def run(self, dataset):
"""Run ring background maker
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Input map dataset.
Returns
-------
dataset_on_off : `~gammapy.datasets.MapDatasetOnOff`
On off dataset.
"""
from gammapy.datasets import MapDatasetOnOff
maps_off = self.make_maps_off(dataset)
maps_off["acceptance"] = dataset.npred_background()
mask_safe = dataset.mask_safe.copy()
not_has_off_acceptance = maps_off["acceptance_off"].data <= 0
mask_safe.data[not_has_off_acceptance] = 0
dataset_on_off = MapDatasetOnOff.from_map_dataset(dataset=dataset,
name=dataset.name,
**maps_off)
dataset_on_off.mask_safe = mask_safe
return dataset_on_off
def run(self, dataset):
"""Run adaptive ring background maker
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Input map dataset.
Returns
-------
dataset_on_off : `~gammapy.datasets.MapDatasetOnOff`
On off dataset.
"""
from gammapy.datasets import MapDatasetOnOff
cubes = self.make_cubes(dataset)
acceptance, acceptance_off, counts_off = self._reduce_cubes(
cubes, dataset)
mask_safe = dataset.mask_safe.copy()
not_has_off_acceptance = acceptance_off.data <= 0
mask_safe.data[not_has_off_acceptance] = 0
dataset_on_off = MapDatasetOnOff.from_map_dataset(
dataset=dataset,
counts_off=counts_off,
acceptance=acceptance,
acceptance_off=acceptance_off,
name=dataset.name,
)
dataset_on_off.mask_safe = mask_safe
return dataset_on_off
def test_map_dataset_on_off_fake(geom):
rad_axis = MapAxis(nodes=np.linspace(0.0, 1.0, 51), unit="deg", name="rad")
energy_true_axis = geom.axes["energy"].copy(name="energy_true")
empty_dataset = MapDataset.create(geom, energy_true_axis, rad_axis=rad_axis)
empty_dataset = MapDatasetOnOff.from_map_dataset(
empty_dataset, acceptance=1, acceptance_off=10.0
)
empty_dataset.acceptance_off.data[0, 50, 50] = 0
background_map = Map.from_geom(geom, data=1)
empty_dataset.fake(background_map, random_state=42)
assert_allclose(empty_dataset.counts.data[0, 50, 50], 0)
assert_allclose(empty_dataset.counts.data.mean(), 0.99445, rtol=1e-3)
assert_allclose(empty_dataset.counts_off.data.mean(), 10.00055, rtol=1e-3)
