22 from __future__
import annotations
24 __all__ = (
"Instrument",
"makeExposureRecordFromObsInfo",
"addUnboundedCalibrationLabel",
"loadCamera")
27 from abc
import ABCMeta, abstractmethod
28 from typing
import Any, Tuple, TYPE_CHECKING
31 from lsst.afw.cameraGeom
import Camera
32 from lsst.daf.butler
import (
44 from .gen2to3
import TranslatorFactory
45 from lsst.daf.butler
import Registry
49 StandardCuratedCalibrationDatasetTypes = {
50 "defects": {
"dimensions": (
"instrument",
"detector",
"calibration_label"),
51 "storageClass":
"Defects"},
52 "qe_curve": {
"dimensions": (
"instrument",
"detector",
"calibration_label"),
53 "storageClass":
"QECurve"},
54 "crosstalk": {
"dimensions": (
"instrument",
"detector",
"calibration_label"),
55 "storageClass":
"CrosstalkCalib"},
60 """Base class for instrument-specific logic for the Gen3 Butler.
62 Concrete instrument subclasses should be directly constructable with no
67 """Paths to config files to read for specific Tasks.
69 The paths in this list should contain files of the form `task.py`, for
70 each of the Tasks that requires special configuration.
74 """Instrument specific name to use when locating a policy or configuration
75 file in the file system."""
78 """Name of the package containing the text curated calibration files.
79 Usually a obs _data package. If `None` no curated calibration files
80 will be read. (`str`)"""
82 standardCuratedDatasetTypes = tuple(StandardCuratedCalibrationDatasetTypes)
83 """The dataset types expected to be obtained from the obsDataPackage.
84 These dataset types are all required to have standard definitions and
85 must be known to the base class. Clearing this list will prevent
86 any of these calibrations from being stored. If a dataset type is not
87 known to a specific instrument it can still be included in this list
88 since the data package is the source of truth.
94 """`~lsst.obs.base.FilterDefinitionCollection`, defining the filters
107 """Return the short (dimension) name for this instrument.
109 This is not (in general) the same as the class name - it's what is used
110 as the value of the "instrument" field in data IDs, and is usually an
111 abbreviation of the full name.
113 raise NotImplementedError()
117 """Retrieve the cameraGeom representation of this instrument.
119 This is a temporary API that should go away once ``obs_`` packages have
120 a standardized approach to writing versioned cameras to a Gen3 repo.
122 raise NotImplementedError()
126 """Insert instrument, physical_filter, and detector entries into a
129 raise NotImplementedError()
133 """The root of the obs package that provides specializations for
134 this instrument (`str`).
145 def fromName(name: str, registry: Registry) -> Instrument:
146 """Given an instrument name and a butler, retrieve a corresponding
147 instantiated instrument object.
152 Name of the instrument (must match the return value of `getName`).
153 registry : `lsst.daf.butler.Registry`
154 Butler registry to query to find the information.
158 instrument : `Instrument`
159 An instance of the relevant `Instrument`.
163 The instrument must be registered in the corresponding butler.
168 Raised if the instrument is not known to the supplied registry.
170 Raised if the class could not be imported. This could mean
171 that the relevant obs package has not been setup.
173 Raised if the class name retrieved is not a string.
175 records = list(registry.queryDimensionRecords(
"instrument", instrument=name))
177 raise LookupError(f
"No registered instrument with name '{name}'.")
178 cls = records[0].class_name
179 if not isinstance(cls, str):
180 raise TypeError(f
"Unexpected class name retrieved from {name} instrument dimension (got {cls})")
181 instrument = doImport(cls)
186 """Import all the instruments known to this registry.
188 This will ensure that all metadata translators have been registered.
192 registry : `lsst.daf.butler.Registry`
193 Butler registry to query to find the information.
197 It is allowed for a particular instrument class to fail on import.
198 This might simply indicate that a particular obs package has
201 records = list(registry.queryDimensionRecords(
"instrument"))
202 for record
in records:
203 cls = record.class_name
209 def _registerFilters(self, registry):
210 """Register the physical and abstract filter Dimension relationships.
211 This should be called in the ``register`` implementation.
215 registry : `lsst.daf.butler.core.Registry`
216 The registry to add dimensions to.
220 if filter.abstract_filter
is None:
221 abstract_filter = filter.physical_filter
223 abstract_filter = filter.abstract_filter
225 registry.insertDimensionData(
"physical_filter",
227 "name": filter.physical_filter,
228 "abstract_filter": abstract_filter
233 """Return the Formatter class that should be used to read a particular
238 dataId : `DataCoordinate`
239 Dimension-based ID for the raw file or files being ingested.
243 formatter : `Formatter` class
244 Class to be used that reads the file into an
245 `lsst.afw.image.Exposure` instance.
247 raise NotImplementedError()
250 """Write human-curated calibration Datasets to the given Butler with
251 the appropriate validity ranges.
255 butler : `lsst.daf.butler.Butler`
256 Butler to use to store these calibrations.
258 Run to use for this collection of calibrations. If `None` the
259 collection name is worked out automatically from the instrument
260 name and other metadata.
264 Expected to be called from subclasses. The base method calls
265 ``writeCameraGeom`` and ``writeStandardTextCuratedCalibrations``.
272 butler.registry.registerCollection(run, type=CollectionType.RUN)
278 """Write additional curated calibrations that might be instrument
279 specific and are not part of the standard set.
281 Default implementation does nothing.
285 butler : `lsst.daf.butler.Butler`
286 Butler to use to store these calibrations.
287 run : `str`, optional
288 Name of the run to use to override the default run associated
294 """Apply instrument-specific overrides for a task config.
299 Name of the object being configured; typically the _DefaultName
301 config : `lsst.pex.config.Config`
302 Config instance to which overrides should be applied.
304 for root
in self.configPaths:
305 path = os.path.join(root, f
"{name}.py")
306 if os.path.exists(path):
310 """Write the default camera geometry to the butler repository
311 with an infinite validity range.
315 butler : `lsst.daf.butler.Butler`
316 Butler to receive these calibration datasets.
317 run : `str`, optional
318 Name of the run to use to override the default run associated
322 datasetType = DatasetType(
"camera", (
"instrument",
"calibration_label"),
"Camera",
323 universe=butler.registry.dimensions)
324 butler.registry.registerDatasetType(datasetType)
327 butler.put(camera, datasetType, unboundedDataId, run=run)
330 """Write the set of standardized curated text calibrations to
335 butler : `lsst.daf.butler.Butler`
336 Butler to receive these calibration datasets.
337 run : `str`, optional
338 Name of the run to use to override the default run associated
344 if datasetTypeName
not in StandardCuratedCalibrationDatasetTypes:
345 raise ValueError(f
"DatasetType {datasetTypeName} not in understood list"
346 f
" [{'.'.join(StandardCuratedCalibrationDatasetTypes)}]")
347 definition = StandardCuratedCalibrationDatasetTypes[datasetTypeName]
348 datasetType = DatasetType(datasetTypeName,
349 universe=butler.registry.dimensions,
353 def _writeSpecificCuratedCalibrationDatasets(self, butler, datasetType, run=None):
354 """Write standardized curated calibration datasets for this specific
355 dataset type from an obs data package.
359 butler : `lsst.daf.butler.Butler`
360 Gen3 butler in which to put the calibrations.
361 datasetType : `lsst.daf.butler.DatasetType`
362 Dataset type to be put.
363 run : `str`, optional
364 Name of the run to use to override the default run associated
369 This method scans the location defined in the ``obsDataPackageDir``
370 class attribute for curated calibrations corresponding to the
371 supplied dataset type. The directory name in the data package must
372 match the name of the dataset type. They are assumed to use the
373 standard layout and can be read by
374 `~lsst.pipe.tasks.read_curated_calibs.read_all` and provide standard
384 if not os.path.exists(calibPath):
388 butler.registry.registerDatasetType(datasetType)
392 from lsst.pipe.tasks.read_curated_calibs
import read_all
395 calibsDict = read_all(calibPath, camera)[0]
396 endOfTime = TIMESPAN_MAX
397 dimensionRecords = []
399 for det
in calibsDict:
400 times = sorted([k
for k
in calibsDict[det]])
401 calibs = [calibsDict[det][time]
for time
in times]
402 times = [astropy.time.Time(t, format=
"datetime", scale=
"utc")
for t
in times]
404 for calib, beginTime, endTime
in zip(calibs, times[:-1], times[1:]):
405 md = calib.getMetadata()
406 calibrationLabel = f
"{datasetType.name}/{md['CALIBDATE']}/{md['DETECTOR']}"
407 dataId = DataCoordinate.standardize(
408 universe=butler.registry.dimensions,
410 calibration_label=calibrationLabel,
411 detector=md[
"DETECTOR"],
413 datasetRecords.append((calib, dataId))
414 dimensionRecords.append({
416 "name": calibrationLabel,
417 "datetime_begin": beginTime,
418 "datetime_end": endTime,
422 with butler.transaction():
423 butler.registry.insertDimensionData(
"calibration_label", *dimensionRecords)
426 for calib, dataId
in datasetRecords:
427 butler.put(calib, datasetType, dataId, run=run)
431 """Return a factory for creating Gen2->Gen3 data ID translators,
432 specialized for this instrument.
434 Derived class implementations should generally call
435 `TranslatorFactory.addGenericInstrumentRules` with appropriate
436 arguments, but are not required to (and may not be able to if their
437 Gen2 raw data IDs are sufficiently different from the HSC/DECam/CFHT
442 factory : `TranslatorFactory`.
443 Factory for `Translator` objects.
445 raise NotImplementedError(
"Must be implemented by derived classes.")
449 """Make the default instrument-specific run collection string for raw
455 Run collection name to be used as the default for ingestion of
462 """Get the instrument-specific collection string to use as derived
463 from the supplied label.
468 String to be combined with the instrument name to form a
474 Collection name to use that includes the instrument name.
476 return f
"{cls.getName()}/{label}"
480 """Construct an exposure DimensionRecord from
481 `astro_metadata_translator.ObservationInfo`.
485 obsInfo : `astro_metadata_translator.ObservationInfo`
486 A `~astro_metadata_translator.ObservationInfo` object corresponding to
488 universe : `DimensionUniverse`
489 Set of all known dimensions.
493 record : `DimensionRecord`
494 A record containing exposure metadata, suitable for insertion into
497 dimension = universe[
"exposure"]
498 return dimension.RecordClass.fromDict({
499 "instrument": obsInfo.instrument,
500 "id": obsInfo.exposure_id,
501 "name": obsInfo.observation_id,
502 "group_name": obsInfo.exposure_group,
503 "group_id": obsInfo.visit_id,
504 "datetime_begin": obsInfo.datetime_begin,
505 "datetime_end": obsInfo.datetime_end,
506 "exposure_time": obsInfo.exposure_time.to_value(
"s"),
507 "dark_time": obsInfo.dark_time.to_value(
"s"),
508 "observation_type": obsInfo.observation_type,
509 "physical_filter": obsInfo.physical_filter,
514 """Add a special 'unbounded' calibration_label dimension entry for the
515 given camera that is valid for any exposure.
517 If such an entry already exists, this function just returns a `DataId`
518 for the existing entry.
522 registry : `Registry`
523 Registry object in which to insert the dimension entry.
524 instrumentName : `str`
525 Name of the instrument this calibration label is associated with.
530 New or existing data ID for the unbounded calibration.
532 d = dict(instrument=instrumentName, calibration_label=
"unbounded")
534 return registry.expandDataId(d)
538 entry[
"datetime_begin"] = TIMESPAN_MIN
539 entry[
"datetime_end"] = TIMESPAN_MAX
540 registry.insertDimensionData(
"calibration_label", entry)
541 return registry.expandDataId(d)
544 def loadCamera(butler: Butler, dataId: DataId, *, collections: Any =
None) -> Tuple[Camera, bool]:
545 """Attempt to load versioned camera geometry from a butler, but fall back
546 to obtaining a nominal camera from the `Instrument` class if that fails.
550 butler : `lsst.daf.butler.Butler`
551 Butler instance to attempt to query for and load a ``camera`` dataset
553 dataId : `dict` or `DataCoordinate`
554 Data ID that identifies at least the ``instrument`` and ``exposure``
556 collections : Any, optional
557 Collections to be searched, overriding ``self.butler.collections``.
558 Can be any of the types supported by the ``collections`` argument
559 to butler construction.
563 camera : `lsst.afw.cameraGeom.Camera`
566 If `True`, the camera was obtained from the butler and should represent
567 a versioned camera from a calibration repository. If `False`, no
568 camera datasets were found, and the returned camera was produced by
569 instantiating the appropriate `Instrument` class and calling
570 `Instrument.getCamera`.
572 if collections
is None:
573 collections = butler.collections
578 dataId = butler.registry.expandDataId(dataId, graph=butler.registry.dimensions[
"exposure"].graph)
579 cameraRefs = list(butler.registry.queryDatasets(
"camera", dataId=dataId, collections=collections,
582 assert len(cameraRefs) == 1,
"Should be guaranteed by deduplicate=True above."
583 return butler.getDirect(cameraRefs[0]),
True
584 instrument = Instrument.fromName(dataId[
"instrument"], butler.registry)
585 return instrument.getCamera(),
False