ObservationsEventsSampler

class gammapy.data.ObservationsEventsSampler

Bases: ParallelMixin

Run event sampling for an ensemble of observations.

Parameters:

sampler_kwargsdict, optional

Arguments passed to MapDatasetEventSampler.

dataset_kwargsdict, optional

Arguments passed to create_map_dataset_from_observation().

outdirstr, optional

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

overwritebool, optional

Overwrite existing file. Default is True.

n_jobsint, optional

Number of processes to run in parallel. By default, the value is 1, unless N_JOBS_DEFAULT has been 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 observations.
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 observations.

Parameters:

observationsObservations

Observations 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.

__init__(sampler_kwargs=None, dataset_kwargs=None, outdir='./simulated_data/', overwrite=True, n_jobs=None, parallel_backend=None)

classmethod __new__(*args, **kwargs)