GeneralizedGaussianSpatialModel¶

class gammapy.modeling.models.GeneralizedGaussianSpatialModel(**kwargs)[source]¶

Bases: gammapy.modeling.models.SpatialModel

Two-dimensional Generealized Gaussian model.

For more information see Generalized Gaussian Spatial Model.

Parameters

lon_0, lat_0Angle: Center position
r_0Angle: Length of the major semiaxis, in angular units.
etafloat: Shape parameter whitin (0, 1]. Special cases for disk: ->0, Gaussian: 0.5, Laplacian:1
efloat: Eccentricity (\(0< e< 1\)).
phiAngle: Rotation angle \(\phi\): of the major semiaxis. Increases counter-clockwise from the North direction.
frame{“icrs”, “galactic”}: Center position coordinate frame

Attributes Summary

`covariance`
`default_parameters`
`e`	A model parameter.
`eta`	A model parameter.
`evaluation_radius`	Evaluation radius (`Angle`).
`is_energy_dependent`
`lat_0`	A model parameter.
`lon_0`	A model parameter.
`parameters`	Parameters (`Parameters`)
`phi`	A model parameter.
`phi_0`
`position`	Spatial model center position
`position_error`	Get 95% containment position error as (`EllipseSkyRegion`)
`r_0`	A model parameter.
`tag`
`type`

Methods Summary

`__call__`(lon, lat[, energy])	Call evaluate method
`copy`()	A deep copy.
`create`(tag[, model_type])	Create a model instance.
`evaluate`(lon, lat, lon_0, lat_0, r_0, eta, …)
`evaluate_geom`(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 (`EllipseSkyRegion`).

Attributes Documentation

covariance¶

default_parameters = <gammapy.modeling.parameter.Parameters object>¶

e¶

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)

eta¶

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)

evaluation_radius¶

Evaluation radius (Angle).

Set as \(5 r_{\rm eff}\).

is_energy_dependent¶

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 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)

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 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)

phi¶

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)

phi_0¶

position¶: Spatial model center position

position_error¶: Get 95% containment position error as (EllipseSkyRegion)

r_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 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 = ['GeneralizedGaussianSpatialModel', 'gauss-general']¶

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

static evaluate(lon, lat, lon_0, lat_0, r_0, eta, e, phi)[source]¶

evaluate_geom(geom)¶

classmethod from_dict(data)¶

classmethod from_parameters(parameters, **kwargs)¶

Create model from parameter list

Parameters

parametersParameters: Parameters for init

Returns

modelModel: Model instance

integrate_geom(geom)¶: Integrate model on Geom.

plot(ax=None, geom=None, **kwargs)¶

Plot spatial model.

Parameters

axAxes, optional: Axis
geomWcsGeom, optional: Geom to use for plotting.
**kwargsdict: Keyword arguments passed to plot()

Returns

axAxes, optional: Axis

plot_error(ax=None, **kwargs)¶

Plot position error

Parameters

axAxes, optional: Axis
**kwargsdict: Keyword arguments passed to plot()

Returns

axAxes, optional: Axis

plot_grid(geom=None, **kwargs)¶

Plot spatial model energy slices in a grid.

Parameters

geomWcsGeom, optional: Geom to use for plotting.
**kwargsdict: Keyword arguments passed to plot()

Returns

axAxes, optional: Axis

plot_interative(ax=None, geom=None, **kwargs)¶

Plot spatial model.

Parameters

axAxes, optional: Axis
geomWcsGeom, optional: Geom to use for plotting.
**kwargsdict: Keyword arguments passed to plot()

Returns

axAxes, optional: Axis

to_dict(full_output=False)¶: Create dict for YAML serilisation

to_region(**kwargs)[source]¶: Model outline (EllipseSkyRegion).