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)