BackgroundModel¶

class gammapy.cube.models.BackgroundModel(background, norm=1, tilt=0, reference='1 TeV', name='background', filename=None, obs_id=None)[source]¶

Bases: gammapy.utils.fitting.Model

Background model.

Create a new map by a tilt and normalisation on the available map

Parameters:	background : `Map` Background model map norm : float Background normalisation tilt : float Additional tilt in the spectrum reference : `Quantity` Reference energy of the tilt.

Attributes Summary

`energy_center`	True energy axis bin centers (`Quantity`)
`filename`
`map`
`name`
`norm`
`obs_id`
`parameters`	Parameters (`Parameters`)
`reference`
`tilt`

Methods Summary

`copy`(self)	A deep copy.
`evaluate`(self)	Evaluate background model.
`from_skymodel`(skymodel, exposure[, edisp, psf])	Create background model from sky model by applying IRFs.
`to_dict`(self[, selection])

Attributes Documentation

energy_center¶: True energy axis bin centers (Quantity)

filename¶

map¶

name¶

norm¶

obs_id¶

parameters¶: Parameters (Parameters)

reference¶

tilt¶

Methods Documentation

copy(self)¶: A deep copy.

evaluate(self)[source]¶

Evaluate background model.

Returns:	background_map : `Map` Background evaluated on the Map

classmethod from_skymodel(skymodel, exposure, edisp=None, psf=None, **kwargs)[source]¶

Create background model from sky model by applying IRFs.

Typically used for diffuse Galactic or constant emission models.

Parameters:	skymodel : `SkyModel` or `SkyDiffuseCube` Sky model exposure : `Map` Exposure map edisp : `EnergyDispersion` Energy dispersion psf : `PSFKernel` PSF kernel

to_dict(self, selection='all')¶