ObservationsEventsSampler

class gammapy.data.ObservationsEventsSampler(sampler_kwargs=None, dataset_kwargs=None, outdir='./simulated_data/', overwrite=True, n_jobs=None, parallel_backend=None)

Bases: gammapy.utils.parallel.ParallelMixin

Run event sampling for an emsemble of observations

Parameters

sampler_kwargsdict, optional

Arguments passed to MapDatasetEventSampler.

dataset_kwargsdict, optional

Arguments passed to create_map_dataset_from_observation().

outdirstr, Path

path of the output files created. Default is “./simulated_data/”. If None a list of Observation is returned.

overwritebool

Overwrite the output files or not

n_jobsint, optional

Number of processes to run in parallel. Default is one, unless N_JOBS_DEFAULT was modified.

parallel_backend{‘multiprocessing’, ‘ray’}, optional

Which backend to use for multiprocessing. Default is None.

Attributes Summary

n_jobs	Number of jobs as an integer.
parallel_backend	Parallel backend as a string.

Methods Summary

run(observations[, models])	Run event sampling for an ensemble of onservations
simulate_observation(observation[, models])	Simulate a single observation.

Attributes Documentation

n_jobs

Number of jobs as an integer.

parallel_backend

Parallel backend as a string.

Methods Documentation

run(observations, models=None)

Run event sampling for an ensemble of onservations

Parameters

observationObservation

Observation to be simulated.

modelsModels, optional

Models to simulate. Can be None to only sample background events. Default is None.

simulate_observation(observation, models=None)

Simulate a single observation.

Parameters

observationObservation

Observation to be simulated.

modelsModels, optional

Models to simulate. Can be None to only sample background events. Default is None.