def models_gauss():
model1 = SkyModel.create("pl", "gauss", name="source-1")
model1.spatial_model.sigma.value = 0.1
model1.spatial_model.lon_0.value = 0
model1.spatial_model.lat_0.value = 0
model2 = SkyModel.create("pl", "gauss", name="source-2")
model2.spatial_model.sigma.value = 0.1
model2.spatial_model.lon_0.value = 1.1
model2.spatial_model.lat_0.value = 0
model3 = SkyModel.create("pl", "gauss", name="source-3")
model3.spatial_model.sigma.value = 0.1
model3.spatial_model.lon_0.value = -1.8
model3.spatial_model.lat_0.value = 0
return Models([model1, model2, model3])
def test_npred_spatial_model(spectrum_dataset):
model = SkyModel.create("pl", "point", name="test")
spectrum_dataset.models = [model]
npred = spectrum_dataset.npred()
assert_allclose(npred.data.sum(), 3000)
assert spectrum_dataset.evaluators["test"].psf is None
def test_npred_spatial_model(spectrum_dataset):
model = SkyModel.create("pl", "gauss", name="test")
spectrum_dataset.models = [model]
npred = spectrum_dataset.npred()
model.spatial_model.sigma.value = 1.0
npred_large_sigma = spectrum_dataset.npred()
assert_allclose(npred.data.sum(), 3000)
assert_allclose(npred_large_sigma.data.sum(), 3000)
assert spectrum_dataset.evaluators["test"].psf is None
def test_flux_estimator_norm_range():
model = SkyModel.create("pl", "gauss", name="test")
model.spectral_model.amplitude.min = 1e-15
model.spectral_model.amplitude.max = 1e-10
estimator = FluxEstimator(source="test",
selection_optional=[],
reoptimize=True)
scale_model = estimator.get_scale_model(Models([model]))
assert_allclose(scale_model.norm.min, 1e-3)
assert_allclose(scale_model.norm.max, 1e2)
assert scale_model.norm.interp == "log"
def test_sky_model_create():
m = SkyModel.create("pl", "point", name="my-source")
assert isinstance(m.spatial_model, PointSpatialModel)
assert isinstance(m.spectral_model, PowerLawSpectralModel)
assert m.name == "my-source"
def reference_model_default(cls):
"""Default reference model: a point source with index = 2 (`SkyModel`)"""
return SkyModel.create("pl", "point")
