Coverage for python/lsst/obs/base/gen2to3/rootRepoConverter.py: 15%
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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
23__all__ = ["RootRepoConverter"]
25import os
26import re
27import itertools
28from typing import TYPE_CHECKING, Dict, Iterator, Mapping, Optional, Tuple, List
30from lsst.skymap import BaseSkyMap
31from lsst.daf.butler import CollectionType, DatasetType, DatasetRef, DimensionGraph, FileDataset
32from .standardRepoConverter import StandardRepoConverter
34SKYMAP_DATASET_TYPES = {
35 coaddName: f"{coaddName}Coadd_skyMap" for coaddName in ("deep", "goodSeeing", "dcr")
36}
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
42def getDataPaths(dataRefs):
43 """Strip HDU identifiers from paths and return a unique set of paths.
45 Parameters
46 ----------
47 dataRefs : `lsst.daf.persistence.ButlerDataRef`
48 The gen2 datarefs to strip "[HDU]" values from.
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
63class RootRepoConverter(StandardRepoConverter):
64 """A specialization of `RepoConverter` for root data repositories.
66 `RootRepoConverter` adds support for raw images (mostly delegated to the
67 parent task's `RawIngestTask` subtask) and reference catalogs.
69 Parameters
70 ----------
71 kwds
72 Keyword arguments are forwarded to (and required by) `RepoConverter`.
73 """
75 def __init__(self, **kwds):
76 super().__init__(run=None, **kwds)
77 self._refCats: Dict[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._rawRefs = []
84 def isDatasetTypeSpecial(self, datasetTypeName: str) -> bool:
85 # Docstring inherited from RepoConverter.
86 return (
87 super().isDatasetTypeSpecial(datasetTypeName)
88 or datasetTypeName in ("raw", "ref_cat", "ref_cat_config")
89 # in Gen2, some of these are in the root repo, not a calib repo
90 or datasetTypeName in self.instrument.getCuratedCalibrationNames()
91 )
93 def getSpecialDirectories(self) -> List[str]:
94 # Docstring inherited from RepoConverter.
95 return super().getSpecialDirectories() + ["CALIB", "ref_cats", "rerun"]
97 def findMatchingSkyMap(self, datasetTypeName: str) -> Tuple[Optional[BaseSkyMap], Optional[str]]:
98 # Docstring inherited from StandardRepoConverter.findMatchingSkyMap.
99 skyMap, name = super().findMatchingSkyMap(datasetTypeName)
100 if skyMap is None and self.task.config.rootSkyMapName is not None:
101 self.task.log.debug(
102 "Assuming configured root skymap with name '%s' for dataset %s.",
103 self.task.config.rootSkyMapName, datasetTypeName
104 )
105 skyMap = self._rootSkyMap
106 name = self.task.config.rootSkyMapName
107 return skyMap, name
109 def runRawIngest(self, pool=None):
110 if self.task.raws is None:
111 self.task.log.info("Skipping raw ingest for %s.", self.root)
112 return
113 self.task.log.info("Finding raws in root %s.", 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 if not self.task.dry_run:
123 self.task.log.info("Ingesting raws from root %s into run %s.",
124 self.root, self.task.raws.butler.run)
125 self._rawRefs.extend(self.task.raws.run(dataPaths, pool=pool))
126 else:
127 self.task.log.info("[dry run] skipping ingesting raws from root %s into run %s.",
128 self.root, self.task.raws.butler.run)
129 self._chain = [self.task.raws.butler.run]
131 def runDefineVisits(self, pool=None):
132 if self.task.defineVisits is None:
133 self.task.log.info("Skipping visit definition for %s.", self.root)
134 return
135 dimensions = DimensionGraph(self.task.universe, names=["exposure"])
136 exposureDataIds = set(ref.dataId.subset(dimensions) for ref in self._rawRefs)
137 if not self.task.dry_run:
138 self.task.log.info("Defining visits from exposures.")
139 self.task.defineVisits.run(exposureDataIds, pool=pool)
140 else:
141 self.task.log.info("[dry run] Skipping defining visits from exposures.")
143 def prep(self):
144 # Docstring inherited from RepoConverter.
145 # Gather information about reference catalogs.
146 if self.task.isDatasetTypeIncluded("ref_cat") and len(self.task.config.refCats) != 0:
147 from lsst.meas.algorithms import DatasetConfig as RefCatDatasetConfig
148 for refCat in os.listdir(os.path.join(self.root, "ref_cats")):
149 path = os.path.join(self.root, "ref_cats", refCat)
150 configFile = os.path.join(path, "config.py")
151 if not os.path.exists(configFile):
152 continue
153 if refCat not in self.task.config.refCats:
154 continue
155 self.task.log.info("Preparing ref_cat %s from root %s.", refCat, self.root)
156 onDiskConfig = RefCatDatasetConfig()
157 onDiskConfig.load(configFile)
158 if onDiskConfig.indexer.name != "HTM":
159 raise ValueError(f"Reference catalog '{refCat}' uses unsupported "
160 f"pixelization '{onDiskConfig.indexer.name}'.")
161 level = onDiskConfig.indexer["HTM"].depth
162 try:
163 dimension = self.task.universe[f"htm{level}"]
164 except KeyError as err:
165 raise ValueError(f"Reference catalog {refCat} uses HTM level {level}, but no htm{level} "
166 f"skypix dimension is configured for this registry.") from err
167 self.task.useSkyPix(dimension)
168 self._refCats[refCat] = dimension
169 if self.task.isDatasetTypeIncluded("brightObjectMask") and self.task.config.rootSkyMapName:
170 self.task.useSkyMap(self._rootSkyMap, self.task.config.rootSkyMapName)
171 super().prep()
173 def iterDatasets(self) -> Iterator[FileDataset]:
174 # Docstring inherited from RepoConverter.
175 # Iterate over reference catalog files.
176 for refCat, dimension in self._refCats.items():
177 datasetType = DatasetType(refCat, dimensions=[dimension], universe=self.task.universe,
178 storageClass="SimpleCatalog")
179 if self.subset is None:
180 regex = re.compile(r"(\d+)\.fits")
181 for fileName in self.progress.wrap(os.listdir(os.path.join(self.root, "ref_cats", refCat)),
182 desc=f"Processing refcat {refCat}"):
183 m = regex.match(fileName)
184 if m is not None:
185 htmId = int(m.group(1))
186 dataId = self.task.registry.expandDataId({dimension: htmId})
187 yield FileDataset(path=os.path.join(self.root, "ref_cats", refCat, fileName),
188 refs=DatasetRef(datasetType, dataId))
189 else:
190 for begin, end in self.progress.wrap(self.subset.skypix[dimension],
191 desc=f"Processing ranges for refcat {refCat}"):
192 for htmId in range(begin, end):
193 dataId = self.task.registry.expandDataId({dimension: htmId})
194 yield FileDataset(path=os.path.join(self.root, "ref_cats", refCat, f"{htmId}.fits"),
195 refs=DatasetRef(datasetType, dataId))
196 yield from super().iterDatasets()
198 def getRun(self, datasetTypeName: str, calibDate: Optional[str] = None) -> str:
199 # Docstring inherited from RepoConverter.
200 if datasetTypeName in self._refCats:
201 return self.instrument.makeRefCatCollectionName("gen2")
202 return super().getRun(datasetTypeName, calibDate)
204 def _finish(self, datasets: Mapping[DatasetType, Mapping[Optional[str], List[FileDataset]]],
205 count: int) -> None:
206 # Docstring inherited from RepoConverter.
207 super()._finish(datasets, count)
208 if self._refCats:
209 # Set up a CHAINED collection named something like "refcats" to
210 # also point to "refcats/gen2". It's conceivable (but unlikely)
211 # that "refcats/gen2" might not exist, if the scanner saw reference
212 # catalog datasets on disk but none overlapped the area of
213 # interest, so we register that here, too (multiple registrations
214 # of collections are fine).
215 chained = self.instrument.makeRefCatCollectionName()
216 child = self.instrument.makeRefCatCollectionName("gen2")
217 self.task.registry.registerCollection(chained, CollectionType.CHAINED)
218 self.task.registry.registerCollection(child, CollectionType.RUN)
219 children = list(self.task.registry.getCollectionChain(chained))
220 children.append(child)
221 self.task.registry.setCollectionChain(chained, children)
222 # Also add "refcats" to the list of collections that contains
223 # everything found in the root repo. Normally this is done in
224 # getRun, but here we want to add the (possibly new) CHAINED
225 # collection instead of the RUN collection.
226 self._chain.append(chained)