优化这段pythonimport numpy as np import matplotlib.pyplot as plt import math # 待测信号 freq = 17.77777 # 信号频率 t = np.linspace(0, 0.2, 1001) Omega =2 * np.pi * freq phi = np.pi A=1 x = A * np.sin(Omega * t + phi) # 加入噪声 noise = 0.2 * np.random.randn(len(t)) x_noise = x + noise # 参考信号 ref0_freq = 17.77777 # 参考信号频率 ref0_Omega =2 * np.pi * ref0_freq ref_0 = 2np.sin(ref0_Omega * t) # 参考信号90°相移信号 ref1_freq = 17.77777 # 参考信号频率 ref1_Omega =2 * np.pi * ref1_freq ref_1 = 2np.cos(ref1_Omega * t) # 混频信号 signal_0 = x_noise * ref_0 signal_1 = x_noise * ref_1 # 绘图 plt.figure(figsize=(13,4)) plt.subplot(2,3,1) plt.plot(t, x_noise) plt.title('input signal', fontsize=13) plt.subplot(2,3,2) plt.plot(t, ref_0) plt.title('reference signal', fontsize=13) plt.subplot(2,3,3) plt.plot(t, ref_1) plt.title('phase-shifted by 90°', fontsize=13) plt.subplot(2,3,4) plt.plot(t, signal_0) plt.title('mixed signal_1', fontsize=13) plt.subplot(2,3,5) plt.plot(t, signal_1) plt.title('mixed signal_2', fontsize=13) plt.tight_layout() # 计算平均值 X = np.mean(signal_0) Y = np.mean(signal_1) print("X=",X) print("Y=",Y) # 计算振幅和相位 X_square =X2 Y_square =Y2 sum_of_squares = X_square + Y_square result = np.sqrt(sum_of_squares) Theta = np.arctan2(Y, X) print("R=", result) print("Theta=", Theta)，把输入信号部分整理成函数：输入参数为t_vec,A,phi,noise;锁相测量部分也整理成代码，输入待测周期信号，以及频率freq,输出为A,phi,不用绘图

优化建议：
1. 可以将绘图部分注释掉，避免在计算时浪费时间和资源。
2. 可以将计算部分整合成一个函数，并返回锁相测量结果，方便后续调用和处理。

修改后的代码如下：

import numpy as np
import math

def lock_in_measurement(t_vec, A, phi, noise, freq):
    # 生成待测信号
    Omega = 2 * np.pi * freq
    x = A * np.sin(Omega * t_vec + phi)
    x_noise = x + noise

    # 生成参考信号
    ref0_Omega = 2 * np.pi * freq
    ref_0 = 2 * np.sin(ref0_Omega * t_vec)
    ref1_Omega = 2 * np.pi * freq
    ref_1 = 2 * np.cos(ref1_Omega * t_vec)

    # 混频信号
    signal_0 = x_noise * ref_0
    signal_1 = x_noise * ref_1

    # 计算平均值
    X = np.mean(signal_0)
    Y = np.mean(signal_1)

    # 计算振幅和相位
    X_square = X ** 2
    Y_square = Y ** 2
    sum_of_squares = X_square + Y_square
    result = np.sqrt(sum_of_squares)
    Theta = np.arctan2(Y, X)

    return result, Theta

使用方法示例：

t = np.linspace(0, 0.2, 1001)
A = 1
phi = np.pi
noise = 0.2 * np.random.randn(len(t))
freq = 17.77777

result, Theta = lock_in_measurement(t, A, phi, noise, freq)

print("R=", result)
print("Theta=", Theta)