[AstroPy] Gaussian models fitter non-functional?

Fri Oct 23 11:40:05 EDT 2015

Thanks Larry.  Sorry I’m coming up to speed on this somewhat haltingly.  I’m an amateur astrophotographer (some of my images here <https://www.flickr.com/photos/jbw/albums/72157631316034648>), as well as a software engineer, and I’m working on an image-cataloging program that, among other things monitors exposures as they are captured and reports things like FHWM, estimated SNR, any correlated PSF elongation, etc., to help tune a capture session.  Astropy has been an absolute boon for this effort.

From what I can see the 2D-Gaussian fit in find_peaks() is in the dev release, which I’ll upgrade to shortly, but it doesn’t seem to report mean or std-devs or orientation of the fitted model, which I think I need for some of the above.  

So, the trick would be to use find_peaks() to get the centroid and then create cut-outs around them that I give to the fitting & modeling functions in astropy, is that right?   Or simply use detect_sources() + segment_properties() to get the cut-outs and covariance matrix from their image-moment analysis to seed the 2D Moffat fit, or whichever model I might use.  How different would you expect say a Gaussian 2D model’s centroids & orientation & standard deviations be from the segment_properties() figures on the same source?

I do hope I’m not overstepping things here as an amateur bugging you guys for help.  

Best,
John.

> On Oct 22, 2015, at 9:19 AM, Larry Bradley <lbradley at stsci.edu> wrote:
> 
> Hi John,
> 
> Just to clarify, the morphological properties calculated by photutils.segment_properties() are derived from the 2D image moments of the source (within the defined segment) -- 2D Gaussian fits are not used there.  Photutils also has a local peak finder (photutils.find_peaks()), which does have an option to use a 2D Gaussian fit for better precision.
> 
> Cheers,
> Larry
> 
> On Thu, Oct 22, 2015 at 12:08 PM, John Wainwright <johnw at lyric.com <mailto:johnw at lyric.com>> wrote:
> Thanks Erik, and sorry, I should have found those.  I realize now I was asking relative to the detect_sources() and segment_properties() functions in the photutils <http://photutils.readthedocs.org/en/latest/index.html> affiliate package, which I think use a 2D Gaussian fit to generate the basic morphological properties I've been getting.   Presumably, I could use the centroids from that data as peak seeds for a Moffat2D model fit.
> 
> Thanks again,
> John.
> 
>> On Oct 20, 2015, at 7:01 AM, Erik Bray <embray at stsci.edu <mailto:embray at stsci.edu>> wrote:
>> 
>> On 10/19/2015 05:56 PM, John Wainwright wrote:
>>> Just on the topic of PSF fitting, is there any plan to implement Moffat PSF
>>> models?  I'm trying to replicate some of the PSF fitting
>>> <http://pixinsight.com/doc/tools/DynamicPSF/DynamicPSF.html <http://pixinsight.com/doc/tools/DynamicPSF/DynamicPSF.html>> capabilities found
>>> in PixInsight <http://pixinsight.com/ <http://pixinsight.com/>> which provides excellent Moffat modeling
>>> support.
>> 
>> Well, there are are already these implementations:
>> 
>> http://docs.astropy.org/en/latest/api/astropy.modeling.functional_models.Moffat1D.html#astropy.modeling.functional_models.Moffat1D <http://docs.astropy.org/en/latest/api/astropy.modeling.functional_models.Moffat1D.html#astropy.modeling.functional_models.Moffat1D>
>> 
>> http://docs.astropy.org/en/latest/api/astropy.modeling.functional_models.Moffat2D.html#astropy.modeling.functional_models.Moffat2D <http://docs.astropy.org/en/latest/api/astropy.modeling.functional_models.Moffat2D.html#astropy.modeling.functional_models.Moffat2D>
>> 
>> So you can check how they compare.  You can also always add your own version:
>> 
>> http://docs.astropy.org/en/latest/modeling/new.html <http://docs.astropy.org/en/latest/modeling/new.html>
>> 
>> 
>> 
>>>> On Oct 19, 2015, at 11:45 AM, Jeff Mangum <jmangum at nrao.edu <mailto:jmangum at nrao.edu>
>>>> <mailto:jmangum at nrao.edu <mailto:jmangum at nrao.edu>>> wrote:
>>>> 
>>>> Thanks for the responses.  Adam Ginsburg and I iterated a bit on this problem
>>>> and found that the issue was the fact that the image I was fitting contained
>>>> nans.  Once I removed the nans the gaussian fit routine works.  Note that if
>>>> the fitter in astropy.models is not able to handle images with nans, then
>>>> perhaps it should issue a warning.
>>>> 
>>>> -- Jeff
>>>> 
>>>> On 10/19/15 11:00 AM, Erik Bray wrote:
>>>>> On 10/16/2015 09:47 AM, Giuliano Iorio wrote:
>>>>>> 
>>>>>> Hi Jeff,
>>>>>> this is the kind of staff that usually happens if the position of the
>>>>>> initial guess is too far away the right one. Did you plot the contour of the
>>>>>> fitted gaussian on the image?
>>>>>> It can be also that the image is too noisy around the target to fit. I
>>>>>> usually use only a little subarray around the target, then I search the
>>>>>> position of the maximum and I set the initial guess of xo and yo to its
>>>>>> position. After I normalize all the subarray to the value of the maximum and
>>>>>> cut all the signal below a certain threshold (usually 0.1).
>>>>>> In this way I am able to obtain a robust fit also in very noisy map.
>>>>> 
>>>>> I agree pretty much with all of Giuliano's advice.  And again it might help
>>>>> to exclude data outside a smaller subarray.  Without actually seeing the data
>>>>> it's hard to provide much other advice.
>>>>> 
>>>>> Have you tried a different optimizer?  There are a couple other non-linear
>>>>> fitters in the package and some have different sensitivities to initial
>>>>> conditions.  But it's certainly not "non-functional".
>>>>> 
>>>>> Erik
>>>>> 
>>>>>>> Hi Jeff--
>>>>>>> 
>>>>>>> The fitted model will not be in g_init (this remains unchanged), but in g.
>>>>>>>  If you take a look in g, you should be able to see the fitted results.
>>>>>>> 
>>>>>>> Cheers
>>>>>>> Steve
>>>>>>> 
>>>>>>> ----- Original Message -----
>>>>>>> From: "Jeff Mangum" <jmangum at nrao.edu <mailto:jmangum at nrao.edu> <mailto:jmangum at nrao.edu <mailto:jmangum at nrao.edu>>>
>>>>>>> To: astropy at scipy.org <mailto:astropy at scipy.org> <mailto:astropy at scipy.org <mailto:astropy at scipy.org>>
>>>>>>> Sent: Friday, October 16, 2015 3:11:56 PM
>>>>>>> Subject: [AstroPy] Gaussian models fitter non-functional?
>>>>>>> 
>>>>>>> Hello,
>>>>>>> 
>>>>>>> Long-time listener but first-time caller.  Have been trying to use the
>>>>>>> 2D Gaussian fitting capabilities within astropy.models to fit a 2D
>>>>>>> Gaussian to an image (in FITS format).  Here is my script:
>>>>>>> 
>>>>>>> import numpy as np
>>>>>>> import radio_beam
>>>>>>> from astropy.modeling import models, fitting
>>>>>>> from astropy import units as u
>>>>>>> from astropy.io <http://astropy.io/> import fits
>>>>>>> import astropy.coordinates as coord
>>>>>>> import astropy.wcs as wcs
>>>>>>> import matplotlib
>>>>>>> import pylab as pl
>>>>>>> 
>>>>>>> # The following is the position of the peak in both J1 and J2, so use
>>>>>>> # it as the starting guess
>>>>>>> ra0 = '13h15m03.50s'
>>>>>>> dec0 = '24d37m08.2s'
>>>>>>> 
>>>>>>> radeg = coord.Angle(ra0, unit=u.hour).degree
>>>>>>> decdeg = coord.Angle(dec0, unit=u.degree)
>>>>>>> 
>>>>>>> # Now open the image
>>>>>>> hdulist = fits.open('IC860CbandCarrayH2COJ1_moment0.fits')
>>>>>>> #hdulist = fits.open('IC860KubandDarrayH2COJ2_moment0.fits')
>>>>>>> w = wcs.WCS(hdulist[0].header, hdulist)
>>>>>>> 
>>>>>>> yy,xx = np.indices(hdulist[0].data.shape)
>>>>>>> 
>>>>>>> x0,y0 = w.wcs_world2pix(radeg, decdeg, 1)
>>>>>>> 
>>>>>>> # Now fit 2D gaussian
>>>>>>> g_init = models.Gaussian2D(amplitude=1., x_mean=x0, y_mean=y0,
>>>>>>> x_stddev=1, y_stddev=1)
>>>>>>> fit_g = fitting.LevMarLSQFitter()
>>>>>>> g = fit_g(g_init, xx, yy, hdulist[0].data)
>>>>>>> 
>>>>>>> 
>>>>>>> When I look at g_init (which is where I believe the fit results should
>>>>>>> be), I see that the fit has not moved from its initial settings.  It
>>>>>>> appears that the fitter just did not run.  With Adam Ginsburg's help I
>>>>>>> have a workaround using gaussfitter, but would certainly like to make
>>>>>>> this work with astropy.models.  Thanks!
>>>>>>> 
>>>>>>> -- Jeff
>> 
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