Coverage for python/lsst/pipe/base/tests/mocks/_storage_class.py: 40%
141 statements
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« prev ^ index » next coverage.py v7.2.7, created at 2023-06-25 09:14 +0000
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)
34from collections.abc import Callable, Iterable, Mapping
35from typing import Any, cast
37import pydantic
38from lsst.daf.butler import (
39 DatasetComponent,
40 Formatter,
41 FormatterFactory,
42 LookupKey,
43 SerializedDataCoordinate,
44 SerializedDatasetRef,
45 SerializedDatasetType,
46 StorageClass,
47 StorageClassDelegate,
48 StorageClassFactory,
49)
50from lsst.daf.butler.formatters.json import JsonFormatter
51from lsst.utils.introspection import get_full_type_name
53_NAME_PREFIX: str = "_mock_"
56def get_mock_name(original: str) -> str:
57 """Return the name of the mock storage class, dataset type, or task label
58 for the given original name.
59 """
60 return _NAME_PREFIX + original
63def get_original_name(mock: str) -> str:
64 """Return the name of the original storage class, dataset type, or task
65 label that corresponds to the given mock name.
66 """
67 assert mock.startswith(_NAME_PREFIX)
68 return mock.removeprefix(_NAME_PREFIX)
71def is_mock_name(name: str) -> bool:
72 """Return whether the given name is that of a mock storage class, dataset
73 type, or task label.
74 """
75 return name.startswith(_NAME_PREFIX)
78# Tests for this module are in the ci_middleware package, where we have easy
79# access to complex real storage classes (and their pytypes) to test against.
82class MockDataset(pydantic.BaseModel):
83 """The in-memory dataset type used by `MockStorageClass`."""
85 ref: SerializedDatasetRef
86 """Reference used to read and write this dataset.
88 This is a `SerializedDatasetRef` instead of a "real" one for two reasons:
90 - the mock dataset may need to be read from disk in a context in which a
91 `~lsst.daf.butler.DimensionUniverse` is unavailable;
92 - we don't want the complexity of having a separate
93 ``SerializedMockDataset``.
95 The downside of this is that we end up effectively reimplementing a few
96 fairly trivial DatasetType/DatasetRef methods that override storage classes
97 and extract components (in `MockStorageClass` and
98 `MockStorageClassDelegate`).
99 """
101 quantum: MockDatasetQuantum | None = None
102 """Description of the quantum that produced this dataset.
103 """
105 output_connection_name: str | None = None
106 """The name of the PipelineTask output connection that produced this
107 dataset.
108 """
110 converted_from: MockDataset | None = None
111 """Another `MockDataset` that underwent a storage class conversion to
112 produce this one.
113 """
115 parent: MockDataset | None = None
116 """Another `MockDataset` from which a component was extract to form this
117 one.
118 """
120 parameters: dict[str, str] | None = None
121 """`repr` of all parameters applied when reading this dataset."""
123 @property
124 def dataset_type(self) -> SerializedDatasetType:
125 return cast(SerializedDatasetType, self.ref.datasetType)
127 @property
128 def storage_class(self) -> str:
129 return cast(str, self.dataset_type.storageClass)
131 def make_derived(self, **kwargs: Any) -> MockDataset:
132 """Return a new MockDataset that represents applying some storage class
133 operation to this one.
135 Keyword arguments are fields of `MockDataset` or
136 `SerializedDatasetType` to override in the result.
137 """
138 dataset_type_updates = {
139 k: kwargs.pop(k) for k in list(kwargs) if k in SerializedDatasetType.__fields__
140 }
141 derived_dataset_type = self.dataset_type.copy(update=dataset_type_updates)
142 derived_ref = self.ref.copy(update=dict(datasetType=derived_dataset_type))
143 # Fields below are those that should not be propagated to the derived
144 # dataset, because they're not about the intrinsic on-disk thing.
145 kwargs.setdefault("converted_from", None)
146 kwargs.setdefault("parent", None)
147 kwargs.setdefault("parameters", None)
148 # Also use setdefault on the ref in case caller wants to override that
149 # directly, but this is expected to be rare enough that it's not worth
150 # it to try to optimize out the work above to make derived_ref.
151 kwargs.setdefault("ref", derived_ref)
152 return self.copy(update=kwargs)
155class MockDatasetQuantum(pydantic.BaseModel):
156 """Description of the quantum that produced a mock dataset."""
158 task_label: str
159 """Label of the producing PipelineTask in its pipeline."""
161 data_id: SerializedDataCoordinate
162 """Data ID for the quantum."""
164 inputs: dict[str, list[MockDataset]]
165 """Mock datasets provided as input to the quantum."""
168MockDataset.update_forward_refs()
171class MockStorageClassDelegate(StorageClassDelegate):
172 """Implementation of the StorageClassDelegate interface for mock datasets.
174 This class does not implement assembly and disassembly just because it's
175 not needed right now. That could be added in the future with some
176 additional tracking attributes in `MockDataset`.
177 """
179 def assemble(self, components: dict[str, Any], pytype: type | None = None) -> MockDataset:
180 # Docstring inherited.
181 raise NotImplementedError("Mock storage classes do not implement assembly.")
183 def getComponent(self, composite: Any, componentName: str) -> Any:
184 # Docstring inherited.
185 assert isinstance(
186 composite, MockDataset
187 ), f"MockStorageClassDelegate given a non-mock dataset {composite!r}."
188 return composite.make_derived(
189 name=f"{composite.dataset_type.name}.{componentName}",
190 storageClass=self.storageClass.allComponents()[componentName].name,
191 parentStorageClass=self.storageClass.name,
192 parent=composite,
193 )
195 def disassemble(
196 self, composite: Any, subset: Iterable | None = None, override: Any | None = None
197 ) -> dict[str, DatasetComponent]:
198 # Docstring inherited.
199 raise NotImplementedError("Mock storage classes do not implement disassembly.")
201 def handleParameters(self, inMemoryDataset: Any, parameters: Mapping[str, Any] | None = None) -> Any:
202 # Docstring inherited.
203 assert isinstance(
204 inMemoryDataset, MockDataset
205 ), f"MockStorageClassDelegate given a non-mock dataset {inMemoryDataset!r}."
206 if not parameters:
207 return inMemoryDataset
208 return inMemoryDataset.make_derived(parameters={k: repr(v) for k, v in parameters.items()})
211class MockStorageClass(StorageClass):
212 """A reimplementation of `lsst.daf.butler.StorageClass` for mock datasets.
214 Each `MockStorageClass` instance corresponds to a real "original" storage
215 class, with components and conversions that are mocks of the original's
216 components and conversions. The `pytype` for all `MockStorageClass`
217 instances is `MockDataset`.
218 """
220 def __init__(self, original: StorageClass, factory: StorageClassFactory | None = None):
221 name = get_mock_name(original.name)
222 if factory is None:
223 factory = StorageClassFactory()
224 super().__init__(
225 name=name,
226 pytype=MockDataset,
227 components={
228 k: self.get_or_register_mock(v.name, factory) for k, v in original.components.items()
229 },
230 derivedComponents={
231 k: self.get_or_register_mock(v.name, factory) for k, v in original.derivedComponents.items()
232 },
233 parameters=frozenset(original.parameters),
234 delegate=get_full_type_name(MockStorageClassDelegate),
235 # Conversions work differently for mock storage classes, since they
236 # all have the same pytype: we use the original storage class being
237 # mocked to see if we can convert, then just make a new MockDataset
238 # that points back to the original.
239 converters={},
240 )
241 self.original = original
242 # Make certain no one tries to use the converters.
243 self._converters = None # type: ignore
245 def _get_converters_by_type(self) -> dict[type, Callable[[Any], Any]]:
246 # Docstring inherited.
247 raise NotImplementedError("MockStorageClass does not use converters.")
249 @classmethod
250 def get_or_register_mock(
251 cls, original: str, factory: StorageClassFactory | None = None
252 ) -> MockStorageClass:
253 """Return a mock storage class for the given original storage class,
254 creating and registering it if necessary.
256 Parameters
257 ----------
258 original : `str`
259 Name of the original storage class to be mocked.
260 factory : `StorageClassFactory`, optional
261 Storage class factory singleton instance.
263 Returns
264 -------
265 mock : `MockStorageClass`
266 New storage class that mocks ``original``.
267 """
268 name = get_mock_name(original)
269 if factory is None:
270 factory = StorageClassFactory()
271 if name in factory:
272 return cast(MockStorageClass, factory.getStorageClass(name))
273 else:
274 result = cls(factory.getStorageClass(original), factory)
275 factory.registerStorageClass(result)
276 return result
278 def allComponents(self) -> Mapping[str, MockStorageClass]:
279 # Docstring inherited.
280 return cast(Mapping[str, MockStorageClass], super().allComponents())
282 @property
283 def components(self) -> Mapping[str, MockStorageClass]:
284 # Docstring inherited.
285 return cast(Mapping[str, MockStorageClass], super().components)
287 @property
288 def derivedComponents(self) -> Mapping[str, MockStorageClass]:
289 # Docstring inherited.
290 return cast(Mapping[str, MockStorageClass], super().derivedComponents)
292 def can_convert(self, other: StorageClass) -> bool:
293 # Docstring inherited.
294 if not isinstance(other, MockStorageClass):
295 return False
296 return self.original.can_convert(other.original)
298 def coerce_type(self, incorrect: Any) -> Any:
299 # Docstring inherited.
300 if not isinstance(incorrect, MockDataset):
301 raise TypeError(
302 f"Mock storage class {self.name!r} can only convert in-memory datasets "
303 f"corresponding to other mock storage classes, not {incorrect!r}."
304 )
305 factory = StorageClassFactory()
306 other_storage_class = factory.getStorageClass(incorrect.storage_class)
307 assert isinstance(other_storage_class, MockStorageClass), "Should not get a MockDataset otherwise."
308 if other_storage_class.name == self.name:
309 return incorrect
310 if not self.can_convert(other_storage_class):
311 raise TypeError(
312 f"Mocked storage class {self.original.name!r} cannot convert from "
313 f"{other_storage_class.original.name!r}."
314 )
315 return incorrect.make_derived(storageClass=self.name, converted_from=incorrect)
318def _monkeypatch_daf_butler() -> None:
319 """Replace methods in daf_butler's StorageClassFactory and FormatterFactory
320 classes to automatically recognize mock storage classes.
322 This monkey-patching is executed when the `lsst.pipe.base.tests.mocks`
323 package is imported, and it affects all butler instances created before or
324 after that imported.
325 """
326 original_get_storage_class = StorageClassFactory.getStorageClass
328 def new_get_storage_class(self: StorageClassFactory, storageClassName: str) -> StorageClass:
329 try:
330 return original_get_storage_class(self, storageClassName)
331 except KeyError:
332 if is_mock_name(storageClassName):
333 return MockStorageClass.get_or_register_mock(get_original_name(storageClassName))
334 raise
336 StorageClassFactory.getStorageClass = new_get_storage_class # type: ignore
338 del new_get_storage_class
340 original_get_formatter_class_with_match = FormatterFactory.getFormatterClassWithMatch
342 def new_get_formatter_class_with_match(
343 self: FormatterFactory, entity: Any
344 ) -> tuple[LookupKey, type[Formatter], dict[str, Any]]:
345 try:
346 return original_get_formatter_class_with_match(self, entity)
347 except KeyError:
348 lookup_keys = (LookupKey(name=entity),) if isinstance(entity, str) else entity._lookupNames()
349 for key in lookup_keys:
350 # This matches mock dataset type names before mock storage
351 # classes, and it would even match some regular dataset types
352 # that are automatic connections (logs, configs, metadata) of
353 # mocked tasks. The latter would be a problem, except that
354 # those should have already matched in the try block above.
355 if is_mock_name(key.name):
356 return (key, JsonFormatter, {})
357 raise
359 FormatterFactory.getFormatterClassWithMatch = new_get_formatter_class_with_match # type: ignore
361 del new_get_formatter_class_with_match
363 original_get_formatter_with_match = FormatterFactory.getFormatterWithMatch
365 def new_get_formatter_with_match(
366 self: FormatterFactory, entity: Any, *args: Any, **kwargs: Any
367 ) -> tuple[LookupKey, Formatter]:
368 try:
369 return original_get_formatter_with_match(self, entity, *args, **kwargs)
370 except KeyError:
371 lookup_keys = (LookupKey(name=entity),) if isinstance(entity, str) else entity._lookupNames()
372 for key in lookup_keys:
373 if is_mock_name(key.name):
374 return (key, JsonFormatter(*args, **kwargs))
375 raise
377 FormatterFactory.getFormatterWithMatch = new_get_formatter_with_match # type: ignore
379 del new_get_formatter_with_match
382_monkeypatch_daf_butler()