167 lines
4.2 KiB
Plaintext
167 lines
4.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1014c5e0",
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"metadata": {},
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"source": [
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"Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e42f3011",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0a834496",
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"metadata": {},
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"outputs": [],
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"source": [
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"path =r\"C:\\Users\\micha\\FAUbox\\WS2526_Fahrsimulator_MSY (Celina Korzer)\\AU_dataset\\output_windowed.parquet\"\n",
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"df = pd.read_parquet(path=path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aa4759fa",
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"metadata": {},
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"outputs": [],
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"source": [
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"high_nback = df[\n",
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" (df[\"STUDY\"]==\"n-back\") &\n",
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" (df[\"LEVEL\"].isin([2, 3, 5, 6])) &\n",
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" (df[\"PHASE\"].isin([\"train\", \"test\"]))\n",
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"]\n",
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"high_nback.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a2aa0596",
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"metadata": {},
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"outputs": [],
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"source": [
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"low_all = df[\n",
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" ((df[\"PHASE\"] == \"baseline\") |\n",
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" ((df[\"STUDY\"] == \"n-back\") & (df[\"PHASE\"] != \"baseline\") & (df[\"LEVEL\"].isin([1,4]))))\n",
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"]\n",
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"print(low_all.shape)\n",
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"high_kdrive = df[\n",
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" (df[\"STUDY\"] == \"k-drive\") & (df[\"PHASE\"] != \"baseline\")\n",
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"]\n",
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"print(high_kdrive.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f7d446a1",
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"metadata": {},
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"outputs": [],
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"source": [
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"print((df.shape[0]==(high_kdrive.shape[0]+high_nback.shape[0]+low_all.shape[0])))\n",
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"print(df.shape[0])\n",
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"print((high_kdrive.shape[0]+high_nback.shape[0]+low_all.shape[0]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "474e144a",
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"metadata": {},
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"outputs": [],
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"source": [
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"high_all = pd.concat([high_nback, high_kdrive])\n",
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"high_all.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5dd585c2",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.dtypes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0bd39d9f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get all columns that start with 'AU'\n",
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"au_columns = [col for col in low_all.columns if col.startswith('AU')]\n",
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"\n",
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"# Calculate number of rows and columns for subplots\n",
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"n_cols = len(au_columns)\n",
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"n_rows = 4\n",
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"n_cols_subplot = 5\n",
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"\n",
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"# Create figure with subplots\n",
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"fig, axes = plt.subplots(n_rows, n_cols_subplot, figsize=(20, 16))\n",
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"axes = axes.flatten()\n",
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"fig.suptitle('Action Unit (AU) Distributions: Low vs High', fontsize=20, fontweight='bold', y=0.995)\n",
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"\n",
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"# Create histogram for each AU column\n",
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"for idx, col in enumerate(au_columns):\n",
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" ax = axes[idx]\n",
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" \n",
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" # Plot overlapping histograms\n",
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" ax.hist(low_all[col].dropna(), bins=30, alpha=0.6, color='blue', label='low_all', edgecolor='black')\n",
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" ax.hist(high_all[col].dropna(), bins=30, alpha=0.6, color='red', label='high_all', edgecolor='black')\n",
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" \n",
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" # Set title and labels\n",
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" ax.set_title(col, fontsize=10, fontweight='bold')\n",
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" ax.set_xlabel('Value', fontsize=8)\n",
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" ax.set_ylabel('Frequency', fontsize=8)\n",
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" ax.legend(fontsize=8)\n",
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" ax.grid(True, alpha=0.3)\n",
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"\n",
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"# Hide any unused subplots\n",
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"for idx in range(len(au_columns), len(axes)):\n",
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" axes[idx].set_visible(False)\n",
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"\n",
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"# Adjust layout\n",
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"plt.tight_layout()\n",
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"plt.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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