Coverage for python/lsst/daf/butler/script/ingest_files.py: 26%

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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 

22 

23__all__ = ("ingest_files",) 

24 

25import logging 

26from collections import defaultdict 

27from typing import TYPE_CHECKING, Any 

28 

29from astropy.table import Table 

30from lsst.resources import ResourcePath 

31from lsst.utils import doImport 

32 

33from .._butler import Butler 

34from ..core import DatasetRef, FileDataset 

35from ..registry import DatasetIdGenEnum 

36 

37if TYPE_CHECKING: 37 ↛ 38line 37 didn't jump to line 38, because the condition on line 37 was never true

38 from ..core import DatasetType, DimensionUniverse 

39 

40log = logging.getLogger(__name__) 

41 

42 

43def ingest_files( 

44 repo: str, 

45 dataset_type: str, 

46 run: str, 

47 table_file: str, 

48 data_id: tuple[str, ...] = (), 

49 formatter: str | None = None, 

50 id_generation_mode: str = "UNIQUE", 

51 prefix: str | None = None, 

52 transfer: str = "auto", 

53) -> None: 

54 """Ingest files from a table. 

55 

56 Parameters 

57 ---------- 

58 repo : `str` 

59 URI string of the Butler repo to use. 

60 dataset_type : `str` 

61 The name of the dataset type for the files to be ingested. This 

62 dataset type must exist. 

63 run : `str` 

64 The run in which the files should be ingested. 

65 table_file : `str` 

66 Path to a table file to read. This file can be in any format that 

67 can be read by Astropy so long as Astropy can determine the format 

68 itself. 

69 data_id : `tuple` of `str` 

70 Tuple of strings of the form ``keyword=value`` that can be used 

71 to define dataId elements that are fixed for all ingested files 

72 found in the table file. This allows those columns to be missing 

73 from the table file. Dimensions given here override table columns. 

74 formatter : `str`, optional 

75 Fully-qualified python class name for the `Formatter` to use 

76 to read the ingested files. If `None` the formatter is read from 

77 datastore configuration based on the dataset type. 

78 id_generation_mode : `str`, optional 

79 Mode to use for generating IDs. Should map to `DatasetGenIdEnum`. 

80 prefix : `str`, optional 

81 Prefix to use when resolving relative paths in table files. The default 

82 is to use the current working directory. 

83 transfer : `str`, optional 

84 Transfer mode to use for ingest. 

85 """ 

86 

87 # Check that the formatter can be imported -- validate this as soon 

88 # as possible before we read a potentially large table file. 

89 if formatter: 

90 doImport(formatter) 

91 else: 

92 formatter = None 

93 

94 # Force empty string prefix (from click) to None for API compatibility. 

95 if not prefix: 

96 prefix = None 

97 

98 # Convert the dataset ID gen mode string to enum. 

99 id_gen_mode = DatasetIdGenEnum.__members__[id_generation_mode] 

100 

101 # Create the butler with the relevant run attached. 

102 butler = Butler(repo, run=run) 

103 

104 datasetType = butler.registry.getDatasetType(dataset_type) 

105 

106 # Convert the k=v strings into a dataId dict. 

107 universe = butler.registry.dimensions 

108 common_data_id = parse_data_id_tuple(data_id, universe) 

109 

110 # Read the table assuming that Astropy can work out the format. 

111 table = Table.read(table_file) 

112 

113 datasets = extract_datasets_from_table(table, common_data_id, datasetType, formatter, prefix) 

114 

115 butler.ingest(*datasets, transfer=transfer, run=run, idGenerationMode=id_gen_mode) 

116 

117 

118def extract_datasets_from_table( 

119 table: Table, 

120 common_data_id: dict, 

121 datasetType: DatasetType, 

122 formatter: str | None = None, 

123 prefix: str | None = None, 

124) -> list[FileDataset]: 

125 """Extract datasets from the supplied table. 

126 

127 Parameters 

128 ---------- 

129 table : `astropy.table.Table` 

130 Table containing the datasets. The first column is assumed to be 

131 the file URI and the remaining columns are dimensions. 

132 common_data_id : `dict` 

133 Data ID values that are common to every row in the table. These 

134 take priority if a dimension in this dataId is also present as 

135 a column in the table. 

136 datasetType : `DatasetType` 

137 The dataset type to be associated with the ingested data. 

138 formatter : `str`, optional 

139 Fully-qualified python class name for the `Formatter` to use 

140 to read the ingested files. If `None` the formatter is read from 

141 datastore configuration based on the dataset type. 

142 prefix : `str` 

143 Prefix to be used for relative paths. Can be `None` for current 

144 working directory. 

145 

146 Returns 

147 ------- 

148 datasets : `list` of `FileDataset` 

149 The `FileDataset` objects corresponding to the rows in the table. 

150 The number of elements in this list can be smaller than the number 

151 of rows in the file because one file can appear in multiple rows 

152 with different dataIds. 

153 """ 

154 # The file is the first column and everything else is assumed to 

155 # be dimensions so we need to know the name of that column. 

156 file_column = table.colnames[0] 

157 

158 # Handle multiple dataIds per file by grouping by file. 

159 refs_by_file = defaultdict(list) 

160 n_dataset_refs = 0 

161 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) 

166 

167 # The command line can override a column. 

168 dataId.update(common_data_id) 

169 

170 # Create the dataset ref that is to be ingested. 

171 ref = DatasetRef(datasetType, dataId) # type: ignore 

172 

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 = ResourcePath(path, root=prefix, forceAbsolute=True) 

177 

178 refs_by_file[path_uri].append(ref) 

179 n_dataset_refs += 1 

180 

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 ] 

189 

190 log.info("Ingesting %d dataset ref(s) from %d file(s)", n_dataset_refs, len(datasets)) 

191 

192 return datasets 

193 

194 

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("=") 

201 

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 

206 

207 # Cast the value to the right python type (since they will be 

208 # strings at this point). 

209 value = dimension.primaryKey.getPythonType()(value) 

210 

211 data_id[dimension_str] = value 

212 return data_id