GaussianSpatialModel¶

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

Bases: gammapy.modeling.models.SpatialModel

Two-dimensional Gaussian model.

For more information see Gaussian Spatial Model.

Parameters

lon_0, lat_0Angle: Center position
sigmaAngle: Length of the major semiaxis of the Gaussian, in angular units.
efloat: Eccentricity of the Gaussian (\(0< e< 1\)).
phiAngle: Rotation angle \(\phi\): of the major semiaxis. Increases counter-clockwise from the North direction.
frame{“icrs”, “galactic”}: Center position coordinate frame

Attributes Summary

`default_parameters`
`e`	A model parameter.
`evaluation_radius`	Evaluation radius (`Angle`).
`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`)
`sigma`	A model parameter.
`tag`

Methods Summary

`__call__`(self, lon, lat)	Call evaluate method
`copy`(self)	A deep copy.
`create`(tag, \args, \\*kwargs)	Create a model instance.
`evaluate`(lon, lat, lon_0, lat_0, sigma, e, phi)	Evaluate model.
`evaluate_geom`(self, geom)	Evaluate model on `Geom`.
`from_dict`(data)
`plot`(self[, ax, geom])	Plot spatial model.
`to_dict`(self)	Create dict for YAML serilisation
`to_region`(self, \\kwargs)	Model outline (`EllipseSkyRegion`).

Attributes Documentation

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
factorfloat or Quantity: Factor
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 \(5\sigma\).

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
factorfloat or Quantity: Factor
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
factorfloat or Quantity: Factor
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
factorfloat or Quantity: Factor
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)

sigma¶

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
factorfloat or Quantity: Factor
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 = 'GaussianSpatialModel'¶

Methods Documentation

__call__(self, lon, lat)¶: Call evaluate method

copy(self)¶: A deep copy.

static create(tag, *args, **kwargs)¶

Create a model instance.

Examples

>>> from gammapy.modeling import Model
>>> spectral_model = Model.create("PowerLaw2SpectralModel", 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, sigma, e, phi)[source]¶: Evaluate model.

evaluate_geom(self, geom)¶: Evaluate model on Geom.

classmethod from_dict(data)¶

plot(self, 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(self)¶: Create dict for YAML serilisation

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