CTA background rates are now per ontime (#2758)

Jürgen Knödlseder
1 parent 15895c1e
Showing 9 changed files with 56 additions and 48 deletions
ChangeLog
NEWS
doc/source/admin/release_history/1.6.rst
test/test_cslightcrv.py
test/test_csworkflow.py
test/test_cterror.py
test/test_ctlike.py
test/test_ctobssim.py
test/test_obsutils.py
-2018-11-07
+2018-11-16
         * Version 1.6.0 released
         ========================
+        CTA background rates are now per ontime (#2758)
         Apply event bin weighting in ctmodel for stacked computation (#2721)
         Enable ctbin to produce counts cubes for a joint binned analysis (#2714)
         Add Jupyter notebooks for H.E.S.S. DR1 (#2712)
 New Features and Important Changes in ctools 1.6.0
-7 November 2018
+16 November 2018
 Introduction
@@ -11,6 +11,10 @@ ctools release with respect to the precedent release.
 Configuration
 -------------
+CTA background rates are now per ontime and not livetime. This makes the IRF
+background consistent with the cube background, and is also consistent with
+the open gamma data format (#2758).
+
 A Code Generator was added to the development directory that can be started
 using dev/codegen.py and that requires GammaLib to be installed. The code
 generator allows adding ctools and cscripts, including unit tests and code
@@ -112,6 +112,8 @@ New features
 Clean up
 --------
+* [`2758 <https://cta-redmine.irap.omp.eu/issues/2758>`_] -
+  CTA background rates are now per ontime
 * Remove ``ctools::is_valid_filename()`` method
 * [`2045 <https://cta-redmine.irap.omp.eu/issues/2045>`_] -
   Remove ``ctools::time_reference`` constant
@@ -241,30 +241,31 @@ class Test(test):
         # 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']      = 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
+        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_irf_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()
@@ -139,10 +139,10 @@ class Test(test):
         models = gammalib.GModels(filename)
         # Set reference values
-        prefactor        = 3.6507089091164e-16
-        index            = 2.39314305030239
-        pre_background   = 1.0405193333791
-        index_background = 0.0100808528325967
+        prefactor        = 3.62775976898809e-16
+        index            = 2.39100016863397
+        pre_background   = 1.05367124323594
+        index_background = 0.0421705243014668
         # Check fit result values
         self.test_value(models['Crab'][2].value(),
@@ -264,8 +264,8 @@ class Test(test):
         prefactor_error        = 1.12920470341148e-16
         index                  = 2.39356360626209
         index_error            = 0.128468611886618
-        pre_background         = 1.01409721534593
-        pre_background_error   = 0.122650321517162
+        pre_background         = 0.993814959041969
+        pre_background_error   = 0.120197277367473
         index_background       = 0.0436264224948547
         index_background_error = 0.110703165254809
@@ -252,8 +252,8 @@ class Test(test):
         prefactor_error        = 1.07582291031224e-16
         index                  = 2.39356360626209
         index_error            = 0.126492171703747
-        pre_background         = 1.01409721534593
-        pre_background_error   = 0.122650321517162
+        pre_background         = 0.993814959041969
+        pre_background_error   = 0.120197277367473
         index_background       = 0.0436264224948547
         index_background_error = 0.110703165254809
@@ -86,7 +86,7 @@ class Test(test):
         # Load counts cube and check content.
         evt = gammalib.GCTAEventList('ctobssim_cmd1.fits')
-        self._test_list(evt, 250)
+        self._test_list(evt, 251)
         # Setup ctobssim command
         cmd = ctobssim+' inmodel="model_that_does_not_exist.xml"'+ \
@@ -152,21 +152,21 @@ class Test(test):
         # Check content of observation
         self._test_observation(sim)
-        self._test_list(sim.obs()[0].events(), 250)
+        self._test_list(sim.obs()[0].events(), 251)
         # Save events
         sim.save()
         # Load counts cube and check content.
         evt = gammalib.GCTAEventList('ctobssim_py1.fits')
-        self._test_list(evt, 250)
+        self._test_list(evt, 251)
         # Copy ctobssim tool
         cpy_sim = sim.copy()
         # Retrieve observation and check content of copy
         self._test_observation(cpy_sim)
-        self._test_list(cpy_sim.obs()[0].events(), 250)
+        self._test_list(cpy_sim.obs()[0].events(), 251)
         # Execute copy of ctobssim tool again, now with a higher chatter
         # level than before
@@ -179,7 +179,7 @@ class Test(test):
         # Load counts cube and check content.
         evt = gammalib.GCTAEventList('ctobssim_py2.fits')
-        self._test_list(evt, 250)
+        self._test_list(evt, 251)
         # Set-up observation container
         pnts = [{'ra': 83.63, 'dec': 21.01},
@@ -206,8 +206,8 @@ class Test(test):
         # Retrieve observation and check content
         self._test_observation(sim, nobs=2, pnts=pnts)
-        self._test_list(sim.obs()[0].events(), 3565)
-        self._test_list(sim.obs()[1].events(), 3523)
+        self._test_list(sim.obs()[0].events(), 3626)
+        self._test_list(sim.obs()[1].events(), 3601)
         # Save events
         sim.save()
@@ -216,8 +216,8 @@ class Test(test):
         obs = gammalib.GObservations('ctobssim_py3.xml')
         # Retrieve observation and check content
-        self._test_list(obs[0].events(), 3565)
-        self._test_list(obs[1].events(), 3523)
+        self._test_list(obs[0].events(), 3626)
+        self._test_list(obs[1].events(), 3601)
         # Return
         return
@@ -217,7 +217,7 @@ class Test(test):
         # Check simulation results
         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(), 19, 'Check number of observed events')
+        self.test_value(res.nobserved(), 20, 'Check number of observed events')
         self.test_value(res.npred(), 0.0, 'Check number of predicted events')
         self.test_value(res[0].eventtype(), 'CountsCube', 'Check event type')
         self.test_value(res[0].events().ebounds().emin().TeV(), 1.0,
@@ -226,7 +226,7 @@ class Test(test):
                         'Check maximum energy')
         self.test_value(res[0].events().ebounds().size(), 5,
                         'Check number of energy bins')
-        self.test_value(res[0].events().number(), 19,
+        self.test_value(res[0].events().number(), 20,
                         'Check number of events in cube')
         # Check energy dispersion flag
@@ -261,7 +261,7 @@ class Test(test):
         # Check simulation results
         self.test_value(res.size(), 2, 'Check number of observations')
         self.test_value(res.models().size(), 2, 'Check number of models')
-        self.test_value(res.nobserved(), 46, 'Check number of observed events')
+        self.test_value(res.nobserved(), 48, 'Check number of observed events')
         self.test_value(res.npred(), 0.0, 'Check number of predicted events')
         # Check results of first observation
@@ -271,7 +271,7 @@ class Test(test):
                         'Check maximum energy of On spectrum')
         self.test_value(res[0].on_spec().ebounds().size(), 5,
                         'Check number of energy bins of On spectrum')
-        self.test_value(res[0].on_spec().counts(), 24,
+        self.test_value(res[0].on_spec().counts(), 26,
                         'Check number of events in of On spectrum')
         self.test_value(res[0].off_spec().ebounds().emin().TeV(), 1.0,
                         'Check minimum energy of Off spectrum')
@@ -315,7 +315,7 @@ class Test(test):
                         'Check maximum energy of Off spectrum')
         self.test_value(res[1].off_spec().ebounds().size(), 5,
                         'Check number of energy bins of Off spectrum')
-        self.test_value(res[1].off_spec().counts(), 0,
+        self.test_value(res[1].off_spec().counts(), 1,
                         'Check number of events in of Off spectrum')
         self.test_value(res[1].arf().ebounds().emin().TeV(), 0.5,
                         'Check minimum energy of ARF')