PointSpatialModel#

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

Bases: SpatialModel

Point Source.

For more information see Point spatial model.

Parameters:

lon_0, lat_0Angle: Center position. Default is “0 deg”, “0 deg”.
frame{“icrs”, “galactic”}: Center position coordinate frame.

Attributes Summary

`covariance`
`default_parameters`
`evaluation_bin_size_min`	Minimal evaluation bin size as an `Angle`.
`evaluation_radius`	Evaluation radius as an `Angle`.
`evaluation_region`	Evaluation region.
`frozen`	Frozen status of a model, True if all parameters are frozen.
`is_energy_dependent`
`lat_0`	A model parameter.
`lon_0`	A model parameter.
`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.
`tag`
`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`.
`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.
`to_region`(**kwargs)	Model outline as a `PointSkyRegion`.
`unfreeze`()	Restore parameters frozen status to default.

Attributes Documentation

covariance#

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

evaluation_bin_size_min#: Minimal evaluation bin size as an Angle.

evaluation_radius#

Evaluation radius as an Angle.

Set as zero degrees.

evaluation_region#: Evaluation region.

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

is_energy_dependent#

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

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

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.

tag = ['PointSpatialModel', 'point']#

type#

Methods Documentation

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

copy(**kwargs)#: Deep copy.

evaluate_geom(geom)[source]#: Evaluate model on Geom.

freeze()#: Freeze all parameters.

classmethod from_dict(data, **kwargs)#

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)#

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.

Parameters:

geomGeom: Map geometry.

Returns:

fluxMap: Predicted flux map.

plot(ax=None, geom=None, **kwargs)#

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)#

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)#

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)#

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)#

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)#: Create dictionary for YAML serilisation.

to_region(**kwargs)[source]#: Model outline as a PointSkyRegion.

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