Coverage for python/lsst/pipe/base/connections.py: 47%

Shortcuts on this page

r m x p   toggle line displays

j k   next/prev highlighted chunk

0   (zero) top of page

1   (one) first highlighted chunk

219 statements  

1# This file is part of pipe_base. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (http://www.lsst.org). 

6# See the COPYRIGHT file at the top-level directory of this distribution 

7# for details of code ownership. 

8# 

9# This program is free software: you can redistribute it and/or modify 

10# it under the terms of the GNU General Public License as published by 

11# the Free Software Foundation, either version 3 of the License, or 

12# (at your option) any later version. 

13# 

14# This program is distributed in the hope that it will be useful, 

15# but WITHOUT ANY WARRANTY; without even the implied warranty of 

16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

17# GNU General Public License for more details. 

18# 

19# You should have received a copy of the GNU General Public License 

20# along with this program. If not, see <http://www.gnu.org/licenses/>. 

21 

22"""Module defining connection classes for PipelineTask. 

23""" 

24 

25from __future__ import annotations 

26 

27__all__ = [ 

28 "AdjustQuantumHelper", 

29 "DeferredDatasetRef", 

30 "InputQuantizedConnection", 

31 "OutputQuantizedConnection", 

32 "PipelineTaskConnections", 

33 "ScalarError", 

34 "iterConnections", 

35 "ScalarError", 

36] 

37 

38import itertools 

39import string 

40import typing 

41from collections import UserDict 

42from dataclasses import dataclass 

43from types import SimpleNamespace 

44from typing import Any, ClassVar, Dict, Iterable, List, Set, Union 

45 

46from lsst.daf.butler import DataCoordinate, DatasetRef, DatasetType, NamedKeyDict, NamedKeyMapping, Quantum 

47 

48from ._status import NoWorkFound 

49from .connectionTypes import ( 

50 BaseConnection, 

51 BaseInput, 

52 InitInput, 

53 InitOutput, 

54 Input, 

55 Output, 

56 PrerequisiteInput, 

57) 

58 

59if typing.TYPE_CHECKING: 59 ↛ 60line 59 didn't jump to line 60, because the condition on line 59 was never true

60 from .config import PipelineTaskConfig 

61 

62 

63class ScalarError(TypeError): 

64 """Exception raised when dataset type is configured as scalar 

65 but there are multiple data IDs in a Quantum for that dataset. 

66 """ 

67 

68 

69class PipelineTaskConnectionDict(UserDict): 

70 """This is a special dict class used by PipelineTaskConnectionMetaclass 

71 

72 This dict is used in PipelineTaskConnection class creation, as the 

73 dictionary that is initially used as __dict__. It exists to 

74 intercept connection fields declared in a PipelineTaskConnection, and 

75 what name is used to identify them. The names are then added to class 

76 level list according to the connection type of the class attribute. The 

77 names are also used as keys in a class level dictionary associated with 

78 the corresponding class attribute. This information is a duplicate of 

79 what exists in __dict__, but provides a simple place to lookup and 

80 iterate on only these variables. 

81 """ 

82 

83 def __init__(self, *args: Any, **kwargs: Any): 

84 super().__init__(*args, **kwargs) 

85 # Initialize class level variables used to track any declared 

86 # class level variables that are instances of 

87 # connectionTypes.BaseConnection 

88 self.data["inputs"] = [] 

89 self.data["prerequisiteInputs"] = [] 

90 self.data["outputs"] = [] 

91 self.data["initInputs"] = [] 

92 self.data["initOutputs"] = [] 

93 self.data["allConnections"] = {} 

94 

95 def __setitem__(self, name: str, value: Any) -> None: 

96 if isinstance(value, Input): 

97 self.data["inputs"].append(name) 

98 elif isinstance(value, PrerequisiteInput): 

99 self.data["prerequisiteInputs"].append(name) 

100 elif isinstance(value, Output): 

101 self.data["outputs"].append(name) 

102 elif isinstance(value, InitInput): 

103 self.data["initInputs"].append(name) 

104 elif isinstance(value, InitOutput): 

105 self.data["initOutputs"].append(name) 

106 # This should not be an elif, as it needs tested for 

107 # everything that inherits from BaseConnection 

108 if isinstance(value, BaseConnection): 

109 object.__setattr__(value, "varName", name) 

110 self.data["allConnections"][name] = value 

111 # defer to the default behavior 

112 super().__setitem__(name, value) 

113 

114 

115class PipelineTaskConnectionsMetaclass(type): 

116 """Metaclass used in the declaration of PipelineTaskConnections classes""" 

117 

118 def __prepare__(name, bases, **kwargs): # noqa: 805 

119 # Create an instance of our special dict to catch and track all 

120 # variables that are instances of connectionTypes.BaseConnection 

121 # Copy any existing connections from a parent class 

122 dct = PipelineTaskConnectionDict() 

123 for base in bases: 

124 if isinstance(base, PipelineTaskConnectionsMetaclass): 124 ↛ 123line 124 didn't jump to line 123, because the condition on line 124 was never false

125 for name, value in base.allConnections.items(): 125 ↛ 126line 125 didn't jump to line 126, because the loop on line 125 never started

126 dct[name] = value 

127 return dct 

128 

129 def __new__(cls, name, bases, dct, **kwargs): 

130 dimensionsValueError = TypeError( 

131 "PipelineTaskConnections class must be created with a dimensions " 

132 "attribute which is an iterable of dimension names" 

133 ) 

134 

135 if name != "PipelineTaskConnections": 

136 # Verify that dimensions are passed as a keyword in class 

137 # declaration 

138 if "dimensions" not in kwargs: 138 ↛ 139line 138 didn't jump to line 139, because the condition on line 138 was never true

139 for base in bases: 

140 if hasattr(base, "dimensions"): 

141 kwargs["dimensions"] = base.dimensions 

142 break 

143 if "dimensions" not in kwargs: 

144 raise dimensionsValueError 

145 try: 

146 if isinstance(kwargs["dimensions"], str): 146 ↛ 147line 146 didn't jump to line 147, because the condition on line 146 was never true

147 raise TypeError( 

148 "Dimensions must be iterable of dimensions, got str," 

149 "possibly omitted trailing comma" 

150 ) 

151 if not isinstance(kwargs["dimensions"], typing.Iterable): 151 ↛ 152line 151 didn't jump to line 152, because the condition on line 151 was never true

152 raise TypeError("Dimensions must be iterable of dimensions") 

153 dct["dimensions"] = set(kwargs["dimensions"]) 

154 except TypeError as exc: 

155 raise dimensionsValueError from exc 

156 # Lookup any python string templates that may have been used in the 

157 # declaration of the name field of a class connection attribute 

158 allTemplates = set() 

159 stringFormatter = string.Formatter() 

160 # Loop over all connections 

161 for obj in dct["allConnections"].values(): 

162 nameValue = obj.name 

163 # add all the parameters to the set of templates 

164 for param in stringFormatter.parse(nameValue): 

165 if param[1] is not None: 

166 allTemplates.add(param[1]) 

167 

168 # look up any template from base classes and merge them all 

169 # together 

170 mergeDict = {} 

171 for base in bases[::-1]: 

172 if hasattr(base, "defaultTemplates"): 172 ↛ 173line 172 didn't jump to line 173, because the condition on line 172 was never true

173 mergeDict.update(base.defaultTemplates) 

174 if "defaultTemplates" in kwargs: 

175 mergeDict.update(kwargs["defaultTemplates"]) 

176 

177 if len(mergeDict) > 0: 

178 kwargs["defaultTemplates"] = mergeDict 

179 

180 # Verify that if templated strings were used, defaults were 

181 # supplied as an argument in the declaration of the connection 

182 # class 

183 if len(allTemplates) > 0 and "defaultTemplates" not in kwargs: 183 ↛ 184line 183 didn't jump to line 184, because the condition on line 183 was never true

184 raise TypeError( 

185 "PipelineTaskConnection class contains templated attribute names, but no " 

186 "defaut templates were provided, add a dictionary attribute named " 

187 "defaultTemplates which contains the mapping between template key and value" 

188 ) 

189 if len(allTemplates) > 0: 

190 # Verify all templates have a default, and throw if they do not 

191 defaultTemplateKeys = set(kwargs["defaultTemplates"].keys()) 

192 templateDifference = allTemplates.difference(defaultTemplateKeys) 

193 if templateDifference: 193 ↛ 194line 193 didn't jump to line 194, because the condition on line 193 was never true

194 raise TypeError(f"Default template keys were not provided for {templateDifference}") 

195 # Verify that templates do not share names with variable names 

196 # used for a connection, this is needed because of how 

197 # templates are specified in an associated config class. 

198 nameTemplateIntersection = allTemplates.intersection(set(dct["allConnections"].keys())) 

199 if len(nameTemplateIntersection) > 0: 199 ↛ 200line 199 didn't jump to line 200, because the condition on line 199 was never true

200 raise TypeError( 

201 f"Template parameters cannot share names with Class attributes" 

202 f" (conflicts are {nameTemplateIntersection})." 

203 ) 

204 dct["defaultTemplates"] = kwargs.get("defaultTemplates", {}) 

205 

206 # Convert all the connection containers into frozensets so they cannot 

207 # be modified at the class scope 

208 for connectionName in ("inputs", "prerequisiteInputs", "outputs", "initInputs", "initOutputs"): 

209 dct[connectionName] = frozenset(dct[connectionName]) 

210 # our custom dict type must be turned into an actual dict to be used in 

211 # type.__new__ 

212 return super().__new__(cls, name, bases, dict(dct)) 

213 

214 def __init__(cls, name, bases, dct, **kwargs): 

215 # This overrides the default init to drop the kwargs argument. Python 

216 # metaclasses will have this argument set if any kwargs are passes at 

217 # class construction time, but should be consumed before calling 

218 # __init__ on the type metaclass. This is in accordance with python 

219 # documentation on metaclasses 

220 super().__init__(name, bases, dct) 

221 

222 

223class QuantizedConnection(SimpleNamespace): 

224 """A Namespace to map defined variable names of connections to the 

225 associated `lsst.daf.butler.DatasetRef` objects. 

226 

227 This class maps the names used to define a connection on a 

228 PipelineTaskConnectionsClass to the corresponding 

229 `lsst.daf.butler.DatasetRef`s provided by a `lsst.daf.butler.Quantum` 

230 instance. This will be a quantum of execution based on the graph created 

231 by examining all the connections defined on the 

232 `PipelineTaskConnectionsClass`. 

233 """ 

234 

235 def __init__(self, **kwargs): 

236 # Create a variable to track what attributes are added. This is used 

237 # later when iterating over this QuantizedConnection instance 

238 object.__setattr__(self, "_attributes", set()) 

239 

240 def __setattr__(self, name: str, value: typing.Union[DatasetRef, typing.List[DatasetRef]]) -> None: 

241 # Capture the attribute name as it is added to this object 

242 self._attributes.add(name) 

243 super().__setattr__(name, value) 

244 

245 def __delattr__(self, name): 

246 object.__delattr__(self, name) 

247 self._attributes.remove(name) 

248 

249 def __iter__( 

250 self, 

251 ) -> typing.Generator[typing.Tuple[str, typing.Union[DatasetRef, typing.List[DatasetRef]]], None, None]: 

252 """Make an Iterator for this QuantizedConnection 

253 

254 Iterating over a QuantizedConnection will yield a tuple with the name 

255 of an attribute and the value associated with that name. This is 

256 similar to dict.items() but is on the namespace attributes rather than 

257 dict keys. 

258 """ 

259 yield from ((name, getattr(self, name)) for name in self._attributes) 

260 

261 def keys(self) -> typing.Generator[str, None, None]: 

262 """Returns an iterator over all the attributes added to a 

263 QuantizedConnection class 

264 """ 

265 yield from self._attributes 

266 

267 

268class InputQuantizedConnection(QuantizedConnection): 

269 pass 

270 

271 

272class OutputQuantizedConnection(QuantizedConnection): 

273 pass 

274 

275 

276@dataclass(frozen=True) 

277class DeferredDatasetRef: 

278 """A wrapper class for `DatasetRef` that indicates that a `PipelineTask` 

279 should receive a `DeferredDatasetHandle` instead of an in-memory dataset. 

280 

281 Parameters 

282 ---------- 

283 datasetRef : `lsst.daf.butler.DatasetRef` 

284 The `lsst.daf.butler.DatasetRef` that will be eventually used to 

285 resolve a dataset 

286 """ 

287 

288 datasetRef: DatasetRef 

289 

290 @property 

291 def datasetType(self) -> DatasetType: 

292 """The dataset type for this dataset.""" 

293 return self.datasetRef.datasetType 

294 

295 @property 

296 def dataId(self) -> DataCoordinate: 

297 """The data ID for this dataset.""" 

298 return self.datasetRef.dataId 

299 

300 

301class PipelineTaskConnections(metaclass=PipelineTaskConnectionsMetaclass): 

302 """PipelineTaskConnections is a class used to declare desired IO when a 

303 PipelineTask is run by an activator 

304 

305 Parameters 

306 ---------- 

307 config : `PipelineTaskConfig` 

308 A `PipelineTaskConfig` class instance whose class has been configured 

309 to use this `PipelineTaskConnectionsClass` 

310 

311 See also 

312 -------- 

313 iterConnections 

314 

315 Notes 

316 ----- 

317 ``PipelineTaskConnection`` classes are created by declaring class 

318 attributes of types defined in `lsst.pipe.base.connectionTypes` and are 

319 listed as follows: 

320 

321 * ``InitInput`` - Defines connections in a quantum graph which are used as 

322 inputs to the ``__init__`` function of the `PipelineTask` corresponding 

323 to this class 

324 * ``InitOuput`` - Defines connections in a quantum graph which are to be 

325 persisted using a butler at the end of the ``__init__`` function of the 

326 `PipelineTask` corresponding to this class. The variable name used to 

327 define this connection should be the same as an attribute name on the 

328 `PipelineTask` instance. E.g. if an ``InitOutput`` is declared with 

329 the name ``outputSchema`` in a ``PipelineTaskConnections`` class, then 

330 a `PipelineTask` instance should have an attribute 

331 ``self.outputSchema`` defined. Its value is what will be saved by the 

332 activator framework. 

333 * ``PrerequisiteInput`` - An input connection type that defines a 

334 `lsst.daf.butler.DatasetType` that must be present at execution time, 

335 but that will not be used during the course of creating the quantum 

336 graph to be executed. These most often are things produced outside the 

337 processing pipeline, such as reference catalogs. 

338 * ``Input`` - Input `lsst.daf.butler.DatasetType` objects that will be used 

339 in the ``run`` method of a `PipelineTask`. The name used to declare 

340 class attribute must match a function argument name in the ``run`` 

341 method of a `PipelineTask`. E.g. If the ``PipelineTaskConnections`` 

342 defines an ``Input`` with the name ``calexp``, then the corresponding 

343 signature should be ``PipelineTask.run(calexp, ...)`` 

344 * ``Output`` - A `lsst.daf.butler.DatasetType` that will be produced by an 

345 execution of a `PipelineTask`. The name used to declare the connection 

346 must correspond to an attribute of a `Struct` that is returned by a 

347 `PipelineTask` ``run`` method. E.g. if an output connection is 

348 defined with the name ``measCat``, then the corresponding 

349 ``PipelineTask.run`` method must return ``Struct(measCat=X,..)`` where 

350 X matches the ``storageClass`` type defined on the output connection. 

351 

352 The process of declaring a ``PipelineTaskConnection`` class involves 

353 parameters passed in the declaration statement. 

354 

355 The first parameter is ``dimensions`` which is an iterable of strings which 

356 defines the unit of processing the run method of a corresponding 

357 `PipelineTask` will operate on. These dimensions must match dimensions that 

358 exist in the butler registry which will be used in executing the 

359 corresponding `PipelineTask`. 

360 

361 The second parameter is labeled ``defaultTemplates`` and is conditionally 

362 optional. The name attributes of connections can be specified as python 

363 format strings, with named format arguments. If any of the name parameters 

364 on connections defined in a `PipelineTaskConnections` class contain a 

365 template, then a default template value must be specified in the 

366 ``defaultTemplates`` argument. This is done by passing a dictionary with 

367 keys corresponding to a template identifier, and values corresponding to 

368 the value to use as a default when formatting the string. For example if 

369 ``ConnectionClass.calexp.name = '{input}Coadd_calexp'`` then 

370 ``defaultTemplates`` = {'input': 'deep'}. 

371 

372 Once a `PipelineTaskConnections` class is created, it is used in the 

373 creation of a `PipelineTaskConfig`. This is further documented in the 

374 documentation of `PipelineTaskConfig`. For the purposes of this 

375 documentation, the relevant information is that the config class allows 

376 configuration of connection names by users when running a pipeline. 

377 

378 Instances of a `PipelineTaskConnections` class are used by the pipeline 

379 task execution framework to introspect what a corresponding `PipelineTask` 

380 will require, and what it will produce. 

381 

382 Examples 

383 -------- 

384 >>> from lsst.pipe.base import connectionTypes as cT 

385 >>> from lsst.pipe.base import PipelineTaskConnections 

386 >>> from lsst.pipe.base import PipelineTaskConfig 

387 >>> class ExampleConnections(PipelineTaskConnections, 

388 ... dimensions=("A", "B"), 

389 ... defaultTemplates={"foo": "Example"}): 

390 ... inputConnection = cT.Input(doc="Example input", 

391 ... dimensions=("A", "B"), 

392 ... storageClass=Exposure, 

393 ... name="{foo}Dataset") 

394 ... outputConnection = cT.Output(doc="Example output", 

395 ... dimensions=("A", "B"), 

396 ... storageClass=Exposure, 

397 ... name="{foo}output") 

398 >>> class ExampleConfig(PipelineTaskConfig, 

399 ... pipelineConnections=ExampleConnections): 

400 ... pass 

401 >>> config = ExampleConfig() 

402 >>> config.connections.foo = Modified 

403 >>> config.connections.outputConnection = "TotallyDifferent" 

404 >>> connections = ExampleConnections(config=config) 

405 >>> assert(connections.inputConnection.name == "ModifiedDataset") 

406 >>> assert(connections.outputConnection.name == "TotallyDifferent") 

407 """ 

408 

409 dimensions: ClassVar[Set[str]] 

410 

411 def __init__(self, *, config: "PipelineTaskConfig" = None): 

412 self.inputs: Set[str] = set(self.inputs) 

413 self.prerequisiteInputs: Set[str] = set(self.prerequisiteInputs) 

414 self.outputs: Set[str] = set(self.outputs) 

415 self.initInputs: Set[str] = set(self.initInputs) 

416 self.initOutputs: Set[str] = set(self.initOutputs) 

417 self.allConnections: Dict[str, BaseConnection] = dict(self.allConnections) 

418 

419 from .config import PipelineTaskConfig # local import to avoid cycle 

420 

421 if config is None or not isinstance(config, PipelineTaskConfig): 

422 raise ValueError( 

423 "PipelineTaskConnections must be instantiated with a PipelineTaskConfig instance" 

424 ) 

425 self.config = config 

426 # Extract the template names that were defined in the config instance 

427 # by looping over the keys of the defaultTemplates dict specified at 

428 # class declaration time 

429 templateValues = { 

430 name: getattr(config.connections, name) for name in getattr(self, "defaultTemplates").keys() 

431 } 

432 # Extract the configured value corresponding to each connection 

433 # variable. I.e. for each connection identifier, populate a override 

434 # for the connection.name attribute 

435 self._nameOverrides = { 

436 name: getattr(config.connections, name).format(**templateValues) 

437 for name in self.allConnections.keys() 

438 } 

439 

440 # connections.name corresponds to a dataset type name, create a reverse 

441 # mapping that goes from dataset type name to attribute identifier name 

442 # (variable name) on the connection class 

443 self._typeNameToVarName = {v: k for k, v in self._nameOverrides.items()} 

444 

445 def buildDatasetRefs( 

446 self, quantum: Quantum 

447 ) -> typing.Tuple[InputQuantizedConnection, OutputQuantizedConnection]: 

448 """Builds QuantizedConnections corresponding to input Quantum 

449 

450 Parameters 

451 ---------- 

452 quantum : `lsst.daf.butler.Quantum` 

453 Quantum object which defines the inputs and outputs for a given 

454 unit of processing 

455 

456 Returns 

457 ------- 

458 retVal : `tuple` of (`InputQuantizedConnection`, 

459 `OutputQuantizedConnection`) Namespaces mapping attribute names 

460 (identifiers of connections) to butler references defined in the 

461 input `lsst.daf.butler.Quantum` 

462 """ 

463 inputDatasetRefs = InputQuantizedConnection() 

464 outputDatasetRefs = OutputQuantizedConnection() 

465 # operate on a reference object and an interable of names of class 

466 # connection attributes 

467 for refs, names in zip( 

468 (inputDatasetRefs, outputDatasetRefs), 

469 (itertools.chain(self.inputs, self.prerequisiteInputs), self.outputs), 

470 ): 

471 # get a name of a class connection attribute 

472 for attributeName in names: 

473 # get the attribute identified by name 

474 attribute = getattr(self, attributeName) 

475 # Branch if the attribute dataset type is an input 

476 if attribute.name in quantum.inputs: 

477 # if the dataset is marked to load deferred, wrap it in a 

478 # DeferredDatasetRef 

479 quantumInputRefs: Union[List[DatasetRef], List[DeferredDatasetRef]] 

480 if attribute.deferLoad: 

481 quantumInputRefs = [ 

482 DeferredDatasetRef(datasetRef=ref) for ref in quantum.inputs[attribute.name] 

483 ] 

484 else: 

485 quantumInputRefs = list(quantum.inputs[attribute.name]) 

486 # Unpack arguments that are not marked multiples (list of 

487 # length one) 

488 if not attribute.multiple: 

489 if len(quantumInputRefs) > 1: 

490 raise ScalarError( 

491 "Received multiple datasets " 

492 f"{', '.join(str(r.dataId) for r in quantumInputRefs)} " 

493 f"for scalar connection {attributeName} " 

494 f"({quantumInputRefs[0].datasetType.name}) " 

495 f"of quantum for {quantum.taskName} with data ID {quantum.dataId}." 

496 ) 

497 if len(quantumInputRefs) == 0: 

498 continue 

499 setattr(refs, attributeName, quantumInputRefs[0]) 

500 else: 

501 # Add to the QuantizedConnection identifier 

502 setattr(refs, attributeName, quantumInputRefs) 

503 # Branch if the attribute dataset type is an output 

504 elif attribute.name in quantum.outputs: 

505 value = quantum.outputs[attribute.name] 

506 # Unpack arguments that are not marked multiples (list of 

507 # length one) 

508 if not attribute.multiple: 

509 setattr(refs, attributeName, value[0]) 

510 else: 

511 setattr(refs, attributeName, value) 

512 # Specified attribute is not in inputs or outputs dont know how 

513 # to handle, throw 

514 else: 

515 raise ValueError( 

516 f"Attribute with name {attributeName} has no counterpoint in input quantum" 

517 ) 

518 return inputDatasetRefs, outputDatasetRefs 

519 

520 def adjustQuantum( 

521 self, 

522 inputs: typing.Dict[str, typing.Tuple[BaseInput, typing.Collection[DatasetRef]]], 

523 outputs: typing.Dict[str, typing.Tuple[Output, typing.Collection[DatasetRef]]], 

524 label: str, 

525 data_id: DataCoordinate, 

526 ) -> typing.Tuple[ 

527 typing.Mapping[str, typing.Tuple[BaseInput, typing.Collection[DatasetRef]]], 

528 typing.Mapping[str, typing.Tuple[Output, typing.Collection[DatasetRef]]], 

529 ]: 

530 """Override to make adjustments to `lsst.daf.butler.DatasetRef` objects 

531 in the `lsst.daf.butler.core.Quantum` during the graph generation stage 

532 of the activator. 

533 

534 Parameters 

535 ---------- 

536 inputs : `dict` 

537 Dictionary whose keys are an input (regular or prerequisite) 

538 connection name and whose values are a tuple of the connection 

539 instance and a collection of associated `DatasetRef` objects. 

540 The exact type of the nested collections is unspecified; it can be 

541 assumed to be multi-pass iterable and support `len` and ``in``, but 

542 it should not be mutated in place. In contrast, the outer 

543 dictionaries are guaranteed to be temporary copies that are true 

544 `dict` instances, and hence may be modified and even returned; this 

545 is especially useful for delegating to `super` (see notes below). 

546 outputs : `Mapping` 

547 Mapping of output datasets, with the same structure as ``inputs``. 

548 label : `str` 

549 Label for this task in the pipeline (should be used in all 

550 diagnostic messages). 

551 data_id : `lsst.daf.butler.DataCoordinate` 

552 Data ID for this quantum in the pipeline (should be used in all 

553 diagnostic messages). 

554 

555 Returns 

556 ------- 

557 adjusted_inputs : `Mapping` 

558 Mapping of the same form as ``inputs`` with updated containers of 

559 input `DatasetRef` objects. Connections that are not changed 

560 should not be returned at all. Datasets may only be removed, not 

561 added. Nested collections may be of any multi-pass iterable type, 

562 and the order of iteration will set the order of iteration within 

563 `PipelineTask.runQuantum`. 

564 adjusted_outputs : `Mapping` 

565 Mapping of updated output datasets, with the same structure and 

566 interpretation as ``adjusted_inputs``. 

567 

568 Raises 

569 ------ 

570 ScalarError 

571 Raised if any `Input` or `PrerequisiteInput` connection has 

572 ``multiple`` set to `False`, but multiple datasets. 

573 NoWorkFound 

574 Raised to indicate that this quantum should not be run; not enough 

575 datasets were found for a regular `Input` connection, and the 

576 quantum should be pruned or skipped. 

577 FileNotFoundError 

578 Raised to cause QuantumGraph generation to fail (with the message 

579 included in this exception); not enough datasets were found for a 

580 `PrerequisiteInput` connection. 

581 

582 Notes 

583 ----- 

584 The base class implementation performs important checks. It always 

585 returns an empty mapping (i.e. makes no adjustments). It should 

586 always called be via `super` by custom implementations, ideally at the 

587 end of the custom implementation with already-adjusted mappings when 

588 any datasets are actually dropped, e.g.:: 

589 

590 def adjustQuantum(self, inputs, outputs, label, data_id): 

591 # Filter out some dataset refs for one connection. 

592 connection, old_refs = inputs["my_input"] 

593 new_refs = [ref for ref in old_refs if ...] 

594 adjusted_inputs = {"my_input", (connection, new_refs)} 

595 # Update the original inputs so we can pass them to super. 

596 inputs.update(adjusted_inputs) 

597 # Can ignore outputs from super because they are guaranteed 

598 # to be empty. 

599 super().adjustQuantum(inputs, outputs, label_data_id) 

600 # Return only the connections we modified. 

601 return adjusted_inputs, {} 

602 

603 Removing outputs here is guaranteed to affect what is actually 

604 passed to `PipelineTask.runQuantum`, but its effect on the larger 

605 graph may be deferred to execution, depending on the context in 

606 which `adjustQuantum` is being run: if one quantum removes an output 

607 that is needed by a second quantum as input, the second quantum may not 

608 be adjusted (and hence pruned or skipped) until that output is actually 

609 found to be missing at execution time. 

610 

611 Tasks that desire zip-iteration consistency between any combinations of 

612 connections that have the same data ID should generally implement 

613 `adjustQuantum` to achieve this, even if they could also run that 

614 logic during execution; this allows the system to see outputs that will 

615 not be produced because the corresponding input is missing as early as 

616 possible. 

617 """ 

618 for name, (input_connection, refs) in inputs.items(): 

619 dataset_type_name = input_connection.name 

620 if not input_connection.multiple and len(refs) > 1: 

621 raise ScalarError( 

622 f"Found multiple datasets {', '.join(str(r.dataId) for r in refs)} " 

623 f"for non-multiple input connection {label}.{name} ({dataset_type_name}) " 

624 f"for quantum data ID {data_id}." 

625 ) 

626 if len(refs) < input_connection.minimum: 

627 if isinstance(input_connection, PrerequisiteInput): 

628 # This branch should only be possible during QG generation, 

629 # or if someone deleted the dataset between making the QG 

630 # and trying to run it. Either one should be a hard error. 

631 raise FileNotFoundError( 

632 f"Not enough datasets ({len(refs)}) found for non-optional connection {label}.{name} " 

633 f"({dataset_type_name}) with minimum={input_connection.minimum} for quantum data ID " 

634 f"{data_id}." 

635 ) 

636 else: 

637 # This branch should be impossible during QG generation, 

638 # because that algorithm can only make quanta whose inputs 

639 # are either already present or should be created during 

640 # execution. It can trigger during execution if the input 

641 # wasn't actually created by an upstream task in the same 

642 # graph. 

643 raise NoWorkFound(label, name, input_connection) 

644 for name, (output_connection, refs) in outputs.items(): 

645 dataset_type_name = output_connection.name 

646 if not output_connection.multiple and len(refs) > 1: 

647 raise ScalarError( 

648 f"Found multiple datasets {', '.join(str(r.dataId) for r in refs)} " 

649 f"for non-multiple output connection {label}.{name} ({dataset_type_name}) " 

650 f"for quantum data ID {data_id}." 

651 ) 

652 return {}, {} 

653 

654 

655def iterConnections( 

656 connections: PipelineTaskConnections, connectionType: Union[str, Iterable[str]] 

657) -> typing.Generator[BaseConnection, None, None]: 

658 """Creates an iterator over the selected connections type which yields 

659 all the defined connections of that type. 

660 

661 Parameters 

662 ---------- 

663 connections: `PipelineTaskConnections` 

664 An instance of a `PipelineTaskConnections` object that will be iterated 

665 over. 

666 connectionType: `str` 

667 The type of connections to iterate over, valid values are inputs, 

668 outputs, prerequisiteInputs, initInputs, initOutputs. 

669 

670 Yields 

671 ------- 

672 connection: `BaseConnection` 

673 A connection defined on the input connections object of the type 

674 supplied. The yielded value Will be an derived type of 

675 `BaseConnection`. 

676 """ 

677 if isinstance(connectionType, str): 

678 connectionType = (connectionType,) 

679 for name in itertools.chain.from_iterable(getattr(connections, ct) for ct in connectionType): 

680 yield getattr(connections, name) 

681 

682 

683@dataclass 

684class AdjustQuantumHelper: 

685 """Helper class for calling `PipelineTaskConnections.adjustQuantum`. 

686 

687 This class holds `input` and `output` mappings in the form used by 

688 `Quantum` and execution harness code, i.e. with `DatasetType` keys, 

689 translating them to and from the connection-oriented mappings used inside 

690 `PipelineTaskConnections`. 

691 """ 

692 

693 inputs: NamedKeyMapping[DatasetType, typing.List[DatasetRef]] 

694 """Mapping of regular input and prerequisite input datasets, grouped by 

695 `DatasetType`. 

696 """ 

697 

698 outputs: NamedKeyMapping[DatasetType, typing.List[DatasetRef]] 

699 """Mapping of output datasets, grouped by `DatasetType`. 

700 """ 

701 

702 inputs_adjusted: bool = False 

703 """Whether any inputs were removed in the last call to `adjust_in_place`. 

704 """ 

705 

706 outputs_adjusted: bool = False 

707 """Whether any outputs were removed in the last call to `adjust_in_place`. 

708 """ 

709 

710 def adjust_in_place( 

711 self, 

712 connections: PipelineTaskConnections, 

713 label: str, 

714 data_id: DataCoordinate, 

715 ) -> None: 

716 """Call `~PipelineTaskConnections.adjustQuantum` and update ``self`` 

717 with its results. 

718 

719 Parameters 

720 ---------- 

721 connections : `PipelineTaskConnections` 

722 Instance on which to call `~PipelineTaskConnections.adjustQuantum`. 

723 label : `str` 

724 Label for this task in the pipeline (should be used in all 

725 diagnostic messages). 

726 data_id : `lsst.daf.butler.DataCoordinate` 

727 Data ID for this quantum in the pipeline (should be used in all 

728 diagnostic messages). 

729 """ 

730 # Translate self's DatasetType-keyed, Quantum-oriented mappings into 

731 # connection-keyed, PipelineTask-oriented mappings. 

732 inputs_by_connection: typing.Dict[str, typing.Tuple[BaseInput, typing.Tuple[DatasetRef, ...]]] = {} 

733 outputs_by_connection: typing.Dict[str, typing.Tuple[Output, typing.Tuple[DatasetRef, ...]]] = {} 

734 for name in itertools.chain(connections.inputs, connections.prerequisiteInputs): 

735 connection = getattr(connections, name) 

736 dataset_type_name = connection.name 

737 inputs_by_connection[name] = (connection, tuple(self.inputs.get(dataset_type_name, ()))) 

738 for name in itertools.chain(connections.outputs): 

739 connection = getattr(connections, name) 

740 dataset_type_name = connection.name 

741 outputs_by_connection[name] = (connection, tuple(self.outputs.get(dataset_type_name, ()))) 

742 # Actually call adjustQuantum. 

743 # MyPy correctly complains that this call is not quite legal, but the 

744 # method docs explain exactly what's expected and it's the behavior we 

745 # want. It'd be nice to avoid this if we ever have to change the 

746 # interface anyway, but not an immediate problem. 

747 adjusted_inputs_by_connection, adjusted_outputs_by_connection = connections.adjustQuantum( 

748 inputs_by_connection, # type: ignore 

749 outputs_by_connection, # type: ignore 

750 label, 

751 data_id, 

752 ) 

753 # Translate adjustments to DatasetType-keyed, Quantum-oriented form, 

754 # installing new mappings in self if necessary. 

755 if adjusted_inputs_by_connection: 

756 adjusted_inputs = NamedKeyDict[DatasetType, typing.List[DatasetRef]](self.inputs) 

757 for name, (connection, updated_refs) in adjusted_inputs_by_connection.items(): 

758 dataset_type_name = connection.name 

759 if not set(updated_refs).issubset(self.inputs[dataset_type_name]): 

760 raise RuntimeError( 

761 f"adjustQuantum implementation for task with label {label} returned {name} " 

762 f"({dataset_type_name}) input datasets that are not a subset of those " 

763 f"it was given for data ID {data_id}." 

764 ) 

765 adjusted_inputs[dataset_type_name] = list(updated_refs) 

766 self.inputs = adjusted_inputs.freeze() 

767 self.inputs_adjusted = True 

768 else: 

769 self.inputs_adjusted = False 

770 if adjusted_outputs_by_connection: 

771 adjusted_outputs = NamedKeyDict[DatasetType, typing.List[DatasetRef]](self.outputs) 

772 for name, (connection, updated_refs) in adjusted_outputs_by_connection.items(): 

773 if not set(updated_refs).issubset(self.outputs[dataset_type_name]): 

774 raise RuntimeError( 

775 f"adjustQuantum implementation for task with label {label} returned {name} " 

776 f"({dataset_type_name}) output datasets that are not a subset of those " 

777 f"it was given for data ID {data_id}." 

778 ) 

779 adjusted_outputs[dataset_type_name] = list(updated_refs) 

780 self.outputs = adjusted_outputs.freeze() 

781 self.outputs_adjusted = True 

782 else: 

783 self.outputs_adjusted = False