/PythonPhot

Simple DAOPHOT-type photometry in Python translated from IDL AstroLib photometry algorithms. Please email me with bugs, suggestions, omissions, etc. The code is citable as part of the Astrophysics Source Code Library (http://ascl.net/1501.010).

Primary LanguagePython

PythonPhot PSF Fitting Photometry Tutorial

getpsf.py : Generates a point-spread function (PSF) from observed stars at specified locations. Uses the family of "peak fit" modules (pkfit, pkfit_noise, pkfit_norecent, etc) to fit a gaussian to each star and define an array of non-gaussian psf residuals. Returns a 5-element vector defining the gaussian, a 2-d array of psf residuals, and the magnitude of the psf. Also writes out the psf model to a fits file with the gaussian parameters in the header and the residuals in the data array.

rdpsf.py : Read the .fits file created by getpsf.py that contains the psf model gaussian parameters and 2-d array of residuals.

pkfit.py : fit a psf model to an isolated point source pkfit_noise : fitting with an input noise image pkfit_norecent : forced photometry (fitting a peak without recentering) pkfit_norecent_noise : forced photometry with an input noise image


EXAMPLE A : Make a psf model

     import getpsf
     import aper
     import numpy as np
     # load FITS image and specify PSF star coordinates
     image = pyfits.getdata(fits_filename)
     xpos,ypos = np.array([1450,1400]),np.array([1550,1600])

     # run aper to get mags and sky values for specified coords
     mag,magerr,flux,fluxerr,sky,skyerr,badflag,outstr = \
            aper.aper(image,xpos,ypos,phpadu=1,apr=5,zeropoint=25,
            skyrad=[40,50],badpix=[-12000,60000],exact=True)

     # use the stars at those coords to generate a PSF model
     gauss,psf,psfmag = \
            getpsf.getpsf(image,xpos,ypos,
                          mag,sky,1,1,np.arange(len(xpos)),
                          5,'output_psf.fits')

EXAMPLE B : fit a psf to isolated stars

 import pyfits
 from PythonPhot import pkfit

 # read in the fits images containing the target sources
 image = pyfits.getdata(fits_filename)
 noiseim = pyfits.getdata(fits_noise_filename)
 maskim = pyfits.getdata(fits_mask_filename)

 # read in the fits image containing the PSF (gaussian model
 # parameters and 2-d residuals array.
 psf = pyfits.getdata(psf_filename)
 hpsf = pyfits.getheader(psf_filename)
 gauss = [hpsf['GAUSS1'],hpsf['GAUSS2'],hpsf['GAUSS3'],hpsf['GAUSS4'],hpsf['GAUSS5']]

 # x and y points for PSF fitting
 xpos,ypos = np.array([1450,1400]),np.array([1550,1600])

 # run 'aper' on x,y coords to get sky values
 mag,magerr,flux,fluxerr,sky,skyerr,badflag,outstr = \
          aper.aper(image,xpos,ypos,phpadu=1,apr=5,zeropoint=25,
          skyrad=[40,50],badpix=[-12000,60000],exact=True)

 # load the pkfit class
 pk = pkfit.pkfit_class(image,gauss,psf,1,1,noiseim,maskim)

 # do the PSF fitting
 for x,y,s in zip(xpos,ypos,sky):
      errmag,chi,sharp,niter,scale = \
          pk.pkfit_norecent_noise(1,x,y,s,5)
      flux = scale*10**(0.4*(25.-hpsf['PSFMAG']))
      dflux = errmag*10**(0.4*(25.-hpsf['PSFMAG']))
      print('PSF fit to coords %.2f,%.2f gives flux %s +/- %s'%(x,y,flux,dflux))