def compute(self):
roi=self.roi
which=self.which
roi.modify(which=which, model=self.model0, keep_old_flux=False)
if not self.quiet: print 'fit source %s with model0 (%s)' % (which,self.model0.name)
self.ll_0 = self.fit()
if not self.quiet:
print roi
sed = PointlikeSED(roi,name=which)
self.sed_points = sed.todict()
# TODO, get SED points
emin,emax=get_full_energy_range(roi)
flux=self.model0.i_flux(emin=emin,emax=emax)
self.model1.set_flux(flux,emin=emin,emax=emax)
self.comprehensive_model = ComprehensiveModel(self.model0.copy(), self.model1.copy())
self.comprehensive_model.theta=0.5
self.comprehensive_model_start = self.comprehensive_model.copy()
if not self.quiet: print 'fit source %s with model1 (%s)' % (which,self.model1.name)
roi.modify(which=self.which, model=self.comprehensive_model, keep_old_flux=False)
self.ll_1 = self.fit()
if not self.quiet:
print roi
self.TS_comp = 2*(self.ll_1-self.ll_0)
def compute(self):
roi = self.roi
which = self.which
roi.modify(which=which, model=self.model0, keep_old_flux=False)
if not self.quiet:
print 'fit source %s with model0 (%s)' % (which, self.model0.name)
self.ll_0 = self.fit()
if not self.quiet:
print roi
sed = PointlikeSED(roi, name=which)
self.sed_points = sed.todict()
# TODO, get SED points
emin, emax = get_full_energy_range(roi)
flux = self.model0.i_flux(emin=emin, emax=emax)
self.model1.set_flux(flux, emin=emin, emax=emax)
self.comprehensive_model = ComprehensiveModel(self.model0.copy(),
self.model1.copy())
self.comprehensive_model.theta = 0.5
self.comprehensive_model_start = self.comprehensive_model.copy()
if not self.quiet:
print 'fit source %s with model1 (%s)' % (which, self.model1.name)
roi.modify(which=self.which,
model=self.comprehensive_model,
keep_old_flux=False)
self.ll_1 = self.fit()
if not self.quiet:
print roi
self.TS_comp = 2 * (self.ll_1 - self.ll_0)
fit(just_prefactor=True)
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))
fit(just_prefactor=True)
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))
assert override_localization['method'] == 'MinuitLocalizer'
m=MinuitLocalizer(roi, name, verbosity=4)
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)
