SpectrumDataset¶

class gammapy.spectrum.SpectrumDataset(model=None, counts=None, livetime=None, mask_fit=None, aeff=None, edisp=None, background=None, mask_safe=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_fit : ndarray: Mask to apply to the likelihood for fitting.
aeff : EffectiveAreaTable: Effective area
edisp : EnergyDispersion: Energy dispersion
background : CountsSpectrum: Background to use for the fit.
mask_safe : ndarray: Mask defining the safe data range.

Attributes Summary

`data_shape`	Shape of the counts data
`energy_range`	Energy range defined by the safe mask
`mask`	Combined fit and safe mask
`model`
`parameters`

Methods Summary

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

Attributes Documentation

data_shape¶: Shape of the counts data

energy_range¶: Energy range defined by the safe mask

mask¶: Combined fit and safe mask

model¶

parameters¶

Methods Documentation

copy(self)¶: A deep copy.

fake(self, 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(self)¶: Total likelihood given the current model parameters.

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

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