PointSpatialModel

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

Bases: gammapy.modeling.models.spatial.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(**kwargs)	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_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 (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.

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

tag = ['PointSpatialModel', 'point']

type

Methods Documentation

__call__(lon, lat, energy=None)

Call evaluate method

copy(**kwargs)

A deep copy.

evaluate_geom(geom)

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

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

Parameters

geomGeom

Map geometry

Returns

fluxMap

Predicted flux map

is_energy_dependent()

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 (PointSkyRegion).

unfreeze()

Restore parameters frozen status to default