def _measure_gals_uw(self):
import fimage
nbin=self.im_stacks.size
for i in xrange(nbin):
print '-'*70
print self.im_stacks['s2n_min'][i], self.im_stacks['s2n_max'][i]
im=self.im_stacks['images'][i,:,:]
res=fimage.fmom(im)
irr=res['cov'][0]-self.psf_res_uw['cov'][0]
irc=res['cov'][1]-self.psf_res_uw['cov'][1]
icc=res['cov'][2]-self.psf_res_uw['cov'][2]
T=irr+icc
e1=(icc-irr)/T
e2=2*irc/T
sh1=0.5*e1/self.Rshear
sh2=0.5*e2/self.Rshear
err=0.16/sqrt(self.im_stacks['nstack'][i])
print 'uw sh1: %.6f +/- %.6f sh2: %.6f +/- %.6f' % (sh1,err,sh2,err)
def _measure_psf_uw(self):
import fimage
res=fimage.fmom(self.psf_stack)
pprint.pprint(res)
self.psf_res_uw=res
def _run_fmom(self, image, psf):
import fimage
res=fimage.fmom(image)
pres=fimage.fmom(psf)
irr = res['cov'][0]-pres['cov'][0]
irc = res['cov'][1]-pres['cov'][1]
icc = res['cov'][2]-pres['cov'][2]
e1=(icc-irr)/(irr+icc)
e2=2.*irc/(irr+icc)
sh1=0.5*e1
sh2=0.5*e2
print 'fmom: ',sh1,sh2
print 'fmom/Rshear:',sh1/self.Rshear,sh2/self.Rshear
return {'e1':e1, 'e2':e2}
def _measure_gals_uw(self):
import fimage
nbin=self.im_stacks.size
for i in xrange(nbin):
print '-'*70
print self.im_stacks['s2n_min'][i], self.im_stacks['s2n_max'][i]
im=self.im_stacks['images'][i,:,:]
res=fimage.fmom(im)
irr=res['cov'][0]-self.psf_res['cov'][0]
irc=res['cov'][1]-self.psf_res['cov'][1]
icc=res['cov'][2]-self.psf_res['cov'][2]
T=irr+icc
e1=(icc-irr)/T
e2=2*irc/T
print e1,e2
def test_turb(ngauss=2, Tfrac=False):
import pprint
import fimage
import admom
from fimage.transform import rebin
import images
from .gmix_fit import print_pars
counts=1.
fwhm=3.3
dims=array([20,20])
#fwhm=10.
#dims=array([60,60])
s2n_psf=1.e9
print 'making image'
expand_fac=5
psfexp=fimage.pixmodel.ogrid_turb_psf(dims*expand_fac,fwhm*expand_fac,
counts=counts)
psf0=rebin(psfexp, expand_fac)
psf,skysig=fimage.noise.add_noise_admom(psf0, s2n_psf)
psfres = admom.admom(psf,
dims[0]/2.,
dims[1]/2.,
sigsky=skysig,
guess=4.,
nsub=1)
"""
psfpars={'model':'turb','psf_fwhm':fwhm}
objpars={'model':'exp','cov':[2.0,0.0,2.0]}
s2n_obj=200.
ci_nonoise = fimage.convolved.ConvolverTurbulence(objpars,psfpars)
ci=fimage.convolved.NoisyConvolvedImage(ci_nonoise, s2n_obj, s2n_psf,
s2n_method='admom')
print ci['cen_uw']
print ci['cov_uw']
print 'running admom'
counts=ci.psf.sum()
psf=ci.psf, skysig=ci['skysig_psf']
psfres = admom.admom(ci.psf,
ci['cen_uw'][0],
ci['cen_uw'][1],
sigsky=ci['skysig_psf'],
guess=4.,
nsub=1)
"""
pprint.pprint(psfres)
if psfres['whyflag'] != 0:
raise ValueError("found admom error: %s" % admom.wrappers.flagmap[psfres['whyflag']])
print 'making prior/guess'
npars=2*ngauss+4
prior=zeros(npars)
width=zeros(npars) + 1000
prior[0]=psfres['row']
prior[1]=psfres['col']
prior[2]=psfres['e1']
prior[3]=psfres['e2']
Tpsf=psfres['Irr']+psfres['Icc']
if Tfrac:
if ngauss==3:
model='coellip-Tfrac'
Tmax = Tpsf*8.3
Tfrac1 = 1.7/8.3
Tfrac2 = 0.8/8.3
prior[4] = Tmax
prior[5] = Tfrac1
prior[6] = Tfrac2
prior[7] = 0.08*counts
prior[8] = 0.38*counts
prior[9] = 0.53*counts
else:
raise ValueError("Do Tfrac ngauss==2")
else:
model='coellip'
if ngauss==3:
Texamp=array([0.46,5.95,2.52])
pexamp=array([0.1,0.7,0.22])
Tfrac=Texamp/Texamp.sum()
pfrac=pexamp/pexamp.sum()
prior[4:4+3] = Tpsf*Tfrac
prior[7:7+3] = counts*pfrac
else:
prior[4] = Tpsf/3.0
prior[5] = Tpsf/3.0
prior[6] = counts/3.
prior[7] = counts/3.
# randomize
prior[0] += 0.01*srandu()
prior[1] += 0.01*srandu()
e1start=prior[2]
e2start=prior[3]
prior[2:2+2] += 0.02*srandu(2)
prior[4:npars] = prior[4:npars]*(1+0.05*srandu(2*ngauss))
print_pars(prior)
print 'doing fit'
gm = gmix_image.GMixFitCoellip(psf, skysig,
prior,width,
model=model,
Tpositive=True)
print_pars( gm.get_pars() )
gmix=gm.get_gmix()
print 'gmix'
pprint.pprint(gmix)
#print 'Tpsf:',Tpsf
moms=fimage.fmom(psf0)
print 'uw T:',moms['cov'][0]+moms['cov'][2]
#print 'unweighted T:',ci['cov_uw'][0]+ci['cov_uw'][1]
print 'T:',gmix.get_T()
model=gm.get_model()
images.compare_images(psf,model)
