def source_selection(self):
# signal-to-noise ratio
self.signal_to_noise()
# local variance
utils.local_variance(
self.imagedata, self.header, self.poscatalog, self.wcs,
self.pixsize, tag=self.snr_tag,local_region=self.local_region,
noise=self.noise, highvariance_factor= self.local_thresh,
high_local_tag=self.high_local_tag, neg_side=True,
setatr=False, prefix=self.prefix, do_high_loc=True)
# correlation
utils.psf_image_correlation(
catalog=self.poscatalog, psfimage=self.psfname,
imagedata=self.imagedata, header=self.header,
wcs=self.wcs, pixelsize=self.pixsize, corr_region=
self.psfcorr_region, thresh=self.high_corr_thresh,
tags=self.high_local_tag, coefftag=self.high_corr_tag,
setatr=False, do_high=True, prefix=self.prefix)
# number of negative detections
self.number_negatives()
return self.poscatalog, self.negcatalog
def get_reliability(self):
# finding sources
self.source_finder(image=self.imagename, lsmname=self.poslsm,
thresh=self.pos_smooth, **self.opts_pos)
self.source_finder(image=self.negativeimage, lsmname=self.neglsm,
thresh=self.neg_smooth, **self.opts_neg)
# removing sources within a specified radius
self.remove_sources_within(catalog=self.poslsm, rel_excl_src=
self.rel_excl_src)
self.remove_sources_within(catalog=self.neglsm, rel_excl_src=
self.rel_excl_src)
# add local variance as a parameter
if self.do_local_var:
utils.local_variance(self.imagedata, self.header,
catalog=self.poslsm, wcs=self.wcs,
pixelsize=self.pixelsize, local_region=
self.local_var_region, savefig=False,
highvariance_factor=None, prefix=self.prefix,
neg_side=True)
utils.local_variance(self.imagedata, self.header,
catalog=self.neglsm, wcs=self.wcs,
pixelsize=self.pixelsize, local_region=
self.local_var_region, savefig=False,
highvariance_factor=None, prefix=self.prefix, neg_side=True)
# compute correlation if only do_psf_corr = True
#and the psf is provided
if self.do_psf_corr and self.psfname:
utils.psf_image_correlation(
catalog=self.poslsm, psfimage=self.psfname,
imagedata=self.imagedata, header=self.header,
wcs=self.wcs, pixelsize=self.pixelsize,
corr_region=self.psf_corr_region, prefix= self.prefix)
utils.psf_image_correlation(
catalog=self.neglsm, psfimage=self.psfname,
imagedata=self.imagedata, header=self.header,
wcs=self.wcs, pixelsize=self.pixelsize,
corr_region=self.psf_corr_region, prefix=self.prefix)
##TODO verbose vs. logging
pmodel = Tigger.load(self.poslsm, verbose=self.loglevel)
nmodel = Tigger.load(self.neglsm, verbose=self.loglevel)
posSources = pmodel.sources
negSources = nmodel.sources
npsrc = len(posSources)
nnsrc = len(negSources)
positive, labels = self.params(posSources, pmodel)
negative, labels = self.params(negSources, nmodel)
# setting up a kernel, Gaussian kernel
bandwidth = []
for plane in negative.T:
bandwidth.append(plane.std())
nplanes = len(labels)
cov = numpy.zeros([nplanes, nplanes])
for i in range(nplanes):
for j in range(nplanes):
if i == j:
cov[i, j] = bandwidth[i]*((4.0/((nplanes+2)*
npsrc))**(1.0/(nplanes+4.0)))
pcov = utils.gaussian_kde_set_covariance(positive.T, cov)
ncov = utils.gaussian_kde_set_covariance(negative.T, cov)
# get number densities
nps = pcov(positive.T) * npsrc
nns = ncov(positive.T) * nnsrc
# define reliability of positive catalog
rel = (nps-nns)/nps
for src, rf in zip(posSources, rel):
src.setAttribute("rel", rf)
out_lsm = self.poslsm
pmodel.save(out_lsm)
if self.makeplots:
savefig = self.prefix + "_planes.png"
utils.plot(positive, negative, rel=rel, labels=labels,
savefig=savefig, prefix=self.prefix)
return self.poslsm, self.neglsm