GenerateDonutFromRefitWcsTask

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

Bases: GenerateDonutCatalogWcsTask

Fit a new WCS to the image from a direct detect Donut Catalog and return the input exposure with the new WCS attached.

Parameters:

kwargs (Any)

Methods Summary

formatDonutCatalog(catalog)	Create a minimal donut catalog in afwTable format from the input direct detect catalog.
run(astromRefCat, exposure, fitDonutCatalog, ...)	Run task algorithm on in-memory data.

Methods Documentation

formatDonutCatalog(catalog)

Create a minimal donut catalog in afwTable format from the input direct detect catalog.

Parameters:

catalog (astropy.table.QTable) – Catalog containing donut sources already detected on the exposure.

Returns:

Minimal catalog needed for astromeryTask to fit WCS.

Return type:

lsst.afw.table.SimpleCatalog

run(astromRefCat, exposure, fitDonutCatalog, photoRefCat)

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:

• astromRefCat (List[SimpleCatalog])

• exposure (Exposure)

• fitDonutCatalog (QTable)

• photoRefCat (List[SimpleCatalog])