fit()
if localize:
paranoid_localize(roi, name, verbosity=4)
if fit_extension:
roi.fit_extension(which=name)
paranoid_localize(roi, name)
fit()
print 'Making pointlike SED for hypothesis %s' % hypothesis
sed = PointlikeSED(roi, name, verbosity=4)
sed.save('%s/sed_pointlike_4bpd_%s_%s.yaml' %
(pipeline.dirdict['seds'], hypothesis, name))
sed.plot('%s/sed_pointlike_4bpd_%s_%s.png' %
(pipeline.dirdict['seds'], hypothesis, name))
print_summary()
p = source_dict(roi, name)
if upper_limit:
pul = PointlikePowerLawUpperLimit(roi, name, cl=.95, verbosity=4)
p['powerlaw_upper_limit'] = pul.todict()
roi.toXML(filename="%s/srcmodel_pointlike_%s_%s.xml" %
(pipeline.dirdict['data'], hypothesis, name))
roi.save('roi_%s_%s.dat' % (hypothesis, name))
return p
fit(fit_bg_first=True)
fit()
if localize:
paranoid_localize(roi, name, verbosity=4)
if fit_extension:
roi.fit_extension(which=name)
paranoid_localize(roi, name)
fit()
print 'Making pointlike SED for hypothesis %s' % hypothesis
sed = PointlikeSED(roi, name, verbosity=4)
sed.save('%s/sed_pointlike_4bpd_%s_%s.yaml' % (pipeline.dirdict['seds'],hypothesis,name))
sed.plot('%s/sed_pointlike_4bpd_%s_%s.png' % (pipeline.dirdict['seds'],hypothesis,name))
print_summary()
p = source_dict(roi, name)
if upper_limit:
pul = PointlikePowerLawUpperLimit(roi, name, cl=.95, verbosity=4)
p['powerlaw_upper_limit']=pul.todict()
roi.toXML(filename="%s/srcmodel_pointlike_%s_%s.xml"%(pipeline.dirdict['data'], hypothesis, name))
roi.save('roi_%s_%s.dat' % (hypothesis,name))
return p
print 'Localization Ellipse:',m.todict()
if fit_extension:
roi.fit_extension(which=name)
ellipse = paranoid_localize(roi, name)
print ellipse
unfreeze_far_away(roi, frozen)
fit()
print 'Making pointlike SED for hypothesis %s' % hypothesis
sed = PointlikeSED(roi, name, verbosity=4)
sed.save('%s/sed_pointlike_4bpd_%s_%s.yaml' % (seddir,hypothesis,name))
sed.plot('%s/sed_pointlike_4bpd_%s_%s.png' % (seddir,hypothesis,name))
print_summary()
p = source_dict(roi, name)
pul = PointlikePowerLawUpperLimit(roi, name, emin=emin, emax=emax, cl=.95, verbosity=4)
p['powerlaw_upper_limit']=pul.todict()
cul = PointlikeCutoffUpperLimit(roi, name, Index=1.7, Cutoff=3e3, b=1, cl=.95, verbosity=4)
p['cutoff_upper_limit']=cul.todict()
if cutoff:
try:
tc = PointlikeCutoffTester(roi,name, cutoff_model=cutoff_model, verbosity=4)
p['test_cutoff']=tc.todict()
tc.plot(sed_results='%s/sed_pointlike_4bpd_%s_%s.yaml' % (seddir,hypothesis,name),
