Coverage for python/lsst/daf/butler/core/dimensions/_graph.py: 33%
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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__ = ["DimensionGraph", "SerializedDimensionGraph"]
26import itertools
27from collections.abc import Iterable, Iterator, Mapping, Set
28from types import MappingProxyType
29from typing import TYPE_CHECKING, Any, ClassVar
31from lsst.utils.classes import cached_getter, immutable
32from pydantic import BaseModel
34from .._topology import TopologicalFamily, TopologicalSpace
35from ..json import from_json_pydantic, to_json_pydantic
36from ..named import NamedValueAbstractSet, NamedValueSet
38if TYPE_CHECKING: # Imports needed only for type annotations; may be circular.
39 from ...registry import Registry
40 from ._elements import Dimension, DimensionElement
41 from ._governor import GovernorDimension
42 from ._universe import DimensionUniverse
45class SerializedDimensionGraph(BaseModel):
46 """Simplified model of a `DimensionGraph` suitable for serialization."""
48 names: list[str]
50 @classmethod
51 def direct(cls, *, names: list[str]) -> SerializedDimensionGraph:
52 """Construct a `SerializedDimensionGraph` directly without validators.
54 This differs from the pydantic "construct" method in that the arguments
55 are explicitly what the model requires, and it will recurse through
56 members, constructing them from their corresponding `direct` methods.
58 This method should only be called when the inputs are trusted.
59 """
60 node = SerializedDimensionGraph.__new__(cls)
61 object.__setattr__(node, "names", names)
62 object.__setattr__(node, "__fields_set__", {"names"})
63 return node
66@immutable
67class DimensionGraph:
68 """An immutable, dependency-complete collection of dimensions.
70 `DimensionGraph` behaves in many respects like a set of `Dimension`
71 instances that maintains several special subsets and supersets of
72 related `DimensionElement` instances. It does not fully implement the
73 `collections.abc.Set` interface, as its automatic expansion of dependencies
74 would make set difference and XOR operations behave surprisingly.
76 It also provides dict-like lookup of `DimensionElement` instances from
77 their names.
79 Parameters
80 ----------
81 universe : `DimensionUniverse`
82 The special graph of all known dimensions of which this graph will be
83 a subset.
84 dimensions : iterable of `Dimension`, optional
85 An iterable of `Dimension` instances that must be included in the
86 graph. All (recursive) dependencies of these dimensions will also
87 be included. At most one of ``dimensions`` and ``names`` must be
88 provided.
89 names : iterable of `str`, optional
90 An iterable of the names of dimensions that must be included in the
91 graph. All (recursive) dependencies of these dimensions will also
92 be included. At most one of ``dimensions`` and ``names`` must be
93 provided.
94 conform : `bool`, optional
95 If `True` (default), expand to include dependencies. `False` should
96 only be used for callers that can guarantee that other arguments are
97 already correctly expanded, and is primarily for internal use.
99 Notes
100 -----
101 `DimensionGraph` should be used instead of other collections in most
102 contexts where a collection of dimensions is required and a
103 `DimensionUniverse` is available. Exceptions include cases where order
104 matters (and is different from the consistent ordering defined by the
105 `DimensionUniverse`), or complete `~collection.abc.Set` semantics are
106 required.
107 """
109 _serializedType = SerializedDimensionGraph
111 def __new__(
112 cls,
113 universe: DimensionUniverse,
114 dimensions: Iterable[Dimension] | None = None,
115 names: Iterable[str] | None = None,
116 conform: bool = True,
117 ) -> DimensionGraph:
118 conformedNames: set[str]
119 if names is None:
120 if dimensions is None:
121 conformedNames = set()
122 else:
123 try:
124 # Optimize for NamedValueSet/NamedKeyDict, though that's
125 # not required.
126 conformedNames = set(dimensions.names) # type: ignore
127 except AttributeError:
128 conformedNames = {d.name for d in dimensions}
129 else:
130 if dimensions is not None:
131 raise TypeError("Only one of 'dimensions' and 'names' may be provided.")
132 conformedNames = set(names)
133 if conform:
134 universe.expandDimensionNameSet(conformedNames)
135 # Look in the cache of existing graphs, with the expanded set of names.
136 cacheKey = frozenset(conformedNames)
137 self = universe._cache.get(cacheKey, None)
138 if self is not None:
139 return self
140 # This is apparently a new graph. Create it, and add it to the cache.
141 self = super().__new__(cls)
142 universe._cache[cacheKey] = self
143 self.universe = universe
144 # Reorder dimensions by iterating over the universe (which is
145 # ordered already) and extracting the ones in the set.
146 self.dimensions = NamedValueSet(universe.sorted(conformedNames)).freeze()
147 # Make a set that includes both the dimensions and any
148 # DimensionElements whose dependencies are in self.dimensions.
149 self.elements = NamedValueSet(
150 e for e in universe.getStaticElements() if e.required.names <= self.dimensions.names
151 ).freeze()
152 self._finish()
153 return self
155 def _finish(self) -> None:
156 # Make a set containing just the governor dimensions in this graph.
157 # Need local import to avoid cycle.
158 from ._governor import GovernorDimension
160 self.governors = NamedValueSet(
161 d for d in self.dimensions if isinstance(d, GovernorDimension)
162 ).freeze()
163 # Split dependencies up into "required" and "implied" subsets.
164 # Note that a dimension may be required in one graph and implied in
165 # another.
166 required: NamedValueSet[Dimension] = NamedValueSet()
167 implied: NamedValueSet[Dimension] = NamedValueSet()
168 for i1, dim1 in enumerate(self.dimensions):
169 for i2, dim2 in enumerate(self.dimensions):
170 if dim1.name in dim2.implied.names:
171 implied.add(dim1)
172 break
173 else:
174 # If no other dimension implies dim1, it's required.
175 required.add(dim1)
176 self.required = required.freeze()
177 self.implied = implied.freeze()
179 self.topology = MappingProxyType(
180 {
181 space: NamedValueSet(e.topology[space] for e in self.elements if space in e.topology).freeze()
182 for space in TopologicalSpace.__members__.values()
183 }
184 )
186 # Build mappings from dimension to index; this is really for
187 # DataCoordinate, but we put it in DimensionGraph because many
188 # (many!) DataCoordinates will share the same DimensionGraph, and
189 # we want them to be lightweight. The order here is what's convenient
190 # for DataCoordinate: all required dimensions before all implied
191 # dimensions.
192 self._dataCoordinateIndices: dict[str, int] = {
193 name: i for i, name in enumerate(itertools.chain(self.required.names, self.implied.names))
194 }
196 def __getnewargs__(self) -> tuple:
197 return (self.universe, None, tuple(self.dimensions.names), False)
199 def __deepcopy__(self, memo: dict) -> DimensionGraph:
200 # DimensionGraph is recursively immutable; see note in @immutable
201 # decorator.
202 return self
204 @property
205 def names(self) -> Set[str]:
206 """Set of the names of all dimensions in the graph (`KeysView`)."""
207 return self.dimensions.names
209 def to_simple(self, minimal: bool = False) -> SerializedDimensionGraph:
210 """Convert this class to a simple python type.
212 This type is suitable for serialization.
214 Parameters
215 ----------
216 minimal : `bool`, optional
217 Use minimal serialization. Has no effect on for this class.
219 Returns
220 -------
221 names : `list`
222 The names of the dimensions.
223 """
224 # Names are all we can serialize.
225 return SerializedDimensionGraph(names=list(self.names))
227 @classmethod
228 def from_simple(
229 cls,
230 names: SerializedDimensionGraph,
231 universe: DimensionUniverse | None = None,
232 registry: Registry | None = None,
233 ) -> DimensionGraph:
234 """Construct a new object from the simplified form.
236 This is assumed to support data data returned from the `to_simple`
237 method.
239 Parameters
240 ----------
241 names : `list` of `str`
242 The names of the dimensions.
243 universe : `DimensionUniverse`
244 The special graph of all known dimensions of which this graph will
245 be a subset. Can be `None` if `Registry` is provided.
246 registry : `lsst.daf.butler.Registry`, optional
247 Registry from which a universe can be extracted. Can be `None`
248 if universe is provided explicitly.
250 Returns
251 -------
252 graph : `DimensionGraph`
253 Newly-constructed object.
254 """
255 if universe is None and registry is None:
256 raise ValueError("One of universe or registry is required to convert names to a DimensionGraph")
257 if universe is None and registry is not None:
258 universe = registry.dimensions
259 if universe is None:
260 # this is for mypy
261 raise ValueError("Unable to determine a usable universe")
263 return cls(names=names.names, universe=universe)
265 to_json = to_json_pydantic
266 from_json: ClassVar = classmethod(from_json_pydantic)
268 def __iter__(self) -> Iterator[Dimension]:
269 """Iterate over all dimensions in the graph.
271 (and true `Dimension` instances only).
272 """
273 return iter(self.dimensions)
275 def __len__(self) -> int:
276 """Return the number of dimensions in the graph.
278 (and true `Dimension` instances only).
279 """
280 return len(self.dimensions)
282 def __contains__(self, element: str | DimensionElement) -> bool:
283 """Return `True` if the given element or element name is in the graph.
285 This test covers all `DimensionElement` instances in ``self.elements``,
286 not just true `Dimension` instances).
287 """
288 return element in self.elements
290 def __getitem__(self, name: str) -> DimensionElement:
291 """Return the element with the given name.
293 This lookup covers all `DimensionElement` instances in
294 ``self.elements``, not just true `Dimension` instances).
295 """
296 return self.elements[name]
298 def get(self, name: str, default: Any = None) -> DimensionElement:
299 """Return the element with the given name.
301 This lookup covers all `DimensionElement` instances in
302 ``self.elements``, not just true `Dimension` instances).
303 """
304 return self.elements.get(name, default)
306 def __str__(self) -> str:
307 return str(self.dimensions)
309 def __repr__(self) -> str:
310 return f"DimensionGraph({str(self)})"
312 def isdisjoint(self, other: DimensionGraph) -> bool:
313 """Test whether the intersection of two graphs is empty.
315 Returns `True` if either operand is the empty.
316 """
317 return self.dimensions.isdisjoint(other.dimensions)
319 def issubset(self, other: DimensionGraph) -> bool:
320 """Test whether all dimensions in ``self`` are also in ``other``.
322 Returns `True` if ``self`` is empty.
323 """
324 return self.dimensions <= other.dimensions
326 def issuperset(self, other: DimensionGraph) -> bool:
327 """Test whether all dimensions in ``other`` are also in ``self``.
329 Returns `True` if ``other`` is empty.
330 """
331 return self.dimensions >= other.dimensions
333 def __eq__(self, other: Any) -> bool:
334 """Test the arguments have exactly the same dimensions & elements."""
335 if isinstance(other, DimensionGraph):
336 return self.dimensions == other.dimensions
337 else:
338 return False
340 def __hash__(self) -> int:
341 return hash(tuple(self.dimensions.names))
343 def __le__(self, other: DimensionGraph) -> bool:
344 """Test whether ``self`` is a subset of ``other``."""
345 return self.dimensions <= other.dimensions
347 def __ge__(self, other: DimensionGraph) -> bool:
348 """Test whether ``self`` is a superset of ``other``."""
349 return self.dimensions >= other.dimensions
351 def __lt__(self, other: DimensionGraph) -> bool:
352 """Test whether ``self`` is a strict subset of ``other``."""
353 return self.dimensions < other.dimensions
355 def __gt__(self, other: DimensionGraph) -> bool:
356 """Test whether ``self`` is a strict superset of ``other``."""
357 return self.dimensions > other.dimensions
359 def union(self, *others: DimensionGraph) -> DimensionGraph:
360 """Construct a new graph with all dimensions in any of the operands.
362 The elements of the returned graph may exceed the naive union of
363 their elements, as some `DimensionElement` instances are included
364 in graphs whenever multiple dimensions are present, and those
365 dependency dimensions could have been provided by different operands.
366 """
367 names = set(self.names).union(*[other.names for other in others])
368 return DimensionGraph(self.universe, names=names)
370 def intersection(self, *others: DimensionGraph) -> DimensionGraph:
371 """Construct a new graph with only dimensions in all of the operands.
373 See also `union`.
374 """
375 names = set(self.names).intersection(*[other.names for other in others])
376 return DimensionGraph(self.universe, names=names)
378 def __or__(self, other: DimensionGraph) -> DimensionGraph:
379 """Construct a new graph with all dimensions in any of the operands.
381 See `union`.
382 """
383 return self.union(other)
385 def __and__(self, other: DimensionGraph) -> DimensionGraph:
386 """Construct a new graph with only dimensions in all of the operands.
388 See `intersection`.
389 """
390 return self.intersection(other)
392 @property
393 @cached_getter
394 def primaryKeyTraversalOrder(self) -> tuple[DimensionElement, ...]:
395 """Return a tuple of all elements in specific order.
397 The order allows records to be
398 found given their primary keys, starting from only the primary keys of
399 required dimensions (`tuple` [ `DimensionRecord` ]).
401 Unlike the table definition/topological order (which is what
402 DimensionUniverse.sorted gives you), when dimension A implies
403 dimension B, dimension A appears first.
404 """
405 done: set[str] = set()
406 order = []
408 def addToOrder(element: DimensionElement) -> None:
409 if element.name in done:
410 return
411 predecessors = set(element.required.names)
412 predecessors.discard(element.name)
413 if not done.issuperset(predecessors):
414 return
415 order.append(element)
416 done.add(element.name)
417 for other in element.implied:
418 addToOrder(other)
420 while not done.issuperset(self.required):
421 for dimension in self.required:
422 addToOrder(dimension)
424 order.extend(element for element in self.elements if element.name not in done)
425 return tuple(order)
427 @property
428 def spatial(self) -> NamedValueAbstractSet[TopologicalFamily]:
429 """Families represented by the spatial elements in this graph."""
430 return self.topology[TopologicalSpace.SPATIAL]
432 @property
433 def temporal(self) -> NamedValueAbstractSet[TopologicalFamily]:
434 """Families represented by the temporal elements in this graph."""
435 return self.topology[TopologicalSpace.TEMPORAL]
437 # Class attributes below are shadowed by instance attributes, and are
438 # present just to hold the docstrings for those instance attributes.
440 universe: DimensionUniverse
441 """The set of all known dimensions, of which this graph is a subset
442 (`DimensionUniverse`).
443 """
445 dimensions: NamedValueAbstractSet[Dimension]
446 """A true `~collections.abc.Set` of all true `Dimension` instances in the
447 graph (`NamedValueAbstractSet` of `Dimension`).
449 This is the set used for iteration, ``len()``, and most set-like operations
450 on `DimensionGraph` itself.
451 """
453 elements: NamedValueAbstractSet[DimensionElement]
454 """A true `~collections.abc.Set` of all `DimensionElement` instances in the
455 graph; a superset of `dimensions` (`NamedValueAbstractSet` of
456 `DimensionElement`).
458 This is the set used for dict-like lookups, including the ``in`` operator,
459 on `DimensionGraph` itself.
460 """
462 governors: NamedValueAbstractSet[GovernorDimension]
463 """A true `~collections.abc.Set` of all true `GovernorDimension` instances
464 in the graph (`NamedValueAbstractSet` of `GovernorDimension`).
465 """
467 required: NamedValueAbstractSet[Dimension]
468 """The subset of `dimensions` whose elements must be directly identified
469 via their primary keys in a data ID in order to identify the rest of the
470 elements in the graph (`NamedValueAbstractSet` of `Dimension`).
471 """
473 implied: NamedValueAbstractSet[Dimension]
474 """The subset of `dimensions` whose elements need not be directly
475 identified via their primary keys in a data ID (`NamedValueAbstractSet` of
476 `Dimension`).
477 """
479 topology: Mapping[TopologicalSpace, NamedValueAbstractSet[TopologicalFamily]]
480 """Families of elements in this graph that can participate in topological
481 relationships (`~collections.abc.Mapping` from `TopologicalSpace` to
482 `NamedValueAbstractSet` of `TopologicalFamily`).
483 """