bvTs4gFoBwkJwDkZGRk3btygL+bOnfvCCy8sX778zp07L7744s6dO5OTk995553PP/98586dhJA33nhj//79H3zwwdatW8PCwkaOHFlQUGA7zd27d3fv3p2+zsvLS0pKSkhIyMjIeOedd9auXfvNN9/Qr7p3737ixAm7UwDQASQkANeNHTs2Nja2R48eL7744o0bN5YsWdKjR4/hw4c/8sgjR48ePXv27O7du5cuXfrYY481a9Zs3rx5DRo0SE1NtZ3OoUOHHn74Yfr6t99+MxqNjz/+eN26dfv3779u3Trxq3bt2hmNxuPHj6u3hAAqQi87ANe1bNmSvqhVq1bt2rXr1q1L39arV89sNp8/f54Q8sknn4i/Ly0ttX3ceGVlZWVlZVBQEH3bq1ev9u3bx8TEPPLIIz169IiJiRGfBms0GgMCAkpKSjy6UABaQUIC8JTq6mo/P79u3bqJn3Tr1q1p06bSf+Xj4/PNN99kZGSkp6f/61//WrFixfTp08ePH0+/ZefxSACKQ0IC8JQGDRpUVlb27ds3LCyMfpKRkVG7dm2rn/n5+RmNxtLSUvr2woUL586de+aZZ/r3708IWbBgwdq1a8WEVFZWFhgYqNYSAKgKZ1sAntK3b9/mzZvPnDmTJps9e/ZMnDixsLDQ9pcPP/wwbd8jhNy4cePNN988fPgwIcRsNufn57dp04Z+deHCBbPZLL4F0BnUkAA8xWg0fvrpp6+//nrXrl39/PwIIdOnTx8wYIDtL/v165eVlUVfR0dHJyUlvfLKK35+flVVVREREcuXL6dfHTt2LDw8PDw8XLVFAFATbowF8LjKysrCwsKwsDBHV4AKCgr69u2bnp4uXmEym83Xr18PDg4OCAgQf5aYmDho0KDRo0erETSA6tBkB+Bxfn5+jRs3luiPEBoaOmbMmC+//FL8xGg0Nm7c2DIb5eTkXLx4MbDF1QMAAABYSURBVD4+3rOxAmgHCQmACa+99lp6evr169cd/eCDDz5ITk6mTX8AuoQmOwBWlJeX+/r6+vj42P22tLS0Tp06KocEoCYkJAAAYAKa7AAAgAlISAAAwIT/B/GqRAuL9ZMhAAAAAElFTkSuQmCC"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function plotERPComparisonMultipleSensors(sensor_names, condition1, condition2, condition1_name, condition2_name)\n",
    "    % Function to plot ERP comparison for multiple sensors\n",
    "    % Inputs:\n",
    "    %   - sensor_names: a cell array of sensor names (e.g., {'AG183', 'AG147'})\n",
    "    %   - condition1: structure containing the ERP data for the first condition (e.g., avgALTL)\n",
    "    %   - condition2: structure containing the ERP data for the second condition (e.g., avgARTL)\n",
    "    %   - condition1_name: name of the first condition (e.g., 'ALTL')\n",
    "    %   - condition2_name: name of the second condition (e.g., 'ARTL')\n",
    "\n",
    "    for i = 1:length(sensor_names)\n",
    "        sensor_name = sensor_names{i};\n",
    "        \n",
    "        % Find the index of the sensor in the layout\n",
    "        sensor_idx = find(strcmp(condition1.label, sensor_name));\n",
    "        \n",
    "        if isempty(sensor_idx)\n",
    "            warning(['Sensor ' sensor_name ' not found in the data. Skipping...']);\n",
    "            continue;\n",
    "        end\n",
    "\n",
    "        % Extract time and data for the sensor from both conditions\n",
    "        time = condition1.time; % Assuming both conditions have the same time vector\n",
    "        data_condition1 = condition1.avg(sensor_idx, :);\n",
    "        data_condition2 = condition2.avg(sensor_idx, :);\n",
    "\n",
    "        % Plot both conditions for the same sensor\n",
    "        figure;\n",
    "        plot(time, data_condition1, 'g', 'LineWidth', 2); % Plot condition1 in green\n",
    "        hold on;\n",
    "        plot(time, data_condition2, 'r', 'LineWidth', 2); % Plot condition2 in red\n",
    "\n",
    "        % Add labels and legend\n",
    "        xlabel('Time (s)');\n",
    "        ylabel('Amplitude');\n",
    "        legend(condition1_name, condition2_name);\n",
    "        title(['Comparison of Conditions for Sensor: ' sensor_name]);\n",
    "\n",
    "        % Add grid for better visualization\n",
    "        grid on;\n",
    "    end\n",
    "end\n",
    "\n",
    "\n",
    "sensor_names = {'AG147', 'AG183'};  % List of sensors\n",
    "plotERPComparisonMultipleSensors(sensor_names, avgALTL, avgARTL, 'ALTL', 'ARTL');\n",
    "\n",
    "sensor_names = {'AG147', 'AG183'};  % List of sensors\n",
    "plotERPComparisonMultipleSensors(sensor_names, avgALTR, avgARTR, 'ALTR', 'ARTR');"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "5f7d18aecb714ff1"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Let us do the same for the ARTR and ALTR and plot the ERP's.",
   "id": "145cc5e6e5b41f57"
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0f49b28b-dc6e-4add-a7f7-0f8daf7470e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the input is timelock data with 1 channels and 1700 timebins\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: sampleinfo in the configuration is inconsistent with the actual dataWarning: reconstructing sampleinfo by assuming that the trials are consecutive segments of a continuous recording"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "constructing single trial from \"avg\"\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "Default cfg.pad='maxperlen' can run slowly. Consider using cfg.pad='nextpow2' for more efficient FFT computation.\n",
      "processing trials\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: output frequencies are different from input frequencies, multiples of the same bin were requested but not given"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trial 1, frequency 11 (14.12 Hz), 1 tapers\n",
      "the call to \"ft_freqanalysis\" took 1 seconds\n",
      "the input is timelock data with 1 channels and 1700 timebins\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: sampleinfo in the configuration is inconsistent with the actual dataWarning: reconstructing sampleinfo by assuming that the trials are consecutive segments of a continuous recording"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "constructing single trial from \"avg\"\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "Default cfg.pad='maxperlen' can run slowly. Consider using cfg.pad='nextpow2' for more efficient FFT computation.\n",
      "processing trials\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: output frequencies are different from input frequencies, multiples of the same bin were requested but not given"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trial 1, frequency 11 (14.12 Hz), 1 tapers\n",
      "the call to \"ft_freqanalysis\" took 1 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the input is timelock data with 1 channels and 1700 timebins\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: sampleinfo in the configuration is inconsistent with the actual dataWarning: reconstructing sampleinfo by assuming that the trials are consecutive segments of a continuous recording"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "constructing single trial from \"avg\"\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "Default cfg.pad='maxperlen' can run slowly. Consider using cfg.pad='nextpow2' for more efficient FFT computation.\n",
      "processing trials\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: output frequencies are different from input frequencies, multiples of the same bin were requested but not given"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trial 1, frequency 11 (14.12 Hz), 1 tapers\n",
      "the call to \"ft_freqanalysis\" took 1 seconds\n",
      "the input is timelock data with 1 channels and 1700 timebins\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: sampleinfo in the configuration is inconsistent with the actual dataWarning: reconstructing sampleinfo by assuming that the trials are consecutive segments of a continuous recording"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "constructing single trial from \"avg\"\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "Default cfg.pad='maxperlen' can run slowly. Consider using cfg.pad='nextpow2' for more efficient FFT computation.\n",
      "processing trials\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: output frequencies are different from input frequencies, multiples of the same bin were requested but not given"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trial 1, frequency 11 (14.12 Hz), 1 tapers\n",
      "the call to \"ft_freqanalysis\" took 1 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the input is timelock data with 1 channels and 1700 timebins\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: sampleinfo in the configuration is inconsistent with the actual dataWarning: reconstructing sampleinfo by assuming that the trials are consecutive segments of a continuous recording"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "constructing single trial from \"avg\"\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "Default cfg.pad='maxperlen' can run slowly. Consider using cfg.pad='nextpow2' for more efficient FFT computation.\n",
      "processing trials\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: output frequencies are different from input frequencies, multiples of the same bin were requested but not given"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trial 1, frequency 11 (14.12 Hz), 1 tapers\n",
      "the call to \"ft_freqanalysis\" took 1 seconds\n",
      "the input is timelock data with 1 channels and 1700 timebins\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: sampleinfo in the configuration is inconsistent with the actual dataWarning: reconstructing sampleinfo by assuming that the trials are consecutive segments of a continuous recording"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "constructing single trial from \"avg\"\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "Default cfg.pad='maxperlen' can run slowly. Consider using cfg.pad='nextpow2' for more efficient FFT computation.\n",
      "processing trials\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: output frequencies are different from input frequencies, multiples of the same bin were requested but not given"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trial 1, frequency 11 (14.12 Hz), 1 tapers\n",
      "the call to \"ft_freqanalysis\" took 1 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "the input is timelock data with 1 channels and 1700 timebins\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: sampleinfo in the configuration is inconsistent with the actual dataWarning: reconstructing sampleinfo by assuming that the trials are consecutive segments of a continuous recording"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "constructing single trial from \"avg\"\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "Default cfg.pad='maxperlen' can run slowly. Consider using cfg.pad='nextpow2' for more efficient FFT computation.\n",
      "processing trials\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: output frequencies are different from input frequencies, multiples of the same bin were requested but not given"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trial 1, frequency 11 (14.12 Hz), 1 tapers\n",
      "the call to \"ft_freqanalysis\" took 1 seconds\n",
      "the input is timelock data with 1 channels and 1700 timebins\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: sampleinfo in the configuration is inconsistent with the actual dataWarning: reconstructing sampleinfo by assuming that the trials are consecutive segments of a continuous recording"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "constructing single trial from \"avg\"\n",
      "the call to \"ft_selectdata\" took 0 seconds\n",
      "Default cfg.pad='maxperlen' can run slowly. Consider using cfg.pad='nextpow2' for more efficient FFT computation.\n",
      "processing trials\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: output frequencies are different from input frequencies, multiples of the same bin were requested but not given"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trial 1, frequency 11 (14.12 Hz), 1 tapers\n",
      "the call to \"ft_freqanalysis\" took 1 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function plotAlphaPowerForTwoConditions(sensor_names, condition1, condition2, condition1_name, condition2_name)\n",
    "    % Function to plot alpha power (8-14 Hz) for multiple sensors across two conditions\n",
    "    % Inputs:\n",
    "    %   - sensor_names: a cell array of sensor names (e.g., {'AG183', 'AG147'})\n",
    "    %   - condition1: structure containing the data for the first condition (e.g., avgALTL)\n",
    "    %   - condition2: structure containing the data for the second condition (e.g., avgARTL)\n",
    "    %   - condition1_name: name of the first condition (e.g., 'ALTL')\n",
    "    %   - condition2_name: name of the second condition (e.g., 'ARTL')\n",
    "\n",
    "    for i = 1:length(sensor_names)\n",
    "        sensor_name = sensor_names{i};\n",
    "        \n",
    "        % Find the index of the sensor in the layout\n",
    "        sensor_idx = find(strcmp(condition1.label, sensor_name));\n",
    "        \n",
    "        if isempty(sensor_idx)\n",
    "            warning(['Sensor ' sensor_name ' not found in the data. Skipping...']);\n",
    "            continue;\n",
    "        end\n",
    "\n",
    "        % Select data for that sensor for both conditions\n",
    "        cfg = [];\n",
    "        cfg.channel = {sensor_name};  % Use only the specific sensor\n",
    "        condition1_sensor = ft_selectdata(cfg, condition1);\n",
    "        condition2_sensor = ft_selectdata(cfg, condition2);\n",
    "\n",
    "        % Perform time-frequency analysis for condition1\n",
    "        cfg = [];\n",
    "        cfg.method     = 'mtmconvol';    % Time-frequency analysis using multitapers\n",
    "        cfg.foi        = 8:0.1:14;      % Frequencies of interest (alpha band 8-14 Hz)\n",
    "        cfg.t_ftimwin  = 3 ./ cfg.foi;   % Time window length\n",
    "        cfg.tapsmofrq  = 2.5;           % Smoothing in frequency domain\n",
    "        cfg.toi        = 'all';         % Time of interest (use all time points)\n",
    "        cfg.output     = 'pow';         % Output power spectrum\n",
    "\n",
    "        % Perform time-frequency analysis for both conditions\n",
    "        freq_condition1_sensor = ft_freqanalysis(cfg, condition1_sensor);\n",
    "        freq_condition2_sensor = ft_freqanalysis(cfg, condition2_sensor);\n",
    "\n",
    "        % Extract the alpha band power (8-14 Hz) and ignore NaN values\n",
    "        alpha_band_idx = find(freq_condition1_sensor.freq >= 8 & freq_condition1_sensor.freq <= 14);\n",
    "        \n",
    "        if isempty(alpha_band_idx)\n",
    "            error('Alpha band (8-14 Hz) not found in the frequency range.');\n",
    "        end\n",
    "\n",
    "        % Calculate the mean alpha power over time for both conditions, ignoring NaNs\n",
    "        alpha_power_condition1 = nanmean(freq_condition1_sensor.powspctrm(1, alpha_band_idx, :), 2);\n",
    "        alpha_power_condition1 = squeeze(alpha_power_condition1);  % Convert to 1D\n",
    "        \n",
    "        alpha_power_condition2 = nanmean(freq_condition2_sensor.powspctrm(1, alpha_band_idx, :), 2);\n",
    "        alpha_power_condition2 = squeeze(alpha_power_condition2);  % Convert to 1D\n",
    "\n",
    "        % Plot both conditions' alpha power on the same plot\n",
    "        figure;\n",
    "        plot(freq_condition1_sensor.time, alpha_power_condition1, 'g', 'LineWidth', 2);  % Condition1 in green\n",
    "        hold on;\n",
    "        plot(freq_condition2_sensor.time, alpha_power_condition2, 'r', 'LineWidth', 2);  % Condition2 in red\n",
    "\n",
    "        % Add labels, legend, and title\n",
    "        xlabel('Time (s)');\n",
    "        ylabel('Alpha Power (8-14 Hz)');\n",
    "        legend(condition1_name, condition2_name);\n",
    "        title(['Alpha Power Over Time for Sensor: ' sensor_name]);\n",
    "\n",
    "        % Add grid for better visualization\n",
    "        grid on;\n",
    "    end\n",
    "end\n",
    "\n",
    "sensor_names = {'AG147', 'AG183'};  % List of sensor names\n",
    "plotAlphaPowerForTwoConditions(sensor_names, avgALTL, avgARTL, 'ALTL', 'ARTL');\n",
    "\n",
    "sensor_names = {'AG147', 'AG183'};  % List of sensor names\n",
    "plotAlphaPowerForTwoConditions(sensor_names, avgARTR, avgALTR, 'ARTR', 'ALTR');"
   ]
  }
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