295 lines
12 KiB
Python
295 lines
12 KiB
Python
from pathlib import Path
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from typing import Union
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import utils
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from .event_map import N2Chn
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import numpy as np
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import pandas as pd
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from .operation_tools import event_mask_2_list
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# 尝试导入 Polars
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try:
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import polars as pl
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HAS_POLARS = True
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except ImportError:
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HAS_POLARS = False
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def read_signal_txt(path: Union[str, Path], dtype, verbose=True, is_peak=False):
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"""
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Read a txt file and return the first column as a numpy array.
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Args:
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:param path:
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:param verbose:
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:param dtype:
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Returns:
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np.ndarray: The first column of the txt file as a numpy array.
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"""
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path = Path(path)
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if not path.exists():
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raise FileNotFoundError(f"File not found: {path}")
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if HAS_POLARS:
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df = pl.read_csv(path, has_header=False, infer_schema_length=0)
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signal_data_raw = df[:, 0].to_numpy().astype(dtype)
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else:
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df = pd.read_csv(path, header=None, dtype=dtype)
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signal_data_raw = df.iloc[:, 0].to_numpy()
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signal_original_length = len(signal_data_raw)
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signal_fs = int(path.stem.split("_")[-1])
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if is_peak:
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signal_second = None
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signal_length = None
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else:
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signal_second = signal_original_length // signal_fs
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# 根据采样率进行截断
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signal_data_raw = signal_data_raw[:signal_second * signal_fs]
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signal_length = len(signal_data_raw)
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if verbose:
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print(f"Signal file read from {path}")
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print(f"signal_fs: {signal_fs}")
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print(f"signal_original_length: {signal_original_length}")
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print(f"signal_after_cut_off_length: {signal_length}")
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print(f"signal_second: {signal_second}")
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return signal_data_raw, signal_length, signal_fs, signal_second
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def read_label_csv(path: Union[str, Path], verbose=True) -> pd.DataFrame:
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"""
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Read a CSV file and return it as a pandas DataFrame.
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Args:
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path (str | Path): Path to the CSV file.
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verbose (bool):
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Returns:
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pd.DataFrame: The content of the CSV file as a pandas DataFrame.
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:param path:
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:param verbose:
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"""
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path = Path(path)
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if not path.exists():
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raise FileNotFoundError(f"File not found: {path}")
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# 直接用pandas读取 包含中文 故指定编码
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df = pd.read_csv(path, encoding="gbk")
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if verbose:
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print(f"Label file read from {path}, number of rows: {len(df)}")
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# 统计打标情况
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# isLabeled=1 表示已打标
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# Event type 有值的为PSG导出的事件
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# Event type 为nan的为手动打标的事件
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# score=1 显著事件， score=2 为受干扰事件 score=3 为非显著应删除事件
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# 确认后的事件在correct_EventsType
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# 输出事件信息 按照总计事件、低通气、中枢性、阻塞性、混合型按行输出 格式为 总计/来自PSG/手动/删除/未标注
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# Columns:
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# Index Event type Stage Time Epoch Date Duration HR bef. HR extr. HR delta O2 bef. O2 min. O2 delta Body Position Validation Start End score remark correct_Start correct_End correct_EventsType isLabeled
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# Event type:
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# Hypopnea
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# Central apnea
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# Obstructive apnea
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# Mixed apnea
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num_total = np.sum((df["isLabeled"] == 1) & (df["score"] != 3))
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num_psg_events = np.sum(df["Event type"].notna())
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num_manual_events = np.sum(df["Event type"].isna())
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num_deleted = np.sum(df["score"] == 3)
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# 统计事件
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num_unlabeled = np.sum(df["isLabeled"] == -1)
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num_psg_hyp = np.sum(df["Event type"] == "Hypopnea")
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num_psg_csa = np.sum(df["Event type"] == "Central apnea")
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num_psg_osa = np.sum(df["Event type"] == "Obstructive apnea")
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num_psg_msa = np.sum(df["Event type"] == "Mixed apnea")
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num_hyp = np.sum((df["correct_EventsType"] == "Hypopnea") & (df["score"] != 3))
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num_csa = np.sum((df["correct_EventsType"] == "Central apnea") & (df["score"] != 3))
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num_osa = np.sum((df["correct_EventsType"] == "Obstructive apnea") & (df["score"] != 3))
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num_msa = np.sum((df["correct_EventsType"] == "Mixed apnea") & (df["score"] != 3))
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num_manual_hyp = np.sum((df["Event type"].isna()) & (df["correct_EventsType"] == "Hypopnea"))
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num_manual_csa = np.sum((df["Event type"].isna()) & (df["correct_EventsType"] == "Central apnea"))
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num_manual_osa = np.sum((df["Event type"].isna()) & (df["correct_EventsType"] == "Obstructive apnea"))
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num_manual_msa = np.sum((df["Event type"].isna()) & (df["correct_EventsType"] == "Mixed apnea"))
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num_deleted_hyp = np.sum((df["score"] == 3) & (df["correct_EventsType"] == "Hypopnea"))
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num_deleted_csa = np.sum((df["score"] == 3) & (df["correct_EventsType"] == "Central apnea"))
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num_deleted_osa = np.sum((df["score"] == 3) & (df["correct_EventsType"] == "Obstructive apnea"))
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num_deleted_msa = np.sum((df["score"] == 3) & (df["correct_EventsType"] == "Mixed apnea"))
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num_unlabeled_hyp = np.sum((df["isLabeled"] == -1) & (df["Event type"] == "Hypopnea"))
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num_unlabeled_csa = np.sum((df["isLabeled"] == -1) & (df["Event type"] == "Central apnea"))
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num_unlabeled_osa = np.sum((df["isLabeled"] == -1) & (df["Event type"] == "Obstructive apnea"))
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num_unlabeled_msa = np.sum((df["isLabeled"] == -1) & (df["Event type"] == "Mixed apnea"))
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num_hyp_1_score = np.sum((df["correct_EventsType"] == "Hypopnea") & (df["score"] == 1))
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num_csa_1_score = np.sum((df["correct_EventsType"] == "Central apnea") & (df["score"] == 1))
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num_osa_1_score = np.sum((df["correct_EventsType"] == "Obstructive apnea") & (df["score"] == 1))
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num_msa_1_score = np.sum((df["correct_EventsType"] == "Mixed apnea") & (df["score"] == 1))
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num_hyp_2_score = np.sum((df["correct_EventsType"] == "Hypopnea") & (df["score"] == 2))
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num_csa_2_score = np.sum((df["correct_EventsType"] == "Central apnea") & (df["score"] == 2))
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num_osa_2_score = np.sum((df["correct_EventsType"] == "Obstructive apnea") & (df["score"] == 2))
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num_msa_2_score = np.sum((df["correct_EventsType"] == "Mixed apnea") & (df["score"] == 2))
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num_hyp_3_score = np.sum((df["correct_EventsType"] == "Hypopnea") & (df["score"] == 3))
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num_csa_3_score = np.sum((df["correct_EventsType"] == "Central apnea") & (df["score"] == 3))
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num_osa_3_score = np.sum((df["correct_EventsType"] == "Obstructive apnea") & (df["score"] == 3))
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num_msa_3_score = np.sum((df["correct_EventsType"] == "Mixed apnea") & (df["score"] == 3))
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num_1_score = np.sum(df["score"] == 1)
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num_2_score = np.sum(df["score"] == 2)
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num_3_score = np.sum(df["score"] == 3)
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if verbose:
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print("Event Statistics:")
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# 格式化输出 总计/来自PSG/手动/删除/未标注 指定宽度
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print(f"Type {'Total':^8s} / {'From PSG':^8s} / {'Manual':^8s} / {'Deleted':^8s} / {'Unlabeled':^8s}")
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print(
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f"Hyp: {num_hyp:^8d} / {num_psg_hyp:^8d} / {num_manual_hyp:^8d} / {num_deleted_hyp:^8d} / {num_unlabeled_hyp:^8d}")
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print(
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f"CSA: {num_csa:^8d} / {num_psg_csa:^8d} / {num_manual_csa:^8d} / {num_deleted_csa:^8d} / {num_unlabeled_csa:^8d}")
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print(
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f"OSA: {num_osa:^8d} / {num_psg_osa:^8d} / {num_manual_osa:^8d} / {num_deleted_osa:^8d} / {num_unlabeled_osa:^8d}")
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print(
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f"MSA: {num_msa:^8d} / {num_psg_msa:^8d} / {num_manual_msa:^8d} / {num_deleted_msa:^8d} / {num_unlabeled_msa:^8d}")
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print(
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f"All: {num_total:^8d} / {num_psg_events:^8d} / {num_manual_events:^8d} / {num_deleted:^8d} / {num_unlabeled:^8d}")
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print("Score Statistics (only for non-deleted events and manual created events):")
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print(f"Type {'Total':^8s} / {'Score 1':^8s} / {'Score 2':^8s} / {'Score 3':^8s}")
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print(f"Hyp: {num_hyp:^8d} / {num_hyp_1_score:^8d} / {num_hyp_2_score:^8d} / {num_hyp_3_score:^8d}")
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print(f"CSA: {num_csa:^8d} / {num_csa_1_score:^8d} / {num_csa_2_score:^8d} / {num_csa_3_score:^8d}")
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print(f"OSA: {num_osa:^8d} / {num_osa_1_score:^8d} / {num_osa_2_score:^8d} / {num_osa_3_score:^8d}")
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print(f"MSA: {num_msa:^8d} / {num_msa_1_score:^8d} / {num_msa_2_score:^8d} / {num_msa_3_score:^8d}")
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print(f"All: {num_total:^8d} / {num_1_score:^8d} / {num_2_score:^8d} / {num_3_score:^8d}")
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df["Start"] = df["Start"].astype(int)
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df["End"] = df["End"].astype(int)
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return df
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def read_disable_excel(path: Union[str, Path]) -> pd.DataFrame:
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"""
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Read an Excel file and return it as a pandas DataFrame.
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Args:
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path (str | Path): Path to the Excel file.
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Returns:
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pd.DataFrame: The content of the Excel file as a pandas DataFrame.
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"""
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path = Path(path)
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if not path.exists():
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raise FileNotFoundError(f"File not found: {path}")
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# 直接用pandas读取
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df = pd.read_excel(path)
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df["id"] = df["id"].astype(int)
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df["start"] = df["start"].astype(int)
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df["end"] = df["end"].astype(int)
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return df
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def read_mask_execl(path: Union[str, Path]):
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"""
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Read an Excel file and return the mask as a numpy array.
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Args:
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path (str | Path): Path to the Excel file.
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Returns:
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np.ndarray: The mask as a numpy array.
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"""
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path = Path(path)
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if not path.exists():
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raise FileNotFoundError(f"File not found: {path}")
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df = pd.read_csv(path)
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event_mask = df.to_dict(orient="list")
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for key in event_mask:
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event_mask[key] = np.array(event_mask[key])
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event_list = {"RespAmpChangeSegment": event_mask_2_list(1 - event_mask["Resp_AmpChange_Label"]),
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"BCGAmpChangeSegment": event_mask_2_list(1 - event_mask["BCG_AmpChange_Label"]),
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"EnableSegment": event_mask_2_list(1 - event_mask["Disable_Label"]),}
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return event_mask, event_list
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def read_psg_channel(path_str: Union[str, Path], channel_number: list[int]):
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"""
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读取PSG文件中特定通道的数据。
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参数:
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path_str (Union[str, Path]): 存放PSG文件的文件夹路径。
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channel_name (str): 需要读取的通道名称。
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返回:
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np.ndarray: 指定通道的数据数组。
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"""
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path = Path(path_str)
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if not path.exists():
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raise FileNotFoundError(f"PSG Dir not found: {path}")
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if not path.is_dir():
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raise NotADirectoryError(f"PSG Dir not found: {path}")
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channel_data = {}
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# 遍历检查通道对应的文件是否存在
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for ch_id in channel_number:
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ch_name = N2Chn[ch_id]
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ch_path = list(path.glob(f"{ch_name}*.txt"))
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if not any(ch_path):
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raise FileNotFoundError(f"PSG Channel file not found: {ch_path}")
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if len(ch_path) > 1:
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print(f"Warning!!! PSG Channel file more than one: {ch_path}")
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if ch_id == 8:
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# sleep stage 特例 读取为整数
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ch_signal, ch_length, ch_fs, ch_second = read_signal_txt(ch_path[0], dtype=str, verbose=True)
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# 转换为整数数组
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for stage_str, stage_number in utils.Stage2N.items():
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np.place(ch_signal, ch_signal == stage_str, stage_number)
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ch_signal = ch_signal.astype(int)
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elif ch_id == 1:
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# Rpeak 特例 读取为整数
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ch_signal, ch_length, ch_fs, ch_second = read_signal_txt(ch_path[0], dtype=int, verbose=True, is_peak=True)
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else:
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ch_signal, ch_length, ch_fs, ch_second = read_signal_txt(ch_path[0], dtype=float, verbose=True)
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channel_data[ch_name] = {
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"name": ch_name,
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"path": ch_path[0],
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"data": ch_signal,
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"length": ch_length,
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"fs": ch_fs,
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"second": ch_second
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}
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return channel_data
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def read_psg_label(path: Union[str, Path], verbose=True):
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path = Path(path)
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if not path.exists():
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raise FileNotFoundError(f"File not found: {path}")
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# 直接用pandas读取 包含中文 故指定编码
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df = pd.read_csv(path, encoding="gbk")
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if verbose:
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print(f"Label file read from {path}, number of rows: {len(df)}")
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# 丢掉Event type为空的行
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df = df.dropna(subset=["Event type"], how='all').reset_index(drop=True)
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return df
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