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
canMultiprocess
inputDataset
outputDataset

Methods Summary

emptyMetadata()	Empty (clear) the metadata for this Task and all sub-Tasks.
getAnalysis(handles[, funcs, band])
getFullMetadata()	Get metadata for all tasks.
getFullName()	Get the task name as a hierarchical name including parent task names.
getFunctors()
getName()	Get the name of the task.
getTaskDict()	Get a dictionary of all tasks as a shallow copy.
makeField(doc)	Make a lsst.pex.config.ConfigurableField for this task.
makeSubtask(name, **keyArgs)	Create a subtask as a new instance as the name attribute of this task.
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.
timer(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.
transform(band, handles, funcs, dataId)

Attributes Documentation

ConfigClass: ClassVar[type[PipelineTaskConfig]]

canMultiprocess: ClassVar[bool] = True

inputDataset

outputDataset

Methods Documentation

emptyMetadata() → None

Empty (clear) the metadata for this Task and all sub-Tasks.

getAnalysis(handles, funcs=None, band=None)

getFullMetadata() → TaskMetadata

Get metadata for all tasks.

Returns:

metadataTaskMetadata

The keys are the full task name. Values are metadata for the top-level task and all subtasks, sub-subtasks, etc.

Notes

The returned metadata includes timing information (if @timer.timeMethod is used) and any metadata set by the task. The name of each item consists of the full task name with . replaced by :, followed by . and the name of the item, e.g.:

topLevelTaskName:subtaskName:subsubtaskName.itemName

using : in the full task name disambiguates the rare situation that a task has a subtask and a metadata item with the same name.

getFullName() → str

Get the task name as a hierarchical name including parent task names.

Returns:

fullNamestr

The full name consists of the name of the parent task and each subtask separated by periods. For example:

• The full name of top-level task “top” is simply “top”.

• The full name of subtask “sub” of top-level task “top” is “top.sub”.

• The full name of subtask “sub2” of subtask “sub” of top-level task “top” is “top.sub.sub2”.

getFunctors()

getName() → str

Get the name of the task.

Returns:

taskNamestr

Name of the task.

See also

getFullName

Get the full name of the task.

getTaskDict() → dict[str, weakref.ReferenceType[lsst.pipe.base.task.Task]]

Get a dictionary of all tasks as a shallow copy.

Returns:

taskDictdict

Dictionary containing full task name: task object for the top-level task and all subtasks, sub-subtasks, etc.

classmethod makeField(doc: str) → ConfigurableField

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:

docstr

Help text for the field.

Returns:

configurableFieldlsst.pex.config.ConfigurableField

A ConfigurableField for this task.

Examples

Provides a convenient way to specify this task is a subtask of another task.

Here is an example of use:

class OtherTaskConfig(lsst.pex.config.Config):
    aSubtask = ATaskClass.makeField("brief description of task")

makeSubtask(name: str, **keyArgs: Any) → None

Create a subtask as a new instance as the name attribute of this task.

Parameters:

namestr

Brief name of the subtask.

**keyArgs

Extra keyword arguments used to construct the task. The following arguments are automatically provided and cannot be overridden:

• config.

• parentTask.

Notes

The subtask must be defined by Task.config.name, an instance of ConfigurableField or RegistryField.

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:

dfpandas.DataFrame

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.

timer(name: str, logLevel: int = 10) → Iterator[None]

Context manager to log performance data for an arbitrary block of code.

Parameters:

namestr

Name of code being timed; data will be logged using item name: Start and End.

logLevelint

A logging level constant.

See also

lsst.utils.timer.logInfo

Implementation function.

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time

transform(band, handles, funcs, dataId)