| Re: fitting mixed gaussians [message #44130 is a reply to message #44114] |
Wed, 18 May 2005 17:37   |
Craig Markwardt
Messages: 1869
Registered: November 1996
|
Senior Member |
|
|
Ben Tupper <btupper@bigelow.org> writes:
> Rob wrote:
>> Thanks for the plug, David. Yep, PAN is just a big GUI wrapper for
>> MPCURVEFIT. In fact the kind of fitting described in the original post
>> is done routinely at our neutron scattering facility where the whole
>> model function can be composed of many Gaussians, Lorentzians,
>> Lognormals, etc.
>>
>
>>>
>>> Just draw and adjust the initial curve parameters on
>>> the data itself, then go fit it with a click of the button.
>>> Of course, it is simply a wrapper for MPFIT. The tutorial
>>> Rob has provided is helpful for getting started.
>>>
> Thanks David and Rob,
>
> Yes I looked at these - the trouble is that I want to perform this on
> literally thousands of images (each image has one object in it).
> Manual seeding is not practical. I think what I am after is described
> here...
>
> http://tinyurl.com/9horr
>
> I have started to translate the MatLab code to IDL - but it is clearly
> over my head.
Long ago I had somebody write to me, asking about how to do this kind
of thing. I don't think he ever succeeded.
You are going to have problems like:
1. uniqueness - there are essentially an infinite number of ways to
add gaussians to reproduce the data; for example, why not have
one gaussian per sample?
2. robustness - the problem is so unconstrained that there is
significant potential for screwed up fits.
I would recommend constraining the analysis as much as possible based
on your problem domain, for example if you know that peaks must be
positive, or the natural widths of the peaks, etc.
One technique would be to find the tallest peak, or perhaps the N
tallest peaks in the data, and fit gaussians to those. Then subtract
that fit and see what the next highest peaks are, and fit another set
of gaussians. You keep doing that until you reach some noise
threshold (i.e. the errors on the amplitudes are comparable to the
amplitudes). Sometimes this is known as "CLEAN" in image space.
But be prepared for the fitting to fail or give whacked results at
least 30% of the time.
Good luck,
Craig
--
------------------------------------------------------------ --------------
Craig B. Markwardt, Ph.D. EMAIL: craigmnet@REMOVEcow.physics.wisc.edu
Astrophysics, IDL, Finance, Derivatives | Remove "net" for better response
------------------------------------------------------------ --------------
|
|
|
|
comp.lang.idl-pvwave archive
Members
Search
Help
Login
Home
