def _test_python(self):
"""
Test csspec from Python
"""
# Set-up unbinned csspec
spec = cscripts.csspec()
spec['inobs'] = self._events
spec['inmodel'] = self._model
spec['srcname'] = 'Crab'
spec['caldb'] = self._caldb
spec['irf'] = self._irf
spec['outfile'] = 'csspec_py1.fits'
spec['ebinalg'] = 'LOG'
spec['enumbins'] = 5
spec['emin'] = 0.1
spec['emax'] = 100.0
spec['logfile'] = 'csspec_py1.log'
spec['chatter'] = 2
# Run csspec script
spec.logFileOpen() # Make sure we get a log file
spec.run()
spec.save()
# Check pull distribution file
self._check_result_file('csspec_py1.fits', 5)
# Set-up binned csspec
spec = cscripts.csspec()
spec['inobs'] = self._inobs
spec['expcube'] = self._expcube
spec['psfcube'] = self._psfcube
spec['edispcube'] = self._edispcube
spec['bkgcube'] = self._bkgcube
spec['inmodel'] = self._model
spec['srcname'] = 'Crab'
spec['outfile'] = 'csspec_py2.fits'
spec['ebinalg'] = 'LOG'
spec['enumbins'] = 2
spec['emin'] = 0.1
spec['emax'] = 100.0
spec['logfile'] = 'csspec_py2.log'
spec['chatter'] = 3
# Execute csspec script
spec.execute()
# Check pull distribution file
self._check_result_file('csspec_py2.fits', 2)
# Return
return
def create_spectrum(datadir):
"""
Simulate events and generate a source spectrum
Returns
-------
datadir : str
Data directory
"""
# Set script parameters
model_name = datadir + '/crab.xml'
caldb = 'prod2'
irf = 'South_0.5h'
ra = 83.63
dec = 22.01
rad_sim = 3.0
tstart = 0.0
tstop = 1800.0
emin = 0.1
emax = 100.0
enumbins = 10
# Simulate events
sim = ctools.ctobssim()
sim['inmodel'] = model_name
sim['caldb'] = caldb
sim['irf'] = irf
sim['ra'] = ra
sim['dec'] = dec
sim['rad'] = rad_sim
sim['tmin'] = tstart
sim['tmax'] = tstop
sim['emin'] = emin
sim['emax'] = emax
sim.run()
# Create spectrum
spec = cscripts.csspec(sim.obs())
spec['srcname'] = 'Crab'
spec['outfile'] = 'example_spectrum.fits'
spec['expcube'] = 'NONE'
spec['psfcube'] = 'NONE'
spec['bkgcube'] = 'NONE'
spec['edisp'] = False
spec['emin'] = emin
spec['emax'] = emax
spec['enumbins'] = enumbins
spec['ebinalg'] = 'LOG'
spec.run()
spec.save()
# Get copy of spectrum
spectrum = spec.spectrum().copy()
# Return
return spectrum
def make_spectrum():
"""
Make a spectrum.
"""
# Set script parameters
model_name = "${CTOOLS}/share/models/crab.xml"
caldb = "prod2"
irf = "South_50h"
ra = 83.63
dec = 22.01
rad_sim = 3.0
tstart = 0.0
tstop = 1800.0
emin = 0.1
emax = 100.0
enumbins = 10
# Simulate events
sim = ctools.ctobssim()
sim["inmodel"] = model_name
sim["caldb"] = caldb
sim["irf"] = irf
sim["ra"] = ra
sim["dec"] = dec
sim["rad"] = rad_sim
sim["tmin"] = tstart
sim["tmax"] = tstop
sim["emin"] = emin
sim["emax"] = emax
sim.run()
# Generate an energy binning
#e_min = gammalib.GEnergy(emin, "TeV")
#e_max = gammalib.GEnergy(emax, "TeV")
#ebounds = gammalib.GEbounds(10, e_min, e_max)
# Setup csspec run
spec = cscripts.csspec(sim.obs())
spec["srcname"] = "Crab"
spec["outfile"] = "spectrum.fits"
spec["expcube"] = "NONE"
spec["psfcube"] = "NONE"
spec["bkgcube"] = "NONE"
spec["edisp"] = False
spec["emin"] = emin
spec["emax"] = emax
spec["enumbins"] = enumbins
spec["binned"] = False
spec.run()
# Get copy of spectrum
spectrum = spec.spectrum().copy()
# Return
return spectrum
def _test_pickeling(self):
"""
Test csspec pickeling
"""
# Perform pickeling tests of empty class
self._pickeling(cscripts.csspec())
# Set-up unbinned csspec
spec = cscripts.csspec()
spec['inobs'] = self._events
spec['inmodel'] = self._model
spec['srcname'] = 'Crab'
spec['caldb'] = self._caldb
spec['irf'] = self._irf
spec['method'] = 'AUTO'
spec['ebinalg'] = 'LOG'
spec['enumbins'] = 2
spec['emin'] = 1.0
spec['emax'] = 100.0
spec['outfile'] = 'csspec_py1_pickle.fits'
spec['logfile'] = 'csspec_py1_pickle.log'
spec['chatter'] = 2
spec['publish'] = True
# Perform pickeling tests of filled class
obj = self._pickeling(spec)
# Run csspec script and save light curve
obj.logFileOpen() # Make sure we get a log file
obj.run()
obj.save()
# Check result file
self._check_result_file('csspec_py1_pickle.fits', 2)
# Return
return
def csspec_run(self,
input_obs_list,
input_models=None,
enumbins=20,
output_file='spectrum.fits',
log_file='csspec.log',
force=False,
save=False):
spec = cscripts.csspec()
if isinstance(input_obs_list, gammalib.GObservations):
spec.obs(input_obs_list)
elif os.path.isfile(input_obs_list) and os.path.isfile(input_models):
# observations list from file
spec["inobs"] = input_obs_list
spec["inmodel"] = input_models
else:
raise Exception('Cannot understand input obs list for csspec')
spec["srcname"] = self.name
spec["caldb"] = self.caldb
spec["irf"] = self.irf
spec["method"] = "AUTO"
spec["emin"] = 0.03
spec["emax"] = 150.0
spec['ebinalg'] = "LOG"
spec["enumbins"] = enumbins
spec['calc_ts'] = True
spec['calc_ulim'] = True
spec['outfile'] = output_file
spec["logfile"] = log_file
spec["nthreads"] = self.nthreads
if force or not os.path.isfile(output_file):
spec.logFileOpen()
spec.run()
elif os.path.isfile(output_file):
spec._fits = gammalib.GFits(output_file)
else:
raise Exception("Cannot proceed with csspec")
saved = False
if (save and force) or (save and not os.path.isfile(output_file)):
spec.save()
saved = True
logger.info("File {} created.".format(output_file))
return spec
def makeSpectralPoints(cfg):
"""
Computes spectrum
"""
outputdir = cfg.getValue('general', 'outputdir')
spec = cscripts.csspec()
if cfg.getValue('general', 'anatype') == 'unbinned':
spec["inobs"] = outputdir + '/' + cfg.getValue('ctselect', 'output')
spec["inmodel"] = outputdir + '/' + cfg.getValue('ctlike', 'output')
elif cfg.getValue('general', 'anatype') == 'binned':
spec["inobs"] = outputdir + '/' + cfg.getValue('ctbin', 'output')
spec["inmodel"] = outputdir + '/' + cfg.getValue('ctlike', 'output')
spec["expcube"] = outputdir + '/' + cfg.getValue('ctexpcube', 'output')
spec["psfcube"] = outputdir + '/' + cfg.getValue('ctpsfcube', 'output')
spec["bkgcube"] = outputdir + '/' + \
cfg.getValue('ctbkgcube', 'output_cube')
else:
Utilities.warning('Unknown type: {}'.format(
cfg.getValue('general', 'anatype')))
sys.exit()
spec["outfile"] = outputdir + '/' + cfg.getValue('csspec', 'output')
spec["emin"] = cfg.getValue('csspec', 'emin')
spec["emax"] = cfg.getValue('csspec', 'emax')
spec["enumbins"] = cfg.getValue('csspec', 'enumbins')
spec["srcname"] = ""
spec["ebinalg"] = "LOG"
if cfg.getValue('general', 'edisp'):
spec["edisp"] = True
else:
spec["edisp"] = True
if cfg.getValue('general', 'debug') is True:
spec["debug"] = True
# spec.run()
spec.execute()
# make copy of spectrum
spectrum = spec.spectrum().copy()
return spectrum
def stackedPipeline(
name="Crab",
obsfile="index.xml",
l=0.01,
b=0.01,
emin=0.1,
emax=100.0,
enumbins=20,
nxpix=200,
nypix=200,
binsz=0.02,
coordsys="CEL",
proj="CAR",
caldb="prod2",
irf="acdc1a",
debug=False,
inmodel="Crab",
outmodel="results",
):
"""
Simulation and stacked analysis pipeline
Parameters
----------
obs : `~gammalib.GObservations`
Observation container
ra : float, optional
Right Ascension of counts cube centre (deg)
dec : float, optional
Declination of Region of counts cube centre (deg)
emin : float, optional
Minimum energy (TeV)
emax : float, optional
Maximum energy (TeV)
enumbins : int, optional
Number of energy bins
nxpix : int, optional
Number of pixels in X axis
nypix : int, optional
Number of pixels in Y axis
binsz : float, optional
Pixel size (deg)
coordsys : str, optional
Coordinate system
proj : str, optional
Coordinate projection
debug : bool, optional
Debug function
"""
# Bin events into counts map
bin = ctools.ctbin()
bin["inobs"] = obsfile
bin["ebinalg"] = "LOG"
bin["emin"] = emin
bin["emax"] = emax
bin["enumbins"] = enumbins
bin["nxpix"] = nxpix
bin["nypix"] = nypix
bin["binsz"] = binsz
bin["coordsys"] = coordsys
bin["proj"] = proj
bin["xref"] = l
bin["yref"] = b
bin["debug"] = debug
bin["outobs"] = "cntcube.fits"
bin.execute()
print("Datacube : done!")
# Create exposure cube
expcube = ctools.ctexpcube()
# expcube['incube']=bin.obs()
expcube["inobs"] = obsfile
expcube["incube"] = "NONE"
expcube["ebinalg"] = "LOG"
expcube["caldb"] = caldb
expcube["irf"] = irf
expcube["emin"] = emin
expcube["emax"] = emax
expcube["enumbins"] = enumbins
expcube["nxpix"] = nxpix
expcube["nypix"] = nypix
expcube["binsz"] = binsz
expcube["coordsys"] = coordsys
expcube["proj"] = proj
expcube["xref"] = l
expcube["yref"] = b
expcube["debug"] = debug
expcube["outcube"] = "cube_exp.fits"
expcube.execute()
print("Expcube : done!")
# Create PSF cube
psfcube = ctools.ctpsfcube()
psfcube["inobs"] = obsfile
psfcube["incube"] = "NONE"
psfcube["ebinalg"] = "LOG"
psfcube["caldb"] = caldb
psfcube["irf"] = irf
psfcube["emin"] = emin
psfcube["emax"] = emax
psfcube["enumbins"] = enumbins
psfcube["nxpix"] = 10
psfcube["nypix"] = 10
psfcube["binsz"] = 1.0
psfcube["coordsys"] = coordsys
psfcube["proj"] = proj
psfcube["xref"] = l
psfcube["yref"] = b
psfcube["debug"] = debug
psfcube["outcube"] = "psf_cube.fits"
psfcube.execute()
print("Psfcube : done!")
edispcube = ctools.ctedispcube()
edispcube["inobs"] = obsfile
edispcube["ebinalg"] = "LOG"
edispcube["incube"] = "NONE"
edispcube["caldb"] = caldb
edispcube["irf"] = irf
edispcube["xref"] = l
edispcube["yref"] = b
edispcube["proj"] = proj
edispcube["coordsys"] = coordsys
edispcube["binsz"] = 1.0
edispcube["nxpix"] = 10
edispcube["nypix"] = 10
edispcube["emin"] = emin
edispcube["emax"] = emax
edispcube["enumbins"] = enumbins
edispcube["outcube"] = "edisp_cube.fits"
edispcube["debug"] = debug
edispcube.execute()
print("Edispcube : done!")
# Create background cube
bkgcube = ctools.ctbkgcube()
bkgcube["inobs"] = obsfile
bkgcube["incube"] = "cntcube.fits"
bkgcube["caldb"] = caldb
bkgcube["irf"] = irf
bkgcube["debug"] = debug
bkgcube["inmodel"] = str(inmodel)
bkgcube["outcube"] = "bkg_cube.fits"
bkgcube["outmodel"] = "bkg_cube.xml"
bkgcube.execute()
print("Bkgcube : done!")
# Fix the instrumental background parameters
bkgcube.models()["BackgroundModel"]["Prefactor"].fix()
bkgcube.models()["BackgroundModel"]["Index"].fix()
#
# # Attach background model to observation container
bin.obs().models(bkgcube.models())
#
#
# # Set Exposure and Psf cube for first CTA observation
# # (ctbin will create an observation with a single container)
bin.obs()[0].response(expcube.expcube(), psfcube.psfcube(),
edispcube.edispcube(), bkgcube.bkgcube())
# Perform maximum likelihood fitting
like = ctools.ctlike(bin.obs())
# like['inmodel']='bkg_cube.xml'
like["edisp"] = True
# like['edispcube'] = 'edisp_cube.fits'
# like['expcube'] = 'cube_exp.fits'
# like['psfcube'] = 'psf_cube.fits'
# like['bkgcube'] = 'bkg_cube.fits'
like["outmodel"] = str(outmodel)
# like['outcovmat']=inmodel+'_covmat.txt'
like["debug"] = debug # Switch this always on for results in console
# like['statistic']='CSTAT'
like.execute()
print("Likelihood : done!")
# Set the best-fit models (from ctlike) for the counts cube
bin.obs().models(like.obs().models())
# Obtain the best-fit butterfly
try:
butterfly = ctools.ctbutterfly(bin.obs())
butterfly["srcname"] = name
butterfly["inmodel"] = str(outmodel)
butterfly["edisp"] = True
butterfly["emin"] = emin
butterfly["emax"] = emax
butterfly["outfile"] = str(outmodel.parent) + "/" + str(
outmodel.stem) + ".txt"
butterfly[
"debug"] = debug # Switch this always on for results in console
# like['statistic']='CSTAT'
butterfly.execute()
print("Butterfly : done!")
except:
print("I COULDN'T CALCULATE THE BUTTERFLY....")
# Extract the spectrum
try:
csspec = cscripts.csspec(bin.obs())
csspec["srcname"] = name
csspec["inmodel"] = str(outmodel)
csspec["method"] = "AUTO"
csspec["ebinalg"] = "LOG"
csspec["emin"] = emin
csspec["emax"] = emax
csspec["enumbins"] = 10
csspec["edisp"] = True
csspec["outfile"] = str(outmodel.parent) + "/" + str(
outmodel.stem) + ".fits"
csspec["debug"] = debug # Switch this always on for results in console
csspec.execute()
print("Csspec : done!")
except:
print("I COULDN'T CALCULATE THE SPECTRUM....")
# 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
debug = True butterfly = ctools.ctbutterfly() butterfly['inobs'] = 'cntcube.fits' butterfly['inmodel'] = 'stacked_results_iem_add3_ext5.xml' butterfly['srcname'] = 'Src001' butterfly['expcube'] = 'expcube.fits' butterfly['psfcube'] = 'psfcube.fits' butterfly['bkgcube'] = 'bkgcube.fits' butterfly['outfile'] = 'butterfly_iem_add3_ext5.txt' butterfly['emin'] = 0.1 butterfly['emax'] = 100.0 butterfly['debug'] = debug butterfly.execute() spec = cscripts.csspec() spec['inobs'] = 'cntcube.fits' spec['inmodel'] = 'stacked_results_iem_add3_ext5.xml' spec['srcname'] = 'Src001' spec['expcube'] = 'expcube.fits' spec['psfcube'] = 'psfcube.fits' spec['bkgcube'] = 'bkgcube.fits' spec['outfile'] = 'spectrum_gc_iem_add3_ext5.fits' spec['method'] = 'AUTO' spec['ebinalg'] = 'LOG' spec['emin'] = 0.1 spec['emax'] = 100.0 spec['enumbins'] = 30 spec['debug'] = debug spec.execute()
def spectrum(inobs,
inmodel,
srcname,
outfile,
emin=1e-2,
emax=1e+3,
enumbins=20,
ebinalg='LOG',
expcube=None,
psfcube=None,
bkgcube=None,
edispcube=None,
caldb=None,
irf=None,
edisp=False,
method='SLICE',
statistic='DEFAULT',
calc_ts=True,
calc_ulim=True,
fix_srcs=True,
fix_bkg=False,
dll_sigstep=1,
dll_sigmax=7,
dll_freenodes=False,
logfile=None,
silent=False):
"""
Compute a spectrum for a given source.
See http://cta.irap.omp.eu/ctools/users/reference_manual/csspec.html
Parameters
----------
- inobs (string): input observation file
- inmodel (string): input model
- srcname (str): name of the source to compute
- outfile (str): output spectrum file
- emin,emax (float) min and max energy considered in TeV
- enumbins (int): number of energy bins
- ebinalg (string): energy bining algorithm
- modcube (string): model map cube
- expcube (string): exposure map cube
- psfcube (string): psfcube
- bkgcube (string): background cube
- edispcube (string): energy dispersion cube
- caldb (string): calibration database
- irf (string): instrument response function
- edisp (bool): apply energy dispersion
- method (str): Spectrum generation method
- statistic (str): Optimization statistic.
- calc_ts (bool): Compute TS for each spectral point?
- calc_ulim (bool): Compute upper limit for each spectral point?
- fix_srcs (bool): Fix other sky model parameters?
- fix_bkg (bool): Fix background model parameters?
- dll_sigstep (float): sigma steps for the likelihood scan
- dll_sigmax (float): sigma max for the likelihood scan
- dll_freenodes (bool): free nodes for method = NODES
- silent (bool): print information or not
Outputs
--------
- create spectrum fits
- return a spectrum object
"""
spec = cscripts.csspec()
spec['inobs'] = inobs
spec['inmodel'] = inmodel
spec['srcname'] = srcname
if expcube is not None: spec['expcube'] = expcube
if psfcube is not None: spec['psfcube'] = psfcube
if edispcube is not None: spec['edispcube'] = edispcube
if bkgcube is not None: spec['bkgcube'] = bkgcube
if caldb is not None: spec['caldb'] = caldb
if irf is not None: spec['irf'] = irf
spec['edisp'] = edisp
spec['outfile'] = outfile
spec['method'] = method
spec['ebinalg'] = ebinalg
spec['emin'] = emin
spec['emax'] = emax
spec['enumbins'] = enumbins
spec['ebinfile'] = 'NONE'
spec['statistic'] = statistic
spec['calc_ts'] = calc_ts
spec['calc_ulim'] = calc_ulim
spec['fix_srcs'] = fix_srcs
spec['fix_bkg'] = fix_bkg
spec['dll_sigstep'] = dll_sigstep
spec['dll_sigmax'] = dll_sigmax
spec['dll_freenodes'] = dll_freenodes
if logfile is not None: spec['logfile'] = logfile
if logfile is not None: spec.logFileOpen()
spec.execute()
if logfile is not None: spec.logFileClose()
if not silent:
print(spec)
print('')
return spec
def _test_python(self):
"""
Test csspec from Python
"""
# Set-up unbinned csspec (no multi-processing)
spec = cscripts.csspec()
spec['inobs'] = self._events
spec['inmodel'] = self._model
spec['srcname'] = 'Crab'
spec['caldb'] = self._caldb
spec['irf'] = self._irf
spec['method'] = 'AUTO'
spec['ebinalg'] = 'LOG'
spec['enumbins'] = 2
spec['emin'] = 1.0
spec['emax'] = 100.0
spec['nthreads'] = 1
spec['outfile'] = 'csspec_py1.fits'
spec['logfile'] = 'csspec_py1.log'
spec['chatter'] = 2
spec['publish'] = True
# Run csspec script
spec.logFileOpen() # Make sure we get a log file
spec.run()
spec.save()
# Check result file
self._check_result_file('csspec_py1.fits', 2)
# Set-up stacked csspec (no multi-processing)
spec = cscripts.csspec()
spec['inobs'] = self._cntcube
spec['expcube'] = self._expcube
spec['psfcube'] = self._psfcube
spec['edispcube'] = self._edispcube
spec['bkgcube'] = self._bkgcube
spec['inmodel'] = self._bkgmodel
spec['srcname'] = 'Crab'
spec['method'] = 'AUTO'
spec['ebinalg'] = 'LOG'
spec['enumbins'] = 2
spec['emin'] = 0.1
spec['emax'] = 100.0
spec['nthreads'] = 1
spec['outfile'] = 'csspec_py2.fits'
spec['logfile'] = 'csspec_py2.log'
spec['chatter'] = 3
spec['publish'] = False
# Execute csspec script
spec.execute()
# Check result file
self._check_result_file('csspec_py2.fits', 2)
# Set-up On/Off csspec (no multi-processing)
spec = cscripts.csspec()
spec['inobs'] = self._inonoff
spec['inmodel'] = self._onoff_model
spec['srcname'] = 'Crab'
spec['method'] = 'AUTO'
spec['ebinalg'] = 'LOG'
spec['enumbins'] = 2
spec['emin'] = 1.0
spec['emax'] = 10.0
spec['nthreads'] = 1
spec['outfile'] = 'csspec_py3.fits'
spec['logfile'] = 'csspec_py3.log'
spec['chatter'] = 4
spec['publish'] = False
# Execute csspec script
spec.execute()
# Check result file
self._check_result_file('csspec_py3.fits', 2)
# Set-up On/Off csspec with NODES method
spec = cscripts.csspec()
spec['inobs'] = self._inonoff
spec['inmodel'] = self._onoff_model
spec['srcname'] = 'Crab'
spec['method'] = 'NODES'
spec['ebinalg'] = 'LOG'
spec['enumbins'] = 2
spec['emin'] = 1.0
spec['emax'] = 10.0
spec['outfile'] = 'csspec_py4.fits'
spec['logfile'] = 'csspec_py4.log'
spec['chatter'] = 4
spec['publish'] = False
# Execute csspec script
spec.execute()
# Check result file
self._check_result_file('csspec_py4.fits', 2)
# Set-up On/Off csspec using WSTAT
spec = cscripts.csspec()
spec['inobs'] = self._inonoff
spec['inmodel'] = self._onoff_model
spec['srcname'] = 'Crab'
spec['method'] = 'AUTO'
spec['statistic'] = 'WSTAT'
spec['ebinalg'] = 'LOG'
spec['enumbins'] = 2
spec['emin'] = 1.0
spec['emax'] = 10.0
spec['outfile'] = 'csspec_py5.fits'
spec['logfile'] = 'csspec_py5.log'
spec['chatter'] = 2
# Execute csspec script
spec.execute()
# Check result file
self._check_result_file('csspec_py5.fits', 2)
# Return
return
import gammalib
def _test_python(self):
"""
Test csspec from Python
"""
# Set-up unbinned csspec
spec = cscripts.csspec()
spec['inobs'] = self._events
spec['inmodel'] = self._model
spec['srcname'] = 'Crab'
spec['caldb'] = self._caldb
spec['irf'] = self._irf
spec['method'] = 'AUTO'
spec['ebinalg'] = 'LOG'
spec['enumbins'] = 5
spec['emin'] = 0.1
spec['emax'] = 100.0
spec['outfile'] = 'csspec_py1.fits'
spec['logfile'] = 'csspec_py1.log'
spec['chatter'] = 2
spec['publish'] = True
# Run csspec script
spec.logFileOpen() # Make sure we get a log file
spec.run()
spec.save()
# Check result file
self._check_result_file('csspec_py1.fits', 5)
# Set-up stacked csspec
spec = cscripts.csspec()
spec['inobs'] = self._inobs
spec['expcube'] = self._expcube
spec['psfcube'] = self._psfcube
spec['edispcube'] = self._edispcube
spec['bkgcube'] = self._bkgcube
spec['inmodel'] = self._bkgmodel
spec['srcname'] = 'Crab'
spec['method'] = 'AUTO'
spec['ebinalg'] = 'LOG'
spec['enumbins'] = 2
spec['emin'] = 0.1
spec['emax'] = 100.0
spec['outfile'] = 'csspec_py2.fits'
spec['logfile'] = 'csspec_py2.log'
spec['chatter'] = 3
spec['publish'] = False
# Execute csspec script
spec.execute()
# Check result file
self._check_result_file('csspec_py2.fits', 2)
# Set-up On/Off csspec
spec = cscripts.csspec()
spec['inobs'] = self._inonoff
spec['inmodel'] = self._onoff_model
spec['srcname'] = 'Crab'
spec['method'] = 'AUTO'
spec['ebinalg'] = 'LOG'
spec['enumbins'] = 10
spec['emin'] = 0.1
spec['emax'] = 10.0
spec['outfile'] = 'csspec_py3.fits'
spec['logfile'] = 'csspec_py3.log'
spec['chatter'] = 4
spec['publish'] = False
# Execute csspec script
spec.execute()
# Check result file
self._check_result_file('csspec_py3.fits', 10)
# Set-up On/Off csspec with NODES method
spec = cscripts.csspec()
spec['inobs'] = self._inonoff
spec['inmodel'] = self._onoff_model
spec['srcname'] = 'Crab'
spec['method'] = 'NODES'
spec['ebinalg'] = 'LOG'
spec['enumbins'] = 10
spec['emin'] = 0.1
spec['emax'] = 10.0
spec['outfile'] = 'csspec_py4.fits'
spec['logfile'] = 'csspec_py4.log'
spec['chatter'] = 4
spec['publish'] = False
# Execute csspec script
spec.execute()
# Check result file
self._check_result_file('csspec_py4.fits', 10)
# Set-up On/Off csspec using WSTAT
spec = cscripts.csspec()
spec['inobs'] = self._inonoff
spec['inmodel'] = self._onoff_model
spec['srcname'] = 'Crab'
spec['method'] = 'AUTO'
spec['statistic'] = 'WSTAT'
spec['ebinalg'] = 'LOG'
spec['enumbins'] = 10
spec['emin'] = 0.1
spec['emax'] = 10.0
spec['outfile'] = 'csspec_py5.fits'
spec['logfile'] = 'csspec_py5.log'
spec['chatter'] = 2
# Execute csspec script
spec.execute()
# Check result file
self._check_result_file('csspec_py5.fits', 10)
# Return
return
