Coverage for python/lsst/obs/base/ingest.py : 27%

Hot-keys on this page
r m x p toggle line displays
j k next/prev highlighted chunk
0 (zero) top of page
1 (one) first highlighted chunk
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/>.
23__all__ = ("RawIngestTask", "RawIngestConfig", "makeTransferChoiceField")
25import os.path
26from dataclasses import dataclass, InitVar
27from typing import List, Iterator, Iterable, Type, Optional, Any
28from collections import defaultdict
29from multiprocessing import Pool
31from astro_metadata_translator import ObservationInfo, merge_headers
32from lsst.afw.fits import readMetadata
33from lsst.daf.butler import (
34 Butler,
35 CollectionType,
36 DataCoordinate,
37 DatasetRef,
38 DatasetType,
39 DimensionRecord,
40 DimensionUniverse,
41 FileDataset,
42 Formatter,
43)
44from lsst.pex.config import Config, ChoiceField
45from lsst.pipe.base import Task
47from ._instrument import Instrument, makeExposureRecordFromObsInfo
48from ._fitsRawFormatterBase import FitsRawFormatterBase
51@dataclass
52class RawFileDatasetInfo:
53 """Structure that holds information about a single dataset within a
54 raw file.
55 """
57 dataId: DataCoordinate
58 """Data ID for this file (`lsst.daf.butler.DataCoordinate`).
59 """
61 obsInfo: ObservationInfo
62 """Standardized observation metadata extracted directly from the file
63 headers (`astro_metadata_translator.ObservationInfo`).
64 """
67@dataclass
68class RawFileData:
69 """Structure that holds information about a single raw file, used during
70 ingest.
71 """
73 datasets: List[RawFileDatasetInfo]
74 """The information describing each dataset within this raw file.
75 (`list` of `RawFileDatasetInfo`)
76 """
78 filename: str
79 """Name of the file this information was extracted from (`str`).
81 This is the path prior to ingest, not the path after ingest.
82 """
84 FormatterClass: Type[FitsRawFormatterBase]
85 """Formatter class that should be used to ingest this file (`type`; as
86 subclass of `FitsRawFormatterBase`).
87 """
89 instrumentClass: Optional[Type[Instrument]]
90 """The `Instrument` class associated with this file. Can be `None`
91 if ``datasets`` is an empty list."""
94@dataclass
95class RawExposureData:
96 """Structure that holds information about a complete raw exposure, used
97 during ingest.
98 """
100 dataId: DataCoordinate
101 """Data ID for this exposure (`lsst.daf.butler.DataCoordinate`).
102 """
104 files: List[RawFileData]
105 """List of structures containing file-level information.
106 """
108 universe: InitVar[DimensionUniverse]
109 """Set of all known dimensions.
110 """
112 record: Optional[DimensionRecord] = None
113 """The exposure `DimensionRecord` that must be inserted into the
114 `~lsst.daf.butler.Registry` prior to file-level ingest (`DimensionRecord`).
115 """
117 def __post_init__(self, universe: DimensionUniverse):
118 # We don't care which file or dataset we read metadata from, because
119 # we're assuming they'll all be the same; just use the first ones.
120 self.record = makeExposureRecordFromObsInfo(self.files[0].datasets[0].obsInfo, universe)
123def makeTransferChoiceField(doc="How to transfer files (None for no transfer).", default="auto"):
124 """Create a Config field with options for how to transfer files between
125 data repositories.
127 The allowed options for the field are exactly those supported by
128 `lsst.daf.butler.Datastore.ingest`.
130 Parameters
131 ----------
132 doc : `str`
133 Documentation for the configuration field.
135 Returns
136 -------
137 field : `lsst.pex.config.ChoiceField`
138 Configuration field.
139 """
140 return ChoiceField(
141 doc=doc,
142 dtype=str,
143 allowed={"move": "move",
144 "copy": "copy",
145 "auto": "choice will depend on datastore",
146 "link": "hard link falling back to symbolic link",
147 "hardlink": "hard link",
148 "symlink": "symbolic (soft) link",
149 "relsymlink": "relative symbolic link",
150 },
151 optional=True,
152 default=default
153 )
156class RawIngestConfig(Config):
157 transfer = makeTransferChoiceField()
160class RawIngestTask(Task):
161 """Driver Task for ingesting raw data into Gen3 Butler repositories.
163 Parameters
164 ----------
165 config : `RawIngestConfig`
166 Configuration for the task.
167 butler : `~lsst.daf.butler.Butler`
168 Writeable butler instance, with ``butler.run`` set to the appropriate
169 `~lsst.daf.butler.CollectionType.RUN` collection for these raw
170 datasets.
171 **kwargs
172 Additional keyword arguments are forwarded to the `lsst.pipe.base.Task`
173 constructor.
175 Notes
176 -----
177 Each instance of `RawIngestTask` writes to the same Butler. Each
178 invocation of `RawIngestTask.run` ingests a list of files.
179 """
181 ConfigClass = RawIngestConfig
183 _DefaultName = "ingest"
185 def getDatasetType(self):
186 """Return the DatasetType of the datasets ingested by this Task.
187 """
188 return DatasetType("raw", ("instrument", "detector", "exposure"), "Exposure",
189 universe=self.butler.registry.dimensions)
191 def __init__(self, config: Optional[RawIngestConfig] = None, *, butler: Butler, **kwargs: Any):
192 config.validate() # Not a CmdlineTask nor PipelineTask, so have to validate the config here.
193 super().__init__(config, **kwargs)
194 self.butler = butler
195 self.universe = self.butler.registry.dimensions
196 self.datasetType = self.getDatasetType()
198 # Import all the instrument classes so that we ensure that we
199 # have all the relevant metadata translators loaded.
200 Instrument.importAll(self.butler.registry)
202 def _reduce_kwargs(self):
203 # Add extra parameters to pickle
204 return dict(**super()._reduce_kwargs(), butler=self.butler)
206 def extractMetadata(self, filename: str) -> RawFileData:
207 """Extract and process metadata from a single raw file.
209 Parameters
210 ----------
211 filename : `str`
212 Path to the file.
214 Returns
215 -------
216 data : `RawFileData`
217 A structure containing the metadata extracted from the file,
218 as well as the original filename. All fields will be populated,
219 but the `RawFileData.dataId` attribute will be a minimal
220 (unexpanded) `DataCoordinate` instance.
222 Notes
223 -----
224 Assumes that there is a single dataset associated with the given
225 file. Instruments using a single file to store multiple datasets
226 must implement their own version of this method.
227 """
229 # We do not want to stop ingest if we are given a bad file.
230 # Instead return a RawFileData with no datasets and allow
231 # the caller to report the failure.
233 try:
234 # Manually merge the primary and "first data" headers here because
235 # we do not know in general if an input file has set INHERIT=T.
236 phdu = readMetadata(filename, 0)
237 header = merge_headers([phdu, readMetadata(filename)], mode="overwrite")
238 datasets = [self._calculate_dataset_info(header, filename)]
239 except Exception as e:
240 self.log.debug("Problem extracting metadata from %s: %s", filename, e)
241 # Indicate to the caller that we failed to read
242 datasets = []
243 FormatterClass = Formatter
244 instrument = None
245 else:
246 self.log.debug("Extracted metadata from file %s", filename)
247 # The data model currently assumes that whilst multiple datasets
248 # can be associated with a single file, they must all share the
249 # same formatter.
250 try:
251 instrument = Instrument.fromName(datasets[0].dataId["instrument"], self.butler.registry)
252 except LookupError:
253 self.log.warning("Instrument %s for file %s not known to registry",
254 datasets[0].dataId["instrument"], filename)
255 datasets = []
256 FormatterClass = Formatter
257 instrument = None
258 else:
259 FormatterClass = instrument.getRawFormatter(datasets[0].dataId)
261 return RawFileData(datasets=datasets, filename=filename,
262 FormatterClass=FormatterClass,
263 instrumentClass=instrument)
265 def _calculate_dataset_info(self, header, filename):
266 """Calculate a RawFileDatasetInfo from the supplied information.
268 Parameters
269 ----------
270 header : `Mapping`
271 Header from the dataset.
272 filename : `str`
273 Filename to use for error messages.
275 Returns
276 -------
277 dataset : `RawFileDatasetInfo`
278 The dataId, and observation information associated with this
279 dataset.
280 """
281 # To ensure we aren't slowed down for no reason, explicitly
282 # list here the properties we need for the schema
283 # Use a dict with values a boolean where True indicates
284 # that it is required that we calculate this property.
285 ingest_subset = {
286 "altaz_begin": False,
287 "boresight_rotation_coord": False,
288 "boresight_rotation_angle": False,
289 "dark_time": False,
290 "datetime_begin": True,
291 "datetime_end": True,
292 "detector_num": True,
293 "exposure_group": False,
294 "exposure_id": True,
295 "exposure_time": True,
296 "instrument": True,
297 "tracking_radec": False,
298 "object": False,
299 "observation_counter": False,
300 "observation_id": True,
301 "observation_reason": False,
302 "observation_type": True,
303 "observing_day": False,
304 "physical_filter": True,
305 "science_program": False,
306 "visit_id": False,
307 }
309 obsInfo = ObservationInfo(header, pedantic=False, filename=filename,
310 required={k for k in ingest_subset if ingest_subset[k]},
311 subset=set(ingest_subset))
313 dataId = DataCoordinate.standardize(instrument=obsInfo.instrument,
314 exposure=obsInfo.exposure_id,
315 detector=obsInfo.detector_num,
316 universe=self.universe)
317 return RawFileDatasetInfo(obsInfo=obsInfo, dataId=dataId)
319 def groupByExposure(self, files: Iterable[RawFileData]) -> List[RawExposureData]:
320 """Group an iterable of `RawFileData` by exposure.
322 Parameters
323 ----------
324 files : iterable of `RawFileData`
325 File-level information to group.
327 Returns
328 -------
329 exposures : `list` of `RawExposureData`
330 A list of structures that group the file-level information by
331 exposure. All fields will be populated. The
332 `RawExposureData.dataId` attributes will be minimal (unexpanded)
333 `DataCoordinate` instances.
334 """
335 exposureDimensions = self.universe["exposure"].graph
336 byExposure = defaultdict(list)
337 for f in files:
338 # Assume that the first dataset is representative for the file
339 byExposure[f.datasets[0].dataId.subset(exposureDimensions)].append(f)
341 return [RawExposureData(dataId=dataId, files=exposureFiles, universe=self.universe)
342 for dataId, exposureFiles in byExposure.items()]
344 def expandDataIds(self, data: RawExposureData) -> RawExposureData:
345 """Expand the data IDs associated with a raw exposure to include
346 additional metadata records.
348 Parameters
349 ----------
350 exposure : `RawExposureData`
351 A structure containing information about the exposure to be
352 ingested. Must have `RawExposureData.records` populated. Should
353 be considered consumed upon return.
355 Returns
356 -------
357 exposure : `RawExposureData`
358 An updated version of the input structure, with
359 `RawExposureData.dataId` and nested `RawFileData.dataId` attributes
360 updated to data IDs for which `DataCoordinate.hasRecords` returns
361 `True`.
362 """
363 # We start by expanded the exposure-level data ID; we won't use that
364 # directly in file ingest, but this lets us do some database lookups
365 # once per exposure instead of once per file later.
366 data.dataId = self.butler.registry.expandDataId(
367 data.dataId,
368 # We pass in the records we'll be inserting shortly so they aren't
369 # looked up from the database. We do expect instrument and filter
370 # records to be retrieved from the database here (though the
371 # Registry may cache them so there isn't a lookup every time).
372 records={
373 self.butler.registry.dimensions["exposure"]: data.record,
374 }
375 )
376 # Now we expand the per-file (exposure+detector) data IDs. This time
377 # we pass in the records we just retrieved from the exposure data ID
378 # expansion.
379 for file in data.files:
380 for dataset in file.datasets:
381 dataset.dataId = self.butler.registry.expandDataId(
382 dataset.dataId,
383 records=dict(data.dataId.records)
384 )
385 return data
387 def prep(self, files, *, pool: Optional[Pool] = None, processes: int = 1) -> Iterator[RawExposureData]:
388 """Perform all ingest preprocessing steps that do not involve actually
389 modifying the database.
391 Parameters
392 ----------
393 files : iterable over `str` or path-like objects
394 Paths to the files to be ingested. Will be made absolute
395 if they are not already.
396 pool : `multiprocessing.Pool`, optional
397 If not `None`, a process pool with which to parallelize some
398 operations.
399 processes : `int`, optional
400 The number of processes to use. Ignored if ``pool`` is not `None`.
402 Yields
403 ------
404 exposure : `RawExposureData`
405 Data structures containing dimension records, filenames, and data
406 IDs to be ingested (one structure for each exposure).
407 bad_files : `list` of `str`
408 List of all the files that could not have metadata extracted.
409 """
410 if pool is None and processes > 1:
411 pool = Pool(processes)
412 mapFunc = map if pool is None else pool.imap_unordered
414 # Extract metadata and build per-detector regions.
415 # This could run in a subprocess so collect all output
416 # before looking at failures.
417 fileData: Iterator[RawFileData] = mapFunc(self.extractMetadata, files)
419 # Filter out all the failed reads and store them for later
420 # reporting
421 good_files = []
422 bad_files = []
423 for fileDatum in fileData:
424 if not fileDatum.datasets:
425 bad_files.append(fileDatum.filename)
426 else:
427 good_files.append(fileDatum)
428 fileData = good_files
430 self.log.info("Successfully extracted metadata from %d file%s with %d failure%s",
431 len(fileData), "" if len(fileData) == 1 else "s",
432 len(bad_files), "" if len(bad_files) == 1 else "s")
434 # Use that metadata to group files (and extracted metadata) by
435 # exposure. Never parallelized because it's intrinsically a gather
436 # step.
437 exposureData: List[RawExposureData] = self.groupByExposure(fileData)
439 # The next operation operates on RawExposureData instances (one at
440 # a time) in-place and then returns the modified instance. We call it
441 # as a pass-through instead of relying on the arguments we pass in to
442 # have been modified because in the parallel case those arguments are
443 # going to be pickled and unpickled, and I'm not certain
444 # multiprocessing is careful enough with that for output arguments to
445 # work.
447 # Expand the data IDs to include all dimension metadata; we need this
448 # because we may need to generate path templates that rely on that
449 # metadata.
450 # This is the first step that involves actual database calls (but just
451 # SELECTs), so if there's going to be a problem with connections vs.
452 # multiple processes, or lock contention (in SQLite) slowing things
453 # down, it'll happen here.
454 return mapFunc(self.expandDataIds, exposureData), bad_files
456 def ingestExposureDatasets(self, exposure: RawExposureData, *, run: Optional[str] = None
457 ) -> List[DatasetRef]:
458 """Ingest all raw files in one exposure.
460 Parameters
461 ----------
462 exposure : `RawExposureData`
463 A structure containing information about the exposure to be
464 ingested. Must have `RawExposureData.records` populated and all
465 data ID attributes expanded.
466 run : `str`, optional
467 Name of a RUN-type collection to write to, overriding
468 ``self.butler.run``.
470 Returns
471 -------
472 refs : `list` of `lsst.daf.butler.DatasetRef`
473 Dataset references for ingested raws.
474 """
475 datasets = [FileDataset(path=os.path.abspath(file.filename),
476 refs=[DatasetRef(self.datasetType, d.dataId) for d in file.datasets],
477 formatter=file.FormatterClass)
478 for file in exposure.files]
479 self.butler.ingest(*datasets, transfer=self.config.transfer, run=run)
480 return [ref for dataset in datasets for ref in dataset.refs]
482 def run(self, files, *, pool: Optional[Pool] = None, processes: int = 1, run: Optional[str] = None):
483 """Ingest files into a Butler data repository.
485 This creates any new exposure or visit Dimension entries needed to
486 identify the ingested files, creates new Dataset entries in the
487 Registry and finally ingests the files themselves into the Datastore.
488 Any needed instrument, detector, and physical_filter Dimension entries
489 must exist in the Registry before `run` is called.
491 Parameters
492 ----------
493 files : iterable over `str` or path-like objects
494 Paths to the files to be ingested. Will be made absolute
495 if they are not already.
496 pool : `multiprocessing.Pool`, optional
497 If not `None`, a process pool with which to parallelize some
498 operations.
499 processes : `int`, optional
500 The number of processes to use. Ignored if ``pool`` is not `None`.
501 run : `str`, optional
502 Name of a RUN-type collection to write to, overriding
503 the default derived from the instrument name.
505 Returns
506 -------
507 refs : `list` of `lsst.daf.butler.DatasetRef`
508 Dataset references for ingested raws.
510 Notes
511 -----
512 This method inserts all datasets for an exposure within a transaction,
513 guaranteeing that partial exposures are never ingested. The exposure
514 dimension record is inserted with `Registry.syncDimensionData` first
515 (in its own transaction), which inserts only if a record with the same
516 primary key does not already exist. This allows different files within
517 the same exposure to be incremented in different runs.
518 """
519 exposureData, bad_files = self.prep(files, pool=pool, processes=processes)
520 # Up to this point, we haven't modified the data repository at all.
521 # Now we finally do that, with one transaction per exposure. This is
522 # not parallelized at present because the performance of this step is
523 # limited by the database server. That may or may not change in the
524 # future once we increase our usage of bulk inserts and reduce our
525 # usage of savepoints; we've tried to get everything but the database
526 # operations done in advance to reduce the time spent inside
527 # transactions.
528 self.butler.registry.registerDatasetType(self.datasetType)
529 refs = []
530 runs = set()
531 n_exposures = 0
532 n_exposures_failed = 0
533 n_ingests_failed = 0
534 for exposure in exposureData:
536 self.log.debug("Attempting to ingest %d file%s from exposure %s:%s",
537 len(exposure.files), "" if len(exposure.files) == 1 else "s",
538 exposure.record.instrument, exposure.record.obs_id)
540 try:
541 self.butler.registry.syncDimensionData("exposure", exposure.record)
542 except Exception as e:
543 n_exposures_failed += 1
544 self.log.warning("Exposure %s:%s could not be registered: %s",
545 exposure.record.instrument, exposure.record.obs_id, e)
546 continue
548 # Override default run if nothing specified explicitly
549 if run is None:
550 instrumentClass = exposure.files[0].instrumentClass
551 this_run = instrumentClass.makeDefaultRawIngestRunName()
552 else:
553 this_run = run
554 if this_run not in runs:
555 self.butler.registry.registerCollection(this_run, type=CollectionType.RUN)
556 runs.add(this_run)
557 try:
558 with self.butler.transaction():
559 refs.extend(self.ingestExposureDatasets(exposure, run=this_run))
560 except Exception as e:
561 n_ingests_failed += 1
562 self.log.warning("Failed to ingest the following for reason: %s", e)
563 for f in exposure.files:
564 self.log.warning("- %s", f.filename)
565 continue
567 # Success for this exposure
568 n_exposures += 1
569 self.log.info("Exposure %s:%s ingested successfully",
570 exposure.record.instrument, exposure.record.obs_id)
572 had_failure = False
574 if bad_files:
575 had_failure = True
576 self.log.warning("Could not extract observation metadata from the following:")
577 for f in bad_files:
578 self.log.warning("- %s", f)
580 self.log.info("Successfully processed data from %d exposure%s with %d failure%s from exposure"
581 " registration and %d failure%s from file ingest.",
582 n_exposures, "" if n_exposures == 1 else "s",
583 n_exposures_failed, "" if n_exposures_failed == 1 else "s",
584 n_ingests_failed, "" if n_ingests_failed == 1 else "s")
585 if n_exposures_failed > 0 or n_ingests_failed > 0:
586 had_failure = True
587 self.log.info("Ingested %d distinct Butler dataset%s",
588 len(refs), "" if len(refs) == 1 else "s")
590 if had_failure:
591 raise RuntimeError("Some failures encountered during ingestion")
593 return refs