ConstantSpatialModel

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

Bases: gammapy.modeling.models.SpatialModel

Spatially constant (isotropic) spatial model.

For more information see Constant Spatial Model.

Parameters

valueQuantity

Value

Attributes Summary

covariance
default_parameters
evaluation_radius
frame
is_energy_dependent
parameters	Parameters (Parameters)
phi_0
position
position_error	Get 95% containment position error as (EllipseSkyRegion)
tag
type
value	A model parameter.

Methods Summary

__call__(lon, lat[, energy])	Call evaluate method
copy()	A deep copy.
create(tag[, model_type])	Create a model instance.
evaluate(lon, lat, value)	Evaluate model.
evaluate_geom(geom)
from_dict(data)
from_parameters(parameters, **kwargs)	Create model from parameter list
integrate_geom(geom)	Integrate model on Geom.
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.
to_dict([full_output])	Create dict for YAML serilisation
to_region(**kwargs)	Model outline (EllipseSkyRegion).

Attributes Documentation

covariance

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

evaluation_radius = None

frame = 'icrs'

is_energy_dependent

parameters

Parameters (Parameters)

phi_0

position = None

position_error

Get 95% containment position error as (EllipseSkyRegion)

tag = ['ConstantSpatialModel', 'const']

type

value

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)

Methods Documentation

__call__(lon, lat, energy=None)

Call evaluate method

copy()

A deep copy.

static create(tag, model_type=None, *args, **kwargs)

Create a model instance.

Examples

>>> from gammapy.modeling.models import Model
>>> spectral_model = Model.create("pl-2", model_type="spectral", amplitude="1e-10 cm-2 s-1", index=3)
>>> type(spectral_model)
gammapy.modeling.models.spectral.PowerLaw2SpectralModel

static evaluate(lon, lat, value)

Evaluate model.

evaluate_geom(geom)

classmethod from_dict(data)

classmethod from_parameters(parameters, **kwargs)

Create model from parameter list

Parameters

parametersParameters

Parameters for init

Returns

modelModel

Model instance

integrate_geom(geom)

Integrate model on Geom.

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

to_dict(full_output=False)

Create dict for YAML serilisation

static to_region(**kwargs)

Model outline (EllipseSkyRegion).