22 """Module defining connection classes for PipelineTask.
25 __all__ = [
"PipelineTaskConnections",
"InputQuantizedConnection",
"OutputQuantizedConnection",
26 "DeferredDatasetRef",
"iterConnections"]
28 from collections
import UserDict, namedtuple
29 from types
import SimpleNamespace
35 from .
import config
as configMod
36 from .connectionTypes
import (InitInput, InitOutput, Input, PrerequisiteInput,
37 Output, BaseConnection)
38 from lsst.daf.butler
import DatasetRef, Quantum
40 if typing.TYPE_CHECKING:
41 from .config
import PipelineTaskConfig
45 """Exception raised when dataset type is configured as scalar
46 but there are multiple data IDs in a Quantum for that dataset.
51 """This is a special dict class used by PipelineTaskConnectionMetaclass
53 This dict is used in PipelineTaskConnection class creation, as the
54 dictionary that is initially used as __dict__. It exists to
55 intercept connection fields declared in a PipelineTaskConnection, and
56 what name is used to identify them. The names are then added to class
57 level list according to the connection type of the class attribute. The
58 names are also used as keys in a class level dictionary associated with
59 the corresponding class attribute. This information is a duplicate of
60 what exists in __dict__, but provides a simple place to lookup and
61 iterate on only these variables.
68 self.data[
'inputs'] = []
69 self.data[
'prerequisiteInputs'] = []
70 self.data[
'outputs'] = []
71 self.data[
'initInputs'] = []
72 self.data[
'initOutputs'] = []
73 self.data[
'allConnections'] = {}
76 if isinstance(value, Input):
77 self.data[
'inputs'].append(name)
78 elif isinstance(value, PrerequisiteInput):
79 self.data[
'prerequisiteInputs'].append(name)
80 elif isinstance(value, Output):
81 self.data[
'outputs'].append(name)
82 elif isinstance(value, InitInput):
83 self.data[
'initInputs'].append(name)
84 elif isinstance(value, InitOutput):
85 self.data[
'initOutputs'].append(name)
88 if isinstance(value, BaseConnection):
89 object.__setattr__(value,
'varName', name)
90 self.data[
'allConnections'][name] = value
96 """Metaclass used in the declaration of PipelineTaskConnections classes
104 if isinstance(base, PipelineTaskConnectionsMetaclass):
105 for name, value
in base.allConnections.items():
110 dimensionsValueError = TypeError(
"PipelineTaskConnections class must be created with a dimensions "
111 "attribute which is an iterable of dimension names")
113 if name !=
'PipelineTaskConnections':
116 if 'dimensions' not in kwargs:
118 if hasattr(base,
'dimensions'):
119 kwargs[
'dimensions'] = base.dimensions
121 if 'dimensions' not in kwargs:
122 raise dimensionsValueError
124 dct[
'dimensions'] = set(kwargs[
'dimensions'])
125 except TypeError
as exc:
126 raise dimensionsValueError
from exc
130 stringFormatter = string.Formatter()
132 for obj
in dct[
'allConnections'].values():
135 for param
in stringFormatter.parse(nameValue):
136 if param[1]
is not None:
137 allTemplates.add(param[1])
142 for base
in bases[::-1]:
143 if hasattr(base,
'defaultTemplates'):
144 mergeDict.update(base.defaultTemplates)
145 if 'defaultTemplates' in kwargs:
146 mergeDict.update(kwargs[
'defaultTemplates'])
148 if len(mergeDict) > 0:
149 kwargs[
'defaultTemplates'] = mergeDict
154 if len(allTemplates) > 0
and 'defaultTemplates' not in kwargs:
155 raise TypeError(
"PipelineTaskConnection class contains templated attribute names, but no "
156 "defaut templates were provided, add a dictionary attribute named "
157 "defaultTemplates which contains the mapping between template key and value")
158 if len(allTemplates) > 0:
160 defaultTemplateKeys = set(kwargs[
'defaultTemplates'].keys())
161 templateDifference = allTemplates.difference(defaultTemplateKeys)
162 if templateDifference:
163 raise TypeError(f
"Default template keys were not provided for {templateDifference}")
167 nameTemplateIntersection = allTemplates.intersection(set(dct[
'allConnections'].keys()))
168 if len(nameTemplateIntersection) > 0:
169 raise TypeError(f
"Template parameters cannot share names with Class attributes")
170 dct[
'defaultTemplates'] = kwargs.get(
'defaultTemplates', {})
174 for connectionName
in (
"inputs",
"prerequisiteInputs",
"outputs",
"initInputs",
"initOutputs"):
175 dct[connectionName] = frozenset(dct[connectionName])
178 return super().
__new__(cls, name, bases, dict(dct))
190 """A Namespace to map defined variable names of connections to their
191 `lsst.daf.buter.DatasetRef`s
193 This class maps the names used to define a connection on a
194 PipelineTaskConnectionsClass to the corresponding
195 `lsst.daf.butler.DatasetRef`s provided by a `lsst.daf.butler.Quantum`
196 instance. This will be a quantum of execution based on the graph created
197 by examining all the connections defined on the
198 `PipelineTaskConnectionsClass`.
203 object.__setattr__(self,
"_attributes", set())
205 def __setattr__(self, name: str, value: typing.Union[DatasetRef, typing.List[DatasetRef]]):
207 self._attributes.add(name)
211 object.__delattr__(self, name)
212 self._attributes.remove(name)
214 def __iter__(self) -> typing.Generator[typing.Tuple[str, typing.Union[DatasetRef,
215 typing.List[DatasetRef]]], None, None]:
216 """Make an Iterator for this QuantizedConnection
218 Iterating over a QuantizedConnection will yield a tuple with the name
219 of an attribute and the value associated with that name. This is
220 similar to dict.items() but is on the namespace attributes rather than
223 yield from ((name, getattr(self, name))
for name
in self._attributes)
225 def keys(self) -> typing.Generator[str, None, None]:
226 """Returns an iterator over all the attributes added to a
227 QuantizedConnection class
229 yield from self._attributes
236 class OutputQuantizedConnection(QuantizedConnection):
241 """Class which denotes that a datasetRef should be treated as deferred when
242 interacting with the butler
246 datasetRef : `lsst.daf.butler.DatasetRef`
247 The `lsst.daf.butler.DatasetRef` that will be eventually used to
254 """PipelineTaskConnections is a class used to declare desired IO when a
255 PipelineTask is run by an activator
259 config : `PipelineTaskConfig`
260 A `PipelineTaskConfig` class instance whose class has been configured
261 to use this `PipelineTaskConnectionsClass`
265 ``PipelineTaskConnection`` classes are created by declaring class
266 attributes of types defined in `lsst.pipe.base.connectionTypes` and are
269 * ``InitInput`` - Defines connections in a quantum graph which are used as
270 inputs to the ``__init__`` function of the `PipelineTask` corresponding
272 * ``InitOuput`` - Defines connections in a quantum graph which are to be
273 persisted using a butler at the end of the ``__init__`` function of the
274 `PipelineTask` corresponding to this class. The variable name used to
275 define this connection should be the same as an attribute name on the
276 `PipelineTask` instance. E.g. if an ``InitOutput`` is declared with
277 the name ``outputSchema`` in a ``PipelineTaskConnections`` class, then
278 a `PipelineTask` instance should have an attribute
279 ``self.outputSchema`` defined. Its value is what will be saved by the
281 * ``PrerequisiteInput`` - An input connection type that defines a
282 `lsst.daf.butler.DatasetType` that must be present at execution time,
283 but that will not be used during the course of creating the quantum
284 graph to be executed. These most often are things produced outside the
285 processing pipeline, such as reference catalogs.
286 * ``Input`` - Input `lsst.daf.butler.DatasetType` objects that will be used
287 in the ``run`` method of a `PipelineTask`. The name used to declare
288 class attribute must match a function argument name in the ``run``
289 method of a `PipelineTask`. E.g. If the ``PipelineTaskConnections``
290 defines an ``Input`` with the name ``calexp``, then the corresponding
291 signature should be ``PipelineTask.run(calexp, ...)``
292 * ``Output`` - A `lsst.daf.butler.DatasetType` that will be produced by an
293 execution of a `PipelineTask`. The name used to declare the connection
294 must correspond to an attribute of a `Struct` that is returned by a
295 `PipelineTask` ``run`` method. E.g. if an output connection is
296 defined with the name ``measCat``, then the corresponding
297 ``PipelineTask.run`` method must return ``Struct(measCat=X,..)`` where
298 X matches the ``storageClass`` type defined on the output connection.
300 The process of declaring a ``PipelineTaskConnection`` class involves
301 parameters passed in the declaration statement.
303 The first parameter is ``dimensions`` which is an iterable of strings which
304 defines the unit of processing the run method of a corresponding
305 `PipelineTask` will operate on. These dimensions must match dimensions that
306 exist in the butler registry which will be used in executing the
307 corresponding `PipelineTask`.
309 The second parameter is labeled ``defaultTemplates`` and is conditionally
310 optional. The name attributes of connections can be specified as python
311 format strings, with named format arguments. If any of the name parameters
312 on connections defined in a `PipelineTaskConnections` class contain a
313 template, then a default template value must be specified in the
314 ``defaultTemplates`` argument. This is done by passing a dictionary with
315 keys corresponding to a template identifier, and values corresponding to
316 the value to use as a default when formatting the string. For example if
317 ``ConnectionClass.calexp.name = '{input}Coadd_calexp'`` then
318 ``defaultTemplates`` = {'input': 'deep'}.
320 Once a `PipelineTaskConnections` class is created, it is used in the
321 creation of a `PipelineTaskConfig`. This is further documented in the
322 documentation of `PipelineTaskConfig`. For the purposes of this
323 documentation, the relevant information is that the config class allows
324 configuration of connection names by users when running a pipeline.
326 Instances of a `PipelineTaskConnections` class are used by the pipeline
327 task execution framework to introspect what a corresponding `PipelineTask`
328 will require, and what it will produce.
332 >>> from lsst.pipe.base import connectionTypes as cT
333 >>> from lsst.pipe.base import PipelineTaskConnections
334 >>> from lsst.pipe.base import PipelineTaskConfig
335 >>> class ExampleConnections(PipelineTaskConnections,
336 ... dimensions=("A", "B"),
337 ... defaultTemplates={"foo": "Example"}):
338 ... inputConnection = cT.Input(doc="Example input",
339 ... dimensions=("A", "B"),
340 ... storageClass=Exposure,
341 ... name="{foo}Dataset")
342 ... outputConnection = cT.Output(doc="Example output",
343 ... dimensions=("A", "B"),
344 ... storageClass=Exposure,
345 ... name="{foo}output")
346 >>> class ExampleConfig(PipelineTaskConfig,
347 ... pipelineConnections=ExampleConnections):
349 >>> config = ExampleConfig()
350 >>> config.connections.foo = Modified
351 >>> config.connections.outputConnection = "TotallyDifferent"
352 >>> connections = ExampleConnections(config=config)
353 >>> assert(connections.inputConnection.name == "ModifiedDataset")
354 >>> assert(connections.outputConnection.name == "TotallyDifferent")
357 def __init__(self, *, config:
'PipelineTaskConfig' =
None):
364 if config
is None or not isinstance(config, configMod.PipelineTaskConfig):
365 raise ValueError(
"PipelineTaskConnections must be instantiated with"
366 " a PipelineTaskConfig instance")
371 templateValues = {name: getattr(config.connections, name)
for name
in getattr(self,
372 'defaultTemplates').keys()}
376 self.
_nameOverrides = {name: getattr(config.connections, name).format(**templateValues)
377 for name
in self.allConnections.keys()}
385 OutputQuantizedConnection]:
386 """Builds QuantizedConnections corresponding to input Quantum
390 quantum : `lsst.daf.butler.Quantum`
391 Quantum object which defines the inputs and outputs for a given
396 retVal : `tuple` of (`InputQuantizedConnection`,
397 `OutputQuantizedConnection`) Namespaces mapping attribute names
398 (identifiers of connections) to butler references defined in the
399 input `lsst.daf.butler.Quantum`
405 for refs, names
in zip((inputDatasetRefs, outputDatasetRefs),
408 for attributeName
in names:
410 attribute = getattr(self, attributeName)
412 if attribute.name
in quantum.predictedInputs:
414 quantumInputRefs = quantum.predictedInputs[attribute.name]
417 if attribute.deferLoad:
421 if not attribute.multiple:
422 if len(quantumInputRefs) > 1:
424 f
"Received multiple datasets "
425 f
"{', '.join(str(r.dataId) for r in quantumInputRefs)} "
426 f
"for scalar connection {attributeName} "
427 f
"({quantumInputRefs[0].datasetType.name}) "
428 f
"of quantum for {quantum.taskName} with data ID {quantum.dataId}."
430 if len(quantumInputRefs) == 0:
432 quantumInputRefs = quantumInputRefs[0]
434 setattr(refs, attributeName, quantumInputRefs)
436 elif attribute.name
in quantum.outputs:
437 value = quantum.outputs[attribute.name]
440 if not attribute.multiple:
443 setattr(refs, attributeName, value)
447 raise ValueError(f
"Attribute with name {attributeName} has no counterpoint "
449 return inputDatasetRefs, outputDatasetRefs
452 """Override to make adjustments to `lsst.daf.butler.DatasetRef` objects
453 in the `lsst.daf.butler.core.Quantum` during the graph generation stage
456 The base class implementation simply checks that input connections with
457 ``multiple`` set to `False` have no more than one dataset.
461 datasetRefMap : `dict`
462 Mapping from dataset type name to `list` of
463 `lsst.daf.butler.DatasetRef` objects
467 datasetRefMap : `dict`
468 Modified mapping of input with possible adjusted
469 `lsst.daf.butler.DatasetRef` objects.
474 Raised if any `Input` or `PrerequisiteInput` connection has
475 ``multiple`` set to `False`, but multiple datasets.
477 Overrides of this function have the option of raising an Exception
478 if a field in the input does not satisfy a need for a corresponding
479 pipelineTask, i.e. no reference catalogs are found.
483 refs = datasetRefMap[connection.name]
484 if not connection.multiple
and len(refs) > 1:
486 f
"Found multiple datasets {', '.join(str(r.dataId) for r in refs)} "
487 f
"for scalar connection {connection.name} ({refs[0].datasetType.name})."
492 def iterConnections(connections: PipelineTaskConnections, connectionType: str) -> typing.Generator:
493 """Creates an iterator over the selected connections type which yields
494 all the defined connections of that type.
498 connections: `PipelineTaskConnections`
499 An instance of a `PipelineTaskConnections` object that will be iterated
501 connectionType: `str`
502 The type of connections to iterate over, valid values are inputs,
503 outputs, prerequisiteInputs, initInputs, initOutputs.
507 connection: `BaseConnection`
508 A connection defined on the input connections object of the type
509 supplied. The yielded value Will be an derived type of
512 for name
in getattr(connections, connectionType):
513 yield getattr(connections, name)