Scipy curve_fit多系列数据，需要为几条曲线设置一组唯一的拟合参数

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

# 假设的数据生成函数
def func(x, a, b):
    return a * x + b

# 生成两组模拟数据
x_data = np.linspace(0, 10, 100)
y1_data = func(x_data, 2.5, 1) + 0.2 * np.random.normal(size=100)
y2_data = func(x_data, 2.5, -1) + 0.2 * np.random.normal(size=100)

# 初始猜测参数
initial_guess = [2, 0]

# 使用curve_fit进行拟合
params, cov_matrix = curve_fit(func, x_data, y1_data, p0=initial_guess)
params2, cov_matrix2 = curve_fit(lambda x, b: func(x, *params[:2]), x_data, y2_data, p0=params[2:])

# 绘制结果
plt.scatter(x_data, y1_data, label='Data 1')
plt.scatter(x_data, y2_data, label='Data 2')
plt.plot(x_data, func(x_data, *params), 'r-', label='Fit 1')
plt.plot(x_data, func(x_data, *params[:2], params2[0]), 'g-', label='Fit 2')
plt.legend()
plt.show()

print('Shared parameters:', params[:2])
print('Unique parameter for second series:', params2[0])