JointcalTask#
- class lsst.jointcal.JointcalTask(**kwargs)#
Bases:
PipelineTaskAstrometricly and photometricly calibrate across multiple visits of the same field.
Methods Summary
run(inputSourceTableVisit, ...[, tract])Run task algorithm on in-memory data.
runQuantum(butlerQC, inputRefs, outputRefs)Do butler IO and transform to provide in memory objects for tasks
runmethod.Methods Documentation
- run(inputSourceTableVisit, inputVisitSummary, inputCamera, tract=None)#
Run task algorithm on in-memory data.
This method should be implemented in a subclass. This method will receive keyword-only arguments whose names will be the same as names of connection fields describing input dataset types. Argument values will be data objects retrieved from data butler. If a dataset type is configured with
multiplefield set toTruethen the argument value will be a list of objects, otherwise it will be a single object.If the task needs to know its input or output DataIds then it also has to override the
runQuantummethod.This method should return a
Structwhose attributes share the same name as the connection fields describing output dataset types.Parameters#
- **kwargs
Any Arbitrary parameters accepted by subclasses.
Returns#
- struct
Struct Struct with attribute names corresponding to output connection fields.
Examples#
Typical implementation of this method may look like:
def run(self, *, input, calib): # "input", "calib", and "output" are the names of the # connection fields. # Assuming that input/calib datasets are `scalar` they are # simple objects, do something with inputs and calibs, produce # output image. image = self.makeImage(input, calib) # If output dataset is `scalar` then return object, not list return Struct(output=image)
- **kwargs
- runQuantum(butlerQC, inputRefs, outputRefs)#
Do butler IO and transform to provide in memory objects for tasks
runmethod.Parameters#
- butlerQC
QuantumContext A butler which is specialized to operate in the context of a
lsst.daf.butler.Quantum.- inputRefs
InputQuantizedConnection Datastructure whose attribute names are the names that identify connections defined in corresponding
PipelineTaskConnectionsclass. The values of these attributes are thelsst.daf.butler.DatasetRefobjects associated with the defined input/prerequisite connections.- outputRefs
OutputQuantizedConnection Datastructure whose attribute names are the names that identify connections defined in corresponding
PipelineTaskConnectionsclass. The values of these attributes are thelsst.daf.butler.DatasetRefobjects associated with the defined output connections.
- butlerQC