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
Attributes Summary
True energy axis bin centers (
Quantity
)A model parameter.
Parameters (
Parameters
)A model parameter.
A model parameter.
Methods Summary
copy
(self[, name])A deep copy.
create
(tag, \*args, \*\*kwargs)Create a model instance.
cutout
(self, position, width[, mode, name])Cutout background model.
evaluate
(self)Evaluate background model.
from_dict
(data)from_parameters
(parameters, \*\*kwargs)Create model from parameter list
stack
(self, other[, weights])Stack background model in place.
to_dict
(self)Create dict for YAML serialisation
Attributes Documentation
-
covariance
¶
-
default_parameters
= <gammapy.modeling.parameter.Parameters object>¶
-
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
ormin
andmax
properties and consider the fact that there is afactor`
andscale
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
andfactor_max
properties, i.e. the optimiser “sees” the well-scaled problem.
-
parameters
¶ Parameters (
Parameters
)
-
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
ormin
andmax
properties and consider the fact that there is afactor`
andscale
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
andfactor_max
properties, i.e. the optimiser “sees” the well-scaled problem.
-
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
ormin
andmax
properties and consider the fact that there is afactor`
andscale
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
andfactor_max
properties, i.e. the optimiser “sees” the well-scaled problem.
Methods Documentation
-
static
create
(tag, *args, **kwargs)¶ Create a model instance.
Examples
>>> from gammapy.modeling.models import Model >>> spectral_model = Model.create("PowerLaw2SpectralModel", amplitude="1e-10 cm-2 s-1", index=3) >>> type(spectral_model) gammapy.modeling.models.spectral.PowerLaw2SpectralModel
-
cutout
(self, position, width, mode='trim', name=None)[source]¶ Cutout background model.
- Parameters
- position
SkyCoord
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.
- position
- Returns
- cutout
BackgroundModel
Cutout background model.
- cutout
-
evaluate
(self)[source]¶ Evaluate background model.
- Returns
- background_map
Map
Background evaluated on the Map
- background_map
-
classmethod
from_parameters
(parameters, **kwargs)¶ Create model from parameter list
- Parameters
- parameters
Parameters
Parameters for init
- parameters
- Returns
- model
Model
Model instance
- model
-
stack
(self, other, weights=None)[source]¶ Stack background model in place.
Stacking the background model resets the current parameters values.
- Parameters
- other
BackgroundModel
Other background model.
- other