109 lines
3.5 KiB
Python
109 lines
3.5 KiB
Python
import numpy as np
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from scipy.stats import pearsonr
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import matplotlib.pyplot as plt
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import seaborn as sns
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from ecg_peaks_val import ecg_peaks_val
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(P_peaks, Q_peaks, R_peaks, S_peaks, T_peaks,
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P_onsets, P_offsets, T_onsets, T_offsets,
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P_peaks_values, Q_peaks_values, R_peaks_values, S_peaks_values, T_peaks_values,
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P_onsets_values, P_offsets_values, T_onsets_values, T_offsets_values,
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PQ_baseline) = ecg_peaks_val()
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from ppg_peaks_val import ppg_peaks_val
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(on, on_toArea, sp, dn, dp,
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on_values, on_toArea_values, sp_values, dn_values, dp_values,
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u, u_toArea, v, w,
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u_values, u_toArea_values, v_values, w_values,
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a, a_toArea, b, c, e, f,
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a_values, a_toArea_values, b_values, c_values, e_values, f_values) = ppg_peaks_val()
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from ecg_time_interval import compute_interval
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intervals_ecg, ratio_pp_tt = compute_interval()
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from ppg_time_interval import ppg_time_interval
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differences_ppg = ppg_time_interval()
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# 读取文件中的数值
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def read_values_from_file(file_path):
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with open(file_path, 'r') as file:
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values = file.readlines()
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# 将字符串转换为浮点数
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values = [float(value.strip()) for value in values]
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return values
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# 文件路径
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file1_path = "D://python_study//big_boss//doc//output//data1.txt" # 第一个文件路径
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file2_path = "D://python_study//big_boss//doc//output//data2.txt" # 第二个文件路径
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# 读取文件中的数值
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data1 = read_values_from_file(file1_path)
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data2 = read_values_from_file(file2_path)
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# 计算皮尔逊相关系数和 p 值
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correlation, p_value = pearsonr(data1, data2)
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# 输出结果
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print(f"Pearson correlation coefficient: {correlation:.4f}")
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print(f"P-value: {p_value:.4f}")
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# 判断显著性
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alpha = 0.05 # 显著性水平
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if p_value < alpha:
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print("The correlation is statistically significant.")
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else:
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print("The correlation is not statistically significant.")
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# 使用 seaborn 绘制散点图并显示回归线
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plt.figure(figsize=(8, 6))
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sns.regplot(x=data1, y=data2, scatter_kws={'alpha': 0.7, 'color': 'blue'}, line_kws={'color': 'red'})
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plt.title('Scatter Plot with Regression Line')
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plt.xlabel('data1 (on-dp)')
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plt.ylabel('data2 (R-T)')
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plt.show()
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# # data1 = np.diff(P_onsets)
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# # data2 = np.diff(on)
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# data1 = differences_ppg['on-dn']
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# print(data1)
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# # 将 data1 的值保存到文件中
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# file_path_1 = "D://python_study//big_boss//doc//output//data1.txt" # 定义文件路径
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# with open(file_path_1, 'a') as file: # 打开文件
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# for value in data1:
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# file.write(f"{value}\n") # 将每个值写入文件，每个值占一行
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# print(f"data1 的值已保存到文件 {file_path_1}")
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# # 将 data2 的值保存到文件中
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# file_path_2 = "D://python_study//big_boss//doc//output//data2.txt" # 定义文件路径
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# # 读取文件中的数值
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# data1 = read_values_from_file(file_path_1)
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# data2 = read_values_from_file(file_path_2)
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# # 计算皮尔逊相关系数和 p 值
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# correlation, p_value = pearsonr(data1, data2)
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# # 输出结果
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# print(f"Pearson correlation coefficient: {correlation:.4f}")
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# print(f"P-value: {p_value:.4f}")
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# # 判断显著性
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# alpha = 0.05 # 显著性水平
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# if p_value < alpha:
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# print("The correlation is statistically significant.")
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# else:
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# print("The correlation is not statistically significant.")
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# # 使用 seaborn 绘制散点图并显示回归线
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# plt.figure(figsize=(8, 6))
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# sns.regplot(x=data1, y=data2, scatter_kws={'alpha': 0.7, 'color': 'blue'}, line_kws={'color': 'red'})
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# plt.title('Scatter Plot with Regression Line')
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# plt.xlabel('data1 (on-dn)')
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# plt.ylabel('data2 (R-T)')
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# plt.show() |