def make_psf_stats(self):
"""
Either generate moments from inputs or read an SDSS PSF
"""
psfmodel = self['psfmodel']
if isinstance(psfmodel, (str,unicode)):
# generate a random orientation
theta = 360.0*numpy.random.random()
Irr, Irc, Icc = admom.ellip2mom(self['Tpsf'], e=self['psf_ellip'], theta=theta)
self['Irr_psf'] = Irr
self['Irc_psf'] = Irc
self['Icc_psf'] = Icc
self.psf = None
else:
# this is a psf generator. We assume the psf has the center at
# the image middle, is normalized
psf = psfmodel.next()
cen = [(psf.shape[0]-1)/2., (psf.shape[1]-1)/2.]
out = admom.admom(psf, cen[0], cen[1])
if out['whyflag'] != 0:
raise RuntimeError("failure measuring psf admom")
self.psf=psf
self['Irr_psf'] = out['Irr']
self['Irc_psf'] = out['Irc']
self['Icc_psf'] = out['Icc']
self['Tpsf'] = out['Irr'] + out['Icc']
def make_object_stats(self):
"""
Generate moments for object from the inputs before convolution
"""
Tobj = self['Tpsf']/self['s2']
theta = 360.0*numpy.random.random()
Irr, Irc, Icc = admom.ellip2mom(Tobj, e=self['obj_ellip'], theta=theta)
self['Irr'] = Irr
self['Irc'] = Irc
self['Icc'] = Icc
def test_regauss(gal_model='gauss',
gal_T=16.0,
gal_e1=0.0,
gal_e2=0.0,
psf_T=4.0,
psf_e1=0.0,
psf_e2=0.0,
show=True):
from pprint import pprint
import fimage
import scipy.signal
nsigma=5
psf_sigma=numpy.sqrt(psf_T/2.)
psf_imsize = int( round(2*nsigma*psf_sigma) )
if (psf_imsize % 2) == 0:
psf_imsize+=1
psf_dims=[psf_imsize]*2
psf_cen=[(psf_imsize-1)/2.]*2
psf_moms = admom.ellip2mom(psf_T,e1=psf_e1,e2=psf_e2)
psf = fimage.model_image('gauss',
psf_dims,
psf_cen,
psf_moms,
nsub=16)
gal_moms = admom.ellip2mom(gal_T,e1=gal_e1,e2=gal_e2)
# for the image size, make it big enough to easily fit
# the *convolved* galaxy
gal_convolved_sigma = admom.mom2sigma(gal_T+psf_T)
gal_imsize = int( round(2*nsigma*gal_convolved_sigma) )
if gal_imsize < psf_imsize:
gal_imsize = psf_imsize
gal_cen = [ (gal_imsize-1)/2. ]*2
gal_dims = [int(gal_imsize)]*2
gal = fimage.model_image(gal_model,
gal_dims,
gal_cen,
gal_moms,
nsub=16)
levels=7
# now convolve with the PSF
imconv = scipy.signal.fftconvolve(gal, psf, mode='same')
if show:
import images
images.multiview(psf, levels=levels)
images.multiview(gal, levels=levels)
images.multiview(imconv, levels=levels)
rg = ReGauss(imconv, gal_cen[0], gal_cen[1], psf, guess=gal_T/2, guess_psf=psf_T/2)
rg.do_admom()
print("admom stats")
pprint(rg['imstats'])
rg.do_psf_admom()
print("psf admom stats")
pprint(rg['psfstats'])
rg.do_basic_corr()
print("admom corrected stats")
pprint(rg['corrstats'])
rg.do_regauss()
print("rg stats")
pprint(rg['rgstats'])
rg.do_rg_corr()
print("rg corrected stats")
pprint(rg['rgcorrstats'])