SpectrumDataset¶

class gammapy.spectrum.SpectrumDataset(model, counts=None, livetime=None, mask=None, aeff=None, edisp=None, background=None)[source]¶

Bases: gammapy.utils.fitting.Dataset

Compute spectral model fit statistic on a CountsSpectrum.

Parameters:	model : `SpectralModel` Fit model counts : `CountsSpectrum` Counts spectrum livetime : float Livetime mask : `ndarray` Mask to apply to the likelihood. aeff : `EffectiveAreaTable` Effective area edisp : `EnergyDispersion` Energy dispersion background : `CountsSpectrum` Background to use for the fit.

Attributes Summary

data_shape Shape of the counts data

Methods Summary

`fake`([random_state])	Simulate a fake `CountsSpectrum`.
`likelihood`(parameters[, mask])	Total likelihood given the current model parameters.
`likelihood_per_bin`()	Likelihood per bin given the current model parameters
`npred`()	Returns npred map (model + background)

Attributes Documentation

data_shape¶: Shape of the counts data

Methods Documentation

fake(random_state='random-seed')[source]¶

Simulate a fake CountsSpectrum.

Parameters:	random_state : {int, ‘random-seed’, ‘global-rng’, `RandomState`} Defines random number generator initialisation. Passed to `get_random_state`.
Returns:	spectrum : `CountsSpectrum` the fake count spectrum

likelihood(parameters, mask=None)[source]¶

Total likelihood given the current model parameters.

Parameters:	mask : `ndarray` Mask to be combined with the dataset mask.

likelihood_per_bin()[source]¶: Likelihood per bin given the current model parameters

npred()[source]¶: Returns npred map (model + background)