Coverage for python/lsst/daf/butler/script/ingest_files.py: 25%
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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/>.
21from __future__ import annotations
23__all__ = ("ingest_files",)
25import logging
26from collections import defaultdict
27from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
29from astropy.table import Table
30from lsst.utils import doImport
32from .._butler import Butler
33from ..core import ButlerURI, DatasetRef, FileDataset
34from ..registry import DatasetIdGenEnum
36if TYPE_CHECKING: 36 ↛ 37line 36 didn't jump to line 37, because the condition on line 36 was never true
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: Optional[str] = None,
49 id_generation_mode: str = "UNIQUE",
50 prefix: Optional[str] = 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 """
86 # Check that the formatter can be imported -- validate this as soon
87 # as possible before we read a potentially large table file.
88 if formatter:
89 doImport(formatter)
90 else:
91 formatter = None
93 # Force empty string prefix (from click) to None for API compatibility.
94 if not prefix:
95 prefix = None
97 # Convert the dataset ID gen mode string to enum.
98 id_gen_mode = DatasetIdGenEnum.__members__[id_generation_mode]
100 # Create the butler with the relevant run attached.
101 butler = Butler(repo, run=run)
103 datasetType = butler.registry.getDatasetType(dataset_type)
105 # Convert the k=v strings into a dataId dict.
106 universe = butler.registry.dimensions
107 common_data_id = parse_data_id_tuple(data_id, universe)
109 # Read the table assuming that Astropy can work out the format.
110 table = Table.read(table_file)
112 datasets = extract_datasets_from_table(table, common_data_id, datasetType, formatter, prefix)
114 butler.ingest(*datasets, transfer=transfer, run=run, idGenerationMode=id_gen_mode)
117def extract_datasets_from_table(
118 table: Table,
119 common_data_id: Dict,
120 datasetType: DatasetType,
121 formatter: Optional[str] = None,
122 prefix: Optional[str] = None,
123) -> List[FileDataset]:
124 """Extract datasets from the supplied table.
126 Parameters
127 ----------
128 table : `astropy.table.Table`
129 Table containing the datasets. The first column is assumed to be
130 the file URI and the remaining columns are dimensions.
131 common_data_id : `dict`
132 Data ID values that are common to every row in the table. These
133 take priority if a dimension in this dataId is also present as
134 a column in the table.
135 datasetType : `DatasetType`
136 The dataset type to be associated with the ingested data.
137 formatter : `str`, optional
138 Fully-qualified python class name for the `Formatter` to use
139 to read the ingested files. If `None` the formatter is read from
140 datastore configuration based on the dataset type.
141 prefix : `str`
142 Prefix to be used for relative paths. Can be `None` for current
143 working directory.
145 Returns
146 -------
147 datasets : `list` of `FileDataset`
148 The `FileDataset` objects corresponding to the rows in the table.
149 The number of elements in this list can be smaller than the number
150 of rows in the file because one file can appear in multiple rows
151 with different dataIds.
152 """
153 # The file is the first column and everything else is assumed to
154 # be dimensions so we need to know the name of that column.
155 file_column = table.colnames[0]
157 # Handle multiple dataIds per file by grouping by file.
158 refs_by_file = defaultdict(list)
159 n_dataset_refs = 0
160 for row in table:
162 # Convert the row to a dataId, remembering to extract the
163 # path column.
164 dataId = dict(row)
165 path = dataId.pop(file_column)
167 # The command line can override a column.
168 dataId.update(common_data_id)
170 # Create the dataset ref that is to be ingested.
171 ref = DatasetRef(datasetType, dataId) # type: ignore
173 # Convert path to absolute (because otherwise system will
174 # assume relative to datastore root and that is almost certainly
175 # never the right default here).
176 path_uri = ButlerURI(path, root=prefix, forceAbsolute=True)
178 refs_by_file[path_uri].append(ref)
179 n_dataset_refs += 1
181 datasets = [
182 FileDataset(
183 path=file_uri,
184 refs=refs,
185 formatter=formatter,
186 )
187 for file_uri, refs in refs_by_file.items()
188 ]
190 log.info("Ingesting %d dataset ref(s) from %d file(s)", n_dataset_refs, len(datasets))
192 return datasets
195def parse_data_id_tuple(data_ids: Tuple[str, ...], universe: DimensionUniverse) -> Dict[str, Any]:
196 # Convert any additional k=v strings in the dataId tuple to dict
197 # form.
198 data_id: Dict[str, Any] = {}
199 for id_str in data_ids:
200 dimension_str, value = id_str.split("=")
202 try:
203 dimension = universe.getStaticDimensions()[dimension_str]
204 except KeyError:
205 raise ValueError(f"DataID dimension '{dimension_str}' is not known to this universe.") from None
207 # Cast the value to the right python type (since they will be
208 # strings at this point).
209 value = dimension.primaryKey.getPythonType()(value)
211 data_id[dimension_str] = value
212 return data_id