1

I'm trying to fit a gaussian to this data

x = [4170.177259096838, 4170.377258006199, 4170.577256915561, 4170.777255824922, 4170.977254734283, 4171.177253643645, 4171.377252553006, 4171.577251462368, 4171.777250371729, 4171.977249281091, 4172.177248190453, 4172.377247099814, 4172.577246009175, 4172.777244918537, 4172.977243827898, 4173.17724273726, 4173.377241646621, 4173.577240555983, 4173.777239465344, 4173.977238374706, 4174.177237284067, 4174.377236193429, 4174.57723510279, 4174.777234012152, 4174.977232921513, 4175.177231830875, 4175.377230740236, 4175.577229649598, 4175.777228558959, 4175.977227468321, 4176.177226377682, 4176.377225287044, 4176.577224196405, 4176.777223105767, 4176.977222015128, 4177.17722092449, 4177.377219833851, 4177.577218743213, 4177.777217652574, 4177.977216561936, 4178.177215471297, 4178.377214380659, 4178.57721329002, 4178.777212199382, 4178.977211108743, 4179.177210018105, 4179.377208927466, 4179.577207836828, 4179.777206746189, 4179.977205655551, 4180.177204564912, 4180.377203474274, 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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)

enter image description here

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?

EternalGenin
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  • You're not getting an inverted gaussian. You're getting a Gaussian with amplitude 1, and a really high width. If you want to get an "inverted gaussian", you need to add another free parameter to your gaussian function to shift your y-axis (like the "b" in y=ax+b). – donghyun208 Jun 15 '17 at 18:26
  • I see. I'll try an extra parameter and see if it get better. Thanks – EternalGenin Jun 15 '17 at 18:56

1 Answers1

5

The easiest way to do this is probably to make use of the built-in models and combine the GaussianModel and ConstantModel. You can use the errors in the fitting using the keyword 'weights' as described here.

You'll probably want to do something like this:

import numpy as np
from lmfit import Model
from lmfit.models import GaussianModel, ConstantModel
import matplotlib.pyplot as plt

xval = np.array(x)
yval = np.array(y)
err = np.array(e)

peak = GaussianModel()
offset = ConstantModel()
model = peak + offset

pars = offset.make_params(c=np.median(y))
pars += peak.guess(yval, x=xval, amplitude=-0.5)

result = model.fit(yval, pars, x=xval, weights=1/err)
print(result.fit_report())

plt.plot(xval, yval, 'ro', ms=6)
plt.plot(xval, result.best_fit, 'b--')
Renee
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  • This works great, thanks! There is just a typo in the definition of pars, median(y) should be median(yval). Could you comment a little about the use of the += operator? And finally, I have to do this for many curves which I'll do with a for loop. Is there a way of store the parameters from the fit_report, lets say all the centres as a list? – EternalGenin Jun 26 '17 at 13:05
  • Ok, so I managed to obtain the values with `result.params['center'].value`. One last thing, the `fit_report ` is printing [Model] and [Variables] but no the [Fit Statistics] and I haven't been able to fix this. Do you know why this might happen? – EternalGenin Jun 30 '17 at 17:28
  • sorry for the late reply... I indeed intended to type `median(yval)` - both will work though as the list is converted to a array. For a detailed description of the usage of `make_params` and `guess`, I'd recommend reading the lmfit manual where this is all described. In short, `make_params` will create a `Parameters` class whereas `guess` will guess starting values of the parameters for the model based on the data. It doesn't really matter which method you use as long as you create a Parameter class for both models and then combine them (which is what the += operator does). – Renee Jun 30 '17 at 20:49
  • `fit_report` should report everything, but you're probably using an older version of `lmfit`. This [issue](https://github.com/lmfit/lmfit-py/issues/407) was introduced is v0.9.6 and fixed in v0.9.7. So please upgrade and see whether that solves the problem. – Renee Jun 30 '17 at 20:54
  • Thanks for your reply, I'll take a better look at the documentation but your answer makes it quite clear. And you are completely right, I had v0.9.6, I've now update it and is working perfect, thanks! – EternalGenin Jun 30 '17 at 21:09