def _test_pickeling(self):
"""
Test csbkgmodel pickeling
"""
# Perform pickeling tests of empty class
self._pickeling(cscripts.csbkgmodel())
# Set-up csbkgmodel
bkgmodel = cscripts.csbkgmodel()
bkgmodel['inobs'] = self._events
bkgmodel['caldb'] = self._caldb
bkgmodel['irf'] = self._irf
bkgmodel['instrument'] = 'CTA'
bkgmodel['spatial'] = 'GAUSS'
bkgmodel['gradient'] = True
bkgmodel['spectral'] = 'NODES'
bkgmodel['ebinalg'] = 'LOG'
bkgmodel['emin'] = 1.0
bkgmodel['emax'] = 100.0
bkgmodel['enumbins'] = 8
bkgmodel['runwise'] = True
bkgmodel['rad'] = 2.0
bkgmodel['chatter'] = 2
bkgmodel['outmodel'] = 'csbkgmodel_py1_pickle.xml'
bkgmodel['logfile'] = 'csbkgmodel_py1_pickle.log'
# Perform pickeling tests of filled class
obj = self._pickeling(bkgmodel)
# Run csbkgmodel script and save background model
obj.logFileOpen() # Make sure we get a log file
obj.run()
obj.save()
# Check background model
self._check_bkg_model('csbkgmodel_py1_pickle.xml')
# Return
return
def get_bkg_model(obs, rad=2.0, suffix=''):
"""
Get background model
Parameters
----------
obs : `~gammalib.GObservations`
Observation container
rad : float, optional
RoI radius (deg)
suffix : str, optional
Background lookup file suffix
Returns
-------
models : `~gammalib.GModels`
Model container
"""
# Get energy range for observation
emin = obs[0].ebounds().emin().TeV()
emax = obs[0].ebounds().emax().TeV()
# Setup task parameters
bkg = cscripts.csbkgmodel(obs)
bkg['instrument'] = 'HESS'
bkg['spatial'] = 'LOOKUP'
bkg['slufile'] = 'bkg_lookup%s.fits' % (suffix)
bkg['snumbins'] = 3
bkg['smin'] = emin
bkg['smax'] = 5.0
bkg['gradient'] = True
bkg['spectral'] = 'NODES'
bkg['ebinalg'] = 'LOG'
bkg['emin'] = emin
bkg['emax'] = emax
bkg['enumbins'] = 8
bkg['runwise'] = True
bkg['rad'] = rad
bkg['debug'] = True
# Generate background model
bkg.run()
# Extract models
models = bkg.models().copy()
# Return models
return models
def prepare(obsname, bkgname, rad=2.0, emin=0.3, emax=50.0, ebins=20):
"""
Prepare events for analysis
Parameters
----------
obsname : str
Observation definition XML file
bkgname : str
Background model definition XML file
rad : float, optional
Selection radius (degrees)
emin : float, optional
Minimum energy for analysis (TeV)
emax : float, optional
Maximum energy for analysis (TeV)
ebins : int, optional
Number of energy bins
"""
# Set filenames
cntcube = 'rx_stacked%2.2d_cntcube.fits' % ebins
expcube = 'rx_stacked%2.2d_expcube.fits' % ebins
psfcube = 'rx_stacked%2.2d_psfcube.fits' % ebins
edispcube = 'rx_stacked%2.2d_edispcube.fits' % ebins
bkgcube = 'rx_stacked%2.2d_bkgcube.fits' % ebins
obsname_binned = add_attribute(obsname, '_binned%2.2d' % ebins)
bkgname_stacked = add_attribute(bkgname, '_stacked')
obsname_stacked = add_attribute(obsname, '_stacked%2.2d' % ebins)
obsname_stacked_edisp = add_attribute(obsname_stacked, '_edisp')
# Generate background lookup
generate_background_lookup()
# Continue only if selected events do not exist
if not os.path.isfile(obsname):
# Setup task parameters
select = ctools.ctselect()
select['inobs'] = '$HESSDATA/obs/obs_rx.xml'
select['outobs'] = obsname
select['ra'] = 'UNDEF'
select['dec'] = 'UNDEF'
select['rad'] = rad
select['tmin'] = 'UNDEF'
select['tmax'] = 'UNDEF'
select['emin'] = emin
select['emax'] = emax
select['usethres'] = 'DEFAULT'
select['logfile'] = 'rx_hess_select_events.log'
select.logFileOpen()
# Select events
select.execute()
# Continue only if background model does not exist
if not os.path.isfile(bkgname):
# Setup task parameters
bkg = cscripts.csbkgmodel()
bkg['inobs'] = '$HESSDATA/obs/obs_rx.xml'
bkg['outmodel'] = bkgname
bkg['instrument'] = 'HESS'
bkg['spatial'] = 'LOOKUP'
bkg['slufile'] = 'off_lookup.fits'
bkg['gradient'] = True
bkg['spectral'] = 'NODES'
bkg['ebinalg'] = 'LOG'
bkg['emin'] = emin
bkg['emax'] = 30.0
bkg['enumbins'] = 8
bkg['runwise'] = True
bkg['rad'] = rad
bkg['logfile'] = 'rx_hess_create_background.log'
bkg.logFileOpen()
# Generate background model
bkg.execute()
# Continue only if counts cube does not exist
if not os.path.isfile(cntcube):
# Setup task parameters
ctbin = ctools.ctbin()
ctbin['inobs'] = obsname
ctbin['outobs'] = cntcube
ctbin['ebinalg'] = 'LOG'
ctbin['emin'] = emin
ctbin['emax'] = emax
ctbin['enumbins'] = ebins
ctbin['coordsys'] = 'CEL'
ctbin['proj'] = 'TAN'
ctbin['xref'] = 258.1125
ctbin['yref'] = -39.6867
ctbin['nxpix'] = 300
ctbin['nypix'] = 300
ctbin['binsz'] = 0.02
ctbin['logfile'] = 'rx_hess_create_cntcube.log'
ctbin.logFileOpen()
# Generate counts cube
ctbin.execute()
# Continue only if counts cubes for binned analysis do not exist
if not os.path.isfile(obsname_binned):
# Setup task parameters
ctbin = ctools.ctbin()
ctbin['inobs'] = obsname
ctbin['outobs'] = obsname_binned
ctbin['stack'] = False
ctbin['usepnt'] = True
ctbin['ebinalg'] = 'LOG'
ctbin['emin'] = emin
ctbin['emax'] = emax
ctbin['enumbins'] = ebins
ctbin['coordsys'] = 'CEL'
ctbin['proj'] = 'TAN'
ctbin['nxpix'] = 200
ctbin['nypix'] = 200
ctbin['binsz'] = 0.02
ctbin['logfile'] = 'rx_hess_create_cntcube_binned.log'
ctbin.logFileOpen()
# Generate counts cubes
ctbin.execute()
# Continue only if exposure cube does not exist
if not os.path.isfile(expcube):
# Setup task parameters
ctexpcube = ctools.ctexpcube()
ctexpcube['inobs'] = obsname
ctexpcube['incube'] = 'NONE'
ctexpcube['ebinalg'] = 'LOG'
ctexpcube['emin'] = 0.1 # Full energy range
ctexpcube['emax'] = 100.0 # Full energy range
ctexpcube['enumbins'] = 300 # Factor ~3 oversampling of IRF
ctexpcube['coordsys'] = 'CEL'
ctexpcube['proj'] = 'TAN'
ctexpcube['xref'] = 258.1125
ctexpcube['yref'] = -39.6867
ctexpcube['nxpix'] = 300
ctexpcube['nypix'] = 300
ctexpcube['binsz'] = 0.02
ctexpcube['outcube'] = expcube
ctexpcube['logfile'] = 'rx_hess_create_expcube.log'
ctexpcube.logFileOpen()
# Generate exposure cube
ctexpcube.execute()
# Continue only if PSF cube does not exist
if not os.path.isfile(psfcube):
# Setup task parameters
ctpsfcube = ctools.ctpsfcube()
ctpsfcube['inobs'] = obsname
ctpsfcube['incube'] = 'NONE'
ctpsfcube['ebinalg'] = 'LOG'
ctpsfcube['emin'] = 0.1 # Full energy range
ctpsfcube['emax'] = 100.0 # Full energy range
ctpsfcube['enumbins'] = 300 # Factor ~3 oversampling of IRF
ctpsfcube['coordsys'] = 'CEL'
ctpsfcube['proj'] = 'TAN'
ctpsfcube['xref'] = 258.1125
ctpsfcube['yref'] = -39.6867
ctpsfcube['nxpix'] = 30
ctpsfcube['nypix'] = 30
ctpsfcube['binsz'] = 0.2
ctpsfcube['amax'] = 0.7 # Full H.E.S.S. PSF range
ctpsfcube['anumbins'] = 300 # Factor ~2 oversampling of IRF
ctpsfcube['outcube'] = psfcube
ctpsfcube['logfile'] = 'rx_hess_create_psfcube.log'
ctpsfcube.logFileOpen()
# Generate PSF cube
ctpsfcube.execute()
# Continue only if energy dispersion cube does not exist
if not os.path.isfile(edispcube):
# Setup task parameters
ctedispcube = ctools.ctedispcube()
ctedispcube['inobs'] = obsname
ctedispcube['incube'] = 'NONE'
ctedispcube['ebinalg'] = 'LOG'
ctedispcube['emin'] = 0.1 # Full energy range
ctedispcube['emax'] = 100.0 # Full energy range
ctedispcube['enumbins'] = 300 # Factor ~3 oversampling of IRF
ctedispcube['coordsys'] = 'CEL'
ctedispcube['proj'] = 'TAN'
ctedispcube['xref'] = 258.1125
ctedispcube['yref'] = -39.6867
ctedispcube['nxpix'] = 30
ctedispcube['nypix'] = 30
ctedispcube['binsz'] = 0.2
ctedispcube['migramax'] = 5.0
ctedispcube['migrabins'] = 300
ctedispcube['outcube'] = edispcube
ctedispcube['logfile'] = 'rx_hess_create_edispcube.log'
ctedispcube.logFileOpen()
# Generate energy dispersion cube
ctedispcube.execute()
# Continue only if background cube does not exist
if not os.path.isfile(bkgcube):
# Setup task parameters
ctbkgcube = ctools.ctbkgcube()
ctbkgcube['inobs'] = obsname
ctbkgcube['incube'] = cntcube
ctbkgcube['inmodel'] = bkgname
ctbkgcube['outcube'] = bkgcube
ctbkgcube['outmodel'] = bkgname_stacked
ctbkgcube['logfile'] = 'rx_hess_create_bkgcube.log'
ctbkgcube.logFileOpen()
# Generate background cube
ctbkgcube.execute()
# Continue only if stacked observation definition XML file does not
# exist
if not os.path.isfile(obsname_stacked):
# Build stacked observation
run = gammalib.GCTAObservation(cntcube, expcube, psfcube, bkgcube)
run.name('RX J1713.7-3946')
run.instrument('HESS')
# Append to observation container
obs = gammalib.GObservations()
obs.append(run)
# Save observation container
obs.save(obsname_stacked)
# Continue only if stacked observation definition XML file with energy
# energy dispersion enabled does not exist
if not os.path.isfile(obsname_stacked_edisp):
# Build stacked observation
run = gammalib.GCTAObservation(cntcube, expcube, psfcube, edispcube,
bkgcube)
run.name('RX J1713.7-3946')
run.instrument('HESS')
# Append to observation container
obs = gammalib.GObservations()
obs.append(run)
# Save observation container
obs.save(obsname_stacked_edisp)
# Continue only if stacked model definition XML file does exist
if os.path.isfile(bkgname_stacked):
# Load model definition XML files
joint = gammalib.GModels(bkgname)
stacked = gammalib.GModels(bkgname_stacked)
# Get spectral component of joint file and remplace it as spectral
# component of stacked file
spectrum = joint[0].spectral()
for i in range(spectrum.nodes()):
spectrum.intensity(i, 1.0)
spectrum.autoscale()
stacked[0].spectral(spectrum)
# Save stacked model
stacked.save(bkgname_stacked)
# Return
return
def spectrum(obsname, bkgname, srcmodel, pars, emin=0.3, emax=20.0, alpha=1.0):
"""
Generate spectrum
Parameters
----------
obsname : str
Observation definition XML file
bkgname : str
Background model definition XML file
srcmodel : str
Source model
pars : dict
Dictionary of analysis parameters
emin : float, optional
Minimum energy (TeV)
emax : float, optional
Maximum energy (TeV)
alpha : float, optional
Template map scaling factor
"""
# Set observation name
_obsname = set_observation(obsname, srcmodel, bkgname, pars)
# Set analysis
analysis = set_analysis(srcmodel, bkgname, 'plaw', pars, alpha=alpha)
# Set file names
outfile = 'rx_spectrum_%s.fits' % (analysis)
logfile = 'rx_spectrum_%s.log' % (analysis)
# Continue only if result file does not exist
if not os.path.isfile(outfile):
# Load observations
obs = gammalib.GObservations(_obsname)
# Set background model for stacked analysis
if pars['stacked']:
bname = add_attribute(bkgname, '_stacked')
models = set_model(srcmodel, 'plaw', bname, alpha=alpha)
for model in models:
if model.type() == 'CTACubeBackground':
model.spectral()['Index'].fix()
# ... otherwise create H.E.S.S. background model with power law
# spectral component
else:
# Setup plaw background model
bkg = cscripts.csbkgmodel()
bkg['inobs'] = '$HESSDATA/obs/obs_rx.xml'
bkg['instrument'] = 'HESS'
bkg['spatial'] = 'LOOKUP'
bkg['slufile'] = 'off_lookup.fits'
bkg['gradient'] = True
bkg['spectral'] = 'PLAW'
bkg['runwise'] = True
bkg['emin'] = emin
bkg['emax'] = emax
bkg['rad'] = 2.0
bkg.run()
# Initialise model container
models = gammalib.GModels()
# Set source model
source = set_source_model(srcmodel, spec='plaw', alpha=alpha)
models.append(source)
# Set background model
bkg_models = bkg.models()
for model in bkg_models:
if model.type() == 'CTABackground':
model.spectral()['Index'].fix()
model.spatial()[0].fix() # Normalization
model.spatial()[1].free() # DETX
model.spatial()[2].free() # DETY
# Append background models
models.extend(bkg_models)
# Append models
obs.models(models)
# Generate spectrum
spec = cscripts.csspec(obs)
spec['srcname'] = 'RX J1713.7-3946'
spec['edisp'] = pars['edisp']
spec['outfile'] = outfile
spec['method'] = 'SLICE'
spec['ebinalg'] = 'LOG'
spec['emin'] = emin
spec['emax'] = emax
spec['enumbins'] = 15
spec['logfile'] = logfile
spec['debug'] = True
spec['chatter'] = 4
spec.logFileOpen()
spec.execute()
# Return
return
def _test_python(self):
"""
Test csbkgmodel from Python
"""
# Set-up csbkgmodel
bkgmodel = cscripts.csbkgmodel()
bkgmodel['inobs'] = self._events
bkgmodel['caldb'] = self._caldb
bkgmodel['irf'] = self._irf
bkgmodel['instrument'] = 'CTA'
bkgmodel['spatial'] = 'GAUSS'
bkgmodel['gradient'] = True
bkgmodel['spectral'] = 'NODES'
bkgmodel['ebinalg'] = 'LOG'
bkgmodel['emin'] = 1.0
bkgmodel['emax'] = 100.0
bkgmodel['enumbins'] = 8
bkgmodel['runwise'] = True
bkgmodel['rad'] = 2.0
bkgmodel['chatter'] = 2
bkgmodel['outmodel'] = 'csbkgmodel_py1.xml'
bkgmodel['logfile'] = 'csbkgmodel_py1.log'
# Run csbkgmodel script and save background model
bkgmodel.logFileOpen() # Make sure we get a log file
bkgmodel.run()
bkgmodel.save()
# Check background model
self._check_bkg_model('csbkgmodel_py1.xml')
# Now test without gradient, power law and not runwise
bkgmodel = cscripts.csbkgmodel()
bkgmodel['inobs'] = self._events
bkgmodel['caldb'] = self._caldb
bkgmodel['irf'] = self._irf
bkgmodel['instrument'] = 'CTA'
bkgmodel['spatial'] = 'GAUSS'
bkgmodel['gradient'] = False
bkgmodel['spectral'] = 'PLAW'
bkgmodel['ebinalg'] = 'LOG'
bkgmodel['emin'] = 1.0
bkgmodel['emax'] = 100.0
bkgmodel['enumbins'] = 8
bkgmodel['runwise'] = False
bkgmodel['rad'] = 2.0
bkgmodel['chatter'] = 3
bkgmodel['outmodel'] = 'csbkgmodel_py2.xml'
bkgmodel['logfile'] = 'csbkgmodel_py2.log'
# Execute csbkgmodel script
bkgmodel.logFileOpen() # Make sure we get a log file
bkgmodel.execute()
# Check background model
self._check_bkg_model('csbkgmodel_py2.xml')
# Now test AEFF model
bkgmodel = cscripts.csbkgmodel()
bkgmodel['inobs'] = self._events
bkgmodel['caldb'] = self._caldb
bkgmodel['irf'] = self._irf
bkgmodel['instrument'] = 'CTA'
bkgmodel['spatial'] = 'AEFF'
bkgmodel['gradient'] = False
bkgmodel['spectral'] = 'PLAW'
bkgmodel['ebinalg'] = 'LOG'
bkgmodel['emin'] = 1.0
bkgmodel['emax'] = 100.0
bkgmodel['enumbins'] = 8
bkgmodel['runwise'] = False
bkgmodel['rad'] = 2.0
bkgmodel['chatter'] = 4
bkgmodel['outmodel'] = 'csbkgmodel_py3.xml'
bkgmodel['logfile'] = 'csbkgmodel_py3.log'
# Execute csbkgmodel script
bkgmodel.logFileOpen() # Make sure we get a log file
bkgmodel.execute()
# Check background model
self._check_bkg_model('csbkgmodel_py3.xml')
# Now test IRF model
bkgmodel = cscripts.csbkgmodel()
bkgmodel['inobs'] = self._events
bkgmodel['caldb'] = self._caldb
bkgmodel['irf'] = self._irf
bkgmodel['instrument'] = 'CTA'
bkgmodel['spatial'] = 'IRF'
bkgmodel['gradient'] = False
bkgmodel['spectral'] = 'PLAW'
bkgmodel['ebinalg'] = 'LOG'
bkgmodel['emin'] = 1.0
bkgmodel['emax'] = 100.0
bkgmodel['enumbins'] = 8
bkgmodel['runwise'] = False
bkgmodel['rad'] = 2.0
bkgmodel['chatter'] = 4
bkgmodel['outmodel'] = 'csbkgmodel_py4.xml'
bkgmodel['logfile'] = 'csbkgmodel_py4.log'
# Execute csbkgmodel script
bkgmodel.logFileOpen() # Make sure we get a log file
bkgmodel.execute()
# Check background model
self._check_bkg_model('csbkgmodel_py4.xml')
# Test with multiple input observations
obs = gammalib.GObservations()
for s, events in enumerate([self._myevents1, self._myevents2]):
run = gammalib.GCTAObservation(events)
run.id(str(s + 1))
run.response(self._irf, gammalib.GCaldb('cta', self._caldb))
obs.append(run)
# Set-up csbkgmodel
bkgmodel = cscripts.csbkgmodel(obs)
bkgmodel['instrument'] = 'CTA'
bkgmodel['spatial'] = 'GAUSS'
bkgmodel['gradient'] = True
bkgmodel['spectral'] = 'NODES'
bkgmodel['ebinalg'] = 'POW'
bkgmodel['emin'] = 1.0
bkgmodel['emax'] = 100.0
bkgmodel['enumbins'] = 8
bkgmodel['ebingamma'] = 1.1
bkgmodel['runwise'] = True
bkgmodel['rad'] = 2.0
bkgmodel['chatter'] = 2
bkgmodel['outmodel'] = 'csbkgmodel_py5.xml'
bkgmodel['logfile'] = 'csbkgmodel_py5.log'
# Execute csbkgmodel script
bkgmodel.logFileOpen() # Make sure we get a log file
bkgmodel.execute()
# Check background model
self._check_bkg_model('csbkgmodel_py5.xml', nmodels=2)
# Test GAUSS(E) spatial model
bkgmodel = cscripts.csbkgmodel()
bkgmodel['inobs'] = self._events
bkgmodel['caldb'] = self._caldb
bkgmodel['irf'] = self._irf
bkgmodel['instrument'] = 'CTA'
bkgmodel['spatial'] = 'GAUSS(E)'
bkgmodel['snumbins'] = 2
bkgmodel['smin'] = 1.0
bkgmodel['smax'] = 10.0
bkgmodel['gradient'] = True
bkgmodel['spectral'] = 'NODES'
bkgmodel['ebinalg'] = 'LOG'
bkgmodel['emin'] = 1.0
bkgmodel['emax'] = 100.0
bkgmodel['enumbins'] = 8
bkgmodel['runwise'] = True
bkgmodel['rad'] = 2.0
bkgmodel['chatter'] = 2
bkgmodel['outmodel'] = 'csbkgmodel_py6.xml'
bkgmodel['logfile'] = 'csbkgmodel_py6.log'
# Run csbkgmodel script and save background model
bkgmodel.logFileOpen() # Make sure we get a log file
bkgmodel.execute()
# Check background model
self._check_bkg_model('csbkgmodel_py6.xml')
# Test LOOKUP spatial model
bkgmodel = cscripts.csbkgmodel()
bkgmodel['inobs'] = self._events
bkgmodel['caldb'] = self._caldb
bkgmodel['irf'] = self._irf
bkgmodel['instrument'] = 'CTA'
bkgmodel['spatial'] = 'LOOKUP'
bkgmodel['slufile'] = self._lookup
bkgmodel['gradient'] = True
bkgmodel['spectral'] = 'NODES'
bkgmodel['ebinalg'] = 'LOG'
bkgmodel['emin'] = 1.0
bkgmodel['emax'] = 100.0
bkgmodel['enumbins'] = 8
bkgmodel['runwise'] = True
bkgmodel['rad'] = 2.0
bkgmodel['chatter'] = 2
bkgmodel['outmodel'] = 'csbkgmodel_py7.xml'
bkgmodel['logfile'] = 'csbkgmodel_py7.log'
# Run csbkgmodel script and save background model
bkgmodel.logFileOpen() # Make sure we get a log file
bkgmodel.execute()
# Check background model
self._check_bkg_model('csbkgmodel_py7.xml')
# Test PROFILE spatial model
bkgmodel = cscripts.csbkgmodel()
bkgmodel['inobs'] = self._events
bkgmodel['caldb'] = self._caldb
bkgmodel['irf'] = self._irf
bkgmodel['instrument'] = 'CTA'
bkgmodel['spatial'] = 'PROFILE'
bkgmodel['gradient'] = True
bkgmodel['spectral'] = 'NODES'
bkgmodel['ebinalg'] = 'LOG'
bkgmodel['emin'] = 1.0
bkgmodel['emax'] = 100.0
bkgmodel['enumbins'] = 8
bkgmodel['runwise'] = True
bkgmodel['rad'] = 2.0
bkgmodel['chatter'] = 2
bkgmodel['outmodel'] = 'csbkgmodel_py8.xml'
bkgmodel['logfile'] = 'csbkgmodel_py8.log'
# Run csbkgmodel script and save background model
bkgmodel.logFileOpen() # Make sure we get a log file
bkgmodel.execute()
# Check background model
self._check_bkg_model('csbkgmodel_py8.xml')
# Test POLYNOM spatial model
bkgmodel = cscripts.csbkgmodel()
bkgmodel['inobs'] = self._events
bkgmodel['caldb'] = self._caldb
bkgmodel['irf'] = self._irf
bkgmodel['instrument'] = 'CTA'
bkgmodel['spatial'] = 'POLYNOM'
bkgmodel['gradient'] = True
bkgmodel['spectral'] = 'NODES'
bkgmodel['ebinalg'] = 'LOG'
bkgmodel['emin'] = 1.0
bkgmodel['emax'] = 100.0
bkgmodel['enumbins'] = 8
bkgmodel['runwise'] = True
bkgmodel['rad'] = 2.0
bkgmodel['chatter'] = 2
bkgmodel['outmodel'] = 'csbkgmodel_py9.xml'
bkgmodel['logfile'] = 'csbkgmodel_py9.log'
# Run csbkgmodel script and save background model
bkgmodel.logFileOpen() # Make sure we get a log file
bkgmodel.execute()
# Check background model
self._check_bkg_model('csbkgmodel_py9.xml')
# Check with data from multiple instruments
new_inst = 'INST2'
obs_multi_inst = gammalib.GObservations(obs)
for run in obs_multi_inst:
run.instrument(new_inst)
obs_multi_inst.extend(obs)
# Set-up csbkgmodel
bkgmodel = cscripts.csbkgmodel(obs_multi_inst)
bkgmodel['instrument'] = new_inst
bkgmodel['spatial'] = 'GAUSS'
bkgmodel['gradient'] = True
bkgmodel['spectral'] = 'NODES'
bkgmodel['ebinalg'] = 'POW'
bkgmodel['emin'] = 1.0
bkgmodel['emax'] = 100.0
bkgmodel['enumbins'] = 8
bkgmodel['ebingamma'] = 1.1
bkgmodel['runwise'] = True
bkgmodel['rad'] = 2.0
bkgmodel['chatter'] = 2
bkgmodel['outmodel'] = 'csbkgmodel_py10.xml'
bkgmodel['logfile'] = 'csbkgmodel_py10.log'
# Execute csbkgmodel script
bkgmodel.logFileOpen() # Make sure we get a log file
bkgmodel.execute()
# Check background model
self._check_bkg_model('csbkgmodel_py10.xml', nmodels=obs.size())
# Return
return
