Coverage for python/lsst/daf/butler/script/ingest_files.py: 22%
53 statements
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« prev ^ index » next coverage.py v7.3.2, created at 2023-10-25 15:14 +0000
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/>.
21from __future__ import annotations
23__all__ = ("ingest_files",)
25import logging
26from collections import defaultdict
27from typing import TYPE_CHECKING, Any
29from astropy.table import Table
30from lsst.resources import ResourcePath
31from lsst.utils import doImport
33from .._butler import Butler
34from ..core import DatasetIdGenEnum, DatasetRef, FileDataset
36if TYPE_CHECKING:
37 from ..core import DatasetType, DimensionUniverse
39log = logging.getLogger(__name__)
42def ingest_files(
43 repo: str,
44 dataset_type: str,
45 run: str,
46 table_file: str,
47 data_id: tuple[str, ...] = (),
48 formatter: str | None = None,
49 id_generation_mode: str = "UNIQUE",
50 prefix: str | None = None,
51 transfer: str = "auto",
52) -> None:
53 """Ingest files from a table.
55 Parameters
56 ----------
57 repo : `str`
58 URI string of the Butler repo to use.
59 dataset_type : `str`
60 The name of the dataset type for the files to be ingested. This
61 dataset type must exist.
62 run : `str`
63 The run in which the files should be ingested.
64 table_file : `str`
65 Path to a table file to read. This file can be in any format that
66 can be read by Astropy so long as Astropy can determine the format
67 itself.
68 data_id : `tuple` of `str`
69 Tuple of strings of the form ``keyword=value`` that can be used
70 to define dataId elements that are fixed for all ingested files
71 found in the table file. This allows those columns to be missing
72 from the table file. Dimensions given here override table columns.
73 formatter : `str`, optional
74 Fully-qualified python class name for the `Formatter` to use
75 to read the ingested files. If `None` the formatter is read from
76 datastore configuration based on the dataset type.
77 id_generation_mode : `str`, optional
78 Mode to use for generating IDs. Should map to `DatasetGenIdEnum`.
79 prefix : `str`, optional
80 Prefix to use when resolving relative paths in table files. The default
81 is to use the current working directory.
82 transfer : `str`, optional
83 Transfer mode to use for ingest.
84 """
85 # Check that the formatter can be imported -- validate this as soon
86 # as possible before we read a potentially large table file.
87 if formatter:
88 doImport(formatter)
89 else:
90 formatter = None
92 # Force empty string prefix (from click) to None for API compatibility.
93 if not prefix:
94 prefix = None
96 # Convert the dataset ID gen mode string to enum.
97 id_gen_mode = DatasetIdGenEnum.__members__[id_generation_mode]
99 # Create the butler with the relevant run attached.
100 butler = Butler(repo, run=run)
102 datasetType = butler.registry.getDatasetType(dataset_type)
104 # Convert the k=v strings into a dataId dict.
105 universe = butler.dimensions
106 common_data_id = parse_data_id_tuple(data_id, universe)
108 # Read the table assuming that Astropy can work out the format.
109 uri = ResourcePath(table_file, forceAbsolute=False)
110 with uri.as_local() as local_file:
111 table = Table.read(local_file.ospath)
113 datasets = extract_datasets_from_table(
114 table, common_data_id, datasetType, run, formatter, prefix, id_gen_mode
115 )
117 butler.ingest(*datasets, transfer=transfer)
120def extract_datasets_from_table(
121 table: Table,
122 common_data_id: dict,
123 datasetType: DatasetType,
124 run: str,
125 formatter: str | None = None,
126 prefix: str | None = None,
127 id_generation_mode: DatasetIdGenEnum = DatasetIdGenEnum.UNIQUE,
128) -> list[FileDataset]:
129 """Extract datasets from the supplied table.
131 Parameters
132 ----------
133 table : `astropy.table.Table`
134 Table containing the datasets. The first column is assumed to be
135 the file URI and the remaining columns are dimensions.
136 common_data_id : `dict`
137 Data ID values that are common to every row in the table. These
138 take priority if a dimension in this dataId is also present as
139 a column in the table.
140 datasetType : `DatasetType`
141 The dataset type to be associated with the ingested data.
142 run : `str`
143 The name of the run that will be receiving these datasets.
144 formatter : `str`, optional
145 Fully-qualified python class name for the `Formatter` to use
146 to read the ingested files. If `None` the formatter is read from
147 datastore configuration based on the dataset type.
148 prefix : `str`, optional
149 Prefix to be used for relative paths. Can be `None` for current
150 working directory.
151 id_generation_mode: `DatasetIdGenEnum`, optional
152 The mode to use when creating the dataset IDs.
154 Returns
155 -------
156 datasets : `list` of `FileDataset`
157 The `FileDataset` objects corresponding to the rows in the table.
158 The number of elements in this list can be smaller than the number
159 of rows in the file because one file can appear in multiple rows
160 with different dataIds.
161 """
162 # The file is the first column and everything else is assumed to
163 # be dimensions so we need to know the name of that column.
164 file_column = table.colnames[0]
166 # Handle multiple dataIds per file by grouping by file.
167 refs_by_file = defaultdict(list)
168 n_dataset_refs = 0
169 for row in table:
170 # Convert the row to a dataId, remembering to extract the
171 # path column.
172 dataId = dict(row)
173 path = dataId.pop(file_column)
175 # The command line can override a column.
176 dataId.update(common_data_id)
178 # Create the dataset ref that is to be ingested.
179 ref = DatasetRef(datasetType, dataId, run=run, id_generation_mode=id_generation_mode) # type: ignore
181 # Convert path to absolute (because otherwise system will
182 # assume relative to datastore root and that is almost certainly
183 # never the right default here).
184 path_uri = ResourcePath(path, root=prefix, forceAbsolute=True)
186 refs_by_file[path_uri].append(ref)
187 n_dataset_refs += 1
189 datasets = [
190 FileDataset(
191 path=file_uri,
192 refs=refs,
193 formatter=formatter,
194 )
195 for file_uri, refs in refs_by_file.items()
196 ]
198 log.info("Ingesting %d dataset ref(s) from %d file(s)", n_dataset_refs, len(datasets))
200 return datasets
203def parse_data_id_tuple(data_ids: tuple[str, ...], universe: DimensionUniverse) -> dict[str, Any]:
204 """Convert any additional k=v strings in the dataId tuple to dict
205 form.
206 """
207 data_id: dict[str, Any] = {}
208 for id_str in data_ids:
209 dimension_str, value = id_str.split("=")
211 try:
212 dimension = universe.getStaticDimensions()[dimension_str]
213 except KeyError:
214 raise ValueError(f"DataID dimension '{dimension_str}' is not known to this universe.") from None
216 # Cast the value to the right python type (since they will be
217 # strings at this point).
218 value = dimension.primaryKey.getPythonType()(value)
220 data_id[dimension_str] = value
221 return data_id