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)

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)

Call self as a function.

contributes(mask, margin='0 deg')

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)

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)

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)

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)

Evaluate model on Geom.

freeze(model_type=None)

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)

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)

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)

Create dictionary for YAML serilisation.

unfreeze(model_type=None)

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.