def set_model(self, likelihoodModel, source_name=None):
"""
Set the model to be used in the joint minimization.
Must be a LikelihoodModel instance.
This method can also set or override a previously set source name.
"""
# with suppress_stdout():
if self._source_name is not None:
if (source_name
is not None) and (source_name != self._source_name):
log.warning('Changing target source from %s to %s' %
(self._source_name, source_name))
self._source_name = source_name
assert self._source_name in likelihoodModel.point_sources, (
'Source %s is not a source in the likelihood model! ' %
self._source_name)
self.lmc = LikelihoodModelConverter(likelihoodModel,
self.irf,
source_name=self._source_name)
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:
#import pdb;pdb.set_trace()
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 _makeFileSpectrum(self, ip):
name = self.likelihoodModel.get_point_source_name(ip)
values = self.likelihoodModel.get_point_source_fluxes(
ip, self.energiesKeV)
tempName = "__%s_%s.txt" % (name, get_random_unique_name())
with open(tempName, "w+") as f:
for e, v in zip(self.energiesKeV, values):
# Gtlike needs energies in MeV and fluxes in ph/MeV/cm2)
f.write("%s %s\n" % (e / 1000.0, v * 1000.0))
# p = fileFunction.parameter("Normalization")
# p.setBounds(1-float(effAreaAllowedSize),1+effAreaAllowedSize)
# Now generate the XML source wrapper
# This is convoluted, but that's the ST way of doing things...
# The final product is a class with a writeXml method
src = "\n".join((
('<source name= "%s" ' % name) + 'type="PointSource">',
' <spectrum type="PowerLaw2"/>',
" <!-- point source units are cm^-2 s^-1 MeV^-1 -->",
' <spatialModel type="SkyDirFunction"/>',
"</source>\n",
))
src = FuncFactory.minidom.parseString(src).getElementsByTagName(
"source")[0]
src = FuncFactory.Source(src)
src.spectrum = FuncFactory.FileFunction()
src.spectrum.file = tempName
src.spectrum.parameters["Normalization"].value = 1.0
src.spectrum.parameters["Normalization"].max = 1.1
src.spectrum.parameters["Normalization"].min = 0.9
src.spectrum.parameters["Normalization"].free = False
src.spectrum.setAttributes()
src.deleteChildElements("spectrum")
src.node.appendChild(src.spectrum.node)
src.spatialModel = FuncFactory.SkyDirFunction()
src.deleteChildElements("spatialModel")
src.node.appendChild(src.spatialModel.node)
ra, dec = self.likelihoodModel.get_point_source_position(ip)
src.spatialModel.RA.value = ra
src.spatialModel.DEC.value = dec
src.spatialModel.setAttributes()
src.setAttributes()
return MyPointSource(src, name, tempName)
def writeXml(self, xmlfile, ra, dec, roi):
# Loop through all the sources in the likelihood model and generate a FileSpectrum
# for all of them. This is necessary to allow the FermiLATLike class
# to update the spectrum in pyLikelihood without having to write and read a .xml file
# on the disk
allSourcesForPyLike = []
temp_files = []
nPtsrc = self.likelihoodModel.get_number_of_point_sources()
for ip in range(nPtsrc):
this_src = self._makeFileSpectrum(ip)
allSourcesForPyLike.append(this_src)
temp_files.append(this_src.temp_file)
# Now the same for extended sources
nExtSrc = self.likelihoodModel.get_number_of_extended_sources()
if nExtSrc > 0:
raise NotImplemented("Cannot support extended sources yet!")
iso = LikelihoodComponent.IsotropicTemplate(self.irfs)
iso.source.spectrum.Normalization.max = 1.5
iso.source.spectrum.Normalization.min = 0.5
iso.source.spectrum.setAttributes()
allSourcesForPyLike.append(iso)
# Get a temporary filename which is guaranteed to be unique
self._unique_filename = get_random_unique_name()
gal = LikelihoodComponent.GalaxyAndExtragalacticDiffuse(
self.irfs, ra, dec, 2.5 * roi, cutout_name=self._unique_filename)
gal.source.spectrum.Value.max = 1.5
gal.source.spectrum.Value.min = 0.5
gal.source.spectrum.setAttributes()
allSourcesForPyLike.append(gal)
# Now generate the xml file with also the Galactic and Isotropic diffuse
# templates
xml = LikelihoodComponent.LikelihoodModel()
xml.addSources(*allSourcesForPyLike)
xml.writeXML(xmlfile)
return temp_files
def writeXml(self, xmlfile, ra, dec, roi):
# Loop through all the sources in the likelihood model and generate a FileSpectrum
# for all of them. This is necessary to allow the FermiLATLike class
# to update the spectrum in pyLikelihood without having to write and read a .xml file
# on the disk
allSourcesForPyLike = []
temp_files = []
nPtsrc = self.likelihoodModel.get_number_of_point_sources()
for ip in range(nPtsrc):
this_src = self._makeFileSpectrum(ip)
allSourcesForPyLike.append(this_src)
temp_files.append(this_src.temp_file)
# Now the same for extended sources
nExtSrc = self.likelihoodModel.get_number_of_extended_sources()
if (nExtSrc > 0):
raise NotImplemented("Cannot support extended sources yet!")
iso = LikelihoodComponent.IsotropicTemplate(self.irfs)
iso.source.spectrum.Normalization.max = 1.5
iso.source.spectrum.Normalization.min = 0.5
iso.source.spectrum.setAttributes()
allSourcesForPyLike.append(iso)
# Get a temporary filename which is guaranteed to be unique
self._unique_filename = get_random_unique_name()
gal = LikelihoodComponent.GalaxyAndExtragalacticDiffuse(
self.irfs, ra, dec, 2.5 * roi, cutout_name=self._unique_filename)
gal.source.spectrum.Value.max = 1.5
gal.source.spectrum.Value.min = 0.5
gal.source.spectrum.setAttributes()
allSourcesForPyLike.append(gal)
# Now generate the xml file with also the Galactic and Isotropic diffuse
# templates
xml = LikelihoodComponent.LikelihoodModel()
xml.addSources(*allSourcesForPyLike)
xml.writeXML(xmlfile)
return temp_files
def _makeFileSpectrum(self, ip):
name = self.likelihoodModel.get_point_source_name(ip)
values = self.likelihoodModel.get_point_source_fluxes(ip,
self.energiesKeV)
tempName = "__%s_%s.txt" % (name, get_random_unique_name())
with open(tempName, "w+") as f:
for e, v in zip(self.energiesKeV, values):
# Gtlike needs energies in MeV and fluxes in ph/MeV/cm2)
f.write("%s %s\n" % (e / 1000.0, v * 1000.0))
# p = fileFunction.parameter("Normalization")
# p.setBounds(1-float(effAreaAllowedSize),1+effAreaAllowedSize)
# Now generate the XML source wrapper
# This is convoluted, but that's the ST way of doing things...
# The final product is a class with a writeXml method
src = '\n'.join((('<source name= "%s" ' % name) + 'type="PointSource">',
' <spectrum type="PowerLaw2"/>',
' <!-- point source units are cm^-2 s^-1 MeV^-1 -->',
' <spatialModel type="SkyDirFunction"/>',
'</source>\n'))
src = FuncFactory.minidom.parseString(src).getElementsByTagName('source')[0]
src = FuncFactory.Source(src)
src.spectrum = FuncFactory.FileFunction()
src.spectrum.file = tempName
src.spectrum.parameters['Normalization'].value = 1.0
src.spectrum.parameters['Normalization'].max = 1.1
src.spectrum.parameters['Normalization'].min = 0.9
src.spectrum.parameters['Normalization'].free = False
src.spectrum.setAttributes()
src.deleteChildElements('spectrum')
src.node.appendChild(src.spectrum.node)
src.spatialModel = FuncFactory.SkyDirFunction()
src.deleteChildElements('spatialModel')
src.node.appendChild(src.spatialModel.node)
ra, dec = self.likelihoodModel.get_point_source_position(ip)
src.spatialModel.RA.value = ra
src.spatialModel.DEC.value = dec
src.spatialModel.setAttributes()
src.setAttributes()
return MyPointSource(src, name, tempName)
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)
