GeneralizedGaussianSpatialModel#

class gammapy.modeling.models.GeneralizedGaussianSpatialModel[source]#

Bases: SpatialModel

Two-dimensional Generalized Gaussian model.

For more information see Generalized gaussian spatial model.

Parameters:

lon_0, lat_0Angle: Center position. Default is “0 deg”, “0 deg”.
r_0Angle: Length of the major semiaxis, in angular units. Default is 1 deg.
etafloat: Shape parameter within (0, 1). Special cases for disk: ->0, Gaussian: 0.5, Laplace:1 Default is 0.5.
efloat: Eccentricity (\(0<= e< =1\)). Default is 0.
phiAngle: Rotation angle \(\phi\): of the major semiaxis (\(-180<=phi<=180\)). Increases counter-clockwise from the North direction. Default is 0 deg.
frame{“icrs”, “galactic”}: Center position coordinate frame.

Attributes Summary

`default_parameters`
`e`	A model parameter.
`eta`	A model parameter.
`evaluation_bin_size_min`	Minimal evaluation bin size as an `Angle`.
`evaluation_radius`	Evaluation radius as an `Angle`.
`evaluation_region`	Evaluation region consistent with evaluation radius.
`lat_0`	A model parameter.
`lon_0`	A model parameter.
`phi`	A model parameter.
`r_0`	A model parameter.
`tag`

Methods Summary

`evaluate`(lon, lat, lon_0, lat_0, r_0, eta, ...)
`to_region`([size_factor])	Model outline at a given number of \(r_0\).

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.
valuefloat or Quantity: Value.
scalefloat, optional: Scale (sometimes used in fitting).
unitUnit or str, optional: Unit. Default is “”.
minfloat, str or quantity, optional: Minimum (sometimes used in fitting). If None, set to numpy.nan. Default is None.
maxfloat, str or quantity, optional: Maximum (sometimes used in fitting). Default is numpy.nan.
frozenbool, optional: Frozen (used in fitting). Default is False.
errorfloat, optional: Parameter error. Default is 0.
scan_minfloat, optional: Minimum value for the parameter scan. Overwrites scan_n_sigma. Default is None.
scan_maxfloat, optional: Maximum value for the parameter scan. Overwrites scan_n_sigma. Default is None.
scan_n_values: int, optional: Number of values to be used for the parameter scan. Default is 11.
scan_n_sigmaint, optional: Number of sigmas to scan. Default is 2.
scan_values: `numpy.array`, optional: Scan values. Overwrites all the scan keywords before. Default is None.
scale_method{‘scale10’, ‘factor1’, None}, optional: Method used to set factor and scale. Default is “scale10”.
interp{“lin”, “sqrt”, “log”}, optional: Parameter scaling to use for the scan. Default is “lin”.
priorPrior, optional: Prior set on the parameter. Default is None.

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. Default is “”.
minfloat, str or quantity, optional: Minimum (sometimes used in fitting). If None, set to numpy.nan. Default is None.
maxfloat, str or quantity, optional: Maximum (sometimes used in fitting). Default is numpy.nan.
frozenbool, optional: Frozen (used in fitting). Default is False.
errorfloat, optional: Parameter error. Default is 0.
scan_minfloat, optional: Minimum value for the parameter scan. Overwrites scan_n_sigma. Default is None.
scan_maxfloat, optional: Maximum value for the parameter scan. Overwrites scan_n_sigma. Default is None.
scan_n_values: int, optional: Number of values to be used for the parameter scan. Default is 11.
scan_n_sigmaint, optional: Number of sigmas to scan. Default is 2.
scan_values: `numpy.array`, optional: Scan values. Overwrites all the scan keywords before. Default is None.
scale_method{‘scale10’, ‘factor1’, None}, optional: Method used to set factor and scale. Default is “scale10”.
interp{“lin”, “sqrt”, “log”}, optional: Parameter scaling to use for the scan. Default is “lin”.
priorPrior, optional: Prior set on the parameter. Default is None.

evaluation_bin_size_min#

Minimal evaluation bin size as an Angle.

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

evaluation_radius#

Evaluation radius as an 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.

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. Default is “”.
minfloat, str or quantity, optional: Minimum (sometimes used in fitting). If None, set to numpy.nan. Default is None.
maxfloat, str or quantity, optional: Maximum (sometimes used in fitting). Default is numpy.nan.
frozenbool, optional: Frozen (used in fitting). Default is False.
errorfloat, optional: Parameter error. Default is 0.
scan_minfloat, optional: Minimum value for the parameter scan. Overwrites scan_n_sigma. Default is None.
scan_maxfloat, optional: Maximum value for the parameter scan. Overwrites scan_n_sigma. Default is None.
scan_n_values: int, optional: Number of values to be used for the parameter scan. Default is 11.
scan_n_sigmaint, optional: Number of sigmas to scan. Default is 2.
scan_values: `numpy.array`, optional: Scan values. Overwrites all the scan keywords before. Default is None.
scale_method{‘scale10’, ‘factor1’, None}, optional: Method used to set factor and scale. Default is “scale10”.
interp{“lin”, “sqrt”, “log”}, optional: Parameter scaling to use for the scan. Default is “lin”.
priorPrior, optional: Prior set on the parameter. Default is None.

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. Default is “”.
minfloat, str or quantity, optional: Minimum (sometimes used in fitting). If None, set to numpy.nan. Default is None.
maxfloat, str or quantity, optional: Maximum (sometimes used in fitting). Default is numpy.nan.
frozenbool, optional: Frozen (used in fitting). Default is False.
errorfloat, optional: Parameter error. Default is 0.
scan_minfloat, optional: Minimum value for the parameter scan. Overwrites scan_n_sigma. Default is None.
scan_maxfloat, optional: Maximum value for the parameter scan. Overwrites scan_n_sigma. Default is None.
scan_n_values: int, optional: Number of values to be used for the parameter scan. Default is 11.
scan_n_sigmaint, optional: Number of sigmas to scan. Default is 2.
scan_values: `numpy.array`, optional: Scan values. Overwrites all the scan keywords before. Default is None.
scale_method{‘scale10’, ‘factor1’, None}, optional: Method used to set factor and scale. Default is “scale10”.
interp{“lin”, “sqrt”, “log”}, optional: Parameter scaling to use for the scan. Default is “lin”.
priorPrior, optional: Prior set on the parameter. Default is None.

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. Default is “”.
minfloat, str or quantity, optional: Minimum (sometimes used in fitting). If None, set to numpy.nan. Default is None.
maxfloat, str or quantity, optional: Maximum (sometimes used in fitting). Default is numpy.nan.
frozenbool, optional: Frozen (used in fitting). Default is False.
errorfloat, optional: Parameter error. Default is 0.
scan_minfloat, optional: Minimum value for the parameter scan. Overwrites scan_n_sigma. Default is None.
scan_maxfloat, optional: Maximum value for the parameter scan. Overwrites scan_n_sigma. Default is None.
scan_n_values: int, optional: Number of values to be used for the parameter scan. Default is 11.
scan_n_sigmaint, optional: Number of sigmas to scan. Default is 2.
scan_values: `numpy.array`, optional: Scan values. Overwrites all the scan keywords before. Default is None.
scale_method{‘scale10’, ‘factor1’, None}, optional: Method used to set factor and scale. Default is “scale10”.
interp{“lin”, “sqrt”, “log”}, optional: Parameter scaling to use for the scan. Default is “lin”.
priorPrior, optional: Prior set on the parameter. Default is None.

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. Default is “”.
minfloat, str or quantity, optional: Minimum (sometimes used in fitting). If None, set to numpy.nan. Default is None.
maxfloat, str or quantity, optional: Maximum (sometimes used in fitting). Default is numpy.nan.
frozenbool, optional: Frozen (used in fitting). Default is False.
errorfloat, optional: Parameter error. Default is 0.
scan_minfloat, optional: Minimum value for the parameter scan. Overwrites scan_n_sigma. Default is None.
scan_maxfloat, optional: Maximum value for the parameter scan. Overwrites scan_n_sigma. Default is None.
scan_n_values: int, optional: Number of values to be used for the parameter scan. Default is 11.
scan_n_sigmaint, optional: Number of sigmas to scan. Default is 2.
scan_values: `numpy.array`, optional: Scan values. Overwrites all the scan keywords before. Default is None.
scale_method{‘scale10’, ‘factor1’, None}, optional: Method used to set factor and scale. Default is “scale10”.
interp{“lin”, “sqrt”, “log”}, optional: Parameter scaling to use for the scan. Default is “lin”.
priorPrior, optional: Prior set on the parameter. Default is None.

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

Methods Documentation

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

to_region(size_factor=1.0, **kwargs)[source]#

Model outline at a given number of \(r_0\).

Parameters:

size_factorfloat, optional: Number of \(r_0\). Default is 1.0.
kwargsdict: Keyword arguments passed to EllipseSkyRegion.

Returns:

regionEllipseSkyRegion: Model outline.

__init__(**kwargs)#

classmethod __new__(*args, **kwargs)#

GeneralizedGaussianSpatialModel#

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