To illustrate the use of curve_fit in weighted and unweighted least squares fitting, the following program fits the Lorentzian line shape function centered at $x_0$ with halfwidth at half-maximum (HWHM), $\gamma$, amplitude, $A$:
$$
f(x) = \frac{A \gamma^2}{\gamma^2 + (x-x_0)^2},
$$
to some artificial noisy data. The fit parameters are $A$, $\gamma$ and $x_0$. The noise is such that a region of the data close to the line centre is much noisier than the rest.
import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
x0, A, gamma = 12, 3, 5
n = 200
x = np.linspace(1, 20, n)
yexact = A * gamma**2 / (gamma**2 + (x-x0)**2)
# Add some noise with a sigma of 0.5 apart from a particularly noisy region
# near x0 where sigma is 3
sigma = np.ones(n)*0.5
sigma[np.abs(x-x0+1)<1] = 3
noise = np.random.randn(n) * sigma
y = yexact + noise
def f(x, x0, A, gamma):
""" The Lorentzian entered at x0 with amplitude A and HWHM gamma. """
return A *gamma**2 / (gamma**2 + (x-x0)**2)
def rms(y, yfit):
return np.sqrt(np.sum((y-yfit)**2))
# Unweighted fit
p0 = 10, 4, 2
popt, pcov = curve_fit(f, x, y, p0)
yfit = f(x, *popt)
print('Unweighted fit parameters:', popt)
print('Covariance matrix:'); print(pcov)
print('rms error in fit:', rms(yexact, yfit))
print()
# Weighted fit
popt2, pcov2 = curve_fit(f, x, y, p0, sigma=sigma, absolute_sigma=True)
yfit2 = f(x, *popt2)
print('Weighted fit parameters:', popt2)
print('Covariance matrix:'); print(pcov2)
print('rms error in fit:', rms(yexact, yfit2))
plt.plot(x, yexact, label='Exact')
plt.plot(x, y, 'o', label='Noisy data')
plt.plot(x, yfit, label='Unweighted fit')
plt.plot(x, yfit2, label='Weighted fit')
plt.ylim(-1,4)
plt.legend(loc='lower center')
plt.show()
As the figure above shows, the unweighted fit is seen to be thrown off by the noisy region. Data in this region are given a lower weight in the weighted fit and so the parameters are closer to their true values and the fit better. The output is:
Unweighted fit parameters: [ 11.61282984 3.64158981 3.93175714]
Covariance matrix:
[[ 0.0686249 -0.00063262 0.00231442]
[-0.00063262 0.06031262 -0.07116127]
[ 0.00231442 -0.07116127 0.16527925]]
rms error in fit: 4.10434012348
Weighted fit parameters: [ 11.90782988 3.0154818 4.7861561 ]
Covariance matrix:
[[ 0.01893474 -0.00333361 0.00639714]
[-0.00333361 0.01233797 -0.02183039]
[ 0.00639714 -0.02183039 0.06062533]]
rms error in fit: 0.694013741786