import numpy as np
from scipy.stats import pearsonr

import matplotlib.pyplot as plt
import seaborn as sns

from ecg_peaks_val import ecg_peaks_val
(P_peaks, Q_peaks, R_peaks, S_peaks, T_peaks,
P_onsets, P_offsets, T_onsets, T_offsets,
P_peaks_values, Q_peaks_values, R_peaks_values, S_peaks_values, T_peaks_values, 
P_onsets_values, P_offsets_values, T_onsets_values, T_offsets_values,
PQ_baseline) = ecg_peaks_val()

from ppg_peaks_val import ppg_peaks_val
(on, on_toArea, sp, dn, dp,
on_values, on_toArea_values, sp_values, dn_values, dp_values,
u, u_toArea, v, w,
u_values, u_toArea_values, v_values, w_values,
a, a_toArea, b, c, e, f,
a_values, a_toArea_values, b_values, c_values, e_values, f_values) = ppg_peaks_val()

from ecg_time_interval import compute_interval
intervals_ecg, ratio_pp_tt = compute_interval()

from ppg_time_interval import ppg_time_interval
differences_ppg = ppg_time_interval()


# 读取文件中的数值
def read_values_from_file(file_path):
    with open(file_path, 'r') as file:
        values = file.readlines()
    # 将字符串转换为浮点数
    values = [float(value.strip()) for value in values]
    return values

# 文件路径
file1_path = "D://python_study//big_boss//doc//output//data1.txt"  # 第一个文件路径
file2_path = "D://python_study//big_boss//doc//output//data2.txt"  # 第二个文件路径

# 读取文件中的数值
data1 = read_values_from_file(file1_path)
data2 = read_values_from_file(file2_path)

# 计算皮尔逊相关系数和 p 值
correlation, p_value = pearsonr(data1, data2)

# 输出结果
print(f"Pearson correlation coefficient: {correlation:.4f}")
print(f"P-value: {p_value:.4f}")

# 判断显著性
alpha = 0.05  # 显著性水平
if p_value < alpha:
    print("The correlation is statistically significant.")
else:
    print("The correlation is not statistically significant.")

# 使用 seaborn 绘制散点图并显示回归线
plt.figure(figsize=(8, 6))
sns.regplot(x=data1, y=data2, scatter_kws={'alpha': 0.7, 'color': 'blue'}, line_kws={'color': 'red'})
plt.title('Scatter Plot with Regression Line')
plt.xlabel('data1 (on-dp)')
plt.ylabel('data2 (R-T)')
plt.show()


# # data1 = np.diff(P_onsets)
# # data2 = np.diff(on)
# data1 = differences_ppg['on-dn']
# print(data1)


# # 将 data1 的值保存到文件中
# file_path_1 = "D://python_study//big_boss//doc//output//data1.txt"  # 定义文件路径
# with open(file_path_1, 'a') as file:  # 打开文件
#     for value in data1:
#         file.write(f"{value}\n")  # 将每个值写入文件，每个值占一行

# print(f"data1 的值已保存到文件 {file_path_1}")

# # 将 data2 的值保存到文件中
# file_path_2 = "D://python_study//big_boss//doc//output//data2.txt"  # 定义文件路径

# # 读取文件中的数值
# data1 = read_values_from_file(file_path_1)
# data2 = read_values_from_file(file_path_2)

# # 计算皮尔逊相关系数和 p 值
# correlation, p_value = pearsonr(data1, data2)

# # 输出结果
# print(f"Pearson correlation coefficient: {correlation:.4f}")
# print(f"P-value: {p_value:.4f}")

# # 判断显著性
# alpha = 0.05  # 显著性水平
# if p_value < alpha:
#     print("The correlation is statistically significant.")
# else:
#     print("The correlation is not statistically significant.")

# # 使用 seaborn 绘制散点图并显示回归线
# plt.figure(figsize=(8, 6))
# sns.regplot(x=data1, y=data2, scatter_kws={'alpha': 0.7, 'color': 'blue'}, line_kws={'color': 'red'})
# plt.title('Scatter Plot with Regression Line')
# plt.xlabel('data1 (on-dn)')
# plt.ylabel('data2 (R-T)')
# plt.show()