lsst.pipe.base  20.0.0-2-g04cfba9+6
Public Member Functions | Public Attributes | List of all members
lsst.pipe.base.connections.PipelineTaskConnections Class Reference
Inheritance diagram for lsst.pipe.base.connections.PipelineTaskConnections:
lsst.pipe.base.connections.PipelineTaskConnectionsMetaclass

Public Member Functions

def __init__ (self, *'PipelineTaskConfig' config=None)
 
typing.Tuple[InputQuantizedConnection, OutputQuantizedConnectionbuildDatasetRefs (self, Quantum quantum)
 
NamedKeyDict[DatasetType, typing.Set[DatasetRef]] adjustQuantum (self, NamedKeyDict[DatasetType, typing.Set[DatasetRef]] datasetRefMap)
 
def __prepare__ (name, bases, **kwargs)
 
def __new__ (cls, name, bases, dct, **kwargs)
 

Public Attributes

 inputs
 
 prerequisiteInputs
 
 outputs
 
 initInputs
 
 initOutputs
 
 config
 

Detailed Description

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

Parameters
----------
config : `PipelineTaskConfig`
    A `PipelineTaskConfig` class instance whose 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 an ``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` objects 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")

Definition at line 254 of file connections.py.

Constructor & Destructor Documentation

◆ __init__()

def lsst.pipe.base.connections.PipelineTaskConnections.__init__ (   self,
*'PipelineTaskConfig'   config = None 
)

Definition at line 358 of file connections.py.

Member Function Documentation

◆ __new__()

def lsst.pipe.base.connections.PipelineTaskConnectionsMetaclass.__new__ (   cls,
  name,
  bases,
  dct,
**  kwargs 
)
inherited

Definition at line 109 of file connections.py.

◆ __prepare__()

def lsst.pipe.base.connections.PipelineTaskConnectionsMetaclass.__prepare__ (   name,
  bases,
**  kwargs 
)
inherited

Definition at line 98 of file connections.py.

◆ adjustQuantum()

NamedKeyDict[DatasetType, typing.Set[DatasetRef]] lsst.pipe.base.connections.PipelineTaskConnections.adjustQuantum (   self,
NamedKeyDict[DatasetType, typing.Set[DatasetRef]]   datasetRefMap 
)
Override to make adjustments to `lsst.daf.butler.DatasetRef` objects
in the `lsst.daf.butler.core.Quantum` during the graph generation stage
of the activator.

The base class implementation simply checks that input connections with
``multiple`` set to `False` have no more than one dataset.

Parameters
----------
datasetRefMap : `NamedKeyDict`
    Mapping from dataset type to a `set` of
    `lsst.daf.butler.DatasetRef` objects

Returns
-------
datasetRefMap : `NamedKeyDict`
    Modified mapping of input with possibly adjusted
    `lsst.daf.butler.DatasetRef` objects.

Raises
------
ScalarError
    Raised if any `Input` or `PrerequisiteInput` connection has
    ``multiple`` set to `False`, but multiple datasets.
Exception
    Overrides of this function have the option of raising an Exception
    if a field in the input does not satisfy a need for a corresponding
    pipelineTask, i.e. no reference catalogs are found.

Definition at line 452 of file connections.py.

◆ buildDatasetRefs()

typing.Tuple[InputQuantizedConnection, OutputQuantizedConnection] lsst.pipe.base.connections.PipelineTaskConnections.buildDatasetRefs (   self,
Quantum  quantum 
)
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`

Definition at line 385 of file connections.py.

Member Data Documentation

◆ config

lsst.pipe.base.connections.PipelineTaskConnections.config

Definition at line 368 of file connections.py.

◆ initInputs

lsst.pipe.base.connections.PipelineTaskConnections.initInputs

Definition at line 362 of file connections.py.

◆ initOutputs

lsst.pipe.base.connections.PipelineTaskConnections.initOutputs

Definition at line 363 of file connections.py.

◆ inputs

lsst.pipe.base.connections.PipelineTaskConnections.inputs

Definition at line 359 of file connections.py.

◆ outputs

lsst.pipe.base.connections.PipelineTaskConnections.outputs

Definition at line 361 of file connections.py.

◆ prerequisiteInputs

lsst.pipe.base.connections.PipelineTaskConnections.prerequisiteInputs

Definition at line 360 of file connections.py.


The documentation for this class was generated from the following file: