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
def get_reliability(self):
# finding sources
self.log.info(" Extracting the sources on both sides ")
pfile = self.prefix + self.catalogue_format + ".fits"
nfile = self.prefix + "_negative" + self.catalogue_format + ".fits"
# i need to catch mmap.mmap error here
# running a source finder
self.source_finder(image=self.negimage,
output=nfile, thresh=self.neg_smooth,
savemask=self.savemaskneg,
prefix=self.prefix, **self.opts_neg)
self.source_finder(image=self.imagename,
output=pfile, thresh=self.pos_smooth,
savemask=self.savemaskpos,
prefix=self.prefix, **self.opts_pos)
self.log.info(" Source Finder completed successfully ")
pmodel, positive, labels = self.params(pfile)
nmodel, negative, labels = self.params(nfile)
# setting up a kernel, Gaussian kernel
bandwidth = []
for plane in negative.T:
bandwidth.append(plane.std())
nplanes = len(labels)
cov = numpy.zeros([nplanes, nplanes])
nnsrc = len(negative)
npsrc = len(positive)
self.log.info(" There are %d positive and %d negtive detections "%(npsrc, nnsrc))
if nnsrc == 0 or npsrc ==0:
self.log.error("The resulting array has length of 0 thus cannot compute"
" the reliability. Aborting.")
self.log.info(" Computing the reliabilities ")
for i in range(nplanes):
for j in range(nplanes):
if i == j:
cov[i, j] = bandwidth[i]*((4.0/((nplanes+2)*
nnsrc))**(1.0/(nplanes+4.0)))
self.log.info("The resulting covariance matrix is %r"%cov)
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(pmodel.sources, rel):
src.setAttribute("rel", rf)
self.log.info(" Saved the reliabilities values.")
# remove sources with poor correlation and high reliability,
# the values are currently arbitrary
if self.do_psf_corr and self.derel:
for s in pmodel.sources:
cf, r = s.correlation_factor, s.rel
if cf < 0.006 and r > 0.60:
s.rel = 0.0
if self.makeplots:
savefig = self.prefix + "_planes.png"
utils.plot(positive, negative, rel=rel, labels=labels,
savefig=savefig, prefix=self.prefix)
# removes sources in a given radius from the phase center
if self.radiusrm:
self.log.info(" Remove sources ra, dec, radius of %r"
" from the phase center" %self.radiusrm)
pmodel = self.remove_sources_within(pmodel)
if not self.savefits:
self.log.info(" Deleting the negative image.")
os.system("rm -r %s"%self.negimage)
# Set field Center
pmodel.ra0, pmodel.dec0 = map(numpy.deg2rad, self.wcs.getCentreWCSCoords())
return pmodel, nmodel, self.locstep
def get_reliability(self):
# finding sources
self.log.info(" Extracting the sources on both sides ")
pfile = self.prefix + self.catalogue_format + ".fits"
nfile = self.prefix + "_negative" + self.catalogue_format + ".fits"
# i need to catch mmap.mmap error here
# running a source finder
self.source_finder(image=self.negimage,
output=nfile,
thresh=self.neg_smooth,
savemask=self.savemaskneg,
prefix=self.prefix,
**self.opts_neg)
self.source_finder(image=self.imagename,
output=pfile,
thresh=self.pos_smooth,
savemask=self.savemaskpos,
prefix=self.prefix,
**self.opts_pos)
self.log.info(" Source Finder completed successfully ")
pmodel, positive, labels = self.params(pfile)
nmodel, negative, labels = self.params(nfile)
# setting up a kernel, Gaussian kernel
bandwidth = []
for plane in negative.T:
bandwidth.append(plane.std())
nplanes = len(labels)
cov = numpy.zeros([nplanes, nplanes])
nnsrc = len(negative)
npsrc = len(positive)
self.log.info(" There are %d positive and %d negtive detections " %
(npsrc, nnsrc))
if nnsrc == 0 or npsrc == 0:
self.log.error(
"The resulting array has length of 0 thus cannot compute"
" the reliability. Aborting.")
self.log.info(" Computing the reliabilities ")
for i in range(nplanes):
for j in range(nplanes):
if i == j:
cov[i, j] = bandwidth[i] * ((4.0 / (
(nplanes + 2) * nnsrc))**(1.0 / (nplanes + 4.0)))
self.log.info("The resulting covariance matrix is %r" % cov)
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(pmodel.sources, rel):
src.setAttribute("rel", rf)
self.log.info(" Saved the reliabilities values.")
# remove sources with poor correlation and high reliability,
# the values are currently arbitrary
if self.do_psf_corr and self.derel:
for s in pmodel.sources:
cf, r = s.correlation_factor, s.rel
if cf < 0.006 and r > 0.60:
s.rel = 0.0
if self.makeplots:
savefig = self.prefix + "_planes.png"
utils.plot(positive,
negative,
rel=rel,
labels=labels,
savefig=savefig,
prefix=self.prefix)
# removes sources in a given radius from the phase center
if self.radiusrm:
self.log.info(" Remove sources ra, dec, radius of %r"
" from the phase center" % self.radiusrm)
pmodel = self.remove_sources_within(pmodel)
if not self.savefits:
self.log.info(" Deleting the negative image.")
os.system("rm -r %s" % self.negimage)
# Set field Center
pmodel.ra0, pmodel.dec0 = map(numpy.deg2rad,
self.wcs.getCentreWCSCoords())
return pmodel, nmodel, self.locstep