GbdesGlobalAstrometricFitConnections#

class lsst.drp.tasks.gbdesAstrometricFit.GbdesGlobalAstrometricFitConnections(*, config: PipelineTaskConfig | None = None)#

Bases: PipelineTaskConnections

Attributes Summary

`allConnections`	Mapping holding all connection attributes.
`camera`	Connection for output dataset.
`colorCatalog`	Class used for declaring PipelineTask input connections.
`dcrCoefficients`	Connection for output dataset.
`defaultTemplates`
`deprecatedTemplates`
`dimensions`	Set of dimension names that define the unit of work for this task.
`initInputs`	Set with the names of all `InitInput` connection attributes.
`initOutputs`	Set with the names of all `InitOutput` connection attributes.
`inputCamera`	Class used for declaring PipelineTask prerequisite connections.
`inputCameraModel`	Class used for declaring PipelineTask prerequisite connections.
`inputVisitSummaries`	Class used for declaring PipelineTask input connections.
`inputs`	Set with the names of all `connectionTypes.Input` connection attributes.
`isolatedStarCatalogs`	Class used for declaring PipelineTask input connections.
`isolatedStarSources`	Class used for declaring PipelineTask input connections.
`modelParams`	Connection for output dataset.
`outputCameraModel`	Connection for output dataset.
`outputCatalog`	Connection for output dataset.
`outputWcs`	Connection for output dataset.
`outputs`	Set with the names of all `Output` connection attributes.
`prerequisiteInputs`	Set with the names of all `PrerequisiteInput` connection attributes.
`referenceCatalog`	Class used for declaring PipelineTask prerequisite connections.
`starCatalog`	Connection for output dataset.

Methods Summary

getSpatialBoundsConnections()

Return the names of regular input and output connections whose data IDs should be used to compute the spatial bounds of this task's quanta.

Attributes Documentation

allConnections: Mapping[str, BaseConnection] = {'camera': Output(name='gbdesGlobalAstrometricFitCamera', storageClass='Camera', doc='Camera object constructed using the per-detector part of the astrometric model', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'physical_filter'), isCalibration=False), 'colorCatalog': Input(name='fgcm_Cycle4_StandardStars', storageClass='SimpleCatalog', doc='The catalog of magnitudes to match to input sources.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument',), isCalibration=False, deferLoad=False, minimum=1, deferGraphConstraint=False, deferBinding=False), 'dcrCoefficients': Output(name='gbdesGlobalAstrometricFit_dcrCoefficients', storageClass='ArrowNumpyDict', doc='Per-visit coefficients for DCR correction.', multiple=False, deprecated=None, _deprecation_context='', dimensions=(), isCalibration=False), 'inputCamera': PrerequisiteInput(name='camera', storageClass='Camera', doc='Input camera to use when constructing camera from astrometric model.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument',), isCalibration=True, deferLoad=False, minimum=1, lookupFunction=None), 'inputCameraModel': PrerequisiteInput(name='gbdesAstrometricFit_cameraModel', storageClass='ArrowNumpyDict', doc="Camera parameters to use for 'device' part of model", multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'physical_filter'), isCalibration=False, deferLoad=False, minimum=1, lookupFunction=None), 'inputVisitSummaries': Input(name='visitSummary', storageClass='ExposureCatalog', doc='Per-visit consolidated exposure metadata built from calexps. These catalogs use detector id for the id and must be sorted for fast lookups of a detector.', multiple=True, deprecated=None, _deprecation_context='', dimensions=('instrument', 'visit'), isCalibration=False, deferLoad=False, minimum=1, deferGraphConstraint=False, deferBinding=False), 'isolatedStarCatalogs': Input(name='isolated_star_presource_associations', storageClass='DataFrame', doc='Catalog of objects corresponding to the isolatedStarSources.', multiple=True, deprecated=None, _deprecation_context='', dimensions=('instrument', 'skymap', 'tract'), isCalibration=False, deferLoad=True, minimum=1, deferGraphConstraint=False, deferBinding=False), 'isolatedStarSources': Input(name='isolated_star_presources', storageClass='DataFrame', doc='Catalog of matched sources.', multiple=True, deprecated=None, _deprecation_context='', dimensions=('instrument', 'skymap', 'tract'), isCalibration=False, deferLoad=True, minimum=1, deferGraphConstraint=False, deferBinding=False), 'modelParams': Output(name='gbdesGlobalAstrometricFit_modelParams', storageClass='ArrowNumpyDict', doc='WCS parameters and covariance.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'physical_filter'), isCalibration=False), 'outputCameraModel': Output(name='gbdesAstrometricFit_cameraModel', storageClass='ArrowNumpyDict', doc="Camera parameters to use for 'device' part of model", multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'physical_filter'), isCalibration=False), 'outputCatalog': Output(name='gbdesGlobalAstrometricFit_fitStars', storageClass='ArrowNumpyDict', doc='Catalog of sources used in fit, along with residuals in pixel coordinates and tangent plane coordinates and chisq values.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'physical_filter'), isCalibration=False), 'outputWcs': Output(name='gbdesGlobalAstrometricFitSkyWcsCatalog', storageClass='ExposureCatalog', doc='Per-visit world coordinate systems derived from the fitted model. These catalogs only contain entries for detectors with an output, and use the detector id for the catalog id, sorted on id for fast lookups of a detector.', multiple=True, deprecated=None, _deprecation_context='', dimensions=('instrument', 'visit'), isCalibration=False), 'referenceCatalog': PrerequisiteInput(name='gaia_dr3_20230707', storageClass='SimpleCatalog', doc='The astrometry reference catalog to match to loaded input catalog sources.', multiple=True, deprecated=None, _deprecation_context='', dimensions=('skypix',), isCalibration=False, deferLoad=True, minimum=1, lookupFunction=None), 'starCatalog': Output(name='gbdesGlobalAstrometricFit_starCatalog', storageClass='ArrowNumpyDict', doc='Catalog of best-fit object positions. Also includes the fit proper motion and parallax if fitProperMotion is True.', multiple=False, deprecated=None, _deprecation_context='', dimensions=('instrument', 'physical_filter'), isCalibration=False)}#

Mapping holding all connection attributes.

This is a read-only view that is automatically updated when connection attributes are added, removed, or replaced in __init__. It is also updated after __init__ completes to reflect changes in inputs, prerequisiteInputs, outputs, initInputs, and initOutputs.

camera#: Connection for output dataset.

colorCatalog#

Class used for declaring PipelineTask input connections.

Attributes#

namestr: The default name used to identify the dataset type.
storageClassstr: The storage class used when (un)/persisting the dataset type.
multiplebool: Indicates if this connection should expect to contain multiple objects of the given dataset type. Tasks with more than one connection with multiple=True with the same dimensions may want to implement PipelineTaskConnections.adjustQuantum to ensure those datasets are consistent (i.e. zip-iterable) in PipelineTask.runQuantum and notify the execution system as early as possible of outputs that will not be produced because the corresponding input is missing.
dimensionsiterable of str: The lsst.daf.butler.Butler lsst.daf.butler.Registry dimensions used to identify the dataset type identified by the specified name.
deferLoadbool: Indicates that this dataset type will be loaded as a lsst.daf.butler.DeferredDatasetHandle. PipelineTasks can use this object to load the object at a later time.
minimumbool: Minimum number of datasets required for this connection, per quantum. This is checked in the base implementation of PipelineTaskConnections.adjustQuantum, which raises NoWorkFound if the minimum is not met for Input connections (causing the quantum to be pruned, skipped, or never created, depending on the context), and FileNotFoundError for PrerequisiteInput connections (causing QuantumGraph generation to fail). PipelineTask implementations may provide custom adjustQuantum implementations for more fine-grained or configuration-driven constraints, as long as they are compatible with this minium.
deferGraphConstraintbool, optional: If True, do not include this dataset type’s existence in the initial query that starts the QuantumGraph generation process. This can be used to make QuantumGraph generation faster by avoiding redundant datasets, and in certain cases it can (along with careful attention to which tasks are included in the same QuantumGraph) be used to work around the QuantumGraph generation algorithm’s inflexible handling of spatial overlaps. This option has no effect when the connection is not an overall input of the pipeline (or subset thereof) for which a graph is being created, and it never affects the ordering of quanta.
deferBindingbool, optional: If True, the dataset will not be automatically included in the pipeline graph, deferGraphConstraint is implied. The custom QuantumGraphBuilder is required to bind it and add a corresponding edge to the pipeline graph. This option allows to have the same dataset type as both input and output of a quantum.

Raises#

TypeError: Raised if minimum is greater than one but multiple=False.
NotImplementedError: Raised if minimum is zero for a regular Input connection; this is not currently supported by our QuantumGraph generation algorithm.

dcrCoefficients#: Connection for output dataset.

defaultTemplates = {}#

deprecatedTemplates = {}#

dimensions: set[str] = {'instrument', 'physical_filter'}#

Set of dimension names that define the unit of work for this task.

Required and implied dependencies will automatically be expanded later and need not be provided.

This may be replaced or modified in __init__ to change the dimensions of the task. After __init__ it will be a frozenset and may not be replaced.

initInputs: set[str] = frozenset({})#

Set with the names of all InitInput connection attributes.

See inputs for additional information.

initOutputs: set[str] = frozenset({})#

Set with the names of all InitOutput connection attributes.

See inputs for additional information.

inputCamera#

Class used for declaring PipelineTask prerequisite connections.

Attributes#

namestr: The default name used to identify the dataset type.
storageClassstr: The storage class used when (un)/persisting the dataset type.
multiplebool: Indicates if this connection should expect to contain multiple objects of the given dataset type. Tasks with more than one connection with multiple=True with the same dimensions may want to implement PipelineTaskConnections.adjustQuantum to ensure those datasets are consistent (i.e. zip-iterable) in PipelineTask.runQuantum and notify the execution system as early as possible of outputs that will not be produced because the corresponding input is missing.
dimensionsiterable of str: The lsst.daf.butler.Butler lsst.daf.butler.Registry dimensions used to identify the dataset type identified by the specified name.
minimumbool: Minimum number of datasets required for this connection, per quantum. This is checked in the base implementation of PipelineTaskConnections.adjustQuantum, which raises FileNotFoundError (causing QuantumGraph generation to fail). PipelineTask implementations may provide custom adjustQuantum implementations for more fine-grained or configuration-driven constraints, as long as they are compatible with this minium.
lookupFunctiontyping.Callable, optional: An optional callable function that will look up PrerequisiteInputs using the DatasetType, registry, quantum dataId, and input collections passed to it. If no function is specified, the default temporal spatial lookup will be used.

Raises#

TypeError: Raised if minimum is greater than one but multiple=False.

Notes#

Prerequisite inputs are used for datasets that must exist in the data repository before a pipeline including this is run; they cannot be produced by another task in the same pipeline.

In exchange for this limitation, they have a number of advantages relative to regular Input connections:

The query used to find them then during QuantumGraph generation can be fully customized by providing a lookupFunction.
Failed searches for prerequisites during QuantumGraph generation will usually generate more helpful diagnostics than those for regular Input connections.
The default query for prerequisite inputs relates the quantum dimensions directly to the dimensions of its dataset type, without being constrained by any of the other dimensions in the pipeline. This allows them to be used for temporal calibration lookups (which regular Input connections cannot do at present) and to work around QuantumGraph generation limitations involving cases where naive spatial overlap relationships between dimensions are not desired (e.g. a task that wants all detectors in each visit for which the visit overlaps a tract, not just those where that detector+visit combination overlaps the tract).
Prerequisite inputs may be optional (regular inputs are never optional).

inputCameraModel#

Class used for declaring PipelineTask prerequisite connections.

Attributes#

namestr: The default name used to identify the dataset type.
storageClassstr: The storage class used when (un)/persisting the dataset type.
multiplebool: Indicates if this connection should expect to contain multiple objects of the given dataset type. Tasks with more than one connection with multiple=True with the same dimensions may want to implement PipelineTaskConnections.adjustQuantum to ensure those datasets are consistent (i.e. zip-iterable) in PipelineTask.runQuantum and notify the execution system as early as possible of outputs that will not be produced because the corresponding input is missing.
dimensionsiterable of str: The lsst.daf.butler.Butler lsst.daf.butler.Registry dimensions used to identify the dataset type identified by the specified name.
minimumbool: Minimum number of datasets required for this connection, per quantum. This is checked in the base implementation of PipelineTaskConnections.adjustQuantum, which raises FileNotFoundError (causing QuantumGraph generation to fail). PipelineTask implementations may provide custom adjustQuantum implementations for more fine-grained or configuration-driven constraints, as long as they are compatible with this minium.
lookupFunctiontyping.Callable, optional: An optional callable function that will look up PrerequisiteInputs using the DatasetType, registry, quantum dataId, and input collections passed to it. If no function is specified, the default temporal spatial lookup will be used.

Raises#

TypeError: Raised if minimum is greater than one but multiple=False.

Notes#

Prerequisite inputs are used for datasets that must exist in the data repository before a pipeline including this is run; they cannot be produced by another task in the same pipeline.

In exchange for this limitation, they have a number of advantages relative to regular Input connections:

The query used to find them then during QuantumGraph generation can be fully customized by providing a lookupFunction.
Failed searches for prerequisites during QuantumGraph generation will usually generate more helpful diagnostics than those for regular Input connections.
The default query for prerequisite inputs relates the quantum dimensions directly to the dimensions of its dataset type, without being constrained by any of the other dimensions in the pipeline. This allows them to be used for temporal calibration lookups (which regular Input connections cannot do at present) and to work around QuantumGraph generation limitations involving cases where naive spatial overlap relationships between dimensions are not desired (e.g. a task that wants all detectors in each visit for which the visit overlaps a tract, not just those where that detector+visit combination overlaps the tract).
Prerequisite inputs may be optional (regular inputs are never optional).

inputVisitSummaries#

Class used for declaring PipelineTask input connections.

Attributes#

namestr: The default name used to identify the dataset type.
storageClassstr: The storage class used when (un)/persisting the dataset type.
multiplebool: Indicates if this connection should expect to contain multiple objects of the given dataset type. Tasks with more than one connection with multiple=True with the same dimensions may want to implement PipelineTaskConnections.adjustQuantum to ensure those datasets are consistent (i.e. zip-iterable) in PipelineTask.runQuantum and notify the execution system as early as possible of outputs that will not be produced because the corresponding input is missing.
dimensionsiterable of str: The lsst.daf.butler.Butler lsst.daf.butler.Registry dimensions used to identify the dataset type identified by the specified name.
deferLoadbool: Indicates that this dataset type will be loaded as a lsst.daf.butler.DeferredDatasetHandle. PipelineTasks can use this object to load the object at a later time.
minimumbool: Minimum number of datasets required for this connection, per quantum. This is checked in the base implementation of PipelineTaskConnections.adjustQuantum, which raises NoWorkFound if the minimum is not met for Input connections (causing the quantum to be pruned, skipped, or never created, depending on the context), and FileNotFoundError for PrerequisiteInput connections (causing QuantumGraph generation to fail). PipelineTask implementations may provide custom adjustQuantum implementations for more fine-grained or configuration-driven constraints, as long as they are compatible with this minium.
deferGraphConstraintbool, optional: If True, do not include this dataset type’s existence in the initial query that starts the QuantumGraph generation process. This can be used to make QuantumGraph generation faster by avoiding redundant datasets, and in certain cases it can (along with careful attention to which tasks are included in the same QuantumGraph) be used to work around the QuantumGraph generation algorithm’s inflexible handling of spatial overlaps. This option has no effect when the connection is not an overall input of the pipeline (or subset thereof) for which a graph is being created, and it never affects the ordering of quanta.
deferBindingbool, optional: If True, the dataset will not be automatically included in the pipeline graph, deferGraphConstraint is implied. The custom QuantumGraphBuilder is required to bind it and add a corresponding edge to the pipeline graph. This option allows to have the same dataset type as both input and output of a quantum.

Raises#

TypeError: Raised if minimum is greater than one but multiple=False.
NotImplementedError: Raised if minimum is zero for a regular Input connection; this is not currently supported by our QuantumGraph generation algorithm.

inputs: set[str] = frozenset({'colorCatalog', 'inputVisitSummaries', 'isolatedStarCatalogs', 'isolatedStarSources'})#

Set with the names of all connectionTypes.Input connection attributes.

This is updated automatically as class attributes are added, removed, or replaced in __init__. Removing entries from this set will cause those connections to be removed after __init__ completes, but this is supported only for backwards compatibility; new code should instead just delete the collection attributed directly. After __init__ this will be a frozenset and may not be replaced.

isolatedStarCatalogs#

Class used for declaring PipelineTask input connections.

Attributes#

namestr: The default name used to identify the dataset type.
storageClassstr: The storage class used when (un)/persisting the dataset type.
multiplebool: Indicates if this connection should expect to contain multiple objects of the given dataset type. Tasks with more than one connection with multiple=True with the same dimensions may want to implement PipelineTaskConnections.adjustQuantum to ensure those datasets are consistent (i.e. zip-iterable) in PipelineTask.runQuantum and notify the execution system as early as possible of outputs that will not be produced because the corresponding input is missing.
dimensionsiterable of str: The lsst.daf.butler.Butler lsst.daf.butler.Registry dimensions used to identify the dataset type identified by the specified name.
deferLoadbool: Indicates that this dataset type will be loaded as a lsst.daf.butler.DeferredDatasetHandle. PipelineTasks can use this object to load the object at a later time.
minimumbool: Minimum number of datasets required for this connection, per quantum. This is checked in the base implementation of PipelineTaskConnections.adjustQuantum, which raises NoWorkFound if the minimum is not met for Input connections (causing the quantum to be pruned, skipped, or never created, depending on the context), and FileNotFoundError for PrerequisiteInput connections (causing QuantumGraph generation to fail). PipelineTask implementations may provide custom adjustQuantum implementations for more fine-grained or configuration-driven constraints, as long as they are compatible with this minium.
deferGraphConstraintbool, optional: If True, do not include this dataset type’s existence in the initial query that starts the QuantumGraph generation process. This can be used to make QuantumGraph generation faster by avoiding redundant datasets, and in certain cases it can (along with careful attention to which tasks are included in the same QuantumGraph) be used to work around the QuantumGraph generation algorithm’s inflexible handling of spatial overlaps. This option has no effect when the connection is not an overall input of the pipeline (or subset thereof) for which a graph is being created, and it never affects the ordering of quanta.
deferBindingbool, optional: If True, the dataset will not be automatically included in the pipeline graph, deferGraphConstraint is implied. The custom QuantumGraphBuilder is required to bind it and add a corresponding edge to the pipeline graph. This option allows to have the same dataset type as both input and output of a quantum.

Raises#

TypeError: Raised if minimum is greater than one but multiple=False.
NotImplementedError: Raised if minimum is zero for a regular Input connection; this is not currently supported by our QuantumGraph generation algorithm.

isolatedStarSources#

Class used for declaring PipelineTask input connections.

Attributes#

namestr: The default name used to identify the dataset type.
storageClassstr: The storage class used when (un)/persisting the dataset type.
multiplebool: Indicates if this connection should expect to contain multiple objects of the given dataset type. Tasks with more than one connection with multiple=True with the same dimensions may want to implement PipelineTaskConnections.adjustQuantum to ensure those datasets are consistent (i.e. zip-iterable) in PipelineTask.runQuantum and notify the execution system as early as possible of outputs that will not be produced because the corresponding input is missing.
dimensionsiterable of str: The lsst.daf.butler.Butler lsst.daf.butler.Registry dimensions used to identify the dataset type identified by the specified name.
deferLoadbool: Indicates that this dataset type will be loaded as a lsst.daf.butler.DeferredDatasetHandle. PipelineTasks can use this object to load the object at a later time.
minimumbool: Minimum number of datasets required for this connection, per quantum. This is checked in the base implementation of PipelineTaskConnections.adjustQuantum, which raises NoWorkFound if the minimum is not met for Input connections (causing the quantum to be pruned, skipped, or never created, depending on the context), and FileNotFoundError for PrerequisiteInput connections (causing QuantumGraph generation to fail). PipelineTask implementations may provide custom adjustQuantum implementations for more fine-grained or configuration-driven constraints, as long as they are compatible with this minium.
deferGraphConstraintbool, optional: If True, do not include this dataset type’s existence in the initial query that starts the QuantumGraph generation process. This can be used to make QuantumGraph generation faster by avoiding redundant datasets, and in certain cases it can (along with careful attention to which tasks are included in the same QuantumGraph) be used to work around the QuantumGraph generation algorithm’s inflexible handling of spatial overlaps. This option has no effect when the connection is not an overall input of the pipeline (or subset thereof) for which a graph is being created, and it never affects the ordering of quanta.
deferBindingbool, optional: If True, the dataset will not be automatically included in the pipeline graph, deferGraphConstraint is implied. The custom QuantumGraphBuilder is required to bind it and add a corresponding edge to the pipeline graph. This option allows to have the same dataset type as both input and output of a quantum.

Raises#

TypeError: Raised if minimum is greater than one but multiple=False.
NotImplementedError: Raised if minimum is zero for a regular Input connection; this is not currently supported by our QuantumGraph generation algorithm.

modelParams#: Connection for output dataset.

outputCameraModel#: Connection for output dataset.

outputCatalog#: Connection for output dataset.

outputWcs#: Connection for output dataset.

outputs: set[str] = frozenset({'camera', 'dcrCoefficients', 'modelParams', 'outputCameraModel', 'outputCatalog', 'outputWcs', 'starCatalog'})#

Set with the names of all Output connection attributes.

See inputs for additional information.

prerequisiteInputs: set[str] = frozenset({'inputCamera', 'inputCameraModel', 'referenceCatalog'})#

Set with the names of all PrerequisiteInput connection attributes.

See inputs for additional information.

referenceCatalog#

Class used for declaring PipelineTask prerequisite connections.

Attributes#

namestr: The default name used to identify the dataset type.
storageClassstr: The storage class used when (un)/persisting the dataset type.
multiplebool: Indicates if this connection should expect to contain multiple objects of the given dataset type. Tasks with more than one connection with multiple=True with the same dimensions may want to implement PipelineTaskConnections.adjustQuantum to ensure those datasets are consistent (i.e. zip-iterable) in PipelineTask.runQuantum and notify the execution system as early as possible of outputs that will not be produced because the corresponding input is missing.
dimensionsiterable of str: The lsst.daf.butler.Butler lsst.daf.butler.Registry dimensions used to identify the dataset type identified by the specified name.
minimumbool: Minimum number of datasets required for this connection, per quantum. This is checked in the base implementation of PipelineTaskConnections.adjustQuantum, which raises FileNotFoundError (causing QuantumGraph generation to fail). PipelineTask implementations may provide custom adjustQuantum implementations for more fine-grained or configuration-driven constraints, as long as they are compatible with this minium.
lookupFunctiontyping.Callable, optional: An optional callable function that will look up PrerequisiteInputs using the DatasetType, registry, quantum dataId, and input collections passed to it. If no function is specified, the default temporal spatial lookup will be used.

Raises#

TypeError: Raised if minimum is greater than one but multiple=False.

Notes#

Prerequisite inputs are used for datasets that must exist in the data repository before a pipeline including this is run; they cannot be produced by another task in the same pipeline.

In exchange for this limitation, they have a number of advantages relative to regular Input connections:

The query used to find them then during QuantumGraph generation can be fully customized by providing a lookupFunction.
Failed searches for prerequisites during QuantumGraph generation will usually generate more helpful diagnostics than those for regular Input connections.
The default query for prerequisite inputs relates the quantum dimensions directly to the dimensions of its dataset type, without being constrained by any of the other dimensions in the pipeline. This allows them to be used for temporal calibration lookups (which regular Input connections cannot do at present) and to work around QuantumGraph generation limitations involving cases where naive spatial overlap relationships between dimensions are not desired (e.g. a task that wants all detectors in each visit for which the visit overlaps a tract, not just those where that detector+visit combination overlaps the tract).
Prerequisite inputs may be optional (regular inputs are never optional).

starCatalog#: Connection for output dataset.

Methods Documentation

getSpatialBoundsConnections()#

Return the names of regular input and output connections whose data IDs should be used to compute the spatial bounds of this task’s quanta.

The spatial bound for a quantum is defined as the union of the regions of all data IDs of all connections returned here, along with the region of the quantum data ID (if the task has spatial dimensions).

Returns#

connection_namescollections.abc.Iterable [ str ]: Names of collections with spatial dimensions. These are the task-internal connection names, not butler dataset type names.

Notes#

The spatial bound is used to search for prerequisite inputs that have skypix dimensions. The default implementation returns an empty iterable, which is usually sufficient for tasks with spatial dimensions, but if a task’s inputs or outputs are associated with spatial regions that extend beyond the quantum data ID’s region, this method may need to be overridden to expand the set of prerequisite inputs found.

Tasks that do not have spatial dimensions that have skypix prerequisite inputs should always override this method, as the default spatial bounds otherwise cover the full sky.