GaussianSpatialModel#
- class gammapy.modeling.models.GaussianSpatialModel[source]#
Bases:
SpatialModel
Two-dimensional Gaussian model.
For more information see Gaussian spatial model.
- Parameters:
- lon_0, lat_0
Angle
Center position. Default is “0 deg”, “0 deg”.
- sigma
Angle
Length of the major semiaxis of the Gaussian, in angular units. Default is 1 deg.
- e
float
Eccentricity of the Gaussian (\(0<= e<= 1\)). Default is 0.
- phi
Angle
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.
- lon_0, lat_0
Attributes Summary
A model parameter.
Minimal evaluation bin size as an
Angle
, chosen as sigma/3.Evaluation radius as an
Angle
.Evaluation region consistent with evaluation radius.
A model parameter.
A model parameter.
A model parameter.
A model parameter.
Methods Summary
evaluate
(lon, lat, lon_0, lat_0, sigma, e, phi)Evaluate model.
to_region
([size_factor])Model outline at a given number of \(\sigma\).
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
ormin
andmax
properties and consider the fact that there is afactor
andscale
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
andfactor_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).
- unit
Unit
or str, optional Unit. Default is “”.
- minfloat, str or
quantity
, optional Minimum (sometimes used in fitting). If
None
, set tonumpy.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
andscale
. Default is “scale10”.- interp{“lin”, “sqrt”, “log”}, optional
Parameter scaling to use for the scan. Default is “lin”.
- prior
Prior
, optional Prior set on the parameter. Default is None.
- 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
ormin
andmax
properties and consider the fact that there is afactor
andscale
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
andfactor_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).
- unit
Unit
or str, optional Unit. Default is “”.
- minfloat, str or
quantity
, optional Minimum (sometimes used in fitting). If
None
, set tonumpy.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
andscale
. Default is “scale10”.- interp{“lin”, “sqrt”, “log”}, optional
Parameter scaling to use for the scan. Default is “lin”.
- prior
Prior
, 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
ormin
andmax
properties and consider the fact that there is afactor
andscale
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
andfactor_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).
- unit
Unit
or str, optional Unit. Default is “”.
- minfloat, str or
quantity
, optional Minimum (sometimes used in fitting). If
None
, set tonumpy.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
andscale
. Default is “scale10”.- interp{“lin”, “sqrt”, “log”}, optional
Parameter scaling to use for the scan. Default is “lin”.
- prior
Prior
, 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
ormin
andmax
properties and consider the fact that there is afactor
andscale
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
andfactor_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).
- unit
Unit
or str, optional Unit. Default is “”.
- minfloat, str or
quantity
, optional Minimum (sometimes used in fitting). If
None
, set tonumpy.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
andscale
. Default is “scale10”.- interp{“lin”, “sqrt”, “log”}, optional
Parameter scaling to use for the scan. Default is “lin”.
- prior
Prior
, optional Prior set on the parameter. Default is None.
- 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
ormin
andmax
properties and consider the fact that there is afactor
andscale
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
andfactor_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).
- unit
Unit
or str, optional Unit. Default is “”.
- minfloat, str or
quantity
, optional Minimum (sometimes used in fitting). If
None
, set tonumpy.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
andscale
. Default is “scale10”.- interp{“lin”, “sqrt”, “log”}, optional
Parameter scaling to use for the scan. Default is “lin”.
- prior
Prior
, optional Prior set on the parameter. Default is None.
- tag = ['GaussianSpatialModel', 'gauss']#
Methods Documentation
- to_region(size_factor=1.0, **kwargs)[source]#
Model outline at a given number of \(\sigma\).
- Parameters:
- size_factorfloat
Number of \(\sigma Default is :math:`1.0\sigma\) which corresponds to about 39% containment for a 2D symmetric Gaussian.
- Returns:
- region
EllipseSkyRegion
Model outline.
- region
- __init__(**kwargs)#
- classmethod __new__(*args, **kwargs)#