def PerformLikelihoodAnalysis(self):
print "\nPerforming likelihood analysis on position: ra=%s, dec=%s" % (
self.xref, self.yref)
# Wait a random amount of time between 1 and 5 minutes before starting in order to not crash the asf/nsf disks at SLAC
waitTime = random.random() * 300
time.sleep(waitTime)
# Defind the scratch directory
JobID = os.environ.get('LSB_JOBID')
Username = getpass.getuser()
ScratchDirectory = "/scratch/%s/%s/" % (Username, JobID)
# Define the pfile directory
if JobID == 'None':
PFILESDirectory = "%s/pfiles_%s/" % (self.outdir, self.binNumber)
else:
PFILESDirectory = "%s/pfiles/" % ScratchDirectory
# Create the output directory if it doesn't already exist
if (os.path.isdir(self.outdir) == False):
print "\n >> Creating Directory: " + self.outdir
cmd = "mkdir " + self.outdir
os.system(cmd)
# Define where to save the results
likelihoodResults = '%s/likelihoodResults_bin%s.txt' % (self.outdir,
self.binNumber)
# Remove any pre-existing pfiles
if (os.path.isdir(PFILESDirectory) == True):
cmd = "rm -r %s" % PFILESDirectory
os.system(cmd)
# Set the new pfiles directory
SetPfilesDirectory(PFILESDirectory)
# Make a copy of the source model
xmlModelWithPutativeSource = '%s/ModelSource_bin%s.xml' % (
self.outdir, self.binNumber)
cmd = "cp " + self.srcmdl + " " + xmlModelWithPutativeSource
print cmd
os.system(cmd)
# Add a putative point source at the requested location
# AddCandidateSource(self.xref, self.yref, xmlModelWithPutativeSource)
ModifySourceModel(xmlModelWithPutativeSource, self.xref, self.yref)
# # Import the necessary gtapps
# gtlike = GtApp('gtlike')
# # Run the likelihood analysis
# print '\nPerforming the likelihood fit:'
# gtlike.run(statistic=self.statistic,
# scfile=self.scfile,
# evfile=self.evfile,
# expmap=self.expmap,
# expcube=self.expcube,
# srcmdl=xmlModelWithPutativeSource,
# irfs=self.irfs,
# optimizer=self.optimizer,
# results=likelihoodResults,
# plot='no',
# save='yes')
# Setup the unbinned likelihood object
print '\nPerforming the likelihood fit:'
try:
obs = UnbinnedObs(self.evfile,
self.scfile,
expMap=self.expmap,
expCube=self.expcube,
irfs=self.irfs)
# Define the likelihood object
#like = UnbinnedAnalysis(obs,xmlModelWithPutativeSource,optimizer=self.optimizer)
like = UnbinnedAnalysis(obs,
xmlModelWithPutativeSource,
optimizer='MINUIT')
# Setup the likelihood parameters
Source = 'CandidateSource'
Integral = like.par_index(Source, 'Integral')
Index = like.par_index(Source, 'Index')
LowerLimit = like.par_index(Source, 'LowerLimit')
UpperLimit = like.par_index(Source, 'UpperLimit')
# Setup the likelihood bounds
like[Integral].setScale(1e-3)
like[Index].setBounds(-5, -0.5)
# like[LowerLimit] = emin
# like[UpperLimit] = emax
# Perform the likelihood fit
#optObject = pyLike.NewMinuit(like.logLike)
#like.fit(verbosity=0,covar=True,tol=0.02,optObject=optObject)
like.fit(verbosity=1,
covar=True,
tol=1e-10,
optimizer='MINUIT',
optObject=None)
# Extract the best fit index
IndexValue = like[Index].value()
IndexError = like[Index].error()
# Extract the best fit flux
FluxValue = like.flux(Source, emin=100, emax=3e5)
FluxError = like.fluxError(Source, emin=100, emax=3e5)
# Extract likelihood fit results
print '\nLikelihood Results:'
print like.model[Source]
print "TS = %s" % like.Ts(Source)
print "Flux = %s +/- %s" % (FluxValue, FluxError)
print "Index = %s +/- %s" % (IndexValue, IndexError)
# Save the xml file
like.writeXml(xmlFile=xmlModelWithPutativeSource)
except Exception, message:
print traceback.format_exc()
def PerformLikelihoodAnalysis(self):
print "\nPerforming likelihood analysis on position: ra=%s, dec=%s" % (self.xref, self.yref)
# Wait a random amount of time between 1 and 5 minutes before starting in order to not crash the asf/nsf disks at SLAC
waitTime = random.random()*300
time.sleep(waitTime)
# Defind the scratch directory
JobID = os.environ.get('LSB_JOBID')
Username = getpass.getuser()
ScratchDirectory = "/scratch/%s/%s/" % (Username, JobID)
# Define the pfile directory
if JobID == 'None':
PFILESDirectory = "%s/pfiles_%s/" % (self.outdir, self.binNumber)
else:
PFILESDirectory = "%s/pfiles/" % ScratchDirectory
# Create the output directory if it doesn't already exist
if(os.path.isdir(self.outdir)==False):
print "\n >> Creating Directory: " + self.outdir
cmd = "mkdir " + self.outdir
os.system(cmd)
# Define where to save the results
likelihoodResults = '%s/likelihoodResults_bin%s.txt' % (self.outdir, self.binNumber)
# Remove any pre-existing pfiles
if(os.path.isdir(PFILESDirectory)==True):
cmd = "rm -r %s" % PFILESDirectory
os.system(cmd)
# Set the new pfiles directory
SetPfilesDirectory(PFILESDirectory)
# Make a copy of the source model
xmlModelWithPutativeSource = '%s/ModelSource_bin%s.xml' % (self.outdir, self.binNumber)
cmd = "cp " + self.srcmdl + " " + xmlModelWithPutativeSource
print cmd
os.system(cmd)
# Add a putative point source at the requested location
# AddCandidateSource(self.xref, self.yref, xmlModelWithPutativeSource)
ModifySourceModel(xmlModelWithPutativeSource, self.xref, self.yref)
# # Import the necessary gtapps
# gtlike = GtApp('gtlike')
# # Run the likelihood analysis
# print '\nPerforming the likelihood fit:'
# gtlike.run(statistic=self.statistic,
# scfile=self.scfile,
# evfile=self.evfile,
# expmap=self.expmap,
# expcube=self.expcube,
# srcmdl=xmlModelWithPutativeSource,
# irfs=self.irfs,
# optimizer=self.optimizer,
# results=likelihoodResults,
# plot='no',
# save='yes')
# Setup the unbinned likelihood object
print '\nPerforming the likelihood fit:'
try:
obs = UnbinnedObs(self.evfile,self.scfile,expMap=self.expmap,expCube=self.expcube,irfs=self.irfs)
# Define the likelihood object
#like = UnbinnedAnalysis(obs,xmlModelWithPutativeSource,optimizer=self.optimizer)
like = UnbinnedAnalysis(obs,xmlModelWithPutativeSource,optimizer='MINUIT')
# Setup the likelihood parameters
Source = 'CandidateSource'
Integral = like.par_index(Source, 'Integral')
Index = like.par_index(Source, 'Index')
LowerLimit = like.par_index(Source, 'LowerLimit')
UpperLimit = like.par_index(Source, 'UpperLimit')
# Setup the likelihood bounds
like[Integral].setScale(1e-3)
like[Index].setBounds(-5, -0.5)
# like[LowerLimit] = emin
# like[UpperLimit] = emax
# Perform the likelihood fit
#optObject = pyLike.NewMinuit(like.logLike)
#like.fit(verbosity=0,covar=True,tol=0.02,optObject=optObject)
like.fit(verbosity=1,covar=True,tol=1e-10,optimizer='MINUIT', optObject=None)
# Extract the best fit index
IndexValue = like[Index].value()
IndexError = like[Index].error()
# Extract the best fit flux
FluxValue = like.flux(Source, emin=100, emax=3e5)
FluxError = like.fluxError(Source, emin=100, emax=3e5)
# Extract likelihood fit results
print '\nLikelihood Results:'
print like.model[Source]
print "TS = %s" % like.Ts(Source)
print "Flux = %s +/- %s" % (FluxValue, FluxError)
print "Index = %s +/- %s" % (IndexValue, IndexError)
# Save the xml file
like.writeXml(xmlFile=xmlModelWithPutativeSource)
except Exception, message:
print traceback.format_exc()
