def test_flux_estimator_norm_range_template():
energy = MapAxis.from_energy_bounds(0.1,
10,
3.0,
unit="TeV",
name="energy_true")
template = WcsNDMap.create(npix=10,
axes=[energy],
unit="cm-2 s-1 sr-1 TeV-1")
spatial = TemplateSpatialModel(template, normalize=False)
spectral = PowerLawNormSpectralModel()
model = SkyModel(spectral_model=spectral,
spatial_model=spatial,
name="test")
model.spectral_model.norm.max = 10
model.spectral_model.norm.min = 0
estimator = FluxEstimator(source="test",
selection_optional=[],
reoptimize=True)
scale_model = estimator.get_scale_model(Models([model]))
assert_allclose(scale_model.norm.min, 0)
assert_allclose(scale_model.norm.max, 10)
assert scale_model.norm.interp == "log"
def test_flux_estimator_norm_range():
model = SkyModel.create("pl", "gauss", name="test")
model.spectral_model.amplitude.min = 1e-15
model.spectral_model.amplitude.max = 1e-10
estimator = FluxEstimator(source="test",
selection_optional=[],
reoptimize=True)
scale_model = estimator.get_scale_model(Models([model]))
assert_allclose(scale_model.norm.min, 1e-3)
assert_allclose(scale_model.norm.max, 1e2)
assert scale_model.norm.interp == "log"
def test_flux_estimator_naima_model():
import naima
ECPL = naima.models.ExponentialCutoffPowerLaw(1e36 * u.Unit("1/eV"),
1 * u.TeV, 2.1, 13 * u.TeV)
IC = naima.models.InverseCompton(ECPL, seed_photon_fields=["CMB"])
naima_model = NaimaSpectralModel(IC)
model = SkyModel(spectral_model=naima_model, name="test")
estimator = FluxEstimator(source="test",
selection_optional=[],
reoptimize=True)
scale_model = estimator.get_scale_model(Models([model]))
assert_allclose(scale_model.norm.min, np.nan)
assert_allclose(scale_model.norm.max, np.nan)
def test_flux_estimator_compound_model():
pl = PowerLawSpectralModel()
pl.amplitude.min = 1e-15
pl.amplitude.max = 1e-10
pln = PowerLawNormSpectralModel()
pln.norm.value = 0.1
spectral_model = pl * pln
model = SkyModel(spectral_model=spectral_model, name="test")
estimator = FluxEstimator(source="test",
selection_optional=[],
reoptimize=True)
scale_model = estimator.get_scale_model(Models([model]))
assert_allclose(scale_model.norm.min, 1e-3)
assert_allclose(scale_model.norm.max, 1e2)