Coverage for python/lsst/daf/butler/core/dimensions/_records.py: 24%

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1# This file is part of daf_butler. 

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 

22from __future__ import annotations 

23 

24__all__ = ("DimensionRecord", "SerializedDimensionRecord") 

25 

26from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Tuple, Type, Union 

27 

28import lsst.sphgeom 

29from lsst.utils.classes import immutable 

30from pydantic import BaseModel, Field, StrictBool, StrictFloat, StrictInt, StrictStr, create_model 

31 

32from .._topology import SpatialRegionDatabaseRepresentation 

33from ..json import from_json_pydantic, to_json_pydantic 

34from ..timespan import Timespan, TimespanDatabaseRepresentation 

35from ._elements import Dimension, DimensionElement 

36 

37if TYPE_CHECKING: # Imports needed only for type annotations; may be circular. 37 ↛ 38line 37 didn't jump to line 38, because the condition on line 37 was never true

38 from ...registry import Registry 

39 from ._coordinate import DataCoordinate 

40 from ._graph import DimensionUniverse 

41 from ._schema import DimensionElementFields 

42 

43 

44def _reconstructDimensionRecord(definition: DimensionElement, mapping: Dict[str, Any]) -> DimensionRecord: 

45 """Unpickle implementation for `DimensionRecord` subclasses. 

46 

47 For internal use by `DimensionRecord`. 

48 """ 

49 return definition.RecordClass(**mapping) 

50 

51 

52def _subclassDimensionRecord(definition: DimensionElement) -> Type[DimensionRecord]: 

53 """Create a dynamic subclass of `DimensionRecord` for the given element. 

54 

55 For internal use by `DimensionRecord`. 

56 """ 

57 from ._schema import DimensionElementFields 

58 

59 fields = DimensionElementFields(definition) 

60 slots = list(fields.standard.names) 

61 if definition.spatial: 

62 slots.append(SpatialRegionDatabaseRepresentation.NAME) 

63 if definition.temporal: 

64 slots.append(TimespanDatabaseRepresentation.NAME) 

65 d = {"definition": definition, "__slots__": tuple(slots), "fields": fields} 

66 return type(definition.name + ".RecordClass", (DimensionRecord,), d) 

67 

68 

69class SpecificSerializedDimensionRecord(BaseModel, extra="forbid"): 

70 """Base model for a specific serialized record content.""" 

71 

72 

73_SIMPLE_RECORD_CLASS_CACHE: Dict[ 

74 Tuple[DimensionElement, DimensionUniverse], Type[SpecificSerializedDimensionRecord] 

75] = {} 

76 

77 

78def _createSimpleRecordSubclass(definition: DimensionElement) -> Type[SpecificSerializedDimensionRecord]: 

79 from ._schema import DimensionElementFields 

80 

81 # Cache on the definition (which hashes as the name) and the 

82 # associated universe. 

83 cache_key = (definition, definition.universe) 

84 if cache_key in _SIMPLE_RECORD_CLASS_CACHE: 

85 return _SIMPLE_RECORD_CLASS_CACHE[cache_key] 

86 

87 fields = DimensionElementFields(definition) 

88 members = {} 

89 # Prefer strict typing for external data 

90 type_map = { 

91 str: StrictStr, 

92 float: StrictFloat, 

93 bool: StrictBool, 

94 int: StrictInt, 

95 } 

96 

97 for field in fields.standard: 

98 field_type = field.getPythonType() 

99 field_type = type_map.get(field_type, field_type) 

100 if field.nullable: 

101 field_type = Optional[field_type] # type: ignore 

102 members[field.name] = (field_type, ...) 

103 if definition.temporal: 

104 members["timespan"] = (Tuple[int, int], ...) # type: ignore 

105 if definition.spatial: 

106 members["region"] = (str, ...) 

107 

108 # mypy does not seem to like create_model 

109 model = create_model( 

110 f"SpecificSerializedDimensionRecord{definition.name.capitalize()}", 

111 __base__=SpecificSerializedDimensionRecord, 

112 **members, # type: ignore 

113 ) 

114 

115 _SIMPLE_RECORD_CLASS_CACHE[cache_key] = model 

116 return model 

117 

118 

119class SerializedDimensionRecord(BaseModel): 

120 """Simplified model for serializing a `DimensionRecord`.""" 

121 

122 definition: str = Field( 

123 ..., 

124 title="Name of dimension associated with this record.", 

125 example="exposure", 

126 ) 

127 

128 # Use strict types to prevent casting 

129 record: Dict[str, Union[None, StrictFloat, StrictStr, StrictBool, StrictInt, Tuple[int, int]]] = Field( 

130 ..., 

131 title="Dimension record keys and values.", 

132 example={ 

133 "definition": "exposure", 

134 "record": {"instrument": "LATISS", "exposure": 2021050300044, "obs_id": "AT_O_20210503_00044"}, 

135 }, 

136 ) 

137 

138 class Config: 

139 """Local configuration overrides for model.""" 

140 

141 schema_extra = { 

142 "example": { 

143 "definition": "detector", 

144 "record": { 

145 "instrument": "HSC", 

146 "id": 72, 

147 "full_name": "0_01", 

148 "name_in_raft": "01", 

149 "raft": "0", 

150 "purpose": "SCIENCE", 

151 }, 

152 } 

153 } 

154 

155 @classmethod 

156 def direct( 

157 cls, 

158 *, 

159 definition: str, 

160 record: Dict[str, Union[None, StrictFloat, StrictStr, StrictBool, StrictInt, Tuple[int, int]]], 

161 ) -> SerializedDimensionRecord: 

162 """Construct a `SerializedDimensionRecord` directly without validators. 

163 

164 This differs from the pydantic "construct" method in that the arguments 

165 are explicitly what the model requires, and it will recurse through 

166 members, constructing them from their corresponding `direct` methods. 

167 

168 This method should only be called when the inputs are trusted. 

169 """ 

170 node = cls.construct(definition=definition, record=record) 

171 node = SerializedDimensionRecord.__new__(cls) 

172 setter = object.__setattr__ 

173 setter(node, "definition", definition) 

174 # This method requires tuples as values of the mapping, but JSON 

175 # readers will read things in as lists. Be kind and transparently 

176 # transform to tuples 

177 setter( 

178 node, "record", {k: v if type(v) != list else tuple(v) for k, v in record.items()} # type: ignore 

179 ) 

180 setter(node, "__fields_set__", {"definition", "record"}) 

181 return node 

182 

183 

184@immutable 

185class DimensionRecord: 

186 """Base class for the Python representation of database records. 

187 

188 Parameters 

189 ---------- 

190 **kwargs 

191 Field values for this record. Unrecognized keys are ignored. If this 

192 is the record for a `Dimension`, its primary key value may be provided 

193 with the actual name of the field (e.g. "id" or "name"), the name of 

194 the `Dimension`, or both. If this record class has a "timespan" 

195 attribute, "datetime_begin" and "datetime_end" keyword arguments may 

196 be provided instead of a single "timespan" keyword argument (but are 

197 ignored if a "timespan" argument is provided). 

198 

199 Notes 

200 ----- 

201 `DimensionRecord` subclasses are created dynamically for each 

202 `DimensionElement` in a `DimensionUniverse`, and are accessible via the 

203 `DimensionElement.RecordClass` attribute. The `DimensionRecord` base class 

204 itself is pure abstract, but does not use the `abc` module to indicate this 

205 because it does not have overridable methods. 

206 

207 Record classes have attributes that correspond exactly to the 

208 `~DimensionElementFields.standard` fields in the related database table, 

209 plus "region" and "timespan" attributes for spatial and/or temporal 

210 elements (respectively). 

211 

212 Instances are usually obtained from a `Registry`, but can be constructed 

213 directly from Python as well. 

214 

215 `DimensionRecord` instances are immutable. 

216 """ 

217 

218 # Derived classes are required to define __slots__ as well, and it's those 

219 # derived-class slots that other methods on the base class expect to see 

220 # when they access self.__slots__. 

221 __slots__ = ("dataId",) 

222 

223 _serializedType = SerializedDimensionRecord 

224 

225 def __init__(self, **kwargs: Any): 

226 # Accept either the dimension name or the actual name of its primary 

227 # key field; ensure both are present in the dict for convenience below. 

228 if isinstance(self.definition, Dimension): 

229 v = kwargs.get(self.definition.primaryKey.name) 

230 if v is None: 

231 v = kwargs.get(self.definition.name) 

232 if v is None: 

233 raise ValueError( 

234 f"No value provided for {self.definition.name}.{self.definition.primaryKey.name}." 

235 ) 

236 kwargs[self.definition.primaryKey.name] = v 

237 else: 

238 v2 = kwargs.setdefault(self.definition.name, v) 

239 if v != v2: 

240 raise ValueError( 

241 f"Multiple inconsistent values for " 

242 f"{self.definition.name}.{self.definition.primaryKey.name}: {v!r} != {v2!r}." 

243 ) 

244 for name in self.__slots__: 

245 object.__setattr__(self, name, kwargs.get(name)) 

246 if self.definition.temporal is not None: 

247 if self.timespan is None: 

248 object.__setattr__( 

249 self, 

250 "timespan", 

251 Timespan( 

252 kwargs.get("datetime_begin"), 

253 kwargs.get("datetime_end"), 

254 ), 

255 ) 

256 

257 from ._coordinate import DataCoordinate 

258 

259 object.__setattr__( 

260 self, 

261 "dataId", 

262 DataCoordinate.fromRequiredValues( 

263 self.definition.graph, 

264 tuple(kwargs[dimension] for dimension in self.definition.required.names), 

265 ), 

266 ) 

267 

268 def __eq__(self, other: Any) -> bool: 

269 if type(other) != type(self): 

270 return False 

271 return self.dataId == other.dataId 

272 

273 def __hash__(self) -> int: 

274 return hash(self.dataId) 

275 

276 def __str__(self) -> str: 

277 lines = [f"{self.definition.name}:"] 

278 lines.extend(f" {name}: {getattr(self, name)!r}" for name in self.__slots__) 

279 return "\n".join(lines) 

280 

281 def __repr__(self) -> str: 

282 return "{}.RecordClass({})".format( 

283 self.definition.name, ", ".join(f"{name}={getattr(self, name)!r}" for name in self.__slots__) 

284 ) 

285 

286 def __reduce__(self) -> tuple: 

287 mapping = {name: getattr(self, name) for name in self.__slots__} 

288 return (_reconstructDimensionRecord, (self.definition, mapping)) 

289 

290 def to_simple(self, minimal: bool = False) -> SerializedDimensionRecord: 

291 """Convert this class to a simple python type. 

292 

293 This makes it suitable for serialization. 

294 

295 Parameters 

296 ---------- 

297 minimal : `bool`, optional 

298 Use minimal serialization. Has no effect on for this class. 

299 

300 Returns 

301 ------- 

302 names : `list` 

303 The names of the dimensions. 

304 """ 

305 # The DataId is sufficient if you are willing to do a deferred 

306 # query. This may not be overly useful since to reconstruct 

307 # a collection of records will require repeated registry queries. 

308 # For now do not implement minimal form. 

309 

310 mapping = {name: getattr(self, name) for name in self.__slots__} 

311 # If the item in mapping supports simplification update it 

312 for k, v in mapping.items(): 

313 try: 

314 mapping[k] = v.to_simple(minimal=minimal) 

315 except AttributeError: 

316 if isinstance(v, lsst.sphgeom.Region): 

317 # YAML serialization specifies the class when it 

318 # doesn't have to. This is partly for explicitness 

319 # and also history. Here use a different approach. 

320 # This code needs to be migrated to sphgeom 

321 mapping[k] = v.encode().hex() 

322 if isinstance(v, bytes): 

323 # We actually can't handle serializing out to bytes for 

324 # hash objects, encode it here to a hex string 

325 mapping[k] = v.hex() 

326 definition = self.definition.to_simple(minimal=minimal) 

327 return SerializedDimensionRecord(definition=definition, record=mapping) 

328 

329 @classmethod 

330 def from_simple( 

331 cls, 

332 simple: SerializedDimensionRecord, 

333 universe: Optional[DimensionUniverse] = None, 

334 registry: Optional[Registry] = None, 

335 ) -> DimensionRecord: 

336 """Construct a new object from the simplified form. 

337 

338 This is generally data returned from the `to_simple` 

339 method. 

340 

341 Parameters 

342 ---------- 

343 simple : `SerializedDimensionRecord` 

344 Value return from `to_simple`. 

345 universe : `DimensionUniverse` 

346 The special graph of all known dimensions of which this graph will 

347 be a subset. Can be `None` if `Registry` is provided. 

348 registry : `lsst.daf.butler.Registry`, optional 

349 Registry from which a universe can be extracted. Can be `None` 

350 if universe is provided explicitly. 

351 

352 Returns 

353 ------- 

354 record : `DimensionRecord` 

355 Newly-constructed object. 

356 """ 

357 if universe is None and registry is None: 

358 raise ValueError("One of universe or registry is required to convert names to a DimensionGraph") 

359 if universe is None and registry is not None: 

360 universe = registry.dimensions 

361 if universe is None: 

362 # this is for mypy 

363 raise ValueError("Unable to determine a usable universe") 

364 

365 definition = DimensionElement.from_simple(simple.definition, universe=universe) 

366 

367 # Create a specialist subclass model with type validation. 

368 # This allows us to do simple checks of external data (possibly 

369 # sent as JSON) since for now _reconstructDimensionRecord does not 

370 # do any validation. 

371 record_model_cls = _createSimpleRecordSubclass(definition) 

372 record_model = record_model_cls(**simple.record) 

373 

374 # Timespan and region have to be converted to native form 

375 # for now assume that those keys are special 

376 rec = record_model.dict() 

377 

378 if (ts := "timespan") in rec: 

379 rec[ts] = Timespan.from_simple(rec[ts], universe=universe, registry=registry) 

380 if (reg := "region") in rec: 

381 encoded = bytes.fromhex(rec[reg]) 

382 rec[reg] = lsst.sphgeom.Region.decode(encoded) 

383 if (hsh := "hash") in rec: 

384 rec[hsh] = bytes.fromhex(rec[hsh].decode()) 

385 

386 return _reconstructDimensionRecord(definition, rec) 

387 

388 to_json = to_json_pydantic 

389 from_json = classmethod(from_json_pydantic) 

390 

391 def toDict(self, splitTimespan: bool = False) -> Dict[str, Any]: 

392 """Return a vanilla `dict` representation of this record. 

393 

394 Parameters 

395 ---------- 

396 splitTimespan : `bool`, optional 

397 If `True` (`False` is default) transform any "timespan" key value 

398 from a `Timespan` instance into a pair of regular 

399 ("datetime_begin", "datetime_end") fields. 

400 """ 

401 results = {name: getattr(self, name) for name in self.__slots__} 

402 if splitTimespan: 

403 timespan = results.pop("timespan", None) 

404 if timespan is not None: 

405 results["datetime_begin"] = timespan.begin 

406 results["datetime_end"] = timespan.end 

407 return results 

408 

409 # DimensionRecord subclasses are dynamically created, so static type 

410 # checkers can't know about them or their attributes. To avoid having to 

411 # put "type: ignore", everywhere, add a dummy __getattr__ that tells type 

412 # checkers not to worry about missing attributes. 

413 def __getattr__(self, name: str) -> Any: 

414 raise AttributeError(name) 

415 

416 # Class attributes below are shadowed by instance attributes, and are 

417 # present just to hold the docstrings for those instance attributes. 

418 

419 dataId: DataCoordinate 

420 """A dict-like identifier for this record's primary keys 

421 (`DataCoordinate`). 

422 """ 

423 

424 definition: ClassVar[DimensionElement] 

425 """The `DimensionElement` whose records this class represents 

426 (`DimensionElement`). 

427 """ 

428 

429 fields: ClassVar[DimensionElementFields] 

430 """A categorized view of the fields in this class 

431 (`DimensionElementFields`). 

432 """