def test_Datasets_mutation():
dat = MyDataset(name="test-1")
dats = Datasets([MyDataset(name="test-2"), MyDataset(name="test-3")])
dats2 = Datasets([MyDataset(name="test-4"), MyDataset(name="test-5")])
dats.insert(0, dat)
assert dats.names == ["test-1", "test-2", "test-3"]
dats.extend(dats2)
assert dats.names == ["test-1", "test-2", "test-3", "test-4", "test-5"]
dat3 = dats[3]
dats.remove(dats[3])
assert dats.names == ["test-1", "test-2", "test-3", "test-5"]
dats.append(dat3)
assert dats.names == ["test-1", "test-2", "test-3", "test-5", "test-4"]
dats.pop(3)
assert dats.names == ["test-1", "test-2", "test-3", "test-4"]
with pytest.raises(ValueError, match="Dataset names must be unique"):
dats.append(dat)
with pytest.raises(ValueError, match="Dataset names must be unique"):
dats.insert(0, dat)
with pytest.raises(ValueError, match="Dataset names must be unique"):
dats.extend(dats2)
def data_prep():
data_store = DataStore.from_dir("$GAMMAPY_DATA/cta-1dc/index/gps/")
OBS_ID = 110380
obs_ids = OBS_ID * np.ones(N_OBS)
observations = data_store.get_observations(obs_ids)
energy_axis = MapAxis.from_bounds(0.1,
10,
nbin=10,
unit="TeV",
name="energy",
interp="log")
geom = WcsGeom.create(
skydir=(0, 0),
binsz=0.02,
width=(10, 8),
frame="galactic",
proj="CAR",
axes=[energy_axis],
)
offset_max = 4 * u.deg
maker = MapDatasetMaker()
safe_mask_maker = SafeMaskMaker(methods=["offset-max"],
offset_max=offset_max)
stacked = MapDataset.create(geom=geom)
spatial_model = PointSpatialModel(lon_0="-0.05 deg",
lat_0="-0.05 deg",
frame="galactic")
spectral_model = ExpCutoffPowerLawSpectralModel(
index=2,
amplitude=3e-12 * u.Unit("cm-2 s-1 TeV-1"),
reference=1.0 * u.TeV,
lambda_=0.1 / u.TeV,
)
model = SkyModel(spatial_model=spatial_model,
spectral_model=spectral_model,
name="gc-source")
datasets = Datasets([])
for idx, obs in enumerate(observations):
cutout = stacked.cutout(obs.pointing_radec,
width=2 * offset_max,
name=f"dataset{idx}")
dataset = maker.run(cutout, obs)
dataset = safe_mask_maker.run(dataset, obs)
dataset.models = model
datasets.append(dataset)
return datasets
