def test_absorbed_extrapolate():
ebl_model = "dominguez"
z = 0.001
absorption = Absorption.read_builtin(ebl_model)
model = absorption.table_model(z)
assert_allclose(model(1 * u.TeV), 1)
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 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)
# Test error propagation
model.spectral_model.parameters.set_error(
amplitude=0.1 * model.spectral_model.amplitude.value)
dnde, dnde_err = model.evaluate_error(1 * u.TeV)
assert_allclose(dnde_err / dnde, 0.1)
This model represents EBL absorption models.
They are usually used as part of `~gammapy.modeling.models.AbsorbedSpectralModel`
"""
# %%
# Example plot
# ------------
# Here we illustrate how to create and plot EBL absorption models for a redshift of 0.5:
from astropy import units as u
import matplotlib.pyplot as plt
from gammapy.modeling.models import Absorption
redshift = 0.5
dominguez = Absorption.read_builtin("dominguez").table_model(redshift)
franceschini = Absorption.read_builtin("franceschini").table_model(redshift)
finke = Absorption.read_builtin("finke").table_model(redshift)
plt.figure()
energy_range = [0.08, 3] * u.TeV
opts = dict(energy_range=energy_range, energy_unit="TeV", flux_unit="")
franceschini.plot(label="Franceschini 2008", **opts)
finke.plot(label="Finke 2010", **opts)
dominguez.plot(label="Dominguez 2011", **opts)
plt.ylabel(r"Absorption coefficient [$\exp{(-\tau(E))}$]")
plt.xlim(energy_range.value)
plt.ylim(1e-4, 2)
plt.title(f"EBL models (z={redshift})")
plt.grid(which="both")
# Example plot
# ------------
# Here is an example plot of the model:
from astropy import units as u
import matplotlib.pyplot as plt
from gammapy.modeling.models import (
AbsorbedSpectralModel,
Absorption,
Models,
PowerLawSpectralModel,
SkyModel,
)
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
import matplotlib.pyplot as plt
import astropy.units as u
from gammapy.modeling.models import Absorption
# Load tables for z=0.5
redshift = 0.5
dominguez = Absorption.read_builtin('dominguez').table_model(redshift)
franceschini = Absorption.read_builtin('franceschini').table_model(redshift)
finke = Absorption.read_builtin('finke').table_model(redshift)
# start customised plot
energy_range = [0.08, 3] * u.TeV
ax = plt.gca()
opts = dict(energy_range=energy_range, energy_unit='TeV', ax=ax, flux_unit='')
franceschini.plot(label='Franceschini 2008', **opts)
finke.plot(label='Finke 2010', **opts)
dominguez.plot(label='Dominguez 2011', **opts)
# tune plot
ax.set_ylabel(r'Absorption coefficient [$\exp{(-\tau(E))}$]')
ax.set_xlim(energy_range.value) # we get ride of units
ax.set_ylim([1.e-4, 2.])
ax.set_yscale('log')
ax.set_title('EBL models (z=' + str(redshift) + ')')
plt.grid(which='both')
plt.legend(loc='best') # legend
# show plot
plt.show()
