您可以 在scipy中使用最小二乘优化函数，以将任意函数拟合到另一个函数。如果拟合
正弦函数，则要拟合的三个参数是偏移量（’a’），幅度（’b’）和相位（’c’）。

只要您对参数进行合理的初步猜测，优化就可以很好地收敛。幸运的是，对于正弦函数，
可以很容易地对其中的2个进行首次估算：可以通过取数据平均值和幅值来估算偏移量。 RMS（3 *标准偏差/ sqrt（2））。

注意：作为以后的编辑，还添加了频率拟合。这不能很好地工作（可能导致极差的拟合）。因此，根据您的
判断，我的建议是除非频率误差小于百分之几，否则不要使用频率拟合。

这将导致以下代码：

import numpy as np
from scipy.optimize import leastsq
import pylab as plt

N = 1000 # number of data points
t = np.linspace(0, 4*np.pi, N)
f = 1.15247 # Optional!! Advised not to use
data = 3.0*np.sin(f*t+0.001) + 0.5 + np.random.randn(N) # create artificial data with noise

guess_mean = np.mean(data)
guess_std = 3*np.std(data)/(2**0.5)/(2**0.5)
guess_phase = 0
guess_freq = 1
guess_amp = 1

# we'll use this to plot our first estimate. This might already be good enough for you
data_first_guess = guess_std*np.sin(t+guess_phase) + guess_mean

# Define the function to optimize, in this case, we want to minimize the difference
# between the actual data and our "guessed" parameters
optimize_func = lambda x: x[0]*np.sin(x[1]*t+x[2]) + x[3] - data
est_amp, est_freq, est_phase, est_mean = leastsq(optimize_func, [guess_amp, guess_freq, guess_phase, guess_mean])[0]

# recreate the fitted curve using the optimized parameters
data_fit = est_amp*np.sin(est_freq*t+est_phase) + est_mean

# recreate the fitted curve using the optimized parameters

fine_t = np.arange(0,max(t),0.1)
data_fit=est_amp*np.sin(est_freq*fine_t+est_phase)+est_mean

plt.plot(t, data, '.')
plt.plot(t, data_first_guess, label='first guess')
plt.plot(fine_t, data_fit, label='after fitting')
plt.legend()
plt.show()

编辑：我假设您知道正弦波中的周期数。如果您不这样做，则安装起来会有些棘手。您可以尝试
通过手动绘制来猜测周期数，并尝试将其优化为第6个参数。