"""
本脚本完成对呼研所数据的处理，包含以下功能：
1. 数据读取与预处理
    从传入路径中，进行数据和标签的读取，并进行初步的预处理
    预处理包括为数据进行滤波、去噪等操作
2. 数据清洗与异常值处理
3. 输出清晰后的统计信息
4. 数据保存
    将处理后的数据保存到指定路径，便于后续使用
    主要是保存切分后的数据位置和标签
5. 可视化
    提供数据处理前后的可视化对比，帮助理解数据变化
    绘制多条可用性趋势图，展示数据的可用区间、体动区间、低幅值区间等

todo: 使用mask 屏蔽无用区间


# 低幅值区间规则标定与剔除
# 高幅值连续体动规则标定与剔除
# 手动标定不可用区间提剔除
"""

from pathlib import Path
from typing import Union
import utils
import numpy as np
import signal_method



def process_one_signal(samp_id):
    signal_path = list((org_signal_root_path / f"{samp_id}").glob("OrgBCG_Sync_*.txt"))
    if not signal_path:
        raise FileNotFoundError(f"OrgBCG_Sync file not found for sample ID: {samp_id}")
    signal_path = signal_path[0]
    print(f"Processing OrgBCG_Sync signal file: {signal_path}")

    label_path = (label_root_path / f"{samp_id}").glob("SA Label_corrected.csv")
    if not label_path:
        raise FileNotFoundError(f"Label_corrected file not found for sample ID: {samp_id}")
    label_path = list(label_path)[0]
    print(f"Processing Label_corrected file: {label_path}")


    signal_data = utils.read_signal_txt(signal_path)
    signal_length = len(signal_data)
    print(f"signal_length: {signal_length}")
    signal_fs = int(signal_path.stem.split("_")[-1])
    print(f"signal_fs: {signal_fs}")
    signal_second = signal_length // signal_fs
    print(f"signal_second: {signal_second}")

    # 滤波
    # 50Hz陷波滤波器
    # signal_data = utils.butterworth(data=signal_data, _type="bandpass", low_cut=0.5, high_cut=45, order=10, sample_rate=signal_fs)
    resp_data = utils.butterworth(data=signal_data, _type=conf["resp"]["filter_type"], low_cut=conf["resp"]["low_cut"],
                                  high_cut=conf["resp"]["high_cut"], order=conf["resp"]["order"], sample_rate=signal_fs)

    bcg_data = utils.butterworth(data=signal_data, _type=conf["bcg"]["filter_type"], low_cut=conf["bcg"]["low_cut"],
                                 high_cut=conf["bcg"]["high_cut"], order=conf["bcg"]["order"], sample_rate=signal_fs)


    label_data = utils.read_label_csv(path=label_path)
    label_mask = utils.generate_event_mask(signal_second=signal_second, event_df=label_data)

    manual_disable_mask = utils.generate_disable_mask(signal_second=signal_second, disable_df=all_samp_disable_df[all_samp_disable_df["id"] == samp_id])
    print(f"disable_mask_shape: {manual_disable_mask.shape}, num_disable: {np.sum(manual_disable_mask == 0)}")

    # 分析Resp的低幅值区间
    resp_low_amp_conf = getattr(conf, "resp_low_amp", None)
    if resp_low_amp_conf is not None:
        resp_low_amp_mask = signal_method.detect_low_amplitude_signal(
            signal_data=resp_data,
            sampling_rate=signal_fs,
            window_size_sec=resp_low_amp_conf["window_size_sec"],
            stride_sec=resp_low_amp_conf["stride_sec"],
            amplitude_threshold=resp_low_amp_conf["amplitude_threshold"],
            merge_gap_sec=resp_low_amp_conf["merge_gap_sec"],
            min_duration_sec=resp_low_amp_conf["min_duration_sec"]
        )
    else:
        resp_low_amp_mask = None

    # 分析Resp的高幅值伪迹区间
    resp_move









if __name__ == '__main__':
    yaml_path = Path("./dataset_config/HYS_config.yaml")
    disable_df_path = Path("./排除区间.xlsx")

    conf = utils.load_dataset_conf(yaml_path)
    select_ids = conf["select_ids"]
    root_path = Path(conf["root_path"])

    print(f"select_ids: {select_ids}")
    print(f"root_path: {root_path}")

    org_signal_root_path = root_path / "OrgBCG_Aligned"
    label_root_path = root_path / "Label"

    all_samp_disable_df = utils.read_disable_excel(disable_df_path)

    process_one_signal(select_ids[0])