def show_residual(self, ci, psfmix, objmix=None):
"""
Show plots of the input compared with the fit gaussian mixtures.
"""
psfmodel = gmix_image.gmix2image(psfmix,ci.psf.shape,
counts=ci.psf.sum())
images.compare_images(ci.psf,psfmodel,
label1='psf',label2='gmix')
if objmix is not None:
skysig=None
if ci['skysig'] > 0:
skysig=ci['skysig']
model0 = gmix_image.gmix2image(objmix,ci.image0.shape,
counts=ci.image0.sum())
model = gmix_image.gmix2image(objmix,ci.image.shape,
psf=psfmix,
counts=ci.image.sum())
images.compare_images(ci.image0,model0,
label1='object0',label2='gmix',
skysig=skysig)
images.compare_images(ci.image,model,
label1='object+psf',label2='gmix',
skysig=skysig)
stop
def make_images(self):
import gmix_image
self.psf = gmix_image.gmix2image(self.psf_gmix,self['dims'],
nsub=self.nsub)
self.image = gmix_image.gmix2image(self.obj_gmix,self['dims'],
nsub=self.nsub)
#self.image0 = gmix_image.gmix2image(self.obj0_gmix,self['dims'],
# nsub=self.nsub)
self.image0=None
def get_normalized_model(self):
"""
For jacobian models
Fit for a flux then run the ydiff on it
"""
from gmix_image.gmix_fit import GMixFitMultiPSFFlux
from . import _render
if self.jacobian is None:
raise ValueError("only works for jacobian")
imlist = [self.image0]
wtlist = [self.image0 * 0 + self.ivar]
jacoblist = [self.jacobian]
gmix_list = [self.get_gmix()]
gm = GMixFitMultiPSFFlux(imlist, wtlist, jacoblist, gmix_list)
res = gm.get_result()
gmix = self.get_gmix()
gmix.set_psum(res["F"])
model = gmix_image.gmix2image(gmix, self.image0.shape, jacob=self.jacobian)
return model
def test():
import images
dguess=[{'p':0.58,'row':15.,'col':15.,'irr':1.3,'irc':-0.05,'icc':1.16},
{'p':0.19,'row':15.,'col':15.,'irr':7.7,'irc':-0.23,'icc':6.8}]
im=gmix_image.gmix2image(dguess, [31,31])
print 'counts:',im.sum()
images.multiview(im)
def show(self):
import images
if hasattr(self.gm,'popt'):
pars=self.gm.popt
else:
pars=self.gm.get_pars()
model=gmix2image(pars,self.psf.shape, coellip=True)
#print((model-self.psf).max())
#images.multiview(self.psf)
#images.multiview(model)
#model *= self['counts']/model.sum()
images.compare_images(self.psf, model)
def process_image(self, image, ngauss, cen, cov, psf=None,
coellip=False, cocenter=False, show=False):
im=image.copy()
# need no zero pixels and sky value
im_min = im.min()
if im_min <= 0:
im -= im_min
sky=0.001*im.max()
im += sky
else:
sky = im_min
# In the iteration, we can sometimes run into negative determinants.
# we will retry a few times with different random offsets in that case
flags = GMIXEM_ERROR_NEGATIVE_DET
ntry=0
while flags == GMIXEM_ERROR_NEGATIVE_DET and ntry < self['max_retry']:
guess = self.get_guess(ngauss, cen, cov)
gm = gmix_image.GMixEM(im,guess,
sky=sky,
maxiter=self['gmix_maxiter'],
tol=self['gmix_tol'],
coellip=coellip,
cocenter=cocenter,
psf=psf,
verbose=self['verbose'])
flags = gm.flags
#stderr.write("gmix niter: %d\n" % gm.numiter)
ntry += 1
#stderr.write("ntry: %d " % ntry)
out={'gmix': gm.pars,
'flags': gm.flags,
'numiter':gm.numiter,
'fdiff':gm.fdiff,
'ntry':ntry}
if flags == 0 and show:
print 'psf'
pprint(psf)
print 'gmix'
pprint(out['gmix'])
imrec=gmix_image.gmix2image(out['gmix'], image.shape,
psf=psf,counts=image.sum())
images.compare_images(image,imrec,
label1='image',label2='%d gauss' % ngauss)
return out
def test_measure_image_width(fwhm=20.0, smooth=0.1, nsub=20, type='erf'):
import gmix_image
print 'testing type:',type
sigma=fwhm/2.3548
dim=2*5*sigma
if (dim % 2)==0:
dim+=1
dims=[dim]*2
cen=[(dim-1)//2]*2
gm=gmix_image.GMix([1.0, cen[0], cen[1], sigma**2, 0.0, sigma**2])
im=gmix_image.gmix2image(gm, dims)
if type=='erf':
widths = measure_image_width(im, 0.5, smooth=smooth)
else:
widths = measure_image_width_interp(im, 0.5, nsub=nsub)
print 'true:',fwhm
print 'meas:',widths
print 'meas/true-1:',widths/fwhm-1