Quality_Relabel/utils/Quality_Relabel.py

# -*- coding: cp936 -*-
# ʹ<><CAB9>gbk<62><6B><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ܶ<EFBFBD><DCB6><EFBFBD>ǩ
"""
@author:Marques
@file:Prepare_Data.py
@email:admin@marques22.com
@email:2021022362@m.scnu.edu.cn
@time:2022/03/26
"""
import time
from typing import List
import logging
import pyedflib
from pathlib import Path
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt, gridspec
from utils.Preprocessing import BCG_Operation
from tqdm import tqdm
from datetime import datetime

plt.rcParams['font.sans-serif'] = ['SimHei']  # <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ʾ<EFBFBD><CABE><EFBFBD>ı<EFBFBD>ǩ
plt.rcParams['axes.unicode_minus'] = False  # <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ʾ<EFBFBD><CABE><EFBFBD><EFBFBD>

# ['EEG F3-A2', 'EEG F4-A1', 'EEG C3-A2', 'EEG C4-A1', 'EEG O1-A2',
# 'EEG O2-A1', 'EOG Right', 'EOG Left', 'EMG Chin', 'ECG I', 'RR',
# 'ECG II', 'Effort Tho', 'Flow Patient', 'Flow Patient', 'Effort Abd',
# 'SpO2', 'Pleth', 'Snore', 'Body', 'Pulse', 'Leg LEG1', 'Leg LEG2',
# 'EEG A1-A2', 'Imp']


# <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>־
logger = logging.getLogger()
logger.setLevel(logging.NOTSET)
realtime = time.strftime('%Y%m%d', time.localtime(time.time()))
fh = logging.FileHandler(Path("history") / (realtime + ".log"), mode='a')
fh.setLevel(logging.NOTSET)
fh.setFormatter(logging.Formatter("%(asctime)s: %(message)s"))
logger.addHandler(fh)

ch = logging.StreamHandler()
ch.setLevel(logging.NOTSET)
ch.setFormatter(logging.Formatter("%(asctime)s: %(message)s"))
logger.addHandler(ch)
logging.getLogger('matplotlib.font_manager').disabled = True
logging.info("------------------------------------")


class Quality_Relabel:
    # <20><>ѡ<EFBFBD><D1A1><EFBFBD><EFBFBD>ͨ<EFBFBD><CDA8>
    base_channel = ['EEG F3-A2', 'EEG F4-A1', 'EEG C3-A2', 'EEG C4-A1', 'EEG O1-A2', 'EEG O2-A1', 'EOG Right',
                    'EOG Left', 'EMG Chin', 'ECG I', 'RR', 'ECG II', 'Effort Tho', 'Flow Patient', 'Flow Patient', 'HR',
                    'Effort Abd', 'SpO2', 'Pleth', 'Snore', 'Body', 'Pulse', 'Leg LEG1', 'Leg LEG2', 'EEG A1-A2', 'Imp']

    # <20><>ʾ<EFBFBD>¼<EFBFBD>
    base_event = ["Hypopnea", "Central apnea", "Obstructive apnea", "Mixed apnea", "Desaturation"]

    # <20>趨<EFBFBD>¼<EFBFBD><C2BC><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ӧ<EFBFBD><D3A6>ɫ
    # event_code  color event
    #      0      <20><>ɫ    <20><><EFBFBD><EFBFBD>
    #      1      <20><>ɫ    <20><>ͨ<EFBFBD><CDA8>
    #      2      <20><>ɫ    <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
    #      3      <20><>ɫ    <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
    #      4      <20><>ɫ    <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
    #      5      <20><>ɫ    Ѫ<><D1AA><EFBFBD><EFBFBD><EFBFBD>Ͷ<EFBFBD><CDB6>½<EFBFBD>
    color_cycle = ["blue", "pink", "orange", "red", "grey", "green"]
    assert len(color_cycle) == len(base_event) + 1, "<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>¼<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ɫ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>һ<EFBFBD><EFBFBD>"

    def __init__(self, sampNo: int, frequency: int = 100, bcg_frequency: int = 1000,
                 channel_list: List[str] = ['Effort Tho', 'Effort Abd', 'SpO2', 'Flow Patient', 'Flow Patient'],
                 focus_event_list: List[str] = ["Obstructive apnea"]):
        """

        :param sampNo: <EFBFBD><EFBFBD><EFBFBD><EFBFBD>ѡ<EFBFBD><EFBFBD>
        :param frequency: <EFBFBD><EFBFBD>ʾ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
        :param bcg_frequency: BCG<EFBFBD>źŲ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
        :param channel_list: <EFBFBD><EFBFBD>ʾ<EFBFBD><EFBFBD>ͨ<EFBFBD><EFBFBD>
        :param focus_event_list: <EFBFBD><EFBFBD>ע<EFBFBD><EFBFBD>ͣ<EFBFBD>¼<EFBFBD>
        """
        self.sampNo = sampNo
        self.channel_list = channel_list
        self.focus_event_list = focus_event_list
        self.frequency = frequency
        self.bcg_frequency = bcg_frequency

        self.ecg_start_time = None

        # <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ʾ<EFBFBD><CABE>ɫʱ<C9AB><CAB1><EFBFBD><EFBFBD>ƥ<EFBFBD><C6A5><EFBFBD>¼<EFBFBD>
        self.ecg_event_label = None
        self.bcg_event_label = None
        self.spo2_event_label = None

        # <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ע<EFBFBD><D7A2>ͣ<EFBFBD>¼<EFBFBD><C2BC><EFBFBD><EFBFBD>б<EFBFBD>
        self.ecg_event_label_filtered_df = None
        self.bcg_event_label_filtered_df = None

        # <20><><EFBFBD><EFBFBD><EFBFBD>¼<EFBFBD><C2BC>б<EFBFBD>
        self.ecg_event_label_df = None
        self.bcg_event_label_df = None

        # <20><>ͨ<EFBFBD><CDA8><EFBFBD>ź<EFBFBD>
        self.signal_select = {}

        self.check_channel()
        self.read_data(frequency, bcg_frequency)
        self.read_event()

    def check_channel(self):
        for i in self.channel_list:
            if i not in self.base_channel:
                logging.debug(f"{i} <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ڳ<EFBFBD><DAB3><EFBFBD>ͨ<EFBFBD><CDA8><EFBFBD><EFBFBD><EFBFBD><EFBFBD>")
        print(f"<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͨ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>{self.channel_list}")

    def read_data(self, frequency: int = 100, bcg_frequency: int = 1000):
        bcg_path = Path(f"Data/BCG/{self.sampNo}samp.npy")
        ecg_path = Path(f"Data/ECG/A{str(self.sampNo).rjust(7, '0')}.edf")

        if not bcg_path.exists():
            logging.error(f"{bcg_path} <20><><EFBFBD><EFBFBD><EFBFBD>ڣ<EFBFBD>")
            raise FileNotFoundError(f"{bcg_path} <20><><EFBFBD><EFBFBD><EFBFBD>ڣ<EFBFBD>")

        if not ecg_path.exists():
            logging.error(f"{ecg_path} <20><><EFBFBD><EFBFBD><EFBFBD>ڣ<EFBFBD>")
            raise FileNotFoundError(f"{ecg_path} <20><><EFBFBD><EFBFBD><EFBFBD>ڣ<EFBFBD>")

        with pyedflib.EdfReader(str(ecg_path.resolve())) as file:
            signal_num = file.signals_in_file
            logging.debug(f"{self.sampNo} EDF file signal number is {signal_num}")

            signal_label = file.getSignalLabels()
            logging.debug(f"{self.sampNo} EDF file signal label : {signal_label}")

            self.ecg_start_time = file.getStartdatetime()

            # <20><><EFBFBD><EFBFBD>PSG<53><47>¼<EFBFBD><C2BC><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>¼<EFBFBD><C2BC><EFBFBD>
            self.ecg_event_label = np.zeros(file.getFileDuration() * self.frequency)
            self.spo2_event_label = np.zeros(file.getFileDuration() * self.frequency)
            # <20><>ӡPSG<53><47>Ϣ
            file.file_info_long()

            # sub_index <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>flow patient
            sub_index = 1

            logging.info("<EFBFBD><EFBFBD>ȡPSG<EFBFBD>źš<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>")
            for i, index in enumerate(signal_label):
                # <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ѡ<EFBFBD>е<EFBFBD>ͨ<EFBFBD><CDA8>
                if index in self.channel_list:
                    # <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>flow patientͨ<74><CDA8>
                    if index == 'Flow Patient':
                        index = index + str(sub_index)
                        sub_index += 1
                    signal = file.readSignal(i)
                    sample_frequency = file.getSampleFrequency(i)
                    # <20><>ȡ<EFBFBD><C8A1><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD>ز<EFBFBD><D8B2><EFBFBD>
                    if sample_frequency < frequency:
                        signal = signal.repeat(frequency / sample_frequency)
                    elif sample_frequency > frequency:
                        signal = signal[::int(sample_frequency / frequency)]
                    self.signal_select[index] = signal
                    logging.info(f"<EFBFBD><EFBFBD><EFBFBD>ɶ<EFBFBD>ȡPSG: {index}")

        # <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ź<EFBFBD>
        logging.info("<EFBFBD><EFBFBD>ȡ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>źš<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>")
        signal = np.load(bcg_path)
        preprocessing = BCG_Operation(sample_rate=bcg_frequency)
        # 20Hz<48><7A>ͨȥ<CDA8><C8A5>
        signal1 = preprocessing.Butterworth(signal, 'lowpass', low_cut=20, order=3)
        # 0.7Hz <20><>ͨ<EFBFBD><CDA8>ȡ<EFBFBD><C8A1><EFBFBD><EFBFBD>
        signal2 = preprocessing.Butterworth(signal, 'lowpass', low_cut=0.7, order=3)

        # <20><><EFBFBD>н<EFBFBD><D0BD><EFBFBD><EFBFBD><EFBFBD>
        signal1 = signal1[::int(bcg_frequency / frequency)]
        signal2 = signal2[::int(bcg_frequency / frequency)]

        # <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>¼<EFBFBD><C2BC><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>¼<EFBFBD><C2BC><EFBFBD>¼
        self.bcg_event_label = np.zeros(len(signal))
        self.signal_select['xin_xiao'] = signal1
        self.signal_select['xin_xiao_respire'] = signal2

    def read_event(self):
        bcg_label_path = Path(f"Data/BCG_label/{self.sampNo}_label_all.csv")
        ecg_label_path = Path(f"Data/ECG_label/export{self.sampNo}.csv")

        if not bcg_label_path.exists():
            logging.error(f"{bcg_label_path} <20><><EFBFBD><EFBFBD><EFBFBD>ڣ<EFBFBD>")
            raise FileNotFoundError(f"{bcg_label_path} <20><><EFBFBD><EFBFBD><EFBFBD>ڣ<EFBFBD>")

        if not ecg_label_path.exists():
            logging.error(f"{ecg_label_path} <20><><EFBFBD><EFBFBD><EFBFBD>ڣ<EFBFBD>")
            raise FileNotFoundError(f"{ecg_label_path} <20><><EFBFBD><EFBFBD><EFBFBD>ڣ<EFBFBD>")

        df = pd.read_csv(ecg_label_path, encoding='gbk')
        self.ecg_event_label_df = df

        # <20><><EFBFBD>˲<EFBFBD><CBB2><EFBFBD>ע<EFBFBD><D7A2><EFBFBD>¼<EFBFBD>
        df2 = df[df["Event type"].isin(self.focus_event_list)]
        # <20><><EFBFBD><EFBFBD>epoch<63><68><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>з<EFBFBD><D0B7><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
        df2 = df2.sort_values(by='Epoch')
        self.ecg_event_label_filtered_df = df2

        logging.info("<EFBFBD><EFBFBD><EFBFBD><EFBFBD>PSG<EFBFBD>¼<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>")
        for one_data in tqdm(df.index, ncols=80):
            one_data = df.loc[one_data]

            # ͨ<><CDA8><EFBFBD><EFBFBD>ʼ<EFBFBD>¼<EFBFBD><C2BC><EFBFBD><EFBFBD><EFBFBD><EFBFBD>¼<EFBFBD><C2BC><EFBFBD>ʼ<EFBFBD><CABC><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
            event_start_time = datetime.strptime(one_data["Date"] + " " + one_data["Time"], '%Y/%m/%d %H:%M:%S')
            SP = (event_start_time - self.ecg_start_time).seconds
            # <20><><EFBFBD><EFBFBD><EFBFBD>Ž<EFBFBD><C5BD><EFBFBD><EFBFBD>з֣<D0B7><D6A3><EFBFBD><EFBFBD><EFBFBD>Duration  20 (20) <20><><EFBFBD>ִ<EFBFBD><D6B4><EFBFBD><EFBFBD>ŵ<EFBFBD><C5B5><EFBFBD><EFBFBD><EFBFBD>
            EP = int(SP + float(one_data["Duration"].split("(")[0]))
            SP *= self.frequency
            EP *= self.frequency

            # <20><><EFBFBD>¼<EFBFBD><C2BC><EFBFBD><EFBFBD>±<EFBFBD><C2B1>벢<EFBFBD>浽<EFBFBD>¼<EFBFBD><C2BC><EFBFBD>¼<EFBFBD><C2BC><EFBFBD><EFBFBD>
            if one_data["Event type"] == "Hypopnea":
                self.ecg_event_label[SP:EP] = 1
            elif one_data["Event type"] == "Central apnea":
                self.ecg_event_label[SP:EP] = 2
            elif one_data["Event type"] == "Obstructive apnea":
                self.ecg_event_label[SP:EP] = 3
            elif one_data["Event type"] == "Mixed apnea":
                self.ecg_event_label[SP:EP] = 4
            elif one_data["Event type"] == "Desaturation":
                self.spo2_event_label[SP:EP] = 5

        # <20><>ȡ<EFBFBD><C8A1><EFBFBD><EFBFBD><EFBFBD>¼<EFBFBD>
        df = pd.read_csv(bcg_label_path, encoding='gbk')
        df["new_start"] = df["new_start"].astype("int")
        df["new_end"] = df["new_end"].astype("int")
        self.bcg_event_label_df = df

        # <20><><EFBFBD>˲<EFBFBD><CBB2><EFBFBD>ע<EFBFBD>¼<EFBFBD>
        df2 = df[df["Event type"].isin(self.focus_event_list)]
        df2 = df2.sort_values(by='Epoch')
        self.bcg_event_label_filtered_df = df2
        logging.info("<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>¼<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>")
        for one_data in tqdm(df.index):
            one_data = df.loc[one_data]
            SP = one_data["new_start"] * self.frequency
            EP = one_data["new_end"] * self.frequency

            if one_data["Event type"] == "Hypopnea":
                self.bcg_event_label[SP:EP] = 1
            elif one_data["Event type"] == "Central apnea":
                self.bcg_event_label[SP:EP] = 2
            elif one_data["Event type"] == "Obstructive apnea":
                self.bcg_event_label[SP:EP] = 3
            elif one_data["Event type"] == "Mixed apnea":
                self.bcg_event_label[SP:EP] = 4

    # assert len(self.ecg_event_label_filtered_df) == len(self.bcg_event_label_filtered_df), \
    #     f"PSG<53><47><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>¼<EFBFBD><C2BC><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>һ<EFBFBD><D2BB>, PSG<53>¼<EFBFBD><C2BC><EFBFBD><EFBFBD><EFBFBD>{len(self.ecg_event_label_filtered_df)},
    #     <20><><EFBFBD><EFBFBD><EFBFBD>¼<EFBFBD><C2BC><EFBFBD><EFBFBD><EFBFBD>{len(self.bcg_event_label_filtered_df)}"

    def show_one_event(self, bcg_index: int, ecg_index: int, front_add_second: int = 60,
                       back_add_second: int = 60):
        """
        :param bcg_index: <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>¼<EFBFBD><EFBFBD><EFBFBD>csv<EFBFBD><EFBFBD><EFBFBD>к<EFBFBD>
        :param ecg_index: PSG<EFBFBD>¼<EFBFBD><EFBFBD><EFBFBD>csv<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
        :param front_add_second: <EFBFBD><EFBFBD>ǰ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ʱ<EFBFBD><EFBFBD>
        :param back_add_second: <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ʱ<EFBFBD><EFBFBD>
        :return:
        """
        # <20><>ȡ<EFBFBD>¼<EFBFBD>ʵ<EFBFBD><CAB5><EFBFBD><EFBFBD>csv<73>ļ<EFBFBD><C4BC>е<EFBFBD><D0B5><EFBFBD><EFBFBD><EFBFBD>
        bcg_real_index = self.bcg_event_label_filtered_df.index[bcg_index],
        ecg_real_index = self.ecg_event_label_filtered_df.index[ecg_index],
        one_bcg_data = self.bcg_event_label_df.loc[bcg_real_index]
        one_ecg_data = self.ecg_event_label_df.loc[ecg_real_index]

        # <20><>ȡECG<43>¼<EFBFBD><C2BC><EFBFBD>ʼ<EFBFBD><CABC><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ʱ<EFBFBD><CAB1>
        event_start_time = datetime.strptime(one_ecg_data["Date"] + " " + one_ecg_data["Time"], '%Y/%m/%d %H:%M:%S')
        ecg_SP = (event_start_time - self.ecg_start_time).seconds
        ecg_duration = int(float(str(one_ecg_data["Duration"]).split("(")[0]) + 0.5)
        ecg_EP = ecg_SP + ecg_duration

        # <20><>ȡBCG<43>¼<EFBFBD><C2BC><EFBFBD>ʼ<EFBFBD><CABC><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ʱ<EFBFBD><CAB1>
        bcg_SP = one_bcg_data["new_start"]
        bcg_EP = one_bcg_data["new_end"]
        bcg_duration = int(float(str(one_bcg_data["Duration"]).split("(")[0]))

        logging.info(f"sampNo:{self.sampNo} "
                     f"bcg[index:{bcg_index} epoch:{one_bcg_data['Epoch']} event:{one_bcg_data['Event type']}] "
                     f"ecg:[index:{ecg_index} epoch:{one_ecg_data['Epoch']} event:{one_ecg_data['Event type']}]")

        if one_bcg_data['Event type'] != one_ecg_data['Event type']:
            logging.error(f"sampNo:{self.sampNo} PSG<53>¼<EFBFBD><C2BC><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ʱ<EFBFBD>䲻һ<E4B2BB>£<EFBFBD><C2A3><EFBFBD><EFBFBD>Ų<EFBFBD>"
                          f"bcg[index:{bcg_index} epoch:{one_bcg_data['Epoch']} event:{one_bcg_data['Event type']}] "
                          f"ecg:[index:{ecg_index} epoch:{one_ecg_data['Epoch']} event:{one_ecg_data['Event type']}]")
            raise ValueError()

        # <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>չ
        ecg_SP = ecg_SP - front_add_second
        ecg_EP = ecg_EP + back_add_second
        bcg_SP = bcg_SP - front_add_second - (ecg_duration - bcg_duration) // 2
        bcg_EP = bcg_EP + back_add_second + (ecg_duration - bcg_duration) // 2

        # <20><>ͼ
        plt.figure(figsize=(12, 6), dpi=150)
        # <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ֮<CDBC><D6AE><EFBFBD>ı<EFBFBD><C4B1><EFBFBD>
        gs = gridspec.GridSpec(7, 1, height_ratios=[1, 1, 1, 1, 1, 3, 1])
        plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
        plt.margins(0, 0)
        plt.tight_layout()

        # <20><><EFBFBD><EFBFBD> Flow1
        plt.subplot(gs[0])
        self.plt_channel(plt_=plt, SP=ecg_SP, EP=ecg_EP, channel="Flow Patient1")

        # <20><><EFBFBD><EFBFBD> Flow2
        plt.subplot(gs[1])
        self.plt_channel(plt_=plt, SP=ecg_SP, EP=ecg_EP, channel="Flow Patient2",
                         title=f"PSG sampNo:{self.sampNo} Epoch:{one_ecg_data['Epoch']} Duration:{one_ecg_data['Duration']}")

        plt.subplot(gs[2])
        self.plt_channel(plt_=plt, SP=ecg_SP, EP=ecg_EP, channel="Effort Tho")

        plt.subplot(gs[3])
        self.plt_channel(plt_=plt, SP=ecg_SP, EP=ecg_EP, channel="Effort Abd")

        plt.subplot(gs[4])
        self.plt_channel(plt_=plt, SP=ecg_SP, EP=ecg_EP, channel="SpO2", event_code=[5])

        plt.subplot(gs[5])
        self.plt_channel(plt_=plt, SP=bcg_SP, EP=bcg_EP, channel="xin_xiao", event_show_under=False)

        plt.subplot(gs[6])
        self.plt_channel(plt_=plt, SP=bcg_SP, EP=bcg_EP, channel="xin_xiao_respire", event_show_under=False,
                         ax_bottom=True,
                         title=f"<EFBFBD><EFBFBD><EFBFBD><EFBFBD> sampNo:{self.sampNo} Epoch:{one_bcg_data['Epoch']} Duration:{one_bcg_data['Duration']}",
                         )

        figManager = plt.get_current_fig_manager()
        figManager.window.showMaximized()
        plt.show()

    def plt_channel(self, plt_, SP, EP, channel, event_code=[1, 2, 3, 4], event_show_under=False,
                    ax_top=False, ax_bottom=False, ax_left=True, ax_right=False, title=None):
        """

        :param plt_:
        :param SP: <EFBFBD><EFBFBD>ʾ<EFBFBD><EFBFBD>ʼ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>
        :param EP: <EFBFBD><EFBFBD>ʾ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
        :param channel: ͨ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
        :param event_code: Ҫ<EFBFBD><EFBFBD>ɫ<EFBFBD><EFBFBD><EFBFBD>¼<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
        :param event_show_under: <EFBFBD>¼<EFBFBD><EFBFBD><EFBFBD>ɫ<EFBFBD><EFBFBD>ʾ<EFBFBD><EFBFBD><EFBFBD>ź<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
        :param ax_top: <EFBFBD><EFBFBD>ʾ<EFBFBD>Ͽ<EFBFBD><EFBFBD><EFBFBD>
        :param ax_bottom: <EFBFBD><EFBFBD>ʾ<EFBFBD>¿<EFBFBD><EFBFBD><EFBFBD>
        :param ax_left: <EFBFBD><EFBFBD>ʾ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
        :param ax_right: <EFBFBD><EFBFBD>ʾ<EFBFBD>ҿ<EFBFBD><EFBFBD><EFBFBD>
        :param title: <EFBFBD><EFBFBD>ʾ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>
        :return:
        """
        plt_.plot(np.linspace(SP, EP, (EP - SP) * self.frequency),
                  self.signal_select[channel][SP * self.frequency:EP * self.frequency], label=channel)

        for j in event_code:
            if channel == "SpO2":
                mask = self.spo2_event_label[SP * self.frequency:EP * self.frequency] == j
            else:
                mask = self.ecg_event_label[SP * self.frequency:EP * self.frequency] == j
            if event_show_under:
                min_point = self.signal_select[channel][SP * self.frequency:EP * self.frequency].min()
                len_segment = EP * self.frequency - SP * self.frequency
                y = (min_point.repeat(len_segment) * mask).astype('float')
                np.place(y, y == 0, np.nan)
            else:
                y = (self.signal_select[channel][SP * self.frequency:EP * self.frequency] * mask).astype('float')
            np.place(y, y == 0, np.nan)
            plt_.plot(np.linspace(SP, EP, (EP - SP) * self.frequency), y, color=self.color_cycle[j])
        plt_.legend(loc=1)

        if title is not None:
            plt_.title(title)
        ax = plt.gca()
        ax.spines["top"].set_visible(ax_top)
        ax.spines["right"].set_visible(ax_right)
        ax.spines["bottom"].set_visible(ax_bottom)
        ax.spines["left"].set_visible(ax_left)
        # xticks = [[]] if xticks else [range(SP, EP, 5), [str(i) for i in range(0, (EP - SP), 5)]]
        # print(xticks)
        # plt_.xticks(*xticks)  # ȥ<><C8A5>x<EFBFBD><78>

    def show_all_event(self, start_bcg_index: int = 0, shifting: int = 0, front_add_second: int = 60,
                       back_add_second: int = 60):

        for index in range(start_bcg_index, len(self.bcg_event_label_filtered_df)):
            self.show_one_event(index,
                                index + shifting,
                                front_add_second=front_add_second,
                                back_add_second=back_add_second
                                )


if __name__ == '__main__':
    prepareData = Quality_Relabel(670)
    prepareData.show_all_event()