I'm trying to fit a gaussian to this data
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I have tried the examples given in Python gaussian fit on simulated gaussian noisy data, and Fitting (a gaussian) with Scipy vs. ROOT et al without luck.
I'm looking to do this with lmfit
because it has several advantages. This attempt was done following lmfit
documentation, here is the code and plot
from numpy import sqrt, pi, exp
from lmfit import Model
import matplotlib.pyplot as plt
def gaussian(x, amp, cen, wid):
"1-d gaussian: gaussian(x, amp, cen, wid)"
return (amp/(sqrt(2*pi)*wid)) * exp(-(x-cen)**2 /(2*wid**2))
gmodel = Model(gaussian)
result = gmodel.fit(y, x=x, amp=-0.5, cen=4200, wid=2)
plt.plot(x, y,'ro', ms=6)
plt.plot(x, result.init_fit, 'g--', lw=2)
plt.plot(x, result.best_fit, 'b-', lw=2)
So in green is the fit with the initial parameters, and in blue is what should be the best fit, and as you can see I get a gaussian shifted from my points and a straight line.
Also, the third row of my data are the errors in the y axis. How can I take the errors into account when fitting the data with lmfit
?