def test_generalized_gaussian_io():
model = GeneralizedGaussianSpatialModel()
assert isinstance(model.to_region(), EllipseSkyRegion)
new_model = GeneralizedGaussianSpatialModel.from_dict(model.to_dict())
assert isinstance(new_model, GeneralizedGaussianSpatialModel)
def test_generalized_gaussian_io():
model = GeneralizedGaussianSpatialModel(e=0.5)
reg = model.to_region()
assert isinstance(reg, EllipseSkyRegion)
assert_allclose(reg.width.value, 1.73205, rtol=1e-5)
new_model = GeneralizedGaussianSpatialModel.from_dict(model.to_dict())
assert isinstance(new_model, GeneralizedGaussianSpatialModel)
def test_generalized_gaussian(eta, r_0, e):
# check normalization is robust for a large set of values
model = GeneralizedGaussianSpatialModel(
eta=eta, r_0=r_0 * u.deg, e=e, frame="galactic"
)
geom = WcsGeom.create(
skydir=(0, 0), binsz=0.02, width=2 * model.evaluation_radius, frame="galactic",
)
integral = model.integrate_geom(geom)
assert integral.unit.is_equivalent("")
assert_allclose(integral.data.sum(), 1.0, atol=5e-3)
def test_generalized_gaussian(eta, r_0, e):
reval = 6
dr = 0.01
geom = WcsGeom.create(
skydir=(0, 0),
binsz=dr,
width=(2 * reval, 2 * reval),
frame="galactic",
)
# check normalization is robust for a large set of values
model = GeneralizedGaussianSpatialModel(eta=eta,
r_0=r_0 * u.deg,
e=e,
frame="galactic")
eval_geom = model.evaluate_geom(geom)
integ_geom = model.integrate_geom(geom)
assert eval_geom.unit.is_equivalent("sr-1")
assert integ_geom.unit.is_equivalent("")
assert_allclose(integ_geom.data.sum(), 1.0, atol=3e-2)
assert isinstance(model.to_region(), EllipseSkyRegion)
new_model = GeneralizedGaussianSpatialModel.from_dict(model.to_dict())
assert isinstance(new_model, GeneralizedGaussianSpatialModel)
assert_allclose(
new_model.integrate_geom(geom).data.sum(), integ_geom.data.sum())
width=(2 * reval, 2 * reval),
frame="galactic",
)
tags = [r"Disk, $\eta=0.01$", r"Gaussian, $\eta=0.5$", r"Laplace, $\eta=1$"]
eta_range = [0.01, 0.5, 1]
r_0 = 1
e = 0.5
phi = 45 * u.deg
fig, axes = plt.subplots(1, 3, figsize=(9, 6))
for ax, eta, tag in zip(axes, eta_range, tags):
model = GeneralizedGaussianSpatialModel(
lon_0=lon_0 * u.deg,
lat_0=lat_0 * u.deg,
eta=eta,
r_0=r_0 * u.deg,
e=e,
phi=phi,
frame="galactic",
)
meval = model.evaluate_geom(geom)
Map.from_geom(geom=geom, data=meval.value, unit=meval.unit).plot(ax=ax)
pixreg = model.to_region().to_pixel(geom.wcs)
pixreg.plot(ax=ax, edgecolor="g", facecolor="none", lw=2)
ax.set_title(tag)
ax.set_xticks([])
ax.set_yticks([])
plt.tight_layout()
# %%
# YAML representation
