SkyDiffuseCube

class gammapy.modeling.models.SkyDiffuseCube(map, norm=<Quantity 1.>, tilt=<Quantity 0.>, reference=<Quantity 1. TeV>, meta=None, interp_kwargs=None, name=None, filename=None, apply_irf=None, datasets_names=None)

Bases: gammapy.modeling.models.SkyModelBase

Cube sky map template model (3D).

This is for a 3D map with an energy axis. Use TemplateSpatialModel for 2D maps.

Parameters

mapMap

Map template

normfloat

Norm parameter (multiplied with map values)

tiltfloat

Additional tilt in the spectrum

referenceQuantity

Reference energy of the tilt.

metadict, optional

Meta information, meta[‘filename’] will be used for serialization

interp_kwargsdict

Interpolation keyword arguments passed to gammapy.maps.Map.interp_by_coord. Default arguments are {‘interp’: ‘linear’, ‘fill_value’: 0}.

Attributes Summary

covariance
default_parameters
evaluation_radius	Angle
frame
name
norm	A model parameter.
parameters	Parameters (Parameters)
position	SkyCoord
reference	A model parameter.
tag
tilt	A model parameter.

Methods Summary

__call__(self, lon, lat, energy[, time])	Call self as a function.
copy(self[, name])	A shallow copy
create(tag, \*args, \*\*kwargs)	Create a model instance.
evaluate(self, lon, lat, energy[, time])	Evaluate model.
evaluate_geom(self, geom[, gti])
from_dict(data)
from_parameters(parameters, \*\*kwargs)	Create model from parameter list
integrate_geom(self, geom[, gti])	Integrate model on Geom.
read(filename[, name])	Read map from FITS file.
to_dict(self)	Create dict for YAML serialisation

Attributes Documentation

covariance

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

evaluation_radius

Angle

frame

name

norm

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)

parameters

Parameters (Parameters)

position

SkyCoord

reference

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)

tag = 'SkyDiffuseCube'

tilt

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__(self, lon, lat, energy, time=None)

Call self as a function.

copy(self, name=None)

A shallow copy

static create(tag, *args, **kwargs)

Create a model instance.

Examples

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

evaluate(self, lon, lat, energy, time=None)

Evaluate model. passing time does not make sense here - passed just to match arguments of SkyModel.evaluate

evaluate_geom(self, geom, gti=None)

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(self, geom, gti=None)

Integrate model on Geom.

Parameters

geomGeom

Map geometry

gtiGTI

GIT table (currently not being used…)

Returns

fluxMap

Predicted flux map

classmethod read(filename, name=None, **kwargs)

Read map from FITS file.

The default unit used if none is found in the file is cm-2 s-1 MeV-1 sr-1.

Parameters

filenamestr

FITS image filename.

namestr

Name of the output model The default used if none is filename.

to_dict(self)

Create dict for YAML serialisation