run_list=None, obs_table=obs_table_with_group_id, ntot_group=obs_groups.n_groups, excluded_sources=exclusion_table, ) # Define the energy and offset binning to use ebounds = EnergyBounds.equal_log_spacing(0.1, 100, 15, 'TeV') #offset = sqrt_space(start=0, stop=2.5, num=20) * u.deg offset=np.linspace(0,2.5,20) * u.deg # Make the model (i.e. stack counts and livetime) bgmaker.make_model("2D", ebounds=ebounds, offset=offset) # Smooth the model bgmaker.smooth_models("2D") # Write the model to disk bgmaker.save_models("2D") bgmaker.save_models(modeltype="2D", smooth=True) #now copy the background files as bkg into the source runs data_dir="data_new" shutil.move(outdir, data_dir) datastore= DataStore.from_dir("$HESS_DATA") datastore.copy_obs(datastore.obs_table,data_dir) group_filename = data_dir + '/background/group-def.fits' data_store = DataStore.from_dir(data_dir) hdu_index_table = bgmaker.make_total_index_table( data_store=data_store, modeltype='2D',
def make_cubes(ereco, etrue, use_etrue, center): tmpdir = os.path.expandvars('$GAMMAPY_EXTRA') + "/test_datasets/cube/data" outdir = tmpdir outdir2 = os.path.expandvars( '$GAMMAPY_EXTRA') + '/test_datasets/cube/background' if os.path.isdir("data"): shutil.rmtree("data") if os.path.isdir("background"): shutil.rmtree("background") Path(outdir2).mkdir() ds = DataStore.from_dir("$GAMMAPY_EXTRA/datasets/hess-crab4-hd-hap-prod2") ds.copy_obs(ds.obs_table, tmpdir) data_store = DataStore.from_dir(tmpdir) # Create a background model from the 4 crab run for the counts ouside the exclusion region. it's just for test, normaly you take 8000 thousands AGN runs to build this kind of model axes = [ObservationGroupAxis('ZEN_PNT', [0, 49, 90], fmt='edges')] obs_groups = ObservationGroups(axes) obs_table_with_group_id = obs_groups.apply(data_store.obs_table) obs_groups.obs_groups_table.write(outdir2 + "/group-def.fits", overwrite=True) # Exclusion sources table cat = SourceCatalogGammaCat() exclusion_table = cat.table exclusion_table.rename_column('ra', 'RA') exclusion_table.rename_column('dec', 'DEC') radius = exclusion_table['morph_sigma'] radius.value[np.isnan(radius)] = 0.3 exclusion_table['Radius'] = radius exclusion_table = Table(exclusion_table) bgmaker = OffDataBackgroundMaker(data_store, outdir2, run_list=None, obs_table=obs_table_with_group_id, ntot_group=obs_groups.n_groups, excluded_sources=exclusion_table) bgmaker.make_model("2D") bgmaker.smooth_models("2D") bgmaker.save_models("2D") bgmaker.save_models(modeltype="2D", smooth=True) shutil.move(str(outdir2), str(outdir)) fn = outdir + '/background/group-def.fits' hdu_index_table = bgmaker.make_total_index_table( data_store=data_store, modeltype='2D', out_dir_background_model="background", filename_obs_group_table=fn, smooth=True) fn = outdir + '/hdu-index.fits.gz' hdu_index_table.write(fn, overwrite=True) offset_band = Angle([0, 2.49], 'deg') ref_cube_images = make_empty_cube(image_size=50, energy=ereco, center=center) ref_cube_exposure = make_empty_cube(image_size=50, energy=etrue, center=center, data_unit="m2 s") data_store = DataStore.from_dir(tmpdir) refheader = ref_cube_images.sky_image_ref.to_image_hdu().header exclusion_mask = SkyMask.read( '$GAMMAPY_EXTRA/datasets/exclusion_masks/tevcat_exclusion.fits') exclusion_mask = exclusion_mask.reproject(reference=refheader) # Pb with the load psftable for one of the run that is not implemented yet... data_store.hdu_table.remove_row(14) cube_maker = StackedObsCubeMaker(empty_cube_images=ref_cube_images, empty_exposure_cube=ref_cube_exposure, offset_band=offset_band, data_store=data_store, obs_table=data_store.obs_table, exclusion_mask=exclusion_mask, save_bkg_scale=True) cube_maker.make_cubes(make_background_image=True, radius=10.) obslist = [data_store.obs(id) for id in data_store.obs_table["OBS_ID"]] ObsList = ObservationList(obslist) mean_psf_cube = make_mean_psf_cube(image_size=50, energy_cube=etrue, center_maps=center, center=center, ObsList=ObsList, spectral_index=2.3) if use_etrue: mean_rmf = make_mean_rmf(energy_true=etrue, energy_reco=ereco, center=center, ObsList=ObsList) filename_mask = 'exclusion_mask.fits' filename_counts = 'counts_cube.fits' filename_bkg = 'bkg_cube.fits' filename_significance = 'significance_cube.fits' filename_excess = 'excess_cube.fits' if use_etrue: filename_exposure = 'exposure_cube_etrue.fits' filename_psf = 'psf_cube_etrue.fits' filename_rmf = 'rmf.fits' mean_rmf.write(filename_rmf, clobber=True) else: filename_exposure = 'exposure_cube.fits' filename_psf = 'psf_cube.fits' exclusion_mask.write(filename_mask, clobber=True) cube_maker.counts_cube.write(filename_counts, format="fermi-counts", clobber=True) cube_maker.bkg_cube.write(filename_bkg, format="fermi-counts", clobber=True) cube_maker.significance_cube.write(filename_significance, format="fermi-counts", clobber=True) cube_maker.excess_cube.write(filename_excess, format="fermi-counts", clobber=True) cube_maker.exposure_cube.write(filename_exposure, format="fermi-counts", clobber=True) mean_psf_cube.write(filename_psf, format="fermi-counts", clobber=True)