如何使用lmfit中的优化参数找到函数(伪Voigt)的区域？

import numpy as np
from scipy.special import wofz
from lmfit import Model

def pseudo_voigt(x, amplitude, center, sigma, fraction):
    """
    伪Voigt函数的定义
    :param x: 自变量
    :param amplitude: 幅值
    :param center: 中心位置
    :param sigma: 高斯部分的标准差
    :param fraction: Lorentz部分在整个函数中的比例
    :return: 函数值
    """
    sigma_g = sigma / np.sqrt(2 * np.log(2))  # 高斯标准差
    sigma_l = sigma  # Lorentz标准差

    # 高斯函数
    gaussian = amplitude * np.exp(-((x - center) ** 2) / (2 * sigma_g ** 2))

    # Lorentz函数
    lorentz = (amplitude * (sigma_l ** 2)) / ((x - center) ** 2 + sigma_l ** 2)

    return (1 - fraction) * gaussian + fraction * lorentz

# 创建模型对象
model = Model(pseudo_voigt)

# 设置参数的初始值
model.set_param_hint('amplitude', value=1.0)
model.set_param_hint('center', value=0.0)
model.set_param_hint('sigma', value=1.0)
model.set_param_hint('fraction', value=0.5)

# 将模型与数据绑定
result = model.fit(y, x=x, amplitude=1.0, center=0.0, sigma=1.0, fraction=0.5)

# 执行参数优化
result = model.fit(data, x=x, amplitude=1.0, center=0.0, sigma=1.0, fraction=0.5)

# 输出拟合结果
print(result.fit_report())

# 获取优化后的参数值
amplitude = result.best_values['amplitude']
center = result.best_values['center']
sigma = result.best_values['sigma']
fraction = result.best_values['fraction']

import matplotlib.pyplot as plt

# 绘制拟合曲线
x_fit = np.linspace(min(x), max(x), 1000)
y_fit = pseudo_voigt(x_fit, amplitude, center, sigma, fraction)

plt.plot(x_fit, y_fit, 'r-', label='Fit')

plt.scatter(x, y, s=10, color='b', label='Data')
plt.legend()
plt.show()