Coverage for python / lsst / pipe / base / _instrument.py: 39%
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28from __future__ import annotations
30__all__ = ("Instrument",)
32import contextlib
33import datetime
34from abc import ABCMeta, abstractmethod
35from collections.abc import Sequence
36from typing import TYPE_CHECKING, Any, Self, cast, final
38from lsst.daf.butler import DataCoordinate, DataId, DimensionPacker, DimensionRecord, Formatter, FormatterV2
39from lsst.daf.butler.registry import DataIdError
40from lsst.pex.config import Config, RegistryField
41from lsst.resources import ResourcePath, ResourcePathExpression
42from lsst.utils import doImportType
43from lsst.utils.introspection import get_full_type_name
45from ._observation_dimension_packer import observation_packer_registry
47if TYPE_CHECKING:
48 from lsst.daf.butler import Registry
51class Instrument(metaclass=ABCMeta):
52 """Base class for instrument-specific logic for the Gen3 Butler.
54 Parameters
55 ----------
56 collection_prefix : `str`, optional
57 Prefix for collection names to use instead of the instrument's own
58 name. This is primarily for use in simulated-data repositories, where
59 the instrument name may not be necessary and/or sufficient to
60 distinguish between collections.
62 Notes
63 -----
64 Concrete instrument subclasses must have the same construction signature as
65 the base class.
66 """
68 configPaths: Sequence[ResourcePathExpression] = ()
69 """Paths to config files to read for specific Tasks.
71 The paths in this list should contain files of the form ``task.py``, for
72 each of the Tasks that requires special configuration.
73 """
75 policyName: str | None = None
76 """Instrument specific name to use when locating a policy or configuration
77 file in the file system."""
79 raw_definition: tuple[str, tuple[str, ...], str] | None = None
80 """Dataset type definition to use for "raw" datasets. This is a tuple
81 of the dataset type name, a tuple of dimension names, and the storage class
82 name. If `None` the ingest system will use its default definition."""
84 def __init__(self, collection_prefix: str | None = None):
85 if collection_prefix is None:
86 collection_prefix = self.getName()
87 self.collection_prefix = collection_prefix
89 @classmethod
90 @abstractmethod
91 def getName(cls) -> str:
92 """Return the short (dimension) name for this instrument.
94 This is not (in general) the same as the class name - it's what is used
95 as the value of the "instrument" field in data IDs, and is usually an
96 abbreviation of the full name.
97 """
98 raise NotImplementedError()
100 @abstractmethod
101 def register(self, registry: Registry, *, update: bool = False) -> None:
102 """Insert instrument, and other relevant records into a butler
103 registry.
105 Parameters
106 ----------
107 registry : `lsst.daf.butler.Registry`
108 Registry client for the data repository to modify.
109 update : `bool`, optional
110 If `True` (`False` is default), update existing records if they
111 differ from the new ones.
113 Returns
114 -------
115 None
117 Raises
118 ------
119 lsst.daf.butler.registry.ConflictingDefinitionError
120 Raised if any existing record has the same key but a different
121 definition as one being registered.
123 Notes
124 -----
125 New records can always be added by calling this method multiple times,
126 as long as no existing records have changed (if existing records have
127 changed, ``update=True`` must be used). Old records can never be
128 removed by this method.
130 Implementations should guarantee that registration is atomic (the
131 registry should not be modified if any error occurs) and idempotent at
132 the level of individual dimension entries; new detectors and filters
133 should be added, but changes to any existing record should not be.
134 This can generally be achieved via a block like
135 """
136 raise NotImplementedError()
138 @classmethod
139 def fromName(cls, name: str, registry: Registry, collection_prefix: str | None = None) -> Self:
140 """Given an instrument name and a butler registry, retrieve a
141 corresponding instantiated instrument object.
143 Parameters
144 ----------
145 name : `str`
146 Name of the instrument (must match the return value of `getName`).
147 registry : `lsst.daf.butler.Registry`
148 Butler registry to query to find the information.
149 collection_prefix : `str`, optional
150 Prefix for collection names to use instead of the instrument's own
151 name. This is primarily for use in simulated-data repositories,
152 where the instrument name may not be necessary and/or sufficient to
153 distinguish between collections.
155 Returns
156 -------
157 instrument : `Instrument`
158 An instance of the relevant `Instrument`.
160 Notes
161 -----
162 The instrument must be registered in the corresponding butler.
164 Raises
165 ------
166 LookupError
167 Raised if the instrument is not known to the supplied registry.
168 ModuleNotFoundError
169 Raised if the class could not be imported. This could mean
170 that the relevant obs package has not been setup.
171 TypeError
172 Raised if the class name retrieved is not a string or the imported
173 symbol is not an `Instrument` subclass.
174 """
175 try:
176 records = list(registry.queryDimensionRecords("instrument", instrument=name))
177 except DataIdError:
178 records = None
179 if not records:
180 raise LookupError(f"No registered instrument with name '{name}'.")
181 cls_name = records[0].class_name
182 if not isinstance(cls_name, str):
183 raise TypeError(
184 f"Unexpected class name retrieved from {name} instrument dimension (got {cls_name})"
185 )
186 return cls._from_cls_name(cls_name, collection_prefix)
188 @classmethod
189 def from_string(
190 cls, name: str, registry: Registry | None = None, collection_prefix: str | None = None
191 ) -> Self:
192 """Return an instance from the short name or class name.
194 If the instrument name is not qualified (does not contain a '.') and a
195 butler registry is provided, this will attempt to load the instrument
196 using `Instrument.fromName()`. Otherwise the instrument will be
197 imported and instantiated.
199 Parameters
200 ----------
201 name : `str`
202 The name or fully-qualified class name of an instrument.
203 registry : `lsst.daf.butler.Registry`, optional
204 Butler registry to query to find information about the instrument,
205 by default `None`.
206 collection_prefix : `str`, optional
207 Prefix for collection names to use instead of the instrument's own
208 name. This is primarily for use in simulated-data repositories,
209 where the instrument name may not be necessary and/or sufficient
210 to distinguish between collections.
212 Returns
213 -------
214 instrument : `Instrument`
215 The instantiated instrument.
217 Raises
218 ------
219 RuntimeError
220 Raised if the instrument can not be imported, instantiated, or
221 obtained from the registry.
222 TypeError
223 Raised if the instrument is not a subclass of
224 `~lsst.pipe.base.Instrument`.
226 See Also
227 --------
228 Instrument.fromName : Constructing Instrument from a name.
229 """
230 if "." not in name and registry is not None:
231 try:
232 instr = cls.fromName(name, registry, collection_prefix=collection_prefix)
233 except Exception as err:
234 raise RuntimeError(
235 f"Could not get instrument from name: {name}. Failed with exception: {err}"
236 ) from err
237 else:
238 try:
239 instr_class = doImportType(name)
240 except Exception as err:
241 raise RuntimeError(
242 f"Could not import instrument: {name}. Failed with exception: {err}"
243 ) from err
244 instr = instr_class(collection_prefix=collection_prefix)
245 if not isinstance(instr, cls):
246 raise TypeError(f"{name} is not a {get_full_type_name(cls)} subclass.")
247 return instr
249 @classmethod
250 def from_data_id(cls, data_id: DataCoordinate, collection_prefix: str | None = None) -> Self:
251 """Instantiate an `Instrument` object from a fully-expanded data ID.
253 Parameters
254 ----------
255 data_id : `~lsst.daf.butler.DataCoordinate`
256 Expanded data ID that includes the instrument dimension.
257 collection_prefix : `str`, optional
258 Prefix for collection names to use instead of the instrument's own
259 name. This is primarily for use in simulated-data repositories,
260 where the instrument name may not be necessary and/or sufficient to
261 distinguish between collections.
263 Returns
264 -------
265 instrument : `Instrument`
266 An instance of the relevant `Instrument`.
268 Raises
269 ------
270 TypeError
271 Raised if the class name retrieved is not a string or the imported
272 symbol is not an `Instrument` subclass.
273 """
274 return cls._from_cls_name(
275 cast(DimensionRecord, data_id.records["instrument"]).class_name, collection_prefix
276 )
278 @classmethod
279 def _from_cls_name(cls, cls_name: str, collection_prefix: str | None = None) -> Self:
280 """Instantiate an `Instrument` object type name.
282 This just provides common error-handling for `fromName` and
283 `from_data_id`
285 Parameters
286 ----------
287 cls_name : `str`
288 Fully-qualified name of the type.
289 collection_prefix : `str`, optional
290 Prefix for collection names to use instead of the instrument's own
291 name. This is primarily for use in simulated-data repositories,
292 where the instrument name may not be necessary and/or sufficient to
293 distinguish between collections.
295 Returns
296 -------
297 instrument : `Instrument`
298 An instance of the relevant `Instrument`.
300 Raises
301 ------
302 TypeError
303 Raised if the class name retrieved is not a string or the imported
304 symbol is not an `Instrument` subclass.
305 """
306 instrument_cls: type = doImportType(cls_name)
307 if not issubclass(instrument_cls, cls):
308 raise TypeError(
309 f"{instrument_cls!r}, obtained from importing {cls_name}, is not a subclass "
310 f"of {get_full_type_name(cls)}."
311 )
312 return instrument_cls(collection_prefix=collection_prefix)
314 @staticmethod
315 def importAll(registry: Registry) -> dict[str, type[Instrument]]:
316 """Import all the instruments known to this registry.
318 This will ensure that all metadata translators have been registered.
320 Parameters
321 ----------
322 registry : `lsst.daf.butler.Registry`
323 Butler registry to query to find the information.
325 Returns
326 -------
327 imported : `dict` [`str`, `type` [`Instrument`]]
328 A mapping containing all the instrument classes that were loaded
329 successfully, keyed by their butler names.
331 Notes
332 -----
333 It is allowed for a particular instrument class to fail on import.
334 This might simply indicate that a particular obs package has
335 not been setup.
336 """
337 imported: dict[str, type[Instrument]] = {}
338 records = list(registry.queryDimensionRecords("instrument"))
339 for record in records:
340 cls = record.class_name
341 instrument_name: str = cast(str, record.name)
342 with contextlib.suppress(Exception):
343 instr = doImportType(cls)
344 assert issubclass(instr, Instrument)
345 imported[instrument_name] = instr
346 return imported
348 @abstractmethod
349 def getRawFormatter(self, dataId: DataId) -> type[Formatter | FormatterV2]:
350 """Return the Formatter class that should be used to read a particular
351 raw file.
353 Parameters
354 ----------
355 dataId : `lsst.daf.butler.DataId`
356 Dimension-based ID for the raw file or files being ingested.
358 Returns
359 -------
360 formatter : `type` \
361 [`lsst.daf.butler.Formatter` | `lsst.daf.butler.FormatterV2` ]
362 Class to be used that reads the file into the correct
363 Python object for the raw data.
364 """
365 raise NotImplementedError()
367 def applyConfigOverrides(self, name: str, config: Config) -> None:
368 """Apply instrument-specific overrides for a task config.
370 Parameters
371 ----------
372 name : `str`
373 Name of the object being configured; typically the _DefaultName
374 of a Task.
375 config : `lsst.pex.config.Config`
376 Config instance to which overrides should be applied.
377 """
378 for root in self.configPaths:
379 resource = ResourcePath(root, forceDirectory=True, forceAbsolute=True)
380 uri = resource.join(f"{name}.py", forceDirectory=False)
381 if uri.exists():
382 config.load(uri)
384 @staticmethod
385 def formatCollectionTimestamp(timestamp: str | datetime.datetime) -> str:
386 """Format a timestamp for use in a collection name.
388 Parameters
389 ----------
390 timestamp : `str` or `datetime.datetime`
391 Timestamp to format. May be a date or datetime string in extended
392 ISO format (assumed UTC), with or without a timezone specifier, a
393 datetime string in basic ISO format with a timezone specifier, a
394 naive `datetime.datetime` instance (assumed UTC) or a
395 timezone-aware `datetime.datetime` instance (converted to UTC).
396 This is intended to cover all forms that string ``CALIBDATE``
397 metadata values have taken in the past, as well as the format this
398 method itself writes out (to enable round-tripping).
400 Returns
401 -------
402 formatted : `str`
403 Standardized string form for the timestamp.
404 """
405 if isinstance(timestamp, str):
406 if "-" in timestamp:
407 # extended ISO format, with - and : delimiters
408 timestamp = datetime.datetime.fromisoformat(timestamp)
409 else:
410 # basic ISO format, with no delimiters (what this method
411 # returns)
412 timestamp = datetime.datetime.strptime(timestamp, "%Y%m%dT%H%M%S%z")
413 if not isinstance(timestamp, datetime.datetime):
414 raise TypeError(f"Unexpected date/time object: {timestamp!r}.")
415 if timestamp.tzinfo is not None:
416 timestamp = timestamp.astimezone(datetime.UTC)
417 return f"{timestamp:%Y%m%dT%H%M%S}Z"
419 @staticmethod
420 def makeCollectionTimestamp() -> str:
421 """Create a timestamp string for use in a collection name from the
422 current time.
424 Returns
425 -------
426 formatted : `str`
427 Standardized string form of the current time.
428 """
429 return Instrument.formatCollectionTimestamp(datetime.datetime.now(tz=datetime.UTC))
431 def makeDefaultRawIngestRunName(self) -> str:
432 """Make the default instrument-specific run collection string for raw
433 data ingest.
435 Returns
436 -------
437 coll : `str`
438 Run collection name to be used as the default for ingestion of
439 raws.
440 """
441 return self.makeCollectionName("raw", "all")
443 def makeUnboundedCalibrationRunName(self, *labels: str) -> str:
444 """Make a RUN collection name appropriate for inserting calibration
445 datasets whose validity ranges are unbounded.
447 Parameters
448 ----------
449 *labels : `str`
450 Extra strings to be included in the base name, using the default
451 delimiter for collection names. Usually this is the name of the
452 ticket on which the calibration collection is being created.
454 Returns
455 -------
456 name : `str`
457 Run collection name.
458 """
459 return self.makeCollectionName("calib", *labels, "unbounded")
461 def makeCuratedCalibrationRunName(self, calibDate: str, *labels: str) -> str:
462 """Make a RUN collection name appropriate for inserting curated
463 calibration datasets with the given ``CALIBDATE`` metadata value.
465 Parameters
466 ----------
467 calibDate : `str`
468 The ``CALIBDATE`` metadata value.
469 *labels : `str`
470 Strings to be included in the collection name (before
471 ``calibDate``, but after all other terms), using the default
472 delimiter for collection names. Usually this is the name of the
473 ticket on which the calibration collection is being created.
475 Returns
476 -------
477 name : `str`
478 Run collection name.
479 """
480 return self.makeCollectionName("calib", *labels, "curated", self.formatCollectionTimestamp(calibDate))
482 def makeCalibrationCollectionName(self, *labels: str) -> str:
483 """Make a CALIBRATION collection name appropriate for associating
484 calibration datasets with validity ranges.
486 Parameters
487 ----------
488 *labels : `str`
489 Strings to be appended to the base name, using the default
490 delimiter for collection names. Usually this is the name of the
491 ticket on which the calibration collection is being created.
493 Returns
494 -------
495 name : `str`
496 Calibration collection name.
497 """
498 return self.makeCollectionName("calib", *labels)
500 @staticmethod
501 def makeRefCatCollectionName(*labels: str) -> str:
502 """Return a global (not instrument-specific) name for a collection that
503 holds reference catalogs.
505 With no arguments, this returns the name of the collection that holds
506 all reference catalogs (usually a ``CHAINED`` collection, at least in
507 long-lived repos that may contain more than one reference catalog).
509 Parameters
510 ----------
511 *labels : `str`
512 Strings to be added to the global collection name, in order to
513 define a collection name for one or more reference catalogs being
514 ingested at the same time.
516 Returns
517 -------
518 name : `str`
519 Collection name.
521 Notes
522 -----
523 This is a ``staticmethod``, not a ``classmethod``, because it should
524 be the same for all instruments.
525 """
526 return "/".join(("refcats",) + labels)
528 def makeUmbrellaCollectionName(self) -> str:
529 """Return the name of the umbrella ``CHAINED`` collection for this
530 instrument that combines all standard recommended input collections.
532 This method should almost never be overridden by derived classes.
534 Returns
535 -------
536 name : `str`
537 Name for the umbrella collection.
538 """
539 return self.makeCollectionName("defaults")
541 def makeCollectionName(self, *labels: str) -> str:
542 """Get the instrument-specific collection string to use as derived
543 from the supplied labels.
545 Parameters
546 ----------
547 *labels : `str`
548 Strings to be combined with the instrument name to form a
549 collection name.
551 Returns
552 -------
553 name : `str`
554 Collection name to use that includes the instrument's recommended
555 prefix.
556 """
557 return "/".join((self.collection_prefix,) + labels)
559 @staticmethod
560 def make_dimension_packer_config_field(
561 doc: str = (
562 "How to pack visit+detector or exposure+detector data IDs into integers. "
563 "The default (None) is to delegate to the Instrument class for which "
564 "registered implementation to use (but still use the nested configuration "
565 "for that implementation)."
566 ),
567 ) -> RegistryField:
568 """Make an `lsst.pex.config.Field` that can be used to configure how
569 data IDs for this instrument are packed.
571 Parameters
572 ----------
573 doc : `str`, optional
574 Documentation for the config field.
576 Returns
577 -------
578 field : `lsst.pex.config.RegistryField`
579 A config field for which calling ``apply`` on the instance
580 attribute constructs an `lsst.daf.butler.DimensionPacker` that
581 defaults to the appropriate one for this instrument.
583 Notes
584 -----
585 This method is expected to be used whenever code requires a single
586 integer that represents the combination of a detector and either a
587 visit or exposure, but in most cases the `lsst.meas.base.IdGenerator`
588 class and its helper configs provide a simpler high-level interface
589 that should be used instead of calling this method directly.
591 This system is designed to work best when the configuration for the ID
592 packer is not overridden at all, allowing the appropriate instrument
593 class to determine the behavior for each data ID encountered. When the
594 configuration does need to be modified (most often when the scheme for
595 packing an instrument's data IDs is undergoing an upgrade), it is
596 important to ensure the overrides are only applied to data IDs with the
597 desired instrument value.
599 Unit tests of code that use a field produced by this method will often
600 want to explicitly set the packer to "observation" and manually set
601 its ``n_detectors`` and ``n_observations`` fields; this will make it
602 unnecessary for tests to provide expanded data IDs.
603 """
604 # The control flow here bounces around a bit when this RegistryField's
605 # apply() method is called, so it merits a thorough walkthrough
606 # somewhere, and that might as well be here:
607 #
608 # - If the config field's name is not `None`, that kind of packer is
609 # constructed and returned with the arguments to `apply`, in just the
610 # way it works with most RegistryFields or ConfigurableFields. But
611 # this is expected to be rare.
612 #
613 # - If the config fields' name is `None`, the `apply` method (which
614 # actually lives on the `pex.config.RegistryInstanceDict` class,
615 # since `RegistryField` is a descriptor), calls
616 # `_make_default_dimension_packer_dispatch` (which is final, and
617 # hence the base class implementation just below is the only one).
618 #
619 # - `_make_default_dimension_packer_dispatch` instantiates an
620 # `Instrument` instance of the type pointed at by the data ID (i.e.
621 # calling `Instrument.from_data_id`), then calls
622 # `_make_default_dimension_packer` on that.
623 #
624 # - The default implementation of `_make_default_dimension_packer` here
625 # in the base class picks the "observation" dimension packer, so if
626 # it's not overridden by a derived class everything proceeds as if
627 # the config field's name was set to that. Note that this sets which
628 # item in the registry is used, but it still pays attention to the
629 # configuration for that entry in the registry field.
630 #
631 # - A subclass implementation of `_make_default_dimension_packer` will
632 # take precedence over the base class, but it's expected that these
633 # will usually just delegate back to ``super()`` while changing the
634 # ``default`` argument to something other than "observation". Once
635 # again, this will control which packer entry in the registry is used
636 # but the result will still reflect the configuration for that packer
637 # in the registry field.
638 #
639 return observation_packer_registry.makeField(
640 doc, default=None, optional=True, on_none=Instrument._make_default_dimension_packer_dispatch
641 )
643 @staticmethod
644 @final
645 def make_default_dimension_packer(
646 data_id: DataCoordinate, is_exposure: bool | None = None
647 ) -> DimensionPacker:
648 """Return the default dimension packer for the given data ID.
650 Parameters
651 ----------
652 data_id : `lsst.daf.butler.DataCoordinate`
653 Data ID that identifies at least the ``instrument`` dimension. Must
654 have dimension records attached.
655 is_exposure : `bool`, optional
656 If `False`, construct a packer for visit+detector data IDs. If
657 `True`, construct a packer for exposure+detector data IDs. If
658 `None`, this is determined based on whether ``visit`` or
659 ``exposure`` is present in ``data_id``, with ``visit`` checked
660 first and hence used if both are present.
662 Returns
663 -------
664 packer : `lsst.daf.butler.DimensionPacker`
665 Object that packs {visit, detector} or {exposure, detector} data
666 IDs into integers.
668 Notes
669 -----
670 When using a dimension packer in task code, using
671 `make_dimension_packer_config_field` to make the packing algorithm
672 configurable is preferred over this method.
674 When obtaining a dimension packer to unpack IDs that were packed by
675 task code, it is similarly preferable to load the configuration for
676 that task and the existing packer configuration field there, to ensure
677 any config overrides are respected. That is sometimes quite difficult,
678 however, and since config overrides for dimension packers are expected
679 to be exceedingly rare, using this simpler method will almost always
680 work.
681 """
683 class _DummyConfig(Config):
684 packer = Instrument.make_dimension_packer_config_field()
686 config = _DummyConfig()
688 return config.packer.apply(data_id, is_exposure=is_exposure) # type: ignore
690 @staticmethod
691 @final
692 def _make_default_dimension_packer_dispatch(
693 config_dict: Any, data_id: DataCoordinate, is_exposure: bool | None = None
694 ) -> DimensionPacker:
695 """Dispatch method used to invoke `_make_dimension_packer`.
697 This method constructs the appropriate `Instrument` subclass from
698 config and then calls its `_make_default_dimension_packer`.
699 It is called when (as usual) the field returned by
700 `make_dimension_packer_config_field` is left to its default selection
701 of `None`.
703 All arguments and return values are the same as
704 `_make_default_dimension_packer.`
705 """
706 instrument = Instrument.from_data_id(data_id)
707 return instrument._make_default_dimension_packer(config_dict, data_id, is_exposure=is_exposure)
709 def _make_default_dimension_packer(
710 self,
711 config_dict: Any,
712 data_id: DataCoordinate,
713 is_exposure: bool | None = None,
714 default: str = "observation",
715 ) -> DimensionPacker:
716 """Construct return the default dimension packer for this instrument.
718 This method is a protected hook for subclasses to override the behavior
719 of `make_dimension_packer_config_field` when the packer is not selected
720 explicitly via configuration.
722 Parameters
723 ----------
724 config_dict
725 Mapping attribute of a `lsst.pex.config.Config` instance that
726 corresponds to a field created by `make_dimension_packer_config`
727 (the actual type of this object is a `lsst.pex.config`
728 implementation detail).
729 data_id : `lsst.daf.butler.DataCoordinate`
730 Data ID that identifies at least the ``instrument`` dimension. For
731 most configurations this must have dimension records attached.
732 is_exposure : `bool`, optional
733 If `False`, construct a packer for visit+detector data IDs. If
734 `True`, construct a packer for exposure+detector data IDs. If
735 `None`, this is determined based on whether ``visit`` or
736 ``exposure`` is present in ``data_id``, with ``visit`` checked
737 first and hence used if both are present.
738 default : `str`, optional
739 Registered name of the dimension packer to select when the
740 configured packer is `None` (as is usually the case). This is
741 intended primarily for derived classes delegating to `super` in
742 reimplementations of this method.
744 Returns
745 -------
746 packer : `lsst.daf.butler.DimensionPacker`
747 Object that packs {visit, detector} or {exposure, detector} data
748 IDs into integers.
749 """
750 return config_dict.apply_with(default, data_id, is_exposure=is_exposure)