lsst.pipe.base

The lsst.pipe.base module provides base classes for the task framework. Tasks package the algorithmic units of the LSST Science Pipelines. You can create, configure, and run tasks with their Python APIs. Some tasks, called pipeline tasks, can be packaged into data processing pipelines that you can run from the command line.

Changes

• lsst-pipe-base v25.0.0 (2023-02-28)
• lsst-pipe-base v24.0.0 (2022-08-26)
• lsst-pipe-base v23.0.1 (2022-02-02)
• lsst-pipe-base v23.0.0 (2021-12-10)
• lsst-pipe-base v22.0 (2021-04-01)

Using lsst.pipe.base

Overview

• Overview of the task framework

Developing tasks and pipeline tasks

• Creating a task
• Testing a pipeline task
• Creating a PipelineTask
• Retargeting subtasks of tasks

Developing Pipelines

• Creating a Pipeline
• Testing pipelines with mocks

Contributing

lsst.pipe.base is developed at https://github.com/lsst/pipe_base. You can find Jira issues for this module under the pipe_base component.

Python API reference

lsst.pipe.base Package

Functions

buildExecutionButler(butler, graph, ...[, ...])	Create an execution butler.
iterConnections(connections, connectionType)	Create an iterator over the selected connections type which yields all the defined connections of that type.

Classes

AdjustQuantumHelper(inputs, outputs[, ...])	Helper class for calling PipelineTaskConnections.adjustQuantum.
ButlerQuantumContext(butler, quantum)	A Butler-like class specialized for a single quantum.
DeferredDatasetRef(datasetRef)	A wrapper class for DatasetRef that indicates that a PipelineTask should receive a DeferredDatasetHandle instead of an in-memory dataset.
GraphBuilder(registry[, skipExistingIn, ...])	GraphBuilder class is responsible for building task execution graph from a Pipeline.
InMemoryDatasetHandle(inMemoryDataset, *[, ...])	An in-memory version of a DeferredDatasetHandle.
IncompatibleGraphError	Exception class to indicate that a lookup by NodeId is impossible due to incompatibilities
InputQuantizedConnection(**kwargs)	Input variant of a QuantizedConnection.
Instrument([collection_prefix])	Base class for instrument-specific logic for the Gen3 Butler.
InvalidQuantumError	Exception that may be raised by PipelineTasks (and code they delegate to) in order to indicate logic bug or configuration problem.
LabelSpecifier([labels, begin, end])	A structure to specify a subset of labels to load
NoWorkFound	An exception raised when a Quantum should not exist because there is no work for it to do.
NodeId(number, buildId)	Deprecated, this class is used with QuantumGraph save formats of 1 and 2 when unpicking objects and must be retained until those formats are considered unloadable.
ObservationDimensionPacker(data_id, config)	A DimensionPacker for visit+detector or exposure+detector.
ObservationDimensionPackerConfig(*args, **kw)	Config associated with a ObservationDimensionPacker.
OutputQuantizedConnection(**kwargs)	Output variant of a QuantizedConnection.
Pipeline(description)	A Pipeline is a representation of a series of tasks to run, and the configuration for those tasks.
PipelineDatasetTypes(initInputs, ...)	An immutable struct that classifies the dataset types used in a Pipeline.
PipelineTask(*[, config, log, initInputs])	Base class for all pipeline tasks.
PipelineTaskConfig(*args, **kw)	Configuration class for PipelineTask
PipelineTaskConnections(*[, config])	PipelineTaskConnections is a class used to declare desired IO when a PipelineTask is run by an activator
QuantumGraph(quanta[, metadata, pruneRefs, ...])	QuantumGraph is a directed acyclic graph of QuantumNode objects
QuantumNode(quantum, taskDef, nodeId)	Class representing a node in the quantum graph.
RepeatableQuantumError	Exception that may be raised by PipelineTasks (and code they delegate to) in order to indicate that a repeatable problem that will not be addressed by retries.
ScalarError	Exception raised when dataset type is configured as scalar but there are multiple data IDs in a Quantum for that dataset.
Struct(**keyArgs)	A container to which you can add fields as attributes.
Task([config, name, parentTask, log])	Base class for data processing tasks.
TaskDatasetTypes(initInputs, initOutputs, ...)	An immutable struct that extracts and classifies the dataset types used by a PipelineTask
TaskDef([taskName, config, taskClass, label])	TaskDef is a collection of information about task needed by Pipeline.
TaskError	Use to report errors for which a traceback is not useful.
TaskFactory()	Abstract base class for task factory.
TaskMetadata(*[, scalars, arrays, metadata])	Dict-like object for storing task metadata.

Class Inheritance Diagram

lsst.pipe.base.testUtils Module

Functions

assertValidInitOutput(task)	Test that a constructed task conforms to its own init-connections.
assertValidOutput(task, result)	Test that the output of a call to run conforms to its own connections.
getInitInputs(butler, config)	Return the initInputs object that would have been passed to a PipelineTask constructor.
lintConnections(connections, *[, ...])	Inspect a connections class for common errors.
makeQuantum(task, butler, dataId, ioDataIds)	Create a Quantum for a particular data ID(s).
runTestQuantum(task, butler, quantum[, mockRun])	Run a PipelineTask on a Quantum.

lsst.pipe.base.connectionTypes Module

Classes

InitInput(name, storageClass[, doc, multiple])	Connection for initInput dataset.
InitOutput(name, storageClass[, doc, multiple])	Connection for initOutput dataset.
Input(name, storageClass[, doc, multiple, ...])	Class used for declaring PipelineTask input connections
PrerequisiteInput(name, storageClass[, doc, ...])	Class used for declaring PipelineTask prerequisite connections.
Output(name, storageClass[, doc, multiple, ...])	Connection for output dataset.
BaseConnection(name, storageClass[, doc, ...])	Base class used for declaring PipelineTask connections.

Class Inheritance Diagram

lsst.pipe.base.pipelineIR Module

Classes

ConfigIR(python, dataId, file, rest)	Intermediate representation of configurations read from a pipeline yaml file.
ContractError	An exception that is raised when a pipeline contract is not satisfied
ContractIR(contract[, msg])	Intermediate representation of configuration contracts read from a pipeline yaml file.
ImportIR(location, include, exclude, ...)	An intermediate representation of imported pipelines
PipelineIR(loaded_yaml)	Intermediate representation of a pipeline definition
TaskIR(label, klass[, config])	Intermediate representation of tasks read from a pipeline yaml file.
LabeledSubset(label, subset, description)	Intermediate representation of named subset of task labels read from a pipeline yaml file.

Class Inheritance Diagram

lsst.pipe.base.tests.mocks Package

A system for replacing the tasks in a pipeline with mocks that just read and write trivial datasets.

See Testing pipelines with mocks for details.

Functions

get_mock_name(original)	Return the name of the mock storage class, dataset type, or task label for the given original name.
get_original_name(mock)	Return the name of the original storage class, dataset type, or task label that corresponds to the given mock name.
is_mock_name(name)	Return whether the given name is that of a mock storage class, dataset type, or task label.
mock_task_defs(originals[, ...])	Create mocks for an iterable of TaskDefs.

Classes

DataIdMatch(expression)	Class that can match DataId against the user-defined string expression.
MockDataset(*, ref[, quantum, ...])	The in-memory dataset type used by MockStorageClass.
MockDatasetQuantum(*, task_label, data_id, ...)	Description of the quantum that produced a mock dataset.
MockPipelineTask(*, config, **kwargs)	Implementation of PipelineTask used for running a mock pipeline.
MockPipelineTaskConfig(*args, **kw)	Configuration class for MockPipelineTask.
MockStorageClass(original[, factory])	A reimplementation of lsst.daf.butler.StorageClass for mock datasets.
MockStorageClassDelegate(storageClass)	Implementation of the StorageClassDelegate interface for mock datasets.

Class Inheritance Diagram