def run(self, dataset, observation=None):
"""Run the event sampler, applying IRF corrections.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Map dataset
observation : `~gammapy.data.Observation`
In memory observation.
edisp : Bool
It allows to include or exclude the Edisp in the simulation.
Returns
-------
events : `~gammapy.data.EventList`
Event list.
"""
if len(dataset.models) > 1:
events_src = self.sample_sources(dataset)
if len(events_src.table) > 0:
if dataset.psf:
events_src = self.sample_psf(dataset.psf, events_src)
else:
events_src.table["RA"] = events_src.table["RA_TRUE"]
events_src.table["DEC"] = events_src.table["DEC_TRUE"]
if dataset.edisp:
events_src = self.sample_edisp(dataset.edisp, events_src)
else:
events_src.table["ENERGY"] = events_src.table[
"ENERGY_TRUE"]
if dataset.background:
events_bkg = self.sample_background(dataset)
events = EventList.from_stack([events_bkg, events_src])
else:
events = events_src
if len(dataset.models) == 1 and dataset.background_model is not None:
events_bkg = self.sample_background(dataset)
events = EventList.from_stack([events_bkg])
events = self.event_det_coords(observation, events)
events.table["EVENT_ID"] = np.arange(len(events.table))
events.table.meta = self.event_list_meta(dataset, observation)
geom = dataset._geom
selection = geom.contains(events.map_coord(geom))
return events.select_row_subset(selection)
def _make_counts(dataset, observation, phases):
event_lists = []
for interval in phases:
events = observation.events.select_parameter(parameter="PHASE",
band=interval)
event_lists.append(events)
events = EventList.from_stack(event_lists)
counts = RegionNDMap.from_geom(dataset.counts.geom)
counts.fill_events(events)
return counts
def sample_sources(self, dataset):
"""Sample source model components.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Map dataset.
Returns
-------
events : `~gammapy.data.EventList`
Event list
"""
events_all = []
for idx, evaluator in enumerate(dataset.evaluators.values()):
if evaluator.needs_update:
evaluator.update(
dataset.exposure,
dataset.psf,
dataset.edisp,
dataset._geom,
dataset.mask,
)
flux = evaluator.compute_flux()
npred = evaluator.apply_exposure(flux)
if evaluator.model.temporal_model is None:
temporal_model = ConstantTemporalModel()
else:
temporal_model = evaluator.model.temporal_model
table = self._sample_coord_time(npred, temporal_model, dataset.gti)
if len(table) > 0:
table["MC_ID"] = idx + 1
else:
mcid = table.Column(name="MC_ID", length=0, dtype=int)
table.add_column(mcid)
events_all.append(EventList(table))
return EventList.from_stack(events_all)
def sample_sources(self, dataset):
"""Sample source model components.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Map dataset.
Returns
-------
events : `~gammapy.data.EventList`
Event list
"""
events_all = []
for idx, model in enumerate(dataset.models):
if isinstance(model, BackgroundModel):
continue
evaluator = dataset.evaluators.get(model)
flux = evaluator.compute_flux()
npred = evaluator.apply_exposure(flux)
if model.temporal_model is None:
temporal_model = ConstantTemporalModel()
else:
temporal_model = model.temporal_model
table = self._sample_coord_time(npred, temporal_model, dataset.gti)
if len(table) > 0:
table["MC_ID"] = idx + 1
else:
mcid = table.Column(name="MC_ID", length=0, dtype=int)
table.add_column(mcid)
events_all.append(EventList(table))
return EventList.from_stack(events_all)
def test_from_stack(self):
event_lists = [self.events] * 2
stacked_list = EventList.from_stack(event_lists)
assert len(stacked_list.table) == 11243 * 2
