SkyModel#

class gammapy.modeling.models.SkyModel(spectral_model, spatial_model=None, temporal_model=None, name=None, apply_irf=None, datasets_names=None)[source]#

Bases: gammapy.modeling.models.core.ModelBase

Sky model component.

This model represents a factorised sky model. It has Parameters combining the spatial and spectral parameters.

Parameters

spectral_modelSpectralModel: Spectral model
spatial_modelSpatialModel: Spatial model (must be normalised to integrate to 1)
temporal_modeltemporalModel: Temporal model
namestr: Model identifier
apply_irfdict: Dictionary declaring which IRFs should be applied to this model. Options are {“exposure”: True, “psf”: True, “edisp”: True}
datasets_nameslist of str: Which datasets this model is applied to.

Attributes Summary

`covariance`
`default_parameters`
`evaluation_bin_size_min`	Minimal spatial bin size for spatial model evaluation.
`evaluation_radius`	`Angle`
`evaluation_region`	`Angle`
`frame`
`frozen`	Frozen status of a model, True if all parameters are frozen
`name`
`parameters`	Parameters (`Parameters`)
`position`	`SkyCoord`
`position_lonlat`	Spatial model center position `(lon, lat)` in rad and frame of the model
`spatial_model`	`SpatialModel`
`spectral_model`	`SpectralModel`
`tag`
`temporal_model`	`TemporalModel`
`type`

Methods Summary

`__call__`(lon, lat, energy[, time])	Call self as a function.
`contributes`(mask[, margin])	Check if a skymodel contributes within a mask map.
`copy`(**kwargs)
`create`(spectral_model[, spatial_model, ...])	Create a model instance.
`evaluate`(lon, lat, energy[, time])	Evaluate the model at given points.
`evaluate_geom`(geom[, gti])	Evaluate model on `Geom`.
`freeze`([model_type])	Freeze parameters depending on model type
`from_dict`(data)	Create SkyModel from dict
`from_parameters`(parameters, **kwargs)	Create model from parameter list
`integrate_geom`(geom[, gti, oversampling_factor])	Integrate model on `Geom`.
`reassign`(datasets_names, new_datasets_names)	Reassign a model from one dataset to another
`to_dict`([full_output])	Create dict for YAML serilisation
`unfreeze`([model_type])	Restore parameters frozen status to default depending on model type

Attributes Documentation

covariance#

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

evaluation_bin_size_min#: Minimal spatial bin size for spatial model evaluation.

evaluation_radius#: Angle

evaluation_region#: Angle

frame#

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

name#

parameters#

position#: SkyCoord

position_lonlat#: Spatial model center position (lon, lat) in rad and frame of the model

spatial_model#: SpatialModel

spectral_model#: SpectralModel

tag = ['SkyModel', 'sky-model']#

temporal_model#: TemporalModel

type#

Methods Documentation

__call__(lon, lat, energy, time=None)[source]#: Call self as a function.

contributes(mask, margin='0 deg')[source]#

Check if a skymodel contributes within a mask map.

Parameters

maskWcsNDMap of boolean type: Map containing a boolean mask
marginQuantity: Add a margin in degree to the source evaluation radius. Used to take into account PSF width.

Returns

modelsDatasetModels: Selected models contributing inside the region where mask==True

copy(**kwargs)#

classmethod create(spectral_model, spatial_model=None, temporal_model=None, **kwargs)[source]#

Create a model instance.

Parameters

spectral_modelstr: Tag to create spectral model
spatial_modelstr: Tag to create spatial model
temporal_modelstr: Tag to create temporal model
**kwargsdict: Keyword arguments passed to SkyModel

Returns

modelSkyModel: Sky model

evaluate(lon, lat, energy, time=None)[source]#

Evaluate the model at given points.

The model evaluation follows numpy broadcasting rules.

Return differential surface brightness cube. At the moment in units: cm-2 s-1 TeV-1 deg-2

Parameters

lon, latQuantity: Spatial coordinates
energyQuantity: Energy coordinate
time: `~astropy.time.Time`: Time coordinate

Returns

valueQuantity: Model value at the given point.

evaluate_geom(geom, gti=None)[source]#: Evaluate model on Geom.

freeze(model_type=None)[source]#

Freeze parameters depending on model type

Parameters

model_type{None, “spatial”, “spectral”, “temporal”}: freeze all parameters or only or only spatial/spectral/temporal. Default is None so all parameters are frozen.

classmethod from_dict(data)[source]#: Create SkyModel from dict

classmethod from_parameters(parameters, **kwargs)#

Create model from parameter list

Parameters

parametersParameters: Parameters for init

Returns

modelModel: Model instance

integrate_geom(geom, gti=None, oversampling_factor=None)[source]#

Integrate model on Geom.

See integrate_geom and integral.

Parameters

geomGeom or RegionGeom: Map geometry
gtiGTI: GIT table
oversampling_factorint or None: The oversampling factor to use for spatial integration. Default is None: the factor is estimated from the model minimal bin size

Returns

fluxMap: Predicted flux map

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.

to_dict(full_output=False)[source]#: Create dict for YAML serilisation

unfreeze(model_type=None)[source]#

Restore parameters frozen status to default depending on model type

Parameters

model_type{None, “spatial”, “spectral”, “temporal”}: restore frozen status to default for all parameters or only spatial/spectral/temporal Default is None so all parameters are restore to default frozen status.