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1# This file is part of obs_base. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (https://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__ = ["RootRepoConverter"] 

24 

25import os 

26import re 

27import itertools 

28from typing import TYPE_CHECKING, Iterator, Optional, Tuple, List, Set 

29 

30from lsst.skymap import BaseSkyMap 

31from lsst.daf.butler import DatasetType, DatasetRef, DimensionGraph, FileDataset 

32from .standardRepoConverter import StandardRepoConverter 

33 

34SKYMAP_DATASET_TYPES = { 

35 coaddName: f"{coaddName}Coadd_skyMap" for coaddName in ("deep", "goodSeeing", "dcr") 

36} 

37 

38if TYPE_CHECKING: 38 ↛ 39line 38 didn't jump to line 39, because the condition on line 38 was never true

39 from lsst.daf.butler import SkyPixDimension 

40 

41 

42def getDataPaths(dataRefs): 

43 """Strip HDU identifiers from paths and return a unique set of paths. 

44 

45 Parameters 

46 ---------- 

47 dataRefs : `lsst.daf.persistence.ButlerDataRef` 

48 The gen2 datarefs to strip "[HDU]" values from. 

49 

50 Returns 

51 ------- 

52 paths : `set` [`str`] 

53 The unique file paths without appended "[HDU]". 

54 """ 

55 paths = set() 

56 for dataRef in dataRefs: 

57 path = dataRef.getUri() 

58 # handle with FITS files with multiple HDUs (e.g. decam raw) 

59 paths.add(path.split('[')[0]) 

60 return paths 

61 

62 

63class RootRepoConverter(StandardRepoConverter): 

64 """A specialization of `RepoConverter` for root data repositories. 

65 

66 `RootRepoConverter` adds support for raw images (mostly delegated to the 

67 parent task's `RawIngestTask` subtask) and reference catalogs. 

68 

69 Parameters 

70 ---------- 

71 kwds 

72 Keyword arguments are forwarded to (and required by) `RepoConverter`. 

73 """ 

74 

75 def __init__(self, **kwds): 

76 super().__init__(run=None, **kwds) 

77 self._refCats: List[Tuple[str, SkyPixDimension]] = [] 

78 if self.task.config.rootSkyMapName is not None: 

79 self._rootSkyMap = self.task.config.skyMaps[self.task.config.rootSkyMapName].skyMap.apply() 

80 else: 

81 self._rootSkyMap = None # All access to _rootSkyMap is guarded 

82 self._chain = {} 

83 self._rawRefs = [] 

84 

85 def isDatasetTypeSpecial(self, datasetTypeName: str) -> bool: 

86 # Docstring inherited from RepoConverter. 

87 return ( 

88 super().isDatasetTypeSpecial(datasetTypeName) 

89 or datasetTypeName in ("raw", "ref_cat", "ref_cat_config") 

90 # in Gen2, some of these are in the root repo, not a calib repo 

91 or datasetTypeName in self.instrument.getCuratedCalibrationNames() 

92 ) 

93 

94 def getSpecialDirectories(self) -> List[str]: 

95 # Docstring inherited from RepoConverter. 

96 return super().getSpecialDirectories() + ["CALIB", "ref_cats", "rerun"] 

97 

98 def findMatchingSkyMap(self, datasetTypeName: str) -> Tuple[Optional[BaseSkyMap], Optional[str]]: 

99 # Docstring inherited from StandardRepoConverter.findMatchingSkyMap. 

100 skyMap, name = super().findMatchingSkyMap(datasetTypeName) 

101 if skyMap is None and self.task.config.rootSkyMapName is not None: 

102 self.task.log.debug( 

103 ("Assuming configured root skymap with name '%s' for dataset %s."), 

104 self.task.config.rootSkyMapName, datasetTypeName 

105 ) 

106 skyMap = self._rootSkyMap 

107 name = self.task.config.rootSkyMapName 

108 return skyMap, name 

109 

110 def runRawIngest(self): 

111 if self.task.raws is None: 

112 return 

113 self.task.log.info(f"Finding raws in root {self.root}.") 

114 if self.subset is not None: 

115 dataRefs = itertools.chain.from_iterable( 

116 self.butler2.subset(self.task.config.rawDatasetType, 

117 visit=visit) for visit in self.subset.visits 

118 ) 

119 else: 

120 dataRefs = self.butler2.subset(self.task.config.rawDatasetType) 

121 dataPaths = getDataPaths(dataRefs) 

122 self.task.log.info("Ingesting raws from root %s into run %s.", self.root, self.task.raws.butler.run) 

123 self._rawRefs.extend(self.task.raws.run(dataPaths)) 

124 self._chain = {self.task.raws.butler.run: {self.task.raws.datasetType.name}} 

125 

126 def runDefineVisits(self): 

127 if self.task.defineVisits is None: 

128 return 

129 dimensions = DimensionGraph(self.task.universe, names=["exposure"]) 

130 exposureDataIds = set(ref.dataId.subset(dimensions) for ref in self._rawRefs) 

131 self.task.log.info("Defining visits from exposures.") 

132 self.task.defineVisits.run(exposureDataIds) 

133 

134 def prep(self): 

135 # Docstring inherited from RepoConverter. 

136 # Gather information about reference catalogs. 

137 if self.task.isDatasetTypeIncluded("ref_cat") and len(self.task.config.refCats) != 0: 

138 from lsst.meas.algorithms import DatasetConfig as RefCatDatasetConfig 

139 for refCat in os.listdir(os.path.join(self.root, "ref_cats")): 

140 path = os.path.join(self.root, "ref_cats", refCat) 

141 configFile = os.path.join(path, "config.py") 

142 if not os.path.exists(configFile): 

143 continue 

144 if refCat not in self.task.config.refCats: 

145 continue 

146 self.task.log.info(f"Preparing ref_cat {refCat} from root {self.root}.") 

147 onDiskConfig = RefCatDatasetConfig() 

148 onDiskConfig.load(configFile) 

149 if onDiskConfig.indexer.name != "HTM": 

150 raise ValueError(f"Reference catalog '{refCat}' uses unsupported " 

151 f"pixelization '{onDiskConfig.indexer.name}'.") 

152 level = onDiskConfig.indexer["HTM"].depth 

153 try: 

154 dimension = self.task.universe[f"htm{level}"] 

155 except KeyError as err: 

156 raise ValueError(f"Reference catalog {refCat} uses HTM level {level}, but no htm{level} " 

157 f"skypix dimension is configured for this registry.") from err 

158 self.task.useSkyPix(dimension) 

159 self._refCats.append((refCat, dimension)) 

160 if self.task.isDatasetTypeIncluded("brightObjectMask") and self.task.config.rootSkyMapName: 

161 self.task.useSkyMap(self._rootSkyMap, self.task.config.rootSkyMapName) 

162 super().prep() 

163 

164 def iterDatasets(self) -> Iterator[FileDataset]: 

165 # Docstring inherited from RepoConverter. 

166 # Iterate over reference catalog files. 

167 for refCat, dimension in self._refCats: 

168 datasetType = DatasetType(refCat, dimensions=[dimension], universe=self.task.universe, 

169 storageClass="SimpleCatalog") 

170 if self.subset is None: 

171 regex = re.compile(r"(\d+)\.fits") 

172 for fileName in os.listdir(os.path.join(self.root, "ref_cats", refCat)): 

173 m = regex.match(fileName) 

174 if m is not None: 

175 htmId = int(m.group(1)) 

176 dataId = self.task.registry.expandDataId({dimension: htmId}) 

177 yield FileDataset(path=os.path.join(self.root, "ref_cats", refCat, fileName), 

178 refs=DatasetRef(datasetType, dataId)) 

179 else: 

180 for begin, end in self.subset.skypix[dimension]: 

181 for htmId in range(begin, end): 

182 dataId = self.task.registry.expandDataId({dimension: htmId}) 

183 yield FileDataset(path=os.path.join(self.root, "ref_cats", refCat, f"{htmId}.fits"), 

184 refs=DatasetRef(datasetType, dataId)) 

185 yield from super().iterDatasets() 

186 

187 def getRun(self, datasetTypeName: str) -> str: 

188 # Docstring inherited from RepoConverter. 

189 run = self.task.config.runs[datasetTypeName] 

190 self._chain.setdefault(run, set()).add(datasetTypeName) 

191 return run 

192 

193 def getCollectionChain(self) -> List[Tuple[str, Set[str]]]: 

194 """Return tuples of run name and associated dataset type names that 

195 can be used to construct a chained collection that refers to the 

196 converted root repository (`list` [ `tuple` ]). 

197 """ 

198 return list(self._chain.items())