如何在python中估计双高斯拟合的参数

import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt

def bimodal_gaussian(x, a1, b1, c1, a2, b2, c2):
    return a1 * np.exp(-(x - b1)**2 / (2 * c1**2)) + a2 * np.exp(-(x - b2)**2 / (2 * c2**2))

x = np.linspace(0, 10, 100)
y = bimodal_gaussian(x, 1, 3, 0.5, 0.5, 7, 1) + np.random.normal(0, 0.05, size=x.shape)

params, params_covariance = curve_fit(bimodal_gaussian, x, y, p0=[1, 3, 1, 0.5, 7, 1])

print("Estimated Parameters:")
print("a1 =", params[0])
print("b1 =", params[1])
print("c1 =", params[2])
print("a2 =", params[3])
print("b2 =", params[4])
print("c2 =", params[5])

plt.plot(x, y, 'bo', label='data')
plt.plot(x, bimodal_gaussian(x, *params), 'r-', label='fit')
plt.legend()
plt.show()