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LSST Data Management Base Package
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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 65 of file isrTask.py.

65def crosstalkSourceLookup(datasetType, registry, quantumDataId, collections):
66 """Lookup function to identify crosstalkSource entries.
67
68 This should return an empty list under most circumstances. Only
69 when inter-chip crosstalk has been identified should this be
70 populated.
71
72 Parameters
73 ----------
74 datasetType : `str`
75 Dataset to lookup.
76 registry : `lsst.daf.butler.Registry`
77 Butler registry to query.
78 quantumDataId : `lsst.daf.butler.DataCoordinate`
79 Expanded data id to transform to identify crosstalkSources. The
80 ``detector`` entry will be stripped.
81 collections : `lsst.daf.butler.CollectionSearch`
82 Collections to search through.
83
84 Returns
85 -------
86 results : `list` [`lsst.daf.butler.DatasetRef`]
87 List of datasets that match the query that will be used as
88 crosstalkSources.
89 """
90 newDataId = quantumDataId.subset(registry.dimensions.conform(["instrument", "exposure"]))
91 results = set(registry.queryDatasets(datasetType, collections=collections, dataId=newDataId,
92 findFirst=True))
93 # In some contexts, calling `.expanded()` to expand all data IDs in the
94 # query results can be a lot faster because it vectorizes lookups. But in
95 # this case, expandDataId shouldn't need to hit the database at all in the
96 # steady state, because only the detector record is unknown and those are
97 # cached in the registry.
98 records = {k: newDataId.records[k] for k in newDataId.dimensions.elements}
99 return [ref.expanded(registry.expandDataId(ref.dataId, records=records)) for ref in results]
100
101