from pathlib import Path

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import os
os.environ["DISPLAY"] = "localhost:10.0"
from func import calculate_hr_spo2_zhihu, ppg2spo2_pipeline, func_1
from spo2_pipeline import spo2_pipeline

def read_data(file_path):
    """
    Read data from a file and return it as a list of floats.

    Args:
        file_path (str or Path): The path to the file to read.
    Returns:
        list of float: The data read from the file.
    """
    df = pd.read_csv(file_path)
    # red = df['red'].values[300:]
    # ir = df['ied'].values[300:]
    red = df['red'].values[50:]
    ir = df['ied'].values[50:]
    return red, ir


def read_labels(file_path):
    """
    Read labels from a file and return them as a list of integers.

    Args:
        file_path (str or Path): The path to the file to read.
    Returns:
        list of int: The labels read from the file.
    """
    df = pd.read_csv(file_path)
    timestamp = df['timestamp'].values
    hr_label = df['心率_值'].values
    spo2_label = df['血氧组_值'].values.astype(float)
    r_value = df['R组_均方根'].values

    np.place(spo2_label, spo2_label == 0, np.nan)

    return timestamp, hr_label, spo2_label, r_value


def draw_compare(red_signal, ir_signal, spo2_values, hr_values, r_values, fs=25):
    """
    Draw comparison plots for red and IR signals along with SpO2 values.

    Args:
        red_signal (array-like): The red light signal data.
        ir_signal (array-like): The infrared light signal data.
        spo2_values (array-like): The computed SpO2 values.
        fs (int): Sampling frequency in Hz. Default is 25.
    """
    time_axis = [i / fs for i in range(len(red_signal))]

    plt.figure(figsize=(15, 10))

    plt.subplot(3, 1, 1)
    plt.plot(time_axis, red_signal, label='Red Signal', color='red')
    plt.plot(time_axis, ir_signal, label='IR Signal', color='blue')
    plt.title('Red and IR Signals')
    plt.xlabel('Time (s)')
    plt.ylabel('Amplitude')
    plt.legend()

    plt.subplot(3, 1, 2)
    plt.plot(spo2_values, label='Computed SpO2', color='green')
    plt.title('Computed SpO2 Values')
    plt.xlabel('Beats')
    plt.ylabel('SpO2 (%)')
    plt.legend()

    plt.subplot(3, 1, 3)
    plt.plot(hr_values, label='Computed Heart Rate', color='orange')
    plt.title('Computed Heart Rate Values')
    plt.xlabel('Beats')
    plt.ylabel('Heart Rate (bpm)')
    plt.legend()

    plt.tight_layout()
    plt.show()




if __name__ == '__main__':
    file_path = Path("./data/spo2hr[42-A3-C5-2F-F7-32]2025.11.17_17.12.15.csv")
    # file_path = "./data/spo2hr[42-A3-C5-2F-F7-32]2025.11.17_17.01.13.csv"
    red_signal, ir_signal = read_data(file_path)

    label_path = file_path.parent / "spo2_alg_info[42_A3_C5_2F_F7_32]2025.11.17_17.12.16.csv"
    timestamp, hr_label, spo2_label, r_value = read_labels(label_path)

    # draw_compare(red_signal, ir_signal, spo2_label, hr_label, r_value, fs=25)
    # df, summary = spo2_pipeline(
    #     red_signal,
    #     ir_signal,
    #     fs=25,
    #     calibrate_with_ref=spo2_label,
    #     A=104.0,
    #     B=17.0
    # )
    # print(summary)
    # print(df)
    # HR_result, SpO2_result = ppg2spo2_pipeline(red_signal, ir_signal, fs=25)

    func_1(red_signal, ir_signal, fs=25)