marques
8ee5980906
- Created a new module `utils/__init__.py` to consolidate utility imports. - Added `event_map.py` for mapping apnea event types to numerical values and colors. - Implemented various filtering functions in `filter_func.py`, including Butterworth, Bessel, downsampling, and notch filters. - Developed `operation_tools.py` for dataset configuration loading, event mask generation, and signal processing utilities. - Introduced `split_method.py` for segmenting data based on movement and amplitude criteria. - Added `statistics_metrics.py` for calculating amplitude metrics and generating confusion matrices. - Included a new Excel file for additional data storage.
233 lines
9.8 KiB
Python
233 lines
9.8 KiB
Python
import multiprocessing
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import sys
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from pathlib import Path
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import os
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import numpy as np
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os.environ['DISPLAY'] = "localhost:10.0"
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sys.path.append(str(Path(__file__).resolve().parent.parent))
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project_root_path = Path(__file__).resolve().parent.parent
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import utils
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import signal_method
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import draw_tools
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import shutil
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def build_HYS_dataset_segment(samp_id, show=False, draw_segment=False, verbose=True, multi_p=None, multi_task_id=None):
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signal_path = list((org_signal_root_path / f"{samp_id}").glob("OrgBCG_Sync_*.txt"))
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if not signal_path:
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raise FileNotFoundError(f"OrgBCG_Sync file not found for sample ID: {samp_id}")
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signal_path = signal_path[0]
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if verbose:
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print(f"Processing OrgBCG_Sync signal file: {signal_path}")
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mask_excel_path = Path(mask_path, f"{samp_id}", f"{samp_id}_Processed_Labels.csv")
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if verbose:
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print(f"mask_excel_path: {mask_excel_path}")
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event_mask, event_list = utils.read_mask_execl(mask_excel_path)
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bcg_signal_raw, signal_length, signal_fs, signal_second = utils.read_signal_txt(signal_path, dtype=float, verbose=verbose)
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bcg_signal_notch, resp_signal, resp_fs, bcg_signal, bcg_fs = signal_method.signal_filter_split(conf, bcg_signal_raw, signal_fs, verbose=verbose)
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normalized_resp_signal = signal_method.normalize_resp_signal_by_segment(resp_signal, resp_fs, event_mask["Resp_Movement_Label"], event_list["RespAmpChangeSegment"])
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# 如果signal_data采样率过，进行降采样
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if signal_fs == 1000:
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bcg_signal_notch = utils.downsample_signal_fast(original_signal=bcg_signal_notch, original_fs=signal_fs, target_fs=100)
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bcg_signal_raw = utils.downsample_signal_fast(original_signal=bcg_signal_raw, original_fs=signal_fs,
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target_fs=100)
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signal_fs = 100
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if bcg_fs == 1000:
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bcg_signal = utils.downsample_signal_fast(original_signal=bcg_signal, original_fs=bcg_fs, target_fs=100)
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bcg_fs = 100
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# draw_tools.draw_signal_with_mask(samp_id=samp_id,
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# signal_data=resp_signal,
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# signal_fs=resp_fs,
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# resp_data=normalized_resp_signal,
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# resp_fs=resp_fs,
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# bcg_data=bcg_signal,
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# bcg_fs=bcg_fs,
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# signal_disable_mask=event_mask["Disable_Label"],
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# resp_low_amp_mask=event_mask["Resp_LowAmp_Label"],
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# resp_movement_mask=event_mask["Resp_Movement_Label"],
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# resp_change_mask=event_mask["Resp_AmpChange_Label"],
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# resp_sa_mask=event_mask["SA_Label"],
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# bcg_low_amp_mask=event_mask["BCG_LowAmp_Label"],
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# bcg_movement_mask=event_mask["BCG_Movement_Label"],
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# bcg_change_mask=event_mask["BCG_AmpChange_Label"],
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# show=show,
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# save_path=None)
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segment_list, disable_segment_list = utils.resp_split(dataset_config, event_mask, event_list, verbose=verbose)
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if verbose:
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print(f"Total segments extracted for sample ID {samp_id}: {len(segment_list)}")
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# 复制mask到processed_Labels文件夹
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save_mask_excel_path = save_processed_label_path / f"{samp_id}_Processed_Labels.csv"
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shutil.copyfile(mask_excel_path, save_mask_excel_path)
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# 复制SA Label_corrected.csv到processed_Labels文件夹
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sa_label_corrected_path = Path(mask_path, f"{samp_id}", f"{samp_id}_SA Label_corrected.csv")
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if sa_label_corrected_path.exists():
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save_sa_label_corrected_path = save_processed_label_path / f"{samp_id}_SA Label_corrected.csv"
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shutil.copyfile(sa_label_corrected_path, save_sa_label_corrected_path)
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else:
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if verbose:
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print(f"Warning: {sa_label_corrected_path} does not exist.")
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# 保存处理后的信号和截取的片段列表
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save_signal_path = save_processed_signal_path / f"{samp_id}_Processed_Signals.npz"
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save_segment_path = save_segment_list_path / f"{samp_id}_Segment_List.npz"
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bcg_data = {
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"bcg_signal_notch": {
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"name": "BCG_Signal_Notch",
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"data": bcg_signal_notch,
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"fs": signal_fs,
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"length": len(bcg_signal_notch),
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"second": len(bcg_signal_notch) // signal_fs
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},
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"bcg_signal":{
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"name": "BCG_Signal_Raw",
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"data": bcg_signal,
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"fs": bcg_fs,
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"length": len(bcg_signal),
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"second": len(bcg_signal) // bcg_fs
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},
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"resp_signal": {
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"name": "Resp_Signal",
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"data": normalized_resp_signal,
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"fs": resp_fs,
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"length": len(normalized_resp_signal),
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"second": len(normalized_resp_signal) // resp_fs
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}
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}
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np.savez_compressed(save_signal_path, **bcg_data)
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np.savez_compressed(save_segment_path,
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segment_list=segment_list,
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disable_segment_list=disable_segment_list)
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if verbose:
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print(f"Saved processed signals to: {save_signal_path}")
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print(f"Saved segment list to: {save_segment_path}")
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if draw_segment:
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psg_data = utils.read_psg_channel(psg_signal_root_path / f"{samp_id}", [1, 2, 3, 4, 5, 6, 7, 8], verbose=verbose)
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psg_data["HR"] = {
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"name": "HR",
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"data": signal_method.rpeak2hr(psg_data["Rpeak"]["data"], psg_data["ECG_Sync"]["length"], psg_data["Rpeak"]["fs"]),
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"fs": psg_data["ECG_Sync"]["fs"],
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"length": psg_data["ECG_Sync"]["length"],
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"second": psg_data["ECG_Sync"]["second"]
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}
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psg_label = utils.read_psg_label(sa_label_corrected_path, verbose=verbose)
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psg_event_mask, _ = utils.generate_event_mask(event_df=psg_label, signal_second=psg_data["ECG_Sync"]["second"], use_correct=False)
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total_len = len(segment_list) + len(disable_segment_list)
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if verbose:
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print(f"Drawing segments for sample ID {samp_id}, total segments (enable + disable): {total_len}")
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# draw_tools.draw_psg_bcg_label(psg_data=psg_data,
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# psg_label=psg_event_mask,
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# bcg_data=bcg_data,
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# event_mask=event_mask,
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# segment_list=segment_list,
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# save_path=visual_path / f"{samp_id}" / "enable",
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# verbose=verbose,
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# multi_p=multi_p,
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# multi_task_id=multi_task_id
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# )
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#
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# draw_tools.draw_psg_bcg_label(
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# psg_data=psg_data,
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# psg_label=psg_event_mask,
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# bcg_data=bcg_data,
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# event_mask=event_mask,
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# segment_list=disable_segment_list,
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# save_path=visual_path / f"{samp_id}" / "disable",
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# verbose=verbose,
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# multi_p=multi_p,
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# multi_task_id=multi_task_id
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# )
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def multiprocess_entry(_progress, task_id, _id):
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build_HYS_dataset_segment(samp_id=_id, show=False, draw_segment=True, verbose=False, multi_p=_progress, multi_task_id=task_id)
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def multiprocess_with_pool(args_list, n_processes):
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"""使用Pool，每个worker处理固定数量任务后重启"""
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from multiprocessing import Pool
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# maxtasksperchild 设置每个worker处理多少任务后重启（释放内存）
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with Pool(processes=n_processes, maxtasksperchild=2) as pool:
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results = []
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for samp_id in args_list:
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result = pool.apply_async(
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build_HYS_dataset_segment,
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args=(samp_id, False, True, False, None, None)
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)
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results.append(result)
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# 等待所有任务完成
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for i, result in enumerate(results):
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try:
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result.get()
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print(f"Completed {i + 1}/{len(args_list)}")
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except Exception as e:
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print(f"Error processing task {i}: {e}")
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pool.close()
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pool.join()
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if __name__ == '__main__':
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yaml_path = project_root_path / "dataset_config/HYS_config.yaml"
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conf = utils.load_dataset_conf(yaml_path)
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select_ids = conf["select_ids"]
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root_path = Path(conf["root_path"])
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mask_path = Path(conf["mask_save_path"])
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save_path = Path(conf["dataset_config"]["dataset_save_path"])
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visual_path = Path(conf["dataset_config"]["dataset_visual_path"])
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dataset_config = conf["dataset_config"]
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visual_path.mkdir(parents=True, exist_ok=True)
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save_processed_signal_path = save_path / "Signals"
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save_processed_signal_path.mkdir(parents=True, exist_ok=True)
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save_segment_list_path = save_path / "Segments_List"
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save_segment_list_path.mkdir(parents=True, exist_ok=True)
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save_processed_label_path = save_path / "Labels"
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save_processed_label_path.mkdir(parents=True, exist_ok=True)
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# print(f"select_ids: {select_ids}")
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# print(f"root_path: {root_path}")
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# print(f"save_path: {save_path}")
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# print(f"visual_path: {visual_path}")
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org_signal_root_path = root_path / "OrgBCG_Aligned"
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psg_signal_root_path = root_path / "PSG_Aligned"
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print(select_ids)
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# build_HYS_dataset_segment(select_ids[3], show=False, draw_segment=True)
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# for samp_id in select_ids:
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# print(f"Processing sample ID: {samp_id}")
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# build_HYS_dataset_segment(samp_id, show=False, draw_segment=True)
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# multiprocess_with_tqdm(args_list=select_ids, n_processes=16)
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multiprocess_with_pool(args_list=select_ids, n_processes=16) |