def make_reco_model():
"""Make a reco bg cube model."""
METHOD = "default"
data_dir = "test_dataset"
overwrite = OVERWRITE
test = TEST
group_id = GROUP_ID
# create output folder
outdir = os.path.join(OUTDIR, "reco")
_create_dir(outdir, overwrite)
# 0. create dummy observation grouping
obs_groups = create_dummy_observation_grouping()
# save
outfile = os.path.join(outdir, "bg_observation_groups.ecsv")
print("Writing {}".format(outfile, overwrite=overwrite))
obs_groups.write(outfile)
# 1. create dummy dataset
# use enough stats so that rebinning (and resmoothing?) doesn't take place
n_obs = 100
if test:
# run fast
n_obs = 2
az_range = AZ_RANGE
alt_range = ALT_RANGE
random_state = np.random.RandomState(seed=0)
sigma = SIGMA
spectral_index = INDEX
make_test_dataset(
outdir=data_dir,
overwrite=overwrite,
observatory_name="HESS",
n_obs=n_obs,
az_range=az_range,
alt_range=alt_range,
date_range=(
Time("2010-01-01T00:00:00", format="isot", scale="utc"),
Time("2015-01-01T00:00:00", format="isot", scale="utc"),
),
n_tels_range=(3, 4),
sigma=sigma,
spectral_index=spectral_index,
random_state=random_state,
)
# 2. get observation table
data_store = DataStore.from_dir(dir=data_dir)
observation_table = data_store.make_observation_table()
outfile = os.path.join(outdir, "bg_observation_table_group{}.fits.gz".format(group_id))
print("Writing {}".format(outfile))
observation_table.write(outfile, overwrite=overwrite)
# 3. build bg model
method = METHOD
bg_cube_model = make_bg_cube_model(
observation_table=observation_table, data_dir=data_dir, method=method, do_not_force_mev_units=True
)
# save
outfile = os.path.join(outdir, "bg_cube_model_group{}".format(group_id))
print("Writing {}".format("{}_table.fits.gz".format(outfile)))
print("Writing {}".format("{}_image.fits.gz".format(outfile)))
bg_cube_model.write("{}_table.fits.gz".format(outfile), format="table", clobber=overwrite)
bg_cube_model.write("{}_image.fits.gz".format(outfile), format="image", clobber=overwrite)
