Coverage for python/lsst/pipe/base/tests/simpleQGraph.py: 26%

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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"""Bunch of common classes and methods for use in unit tests. 

23""" 

24from __future__ import annotations 

25 

26__all__ = ["AddTaskConfig", "AddTask", "AddTaskFactoryMock"] 

27 

28import itertools 

29import logging 

30from collections.abc import Iterable, Mapping, MutableMapping 

31from typing import TYPE_CHECKING, Any, cast 

32 

33import lsst.daf.butler.tests as butlerTests 

34import lsst.pex.config as pexConfig 

35import numpy 

36from lsst.daf.butler import Butler, Config, DataId, DatasetRef, DatasetType, Formatter, LimitedButler 

37from lsst.daf.butler.core.logging import ButlerLogRecords 

38from lsst.resources import ResourcePath 

39from lsst.utils import doImportType 

40from lsst.utils.introspection import get_full_type_name 

41 

42from .. import connectionTypes as cT 

43from .._instrument import Instrument 

44from ..config import PipelineTaskConfig 

45from ..connections import PipelineTaskConnections 

46from ..graph import QuantumGraph 

47from ..graphBuilder import DatasetQueryConstraintVariant as DSQVariant 

48from ..graphBuilder import GraphBuilder 

49from ..pipeline import Pipeline, TaskDatasetTypes, TaskDef 

50from ..pipelineTask import PipelineTask 

51from ..struct import Struct 

52from ..task import _TASK_FULL_METADATA_TYPE 

53from ..taskFactory import TaskFactory 

54 

55if TYPE_CHECKING: 

56 from lsst.daf.butler import Registry 

57 

58_LOG = logging.getLogger(__name__) 

59 

60 

61class SimpleInstrument(Instrument): 

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

63 pass 

64 

65 @staticmethod 

66 def getName() -> str: 

67 return "INSTRU" 

68 

69 def getRawFormatter(self, dataId: DataId) -> type[Formatter]: 

70 return Formatter 

71 

72 def register(self, registry: Registry, *, update: bool = False) -> None: 

73 pass 

74 

75 

76class AddTaskConnections( 

77 PipelineTaskConnections, 

78 dimensions=("instrument", "detector"), 

79 defaultTemplates={"in_tmpl": "_in", "out_tmpl": "_out"}, 

80): 

81 """Connections for AddTask, has one input and two outputs, 

82 plus one init output. 

83 """ 

84 

85 input = cT.Input( 

86 name="add_dataset{in_tmpl}", 

87 dimensions=["instrument", "detector"], 

88 storageClass="NumpyArray", 

89 doc="Input dataset type for this task", 

90 ) 

91 output = cT.Output( 

92 name="add_dataset{out_tmpl}", 

93 dimensions=["instrument", "detector"], 

94 storageClass="NumpyArray", 

95 doc="Output dataset type for this task", 

96 ) 

97 output2 = cT.Output( 

98 name="add2_dataset{out_tmpl}", 

99 dimensions=["instrument", "detector"], 

100 storageClass="NumpyArray", 

101 doc="Output dataset type for this task", 

102 ) 

103 initout = cT.InitOutput( 

104 name="add_init_output{out_tmpl}", 

105 storageClass="NumpyArray", 

106 doc="Init Output dataset type for this task", 

107 ) 

108 

109 

110class AddTaskConfig(PipelineTaskConfig, pipelineConnections=AddTaskConnections): 

111 """Config for AddTask.""" 

112 

113 addend = pexConfig.Field[int](doc="amount to add", default=3) 

114 

115 

116class AddTask(PipelineTask): 

117 """Trivial PipelineTask for testing, has some extras useful for specific 

118 unit tests. 

119 """ 

120 

121 ConfigClass = AddTaskConfig 

122 _DefaultName = "add_task" 

123 

124 initout = numpy.array([999]) 

125 """InitOutputs for this task""" 

126 

127 taskFactory: AddTaskFactoryMock | None = None 

128 """Factory that makes instances""" 

129 

130 def run(self, input: int) -> Struct: # type: ignore 

131 if self.taskFactory: 

132 # do some bookkeeping 

133 if self.taskFactory.stopAt == self.taskFactory.countExec: 

134 raise RuntimeError("pretend something bad happened") 

135 self.taskFactory.countExec += 1 

136 

137 self.config = cast(AddTaskConfig, self.config) 

138 self.metadata.add("add", self.config.addend) 

139 output = input + self.config.addend 

140 output2 = output + self.config.addend 

141 _LOG.info("input = %s, output = %s, output2 = %s", input, output, output2) 

142 return Struct(output=output, output2=output2) 

143 

144 

145class AddTaskFactoryMock(TaskFactory): 

146 """Special task factory that instantiates AddTask. 

147 

148 It also defines some bookkeeping variables used by AddTask to report 

149 progress to unit tests. 

150 """ 

151 

152 def __init__(self, stopAt: int = -1): 

153 self.countExec = 0 # incremented by AddTask 

154 self.stopAt = stopAt # AddTask raises exception at this call to run() 

155 

156 def makeTask( 

157 self, taskDef: TaskDef, butler: LimitedButler, initInputRefs: Iterable[DatasetRef] | None 

158 ) -> PipelineTask: 

159 taskClass = taskDef.taskClass 

160 assert taskClass is not None 

161 task = taskClass(config=taskDef.config, initInputs=None, name=taskDef.label) 

162 task.taskFactory = self # type: ignore 

163 return task 

164 

165 

166def registerDatasetTypes(registry: Registry, pipeline: Pipeline | Iterable[TaskDef]) -> None: 

167 """Register all dataset types used by tasks in a registry. 

168 

169 Copied and modified from `PreExecInit.initializeDatasetTypes`. 

170 

171 Parameters 

172 ---------- 

173 registry : `~lsst.daf.butler.Registry` 

174 Registry instance. 

175 pipeline : `typing.Iterable` of `TaskDef` 

176 Iterable of TaskDef instances, likely the output of the method 

177 `Pipelines.toExpandedPipeline` on a `~lsst.pipe.base.Pipeline` object. 

178 """ 

179 for taskDef in pipeline: 

180 configDatasetType = DatasetType( 

181 taskDef.configDatasetName, {}, storageClass="Config", universe=registry.dimensions 

182 ) 

183 storageClass = "Packages" 

184 packagesDatasetType = DatasetType( 

185 "packages", {}, storageClass=storageClass, universe=registry.dimensions 

186 ) 

187 datasetTypes = TaskDatasetTypes.fromTaskDef(taskDef, registry=registry) 

188 for datasetType in itertools.chain( 

189 datasetTypes.initInputs, 

190 datasetTypes.initOutputs, 

191 datasetTypes.inputs, 

192 datasetTypes.outputs, 

193 datasetTypes.prerequisites, 

194 [configDatasetType, packagesDatasetType], 

195 ): 

196 _LOG.info("Registering %s with registry", datasetType) 

197 # this is a no-op if it already exists and is consistent, 

198 # and it raises if it is inconsistent. But components must be 

199 # skipped 

200 if not datasetType.isComponent(): 

201 registry.registerDatasetType(datasetType) 

202 

203 

204def makeSimplePipeline(nQuanta: int, instrument: str | None = None) -> Pipeline: 

205 """Make a simple Pipeline for tests. 

206 

207 This is called by `makeSimpleQGraph()` if no pipeline is passed to that 

208 function. It can also be used to customize the pipeline used by 

209 `makeSimpleQGraph()` function by calling this first and passing the result 

210 to it. 

211 

212 Parameters 

213 ---------- 

214 nQuanta : `int` 

215 The number of quanta to add to the pipeline. 

216 instrument : `str` or `None`, optional 

217 The importable name of an instrument to be added to the pipeline or 

218 if no instrument should be added then an empty string or `None`, by 

219 default None 

220 

221 Returns 

222 ------- 

223 pipeline : `~lsst.pipe.base.Pipeline` 

224 The created pipeline object. 

225 """ 

226 pipeline = Pipeline("test pipeline") 

227 # make a bunch of tasks that execute in well defined order (via data 

228 # dependencies) 

229 for lvl in range(nQuanta): 

230 pipeline.addTask(AddTask, f"task{lvl}") 

231 pipeline.addConfigOverride(f"task{lvl}", "connections.in_tmpl", lvl) 

232 pipeline.addConfigOverride(f"task{lvl}", "connections.out_tmpl", lvl + 1) 

233 if instrument: 

234 pipeline.addInstrument(instrument) 

235 return pipeline 

236 

237 

238def makeSimpleButler( 

239 root: str, run: str = "test", inMemory: bool = True, config: Config | str | None = None 

240) -> Butler: 

241 """Create new data butler instance. 

242 

243 Parameters 

244 ---------- 

245 root : `str` 

246 Path or URI to the root location of the new repository. 

247 run : `str`, optional 

248 Run collection name. 

249 inMemory : `bool`, optional 

250 If true make in-memory repository. 

251 config : `~lsst.daf.butler.Config`, optional 

252 Configuration to use for new Butler, if `None` then default 

253 configuration is used. If ``inMemory`` is `True` then configuration 

254 is updated to use SQLite registry and file datastore in ``root``. 

255 

256 Returns 

257 ------- 

258 butler : `~lsst.daf.butler.Butler` 

259 Data butler instance. 

260 """ 

261 root_path = ResourcePath(root, forceDirectory=True) 

262 if not root_path.isLocal: 

263 raise ValueError(f"Only works with local root not {root_path}") 

264 butler_config = Config() 

265 if config: 

266 butler_config.update(Config(config)) 

267 if not inMemory: 

268 butler_config["registry", "db"] = f"sqlite:///{root_path.ospath}/gen3.sqlite" 

269 butler_config["datastore", "cls"] = "lsst.daf.butler.datastores.fileDatastore.FileDatastore" 

270 repo = butlerTests.makeTestRepo(str(root_path), {}, config=butler_config) 

271 butler = Butler(butler=repo, run=run) 

272 return butler 

273 

274 

275def populateButler( 

276 pipeline: Pipeline, butler: Butler, datasetTypes: dict[str | None, list[str]] | None = None 

277) -> None: 

278 """Populate data butler with data needed for test. 

279 

280 Initializes data butler with a bunch of items: 

281 - registers dataset types which are defined by pipeline 

282 - create dimensions data for (instrument, detector) 

283 - adds datasets based on ``datasetTypes`` dictionary, if dictionary is 

284 missing then a single dataset with type "add_dataset0" is added 

285 

286 All datasets added to butler have ``dataId={instrument=instrument, 

287 detector=0}`` where ``instrument`` is extracted from pipeline, "INSTR" is 

288 used if pipeline is missing instrument definition. Type of the dataset is 

289 guessed from dataset type name (assumes that pipeline is made of `AddTask` 

290 tasks). 

291 

292 Parameters 

293 ---------- 

294 pipeline : `~lsst.pipe.base.Pipeline` 

295 Pipeline instance. 

296 butler : `~lsst.daf.butler.Butler` 

297 Data butler instance. 

298 datasetTypes : `dict` [ `str`, `list` ], optional 

299 Dictionary whose keys are collection names and values are lists of 

300 dataset type names. By default a single dataset of type "add_dataset0" 

301 is added to a ``butler.run`` collection. 

302 """ 

303 # Add dataset types to registry 

304 taskDefs = list(pipeline.toExpandedPipeline()) 

305 registerDatasetTypes(butler.registry, taskDefs) 

306 

307 instrument = pipeline.getInstrument() 

308 if instrument is not None: 

309 instrument_class = doImportType(instrument) 

310 instrumentName = instrument_class.getName() 

311 instrumentClass = get_full_type_name(instrument_class) 

312 else: 

313 instrumentName = "INSTR" 

314 instrumentClass = None 

315 

316 # Add all needed dimensions to registry 

317 butler.registry.insertDimensionData("instrument", dict(name=instrumentName, class_name=instrumentClass)) 

318 butler.registry.insertDimensionData("detector", dict(instrument=instrumentName, id=0, full_name="det0")) 

319 

320 taskDefMap = dict((taskDef.label, taskDef) for taskDef in taskDefs) 

321 # Add inputs to butler 

322 if not datasetTypes: 

323 datasetTypes = {None: ["add_dataset0"]} 

324 for run, dsTypes in datasetTypes.items(): 

325 if run is not None: 

326 butler.registry.registerRun(run) 

327 for dsType in dsTypes: 

328 if dsType == "packages": 

329 # Version is intentionally inconsistent. 

330 # Dict is convertible to Packages if Packages is installed. 

331 data: Any = {"python": "9.9.99"} 

332 butler.put(data, dsType, run=run) 

333 else: 

334 if dsType.endswith("_config"): 

335 # find a config from matching task name or make a new one 

336 taskLabel, _, _ = dsType.rpartition("_") 

337 taskDef = taskDefMap.get(taskLabel) 

338 if taskDef is not None: 

339 data = taskDef.config 

340 else: 

341 data = AddTaskConfig() 

342 elif dsType.endswith("_metadata"): 

343 data = _TASK_FULL_METADATA_TYPE() 

344 elif dsType.endswith("_log"): 

345 data = ButlerLogRecords.from_records([]) 

346 else: 

347 data = numpy.array([0.0, 1.0, 2.0, 5.0]) 

348 butler.put(data, dsType, run=run, instrument=instrumentName, detector=0) 

349 

350 

351def makeSimpleQGraph( 

352 nQuanta: int = 5, 

353 pipeline: Pipeline | None = None, 

354 butler: Butler | None = None, 

355 root: str | None = None, 

356 callPopulateButler: bool = True, 

357 run: str = "test", 

358 instrument: str | None = None, 

359 skipExistingIn: Any = None, 

360 inMemory: bool = True, 

361 userQuery: str = "", 

362 datasetTypes: dict[str | None, list[str]] | None = None, 

363 datasetQueryConstraint: DSQVariant = DSQVariant.ALL, 

364 makeDatastoreRecords: bool = False, 

365 bind: Mapping[str, Any] | None = None, 

366 metadata: MutableMapping[str, Any] | None = None, 

367) -> tuple[Butler, QuantumGraph]: 

368 """Make simple `QuantumGraph` for tests. 

369 

370 Makes simple one-task pipeline with AddTask, sets up in-memory registry 

371 and butler, fills them with minimal data, and generates QuantumGraph with 

372 all of that. 

373 

374 Parameters 

375 ---------- 

376 nQuanta : `int` 

377 Number of quanta in a graph, only used if ``pipeline`` is None. 

378 pipeline : `~lsst.pipe.base.Pipeline` 

379 If `None` then a pipeline is made with `AddTask` and default 

380 `AddTaskConfig`. 

381 butler : `~lsst.daf.butler.Butler`, optional 

382 Data butler instance, if None then new data butler is created by 

383 calling `makeSimpleButler`. 

384 callPopulateButler : `bool`, optional 

385 If True insert datasets into the butler prior to building a graph. 

386 If False butler argument must not be None, and must be pre-populated. 

387 Defaults to True. 

388 root : `str` 

389 Path or URI to the root location of the new repository. Only used if 

390 ``butler`` is None. 

391 run : `str`, optional 

392 Name of the RUN collection to add to butler, only used if ``butler`` 

393 is None. 

394 instrument : `str` or `None`, optional 

395 The importable name of an instrument to be added to the pipeline or 

396 if no instrument should be added then an empty string or `None`, by 

397 default `None`. Only used if ``pipeline`` is `None`. 

398 skipExistingIn 

399 Expressions representing the collections to search for existing 

400 output datasets that should be skipped. See 

401 :ref:`daf_butler_ordered_collection_searches`. 

402 inMemory : `bool`, optional 

403 If true make in-memory repository, only used if ``butler`` is `None`. 

404 userQuery : `str`, optional 

405 The user query to pass to ``makeGraph``, by default an empty string. 

406 datasetTypes : `dict` [ `str`, `list` ], optional 

407 Dictionary whose keys are collection names and values are lists of 

408 dataset type names. By default a single dataset of type "add_dataset0" 

409 is added to a ``butler.run`` collection. 

410 datasetQueryQConstraint : `DatasetQueryConstraintVariant` 

411 The query constraint variant that should be used to constrain the 

412 query based on dataset existence, defaults to 

413 `DatasetQueryConstraintVariant.ALL`. 

414 makeDatastoreRecords : `bool`, optional 

415 If `True` then add datstore records to generated quanta. 

416 bind : `~collections.abc.Mapping`, optional 

417 Mapping containing literal values that should be injected into the 

418 ``userQuery`` expression, keyed by the identifiers they replace. 

419 metadata : `~collections.abc.Mapping`, optional 

420 Optional graph metadata. 

421 

422 Returns 

423 ------- 

424 butler : `~lsst.daf.butler.Butler` 

425 Butler instance 

426 qgraph : `~lsst.pipe.base.QuantumGraph` 

427 Quantum graph instance 

428 """ 

429 if pipeline is None: 

430 pipeline = makeSimplePipeline(nQuanta=nQuanta, instrument=instrument) 

431 

432 if butler is None: 

433 if root is None: 

434 raise ValueError("Must provide `root` when `butler` is None") 

435 if callPopulateButler is False: 

436 raise ValueError("populateButler can only be False when butler is supplied as an argument") 

437 butler = makeSimpleButler(root, run=run, inMemory=inMemory) 

438 

439 if callPopulateButler: 

440 populateButler(pipeline, butler, datasetTypes=datasetTypes) 

441 

442 # Make the graph 

443 _LOG.debug("Instantiating GraphBuilder, skipExistingIn=%s", skipExistingIn) 

444 builder = GraphBuilder( 

445 registry=butler.registry, 

446 skipExistingIn=skipExistingIn, 

447 datastore=butler.datastore if makeDatastoreRecords else None, 

448 ) 

449 if not run: 

450 assert butler.run is not None, "Butler must have run defined" 

451 run = butler.run 

452 _LOG.debug( 

453 "Calling GraphBuilder.makeGraph, collections=%r, run=%r, userQuery=%r bind=%s", 

454 butler.collections, 

455 run, 

456 userQuery, 

457 bind, 

458 ) 

459 if not metadata: 

460 metadata = {} 

461 metadata["output_run"] = run 

462 

463 qgraph = builder.makeGraph( 

464 pipeline, 

465 collections=butler.collections, 

466 run=run, 

467 userQuery=userQuery, 

468 datasetQueryConstraint=datasetQueryConstraint, 

469 bind=bind, 

470 metadata=metadata, 

471 ) 

472 

473 return butler, qgraph