MapDatasetEventSampler

class gammapy.datasets.MapDatasetEventSampler(random_state='random-seed', oversample_energy_factor=10, t_delta=<Quantity 0.5 s>, keep_mc_id=True, n_event_bunch=10000)

Bases: object

Sample events from a map dataset.

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.

t_deltaQuantity, optional

Time interval used to sample the time-dependent source.

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.

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(dataset[, observation])	Run the event sampler, applying IRF corrections.
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[, psf_update])	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(dataset, observation=None)

Run the event sampler, applying IRF corrections.

Parameters:

datasetMapDataset

Map dataset.

observationObservation, optional

In memory observation. Default is None.

Returns:

eventsEventList

Event list.

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, psf_update=False)

Sample source model components.

Parameters:

datasetMapDataset

Map dataset.

psf_updatebool

Parameter to switch-off (on) the update of the PSF in the dataset; default is False.

Returns:

eventsEventList

Event list.