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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/>.
22"""
23Butler top level classes.
24"""
25from __future__ import annotations
27__all__ = (
28 "Butler",
29 "ButlerValidationError",
30 "PruneCollectionsArgsError",
31 "PurgeWithoutUnstorePruneCollectionsError",
32 "RunWithoutPurgePruneCollectionsError",
33 "PurgeUnsupportedPruneCollectionsError",
34)
37from collections import defaultdict
38import contextlib
39import logging
40import numbers
41import os
42from typing import (
43 Any,
44 ClassVar,
45 Counter,
46 Dict,
47 Iterable,
48 Iterator,
49 List,
50 MutableMapping,
51 Optional,
52 Set,
53 TextIO,
54 Tuple,
55 Type,
56 Union,
57)
59try:
60 import boto3
61except ImportError:
62 boto3 = None
64from lsst.utils import doImport
65from .core import (
66 AmbiguousDatasetError,
67 ButlerURI,
68 Config,
69 ConfigSubset,
70 DataCoordinate,
71 DataId,
72 DataIdValue,
73 DatasetRef,
74 DatasetType,
75 Datastore,
76 Dimension,
77 DimensionConfig,
78 FileDataset,
79 Progress,
80 StorageClassFactory,
81 Timespan,
82 ValidationError,
83)
84from .core.repoRelocation import BUTLER_ROOT_TAG
85from .core.utils import transactional, getClassOf
86from ._deferredDatasetHandle import DeferredDatasetHandle
87from ._butlerConfig import ButlerConfig
88from .registry import (
89 Registry,
90 RegistryConfig,
91 RegistryDefaults,
92 CollectionSearch,
93 CollectionType,
94 ConflictingDefinitionError,
95 DatasetIdGenEnum,
96)
97from .transfers import RepoExportContext
99log = logging.getLogger(__name__)
102class ButlerValidationError(ValidationError):
103 """There is a problem with the Butler configuration."""
104 pass
107class PruneCollectionsArgsError(TypeError):
108 """Base class for errors relating to Butler.pruneCollections input
109 arguments.
110 """
111 pass
114class PurgeWithoutUnstorePruneCollectionsError(PruneCollectionsArgsError):
115 """Raised when purge and unstore are both required to be True, and
116 purge is True but unstore is False.
117 """
119 def __init__(self) -> None:
120 super().__init__("Cannot pass purge=True without unstore=True.")
123class RunWithoutPurgePruneCollectionsError(PruneCollectionsArgsError):
124 """Raised when pruning a RUN collection but purge is False."""
126 def __init__(self, collectionType: CollectionType):
127 self.collectionType = collectionType
128 super().__init__(f"Cannot prune RUN collection {self.collectionType.name} without purge=True.")
131class PurgeUnsupportedPruneCollectionsError(PruneCollectionsArgsError):
132 """Raised when purge is True but is not supported for the given
133 collection."""
135 def __init__(self, collectionType: CollectionType):
136 self.collectionType = collectionType
137 super().__init__(
138 f"Cannot prune {self.collectionType} collection {self.collectionType.name} with purge=True.")
141class Butler:
142 """Main entry point for the data access system.
144 Parameters
145 ----------
146 config : `ButlerConfig`, `Config` or `str`, optional.
147 Configuration. Anything acceptable to the
148 `ButlerConfig` constructor. If a directory path
149 is given the configuration will be read from a ``butler.yaml`` file in
150 that location. If `None` is given default values will be used.
151 butler : `Butler`, optional.
152 If provided, construct a new Butler that uses the same registry and
153 datastore as the given one, but with the given collection and run.
154 Incompatible with the ``config``, ``searchPaths``, and ``writeable``
155 arguments.
156 collections : `str` or `Iterable` [ `str` ], optional
157 An expression specifying the collections to be searched (in order) when
158 reading datasets.
159 This may be a `str` collection name or an iterable thereof.
160 See :ref:`daf_butler_collection_expressions` for more information.
161 These collections are not registered automatically and must be
162 manually registered before they are used by any method, but they may be
163 manually registered after the `Butler` is initialized.
164 run : `str`, optional
165 Name of the `~CollectionType.RUN` collection new datasets should be
166 inserted into. If ``collections`` is `None` and ``run`` is not `None`,
167 ``collections`` will be set to ``[run]``. If not `None`, this
168 collection will automatically be registered. If this is not set (and
169 ``writeable`` is not set either), a read-only butler will be created.
170 searchPaths : `list` of `str`, optional
171 Directory paths to search when calculating the full Butler
172 configuration. Not used if the supplied config is already a
173 `ButlerConfig`.
174 writeable : `bool`, optional
175 Explicitly sets whether the butler supports write operations. If not
176 provided, a read-write butler is created if any of ``run``, ``tags``,
177 or ``chains`` is non-empty.
178 inferDefaults : `bool`, optional
179 If `True` (default) infer default data ID values from the values
180 present in the datasets in ``collections``: if all collections have the
181 same value (or no value) for a governor dimension, that value will be
182 the default for that dimension. Nonexistent collections are ignored.
183 If a default value is provided explicitly for a governor dimension via
184 ``**kwargs``, no default will be inferred for that dimension.
185 **kwargs : `str`
186 Default data ID key-value pairs. These may only identify "governor"
187 dimensions like ``instrument`` and ``skymap``.
189 Examples
190 --------
191 While there are many ways to control exactly how a `Butler` interacts with
192 the collections in its `Registry`, the most common cases are still simple.
194 For a read-only `Butler` that searches one collection, do::
196 butler = Butler("/path/to/repo", collections=["u/alice/DM-50000"])
198 For a read-write `Butler` that writes to and reads from a
199 `~CollectionType.RUN` collection::
201 butler = Butler("/path/to/repo", run="u/alice/DM-50000/a")
203 The `Butler` passed to a ``PipelineTask`` is often much more complex,
204 because we want to write to one `~CollectionType.RUN` collection but read
205 from several others (as well)::
207 butler = Butler("/path/to/repo", run="u/alice/DM-50000/a",
208 collections=["u/alice/DM-50000/a",
209 "u/bob/DM-49998",
210 "HSC/defaults"])
212 This butler will `put` new datasets to the run ``u/alice/DM-50000/a``.
213 Datasets will be read first from that run (since it appears first in the
214 chain), and then from ``u/bob/DM-49998`` and finally ``HSC/defaults``.
216 Finally, one can always create a `Butler` with no collections::
218 butler = Butler("/path/to/repo", writeable=True)
220 This can be extremely useful when you just want to use ``butler.registry``,
221 e.g. for inserting dimension data or managing collections, or when the
222 collections you want to use with the butler are not consistent.
223 Passing ``writeable`` explicitly here is only necessary if you want to be
224 able to make changes to the repo - usually the value for ``writeable`` can
225 be guessed from the collection arguments provided, but it defaults to
226 `False` when there are not collection arguments.
227 """
228 def __init__(self, config: Union[Config, str, None] = None, *,
229 butler: Optional[Butler] = None,
230 collections: Any = None,
231 run: Optional[str] = None,
232 searchPaths: Optional[List[str]] = None,
233 writeable: Optional[bool] = None,
234 inferDefaults: bool = True,
235 **kwargs: str,
236 ):
237 defaults = RegistryDefaults(collections=collections, run=run, infer=inferDefaults, **kwargs)
238 # Load registry, datastore, etc. from config or existing butler.
239 if butler is not None:
240 if config is not None or searchPaths is not None or writeable is not None:
241 raise TypeError("Cannot pass 'config', 'searchPaths', or 'writeable' "
242 "arguments with 'butler' argument.")
243 self.registry = butler.registry.copy(defaults)
244 self.datastore = butler.datastore
245 self.storageClasses = butler.storageClasses
246 self._config: ButlerConfig = butler._config
247 else:
248 self._config = ButlerConfig(config, searchPaths=searchPaths)
249 if "root" in self._config:
250 butlerRoot = self._config["root"]
251 else:
252 butlerRoot = self._config.configDir
253 if writeable is None:
254 writeable = run is not None
255 self.registry = Registry.fromConfig(self._config, butlerRoot=butlerRoot, writeable=writeable,
256 defaults=defaults)
257 self.datastore = Datastore.fromConfig(self._config, self.registry.getDatastoreBridgeManager(),
258 butlerRoot=butlerRoot)
259 self.storageClasses = StorageClassFactory()
260 self.storageClasses.addFromConfig(self._config)
261 if "run" in self._config or "collection" in self._config:
262 raise ValueError("Passing a run or collection via configuration is no longer supported.")
264 GENERATION: ClassVar[int] = 3
265 """This is a Generation 3 Butler.
267 This attribute may be removed in the future, once the Generation 2 Butler
268 interface has been fully retired; it should only be used in transitional
269 code.
270 """
272 @staticmethod
273 def makeRepo(root: str, config: Union[Config, str, None] = None,
274 dimensionConfig: Union[Config, str, None] = None, standalone: bool = False,
275 searchPaths: Optional[List[str]] = None, forceConfigRoot: bool = True,
276 outfile: Optional[str] = None, overwrite: bool = False) -> Config:
277 """Create an empty data repository by adding a butler.yaml config
278 to a repository root directory.
280 Parameters
281 ----------
282 root : `str` or `ButlerURI`
283 Path or URI to the root location of the new repository. Will be
284 created if it does not exist.
285 config : `Config` or `str`, optional
286 Configuration to write to the repository, after setting any
287 root-dependent Registry or Datastore config options. Can not
288 be a `ButlerConfig` or a `ConfigSubset`. If `None`, default
289 configuration will be used. Root-dependent config options
290 specified in this config are overwritten if ``forceConfigRoot``
291 is `True`.
292 dimensionConfig : `Config` or `str`, optional
293 Configuration for dimensions, will be used to initialize registry
294 database.
295 standalone : `bool`
296 If True, write all expanded defaults, not just customized or
297 repository-specific settings.
298 This (mostly) decouples the repository from the default
299 configuration, insulating it from changes to the defaults (which
300 may be good or bad, depending on the nature of the changes).
301 Future *additions* to the defaults will still be picked up when
302 initializing `Butlers` to repos created with ``standalone=True``.
303 searchPaths : `list` of `str`, optional
304 Directory paths to search when calculating the full butler
305 configuration.
306 forceConfigRoot : `bool`, optional
307 If `False`, any values present in the supplied ``config`` that
308 would normally be reset are not overridden and will appear
309 directly in the output config. This allows non-standard overrides
310 of the root directory for a datastore or registry to be given.
311 If this parameter is `True` the values for ``root`` will be
312 forced into the resulting config if appropriate.
313 outfile : `str`, optional
314 If not-`None`, the output configuration will be written to this
315 location rather than into the repository itself. Can be a URI
316 string. Can refer to a directory that will be used to write
317 ``butler.yaml``.
318 overwrite : `bool`, optional
319 Create a new configuration file even if one already exists
320 in the specified output location. Default is to raise
321 an exception.
323 Returns
324 -------
325 config : `Config`
326 The updated `Config` instance written to the repo.
328 Raises
329 ------
330 ValueError
331 Raised if a ButlerConfig or ConfigSubset is passed instead of a
332 regular Config (as these subclasses would make it impossible to
333 support ``standalone=False``).
334 FileExistsError
335 Raised if the output config file already exists.
336 os.error
337 Raised if the directory does not exist, exists but is not a
338 directory, or cannot be created.
340 Notes
341 -----
342 Note that when ``standalone=False`` (the default), the configuration
343 search path (see `ConfigSubset.defaultSearchPaths`) that was used to
344 construct the repository should also be used to construct any Butlers
345 to avoid configuration inconsistencies.
346 """
347 if isinstance(config, (ButlerConfig, ConfigSubset)):
348 raise ValueError("makeRepo must be passed a regular Config without defaults applied.")
350 # Ensure that the root of the repository exists or can be made
351 uri = ButlerURI(root, forceDirectory=True)
352 uri.mkdir()
354 config = Config(config)
356 # If we are creating a new repo from scratch with relative roots,
357 # do not propagate an explicit root from the config file
358 if "root" in config:
359 del config["root"]
361 full = ButlerConfig(config, searchPaths=searchPaths) # this applies defaults
362 datastoreClass: Type[Datastore] = doImport(full["datastore", "cls"])
363 datastoreClass.setConfigRoot(BUTLER_ROOT_TAG, config, full, overwrite=forceConfigRoot)
365 # if key exists in given config, parse it, otherwise parse the defaults
366 # in the expanded config
367 if config.get(("registry", "db")):
368 registryConfig = RegistryConfig(config)
369 else:
370 registryConfig = RegistryConfig(full)
371 defaultDatabaseUri = registryConfig.makeDefaultDatabaseUri(BUTLER_ROOT_TAG)
372 if defaultDatabaseUri is not None:
373 Config.updateParameters(RegistryConfig, config, full,
374 toUpdate={"db": defaultDatabaseUri},
375 overwrite=forceConfigRoot)
376 else:
377 Config.updateParameters(RegistryConfig, config, full, toCopy=("db",),
378 overwrite=forceConfigRoot)
380 if standalone:
381 config.merge(full)
382 else:
383 # Always expand the registry.managers section into the per-repo
384 # config, because after the database schema is created, it's not
385 # allowed to change anymore. Note that in the standalone=True
386 # branch, _everything_ in the config is expanded, so there's no
387 # need to special case this.
388 Config.updateParameters(RegistryConfig, config, full, toCopy=("managers",), overwrite=False)
389 configURI: Union[str, ButlerURI]
390 if outfile is not None:
391 # When writing to a separate location we must include
392 # the root of the butler repo in the config else it won't know
393 # where to look.
394 config["root"] = uri.geturl()
395 configURI = outfile
396 else:
397 configURI = uri
398 config.dumpToUri(configURI, overwrite=overwrite)
400 # Create Registry and populate tables
401 registryConfig = RegistryConfig(config.get("registry"))
402 dimensionConfig = DimensionConfig(dimensionConfig)
403 Registry.createFromConfig(registryConfig, dimensionConfig=dimensionConfig, butlerRoot=root)
405 return config
407 @classmethod
408 def _unpickle(cls, config: ButlerConfig, collections: Optional[CollectionSearch], run: Optional[str],
409 defaultDataId: Dict[str, str], writeable: bool) -> Butler:
410 """Callable used to unpickle a Butler.
412 We prefer not to use ``Butler.__init__`` directly so we can force some
413 of its many arguments to be keyword-only (note that ``__reduce__``
414 can only invoke callables with positional arguments).
416 Parameters
417 ----------
418 config : `ButlerConfig`
419 Butler configuration, already coerced into a true `ButlerConfig`
420 instance (and hence after any search paths for overrides have been
421 utilized).
422 collections : `CollectionSearch`
423 Names of the default collections to read from.
424 run : `str`, optional
425 Name of the default `~CollectionType.RUN` collection to write to.
426 defaultDataId : `dict` [ `str`, `str` ]
427 Default data ID values.
428 writeable : `bool`
429 Whether the Butler should support write operations.
431 Returns
432 -------
433 butler : `Butler`
434 A new `Butler` instance.
435 """
436 # MyPy doesn't recognize that the kwargs below are totally valid; it
437 # seems to think '**defaultDataId* is a _positional_ argument!
438 return cls(config=config, collections=collections, run=run, writeable=writeable,
439 **defaultDataId) # type: ignore
441 def __reduce__(self) -> tuple:
442 """Support pickling.
443 """
444 return (Butler._unpickle, (self._config, self.collections, self.run,
445 self.registry.defaults.dataId.byName(),
446 self.registry.isWriteable()))
448 def __str__(self) -> str:
449 return "Butler(collections={}, run={}, datastore='{}', registry='{}')".format(
450 self.collections, self.run, self.datastore, self.registry)
452 def isWriteable(self) -> bool:
453 """Return `True` if this `Butler` supports write operations.
454 """
455 return self.registry.isWriteable()
457 @contextlib.contextmanager
458 def transaction(self) -> Iterator[None]:
459 """Context manager supporting `Butler` transactions.
461 Transactions can be nested.
462 """
463 with self.registry.transaction():
464 with self.datastore.transaction():
465 yield
467 def _standardizeArgs(self, datasetRefOrType: Union[DatasetRef, DatasetType, str],
468 dataId: Optional[DataId] = None, **kwds: Any
469 ) -> Tuple[DatasetType, Optional[DataId]]:
470 """Standardize the arguments passed to several Butler APIs.
472 Parameters
473 ----------
474 datasetRefOrType : `DatasetRef`, `DatasetType`, or `str`
475 When `DatasetRef` the `dataId` should be `None`.
476 Otherwise the `DatasetType` or name thereof.
477 dataId : `dict` or `DataCoordinate`
478 A `dict` of `Dimension` link name, value pairs that label the
479 `DatasetRef` within a Collection. When `None`, a `DatasetRef`
480 should be provided as the second argument.
481 kwds
482 Additional keyword arguments used to augment or construct a
483 `DataCoordinate`. See `DataCoordinate.standardize`
484 parameters.
486 Returns
487 -------
488 datasetType : `DatasetType`
489 A `DatasetType` instance extracted from ``datasetRefOrType``.
490 dataId : `dict` or `DataId`, optional
491 Argument that can be used (along with ``kwds``) to construct a
492 `DataId`.
494 Notes
495 -----
496 Butler APIs that conceptually need a DatasetRef also allow passing a
497 `DatasetType` (or the name of one) and a `DataId` (or a dict and
498 keyword arguments that can be used to construct one) separately. This
499 method accepts those arguments and always returns a true `DatasetType`
500 and a `DataId` or `dict`.
502 Standardization of `dict` vs `DataId` is best handled by passing the
503 returned ``dataId`` (and ``kwds``) to `Registry` APIs, which are
504 generally similarly flexible.
505 """
506 externalDatasetType: Optional[DatasetType] = None
507 internalDatasetType: Optional[DatasetType] = None
508 if isinstance(datasetRefOrType, DatasetRef):
509 if dataId is not None or kwds:
510 raise ValueError("DatasetRef given, cannot use dataId as well")
511 externalDatasetType = datasetRefOrType.datasetType
512 dataId = datasetRefOrType.dataId
513 else:
514 # Don't check whether DataId is provided, because Registry APIs
515 # can usually construct a better error message when it wasn't.
516 if isinstance(datasetRefOrType, DatasetType):
517 externalDatasetType = datasetRefOrType
518 else:
519 internalDatasetType = self.registry.getDatasetType(datasetRefOrType)
521 # Check that they are self-consistent
522 if externalDatasetType is not None:
523 internalDatasetType = self.registry.getDatasetType(externalDatasetType.name)
524 if externalDatasetType != internalDatasetType:
525 raise ValueError(f"Supplied dataset type ({externalDatasetType}) inconsistent with "
526 f"registry definition ({internalDatasetType})")
528 assert internalDatasetType is not None
529 return internalDatasetType, dataId
531 def _findDatasetRef(self, datasetRefOrType: Union[DatasetRef, DatasetType, str],
532 dataId: Optional[DataId] = None, *,
533 collections: Any = None,
534 allowUnresolved: bool = False,
535 **kwds: Any) -> DatasetRef:
536 """Shared logic for methods that start with a search for a dataset in
537 the registry.
539 Parameters
540 ----------
541 datasetRefOrType : `DatasetRef`, `DatasetType`, or `str`
542 When `DatasetRef` the `dataId` should be `None`.
543 Otherwise the `DatasetType` or name thereof.
544 dataId : `dict` or `DataCoordinate`, optional
545 A `dict` of `Dimension` link name, value pairs that label the
546 `DatasetRef` within a Collection. When `None`, a `DatasetRef`
547 should be provided as the first argument.
548 collections : Any, optional
549 Collections to be searched, overriding ``self.collections``.
550 Can be any of the types supported by the ``collections`` argument
551 to butler construction.
552 allowUnresolved : `bool`, optional
553 If `True`, return an unresolved `DatasetRef` if finding a resolved
554 one in the `Registry` fails. Defaults to `False`.
555 kwds
556 Additional keyword arguments used to augment or construct a
557 `DataId`. See `DataId` parameters.
559 Returns
560 -------
561 ref : `DatasetRef`
562 A reference to the dataset identified by the given arguments.
564 Raises
565 ------
566 LookupError
567 Raised if no matching dataset exists in the `Registry` (and
568 ``allowUnresolved is False``).
569 ValueError
570 Raised if a resolved `DatasetRef` was passed as an input, but it
571 differs from the one found in the registry.
572 TypeError
573 Raised if no collections were provided.
574 """
575 datasetType, dataId = self._standardizeArgs(datasetRefOrType, dataId, **kwds)
576 if isinstance(datasetRefOrType, DatasetRef):
577 idNumber = datasetRefOrType.id
578 else:
579 idNumber = None
580 timespan: Optional[Timespan] = None
582 # Process dimension records that are using record information
583 # rather than ids
584 newDataId: Dict[str, DataIdValue] = {}
585 byRecord: Dict[str, Dict[str, Any]] = defaultdict(dict)
587 # if all the dataId comes from keyword parameters we do not need
588 # to do anything here because they can't be of the form
589 # exposure.obs_id because a "." is not allowed in a keyword parameter.
590 if dataId:
591 for k, v in dataId.items():
592 # If we have a Dimension we do not need to do anything
593 # because it cannot be a compound key.
594 if isinstance(k, str) and "." in k:
595 # Someone is using a more human-readable dataId
596 dimensionName, record = k.split(".", 1)
597 byRecord[dimensionName][record] = v
598 elif isinstance(k, Dimension):
599 newDataId[k.name] = v
600 else:
601 newDataId[k] = v
603 # Go through the updated dataId and check the type in case someone is
604 # using an alternate key. We have already filtered out the compound
605 # keys dimensions.record format.
606 not_dimensions = {}
608 # Will need to look in the dataId and the keyword arguments
609 # and will remove them if they need to be fixed or are unrecognized.
610 for dataIdDict in (newDataId, kwds):
611 # Use a list so we can adjust the dict safely in the loop
612 for dimensionName in list(dataIdDict):
613 value = dataIdDict[dimensionName]
614 try:
615 dimension = self.registry.dimensions.getStaticDimensions()[dimensionName]
616 except KeyError:
617 # This is not a real dimension
618 not_dimensions[dimensionName] = value
619 del dataIdDict[dimensionName]
620 continue
622 # Convert an integral type to an explicit int to simplify
623 # comparisons here
624 if isinstance(value, numbers.Integral):
625 value = int(value)
627 if not isinstance(value, dimension.primaryKey.getPythonType()):
628 for alternate in dimension.alternateKeys:
629 if isinstance(value, alternate.getPythonType()):
630 byRecord[dimensionName][alternate.name] = value
631 del dataIdDict[dimensionName]
632 log.debug("Converting dimension %s to %s.%s=%s",
633 dimensionName, dimensionName, alternate.name, value)
634 break
635 else:
636 log.warning("Type mismatch found for value '%r' provided for dimension %s. "
637 "Could not find matching alternative (primary key has type %s) "
638 "so attempting to use as-is.",
639 value, dimensionName, dimension.primaryKey.getPythonType())
641 # If we have some unrecognized dimensions we have to try to connect
642 # them to records in other dimensions. This is made more complicated
643 # by some dimensions having records with clashing names. A mitigation
644 # is that we can tell by this point which dimensions are missing
645 # for the DatasetType but this does not work for calibrations
646 # where additional dimensions can be used to constrain the temporal
647 # axis.
648 if not_dimensions:
649 # Calculate missing dimensions
650 provided = set(newDataId) | set(kwds) | set(byRecord)
651 missingDimensions = datasetType.dimensions.names - provided
653 # For calibrations we may well be needing temporal dimensions
654 # so rather than always including all dimensions in the scan
655 # restrict things a little. It is still possible for there
656 # to be confusion over day_obs in visit vs exposure for example.
657 # If we are not searching calibration collections things may
658 # fail but they are going to fail anyway because of the
659 # ambiguousness of the dataId...
660 candidateDimensions: Set[str] = set()
661 candidateDimensions.update(missingDimensions)
662 if datasetType.isCalibration():
663 for dim in self.registry.dimensions.getStaticDimensions():
664 if dim.temporal:
665 candidateDimensions.add(str(dim))
667 # Look up table for the first association with a dimension
668 guessedAssociation: Dict[str, Dict[str, Any]] = defaultdict(dict)
670 # Keep track of whether an item is associated with multiple
671 # dimensions.
672 counter: Counter[str] = Counter()
673 assigned: Dict[str, Set[str]] = defaultdict(set)
675 # Go through the missing dimensions and associate the
676 # given names with records within those dimensions
677 for dimensionName in candidateDimensions:
678 dimension = self.registry.dimensions.getStaticDimensions()[dimensionName]
679 fields = dimension.metadata.names | dimension.uniqueKeys.names
680 for field in not_dimensions:
681 if field in fields:
682 guessedAssociation[dimensionName][field] = not_dimensions[field]
683 counter[dimensionName] += 1
684 assigned[field].add(dimensionName)
686 # There is a chance we have allocated a single dataId item
687 # to multiple dimensions. Need to decide which should be retained.
688 # For now assume that the most popular alternative wins.
689 # This means that day_obs with seq_num will result in
690 # exposure.day_obs and not visit.day_obs
691 # Also prefer an explicitly missing dimension over an inferred
692 # temporal dimension.
693 for fieldName, assignedDimensions in assigned.items():
694 if len(assignedDimensions) > 1:
695 # Pick the most popular (preferring mandatory dimensions)
696 requiredButMissing = assignedDimensions.intersection(missingDimensions)
697 if requiredButMissing:
698 candidateDimensions = requiredButMissing
699 else:
700 candidateDimensions = assignedDimensions
702 # Select the relevant items and get a new restricted
703 # counter.
704 theseCounts = {k: v for k, v in counter.items() if k in candidateDimensions}
705 duplicatesCounter: Counter[str] = Counter()
706 duplicatesCounter.update(theseCounts)
708 # Choose the most common. If they are equally common
709 # we will pick the one that was found first.
710 # Returns a list of tuples
711 selected = duplicatesCounter.most_common(1)[0][0]
713 log.debug("Ambiguous dataId entry '%s' associated with multiple dimensions: %s."
714 " Removed ambiguity by choosing dimension %s.",
715 fieldName, ", ".join(assignedDimensions), selected)
717 for candidateDimension in assignedDimensions:
718 if candidateDimension != selected:
719 del guessedAssociation[candidateDimension][fieldName]
721 # Update the record look up dict with the new associations
722 for dimensionName, values in guessedAssociation.items():
723 if values: # A dict might now be empty
724 log.debug("Assigned non-dimension dataId keys to dimension %s: %s",
725 dimensionName, values)
726 byRecord[dimensionName].update(values)
728 if byRecord:
729 # Some record specifiers were found so we need to convert
730 # them to the Id form
731 for dimensionName, values in byRecord.items():
732 if dimensionName in newDataId:
733 log.warning("DataId specified explicit %s dimension value of %s in addition to"
734 " general record specifiers for it of %s. Ignoring record information.",
735 dimensionName, newDataId[dimensionName], str(values))
736 continue
738 # Build up a WHERE expression -- use single quotes
739 def quote(s: Any) -> str:
740 if isinstance(s, str):
741 return f"'{s}'"
742 else:
743 return s
745 where = " AND ".join(f"{dimensionName}.{k} = {quote(v)}"
746 for k, v in values.items())
748 # Hopefully we get a single record that matches
749 records = set(self.registry.queryDimensionRecords(dimensionName, dataId=newDataId,
750 where=where, **kwds))
752 if len(records) != 1:
753 if len(records) > 1:
754 log.debug("Received %d records from constraints of %s", len(records), str(values))
755 for r in records:
756 log.debug("- %s", str(r))
757 raise RuntimeError(f"DataId specification for dimension {dimensionName} is not"
758 f" uniquely constrained to a single dataset by {values}."
759 f" Got {len(records)} results.")
760 raise RuntimeError(f"DataId specification for dimension {dimensionName} matched no"
761 f" records when constrained by {values}")
763 # Get the primary key from the real dimension object
764 dimension = self.registry.dimensions.getStaticDimensions()[dimensionName]
765 if not isinstance(dimension, Dimension):
766 raise RuntimeError(
767 f"{dimension.name} is not a true dimension, and cannot be used in data IDs."
768 )
769 newDataId[dimensionName] = getattr(records.pop(), dimension.primaryKey.name)
771 # We have modified the dataId so need to switch to it
772 dataId = newDataId
774 if datasetType.isCalibration():
775 # Because this is a calibration dataset, first try to make a
776 # standardize the data ID without restricting the dimensions to
777 # those of the dataset type requested, because there may be extra
778 # dimensions that provide temporal information for a validity-range
779 # lookup.
780 dataId = DataCoordinate.standardize(dataId, universe=self.registry.dimensions,
781 defaults=self.registry.defaults.dataId, **kwds)
782 if dataId.graph.temporal:
783 dataId = self.registry.expandDataId(dataId)
784 timespan = dataId.timespan
785 else:
786 # Standardize the data ID to just the dimensions of the dataset
787 # type instead of letting registry.findDataset do it, so we get the
788 # result even if no dataset is found.
789 dataId = DataCoordinate.standardize(dataId, graph=datasetType.dimensions,
790 defaults=self.registry.defaults.dataId, **kwds)
791 # Always lookup the DatasetRef, even if one is given, to ensure it is
792 # present in the current collection.
793 ref = self.registry.findDataset(datasetType, dataId, collections=collections, timespan=timespan)
794 if ref is None:
795 if allowUnresolved:
796 return DatasetRef(datasetType, dataId)
797 else:
798 if collections is None:
799 collections = self.registry.defaults.collections
800 raise LookupError(f"Dataset {datasetType.name} with data ID {dataId} "
801 f"could not be found in collections {collections}.")
802 if idNumber is not None and idNumber != ref.id:
803 if collections is None:
804 collections = self.registry.defaults.collections
805 raise ValueError(f"DatasetRef.id provided ({idNumber}) does not match "
806 f"id ({ref.id}) in registry in collections {collections}.")
807 return ref
809 @transactional
810 def put(self, obj: Any, datasetRefOrType: Union[DatasetRef, DatasetType, str],
811 dataId: Optional[DataId] = None, *,
812 run: Optional[str] = None,
813 **kwds: Any) -> DatasetRef:
814 """Store and register a dataset.
816 Parameters
817 ----------
818 obj : `object`
819 The dataset.
820 datasetRefOrType : `DatasetRef`, `DatasetType`, or `str`
821 When `DatasetRef` is provided, ``dataId`` should be `None`.
822 Otherwise the `DatasetType` or name thereof.
823 dataId : `dict` or `DataCoordinate`
824 A `dict` of `Dimension` link name, value pairs that label the
825 `DatasetRef` within a Collection. When `None`, a `DatasetRef`
826 should be provided as the second argument.
827 run : `str`, optional
828 The name of the run the dataset should be added to, overriding
829 ``self.run``.
830 kwds
831 Additional keyword arguments used to augment or construct a
832 `DataCoordinate`. See `DataCoordinate.standardize`
833 parameters.
835 Returns
836 -------
837 ref : `DatasetRef`
838 A reference to the stored dataset, updated with the correct id if
839 given.
841 Raises
842 ------
843 TypeError
844 Raised if the butler is read-only or if no run has been provided.
845 """
846 log.debug("Butler put: %s, dataId=%s, run=%s", datasetRefOrType, dataId, run)
847 if not self.isWriteable():
848 raise TypeError("Butler is read-only.")
849 datasetType, dataId = self._standardizeArgs(datasetRefOrType, dataId, **kwds)
850 if isinstance(datasetRefOrType, DatasetRef) and datasetRefOrType.id is not None:
851 raise ValueError("DatasetRef must not be in registry, must have None id")
853 # Add Registry Dataset entry.
854 dataId = self.registry.expandDataId(dataId, graph=datasetType.dimensions, **kwds)
855 ref, = self.registry.insertDatasets(datasetType, run=run, dataIds=[dataId])
857 # Add Datastore entry.
858 self.datastore.put(obj, ref)
860 return ref
862 def getDirect(self, ref: DatasetRef, *, parameters: Optional[Dict[str, Any]] = None) -> Any:
863 """Retrieve a stored dataset.
865 Unlike `Butler.get`, this method allows datasets outside the Butler's
866 collection to be read as long as the `DatasetRef` that identifies them
867 can be obtained separately.
869 Parameters
870 ----------
871 ref : `DatasetRef`
872 Resolved reference to an already stored dataset.
873 parameters : `dict`
874 Additional StorageClass-defined options to control reading,
875 typically used to efficiently read only a subset of the dataset.
877 Returns
878 -------
879 obj : `object`
880 The dataset.
881 """
882 return self.datastore.get(ref, parameters=parameters)
884 def getDirectDeferred(self, ref: DatasetRef, *,
885 parameters: Union[dict, None] = None) -> DeferredDatasetHandle:
886 """Create a `DeferredDatasetHandle` which can later retrieve a dataset,
887 from a resolved `DatasetRef`.
889 Parameters
890 ----------
891 ref : `DatasetRef`
892 Resolved reference to an already stored dataset.
893 parameters : `dict`
894 Additional StorageClass-defined options to control reading,
895 typically used to efficiently read only a subset of the dataset.
897 Returns
898 -------
899 obj : `DeferredDatasetHandle`
900 A handle which can be used to retrieve a dataset at a later time.
902 Raises
903 ------
904 AmbiguousDatasetError
905 Raised if ``ref.id is None``, i.e. the reference is unresolved.
906 """
907 if ref.id is None:
908 raise AmbiguousDatasetError(
909 f"Dataset of type {ref.datasetType.name} with data ID {ref.dataId} is not resolved."
910 )
911 return DeferredDatasetHandle(butler=self, ref=ref, parameters=parameters)
913 def getDeferred(self, datasetRefOrType: Union[DatasetRef, DatasetType, str],
914 dataId: Optional[DataId] = None, *,
915 parameters: Union[dict, None] = None,
916 collections: Any = None,
917 **kwds: Any) -> DeferredDatasetHandle:
918 """Create a `DeferredDatasetHandle` which can later retrieve a dataset,
919 after an immediate registry lookup.
921 Parameters
922 ----------
923 datasetRefOrType : `DatasetRef`, `DatasetType`, or `str`
924 When `DatasetRef` the `dataId` should be `None`.
925 Otherwise the `DatasetType` or name thereof.
926 dataId : `dict` or `DataCoordinate`, optional
927 A `dict` of `Dimension` link name, value pairs that label the
928 `DatasetRef` within a Collection. When `None`, a `DatasetRef`
929 should be provided as the first argument.
930 parameters : `dict`
931 Additional StorageClass-defined options to control reading,
932 typically used to efficiently read only a subset of the dataset.
933 collections : Any, optional
934 Collections to be searched, overriding ``self.collections``.
935 Can be any of the types supported by the ``collections`` argument
936 to butler construction.
937 kwds
938 Additional keyword arguments used to augment or construct a
939 `DataId`. See `DataId` parameters.
941 Returns
942 -------
943 obj : `DeferredDatasetHandle`
944 A handle which can be used to retrieve a dataset at a later time.
946 Raises
947 ------
948 LookupError
949 Raised if no matching dataset exists in the `Registry` (and
950 ``allowUnresolved is False``).
951 ValueError
952 Raised if a resolved `DatasetRef` was passed as an input, but it
953 differs from the one found in the registry.
954 TypeError
955 Raised if no collections were provided.
956 """
957 ref = self._findDatasetRef(datasetRefOrType, dataId, collections=collections, **kwds)
958 return DeferredDatasetHandle(butler=self, ref=ref, parameters=parameters)
960 def get(self, datasetRefOrType: Union[DatasetRef, DatasetType, str],
961 dataId: Optional[DataId] = None, *,
962 parameters: Optional[Dict[str, Any]] = None,
963 collections: Any = None,
964 **kwds: Any) -> Any:
965 """Retrieve a stored dataset.
967 Parameters
968 ----------
969 datasetRefOrType : `DatasetRef`, `DatasetType`, or `str`
970 When `DatasetRef` the `dataId` should be `None`.
971 Otherwise the `DatasetType` or name thereof.
972 dataId : `dict` or `DataCoordinate`
973 A `dict` of `Dimension` link name, value pairs that label the
974 `DatasetRef` within a Collection. When `None`, a `DatasetRef`
975 should be provided as the first argument.
976 parameters : `dict`
977 Additional StorageClass-defined options to control reading,
978 typically used to efficiently read only a subset of the dataset.
979 collections : Any, optional
980 Collections to be searched, overriding ``self.collections``.
981 Can be any of the types supported by the ``collections`` argument
982 to butler construction.
983 kwds
984 Additional keyword arguments used to augment or construct a
985 `DataCoordinate`. See `DataCoordinate.standardize`
986 parameters.
988 Returns
989 -------
990 obj : `object`
991 The dataset.
993 Raises
994 ------
995 ValueError
996 Raised if a resolved `DatasetRef` was passed as an input, but it
997 differs from the one found in the registry.
998 LookupError
999 Raised if no matching dataset exists in the `Registry`.
1000 TypeError
1001 Raised if no collections were provided.
1003 Notes
1004 -----
1005 When looking up datasets in a `~CollectionType.CALIBRATION` collection,
1006 this method requires that the given data ID include temporal dimensions
1007 beyond the dimensions of the dataset type itself, in order to find the
1008 dataset with the appropriate validity range. For example, a "bias"
1009 dataset with native dimensions ``{instrument, detector}`` could be
1010 fetched with a ``{instrument, detector, exposure}`` data ID, because
1011 ``exposure`` is a temporal dimension.
1012 """
1013 log.debug("Butler get: %s, dataId=%s, parameters=%s", datasetRefOrType, dataId, parameters)
1014 ref = self._findDatasetRef(datasetRefOrType, dataId, collections=collections, **kwds)
1015 return self.getDirect(ref, parameters=parameters)
1017 def getURIs(self, datasetRefOrType: Union[DatasetRef, DatasetType, str],
1018 dataId: Optional[DataId] = None, *,
1019 predict: bool = False,
1020 collections: Any = None,
1021 run: Optional[str] = None,
1022 **kwds: Any) -> Tuple[Optional[ButlerURI], Dict[str, ButlerURI]]:
1023 """Returns the URIs associated with the dataset.
1025 Parameters
1026 ----------
1027 datasetRefOrType : `DatasetRef`, `DatasetType`, or `str`
1028 When `DatasetRef` the `dataId` should be `None`.
1029 Otherwise the `DatasetType` or name thereof.
1030 dataId : `dict` or `DataCoordinate`
1031 A `dict` of `Dimension` link name, value pairs that label the
1032 `DatasetRef` within a Collection. When `None`, a `DatasetRef`
1033 should be provided as the first argument.
1034 predict : `bool`
1035 If `True`, allow URIs to be returned of datasets that have not
1036 been written.
1037 collections : Any, optional
1038 Collections to be searched, overriding ``self.collections``.
1039 Can be any of the types supported by the ``collections`` argument
1040 to butler construction.
1041 run : `str`, optional
1042 Run to use for predictions, overriding ``self.run``.
1043 kwds
1044 Additional keyword arguments used to augment or construct a
1045 `DataCoordinate`. See `DataCoordinate.standardize`
1046 parameters.
1048 Returns
1049 -------
1050 primary : `ButlerURI`
1051 The URI to the primary artifact associated with this dataset.
1052 If the dataset was disassembled within the datastore this
1053 may be `None`.
1054 components : `dict`
1055 URIs to any components associated with the dataset artifact.
1056 Can be empty if there are no components.
1057 """
1058 ref = self._findDatasetRef(datasetRefOrType, dataId, allowUnresolved=predict,
1059 collections=collections, **kwds)
1060 if ref.id is None: # only possible if predict is True
1061 if run is None:
1062 run = self.run
1063 if run is None:
1064 raise TypeError("Cannot predict location with run=None.")
1065 # Lie about ID, because we can't guess it, and only
1066 # Datastore.getURIs() will ever see it (and it doesn't use it).
1067 ref = ref.resolved(id=0, run=run)
1068 return self.datastore.getURIs(ref, predict)
1070 def getURI(self, datasetRefOrType: Union[DatasetRef, DatasetType, str],
1071 dataId: Optional[DataId] = None, *,
1072 predict: bool = False,
1073 collections: Any = None,
1074 run: Optional[str] = None,
1075 **kwds: Any) -> ButlerURI:
1076 """Return the URI to the Dataset.
1078 Parameters
1079 ----------
1080 datasetRefOrType : `DatasetRef`, `DatasetType`, or `str`
1081 When `DatasetRef` the `dataId` should be `None`.
1082 Otherwise the `DatasetType` or name thereof.
1083 dataId : `dict` or `DataCoordinate`
1084 A `dict` of `Dimension` link name, value pairs that label the
1085 `DatasetRef` within a Collection. When `None`, a `DatasetRef`
1086 should be provided as the first argument.
1087 predict : `bool`
1088 If `True`, allow URIs to be returned of datasets that have not
1089 been written.
1090 collections : Any, optional
1091 Collections to be searched, overriding ``self.collections``.
1092 Can be any of the types supported by the ``collections`` argument
1093 to butler construction.
1094 run : `str`, optional
1095 Run to use for predictions, overriding ``self.run``.
1096 kwds
1097 Additional keyword arguments used to augment or construct a
1098 `DataCoordinate`. See `DataCoordinate.standardize`
1099 parameters.
1101 Returns
1102 -------
1103 uri : `ButlerURI`
1104 URI pointing to the Dataset within the datastore. If the
1105 Dataset does not exist in the datastore, and if ``predict`` is
1106 `True`, the URI will be a prediction and will include a URI
1107 fragment "#predicted".
1108 If the datastore does not have entities that relate well
1109 to the concept of a URI the returned URI string will be
1110 descriptive. The returned URI is not guaranteed to be obtainable.
1112 Raises
1113 ------
1114 LookupError
1115 A URI has been requested for a dataset that does not exist and
1116 guessing is not allowed.
1117 ValueError
1118 Raised if a resolved `DatasetRef` was passed as an input, but it
1119 differs from the one found in the registry.
1120 TypeError
1121 Raised if no collections were provided.
1122 RuntimeError
1123 Raised if a URI is requested for a dataset that consists of
1124 multiple artifacts.
1125 """
1126 primary, components = self.getURIs(datasetRefOrType, dataId=dataId, predict=predict,
1127 collections=collections, run=run, **kwds)
1129 if primary is None or components:
1130 raise RuntimeError(f"Dataset ({datasetRefOrType}) includes distinct URIs for components. "
1131 "Use Butler.getURIs() instead.")
1132 return primary
1134 def retrieveArtifacts(self, refs: Iterable[DatasetRef],
1135 destination: Union[str, ButlerURI], transfer: str = "auto",
1136 preserve_path: bool = True,
1137 overwrite: bool = False) -> List[ButlerURI]:
1138 """Retrieve the artifacts associated with the supplied refs.
1140 Parameters
1141 ----------
1142 refs : iterable of `DatasetRef`
1143 The datasets for which artifacts are to be retrieved.
1144 A single ref can result in multiple artifacts. The refs must
1145 be resolved.
1146 destination : `ButlerURI` or `str`
1147 Location to write the artifacts.
1148 transfer : `str`, optional
1149 Method to use to transfer the artifacts. Must be one of the options
1150 supported by `ButlerURI.transfer_from()`. "move" is not allowed.
1151 preserve_path : `bool`, optional
1152 If `True` the full path of the artifact within the datastore
1153 is preserved. If `False` the final file component of the path
1154 is used.
1155 overwrite : `bool`, optional
1156 If `True` allow transfers to overwrite existing files at the
1157 destination.
1159 Returns
1160 -------
1161 targets : `list` of `ButlerURI`
1162 URIs of file artifacts in destination location. Order is not
1163 preserved.
1165 Notes
1166 -----
1167 For non-file datastores the artifacts written to the destination
1168 may not match the representation inside the datastore. For example
1169 a hierarchical data structure in a NoSQL database may well be stored
1170 as a JSON file.
1171 """
1172 return self.datastore.retrieveArtifacts(refs, ButlerURI(destination), transfer=transfer,
1173 preserve_path=preserve_path, overwrite=overwrite)
1175 def datasetExists(self, datasetRefOrType: Union[DatasetRef, DatasetType, str],
1176 dataId: Optional[DataId] = None, *,
1177 collections: Any = None,
1178 **kwds: Any) -> bool:
1179 """Return True if the Dataset is actually present in the Datastore.
1181 Parameters
1182 ----------
1183 datasetRefOrType : `DatasetRef`, `DatasetType`, or `str`
1184 When `DatasetRef` the `dataId` should be `None`.
1185 Otherwise the `DatasetType` or name thereof.
1186 dataId : `dict` or `DataCoordinate`
1187 A `dict` of `Dimension` link name, value pairs that label the
1188 `DatasetRef` within a Collection. When `None`, a `DatasetRef`
1189 should be provided as the first argument.
1190 collections : Any, optional
1191 Collections to be searched, overriding ``self.collections``.
1192 Can be any of the types supported by the ``collections`` argument
1193 to butler construction.
1194 kwds
1195 Additional keyword arguments used to augment or construct a
1196 `DataCoordinate`. See `DataCoordinate.standardize`
1197 parameters.
1199 Raises
1200 ------
1201 LookupError
1202 Raised if the dataset is not even present in the Registry.
1203 ValueError
1204 Raised if a resolved `DatasetRef` was passed as an input, but it
1205 differs from the one found in the registry.
1206 TypeError
1207 Raised if no collections were provided.
1208 """
1209 ref = self._findDatasetRef(datasetRefOrType, dataId, collections=collections, **kwds)
1210 return self.datastore.exists(ref)
1212 def removeRuns(self, names: Iterable[str], unstore: bool = True) -> None:
1213 """Remove one or more `~CollectionType.RUN` collections and the
1214 datasets within them.
1216 Parameters
1217 ----------
1218 names : `Iterable` [ `str` ]
1219 The names of the collections to remove.
1220 unstore : `bool`, optional
1221 If `True` (default), delete datasets from all datastores in which
1222 they are present, and attempt to rollback the registry deletions if
1223 datastore deletions fail (which may not always be possible). If
1224 `False`, datastore records for these datasets are still removed,
1225 but any artifacts (e.g. files) will not be.
1227 Raises
1228 ------
1229 TypeError
1230 Raised if one or more collections are not of type
1231 `~CollectionType.RUN`.
1232 """
1233 if not self.isWriteable():
1234 raise TypeError("Butler is read-only.")
1235 names = list(names)
1236 refs: List[DatasetRef] = []
1237 for name in names:
1238 collectionType = self.registry.getCollectionType(name)
1239 if collectionType is not CollectionType.RUN:
1240 raise TypeError(f"The collection type of '{name}' is {collectionType.name}, not RUN.")
1241 refs.extend(self.registry.queryDatasets(..., collections=name, findFirst=True))
1242 with self.registry.transaction():
1243 if unstore:
1244 for ref in refs:
1245 if self.datastore.exists(ref):
1246 self.datastore.trash(ref)
1247 else:
1248 self.datastore.forget(refs)
1249 for name in names:
1250 self.registry.removeCollection(name)
1251 if unstore:
1252 # Point of no return for removing artifacts
1253 self.datastore.emptyTrash()
1255 def pruneCollection(self, name: str, purge: bool = False, unstore: bool = False,
1256 unlink: Optional[List[str]] = None) -> None:
1257 """Remove a collection and possibly prune datasets within it.
1259 Parameters
1260 ----------
1261 name : `str`
1262 Name of the collection to remove. If this is a
1263 `~CollectionType.TAGGED` or `~CollectionType.CHAINED` collection,
1264 datasets within the collection are not modified unless ``unstore``
1265 is `True`. If this is a `~CollectionType.RUN` collection,
1266 ``purge`` and ``unstore`` must be `True`, and all datasets in it
1267 are fully removed from the data repository.
1268 purge : `bool`, optional
1269 If `True`, permit `~CollectionType.RUN` collections to be removed,
1270 fully removing datasets within them. Requires ``unstore=True`` as
1271 well as an added precaution against accidental deletion. Must be
1272 `False` (default) if the collection is not a ``RUN``.
1273 unstore: `bool`, optional
1274 If `True`, remove all datasets in the collection from all
1275 datastores in which they appear.
1276 unlink: `list` [`str`], optional
1277 Before removing the given `collection` unlink it from from these
1278 parent collections.
1280 Raises
1281 ------
1282 TypeError
1283 Raised if the butler is read-only or arguments are mutually
1284 inconsistent.
1285 """
1287 # See pruneDatasets comments for more information about the logic here;
1288 # the cases are almost the same, but here we can rely on Registry to
1289 # take care everything but Datastore deletion when we remove the
1290 # collection.
1291 if not self.isWriteable():
1292 raise TypeError("Butler is read-only.")
1293 collectionType = self.registry.getCollectionType(name)
1294 if purge and not unstore:
1295 raise PurgeWithoutUnstorePruneCollectionsError()
1296 if collectionType is CollectionType.RUN and not purge:
1297 raise RunWithoutPurgePruneCollectionsError(collectionType)
1298 if collectionType is not CollectionType.RUN and purge:
1299 raise PurgeUnsupportedPruneCollectionsError(collectionType)
1301 def remove(child: str, parent: str) -> None:
1302 """Remove a child collection from a parent collection."""
1303 # Remove child from parent.
1304 chain = list(self.registry.getCollectionChain(parent))
1305 try:
1306 chain.remove(name)
1307 except ValueError as e:
1308 raise RuntimeError(f"{name} is not a child of {parent}") from e
1309 self.registry.setCollectionChain(parent, chain)
1311 with self.registry.transaction():
1312 if (unlink):
1313 for parent in unlink:
1314 remove(name, parent)
1315 if unstore:
1316 for ref in self.registry.queryDatasets(..., collections=name, findFirst=True):
1317 if self.datastore.exists(ref):
1318 self.datastore.trash(ref)
1319 self.registry.removeCollection(name)
1320 if unstore:
1321 # Point of no return for removing artifacts
1322 self.datastore.emptyTrash()
1324 def pruneDatasets(self, refs: Iterable[DatasetRef], *,
1325 disassociate: bool = True,
1326 unstore: bool = False,
1327 tags: Iterable[str] = (),
1328 purge: bool = False,
1329 run: Optional[str] = None) -> None:
1330 """Remove one or more datasets from a collection and/or storage.
1332 Parameters
1333 ----------
1334 refs : `~collections.abc.Iterable` of `DatasetRef`
1335 Datasets to prune. These must be "resolved" references (not just
1336 a `DatasetType` and data ID).
1337 disassociate : `bool`, optional
1338 Disassociate pruned datasets from ``tags``, or from all collections
1339 if ``purge=True``.
1340 unstore : `bool`, optional
1341 If `True` (`False` is default) remove these datasets from all
1342 datastores known to this butler. Note that this will make it
1343 impossible to retrieve these datasets even via other collections.
1344 Datasets that are already not stored are ignored by this option.
1345 tags : `Iterable` [ `str` ], optional
1346 `~CollectionType.TAGGED` collections to disassociate the datasets
1347 from. Ignored if ``disassociate`` is `False` or ``purge`` is
1348 `True`.
1349 purge : `bool`, optional
1350 If `True` (`False` is default), completely remove the dataset from
1351 the `Registry`. To prevent accidental deletions, ``purge`` may
1352 only be `True` if all of the following conditions are met:
1354 - All given datasets are in the given run.
1355 - ``disassociate`` is `True`;
1356 - ``unstore`` is `True`.
1358 This mode may remove provenance information from datasets other
1359 than those provided, and should be used with extreme care.
1361 Raises
1362 ------
1363 TypeError
1364 Raised if the butler is read-only, if no collection was provided,
1365 or the conditions for ``purge=True`` were not met.
1366 """
1367 if not self.isWriteable():
1368 raise TypeError("Butler is read-only.")
1369 if purge:
1370 if not disassociate:
1371 raise TypeError("Cannot pass purge=True without disassociate=True.")
1372 if not unstore:
1373 raise TypeError("Cannot pass purge=True without unstore=True.")
1374 elif disassociate:
1375 tags = tuple(tags)
1376 if not tags:
1377 raise TypeError("No tags provided but disassociate=True.")
1378 for tag in tags:
1379 collectionType = self.registry.getCollectionType(tag)
1380 if collectionType is not CollectionType.TAGGED:
1381 raise TypeError(f"Cannot disassociate from collection '{tag}' "
1382 f"of non-TAGGED type {collectionType.name}.")
1383 # Transform possibly-single-pass iterable into something we can iterate
1384 # over multiple times.
1385 refs = list(refs)
1386 # Pruning a component of a DatasetRef makes no sense since registry
1387 # doesn't know about components and datastore might not store
1388 # components in a separate file
1389 for ref in refs:
1390 if ref.datasetType.component():
1391 raise ValueError(f"Can not prune a component of a dataset (ref={ref})")
1392 # We don't need an unreliable Datastore transaction for this, because
1393 # we've been extra careful to ensure that Datastore.trash only involves
1394 # mutating the Registry (it can _look_ at Datastore-specific things,
1395 # but shouldn't change them), and hence all operations here are
1396 # Registry operations.
1397 with self.registry.transaction():
1398 if unstore:
1399 for ref in refs:
1400 # There is a difference between a concrete composite
1401 # and virtual composite. In a virtual composite the
1402 # datastore is never given the top level DatasetRef. In
1403 # the concrete composite the datastore knows all the
1404 # refs and will clean up itself if asked to remove the
1405 # parent ref. We can not check configuration for this
1406 # since we can not trust that the configuration is the
1407 # same. We therefore have to ask if the ref exists or
1408 # not. This is consistent with the fact that we want
1409 # to ignore already-removed-from-datastore datasets
1410 # anyway.
1411 if self.datastore.exists(ref):
1412 self.datastore.trash(ref)
1413 if purge:
1414 self.registry.removeDatasets(refs)
1415 elif disassociate:
1416 assert tags, "Guaranteed by earlier logic in this function."
1417 for tag in tags:
1418 self.registry.disassociate(tag, refs)
1419 # We've exited the Registry transaction, and apparently committed.
1420 # (if there was an exception, everything rolled back, and it's as if
1421 # nothing happened - and we never get here).
1422 # Datastore artifacts are not yet gone, but they're clearly marked
1423 # as trash, so if we fail to delete now because of (e.g.) filesystem
1424 # problems we can try again later, and if manual administrative
1425 # intervention is required, it's pretty clear what that should entail:
1426 # deleting everything on disk and in private Datastore tables that is
1427 # in the dataset_location_trash table.
1428 if unstore:
1429 # Point of no return for removing artifacts
1430 self.datastore.emptyTrash()
1432 @transactional
1433 def ingest(self, *datasets: FileDataset, transfer: Optional[str] = "auto", run: Optional[str] = None,
1434 idGenerationMode: DatasetIdGenEnum = DatasetIdGenEnum.UNIQUE,
1435 ) -> None:
1436 """Store and register one or more datasets that already exist on disk.
1438 Parameters
1439 ----------
1440 datasets : `FileDataset`
1441 Each positional argument is a struct containing information about
1442 a file to be ingested, including its path (either absolute or
1443 relative to the datastore root, if applicable), a `DatasetRef`,
1444 and optionally a formatter class or its fully-qualified string
1445 name. If a formatter is not provided, the formatter that would be
1446 used for `put` is assumed. On successful return, all
1447 `FileDataset.ref` attributes will have their `DatasetRef.id`
1448 attribute populated and all `FileDataset.formatter` attributes will
1449 be set to the formatter class used. `FileDataset.path` attributes
1450 may be modified to put paths in whatever the datastore considers a
1451 standardized form.
1452 transfer : `str`, optional
1453 If not `None`, must be one of 'auto', 'move', 'copy', 'direct',
1454 'hardlink', 'relsymlink' or 'symlink', indicating how to transfer
1455 the file.
1456 run : `str`, optional
1457 The name of the run ingested datasets should be added to,
1458 overriding ``self.run``.
1459 idGenerationMode : `DatasetIdGenEnum`, optional
1460 Specifies option for generating dataset IDs. By default unique IDs
1461 are generated for each inserted dataset.
1463 Raises
1464 ------
1465 TypeError
1466 Raised if the butler is read-only or if no run was provided.
1467 NotImplementedError
1468 Raised if the `Datastore` does not support the given transfer mode.
1469 DatasetTypeNotSupportedError
1470 Raised if one or more files to be ingested have a dataset type that
1471 is not supported by the `Datastore`..
1472 FileNotFoundError
1473 Raised if one of the given files does not exist.
1474 FileExistsError
1475 Raised if transfer is not `None` but the (internal) location the
1476 file would be moved to is already occupied.
1478 Notes
1479 -----
1480 This operation is not fully exception safe: if a database operation
1481 fails, the given `FileDataset` instances may be only partially updated.
1483 It is atomic in terms of database operations (they will either all
1484 succeed or all fail) providing the database engine implements
1485 transactions correctly. It will attempt to be atomic in terms of
1486 filesystem operations as well, but this cannot be implemented
1487 rigorously for most datastores.
1488 """
1489 if not self.isWriteable():
1490 raise TypeError("Butler is read-only.")
1491 progress = Progress("lsst.daf.butler.Butler.ingest", level=logging.DEBUG)
1492 # Reorganize the inputs so they're grouped by DatasetType and then
1493 # data ID. We also include a list of DatasetRefs for each FileDataset
1494 # to hold the resolved DatasetRefs returned by the Registry, before
1495 # it's safe to swap them into FileDataset.refs.
1496 # Some type annotation aliases to make that clearer:
1497 GroupForType = Dict[DataCoordinate, Tuple[FileDataset, List[DatasetRef]]]
1498 GroupedData = MutableMapping[DatasetType, GroupForType]
1499 # The actual data structure:
1500 groupedData: GroupedData = defaultdict(dict)
1501 # And the nested loop that populates it:
1502 for dataset in progress.wrap(datasets, desc="Grouping by dataset type"):
1503 # This list intentionally shared across the inner loop, since it's
1504 # associated with `dataset`.
1505 resolvedRefs: List[DatasetRef] = []
1506 for ref in dataset.refs:
1507 if ref.dataId in groupedData[ref.datasetType]:
1508 raise ConflictingDefinitionError(f"Ingest conflict. Dataset {dataset.path} has same"
1509 " DataId as other ingest dataset"
1510 f" {groupedData[ref.datasetType][ref.dataId][0].path} "
1511 f" ({ref.dataId})")
1512 groupedData[ref.datasetType][ref.dataId] = (dataset, resolvedRefs)
1514 # Now we can bulk-insert into Registry for each DatasetType.
1515 allResolvedRefs: List[DatasetRef] = []
1516 for datasetType, groupForType in progress.iter_item_chunks(groupedData.items(),
1517 desc="Bulk-inserting datasets by type"):
1518 refs = self.registry.insertDatasets(
1519 datasetType,
1520 dataIds=groupForType.keys(),
1521 run=run,
1522 expand=self.datastore.needs_expanded_data_ids(transfer, datasetType),
1523 idGenerationMode=idGenerationMode,
1524 )
1525 # Append those resolved DatasetRefs to the new lists we set up for
1526 # them.
1527 for ref, (_, resolvedRefs) in zip(refs, groupForType.values()):
1528 resolvedRefs.append(ref)
1530 # Go back to the original FileDatasets to replace their refs with the
1531 # new resolved ones, and also build a big list of all refs.
1532 allResolvedRefs = []
1533 for groupForType in progress.iter_chunks(groupedData.values(),
1534 desc="Reassociating resolved dataset refs with files"):
1535 for dataset, resolvedRefs in groupForType.values():
1536 dataset.refs = resolvedRefs
1537 allResolvedRefs.extend(resolvedRefs)
1539 # Bulk-insert everything into Datastore.
1540 self.datastore.ingest(*datasets, transfer=transfer)
1542 @contextlib.contextmanager
1543 def export(self, *, directory: Optional[str] = None,
1544 filename: Optional[str] = None,
1545 format: Optional[str] = None,
1546 transfer: Optional[str] = None) -> Iterator[RepoExportContext]:
1547 """Export datasets from the repository represented by this `Butler`.
1549 This method is a context manager that returns a helper object
1550 (`RepoExportContext`) that is used to indicate what information from
1551 the repository should be exported.
1553 Parameters
1554 ----------
1555 directory : `str`, optional
1556 Directory dataset files should be written to if ``transfer`` is not
1557 `None`.
1558 filename : `str`, optional
1559 Name for the file that will include database information associated
1560 with the exported datasets. If this is not an absolute path and
1561 ``directory`` is not `None`, it will be written to ``directory``
1562 instead of the current working directory. Defaults to
1563 "export.{format}".
1564 format : `str`, optional
1565 File format for the database information file. If `None`, the
1566 extension of ``filename`` will be used.
1567 transfer : `str`, optional
1568 Transfer mode passed to `Datastore.export`.
1570 Raises
1571 ------
1572 TypeError
1573 Raised if the set of arguments passed is inconsistent.
1575 Examples
1576 --------
1577 Typically the `Registry.queryDataIds` and `Registry.queryDatasets`
1578 methods are used to provide the iterables over data IDs and/or datasets
1579 to be exported::
1581 with butler.export("exports.yaml") as export:
1582 # Export all flats, but none of the dimension element rows
1583 # (i.e. data ID information) associated with them.
1584 export.saveDatasets(butler.registry.queryDatasets("flat"),
1585 elements=())
1586 # Export all datasets that start with "deepCoadd_" and all of
1587 # their associated data ID information.
1588 export.saveDatasets(butler.registry.queryDatasets("deepCoadd_*"))
1589 """
1590 if directory is None and transfer is not None:
1591 raise TypeError("Cannot transfer without providing a directory.")
1592 if transfer == "move":
1593 raise TypeError("Transfer may not be 'move': export is read-only")
1594 if format is None:
1595 if filename is None:
1596 raise TypeError("At least one of 'filename' or 'format' must be provided.")
1597 else:
1598 _, format = os.path.splitext(filename)
1599 elif filename is None:
1600 filename = f"export.{format}"
1601 if directory is not None:
1602 filename = os.path.join(directory, filename)
1603 BackendClass = getClassOf(self._config["repo_transfer_formats"][format]["export"])
1604 with open(filename, 'w') as stream:
1605 backend = BackendClass(stream)
1606 try:
1607 helper = RepoExportContext(self.registry, self.datastore, backend=backend,
1608 directory=directory, transfer=transfer)
1609 yield helper
1610 except BaseException:
1611 raise
1612 else:
1613 helper._finish()
1615 def import_(self, *, directory: Optional[str] = None,
1616 filename: Union[str, TextIO, None] = None,
1617 format: Optional[str] = None,
1618 transfer: Optional[str] = None,
1619 skip_dimensions: Optional[Set] = None,
1620 idGenerationMode: DatasetIdGenEnum = DatasetIdGenEnum.UNIQUE,
1621 reuseIds: bool = False) -> None:
1622 """Import datasets into this repository that were exported from a
1623 different butler repository via `~lsst.daf.butler.Butler.export`.
1625 Parameters
1626 ----------
1627 directory : `str`, optional
1628 Directory containing dataset files to import from. If `None`,
1629 ``filename`` and all dataset file paths specified therein must
1630 be absolute.
1631 filename : `str` or `TextIO`, optional
1632 A stream or name of file that contains database information
1633 associated with the exported datasets, typically generated by
1634 `~lsst.daf.butler.Butler.export`. If this a string (name) and
1635 is not an absolute path, does not exist in the current working
1636 directory, and ``directory`` is not `None`, it is assumed to be in
1637 ``directory``. Defaults to "export.{format}".
1638 format : `str`, optional
1639 File format for ``filename``. If `None`, the extension of
1640 ``filename`` will be used.
1641 transfer : `str`, optional
1642 Transfer mode passed to `~lsst.daf.butler.Datastore.ingest`.
1643 skip_dimensions : `set`, optional
1644 Names of dimensions that should be skipped and not imported.
1645 idGenerationMode : `DatasetIdGenEnum`, optional
1646 Specifies option for generating dataset IDs when IDs are not
1647 provided or their type does not match backend type. By default
1648 unique IDs are generated for each inserted dataset.
1649 reuseIds : `bool`, optional
1650 If `True` then forces re-use of imported dataset IDs for integer
1651 IDs which are normally generated as auto-incremented; exception
1652 will be raised if imported IDs clash with existing ones. This
1653 option has no effect on the use of globally-unique IDs which are
1654 always re-used (or generated if integer IDs are being imported).
1656 Raises
1657 ------
1658 TypeError
1659 Raised if the set of arguments passed is inconsistent, or if the
1660 butler is read-only.
1661 """
1662 if not self.isWriteable():
1663 raise TypeError("Butler is read-only.")
1664 if format is None:
1665 if filename is None:
1666 raise TypeError("At least one of 'filename' or 'format' must be provided.")
1667 else:
1668 _, format = os.path.splitext(filename) # type: ignore
1669 elif filename is None:
1670 filename = f"export.{format}"
1671 if isinstance(filename, str) and directory is not None and not os.path.exists(filename):
1672 filename = os.path.join(directory, filename)
1673 BackendClass = getClassOf(self._config["repo_transfer_formats"][format]["import"])
1675 def doImport(importStream: TextIO) -> None:
1676 backend = BackendClass(importStream, self.registry)
1677 backend.register()
1678 with self.transaction():
1679 backend.load(self.datastore, directory=directory, transfer=transfer,
1680 skip_dimensions=skip_dimensions, idGenerationMode=idGenerationMode,
1681 reuseIds=reuseIds)
1683 if isinstance(filename, str):
1684 with open(filename, "r") as stream:
1685 doImport(stream)
1686 else:
1687 doImport(filename)
1689 def validateConfiguration(self, logFailures: bool = False,
1690 datasetTypeNames: Optional[Iterable[str]] = None,
1691 ignore: Iterable[str] = None) -> None:
1692 """Validate butler configuration.
1694 Checks that each `DatasetType` can be stored in the `Datastore`.
1696 Parameters
1697 ----------
1698 logFailures : `bool`, optional
1699 If `True`, output a log message for every validation error
1700 detected.
1701 datasetTypeNames : iterable of `str`, optional
1702 The `DatasetType` names that should be checked. This allows
1703 only a subset to be selected.
1704 ignore : iterable of `str`, optional
1705 Names of DatasetTypes to skip over. This can be used to skip
1706 known problems. If a named `DatasetType` corresponds to a
1707 composite, all components of that `DatasetType` will also be
1708 ignored.
1710 Raises
1711 ------
1712 ButlerValidationError
1713 Raised if there is some inconsistency with how this Butler
1714 is configured.
1715 """
1716 if datasetTypeNames:
1717 datasetTypes = [self.registry.getDatasetType(name) for name in datasetTypeNames]
1718 else:
1719 datasetTypes = list(self.registry.queryDatasetTypes())
1721 # filter out anything from the ignore list
1722 if ignore:
1723 ignore = set(ignore)
1724 datasetTypes = [e for e in datasetTypes
1725 if e.name not in ignore and e.nameAndComponent()[0] not in ignore]
1726 else:
1727 ignore = set()
1729 # Find all the registered instruments
1730 instruments = set(
1731 record.name for record in self.registry.queryDimensionRecords("instrument")
1732 )
1734 # For each datasetType that has an instrument dimension, create
1735 # a DatasetRef for each defined instrument
1736 datasetRefs = []
1738 for datasetType in datasetTypes:
1739 if "instrument" in datasetType.dimensions:
1740 for instrument in instruments:
1741 datasetRef = DatasetRef(datasetType, {"instrument": instrument}, # type: ignore
1742 conform=False)
1743 datasetRefs.append(datasetRef)
1745 entities: List[Union[DatasetType, DatasetRef]] = []
1746 entities.extend(datasetTypes)
1747 entities.extend(datasetRefs)
1749 datastoreErrorStr = None
1750 try:
1751 self.datastore.validateConfiguration(entities, logFailures=logFailures)
1752 except ValidationError as e:
1753 datastoreErrorStr = str(e)
1755 # Also check that the LookupKeys used by the datastores match
1756 # registry and storage class definitions
1757 keys = self.datastore.getLookupKeys()
1759 failedNames = set()
1760 failedDataId = set()
1761 for key in keys:
1762 if key.name is not None:
1763 if key.name in ignore:
1764 continue
1766 # skip if specific datasetType names were requested and this
1767 # name does not match
1768 if datasetTypeNames and key.name not in datasetTypeNames:
1769 continue
1771 # See if it is a StorageClass or a DatasetType
1772 if key.name in self.storageClasses:
1773 pass
1774 else:
1775 try:
1776 self.registry.getDatasetType(key.name)
1777 except KeyError:
1778 if logFailures:
1779 log.critical("Key '%s' does not correspond to a DatasetType or StorageClass", key)
1780 failedNames.add(key)
1781 else:
1782 # Dimensions are checked for consistency when the Butler
1783 # is created and rendezvoused with a universe.
1784 pass
1786 # Check that the instrument is a valid instrument
1787 # Currently only support instrument so check for that
1788 if key.dataId:
1789 dataIdKeys = set(key.dataId)
1790 if set(["instrument"]) != dataIdKeys:
1791 if logFailures:
1792 log.critical("Key '%s' has unsupported DataId override", key)
1793 failedDataId.add(key)
1794 elif key.dataId["instrument"] not in instruments:
1795 if logFailures:
1796 log.critical("Key '%s' has unknown instrument", key)
1797 failedDataId.add(key)
1799 messages = []
1801 if datastoreErrorStr:
1802 messages.append(datastoreErrorStr)
1804 for failed, msg in ((failedNames, "Keys without corresponding DatasetType or StorageClass entry: "),
1805 (failedDataId, "Keys with bad DataId entries: ")):
1806 if failed:
1807 msg += ", ".join(str(k) for k in failed)
1808 messages.append(msg)
1810 if messages:
1811 raise ValidationError(";\n".join(messages))
1813 @property
1814 def collections(self) -> CollectionSearch:
1815 """The collections to search by default, in order (`CollectionSearch`).
1817 This is an alias for ``self.registry.defaults.collections``. It cannot
1818 be set directly in isolation, but all defaults may be changed together
1819 by assigning a new `RegistryDefaults` instance to
1820 ``self.registry.defaults``.
1821 """
1822 return self.registry.defaults.collections
1824 @property
1825 def run(self) -> Optional[str]:
1826 """Name of the run this butler writes outputs to by default (`str` or
1827 `None`).
1829 This is an alias for ``self.registry.defaults.run``. It cannot be set
1830 directly in isolation, but all defaults may be changed together by
1831 assigning a new `RegistryDefaults` instance to
1832 ``self.registry.defaults``.
1833 """
1834 return self.registry.defaults.run
1836 registry: Registry
1837 """The object that manages dataset metadata and relationships (`Registry`).
1839 Most operations that don't involve reading or writing butler datasets are
1840 accessible only via `Registry` methods.
1841 """
1843 datastore: Datastore
1844 """The object that manages actual dataset storage (`Datastore`).
1846 Direct user access to the datastore should rarely be necessary; the primary
1847 exception is the case where a `Datastore` implementation provides extra
1848 functionality beyond what the base class defines.
1849 """
1851 storageClasses: StorageClassFactory
1852 """An object that maps known storage class names to objects that fully
1853 describe them (`StorageClassFactory`).
1854 """