def simulate_spectrum_dataset(model, random_state=0):
energy_edges = np.logspace(-0.5, 1.5, 21) * u.TeV
energy_axis = MapAxis.from_edges(energy_edges, interp="log", name="energy")
energy_axis_true = energy_axis.copy(name="energy_true")
aeff = EffectiveAreaTable2D.from_parametrization(
energy_axis_true=energy_axis_true)
bkg_model = SkyModel(
spectral_model=PowerLawSpectralModel(index=2.5,
amplitude="1e-12 cm-2 s-1 TeV-1"),
name="background",
)
bkg_model.spectral_model.amplitude.frozen = True
bkg_model.spectral_model.index.frozen = True
geom = RegionGeom.create(region="icrs;circle(0, 0, 0.1)",
axes=[energy_axis])
acceptance = RegionNDMap.from_geom(geom=geom, data=1)
edisp = EDispKernelMap.from_diagonal_response(
energy_axis=energy_axis,
energy_axis_true=energy_axis_true,
geom=geom,
)
geom_true = RegionGeom.create(region="icrs;circle(0, 0, 0.1)",
axes=[energy_axis_true])
exposure = make_map_exposure_true_energy(pointing=SkyCoord("0d", "0d"),
aeff=aeff,
livetime=100 * u.h,
geom=geom_true)
mask_safe = RegionNDMap.from_geom(geom=geom, dtype=bool)
mask_safe.data += True
acceptance_off = RegionNDMap.from_geom(geom=geom, data=5)
dataset = SpectrumDatasetOnOff(
name="test_onoff",
exposure=exposure,
acceptance=acceptance,
acceptance_off=acceptance_off,
edisp=edisp,
mask_safe=mask_safe,
)
dataset.models = bkg_model
bkg_npred = dataset.npred_signal()
dataset.models = model
dataset.fake(
random_state=random_state,
npred_background=bkg_npred,
)
return dataset
def simulate_spectrum_dataset(model, random_state=0):
energy_edges = np.logspace(-0.5, 1.5, 21) * u.TeV
energy_axis = MapAxis.from_edges(energy_edges, interp="log", name="energy")
aeff = EffectiveAreaTable.from_parametrization(energy=energy_edges).to_region_map()
bkg_model = SkyModel(
spectral_model=PowerLawSpectralModel(
index=2.5, amplitude="1e-12 cm-2 s-1 TeV-1"
),
name="background",
)
bkg_model.spectral_model.amplitude.frozen = True
bkg_model.spectral_model.index.frozen = True
geom = RegionGeom(region=None, axes=[energy_axis])
acceptance = RegionNDMap.from_geom(geom=geom, data=1)
edisp = EDispKernelMap.from_diagonal_response(
energy_axis=energy_axis,
energy_axis_true=energy_axis.copy(name="energy_true"),
geom=geom,
)
livetime = 100 * u.h
exposure = aeff * livetime
mask_safe = RegionNDMap.from_geom(geom=geom, dtype=bool)
mask_safe.data += True
dataset = SpectrumDatasetOnOff(
name="test_onoff",
exposure=exposure,
acceptance=acceptance,
acceptance_off=5,
edisp=edisp,
mask_safe=mask_safe
)
dataset.models = bkg_model
bkg_npred = dataset.npred_signal()
dataset.models = model
dataset.fake(
random_state=random_state, npred_background=bkg_npred,
)
return dataset
def test_npred_no_edisp(self):
const = 1 * u.Unit("cm-2 s-1 TeV-1")
model = SkyModel(spectral_model=ConstantSpectralModel(const=const))
livetime = 1 * u.s
aeff = RegionNDMap.create(
region=self.on_region,
unit="cm2",
axes=[self.e_reco.copy(name="energy_true")],
)
aeff.data += 1
dataset = SpectrumDatasetOnOff(
counts=self.on_counts,
counts_off=self.off_counts,
exposure=aeff * livetime,
models=model,
)
energy = aeff.geom.axes[0].edges
expected = aeff.data[0] * (energy[-1] - energy[0]) * const * livetime
assert_allclose(dataset.npred_signal().data.sum(), expected.value)
