MatchedBaseTaskConnections

class lsst.faro.base.MatchedBaseTaskConnections(*, config=None)

Bases: lsst.pipe.base.PipelineTaskConnections

Attributes Summary

allConnections
astrom_calibs
defaultTemplates
dimensions
initInputs
initOutputs
inputs
outputs
photo_calibs
prerequisiteInputs
skyMap
source_catalogs

Methods Summary

adjustQuantum(datasetRefMap, …)	Override to make adjustments to lsst.daf.butler.DatasetRef objects in the lsst.daf.butler.core.Quantum during the graph generation stage of the activator.
buildDatasetRefs(quantum)	Builds QuantizedConnections corresponding to input Quantum

Attributes Documentation

allConnections = {'astrom_calibs': Input(name='{wcsName}', storageClass='Wcs', doc='WCS for the catalog.', multiple=True, dimensions=('instrument', 'visit', 'detector', 'band'), isCalibration=False, deferLoad=False), 'photo_calibs': Input(name='{photoCalibName}', storageClass='PhotoCalib', doc='Photometric calibration object.', multiple=True, dimensions=('instrument', 'visit', 'detector', 'band'), isCalibration=False, deferLoad=False), 'skyMap': Input(name='skyMap', storageClass='SkyMap', doc='Input definition of geometry/bbox and projection/wcs for warped exposures', multiple=False, dimensions=('skymap',), isCalibration=False, deferLoad=False), 'source_catalogs': Input(name='src', storageClass='SourceCatalog', doc='Source catalogs to match up.', multiple=True, dimensions=('instrument', 'visit', 'detector', 'band'), isCalibration=False, deferLoad=False)}

astrom_calibs

defaultTemplates = {'coaddName': 'deep', 'photoCalibName': 'calexp.photoCalib', 'wcsName': 'calexp.wcs'}

dimensions = set()

initInputs = frozenset()

initOutputs = frozenset()

inputs = frozenset({'source_catalogs', 'astrom_calibs', 'photo_calibs', 'skyMap'})

outputs = frozenset()

photo_calibs

prerequisiteInputs = frozenset()

skyMap

source_catalogs

Methods Documentation

adjustQuantum(datasetRefMap: lsst.daf.butler.core.named.NamedKeyDict[lsst.daf.butler.core.datasets.type.DatasetType, typing.Set[lsst.daf.butler.core.datasets.ref.DatasetRef]][lsst.daf.butler.core.datasets.type.DatasetType, Set[lsst.daf.butler.core.datasets.ref.DatasetRef]]) → lsst.daf.butler.core.named.NamedKeyDict[lsst.daf.butler.core.datasets.type.DatasetType, typing.Set[lsst.daf.butler.core.datasets.ref.DatasetRef]][lsst.daf.butler.core.datasets.type.DatasetType, Set[lsst.daf.butler.core.datasets.ref.DatasetRef]]

Override to make adjustments to lsst.daf.butler.DatasetRef objects in the lsst.daf.butler.core.Quantum during the graph generation stage of the activator.

The base class implementation simply checks that input connections with multiple set to False have no more than one dataset.

Parameters:

datasetRefMap : NamedKeyDict

Mapping from dataset type to a set of lsst.daf.butler.DatasetRef objects

Returns:

datasetRefMap : NamedKeyDict

Modified mapping of input with possibly adjusted lsst.daf.butler.DatasetRef objects.

Raises:

ScalarError

Raised if any Input or PrerequisiteInput connection has multiple set to False, but multiple datasets.

Exception

Overrides of this function have the option of raising an Exception if a field in the input does not satisfy a need for a corresponding pipelineTask, i.e. no reference catalogs are found.

buildDatasetRefs(quantum: lsst.daf.butler.core.quantum.Quantum) → Tuple[lsst.pipe.base.connections.InputQuantizedConnection, lsst.pipe.base.connections.OutputQuantizedConnection]

Builds QuantizedConnections corresponding to input Quantum

Parameters:

quantum : lsst.daf.butler.Quantum

Quantum object which defines the inputs and outputs for a given unit of processing

Returns:

retVal : tuple of (InputQuantizedConnection,

OutputQuantizedConnection) Namespaces mapping attribute names (identifiers of connections) to butler references defined in the input lsst.daf.butler.Quantum