在Python中解卷积Voigt拟合以提取洛伦兹半高宽？

import numpy as np
from scipy.signal import fftconvolve
from scipy.optimize import curve_fit
from scipy.special import wofz

def voigt(x, sigma, gamma):
    z = (x + 1j*gamma) / (sigma * np.sqrt(2))
    return np.real(wofz(z).real)

def lorentz(x, x0, gamma):
    return (gamma / np.pi) / ((x - x0)**2 + gamma**2)

def convolve_voigt(x, y, sigma, gamma):
    voigt_y = voigt(x, sigma, gamma)
    convolved = fftconvolve(y, voigt_y, mode='same')
    return convolved

def fit_voigt(x, y):
    p0 = [1, 1]  # 初始参数猜测值
    popt, pcov = curve_fit(convolve_voigt, x, y, p0=p0)
    return popt[0], popt[1]  # 返回拟合得到的sigma和gamma值

x = np.linspace(-10, 10, 1000)  # x轴范围
y = lorentz(x, 0, 1)  # 生成洛伦兹函数曲线
y_noisy = y + np.random.normal(0, 0.01, len(x))  # 添加噪声
sigma, gamma = fit_voigt(x, y_noisy)  # 进行Voigt拟合