import matplotlib.pyplot as plt import os from global_var import global_var_init cycle, fs, record_name, data_path = global_var_init() from processing import processing (signal_dwt , waves_dwt , rpeaks , ecg_signal, signal, signal.v, signal.fs, rows_to_extract, t_ecg, t_ppg, on_column_list, on_column_list_toArea, on_values, on_values_toArea, sp_column_list, sp_values, dn_column_list, dn_values, dp_column_list, dp_values, u_column_list, u_column_list_toArea, u_values, u_values_toArea, v_column_list, v_values, w_column_list, w_values, a_column_list, a_column_list_toArea, a_values, a_values_toArea, b_column_list, b_values, c_column_list, c_values, e_column_list, e_values, f_column_list, f_values, signal.ppg, signal.vpg, signal.apg, signal.jpg) = processing() 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() # 创建一个字典来存储所有的特征点(用于测试) features = { "P_peaks_values": P_peaks_values, "Q_peaks_values": Q_peaks_values, "R_peaks_values": R_peaks_values, "S_peaks_values": S_peaks_values, "T_peaks_values": T_peaks_values, "P_onsets_values": P_onsets_values, "P_offsets_values": P_offsets_values, "T_onsets_values": T_onsets_values, "T_offsets_values": T_offsets_values, "on_values": on_values, "on_toArea_values": on_toArea_values, "sp_values": sp_values, "dn_values": dn_values, "dp_values": dp_values, "u_values": u_values, "u_toArea_values": u_toArea_values, "v_values": v_values, "w_values": w_values, "a_values": a_values, "a_toArea_values": a_toArea_values, "b_values": b_values, "c_values": c_values, "e_values": e_values, "f_values": f_values, } # 遍历 features 字典并打印每个键对应的时间序列长度(用于测试) for label, times in features.items(): print(f"{label}: {len(times)}") # 设置保存目录 save_directory = "D://python_study//big_boss//doc//output" # 文件名 file_name = "coordinates.txt" # 文件名及路径 output_file = os.path.join(save_directory, file_name) # 清空文件（如果存在） open(output_file, "w").close() # 状态变量：是否按下空格键 space_pressed = False def on_key_press(event): """处理键盘按键事件，检查是否按下空格键。""" global space_pressed if event.key == ' ': # 检查是否按下空格键 space_pressed = True def on_key_release(event): """处理键盘松开事件，松开空格键时取消按下状态。""" global space_pressed if event.key == ' ': # 检查是否松开空格键 space_pressed = False def onclick(event): """处理鼠标点击事件，记录横坐标到文件，只有在按下空格键时才触发。""" global space_pressed if event.xdata is not None and space_pressed: # 判断是否按下空格键 x_coord = int(event.xdata*fs) print(f"{x_coord},") with open(output_file, "a") as f: f.write(f"{x_coord},") # 将横坐标写入文件 #-------------------------------------------------------------------------------------------------------- #-------------------------------------------------------------------------------------------------------- #-------------------------------------------------------------------------------------------------------- #-------------------------------------------------------------------------------------------------------- #----------------------------------- 创建绘图函数 ---------------------------------------- #-------------------------------------------------------------------------------------------------------- #-------------------------------------------------------------------------------------------------------- #-------------------------------------------------------------------------------------------------------- #-------------------------------------------------------------------------------------------------------- def create_plot_with_draggable_points(save_path = None): fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, sharex=True) # 绘制归一化后的ECG信号 ax1.plot(t_ecg, ecg_signal) ax1.set(xlabel='', ylabel='ECG') # 绘制归一化后的PPG信号 ax2.plot(t_ppg, signal.ppg) ax2.set(xlabel='', ylabel='PPG') # 绘制归一化后的一阶导数 ax3.plot(t_ppg, signal.vpg) ax3.set(xlabel='', ylabel='vpg') # 绘制归一化后的二阶导数 ax4.plot(t_ppg, signal.apg) ax4.set(xlabel='Time (s)', ylabel='apg') # 存储并绘制这些点对象 points_1 = [ax1.plot(x, y, 'o')[0] for x, y in zip(t_ecg[P_peaks], P_peaks_values)] points_1 += [ax1.plot(x, y, 'o', color='cyan')[0] for x, y in zip(t_ecg[Q_peaks], Q_peaks_values)] points_1 += [ax1.plot(x, y, 'o', color='purple')[0] for x, y in zip(t_ecg[R_peaks], R_peaks_values)] points_1 += [ax1.plot(x, y, 'o', color='blue')[0] for x, y in zip(t_ecg[S_peaks], S_peaks_values)] points_1 += [ax1.plot(x, y, 'o', color='black')[0] for x, y in zip(t_ecg[T_peaks], T_peaks_values)] points_1 += [ax1.plot(x, y, 'x', color='red')[0] for x, y in zip(t_ecg[P_onsets], P_onsets_values)] points_1 += [ax1.plot(x, y, 'x', color='magenta')[0] for x, y in zip(t_ecg[P_offsets], P_offsets_values)] points_1 += [ax1.plot(x, y, 'x', color='black')[0] for x, y in zip(t_ecg[T_onsets], T_onsets_values)] points_1 += [ax1.plot(x, y, 'o', color='red')[0] for x, y in zip(t_ecg[T_offsets], T_offsets_values)] points_2 = [ax2.plot(x, y, 'o', color='red')[0] for x, y in zip(t_ppg[on], on_values)] points_2 += [ax2.plot(x, y, 'o', color='blue')[0] for x, y in zip(t_ppg[sp], sp_values)] points_2 += [ax2.plot(x, y, 'x', color='orange')[0] for x, y in zip(t_ppg[dn], dn_values)] points_2 += [ax2.plot(x, y, 'o', color='purple')[0] for x, y in zip(t_ppg[dp], dp_values)] points_3 = [ax3.plot(x, y, 'o', color='cyan')[0] for x, y in zip(t_ppg[u], u_values)] points_3 += [ax3.plot(x, y, 'o', color='magenta')[0] for x, y in zip(t_ppg[v], v_values)] points_3 += [ax3.plot(x, y, 'o', color='black')[0] for x, y in zip(t_ppg[w], w_values)] points_4 = [ax4.plot(x, y, 'o', color='black')[0] for x, y in zip(t_ppg[a], a_values)] points_4 += [ax4.plot(x, y, 'o', color='blue')[0] for x, y in zip(t_ppg[b], b_values)] points_4 += [ax4.plot(x, y, 'o', color='green')[0] for x, y in zip(t_ppg[c], c_values)] points_4 += [ax4.plot(x, y, 'o', color='orange')[0] for x, y in zip(t_ppg[e], e_values)] points_4 += [ax4.plot(x, y, 'o', color='purple')[0] for x, y in zip(t_ppg[f], f_values)] # 绑定事件 fig.canvas.mpl_connect('button_press_event', onclick) fig.canvas.mpl_connect('key_press_event', on_key_press) fig.canvas.mpl_connect('key_release_event', on_key_release) # 如果指定了保存路径，则保存图像 if save_path: plt.savefig(save_path) plt.tight_layout() plt.show() # 将图形嵌入到 Qt 的 canvas 中 # canvas.figure = fig # canvas.draw() #-------------------------------------------------------------------------------------------------------- #-------------------------------------------------------------------------------------------------------- #-------------------------------------------------------------------------------------------------------- #-------------------------------------------------------------------------------------------------------- #----------------------------------- 调用绘图函数 ---------------------------------------- #-------------------------------------------------------------------------------------------------------- #-------------------------------------------------------------------------------------------------------- #-------------------------------------------------------------------------------------------------------- #-------------------------------------------------------------------------------------------------------- if __name__ == '__main__': create_plot_with_draggable_points()