def test_datasets_stack_reduce():
obs_ids = [23523, 23526, 23559, 23592]
dataset_list = []
for obs in obs_ids:
filename = "$GAMMAPY_DATA/joint-crab/spectra/hess/pha_obs{}.fits"
ds = SpectrumDatasetOnOff.from_ogip_files(filename.format(obs))
dataset_list.append(ds)
datasets = Datasets(dataset_list)
stacked = datasets.stack_reduce(name="stacked")
assert_allclose(stacked.livetime.to_value("s"), 6313.8116406202325)
info_table = datasets.info_table()
assert_allclose(info_table["n_on"], [124, 126, 119, 90])
info_table_cum = datasets.info_table(cumulative=True)
assert_allclose(info_table_cum["n_on"], [124, 250, 369, 459])
assert stacked.name == "stacked"
def test_datasets_stack_reduce():
datasets = Datasets()
obs_ids = [23523, 23526, 23559, 23592]
for obs_id in obs_ids:
filename = f"$GAMMAPY_DATA/joint-crab/spectra/hess/pha_obs{obs_id}.fits"
ds = SpectrumDatasetOnOff.from_ogip_files(filename)
datasets.append(ds)
stacked = datasets.stack_reduce(name="stacked")
assert_allclose(stacked.exposure.meta["livetime"].to_value("s"), 6313.8116406202325)
info_table = datasets.info_table()
assert_allclose(info_table["counts"], [124, 126, 119, 90])
info_table_cum = datasets.info_table(cumulative=True)
assert_allclose(info_table_cum["counts"], [124, 250, 369, 459])
assert stacked.name == "stacked"
for idx in range(n_obs):
dataset = maker.run(dataset_empty, obs)
# Set the model on the dataset, and fake the counts
dataset.models = model
dataset.fake(random_state=idx)
# Set off regions
dataset_on_off = SpectrumDatasetOnOff.from_spectrum_dataset(
dataset=dataset, acceptance=1, acceptance_off=3)
dataset_on_off.fake(random_state=idx,
npred_background=dataset.npred_background())
dataset_fake = dataset_on_off.copy(name=f"obs-{idx}")
dataset_fake.meta_table["OBS_ID"] = [idx]
datasets.append(dataset_fake)
# Save the data from the simulations
table = datasets.info_table()
table.write('observations.txt', format='ascii')
# Check counts in one selected realization
fig_2 = plt.figure(1)
datasets[0].npred().plot_hist(label='Predicted S+B')
datasets[0].npred_signal().plot_hist(label='Predicted S')
datasets[0].npred_background().plot_hist(label='Predicted B')
plt.legend()
form = plt.FormatStrFormatter('$%g$')
gca().xaxis.set_major_formatter(form)
plt.close(fig_2)
fig_2.savefig('obs_counts_one_simu.png', quality=95, dpi=1000)
# # Check consistency in the sample of observations
