def set_model(self, likelihoodModel):
"""
Set the model to be used in the joint minimization.
Must be a LikelihoodModel instance.
"""
with suppress_stdout():
self.lmc = LikelihoodModelConverter(likelihoodModel, self.irf)
self.lmc.setFileSpectrumEnergies(self.emin, self.emax,
self.Nenergies)
xmlFile = str("%s.xml" % get_random_unique_name())
temp_files = self.lmc.writeXml(xmlFile, self.ra, self.dec,
self.rad)
if self.kind == "BINNED":
self.like = BinnedAnalysis.BinnedAnalysis(self.obs,
xmlFile,
optimizer="DRMNFB")
else:
self.like = UnbinnedAnalysis.UnbinnedAnalysis(self.obs,
xmlFile,
optimizer="DRMNFB")
self.likelihoodModel = likelihoodModel
# Here we need also to compute the logLike value, so that the model
# in the XML file will be chanded if needed
dumb = self.get_log_like()
# Since now the Galactic template is in RAM, we can remove the temporary file
os.remove(self.lmc._unique_filename)
os.remove(xmlFile)
# Delete temporary spectral files
for temp_file in temp_files:
os.remove(temp_file)
# Build the list of the nuisance parameters
new_nuisance_parameters = self._setNuisanceParameters()
self.update_nuisance_parameters(new_nuisance_parameters)
def set_model(self, likelihoodModel):
'''
Set the model to be used in the joint minimization.
Must be a LikelihoodModel instance.
'''
with suppress_stdout():
self.lmc = LikelihoodModelConverter(likelihoodModel,
self.irf)
self.lmc.setFileSpectrumEnergies(self.emin, self.emax, self.Nenergies)
xmlFile = '%s.xml' % get_random_unique_name()
temp_files = self.lmc.writeXml(xmlFile, self.ra, self.dec, self.rad)
if (self.kind == "BINNED"):
self.like = BinnedAnalysis.BinnedAnalysis(self.obs,
xmlFile,
optimizer='DRMNFB')
else:
self.like = UnbinnedAnalysis.UnbinnedAnalysis(self.obs,
xmlFile,
optimizer='DRMNFB')
self.likelihoodModel = likelihoodModel
# Here we need also to compute the logLike value, so that the model
# in the XML file will be chanded if needed
dumb = self.get_log_like()
# Since now the Galactic template is in RAM, we can remove the temporary file
os.remove(self.lmc._unique_filename)
os.remove(xmlFile)
# Delete temporary spectral files
for temp_file in temp_files:
os.remove(temp_file)
# Build the list of the nuisance parameters
new_nuisance_parameters = self._setNuisanceParameters()
self.update_nuisance_parameters(new_nuisance_parameters)
def _minimize(self):
"""
Minimize the function using the Multinest sampler
"""
n_dim = len(self.parameters)
# We need to check if the MCMC
# chains will have a place on
# the disk to write and if not,
# create one
# chain_name = "multinest_minimizer/fit-"
#
# mcmc_chains_out_dir = ""
# tmp = chain_name.split('/')
# for s in tmp[:-1]:
# mcmc_chains_out_dir += s + '/'
#
# if not os.path.exists(mcmc_chains_out_dir):
#
# os.makedirs(mcmc_chains_out_dir)
with temporary_directory(
prefix="multinest-",
within_directory=os.getcwd()) as mcmc_chains_out_dir:
outputfiles_basename = os.path.join(mcmc_chains_out_dir, "fit-")
# print("\nMultinest is exploring the parameter space...\n")
# Reset the likelihood values
self._log_like_values = []
sampler = pymultinest.run(
self._func_wrapper,
self._prior,
n_dim,
n_dim,
outputfiles_basename=outputfiles_basename,
n_live_points=self._setup_dict["live_points"],
multimodal=True,
resume=False,
)
# Use PyMULTINEST analyzer to gather parameter info
# NOTE: I encapsulate this to avoid the output in the constructor of Analyzer
with suppress_stdout():
multinest_analyzer = pymultinest.analyse.Analyzer(
n_params=n_dim, outputfiles_basename=outputfiles_basename)
# Get the function value from the chain
func_values = multinest_analyzer.get_equal_weighted_posterior()[:,
-1]
# Store the sample for further use (if needed)
self._sampler = sampler
# Get the samples from the sampler
_raw_samples = multinest_analyzer.get_equal_weighted_posterior(
)[:, :-1]
# Find the minimum of the function (i.e. the maximum of func_wrapper)
idx = func_values.argmax()
best_fit_values = _raw_samples[idx]
minimum = func_values[idx] * (-1)
return best_fit_values, minimum