DiaPipelineTask¶

class lsst.ap.association.DiaPipelineTask(initInputs=None, **kwargs)¶

Bases: PipelineTask

Task for loading, associating and storing Difference Image Analysis (DIA) Objects and Sources.

Attributes Summary

canMultiprocess

Methods Summary

`associateDiaSources`(diaSourceTable, ...)	Associate DiaSources with DiaObjects.
`checkTableIndex`(dataFrame, index)
`createNewDiaObjects`(unassociatedDiaSources)	Loop through the set of DiaSources and create new DiaObjects for unassociated DiaSources.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`filterSources`(sources[, snrThreshold, ...])	Select good sources out of a catalog.
`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.
`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.
`mergeAssociatedCatalogs`(preloadedDiaSources, ...)	Merge the associated diaSource and diaObjects to their previous history.
`mergeCatalogs`(originalCatalog, newCatalog, ...)	Combine two catalogs, ensuring that the columns of the new catalog have the same dtype as the original.
`purgeDiaObjects`(bbox, wcs, diaObjCat[, ...])	Drop diaObjects that are outside the exposure bounding box.
`run`(diaSourceTable, legacySolarSystemTable, ...)	Process DiaSources and DiaObjects.
`runForcedMeasurement`(diaObjects, ...)	Forced Source Measurement
`runQuantum`(butlerQC, inputRefs, outputRefs)	Do butler IO and transform to provide in memory objects for tasks `run` method.
`testDataFrameIndex`(df)	Test the sorted DataFrame index for duplicates.
`timer`(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.
`updateObjectTable`(diaObjects, diaSources)	Update the diaObject table with the new diaSource records.
`writeToApdb`(updatedDiaObjects, ...)	Write to the Alert Production Database (Apdb).

Attributes Documentation

canMultiprocess: ClassVar[bool] = True¶

Methods Documentation

associateDiaSources(diaSourceTable, solarSystemObjectTable, diffIm, diaObjects)¶

Associate DiaSources with DiaObjects.

Associate new DiaSources with existing DiaObjects. Create new DiaObjects fron unassociated DiaSources. Index DiaSource catalogue after associations. Append new DiaObjects and DiaSources to their previous history. Test for DiaSource and DiaObject duplications. Compute DiaObject Summary statistics from their full DiaSource history. Test for duplication in the updated DiaObjects.

Parameters:

diaSourceTablepandas.DataFrame: Newly detected DiaSources.
solarSystemObjectTableastropy.table.Table: Preloaded Solar System objects expected to be visible in the image.
diffImlsst.afw.image.ExposureF: Difference image exposure in which the sources in diaSourceCat were detected.
diaObjectspandas.DataFrame: Table of DiaObjects from preloaded DiaObjects.

Returns:

resultslsst.pipe.base.Struct

Results struct containing:

associatedDiaSourcespandas.DataFrame
Associated DiaSources with DiaObjects.
newDiaObjectspandas.DataFrame
Table of new DiaObjects after association.
associatedSsSourcesastropy.table.Table
Table of new ssSources after association.
unassociatedSsObjectsastropy.table.Table
Table of expected ssSources that were not associated with a diaSource.
newDiaSourcespandas.DataFrame
Subset of associatedDiaSources consisting of only the unassociated diaSources that were added as new diaObjects.
marginalDiaSourcespandas.DataFrame
Unassociated diaSources with marginal detections, which were removed from associatedDiaSources and were not added as new diaObjects.

static checkTableIndex(dataFrame, index)¶

createNewDiaObjects(unassociatedDiaSources)¶

Loop through the set of DiaSources and create new DiaObjects for unassociated DiaSources.

Parameters:

unassociatedDiaSourcespandas.DataFrame: Set of DiaSources to create new DiaObjects from.

Returns:

resultslsst.pipe.base.Struct

Results struct containing:

diaSourcespandas.DataFrame
DiaSource catalog with updated DiaObject ids.
newDiaObjectspandas.DataFrame
Newly created DiaObjects from the unassociated DiaSources.
nNewDiaObjectsint
Number of newly created diaObjects.
marginalDiaSourcespandas.DataFrame
Unassociated diaSources with low signal-to-noise and/or reliability, which are excluded from the new DiaObjects.

emptyMetadata() → None¶: Empty (clear) the metadata for this Task and all sub-Tasks.

filterSources(sources, snrThreshold=None, lowReliabilitySnrThreshold=None, reliabilityThreshold=None, lowSnrReliabilityThreshold=None, badFlags=None)¶

Select good sources out of a catalog.

Parameters:

sourcespandas.DataFrame: Set of DiaSources to check.
snrThresholdfloat, optional: The minimum signal to noise diaSource to make a new diaObject. Included for unit tests. Uses the task config value if not set.
lowReliabilitySnrThresholdfloat, optional: Use lowSnrReliabilityThreshold as the reliability threshold for diaSources with snrThreshold < SNR < lowReliabilitySnrThreshold Included for unit tests. Uses the task config value if not set.
reliabilityThresholdfloat, optional: The minimum reliability score diaSource to make a new diaObject Included for unit tests. Uses the task config value if not set.
lowSnrReliabilityThresholdfloat, optional: Use lowSnrReliabilityThreshold as the reliability threshold for diaSources with snrThreshold < SNR < lowReliabilitySnrThreshold Included for unit tests. Uses the task config value if not set.
badFlagslist of str, optional: Do not create new diaObjects for any diaSource with any of these flags set. Included for unit tests. Uses the task config value if not set.

Returns:

resultslsst.pipe.base.Struct

Results struct containing:

goodSourcespandas.DataFrame
Subset of the input sources that pass all checks.
badSourcespandas.DataFrame
Subset of the input sources that fail any check.

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”.

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.

mergeAssociatedCatalogs(preloadedDiaSources, associatedDiaSources, diaObjects, newDiaObjects, diffIm)¶

Merge the associated diaSource and diaObjects to their previous history.

Parameters:

preloadedDiaSourcespandas.DataFrame: Previously detected DiaSources, loaded from the APDB.
associatedDiaSourcespandas.DataFrame: Associated DiaSources with DiaObjects.
diaObjectspandas.DataFrame: Table of DiaObjects from preloaded DiaObjects.
newDiaObjectspandas.DataFrame: Table of new DiaObjects after association.

Returns:

mergedDiaSourceHistorypandas.DataFrame: The combined catalog, with all of the rows from preloadedDiaSources catalog ordered before the rows of associatedDiaSources catalog.
mergedDiaObjectspandas.DataFrame: Table of new DiaObjects merged with their history.
updatedDiaObjectIdsnumpy.Array: Object Id’s from associated diaSources.

Raises:

RuntimeError: Raised if duplicate DiaObjects or duplicate DiaSources are found.

mergeCatalogs(originalCatalog, newCatalog, catalogName)¶

Combine two catalogs, ensuring that the columns of the new catalog have the same dtype as the original.

Parameters:

originalCatalogpandas.DataFrame: The original catalog to be added to.
newCatalogpandas.DataFrame: The new catalog to append to originalCatalog
catalogNamestr, optional: The name of the catalog to use for logging messages.

Returns:

mergedCatalogpandas.DataFrame: The combined catalog, with all of the rows from originalCatalog ordered before the rows of newCatalog

purgeDiaObjects(bbox, wcs, diaObjCat, diaObjectIds=None, buffer=0)¶

Drop diaObjects that are outside the exposure bounding box.

Parameters:

bboxlsst.geom.Box2I: Bounding box of the exposure.
wcslsst.afw.geom.SkyWcs: Coordinate system definition (wcs) for the exposure.
diaObjCatpandas.DataFrame: DiaObjects loaded from the Apdb.
bufferint, optional: Width, in pixels, to pad the exposure bounding box.

Returns:

diaObjCatpandas.DataFrame: DiaObjects loaded from the Apdb, restricted to the exposure bounding box.

run(diaSourceTable, legacySolarSystemTable, diffIm, exposure, template, preloadedDiaObjects, preloadedDiaSources, preloadedDiaForcedSources, band, idGenerator, solarSystemObjectTable=None)¶

Process DiaSources and DiaObjects.

Load previous DiaObjects and their DiaSource history. Calibrate the values in the diaSourceCat. Associate new DiaSources with previous DiaObjects. Run forced photometry at the updated DiaObject locations. Store the results in the Alert Production Database (Apdb).

Parameters:

diaSourceTablepandas.DataFrame: Newly detected DiaSources.
legacySolarSystemTablepandas.DataFrame: Not used
diffImlsst.afw.image.ExposureF: Difference image exposure in which the sources in diaSourceCat were detected.
exposurelsst.afw.image.ExposureF: Calibrated exposure differenced with a template to create diffIm.
templatelsst.afw.image.ExposureF: Template exposure used to create diffIm.
preloadedDiaObjectspandas.DataFrame: Previously detected DiaObjects, loaded from the APDB.
preloadedDiaSourcespandas.DataFrame: Previously detected DiaSources, loaded from the APDB.
preloadedDiaForcedSourcespandas.DataFrame: Catalog of previously detected forced DiaSources, from the APDB
bandstr: The band in which the new DiaSources were detected.
idGeneratorlsst.meas.base.IdGenerator: Object that generates source IDs and random number generator seeds.
solarSystemObjectTableastropy.table.Table: Preloaded Solar System objects expected to be visible in the image.

Returns:

resultslsst.pipe.base.Struct

Results struct with components.

apdbMarker : Marker dataset to store in the Butler indicating that this ccdVisit has completed successfully. (lsst.dax.apdb.ApdbConfig)
associatedDiaSources : Catalog of newly associated DiaSources. (pandas.DataFrame)
diaForcedSources : Catalog of new and previously detected forced DiaSources. (pandas.DataFrame)
diaObjects : Updated table of DiaObjects. (pandas.DataFrame)
associatedSsSources : Catalog of ssSource records. (pandas.DataFrame)

Raises:

RuntimeError: Raised if duplicate DiaObjects or duplicate DiaSources are found.

runForcedMeasurement(diaObjects, updatedDiaObjects, exposure, diffIm, idGenerator)¶

Forced Source Measurement

Forced photometry on the difference and calibrated exposures using the new and updated DiaObject locations.

Parameters:

diaObjectspandas.DataFrame: Catalog of DiaObjects.
updatedDiaObjectspandas.DataFrame: Catalog of updated DiaObjects.
exposurelsst.afw.image.ExposureF: Calibrated exposure differenced with a template to create diffIm.
diffImlsst.afw.image.ExposureF: Difference image exposure in which the sources in diaSourceCat were detected.
idGeneratorlsst.meas.base.IdGenerator: Object that generates source IDs and random number generator seeds.

Returns:

diaForcedSourcespandas.DataFrame: Catalog of calibrated forced photometered fluxes on both the difference and direct images at DiaObject locations.

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.

testDataFrameIndex(df)¶

Test the sorted DataFrame index for duplicates.

Wrapped as a separate function to allow for mocking of the this task in unittesting. Default of a mock return for this test is True.

Parameters:

dfpandas.DataFrame: DataFrame to text.

Returns:

bool: True if DataFrame contains duplicate rows.

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.

Navigation

DiaPipelineTask¶