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fedepupfedepup
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updated Notebooks and added new example
1 parent 1548188 commit e573df1

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‎Notebooks/Augmentation_guide.ipynb

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‎Notebooks/Dataload_guide.ipynb

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"cell_type": "code",
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"execution_count": null,
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"id": "31f8ad6a",
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"outputs": [],
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"cell_type": "code",
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"id": "669a9877-adc2-4741-b291-2ff0d5a11396",
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"metadata": {},
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"id": "0fe92ca6",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>full_path</th>\n",
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" <th>file_name</th>\n",
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" <th>N_samples</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Simulated_EEG/1_10_1_1.pickle</td>\n",
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" <td>1_10_1_1.pickle</td>\n",
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" <td>15</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Simulated_EEG/1_10_2_1.pickle</td>\n",
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" <td>1_10_2_1.pickle</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Simulated_EEG/1_10_3_1.pickle</td>\n",
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" <td>1_10_3_1.pickle</td>\n",
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" <td>7</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Simulated_EEG/1_10_4_1.pickle</td>\n",
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" <td>1_10_4_1.pickle</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Simulated_EEG/1_10_5_1.pickle</td>\n",
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" <td>1_10_5_1.pickle</td>\n",
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" <td>6</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" full_path file_name N_samples\n",
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"0 Simulated_EEG/1_10_1_1.pickle 1_10_1_1.pickle 15\n",
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"1 Simulated_EEG/1_10_2_1.pickle 1_10_2_1.pickle 5\n",
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"2 Simulated_EEG/1_10_3_1.pickle 1_10_3_1.pickle 7\n",
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"3 Simulated_EEG/1_10_4_1.pickle 1_10_4_1.pickle 12\n",
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"4 Simulated_EEG/1_10_5_1.pickle 1_10_5_1.pickle 6"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"# Define partition spec\n",
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"eegpath = 'Simulated_EEG'\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aabf2f21",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"train ratio: 0.80\n",
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"validation ratio: 0.10\n",
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"test ratio: 0.10\n",
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"\n",
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"train labels ratio: 1=0.239, 2=0.244, 3=0.252, 4=0.265, \n",
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"val labels ratio: 1=0.239, 2=0.244, 3=0.252, 4=0.265, \n",
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"test labels ratio: 1=0.239, 2=0.244, 3=0.252, 4=0.265, \n",
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"\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# for stratified split we need to create an array with the labels\n",
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"# associated to each eeg file\n",
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"text": [
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"train ratio: 0.80\n",
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"validation ratio: 0.10\n",
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"test ratio: 0.10\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"EEGsplit2 = dl.GetEEGSplitTable(EEGlen, \n",
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" test_ratio=0.1, val_ratio=0.1,\n",
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"text": [
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"torch.Size([2, 256])\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"dataset_pretrain = dl.EEGDataset(EEGlen, EEGsplit, \n",
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" [freq, window, overlap], # split parameters must be given as list\n",
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"torch.Size([2, 256]) 1\n"
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"outputs": [],
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"source": [
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"dataset_finetune = dl.EEGDataset(EEGlen, EEGsplit, \n",
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" [freq, window, overlap], # split parameters must be given as list\n",
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"cell_type": "code",
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"outputs": [],
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"metadata": {},
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"outputs": [
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"text": [
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"torch.Size([16, 2, 256])\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"Final_Dataloader = DataLoader( dataset = dataset_pretrain, \n",
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" batch_size= 16, \n",

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