def _test_get_onoff_obs(self):
"""
Test get_onoff_obs() function
"""
# Set-up unbinned cslightcrv (is not run here!!!)
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._events
lcrv['inmodel'] = self._model
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'LIN'
lcrv['tmin'] = '2020-01-01T00:00:00'
lcrv['tmax'] = '2020-01-01T00:05:00'
lcrv['tbins'] = 2
lcrv['method'] = 'ONOFF'
lcrv['emin'] = 1.0
lcrv['emax'] = 100.0
lcrv['enumbins'] = 2
lcrv['coordsys'] = 'CEL'
lcrv['xref'] = 83.63
lcrv['yref'] = 22.01
lcrv['rad'] = 0.2
lcrv['outfile'] = 'obsutils_cslightcrv_py2.fits'
lcrv['logfile'] = 'obsutils_cslightcrv_py2.log'
lcrv._get_parameters()
# Get on/off observation container
res = obsutils.get_onoff_obs(lcrv, lcrv.obs())
# Check result
self.test_value(res.size(), 1, 'Check number of observations')
self.test_value(res.models().size(), 2, 'Check number of models')
self.test_value(res.nobserved(), 91, 'Check number of observed events')
self.test_value(res.npred(), 0.0, 'Check number of predicted events')
# Use galactic coordinates
lcrv['coordsys'] = 'GAL'
lcrv['xref'] = 184.5597
lcrv['yref'] = -5.7892
lcrv['chatter'] = 4
# Get on/off observation container
res = obsutils.get_onoff_obs(lcrv, lcrv.obs())
# Check result
self.test_value(res.size(), 1, 'Check number of observations')
self.test_value(res.models().size(), 2, 'Check number of models')
self.test_value(res.nobserved(), 91, 'Check number of observed events')
self.test_value(res.npred(), 0.0, 'Check number of predicted events')
# Return
return
def _test_pickeling(self):
"""
Test cslightcrv pickeling
"""
# Perform pickeling tests of empty class
self._pickeling(cscripts.cslightcrv())
# Set-up unbinned cslightcrv
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._events
lcrv['inmodel'] = self._model
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'LIN'
lcrv['tmin'] = '2020-01-01T00:00:00'
lcrv['tmax'] = '2020-01-01T00:05:00'
lcrv['tbins'] = 2
lcrv['method'] = '3D'
lcrv['enumbins'] = 0
lcrv['emin'] = 1.0
lcrv['emax'] = 100.0
lcrv['outfile'] = 'cslightcrv_py1_pickle.fits'
lcrv['logfile'] = 'cslightcrv_py1_pickle.log'
lcrv['chatter'] = 2
lcrv['publish'] = True
# Perform pickeling tests of filled class
obj = self._pickeling(lcrv)
# Run cslightcrv script and save light curve
obj.logFileOpen() # Make sure we get a log file
obj.run()
obj.save()
# Check light curve
self._check_light_curve('cslightcrv_py1_pickle.fits', 2)
# Return
return
def lightcurve(inobs,
inmodel,
srcname,
outfile,
map_reso,
map_coord,
map_fov,
xref,
yref,
caldb=None,
irf=None,
inexclusion=None,
edisp=False,
tbinalg='LIN',
tmin=None,
tmax=None,
mjdref=51544.5,
tbins=20,
tbinfile=None,
method='3D',
emin=1e-2,
emax=1e+3,
enumbins=20,
rad=1.0,
bkgregmin=2,
use_model_bkg=True,
maxoffset=4.0,
etruemin=0.01,
etruemax=300,
etruebins=30,
statistic='DEFAULT',
calc_ts=True,
calc_ulim=True,
fix_srcs=True,
fix_bkg=False,
logfile=None,
silent=False):
"""
Compute a lightcurve for a given source.
See http://cta.irap.omp.eu/ctools/users/reference_manual/cslightcrv.html
Parameters
----------
- inobs (str): Input event list or observation definition XML file.
- inmodel (string): input model
- srcname (str): name of the source to compute
- outfile (str): output lightcurve file
- map_reso (float): the resolution of the map (can be an
astropy.unit object, or in deg)
- map_coord (float): a skycoord object that give the center of the map
- map_fov (float): the field of view of the map (can be an
astropy.unit object, or in deg)
- xref/yref (float): the coordinates of the source (on/off) or the map.
- caldb (string): calibration database
- irf (string): instrument response function
- inexclusion (string): Optional FITS file containing a WCS map that
defines sky regions not to be used for background estimation (where
map value != 0). If the file contains multiple extensions the user may
specify which one to use. Otherwise, the extention EXCLUSION will be
used if available, or else the primary extension will be used.
- edisp (bool): apply energy dispersion
- tbinalg (string): <FILE|LIN|GTI> Algorithm for defining time bins.
- tmin/tmax: Lightcurve start/stop time (UTC string, JD, MJD or MET in seconds).
- mjdref (float): Reference Modified Julian Day (MJD) for Mission Elapsed Time (MET).
- tbins (int): Number of time bins.
- tbinfile (str): File defining the time binning.
- method (string): <3D|ONOFF> Selects between 3D analysis (3D spatial/energy
likelihood) and ONOFF analysis (1D likelihood with background from Off regions).
- emin,emax (float) min and max energy considered in TeV
- enumbins (int): number of energy bins
- rad (float): Radius of source region circle for On/Off analysis (deg)
- bkgregmin (int): Minimum number of background regions that are required
for an observation in ONOFF analysis. If this number of background regions
is not available the observation is skipped.
- use_model_bkg (bool): Specifies whether the background model should
be used for the computation of the alpha parameter and the predicted
background rate in the Off region that is stored in the BACKRESP column
of the Off spectrum when using the ONOFF method.
- maxoffset(float): Maximum offset in degrees of source from camera
center to accept the observation for On/Off analysis.
- etruemin (float): Minimum true energy to evaluate instrumental response
in On/Off analysis (TeV).
- etruemax (float): Maximum true energy to evaluate instrumental response
in On/Off analysis (TeV).
- etruebins (float): Number of bins per decade for true energy bins to
evaluate instrumental response in On/Off analysis.
- 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?
- silent (bool): print information or not
Outputs
--------
- create lightcurve fits
- return a lightcurve object
"""
npix = utilities.npix_from_fov_def(map_fov, map_reso)
lc = cscripts.cslightcrv()
lc['inobs'] = inobs
lc['inmodel'] = inmodel
lc['srcname'] = srcname
lc['outfile'] = outfile
if caldb is not None: lc['caldb'] = caldb
if irf is not None: lc['irf'] = irf
if inexclusion is not None: lc['inexclusion'] = inexclusion
lc['edisp'] = edisp
lc['tbinalg'] = tbinalg
if tmin is not None: lc['tmin'] = tmin
if tmax is not None: lc['tmax'] = tmax
if tbinfile is not None: lc['tbinfile'] = tbinfile
lc['mjdref'] = mjdref
lc['tbins'] = tbins
lc['method'] = method
lc['emin'] = emin
lc['emax'] = emax
lc['enumbins'] = enumbins
lc['nxpix'] = npix
lc['nypix'] = npix
lc['binsz'] = map_reso.to_value('deg')
lc['coordsys'] = 'CEL'
lc['proj'] = 'TAN'
lc['xref'] = xref
lc['yref'] = yref
lc['srcshape'] = 'CIRCLE'
lc['rad'] = rad
lc['bkgmethod'] = 'REFLECTED'
lc['bkgregmin'] = bkgregmin
lc['use_model_bkg'] = use_model_bkg
lc['maxoffset'] = maxoffset
lc['etruemin'] = etruemin
lc['etruemax'] = etruemax
lc['etruebins'] = etruebins
lc['statistic'] = statistic
lc['calc_ts'] = calc_ts
lc['calc_ulim'] = calc_ulim
lc['fix_srcs'] = fix_srcs
lc['fix_bkg'] = fix_bkg
if logfile is not None: lc['logfile'] = logfile
if logfile is not None: lc.logFileOpen()
lc.execute()
if logfile is not None: lc.logFileClose()
if not silent:
print(lc)
print('')
return lc
def lightcurve(obsname, bkgname, srcmodel, spec, method, pars,
emin=0.7, emax=10.0):
"""
Generate lightcurve
Parameters
----------
obsname : str
Observation definition XML file
bkgname : str
Background model definition XML file
srcmodel : str
Source model
spec : str
Spectral model
method : str
Light curve method
pars : dict
Dictionary of analysis parameters
emin : float, optional
Minimum energy (TeV)
emax : float, optional
Maximum energy (TeV)
"""
# Set observation name
_obsname = set_observation(obsname, srcmodel, bkgname, pars)
# Set analysis
analysis = set_analysis(srcmodel, bkgname, spec, pars)
# Set file names
if method == 'ONOFF':
outfile = 'pks_lightcrv_%s_%s_%dbins.fits' % (analysis, method, pars['ebins'])
logfile = 'pks_lightcrv_%s_%s_%dbins.log' % (analysis, method, pars['ebins'])
else:
outfile = 'pks_lightcrv_%s_%s.fits' % (analysis, method)
logfile = 'pks_lightcrv_%s_%s.log' % (analysis, method)
# Continue only if result file does not exist
if not os.path.isfile(outfile):
# Create GTIs for light curve
create_lightcurve_gti(obsname_t700, 'lightcurve_gti.fits')
# Load observations
obs = gammalib.GObservations(_obsname)
# Set models
models = set_model(srcmodel, spec, bkgname)
# Set and fix source position
models['PKS 2155-304']['RA'].value(329.71694) # true
models['PKS 2155-304']['DEC'].value(-30.225588) # true
models['PKS 2155-304']['RA'].fix()
models['PKS 2155-304']['DEC'].fix()
# Set and fix spectral index
models['PKS 2155-304']['Index'].value(-3.4)
models['PKS 2155-304']['Index'].fix()
# Attach models
obs.models(models)
# Generate light curve
lightcurve = cscripts.cslightcrv(obs)
lightcurve['srcname'] = 'PKS 2155-304'
lightcurve['edisp'] = pars['edisp']
lightcurve['outfile'] = outfile
lightcurve['tbinalg'] = 'FILE'
lightcurve['tbinfile'] = 'lightcurve_gti.fits'
lightcurve['method'] = method
lightcurve['emin'] = emin
lightcurve['emax'] = emax
lightcurve['coordsys'] = 'CEL'
lightcurve['xref'] = 329.71694
lightcurve['yref'] = -30.22559
lightcurve['rad'] = 0.2
lightcurve['enumbins'] = pars['ebins']
lightcurve['etruebins'] = 100
lightcurve['logfile'] = logfile
lightcurve['clobber'] = 4
lightcurve['debug'] = True
if method == 'ONOFF':
lightcurve['statistic'] = 'WSTAT'
lightcurve['use_model_bkg'] = False
lightcurve.logFileOpen()
lightcurve.execute()
# Return
return
def _test_python(self):
"""
Test cslightcrv from Python
"""
# Set-up unbinned cslightcrv
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._events
lcrv['inmodel'] = self._model
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'LIN'
lcrv['tmin'] = '2020-01-01T00:00:00'
lcrv['tmax'] = '2020-01-01T00:05:00'
lcrv['tbins'] = 3
lcrv['method'] = '3D'
lcrv['enumbins'] = 0
lcrv['emin'] = 0.1
lcrv['emax'] = 100.0
lcrv['outfile'] = 'cslightcrv_py1.fits'
lcrv['logfile'] = 'cslightcrv_py1.log'
lcrv['chatter'] = 2
lcrv['publish'] = True
# Run cslightcrv script and save light curve
lcrv.logFileOpen() # Make sure we get a log file
lcrv.run()
lcrv.save()
# Check light curve
self._check_light_curve('cslightcrv_py1.fits', 3)
# Now use FILE as time bin algorithm. For this we need first to
# create an ASCII file. We use now 6 time bins. The ASCII file
# is saved into the file "lightcurve_py2.dat".
csv = gammalib.GCsv(2,2)
tmin = 58849.00
tdelta = 0.0017361
for i in range(csv.nrows()):
csv[i,0] = '%.5f' % (tmin + i * tdelta)
csv[i,1] = '%.5f' % (tmin + (i+1) * tdelta)
csv.save('cslightcrv_py2.dat', ' ', True)
# Set-up unbinned cslightcrv
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._events
lcrv['inmodel'] = self._model
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'FILE'
lcrv['tbinfile'] = 'cslightcrv_py2.dat'
lcrv['method'] = '3D'
lcrv['enumbins'] = 0
lcrv['emin'] = 0.1
lcrv['emax'] = 100.0
lcrv['fix_bkg'] = True
lcrv['outfile'] = 'cslightcrv_py2.fits'
lcrv['logfile'] = 'cslightcrv_py2.log'
lcrv['chatter'] = 3
# Execute cslightcrv script
lcrv.execute()
# Check light curve
self._check_light_curve('cslightcrv_py2.fits', 2)
# Now we setup an observation container on input. We attached the
# model to the observation container so that cslightcrv should
# no longer query for the parameter.
cta = gammalib.GCTAObservation(self._events)
obs = gammalib.GObservations()
obs.append(cta)
obs.models(self._model)
# Set-up unbinned cslightcrv from observation container. Now use
# the GTI algorithm so that we test all timing algorithms.
lcrv = cscripts.cslightcrv(obs)
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'GTI'
lcrv['method'] = '3D'
lcrv['enumbins'] = 0
lcrv['emin'] = 0.1
lcrv['emax'] = 100.0
lcrv['outfile'] = 'cslightcrv_py3.fits'
lcrv['logfile'] = 'cslightcrv_py3.log'
lcrv['chatter'] = 4
# Execute cslightcrv script
lcrv.execute()
# Check light curve
self._check_light_curve('cslightcrv_py3.fits', 1)
# Binned cslightcrv
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._events
lcrv['inmodel'] = self._model
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'LIN'
lcrv['tmin'] = '2020-01-01T00:00:00'
lcrv['tmax'] = '2020-01-01T00:05:00'
lcrv['tbins'] = 2
lcrv['method'] = '3D'
lcrv['emin'] = 0.1
lcrv['emax'] = 100.0
lcrv['enumbins'] = 10
lcrv['coordsys'] = 'CEL'
lcrv['proj'] = 'TAN'
lcrv['xref'] = 83.63
lcrv['yref'] = 22.01
lcrv['nxpix'] = 20
lcrv['nypix'] = 20
lcrv['binsz'] = 0.02
lcrv['outfile'] = 'cslightcrv_py4.fits'
lcrv['logfile'] = 'cslightcrv_py4.log'
lcrv['chatter'] = 4
# Execute cslightcrv script
lcrv.execute()
# Check light curve
self._check_light_curve('cslightcrv_py4.fits', 2)
# cslightcrv with classical analysis
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._offaxis_events
lcrv['inmodel'] = self._model_onoff
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'LIN'
lcrv['tmin'] = '2020-01-01T00:00:00'
lcrv['tmax'] = '2020-01-01T00:05:00'
lcrv['tbins'] = 2
lcrv['method'] = 'ONOFF'
lcrv['emin'] = 0.1
lcrv['emax'] = 100.0
lcrv['enumbins'] = 10
lcrv['coordsys'] = 'CEL'
lcrv['xref'] = 83.63
lcrv['yref'] = 22.01
lcrv['rad'] = 0.2
lcrv['etruemin'] = 0.05
lcrv['etruemax'] = 150.0
lcrv['etruebins'] = 5
lcrv['statistic'] = 'WSTAT'
lcrv['outfile'] = 'cslightcrv_py5.fits'
lcrv['logfile'] = 'cslightcrv_py5.log'
lcrv['chatter'] = 4
# Execute cslightcrv script
lcrv.execute()
# Check light curve
self._check_light_curve('cslightcrv_py5.fits', 2)
# Return
return
def _test_python(self):
"""
Test cslightcrv from Python
"""
# Set-up unbinned cslightcrv
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._events
lcrv['inmodel'] = self._model
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'LIN'
lcrv['tmin'] = '2020-01-01T00:00:00'
lcrv['tmax'] = '2020-01-01T00:05:00'
lcrv['tbins'] = 2
lcrv['method'] = '3D'
lcrv['enumbins'] = 0
lcrv['emin'] = 1.0
lcrv['emax'] = 100.0
lcrv['outfile'] = 'cslightcrv_py1.fits'
lcrv['logfile'] = 'cslightcrv_py1.log'
lcrv['chatter'] = 2
lcrv['publish'] = True
# Run cslightcrv script and save light curve
lcrv.logFileOpen() # Make sure we get a log file
lcrv.run()
lcrv.save()
# Check light curve
self._check_light_curve('cslightcrv_py1.fits', 2)
# Now use FILE as time bin algorithm. For this we need first to
# create an ASCII file. We use now 2 time bins. The ASCII file
# is saved into the file "lightcurve_py2.dat".
csv = gammalib.GCsv(2, 2)
tmin = 58849.00
tdelta = 0.0017361
for i in range(csv.nrows()):
csv[i, 0] = '%.5f' % (tmin + i * tdelta)
csv[i, 1] = '%.5f' % (tmin + (i + 1) * tdelta)
csv.save('cslightcrv_py2.dat', ' ', True)
# Set-up unbinned cslightcrv
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._events
lcrv['inmodel'] = self._model
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'FILE'
lcrv['tbinfile'] = 'cslightcrv_py2.dat'
lcrv['method'] = '3D'
lcrv['enumbins'] = 0
lcrv['emin'] = 1.0
lcrv['emax'] = 100.0
lcrv['fix_bkg'] = True
lcrv['outfile'] = 'cslightcrv_py2.fits'
lcrv['logfile'] = 'cslightcrv_py2.log'
lcrv['chatter'] = 3
# Execute cslightcrv script
lcrv.execute()
# Check light curve
self._check_light_curve('cslightcrv_py2.fits', 2)
# Now we setup an observation container on input. We attached the
# model to the observation container so that cslightcrv should
# no longer query for the parameter.
cta = gammalib.GCTAObservation(self._events)
obs = gammalib.GObservations()
obs.append(cta)
obs.models(self._model)
# Set-up unbinned cslightcrv from observation container. Now use
# the GTI algorithm so that we test all timing algorithms.
lcrv = cscripts.cslightcrv(obs)
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'GTI'
lcrv['method'] = '3D'
lcrv['enumbins'] = 0
lcrv['emin'] = 1.0
lcrv['emax'] = 100.0
lcrv['outfile'] = 'cslightcrv_py3.fits'
lcrv['logfile'] = 'cslightcrv_py3.log'
lcrv['chatter'] = 4
# Execute cslightcrv script
lcrv.execute()
# Check light curve
self._check_light_curve('cslightcrv_py3.fits', 1)
# Binned cslightcrv
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._events
lcrv['inmodel'] = self._model
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'LIN'
lcrv['tmin'] = '2020-01-01T00:00:00'
lcrv['tmax'] = '2020-01-01T00:05:00'
lcrv['tbins'] = 2
lcrv['method'] = '3D'
lcrv['emin'] = 1.0
lcrv['emax'] = 100.0
lcrv['enumbins'] = 3
lcrv['coordsys'] = 'CEL'
lcrv['proj'] = 'TAN'
lcrv['xref'] = 83.63
lcrv['yref'] = 22.01
lcrv['nxpix'] = 10
lcrv['nypix'] = 10
lcrv['binsz'] = 0.04
lcrv['outfile'] = 'cslightcrv_py4.fits'
lcrv['logfile'] = 'cslightcrv_py4.log'
lcrv['chatter'] = 4
# Execute cslightcrv script
lcrv.execute()
# Check light curve
self._check_light_curve('cslightcrv_py4.fits', 2)
# cslightcrv with classical analysis
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._offaxis_events
lcrv['inmodel'] = self._model_onoff
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'LIN'
lcrv['tmin'] = '2020-01-01T00:00:00'
lcrv['tmax'] = '2020-01-01T00:05:00'
lcrv['tbins'] = 2
lcrv['method'] = 'ONOFF'
lcrv['use_model_bkg'] = False # Needed even if WSTAT
lcrv['emin'] = 1.0
lcrv['emax'] = 100.0
lcrv['enumbins'] = 2
lcrv['coordsys'] = 'CEL'
lcrv['xref'] = 83.63
lcrv['yref'] = 22.01
lcrv['rad'] = 0.2
lcrv['etruemin'] = 1.0
lcrv['etruemax'] = 100.0
lcrv['etruebins'] = 5
lcrv['statistic'] = 'WSTAT'
lcrv['outfile'] = 'cslightcrv_py5.fits'
lcrv['logfile'] = 'cslightcrv_py5.log'
lcrv['chatter'] = 4
# Execute cslightcrv script
lcrv.execute()
# Check light curve
self._check_light_curve('cslightcrv_py5.fits', 2)
# Set-up cslightcrv without multiprocessing
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._events
lcrv['inmodel'] = self._model
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'LIN'
lcrv['tmin'] = '2020-01-01T00:00:00'
lcrv['tmax'] = '2020-01-01T00:05:00'
lcrv['tbins'] = 2
lcrv['method'] = '3D'
lcrv['enumbins'] = 0
lcrv['emin'] = 1.0
lcrv['emax'] = 100.0
lcrv['outfile'] = 'cslightcrv_py6.fits'
lcrv['logfile'] = 'cslightcrv_py6.log'
lcrv['chatter'] = 2
lcrv['publish'] = True
lcrv['nthreads'] = 1
# Run cslightcrv script and save light curve
lcrv.logFileOpen() # Make sure we get a log file
lcrv.run()
lcrv.save()
# Check light curve
self._check_light_curve('cslightcrv_py6.fits', 2)
# Return
return
def _test_python(self):
"""
Test cslightcrv from Python
"""
# Set-up unbinned cslightcrv
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._events
lcrv['inmodel'] = self._model
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'LIN'
lcrv['tmin'] = 51544.50
lcrv['tmax'] = 51544.53
lcrv['tbins'] = 3
lcrv['enumbins'] = 0
lcrv['emin'] = 0.1
lcrv['emax'] = 100.0
lcrv['outfile'] = 'cslightcrv_py1.fits'
lcrv['logfile'] = 'cslightcrv_py1.log'
lcrv['chatter'] = 2
# Run cslightcrv script and save light curve
lcrv.logFileOpen() # Make sure we get a log file
lcrv.run()
lcrv.save()
# Check light curve
self._check_light_curve('cslightcrv_py1.fits', 3)
# Now use FILE as time bin algorithm. For this we need first to
# create an ASCII file. We use now 6 time bins. The ASCII file
# is saved into the file "lightcurve_py2.dat".
csv = gammalib.GCsv(2, 2)
tmin = 51544.50
tdelta = 0.01
for i in range(csv.nrows()):
csv[i, 0] = '%.5f' % (tmin + i * tdelta)
csv[i, 1] = '%.5f' % (tmin + (i + 1) * tdelta)
csv.save('cslightcrv_py2.dat', ' ', True)
# Set-up unbinned cslightcrv
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._events
lcrv['inmodel'] = self._model
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'FILE'
lcrv['tbinfile'] = 'cslightcrv_py2.dat'
lcrv['enumbins'] = 0
lcrv['emin'] = 0.1
lcrv['emax'] = 100.0
lcrv['fix_bkg'] = True
lcrv['outfile'] = 'cslightcrv_py2.fits'
lcrv['logfile'] = 'cslightcrv_py2.log'
lcrv['chatter'] = 3
# Execute cslightcrv script
lcrv.execute()
# Check light curve
self._check_light_curve('cslightcrv_py2.fits', 2)
# Now we setup an observation container on input. We attached the
# model to the observation container so that cslightcrv should
# no longer query for the parameter.
cta = gammalib.GCTAObservation(self._events)
obs = gammalib.GObservations()
obs.append(cta)
obs.models(self._model)
# Set-up unbinned cslightcrv from observation container. Now use
# the GTI algorithm so that we test all timing algorithms.
lcrv = cscripts.cslightcrv(obs)
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'GTI'
lcrv['enumbins'] = 0
lcrv['emin'] = 0.1
lcrv['emax'] = 100.0
lcrv['outfile'] = 'cslightcrv_py3.fits'
lcrv['logfile'] = 'cslightcrv_py3.log'
lcrv['chatter'] = 4
# Execute cslightcrv script
lcrv.execute()
# Check light curve
self._check_light_curve('cslightcrv_py3.fits', 1)
# Finally we set-up a binned cslightcrv
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._events
lcrv['inmodel'] = self._model
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'LIN'
lcrv['tmin'] = 51544.50
lcrv['tmax'] = 51544.53
lcrv['tbins'] = 2
lcrv['emin'] = 0.1
lcrv['emax'] = 100.0
lcrv['enumbins'] = 10
lcrv['coordsys'] = 'CEL'
lcrv['proj'] = 'TAN'
lcrv['xref'] = 83.63
lcrv['yref'] = 22.01
lcrv['nxpix'] = 20
lcrv['nypix'] = 20
lcrv['binsz'] = 0.02
lcrv['outfile'] = 'cslightcrv_py4.fits'
lcrv['logfile'] = 'cslightcrv_py4.log'
lcrv['chatter'] = 4
# Execute cslightcrv script
lcrv.execute()
# Check light curve
self._check_light_curve('cslightcrv_py4.fits', 2)
# Return
return
def _test_get_stacked_obs(self):
"""
Test get_stacked_obs() function
"""
# Set-up unbinned cslightcrv (is not run here!!!)
lcrv = cscripts.cslightcrv()
lcrv['inobs'] = self._events
lcrv['inmodel'] = self._model
lcrv['srcname'] = 'Crab'
lcrv['caldb'] = self._caldb
lcrv['irf'] = self._irf
lcrv['tbinalg'] = 'LIN'
lcrv['tmin'] = '2020-01-01T00:00:00'
lcrv['tmax'] = '2020-01-01T00:05:00'
lcrv['tbins'] = 2
lcrv['method'] = '3D'
lcrv['emin'] = 1.0
lcrv['emax'] = 100.0
lcrv['enumbins'] = 3
lcrv['coordsys'] = 'CEL'
lcrv['proj'] = 'TAN'
lcrv['xref'] = 83.63
lcrv['yref'] = 22.01
lcrv['binsz'] = 0.1
lcrv['nxpix'] = 10
lcrv['nypix'] = 10
lcrv['outfile'] = 'obsutils_cslightcrv_py1.fits'
lcrv['logfile'] = 'obsutils_cslightcrv_py1.log'
lcrv._get_parameters()
# Get stacked observation container
res = obsutils.get_stacked_obs(lcrv, lcrv.obs())
# Apply energy dispersion
res[0].response().apply_edisp(True)
# Check result
self.test_value(res.size(), 1, 'Check number of observations')
self.test_value(res.models().size(), 2, 'Check number of models')
self.test_value(res.nobserved(), 96, 'Check number of observed events')
self.test_value(res.npred(), 0.0, 'Check number of predicted events')
self.test_assert(not res[0].response().use_edisp(),
'Check that no energy dispersion is used')
# Set energy dispersion
lcrv['edisp'] = True
# Get stacked observation container
res = obsutils.get_stacked_obs(lcrv, lcrv.obs())
# Apply energy dispersion
res[0].response().apply_edisp(True)
# Check result
self.test_value(res.size(), 1, 'Check number of observations')
self.test_value(res.models().size(), 2, 'Check number of models')
self.test_value(res.nobserved(), 96, 'Check number of observed events')
self.test_value(res.npred(), 0.0, 'Check number of predicted events')
self.test_assert(res[0].response().use_edisp(),
'Check that energy dispersion is used')
# Increase chatter level
lcrv['edisp'] = False
lcrv['chatter'] = 4
# Get stacked observation container
res = obsutils.get_stacked_obs(lcrv, lcrv.obs())
# Apply energy dispersion
res[0].response().apply_edisp(True)
# Check result
self.test_value(res.size(), 1, 'Check number of observations')
self.test_value(res.models().size(), 2, 'Check number of models')
self.test_value(res.nobserved(), 96, 'Check number of observed events')
self.test_value(res.npred(), 0.0, 'Check number of predicted events')
self.test_assert(not res[0].response().use_edisp(),
'Check that no energy dispersion is used')
# Return
return