def set_model(self, likelihood_model_instance):
"""
Set the model to be used in the joint minimization. Must be a LikelihoodModel instance.
"""
# Instance the python - C++ bridge
self._model = likelihood_model_instance
self._pymodel = pyToCppModelInterfaceCache()
# Set boundaries for extended source
# NOTE: we assume that these boundaries do not change during the fit
for id in range(self._model.get_number_of_extended_sources()):
(
lon_min,
lon_max,
lat_min,
lat_max,
) = self._model.get_extended_source_boundaries(id)
self._pymodel.setExtSourceBoundaries(id, lon_min, lon_max, lat_min, lat_max)
# Set positions for point source
# NOTE: this should not change so much that the response is not valid anymore
n_point_sources = self._model.get_number_of_point_sources()
for id in range(n_point_sources):
this_ra, this_dec = self._model.get_point_source_position(id)
self._pymodel.setPtsSourcePosition(id, this_ra, this_dec)
# Now init the HAWC LIFF software
try:
# Load all sky
# (ROI will be defined later)
if self._n_transits is None:
self._theLikeHAWC = liff_3ML.LikeHAWC(
self._maptree,
self._response,
self._pymodel,
self._bin_list,
self._fullsky,
)
else:
self._theLikeHAWC = liff_3ML.LikeHAWC(
self._maptree,
self._n_transits,
self._response,
self._pymodel,
self._bin_list,
self._fullsky,
)
except:
print(
"Could not instance the LikeHAWC class from LIFF. "
+ "Check that HAWC software is working"
)
raise
else:
self._instanced = True
# If fullsky=True, the user *must* use one of the set_ROI methods
if self._fullsky:
if self._roi_ra is None and self._roi_fits is None:
raise RuntimeError("You have to define a ROI with the setROI method")
# Now if an ROI is set, try to use it
if self._roi_ra is not None:
if not isinstance(self._roi_ra, list):
self._theLikeHAWC.SetROI(
self._roi_ra,
self._roi_dec,
self._roi_radius,
self._fixed_ROI,
self._roi_galactic,
)
elif len(self._roi_ra) == 2:
self._theLikeHAWC.SetROI(
self._roi_ra[0],
self._roi_ra[1],
self._roi_dec[0],
self._roi_dec[1],
self._fixed_ROI,
self._roi_galactic,
)
elif len(self._roi_ra) > 2:
self._theLikeHAWC.SetROI(
self._roi_ra, self._roi_dec, self._fixed_ROI, self._roi_galactic
)
else:
raise RuntimeError(
"Only one point is found, use set_ROI(float ra, float dec, float radius, bool fixedROI, bool galactic)."
)
elif self._roi_fits is not None:
self._theLikeHAWC.SetROI(
self._roi_fits, self._roi_threshold, self._fixed_ROI
)
# Now set a callback in the CommonNorm parameter, so that if the user or the fit
# engine or the Bayesian sampler change the CommonNorm value, the change will be
# propagated to the LikeHAWC instance
list(self._nuisance_parameters.values())[0].add_callback(
self._CommonNormCallback
)
# Update to start the computation of positions and energies inside LiFF
self._theLikeHAWC.UpdateSources()
# Get the energies needed by LiFF (the same for all sources)
# (note that the output is in MeV, while we need keV)
self._energies = np.array(self._theLikeHAWC.GetEnergies(False)) * 1000.0
# make sure the model maps etc. get filled
self.get_log_like()
def set_model(self, likelihood_model_instance):
"""
Set the model to be used in the joint minimization. Must be a LikelihoodModel instance.
"""
# Instance the python - C++ bridge
self._model = likelihood_model_instance
self._pymodel = pyToCppModelInterfaceCache()
# Set boundaries for extended source
# NOTE: we assume that these boundaries do not change during the fit
for id in range(self._model.get_number_of_extended_sources()):
lon_min, lon_max, lat_min, lat_max = self._model.get_extended_source_boundaries(id)
self._pymodel.setExtSourceBoundaries(id, lon_min, lon_max, lat_min, lat_max)
# Set positions for point source
# NOTE: this should not change so much that the response is not valid anymore
n_point_sources = self._model.get_number_of_point_sources()
for id in range(n_point_sources):
this_ra, this_dec = self._model.get_point_source_position(id)
self._pymodel.setPtsSourcePosition(id, this_ra, this_dec)
# Now init the HAWC LIFF software
try:
# Load all sky
# (ROI will be defined later)
if self._n_transits is None:
self._theLikeHAWC = liff_3ML.LikeHAWC(self._maptree,
self._response,
self._pymodel,
self._bin_list,
self._fullsky)
else:
self._theLikeHAWC = liff_3ML.LikeHAWC(self._maptree,
self._n_transits,
self._response,
self._pymodel,
self._bin_list,
self._fullsky)
except:
print("Could not instance the LikeHAWC class from LIFF. " +
"Check that HAWC software is working")
raise
else:
self._instanced = True
# If fullsky=True, the user *must* use one of the set_ROI methods
if self._fullsky:
if self._roi_ra is None and self._roi_fits is None:
raise RuntimeError("You have to define a ROI with the setROI method")
# Now if an ROI is set, try to use it
if self._roi_ra is not None:
if not isinstance(self._roi_ra, list):
self._theLikeHAWC.SetROI(self._roi_ra, self._roi_dec, self._roi_radius, self._fixed_ROI,
self._roi_galactic)
elif len(self._roi_ra) == 2:
self._theLikeHAWC.SetROI(self._roi_ra[0], self._roi_ra[1], self._roi_dec[0], self._roi_dec[1],
self._fixed_ROI, self._roi_galactic)
elif len(self._roi_ra) > 2:
self._theLikeHAWC.SetROI(self._roi_ra, self._roi_dec, self._fixed_ROI, self._roi_galactic)
else:
raise RuntimeError(
"Only one point is found, use set_ROI(float ra, float dec, float radius, bool fixedROI, bool galactic).")
elif self._roi_fits is not None:
self._theLikeHAWC.SetROI(self._roi_fits, self._roi_threshold, self._fixed_ROI)
# Now set a callback in the CommonNorm parameter, so that if the user or the fit
# engine or the Bayesian sampler change the CommonNorm value, the change will be
# propagated to the LikeHAWC instance
self._nuisance_parameters.values()[0].add_callback(self._CommonNormCallback)
# Update to start the computation of positions and energies inside LiFF
self._theLikeHAWC.UpdateSources()
# Get the energies needed by LiFF (the same for all sources)
# (note that the output is in MeV, while we need keV)
self._energies = np.array(self._theLikeHAWC.GetEnergies(False)) * 1000.0
#make sure the model maps etc. get filled
self.get_log_like()