Coverage for python/lsst/obs/base/instrument.py : 24%

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1# This file is part of obs_base.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <http://www.gnu.org/licenses/>.
22from __future__ import annotations
24__all__ = ("Instrument", "makeExposureRecordFromObsInfo", "addUnboundedCalibrationLabel", "loadCamera")
26import os.path
27from abc import ABCMeta, abstractmethod
28from typing import Any, Tuple, TYPE_CHECKING
29import astropy.time
31from lsst.afw.cameraGeom import Camera
32from lsst.daf.butler import Butler, DataId, TIMESPAN_MIN, TIMESPAN_MAX, DatasetType, DataCoordinate
33from lsst.utils import getPackageDir, doImport
35if TYPE_CHECKING: 35 ↛ 36line 35 didn't jump to line 36, because the condition on line 35 was never true
36 from .gen2to3 import TranslatorFactory
38# To be a standard text curated calibration means that we use a
39# standard definition for the corresponding DatasetType.
40StandardCuratedCalibrationDatasetTypes = {
41 "defects": {"dimensions": ("instrument", "detector", "calibration_label"),
42 "storageClass": "Defects"},
43 "qe_curve": {"dimensions": ("instrument", "detector", "calibration_label"),
44 "storageClass": "QECurve"},
45}
48class Instrument(metaclass=ABCMeta):
49 """Base class for instrument-specific logic for the Gen3 Butler.
51 Concrete instrument subclasses should be directly constructable with no
52 arguments.
53 """
55 configPaths = ()
56 """Paths to config files to read for specific Tasks.
58 The paths in this list should contain files of the form `task.py`, for
59 each of the Tasks that requires special configuration.
60 """
62 policyName = None
63 """Instrument specific name to use when locating a policy or configuration
64 file in the file system."""
66 obsDataPackage = None
67 """Name of the package containing the text curated calibration files.
68 Usually a obs _data package. If `None` no curated calibration files
69 will be read. (`str`)"""
71 standardCuratedDatasetTypes = tuple(StandardCuratedCalibrationDatasetTypes)
72 """The dataset types expected to be obtained from the obsDataPackage.
73 These dataset types are all required to have standard definitions and
74 must be known to the base class. Clearing this list will prevent
75 any of these calibrations from being stored. If a dataset type is not
76 known to a specific instrument it can still be included in this list
77 since the data package is the source of truth.
78 """
80 @property
81 @abstractmethod
82 def filterDefinitions(self):
83 """`~lsst.obs.base.FilterDefinitionCollection`, defining the filters
84 for this instrument.
85 """
86 return None
88 def __init__(self, *args, **kwargs):
89 self.filterDefinitions.reset()
90 self.filterDefinitions.defineFilters()
91 self._obsDataPackageDir = None
93 @classmethod
94 @abstractmethod
95 def getName(cls):
96 """Return the short (dimension) name for this instrument.
98 This is not (in general) the same as the class name - it's what is used
99 as the value of the "instrument" field in data IDs, and is usually an
100 abbreviation of the full name.
101 """
102 raise NotImplementedError()
104 @abstractmethod
105 def getCamera(self):
106 """Retrieve the cameraGeom representation of this instrument.
108 This is a temporary API that should go away once obs_ packages have
109 a standardized approach to writing versioned cameras to a Gen3 repo.
110 """
111 raise NotImplementedError()
113 @abstractmethod
114 def register(self, registry):
115 """Insert instrument, physical_filter, and detector entries into a
116 `Registry`.
117 """
118 raise NotImplementedError()
120 @property
121 def obsDataPackageDir(self):
122 """The root of the obs package that provides specializations for
123 this instrument (`str`).
124 """
125 if self.obsDataPackage is None:
126 return None
127 if self._obsDataPackageDir is None:
128 # Defer any problems with locating the package until
129 # we need to find it.
130 self._obsDataPackageDir = getPackageDir(self.obsDataPackage)
131 return self._obsDataPackageDir
133 @classmethod
134 def fromName(cls, name, registry):
135 """Given an instrument name and a butler, retrieve a corresponding
136 instantiated instrument object.
138 Parameters
139 ----------
140 name : `str`
141 Name of the instrument (must match the return value of `getName`).
142 registry : `lsst.daf.butler.Registry`
143 Butler registry to query to find the information.
145 Returns
146 -------
147 instrument : `Instrument`
148 An instance of the relevant `Instrument`.
150 Notes
151 -----
152 The instrument must be registered in the corresponding butler.
154 Raises
155 ------
156 LookupError
157 Raised if the instrument is not known to the supplied registry.
158 ModuleNotFoundError
159 Raised if the class could not be imported. This could mean
160 that the relevant obs package has not been setup.
161 TypeError
162 Raised if the class name retrieved is not a string.
163 """
164 dimensions = list(registry.queryDimensions("instrument", dataId={"instrument": name}))
165 cls = dimensions[0].records["instrument"].class_name
166 if not isinstance(cls, str):
167 raise TypeError(f"Unexpected class name retrieved from {name} instrument dimension (got {cls})")
168 instrument = doImport(cls)
169 return instrument()
171 def _registerFilters(self, registry):
172 """Register the physical and abstract filter Dimension relationships.
173 This should be called in the ``register`` implementation.
175 Parameters
176 ----------
177 registry : `lsst.daf.butler.core.Registry`
178 The registry to add dimensions to.
179 """
180 for filter in self.filterDefinitions:
181 # fix for undefined abstract filters causing trouble in the registry:
182 if filter.abstract_filter is None:
183 abstract_filter = filter.physical_filter
184 else:
185 abstract_filter = filter.abstract_filter
187 registry.insertDimensionData("physical_filter",
188 {"instrument": self.getName(),
189 "name": filter.physical_filter,
190 "abstract_filter": abstract_filter
191 })
193 @abstractmethod
194 def getRawFormatter(self, dataId):
195 """Return the Formatter class that should be used to read a particular
196 raw file.
198 Parameters
199 ----------
200 dataId : `DataCoordinate`
201 Dimension-based ID for the raw file or files being ingested.
203 Returns
204 -------
205 formatter : `Formatter` class
206 Class to be used that reads the file into an
207 `lsst.afw.image.Exposure` instance.
208 """
209 raise NotImplementedError()
211 def writeCuratedCalibrations(self, butler):
212 """Write human-curated calibration Datasets to the given Butler with
213 the appropriate validity ranges.
215 Parameters
216 ----------
217 butler : `lsst.daf.butler.Butler`
218 Butler to use to store these calibrations.
220 Notes
221 -----
222 Expected to be called from subclasses. The base method calls
223 ``writeCameraGeom`` and ``writeStandardTextCuratedCalibrations``.
224 """
225 self.writeCameraGeom(butler)
226 self.writeStandardTextCuratedCalibrations(butler)
228 def applyConfigOverrides(self, name, config):
229 """Apply instrument-specific overrides for a task config.
231 Parameters
232 ----------
233 name : `str`
234 Name of the object being configured; typically the _DefaultName
235 of a Task.
236 config : `lsst.pex.config.Config`
237 Config instance to which overrides should be applied.
238 """
239 for root in self.configPaths:
240 path = os.path.join(root, f"{name}.py")
241 if os.path.exists(path):
242 config.load(path)
244 def writeCameraGeom(self, butler):
245 """Write the default camera geometry to the butler repository
246 with an infinite validity range.
248 Parameters
249 ----------
250 butler : `lsst.daf.butler.Butler`
251 Butler to receive these calibration datasets.
252 """
254 datasetType = DatasetType("camera", ("instrument", "calibration_label"), "Camera",
255 universe=butler.registry.dimensions)
256 butler.registry.registerDatasetType(datasetType)
257 unboundedDataId = addUnboundedCalibrationLabel(butler.registry, self.getName())
258 camera = self.getCamera()
259 butler.put(camera, datasetType, unboundedDataId)
261 def writeStandardTextCuratedCalibrations(self, butler):
262 """Write the set of standardized curated text calibrations to
263 the repository.
265 Parameters
266 ----------
267 butler : `lsst.daf.butler.Butler`
268 Butler to receive these calibration datasets.
269 """
271 for datasetTypeName in self.standardCuratedDatasetTypes:
272 # We need to define the dataset types.
273 if datasetTypeName not in StandardCuratedCalibrationDatasetTypes:
274 raise ValueError(f"DatasetType {datasetTypeName} not in understood list"
275 f" [{'.'.join(StandardCuratedCalibrationDatasetTypes)}]")
276 definition = StandardCuratedCalibrationDatasetTypes[datasetTypeName]
277 datasetType = DatasetType(datasetTypeName,
278 universe=butler.registry.dimensions,
279 **definition)
280 self._writeSpecificCuratedCalibrationDatasets(butler, datasetType)
282 def _writeSpecificCuratedCalibrationDatasets(self, butler, datasetType):
283 """Write standardized curated calibration datasets for this specific
284 dataset type from an obs data package.
286 Parameters
287 ----------
288 butler : `lsst.daf.butler.Butler`
289 Gen3 butler in which to put the calibrations.
290 datasetType : `lsst.daf.butler.DatasetType`
291 Dataset type to be put.
293 Notes
294 -----
295 This method scans the location defined in the ``obsDataPackageDir``
296 class attribute for curated calibrations corresponding to the
297 supplied dataset type. The directory name in the data package must
298 match the name of the dataset type. They are assumed to use the
299 standard layout and can be read by
300 `~lsst.pipe.tasks.read_curated_calibs.read_all` and provide standard
301 metadata.
302 """
303 if self.obsDataPackageDir is None:
304 # if there is no data package then there can't be datasets
305 return
307 calibPath = os.path.join(self.obsDataPackageDir, self.policyName,
308 datasetType.name)
310 if not os.path.exists(calibPath):
311 return
313 # Register the dataset type
314 butler.registry.registerDatasetType(datasetType)
316 # obs_base can't depend on pipe_tasks but concrete obs packages
317 # can -- we therefore have to defer import
318 from lsst.pipe.tasks.read_curated_calibs import read_all
320 camera = self.getCamera()
321 calibsDict = read_all(calibPath, camera)[0] # second return is calib type
322 endOfTime = TIMESPAN_MAX
323 dimensionRecords = []
324 datasetRecords = []
325 for det in calibsDict:
326 times = sorted([k for k in calibsDict[det]])
327 calibs = [calibsDict[det][time] for time in times]
328 times = [astropy.time.Time(t, format="datetime", scale="utc") for t in times]
329 times += [endOfTime]
330 for calib, beginTime, endTime in zip(calibs, times[:-1], times[1:]):
331 md = calib.getMetadata()
332 calibrationLabel = f"{datasetType.name}/{md['CALIBDATE']}/{md['DETECTOR']}"
333 dataId = DataCoordinate.standardize(
334 universe=butler.registry.dimensions,
335 instrument=self.getName(),
336 calibration_label=calibrationLabel,
337 detector=md["DETECTOR"],
338 )
339 datasetRecords.append((calib, dataId))
340 dimensionRecords.append({
341 "instrument": self.getName(),
342 "name": calibrationLabel,
343 "datetime_begin": beginTime,
344 "datetime_end": endTime,
345 })
347 # Second loop actually does the inserts and filesystem writes.
348 with butler.transaction():
349 butler.registry.insertDimensionData("calibration_label", *dimensionRecords)
350 # TODO: vectorize these puts, once butler APIs for that become
351 # available.
352 for calib, dataId in datasetRecords:
353 butler.put(calib, datasetType, dataId)
355 @abstractmethod
356 def makeDataIdTranslatorFactory(self) -> TranslatorFactory:
357 """Return a factory for creating Gen2->Gen3 data ID translators,
358 specialized for this instrument.
360 Derived class implementations should generally call
361 `TranslatorFactory.addGenericInstrumentRules` with appropriate
362 arguments, but are not required to (and may not be able to if their
363 Gen2 raw data IDs are sufficiently different from the HSC/DECam/CFHT
364 norm).
366 Returns
367 -------
368 factory : `TranslatorFactory`.
369 Factory for `Translator` objects.
370 """
371 raise NotImplementedError("Must be implemented by derived classes.")
374def makeExposureRecordFromObsInfo(obsInfo, universe):
375 """Construct an exposure DimensionRecord from
376 `astro_metadata_translator.ObservationInfo`.
378 Parameters
379 ----------
380 obsInfo : `astro_metadata_translator.ObservationInfo`
381 A `~astro_metadata_translator.ObservationInfo` object corresponding to
382 the exposure.
383 universe : `DimensionUniverse`
384 Set of all known dimensions.
386 Returns
387 -------
388 record : `DimensionRecord`
389 A record containing exposure metadata, suitable for insertion into
390 a `Registry`.
391 """
392 dimension = universe["exposure"]
393 return dimension.RecordClass.fromDict({
394 "instrument": obsInfo.instrument,
395 "id": obsInfo.exposure_id,
396 "name": obsInfo.observation_id,
397 "group_name": obsInfo.exposure_group,
398 "group_id": obsInfo.visit_id,
399 "datetime_begin": obsInfo.datetime_begin,
400 "datetime_end": obsInfo.datetime_end,
401 "exposure_time": obsInfo.exposure_time.to_value("s"),
402 "dark_time": obsInfo.dark_time.to_value("s"),
403 "observation_type": obsInfo.observation_type,
404 "physical_filter": obsInfo.physical_filter,
405 })
408def addUnboundedCalibrationLabel(registry, instrumentName):
409 """Add a special 'unbounded' calibration_label dimension entry for the
410 given camera that is valid for any exposure.
412 If such an entry already exists, this function just returns a `DataId`
413 for the existing entry.
415 Parameters
416 ----------
417 registry : `Registry`
418 Registry object in which to insert the dimension entry.
419 instrumentName : `str`
420 Name of the instrument this calibration label is associated with.
422 Returns
423 -------
424 dataId : `DataId`
425 New or existing data ID for the unbounded calibration.
426 """
427 d = dict(instrument=instrumentName, calibration_label="unbounded")
428 try:
429 return registry.expandDataId(d)
430 except LookupError:
431 pass
432 entry = d.copy()
433 entry["datetime_begin"] = TIMESPAN_MIN
434 entry["datetime_end"] = TIMESPAN_MAX
435 registry.insertDimensionData("calibration_label", entry)
436 return registry.expandDataId(d)
439def loadCamera(butler: Butler, dataId: DataId, *, collections: Any = None) -> Tuple[Camera, bool]:
440 """Attempt to load versioned camera geometry from a butler, but fall back
441 to obtaining a nominal camera from the `Instrument` class if that fails.
443 Parameters
444 ----------
445 butler : `lsst.daf.butler.Butler`
446 Butler instance to attempt to query for and load a ``camera`` dataset
447 from.
448 dataId : `dict` or `DataCoordinate`
449 Data ID that identifies at least the ``instrument`` and ``exposure``
450 dimensions.
451 collections : Any, optional
452 Collections to be searched, overriding ``self.butler.collections``.
453 Can be any of the types supported by the ``collections`` argument
454 to butler construction.
456 Returns
457 -------
458 camera : `lsst.afw.cameraGeom.Camera`
459 Camera object.
460 versioned : `bool`
461 If `True`, the camera was obtained from the butler and should represent
462 a versioned camera from a calibration repository. If `False`, no
463 camera datasets were found, and the returned camera was produced by
464 instantiating the appropriate `Instrument` class and calling
465 `Instrument.getCamera`.
466 """
467 if collections is None:
468 collections = butler.collections
469 # Registry would do data ID expansion internally if we didn't do it first,
470 # but we might want an expanded data ID ourselves later, so we do it here
471 # to ensure it only happens once.
472 # This will also catch problems with the data ID not having keys we need.
473 dataId = butler.registry.expandDataId(dataId, graph=butler.registry.dimensions["exposure"].graph)
474 cameraRefs = list(butler.registry.queryDatasets("camera", dataId=dataId, collections=collections,
475 deduplicate=True))
476 if cameraRefs:
477 assert len(cameraRefs) == 1, "Should be guaranteed by deduplicate=True above."
478 return butler.getDirect(cameraRefs[0]), True
479 instrument = Instrument.fromName(dataId["instrument"], butler.registry)
480 return instrument.getCamera(), False