PointSpatialModel¶
-
class
gammapy.modeling.models.
PointSpatialModel
(**kwargs)[source]¶ Bases:
gammapy.modeling.models.SpatialModel
Point Source.
For more information see Point Spatial Model.
- Parameters
- lon_0, lat_0
Angle
Center position
- frame{“icrs”, “galactic”}
Center position coordinate frame
- lon_0, lat_0
Attributes Summary
Evaluation radius (
Angle
).A model parameter.
A model parameter.
Parameters (
Parameters
)Spatial model center position
Get 95% containment position error as (
EllipseSkyRegion
)Methods Summary
__call__
(lon, lat[, energy])Call evaluate method
copy
()A deep copy.
create
(tag[, model_type])Create a model instance.
evaluate_geom
(geom)Evaluate model on
Geom
.from_dict
(data)from_parameters
(parameters, **kwargs)Create model from parameter list
integrate_geom
(geom)Integrate model on
Geom
plot
([ax, geom])Plot spatial model.
plot_error
([ax])Plot position error
plot_grid
([geom])Plot spatial model energy slices in a grid.
plot_interative
([ax, geom])Plot spatial model.
to_dict
([full_output])Create dict for YAML serilisation
to_region
(**kwargs)Model outline (
PointSkyRegion
).Attributes Documentation
-
covariance
¶
-
default_parameters
= <gammapy.modeling.parameter.Parameters object>¶
-
is_energy_dependent
= False¶
-
lat_0
¶ 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.
-
lon_0
¶ 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
)
-
phi_0
¶
-
position
¶ Spatial model center position
-
position_error
¶ Get 95% containment position error as (
EllipseSkyRegion
)
-
tag
= ['PointSpatialModel', 'point']¶
-
type
¶
Methods Documentation
-
__call__
(lon, lat, energy=None)¶ Call evaluate method
-
copy
()¶ 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
-
classmethod
from_dict
(data)¶
-
classmethod
from_parameters
(parameters, **kwargs)¶ Create model from parameter list
- Parameters
- parameters
Parameters
Parameters for init
- parameters
- Returns
- model
Model
Model instance
- model
-
integrate_geom
(geom)[source]¶ Integrate model on
Geom
- Parameters
- geom
Geom
Map geometry
- geom
- Returns
- flux
Map
Predicted flux map
- flux
-
plot
(ax=None, geom=None, **kwargs)¶ Plot spatial model.
-
plot_error
(ax=None, **kwargs)¶ Plot position error
-
plot_grid
(geom=None, **kwargs)¶ Plot spatial model energy slices in a grid.
-
plot_interative
(ax=None, geom=None, **kwargs)¶ Plot spatial model.
-
to_dict
(full_output=False)¶ Create dict for YAML serilisation
-
to_region
(**kwargs)[source]¶ Model outline (
PointSkyRegion
).