Reorganise CTA test scripts

Jürgen Knödlseder
1 parent 9f0f5511
Showing 10 changed files with 84 additions and 70 deletions
.gitignore
inst/cta/test/README.md
inst/cta/test/README → inst/cta/test/dev/README.md
inst/cta/test/dev/example_binned_ml_fit.py
inst/cta/test/dev/example_make_model.py
inst/cta/test/dev/example_sim_photons.py
inst/cta/test/dev/test_model.py
inst/cta/test/dev/test_sim_edisp.py
inst/cta/test/dev/test_sim_psf.py
testall.sh
@@ -11,6 +11,7 @@ examples/python/*.fits
 inst/cta/test/*.xml
 inst/cta/test/*.fits
 inst/cta/test/*.fits.gz
+inst/cta/test/dev/*.fits
 inst/spi/test/data/*.xml
  
 # Generated files
@@ -88,6 +89,8 @@ test/region.reg
 test/test_python.sh
 test/test_examples.sh
 test/test_csv*.dat
+test/dev/*.fits
+test/dev/*.xml
  
 examples/python/*.xml
 examples/python/*.html
+CTA module unit tests
+=====================
+This folder contains scripts and executables that are used for CTA
+module unit testing.
+
+benchmark_irf_computing.py
+  This script performs a benchmark of the response computation for all
+  different analysis methods (unbinned, binned, stacked). Specifically it
+  compares the total number of modelled counts to the expected number from
+  Monte-Carlo simulations. This script is used to validate the absolute
+  normalization of the CTA response computations.
+
+benchmark_ml_fitting.py
+  This script performs a benchmark for maximum likelihood fitting of CTA
+  data.
+
+test_CTA.cpp
+  Unit test C++ file for CTA specific GammaLib components.
+
+test_CTA.py
+  Unit test Python script for CTA specific GammaLib components.
-This folder contains files used for unit testing and files that serve
-as example scripts for the CTA interface of GammaLib.
-
-benchmark_irf_computing.py
-  This script performs a benchmark of the response computation for all
-  different analysis methods (unbinned, binned, stacked). Specifically it
-  compares the total number of modelled counts to the expected number from
-  Monte-Carlo simulations. This script is used to validate the absolute
-  normalization of the CTA response computations.
-
-benchmark_ml_fitting.py
-  This script performs a benchmark for maximum likelihood fitting of CTA
-  data.
+Developer test scripts
+======================
+This folder contains scripts that are used for various testing purposes
+during code development. None of the scripts actually needs to work, they
+may actually be outdated, but could still be useful for further testing
+if needed.
  
 example_binned_ml_fit.py
   Illustrates a binned maximum likelihood fit of CTA data.
@@ -27,9 +20,6 @@ example_sim_photons.py
   matplotlib is installed, the simulated photons are shown as
   a spectrum.
  
-test_CTA.cpp
-  Unit test C++ file for CTA specific GammaLib components.
-
 test_gauss.py
   Test Gaussian.
  
@@ -209,30 +209,26 @@ def stacked_analysis(model, cntmap, expcube, psfcube, bkgcube):
 # Main routine entry point #
 #==========================#
 if __name__ == '__main__':
-    """
-    Example illustrating binned data analyses.
-    """
+
     # Dump header
-    print("")
-    print("**************************************************")
-    print("* Perform maximum likelihood fitting of CTA data *")
-    print("**************************************************")
-    print("... please wait for a few seconds")
-
-    # Set parameters
-    irf     = "cta_dummy_irf"
-    caldb   = "../caldb"
-    model   = "data/crab.xml"
-    cntmap  = "data/crab_cntmap.fits"
-    expcube = "data/expcube.fits"
-    psfcube = "data/psfcube.fits"
-    bkgcube = "data/bkgcube.fits"
+    print('')
+    print('**************************************************')
+    print('* Perform maximum likelihood fitting of CTA data *')
+    print('**************************************************')
+    print('... please wait for a few seconds')
  
     # Perform binned analysis
-    results_binned = binned_analysis(model, cntmap, irf, gammalib.GCaldb(caldb))
+    results_binned = binned_analysis('../data/crab.xml',
+                                     '../data/crab_cntmap.fits',
+                                     'cta_dummy_irf',
+                                     gammalib.GCaldb('../caldb'))
  
     # Perform stacked analysis
-    results_cube = stacked_analysis(model, cntmap, expcube, psfcube, bkgcube)
+    results_cube = stacked_analysis('../data/stacked_model.xml',
+                                    '../data/stacked_cntcube.fits',
+                                    '../data/stacked_expcube.fits',
+                                    '../data/stacked_psfcube.fits',
+                                    '../data/stacked_bkgcube.fits')
  
     # Print model results
     #print(result)
@@ -56,22 +56,20 @@ def make_model_for_cntmap(cntmap, modname, xmlname, irf, caldb, clobber=True):
 # Main routine entry point #
 #==========================#
 if __name__ == '__main__':
-    """
-    Example illustrating how to create a model map for a counts map.
-    """
+
     # Dump header
-    print("")
-    print("***************************************************")
-    print("* Create model map for counts map using XML model *")
-    print("***************************************************")
-    print("... please wait for a few seconds")
+    print('')
+    print('***************************************************')
+    print('* Create model map for counts map using XML model *')
+    print('***************************************************')
+    print('... please wait for a few seconds')
  
     # Set parameters
-    irf     = "cta_dummy_irf"
-    caldb   = "./caldb"
-    xmlname = "data/crab.xml"
-    cntmap  = "data/crab_cntmap.fits"
-    modname = "model.fits"
+    irf     = 'cta_dummy_irf'
+    caldb   = '../caldb'
+    xmlname = '../data/crab.xml'
+    cntmap  = '../data/crab_cntmap.fits'
+    modname = 'model.fits'
  
     # Make the model
     make_model_for_cntmap(cntmap, modname, xmlname, irf, gammalib.GCaldb(caldb))
@@ -143,20 +143,18 @@ def show_photons(photons, xmlname, e_min, e_max, area, duration, ebins=30):
 # Main routine entry point #
 #==========================#
 if __name__ == '__main__':
-    """
-    Simulate photons.
-    """
+
     # Dump header
-    print("")
-    print("********************")
-    print("* Simulate photons *")
-    print("********************")
+    print('')
+    print('********************')
+    print('* Simulate photons *')
+    print('********************')
  
     # Set XML names
-    xmlnames = ["data/crab.xml",
-                "data/crab_eplaw.xml",
-                "data/crab_file_function.xml",
-                "data/crab_file_function_mod.xml"]
+    xmlnames = ['../data/crab.xml',
+                '../data/crab_eplaw.xml']
+    #            '../data/crab_file_function.xml',
+    #            '../data/crab_file_function_mod.xml']
  
     # Set simulation parameters
     e_min    = 0.1                # 0.1 TeV
@@ -174,9 +174,9 @@ def observation(ra=83.6331, dec=22.0145, emin=0.1, emax=100.0,
     obs = gammalib.GCTAObservation()
  
     # Set response
-    exposure   = gammalib.GCTACubeExposure("data/expcube.fits")
-    psf        = gammalib.GCTACubePsf("data/psfcube.fits")
-    background = gammalib.GCTACubeBackground("data/bkgcube.fits")
+    exposure   = gammalib.GCTACubeExposure("../data/stacked_expcube.fits")
+    psf        = gammalib.GCTACubePsf("../data/stacked_psfcube.fits")
+    background = gammalib.GCTACubeBackground("../data/stacked_bkgcube.fits")
     obs.response(exposure, psf, background)
  
     # Set pointing
@@ -144,7 +144,7 @@ if __name__ == &#39;__main__&#39;:
     # Load edisp
     #edisp = gammalib.GCTAEdispRmf("./caldb/dc1/rmf.fits")
     #edisp = gammalib.GCTAEdispPerfTable("./caldb/cta_dummy_irf.dat")
-    edisp = gammalib.GCTAEdisp2D("./caldb/edisp_matrix.fits")
+    edisp = gammalib.GCTAEdisp2D("../caldb/edisp_matrix.fits")
  
     # Simulate Edisp
     sim_edisp(edisp, 20.0) # Energy in TeV
@@ -121,7 +121,7 @@ if __name__ == &#39;__main__&#39;:
     #psf = gammalib.GCTAPsfKing('../caldb/data/cta/e/bcf/IFAE20120510_50h_King/irf_file.fits')
     #psf = gammalib.GCTAPsf2D('../caldb/data/cta/e/bcf/IFAE20120510_50h/irf_file.fits')
     #psf = gammalib.GCTAPsfPerfTable('../caldb/cta_dummy_irf.dat')
-    psf = gammalib.GCTAPsfTable('../test/caldb/psf_table.fits[PSF_2D_TABLE]')
+    psf = gammalib.GCTAPsfTable('../caldb/psf_table.fits[PSF_2D_TABLE]')
  
     # Simulate PSF
     sim_psf(psf, 1.0, r_max=0.5)
@@ -19,13 +19,21 @@
 # =====================================================================
 base=$PWD
  
+# test/dev
+# ========
+echo
+echo "=====> test/dev"
+cd test/dev
+./example_model_xml_io.py
+./example_radial_models.py
+cd $base
+
+
 # examples/python
 # ===============
 echo
 echo "=====> examples/python"
 cd examples/python
-./example_model_xml_io.py
-./example_radial_models.py
 ./matrix_howto.py
 ./models_howto.py
 ./xml_howto.py
@@ -33,11 +41,11 @@ cd examples/python
 cd $base
  
  
-# inst/cta/test
-# =============
+# inst/cta/test/dev
+# =================
 echo
-echo "=====> inst/cta/test"
-cd inst/cta/test
+echo "=====> inst/cta/test/dev"
+cd inst/cta/test/dev
 ./example_binned_ml_fit.py
 ./example_make_model.py
 ./example_sim_photons.py