ShellSpatialModel

class gammapy.modeling.models.ShellSpatialModel(**kwargs)

Bases: gammapy.modeling.models.spatial.SpatialModel

Shell model.

For more information see Shell spatial model.

Parameters

lon_0, lat_0Angle

Center position

radiusAngle

Inner radius, \(r_{in}\)

widthAngle

Shell width

frame{“icrs”, “galactic”}

Center position coordinate frame

See also

Shell2SpatialModel

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
is_energy_dependent
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
radius	A model parameter.
tag
type
width	A model parameter.

Methods Summary

__call__(lon, lat[, energy])	Call evaluate method
copy(**kwargs)	A deep copy.
evaluate(lon, lat, lon_0, lat_0, radius, width)	Evaluate model.
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 or RegionGeom.
plot([ax, geom])	Plot spatial model.
plot_error([ax])	Plot position error
plot_grid([geom])	Plot spatial model energy slices in a grid.
plot_interactive([ax, geom])	Plot spatial model.
plot_interative([ax, geom])	Deprecated since version v1.0.1.
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 (CircleAnnulusSkyRegion).
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).

The bin min size is defined as the shell width.

evaluation_radius

Evaluation radius (Angle).

Set to \(r_\text{out}\).

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 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.

is_normbool

Whether the parameter represents the flux norm of the model.

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.

is_normbool

Whether the parameter represents the flux norm of the model.

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

radius

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.

is_normbool

Whether the parameter represents the flux norm of the model.

tag = ['ShellSpatialModel', 'shell']

type

width

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.

is_normbool

Whether the parameter represents the flux norm of the model.

Methods Documentation

__call__(lon, lat, energy=None)

Call evaluate method

copy(**kwargs)

A deep copy.

static evaluate(lon, lat, lon_0, lat_0, radius, width)

Evaluate model.

evaluate_geom(geom)

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)

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

The model will be created in the frame of the sky coord

positionSkyCoord

Position

Returns

modelSpatialModel

Spatial model

integrate_geom(geom, oversampling_factor=None)

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 minimimal bin size

Returns

mapMap or gammapy.maps.RegionNDMap

Map containing the integral value in each spatial bin.

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_interactive(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_interative(ax=None, geom=None, **kwargs)

Deprecated since version v1.0.1: The plot_interative function is deprecated and may be removed in a future version. Use plot_interactive instead.

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)

Model outline (CircleAnnulusSkyRegion).

unfreeze()

Restore parameters frozen status to default