BackgroundModel

class gammapy.modeling.models.BackgroundModel(map, norm=<Quantity 1.>, tilt=<Quantity 0.>, reference=<Quantity 1. TeV>, name=None, filename=None, datasets_names=None)[source]

Bases: gammapy.modeling.models.core.Model

Background model.

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

Parameters
mapMap

Background model map

normfloat

Background normalization

tiltfloat

Additional tilt in the spectrum

referenceQuantity

Reference energy of the tilt.

Attributes Summary

covariance

default_parameters

energy_center

True energy axis bin centers (Quantity)

evaluation_radius

Angle

map

A lazy FITS data descriptor.

name

norm

A model parameter.

parameters

Parameters (Parameters)

position

SkyCoord

reference

A model parameter.

tag

tilt

A model parameter.

Methods Summary

copy([name])

A deep copy.

create(tag[, model_type])

Create a model instance.

cutout(position, width[, mode, name])

Cutout background model.

evaluate()

Evaluate background model.

from_dict(data)

from_parameters(parameters, **kwargs)

Create model from parameter list

stack(other[, weights])

Stack background model in place.

to_dict()

Create dict for YAML serialisation

Attributes Documentation

covariance
default_parameters = <gammapy.modeling.parameter.Parameters object>
energy_center

True energy axis bin centers (Quantity)

evaluation_radius

Angle

map

A lazy FITS data descriptor.

Parameters
cachebool

Whether to cache the data.

name
norm

A model parameter.

Note that the parameter value has been split into a factor and scale like this:

value = factor x scale

Users should interact with the value, quantity or min and max properties and consider the fact that there is a factor` and scale an implementation detail.

That was introduced for numerical stability in parameter and error estimation methods, only in the Gammapy optimiser interface do we interact with the factor, factor_min and factor_max properties, i.e. the optimiser “sees” the well-scaled problem.

Parameters
namestr

Name

valuefloat or Quantity

Value

scalefloat, optional

Scale (sometimes used in fitting)

unitUnit or str, optional

Unit

minfloat, optional

Minimum (sometimes used in fitting)

maxfloat, optional

Maximum (sometimes used in fitting)

frozenbool, optional

Frozen? (used in fitting)

parameters

Parameters (Parameters)

position

SkyCoord

reference

A model parameter.

Note that the parameter value has been split into a factor and scale like this:

value = factor x scale

Users should interact with the value, quantity or min and max properties and consider the fact that there is a factor` and scale an implementation detail.

That was introduced for numerical stability in parameter and error estimation methods, only in the Gammapy optimiser interface do we interact with the factor, factor_min and factor_max properties, i.e. the optimiser “sees” the well-scaled problem.

Parameters
namestr

Name

valuefloat or Quantity

Value

scalefloat, optional

Scale (sometimes used in fitting)

unitUnit or str, optional

Unit

minfloat, optional

Minimum (sometimes used in fitting)

maxfloat, optional

Maximum (sometimes used in fitting)

frozenbool, optional

Frozen? (used in fitting)

tag = 'BackgroundModel'
tilt

A model parameter.

Note that the parameter value has been split into a factor and scale like this:

value = factor x scale

Users should interact with the value, quantity or min and max properties and consider the fact that there is a factor` and scale an implementation detail.

That was introduced for numerical stability in parameter and error estimation methods, only in the Gammapy optimiser interface do we interact with the factor, factor_min and factor_max properties, i.e. the optimiser “sees” the well-scaled problem.

Parameters
namestr

Name

valuefloat or Quantity

Value

scalefloat, optional

Scale (sometimes used in fitting)

unitUnit or str, optional

Unit

minfloat, optional

Minimum (sometimes used in fitting)

maxfloat, optional

Maximum (sometimes used in fitting)

frozenbool, optional

Frozen? (used in fitting)

Methods Documentation

copy(name=None)[source]

A deep copy.

static create(tag, model_type=None, *args, **kwargs)

Create a model instance.

Examples

>>> from gammapy.modeling.models import Model
>>> spectral_model = Model.create("pl-2", model_type="spectral", amplitude="1e-10 cm-2 s-1", index=3)
>>> type(spectral_model)
gammapy.modeling.models.spectral.PowerLaw2SpectralModel
cutout(position, width, mode='trim', name=None)[source]

Cutout background model.

Parameters
positionSkyCoord

Center position of the cutout region.

widthtuple of Angle

Angular sizes of the region in (lon, lat) in that specific order. If only one value is passed, a square region is extracted.

mode{‘trim’, ‘partial’, ‘strict’}

Mode option for Cutout2D, for details see Cutout2D.

namestr

Name of the returned background model.

Returns
cutoutBackgroundModel

Cutout background model.

evaluate()[source]

Evaluate background model.

Returns
background_mapMap

Background evaluated on the Map

classmethod from_dict(data)[source]
classmethod from_parameters(parameters, **kwargs)

Create model from parameter list

Parameters
parametersParameters

Parameters for init

Returns
modelModel

Model instance

stack(other, weights=None)[source]

Stack background model in place.

Stacking the background model resets the current parameters values.

Parameters
otherBackgroundModel

Other background model.

to_dict()[source]

Create dict for YAML serialisation