| { |
| "cells": [ |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "import pandas as pd\n", |
| "import torch\n", |
| "import numpy as np\n", |
| "import os" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 11, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "df = pd.DataFrame()\n", |
| "\n", |
| "for file in os.listdir(\"./dataset/train\"):\n", |
| " df = pd.concat([df, pd.read_parquet(f\"./dataset/train/{file}\")])\n", |
| " \n", |
| "grouped_df = df.groupby('subject_id', group_keys=False)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 13, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Processing subject_id: 1\n", |
| "Finished processing subject_id: 1\n", |
| "Processing subject_id: 2\n", |
| "Finished processing subject_id: 2\n", |
| "Processing subject_id: 3\n", |
| "Finished processing subject_id: 3\n", |
| "Processing subject_id: 4\n", |
| "Finished processing subject_id: 4\n", |
| "Processing subject_id: 5\n", |
| "Finished processing subject_id: 5\n", |
| "Processing subject_id: 6\n", |
| "Finished processing subject_id: 6\n", |
| "Processing subject_id: 7\n", |
| "Finished processing subject_id: 7\n", |
| "Processing subject_id: 8\n", |
| "Finished processing subject_id: 8\n" |
| ] |
| } |
| ], |
| "source": [ |
| "for subject_id, group in grouped_df:\n", |
| " print(\"Processing subject_id: \", subject_id)\n", |
| " arr = np.array([np.array([y for y in x]) for x in group[\"EEG\"].values])\n", |
| " mean_each_channel = arr.mean(axis=2)\n", |
| " std_each_channel = arr.std(axis=2)\n", |
| " # Reshape mean and std to match arr dimensions for broadcasting\n", |
| " mean_each_channel = mean_each_channel[:, :, np.newaxis]\n", |
| " std_each_channel = std_each_channel[:, :, np.newaxis]\n", |
| " normalized_arr = (arr - mean_each_channel) / std_each_channel\n", |
| " \n", |
| " normalized_df = pd.DataFrame()\n", |
| " normalized_df[\"EEG\"] = normalized_arr.tolist()\n", |
| " normalized_df[\"subject_id\"] = subject_id\n", |
| " normalized_df[\"coco_id\"] = group[\"coco_id\"].values\n", |
| " normalized_df[\"curr_time\"] = group[\"curr_time\"].values\n", |
| " normalized_df[\"session\"] = group[\"session\"].values\n", |
| " normalized_df[\"trial\"] = group[\"trial\"].values\n", |
| " normalized_df[\"block\"] = group[\"block\"].values\n", |
| " normalized_df[\"73k_id\"] = group[\"73k_id\"].values\n", |
| " \n", |
| " normalized_df.to_parquet(f\"./dataset/normalized_subject/subject_{subject_id}.parquet\")\n", |
| " \n", |
| " print(\"Finished processing subject_id: \", subject_id)\n" |
| ] |
| } |
| ], |
| "metadata": { |
| "kernelspec": { |
| "display_name": ".venv", |
| "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": "3.12.8" |
| } |
| }, |
| "nbformat": 4, |
| "nbformat_minor": 2 |
| } |
|
|
