def test_absorption():
# absorption values for given redshift
redshift = 0.117
absorption = Absorption.read_builtin("dominguez")
# Spectral model corresponding to PKS 2155-304 (quiescent state)
index = 3.53
amplitude = 1.81 * 1e-12 * u.Unit("cm-2 s-1 TeV-1")
reference = 1 * u.TeV
pwl = PowerLawSpectralModel(index=index,
amplitude=amplitude,
reference=reference)
# EBL + PWL model
model = AbsorbedSpectralModel(spectral_model=pwl,
absorption=absorption,
parameter=redshift)
desired = u.Quantity(5.140765e-13, "TeV-1 s-1 cm-2")
assert_quantity_allclose(model(1 * u.TeV), desired, rtol=1e-3)
assert model.alpha_norm.value == 1.0
# EBL + PWL model: test if norm of EBL=0: it mean model =pwl
model = AbsorbedSpectralModel(spectral_model=pwl,
absorption=absorption,
alpha_norm=0,
parameter=redshift)
assert_quantity_allclose(model(1 * u.TeV), pwl(1 * u.TeV), rtol=1e-3)
# EBL + PWL model: Test with a norm different of 1
model = AbsorbedSpectralModel(spectral_model=pwl,
absorption=absorption,
alpha_norm=1.5,
parameter=redshift)
desired = u.Quantity(2.739695e-13, "TeV-1 s-1 cm-2")
assert model.alpha_norm.value == 1.5
assert_quantity_allclose(model(1 * u.TeV), desired, rtol=1e-3)
def test_absorption_io(tmp_path):
dominguez = Absorption.read_builtin("dominguez")
model = AbsorbedSpectralModel(
spectral_model=Model.create("PowerLawSpectralModel"),
absorption=dominguez,
parameter=0.5,
parameter_name="redshift",
)
assert len(model.parameters) == 5
model_dict = model.to_dict()
parnames = [_["name"] for _ in model_dict["parameters"]]
assert parnames == ["redshift", "alpha_norm"]
new_model = AbsorbedSpectralModel.from_dict(model_dict)
assert new_model.parameter == 0.5
assert new_model.parameter_name == "redshift"
assert new_model.alpha_norm.name == "alpha_norm"
assert new_model.alpha_norm.value == 1
assert new_model.spectral_model.tag == "PowerLawSpectralModel"
assert_allclose(new_model.absorption.energy, dominguez.energy)
assert_allclose(new_model.absorption.param, dominguez.param)
assert len(new_model.parameters) == 5
test_absorption = Absorption(
u.Quantity(range(3), "keV"),
u.Quantity(range(2), ""),
u.Quantity(np.ones((2, 3)), ""),
)
model = AbsorbedSpectralModel(
spectral_model=Model.create("PowerLawSpectralModel"),
absorption=test_absorption,
parameter=0.5,
parameter_name="redshift",
)
model_dict = model.to_dict()
new_model = AbsorbedSpectralModel.from_dict(model_dict)
assert_allclose(new_model.absorption.energy, test_absorption.energy)
assert_allclose(new_model.absorption.param, test_absorption.param)
write_yaml(model_dict, tmp_path / "tmp.yaml")
read_yaml(tmp_path / "tmp.yaml")
def _set_model_dataset(self, ch, mass):
"""Attach to loop dataset the model to fit."""
jfactor = self.flux_model.parameters[
"jfactor"].value * self.flux_model.parameters["jfactor"].unit
flux_model = DarkMatterAnnihilationSpectralModel(mass=mass,
channel=ch,
sv=1,
jfactor=jfactor,
z=self.flux_model.z,
k=self.flux_model.k)
if isinstance(self.dataset.models[0].spectral_model,
AbsorbedSpectralModel):
flux_model = AbsorbedSpectralModel(
flux_model, self.dataset.models[0].spectral_model.absorption,
self.flux_model.z)
ds = self.dataset.copy()
ds.models[0].spectral_model = flux_model
return ds
)
redshift = 0.117
absorption = Absorption.read_builtin("dominguez")
# Spectral model corresponding to PKS 2155-304 (quiescent state)
index = 3.53
amplitude = 1.81 * 1e-12 * u.Unit("cm-2 s-1 TeV-1")
reference = 1 * u.TeV
pwl = PowerLawSpectralModel(index=index,
amplitude=amplitude,
reference=reference)
# EBL + PWL model
model = AbsorbedSpectralModel(spectral_model=pwl,
absorption=absorption,
redshift=redshift)
energy_range = [0.1, 100] * u.TeV
model.plot(energy_range)
plt.grid(which="both")
plt.ylim(1e-24, 1e-8)
# %%
# YAML representation
# -------------------
# Here is an example YAML file using the model:
model = SkyModel(spectral_model=model, name="absorbed-model")
models = Models([model])