import utils
import pandas as pd
import numpy as np
from scipy import signal

def normalize_resp_signal(resp_signal: np.ndarray, resp_fs, movement_mask, enable_list):
    # 根据呼吸信号的幅值改变区间，对每段进行Z-Score标准化
    normalized_resp_signal = np.zeros_like(resp_signal)
    # 全部填成nan
    normalized_resp_signal[:] = np.nan

    resp_signal_no_movement = resp_signal.copy()


    resp_signal_no_movement[np.array(movement_mask == 1).repeat(resp_fs)] = np.nan


    for i in range(len(enable_list)):
        enable_start = enable_list[i][0] * resp_fs
        enable_end = enable_list[i][1] * resp_fs
        segment = resp_signal_no_movement[enable_start:enable_end]

        # print(f"Normalizing segment {i+1}/{len(enable_list)}: start={enable_start}, end={enable_end}, length={len(segment)}")

        segment_mean = np.nanmean(segment)
        segment_std = np.nanstd(segment)
        if segment_std == 0:
            raise ValueError(f"segment_std is zero! segment_start: {enable_start}, segment_end: {enable_end}")

        # 同下一个enable区间的体动一起进行标准化
        if i <= len(enable_list) - 2:
            enable_end = enable_list[i + 1][0] * resp_fs
        raw_segment = resp_signal[enable_start:enable_end]
        normalized_resp_signal[enable_start:enable_end] = (raw_segment - segment_mean) / segment_std

    return normalized_resp_signal