#! /usr/bin/env python # ========================================================================== # This scripts performs unit tests for the ctlike tool. # # Copyright (C) 2014-2018 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 . # # ========================================================================== import gammalib import ctools from testing import test # ========================== # # Test class for ctlike tool # # ========================== # class Test(test): """ Test class for ctlike tool """ # Constructor def __init__(self): """ Constructor """ # Call base class constructor test.__init__(self) # Return return # Set test functions def set(self): """ Set all test functions """ # Set test name self.name('ctlike') # Append tests self.append(self._test_cmd, 'Test ctlike on command line') self.append(self._test_python, 'Test ctlike from Python') # Return return # Test ctlike on command line def _test_cmd(self): """ Test ctlike on the command line """ # Set tool name ctlike = self._tool('ctlike') # Setup ctlike command cmd = ctlike+' inobs="'+self._events+'"'+ \ ' inmodel="'+self._model+'"'+ \ ' caldb="'+self._caldb+'" irf="'+self._irf+'"'+ \ ' outmodel="ctlike_cmd1.xml"'+ \ ' logfile="ctlike_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('ctlike_cmd1.xml') # Setup ctlike command cmd = ctlike+' inobs="event_file_that_does_not_exist.fits"'+ \ ' inmodel="'+self._model+'"'+ \ ' caldb="'+self._caldb+'" irf="'+self._irf+'"'+ \ ' outmodel="ctlike_cmd2.xml"'+ \ ' logfile="ctlike_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 ctlike --help self._check_help(ctlike) # Return return # Test ctlike from Python def _test_python(self): """ Test ctlike from Python """ # Allocate ctlike like = ctools.ctlike() # Check that empty ctlike tool holds an empty observation and # optimizer self._check_obs(like.obs(), nobs=0, nmodels=0) self._check_opt(like.opt()) # Check that saving saves an empty model definition file like['outmodel'] = 'ctlike_py0.xml' like['logfile'] = 'ctlike_py0.log' like.logFileOpen() like.save() self._check_result_file('ctlike_py0.xml', nmodels=0) # Check that clearing does not lead to an exception or segfault like.clear() # Now set ctlike parameters like['inobs'] = self._events like['inmodel'] = self._model like['caldb'] = self._caldb like['irf'] = self._irf like['outmodel'] = 'ctlike_py1.xml' like['logfile'] = 'ctlike_py1.log' like['chatter'] = 2 # Run ctlike tool like.logFileOpen() # Make sure we get a log file like.run() like.save() # Check result file self._check_result_file('ctlike_py1.xml') # Copy ctlike tool cpy_like = like.copy() # Check observation and optimizer of copy self._check_obs(cpy_like.obs()) self._check_opt(cpy_like.opt()) # Execute copy of ctlike tool again, now with a higher chatter # level than before cpy_like['outmodel'] = 'ctlike_py2.xml' cpy_like['logfile'] = 'ctlike_py2.log' cpy_like['chatter'] = 3 cpy_like.logFileOpen() # Needed to get a new log file cpy_like.execute() # Check result file self._check_result_file('ctlike_py2.xml') # Now clear copy of ctlike tool cpy_like.clear() # Check that the cleared copy has also cleared the observations self._check_obs(cpy_like.obs(), nobs=0, nmodels=0) # Prepare observation container with a single event list obs = gammalib.GObservations() obs.append(gammalib.GCTAObservation(self._events)) # Prepare a special model to enforce TS computation models = gammalib.GModels(self._model) models['Crab'].tscalc(True) obs.models(models) # Allocate ctlike tool from observation container like = ctools.ctlike(obs) like['caldb'] = self._caldb like['irf'] = self._irf like['refit'] = True like['fix_spat_for_ts'] = True like['outmodel'] = 'ctlike_py3.xml' like['logfile'] = 'ctlike_py3.log' like['chatter'] = 4 # Execute ctlike like.logFileOpen() # Needed to get a new log file like.execute() # Check result file self._check_result_file('ctlike_py3.xml') # Return return # Check result file def _check_result_file(self, filename, nmodels=2): """ Check result file Parameters ---------- filename : str Model definition XML file nmodels : int, optional Expected number of models """ # Open models from result file models = gammalib.GModels(filename) # Check models self._check_models(models, nmodels=nmodels) # Return return # Check observation and models def _check_obs(self, obs, nobs=1, nmodels=2): """ Check observation and models Parameters ---------- obs : `~gammalib.GObservations` Models nobs : int, optional Expected number of observations nmodels : int, optional Expected number of models """ # Check number of observations self.test_value(obs.size(), nobs, 'Check number of observations') # Check models self._check_models(obs.models(), nmodels=nmodels) # Return return # Check models def _check_models(self, models, nmodels=2): """ Check observation and models Parameters ---------- models : `~gammalib.GModels` Models nmodels : int, optional Expected number of models """ # Check number of models self.test_value(models.size(), nmodels, 'Check number of models') # If there are models in the container then check them if nmodels > 0: self.test_value(models['Crab']['Prefactor'].value(), 5.6530206356786e-16, 1.0e-4 * 5.6530206356786e-16, 'Check fitted Crab Prefactor') self.test_value(models['Crab']['Prefactor'].error(), 2.48304276536022e-17, 1.0e-4 * 2.48304276536022e-17, 'Check Crab Prefactor error') self.test_value(models['Crab']['Index'].value(), -2.45716228083265, 1.0e-4 * 2.45716228083265, 'Check fitted Crab Index') self.test_value(models['Crab']['Index'].error(), 0.0386777873333435, 1.0e-4 * 0.0386777873333435, 'Check Crab Index error') self.test_value(models['Background']['Prefactor'].value(), 1.01525433446138, 1.0e-4 * 1.01525433446138, 'Check fitted background Prefactor') self.test_value(models['Background']['Prefactor'].error(), 0.0334412516391298, 1.0e-4 * 0.0334412516391298, 'Check background Prefactor error') self.test_value(models['Background']['Index'].value(), -0.005132848897274, 1.0e-3 * 0.005132848897274, 'Check fitted background Index') self.test_value(models['Background']['Index'].error(), 0.0200624490548333, 1.0e-4 * 0.0200624490548333, 'Check background Index error') # Return return # Check optimizer def _check_opt(self, opt): """ Check optimizer Parameters ---------- opt : `~gammalib.GOptimizerLM` Optimizer """ # Check optimizer self.test_value(opt.status(), 0, 'Check optimizer status') # Return return