Coverage for python/lsst/obs/base/gen2to3/convertRepo.py : 32%

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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 formatterClasses = DictField(
146 "Mapping from dataset type name to formatter class. "
147 "By default these are derived from the formatters listed in the"
148 " Gen3 datastore configuration.",
149 keytype=str,
150 itemtype=str,
151 default={}
152 )
153 targetHandlerClasses = DictField(
154 "Mapping from dataset type name to target handler class.",
155 keytype=str,
156 itemtype=str,
157 default={}
158 )
159 doRegisterInstrument = Field(
160 "If True (default), add dimension records for the Instrument and its "
161 "filters and detectors to the registry instead of assuming they are "
162 "already present.",
163 dtype=bool,
164 default=True,
165 )
166 doWriteCuratedCalibrations = Field(
167 "If True (default), ingest human-curated calibrations directly via "
168 "the Instrument interface. Note that these calibrations are never "
169 "converted from Gen2 repositories.",
170 dtype=bool,
171 default=True,
172 )
173 refCats = ListField(
174 "The names of reference catalogs (subdirectories under ref_cats) to "
175 "be converted",
176 dtype=str,
177 default=[]
178 )
179 fileIgnorePatterns = ListField(
180 "Filename globs that should be ignored instead of being treated as "
181 "datasets.",
182 dtype=str,
183 default=["README.txt", "*~?", "butler.yaml", "gen3.sqlite3",
184 "registry.sqlite3", "calibRegistry.sqlite3", "_mapper",
185 "_parent", "repositoryCfg.yaml"]
186 )
187 datasetIncludePatterns = ListField(
188 "Glob-style patterns for dataset type names that should be converted.",
189 dtype=str,
190 default=["*"]
191 )
192 datasetIgnorePatterns = ListField(
193 "Glob-style patterns for dataset type names that should not be "
194 "converted despite matching a pattern in datasetIncludePatterns.",
195 dtype=str,
196 default=[]
197 )
198 ccdKey = Field(
199 "Key used for the Gen2 equivalent of 'detector' in data IDs.",
200 dtype=str,
201 default="ccd",
202 )
203 relatedOnly = Field(
204 "If True (default), only convert datasets that are related to the "
205 "ingested visits. Ignored unless a list of visits is passed to "
206 "run().",
207 dtype=bool,
208 default=False,
209 )
210 curatedCalibrations = ListField(
211 "Dataset types that are handled by `Instrument.writeCuratedCalibrations()` "
212 "and thus should not be converted using the standard calibration "
213 "conversion system.",
214 dtype=str,
215 default=["camera",
216 "transmission_sensor",
217 "transmission_filter",
218 "transmission_optics",
219 "transmission_atmosphere",
220 "bfKernel"]
221 )
223 @property
224 def transfer(self):
225 return self.raws.transfer
227 @transfer.setter
228 def transfer(self, value):
229 self.raws.transfer = value
231 @property
232 def instrument(self):
233 return self.raws.instrument
235 @instrument.setter
236 def instrument(self, value):
237 self.raws.instrument = value
239 def setDefaults(self):
240 self.transfer = None
242 # TODO: check that there are no collection overrides for curated
243 # calibrations, since we don't have a good way to utilize them.
246class ConvertRepoTask(Task):
247 """A task that converts one or more related Gen2 data repositories to a
248 single Gen3 data repository (with multiple collections).
250 Parameters
251 ----------
252 config: `ConvertRepoConfig`
253 Configuration for this task.
254 butler3: `lsst.daf.butler.Butler`
255 Gen3 Butler instance that represents the data repository datasets will
256 be ingested into. The collection and/or run associated with this
257 Butler will be ignored in favor of collections/runs passed via config
258 or to `run`.
259 kwds
260 Other keyword arguments are forwarded to the `Task` constructor.
262 Notes
263 -----
264 Most of the work of converting repositories is delegated to instances of
265 the `RepoConverter` hierarchy. The `ConvertRepoTask` instance itself holds
266 only state that is relevant for all Gen2 repositories being ingested, while
267 each `RepoConverter` instance holds only state relevant for the conversion
268 of a single Gen2 repository. Both the task and the `RepoConverter`
269 instances are single use; `ConvertRepoTask.run` and most `RepoConverter`
270 methods may only be called once on a particular instance.
271 """
273 ConfigClass = ConvertRepoConfig
275 _DefaultName = "convertRepo"
277 def __init__(self, config=None, *, butler3: Butler3, **kwds):
278 config.validate() # Not a CmdlineTask nor PipelineTask, so have to validate the config here.
279 super().__init__(config, **kwds)
280 self.butler3 = butler3
281 self.registry = self.butler3.registry
282 self.universe = self.registry.dimensions
283 if self.isDatasetTypeIncluded("raw"):
284 self.makeSubtask("raws", butler=butler3)
285 self.instrument = self.raws.instrument
286 else:
287 self.raws = None
288 self.instrument = doImport(self.config.instrument)()
289 self._configuredSkyMapsBySha1 = {}
290 self._configuredSkyMapsByName = {}
291 for name, config in self.config.skyMaps.items():
292 instance = config.skyMap.apply()
293 self._populateSkyMapDicts(name, instance)
294 self._usedSkyPix = set()
296 def _populateSkyMapDicts(self, name, instance):
297 struct = ConfiguredSkyMap(name=name, sha1=instance.getSha1(), instance=instance)
298 self._configuredSkyMapsBySha1[struct.sha1] = struct
299 self._configuredSkyMapsByName[struct.name] = struct
301 def isDatasetTypeIncluded(self, datasetTypeName: str):
302 """Return `True` if configuration indicates that the given dataset type
303 should be converted.
305 This method is intended to be called primarily by the
306 `RepoConverter` instances used interally by the task.
308 Parameters
309 ----------
310 datasetTypeName: str
311 Name of the dataset type.
313 Returns
314 -------
315 included : `bool`
316 Whether the dataset should be included in the conversion.
317 """
318 return (
319 any(fnmatch.fnmatchcase(datasetTypeName, pattern)
320 for pattern in self.config.datasetIncludePatterns)
321 and not any(fnmatch.fnmatchcase(datasetTypeName, pattern)
322 for pattern in self.config.datasetIgnorePatterns)
323 )
325 def useSkyMap(self, skyMap: BaseSkyMap, skyMapName: str) -> str:
326 """Indicate that a repository uses the given SkyMap.
328 This method is intended to be called primarily by the
329 `RepoConverter` instances used interally by the task.
331 Parameters
332 ----------
333 skyMap : `lsst.skymap.BaseSkyMap`
334 SkyMap instance being used, typically retrieved from a Gen2
335 data repository.
336 skyMapName : `str`
337 The name of the gen2 skymap, for error reporting.
339 Returns
340 -------
341 name : `str`
342 The name of the skymap in Gen3 data IDs.
344 Raises
345 ------
346 LookupError
347 Raised if the specified skymap cannot be found.
348 """
349 sha1 = skyMap.getSha1()
350 if sha1 not in self._configuredSkyMapsBySha1:
351 self._populateSkyMapDicts(skyMapName, skyMap)
352 try:
353 struct = self._configuredSkyMapsBySha1[sha1]
354 except KeyError as err:
355 msg = f"SkyMap '{skyMapName}' with sha1={sha1} not included in configuration."
356 raise LookupError(msg) from err
357 struct.used = True
358 return struct.name
360 def registerUsedSkyMaps(self, subset: Optional[ConversionSubset]):
361 """Register all skymaps that have been marked as used.
363 This method is intended to be called primarily by the
364 `RepoConverter` instances used interally by the task.
366 Parameters
367 ----------
368 subset : `ConversionSubset`, optional
369 Object that will be used to filter converted datasets by data ID.
370 If given, it will be updated with the tracts of this skymap that
371 overlap the visits in the subset.
372 """
373 for struct in self._configuredSkyMapsBySha1.values():
374 if struct.used:
375 struct.instance.register(struct.name, self.registry)
376 if subset is not None and self.config.relatedOnly:
377 subset.addSkyMap(self.registry, struct.name)
379 def useSkyPix(self, dimension: SkyPixDimension):
380 """Indicate that a repository uses the given SkyPix dimension.
382 This method is intended to be called primarily by the
383 `RepoConverter` instances used interally by the task.
385 Parameters
386 ----------
387 dimension : `lsst.daf.butler.SkyPixDimension`
388 Dimension represening a pixelization of the sky.
389 """
390 self._usedSkyPix.add(dimension)
392 def registerUsedSkyPix(self, subset: Optional[ConversionSubset]):
393 """Register all skymaps that have been marked as used.
395 This method is intended to be called primarily by the
396 `RepoConverter` instances used interally by the task.
398 Parameters
399 ----------
400 subset : `ConversionSubset`, optional
401 Object that will be used to filter converted datasets by data ID.
402 If given, it will be updated with the pixelization IDs that
403 overlap the visits in the subset.
404 """
405 if subset is not None and self.config.relatedOnly:
406 for dimension in self._usedSkyPix:
407 subset.addSkyPix(self.registry, dimension)
409 def run(self, root: str, collections: List[str], *,
410 calibs: Dict[str, List[str]] = None,
411 reruns: Dict[str, List[str]] = None,
412 visits: Optional[Iterable[int]] = None):
413 """Convert a group of related data repositories.
415 Parameters
416 ----------
417 root : `str`
418 Complete path to the root Gen2 data repository. This should be
419 a data repository that includes a Gen2 registry and any raw files
420 and/or reference catalogs.
421 collections : `list` of `str`
422 Gen3 collections that datasets from the root repository should be
423 associated with. This should include any rerun collection that
424 these datasets should also be considered to be part of; because of
425 structural difference between Gen2 parent/child relationships and
426 Gen3 collections, these cannot be reliably inferred.
427 calibs : `dict`
428 Dictionary mapping calibration repository path to the collections
429 that the repository's datasets should be associated with. The path
430 may be relative to ``root`` or absolute. Collections should
431 include child repository collections as appropriate (see
432 documentation for ``collections``).
433 reruns : `dict`
434 Dictionary mapping rerun repository path to the collections that
435 the repository's datasets should be associated with. The path may
436 be relative to ``root`` or absolute. Collections should include
437 child repository collections as appropriate (see documentation for
438 ``collections``).
439 visits : iterable of `int`, optional
440 The integer IDs of visits to convert. If not provided, all visits
441 in the Gen2 root repository will be converted.
442 """
444 if calibs is None:
445 calibs = {}
446 if reruns is None:
447 reruns = {}
448 if visits is not None:
449 subset = ConversionSubset(instrument=self.instrument.getName(), visits=frozenset(visits))
450 else:
451 if self.config.relatedOnly:
452 self.log.warn("config.relatedOnly is True but all visits are being ingested; "
453 "no filtering will be done.")
454 subset = None
456 # We can't wrap database writes sanely in transactions (yet) because we
457 # keep initializing new Butler instances just so we can write into new
458 # runs/collections, and transactions are managed at the Butler level.
459 # DM-21246 should let us fix this, assuming we actually want to keep
460 # the transaction open that long.
461 if self.config.doRegisterInstrument:
462 self.instrument.register(self.registry)
464 # Make and prep converters for all Gen2 repos. This should not modify
465 # the Registry database or filesystem at all, though it may query it.
466 # The prep() calls here will be some of the slowest ones, because
467 # that's when we walk the filesystem.
468 converters = []
469 rootConverter = RootRepoConverter(task=self, root=root, collections=collections, subset=subset)
470 rootConverter.prep()
471 converters.append(rootConverter)
473 for root, collections in calibs.items():
474 if not os.path.isabs(root):
475 root = os.path.join(rootConverter.root, root)
476 converter = CalibRepoConverter(task=self, root=root, collections=collections,
477 mapper=rootConverter.mapper,
478 subset=rootConverter.subset)
479 converter.prep()
480 converters.append(converter)
482 for root, collections in reruns.items():
483 if not os.path.isabs(root):
484 root = os.path.join(rootConverter.root, root)
485 converter = StandardRepoConverter(task=self, root=root, collections=collections,
486 subset=rootConverter.subset)
487 converter.prep()
488 converters.append(converter)
490 # Actual database writes start here. We can't wrap these sanely in
491 # transactions (yet) because we keep initializing new Butler instances
492 # just so we can write into new runs/collections, and transactions
493 # are managed at the Butler level (DM-21246 should let us fix this).
495 # Insert dimensions needed by any converters. These are only the
496 # dimensions that a converter expects to be uniquely derived from the
497 # Gen2 repository it is reponsible for - e.g. visits, exposures, and
498 # calibration_labels.
499 #
500 # Note that we do not try to filter dimensions down to just those
501 # related to the given visits, even if config.relatedOnly is True; we
502 # need them in the Gen3 repo in order to be able to know which datasets
503 # to convert, because Gen2 alone doesn't know enough about the
504 # relationships between data IDs.
505 for converter in converters:
506 converter.insertDimensionData()
508 # Insert dimensions that are potentially shared by all Gen2
509 # repositories (and are hence managed directly by the Task, rather
510 # than a converter instance).
511 # This also finishes setting up the (shared) converter.subsets object
512 # that is used to filter data IDs for config.relatedOnly.
513 self.registerUsedSkyMaps(rootConverter.subset)
514 self.registerUsedSkyPix(rootConverter.subset)
516 # Look for datasets, generally by scanning the filesystem.
517 # This requires dimensions to have already been inserted so we can use
518 # dimension information to identify related datasets.
519 for converter in converters:
520 converter.findDatasets()
522 # Expand data IDs.
523 for converter in converters:
524 converter.expandDataIds()
526 # Actually ingest datasets.
527 for converter in converters:
528 converter.ingest()