def test_fake(self):
"""Test the fake dataset"""
source_model = SkyModel(spectral_model=PowerLawSpectralModel())
dataset = SpectrumDatasetOnOff(
counts=self.on_counts,
counts_off=self.off_counts,
models=source_model,
aeff=self.aeff,
livetime=self.livetime,
edisp=self.edisp,
acceptance=1,
acceptance_off=10,
)
real_dataset = dataset.copy()
# Define background model counts
elo = self.on_counts.energy.edges[:-1]
ehi = self.on_counts.energy.edges[1:]
data = np.ones(self.on_counts.data.shape)
background_model = CountsSpectrum(elo, ehi, data)
dataset.fake(background_model=background_model,
random_state=314,
name="fake")
assert real_dataset.counts.data.shape == dataset.counts.data.shape
assert real_dataset.counts_off.data.shape == dataset.counts_off.data.shape
assert (real_dataset.counts.energy.center.mean() ==
dataset.counts.energy.center.mean())
assert real_dataset.acceptance.mean() == dataset.acceptance.mean()
assert real_dataset.acceptance_off.mean(
) == dataset.acceptance_off.mean()
assert dataset.counts_off.data.sum() == 39
assert dataset.counts.data.sum() == 5
assert dataset.name == "fake"
def simulate_spectrum_dataset(model, random_state=0):
energy = np.logspace(-0.5, 1.5, 21) * u.TeV
aeff = EffectiveAreaTable.from_parametrization(energy=energy)
bkg_model = PowerLawSpectralModel(index=2.5, amplitude="1e-12 cm-2 s-1 TeV-1")
dataset = SpectrumDatasetOnOff(
aeff=aeff, model=model, livetime=100 * u.h, acceptance=1, acceptance_off=5
)
eval = SpectrumEvaluator(model=bkg_model, aeff=aeff, livetime=100 * u.h)
bkg_model = eval.compute_npred()
dataset.fake(random_state=random_state, background_model=bkg_model)
return dataset
# ### OnOff analysis
#
# To do `OnOff` spectral analysis, which is the usual science case, the standard would be to use `SpectrumDatasetOnOff`, which uses the acceptance to fake off-counts
# In[ ]:
dataset_onoff = SpectrumDatasetOnOff(
aeff=dataset.aeff,
edisp=dataset.edisp,
models=model,
livetime=livetime,
acceptance=1,
acceptance_off=5,
)
dataset_onoff.fake(background_model=dataset.background)
print(dataset_onoff)
# You can see that off counts are now simulated as well. We now simulate several spectra using the same set of observation conditions.
# In[ ]:
get_ipython().run_cell_magic(
'time', '',
'\nn_obs = 100\ndatasets = []\n\nfor idx in range(n_obs):\n dataset_onoff.fake(random_state=idx, background_model=dataset.background)\n dataset_onoff.name = f"obs_{idx}"\n datasets.append(dataset_onoff.copy())'
)
# Before moving on to the fit let's have a look at the simulated observations.
# In[ ]:
