TransformCatalogBaseTask#

class lsst.pipe.tasks.postprocess.TransformCatalogBaseTask(*args, **kwargs)#

Bases: PipelineTask

Base class for transforming/standardizing a catalog by applying functors that convert units and apply calibrations.

The purpose of this task is to perform a set of computations on an input DeferredDatasetHandle or InMemoryDatasetHandle that holds a DataFrame dataset (such as deepCoadd_obj), and write the results to a new dataset (which needs to be declared in an outputDataset attribute).

The calculations to be performed are defined in a YAML file that specifies a set of functors to be computed, provided as a --functorFile config parameter. An example of such a YAML file is the following:

funcs:
sourceId:

functor: Index

x:

functor: Column args: slot_Centroid_x

y:

functor: Column args: slot_Centroid_y

psfFlux:

functor: LocalNanojansky args:

  • slot_PsfFlux_instFlux

  • slot_PsfFlux_instFluxErr

  • base_LocalPhotoCalib

  • base_LocalPhotoCalibErr

psfFluxErr:

functor: LocalNanojanskyErr args:

  • slot_PsfFlux_instFlux

  • slot_PsfFlux_instFluxErr

  • base_LocalPhotoCalib

  • base_LocalPhotoCalibErr

flags:

  • detect_isPrimary

The names for each entry under “func” will become the names of columns in the output dataset. All the functors referenced are defined in functors. Positional arguments to be passed to each functor are in the args list, and any additional entries for each column other than “functor” or “args” (e.g., 'filt', 'dataset') are treated as keyword arguments to be passed to the functor initialization.

The “flags” entry is the default shortcut for Column functors. All columns listed under “flags” will be copied to the output table untransformed. They can be of any datatype. In the special case of transforming a multi-level oject table with band and dataset indices (deepCoadd_obj), these will be taked from the meas dataset and exploded out per band.

There are two special shortcuts that only apply when transforming multi-level Object (deepCoadd_obj) tables:

  • The “refFlags” entry is shortcut for Column functor taken from the ref dataset if transforming an ObjectTable.

  • The “forcedFlags” entry is shortcut for Column functors. taken from the forced_src dataset if transforming an ObjectTable. These are expanded out per band.

This task uses the lsst.pipe.tasks.postprocess.PostprocessAnalysis object to organize and excecute the calculations.

Attributes Summary

ConfigClass

inputDataset

outputDataset

Methods Summary

getAnalysis(handles[, funcs, band])

getFunctors()

run(handle[, funcs, dataId, band])

Do postprocessing calculations

runQuantum(butlerQC, inputRefs, outputRefs)

Do butler IO and transform to provide in memory objects for tasks run method.

transform(band, handles, funcs, dataId)

Attributes Documentation

ConfigClass: ClassVar[type[PipelineTaskConfig]]#
inputDataset#
outputDataset#

Methods Documentation

getAnalysis(handles, funcs=None, band=None)#
getFunctors()#
run(handle, funcs=None, dataId=None, band=None)#

Do postprocessing calculations

Takes a DeferredDatasetHandle or InMemoryDatasetHandle or DataFrame object and dataId, returns a dataframe with results of postprocessing calculations.

Parameters#

handlesDeferredDatasetHandle or

InMemoryDatasetHandle or DataFrame, or list of these.

DataFrames from which calculations are done.

funcsFunctor

Functors to apply to the table’s columns

dataIddict, optional

Used to add a patchId column to the output dataframe.

bandstr, optional

Filter band that is being processed.

Returns#

resultlsst.pipe.base.Struct

Result struct, with a single outputCatalog attribute holding the transformed catalog.

runQuantum(butlerQC, inputRefs, outputRefs)#

Do butler IO and transform to provide in memory objects for tasks run method.

Parameters#

butlerQCQuantumContext

A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.

inputRefsInputQuantizedConnection

Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined input/prerequisite connections.

outputRefsOutputQuantizedConnection

Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined output connections.

transform(band, handles, funcs, dataId)#