(Top) - Original images. (Bottom) - Residuals images after GALFIT (F160W) and PyGFIT modeling (R,J,[3.6]).

The Python Galaxy Fitter (PyGFIT) is a code designed to yield matched photometry for multiresolution data sets. In an era where quantitative morphological fitting is commonplace and automated, we have designed PyGFIT with the aim of providing fast, easy, and robust matched photometry for data sets where quantitative morphologies have already been derived from the highest resolution imaging. The code is described in detail in an upcoming paper (Mancone et al. 2013, in prep). This paper also discusses issues such as morphological k­corrections of which the user should be aware. Here we aim to provide a cookbook to guide users through the process from start to finish. In this example we assume that the quantitative morphologies will be derived with Galapagos/GALFIT, which is currently the most commonly used combination for automated quantitative morphologies. The PyGFIT code is sufficiently general however that it can handle any analytic parameterizations of galaxy structure through the inclusion of additional modules beyond the current Sersic and point source models. We encourage users who either develop or need additional structural models to contact us.

Source Code

The code can be downloaded here. A PyGFit example is also available for testing the installation, along with the complete expected output .

The following python libraries must be installed prior to pygfit: scipy, numpy, maplotlib, and pyfits. The first three can be found at the Scipy web page; while STScI hosts the Pyfits web page.

Installation of SExtractor (Bertin & Arnouts 1996) is also required. Mac users wishing to install SExtractor may wish to look at the following pages:
Scisoft OSX (distribution includes SExtractor) (Notes on installation on OS X 10.7)


In addition to the paper there is a cookbook to aid first time PyGFIT users. It can be found here.