def gtlike_source_dict(like, name, emin=None, emax=None,
flux_units='erg', energy_units='MeV',
errors=True, minos_errors=False, covariance_matrix=True,
save_TS=True, add_diffuse_dict=True,
verbosity=True):
if emin is None and emax is None:
emin, emax = get_full_energy_range(like)
d=dict(
logLikelihood=like.logLike.value(),
)
d['energy'] = energy_dict(emin=emin, emax=emax, energy_units=energy_units)
d['spectrum']= name_to_spectral_dict(like, name, errors=errors,
minos_errors=minos_errors, covariance_matrix=covariance_matrix)
if save_TS:
d['TS']=gtlike_ts_dict(like, name, verbosity=verbosity)
d['flux']=gtlike_flux_dict(like,name,
emin=emin, emax=emax,
flux_units=flux_units, energy_units=energy_units, errors=errors)
# if add_diffuse_dict:
# d['diffuse'] = diffuse_dict(like)
return tolist(d)
def diffuse_dict(like):
""" Save out all diffuse sources. """
f = dict()
bgs = get_background(like)
for name in bgs:
f[name] = name_to_spectral_dict(like, name, errors=True)
return tolist(f)
def diffuse_dict(like):
""" Save out all diffuse sources. """
f = dict()
bgs = get_background(like)
for name in bgs:
f[name] = name_to_spectral_dict(like, name, errors=True)
return tolist(f)
def gtlike_source_dict(like,
name,
emin=None,
emax=None,
flux_units='erg',
energy_units='MeV',
errors=True,
minos_errors=False,
covariance_matrix=True,
save_TS=True,
add_diffuse_dict=True,
verbosity=True):
if emin is None and emax is None:
emin, emax = get_full_energy_range(like)
d = dict(logLikelihood=like.logLike.value(), )
d['energy'] = energy_dict(emin=emin, emax=emax, energy_units=energy_units)
d['spectrum'] = name_to_spectral_dict(like,
name,
errors=errors,
minos_errors=minos_errors,
covariance_matrix=covariance_matrix)
if save_TS:
d['TS'] = gtlike_ts_dict(like, name, verbosity=verbosity)
d['flux'] = gtlike_flux_dict(like,
name,
emin=emin,
emax=emax,
flux_units=flux_units,
energy_units=energy_units,
errors=errors)
# if add_diffuse_dict:
# d['diffuse'] = diffuse_dict(like)
return tolist(d)
print 'ERROR calculating flux error: ', ex
traceback.print_exc(file=sys.stdout)
f['flux_err'] = -1
f['eflux_err'] = -1
if include_prefactor:
assert prefactor_energy is not None
source = get_gtlike_source(like, name)
# source = like.logLike.getSource(name)
spectrum = source.spectrum()
cp = lambda e: units.convert(e, '1/MeV', '1/%s' % flux_units)
f['prefactor'] = cp(
SpectrumPlotter.get_dnde_mev(spectrum, prefactor_energy))
f['prefactor_units'] = 'ph/cm^2/s/%s' % flux_units
f['prefactor_energy'] = ce(prefactor_energy)
return tolist(f)
def energy_dict(emin, emax, energy_units='MeV'):
ce = lambda e: units.convert(e, 'MeV', energy_units)
return dict(emin=ce(emin),
emax=ce(emax),
emiddle=ce(np.sqrt(emin * emax)),
energy_units=energy_units)
def gtlike_ts_dict(like, name, verbosity=True):
return dict(reoptimize=like.Ts(name, reoptimize=True, verbosity=verbosity),
noreoptimize=like.Ts(name,
reoptimize=False,
verbosity=verbosity))
except Exception, ex:
print 'ERROR calculating flux error: ', ex
traceback.print_exc(file=sys.stdout)
f['flux_err']=-1
f['eflux_err']=-1
if include_prefactor:
assert prefactor_energy is not None
source = get_gtlike_source(like,name)
# source = like.logLike.getSource(name)
spectrum = source.spectrum()
cp = lambda e: units.convert(e,'1/MeV','1/%s' % flux_units)
f['prefactor'] = cp(SpectrumPlotter.get_dnde_mev(spectrum,prefactor_energy))
f['prefactor_units'] = 'ph/cm^2/s/%s' % flux_units
f['prefactor_energy'] = ce(prefactor_energy)
return tolist(f)
def energy_dict(emin, emax, energy_units='MeV'):
ce=lambda e: units.convert(e,'MeV',energy_units)
return dict(emin=ce(emin),
emax=ce(emax),
emiddle=ce(np.sqrt(emin*emax)),
energy_units=energy_units)
def gtlike_ts_dict(like, name, verbosity=True):
return dict(
reoptimize=like.Ts(name,reoptimize=True, verbosity=verbosity),
noreoptimize=like.Ts(name,reoptimize=False, verbosity=verbosity)
)
def gtlike_spectrum_to_dict(spectrum, errors=False):
