DiaPipelineTask¶

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

Bases: lsst.pipe.base.PipelineTask

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

Attributes Summary

canMultiprocess

Methods Summary

`createNewDiaObjects`(diaSources)	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.
`getAllSchemaCatalogs`()	Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
`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.
`getSchemaCatalogs`()	Get the schemas generated by 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.
`run`(diaSourceTable, diffIm, exposure, …)	Process DiaSources and DiaObjects.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Method to do butler IO and or transforms 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.

Attributes Documentation

canMultiprocess = True¶

Methods Documentation

createNewDiaObjects(diaSources)¶

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

Parameters:	diaSources : `pandas.DataFrame` Set of DiaSources to create new DiaObjects from.
Returns:	results : `lsst.pipe.base.Struct` Results struct containing: `diaSources` : DiaSource catalog with updated DiaObject ids. (`pandas.DataFrame`) `newDiaObjects` : Newly created DiaObjects from the unassociated DiaSources. (`pandas.DataFrame`)

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

getAllSchemaCatalogs()¶

Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.

Returns:	schemacatalogs : `dict` Keys are butler dataset type, values are a empty catalog (an instance of the appropriate `lsst.afw.table` Catalog type) for all tasks in the hierarchy, from the top-level task down through all subtasks.

Notes

This method may be called on any task in the hierarchy; it will return the same answer, regardless.

The default implementation should always suffice. If your subtask uses schemas the override Task.getSchemaCatalogs, not this method.

getFullMetadata()¶

Get metadata for all tasks.

Returns:	metadata : `lsst.daf.base.PropertySet` The `PropertySet` 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()¶

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()¶

Get the name of the task.

Returns:	taskName : `str` Name of the task.

See also

Task.getAllSchemaCatalogs

Notes

Warning

Subclasses that use schemas must override this method. The default implementation returns an empty dict.

This method may be called at any time after the Task is constructed, which means that all task schemas should be computed at construction time, not when data is actually processed. This reflects the philosophy that the schema should not depend on the data.

Returning catalogs rather than just schemas allows us to save e.g. slots for SourceCatalog as well.

getTaskDict()¶

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)¶

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, **keyArgs)¶

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.

run(diaSourceTable, diffIm, exposure, warpedExposure, ccdExposureIdBits, band)¶

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:

diaSourceTable : pandas.DataFrame: Newly detected DiaSources.
diffIm : lsst.afw.image.ExposureF: Difference image exposure in which the sources in diaSourceCat were detected.
exposure : lsst.afw.image.ExposureF: Calibrated exposure differenced with a template to create diffIm.
warpedExposure : lsst.afw.image.ExposureF: Template exposure used to create diffIm.
ccdExposureIdBits : int: Number of bits used for a unique ccdVisitId.
band : str: The band in which the new DiaSources were detected.

Returns:

results : lsst.pipe.base.Struct

Results struct with components.

apdbMaker : 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)

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.

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:	df : `pandas.DataFrame` DataFrame to text.
Returns:	`bool` True if DataFrame contains duplicate rows.

timer(name, logLevel=10)¶

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.

Navigation

DiaPipelineTask¶