PrerequisiteInput¶
-
class
lsst.pipe.base.connectionTypes.
PrerequisiteInput
(name: str, storageClass: str, doc: str = '', multiple: bool = False, dimensions: Iterable[str] = (), isCalibration: bool = False, deferLoad: bool = False, lookupFunction: Optional[Callable[[lsst.daf.butler.core.datasets.type.DatasetType, lsst.daf.butler.registry._registry.Registry, lsst.daf.butler.core.dimensions._coordinate.DataCoordinate, lsst.daf.butler.registry.wildcards.CollectionSearch], Iterable[lsst.daf.butler.core.datasets.ref.DatasetRef]]] = None)¶ Bases:
lsst.pipe.base.connectionTypes.BaseInput
Class used for declaring PipelineTask prerequisite connections
Parameters: - name :
str
The default name used to identify the dataset type
- storageClass :
str
The storage class used when (un)/persisting the dataset type
- multiple :
bool
Indicates if this connection should expect to contain multiple objects of the given dataset type
- dimensions : iterable of
str
The
lsst.daf.butler.Butler
lsst.daf.butler.Registry
dimensions used to identify the dataset type identified by the specified name- deferLoad :
bool
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.- lookupFunction: `typing.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.
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 alookupFunction
. - Failed searches for prerequisites during
QuantumGraph
generation will usually generate more helpful diagnostics than those for regularInput
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 aroundQuantumGraph
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).
Attributes Summary
deferLoad
dimensions
doc
isCalibration
lookupFunction
multiple
Methods Summary
makeDatasetType
(universe, parentStorageClass)Construct a true DatasetType
instance with normalized dimensions.Attributes Documentation
-
deferLoad
= False¶
-
dimensions
= ()¶
-
doc
= ''¶
-
isCalibration
= False¶
-
lookupFunction
= None¶
-
multiple
= False¶
Methods Documentation
-
makeDatasetType
(universe: lsst.daf.butler.core.dimensions._universe.DimensionUniverse, parentStorageClass: Optional[lsst.daf.butler.core.storageClass.StorageClass] = None)¶ Construct a true
DatasetType
instance with normalized dimensions.Parameters: - universe :
lsst.daf.butler.DimensionUniverse
Set of all known dimensions to be used to normalize the dimension names specified in config.
- parentStorageClass :
lsst.daf.butler.StorageClass
, optional Parent storage class for component datasets;
None
otherwise.
Returns: - datasetType :
DatasetType
The
DatasetType
defined by this connection.
- universe :
- name :