def gtlike_analysis(roi, hypothesis, upper_limit=False, cutoff=False):
print 'Performing Gtlike crosscheck for %s' % hypothesis
gtlike=Gtlike(roi)
like=gtlike.like
like.fit(covar=True)
r=sourcedict(like, name)
if upper_limit:
r['upper_limit'] = powerlaw_upper_limit(like, name, emin=emin, emax=emax, cl=.95)
if cutoff:
r['test_cutoff']=test_cutoff(like,name)
for kind, kwargs in [['4bpd',dict(bin_edges=np.logspace(2,5,13))],
['1bpd',dict(bin_edges=np.logspace(2,5,4))]]:
print 'Making %s SED' % kind
sed = SED(like, name, **kwargs)
sed.plot('sed_gtlike_%s_%s_%s.png' % (kind,hypothesis,name))
sed.verbosity=True
sed.save('sed_gtlike_%s_%s_%s.dat' % (kind,hypothesis,name))
return r
def gtlike_analysis(roi, hypothesis, upper_limit=False, cutoff=False):
print "Performing Gtlike crosscheck for %s" % hypothesis
gtlike = Gtlike(roi)
like = gtlike.like
like.fit(covar=True)
r = sourcedict(like, name)
if upper_limit:
r["upper_limit"] = powerlaw_upper_limit(like, name, emin=emin, emax=emax, cl=0.95)
if cutoff:
r["test_cutoff"] = test_cutoff(like, name)
for kind, kwargs in [
["4bpd", dict(bin_edges=np.logspace(2, 5, 13))],
["1bpd", dict(bin_edges=np.logspace(2, 5, 4))],
]:
print "Making %s SED" % kind
sed = SED(like, name, **kwargs)
sed.plot("sed_gtlike_%s_%s_%s.png" % (kind, hypothesis, name))
sed.verbosity = True
sed.save("sed_gtlike_%s_%s_%s.dat" % (kind, hypothesis, name))
return r
like.fit(covar=True)
r['gtlike']=sourcedict(like,name,'_at_pulsar')
# calculate gtlike upper limits
# calculate TScutoff
roi.save('roi_%s.dat' % name)
open('results_%s.yaml' % name,'w').write(
yaml.dump(
tolist(results)
)
)
# save stuff out
roi.plot_tsmap(filename='residual_tsmap_%s.pdf' % name, size=8)
roi.zero_source(which=name)
roi.plot_tsmap(filename='source_tsmap_%s.pdf' % name, size=8)
roi.unzero_source(which=name)
roi.plot_sources(filename='sources_%s.pdf' % name, size=8, label_psf=False)
sed = SED(like,name)
sed.save('sed_gtlike_%s.dat' % name)
sed.plot('sed_gtlike_%s.png' % name)
