Coverage for python/lsst/pipe/base/tests/simpleQGraph.py: 27%
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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/>.
22"""Bunch of common classes and methods for use in unit tests.
23"""
24from __future__ import annotations
26__all__ = ["AddTaskConfig", "AddTask", "AddTaskFactoryMock"]
28import itertools
29import logging
30from collections.abc import Iterable, Mapping, MutableMapping
31from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, Union, cast
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
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
55if TYPE_CHECKING: 55 ↛ 56line 55 didn't jump to line 56, because the condition on line 55 was never true
56 from lsst.daf.butler import Registry
58_LOG = logging.getLogger(__name__)
61class SimpleInstrument(Instrument):
62 def __init__(self, *args: Any, **kwargs: Any):
63 pass
65 @staticmethod
66 def getName() -> str:
67 return "INSTRU"
69 def getRawFormatter(self, dataId: DataId) -> Type[Formatter]:
70 return Formatter
72 def register(self, registry: Registry, *, update: bool = False) -> None:
73 pass
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 """
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 )
110class AddTaskConfig(PipelineTaskConfig, pipelineConnections=AddTaskConnections):
111 """Config for AddTask."""
113 addend = pexConfig.Field[int](doc="amount to add", default=3)
116class AddTask(PipelineTask):
117 """Trivial PipelineTask for testing, has some extras useful for specific
118 unit tests.
119 """
121 ConfigClass = AddTaskConfig
122 _DefaultName = "add_task"
124 initout = numpy.array([999])
125 """InitOutputs for this task"""
127 taskFactory: Optional[AddTaskFactoryMock] = None
128 """Factory that makes instances"""
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
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)
145class AddTaskFactoryMock(TaskFactory):
146 """Special task factory that instantiates AddTask.
148 It also defines some bookkeeping variables used by AddTask to report
149 progress to unit tests.
150 """
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()
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
166def registerDatasetTypes(registry: Registry, pipeline: Union[Pipeline, Iterable[TaskDef]]) -> None:
167 """Register all dataset types used by tasks in a registry.
169 Copied and modified from `PreExecInit.initializeDatasetTypes`.
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 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)
204def makeSimplePipeline(nQuanta: int, instrument: Optional[str] = None) -> Pipeline:
205 """Make a simple Pipeline for tests.
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.
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
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
238def makeSimpleButler(
239 root: str, run: str = "test", inMemory: bool = True, config: Config | str | None = None
240) -> Butler:
241 """Create new data butler instance.
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``.
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
275def populateButler(
276 pipeline: Pipeline, butler: Butler, datasetTypes: Dict[Optional[str], List[str]] | None = None
277) -> None:
278 """Populate data butler with data needed for test.
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
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).
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 """
304 # Add dataset types to registry
305 taskDefs = list(pipeline.toExpandedPipeline())
306 registerDatasetTypes(butler.registry, taskDefs)
308 instrument = pipeline.getInstrument()
309 if instrument is not None:
310 instrument_class = doImportType(instrument)
311 instrumentName = instrument_class.getName()
312 instrumentClass = get_full_type_name(instrument_class)
313 else:
314 instrumentName = "INSTR"
315 instrumentClass = None
317 # Add all needed dimensions to registry
318 butler.registry.insertDimensionData("instrument", dict(name=instrumentName, class_name=instrumentClass))
319 butler.registry.insertDimensionData("detector", dict(instrument=instrumentName, id=0, full_name="det0"))
321 taskDefMap = dict((taskDef.label, taskDef) for taskDef in taskDefs)
322 # Add inputs to butler
323 if not datasetTypes:
324 datasetTypes = {None: ["add_dataset0"]}
325 for run, dsTypes in datasetTypes.items():
326 if run is not None:
327 butler.registry.registerRun(run)
328 for dsType in dsTypes:
329 if dsType == "packages":
330 # Version is intentionally inconsistent.
331 # Dict is convertible to Packages if Packages is installed.
332 data: Any = {"python": "9.9.99"}
333 butler.put(data, dsType, run=run)
334 else:
335 if dsType.endswith("_config"):
336 # find a config from matching task name or make a new one
337 taskLabel, _, _ = dsType.rpartition("_")
338 taskDef = taskDefMap.get(taskLabel)
339 if taskDef is not None:
340 data = taskDef.config
341 else:
342 data = AddTaskConfig()
343 elif dsType.endswith("_metadata"):
344 data = _TASK_FULL_METADATA_TYPE()
345 elif dsType.endswith("_log"):
346 data = ButlerLogRecords.from_records([])
347 else:
348 data = numpy.array([0.0, 1.0, 2.0, 5.0])
349 butler.put(data, dsType, run=run, instrument=instrumentName, detector=0)
352def makeSimpleQGraph(
353 nQuanta: int = 5,
354 pipeline: Optional[Pipeline] = None,
355 butler: Optional[Butler] = None,
356 root: Optional[str] = None,
357 callPopulateButler: bool = True,
358 run: str = "test",
359 instrument: Optional[str] = None,
360 skipExistingIn: Any = None,
361 inMemory: bool = True,
362 userQuery: str = "",
363 datasetTypes: Optional[Dict[Optional[str], List[str]]] = None,
364 datasetQueryConstraint: DSQVariant = DSQVariant.ALL,
365 makeDatastoreRecords: bool = False,
366 bind: Optional[Mapping[str, Any]] = None,
367 metadata: Optional[MutableMapping[str, Any]] = None,
368) -> Tuple[Butler, QuantumGraph]:
369 """Make simple QuantumGraph for tests.
371 Makes simple one-task pipeline with AddTask, sets up in-memory registry
372 and butler, fills them with minimal data, and generates QuantumGraph with
373 all of that.
375 Parameters
376 ----------
377 nQuanta : `int`
378 Number of quanta in a graph, only used if ``pipeline`` is None.
379 pipeline : `~lsst.pipe.base.Pipeline`
380 If `None` then a pipeline is made with `AddTask` and default
381 `AddTaskConfig`.
382 butler : `~lsst.daf.butler.Butler`, optional
383 Data butler instance, if None then new data butler is created by
384 calling `makeSimpleButler`.
385 callPopulateButler : `bool`, optional
386 If True insert datasets into the butler prior to building a graph.
387 If False butler argument must not be None, and must be pre-populated.
388 Defaults to True.
389 root : `str`
390 Path or URI to the root location of the new repository. Only used if
391 ``butler`` is None.
392 run : `str`, optional
393 Name of the RUN collection to add to butler, only used if ``butler``
394 is None.
395 instrument : `str` or `None`, optional
396 The importable name of an instrument to be added to the pipeline or
397 if no instrument should be added then an empty string or `None`, by
398 default `None`. Only used if ``pipeline`` is `None`.
399 skipExistingIn
400 Expressions representing the collections to search for existing
401 output datasets that should be skipped. See
402 :ref:`daf_butler_ordered_collection_searches`.
403 inMemory : `bool`, optional
404 If true make in-memory repository, only used if ``butler`` is `None`.
405 userQuery : `str`, optional
406 The user query to pass to ``makeGraph``, by default an empty string.
407 datasetTypes : `dict` [ `str`, `list` ], optional
408 Dictionary whose keys are collection names and values are lists of
409 dataset type names. By default a single dataset of type "add_dataset0"
410 is added to a ``butler.run`` collection.
411 datasetQueryQConstraint : `DatasetQueryConstraintVariant`
412 The query constraint variant that should be used to constrain the
413 query based on dataset existence, defaults to
414 `DatasetQueryConstraintVariant.ALL`.
415 makeDatastoreRecords : `bool`, optional
416 If `True` then add datstore records to generated quanta.
417 bind : `Mapping`, optional
418 Mapping containing literal values that should be injected into the
419 ``userQuery`` expression, keyed by the identifiers they replace.
420 metadata : `Mapping`, optional
421 Optional graph metadata.
423 Returns
424 -------
425 butler : `~lsst.daf.butler.Butler`
426 Butler instance
427 qgraph : `~lsst.pipe.base.QuantumGraph`
428 Quantum graph instance
429 """
431 if pipeline is None:
432 pipeline = makeSimplePipeline(nQuanta=nQuanta, instrument=instrument)
434 if butler is None:
435 if root is None:
436 raise ValueError("Must provide `root` when `butler` is None")
437 if callPopulateButler is False:
438 raise ValueError("populateButler can only be False when butler is supplied as an argument")
439 butler = makeSimpleButler(root, run=run, inMemory=inMemory)
441 if callPopulateButler:
442 populateButler(pipeline, butler, datasetTypes=datasetTypes)
444 # Make the graph
445 _LOG.debug("Instantiating GraphBuilder, skipExistingIn=%s", skipExistingIn)
446 builder = GraphBuilder(
447 registry=butler.registry,
448 skipExistingIn=skipExistingIn,
449 datastore=butler.datastore if makeDatastoreRecords else None,
450 )
451 if not run:
452 assert butler.run is not None, "Butler must have run defined"
453 run = butler.run
454 _LOG.debug(
455 "Calling GraphBuilder.makeGraph, collections=%r, run=%r, userQuery=%r bind=%s",
456 butler.collections,
457 run,
458 userQuery,
459 bind,
460 )
461 if not metadata:
462 metadata = {}
463 metadata["output_run"] = run
465 qgraph = builder.makeGraph(
466 pipeline,
467 collections=butler.collections,
468 run=run,
469 userQuery=userQuery,
470 datasetQueryConstraint=datasetQueryConstraint,
471 bind=bind,
472 metadata=metadata,
473 )
475 return butler, qgraph