SpatialModel#

class gammapy.modeling.models.SpatialModel(**kwargs)[source]#

Bases: ModelBase

Spatial model base class.

Attributes Summary

`covariance`
`default_parameters`
`evaluation_bin_size_min`
`evaluation_radius`	Evaluation radius.
`evaluation_region`	Evaluation region.
`frozen`	Frozen status of a model, True if all parameters are frozen.
`is_energy_dependent`
`parameters`	Parameters as a `Parameters` object.
`parameters_unique_names`
`phi_0`
`position`	Spatial model center position as a `SkyCoord`.
`position_error`	Get 95% containment position error as `EllipseSkyRegion`.
`position_lonlat`	Spatial model center position `(lon, lat)` in radians and frame of the model.
`type`

Methods Summary

`__call__`(lon, lat[, energy])	Call evaluate method.
`copy`(**kwargs)	Deep copy.
`evaluate_geom`(geom)	Evaluate model on `Geom`.
`freeze`()	Freeze all parameters.
`from_dict`(data, **kwargs)	Create a spatial model from a dictionary.
`from_parameters`(parameters, **kwargs)	Create model from parameter list.
`from_position`(position, **kwargs)	Define the position of the model using a `SkyCoord`.
`integrate_geom`(geom[, oversampling_factor])	Integrate model on `Geom` or `RegionGeom`.
`plot`([ax, geom])	Plot spatial model.
`plot_error`([ax, which, kwargs_position, ...])	Plot the errors of the spatial model.
`plot_grid`([geom])	Plot spatial model energy slices in a grid.
`plot_interactive`([ax, geom])	Plot spatial model.
`plot_position_error`([ax])	Plot position error.
`reassign`(datasets_names, new_datasets_names)	Reassign a model from one dataset to another.
`to_dict`([full_output])	Create dictionary for YAML serilisation.
`unfreeze`()	Restore parameters frozen status to default.

Attributes Documentation

covariance#

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

evaluation_bin_size_min#

evaluation_radius#: Evaluation radius.

evaluation_region#: Evaluation region.

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

is_energy_dependent#

parameters#: Parameters as a Parameters object.

parameters_unique_names#

phi_0#

position#: Spatial model center position as a SkyCoord.

position_error#: Get 95% containment position error as EllipseSkyRegion.

position_lonlat#: Spatial model center position (lon, lat) in radians and frame of the model.

type#

Methods Documentation

__call__(lon, lat, energy=None)[source]#: Call evaluate method.

copy(**kwargs)#: Deep copy.

evaluate_geom(geom)[source]#

Evaluate model on Geom.

Parameters:

geomWcsGeom: Map geometry.

Returns:

mapMap: Map containing the value in each spatial bin.

freeze()#: Freeze all parameters.

classmethod from_dict(data, **kwargs)[source]#

Create a spatial model from a dictionary.

Parameters:

datadict: Dictionary containing model parameters.
kwargsdict: Keyword arguments passed to from_parameters.

classmethod from_parameters(parameters, **kwargs)#

Create model from parameter list.

Parameters:

parametersParameters: Parameters for init.

Returns:

modelModel: Model instance.

classmethod from_position(position, **kwargs)[source]#

Define the position of the model using a SkyCoord.

The model will be created in the frame of the SkyCoord.

Parameters:

positionSkyCoord: Position.

Returns:

modelSpatialModel: Spatial model.

integrate_geom(geom, oversampling_factor=None)[source]#

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 minimal bin size.

Returns:

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

plot(ax=None, geom=None, **kwargs)[source]#

Plot spatial model.

Parameters:

axAxes, optional: Matplotlib axes. Default is None.
geomWcsGeom, optional: Geometry to use for plotting. Default is None.
**kwargsdict: Keyword arguments passed to plot().

Returns:

axAxes, optional: Matplotlib axes.

plot_error(ax=None, which='position', kwargs_position=None, kwargs_extension=None)[source]#

Plot the errors of the spatial model.

Parameters:

axAxes, optional

Matplotlib axes to plot the errors on. Default is None.

which: list of str

Which errors to plot. Available options are:

“all”: all the optional steps are plotted

“position”: plot the position error of the spatial model

“extension”: plot the extension error of the spatial model

kwargs_positiondict, optional

Keyword arguments passed to plot_position_error. Default is None.

kwargs_extensiondict, optional

Keyword arguments passed to plot_extension_error. Default is None.

Returns:

axAxes, optional: Matplotlib axes.

plot_grid(geom=None, **kwargs)[source]#

Plot spatial model energy slices in a grid.

Parameters:

geomWcsGeom, optional: Geometry to use for plotting. Default is None.
**kwargsdict: Keyword arguments passed to plot().

Returns:

axAxes, optional: Matplotlib axes.

plot_interactive(ax=None, geom=None, **kwargs)[source]#

Plot spatial model.

Parameters:

axAxes, optional: Matplotlib axes. Default is None.
geomWcsGeom, optional: Geom to use for plotting. Default is None.
**kwargsdict: Keyword arguments passed to plot().

Returns:

axAxes, optional: Matplotlib axes.

plot_position_error(ax=None, **kwargs)[source]#

Plot position error.

Parameters:

axAxes, optional: Matplotlib axes to plot the position error on. Default is None.
**kwargsdict: Keyword arguments passed to plot().

Returns:

axAxes, optional: Matplotlib axes.

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)[source]#: Create dictionary for YAML serilisation.

unfreeze()#: Restore parameters frozen status to default.