Coverage for python/lsst/pipe/base/tests/mocks/_storage_class.py: 43%
171 statements
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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/>.
22from __future__ import annotations
24__all__ = (
25 "MockDataset",
26 "MockStorageClass",
27 "MockDatasetQuantum",
28 "MockStorageClassDelegate",
29 "get_mock_name",
30 "get_original_name",
31 "is_mock_name",
32)
34import uuid
35from collections.abc import Callable, Iterable, Mapping
36from typing import Any, cast
38from lsst.daf.butler import (
39 DataIdValue,
40 DatasetComponent,
41 DatasetRef,
42 DatasetType,
43 Formatter,
44 FormatterFactory,
45 LookupKey,
46 SerializedDatasetType,
47 StorageClass,
48 StorageClassDelegate,
49 StorageClassFactory,
50)
51from lsst.daf.butler._compat import _BaseModelCompat
52from lsst.daf.butler.formatters.json import JsonFormatter
53from lsst.utils.introspection import get_full_type_name
55_NAME_PREFIX: str = "_mock_"
58def get_mock_name(original: str) -> str:
59 """Return the name of the mock storage class, dataset type, or task label
60 for the given original name.
61 """
62 return _NAME_PREFIX + original
65def get_original_name(mock: str) -> str:
66 """Return the name of the original storage class, dataset type, or task
67 label that corresponds to the given mock name.
68 """
69 assert mock.startswith(_NAME_PREFIX)
70 return mock.removeprefix(_NAME_PREFIX)
73def is_mock_name(name: str) -> bool:
74 """Return whether the given name is that of a mock storage class, dataset
75 type, or task label.
76 """
77 return name.startswith(_NAME_PREFIX)
80# Tests for this module are in the ci_middleware package, where we have easy
81# access to complex real storage classes (and their pytypes) to test against.
84class MockDataset(_BaseModelCompat):
85 """The in-memory dataset type used by `MockStorageClass`."""
87 dataset_id: uuid.UUID | None
88 """Universal unique identifier for this dataset."""
90 dataset_type: SerializedDatasetType
91 """Butler dataset type or this dataset.
93 See the documentation for ``data_id`` for why this is a
94 `~lsst.daf.butler.SerializedDatasetType` instead of a "real" one.
95 """
97 data_id: dict[str, DataIdValue]
98 """Butler data ID for this dataset.
100 This is a `~lsst.daf.butler.SerializedDataCoordinate` instead of a "real"
101 one for two reasons:
103 - the mock dataset may need to be read from disk in a context in which a
104 `~lsst.daf.butler.DimensionUniverse` is unavailable;
105 - we don't want the complexity of having a separate
106 ``SerializedMockDataCoordinate``.
107 """
109 run: str | None
110 """`~lsst.daf.butler.CollectionType.RUN` collection this dataset belongs
111 to.
112 """
114 quantum: MockDatasetQuantum | None = None
115 """Description of the quantum that produced this dataset.
116 """
118 output_connection_name: str | None = None
119 """The name of the PipelineTask output connection that produced this
120 dataset.
121 """
123 converted_from: MockDataset | None = None
124 """Another `MockDataset` that underwent a storage class conversion to
125 produce this one.
126 """
128 parent: MockDataset | None = None
129 """Another `MockDataset` from which a component was extract to form this
130 one.
131 """
133 parameters: dict[str, str] | None = None
134 """`repr` of all parameters applied when reading this dataset."""
136 @property
137 def storage_class(self) -> str:
138 return cast(str, self.dataset_type.storageClass)
140 def make_derived(self, **kwargs: Any) -> MockDataset:
141 """Return a new MockDataset that represents applying some storage class
142 operation to this one.
144 Keyword arguments are fields of `MockDataset` or
145 `~lsst.daf.butler.SerializedDatasetType` to override in the result.
146 """
147 dataset_type_updates = {
148 k: kwargs.pop(k) for k in list(kwargs) if k in SerializedDatasetType.model_fields # type: ignore
149 }
150 kwargs.setdefault("dataset_type", self.dataset_type.copy(update=dataset_type_updates))
151 # Fields below are those that should not be propagated to the derived
152 # dataset, because they're not about the intrinsic on-disk thing.
153 kwargs.setdefault("converted_from", None)
154 kwargs.setdefault("parent", None)
155 kwargs.setdefault("parameters", None)
156 # Also use setdefault on the ref in case caller wants to override that
157 # directly, but this is expected to be rare enough that it's not worth
158 # it to try to optimize out the work above to make derived_ref.
159 return self.copy(update=kwargs)
162class MockDatasetQuantum(_BaseModelCompat):
163 """Description of the quantum that produced a mock dataset.
165 This is also used to represent task-init operations for init-output mock
166 datasets.
167 """
169 task_label: str
170 """Label of the producing PipelineTask in its pipeline."""
172 data_id: dict[str, DataIdValue]
173 """Data ID for the quantum."""
175 inputs: dict[str, list[MockDataset]]
176 """Mock datasets provided as input to the quantum.
178 Keys are task-internal connection names, not dataset type names.
179 """
182MockDataset.model_rebuild()
185class MockStorageClassDelegate(StorageClassDelegate):
186 """Implementation of the StorageClassDelegate interface for mock datasets.
188 This class does not implement assembly and disassembly just because it's
189 not needed right now. That could be added in the future with some
190 additional tracking attributes in `MockDataset`.
191 """
193 def assemble(self, components: dict[str, Any], pytype: type | None = None) -> MockDataset:
194 # Docstring inherited.
195 raise NotImplementedError("Mock storage classes do not implement assembly.")
197 def getComponent(self, composite: Any, componentName: str) -> Any:
198 # Docstring inherited.
199 assert isinstance(
200 composite, MockDataset
201 ), f"MockStorageClassDelegate given a non-mock dataset {composite!r}."
202 return composite.make_derived(
203 name=f"{composite.dataset_type.name}.{componentName}",
204 storageClass=self.storageClass.allComponents()[componentName].name,
205 parentStorageClass=self.storageClass.name,
206 parent=composite,
207 )
209 def disassemble(
210 self, composite: Any, subset: Iterable | None = None, override: Any | None = None
211 ) -> dict[str, DatasetComponent]:
212 # Docstring inherited.
213 raise NotImplementedError("Mock storage classes do not implement disassembly.")
215 def handleParameters(self, inMemoryDataset: Any, parameters: Mapping[str, Any] | None = None) -> Any:
216 # Docstring inherited.
217 assert isinstance(
218 inMemoryDataset, MockDataset
219 ), f"MockStorageClassDelegate given a non-mock dataset {inMemoryDataset!r}."
220 if not parameters:
221 return inMemoryDataset
222 return inMemoryDataset.make_derived(parameters={k: repr(v) for k, v in parameters.items()})
225class MockStorageClass(StorageClass):
226 """A reimplementation of `lsst.daf.butler.StorageClass` for mock datasets.
228 Each `MockStorageClass` instance corresponds to a real "original" storage
229 class, with components and conversions that are mocks of the original's
230 components and conversions. The `pytype` for all `MockStorageClass`
231 instances is `MockDataset`.
232 """
234 def __init__(self, original: StorageClass, factory: StorageClassFactory | None = None):
235 name = get_mock_name(original.name)
236 if factory is None:
237 factory = StorageClassFactory()
238 super().__init__(
239 name=name,
240 pytype=MockDataset,
241 components={
242 k: self.get_or_register_mock(v.name, factory) for k, v in original.components.items()
243 },
244 derivedComponents={
245 k: self.get_or_register_mock(v.name, factory) for k, v in original.derivedComponents.items()
246 },
247 parameters=frozenset(original.parameters),
248 delegate=get_full_type_name(MockStorageClassDelegate),
249 # Conversions work differently for mock storage classes, since they
250 # all have the same pytype: we use the original storage class being
251 # mocked to see if we can convert, then just make a new MockDataset
252 # that points back to the original.
253 converters={},
254 )
255 self.original = original
256 # Make certain no one tries to use the converters.
257 self._converters = None # type: ignore
259 def _get_converters_by_type(self) -> dict[type, Callable[[Any], Any]]:
260 # Docstring inherited.
261 raise NotImplementedError("MockStorageClass does not use converters.")
263 @classmethod
264 def get_or_register_mock(
265 cls, original: str, factory: StorageClassFactory | None = None
266 ) -> MockStorageClass:
267 """Return a mock storage class for the given original storage class,
268 creating and registering it if necessary.
270 Parameters
271 ----------
272 original : `str`
273 Name of the original storage class to be mocked.
274 factory : `~lsst.daf.butler.StorageClassFactory`, optional
275 Storage class factory singleton instance.
277 Returns
278 -------
279 mock : `MockStorageClass`
280 New storage class that mocks ``original``.
281 """
282 name = get_mock_name(original)
283 if factory is None:
284 factory = StorageClassFactory()
285 if name in factory:
286 return cast(MockStorageClass, factory.getStorageClass(name))
287 else:
288 result = cls(factory.getStorageClass(original), factory)
289 factory.registerStorageClass(result)
290 return result
292 def allComponents(self) -> Mapping[str, MockStorageClass]:
293 # Docstring inherited.
294 return cast(Mapping[str, MockStorageClass], super().allComponents())
296 @property
297 def components(self) -> Mapping[str, MockStorageClass]:
298 # Docstring inherited.
299 return cast(Mapping[str, MockStorageClass], super().components)
301 @property
302 def derivedComponents(self) -> Mapping[str, MockStorageClass]:
303 # Docstring inherited.
304 return cast(Mapping[str, MockStorageClass], super().derivedComponents)
306 def can_convert(self, other: StorageClass) -> bool:
307 # Docstring inherited.
308 if not isinstance(other, MockStorageClass):
309 return False
310 return self.original.can_convert(other.original)
312 def coerce_type(self, incorrect: Any) -> Any:
313 # Docstring inherited.
314 if not isinstance(incorrect, MockDataset):
315 raise TypeError(
316 f"Mock storage class {self.name!r} can only convert in-memory datasets "
317 f"corresponding to other mock storage classes, not {incorrect!r}."
318 )
319 factory = StorageClassFactory()
320 other_storage_class = factory.getStorageClass(incorrect.storage_class)
321 assert isinstance(other_storage_class, MockStorageClass), "Should not get a MockDataset otherwise."
322 if other_storage_class.name == self.name:
323 return incorrect
324 if not self.can_convert(other_storage_class):
325 raise TypeError(
326 f"Mocked storage class {self.original.name!r} cannot convert from "
327 f"{other_storage_class.original.name!r}."
328 )
329 return incorrect.make_derived(storageClass=self.name, converted_from=incorrect)
331 @staticmethod
332 def mock_dataset_type(original_type: DatasetType) -> DatasetType:
333 """Replace a dataset type with a version that uses a mock storage class
334 and name.
336 Parameters
337 ----------
338 original_type : `lsst.daf.butler.DatasetType`
339 Original dataset type to be mocked.
341 Returns
342 -------
343 mock_type : `lsst.daf.butler.DatasetType`
344 A mock version of the dataset type, with name and storage class
345 changed and everything else unchanged.
346 """
347 mock_storage_class = MockStorageClass.get_or_register_mock(original_type.storageClass_name)
348 mock_parent_storage_class = None
349 if original_type.parentStorageClass is not None:
350 mock_parent_storage_class = MockStorageClass.get_or_register_mock(
351 original_type.parentStorageClass.name
352 )
353 return DatasetType(
354 get_mock_name(original_type.name),
355 original_type.dimensions,
356 mock_storage_class,
357 isCalibration=original_type.isCalibration(),
358 parentStorageClass=mock_parent_storage_class,
359 )
361 @staticmethod
362 def mock_dataset_refs(original_refs: Iterable[DatasetRef]) -> list[DatasetRef]:
363 """Replace dataset references with versions that uses a mock storage
364 class and dataset type name.
366 Parameters
367 ----------
368 original_refs : `~collections.abc.Iterable` [ \
369 `lsst.daf.butler.DatasetRef` ]
370 Original dataset references to be mocked.
372 Returns
373 -------
374 mock_refs : `list` [ `lsst.daf.butler.DatasetRef` ]
375 Mocked version of the dataset references, with dataset type name
376 and storage class changed and everything else unchanged.
377 """
378 original_refs = list(original_refs)
379 if not original_refs:
380 return original_refs
381 dataset_type = MockStorageClass.mock_dataset_type(original_refs[0].datasetType)
382 return [
383 DatasetRef(dataset_type, original_ref.dataId, run=original_ref.run, id=original_ref.id)
384 for original_ref in original_refs
385 ]
387 @staticmethod
388 def unmock_dataset_type(mock_type: DatasetType) -> DatasetType:
389 """Replace a mock dataset type with the original one it was created
390 from.
392 Parameters
393 ----------
394 mock_type : `lsst.daf.butler.DatasetType`
395 A dataset type with a mocked name and storage class.
397 Returns
398 -------
399 original_type : `lsst.daf.butler.DatasetType`
400 The original dataset type.
401 """
402 mock_storage_class = cast(MockStorageClass, mock_type.storageClass)
403 original_parent_storage_class = None
404 if mock_type.parentStorageClass is not None:
405 original_parent_storage_class = cast(MockStorageClass, mock_type.parentStorageClass).original
406 return DatasetType(
407 get_original_name(mock_type.name),
408 mock_type.dimensions,
409 mock_storage_class.original,
410 isCalibration=mock_type.isCalibration(),
411 parentStorageClass=original_parent_storage_class,
412 )
414 @staticmethod
415 def unmock_dataset_refs(mock_refs: Iterable[DatasetRef]) -> list[DatasetRef]:
416 """Replace dataset references with versions that do not use a mock
417 storage class and dataset type name.
419 Parameters
420 ----------
421 mock_refs : `~collections.abc.Iterable` [ \
422 `lsst.daf.butler.DatasetRef` ]
423 Dataset references that use a mocked dataset type name and storage
424 class.
426 Returns
427 -------
428 original_refs : `list` [ `lsst.daf.butler.DatasetRef` ]
429 The original dataset references.
430 """
431 mock_refs = list(mock_refs)
432 if not mock_refs:
433 return mock_refs
434 dataset_type = MockStorageClass.unmock_dataset_type(mock_refs[0].datasetType)
435 return [
436 DatasetRef(dataset_type, mock_ref.dataId, run=mock_ref.run, id=mock_ref.id)
437 for mock_ref in mock_refs
438 ]
441def _monkeypatch_daf_butler() -> None:
442 """Replace methods in daf_butler's StorageClassFactory and FormatterFactory
443 classes to automatically recognize mock storage classes.
445 This monkey-patching is executed when the `lsst.pipe.base.tests.mocks`
446 package is imported, and it affects all butler instances created before or
447 after that imported.
448 """
449 original_get_storage_class = StorageClassFactory.getStorageClass
451 def new_get_storage_class(self: StorageClassFactory, storageClassName: str) -> StorageClass:
452 try:
453 return original_get_storage_class(self, storageClassName)
454 except KeyError:
455 if is_mock_name(storageClassName):
456 return MockStorageClass.get_or_register_mock(get_original_name(storageClassName))
457 raise
459 StorageClassFactory.getStorageClass = new_get_storage_class # type: ignore
461 del new_get_storage_class
463 original_get_formatter_class_with_match = FormatterFactory.getFormatterClassWithMatch
465 def new_get_formatter_class_with_match(
466 self: FormatterFactory, entity: Any
467 ) -> tuple[LookupKey, type[Formatter], dict[str, Any]]:
468 try:
469 return original_get_formatter_class_with_match(self, entity)
470 except KeyError:
471 lookup_keys = (LookupKey(name=entity),) if isinstance(entity, str) else entity._lookupNames()
472 for key in lookup_keys:
473 # This matches mock dataset type names before mock storage
474 # classes, and it would even match some regular dataset types
475 # that are automatic connections (logs, configs, metadata) of
476 # mocked tasks. The latter would be a problem, except that
477 # those should have already matched in the try block above.
478 if is_mock_name(key.name):
479 return (key, JsonFormatter, {})
480 raise
482 FormatterFactory.getFormatterClassWithMatch = new_get_formatter_class_with_match # type: ignore
484 del new_get_formatter_class_with_match
486 original_get_formatter_with_match = FormatterFactory.getFormatterWithMatch
488 def new_get_formatter_with_match(
489 self: FormatterFactory, entity: Any, *args: Any, **kwargs: Any
490 ) -> tuple[LookupKey, Formatter]:
491 try:
492 return original_get_formatter_with_match(self, entity, *args, **kwargs)
493 except KeyError:
494 lookup_keys = (LookupKey(name=entity),) if isinstance(entity, str) else entity._lookupNames()
495 for key in lookup_keys:
496 if is_mock_name(key.name):
497 return (key, JsonFormatter(*args, **kwargs))
498 raise
500 FormatterFactory.getFormatterWithMatch = new_get_formatter_with_match # type: ignore
502 del new_get_formatter_with_match
505_monkeypatch_daf_butler()