Coverage for python/lsst/daf/butler/registry/_collection_summary.py: 28%
58 statements
« prev ^ index » next coverage.py v7.4.4, created at 2024-03-26 02:48 -0700
« prev ^ index » next coverage.py v7.4.4, created at 2024-03-26 02:48 -0700
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 software is dual licensed under the GNU General Public License and also
10# under a 3-clause BSD license. Recipients may choose which of these licenses
11# to use; please see the files gpl-3.0.txt and/or bsd_license.txt,
12# respectively. If you choose the GPL option then the following text applies
13# (but note that there is still no warranty even if you opt for BSD instead):
14#
15# This program is free software: you can redistribute it and/or modify
16# it under the terms of the GNU General Public License as published by
17# the Free Software Foundation, either version 3 of the License, or
18# (at your option) any later version.
19#
20# This program is distributed in the hope that it will be useful,
21# but WITHOUT ANY WARRANTY; without even the implied warranty of
22# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
23# GNU General Public License for more details.
24#
25# You should have received a copy of the GNU General Public License
26# along with this program. If not, see <http://www.gnu.org/licenses/>.
27from __future__ import annotations
29__all__ = ("CollectionSummary",)
31import dataclasses
32from collections.abc import Generator, Iterable, Mapping, Set
33from typing import cast
35from .._dataset_ref import DatasetRef
36from .._dataset_type import DatasetType
37from .._named import NamedValueSet
38from ..dimensions import DataCoordinate
41@dataclasses.dataclass
42class CollectionSummary:
43 """A summary of the datasets that can be found in a collection."""
45 def copy(self) -> CollectionSummary:
46 """Return a deep copy of this object.
48 Returns
49 -------
50 copy : `CollectionSummary`
51 A copy of ``self`` that can be modified without modifying ``self``
52 at all.
53 """
54 return CollectionSummary(
55 dataset_types=self.dataset_types.copy(),
56 governors={k: v.copy() for k, v in self.governors.items()},
57 )
59 def add_datasets_generator(self, refs: Iterable[DatasetRef]) -> Generator[DatasetRef, None, None]:
60 """Include the given datasets in the summary, yielding them back as a
61 generator.
63 Parameters
64 ----------
65 refs : `~collections.abc.Iterable` [ `DatasetRef` ]
66 Datasets to include.
68 Yields
69 ------
70 ref : `DatasetRef`
71 The same dataset references originally passed in.
73 Notes
74 -----
75 As a generator, this method does nothing if its return iterator is not
76 used. Call `add_datasets` instead to avoid this; this method is
77 intended for the case where the given iterable may be single-pass and a
78 copy is not desired, but other processing needs to be done on its
79 elements.
80 """
81 for ref in refs:
82 self.dataset_types.add(ref.datasetType)
83 for gov in ref.dataId.dimensions.governors:
84 self.governors.setdefault(gov, set()).add(cast(str, ref.dataId[gov]))
85 yield ref
87 def add_datasets(self, refs: Iterable[DatasetRef]) -> None:
88 """Include the given datasets in the summary.
90 Parameters
91 ----------
92 refs : `~collections.abc.Iterable` [ `DatasetRef` ]
93 Datasets to include.
94 """
95 for _ in self.add_datasets_generator(refs):
96 pass
98 def add_data_ids_generator(
99 self, dataset_type: DatasetType, data_ids: Iterable[DataCoordinate]
100 ) -> Generator[DataCoordinate, None, None]:
101 """Include the given dataset type and data IDs in the summary, yielding
102 them back as a generator.
104 Parameters
105 ----------
106 dataset_type : `DatasetType`
107 Dataset type to include.
108 data_ids : `~collections.abc.Iterable` [ `DataCoordinate` ]
109 Data IDs to include.
111 Yields
112 ------
113 data_id : `DataCoordinate`
114 The same data IDs originally passed in.
116 Notes
117 -----
118 As a generator, this method does nothing if its return iterator is not
119 used. Call `add_data_ids` instead to avoid this; this method is
120 intended for the case where the given iterable may be single-pass and a
121 copy is not desired, but other processing needs to be done on its
122 elements.
123 """
124 self.dataset_types.add(dataset_type)
125 for data_id in data_ids:
126 for gov in data_id.dimensions.governors:
127 self.governors.setdefault(gov, set()).add(cast(str, data_id[gov]))
128 yield data_id
130 def add_data_ids(self, dataset_type: DatasetType, data_ids: Iterable[DataCoordinate]) -> None:
131 """Include the given dataset type and data IDs in the summary.
133 Parameters
134 ----------
135 dataset_type : `DatasetType`
136 Dataset type to include.
137 data_ids : `~collections.abc.Iterable` [ `DataCoordinate` ]
138 Data IDs to include.
139 """
140 for _ in self.add_data_ids_generator(dataset_type, data_ids):
141 pass
143 def update(self, *args: CollectionSummary) -> None:
144 """Update this summary with dataset types and governor dimension values
145 from other summaries.
147 Parameters
148 ----------
149 *args : `CollectionSummary`
150 Summaries to include in ``self``.
151 """
152 for arg in args:
153 self.dataset_types.update(arg.dataset_types)
154 for gov, values in arg.governors.items():
155 self.governors.setdefault(gov, set()).update(values)
157 def union(*args: CollectionSummary) -> CollectionSummary:
158 """Construct a summary that contains all dataset types and governor
159 dimension values in any of the inputs.
161 Parameters
162 ----------
163 *args : `CollectionSummary`
164 Summaries to combine.
166 Returns
167 -------
168 unioned : `CollectionSummary`
169 New summary object that represents the union of the given ones.
170 """
171 result = CollectionSummary()
172 result.update(*args)
173 return result
175 def is_compatible_with(
176 self,
177 dataset_type: DatasetType,
178 dimensions: Mapping[str, Set[str]],
179 rejections: list[str] | None = None,
180 name: str | None = None,
181 ) -> bool:
182 """Test whether the collection summarized by this object should be
183 queried for a given dataset type and governor dimension values.
185 Parameters
186 ----------
187 dataset_type : `DatasetType`
188 Dataset type being queried. If this collection has no instances of
189 this dataset type (or its parent dataset type, if it is a
190 component), `False` will always be returned.
191 dimensions : `~collections.abc.Mapping`
192 Bounds on the values governor dimensions can take in the query,
193 usually from a WHERE expression, as a mapping from dimension name
194 to a set of `str` governor dimension values.
195 rejections : `list` [ `str` ], optional
196 If provided, a list that will be populated with a log- or
197 exception-friendly message explaining why this dataset is
198 incompatible with this collection when `False` is returned.
199 name : `str`, optional
200 Name of the collection this object summarizes, for use in messages
201 appended to ``rejections``. Ignored if ``rejections`` is `None`.
203 Returns
204 -------
205 compatible : `bool`
206 `True` if the dataset query described by this summary and the given
207 arguments might yield non-empty results; `False` if the result from
208 such a query is definitely empty.
209 """
210 parent = dataset_type if not dataset_type.isComponent() else dataset_type.makeCompositeDatasetType()
211 if parent.name not in self.dataset_types.names:
212 if rejections is not None:
213 rejections.append(f"No datasets of type {parent.name} in collection {name!r}.")
214 return False
215 for gov_name in self.governors.keys() & dataset_type.dimensions.names & dimensions.keys():
216 values_in_collection = self.governors[gov_name]
217 values_given = dimensions[gov_name]
218 if values_in_collection.isdisjoint(values_given):
219 if rejections is not None:
220 rejections.append(
221 f"No datasets with {gov_name} in {values_given} in collection {name!r}."
222 )
223 return False
224 return True
226 dataset_types: NamedValueSet[DatasetType] = dataclasses.field(default_factory=NamedValueSet)
227 """Dataset types that may be present in the collection
228 (`NamedValueSet` [ `DatasetType` ]).
230 A dataset type not in this set is definitely not in the collection, but
231 the converse is not necessarily true.
232 """
234 governors: dict[str, set[str]] = dataclasses.field(default_factory=dict)
235 """Governor data ID values that are present in the collection's dataset
236 data IDs (`dict` [ `str`, `set` [ `str` ] ]).
238 A data ID value not in this restriction is not necessarily inconsistent
239 with a query in the collection; such a search may only involve dataset
240 types that do not include one or more governor dimensions in their data
241 IDs, and hence the values of those data IDs are unconstrained by this
242 collection in the query.
243 """