Pipeline¶

class lsst.pipe.base.Pipeline(description: str)¶

Bases: object

A Pipeline is a representation of a series of tasks to run, and the configuration for those tasks.

Parameters:

descriptionstr: A description of that this pipeline does.

Methods Summary

`addConfigFile`(label, filename)	Add overrides from a specified file.
`addConfigOverride`(label, key, value)	Apply single config override.
`addConfigPython`(label, pythonString)	Add Overrides by running a snippet of python code against a config.
`addInstrument`(instrument)	Add an instrument to the pipeline, or replace an instrument that is already defined.
`addLabelToSubset`(subset, label)	Add a task label from the specified subset.
`addTask`(task, label)	Add a new task to the pipeline, or replace a task that is already associated with the supplied label.
`findSubsetsWithLabel`(label)	Find any subsets which may contain the specified label.
`fromFile`(filename)	Load a pipeline defined in a pipeline yaml file.
`fromIR`(deserialized_pipeline)	Create a pipeline from an already created `PipelineIR` object.
`fromPipeline`(pipeline)	Create a new pipeline by copying an already existing `Pipeline`.
`fromString`(pipeline_string)	Create a pipeline from string formatted as a pipeline document.
`from_uri`(uri)	Load a pipeline defined in a pipeline yaml file at a location specified by a URI.
`getInstrument`()	Get the instrument from the pipeline.
`get_data_id`(universe)	Return a data ID with all dimension constraints embedded in the pipeline.
`mergePipeline`(pipeline)	Merge another in-memory `Pipeline` object into this one.
`removeLabelFromSubset`(subset, label)	Remove a task label from the specified subset.
`removeTask`(label)	Remove a task from the pipeline.
`subsetFromLabels`(labelSpecifier)	Subset a pipeline to contain only labels specified in labelSpecifier
`toExpandedPipeline`()	Return a generator of `TaskDef`s which can be used to create quantum graphs.
`to_graph`([registry])	Construct a pipeline graph from this pipeline.
`write_to_uri`(uri)	Write the pipeline to a file or directory.

Methods Documentation

addConfigFile(label: str, filename: str) → None¶

Add overrides from a specified file.

Parameters:

labelstr: The label used to identify the task associated with config to modify
filenamestr: Path to the override file.

addConfigOverride(label: str, key: str, value: object) → None¶

Apply single config override.

Parameters:

labelstr: Label of the task.
key: `str`: Fully-qualified field name.
valueobject: Value to be given to a field.

addConfigPython(label: str, pythonString: str) → None¶

Add Overrides by running a snippet of python code against a config.

Parameters:

labelstr: The label used to identity the task associated with config to modify.
pythonString: `str`: A string which is valid python code to be executed. This is done with config as the only local accessible value.

addInstrument(instrument: Instrument | str) → None¶

Add an instrument to the pipeline, or replace an instrument that is already defined.

Parameters:

instrumentInstrument or str: Either a derived class object of a lsst.daf.butler.instrument or a string corresponding to a fully qualified lsst.daf.butler.instrument name.

addLabelToSubset(subset: str, label: str) → None¶

Add a task label from the specified subset.

Parameters:

subsetstr: The labeled subset to modify
labelstr: The task label to add to the specified subset.

Raises:

ValueError: Raised if the specified subset does not exist within the pipeline. Raised if the specified label does not exist within the pipeline.

addTask(task: type[lsst.pipe.base.pipelineTask.PipelineTask] | str, label: str) → None¶

Add a new task to the pipeline, or replace a task that is already associated with the supplied label.

Parameters:

task: `PipelineTask` or `str`: Either a derived class object of a PipelineTask or a string corresponding to a fully qualified PipelineTask name.
label: `str`: A label that is used to identify the PipelineTask being added

findSubsetsWithLabel(label: str) → set[str]¶

Find any subsets which may contain the specified label.

This function returns the name of subsets which return the specified label. May return an empty set if there are no subsets, or no subsets containing the specified label.

Parameters:

labelstr: The task label to use in membership check

Returns:

subsetsset of str: Returns a set (possibly empty) of subsets names which contain the specified label.

Raises:

ValueError: Raised if the specified label does not exist within this pipeline.

classmethod fromFile(filename: str) → Pipeline¶

Load a pipeline defined in a pipeline yaml file.

Parameters:

filename: `str`: A path that points to a pipeline defined in yaml format. This filename may also supply additional labels to be used in subsetting the loaded Pipeline. These labels are separated from the path by a #, and may be specified as a comma separated list, or a range denoted as beginning..end. Beginning or end may be empty, in which case the range will be a half open interval. Unlike python iteration bounds, end bounds are INCLUDED. Note that range based selection is not well defined for pipelines that are not linear in nature, and correct behavior is not guaranteed, or may vary from run to run.

Returns:

pipeline: Pipeline: The pipeline loaded from specified location with appropriate (if any) subsetting.

Notes

This method attempts to prune any contracts that contain labels which are not in the declared subset of labels. This pruning is done using a string based matching due to the nature of contracts and may prune more than it should.

classmethod fromIR(deserialized_pipeline: PipelineIR) → Pipeline¶

Create a pipeline from an already created PipelineIR object.

Parameters:

deserialized_pipeline: `PipelineIR`: An already created pipeline intermediate representation object

Returns:

pipeline: Pipeline

classmethod fromPipeline(pipeline: Pipeline) → Pipeline¶

Create a new pipeline by copying an already existing Pipeline.

Parameters:

pipeline: `Pipeline`: An already created pipeline intermediate representation object

Returns:

pipeline: Pipeline

classmethod fromString(pipeline_string: str) → Pipeline¶

Create a pipeline from string formatted as a pipeline document.

Parameters:

pipeline_stringstr: A string that is formatted according like a pipeline document

Returns:

pipeline: Pipeline

classmethod from_uri(uri: str | ParseResult | ResourcePath | Path) → Pipeline¶

Load a pipeline defined in a pipeline yaml file at a location specified by a URI.

Parameters:

uriconvertible to ResourcePath: If a string is supplied this should be a URI path that points to a pipeline defined in yaml format, either as a direct path to the yaml file, or as a directory containing a pipeline.yaml file the form used by write_to_uri with expand=True). This uri may also supply additional labels to be used in subsetting the loaded Pipeline. These labels are separated from the path by a #, and may be specified as a comma separated list, or a range denoted as beginning..end. Beginning or end may be empty, in which case the range will be a half open interval. Unlike python iteration bounds, end bounds are INCLUDED. Note that range based selection is not well defined for pipelines that are not linear in nature, and correct behavior is not guaranteed, or may vary from run to run. The same specifiers can be used with a ResourcePath object, by being the sole contents in the fragments attribute.

Returns:

pipelinePipeline: The pipeline loaded from specified location with appropriate (if any) subsetting.

Notes

This method attempts to prune any contracts that contain labels which are not in the declared subset of labels. This pruning is done using a string based matching due to the nature of contracts and may prune more than it should.

getInstrument() → str | None¶

Get the instrument from the pipeline.

Returns:

instrumentstr, or None: The fully qualified name of a lsst.obs.base.Instrument subclass, name, or None if the pipeline does not have an instrument.

get_data_id(universe: DimensionUniverse) → DataCoordinate¶

Return a data ID with all dimension constraints embedded in the pipeline.

Parameters:

universelsst.daf.butler.DimensionUniverse: Object that defines all dimensions.

Returns:

data_idlsst.daf.butler.DataCoordinate: Data ID with all dimension constraints embedded in the pipeline.

mergePipeline(pipeline: Pipeline) → None¶

Merge another in-memory Pipeline object into this one.

This merges another pipeline into this object, as if it were declared in the import block of the yaml definition of this pipeline. This modifies this pipeline in place.

Parameters:

pipelinePipeline: The Pipeline object that is to be merged into this object.

removeLabelFromSubset(subset: str, label: str) → None¶

Remove a task label from the specified subset.

Parameters:

subsetstr: The labeled subset to modify
labelstr: The task label to remove from the specified subset.

Raises:

ValueError: Raised if the specified subset does not exist in the pipeline. Raised if the specified label does not exist within the specified subset.

removeTask(label: str) → None¶

Remove a task from the pipeline.

Parameters:

labelstr: The label used to identify the task that is to be removed

Raises:

KeyError: If no task with that label exists in the pipeline

subsetFromLabels(labelSpecifier: LabelSpecifier) → Pipeline¶

Subset a pipeline to contain only labels specified in labelSpecifier

Parameters:

labelSpecifierlabelSpecifier: Object containing labels that describes how to subset a pipeline.

Returns:

pipelinePipeline: A new pipeline object that is a subset of the old pipeline

Raises:

ValueError: Raised if there is an issue with specified labels

Notes

This method attempts to prune any contracts that contain labels which are not in the declared subset of labels. This pruning is done using a string based matching due to the nature of contracts and may prune more than it should.

toExpandedPipeline() → Generator[TaskDef, None, None]¶

Return a generator of TaskDefs which can be used to create quantum graphs.

Returns:

generatorgenerator of TaskDef: The generator returned will be the sorted iterator of tasks which are to be used in constructing a quantum graph.

Raises:

NotImplementedError: If a dataId is supplied in a config block. This is in place for future use

to_graph(registry: Registry | None = None) → PipelineGraph¶

Construct a pipeline graph from this pipeline.

Constructing a graph applies all configuration overrides, freezes all configuration, checks all contracts, and checks for dataset type consistency between tasks (as much as possible without access to a data repository). It cannot be reversed.

Parameters:

registrylsst.daf.butler.Registry, optional: Data repository client. If provided, the graph’s dataset types and dimensions will be resolved (see PipelineGraph.resolve).

Returns:

graphpipeline_graph.PipelineGraph: Representation of the pipeline as a graph.

write_to_uri(uri: str | ParseResult | ResourcePath | Path) → None¶

Write the pipeline to a file or directory.

Parameters:

uriconvertible to ResourcePath: URI to write to; may have any scheme with ResourcePath write support or no scheme for a local file/directory. Should have a .yaml extension.

Navigation

Pipeline¶