added notebook EDA
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weigmi87303 committed
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commit
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1 file changed
+182
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "7440a5b3",
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"metadata": {},
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"outputs": [],
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"source": [
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"import h5py\n",
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"import os\n",
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"import warnings\n",
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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\n",
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"from pathlib import 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": 2,
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"id": "2401aaef",
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"metadata": {},
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"outputs": [],
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"source": [
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"file_path = \"adabase-public-0001-v_0_0_2.h5py\""
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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": 9,
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"id": "46280999",
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"metadata": {},
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"outputs": [],
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"source": [
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"SKT_SR = 100\n",
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"ECG_SR = 500\n",
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"RSP_SR = 250\n",
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"EMG_SR = 1000\n",
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"EDA_SR = 500\n",
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"EYE_SR = 250"
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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": 5,
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"id": "e23eb552",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_signals = pd.read_hdf(file_path, \"SIGNALS\", mode=\"r\")\n"
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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": 7,
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"id": "f30b8814",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(9647884, 4)"
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]
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},
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"execution_count": 7,
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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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"source": [
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"# df_signals_ecg = pd.read_hdf(file_path, \"SIGNALS\", mode=\"r\", columns=[\"STUDY\",\"LEVEL\", \"PHASE\", 'RAW_ECG_I'])\n",
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"df_signals_ecg = df_signals[[\"STUDY\",\"LEVEL\", \"PHASE\", 'RAW_ECG_I']]\n",
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"df_signals_ecg.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": 13,
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"id": "ee80fd79",
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"metadata": {},
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"outputs": [],
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"source": [
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"study_filter = df_signals[\"STUDY\"] == \"n-back\""
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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": 16,
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"id": "3ef29446",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.legend.Legend at 0x15a971bb650>"
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]
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},
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"execution_count": 16,
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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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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 1600x200 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1600x200 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots(figsize=(16, 2))\n",
|
||||
"# Set the number of seconds to plot\n",
|
||||
"seconds = 20\n",
|
||||
"# Get the ECG signal data\n",
|
||||
"ecg_signal = df_signals.loc[study_filter, \"RAW_ECG_I\"].dropna()\n",
|
||||
"# Set the x-axis limits to the number of samples in the specified time range\n",
|
||||
"num_samples = ECG_SR * seconds\n",
|
||||
"# Plot the ECG signal\n",
|
||||
"ax.plot(ecg_signal.index[:num_samples]/1000, ecg_signal[:num_samples]);\n",
|
||||
"ax.set_title(\"ECG I\");\n",
|
||||
"ax.set_xlabel('Seconds');\n",
|
||||
"# Set figure size with a 16:6 aspect ratio\n",
|
||||
"fig, ax = plt.subplots(figsize=(16, 2))\n",
|
||||
"# Set the number of seconds to plot\n",
|
||||
"start_second = 0\n",
|
||||
"end_second = 60*30\n",
|
||||
"# Get the EYE signal data - we replace inf with nan to get the original signal.␣\n",
|
||||
"\n",
|
||||
"eye_left_signal = df_signals.loc[study_filter, \"LEFT_PUPIL_DIAMETER\"].dropna()\n",
|
||||
"eye_right_signal = df_signals.loc[study_filter, \"RIGHT_PUPIL_DIAMETER\"].dropna()\n",
|
||||
"#eye_left_signal = df_signals.loc[:, \"LEFT_PUPIL_DIAMETER\"].replace([np.inf],␣\n",
|
||||
"\n",
|
||||
"#eye_right_signal = df_signals.loc[:, \"RIGHT_PUPIL_DIAMETER\"].replace([np.inf],␣\n",
|
||||
"\n",
|
||||
"# Set the x-axis limits to the number of samples in the specified time range\n",
|
||||
"num_samples_start = EYE_SR * start_second\n",
|
||||
"num_samples_end = EYE_SR * end_second\n",
|
||||
"ax.plot(eye_left_signal.index[num_samples_start:num_samples_end]/1000,eye_left_signal[num_samples_start:num_samples_end], label=\"Left\")\n",
|
||||
"ax.plot(eye_right_signal.index[num_samples_start:num_samples_end]/1000,eye_right_signal[num_samples_start:num_samples_end], label=\"Right\")\n",
|
||||
"ax.set_title(\"Pupil Dilation\")\n",
|
||||
"ax.set_xlabel('Seconds')\n",
|
||||
"ax.legend()\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.12.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in new issue
Block a user