def _test_python(self):
"""
Test cssens from Python
"""
# Set-up cssens for differential sensitivity computation with
# multiprocessing
sens = cscripts.cssens()
sens['inobs'] = 'NONE'
sens['inmodel'] = self._model
sens['srcname'] = 'Crab'
sens['caldb'] = self._caldb
sens['irf'] = self._irf
sens['outfile'] = 'cssens_py1.dat'
sens['duration'] = 180.0
sens['rad'] = 3.0
sens['emin'] = 1.0
sens['emax'] = 10.0
sens['bins'] = 2
sens['logfile'] = 'cssens_py1.log'
sens['chatter'] = 4
# Run cssens script
sens.logFileOpen() # Make sure we get a log file
sens.run()
sens.save()
# Check pull distribution file
self._check_result_file('cssens_py1.dat', nrows=3)
# Set-up cssens for integral sensitivity computation without
# multiprocessing
sens = cscripts.cssens()
sens['inobs'] = 'NONE'
sens['inmodel'] = self._model
sens['srcname'] = 'Crab'
sens['caldb'] = self._caldb
sens['irf'] = self._irf
sens['outfile'] = 'cssens_py2.dat'
sens['duration'] = 180.0
sens['rad'] = 3.0
sens['emin'] = 1.0
sens['emax'] = 10.0
sens['bins'] = 1
sens['type'] = 'Integral'
sens['logfile'] = 'cssens_py2.log'
sens['chatter'] = 4
sens['nthreads'] = 1
# Execute cssens script
sens.execute()
# Check pull distribution file
self._check_result_file('cssens_py2.dat')
# Return
return
def _test_python(self):
"""
Test cssens from Python
"""
# Set-up cssens
sens = cscripts.cssens()
sens['inobs'] = 'NONE'
sens['inmodel'] = self._model
sens['srcname'] = 'Crab'
sens['caldb'] = self._caldb
sens['irf'] = self._irf
sens['outfile'] = 'cssens_py1.dat'
sens['duration'] = 1800.0
sens['rad'] = 3.0
sens['emin'] = 1.0
sens['emax'] = 10.0
sens['bins'] = 1
sens['logfile'] = 'cssens_py1.log'
sens['chatter'] = 2
# Run cssens script
sens.logFileOpen() # Make sure we get a log file
sens.run()
sens.save()
# Check pull distribution file
self._check_result_file('cssens_py1.dat')
# Return
return
def _test_pickeling(self):
"""
Test cssens pickeling
"""
# Perform pickeling tests of empty class
self._pickeling(cscripts.cssens())
# Set-up unbinned cssens
sens = cscripts.cssens()
sens['inobs'] = 'NONE'
sens['inmodel'] = self._model
sens['srcname'] = 'Crab'
sens['caldb'] = self._caldb
sens['irf'] = self._irf
sens['outfile'] = 'cssens_py1_pickle.dat'
sens['duration'] = 180.0
sens['rad'] = 3.0
sens['emin'] = 1.0
sens['emax'] = 10.0
sens['bins'] = 2
sens['logfile'] = 'cssens_py1_pickle.log'
sens['chatter'] = 4
# Perform pickeling tests of filled class
obj = self._pickeling(sens)
# 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('cssens_py1_pickle.dat', nrows=3)
# Return
return
default=10 ,\
metavar='20' )
source.add_argument( '--emin' ,\
help='Minimum energy for events (TeV)' ,\
type=float ,\
default=0.05 ,\
metavar='0.01' )
source.add_argument( '--emax' ,\
help='Maximum energy for events (TeV)' ,\
type=float ,\
default=10.0 ,\
metavar='100.0' )
args = options.parse_args()
sensitivity = cscripts.cssens()
sensitivity['inmodel'] = args.inmodel
sensitivity['srcname'] = args.srcname
sensitivity['caldb'] = args.caldb
sensitivity['irf'] = args.irf
sensitivity['outfile'] = args.srcname + 'Sens{:.1f}h.txt'.format(args.hours)
sensitivity['duration'] = args.hours * 3600.
sensitivity['rad'] = args.rad
sensitivity['emin'] = args.emin
sensitivity['emax'] = args.emax
sensitivity['bins'] = args.enumbins
sensitivity['enumbins'] = args.enumbins
sensitivity['nthreads'] = 2
sensitivity.execute()
def _calculate_sensitivity(
self,
job_number,
duration,
cwd=None,
parallel_results=None,
nthreads=1,
load_results=False,
verbose=True,
):
"""Run the `cssens` ctools module based on the given input."""
# set duration to a float
duration = float(duration)
if verbose:
print(f"Running `cssens` job #{job_number} for "
f"{self.params['src_name']} for a duration of {duration}s")
# create cssens object
sen = cscripts.cssens()
outfile = (f"{cwd}/cssens_outputs/grbsens-{self.params['sigma']}"
f"sigma_obstime-{duration}_irf-{self.params['irf']}.txt")
logfile = (f"{cwd}/cssens_logs/grbsens-{self.params['sigma']}"
f"sigma_obstime-{duration}_irf-{self.params['irf']}.log")
# run cssens
if not load_results or not Path(outfile).is_file():
# load input model
sen["inmodel"] = self.input_model
# set parameters that change each loop
sen["duration"] = duration
sen["outfile"] = outfile
sen["logfile"] = logfile
# set global parameters
sen["srcname"] = self.params["src_name"]
sen["caldb"] = self.params["caldb"]
sen["irf"] = self.params["irf"]
sen["rad"] = self.params["rad"]
sen["emin"] = self.params["emin"]
sen["emax"] = self.params["emax"]
sen["type"] = self.params["sens_type"]
sen["sigma"] = self.params["sigma"]
sen["bins"] = self.params["bins"]
sen["binsz"] = self.params["binsz"]
sen["offset"] = self.params["offset"]
sen["npix"] = self.params["npix"]
# set number of cores used for energy bins
sen["nthreads"] = nthreads
# set chatter to max
sen["chatter"] = 4
sen.execute()
# format results into pandas DataFrame
results = self._results_to_df(outfile, duration, job_number, logfile)
# add output pandas df to results dictionary
self._save_results(results=results,
job_number=job_number,
parallel_results=parallel_results)
if verbose:
# print success message
print(f"Done with job #{job_number}, duration={duration}s\n")