Coverage for python/lsst/obs/base/gen2to3/convertRepo.py : 31%

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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__ = ["ConvertRepoConfig", "ConvertRepoTask", "ConvertRepoSkyMapConfig"]
25import os
26import fnmatch
27from dataclasses import dataclass
28from typing import Iterable, Optional, List, Dict
30from lsst.utils import doImport
31from lsst.daf.butler import (
32 Butler as Butler3,
33 SkyPixDimension
34)
35from lsst.pex.config import Config, ConfigurableField, ConfigDictField, DictField, ListField, Field
36from lsst.pipe.base import Task
37from lsst.skymap import skyMapRegistry, BaseSkyMap
39from ..ingest import RawIngestTask
40from .repoConverter import ConversionSubset
41from .rootRepoConverter import RootRepoConverter
42from .calibRepoConverter import CalibRepoConverter
43from .standardRepoConverter import StandardRepoConverter
46@dataclass
47class ConfiguredSkyMap:
48 """Struct containing information about a skymap that may appear in a Gen2
49 repository.
50 """
52 name: str
53 """Name of the skymap used in Gen3 data IDs.
54 """
56 sha1: bytes
57 """Hash computed by `BaseSkyMap.getSha1`.
58 """
60 instance: BaseSkyMap
61 """Name of the skymap used in Gen3 data IDs.
62 """
64 used: bool = False
65 """Whether this skymap has been found in at least one repository being
66 converted.
67 """
70class ConvertRepoSkyMapConfig(Config):
71 """Sub-config used to hold the parameters of a SkyMap.
73 Notes
74 -----
75 This config only needs to exist because we can't put a
76 `~lsst.pex.config.RegistryField` directly inside a
77 `~lsst.pex.config.ConfigDictField`.
79 It needs to have its only field named "skyMap" for compatibility with the
80 configuration of `lsst.pipe.tasks.MakeSkyMapTask`, which we want so we can
81 use one config file in an obs package to configure both.
83 This name leads to unfortunate repetition with the field named
84 "skymap" that holds it - "skyMap[name].skyMap" - but that seems
85 unavoidable.
86 """
87 skyMap = skyMapRegistry.makeField(
88 doc="Type and parameters for the SkyMap itself.",
89 default="dodeca",
90 )
93class ConvertRepoConfig(Config):
94 raws = ConfigurableField(
95 "Configuration for subtask responsible for ingesting raws and adding "
96 "visit and exposure dimension entries.",
97 target=RawIngestTask,
98 )
99 skyMaps = ConfigDictField(
100 "Mapping from Gen3 skymap name to the parameters used to construct a "
101 "BaseSkyMap instance. This will be used to associate names with "
102 "existing skymaps found in the Gen2 repo.",
103 keytype=str,
104 itemtype=ConvertRepoSkyMapConfig,
105 default={}
106 )
107 rootSkyMapName = Field(
108 "Name of a Gen3 skymap (an entry in ``self.skyMaps``) to assume for "
109 "datasets in the root repository when no SkyMap is found there. ",
110 dtype=str,
111 optional=True,
112 default=None,
113 )
114 collections = DictField(
115 "Special collections (values) for certain dataset types (keys). "
116 "These are used in addition to rerun collections for datasets in "
117 "reruns. The 'raw' dataset must have an entry here if it is to be "
118 "converted.",
119 keytype=str,
120 itemtype=str,
121 default={
122 "deepCoadd_skyMap": "skymaps",
123 "brightObjectMask": "masks",
124 }
125 )
126 storageClasses = DictField(
127 "Mapping from dataset type name or Gen2 policy entry (e.g. 'python' "
128 "or 'persistable') to the Gen3 StorageClass name.",
129 keytype=str,
130 itemtype=str,
131 default={
132 "bias": "ExposureF",
133 "dark": "ExposureF",
134 "flat": "ExposureF",
135 "defects": "Defects",
136 "BaseSkyMap": "SkyMap",
137 "BaseCatalog": "Catalog",
138 "BackgroundList": "Background",
139 "raw": "Exposure",
140 "MultilevelParquetTable": "DataFrame",
141 "ParquetTable": "DataFrame",
142 "SkyWcs": "Wcs",
143 }
144 )
145 doRegisterInstrument = Field(
146 "If True (default), add dimension records for the Instrument and its "
147 "filters and detectors to the registry instead of assuming they are "
148 "already present.",
149 dtype=bool,
150 default=True,
151 )
152 doWriteCuratedCalibrations = Field(
153 "If True (default), ingest human-curated calibrations directly via "
154 "the Instrument interface. Note that these calibrations are never "
155 "converted from Gen2 repositories.",
156 dtype=bool,
157 default=True,
158 )
159 refCats = ListField(
160 "The names of reference catalogs (subdirectories under ref_cats) to "
161 "be converted",
162 dtype=str,
163 default=[]
164 )
165 fileIgnorePatterns = ListField(
166 "Filename globs that should be ignored instead of being treated as "
167 "datasets.",
168 dtype=str,
169 default=["README.txt", "*~?", "butler.yaml", "gen3.sqlite3",
170 "registry.sqlite3", "calibRegistry.sqlite3", "_mapper",
171 "_parent", "repositoryCfg.yaml"]
172 )
173 datasetIncludePatterns = ListField(
174 "Glob-style patterns for dataset type names that should be converted.",
175 dtype=str,
176 default=["*"]
177 )
178 datasetIgnorePatterns = ListField(
179 "Glob-style patterns for dataset type names that should not be "
180 "converted despite matching a pattern in datasetIncludePatterns.",
181 dtype=str,
182 default=[]
183 )
184 ccdKey = Field(
185 "Key used for the Gen2 equivalent of 'detector' in data IDs.",
186 dtype=str,
187 default="ccd",
188 )
189 relatedOnly = Field(
190 "If True (default), only convert datasets that are related to the "
191 "ingested visits. Ignored unless a list of visits is passed to "
192 "run().",
193 dtype=bool,
194 default=False,
195 )
196 curatedCalibrations = ListField(
197 "Dataset types that are handled by `Instrument.writeCuratedCalibrations()` "
198 "and thus should not be converted using the standard calibration "
199 "conversion system.",
200 dtype=str,
201 default=["camera",
202 "transmission_sensor",
203 "transmission_filter",
204 "transmission_optics",
205 "transmission_atmosphere",
206 "bfKernel"]
207 )
209 @property
210 def transfer(self):
211 return self.raws.transfer
213 @transfer.setter
214 def transfer(self, value):
215 self.raws.transfer = value
217 @property
218 def instrument(self):
219 return self.raws.instrument
221 @instrument.setter
222 def instrument(self, value):
223 self.raws.instrument = value
225 def setDefaults(self):
226 self.transfer = None
228 # TODO: check that there are no collection overrides for curated
229 # calibrations, since we don't have a good way to utilize them.
232class ConvertRepoTask(Task):
233 """A task that converts one or more related Gen2 data repositories to a
234 single Gen3 data repository (with multiple collections).
236 Parameters
237 ----------
238 config: `ConvertRepoConfig`
239 Configuration for this task.
240 butler3: `lsst.daf.butler.Butler`
241 Gen3 Butler instance that represents the data repository datasets will
242 be ingested into. The collection and/or run associated with this
243 Butler will be ignored in favor of collections/runs passed via config
244 or to `run`.
245 kwds
246 Other keyword arguments are forwarded to the `Task` constructor.
248 Notes
249 -----
250 Most of the work of converting repositories is delegated to instances of
251 the `RepoConverter` hierarchy. The `ConvertRepoTask` instance itself holds
252 only state that is relevant for all Gen2 repositories being ingested, while
253 each `RepoConverter` instance holds only state relevant for the conversion
254 of a single Gen2 repository. Both the task and the `RepoConverter`
255 instances are single use; `ConvertRepoTask.run` and most `RepoConverter`
256 methods may only be called once on a particular instance.
257 """
259 ConfigClass = ConvertRepoConfig
261 _DefaultName = "convertRepo"
263 def __init__(self, config=None, *, butler3: Butler3, **kwds):
264 config.validate() # Not a CmdlineTask nor PipelineTask, so have to validate the config here.
265 super().__init__(config, **kwds)
266 self.butler3 = butler3
267 self.registry = self.butler3.registry
268 self.universe = self.registry.dimensions
269 if self.isDatasetTypeIncluded("raw"):
270 self.makeSubtask("raws", butler=butler3)
271 self.instrument = self.raws.instrument
272 else:
273 self.raws = None
274 self.instrument = doImport(self.config.instrument)()
275 self._configuredSkyMapsBySha1 = {}
276 self._configuredSkyMapsByName = {}
277 for name, config in self.config.skyMaps.items():
278 instance = config.skyMap.apply()
279 self._populateSkyMapDicts(name, instance)
280 self._usedSkyPix = set()
282 def _populateSkyMapDicts(self, name, instance):
283 struct = ConfiguredSkyMap(name=name, sha1=instance.getSha1(), instance=instance)
284 self._configuredSkyMapsBySha1[struct.sha1] = struct
285 self._configuredSkyMapsByName[struct.name] = struct
287 def isDatasetTypeIncluded(self, datasetTypeName: str):
288 """Return `True` if configuration indicates that the given dataset type
289 should be converted.
291 This method is intended to be called primarily by the
292 `RepoConverter` instances used interally by the task.
294 Parameters
295 ----------
296 datasetTypeName: str
297 Name of the dataset type.
299 Returns
300 -------
301 included : `bool`
302 Whether the dataset should be included in the conversion.
303 """
304 return (
305 any(fnmatch.fnmatchcase(datasetTypeName, pattern)
306 for pattern in self.config.datasetIncludePatterns)
307 and not any(fnmatch.fnmatchcase(datasetTypeName, pattern)
308 for pattern in self.config.datasetIgnorePatterns)
309 )
311 def useSkyMap(self, skyMap: BaseSkyMap, skyMapName: str) -> str:
312 """Indicate that a repository uses the given SkyMap.
314 This method is intended to be called primarily by the
315 `RepoConverter` instances used interally by the task.
317 Parameters
318 ----------
319 skyMap : `lsst.skymap.BaseSkyMap`
320 SkyMap instance being used, typically retrieved from a Gen2
321 data repository.
322 skyMapName : `str`
323 The name of the gen2 skymap, for error reporting.
325 Returns
326 -------
327 name : `str`
328 The name of the skymap in Gen3 data IDs.
330 Raises
331 ------
332 LookupError
333 Raised if the specified skymap cannot be found.
334 """
335 sha1 = skyMap.getSha1()
336 if sha1 not in self._configuredSkyMapsBySha1:
337 self._populateSkyMapDicts(skyMapName, skyMap)
338 try:
339 struct = self._configuredSkyMapsBySha1[sha1]
340 except KeyError as err:
341 msg = f"SkyMap '{skyMapName}' with sha1={sha1} not included in configuration."
342 raise LookupError(msg) from err
343 struct.used = True
344 return struct.name
346 def registerUsedSkyMaps(self, subset: Optional[ConversionSubset]):
347 """Register all skymaps that have been marked as used.
349 This method is intended to be called primarily by the
350 `RepoConverter` instances used interally by the task.
352 Parameters
353 ----------
354 subset : `ConversionSubset`, optional
355 Object that will be used to filter converted datasets by data ID.
356 If given, it will be updated with the tracts of this skymap that
357 overlap the visits in the subset.
358 """
359 for struct in self._configuredSkyMapsBySha1.values():
360 if struct.used:
361 struct.instance.register(struct.name, self.registry)
362 if subset is not None and self.config.relatedOnly:
363 subset.addSkyMap(self.registry, struct.name)
365 def useSkyPix(self, dimension: SkyPixDimension):
366 """Indicate that a repository uses the given SkyPix dimension.
368 This method is intended to be called primarily by the
369 `RepoConverter` instances used interally by the task.
371 Parameters
372 ----------
373 dimension : `lsst.daf.butler.SkyPixDimension`
374 Dimension represening a pixelization of the sky.
375 """
376 self._usedSkyPix.add(dimension)
378 def registerUsedSkyPix(self, subset: Optional[ConversionSubset]):
379 """Register all skymaps that have been marked as used.
381 This method is intended to be called primarily by the
382 `RepoConverter` instances used interally by the task.
384 Parameters
385 ----------
386 subset : `ConversionSubset`, optional
387 Object that will be used to filter converted datasets by data ID.
388 If given, it will be updated with the pixelization IDs that
389 overlap the visits in the subset.
390 """
391 if subset is not None and self.config.relatedOnly:
392 for dimension in self._usedSkyPix:
393 subset.addSkyPix(self.registry, dimension)
395 def run(self, root: str, collections: List[str], *,
396 calibs: Dict[str, List[str]] = None,
397 reruns: Dict[str, List[str]] = None,
398 visits: Optional[Iterable[int]] = None):
399 """Convert a group of related data repositories.
401 Parameters
402 ----------
403 root : `str`
404 Complete path to the root Gen2 data repository. This should be
405 a data repository that includes a Gen2 registry and any raw files
406 and/or reference catalogs.
407 collections : `list` of `str`
408 Gen3 collections that datasets from the root repository should be
409 associated with. This should include any rerun collection that
410 these datasets should also be considered to be part of; because of
411 structural difference between Gen2 parent/child relationships and
412 Gen3 collections, these cannot be reliably inferred.
413 calibs : `dict`
414 Dictionary mapping calibration repository path to the collections
415 that the repository's datasets should be associated with. The path
416 may be relative to ``root`` or absolute. Collections should
417 include child repository collections as appropriate (see
418 documentation for ``collections``).
419 reruns : `dict`
420 Dictionary mapping rerun repository path to the collections that
421 the repository's datasets should be associated with. The path may
422 be relative to ``root`` or absolute. Collections should include
423 child repository collections as appropriate (see documentation for
424 ``collections``).
425 visits : iterable of `int`, optional
426 The integer IDs of visits to convert. If not provided, all visits
427 in the Gen2 root repository will be converted.
428 """
430 if calibs is None:
431 calibs = {}
432 if reruns is None:
433 reruns = {}
434 if visits is not None:
435 subset = ConversionSubset(instrument=self.instrument.getName(), visits=frozenset(visits))
436 else:
437 if self.config.relatedOnly:
438 self.log.warn("config.relatedOnly is True but all visits are being ingested; "
439 "no filtering will be done.")
440 subset = None
442 # We can't wrap database writes sanely in transactions (yet) because we
443 # keep initializing new Butler instances just so we can write into new
444 # runs/collections, and transactions are managed at the Butler level.
445 # DM-21246 should let us fix this, assuming we actually want to keep
446 # the transaction open that long.
447 if self.config.doRegisterInstrument:
448 self.instrument.register(self.registry)
450 # Make and prep converters for all Gen2 repos. This should not modify
451 # the Registry database or filesystem at all, though it may query it.
452 # The prep() calls here will be some of the slowest ones, because
453 # that's when we walk the filesystem.
454 converters = []
455 rootConverter = RootRepoConverter(task=self, root=root, collections=collections, subset=subset)
456 rootConverter.prep()
457 converters.append(rootConverter)
459 for root, collections in calibs.items():
460 if not os.path.isabs(root):
461 root = os.path.join(rootConverter.root, root)
462 converter = CalibRepoConverter(task=self, root=root, collections=collections,
463 mapper=rootConverter.mapper,
464 subset=rootConverter.subset)
465 converter.prep()
466 converters.append(converter)
468 for root, collections in reruns.items():
469 if not os.path.isabs(root):
470 root = os.path.join(rootConverter.root, root)
471 converter = StandardRepoConverter(task=self, root=root, collections=collections,
472 subset=rootConverter.subset)
473 converter.prep()
474 converters.append(converter)
476 # Actual database writes start here. We can't wrap these sanely in
477 # transactions (yet) because we keep initializing new Butler instances
478 # just so we can write into new runs/collections, and transactions
479 # are managed at the Butler level (DM-21246 should let us fix this).
481 # Insert dimensions needed by any converters. These are only the
482 # dimensions that a converter expects to be uniquely derived from the
483 # Gen2 repository it is reponsible for - e.g. visits, exposures, and
484 # calibration_labels.
485 #
486 # Note that we do not try to filter dimensions down to just those
487 # related to the given visits, even if config.relatedOnly is True; we
488 # need them in the Gen3 repo in order to be able to know which datasets
489 # to convert, because Gen2 alone doesn't know enough about the
490 # relationships between data IDs.
491 for converter in converters:
492 converter.insertDimensionData()
494 # Insert dimensions that are potentially shared by all Gen2
495 # repositories (and are hence managed directly by the Task, rather
496 # than a converter instance).
497 # This also finishes setting up the (shared) converter.subsets object
498 # that is used to filter data IDs for config.relatedOnly.
499 self.registerUsedSkyMaps(rootConverter.subset)
500 self.registerUsedSkyPix(rootConverter.subset)
502 # Look for datasets, generally by scanning the filesystem.
503 # This requires dimensions to have already been inserted so we can use
504 # dimension information to identify related datasets.
505 for converter in converters:
506 converter.findDatasets()
508 # Expand data IDs.
509 for converter in converters:
510 converter.expandDataIds()
512 # Actually ingest datasets.
513 for converter in converters:
514 converter.ingest()