def CreateLikeObject(self):
"""Create an UnbinnedAnalysis or a BinnedAnalysis and retrun it.
By default, the optimizer is DRMNGB """
#create binnedAnalysis object
if self.config['analysis']['likelihood'] == 'binned':
Obs = BinnedObs(srcMaps=self.obs.scrMap,
expCube=self.obs.Cubename,
binnedExpMap=self.obs.BinnedMapfile,
irfs=self.obs.irfs)
Fit = BinnedAnalysis(Obs, self.obs.xmlfile, optimizer='DRMNGB')
#create a unbinnedAnalysis object
if self.config['analysis']['likelihood'] == 'unbinned':
Obs = UnbinnedObs(self.obs.eventfile,
self.obs.ft2,
expMap=self.obs.Mapname,
expCube=self.obs.Cubename,
irfs=self.obs.irfs)
Fit = UnbinnedAnalysis(Obs, self.obs.xmlfile, optimizer='DRMNGB')
if float(self.config['Spectrum']['FrozenSpectralIndex']) > 0:
if Fit.model.srcs[self.obs.srcname].spectrum().genericName(
) == "PowerLaw" or Fit.model.srcs[
self.obs.srcname].spectrum().genericName() == "PowerLaw2":
PhIndex = Fit.par_index(self.obs.srcname, 'Index')
Fit[PhIndex] = -float(
self.config['Spectrum']['FrozenSpectralIndex'])
Fit.freeze(PhIndex)
print "Freezing spectral index at ", -float(
self.config['Spectrum']['FrozenSpectralIndex'])
else:
log.warning(
"The model is not a PowerLaw. Cannot freeze the index.")
return Fit #return the BinnedAnalysis or UnbinnedAnalysis object.
def CreateLikeObject(self):
"""Create an UnbinnedAnalysis or a BinnedAnalysis and retrun it."""
#create binnedAnalysis object
if self.config['analysis']['likelihood'] == 'binned':
Obs = BinnedObs(srcMaps=self.obs.srcMap,
expCube=self.obs.Cubename,
binnedExpMap=self.obs.BinnedMapfile,
irfs=self.obs.irfs)
Fit = BinnedAnalysis(Obs,
self.obs.xmlfile,
optimizer=self.config['fitting']['optimizer'])
#create a unbinnedAnalysis object
if self.config['analysis']['likelihood'] == 'unbinned':
Obs = UnbinnedObs(self.obs.mktimefile,
self.obs.ft2,
expMap=self.obs.Mapname,
expCube=self.obs.Cubename,
irfs=self.obs.irfs)
Fit = UnbinnedAnalysis(
Obs,
self.obs.xmlfile,
optimizer=self.config['fitting']['optimizer'])
# Fix this, EBL absorbed models use LogParabola with b=0 instead of PowerLaw, we may want to allow fixed shape for that case
if float(self.config['Spectrum']['FrozenSpectralIndex'] > 0
) and self.config['target']['spectrum'] == "PowerLaw":
parameters = dict()
parameters['Index'] = -float(
self.config['Spectrum']['FrozenSpectralIndex'])
# parameters['alpha'] = +float(self.config['Spectrum']['FrozenSpectralIndex'])
# parameters['Index1'] = -float(self.config['Spectrum']['FrozenSpectralIndex'])
# parameters['beta'] = 0
# parameters['Index2'] = 2.
# parameters['Cutoff'] = 30000. # set the cutoff to be high
for key in parameters.keys():
try:
IdGamma = utils.getParamIndx(Fit, self.obs.srcname, key)
Fit[IdGamma] = parameters[key] # set the parameter
Fit[IdGamma].setFree(
0) #the variable index is frozen to compute the UL
except:
continue
else:
self.info("Freezing %s at %s"\
%(key,str(self.config['Spectrum']['FrozenSpectralIndex'])))
return Fit #return the BinnedAnalysis or UnbinnedAnalysis object.
def CreateLikeObject(self):
"""Create an UnbinnedAnalysis or a BinnedAnalysis and retrun it."""
#create binnedAnalysis object
if self.config['analysis']['likelihood'] == 'binned':
use_edisp = self.config['analysis']['EnergyDispersion'] == 'yes'
edisp_bins = -2 if use_edisp==True else 0
Obs = BinnedObs(srcMaps=self.obs.srcMap,
expCube=self.obs.Cubename,
binnedExpMap=self.obs.BinnedMapfile,
irfs=self.obs.irfs)
Cfg = BinnedConfig(use_edisp=use_edisp,
edisp_bins=edisp_bins)
Fit = BinnedAnalysis(Obs, self.obs.xmlfile, config=Cfg,
optimizer=self.config['fitting']['optimizer'])
Fit.setEnergyRange(self.obs.Emin,self.obs.Emax)
print("Is edisp enabled? {0}".format(str(Fit.logLike.use_edisp())))
#create a unbinnedAnalysis object
if self.config['analysis']['likelihood'] == 'unbinned':
Obs = UnbinnedObs(self.obs.mktimefile,
self.obs.ft2,
expMap=self.obs.Mapname,
expCube=self.obs.Cubename,
irfs=self.obs.irfs)
Fit = UnbinnedAnalysis(Obs, self.obs.xmlfile,
optimizer=self.config['fitting']['optimizer'])
# Fix this, EBL absorbed models use LogParabola with b=0 instead of PowerLaw,
# we may want to allow fixed shape for that case
if float(self.config['Spectrum']['FrozenSpectralIndex']!=0):
parameters = dict()
parameters['Index'] = -float(self.config['Spectrum']['FrozenSpectralIndex'])
parameters['alpha'] = +float(self.config['Spectrum']['FrozenSpectralIndex'])
parameters['Index1'] = -float(self.config['Spectrum']['FrozenSpectralIndex'])
parameters['beta'] = 0
parameters['Index2'] = 2.
parameters['Cutoff'] = 30000. # set the cutoff to be high
for key in parameters.keys():
IdGamma = utils.getParamIndx(Fit, self.obs.srcname, key)
if (IdGamma == -1):
continue
else:
self.info("Freezing %s = %s" %(str(key),str(parameters[key])))
Fit[IdGamma] = parameters[key] # set the parameter
Fit[IdGamma].setFree(False)#the variable index is frozen to compute the UL
return Fit #return the BinnedAnalysis or UnbinnedAnalysis object.
def CreateLikeObject(self):
"""Create an UnbinnedAnalysis or a BinnedAnalysis and retrun it."""
#create binnedAnalysis object
if self.config['analysis']['likelihood'] == 'binned':
Obs = BinnedObs(srcMaps=self.obs.srcMap,
expCube=self.obs.Cubename,
binnedExpMap=self.obs.BinnedMapfile,
irfs=self.obs.irfs)
Fit = BinnedAnalysis(Obs, self.obs.xmlfile,
optimizer=self.config['fitting']['optimizer'])
#create a unbinnedAnalysis object
if self.config['analysis']['likelihood'] == 'unbinned':
Obs = UnbinnedObs(self.obs.eventfile,
self.obs.ft2,
expMap=self.obs.Mapname,
expCube=self.obs.Cubename,
irfs=self.obs.irfs)
Fit = UnbinnedAnalysis(Obs, self.obs.xmlfile,
optimizer=self.config['fitting']['optimizer'])
if float(self.config['Spectrum']['FrozenSpectralIndex']) != 0:
parameters = dict()
parameters['Index'] = -float(self.config['Spectrum']['FrozenSpectralIndex'])
parameters['alpha'] = +float(self.config['Spectrum']['FrozenSpectralIndex'])
parameters['Index1'] = -float(self.config['Spectrum']['FrozenSpectralIndex'])
parameters['beta'] = 0
parameters['Index2'] = 2.
parameters['Cutoff'] = 30000. # set the cutoff to be high
for key in parameters.keys():
try:
IdGamma = utils.getParamIndx(Fit, self.obs.srcname, key)
Fit[IdGamma] = parameters[key] # set the parameter
Fit[IdGamma].setFree(0)#the variable index is frozen to compute the UL
except:
continue
else:
self.info("Freezing %s at %s"\
%(key,str(self.config['Spectrum']['FrozenSpectralIndex'])))
return Fit #return the BinnedAnalysis or UnbinnedAnalysis object.
def __init__(self, roi, **kwargs):
""" Build a gtlike pyLikelihood object
which is consistent with a pointlike roi. """
keyword_options.process(self, kwargs)
self.roi = roi
if not roi.quiet: print 'Running a gtlike followup'
self.old_dir = os.getcwd()
if self.savedir is not None:
self.savedata = True
if not os.path.exists(self.savedir):
os.makedirs(self.savedir)
else:
self.savedata = False
self.savedir = mkdtemp(prefix=self.savedir_prefix)
# put pfiles into savedir
os.environ['PFILES'] = self.savedir + ';' + os.environ['PFILES'].split(
';')[-1]
if not roi.quiet: print 'Saving files to ', self.savedir
cut_ft1 = join(self.savedir, "ft1_cut.fits")
input_srcmdl_file = join(self.savedir, 'srcmdl.xml')
expmap = join(self.savedir, "expmap.fits")
ltcube = roi.sa.pixeldata.ltcube
ft2 = Gtlike.get_ft2(roi)
irfs = Gtlike.get_gtlike_irfs(roi)
ct = roi.sa.pixeldata.conv_type
radius = roi.sa.maxROI
ra = roi.roi_dir.ra()
dec = roi.roi_dir.dec()
emin, emax = roi.bin_edges[0], roi.bin_edges[-1]
Gtlike.save_xml(roi, input_srcmdl_file)
evfile = Gtlike.make_evfile(roi, self.savedir)
if not os.path.exists(cut_ft1):
if not roi.quiet: print 'Running gtselect'
gtselect = GtApp('gtselect', 'dataSubselector')
gtselect.run(infile=evfile,
outfile=cut_ft1,
ra=ra,
dec=dec,
rad=radius,
tmin=0,
tmax=0,
emin=emin,
emax=emax,
zmax=180,
convtype=ct,
chatter=self.chatter)
else:
if not roi.quiet: print '... Skiping gtselect'
if not os.path.exists(expmap):
# Run gtexpmap following suggestions from tutorial
# pad 10deg on radius to account for nearby sources,
# nlat has half degree pixels
if not roi.quiet: print 'Running gtexpmap'
gtexpmap = GtApp('gtexpmap')
gtexpmap.run(
evfile=cut_ft1,
scfile=ft2,
expcube=ltcube,
outfile=expmap,
irfs=irfs,
srcrad=radius + 10,
nlong=int(np.ceil(0.5 * (radius + 10) * 2)),
nlat=int(np.ceil(0.5 * (radius + 10) * 2)),
nenergies=int(np.ceil(np.log10(emax) - np.log10(emin))) * 4,
chatter=self.chatter,
)
else:
if not roi.quiet: print '... Skiping gtexpmap'
gtdiffrsp = GtApp('gtdiffrsp')
if not roi.quiet: print 'Running gtdiffrsp'
gtdiffrsp.run(
evfile=cut_ft1,
scfile=ft2,
srcmdl=input_srcmdl_file,
irfs=irfs,
chatter=self.chatter,
)
if not roi.quiet: print 'Creating Unbinned LIKE'
obs = UnbinnedObs(eventFile=cut_ft1,
scFile=ft2,
expMap=expmap,
expCube=ltcube,
irfs=irfs)
self.like = UnbinnedAnalysis(observation=obs,
srcModel=input_srcmdl_file,
optimizer=self.optimizer)
if not roi.quiet: print 'Unbinned LIKE Created!'