TemplateSpatialModel

class gammapy.modeling.models.TemplateSpatialModel(map, norm=<Quantity 1.>, meta=None, normalize=True, interp_kwargs=None, filename=None)[source]

Bases: gammapy.modeling.models.SpatialModel

Spatial sky map template model (2D).

This is for a 2D image. Use SkyDiffuseCube for 3D cubes with an energy axis.

Parameters:
mapMap

Map template

normfloat

Norm parameter (multiplied with map values)

metadict, optional

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

normalizebool

Normalize the input map so that it integrates to unity.

interp_kwargsdict

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

Attributes Summary

default_parameters
evaluation_radius Evaluation radius (Angle).
frame
norm A model parameter.
parameters Parameters (Parameters)
phi_0
position SkyCoord
position_error Get 95% containment position error as (EllipseSkyRegion)
tag

Methods Summary

__call__(self, lon, lat) Call evaluate method
copy(self) A deep copy.
create(tag, \*args, \*\*kwargs) Create a model instance.
evaluate(self, lon, lat, norm) Evaluate model.
evaluate_geom(self, geom) Evaluate model on Geom.
from_dict(data)
read(filename[, normalize]) Read spatial template model from FITS image.
to_dict(self) Create dict for YAML serilisation
to_region(self, \*\*kwargs) Model outline (PolygonSkyRegion).

Attributes Documentation

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

Evaluation radius (Angle).

Set to half of the maximal dimension of the map.

frame
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

factorfloat or Quantity

Factor

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)

phi_0
position

SkyCoord

position_error

Get 95% containment position error as (EllipseSkyRegion)

tag = 'TemplateSpatialModel'

Methods Documentation

__call__(self, lon, lat)

Call evaluate method

copy(self)

A deep copy.

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

Create a model instance.

Examples

>>> from gammapy.modeling 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, norm)[source]

Evaluate model.

evaluate_geom(self, geom)

Evaluate model on Geom.

classmethod from_dict(data)[source]
classmethod read(filename, normalize=True, **kwargs)[source]

Read spatial template model from FITS image.

The default unit used if none is found in the file is sr-1.

Parameters:
filenamestr

FITS image filename.

normalizebool

Normalize the input map so that it integrates to unity.

kwargsdict

Keyword arguments passed to Map.read().

to_dict(self)[source]

Create dict for YAML serilisation

to_region(self, **kwargs)[source]

Model outline (PolygonSkyRegion).