ObservationEventSampler#

class gammapy.datasets.ObservationEventSampler(random_state='random-seed', oversample_energy_factor=10, t_delta=<Quantity 0.5 s>, keep_mc_id=True, n_event_bunch=10000, dataset_kwargs=None)[source]#

Bases: gammapy.datasets.simulate.MapDatasetEventSampler

Sample event lists for a given observation and signal models.

Signal events are sampled from the predicted counts distribution given by the product of the sky models and the expected exposure. They are then folded with the instrument response functions. To improve performance, IRFs are evaluated on a pre-defined binning, not at each individual event energy / coordinate.

Parameters

random_state{int, ‘random-seed’, ‘global-rng’, RandomState}, optional: Defines random number generator initialisation via the get_random_state function.
oversample_energy_factorint, optional: Defines an oversampling factor for the energies; it is used only when sampling an energy-dependent time-varying source. Default is 10.
t_deltaQuantity, optional: Time interval used to sample the time-dependent source. Default is 0.5 s.
keep_mc_idbool, optional: Flag to tag sampled events from a given model with a Montecarlo identifier. Default is True. If set to False, no identifier will be assigned.
n_event_bunchint: Size of events bunches to sample. If None, sample all events in memory. Default is 10000.
dataset_kwargsdict, optional: Arguments passed to create_map_dataset_from_observation()

Methods Summary

`event_det_coords`(observation, events)	Add columns of detector coordinates (DETX-DETY) to the event list.
`event_list_meta`(dataset, observation[, ...])	Event list meta info.
`run`(observation[, models, dataset_name])	Sample events for given observation and signal models.
`sample_background`(dataset)	Sample background.
`sample_edisp`(edisp_map, events)	Sample energy dispersion map.
`sample_psf`(psf_map, events)	Sample PSF map.
`sample_sources`(dataset)	Sample source model components.

Methods Documentation

static event_det_coords(observation, events)#

Add columns of detector coordinates (DETX-DETY) to the event list.

Parameters

observationObservation: In memory observation.
eventsEventList: Event list.

Returns

eventsEventList: Event list with columns of event detector coordinates.

static event_list_meta(dataset, observation, keep_mc_id=True)#

Event list meta info. Please, note that this function will be updated in the future.

Parameters

datasetMapDataset: Map dataset.
observationObservation: In memory observation.
keep_mc_idbool: Flag to tag sampled events from a given model with a Montecarlo identifier. Default is True. If set to False, no identifier will be assigned.

Returns

metadict: Meta dictionary.

run(observation, models=None, dataset_name=None)[source]#

Sample events for given observation and signal models.

The signal distribution is sampled from the given models in true coordinates and energy. The true quantities are then folded with the IRFs to obtain the observable quantities.

Parameters

observationObservation: Observation to be simulated.
modelsModels, optional: Models to simulate. Can be None to only sample background events. Default is None.
dataset_namestr, optional: If models contains one or multiple FoVBackgroundModel it should match the dataset_name of the background model to use. Default is None.
Returns
——-
observationObservation: A copy of the input observation with event list filled.

sample_background(dataset)#

Sample background.

Parameters

datasetMapDataset: Map dataset.

Returns

eventsgammapy.data.EventList: Background events.

sample_edisp(edisp_map, events)#

Sample energy dispersion map.

Parameters

edisp_mapEDispMap: Energy dispersion map.
eventsEventList: Event list with the true energies.

Returns

eventsEventList: Event list with reconstructed energy column.

sample_psf(psf_map, events)#

Sample PSF map.

Parameters

psf_mapPSFMap: PSF map.
eventsEventList: Event list.

Returns

eventsEventList: Event list with reconstructed position columns.

sample_sources(dataset)#

Sample source model components.

Parameters

datasetMapDataset: Map dataset.

Returns

eventsEventList: Event list.