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, Laplace: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_bin_size_min`	Minimal evaluation bin size (`Angle`).
`evaluation_radius`	Evaluation radius (`Angle`).
`evaluation_region`	Evaluation region consistent with evaluation radius
`frozen`	Frozen status of a model, True if all parameters are frozen
`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 (`SkyCoord`)
`position_error`	Get 95% containment position error as (`EllipseSkyRegion`)
`position_lonlat`	Spatial model center position `(lon, lat)` in rad and frame of the model
`r_0`	A model parameter.
`tag`
`type`

Methods Summary

`__call__`(lon, lat[, energy])	Call evaluate method
`copy`()	A deep copy.
`evaluate`(lon, lat, lon_0, lat_0, r_0, eta, …)
`evaluate_geom`(geom)	Evaluate model on `Geom`
`freeze`()	Freeze all parameters
`from_dict`(data)
`from_parameters`(parameters, **kwargs)	Create model from parameter list
`from_position`(position, **kwargs)	Define the position of the model using a sky coord
`integrate_geom`(geom[, oversampling_factor])	Integrate model on `Geom` or `RegionGeom`.
`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.
`reassign`(datasets_names, new_datasets_names)	Reassign a model from one dataset to another
`to_dict`([full_output])	Create dict for YAML serilisation
`to_region`([x_r_0])	Model outline at a given number of r_0.
`unfreeze`()	Restore parameters frozen status to default

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)
errorfloat: Parameter error
scan_minfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_maxfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_n_values: int: Number of values to be used for the parameter scan.
scan_n_sigmaint: Number of sigmas to scan.
scan_values: `numpy.array`: Scan values. Overwrites all of the scan keywords before.
scale_method{‘scale10’, ‘factor1’, None}: Method used to set factor and scale
interp{“lin”, “sqrt”, “log”}: Parameter scaling to use for the scan.

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)
errorfloat: Parameter error
scan_minfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_maxfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_n_values: int: Number of values to be used for the parameter scan.
scan_n_sigmaint: Number of sigmas to scan.
scan_values: `numpy.array`: Scan values. Overwrites all of the scan keywords before.
scale_method{‘scale10’, ‘factor1’, None}: Method used to set factor and scale
interp{“lin”, “sqrt”, “log”}: Parameter scaling to use for the scan.

evaluation_bin_size_min¶

Minimal evaluation bin size (Angle).

The bin min size is defined as r_0/(3+8*eta)/(e+1).

evaluation_radius¶: Evaluation radius (Angle). The evaluation radius is defined as r_eval = r_0*(1+8*eta) so it verifies: r_eval -> r_0 if eta -> 0 r_eval = 5*r_0 > 5*sigma_gauss = 5*r_0/sqrt(2) ~ 3.5*r_0 if eta=0.5 r_eval = 9*r_0 > 5*sigma_laplace = 5*sqrt(2)*r_0 ~ 7*r_0 if eta = 1 r_eval -> inf if eta -> inf

evaluation_region¶: Evaluation region consistent with evaluation radius

frozen¶: Frozen status of a model, True if all parameters are frozen

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)
errorfloat: Parameter error
scan_minfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_maxfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_n_values: int: Number of values to be used for the parameter scan.
scan_n_sigmaint: Number of sigmas to scan.
scan_values: `numpy.array`: Scan values. Overwrites all of the scan keywords before.
scale_method{‘scale10’, ‘factor1’, None}: Method used to set factor and scale
interp{“lin”, “sqrt”, “log”}: Parameter scaling to use for the scan.

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)
errorfloat: Parameter error
scan_minfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_maxfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_n_values: int: Number of values to be used for the parameter scan.
scan_n_sigmaint: Number of sigmas to scan.
scan_values: `numpy.array`: Scan values. Overwrites all of the scan keywords before.
scale_method{‘scale10’, ‘factor1’, None}: Method used to set factor and scale
interp{“lin”, “sqrt”, “log”}: Parameter scaling to use for the scan.

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)
errorfloat: Parameter error
scan_minfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_maxfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_n_values: int: Number of values to be used for the parameter scan.
scan_n_sigmaint: Number of sigmas to scan.
scan_values: `numpy.array`: Scan values. Overwrites all of the scan keywords before.
scale_method{‘scale10’, ‘factor1’, None}: Method used to set factor and scale
interp{“lin”, “sqrt”, “log”}: Parameter scaling to use for the scan.

phi_0¶

position¶: Spatial model center position (SkyCoord)

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

position_lonlat¶: Spatial model center position (lon, lat) in rad and frame of the model

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)
errorfloat: Parameter error
scan_minfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_maxfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_n_values: int: Number of values to be used for the parameter scan.
scan_n_sigmaint: Number of sigmas to scan.
scan_values: `numpy.array`: Scan values. Overwrites all of the scan keywords before.
scale_method{‘scale10’, ‘factor1’, None}: Method used to set factor and scale
interp{“lin”, “sqrt”, “log”}: Parameter scaling to use for the scan.

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

type¶

Methods Documentation

__call__(lon, lat, energy=None)¶: Call evaluate method

copy()¶: A deep copy.

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

evaluate_geom(geom)¶

Evaluate model on Geom

Parameters

geomWcsGeom

Returns

Map

freeze()¶: Freeze all parameters

classmethod from_dict(data)¶

classmethod from_parameters(parameters, **kwargs)¶

Create model from parameter list

Parameters

parametersParameters: Parameters for init

Returns

modelModel: Model instance

classmethod from_position(position, **kwargs)¶

Define the position of the model using a sky coord

Parameters

positionSkyCoord: Position

Returns

modelSpatialModel: Spatial model

integrate_geom(geom, oversampling_factor=None)¶

Integrate model on Geom or RegionGeom.

Integration is performed by simple rectangle approximation, the pixel center model value is multiplied by the pixel solid angle. An oversampling factor can be used for precision. By default, this parameter is set to None and an oversampling factor is automatically estimated based on the model estimation maximal bin width.

For a RegionGeom, the model is integrated on a tangent WCS projection in the region.

Parameters

geomWcsGeom or RegionGeom: The geom on which the integration is performed
oversampling_factorint or None: The oversampling factor to use for integration. Default is None: the factor is estimated from the model minimimal bin size

Returns

Map or gammapy.maps.RegionNDMap, containing: the integral value in each spatial bin.

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

reassign(datasets_names, new_datasets_names)¶

Reassign a model from one dataset to another

Parameters

datasets_namesstr or list: Name of the datasets where the model is currently defined
new_datasets_namesstr or list: Name of the datasets where the model should be defined instead. If multiple names are given the two list must have the save length, as the reassignment is element-wise.

Returns

modelModel: Reassigned model.

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

to_region(x_r_0=1, **kwargs)[source]¶

Model outline at a given number of r_0.

Parameters

x_r_0float: Number of r_0 (Default is 1).

Returns

regionEllipseSkyRegion: Model outline.

unfreeze()¶: Restore parameters frozen status to default