lsst.ip.isr.isrTask Namespace Reference

Classes
class	FakeAmp
class	IsrTask
class	IsrTaskConfig
class	IsrTaskConnections

Functions
	crosstalkSourceLookup (datasetType, registry, quantumDataId, collections)

Function Documentation

crosstalkSourceLookup()

lsst.ip.isr.isrTask.crosstalkSourceLookup	(		datasetType,
			registry,
			quantumDataId,
			collections )

Lookup function to identify crosstalkSource entries.

This should return an empty list under most circumstances.  Only
when inter-chip crosstalk has been identified should this be
populated.

Parameters
----------
datasetType : `str`
    Dataset to lookup.
registry : `lsst.daf.butler.Registry`
    Butler registry to query.
quantumDataId : `lsst.daf.butler.DataCoordinate`
    Expanded data id to transform to identify crosstalkSources.  The
    ``detector`` entry will be stripped.
collections : `lsst.daf.butler.CollectionSearch`
    Collections to search through.

Returns
-------
results : `list` [`lsst.daf.butler.DatasetRef`]
    List of datasets that match the query that will be used as
    crosstalkSources.

Definition at line 64 of file isrTask.py.

def crosstalkSourceLookup(datasetType, registry, quantumDataId, collections):
    """Lookup function to identify crosstalkSource entries.
 
    This should return an empty list under most circumstances.  Only
    when inter-chip crosstalk has been identified should this be
    populated.
 
    Parameters
    ----------
    datasetType : `str`
        Dataset to lookup.
    registry : `lsst.daf.butler.Registry`
        Butler registry to query.
    quantumDataId : `lsst.daf.butler.DataCoordinate`
        Expanded data id to transform to identify crosstalkSources.  The
        ``detector`` entry will be stripped.
    collections : `lsst.daf.butler.CollectionSearch`
        Collections to search through.
 
    Returns
    -------
    results : `list` [`lsst.daf.butler.DatasetRef`]
        List of datasets that match the query that will be used as
        crosstalkSources.
    """
    newDataId = quantumDataId.subset(registry.dimensions.conform(["instrument", "exposure"]))
    results = set(registry.queryDatasets(datasetType, collections=collections, dataId=newDataId,
                                         findFirst=True))
    # In some contexts, calling `.expanded()` to expand all data IDs in the
    # query results can be a lot faster because it vectorizes lookups.  But in
    # this case, expandDataId shouldn't need to hit the database at all in the
    # steady state, because only the detector record is unknown and those are
    # cached in the registry.
    records = {k: newDataId.records[k] for k in newDataId.dimensions.elements}
    return [ref.expanded(registry.expandDataId(ref.dataId, records=records)) for ref in results]