test_csspec.py
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#! /usr/bin/env python
# ==========================================================================
# This scripts performs unit tests for the csspec script.
#
# Copyright (C) 2016-2020 Juergen Knoedlseder
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# ==========================================================================
import gammalib
import cscripts
from testing import test
# ============================ #
# Test class for csspec script #
# ============================ #
class Test(test):
"""
Test class for csspec script
This test class makes unit tests for the csspec script by using it
from the command line and from Python.
"""
# Constructor
def __init__(self):
"""
Constructor
"""
# Call base class constructor
test.__init__(self)
# Set members
self._inonoff = self._datadir + '/onoff_obs.xml'
self._expcube = self._datadir + '/crab_expcube.fits'
self._psfcube = self._datadir + '/crab_psfcube.fits'
self._edispcube = self._datadir + '/crab_edispcube.fits'
self._bkgcube = self._datadir + '/crab_bkgcube.fits'
self._bkgmodel = self._datadir + '/crab_bkgcube.xml'
self._onoff_model = self._datadir + '/onoff_model.xml'
# Return
return
# Set test functions
def set(self):
"""
Set all test functions
"""
# Set test name
self.name('csspec')
# Append tests
self.append(self._test_cmd, 'Test csspec on command line')
self.append(self._test_python, 'Test csspec from Python')
self.append(self._test_pickeling, 'Test csspec pickeling')
# Return
return
# Test csspec on command line
def _test_cmd(self):
"""
Test csspec on the command line
"""
# Set script name
csspec = self._script('csspec')
# Setup csspec command
cmd = csspec+' inobs="'+self._events+'"'+ \
' inmodel="'+self._model+'"'+ \
' srcname="Crab" method="AUTO"'+ \
' caldb="'+self._caldb+'" irf="'+self._irf+'"'+ \
' ebinalg="LOG" enumbins=2 emin=1.0 emax=100.0'+ \
' outfile="csspec_cmd1.fits"'+ \
' logfile="csspec_cmd1.log" chatter=1'
# Check if execution was successful
self.test_assert(self._execute(cmd) == 0,
'Check successful execution from command line')
# Check result file
self._check_result_file('csspec_cmd1.fits', 2)
# Setup csspec command
cmd = csspec+' inobs="input_file_that_does_not_exist.fits"'+ \
' inmodel="'+self._model+'"'+ \
' srcname="Crab" method="AUTO"'+ \
' caldb="'+self._caldb+'" irf="'+self._irf+'"'+ \
' ebinalg="LOG" enumbins=2 emin=1.0 emax=100.0'+ \
' outfile="csspec_cmd2.fits"'+ \
' logfile="csspec_cmd2.log" debug=yes chatter=1'
# Check if execution failed
self.test_assert(self._execute(cmd, success=False) != 0,
'Check invalid input file when executed from command line')
# Check csspec --help
self._check_help(csspec)
# Return
return
# Test csspec from Python
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'] = 'POW'
spec['enumbins'] = 2
spec['emin'] = 1.0
spec['emax'] = 10.0
spec['ebingamma'] = 1.0
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)
# Set-up unbinned csspec with likelihood profile (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['dll_sigstep'] = 2.5
spec['dll_sigmax'] = 5
spec['outfile'] = 'csspec_py6.fits'
spec['logfile'] = 'csspec_py6.log'
spec['chatter'] = 2
spec['publish'] = True
# Run csspec script
spec.execute()
# Check result file
self._check_result_file('csspec_py6.fits', 2, 5)
# Return
return
# Test csspec pickeling
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
# Check result file
def _check_result_file(self, filename, bins, lbins=0):
"""
Check result file
"""
# Open result file
fits = gammalib.GFits(filename)
# Get spectrum table
spectrum = fits['SPECTRUM']
# Check dimensions
self.test_value(spectrum.nrows(), bins,
'Check for %d rows in spectrum' % bins)
# Make sure that the header keywords are present
req_keywords = ['SED_TYPE', 'UL_CONF']
for keyword in req_keywords:
self.test_assert(spectrum.has_card(keyword),
'Check that SED has \'%s\' keyword' % keyword)
# Do checks specific to likelihood profile table
flux_keys = spectrum.card('SED_TYPE').string()
flux_keys = flux_keys.split(',')
if lbins > 0:
# Should be 13 columns
self.test_value(spectrum.ncols(), 13,
'Check for 13 columns in spectrum')
self.test_value(flux_keys[0], 'likelihood',
'Check that \'SED_TYPE\' card is \'likelihood\'')
# Make sure the profile has the appropriate entries
self.test_value(spectrum['norm_scan'].elements(0), lbins,
'Check for %d rows in flux scan profile' % lbins)
self.test_value(spectrum['dloglike_scan'].elements(0), lbins,
'Check for %d rows in likelihood profile' % lbins)
else:
# Should be 10 columns
self.test_value(spectrum.ncols(), 10,
'Check for 10 columns in spectrum')
self.test_value(flux_keys[0], 'norm',
'Check that \'SED_TYPE\' card is \'norm\'')
# Return
return