def simulate_spectrum_dataset(model, random_state=0):
edges = np.logspace(-0.5, 1.5, 21) * u.TeV
energy_axis = MapAxis.from_edges(edges, interp="log", name="energy")
aeff = EffectiveAreaTable.from_parametrization(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
)
dataset = SpectrumDatasetOnOff(
aeff=aeff, livetime=100 * u.h, acceptance=acceptance, acceptance_off=5, edisp=edisp
)
dataset.models = bkg_model
bkg_npred = dataset.npred_sig()
dataset.models = model
dataset.fake(random_state=random_state, background_model=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")
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 test_wstat(self):
"""WStat with on source and background spectrum"""
on_vector = self.src.copy()
on_vector.data += self.bkg.data
acceptance = RegionNDMap.from_geom(self.src.geom, data=1)
acceptance_off = RegionNDMap.from_geom(self.bkg.geom,
data=1 / self.alpha)
dataset = SpectrumDatasetOnOff(
counts=on_vector,
counts_off=self.off,
exposure=self.exposure,
acceptance=acceptance,
acceptance_off=acceptance_off,
)
dataset.models = self.source_model
self.source_model.parameters.index = 1.12
fit = Fit()
result = fit.run(datasets=[dataset])
pars = self.source_model.parameters
assert_allclose(pars["index"].value, 1.997342, rtol=1e-3)
assert_allclose(pars["amplitude"].value, 100245.187067, rtol=1e-3)
assert_allclose(result.total_stat, 30.022316, rtol=1e-3)
def test_wstat(self):
"""WStat with on source and background spectrum"""
on_vector = self.src.copy()
on_vector.data += self.bkg.data
dataset = SpectrumDatasetOnOff(
counts=on_vector,
counts_off=self.off,
exposure=self.aeff * self.livetime,
acceptance=1,
acceptance_off=1 / self.alpha,
)
dataset.models = self.source_model
self.source_model.parameters.index = 1.12
fit = Fit([dataset])
result = fit.run()
pars = self.source_model.parameters
assert_allclose(pars["index"].value, 1.997342, rtol=1e-3)
assert_allclose(pars["amplitude"].value, 100245.187067, rtol=1e-3)
assert_allclose(result.total_stat, 30.022316, rtol=1e-3)
