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)