Coverage for python/lsst/daf/butler/core/_column_categorization.py: 41%
35 statements
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« prev ^ index » next coverage.py v7.2.5, created at 2023-05-05 03:17 -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 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__ = ("ColumnCategorization",)
26import dataclasses
27from collections import defaultdict
28from collections.abc import Iterable, Iterator
29from typing import Any
31from ._column_tags import DatasetColumnTag, DimensionKeyColumnTag, DimensionRecordColumnTag
32from .dimensions import DimensionUniverse, GovernorDimension, SkyPixDimension
35@dataclasses.dataclass
36class ColumnCategorization:
37 dimension_keys: set[str] = dataclasses.field(default_factory=set)
38 dimension_records: defaultdict[str, set[str]] = dataclasses.field( 38 ↛ exitline 38 didn't jump to the function exit
39 default_factory=lambda: defaultdict(set)
40 )
41 datasets: defaultdict[str, set[str]] = dataclasses.field(default_factory=lambda: defaultdict(set)) 41 ↛ exitline 41 didn't run the lambda on line 41
43 @classmethod
44 def from_iterable(cls, iterable: Iterable[Any]) -> ColumnCategorization:
45 result = cls()
46 for tag in iterable:
47 match tag:
48 case DimensionKeyColumnTag(dimension=dimension):
49 result.dimension_keys.add(dimension)
50 case DimensionRecordColumnTag(element=element, column=column):
51 result.dimension_records[element].add(column)
52 case DatasetColumnTag(dataset_type=dataset_type, column=column):
53 result.datasets[dataset_type].add(column)
54 return result
56 def filter_skypix(self, universe: DimensionUniverse) -> Iterator[SkyPixDimension]:
57 return (
58 dimension
59 for name in self.dimension_keys
60 if isinstance(dimension := universe[name], SkyPixDimension)
61 )
63 def filter_governors(self, universe: DimensionUniverse) -> Iterator[GovernorDimension]:
64 return (
65 dimension
66 for name in self.dimension_keys
67 if isinstance(dimension := universe[name], GovernorDimension)
68 )
70 def filter_timespan_dataset_types(self) -> Iterator[str]:
71 return (dataset_type for dataset_type, columns in self.datasets.items() if "timespan" in columns)
73 def filter_timespan_dimension_elements(self) -> Iterator[str]:
74 return (element for element, columns in self.dimension_records.items() if "timespan" in columns)
76 def filter_spatial_region_dimension_elements(self) -> Iterator[str]:
77 return (element for element, columns in self.dimension_records.items() if "region" in columns)