PointSpatialModel¶

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

Bases: gammapy.modeling.models.SpatialModel

Point Source.

For more information see Point spatial model.

Parameters

lon_0, lat_0Angle: Center position
frame{“icrs”, “galactic”}: Center position coordinate frame

Attributes Summary

`covariance`
`default_parameters`
`evaluation_bin_size_min`	Minimal evaluation bin size (`Angle`).
`evaluation_radius`	Evaluation radius (`Angle`).
`evaluation_region`	Evaluation region
`frozen`	Frozen status of a model, True if all parameters are frozen
`lat_0`	A model parameter.
`lon_0`	A model parameter.
`parameters`	Parameters (`Parameters`)
`phi_0`
`position`	Spatial model center position (`SkyCoord`)
`position_error`	Get 95% containment position error as (`EllipseSkyRegion`)
`position_lonlat`	Spatial model center position `(lon, lat)` in rad and frame of the model
`tag`
`type`

Methods Summary

`__call__`(lon, lat[, energy])	Call evaluate method
`copy`()	A deep copy.
`evaluate_geom`(geom)	Evaluate model on `Geom`.
`freeze`()	Freeze all parameters
`from_dict`(data)
`from_parameters`(parameters, **kwargs)	Create model from parameter list
`from_position`(position, **kwargs)	Define the position of the model using a sky coord
`integrate_geom`(geom[, oversampling_factor])	Integrate model on `Geom`
`is_energy_dependent`()
`plot`([ax, geom])	Plot spatial model.
`plot_error`([ax])	Plot position error
`plot_grid`([geom])	Plot spatial model energy slices in a grid.
`plot_interative`([ax, geom])	Plot spatial model.
`reassign`(datasets_names, new_datasets_names)	Reassign a model from one dataset to another
`to_dict`([full_output])	Create dict for YAML serilisation
`to_region`(**kwargs)	Model outline (`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 (Angle).

evaluation_radius¶

Evaluation radius (Angle).

Set as zero degrees.

evaluation_region¶: Evaluation region

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

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 of 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.

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 of 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.

parameters¶: Parameters (Parameters)

phi_0¶

position¶: Spatial model center position (SkyCoord)

position_error¶: Get 95% containment position error as (EllipseSkyRegion)

position_lonlat¶: Spatial model center position (lon, lat) in rad and frame of the model

tag = ['PointSpatialModel', 'point']¶

type¶

Methods Documentation

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

copy()¶: A deep copy.

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

freeze()¶: Freeze all parameters

classmethod from_dict(data)¶

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 sky coord

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

is_energy_dependent()[source]¶

plot(ax=None, geom=None, **kwargs)¶

Plot spatial model.

Parameters

axAxes, optional: Axis
geomWcsGeom, optional: Geom to use for plotting.
**kwargsdict: Keyword arguments passed to plot()

Returns

axAxes, optional: Axis

plot_error(ax=None, **kwargs)¶

Plot position error

Parameters

axAxes, optional: Axis
**kwargsdict: Keyword arguments passed to plot()

Returns

axAxes, optional: Axis

plot_grid(geom=None, **kwargs)¶

Plot spatial model energy slices in a grid.

Parameters

geomWcsGeom, optional: Geom to use for plotting.
**kwargsdict: Keyword arguments passed to plot()

Returns

axAxes, optional: Axis

plot_interative(ax=None, geom=None, **kwargs)¶

Plot spatial model.

Parameters

axAxes, optional: Axis
geomWcsGeom, optional: Geom to use for plotting.
**kwargsdict: Keyword arguments passed to plot()

Returns

axAxes, optional: Axis

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 dict for YAML serilisation

to_region(**kwargs)[source]¶: Model outline (PointSkyRegion).

unfreeze()¶: Restore parameters frozen status to default