CalcZernikesUnpairedTask#
- class lsst.ts.wep.task.CalcZernikesUnpairedTask(**kwargs)#
Bases:
CalcZernikesTaskCalculate Zernikes using unpaired donuts.
- Parameters:
kwargs (
Any)
Methods Summary
run(donutStamps, intrinsicZernikes[, numCores])Run task algorithm on in-memory data.
runQuantum(butlerQC, inputRefs, outputRefs)Need to override the runQuantum method to handle the unpaired nature of the inputs.
Methods Documentation
- run(donutStamps, intrinsicZernikes, numCores=1)#
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 with attribute names corresponding to output connection fields.
- Return type:
Struct
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)
- Parameters:
donutStamps (
DonutStamps)intrinsicZernikes (
IntrinsicZernikes)numCores (
int, default:1)