118 lines
5.0 KiB
Plaintext
118 lines
5.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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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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"Reading SB\n",
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"Length of SB: 16559\n",
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"Reading AFIB\n",
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"Length of AFIB: 9839\n",
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"Reading GSVT\n",
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"Length of GSVT: 948\n",
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"Reading SR\n",
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"Length of SR: 9720\n"
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]
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}
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],
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"source": [
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"import pickle\n",
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"# read pickle files and check len and print first record and first record keys\n",
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"\n",
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"path = \"C:/Studium/dsa/data\"\n",
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"\n",
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"categories_dict = {\n",
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"'SB': [426177001],\n",
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"'AFIB': [164889003, 164890007],\n",
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"'GSVT': [426761007, 713422000, 233896004, 233897008, 713422000],\n",
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"'SR': [426783006, 427393009]\n",
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"}\n",
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"\n",
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"\n",
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"data = {}\n",
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"for cat_name in categories_dict.keys():\n",
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" print(f\"Reading {cat_name}\")\n",
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" with open(f'{path}/{cat_name}.pkl', 'rb') as f:\n",
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" records = pickle.load(f)\n",
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" data[cat_name] = records\n",
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" print(f\"Length of {cat_name}: {len(records)}\")"
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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": 31,
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"metadata": {},
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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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"Keys of first record of SB: dict_keys(['record_name', 'n_sig', 'fs', 'counter_freq', 'base_counter', 'sig_len', 'base_time', 'base_date', 'comments', 'sig_name', 'p_signal', 'd_signal', 'e_p_signal', 'e_d_signal', 'file_name', 'fmt', 'samps_per_frame', 'skew', 'byte_offset', 'adc_gain', 'baseline', 'units', 'adc_res', 'adc_zero', 'init_value', 'checksum', 'block_size'])\n",
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"Number of records with p_signal: 9720\n",
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"Number of records with e_p_signal: 0\n",
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"Number of records with d_signal: 0\n",
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"Number of records with e_d_signal: 0\n",
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"{'record_name': 'JS00002', 'n_sig': 12, 'fs': 500, 'counter_freq': None, 'base_counter': None, 'sig_len': 5000, 'base_time': None, 'base_date': None, 'comments': ['Age: 59', 'Sex: Female', 'Dx: 426177001,164934002', 'Rx: Unknown', 'Hx: Unknown', 'Sx: Unknown'], 'sig_name': ['I', 'II', 'III', 'aVR', 'aVL', 'aVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6'], 'p_signal': array([[-0.01 , 0.01 , 0.02 , ..., 0.054, 0.049, 0. ],\n",
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" [-0.024, -0.02 , 0.005, ..., 0.034, 0.034, -0.015],\n",
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" [-0.02 , -0.02 , 0. , ..., 0.034, 0.034, -0.01 ],\n",
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" ...,\n",
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" [ 0.015, 0.01 , -0.005, ..., -0.015, -0.02 , 0.005],\n",
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" [ 0.01 , 0.01 , 0. , ..., -0.02 , -0.024, 0. ],\n",
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" [ 0.01 , 0.01 , 0. , ..., -0.024, -0.029, -0.005]]), 'd_signal': None, 'e_p_signal': None, 'e_d_signal': None, 'file_name': ['JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat'], 'fmt': ['16', '16', '16', '16', '16', '16', '16', '16', '16', '16', '16', '16'], 'samps_per_frame': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'skew': [None, None, None, None, None, None, None, None, None, None, None, None], 'byte_offset': [24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24], 'adc_gain': [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0], 'baseline': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'units': ['mV', 'mV', 'mV', 'mV', 'mV', 'mV', 'mV', 'mV', 'mV', 'mV', 'mV', 'mV'], 'adc_res': [16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16], 'adc_zero': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'init_value': [-10, 10, 20, 0, -15, 15, -10, -54, 63, 54, 49, 0], 'checksum': [12346, 26962, 14528, -13537, -6991, 14569, -383, 16468, -29512, -7548, -22267, 31542], 'block_size': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}\n"
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]
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}
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],
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"source": [
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"# print keys of first record\n",
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"print(f\"Keys of first record of SB: {data['SB'][0].__dict__.keys()}\")\n",
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"\n",
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"counter_p = 0\n",
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"counter_e_p = 0\n",
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"counter_d = 0\n",
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"counter_e_d = 0\n",
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"for record in data['SR']:\n",
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" if record.p_signal is not None:\n",
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" counter_p += 1\n",
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" if record.e_p_signal is not None:\n",
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" counter_e_p += 1\n",
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" if record.d_signal is not None:\n",
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" counter_d += 1\n",
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"\n",
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"print(f\"Number of records with p_signal: {counter_p}\")\n",
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"print(f\"Number of records with e_p_signal: {counter_e_p}\")\n",
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"print(f\"Number of records with d_signal: {counter_d}\")\n",
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"print(f\"Number of records with e_d_signal: {counter_e_d}\")\n",
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"\n",
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"\n",
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"print(data['SB'][0].__dict__)"
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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": "Python 3",
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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": "3.10.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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