GenerateDonutCatalogWcsTask

class lsst.ts.wep.task.GenerateDonutCatalogWcsTask(**kwargs)

Bases: PipelineTask

Create a WCS from boresight info and then use this with a reference catalog to select sources on the detectors for AOS.

Parameters:

kwargs (Any)

Methods Summary

getRefObjLoader(refCatalogList)	Create a ReferenceObjectLoader from available reference catalogs in the repository.
run(refCatalogs, exposure)	Run task algorithm on in-memory data.

Methods Documentation

getRefObjLoader(refCatalogList)

Create a ReferenceObjectLoader from available reference catalogs in the repository.

Parameters:

refCatalogList (list) – List of deferred butler references for the reference catalogs.

Returns:

Object to conduct spatial searches through the reference catalogs

Return type:

lsst.meas.algorithms.ReferenceObjectsLoader

run(refCatalogs, exposure)

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 multiple field set to True then 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 runQuantum method.

This method should return a Struct whose 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:

• refCatalogs (list[SimpleCatalog])

• exposure (Exposure)