PiecewiseNormSpatialModel

class gammapy.modeling.models.PiecewiseNormSpatialModel

Bases: SpatialModel

Piecewise spatial correction with a free normalization at each fixed nodes.

For more information see Piecewise norm spatial model.

Parameters:

coordgammapy.maps.MapCoord

Flat coordinates list at which the model values are given (nodes).

normsndarray or list of gammapy.modeling.Parameter

Array with the initial norms of the model at energies energy. Normalisation parameters are created for each value. Default is one at each node.

interp{“lin”, “log”}

Interpolation scaling. Default is “lin”.

Attributes Summary

coords	Energy nodes.
default_parameters
is_energy_dependent
norms	Norm values.
tag

Methods Summary

evaluate(lon, lat[, energy])	Evaluate the model at given coordinates.
evaluate_geom(geom)	Evaluate model on Geom.
from_dict(data)	Create model from dictionary.
from_parameters(parameters, **kwargs)	Create model from parameters.
to_dict([full_output])	Create dictionary for YAML serilisation.

Attributes Documentation

coords

Energy nodes.

default_parameters = <gammapy.modeling.parameter.Parameters object>

is_energy_dependent

norms

Norm values.

tag = ['PiecewiseNormSpatialModel', 'piecewise-norm']

Methods Documentation

evaluate(lon, lat, energy=None, **norms)

Evaluate the model at given coordinates.

evaluate_geom(geom)

Evaluate model on Geom.

Parameters:

geomWcsGeom

Map geometry.

Returns:

mapMap

Map containing the value in each spatial bin.

classmethod from_dict(data)

Create model from dictionary.

classmethod from_parameters(parameters, **kwargs)

Create model from parameters.

to_dict(full_output=False)

Create dictionary for YAML serilisation.

__init__(coords, norms=None, interp='lin', **kwargs)

classmethod __new__(*args, **kwargs)