def test_names(geom, geom_etrue, sky_model):
m = Map.from_geom(geom)
m.quantity = 0.2 * np.ones(m.data.shape)
background_model1 = BackgroundModel(m, name="bkg1")
assert background_model1.name == "bkg1"
c_map1 = Map.from_geom(geom)
c_map1.quantity = 0.3 * np.ones(c_map1.data.shape)
model1 = sky_model.copy()
assert model1.name != sky_model.name
model1 = sky_model.copy(name="model1")
assert model1.name == "model1"
model2 = sky_model.copy(name="model2")
dataset1 = MapDataset(
counts=c_map1,
background_model=background_model1,
models=Models([model1, model2]),
exposure=get_exposure(geom_etrue),
)
dataset2 = dataset1.copy()
assert dataset2.name != dataset1.name
assert dataset2.background_model
dataset2 = dataset1.copy(name="dataset2")
assert dataset2.name == "dataset2"
assert dataset2.background_model.name == "bkg1"
assert dataset1.background_model is not dataset2.background_model
assert dataset1.models.names == dataset2.models.names
assert dataset1.models is not dataset2.models
def test_to_image(geom):
counts = Map.read(
"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-counts-cube.fits.gz")
background = Map.read(
"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-background-cube.fits.gz")
background = BackgroundModel(background)
exposure = Map.read(
"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-exposure-cube.fits.gz")
exposure = exposure.sum_over_axes(keepdims=True)
dataset = MapDataset(counts=counts,
background_model=background,
exposure=exposure)
dataset_im = dataset.to_image()
assert dataset_im.mask_safe is None
assert dataset_im.counts.data.sum() == dataset.counts.data.sum()
assert_allclose(dataset_im.background_model.map.data.sum(),
28548.625,
rtol=1e-5)
ebounds = np.logspace(-1.0, 1.0, 3)
axis = MapAxis.from_edges(ebounds, name="energy", unit=u.TeV, interp="log")
geom = WcsGeom.create(skydir=(0, 0),
binsz=0.5,
width=(1, 1),
frame="icrs",
axes=[axis])
dataset = MapDataset.create(geom)
# Check map_safe handling
data = np.array([[[False, True], [True, True]],
[[False, False], [True, True]]])
dataset.mask_safe = WcsNDMap.from_geom(geom=geom, data=data)
dataset_im = dataset.to_image()
assert dataset_im.mask_safe.data.dtype == bool
desired = np.array([[False, True], [True, True]])
assert (dataset_im.mask_safe.data == desired).all()
# Check that missing entries in the dataset do not break
dataset_copy = dataset.copy()
dataset_copy.exposure = None
dataset_copy.background_model = None
dataset_im = dataset_copy.to_image()
assert dataset_im.exposure is None
assert dataset_im.background_model is None
dataset_copy = dataset.copy()
dataset_copy.counts = None
dataset_im = dataset_copy.to_image()
assert dataset_im.counts is None
