AssembleCoaddConnections

class lsst.pipe.tasks.assembleCoadd.AssembleCoaddConnections(*, config=None)

Bases: lsst.pipe.base.PipelineTaskConnections

Attributes Summary

allConnections
brightObjectMask
coaddExposure
defaultTemplates
dimensions
initInputs
initOutputs
inputWarps
inputs
nImage
outputs
prerequisiteInputs
skyMap

Methods Summary

adjustQuantum(datasetRefMap)	Override to make adjustments to lsst.daf.butler.DatasetRef`s 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 = {'brightObjectMask': PrerequisiteInput(name='brightObjectMask', storageClass='ObjectMaskCatalog', doc='Input Bright Object Mask mask produced with external catalogs to be applied to the mask plane BRIGHT_OBJECT.', multiple=False, dimensions=('tract', 'patch', 'skymap', 'abstract_filter'), deferLoad=False), 'coaddExposure': Output(name='{fakesType}{outputCoaddName}Coadd{warpTypeSuffix}', storageClass='ExposureF', doc='Output coadded exposure, produced by stacking input warps', multiple=False, dimensions=('tract', 'patch', 'skymap', 'abstract_filter')), 'inputWarps': Input(name='{inputCoaddName}Coadd_{warpType}Warp', storageClass='ExposureF', doc='Input list of warps to be assemebled i.e. stacked.WarpType (e.g. direct, psfMatched) is controlled by the warpType config parameter', multiple=True, dimensions=('tract', 'patch', 'skymap', 'visit', 'instrument'), deferLoad=True), 'nImage': Output(name='{outputCoaddName}Coadd_nImage', storageClass='ImageU', doc='Output image of number of input images per pixel', multiple=False, dimensions=('tract', 'patch', 'skymap', 'abstract_filter')), 'skyMap': Input(name='{inputCoaddName}Coadd_skyMap', storageClass='SkyMap', doc='Input definition of geometry/bbox and projection/wcs for coadded exposures', multiple=False, dimensions=('skymap',), deferLoad=False)}

brightObjectMask

coaddExposure

defaultTemplates = {'fakesType': '', 'inputCoaddName': 'deep', 'outputCoaddName': 'deep', 'warpType': 'direct', 'warpTypeSuffix': ''}

dimensions = {'abstract_filter', 'patch', 'tract', 'skymap'}

initInputs = frozenset()

initOutputs = frozenset()

inputWarps

inputs = frozenset({'inputWarps', 'skyMap'})

nImage

outputs = frozenset({'coaddExposure', 'nImage'})

prerequisiteInputs = frozenset({'brightObjectMask'})

skyMap

Methods Documentation

adjustQuantum(datasetRefMap: lsst.pipe.base.connections.InputQuantizedConnection)

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

Parameters:

datasetRefMap : dict

Mapping with keys of dataset type name to list of `lsst.daf.butler.DatasetRef`s

Returns:

datasetRefMap : dict

Modified mapping of input with possible adjusted `lsst.daf.butler.DatasetRef`s

Raises:

Exception

Overrides of this function have the option of raising and 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