forked from gammapy/gammapy
/
test_models.py
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test_models.py
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# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function, unicode_literals
import pytest
import numpy as np
import astropy.units as u
from ...utils.energy import EnergyBounds
from ...utils.testing import assert_quantity_allclose
from ...utils.testing import requires_dependency, requires_data, mpl_plot_check
from ..models import (
PowerLaw,
PowerLaw2,
ExponentialCutoffPowerLaw,
ExponentialCutoffPowerLaw3FGL,
LogParabola,
TableModel,
AbsorbedSpectralModel,
Absorption,
ConstantModel,
)
def table_model():
energy_edges = EnergyBounds.equal_log_spacing(0.1 * u.TeV, 100 * u.TeV, 1000)
energy = energy_edges.log_centers
index = 2.3 * u.Unit("")
amplitude = 4 / u.cm ** 2 / u.s / u.TeV
reference = 1 * u.TeV
pl = PowerLaw(index, amplitude, reference)
flux = pl(energy)
return TableModel(energy, flux, 1 * u.Unit(""))
TEST_MODELS = [
dict(
name="powerlaw",
model=PowerLaw(
index=2.3 * u.Unit(""),
amplitude=4 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
),
val_at_2TeV=u.Quantity(4 * 2.0 ** (-2.3), "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(2.9227116204223784, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(6.650836884969039, "TeV cm-2 s-1"),
),
dict(
name="powerlaw",
model=PowerLaw(
index=2 * u.Unit(""),
amplitude=4 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
),
val_at_2TeV=u.Quantity(1.0, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(3.6, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(9.210340371976184, "TeV cm-2 s-1"),
),
dict(
name="powerlaw2",
model=PowerLaw2(
amplitude=u.Quantity(2.9227116204223784, "cm-2 s-1"),
index=2.3 * u.Unit(""),
emin=1 * u.TeV,
emax=10 * u.TeV,
),
val_at_2TeV=u.Quantity(4 * 2.0 ** (-2.3), "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(2.9227116204223784, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(6.650836884969039, "TeV cm-2 s-1"),
),
dict(
name="ecpl",
model=ExponentialCutoffPowerLaw(
index=1.6 * u.Unit(""),
amplitude=4 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
lambda_=0.1 / u.TeV,
),
val_at_2TeV=u.Quantity(1.080321705479446, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(3.765838739678921, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(9.901735870666526, "TeV cm-2 s-1"),
e_peak=4 * u.TeV,
),
dict(
name="ecpl_3fgl",
model=ExponentialCutoffPowerLaw3FGL(
index=2.3 * u.Unit(""),
amplitude=4 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
ecut=10 * u.TeV,
),
val_at_2TeV=u.Quantity(0.7349563611124971, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(2.6034046173089, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(5.340285560055799, "TeV cm-2 s-1"),
),
dict(
name="logpar",
model=LogParabola(
alpha=2.3 * u.Unit(""),
amplitude=4 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
beta=0.5 * u.Unit(""),
),
val_at_2TeV=u.Quantity(0.6387956571420305, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(2.255689748270628, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(3.9586515834989267, "TeV cm-2 s-1"),
e_peak=0.74082 * u.TeV,
),
dict(
name="logpar10",
model=LogParabola.from_log10(
alpha=2.3 * u.Unit(""),
amplitude=4 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
beta=1.151292546497023 * u.Unit(""),
),
val_at_2TeV=u.Quantity(0.6387956571420305, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(2.255689748270628, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(3.9586515834989267, "TeV cm-2 s-1"),
e_peak=0.74082 * u.TeV,
),
dict(
name="constant",
model=ConstantModel(const=4 / u.cm ** 2 / u.s / u.TeV),
val_at_2TeV=u.Quantity(4, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(35.9999999999999, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(198.00000000000006, "TeV cm-2 s-1"),
),
]
# Add compound models
TEST_MODELS.append(
dict(
name="compound1",
model=TEST_MODELS[0]["model"] * 5,
val_at_2TeV=TEST_MODELS[0]["val_at_2TeV"] * 5,
integral_1_10TeV=TEST_MODELS[0]["integral_1_10TeV"] * 5,
eflux_1_10TeV=TEST_MODELS[0]["eflux_1_10TeV"] * 5,
)
)
TEST_MODELS.append(
dict(
name="compound2",
model=5 * TEST_MODELS[0]["model"],
val_at_2TeV=TEST_MODELS[0]["val_at_2TeV"] * 5,
integral_1_10TeV=TEST_MODELS[0]["integral_1_10TeV"] * 5,
eflux_1_10TeV=TEST_MODELS[0]["eflux_1_10TeV"] * 5,
)
)
TEST_MODELS.append(
dict(
name="compound3",
model=TEST_MODELS[0]["model"] + TEST_MODELS[0]["model"],
val_at_2TeV=TEST_MODELS[0]["val_at_2TeV"] * 2,
integral_1_10TeV=TEST_MODELS[0]["integral_1_10TeV"] * 2,
eflux_1_10TeV=TEST_MODELS[0]["eflux_1_10TeV"] * 2,
)
)
TEST_MODELS.append(
dict(
name="compound4",
model=TEST_MODELS[0]["model"] - 0.1 * TEST_MODELS[0]["val_at_2TeV"],
val_at_2TeV=0.9 * TEST_MODELS[0]["val_at_2TeV"],
integral_1_10TeV=2.1919819216346936 * u.Unit("cm-2 s-1"),
eflux_1_10TeV=2.6322140512045697 * u.Unit("TeV cm-2 s-1"),
)
)
TEST_MODELS.append(
dict(
name="compound5",
model=TEST_MODELS[0]["model"] - TEST_MODELS[0]["model"] / 2.0,
val_at_2TeV=0.5 * TEST_MODELS[0]["val_at_2TeV"],
integral_1_10TeV=TEST_MODELS[0]["integral_1_10TeV"] * 0.5,
eflux_1_10TeV=TEST_MODELS[0]["eflux_1_10TeV"] * 0.5,
)
)
# The table model imports scipy.interpolate in `__init__`,
# so we skip it if scipy is not available
try:
TEST_MODELS.append(
dict(
name="table_model",
model=table_model(),
# Values took from power law expectation
val_at_2TeV=u.Quantity(4 * 2.0 ** (-2.3), "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(2.9227116204223784, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(6.650836884969039, "TeV cm-2 s-1"),
)
)
except ImportError:
pass
@requires_dependency("uncertainties")
@pytest.mark.parametrize("spectrum", TEST_MODELS, ids=[_["name"] for _ in TEST_MODELS])
def test_models(spectrum):
model = spectrum["model"]
energy = 2 * u.TeV
value = model(energy)
assert_quantity_allclose(value, spectrum["val_at_2TeV"])
emin = 1 * u.TeV
emax = 10 * u.TeV
assert_quantity_allclose(
model.integral(emin=emin, emax=emax), spectrum["integral_1_10TeV"]
)
assert_quantity_allclose(
model.energy_flux(emin=emin, emax=emax), spectrum["eflux_1_10TeV"]
)
if "e_peak" in spectrum:
assert_quantity_allclose(model.e_peak, spectrum["e_peak"], rtol=1e-2)
# inverse for TableModel is not implemented
if not (isinstance(model, TableModel) or isinstance(model, ConstantModel)):
assert_quantity_allclose(model.inverse(value), 2 * u.TeV, rtol=0.05)
model.to_dict()
assert "" in str(model)
# check that an array evaluation works (otherwise e.g. plotting raises an error)
e_array = [2, 10, 20] * u.TeV
e_array = e_array[:, np.newaxis, np.newaxis]
val = model(e_array)
assert val.shape == e_array.shape
assert_quantity_allclose(val[0], spectrum["val_at_2TeV"])
def test_model_unit():
pwl = PowerLaw()
value = pwl(500 * u.MeV)
assert value.unit == "cm-2 s-1 TeV-1"
@requires_dependency("matplotlib")
@requires_data("gammapy-extra")
def test_table_model_from_file():
filename = "$GAMMAPY_EXTRA/datasets/ebl/ebl_franceschini.fits.gz"
absorption_z03 = TableModel.read_xspec_model(filename=filename, param=0.3)
with mpl_plot_check():
absorption_z03.plot(energy_range=(0.03, 10), energy_unit=u.TeV, flux_unit="")
@requires_data("gammapy-extra")
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 = PowerLaw(index=index, amplitude=amplitude, reference=reference)
# EBL + PWL model
model = AbsorbedSpectralModel(
spectral_model=pwl, absorption=absorption, parameter=redshift
)
# Test if the absorption factor at the reference energy
# corresponds to the ratio of the absorbed flux
# divided by the flux of the spectral model
kwargs = dict(
index=index, amplitude=amplitude, reference=reference, redshift=redshift
)
model_ref_energy = model.evaluate(energy=reference, **kwargs)
pwl_ref_energy = pwl.evaluate(
energy=reference, index=index, amplitude=amplitude, reference=reference
)
desired = absorption.evaluate(energy=reference, parameter=redshift)
actual = model_ref_energy / pwl_ref_energy
assert_quantity_allclose(actual, desired)
@requires_dependency("uncertainties")
def test_pwl_index_2_error():
pars, errs = {}, {}
pars["amplitude"] = 1e-12 * u.Unit("TeV-1 cm-2 s-1")
pars["reference"] = 1 * u.Unit("TeV")
pars["index"] = 2 * u.Unit("")
errs["amplitude"] = 0.1e-12 * u.Unit("TeV-1 cm-2 s-1")
pwl = PowerLaw(**pars)
pwl.parameters.set_parameter_errors(errs)
val, val_err = pwl.evaluate_error(1 * u.TeV)
assert_quantity_allclose(val, 1e-12 * u.Unit("TeV-1 cm-2 s-1"))
assert_quantity_allclose(val_err, 0.1e-12 * u.Unit("TeV-1 cm-2 s-1"))
flux, flux_err = pwl.integral_error(1 * u.TeV, 10 * u.TeV)
assert_quantity_allclose(flux, 9e-13 * u.Unit("cm-2 s-1"))
assert_quantity_allclose(flux_err, 9e-14 * u.Unit("cm-2 s-1"))
eflux, eflux_err = pwl.energy_flux_error(1 * u.TeV, 10 * u.TeV)
assert_quantity_allclose(eflux, 2.302585e-12 * u.Unit("TeV cm-2 s-1"))
assert_quantity_allclose(eflux_err, 0.2302585e-12 * u.Unit("TeV cm-2 s-1"))
@requires_data("gammapy-extra")
def test_fermi_isotropic():
filename = "$GAMMAPY_EXTRA/datasets/fermi_3fhl/iso_P8R2_SOURCE_V6_v06.txt"
model = TableModel.read_fermi_isotropic_model(filename)
assert_quantity_allclose(
model(50 * u.GeV), 1.463 * u.Unit("1e-13 MeV-1 cm-2 s-1 sr-1"), rtol=1e-3
)