def _calculate(self):
""" Compute the flux data points for each energy. """
like = self.like
name = self.name
# Freeze all sources except one to make sed of.
all_sources = like.sourceNames()
if name not in all_sources:
raise Exception("Cannot find source %s in list of sources" % name)
saved_state = SuperState(like)
self.results = dict(
name=name,
bands=[],
min_ts=self.min_ts,
)
for i,(emin,emax,e_middle) in enumerate(zip(self.lower_energy,self.upper_energy,self.middle_energy)):
if self.verbosity: print 'Calculating bandfits from %.0dMeV to %.0dMeV' % (emin,emax)
like.setEnergyRange(float(emin)+1, float(emax)-1)
# Scale the powerlaw to the input spectral model => helps with convergence
old_flux = self.init_model.i_flux(emin=emin, emax=emax)
model = PowerLaw(index=2, e0=e_middle)
model.set_flux(old_flux, emin=emin, emax=emax)
norm = model['norm']
model.set_limits('norm',norm/float(self.fit_range),norm*self.fit_range, scale=norm)
model.set_limits('index',-5,5)
spectrum = build_gtlike_spectrum(model)
like.setSpectrum(name,spectrum)
like.syncSrcParams(name)
if self.verbosity:
print 'Before bandfits fitting from %.0dMeV to %.0dMeV' % (emin,emax)
print summary(like)
paranoid_gtlike_fit(like, verbosity=self.verbosity)
if self.verbosity:
print 'After bandfits fitting from %.0dMeV to %.0dMeV' % (emin,emax)
print summary(like)
r = source_dict(like, name, emin=emin, emax=emax,
flux_units=self.flux_units,
energy_units=self.energy_units,
verbosity=self.verbosity)
if self.verbosity: print 'Calculating bandfits upper limit from %.0dMeV to %.0dMeV' % (emin,emax)
g = GtlikePowerLawUpperLimit(like, name,
powerlaw_index=self.upper_limit_index,
cl=self.ul_confidence,
emin=emin,emax=emax,
flux_units=self.flux_units,
energy_units=self.energy_units,
upper_limit_kwargs=self.upper_limit_kwargs,
include_prefactor=True,
prefactor_energy=e_middle,
verbosity=self.verbosity)
r['upper_limit'] = g.todict()
r['prefactor'] = powerlaw_prefactor_dict(like, name, errors=True, minos_errors=False,
flux_units=self.flux_units)
r['significant']=r['TS']['reoptimize']>self.min_ts
self.results['bands'].append(r)
# revert to old model
like.setEnergyRange(*self.init_energes)
saved_state.restore()
def build_roi(self, name, fast):
if fast:
roi_size=5
binsperdec = 2
max_free=2
free_radius=2
else:
roi_size = 10
binsperdec = 4
max_free=5
free_radius=5
catalog = Catalog2FGL('$FERMI/catalogs/gll_psc_v05.fit',
latextdir='$FERMI/extended_archives/gll_psc_v05_templates',
prune_radius=0,
max_free=max_free,
free_radius=free_radius,
limit_parameters=True)
ft1 = self.radiopsr_loader.get_ft1(name)
ft2 = self.radiopsr_loader.get_ft2(name)
ltcube = self.radiopsr_loader.get_ltcube(name)
binfile = self.radiopsr_loader.get_binfile(name, binsperdec)
roi_dir = self.radiopsr_loader.get_skydir(name)
ds = DataSpecification(
ft1files = ft1,
ft2files = ft2,
ltcube = ltcube,
binfile = binfile)
sa = SpectralAnalysis(ds,
binsperdec = binsperdec,
emin = 100,
emax = 1000000,
irf = "P7SOURCE_V6",
roi_dir = roi_dir,
maxROI = roi_size,
minROI = roi_size,
event_class= 0)
fit_emin = 1e2
fit_emax = 10**5.5
model=PowerLaw(index=2, e0=np.sqrt(fit_emin*fit_emax))
model.set_limits('index',-5,5)
ps = PointSource(name=name, model=model, skydir=roi_dir)
point_sources = [ps]
diffuse_sources = get_default_diffuse(diffdir="/afs/slac/g/glast/groups/diffuse/rings/2year",
gfile="ring_2year_P76_v0.fits",
ifile="isotrop_2year_P76_source_v0.txt",
limit_parameters=True)
roi=sa.roi(point_sources=point_sources,
diffuse_sources=diffuse_sources,
catalogs=catalog,
fit_emin=fit_emin, fit_emax=fit_emax)
return roi
def _calculate(self,*args,**kwargs):
""" Convert all units into sympy arrays after the initial calculation. """
like = self.like
name = self.name
init_energes = like.energies[[0,-1]]
# Freeze all sources except one to make sed of.
all_sources = like.sourceNames()
if name not in all_sources:
raise Exception("Cannot find source %s in list of sources" % name)
# make copy of parameter values + free parameters
saved_state = SuperState(like)
if self.verbosity: print 'Freezing background sources'
for other_name in get_background(like):
if self.freeze_bg_diffuse:
if self.verbosity: print ' * Freezing diffuse source %s' % other_name
modify(like, other_name, free=False)
else:
if self.verbosity: print ' * Freezing spectral shape for diffuse source %s' % other_name
modify(like, other_name, freeze_spectral_shape=True)
for other_name in get_sources(like):
if self.freeze_bg_sources:
if self.verbosity: print ' * Freezing bg source %s' % other_name
modify(like, other_name, free=False)
else:
if self.verbosity: print ' * Freezing spectral shape for bg source %s' % other_name
modify(like, other_name, freeze_spectral_shape=True)
self.raw_results = []
for i,(lower,upper) in enumerate(zip(self.lower,self.upper)):
like.setEnergyRange(float(lower)+1, float(upper)-1)
e = np.sqrt(lower*upper)
if self.verbosity: print 'Calculating SED from %.0dMeV to %.0dMeV' % (lower,upper)
""" Note, the most robust method I have found for computing SEDs in gtlike is:
(a) Create a generic spectral model with a fixed spectral index.
(b) Set the 'Scale' to sqrt(emin*emax) so the prefactor is dNdE in the middle
of the sed bin.
(b) Set the limits to go from norm/fit_range to norm*fit_range and set the scale to 'norm'
"""
old_flux = self.init_model.i_flux(emin=lower,emax=upper)
model = PowerLaw(index=self.powerlaw_index, e0=e)
model.set_flux(old_flux, emin=lower, emax=upper)
norm = model['norm']
model.set_limits('norm',norm/float(self.fit_range),norm*self.fit_range, scale=norm)
model.set_limits('index',-5,5)
model.freeze('index')
spectrum = build_gtlike_spectrum(model)
like.setSpectrum(name,spectrum)
like.syncSrcParams(name)
if self.verbosity:
print 'Before fitting SED from %.0dMeV to %.0dMeV' % (lower,upper)
print summary(like)
paranoid_gtlike_fit(like, verbosity=self.verbosity)
if self.verbosity:
print 'After fitting SED from %.0dMeV to %.0dMeV' % (lower,upper)
print summary(like)
d = dict()
self.raw_results.append(d)
d['energy'] = energy_dict(emin=lower, emax=upper, energy_units=self.energy_units)
d['flux'] = flux_dict(like, name, emin=lower,emax=upper, flux_units=self.flux_units,
errors=True, include_prefactor=True, prefactor_energy=e)
d['prefactor'] = powerlaw_prefactor_dict(like, name, errors=self.save_hesse_errors, minos_errors=True,
flux_units=self.flux_units)
d['TS'] = ts_dict(like, name, verbosity=self.verbosity)
if self.verbosity: print 'Calculating SED upper limit from %.0dMeV to %.0dMeV' % (lower,upper)
if self.always_upper_limit or d['TS']['reoptimize'] < self.min_ts:
ul = GtlikePowerLawUpperLimit(like, name,
cl=self.ul_confidence,
emin=lower,emax=upper,
flux_units=self.flux_units,
energy_units=self.energy_units,
upper_limit_kwargs=self.upper_limit_kwargs,
include_prefactor=True,
prefactor_energy=e,
verbosity=self.verbosity,
)
d['upper_limit'] = ul.todict()
# revert to old model
like.setEnergyRange(*init_energes)
saved_state.restore()
self._condense_results()
def _calculate(self, *args, **kwargs):
""" Convert all units into sympy arrays after the initial calculation. """
like = self.like
name = self.name
init_energes = like.energies[[0, -1]]
# Freeze all sources except one to make sed of.
all_sources = like.sourceNames()
if name not in all_sources:
raise Exception("Cannot find source %s in list of sources" % name)
# make copy of parameter values + free parameters
saved_state = SuperState(like)
if self.verbosity: print 'Freezing background sources'
for other_name in get_background(like):
if self.freeze_bg_diffuse:
if self.verbosity:
print ' * Freezing diffuse source %s' % other_name
modify(like, other_name, free=False)
else:
if self.verbosity:
print ' * Freezing spectral shape for diffuse source %s' % other_name
modify(like, other_name, freeze_spectral_shape=True)
for other_name in get_sources(like):
if self.freeze_bg_sources:
if self.verbosity:
print ' * Freezing bg source %s' % other_name
modify(like, other_name, free=False)
else:
if self.verbosity:
print ' * Freezing spectral shape for bg source %s' % other_name
modify(like, other_name, freeze_spectral_shape=True)
self.raw_results = []
for i, (lower, upper) in enumerate(zip(self.lower, self.upper)):
like.setEnergyRange(float(lower) + 1, float(upper) - 1)
e = np.sqrt(lower * upper)
if self.verbosity:
print 'Calculating SED from %.0dMeV to %.0dMeV' % (lower,
upper)
""" Note, the most robust method I have found for computing SEDs in gtlike is:
(a) Create a generic spectral model with a fixed spectral index.
(b) Set the 'Scale' to sqrt(emin*emax) so the prefactor is dNdE in the middle
of the sed bin.
(b) Set the limits to go from norm/fit_range to norm*fit_range and set the scale to 'norm'
"""
old_flux = self.init_model.i_flux(emin=lower, emax=upper)
model = PowerLaw(index=self.powerlaw_index, e0=e)
model.set_flux(old_flux, emin=lower, emax=upper)
norm = model['norm']
model.set_limits('norm',
norm / float(self.fit_range),
norm * self.fit_range,
scale=norm)
model.set_limits('index', -5, 5)
model.freeze('index')
spectrum = build_gtlike_spectrum(model)
like.setSpectrum(name, spectrum)
like.syncSrcParams(name)
if self.verbosity:
print 'Before fitting SED from %.0dMeV to %.0dMeV' % (lower,
upper)
print summary(like)
paranoid_gtlike_fit(like, verbosity=self.verbosity)
if self.verbosity:
print 'After fitting SED from %.0dMeV to %.0dMeV' % (lower,
upper)
print summary(like)
d = dict()
self.raw_results.append(d)
d['energy'] = energy_dict(emin=lower,
emax=upper,
energy_units=self.energy_units)
d['flux'] = flux_dict(like,
name,
emin=lower,
emax=upper,
flux_units=self.flux_units,
errors=True,
include_prefactor=True,
prefactor_energy=e)
d['prefactor'] = powerlaw_prefactor_dict(
like,
name,
errors=self.save_hesse_errors,
minos_errors=True,
flux_units=self.flux_units)
d['TS'] = ts_dict(like, name, verbosity=self.verbosity)
if self.verbosity:
print 'Calculating SED upper limit from %.0dMeV to %.0dMeV' % (
lower, upper)
if self.always_upper_limit or d['TS']['reoptimize'] < self.min_ts:
ul = GtlikePowerLawUpperLimit(
like,
name,
cl=self.ul_confidence,
emin=lower,
emax=upper,
flux_units=self.flux_units,
energy_units=self.energy_units,
upper_limit_kwargs=self.upper_limit_kwargs,
include_prefactor=True,
prefactor_energy=e,
verbosity=self.verbosity,
)
d['upper_limit'] = ul.todict()
# revert to old model
like.setEnergyRange(*init_energes)
saved_state.restore()
self._condense_results()
