WriteRecalibratedSourceTableTask

class lsst.pipe.tasks.postprocess.WriteRecalibratedSourceTableTask(*, config: Optional[PipelineTaskConfig] = None, log: Optional[Union[logging.Logger, LsstLogAdapter]] = None, initInputs: Optional[Dict[str, Any]] = None, **kwargs)

Bases: lsst.pipe.tasks.postprocess.WriteSourceTableTask

Write source table to parquet

Attributes Summary

canMultiprocess

Methods Summary

addCalibColumns(catalog, exposure, …)	Add replace columns with calibs evaluated at each centroid
attachCalibs(inputRefs, skyMap, exposure[, …])	Apply external calibrations to exposure per configuration
emptyMetadata()	Empty (clear) the metadata for this Task and all sub-Tasks.
getClosestTract(tracts, skyMap, bbox, wcs)	Find the index of the tract closest to detector from list of tractIds
getFullMetadata()	Get metadata for all tasks.
getFullName()	Get the task name as a hierarchical name including parent task names.
getName()	Get the name of the task.
getResourceConfig()	Return resource configuration for this 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.
prepareCalibratedExposure(exposure[, …])	Prepare a calibrated exposure and apply external calibrations if so configured.
run(catalog[, ccdVisitId])	Convert src catalog to parquet
runQuantum(butlerQC, inputRefs, outputRefs)	Method to do butler IO and or transforms to provide in memory objects for tasks run method
timer(name, logLevel)	Context manager to log performance data for an arbitrary block of code.

Attributes Documentation

canMultiprocess = True

Methods Documentation

addCalibColumns(catalog, exposure, exposureIdInfo, **kwargs)

Add replace columns with calibs evaluated at each centroid

Add or replace ‘base_LocalWcs’ `base_LocalPhotoCalib’ columns in a a source catalog, by rerunning the plugins.

Parameters:

catalog : lsst.afw.table.SourceCatalog

catalog to which calib columns will be added

exposure : lsst.afw.image.exposure.Exposure

Exposure with attached PhotoCalibs and SkyWcs attributes to be reevaluated at local centroids. Pixels are not required.

exposureIdInfo : lsst.obs.base.ExposureIdInfo

Returns:

newCat: lsst.afw.table.SourceCatalog

Source Catalog with requested local calib columns

attachCalibs(inputRefs, skyMap, exposure, externalSkyWcsGlobalCatalog=None, externalSkyWcsTractCatalog=None, externalPhotoCalibGlobalCatalog=None, externalPhotoCalibTractCatalog=None, **kwargs)

Apply external calibrations to exposure per configuration

When multiple tract-level calibrations overlap, select the one with the center closest to detector.

Parameters:

inputRefs : lsst.pipe.base.InputQuantizedConnection, for dataIds of

tract-level calibs.

skyMap : lsst.skymap.SkyMap

exposure : lsst.afw.image.exposure.Exposure

Input exposure to adjust calibrations.

externalSkyWcsGlobalCatalog : lsst.afw.table.ExposureCatalog, optional

Exposure catalog with external skyWcs to be applied per config

externalSkyWcsTractCatalog : lsst.afw.table.ExposureCatalog, optional

Exposure catalog with external skyWcs to be applied per config

externalPhotoCalibGlobalCatalog : lsst.afw.table.ExposureCatalog, optional

Exposure catalog with external photoCalib to be applied per config

externalPhotoCalibTractCatalog : lsst.afw.table.ExposureCatalog, optional

Returns:

exposure : lsst.afw.image.exposure.Exposure

Exposure with adjusted calibrations.

emptyMetadata() → None

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

getClosestTract(tracts, skyMap, bbox, wcs)

Find the index of the tract closest to detector from list of tractIds

Parameters:

tracts: `list` [`int`]

Iterable of integer tractIds

skyMap : lsst.skymap.SkyMap

skyMap to lookup tract geometry and wcs

bbox : lsst.geom.Box2I

Detector bbox, center of which will compared to tract centers

wcs : lsst.afw.geom.SkyWcs

Detector Wcs object to map the detector center to SkyCoord

Returns:

index : int

getFullMetadata() → lsst.pipe.base._task_metadata.TaskMetadata

Get metadata for all tasks.

Returns:

metadata : TaskMetadata

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:

fullName : str

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”.

getName() → str

Get the name of the task.

Returns:

taskName : str

Name of the task.

See also

getFullName

getResourceConfig() → Optional[ResourceConfig]

Return resource configuration for this task.

Returns:

Object of type ResourceConfig or None if resource

configuration is not defined for this task.

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

Get a dictionary of all tasks as a shallow copy.

Returns:

taskDict : dict

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

classmethod makeField(doc: str) → lsst.pex.config.configurableField.ConfigurableField

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

Parameters:

doc : str

Help text for the field.

Returns:

configurableField : lsst.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) → None

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

Parameters:

name : str

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.

prepareCalibratedExposure(exposure, externalSkyWcsCatalog=None, externalPhotoCalibCatalog=None)

Prepare a calibrated exposure and apply external calibrations if so configured.

Parameters:

exposure : lsst.afw.image.exposure.Exposure

Input exposure to adjust calibrations.

externalSkyWcsCatalog : lsst.afw.table.ExposureCatalog, optional

Exposure catalog with external skyWcs to be applied if config.doApplyExternalSkyWcs=True. Catalog uses the detector id for the catalog id, sorted on id for fast lookup.

externalPhotoCalibCatalog : lsst.afw.table.ExposureCatalog, optional

Exposure catalog with external photoCalib to be applied if config.doApplyExternalPhotoCalib=True. Catalog uses the detector id for the catalog id, sorted on id for fast lookup.

Returns:

exposure : lsst.afw.image.exposure.Exposure

Exposure with adjusted calibrations.

run(catalog, ccdVisitId=None, **kwargs)

Convert src catalog to parquet

Parameters:

catalog: `afwTable.SourceCatalog`

catalog to be converted

ccdVisitId: `int`

ccdVisitId to be added as a column

Returns:

result : lsst.pipe.base.Struct

table

ParquetTable version of the input catalog

runQuantum(butlerQC, inputRefs, outputRefs)

Method to do butler IO and or transforms to provide in memory objects for tasks run method

Parameters:

butlerQC : ButlerQuantumContext

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

inputRefs : InputQuantizedConnection

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.

outputRefs : OutputQuantizedConnection

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:

name : str

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

logLevel

A logging level constant.

See also

timer.logInfo

Examples

Creating a timer context:

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