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 test_fake(self): """Test the fake dataset""" source_model = SkyModel(spectral_model=PowerLawSpectralModel()) dataset = SpectrumDatasetOnOff( name="test", counts=self.on_counts, counts_off=self.off_counts, models=source_model, exposure=self.aeff * self.livetime, edisp=self.edisp, acceptance=1, acceptance_off=10, ) real_dataset = dataset.copy() background = RegionNDMap.from_geom(dataset.counts.geom) background.data += 1 background_model = BackgroundModel(background, name="test-bkg", datasets_names="test") dataset.fake(background_model=background_model, random_state=314) assert real_dataset.counts.data.shape == dataset.counts.data.shape assert real_dataset.counts_off.data.shape == dataset.counts_off.data.shape assert dataset.counts_off.data.sum() == 39 assert dataset.counts.data.sum() == 5
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