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

covariance

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__(lon, lat)

Call evaluate method

copy()

A deep copy.

create(tag[, model_type])

Create a model instance.

evaluate(lon, lat, lon_0, lat_0, sigma, e, phi)

Evaluate model.

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

to_dict()

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)

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

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)

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

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 = ['GaussianSpatialModel', 'gauss']

Methods Documentation

__call__(lon, lat)

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, sigma, e, phi)[source]

Evaluate model.

evaluate_geom(geom)

Evaluate model on 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

to_dict()

Create dict for YAML serilisation

to_region(**kwargs)[source]

Model outline (EllipseSkyRegion).