[SciPy-User] Asymmetric peak fitting

Thu Mar 19 07:04:17 EDT 2015

Hi,

Ok, now I have a problem. In the attached script I try to fit some example
experimental data I have. The peak is actually a numerical derivative of a
step function - this is why the values are so large. I tried different
models, all except the ExponentialGaussianModel can handle the fit pretty
well using the automatically guessed parameters. I don't really understand
why. Is there something I'm missing here?

Cheers,

Pawel

2015-03-19 10:57 GMT+01:00 Paweł Kwaśniewski <pawel.kw at gmail.com>:

> Matt,
>
> I have one more question: the weights for fitting a model should be
> 1./error_bar or the error_bar? The name "weights" suggests the former, but
> I'd like to be sure.
>
> Cheers,
>
> Paweł
>
> 2015-03-19 9:09 GMT+01:00 Paweł Kwaśniewski <pawel.kw at gmail.com>:
>
>> Jonathan, Matt,
>>
>> Thank you for your answers. I must say that lmfit is already a good
>> friend of mine - I use it for quite some time for fitting. Somehow I didn't
>> think of looking there for asymmetric peak models. Thanks for pointing this
>> out! I suppose this will solve my problem.
>>
>> Cheers,
>>
>> Pawel
>>
>> 2015-03-19 2:16 GMT+01:00 Matt Newville <newville at cars.uchicago.edu>:
>>
>>> Pawel,
>>>
>>> On Wed, Mar 18, 2015 at 10:06 AM, Paweł Kwaśniewski <pawel.kw at gmail.com>
>>> wrote:
>>>
>>>> Hi All,
>>>>
>>>> I'm currently trying to fit some experimental data in the form of
>>>> asymmetric peaks. In principle it's difficult to find a distribution
>>>> describing this kind of data. My goal is to get the full width half maximum
>>>> of the peak and the peak position. I tried to look for some ready solutions
>>>> within scipy but could not find any. I did find a nice paper though:
>>>>
>>>> http://arxiv.org/abs/0711.4449
>>>>
>>>> Since it's not so trivial (at least not for me) to implement this, I'd
>>>> like to ask if anyone in the community has already done this (or something
>>>> similar) and would like to share the code.
>>>>
>>>> Cheers,
>>>>
>>>> Pawel Kwasniewski
>>>>
>>>
>>> You might find the lmfit module (http://lmfit.github.io/lmfit-py/)
>>> useful.  This is a module for least-squares minimization and curve-fitting,
>>> built on top of scipy.optimize.  It includes several built-in Models, a few
>>> representing asymmetric peaks such as an exponentially damped Gaussian (
>>> http://lmfit.github.io/lmfit-py/builtin_models.html#exponentialgaussianmodel).
>>> A quick look at the paper you referenced suggests this function might be
>>> sufficient, but if this or one of the other built-in Models isn't exactly
>>> what you're looking forward, it's very easy to make a new Model class from
>>> a Python function that calculates and returns the model function.  Lmfit
>>> Models can also be added  or multiplied together to make composite models
>>> (say, Gaussian + Step + Quadratic), and allow you to place bounds and/or
>>> constraints on any of the Parameters in the fit.
>>>
>>> Though there are many features you can use to set up models and their
>>> parameters, using these models (either built-in or one that you define
>>> yourself) for curve-fitting data is pretty straightforward.  See
>>> http://lmfit.github.io/lmfit-py/builtin_models.html#example-1-fit-peaked-data-to-gaussian-lorentzian-and-voigt-profiles
>>> for a simple example.   That example uses symmetric peaks, but the use of
>>> any Model for curve-fitting is basically the same.
>>>
>>> Hope that helps,
>>>
>>> --Matt Newville
>>>
>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>>
>>
>
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import lmfit
import pylab as pl

data = pl.loadtxt('example_data.txt')

x = data[:,0]
y = data[:,1]
#model = lmfit.models.ExponentialGaussianModel()
model = lmfit.models.GaussianModel()
#model = lmfit.models.SkewedGaussianModel()
#model = lmfit.models.VoigtModel()

pars = model.guess(y,x=x)
initguess = model.eval(params=pars,x=x)
out = model.fit(y,pars,x=x)
print(out.fit_report(min_correl=0.25))
fitres = model.eval(params=out.params,x=x)
fig = pl.figure()
ax = pl.subplot(111)
ax.plot(x,y,'o')
ax.plot(x,fitres,'-',lw=1)
ax.plot(x,initguess,'--k',lw=1)
pl.show()

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