09-Task-Evaluate_Data/03d2_EVAL_FRP_Permutation_sdiff_01-10_100Hz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9a9ba0-1fa4-467c-be6d-de2c92c50a30",
   "metadata": {},
   "source": [
    "# FRP Mass Univariate ANOVA pairwise permutation test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed68a5f5-cc8e-44cd-b916-6c4c8f4a9809",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "774c436e-1ab4-477b-ba08-dcf2008f1774",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tqdm.notebook import tqdm\n",
    "from utils.utils import *\n",
    "from mne.stats import *\n",
    "import mne\n",
    "\n",
    "import scipy\n",
    "mne.set_log_level('CRITICAL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2bd4e487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'first_fixation_aoi_small',\n",
       "  'snippet_group': 'CalcDiff',\n",
       "  'snippet_numbers': [0, 10, 11, 12, 13, 14, 15, 30, 5, 54, 7, 8, 9],\n",
       "  'participants': ['003',\n",
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       "   '101',\n",
       "   '102',\n",
       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S191',\n",
       "   'confusing': 'Stimulus/S192'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'combined_approach_small',\n",
       "  'snippet_group': 'CalcDiff',\n",
       "  'snippet_numbers': [0, 10, 11, 12, 13, 14, 15, 30, 5, 54, 7, 8, 9],\n",
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       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S171',\n",
       "   'confusing': 'Stimulus/S172'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'first_fixation_aoi_small',\n",
       "  'snippet_group': 'CalcEqual',\n",
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       "  'conditional_stimuli': {'clean': 'Stimulus/S191',\n",
       "   'confusing': 'Stimulus/S192'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'combined_approach_small',\n",
       "  'snippet_group': 'CalcEqual',\n",
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       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S171',\n",
       "   'confusing': 'Stimulus/S172'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'longest_fixation',\n",
       "  'snippet_group': 'all',\n",
       "  'participants': ['003',\n",
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       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S 31',\n",
       "   'confusing': 'Stimulus/S 32'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'first_of_most_fixated',\n",
       "  'snippet_group': 'all',\n",
       "  'participants': ['003',\n",
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       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S 41',\n",
       "   'confusing': 'Stimulus/S 42'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'longest_refixation',\n",
       "  'snippet_group': 'all',\n",
       "  'participants': ['003',\n",
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       "  'conditional_stimuli': {'clean': 'Stimulus/S 51',\n",
       "   'confusing': 'Stimulus/S 52'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'combined_approach_no',\n",
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       "  'conditional_stimuli': {'clean': 'Stimulus/S 21',\n",
       "   'confusing': 'Stimulus/S 22'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'longest_fixation_aoi_no',\n",
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       "  'conditional_stimuli': {'clean': 'Stimulus/S 61',\n",
       "   'confusing': 'Stimulus/S 62'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'first_fixation_aoi_no',\n",
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       "  'conditional_stimuli': {'clean': 'Stimulus/S 71',\n",
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       "  'plot': False,\n",
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       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'longest_refixation_aoi_no',\n",
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       "  'conditional_stimuli': {'clean': 'Stimulus/S 81',\n",
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       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
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       "  'conditional_stimuli': {'clean': 'Stimulus/S171',\n",
       "   'confusing': 'Stimulus/S172'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'longest_fixation_aoi_small',\n",
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       "  'conditional_stimuli': {'clean': 'Stimulus/S181',\n",
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       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'first_fixation_aoi_small',\n",
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       "  'conditional_stimuli': {'clean': 'Stimulus/S191',\n",
       "   'confusing': 'Stimulus/S192'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'longest_refixation_aoi_small',\n",
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       "  'conditional_stimuli': {'clean': 'Stimulus/S201',\n",
       "   'confusing': 'Stimulus/S202'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'combined_approach_med',\n",
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       "  'conditional_stimuli': {'clean': 'Stimulus/S151',\n",
       "   'confusing': 'Stimulus/S152'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'longest_fixation_aoi_med',\n",
       "  'snippet_group': 'all',\n",
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       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S 91',\n",
       "   'confusing': 'Stimulus/S 92'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'first_fixation_aoi_med',\n",
       "  'snippet_group': 'all',\n",
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       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S101',\n",
       "   'confusing': 'Stimulus/S102'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'longest_refixation_aoi_med',\n",
       "  'snippet_group': 'all',\n",
       "  'participants': ['003',\n",
       "   '004',\n",
       "   '005',\n",
       "   '007',\n",
       "   '008',\n",
       "   '009',\n",
       "   '010',\n",
       "   '011',\n",
       "   '012',\n",
       "   '013',\n",
       "   '014',\n",
       "   '015',\n",
       "   '016',\n",
       "   '017',\n",
       "   '018',\n",
       "   '019',\n",
       "   '020',\n",
       "   '021',\n",
       "   '022',\n",
       "   '023',\n",
       "   '024',\n",
       "   '101',\n",
       "   '102',\n",
       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S111',\n",
       "   'confusing': 'Stimulus/S112'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'combined_approach_ml',\n",
       "  'snippet_group': 'all',\n",
       "  'participants': ['003',\n",
       "   '004',\n",
       "   '005',\n",
       "   '007',\n",
       "   '008',\n",
       "   '009',\n",
       "   '010',\n",
       "   '011',\n",
       "   '012',\n",
       "   '013',\n",
       "   '014',\n",
       "   '015',\n",
       "   '016',\n",
       "   '017',\n",
       "   '018',\n",
       "   '019',\n",
       "   '020',\n",
       "   '021',\n",
       "   '022',\n",
       "   '023',\n",
       "   '024',\n",
       "   '101',\n",
       "   '102',\n",
       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S211',\n",
       "   'confusing': 'Stimulus/S212'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'longest_fixation_aoi_ml',\n",
       "  'snippet_group': 'all',\n",
       "  'participants': ['003',\n",
       "   '004',\n",
       "   '005',\n",
       "   '007',\n",
       "   '008',\n",
       "   '009',\n",
       "   '010',\n",
       "   '011',\n",
       "   '012',\n",
       "   '013',\n",
       "   '014',\n",
       "   '015',\n",
       "   '016',\n",
       "   '017',\n",
       "   '018',\n",
       "   '019',\n",
       "   '020',\n",
       "   '021',\n",
       "   '022',\n",
       "   '023',\n",
       "   '024',\n",
       "   '101',\n",
       "   '102',\n",
       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S221',\n",
       "   'confusing': 'Stimulus/S222'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'first_fixation_aoi_ml',\n",
       "  'snippet_group': 'all',\n",
       "  'participants': ['003',\n",
       "   '004',\n",
       "   '005',\n",
       "   '007',\n",
       "   '008',\n",
       "   '009',\n",
       "   '010',\n",
       "   '011',\n",
       "   '012',\n",
       "   '013',\n",
       "   '014',\n",
       "   '015',\n",
       "   '016',\n",
       "   '017',\n",
       "   '018',\n",
       "   '019',\n",
       "   '020',\n",
       "   '021',\n",
       "   '022',\n",
       "   '023',\n",
       "   '024',\n",
       "   '101',\n",
       "   '102',\n",
       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S231',\n",
       "   'confusing': 'Stimulus/S232'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'longest_refixation_aoi_ml',\n",
       "  'snippet_group': 'all',\n",
       "  'participants': ['003',\n",
       "   '004',\n",
       "   '005',\n",
       "   '007',\n",
       "   '008',\n",
       "   '009',\n",
       "   '010',\n",
       "   '011',\n",
       "   '012',\n",
       "   '013',\n",
       "   '014',\n",
       "   '015',\n",
       "   '016',\n",
       "   '017',\n",
       "   '018',\n",
       "   '019',\n",
       "   '020',\n",
       "   '021',\n",
       "   '022',\n",
       "   '023',\n",
       "   '024',\n",
       "   '101',\n",
       "   '102',\n",
       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S241',\n",
       "   'confusing': 'Stimulus/S242'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'combined_approach_large',\n",
       "  'snippet_group': 'all',\n",
       "  'participants': ['003',\n",
       "   '004',\n",
       "   '005',\n",
       "   '007',\n",
       "   '008',\n",
       "   '009',\n",
       "   '010',\n",
       "   '011',\n",
       "   '012',\n",
       "   '013',\n",
       "   '014',\n",
       "   '015',\n",
       "   '016',\n",
       "   '017',\n",
       "   '018',\n",
       "   '019',\n",
       "   '020',\n",
       "   '021',\n",
       "   '022',\n",
       "   '023',\n",
       "   '024',\n",
       "   '101',\n",
       "   '102',\n",
       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S161',\n",
       "   'confusing': 'Stimulus/S162'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'longest_fixation_aoi_large',\n",
       "  'snippet_group': 'all',\n",
       "  'participants': ['003',\n",
       "   '004',\n",
       "   '005',\n",
       "   '007',\n",
       "   '008',\n",
       "   '009',\n",
       "   '010',\n",
       "   '011',\n",
       "   '012',\n",
       "   '013',\n",
       "   '014',\n",
       "   '015',\n",
       "   '016',\n",
       "   '017',\n",
       "   '018',\n",
       "   '019',\n",
       "   '020',\n",
       "   '021',\n",
       "   '022',\n",
       "   '023',\n",
       "   '024',\n",
       "   '101',\n",
       "   '102',\n",
       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S121',\n",
       "   'confusing': 'Stimulus/S122'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'first_fixation_aoi_large',\n",
       "  'snippet_group': 'all',\n",
       "  'participants': ['003',\n",
       "   '004',\n",
       "   '005',\n",
       "   '007',\n",
       "   '008',\n",
       "   '009',\n",
       "   '010',\n",
       "   '011',\n",
       "   '012',\n",
       "   '013',\n",
       "   '014',\n",
       "   '015',\n",
       "   '016',\n",
       "   '017',\n",
       "   '018',\n",
       "   '019',\n",
       "   '020',\n",
       "   '021',\n",
       "   '022',\n",
       "   '023',\n",
       "   '024',\n",
       "   '101',\n",
       "   '102',\n",
       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S131',\n",
       "   'confusing': 'Stimulus/S132'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'longest_refixation_aoi_large',\n",
       "  'snippet_group': 'all',\n",
       "  'participants': ['003',\n",
       "   '004',\n",
       "   '005',\n",
       "   '007',\n",
       "   '008',\n",
       "   '009',\n",
       "   '010',\n",
       "   '011',\n",
       "   '012',\n",
       "   '013',\n",
       "   '014',\n",
       "   '015',\n",
       "   '016',\n",
       "   '017',\n",
       "   '018',\n",
       "   '019',\n",
       "   '020',\n",
       "   '021',\n",
       "   '022',\n",
       "   '023',\n",
       "   '024',\n",
       "   '101',\n",
       "   '102',\n",
       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S141',\n",
       "   'confusing': 'Stimulus/S142'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]},\n",
       " {'epoch_interval': [-0.3, 1.0],\n",
       "  'erp_frp': 'first',\n",
       "  'snippet_group': 'all',\n",
       "  'participants': ['003',\n",
       "   '004',\n",
       "   '005',\n",
       "   '007',\n",
       "   '008',\n",
       "   '009',\n",
       "   '010',\n",
       "   '011',\n",
       "   '012',\n",
       "   '013',\n",
       "   '014',\n",
       "   '015',\n",
       "   '016',\n",
       "   '017',\n",
       "   '018',\n",
       "   '019',\n",
       "   '020',\n",
       "   '021',\n",
       "   '022',\n",
       "   '023',\n",
       "   '024',\n",
       "   '101',\n",
       "   '102',\n",
       "   '106'],\n",
       "  'conditional_stimuli': {'clean': 'Stimulus/S251',\n",
       "   'confusing': 'Stimulus/S252'},\n",
       "  'plot': False,\n",
       "  'correct_data_only': False,\n",
       "  'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]}]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# erp parameter combinations\n",
    "required_parameters = [ERP_PARAMETER_EPOCH_INTERVAL,\n",
    "                       ERP_PARAMETER_CORRECT_TRIALS_ONLY, ERP_PARAMETER_PARTICIPANTS, ERP_PARAMETER_ERP_FRP]\n",
    "with open(get_erp_parameters_path(False), 'r') as f:\n",
    "    all_frp_parameter_combinations = json.load(f)\n",
    "all_frp_parameter_combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5784a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "864ae46f682242cd97274d1917b95db1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch_interval': [-0.3, 1.0], 'erp_frp': 'first_fixation_aoi_small', 'snippet_group': 'all', 'participants': ['003', '004', '005', '007', '008', '009', '010', '011', '012', '013', '014', '015', '016', '017', '018', '019', '020', '021', '022', '023', '024', '101', '102', '106'], 'conditional_stimuli': {'clean': 'Stimulus/S191', 'confusing': 'Stimulus/S192'}, 'plot': False, 'correct_data_only': False, 'topomap_times': [0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.98]}\n",
      "faois_-300_1000_all\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f27d453cf8b4099a214627220940999",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "multiple channels\n",
      "\tsubjects x times (x electrodes) (24, 90, 23)\n",
      "\tTest greater\n",
      "\t\tPermutation count 1000.0\n",
      "Using a threshold of 1.713872\n",
      "stat_fun(H1): min=-2.483113 max=5.004061\n",
      "Running initial clustering …\n",
      "Found 18 clusters\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "767503780fc54d9bb346c128464742d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | Permuting : 0/999 [00:00<?,       ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\t P values [0.196 0.912 0.738 0.587 0.7   0.88  0.421 0.793 0.899 0.732 0.045 0.636\n",
      " 0.873 0.9   0.205 0.908 0.106 0.796]\n",
      "\t\t\t positive clusters [10]\n",
      "\tTest less\n",
      "\t\tPermutation count 1000.0\n",
      "Using a threshold of -1.713872\n",
      "stat_fun(H1): min=-2.483113 max=5.004061\n",
      "Running initial clustering …\n",
      "Found 2 clusters\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b37e25ae98e045a589898f0a06e43792",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | Permuting : 0/999 [00:00<?,       ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\t P values [0.724 0.876]\n",
      "\t\t\t positive clusters []\n",
      "(368, 900, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare data\n",
    "for frp_parameters in tqdm(all_frp_parameter_combinations[13:14]):\n",
    "    print(frp_parameters)\n",
    "    frp_parameters[ERP_PARAMETER_CORRECT_TRIALS_ONLY]=False\n",
    "    perform_cluster_permutation_test_diff(frp_parameters[ERP_PARAMETER_ERP_FRP], frp_parameters[ERP_PARAMETER_SNIPPET_GROUP] if ERP_PARAMETER_SNIPPET_GROUP in frp_parameters else SNIPPET_GROUP_ALL, frp_parameters[ERP_PARAMETER_CORRECT_TRIALS_ONLY],\n",
    "                                          frp_parameters[ERP_PARAMETER_EPOCH_INTERVAL], frp_parameters[ERP_PARAMETER_PARTICIPANTS], \n",
    "                                          subinterval=[0.1, 1.0], downsample_to=100, ignore_channels=ERP_PERM_CHANNELS_TO_IGNORE, each_channel=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}