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: 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. Default is True. Use 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 as a Quantity.
evaluation_radius	Evaluation radius as a Angle.
frozen	Frozen status of a model, True if all parameters are frozen.
map	A lazy FITS data descriptor.
name
parameters	Parameters as a Parameters object.
parameters_unique_names	List of unique parameter names.
position	Position as a SkyCoord.
spectral_model	Spectral model as a SpectralModel object.
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, **kwargs)
from_parameters(parameters, **kwargs)	Create model from parameter list.
reassign(datasets_names, new_datasets_names)	Reassign a model from one dataset to another.
slice_by_energy([energy_min, energy_max, name])	Select and slice model template in energy range
stack(other[, weights, nan_to_num])	Stack background model in place.
to_dict([full_output])	Create dictionary for YAML serialisation.
unfreeze([model_type])	Restore parameters frozen status to default.
write([overwrite])	Write the map.

Attributes Documentation

covariance

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

energy_center

True energy axis bin centers as a Quantity.

evaluation_radius

Evaluation radius as a 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

parameters_unique_names

List of unique parameter names. Return formatted as par_type.par_name.

position

Position as a SkyCoord.

spectral_model

Spectral model as a SpectralModel object.

tag = 'TemplateNPredModel'

type

Methods Documentation

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

Copy template npred model.

Parameters:

namestr, optional

Assign a new name to the copied model. Default is None.

copy_databool, optional

Copy the data arrays attached to models. Default is False.

**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. Default is “trim”.

namestr, optional

Name of the returned background model. Default is None.

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, **kwargs)

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.

slice_by_energy(energy_min=None, energy_max=None, name=None)

Select and slice model template in energy range

Parameters:

energy_min, energy_maxQuantity

Energy bounds of the slice. Default is None.

namestr

Name of the sliced model. Default is None.

Returns:

modelTemplateNpredModel

Sliced 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.

weightsfloat, optional

Weights. Default is None.

nan_to_num: bool, optional

Non-finite values are replaced by zero if True. Default is True.

to_dict(full_output=False)

Create dictionary for YAML serialisation.

unfreeze(model_type='spectral')

Restore parameters frozen status to default.

write(overwrite=False)

Write the map.

Parameters:

overwrite: bool, optional

Overwrite existing file. Default is False, which will raise a warning if the template file exists already.