PipelineTaskConnections¶

class lsst.pipe.base.PipelineTaskConnections(*, config: PipelineTaskConfig = None)¶

Bases: object

PipelineTaskConnections is a class used to declare desired IO when a PipelineTask is run by an activator

Parameters:

config : PipelineTaskConfig A PipelineTaskConfig class instance who’s
class has been configured to use this `PipelineTaskConnectionsClass`
Notes —– PipelineTaskConnection classes are created by declaring class
attributes of types defined in lsst.pipe.base.connectionTypes and are
listed as follows:
* InitInput - Defines connections in a quantum graph which are used as
inputs to the __init__ function of the PipelineTask corresponding to this
class
* InitOuput - Defines connections in a quantum graph which are to be
persisted using a butler at the end of the __init__ function of the
PipelineTask corresponding to this class. The variable name used to define
this connection should be the same as an attribute name on the PipelineTask
instance. E.g. if a InitOutput is declared with the name outputSchema in a
PipelineTaskConnections class, then a PipelineTask instance should have an
attribute self.outputSchema defined. Its value is what will be saved by the
activator framework.
* PrerequisiteInput - An input connection type that defines a
`lsst.daf.butler.DatasetType` that must be present at execution time, but
that will not be used during the course of creating the quantum graph to be
executed. These most often are things produced outside the processing
pipeline, such as reference catalogs.
* Input - Input `lsst.daf.butler.DatasetType`s that will be used in the run
method of a PipelineTask. The name used to declare class attribute must
match a function argument name in the run method of a PipelineTask. E.g. If
the PipelineTaskConnections defines an Input with the name calexp, then the
corresponding signature should be PipelineTask.run(calexp, …)
* Output - A `lsst.daf.butler.DatasetType` that will be produced by an
execution of a PipelineTask. The name used to declare the connection must
correspond to an attribute of a `Struct` that is returned by a
`PipelineTask` run method. E.g. if an output connection is defined with
the name measCat, then the corresponding PipelineTask.run method must
return Struct(measCat=X,..) where X matches the storageClass type defined
on the output connection.
The process of declaring a PipelineTaskConnection class involves parameters
passed in the declaration statement.
The first parameter is dimensions which is an iterable of strings which
defines the unit of processing the run method of a corresponding
`PipelineTask` will operate on. These dimensions must match dimensions that
exist in the butler registry which will be used in executing the
corresponding `PipelineTask`.
The second parameter is labeled ``defaultTemplates`` and is conditionally
optional. The name attributes of connections can be specified as python
format strings, with named format arguments. If any of the name parameters
on connections defined in a `PipelineTaskConnections` class contain a
template, then a default template value must be specified in the
``defaultTemplates`` argument. This is done by passing a dictionary with
keys corresponding to a template identifier, and values corresponding to
the value to use as a default when formatting the string. For example if
ConnectionClass.calexp.name = ‘{input}Coadd_calexp’ then
``defaultTemplates`` = {‘input’: ‘deep’}.
Once a `PipelineTaskConnections` class is created, it is used in the
creation of a `PipelineTaskConfig`. This is further documented in the
documentation of `PipelineTaskConfig`. For the purposes of this
documentation, the relevant information is that the config class allows
configuration of connection names by users when running a pipeline.
Instances of a `PipelineTaskConnections` class are used by the pipeline
task execution framework to introspect what a corresponding `PipelineTask`
will require, and what it will produce.

Examples

>>> from lsst.pipe.base import connectionTypes as cT
>>> from lsst.pipe.base import PipelineTaskConnections
>>> from lsst.pipe.base import PipelineTaskConfig
>>> class ExampleConnections(PipelineTaskConnections,
...                          dimensions=("A", "B"),
...                          defaultTemplates={"foo": "Example"}):
...     inputConnection = cT.Input(doc="Example input",
...                                dimensions=("A", "B"),
...                                storageClass=Exposure,
...                                name="{foo}Dataset")
...     outputConnection = cT.Output(doc="Example output",
...                                  dimensions=("A", "B"),
...                                  storageClass=Exposure,
...                                  name="{foo}output")
>>> class ExampleConfig(PipelineTaskConfig,
...                     pipelineConnections=ExampleConnections):
...    pass
>>> config = ExampleConfig()
>>> config.connections.foo = Modified
>>> config.connections.outputConnection = "TotallyDifferent"
>>> connections = ExampleConnections(config=config)
>>> assert(connections.inputConnection.name == "ModifiedDataset")
>>> assert(connections.outputCOnnection.name == "TotallyDifferent")

Attributes Summary

`allConnections`
`initInputs`
`initOutputs`
`inputs`
`outputs`
`prerequisiteInputs`

Methods Summary

`adjustQuantum`(datasetRefMap)	Override to make adjustments to lsst.daf.butler.DatasetRef`s in the `lsst.daf.butler.core.Quantum during the graph generation stage of the activator.
`buildDatasetRefs`(quantum)	Builds QuantizedConnections corresponding to input Quantum

Attributes Documentation

allConnections = {}¶

initInputs = frozenset()¶

initOutputs = frozenset()¶

inputs = frozenset()¶

outputs = frozenset()¶

prerequisiteInputs = frozenset()¶

Methods Documentation

adjustQuantum(datasetRefMap: lsst.pipe.base.connections.InputQuantizedConnection)¶

Override to make adjustments to lsst.daf.butler.DatasetRef`s in the `lsst.daf.butler.core.Quantum during the graph generation stage of the activator.

Parameters:	datasetRefMap : `dict` Mapping with keys of dataset type name to `list` of `lsst.daf.butler.DatasetRef`s
Returns:	datasetRefMap : `dict` Modified mapping of input with possible adjusted `lsst.daf.butler.DatasetRef`s
Raises:	Exception Overrides of this function have the option of raising and Exception if a field in the input does not satisfy a need for a corresponding pipelineTask, i.e. no reference catalogs are found.

buildDatasetRefs(quantum: lsst.daf.butler.core.quantum.Quantum) → Tuple[lsst.pipe.base.connections.InputQuantizedConnection, lsst.pipe.base.connections.OutputQuantizedConnection]¶

Builds QuantizedConnections corresponding to input Quantum

Parameters:	quantum : `lsst.daf.butler.Quantum` Quantum object which defines the inputs and outputs for a given unit of processing
Returns:	retVal : `tuple` of (`InputQuantizedConnection`, `OutputQuantizedConnection`) Namespaces mapping attribute names (identifiers of connections) to butler references defined in the input `lsst.daf.butler.Quantum`

Navigation

PipelineTaskConnections¶