在Python中拟合Voigt函数与数据

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

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

def wofz(z):
    return np.real(wof(z) + 1j*wof(z, 1))

def wof(z, n=0):
    a = [1.0000000, 0.57721566, -0.655878071, -0.042002635, 0.166538611, -0.042197734, -0.0096219715]
    b = [0.99999999999999709182, 0.57721566490153286060, 0.076939922174348439382, -0.0020353708497186932755, 0.83643535818340344505, -0.22477354587992980704, 0.0063152639836473649675, -0.0020568344804678375822, 0.0011330272319816958824, -0.000092825493827160327194, 0.000010253077892675368384]
    z = np.asarray(z)
    if n == 0:
        return np.exp(-z**2) * np.polyval(a[::-1], z**2)
    else:
        return np.exp(-z**2) * np.polyval(b[::-1], z**2) / (z**2 + 1)

x = np.linspace(-10, 10, 100)
y = voigt(x, 1, 1) + np.random.normal(0, 0.1, len(x))

popt, pcov = optimize.curve_fit(voigt, x, y, p0=[1, 1])

plt.plot(x, y, 'b-', label='data')
plt.plot(x, voigt(x, *popt), 'r-', label='fit')
plt.legend()
plt.show()