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/>.
22from __future__ import annotations
24__all__ = ("DimensionRecord", "SerializedDimensionRecord")
26from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Tuple, Type, Union
28import lsst.sphgeom
29from lsst.utils.classes import immutable
30from pydantic import BaseModel, Field, StrictBool, StrictFloat, StrictInt, StrictStr, create_model
32from .._topology import SpatialRegionDatabaseRepresentation
33from ..json import from_json_pydantic, to_json_pydantic
34from ..timespan import Timespan, TimespanDatabaseRepresentation
35from ._elements import Dimension, DimensionElement
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
44def _reconstructDimensionRecord(definition: DimensionElement, mapping: Dict[str, Any]) -> DimensionRecord:
45 """Unpickle implementation for `DimensionRecord` subclasses.
47 For internal use by `DimensionRecord`.
48 """
49 return definition.RecordClass(**mapping)
52def _subclassDimensionRecord(definition: DimensionElement) -> Type[DimensionRecord]:
53 """Create a dynamic subclass of `DimensionRecord` for the given element.
55 For internal use by `DimensionRecord`.
56 """
57 from ._schema import DimensionElementFields
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)
69class SpecificSerializedDimensionRecord(BaseModel, extra="forbid"):
70 """Base model for a specific serialized record content."""
73_SIMPLE_RECORD_CLASS_CACHE: Dict[
74 Tuple[DimensionElement, DimensionUniverse], Type[SpecificSerializedDimensionRecord]
75] = {}
78def _createSimpleRecordSubclass(definition: DimensionElement) -> Type[SpecificSerializedDimensionRecord]:
79 from ._schema import DimensionElementFields
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]
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 }
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, ...)
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 )
115 _SIMPLE_RECORD_CLASS_CACHE[cache_key] = model
116 return model
119class SerializedDimensionRecord(BaseModel):
120 """Simplified model for serializing a `DimensionRecord`."""
122 definition: str = Field(
123 ...,
124 title="Name of dimension associated with this record.",
125 example="exposure",
126 )
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 )
138 class Config:
139 """Local configuration overrides for model."""
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 }
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.
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.
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
184@immutable
185class DimensionRecord:
186 """Base class for the Python representation of database records.
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).
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.
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).
212 Instances are usually obtained from a `Registry`, but can be constructed
213 directly from Python as well.
215 `DimensionRecord` instances are immutable.
216 """
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",)
223 _serializedType = SerializedDimensionRecord
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 )
257 from ._coordinate import DataCoordinate
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 )
268 def __eq__(self, other: Any) -> bool:
269 if type(other) != type(self):
270 return False
271 return self.dataId == other.dataId
273 def __hash__(self) -> int:
274 return hash(self.dataId)
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)
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 )
286 def __reduce__(self) -> tuple:
287 mapping = {name: getattr(self, name) for name in self.__slots__}
288 return (_reconstructDimensionRecord, (self.definition, mapping))
290 def _repr_html_(self) -> str:
291 """Override the default representation in IPython/Jupyter notebooks.
293 This gives a more readable output that understands embedded newlines.
294 """
295 return f"<pre>{self}<pre>"
297 def to_simple(self, minimal: bool = False) -> SerializedDimensionRecord:
298 """Convert this class to a simple python type.
300 This makes it suitable for serialization.
302 Parameters
303 ----------
304 minimal : `bool`, optional
305 Use minimal serialization. Has no effect on for this class.
307 Returns
308 -------
309 names : `list`
310 The names of the dimensions.
311 """
312 # The DataId is sufficient if you are willing to do a deferred
313 # query. This may not be overly useful since to reconstruct
314 # a collection of records will require repeated registry queries.
315 # For now do not implement minimal form.
317 mapping = {name: getattr(self, name) for name in self.__slots__}
318 # If the item in mapping supports simplification update it
319 for k, v in mapping.items():
320 try:
321 mapping[k] = v.to_simple(minimal=minimal)
322 except AttributeError:
323 if isinstance(v, lsst.sphgeom.Region):
324 # YAML serialization specifies the class when it
325 # doesn't have to. This is partly for explicitness
326 # and also history. Here use a different approach.
327 # This code needs to be migrated to sphgeom
328 mapping[k] = v.encode().hex()
329 if isinstance(v, bytes):
330 # We actually can't handle serializing out to bytes for
331 # hash objects, encode it here to a hex string
332 mapping[k] = v.hex()
333 definition = self.definition.to_simple(minimal=minimal)
334 return SerializedDimensionRecord(definition=definition, record=mapping)
336 @classmethod
337 def from_simple(
338 cls,
339 simple: SerializedDimensionRecord,
340 universe: Optional[DimensionUniverse] = None,
341 registry: Optional[Registry] = None,
342 ) -> DimensionRecord:
343 """Construct a new object from the simplified form.
345 This is generally data returned from the `to_simple`
346 method.
348 Parameters
349 ----------
350 simple : `SerializedDimensionRecord`
351 Value return from `to_simple`.
352 universe : `DimensionUniverse`
353 The special graph of all known dimensions of which this graph will
354 be a subset. Can be `None` if `Registry` is provided.
355 registry : `lsst.daf.butler.Registry`, optional
356 Registry from which a universe can be extracted. Can be `None`
357 if universe is provided explicitly.
359 Returns
360 -------
361 record : `DimensionRecord`
362 Newly-constructed object.
363 """
364 if universe is None and registry is None:
365 raise ValueError("One of universe or registry is required to convert names to a DimensionGraph")
366 if universe is None and registry is not None:
367 universe = registry.dimensions
368 if universe is None:
369 # this is for mypy
370 raise ValueError("Unable to determine a usable universe")
372 definition = DimensionElement.from_simple(simple.definition, universe=universe)
374 # Create a specialist subclass model with type validation.
375 # This allows us to do simple checks of external data (possibly
376 # sent as JSON) since for now _reconstructDimensionRecord does not
377 # do any validation.
378 record_model_cls = _createSimpleRecordSubclass(definition)
379 record_model = record_model_cls(**simple.record)
381 # Timespan and region have to be converted to native form
382 # for now assume that those keys are special
383 rec = record_model.dict()
385 if (ts := "timespan") in rec:
386 rec[ts] = Timespan.from_simple(rec[ts], universe=universe, registry=registry)
387 if (reg := "region") in rec:
388 encoded = bytes.fromhex(rec[reg])
389 rec[reg] = lsst.sphgeom.Region.decode(encoded)
390 if (hsh := "hash") in rec:
391 rec[hsh] = bytes.fromhex(rec[hsh].decode())
393 return _reconstructDimensionRecord(definition, rec)
395 to_json = to_json_pydantic
396 from_json = classmethod(from_json_pydantic)
398 def toDict(self, splitTimespan: bool = False) -> Dict[str, Any]:
399 """Return a vanilla `dict` representation of this record.
401 Parameters
402 ----------
403 splitTimespan : `bool`, optional
404 If `True` (`False` is default) transform any "timespan" key value
405 from a `Timespan` instance into a pair of regular
406 ("datetime_begin", "datetime_end") fields.
407 """
408 results = {name: getattr(self, name) for name in self.__slots__}
409 if splitTimespan:
410 timespan = results.pop("timespan", None)
411 if timespan is not None:
412 results["datetime_begin"] = timespan.begin
413 results["datetime_end"] = timespan.end
414 return results
416 # DimensionRecord subclasses are dynamically created, so static type
417 # checkers can't know about them or their attributes. To avoid having to
418 # put "type: ignore", everywhere, add a dummy __getattr__ that tells type
419 # checkers not to worry about missing attributes.
420 def __getattr__(self, name: str) -> Any:
421 raise AttributeError(name)
423 # Class attributes below are shadowed by instance attributes, and are
424 # present just to hold the docstrings for those instance attributes.
426 dataId: DataCoordinate
427 """A dict-like identifier for this record's primary keys
428 (`DataCoordinate`).
429 """
431 definition: ClassVar[DimensionElement]
432 """The `DimensionElement` whose records this class represents
433 (`DimensionElement`).
434 """
436 fields: ClassVar[DimensionElementFields]
437 """A categorized view of the fields in this class
438 (`DimensionElementFields`).
439 """