如何使用python分离两条高斯曲线？

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

def gaussian(x, a, x0, sigma):
    return a * np.exp(-(x - x0) ** 2 / (2 * sigma ** 2))

x = np.linspace(-10, 10, 100)
y1 = gaussian(x, 1, -2, 1)  # 第一条高斯曲线
y2 = gaussian(x, 0.5, 2, 0.5)  # 第二条高斯曲线
y = y1 + y2  # 两条高斯曲线的叠加

# 定义拟合函数
def fit_function(x, a1, x1, sigma1, a2, x2, sigma2):
    return gaussian(x, a1, x1, sigma1) + gaussian(x, a2, x2, sigma2)

# 初始参数猜测值
initial_guess = [1, -2, 1, 0.5, 2, 0.5]

# 拟合曲线
params, _ = curve_fit(fit_function, x, y, p0=initial_guess)

# 分离两条高斯曲线
y1_fit = gaussian(x, params[0], params[1], params[2])  # 第一条高斯曲线
y2_fit = gaussian(x, params[3], params[4], params[5])  # 第二条高斯曲线

plt.plot(x, y, 'b', label='Original Data')
plt.plot(x, y1_fit, 'r--', label='Gaussian 1')
plt.plot(x, y2_fit, 'g--', label='Gaussian 2')
plt.legend()
plt.xlabel('x')
plt.ylabel('y')
plt.show()