def test_sky_diffuse_constant():
model = ConstantSpatialModel(value="42 sr-1")
lon = [1, 2] * u.deg
lat = 45 * u.deg
val = model(lon, lat)
assert val.unit == "sr-1"
assert_allclose(val.value, 42)
radius = model.evaluation_radius
assert radius is None
assert isinstance(model.to_region(), EllipseSkyRegion)
def test_flux_point_dataset_serialization(tmp_path):
path = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits"
data = FluxPoints.read(path)
data.table["e_ref"] = data.e_ref.to("TeV")
# TODO: remove duplicate definition this once model is redefine as skymodel
spatial_model = ConstantSpatialModel()
spectral_model = PowerLawSpectralModel(index=2.3,
amplitude="2e-13 cm-2 s-1 TeV-1",
reference="1 TeV")
model = SkyModel(spatial_model, spectral_model, name="test_model")
dataset = FluxPointsDataset(SkyModels([model]), data, name="test_dataset")
Datasets([dataset]).to_yaml(tmp_path, prefix="tmp")
datasets = Datasets.from_yaml(tmp_path / "tmp_datasets.yaml",
tmp_path / "tmp_models.yaml")
new_dataset = datasets[0]
assert_allclose(new_dataset.data.table["dnde"], dataset.data.table["dnde"],
1e-4)
if dataset.mask_fit is None:
assert np.all(new_dataset.mask_fit == dataset.mask_safe)
assert np.all(new_dataset.mask_safe == dataset.mask_safe)
assert new_dataset.name == "test_dataset"
# %%
# Example plot
# ------------
# Here is an example plot of the model:
from gammapy.maps import WcsGeom
from gammapy.modeling.models import (
ConstantSpatialModel,
Models,
PowerLawSpectralModel,
SkyModel,
)
geom = WcsGeom.create(npix=(100, 100), binsz=0.1)
model = ConstantSpatialModel(value="42 sr-1")
model.plot(geom=geom, add_cbar=True)
# %%
# YAML representation
# -------------------
# Here is an example YAML file using the model:
pwl = PowerLawSpectralModel()
constant = ConstantSpatialModel()
model = SkyModel(spectral_model=pwl,
spatial_model=constant,
name="pwl-constant-model")
models = Models([model])
# In[ ]:
model_diffuse = SkyDiffuseCube(diffuse_galactic, name="diffuse")
eval_diffuse = MapEvaluator(model=model_diffuse,
exposure=exposure,
psf=psf_kernel,
edisp=edisp)
background_gal = eval_diffuse.compute_npred()
background_gal.sum_over_axes().plot()
print("Background counts from Galactic diffuse: ", background_gal.data.sum())
# In[ ]:
model_iso = SkyModel(ConstantSpatialModel(), diffuse_iso, name="diffuse-iso")
eval_iso = MapEvaluator(model=model_iso, exposure=exposure, edisp=edisp)
background_iso = eval_iso.compute_npred()
background_iso.sum_over_axes().plot(add_cbar=True)
print("Background counts from isotropic diffuse: ", background_iso.data.sum())
# In[ ]:
background_total = background_iso + background_gal
# ## Excess and flux
#
# Let's compute an excess and flux image, by subtracting the background, and summing over the energy axis.