def _compute(self):
if self.verbosity: print 'calculating pointlike cutoff upper limit'
roi = self.roi
name = self.name
saved_state = PointlikeState(roi)
cutoff_model = PLSuperExpCutoff(Index=self.Index, Cutoff=self.Cutoff, b=self.b)
roi.modify(which=name, model=cutoff_model, keep_old_flux=True)
super(PointlikeCutoffUpperLimit,self)._compute()
saved_state.restore(just_spectra=True)
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()
class PointlikeCutoffTester(CutoffTester):
defaults = CutoffTester.defaults + (
('cutoff_model',None,'starting value of spectral model'),
('fit_kwargs',dict(),'kwargs to pass into roi.fit()'),
)
@keyword_options.decorate(defaults)
def __init__(self, roi, name, *args, **kwargs):
keyword_options.process(self, kwargs)
self.roi = roi
self.name = name
self._calculate()
def _calculate(self):
roi = self.roi
name = self.name
if self.verbosity: print 'Testing cutoff in pointlike'
emin,emax=get_full_energy_range(roi)
self.results = d = dict(
energy = energy_dict(emin=emin, emax=emax, energy_units=self.energy_units)
)
saved_state = PointlikeState(roi)
old_flux = roi.get_model(name).i_flux(emin,emax)
if not isinstance(roi.get_model(name),PowerLaw):
powerlaw_model=PowerLaw(norm=1e-11, index=2, e0=np.sqrt(emin*emax))
powerlaw_model.set_mapper('Index', PowerLaw.default_limits['Index'])
powerlaw_model.set_flux(old_flux,emin=emin,emax=emax)
if self.verbosity: print "powerlaw_model is ",powerlaw_model
roi.modify(which=name, model=powerlaw_model, keep_old_flux=False)
fit = lambda: roi.fit(**self.fit_kwargs)
def ts():
old_quiet = roi.quiet; roi.quiet=True
ts = roi.TS(name,quick=False)
roi.quiet = old_quiet
return ts
spectrum = lambda: spectrum_to_dict(roi.get_model(name), errors=True)
if self.verbosity:
print 'About to fit powerlaw_model'
roi.print_summary()
fit()
if self.verbosity:
print 'Done fitting powerlaw_model'
roi.print_summary()
d['hypothesis_0'] = source_dict(roi, name, emin=emin, emax=emax,
flux_units=self.flux_units,
energy_units=self.energy_units,
verbosity=self.verbosity)
if self.cutoff_model is not None:
pass
else:
self.cutoff_model=PLSuperExpCutoff(norm=1e-9, index=1, cutoff=1000, e0=1000, b=1)
# Note, don't limit the normalization parameter
for p in ['Index', 'Cutoff', 'b']:
self.cutoff_model.set_mapper(p, PLSuperExpCutoff.default_limits[p])
self.cutoff_model.set_free('b', False)
self.cutoff_model.set_flux(old_flux,emin=emin,emax=emax)
if self.verbosity: print "cutoff_model is ",self.cutoff_model
roi.modify(which=name, model=self.cutoff_model, keep_old_flux=False)
if self.verbosity:
print 'About to fit cutoff_model'
roi.print_summary()
fit()
ll = -roi.logLikelihood(roi.parameters())
if ll < d['hypothesis_0']['logLikelihood']:
# if fit is worse than PowerLaw fit, then
# restart fit with parameters almost
# equal to best fit powerlaw
self.cutoff_plaw=PLSuperExpCutoff(b=1)
self.cutoff_plaw.set_free('b', False)
self.cutoff_plaw.setp('norm', d['hypothesis_0']['spectrum']['Norm'])
self.cutoff_plaw.setp('index', d['hypothesis_0']['spectrum']['Index'])
self.cutoff_plaw.setp('e0', d['hypothesis_0']['spectrum']['e0'])
self.cutoff_plaw.setp('cutoff', 1e6)
roi.modify(which=name, model=self.cutoff_plaw, keep_old_flux=False)
fit()
if self.verbosity:
print 'Redoing fit with cutoff same as plaw'
print 'Before:'
roi.print_summary()
print fit()
if self.verbosity:
print 'Done fitting cutoff_model'
roi.print_summary()
d['hypothesis_1'] = source_dict(roi, name, emin=emin, emax=emax,
flux_units=self.flux_units,
energy_units=self.energy_units,
verbosity=self.verbosity)
d['TS_cutoff']=d['hypothesis_1']['TS']['noquick']-d['hypothesis_0']['TS']['noquick']
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
def _calculate(self):
roi = self.roi
name = self.name
if self.verbosity: print 'Testing cutoff in pointlike'
emin, emax = get_full_energy_range(roi)
self.results = d = dict(energy=energy_dict(
emin=emin, emax=emax, energy_units=self.energy_units))
saved_state = PointlikeState(roi)
old_flux = roi.get_model(name).i_flux(emin, emax)
if not isinstance(roi.get_model(name), PowerLaw):
powerlaw_model = PowerLaw(norm=1e-11,
index=2,
e0=np.sqrt(emin * emax))
powerlaw_model.set_mapper('Index',
PowerLaw.default_limits['Index'])
powerlaw_model.set_flux(old_flux, emin=emin, emax=emax)
if self.verbosity: print "powerlaw_model is ", powerlaw_model
roi.modify(which=name, model=powerlaw_model, keep_old_flux=False)
fit = lambda: roi.fit(**self.fit_kwargs)
def ts():
old_quiet = roi.quiet
roi.quiet = True
ts = roi.TS(name, quick=False)
roi.quiet = old_quiet
return ts
spectrum = lambda: spectrum_to_dict(roi.get_model(name), errors=True)
if self.verbosity:
print 'About to fit powerlaw_model'
roi.print_summary()
fit()
if self.verbosity:
print 'Done fitting powerlaw_model'
roi.print_summary()
d['hypothesis_0'] = source_dict(roi,
name,
emin=emin,
emax=emax,
flux_units=self.flux_units,
energy_units=self.energy_units,
verbosity=self.verbosity)
if self.cutoff_model is not None:
pass
else:
self.cutoff_model = PLSuperExpCutoff(norm=1e-9,
index=1,
cutoff=1000,
e0=1000,
b=1)
# Note, don't limit the normalization parameter
for p in ['Index', 'Cutoff', 'b']:
self.cutoff_model.set_mapper(
p, PLSuperExpCutoff.default_limits[p])
self.cutoff_model.set_free('b', False)
self.cutoff_model.set_flux(old_flux, emin=emin, emax=emax)
if self.verbosity: print "cutoff_model is ", self.cutoff_model
roi.modify(which=name, model=self.cutoff_model, keep_old_flux=False)
if self.verbosity:
print 'About to fit cutoff_model'
roi.print_summary()
fit()
ll = -roi.logLikelihood(roi.parameters())
if ll < d['hypothesis_0']['logLikelihood']:
# if fit is worse than PowerLaw fit, then
# restart fit with parameters almost
# equal to best fit powerlaw
self.cutoff_plaw = PLSuperExpCutoff(b=1)
self.cutoff_plaw.set_free('b', False)
self.cutoff_plaw.setp('norm',
d['hypothesis_0']['spectrum']['Norm'])
self.cutoff_plaw.setp('index',
d['hypothesis_0']['spectrum']['Index'])
self.cutoff_plaw.setp('e0', d['hypothesis_0']['spectrum']['e0'])
self.cutoff_plaw.setp('cutoff', 1e6)
roi.modify(which=name, model=self.cutoff_plaw, keep_old_flux=False)
fit()
if self.verbosity:
print 'Redoing fit with cutoff same as plaw'
print 'Before:'
roi.print_summary()
print fit()
if self.verbosity:
print 'Done fitting cutoff_model'
roi.print_summary()
d['hypothesis_1'] = source_dict(roi,
name,
emin=emin,
emax=emax,
flux_units=self.flux_units,
energy_units=self.energy_units,
verbosity=self.verbosity)
d['TS_cutoff'] = d['hypothesis_1']['TS']['noquick'] - d[
'hypothesis_0']['TS']['noquick']
saved_state.restore()
