SkyModel#

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

Bases: CovarianceMixin, 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`	Covariance as a `Covariance` object.
`default_parameters`
`evaluation_bin_size_min`	Minimal spatial bin size for spatial model evaluation.
`evaluation_radius`	Evaluation radius as an `Angle`.
`evaluation_region`	Evaluation region as an `Angle`.
`frame`
`frozen`	Frozen status of a model, True if all parameters are frozen.
`name`
`parameters`	Parameters as a `Parameters` object.
`parameters_unique_names`	List of unique parameter names.
`position`	Position as a `SkyCoord`.
`position_lonlat`	Spatial model center position `(lon, lat)` in radians and frame of the model.
`spatial_model`	Spatial model as a `SpatialModel` object.
`spectral_model`	Spectral model as a `SpectralModel` object.
`tag`
`temporal_model`	Temporal model as a `TemporalModel` object.
`type`

Methods Summary

`__call__`(lon, lat, energy[, time])	Call self as a function.
`contributes`(mask[, margin])	Check if a sky model contributes within a mask map.
`copy`([name, copy_data])	Copy sky model.
`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, **kwargs)	Create SkyModel from dictionary.
`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 dictionary for YAML serilisation.
`unfreeze`([model_type])	Restore parameters frozen status to default depending on model type.

Attributes Documentation

covariance#: Covariance as a Covariance object.

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

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

evaluation_radius#: Evaluation radius as an Angle.

evaluation_region#: Evaluation region as an Angle.

frame#

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

name#

parameters#

parameters_unique_names#: List of unique parameter names. Return formatted as par_type.par_name.

position#: Position as a SkyCoord.

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

spatial_model#: Spatial model as a SpatialModel object.

spectral_model#: Spectral model as a SpectralModel object.

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

temporal_model#: Temporal model as a TemporalModel object.

type#

Methods Documentation

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

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

Check if a sky model 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 is True.

copy(name=None, copy_data=False, **kwargs)[source]#

Copy sky 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 SkyModel.

Returns:

modelSkyModel: Copied sky model.

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, optional: Tag to create spatial model. Default is None.
temporal_modelstr, optional: Tag to create temporal model. Default is None.
**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`, optional: Time coordinate. Default is None.

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 spatial/spectral/temporal. Default is None, such that all parameters are frozen.

classmethod from_dict(data, **kwargs)[source]#: Create SkyModel from dictionary.

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, optional: GIT table. Default is None.
oversampling_factorint, optional: 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 dictionary 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, such that all parameters are restored to default frozen status.