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
def gtlike_analysis(roi, name, emin, emax, hypothesis, snrsize, upper_limit=False):
""" perform spectral fit with gtlike to crosscheck the point-like anlaysis. """
print '\n\nPerforming Gtlike analysis\n\n'
gtlike=Gtlike(roi,binsz=1./8)
like=gtlike.like
like.fit(covar=True)
results = sourcedict(like,name,emin=emin,emax=emax)
if upper_limit:
# *) Perform upper limits assuming spectral index 2
# N.B., for the E>10GeV analysis we are very much in the Poisson instead
# of Gaussian regime. The likelihood function will be VERY linear. As a result,
# delta_log_like_limits = 50 should be much more reasonable (not quite sure
# how to quantify this right now...)
results['upper_limit'] = powerlaw_upper_limit(like,name, delta_log_like_limits=50, verbosity=2, emin=emin, emax=emax)
return results
if fit_extension:
try:
roi.fit_extension(name)
roi.localize(name, update=True)
except Exception, ex:
print 'ERROR localizing: ', ex
fit()
p = sourcedict(roi, name)
if extension_ul:
print 'UNABLE To Calculate Extension Upper limit'
if upper_limit:
p['upper_limit'] = powerlaw_upper_limit(roi, name, emin=emin, emax=emax, cl=.95)
if cutoff:
p['test_cutoff']=test_cutoff(roi,name)
roi.plot_sed(which=name,filename='sed_pointlike_%s_%s.pdf' % (hypothesis,name), use_ergs=True)
roi.save('roi_%s_%s.dat' % (hypothesis,name))
#plot(roi, hypothesis)
return p
def gtlike_analysis(roi, hypothesis, upper_limit=False, cutoff=False):
print 'Performing Gtlike crosscheck for %s' % hypothesis
gtlike=Gtlike(roi)
like=gtlike.like
roi.fit_extension(name)
roi.localize(name, update=True)
except Exception, ex:
print "ERROR localizing: ", ex
fit()
p = sourcedict(roi, name)
if extension_upper_limit:
print "Calculating extension upper limit"
p["extension_upper_limit"] = roi.extension_upper_limit(
which=name, confidence=0.95, spatial_model=Gaussian(), npoints=10
)
if upper_limit:
p["upper_limit"] = powerlaw_upper_limit(roi, name, emin=emin, emax=emax, cl=0.95)
if cutoff:
p["test_cutoff"] = test_cutoff(roi, name)
roi.plot_sed(which=name, filename="sed_pointlike_%s_%s.pdf" % (hypothesis, name), use_ergs=True)
roi.save("roi_%s_%s.dat" % (hypothesis, name))
if do_plots:
plots(roi, hypothesis)
return p
def gtlike_analysis(roi, hypothesis, upper_limit=False, cutoff=False):
print "Performing Gtlike crosscheck for %s" % hypothesis
try:
print 'First, localize with GridLocalize (helps with convergence)'
size=max(snrsize,0.5)
grid=GridLocalize(roi,which=name,size=size,pixelsize=size/10)
skydir = grid.best_position()
print 'Using Grid Localize, best position is (l,b)=(%.3f,%.3f)' % (skydir.l(),skydir.b())
roi.modify(which=name, skydir=skydir)
roi.localize(which=name, update=True)
except Exception, ex:
print 'ERROR localizing: ',ex
if fit_extension:
fit()
roi.fit_extension(which=name)
fit()
print 'Final Spectral Model for %s hypothesis:' % hypothesis
roi.print_summary(galactic=True)
results=sourcedict(roi,name,emin=emin,emax=emax)
if upper_limit:
results['upper_limit'] = powerlaw_upper_limit(roi,name, emin=emin, emax=emax, verbosity=2)
roi.save('roi_%s.dat' % hypothesis)
return results