SpectrumSimulation

class gammapy.spectrum.SpectrumSimulation(livetime, source_model, aeff=None, edisp=None, e_true=None, background_model=None, alpha=None)

Bases: object

Simulate SpectrumObservation.

For a usage example see spectrum_simulation.html

Parameters:

livetime : Quantity

Livetime

source_model : SpectralModel

Source model

aeff : EffectiveAreaTable, optional

Effective Area

edisp : EnergyDispersion, optional

Energy Dispersion

e_true : Quantity, optional

Desired energy axis of the prediced counts vector if no IRFs are given

background_model : SpectralModel, optional

Background model

alpha : float, optional

Exposure ratio between source and background

Attributes Summary

e_reco	Reconstructed energy binning.
npred_background	Predicted background (CountsSpectrum).
npred_source	Predicted source CountsSpectrum.

Methods Summary

reset(self)	Clear all results.
run(self, seed)	Simulate list of SpectrumDatasetOnOff.
simulate_background_counts(self, rand)	Simulate background PHACountsSpectrum.
simulate_obs(self, obs_id[, seed])	Simulate one SpectrumObservation.
simulate_source_counts(self, rand)	Simulate source PHACountsSpectrum.

Attributes Documentation

e_reco

Reconstructed energy binning.

npred_background

Predicted background (CountsSpectrum).

Calls gammapy.spectrum.utils.SpectrumEvaluator().

npred_source

Predicted source CountsSpectrum.

Calls gammapy.spectrum.utils.SpectrumEvaluator().

Methods Documentation

reset(self)

Clear all results.

run(self, seed)

Simulate list of SpectrumDatasetOnOff.

The seeds will be set as observation ID. Previously produced results will be overwritten.

Parameters:

seed : array of ints

Random number generator seeds

simulate_background_counts(self, rand)

Simulate background PHACountsSpectrum.

Background counts are added to the on vector. Furthermore background counts divided by alpha are added to the off vector.

TODO: At the moment the source IRFs are used. Make it possible to pass dedicated background IRFs.

Parameters:

rand : RandomState

random state

simulate_obs(self, obs_id, seed='random-seed')

Simulate one SpectrumObservation.

The result is stored as obs attribute

Parameters:

obs_id : int

Observation identifier

seed : {int, ‘random-seed’, ‘global-rng’, RandomState}

see :func:~`gammapy.utils.random.get_random_state`

simulate_source_counts(self, rand)

Simulate source PHACountsSpectrum.

Source counts are added to the on vector.

Parameters:

rand : RandomState

random state