TemplateNPredModel

class gammapy.modeling.models.TemplateNPredModel(map, spectral_model=None, name=None, filename=None, datasets_names=None, copy_data=True, spatial_model=None, covariance_data=None)

Bases: gammapy.modeling.models.core.ModelBase

Background model.

Create a new map by a tilt and normalization on the available map

Parameters

mapMap

Background model map

spectral_modelSpectralModel

Normalized spectral model, default is PowerLawNormSpectralModel

copy_databool

Create a deepcopy of the map data or directly use the original. True by default, can be turned to False to save memory in case of large maps.

spatial_modelSpatialModel

Unitless Spatial model (unit is dropped on evaluation if defined). Default is None.

Attributes Summary

covariance
default_parameters
energy_center	True energy axis bin centers (Quantity)
evaluation_radius	Angle
frozen	Frozen status of a model, True if all parameters are frozen
map	A lazy FITS data descriptor.
name
parameters	Parameters (Parameters)
position	SkyCoord
spectral_model	SpectralModel
tag
type

Methods Summary

copy([name, copy_data])	Copy template npred model.
cutout(position, width[, mode, name])	Cutout background model.
evaluate()	Evaluate background model.
freeze([model_type])	Freeze model parameters
from_dict(data)
from_parameters(parameters, **kwargs)	Create model from parameter list
reassign(datasets_names, new_datasets_names)	Reassign a model from one dataset to another
stack(other[, weights, nan_to_num])	Stack background model in place.
to_dict([full_output])	Create dict for YAML serialisation
unfreeze([model_type])	Restore parameters frozen status to default
write([overwrite])

Attributes Documentation

covariance

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

energy_center

True energy axis bin centers (Quantity)

evaluation_radius

Angle

frozen

Frozen status of a model, True if all parameters are frozen

map

A lazy FITS data descriptor.

Parameters

cachebool

Whether to cache the data.

name

parameters

position

SkyCoord

spectral_model

SpectralModel

tag = 'TemplateNPredModel'

type

Methods Documentation

copy(name=None, copy_data=False, **kwargs)

Copy template npred model.

Parameters

namestr

Assign a new name to the copied model.

copy_databool

Copy the data arrays attached to models.

**kwargsdict

Keyword arguments forwarded to TemplateNPredModel

Returns

modelTemplateNPredModel

Copied template npred model.

cutout(position, width, mode='trim', name=None)

Cutout background model.

Parameters

positionSkyCoord

Center position of the cutout region.

widthtuple of Angle

Angular sizes of the region in (lon, lat) in that specific order. If only one value is passed, a square region is extracted.

mode{‘trim’, ‘partial’, ‘strict’}

Mode option for Cutout2D, for details see Cutout2D.

namestr

Name of the returned background model.

Returns

cutoutTemplateNPredModel

Cutout background model.

evaluate()

Evaluate background model.

Returns

background_mapMap

Background evaluated on the Map

freeze(model_type='spectral')

Freeze model parameters

classmethod from_dict(data)

classmethod from_parameters(parameters, **kwargs)

Create model from parameter list

Parameters

parametersParameters

Parameters for init

Returns

modelModel

Model instance

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.

stack(other, weights=None, nan_to_num=True)

Stack background model in place.

Stacking the background model resets the current parameters values.

Parameters

otherTemplateNPredModel

Other background model.

nan_to_num: bool

Non-finite values are replaced by zero if True (default).

to_dict(full_output=False)

Create dict for YAML serialisation

unfreeze(model_type='spectral')

Restore parameters frozen status to default

write(overwrite=False)