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, **kwargs)[source]#

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

Parameters:
size_factorfloat, optional

Number of \(r_0\). Default is 1.0

Returns:
regionEllipseSkyRegion

Model outline.

__init__(**kwargs)#
classmethod __new__(*args, **kwargs)#