def test_one(class_type):
instance = class_type()
if not instance.is_prior:
# if we have fixed x_units then we will use those
# in the test
if instance.has_fixed_units():
x_unit_to_use = instance.fixed_units[0]
else:
x_unit_to_use = u.keV
# Use the function as a spectrum
ps = PointSource("test", 0, 0, instance)
if instance.name in ["Synchrotron", "_ComplexTestFunction"]:
particleSource = ParticleSource("particles", Powerlaw())
instance.set_particle_distribution(
particleSource.spectrum.main.shape)
result = ps(1.0)
assert isinstance(result, float)
result = ps(1.0 * x_unit_to_use)
assert isinstance(result, u.Quantity)
result = ps(np.array([1, 2, 3]) * x_unit_to_use)
assert isinstance(result, u.Quantity)
if instance.name in ["Synchrotron", "_ComplexTestFunction"]:
model = Model(particleSource, ps)
else:
model = Model(ps)
new_model = clone_model(model)
new_result = new_model["test"](np.array([1, 2, 3]) * x_unit_to_use)
assert np.all(new_result == result)
model.save("__test.yml", overwrite=True)
new_model = load_model("__test.yml")
new_result = new_model["test"](np.array([1, 2, 3]) * x_unit_to_use)
assert np.all(new_result == result)
else:
print('Skipping prior function')
def test_one(class_type, name):
print("testing %s ..." % name)
shape = class_type()
source = ExtendedSource('test_source_%s' % name,
spatial_shape=shape,
components=[c1, c2])
if name != "SpatialTemplate_2D":
shape.lon0 = ra * u.degree
shape.lat0 = dec * u.degree
else:
make_test_template(ra, dec, "__test.fits")
shape.load_file("__test.fits")
shape.K = 1.0
assert np.all(
source.spectrum.component1([1, 2, 3] * u.keV) == po1([1, 2, 3] *
u.keV))
assert np.all(
source.spectrum.component2([1, 2, 3] * u.keV) == po2([1, 2, 3] *
u.keV))
one = source.spectrum.component1([1, 2, 3] * u.keV)
two = source.spectrum.component2([1, 2, 3] * u.keV)
#check spectral components
assert np.all(
np.abs(one + two -
source.get_spatially_integrated_flux([1, 2, 3] *
u.keV)) == 0)
#check spectral and spatial components
#spatial = source.spatial_shape( ra*u.deg,dec*u.deg )
spatial = source.spatial_shape([ra, ra, ra] * u.deg,
[dec, dec, dec] * u.deg)
total = source([ra, ra, ra] * u.deg, [dec, dec, dec] * u.deg,
[1, 2, 3] * u.keV)
spectrum = one + two
assert np.all(np.abs(total - spectrum * spatial) == 0)
total = source([ra * 1.01] * 3 * u.deg, [dec * 1.01] * 3 * u.deg,
[1, 2, 3] * u.keV)
spectrum = one + two
spatial = source.spatial_shape([ra * 1.01] * 3 * u.deg,
[dec * 1.01] * 3 * u.deg)
assert np.all(np.abs(total - spectrum * spatial) == 0)
model = Model(source)
new_model = clone_model(model)
new_total = new_model['test_source_%s' % name](
[ra * 1.01] * 3 * u.deg, [dec * 1.01] * 3 * u.deg,
[1, 2, 3] * u.keV)
assert np.all(np.abs(total - new_total) == 0)
