lsst.fgcmcal g5158719beb+d560493a6f
fgcmBuildStarsBase.py
Go to the documentation of this file.
1# This file is part of fgcmcal.
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 <https://www.gnu.org/licenses/>.
21"""Base class for BuildStars using src tables or sourceTable_visit tables.
22"""
23
24import os
25import sys
26import traceback
27import abc
28
29import numpy as np
30
31import lsst.daf.persistence as dafPersist
32import lsst.pex.config as pexConfig
33import lsst.pipe.base as pipeBase
34import lsst.afw.table as afwTable
35import lsst.geom as geom
36from lsst.daf.base import PropertyList
37from lsst.daf.base.dateTime import DateTime
38from lsst.meas.algorithms.sourceSelector import sourceSelectorRegistry
39from lsst.utils.timer import timeMethod
40
41from .utilities import computeApertureRadiusFromDataRef
42from .fgcmLoadReferenceCatalog import FgcmLoadReferenceCatalogTask
43
44import fgcm
45
46REFSTARS_FORMAT_VERSION = 1
47
48__all__ = ['FgcmBuildStarsConfigBase', 'FgcmBuildStarsRunner', 'FgcmBuildStarsBaseTask']
49
50
51class FgcmBuildStarsConfigBase(pexConfig.Config):
52 """Base config for FgcmBuildStars tasks"""
53
54 instFluxField = pexConfig.Field(
55 doc=("Faull name of the source instFlux field to use, including 'instFlux'. "
56 "The associated flag will be implicitly included in badFlags"),
57 dtype=str,
58 default='slot_CalibFlux_instFlux',
59 )
60 minPerBand = pexConfig.Field(
61 doc="Minimum observations per band",
62 dtype=int,
63 default=2,
64 )
65 matchRadius = pexConfig.Field(
66 doc="Match radius (arcseconds)",
67 dtype=float,
68 default=1.0,
69 )
70 isolationRadius = pexConfig.Field(
71 doc="Isolation radius (arcseconds)",
72 dtype=float,
73 default=2.0,
74 )
75 densityCutNside = pexConfig.Field(
76 doc="Density cut healpix nside",
77 dtype=int,
78 default=128,
79 )
80 densityCutMaxPerPixel = pexConfig.Field(
81 doc="Density cut number of stars per pixel",
82 dtype=int,
83 default=1000,
84 )
85 randomSeed = pexConfig.Field(
86 doc="Random seed for high density down-sampling.",
87 dtype=int,
88 default=None,
89 optional=True,
90 )
91 matchNside = pexConfig.Field(
92 doc="Healpix Nside for matching",
93 dtype=int,
94 default=4096,
95 )
96 coarseNside = pexConfig.Field(
97 doc="Healpix coarse Nside for partitioning matches",
98 dtype=int,
99 default=8,
100 )
101 # The following config will not be necessary after Gen2 retirement.
102 # In the meantime, obs packages should set to 'filterDefinitions.filter_to_band'
103 # which is easiest to access in the config file.
104 physicalFilterMap = pexConfig.DictField(
105 doc="Mapping from 'physicalFilter' to band.",
106 keytype=str,
107 itemtype=str,
108 default={},
109 )
110 requiredBands = pexConfig.ListField(
111 doc="Bands required for each star",
112 dtype=str,
113 default=(),
114 )
115 primaryBands = pexConfig.ListField(
116 doc=("Bands for 'primary' star matches. "
117 "A star must be observed in one of these bands to be considered "
118 "as a calibration star."),
119 dtype=str,
120 default=None
121 )
122 visitDataRefName = pexConfig.Field(
123 doc="dataRef name for the 'visit' field, usually 'visit'.",
124 dtype=str,
125 default="visit"
126 )
127 ccdDataRefName = pexConfig.Field(
128 doc="dataRef name for the 'ccd' field, usually 'ccd' or 'detector'.",
129 dtype=str,
130 default="ccd"
131 )
132 doApplyWcsJacobian = pexConfig.Field(
133 doc="Apply the jacobian of the WCS to the star observations prior to fit?",
134 dtype=bool,
135 default=True
136 )
137 doModelErrorsWithBackground = pexConfig.Field(
138 doc="Model flux errors with background term?",
139 dtype=bool,
140 default=True
141 )
142 psfCandidateName = pexConfig.Field(
143 doc="Name of field with psf candidate flag for propagation",
144 dtype=str,
145 default="calib_psf_candidate"
146 )
147 doSubtractLocalBackground = pexConfig.Field(
148 doc=("Subtract the local background before performing calibration? "
149 "This is only supported for circular aperture calibration fluxes."),
150 dtype=bool,
151 default=False
152 )
153 localBackgroundFluxField = pexConfig.Field(
154 doc="Full name of the local background instFlux field to use.",
155 dtype=str,
156 default='base_LocalBackground_instFlux'
157 )
158 sourceSelector = sourceSelectorRegistry.makeField(
159 doc="How to select sources",
160 default="science"
161 )
162 apertureInnerInstFluxField = pexConfig.Field(
163 doc=("Full name of instFlux field that contains inner aperture "
164 "flux for aperture correction proxy"),
165 dtype=str,
166 default='base_CircularApertureFlux_12_0_instFlux'
167 )
168 apertureOuterInstFluxField = pexConfig.Field(
169 doc=("Full name of instFlux field that contains outer aperture "
170 "flux for aperture correction proxy"),
171 dtype=str,
172 default='base_CircularApertureFlux_17_0_instFlux'
173 )
174 doReferenceMatches = pexConfig.Field(
175 doc="Match reference catalog as additional constraint on calibration",
176 dtype=bool,
177 default=True,
178 )
179 fgcmLoadReferenceCatalog = pexConfig.ConfigurableField(
180 target=FgcmLoadReferenceCatalogTask,
181 doc="FGCM reference object loader",
182 )
183 nVisitsPerCheckpoint = pexConfig.Field(
184 doc="Number of visits read between checkpoints",
185 dtype=int,
186 default=500,
187 )
188
189 def setDefaults(self):
190 sourceSelector = self.sourceSelectorsourceSelector["science"]
191 sourceSelector.setDefaults()
192
193 sourceSelector.doFlags = True
194 sourceSelector.doUnresolved = True
195 sourceSelector.doSignalToNoise = True
196 sourceSelector.doIsolated = True
197
198 sourceSelector.signalToNoise.minimum = 10.0
199 sourceSelector.signalToNoise.maximum = 1000.0
200
201 # FGCM operates on unresolved sources, and this setting is
202 # appropriate for the current base_ClassificationExtendedness
203 sourceSelector.unresolved.maximum = 0.5
204
205
206class FgcmBuildStarsRunner(pipeBase.ButlerInitializedTaskRunner):
207 """Subclass of TaskRunner for FgcmBuildStars tasks
208
209 fgcmBuildStarsTask.run() and fgcmBuildStarsTableTask.run() take a number of
210 arguments, one of which is the butler (for persistence and mapper data),
211 and a list of dataRefs extracted from the command line. Note that FGCM
212 runs on a large set of dataRefs, and not on single dataRef/tract/patch.
213 This class transforms the process arguments generated by the ArgumentParser
214 into the arguments expected by FgcmBuildStarsTask.run(). This runner does
215 not use any parallelization.
216 """
217 @staticmethod
218 def getTargetList(parsedCmd):
219 """
220 Return a list with one element: a tuple with the butler and
221 list of dataRefs
222 """
223 # we want to combine the butler with any (or no!) dataRefs
224 return [(parsedCmd.butler, parsedCmd.id.refList)]
225
226 def __call__(self, args):
227 """
228 Parameters
229 ----------
230 args: `tuple` with (butler, dataRefList)
231
232 Returns
233 -------
234 exitStatus: `list` with `lsst.pipe.base.Struct`
235 exitStatus (0: success; 1: failure)
236 """
237 butler, dataRefList = args
238
239 task = self.TaskClass(config=self.config, log=self.log)
240
241 exitStatus = 0
242 if self.doRaise:
243 task.runDataRef(butler, dataRefList)
244 else:
245 try:
246 task.runDataRef(butler, dataRefList)
247 except Exception as e:
248 exitStatus = 1
249 task.log.fatal("Failed: %s" % e)
250 if not isinstance(e, pipeBase.TaskError):
251 traceback.print_exc(file=sys.stderr)
252
253 task.writeMetadata(butler)
254
255 # The task does not return any results:
256 return [pipeBase.Struct(exitStatus=exitStatus)]
257
258 def run(self, parsedCmd):
259 """
260 Run the task, with no multiprocessing
261
262 Parameters
263 ----------
264 parsedCmd: `lsst.pipe.base.ArgumentParser` parsed command line
265 """
266
267 resultList = []
268
269 if self.precall(parsedCmd):
270 targetList = self.getTargetListgetTargetList(parsedCmd)
271 resultList = self(targetList[0])
272
273 return resultList
274
275
276class FgcmBuildStarsBaseTask(pipeBase.PipelineTask, pipeBase.CmdLineTask, abc.ABC):
277 """
278 Base task to build stars for FGCM global calibration
279
280 Parameters
281 ----------
282 butler : `lsst.daf.persistence.Butler`
283 """
284 def __init__(self, initInputs=None, butler=None, **kwargs):
285 super().__init__(**kwargs)
286
287 self.makeSubtask("sourceSelector")
288 # Only log warning and fatal errors from the sourceSelector
289 self.sourceSelector.log.setLevel(self.sourceSelector.log.WARN)
290
291 # no saving of metadata for now
292 def _getMetadataName(self):
293 return None
294
295 @timeMethod
296 def runDataRef(self, butler, dataRefs):
297 """
298 Cross-match and make star list for FGCM Input
299
300 Parameters
301 ----------
302 butler: `lsst.daf.persistence.Butler`
303 dataRefs: `list` of `lsst.daf.persistence.ButlerDataRef`
304 Source data references for the input visits.
305
306 Raises
307 ------
308 RuntimeErrror: Raised if `config.doReferenceMatches` is set and
309 an fgcmLookUpTable is not available, or if computeFluxApertureRadius()
310 fails if the calibFlux is not a CircularAperture flux.
311 """
312 datasetType = dataRefs[0].butlerSubset.datasetType
313 self.log.info("Running with %d %s dataRefs", len(dataRefs), datasetType)
314
315 if self.config.doReferenceMatches:
316 self.makeSubtask("fgcmLoadReferenceCatalog", butler=butler)
317 # Ensure that we have a LUT
318 if not butler.datasetExists('fgcmLookUpTable'):
319 raise RuntimeError("Must have fgcmLookUpTable if using config.doReferenceMatches")
320 # Compute aperture radius if necessary. This is useful to do now before
321 # any heavy lifting has happened (fail early).
322 calibFluxApertureRadius = None
323 if self.config.doSubtractLocalBackground:
324 try:
325 calibFluxApertureRadius = computeApertureRadiusFromDataRef(dataRefs[0],
326 self.config.instFluxField)
327 except RuntimeError as e:
328 raise RuntimeError("Could not determine aperture radius from %s. "
329 "Cannot use doSubtractLocalBackground." %
330 (self.config.instFluxField)) from e
331
332 camera = butler.get('camera')
333 groupedDataRefs = self._findAndGroupDataRefsGen2_findAndGroupDataRefsGen2(butler, camera, dataRefs)
334
335 # Make the visit catalog if necessary
336 # First check if the visit catalog is in the _current_ path
337 # We cannot use Gen2 datasetExists() because that checks all parent
338 # directories as well, which would make recovering from faults
339 # and fgcmcal reruns impossible.
340 visitCatDataRef = butler.dataRef('fgcmVisitCatalog')
341 filename = visitCatDataRef.get('fgcmVisitCatalog_filename')[0]
342 if os.path.exists(filename):
343 # This file exists and we should continue processing
344 inVisitCat = visitCatDataRef.get()
345 if len(inVisitCat) != len(groupedDataRefs):
346 raise RuntimeError("Existing visitCatalog found, but has an inconsistent "
347 "number of visits. Cannot continue.")
348 else:
349 inVisitCat = None
350
351 visitCat = self.fgcmMakeVisitCatalogfgcmMakeVisitCatalog(camera, groupedDataRefs,
352 visitCatDataRef=visitCatDataRef,
353 inVisitCat=inVisitCat)
354
355 # Persist the visitCat as a checkpoint file.
356 visitCatDataRef.put(visitCat)
357
358 starObsDataRef = butler.dataRef('fgcmStarObservations')
359 filename = starObsDataRef.get('fgcmStarObservations_filename')[0]
360 if os.path.exists(filename):
361 inStarObsCat = starObsDataRef.get()
362 else:
363 inStarObsCat = None
364
365 rad = calibFluxApertureRadius
366 sourceSchemaDataRef = butler.dataRef('src_schema')
367 sourceSchema = sourceSchemaDataRef.get('src_schema', immediate=True).schema
368 fgcmStarObservationCat = self.fgcmMakeAllStarObservationsfgcmMakeAllStarObservations(groupedDataRefs,
369 visitCat,
370 sourceSchema,
371 camera,
372 calibFluxApertureRadius=rad,
373 starObsDataRef=starObsDataRef,
374 visitCatDataRef=visitCatDataRef,
375 inStarObsCat=inStarObsCat)
376 visitCatDataRef.put(visitCat)
377 starObsDataRef.put(fgcmStarObservationCat)
378
379 # Always do the matching.
380 if self.config.doReferenceMatches:
381 lutDataRef = butler.dataRef('fgcmLookUpTable')
382 else:
383 lutDataRef = None
384 fgcmStarIdCat, fgcmStarIndicesCat, fgcmRefCat = self.fgcmMatchStarsfgcmMatchStars(visitCat,
385 fgcmStarObservationCat,
386 lutDataRef=lutDataRef)
387
388 # Persist catalogs via the butler
389 butler.put(fgcmStarIdCat, 'fgcmStarIds')
390 butler.put(fgcmStarIndicesCat, 'fgcmStarIndices')
391 if fgcmRefCat is not None:
392 butler.put(fgcmRefCat, 'fgcmReferenceStars')
393
394 @abc.abstractmethod
395 def _findAndGroupDataRefsGen2(self, butler, camera, dataRefs):
396 """
397 Find and group dataRefs (by visit); Gen2 only.
398
399 Parameters
400 ----------
401 butler : `lsst.daf.persistence.Butler`
402 Gen2 butler.
403 camera : `lsst.afw.cameraGeom.Camera`
404 Camera from the butler.
405 dataRefs : `list` of `lsst.daf.persistence.ButlerDataRef`
406 Data references for the input visits.
407
408 Returns
409 -------
410 groupedDataRefs : `dict` [`int`, `list`]
411 Dictionary with sorted visit keys, and `list`s of
412 `lsst.daf.persistence.ButlerDataRef`
413 """
414 raise NotImplementedError("_findAndGroupDataRefsGen2 not implemented.")
415
416 @abc.abstractmethod
417 def fgcmMakeAllStarObservations(self, groupedDataRefs, visitCat,
418 sourceSchema,
419 camera,
420 calibFluxApertureRadius=None,
421 visitCatDataRef=None,
422 starObsDataRef=None,
423 inStarObsCat=None):
424 """
425 Compile all good star observations from visits in visitCat. Checkpoint files
426 will be stored if both visitCatDataRef and starObsDataRef are not None.
427
428 Parameters
429 ----------
430 groupedDataRefs : `dict` of `list`s
431 Lists of `~lsst.daf.persistence.ButlerDataRef` or
432 `~lsst.daf.butler.DeferredDatasetHandle`, grouped by visit.
433 visitCat : `~afw.table.BaseCatalog`
434 Catalog with visit data for FGCM
435 sourceSchema : `~lsst.afw.table.Schema`
436 Schema for the input src catalogs.
437 camera : `~lsst.afw.cameraGeom.Camera`
438 calibFluxApertureRadius : `float`, optional
439 Aperture radius for calibration flux.
440 visitCatDataRef : `~lsst.daf.persistence.ButlerDataRef`, optional
441 Dataref to write visitCat for checkpoints
442 starObsDataRef : `~lsst.daf.persistence.ButlerDataRef`, optional
443 Dataref to write the star observation catalog for checkpoints.
444 inStarObsCat : `~afw.table.BaseCatalog`
445 Input observation catalog. If this is incomplete, observations
446 will be appended from when it was cut off.
447
448 Returns
449 -------
450 fgcmStarObservations : `afw.table.BaseCatalog`
451 Full catalog of good observations.
452
453 Raises
454 ------
455 RuntimeError: Raised if doSubtractLocalBackground is True and
456 calibFluxApertureRadius is not set.
457 """
458 raise NotImplementedError("fgcmMakeAllStarObservations not implemented.")
459
460 def fgcmMakeVisitCatalog(self, camera, groupedDataRefs, bkgDataRefDict=None,
461 visitCatDataRef=None, inVisitCat=None):
462 """
463 Make a visit catalog with all the keys from each visit
464
465 Parameters
466 ----------
467 camera: `lsst.afw.cameraGeom.Camera`
468 Camera from the butler
469 groupedDataRefs: `dict`
470 Dictionary with visit keys, and `list`s of
471 `lsst.daf.persistence.ButlerDataRef`
472 bkgDataRefDict: `dict`, optional
473 Dictionary of gen3 dataRefHandles for background info.
474 visitCatDataRef: `lsst.daf.persistence.ButlerDataRef`, optional
475 Dataref to write visitCat for checkpoints
476 inVisitCat: `afw.table.BaseCatalog`, optional
477 Input (possibly incomplete) visit catalog
478
479 Returns
480 -------
481 visitCat: `afw.table.BaseCatalog`
482 """
483
484 self.log.info("Assembling visitCatalog from %d %ss" %
485 (len(groupedDataRefs), self.config.visitDataRefName))
486
487 nCcd = len(camera)
488
489 if inVisitCat is None:
490 schema = self._makeFgcmVisitSchema_makeFgcmVisitSchema(nCcd)
491
492 visitCat = afwTable.BaseCatalog(schema)
493 visitCat.reserve(len(groupedDataRefs))
494 visitCat.resize(len(groupedDataRefs))
495
496 visitCat['visit'] = list(groupedDataRefs.keys())
497 visitCat['used'] = 0
498 visitCat['sources_read'] = False
499 else:
500 visitCat = inVisitCat
501
502 # No matter what, fill the catalog. This will check if it was
503 # already read.
504 self._fillVisitCatalog_fillVisitCatalog(visitCat, groupedDataRefs,
505 bkgDataRefDict=bkgDataRefDict,
506 visitCatDataRef=visitCatDataRef)
507
508 return visitCat
509
510 def _fillVisitCatalog(self, visitCat, groupedDataRefs, bkgDataRefDict=None,
511 visitCatDataRef=None):
512 """
513 Fill the visit catalog with visit metadata
514
515 Parameters
516 ----------
517 visitCat : `afw.table.BaseCatalog`
518 Visit catalog. See _makeFgcmVisitSchema() for schema definition.
519 groupedDataRefs : `dict`
520 Dictionary with visit keys, and `list`s of
521 `lsst.daf.persistence.ButlerDataRef` or
522 `lsst.daf.butler.DeferredDatasetHandle`
523 visitCatDataRef : `lsst.daf.persistence.ButlerDataRef`, optional
524 Dataref to write ``visitCat`` for checkpoints. Gen2 only.
525 bkgDataRefDict : `dict`, optional
526 Dictionary of Gen3 `lsst.daf.butler.DeferredDatasetHandle`
527 for background info.
528 """
529 bbox = geom.BoxI(geom.PointI(0, 0), geom.PointI(1, 1))
530
531 for i, visit in enumerate(groupedDataRefs):
532 # We don't use the bypasses since we need the psf info which does
533 # not have a bypass
534 # TODO: When DM-15500 is implemented in the Gen3 Butler, this
535 # can be fixed
536
537 # Do not read those that have already been read
538 if visitCat['used'][i]:
539 continue
540
541 if (i % self.config.nVisitsPerCheckpoint) == 0:
542 self.log.info("Retrieving metadata for %s %d (%d/%d)" %
543 (self.config.visitDataRefName, visit, i, len(groupedDataRefs)))
544 # Save checkpoint if desired
545 if visitCatDataRef is not None:
546 visitCatDataRef.put(visitCat)
547
548 dataRef = groupedDataRefs[visit][0]
549 if isinstance(dataRef, dafPersist.ButlerDataRef):
550 # Gen2: calexp dataRef
551 # The first dataRef in the group will be the reference ccd (if available)
552 exp = dataRef.get(datasetType='calexp_sub', bbox=bbox)
553 visitInfo = exp.getInfo().getVisitInfo()
554 label = dataRef.get(datasetType='calexp_filterLabel')
555 physicalFilter = label.physicalLabel
556 psf = exp.getPsf()
557 bbox = dataRef.get(datasetType='calexp_bbox')
558 psfSigma = psf.computeShape(bbox.getCenter()).getDeterminantRadius()
559 else:
560 # Gen3: use the visitSummary dataRef
561 summary = dataRef.get()
562
563 summaryRow = summary.find(self.config.referenceCCD)
564 if summaryRow is None:
565 # Take the first available ccd if reference isn't available
566 summaryRow = summary[0]
567
568 summaryDetector = summaryRow['id']
569 visitInfo = summaryRow.getVisitInfo()
570 physicalFilter = summaryRow['physical_filter']
571 # Compute the median psf sigma if possible
572 goodSigma, = np.where(summary['psfSigma'] > 0)
573 if goodSigma.size > 2:
574 psfSigma = np.median(summary['psfSigma'][goodSigma])
575 elif goodSigma > 0:
576 psfSigma = np.mean(summary['psfSigma'][goodSigma])
577 else:
578 psfSigma = 0.0
579
580 rec = visitCat[i]
581 rec['visit'] = visit
582 rec['physicalFilter'] = physicalFilter
583 # TODO DM-26991: when gen2 is removed, gen3 workflow will make it
584 # much easier to get the wcs's necessary to recompute the pointing
585 # ra/dec at the center of the camera.
586 radec = visitInfo.getBoresightRaDec()
587 rec['telra'] = radec.getRa().asDegrees()
588 rec['teldec'] = radec.getDec().asDegrees()
589 rec['telha'] = visitInfo.getBoresightHourAngle().asDegrees()
590 rec['telrot'] = visitInfo.getBoresightRotAngle().asDegrees()
591 rec['mjd'] = visitInfo.getDate().get(system=DateTime.MJD)
592 rec['exptime'] = visitInfo.getExposureTime()
593 # convert from Pa to millibar
594 # Note that I don't know if this unit will need to be per-camera config
595 rec['pmb'] = visitInfo.getWeather().getAirPressure() / 100
596 # Flag to signify if this is a "deep" field. Not currently used
597 rec['deepFlag'] = 0
598 # Relative flat scaling (1.0 means no relative scaling)
599 rec['scaling'][:] = 1.0
600 # Median delta aperture, to be measured from stars
601 rec['deltaAper'] = 0.0
602 rec['psfSigma'] = psfSigma
603
604 if self.config.doModelErrorsWithBackground:
605 foundBkg = False
606 if isinstance(dataRef, dafPersist.ButlerDataRef):
607 # Gen2-style dataRef
608 det = dataRef.dataId[self.config.ccdDataRefName]
609 if dataRef.datasetExists(datasetType='calexpBackground'):
610 bgList = dataRef.get(datasetType='calexpBackground')
611 foundBkg = True
612 else:
613 # Gen3-style dataRef
614 try:
615 # Use the same detector used from the summary.
616 bkgRef = bkgDataRefDict[(visit, summaryDetector)]
617 bgList = bkgRef.get()
618 foundBkg = True
619 except KeyError:
620 pass
621
622 if foundBkg:
623 bgStats = (bg[0].getStatsImage().getImage().array
624 for bg in bgList)
625 rec['skyBackground'] = sum(np.median(bg[np.isfinite(bg)]) for bg in bgStats)
626 else:
627 self.log.warning('Sky background not found for visit %d / ccd %d' %
628 (visit, det))
629 rec['skyBackground'] = -1.0
630 else:
631 rec['skyBackground'] = -1.0
632
633 rec['used'] = 1
634
635 def _makeSourceMapper(self, sourceSchema):
636 """
637 Make a schema mapper for fgcm sources
638
639 Parameters
640 ----------
641 sourceSchema: `afwTable.Schema`
642 Default source schema from the butler
643
644 Returns
645 -------
646 sourceMapper: `afwTable.schemaMapper`
647 Mapper to the FGCM source schema
648 """
649
650 # create a mapper to the preferred output
651 sourceMapper = afwTable.SchemaMapper(sourceSchema)
652
653 # map to ra/dec
654 sourceMapper.addMapping(sourceSchema['coord_ra'].asKey(), 'ra')
655 sourceMapper.addMapping(sourceSchema['coord_dec'].asKey(), 'dec')
656 sourceMapper.addMapping(sourceSchema['slot_Centroid_x'].asKey(), 'x')
657 sourceMapper.addMapping(sourceSchema['slot_Centroid_y'].asKey(), 'y')
658 # Add the mapping if the field exists in the input catalog.
659 # If the field does not exist, simply add it (set to False).
660 # This field is not required for calibration, but is useful
661 # to collate if available.
662 try:
663 sourceMapper.addMapping(sourceSchema[self.config.psfCandidateName].asKey(),
664 'psf_candidate')
665 except LookupError:
666 sourceMapper.editOutputSchema().addField(
667 "psf_candidate", type='Flag',
668 doc=("Flag set if the source was a candidate for PSF determination, "
669 "as determined by the star selector."))
670
671 # and add the fields we want
672 sourceMapper.editOutputSchema().addField(
673 "visit", type=np.int32, doc="Visit number")
674 sourceMapper.editOutputSchema().addField(
675 "ccd", type=np.int32, doc="CCD number")
676 sourceMapper.editOutputSchema().addField(
677 "instMag", type=np.float32, doc="Instrumental magnitude")
678 sourceMapper.editOutputSchema().addField(
679 "instMagErr", type=np.float32, doc="Instrumental magnitude error")
680 sourceMapper.editOutputSchema().addField(
681 "jacobian", type=np.float32, doc="Relative pixel scale from wcs jacobian")
682 sourceMapper.editOutputSchema().addField(
683 "deltaMagBkg", type=np.float32, doc="Change in magnitude due to local background offset")
684
685 return sourceMapper
686
687 def fgcmMatchStars(self, visitCat, obsCat, lutDataRef=None):
688 """
689 Use FGCM code to match observations into unique stars.
690
691 Parameters
692 ----------
693 visitCat: `afw.table.BaseCatalog`
694 Catalog with visit data for fgcm
695 obsCat: `afw.table.BaseCatalog`
696 Full catalog of star observations for fgcm
697 lutDataRef: `lsst.daf.persistence.ButlerDataRef` or
698 `lsst.daf.butler.DeferredDatasetHandle`, optional
699 Data reference to fgcm look-up table (used if matching reference stars).
700
701 Returns
702 -------
703 fgcmStarIdCat: `afw.table.BaseCatalog`
704 Catalog of unique star identifiers and index keys
705 fgcmStarIndicesCat: `afwTable.BaseCatalog`
706 Catalog of unique star indices
707 fgcmRefCat: `afw.table.BaseCatalog`
708 Catalog of matched reference stars.
709 Will be None if `config.doReferenceMatches` is False.
710 """
711 # get filter names into a numpy array...
712 # This is the type that is expected by the fgcm code
713 visitFilterNames = np.zeros(len(visitCat), dtype='a30')
714 for i in range(len(visitCat)):
715 visitFilterNames[i] = visitCat[i]['physicalFilter']
716
717 # match to put filterNames with observations
718 visitIndex = np.searchsorted(visitCat['visit'],
719 obsCat['visit'])
720
721 obsFilterNames = visitFilterNames[visitIndex]
722
723 if self.config.doReferenceMatches:
724 # Get the reference filter names, using the LUT
725 lutCat = lutDataRef.get()
726
727 stdFilterDict = {filterName: stdFilter for (filterName, stdFilter) in
728 zip(lutCat[0]['physicalFilters'].split(','),
729 lutCat[0]['stdPhysicalFilters'].split(','))}
730 stdLambdaDict = {stdFilter: stdLambda for (stdFilter, stdLambda) in
731 zip(lutCat[0]['stdPhysicalFilters'].split(','),
732 lutCat[0]['lambdaStdFilter'])}
733
734 del lutCat
735
736 referenceFilterNames = self._getReferenceFilterNames_getReferenceFilterNames(visitCat,
737 stdFilterDict,
738 stdLambdaDict)
739 self.log.info("Using the following reference filters: %s" %
740 (', '.join(referenceFilterNames)))
741
742 else:
743 # This should be an empty list
744 referenceFilterNames = []
745
746 # make the fgcm starConfig dict
747 starConfig = {'logger': self.log,
748 'filterToBand': self.config.physicalFilterMap,
749 'requiredBands': self.config.requiredBands,
750 'minPerBand': self.config.minPerBand,
751 'matchRadius': self.config.matchRadius,
752 'isolationRadius': self.config.isolationRadius,
753 'matchNSide': self.config.matchNside,
754 'coarseNSide': self.config.coarseNside,
755 'densNSide': self.config.densityCutNside,
756 'densMaxPerPixel': self.config.densityCutMaxPerPixel,
757 'randomSeed': self.config.randomSeed,
758 'primaryBands': self.config.primaryBands,
759 'referenceFilterNames': referenceFilterNames}
760
761 # initialize the FgcmMakeStars object
762 fgcmMakeStars = fgcm.FgcmMakeStars(starConfig)
763
764 # make the primary stars
765 # note that the ra/dec native Angle format is radians
766 # We determine the conversion from the native units (typically
767 # radians) to degrees for the first observation. This allows us
768 # to treate ra/dec as numpy arrays rather than Angles, which would
769 # be approximately 600x slower.
770 conv = obsCat[0]['ra'].asDegrees() / float(obsCat[0]['ra'])
771 fgcmMakeStars.makePrimaryStars(obsCat['ra'] * conv,
772 obsCat['dec'] * conv,
773 filterNameArray=obsFilterNames,
774 bandSelected=False)
775
776 # and match all the stars
777 fgcmMakeStars.makeMatchedStars(obsCat['ra'] * conv,
778 obsCat['dec'] * conv,
779 obsFilterNames)
780
781 if self.config.doReferenceMatches:
782 fgcmMakeStars.makeReferenceMatches(self.fgcmLoadReferenceCatalog)
783
784 # now persist
785
786 objSchema = self._makeFgcmObjSchema_makeFgcmObjSchema()
787
788 # make catalog and records
789 fgcmStarIdCat = afwTable.BaseCatalog(objSchema)
790 fgcmStarIdCat.reserve(fgcmMakeStars.objIndexCat.size)
791 for i in range(fgcmMakeStars.objIndexCat.size):
792 fgcmStarIdCat.addNew()
793
794 # fill the catalog
795 fgcmStarIdCat['fgcm_id'][:] = fgcmMakeStars.objIndexCat['fgcm_id']
796 fgcmStarIdCat['ra'][:] = fgcmMakeStars.objIndexCat['ra']
797 fgcmStarIdCat['dec'][:] = fgcmMakeStars.objIndexCat['dec']
798 fgcmStarIdCat['obsArrIndex'][:] = fgcmMakeStars.objIndexCat['obsarrindex']
799 fgcmStarIdCat['nObs'][:] = fgcmMakeStars.objIndexCat['nobs']
800
801 obsSchema = self._makeFgcmObsSchema_makeFgcmObsSchema()
802
803 fgcmStarIndicesCat = afwTable.BaseCatalog(obsSchema)
804 fgcmStarIndicesCat.reserve(fgcmMakeStars.obsIndexCat.size)
805 for i in range(fgcmMakeStars.obsIndexCat.size):
806 fgcmStarIndicesCat.addNew()
807
808 fgcmStarIndicesCat['obsIndex'][:] = fgcmMakeStars.obsIndexCat['obsindex']
809
810 if self.config.doReferenceMatches:
811 refSchema = self._makeFgcmRefSchema_makeFgcmRefSchema(len(referenceFilterNames))
812
813 fgcmRefCat = afwTable.BaseCatalog(refSchema)
814 fgcmRefCat.reserve(fgcmMakeStars.referenceCat.size)
815
816 for i in range(fgcmMakeStars.referenceCat.size):
817 fgcmRefCat.addNew()
818
819 fgcmRefCat['fgcm_id'][:] = fgcmMakeStars.referenceCat['fgcm_id']
820 fgcmRefCat['refMag'][:, :] = fgcmMakeStars.referenceCat['refMag']
821 fgcmRefCat['refMagErr'][:, :] = fgcmMakeStars.referenceCat['refMagErr']
822
823 md = PropertyList()
824 md.set("REFSTARS_FORMAT_VERSION", REFSTARS_FORMAT_VERSION)
825 md.set("FILTERNAMES", referenceFilterNames)
826 fgcmRefCat.setMetadata(md)
827
828 else:
829 fgcmRefCat = None
830
831 return fgcmStarIdCat, fgcmStarIndicesCat, fgcmRefCat
832
833 def _makeFgcmVisitSchema(self, nCcd):
834 """
835 Make a schema for an fgcmVisitCatalog
836
837 Parameters
838 ----------
839 nCcd: `int`
840 Number of CCDs in the camera
841
842 Returns
843 -------
844 schema: `afwTable.Schema`
845 """
846
847 schema = afwTable.Schema()
848 schema.addField('visit', type=np.int32, doc="Visit number")
849 schema.addField('physicalFilter', type=str, size=30, doc="Physical filter")
850 schema.addField('telra', type=np.float64, doc="Pointing RA (deg)")
851 schema.addField('teldec', type=np.float64, doc="Pointing Dec (deg)")
852 schema.addField('telha', type=np.float64, doc="Pointing Hour Angle (deg)")
853 schema.addField('telrot', type=np.float64, doc="Camera rotation (deg)")
854 schema.addField('mjd', type=np.float64, doc="MJD of visit")
855 schema.addField('exptime', type=np.float32, doc="Exposure time")
856 schema.addField('pmb', type=np.float32, doc="Pressure (millibar)")
857 schema.addField('psfSigma', type=np.float32, doc="PSF sigma (reference CCD)")
858 schema.addField('deltaAper', type=np.float32, doc="Delta-aperture")
859 schema.addField('skyBackground', type=np.float32, doc="Sky background (ADU) (reference CCD)")
860 # the following field is not used yet
861 schema.addField('deepFlag', type=np.int32, doc="Deep observation")
862 schema.addField('scaling', type='ArrayD', doc="Scaling applied due to flat adjustment",
863 size=nCcd)
864 schema.addField('used', type=np.int32, doc="This visit has been ingested.")
865 schema.addField('sources_read', type='Flag', doc="This visit had sources read.")
866
867 return schema
868
869 def _makeFgcmObjSchema(self):
870 """
871 Make a schema for the objIndexCat from fgcmMakeStars
872
873 Returns
874 -------
875 schema: `afwTable.Schema`
876 """
877
878 objSchema = afwTable.Schema()
879 objSchema.addField('fgcm_id', type=np.int32, doc='FGCM Unique ID')
880 # Will investigate making these angles...
881 objSchema.addField('ra', type=np.float64, doc='Mean object RA (deg)')
882 objSchema.addField('dec', type=np.float64, doc='Mean object Dec (deg)')
883 objSchema.addField('obsArrIndex', type=np.int32,
884 doc='Index in obsIndexTable for first observation')
885 objSchema.addField('nObs', type=np.int32, doc='Total number of observations')
886
887 return objSchema
888
889 def _makeFgcmObsSchema(self):
890 """
891 Make a schema for the obsIndexCat from fgcmMakeStars
892
893 Returns
894 -------
895 schema: `afwTable.Schema`
896 """
897
898 obsSchema = afwTable.Schema()
899 obsSchema.addField('obsIndex', type=np.int32, doc='Index in observation table')
900
901 return obsSchema
902
903 def _makeFgcmRefSchema(self, nReferenceBands):
904 """
905 Make a schema for the referenceCat from fgcmMakeStars
906
907 Parameters
908 ----------
909 nReferenceBands: `int`
910 Number of reference bands
911
912 Returns
913 -------
914 schema: `afwTable.Schema`
915 """
916
917 refSchema = afwTable.Schema()
918 refSchema.addField('fgcm_id', type=np.int32, doc='FGCM Unique ID')
919 refSchema.addField('refMag', type='ArrayF', doc='Reference magnitude array (AB)',
920 size=nReferenceBands)
921 refSchema.addField('refMagErr', type='ArrayF', doc='Reference magnitude error array',
922 size=nReferenceBands)
923
924 return refSchema
925
926 def _getReferenceFilterNames(self, visitCat, stdFilterDict, stdLambdaDict):
927 """
928 Get the reference filter names, in wavelength order, from the visitCat and
929 information from the look-up-table.
930
931 Parameters
932 ----------
933 visitCat: `afw.table.BaseCatalog`
934 Catalog with visit data for FGCM
935 stdFilterDict: `dict`
936 Mapping of filterName to stdFilterName from LUT
937 stdLambdaDict: `dict`
938 Mapping of stdFilterName to stdLambda from LUT
939
940 Returns
941 -------
942 referenceFilterNames: `list`
943 Wavelength-ordered list of reference filter names
944 """
945
946 # Find the unique list of filter names in visitCat
947 filterNames = np.unique(visitCat.asAstropy()['physicalFilter'])
948
949 # Find the unique list of "standard" filters
950 stdFilterNames = {stdFilterDict[filterName] for filterName in filterNames}
951
952 # And sort these by wavelength
953 referenceFilterNames = sorted(stdFilterNames, key=stdLambdaDict.get)
954
955 return referenceFilterNames
def fgcmMatchStars(self, visitCat, obsCat, lutDataRef=None)
def _getReferenceFilterNames(self, visitCat, stdFilterDict, stdLambdaDict)
def _fillVisitCatalog(self, visitCat, groupedDataRefs, bkgDataRefDict=None, visitCatDataRef=None)
def fgcmMakeAllStarObservations(self, groupedDataRefs, visitCat, sourceSchema, camera, calibFluxApertureRadius=None, visitCatDataRef=None, starObsDataRef=None, inStarObsCat=None)
def __init__(self, initInputs=None, butler=None, **kwargs)
def fgcmMakeVisitCatalog(self, camera, groupedDataRefs, bkgDataRefDict=None, visitCatDataRef=None, inVisitCat=None)
def _findAndGroupDataRefsGen2(self, butler, camera, dataRefs)
def computeApertureRadiusFromDataRef(dataRef, fluxField)
Definition: utilities.py:812