def __init__(self, like, name, *args, **kwargs):
keyword_options.process(self, kwargs)
self.like = like
self.name = name
if self.ul_algorithm not in BaseGtlikeSED.ul_choices:
raise Exception("Upper Limit Algorithm %s not in %s" % (self.ul_algorithm,str(BaseGtlikeSED.ul_choices)))
if self.bin_edges is not None:
if not BaseGtlikeSED.good_binning(like, self.bin_edges):
raise Exception("bin_edges is not commensurate with the underlying energy binning of pyLikelihood.")
self.bin_edges = np.asarray(self.bin_edges)
energy = np.sqrt(self.bin_edges[1:]*self.bin_edges[:-1])
else:
# These energies are always in MeV
self.bin_edges = like.energies
energy = like.e_vals
self.lower=self.bin_edges[:-1]
self.upper=self.bin_edges[1:]
source=self.like.logLike.getSource(name)
self.init_spectrum=source.spectrum()
self.init_model=build_pointlike_model(self.init_spectrum)
self.results = dict(
Name=name,
spectrum=name_to_spectral_dict(like,name, errors=True, covariance_matrix=True),
)
self._calculate(like)
super(GtlikeSED,self).__init__(self.results, **keyword_options.defaults_to_kwargs(self, SED))
def __init__(self, like, name, bin_edges, **kwargs):
""" Parameters:
* like - pyLikelihood object
* name - source to make an SED for
* bin_edges - if specified, calculate the SED in these bins.
"""
keyword_options.process(self, kwargs)
self.like = like
self.name = name
if not BaseGtlikeSED.good_binning(self.like, bin_edges):
raise Exception("bin_edges is not commensurate with the underlying energy binning of pyLikelihood.")
source=self.like.logLike.getSource(name)
self.init_spectrum=source.spectrum()
self.init_model=build_pointlike_model(self.init_spectrum)
self.init_energes = self.like.energies[[0,-1]]
bin_edges = np.asarray(bin_edges)
self.lower_energy=bin_edges[:-1]
self.upper_energy=bin_edges[1:]
self.middle_energy=np.sqrt(self.lower_energy*self.upper_energy)
if self.ul_algorithm not in self.ul_choices:
raise Exception("Upper Limit Algorithm %s not in %s" % (self.ul_algorithm,str(self.ul_choices)))
empty = lambda: np.empty_like(self.middle_energy)
self._calculate()
def __init__(self, like, name, *args, **kwargs):
keyword_options.process(self, kwargs)
self.like = like
self.name = name
if self.ul_algorithm not in BaseGtlikeSED.ul_choices:
raise Exception("Upper Limit Algorithm %s not in %s" %
(self.ul_algorithm, str(BaseGtlikeSED.ul_choices)))
if self.bin_edges is not None:
if not BaseGtlikeSED.good_binning(like, self.bin_edges):
raise Exception(
"bin_edges is not commensurate with the underlying energy binning of pyLikelihood."
)
self.bin_edges = np.asarray(self.bin_edges)
energy = np.sqrt(self.bin_edges[1:] * self.bin_edges[:-1])
else:
# These energies are always in MeV
self.bin_edges = like.energies
energy = like.e_vals
self.lower = self.bin_edges[:-1]
self.upper = self.bin_edges[1:]
source = self.like.logLike.getSource(name)
self.init_spectrum = source.spectrum()
self.init_model = build_pointlike_model(self.init_spectrum)
self.results = dict(
Name=name,
spectrum=name_to_spectral_dict(like,
name,
errors=True,
covariance_matrix=True),
)
self._calculate(like)
super(GtlikeSED,
self).__init__(self.results,
**keyword_options.defaults_to_kwargs(self, SED))
