def _compute(self):
if self.verbosity: print 'calculating gtlike cutoff upper limit'
like = self.like
name = self.name
saved_state = SuperState(like)
old_flux = like.flux(name, self.emin, self.emax)
cutoff_model = PLSuperExpCutoff(Index=self.Index, Cutoff=self.Cutoff, b=self.b)
cutoff_model.set_flux(old_flux, emin=self.emin, emax=self.emax, strict=False)
cutoff_model.set_default_limits(oomp_limits=True)
cutoff_spectrum = build_gtlike_spectrum(cutoff_model)
like.setSpectrum(name,cutoff_spectrum)
like.syncSrcParams(name)
super(GtlikeCutoffUpperLimit,self)._compute()
saved_state.restore()
def _calculate(self):
like = self.like
name = self.name
if self.verbosity: print 'Testing cutoff in gtlike'
saved_state = SuperState(like)
emin, emax = get_full_energy_range(like)
self.results = d = dict(
energy = energy_dict(emin=emin, emax=emax, energy_units=self.energy_units)
)
try:
def get_flux():
return like.flux(name, emin, emax)
def spectrum():
source = like.logLike.getSource(name)
s=source.spectrum()
return spectrum_to_dict(s, errors=True)
old_flux = get_flux()
if spectrum()['name'] == 'PowerLaw':
pass
else:
powerlaw_model=PowerLaw(norm=1e-11, index=2, e0=np.sqrt(emin*emax))
powerlaw_model.set_flux(old_flux,emin=emin,emax=emax)
powerlaw_model.set_default_limits(oomp_limits=True)
if self.verbosity: print 'powerlaw_model is',powerlaw_model
powerlaw_spectrum=build_gtlike_spectrum(powerlaw_model)
like.setSpectrum(name,powerlaw_spectrum)
if self.verbosity:
print 'About to fit powerlaw_spectrum'
print summary(like)
paranoid_gtlike_fit(like, verbosity=self.verbosity)
if self.verbosity:
print 'Done fitting powerlaw_spectrum'
print summary(like)
d['hypothesis_0'] = source_dict(like, name, emin=emin, emax=emax,
flux_units=self.flux_units,
energy_units=self.energy_units,
verbosity=self.verbosity)
if self.cutoff_model is None:
self.cutoff_model=PLSuperExpCutoff(norm=1e-9, index=1, cutoff=1000, e0=1000, b=1)
self.cutoff_model.set_free('b', False)
self.cutoff_model.set_flux(old_flux,emin=emin,emax=emax)
self.cutoff_model.set_default_limits(oomp_limits=True)
if self.verbosity:
print 'cutoff_model is',self.cutoff_model
cutoff_spectrum=build_gtlike_spectrum(self.cutoff_model)
like.setSpectrum(name,cutoff_spectrum)
if self.verbosity:
print 'About to fit cutoff_model'
print summary(like)
paranoid_gtlike_fit(like, verbosity=self.verbosity)
ll = like.logLike.value()
if ll < d['hypothesis_0']['logLikelihood']:
# if fit is worse than PowerLaw fit, then
# restart fit with parameters almost
# equal to best fit powerlaw
cutoff_plaw=PLSuperExpCutoff(b=1)
cutoff_plaw.set_free('b', False)
cutoff_plaw.setp_gtlike('norm', d['hypothesis_0']['spectrum']['Prefactor'])
cutoff_plaw.setp_gtlike('index', d['hypothesis_0']['spectrum']['Index'])
cutoff_plaw.setp_gtlike('e0', d['hypothesis_0']['spectrum']['Scale'])
cutoff_plaw.setp_gtlike('cutoff', 1e6)
cutoff_plaw.set_default_limits(oomp_limits=True)
temp=build_gtlike_spectrum(cutoff_plaw)
like.setSpectrum(name,temp)
if self.verbosity:
print 'Redoing fit with cutoff same as plaw'
print summary(like)
paranoid_gtlike_fit(like, verbosity=self.verbosity)
if self.verbosity:
print 'Done fitting cutoff_spectrum'
print summary(like)
d['hypothesis_1'] = source_dict(like, name, emin=emin, emax=emax,
flux_units=self.flux_units,
energy_units=self.energy_units,
verbosity=self.verbosity)
if self.cutoff_xml_name is not None:
like.writeXml(self.cutoff_xml_name)
d['TS_cutoff']=d['hypothesis_1']['TS']['reoptimize']-d['hypothesis_0']['TS']['reoptimize']
if self.verbosity:
print 'For cutoff test, TS_cutoff = ', d['TS_cutoff']
except Exception, ex:
print 'ERROR gtlike test cutoff: ', ex
traceback.print_exc(file=sys.stdout)
self.results = None
def _calculate(self):
like = self.like
name = self.name
if self.verbosity: print 'Testing cutoff in gtlike'
saved_state = SuperState(like)
emin, emax = get_full_energy_range(like)
self.results = d = dict(energy=energy_dict(
emin=emin, emax=emax, energy_units=self.energy_units))
try:
def get_flux():
return like.flux(name, emin, emax)
def spectrum():
source = like.logLike.getSource(name)
s = source.spectrum()
return spectrum_to_dict(s, errors=True)
old_flux = get_flux()
if spectrum()['name'] == 'PowerLaw':
pass
else:
powerlaw_model = PowerLaw(norm=1e-11,
index=2,
e0=np.sqrt(emin * emax))
powerlaw_model.set_flux(old_flux, emin=emin, emax=emax)
powerlaw_model.set_default_limits(oomp_limits=True)
if self.verbosity: print 'powerlaw_model is', powerlaw_model
powerlaw_spectrum = build_gtlike_spectrum(powerlaw_model)
like.setSpectrum(name, powerlaw_spectrum)
if self.verbosity:
print 'About to fit powerlaw_spectrum'
print summary(like)
paranoid_gtlike_fit(like, verbosity=self.verbosity)
if self.verbosity:
print 'Done fitting powerlaw_spectrum'
print summary(like)
d['hypothesis_0'] = source_dict(like,
name,
emin=emin,
emax=emax,
flux_units=self.flux_units,
energy_units=self.energy_units,
verbosity=self.verbosity)
if self.cutoff_model is None:
self.cutoff_model = PLSuperExpCutoff(norm=1e-9,
index=1,
cutoff=1000,
e0=1000,
b=1)
self.cutoff_model.set_free('b', False)
self.cutoff_model.set_flux(old_flux, emin=emin, emax=emax)
self.cutoff_model.set_default_limits(oomp_limits=True)
if self.verbosity:
print 'cutoff_model is', self.cutoff_model
cutoff_spectrum = build_gtlike_spectrum(self.cutoff_model)
like.setSpectrum(name, cutoff_spectrum)
if self.verbosity:
print 'About to fit cutoff_model'
print summary(like)
paranoid_gtlike_fit(like, verbosity=self.verbosity)
ll = like.logLike.value()
if ll < d['hypothesis_0']['logLikelihood']:
# if fit is worse than PowerLaw fit, then
# restart fit with parameters almost
# equal to best fit powerlaw
cutoff_plaw = PLSuperExpCutoff(b=1)
cutoff_plaw.set_free('b', False)
cutoff_plaw.setp_gtlike(
'norm', d['hypothesis_0']['spectrum']['Prefactor'])
cutoff_plaw.setp_gtlike('index',
d['hypothesis_0']['spectrum']['Index'])
cutoff_plaw.setp_gtlike('e0',
d['hypothesis_0']['spectrum']['Scale'])
cutoff_plaw.setp_gtlike('cutoff', 1e6)
cutoff_plaw.set_default_limits(oomp_limits=True)
temp = build_gtlike_spectrum(cutoff_plaw)
like.setSpectrum(name, temp)
if self.verbosity:
print 'Redoing fit with cutoff same as plaw'
print summary(like)
paranoid_gtlike_fit(like, verbosity=self.verbosity)
if self.verbosity:
print 'Done fitting cutoff_spectrum'
print summary(like)
d['hypothesis_1'] = source_dict(like,
name,
emin=emin,
emax=emax,
flux_units=self.flux_units,
energy_units=self.energy_units,
verbosity=self.verbosity)
if self.cutoff_xml_name is not None:
like.writeXml(self.cutoff_xml_name)
d['TS_cutoff'] = d['hypothesis_1']['TS']['reoptimize'] - d[
'hypothesis_0']['TS']['reoptimize']
if self.verbosity:
print 'For cutoff test, TS_cutoff = ', d['TS_cutoff']
except Exception, ex:
print 'ERROR gtlike test cutoff: ', ex
traceback.print_exc(file=sys.stdout)
self.results = None