Coverage for python/lsst/fgcmcal/fgcmBuildStarsBase.py : 17%
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 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"""
24import os
25import sys
26import traceback
27import abc
29import numpy as np
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
40from .utilities import computeApertureRadiusFromDataRef
41from .fgcmLoadReferenceCatalog import FgcmLoadReferenceCatalogTask
43import fgcm
45REFSTARS_FORMAT_VERSION = 1
47__all__ = ['FgcmBuildStarsConfigBase', 'FgcmBuildStarsRunner', 'FgcmBuildStarsBaseTask']
50class FgcmBuildStarsConfigBase(pexConfig.Config):
51 """Base config for FgcmBuildStars tasks"""
53 instFluxField = pexConfig.Field(
54 doc=("Faull name of the source instFlux field to use, including 'instFlux'. "
55 "The associated flag will be implicitly included in badFlags"),
56 dtype=str,
57 default='slot_CalibFlux_instFlux',
58 )
59 minPerBand = pexConfig.Field(
60 doc="Minimum observations per band",
61 dtype=int,
62 default=2,
63 )
64 matchRadius = pexConfig.Field(
65 doc="Match radius (arcseconds)",
66 dtype=float,
67 default=1.0,
68 )
69 isolationRadius = pexConfig.Field(
70 doc="Isolation radius (arcseconds)",
71 dtype=float,
72 default=2.0,
73 )
74 densityCutNside = pexConfig.Field(
75 doc="Density cut healpix nside",
76 dtype=int,
77 default=128,
78 )
79 densityCutMaxPerPixel = pexConfig.Field(
80 doc="Density cut number of stars per pixel",
81 dtype=int,
82 default=1000,
83 )
84 matchNside = pexConfig.Field(
85 doc="Healpix Nside for matching",
86 dtype=int,
87 default=4096,
88 )
89 coarseNside = pexConfig.Field(
90 doc="Healpix coarse Nside for partitioning matches",
91 dtype=int,
92 default=8,
93 )
94 filterMap = pexConfig.DictField(
95 doc="Mapping from 'filterName' to band.",
96 keytype=str,
97 itemtype=str,
98 default={},
99 deprecated=("This field is no longer used, and has been deprecated by "
100 "DM-28088. It will be removed after v22. Use "
101 "physicalFilterMap instead.")
102 )
103 # The following config will not be necessary after Gen2 retirement.
104 # In the meantime, obs packages should set to 'filterDefinitions.filter_to_band'
105 # which is easiest to access in the config file.
106 physicalFilterMap = pexConfig.DictField(
107 doc="Mapping from 'physicalFilter' to band.",
108 keytype=str,
109 itemtype=str,
110 default={},
111 )
112 requiredBands = pexConfig.ListField(
113 doc="Bands required for each star",
114 dtype=str,
115 default=(),
116 )
117 primaryBands = pexConfig.ListField(
118 doc=("Bands for 'primary' star matches. "
119 "A star must be observed in one of these bands to be considered "
120 "as a calibration star."),
121 dtype=str,
122 default=None
123 )
124 visitDataRefName = pexConfig.Field(
125 doc="dataRef name for the 'visit' field, usually 'visit'.",
126 dtype=str,
127 default="visit"
128 )
129 ccdDataRefName = pexConfig.Field(
130 doc="dataRef name for the 'ccd' field, usually 'ccd' or 'detector'.",
131 dtype=str,
132 default="ccd"
133 )
134 doApplyWcsJacobian = pexConfig.Field(
135 doc="Apply the jacobian of the WCS to the star observations prior to fit?",
136 dtype=bool,
137 default=True
138 )
139 doModelErrorsWithBackground = pexConfig.Field(
140 doc="Model flux errors with background term?",
141 dtype=bool,
142 default=True
143 )
144 psfCandidateName = pexConfig.Field(
145 doc="Name of field with psf candidate flag for propagation",
146 dtype=str,
147 default="calib_psf_candidate"
148 )
149 doSubtractLocalBackground = pexConfig.Field(
150 doc=("Subtract the local background before performing calibration? "
151 "This is only supported for circular aperture calibration fluxes."),
152 dtype=bool,
153 default=False
154 )
155 localBackgroundFluxField = pexConfig.Field(
156 doc="Full name of the local background instFlux field to use.",
157 dtype=str,
158 default='base_LocalBackground_instFlux'
159 )
160 sourceSelector = sourceSelectorRegistry.makeField(
161 doc="How to select sources",
162 default="science"
163 )
164 apertureInnerInstFluxField = pexConfig.Field(
165 doc=("Full name of instFlux field that contains inner aperture "
166 "flux for aperture correction proxy"),
167 dtype=str,
168 default='base_CircularApertureFlux_12_0_instFlux'
169 )
170 apertureOuterInstFluxField = pexConfig.Field(
171 doc=("Full name of instFlux field that contains outer aperture "
172 "flux for aperture correction proxy"),
173 dtype=str,
174 default='base_CircularApertureFlux_17_0_instFlux'
175 )
176 doReferenceMatches = pexConfig.Field(
177 doc="Match reference catalog as additional constraint on calibration",
178 dtype=bool,
179 default=True,
180 )
181 fgcmLoadReferenceCatalog = pexConfig.ConfigurableField(
182 target=FgcmLoadReferenceCatalogTask,
183 doc="FGCM reference object loader",
184 )
185 nVisitsPerCheckpoint = pexConfig.Field(
186 doc="Number of visits read between checkpoints",
187 dtype=int,
188 default=500,
189 )
191 def setDefaults(self):
192 sourceSelector = self.sourceSelector["science"]
193 sourceSelector.setDefaults()
195 sourceSelector.doFlags = True
196 sourceSelector.doUnresolved = True
197 sourceSelector.doSignalToNoise = True
198 sourceSelector.doIsolated = True
200 sourceSelector.signalToNoise.minimum = 10.0
201 sourceSelector.signalToNoise.maximum = 1000.0
203 # FGCM operates on unresolved sources, and this setting is
204 # appropriate for the current base_ClassificationExtendedness
205 sourceSelector.unresolved.maximum = 0.5
208class FgcmBuildStarsRunner(pipeBase.ButlerInitializedTaskRunner):
209 """Subclass of TaskRunner for FgcmBuildStars tasks
211 fgcmBuildStarsTask.run() and fgcmBuildStarsTableTask.run() take a number of
212 arguments, one of which is the butler (for persistence and mapper data),
213 and a list of dataRefs extracted from the command line. Note that FGCM
214 runs on a large set of dataRefs, and not on single dataRef/tract/patch.
215 This class transforms the process arguments generated by the ArgumentParser
216 into the arguments expected by FgcmBuildStarsTask.run(). This runner does
217 not use any parallelization.
218 """
219 @staticmethod
220 def getTargetList(parsedCmd):
221 """
222 Return a list with one element: a tuple with the butler and
223 list of dataRefs
224 """
225 # we want to combine the butler with any (or no!) dataRefs
226 return [(parsedCmd.butler, parsedCmd.id.refList)]
228 def __call__(self, args):
229 """
230 Parameters
231 ----------
232 args: `tuple` with (butler, dataRefList)
234 Returns
235 -------
236 exitStatus: `list` with `lsst.pipe.base.Struct`
237 exitStatus (0: success; 1: failure)
238 """
239 butler, dataRefList = args
241 task = self.TaskClass(config=self.config, log=self.log)
243 exitStatus = 0
244 if self.doRaise:
245 task.runDataRef(butler, dataRefList)
246 else:
247 try:
248 task.runDataRef(butler, dataRefList)
249 except Exception as e:
250 exitStatus = 1
251 task.log.fatal("Failed: %s" % e)
252 if not isinstance(e, pipeBase.TaskError):
253 traceback.print_exc(file=sys.stderr)
255 task.writeMetadata(butler)
257 # The task does not return any results:
258 return [pipeBase.Struct(exitStatus=exitStatus)]
260 def run(self, parsedCmd):
261 """
262 Run the task, with no multiprocessing
264 Parameters
265 ----------
266 parsedCmd: `lsst.pipe.base.ArgumentParser` parsed command line
267 """
269 resultList = []
271 if self.precall(parsedCmd):
272 targetList = self.getTargetList(parsedCmd)
273 resultList = self(targetList[0])
275 return resultList
278class FgcmBuildStarsBaseTask(pipeBase.PipelineTask, pipeBase.CmdLineTask, abc.ABC):
279 """
280 Base task to build stars for FGCM global calibration
282 Parameters
283 ----------
284 butler : `lsst.daf.persistence.Butler`
285 """
286 def __init__(self, initInputs=None, butler=None, **kwargs):
287 super().__init__(**kwargs)
289 self.makeSubtask("sourceSelector")
290 # Only log warning and fatal errors from the sourceSelector
291 self.sourceSelector.log.setLevel(self.sourceSelector.log.WARN)
293 # no saving of metadata for now
294 def _getMetadataName(self):
295 return None
297 @pipeBase.timeMethod
298 def runDataRef(self, butler, dataRefs):
299 """
300 Cross-match and make star list for FGCM Input
302 Parameters
303 ----------
304 butler: `lsst.daf.persistence.Butler`
305 dataRefs: `list` of `lsst.daf.persistence.ButlerDataRef`
306 Source data references for the input visits.
308 Raises
309 ------
310 RuntimeErrror: Raised if `config.doReferenceMatches` is set and
311 an fgcmLookUpTable is not available, or if computeFluxApertureRadius()
312 fails if the calibFlux is not a CircularAperture flux.
313 """
314 datasetType = dataRefs[0].butlerSubset.datasetType
315 self.log.info("Running with %d %s dataRefs", len(dataRefs), datasetType)
317 if self.config.doReferenceMatches:
318 self.makeSubtask("fgcmLoadReferenceCatalog", butler=butler)
319 # Ensure that we have a LUT
320 if not butler.datasetExists('fgcmLookUpTable'):
321 raise RuntimeError("Must have fgcmLookUpTable if using config.doReferenceMatches")
322 # Compute aperture radius if necessary. This is useful to do now before
323 # any heavy lifting has happened (fail early).
324 calibFluxApertureRadius = None
325 if self.config.doSubtractLocalBackground:
326 try:
327 calibFluxApertureRadius = computeApertureRadiusFromDataRef(dataRefs[0],
328 self.config.instFluxField)
329 except RuntimeError as e:
330 raise RuntimeError("Could not determine aperture radius from %s. "
331 "Cannot use doSubtractLocalBackground." %
332 (self.config.instFluxField)) from e
334 camera = butler.get('camera')
335 groupedDataRefs = self._findAndGroupDataRefsGen2(butler, camera, dataRefs)
337 # Make the visit catalog if necessary
338 # First check if the visit catalog is in the _current_ path
339 # We cannot use Gen2 datasetExists() because that checks all parent
340 # directories as well, which would make recovering from faults
341 # and fgcmcal reruns impossible.
342 visitCatDataRef = butler.dataRef('fgcmVisitCatalog')
343 filename = visitCatDataRef.get('fgcmVisitCatalog_filename')[0]
344 if os.path.exists(filename):
345 # This file exists and we should continue processing
346 inVisitCat = visitCatDataRef.get()
347 if len(inVisitCat) != len(groupedDataRefs):
348 raise RuntimeError("Existing visitCatalog found, but has an inconsistent "
349 "number of visits. Cannot continue.")
350 else:
351 inVisitCat = None
353 visitCat = self.fgcmMakeVisitCatalog(camera, groupedDataRefs,
354 visitCatDataRef=visitCatDataRef,
355 inVisitCat=inVisitCat)
357 # Persist the visitCat as a checkpoint file.
358 visitCatDataRef.put(visitCat)
360 starObsDataRef = butler.dataRef('fgcmStarObservations')
361 filename = starObsDataRef.get('fgcmStarObservations_filename')[0]
362 if os.path.exists(filename):
363 inStarObsCat = starObsDataRef.get()
364 else:
365 inStarObsCat = None
367 rad = calibFluxApertureRadius
368 sourceSchemaDataRef = butler.dataRef('src_schema')
369 sourceSchema = sourceSchemaDataRef.get('src_schema', immediate=True).schema
370 fgcmStarObservationCat = self.fgcmMakeAllStarObservations(groupedDataRefs,
371 visitCat,
372 sourceSchema,
373 camera,
374 calibFluxApertureRadius=rad,
375 starObsDataRef=starObsDataRef,
376 visitCatDataRef=visitCatDataRef,
377 inStarObsCat=inStarObsCat)
378 visitCatDataRef.put(visitCat)
379 starObsDataRef.put(fgcmStarObservationCat)
381 # Always do the matching.
382 if self.config.doReferenceMatches:
383 lutDataRef = butler.dataRef('fgcmLookUpTable')
384 else:
385 lutDataRef = None
386 fgcmStarIdCat, fgcmStarIndicesCat, fgcmRefCat = self.fgcmMatchStars(visitCat,
387 fgcmStarObservationCat,
388 lutDataRef=lutDataRef)
390 # Persist catalogs via the butler
391 butler.put(fgcmStarIdCat, 'fgcmStarIds')
392 butler.put(fgcmStarIndicesCat, 'fgcmStarIndices')
393 if fgcmRefCat is not None:
394 butler.put(fgcmRefCat, 'fgcmReferenceStars')
396 @abc.abstractmethod
397 def _findAndGroupDataRefsGen2(self, butler, camera, dataRefs):
398 """
399 Find and group dataRefs (by visit); Gen2 only.
401 Parameters
402 ----------
403 butler : `lsst.daf.persistence.Butler`
404 Gen2 butler.
405 camera : `lsst.afw.cameraGeom.Camera`
406 Camera from the butler.
407 dataRefs : `list` of `lsst.daf.persistence.ButlerDataRef`
408 Data references for the input visits.
410 Returns
411 -------
412 groupedDataRefs : `dict` [`int`, `list`]
413 Dictionary with sorted visit keys, and `list`s of
414 `lsst.daf.persistence.ButlerDataRef`
415 """
416 raise NotImplementedError("_findAndGroupDataRefsGen2 not implemented.")
418 @abc.abstractmethod
419 def fgcmMakeAllStarObservations(self, groupedDataRefs, visitCat,
420 sourceSchema,
421 camera,
422 calibFluxApertureRadius=None,
423 visitCatDataRef=None,
424 starObsDataRef=None,
425 inStarObsCat=None):
426 """
427 Compile all good star observations from visits in visitCat. Checkpoint files
428 will be stored if both visitCatDataRef and starObsDataRef are not None.
430 Parameters
431 ----------
432 groupedDataRefs : `dict` of `list`s
433 Lists of `~lsst.daf.persistence.ButlerDataRef` or
434 `~lsst.daf.butler.DeferredDatasetHandle`, grouped by visit.
435 visitCat : `~afw.table.BaseCatalog`
436 Catalog with visit data for FGCM
437 sourceSchema : `~lsst.afw.table.Schema`
438 Schema for the input src catalogs.
439 camera : `~lsst.afw.cameraGeom.Camera`
440 calibFluxApertureRadius : `float`, optional
441 Aperture radius for calibration flux.
442 visitCatDataRef : `~lsst.daf.persistence.ButlerDataRef`, optional
443 Dataref to write visitCat for checkpoints
444 starObsDataRef : `~lsst.daf.persistence.ButlerDataRef`, optional
445 Dataref to write the star observation catalog for checkpoints.
446 inStarObsCat : `~afw.table.BaseCatalog`
447 Input observation catalog. If this is incomplete, observations
448 will be appended from when it was cut off.
450 Returns
451 -------
452 fgcmStarObservations : `afw.table.BaseCatalog`
453 Full catalog of good observations.
455 Raises
456 ------
457 RuntimeError: Raised if doSubtractLocalBackground is True and
458 calibFluxApertureRadius is not set.
459 """
460 raise NotImplementedError("fgcmMakeAllStarObservations not implemented.")
462 def fgcmMakeVisitCatalog(self, camera, groupedDataRefs, bkgDataRefDict=None,
463 visitCatDataRef=None, inVisitCat=None):
464 """
465 Make a visit catalog with all the keys from each visit
467 Parameters
468 ----------
469 camera: `lsst.afw.cameraGeom.Camera`
470 Camera from the butler
471 groupedDataRefs: `dict`
472 Dictionary with visit keys, and `list`s of
473 `lsst.daf.persistence.ButlerDataRef`
474 bkgDataRefDict: `dict`, optional
475 Dictionary of gen3 dataRefHandles for background info.
476 visitCatDataRef: `lsst.daf.persistence.ButlerDataRef`, optional
477 Dataref to write visitCat for checkpoints
478 inVisitCat: `afw.table.BaseCatalog`, optional
479 Input (possibly incomplete) visit catalog
481 Returns
482 -------
483 visitCat: `afw.table.BaseCatalog`
484 """
486 self.log.info("Assembling visitCatalog from %d %ss" %
487 (len(groupedDataRefs), self.config.visitDataRefName))
489 nCcd = len(camera)
491 if inVisitCat is None:
492 schema = self._makeFgcmVisitSchema(nCcd)
494 visitCat = afwTable.BaseCatalog(schema)
495 visitCat.reserve(len(groupedDataRefs))
496 visitCat.resize(len(groupedDataRefs))
498 visitCat['visit'] = list(groupedDataRefs.keys())
499 visitCat['used'] = 0
500 visitCat['sources_read'] = False
501 else:
502 visitCat = inVisitCat
504 # No matter what, fill the catalog. This will check if it was
505 # already read.
506 self._fillVisitCatalog(visitCat, groupedDataRefs,
507 bkgDataRefDict=bkgDataRefDict,
508 visitCatDataRef=visitCatDataRef)
510 return visitCat
512 def _fillVisitCatalog(self, visitCat, groupedDataRefs, bkgDataRefDict=None,
513 visitCatDataRef=None):
514 """
515 Fill the visit catalog with visit metadata
517 Parameters
518 ----------
519 visitCat : `afw.table.BaseCatalog`
520 Visit catalog. See _makeFgcmVisitSchema() for schema definition.
521 groupedDataRefs : `dict`
522 Dictionary with visit keys, and `list`s of
523 `lsst.daf.persistence.ButlerDataRef` or
524 `lsst.daf.butler.DeferredDatasetHandle`
525 visitCatDataRef : `lsst.daf.persistence.ButlerDataRef`, optional
526 Dataref to write ``visitCat`` for checkpoints. Gen2 only.
527 bkgDataRefDict : `dict`, optional
528 Dictionary of Gen3 `lsst.daf.butler.DeferredDatasetHandle`
529 for background info.
530 """
531 bbox = geom.BoxI(geom.PointI(0, 0), geom.PointI(1, 1))
533 for i, visit in enumerate(groupedDataRefs):
534 # We don't use the bypasses since we need the psf info which does
535 # not have a bypass
536 # TODO: When DM-15500 is implemented in the Gen3 Butler, this
537 # can be fixed
539 # Do not read those that have already been read
540 if visitCat['used'][i]:
541 continue
543 if (i % self.config.nVisitsPerCheckpoint) == 0:
544 self.log.info("Retrieving metadata for %s %d (%d/%d)" %
545 (self.config.visitDataRefName, visit, i, len(groupedDataRefs)))
546 # Save checkpoint if desired
547 if visitCatDataRef is not None:
548 visitCatDataRef.put(visitCat)
550 dataRef = groupedDataRefs[visit][0]
551 if isinstance(dataRef, dafPersist.ButlerDataRef):
552 # Gen2: calexp dataRef
553 # The first dataRef in the group will be the reference ccd (if available)
554 exp = dataRef.get(datasetType='calexp_sub', bbox=bbox)
555 visitInfo = exp.getInfo().getVisitInfo()
556 label = dataRef.get(datasetType='calexp_filterLabel')
557 physicalFilter = label.physicalLabel
558 psf = exp.getPsf()
559 psfSigma = psf.computeShape().getDeterminantRadius()
560 else:
561 # Gen3: use the visitSummary dataRef
562 summary = dataRef.get()
564 summaryRow = summary.find(self.config.referenceCCD)
565 if summaryRow is None:
566 # Take the first available ccd if reference isn't available
567 summaryRow = summary[0]
569 summaryDetector = summaryRow['id']
570 visitInfo = summaryRow.getVisitInfo()
571 physicalFilter = summaryRow['physical_filter']
572 # Compute the median psf sigma if possible
573 goodSigma, = np.where(summary['psfSigma'] > 0)
574 if goodSigma.size > 2:
575 psfSigma = np.median(summary['psfSigma'][goodSigma])
576 elif goodSigma > 0:
577 psfSigma = np.mean(summary['psfSigma'][goodSigma])
578 else:
579 psfSigma = 0.0
581 rec = visitCat[i]
582 rec['visit'] = visit
583 rec['physicalFilter'] = physicalFilter
584 # TODO DM-26991: when gen2 is removed, gen3 workflow will make it
585 # much easier to get the wcs's necessary to recompute the pointing
586 # ra/dec at the center of the camera.
587 radec = visitInfo.getBoresightRaDec()
588 rec['telra'] = radec.getRa().asDegrees()
589 rec['teldec'] = radec.getDec().asDegrees()
590 rec['telha'] = visitInfo.getBoresightHourAngle().asDegrees()
591 rec['telrot'] = visitInfo.getBoresightRotAngle().asDegrees()
592 rec['mjd'] = visitInfo.getDate().get(system=DateTime.MJD)
593 rec['exptime'] = visitInfo.getExposureTime()
594 # convert from Pa to millibar
595 # Note that I don't know if this unit will need to be per-camera config
596 rec['pmb'] = visitInfo.getWeather().getAirPressure() / 100
597 # Flag to signify if this is a "deep" field. Not currently used
598 rec['deepFlag'] = 0
599 # Relative flat scaling (1.0 means no relative scaling)
600 rec['scaling'][:] = 1.0
601 # Median delta aperture, to be measured from stars
602 rec['deltaAper'] = 0.0
603 rec['psfSigma'] = psfSigma
605 if self.config.doModelErrorsWithBackground:
606 foundBkg = False
607 if isinstance(dataRef, dafPersist.ButlerDataRef):
608 # Gen2-style dataRef
609 det = dataRef.dataId[self.config.ccdDataRefName]
610 if dataRef.datasetExists(datasetType='calexpBackground'):
611 bgList = dataRef.get(datasetType='calexpBackground')
612 foundBkg = True
613 else:
614 # Gen3-style dataRef
615 try:
616 # Use the same detector used from the summary.
617 bkgRef = bkgDataRefDict[(visit, summaryDetector)]
618 bgList = bkgRef.get()
619 foundBkg = True
620 except KeyError:
621 pass
623 if foundBkg:
624 bgStats = (bg[0].getStatsImage().getImage().array
625 for bg in bgList)
626 rec['skyBackground'] = sum(np.median(bg[np.isfinite(bg)]) for bg in bgStats)
627 else:
628 self.log.warn('Sky background not found for visit %d / ccd %d' %
629 (visit, det))
630 rec['skyBackground'] = -1.0
631 else:
632 rec['skyBackground'] = -1.0
634 rec['used'] = 1
636 def _makeSourceMapper(self, sourceSchema):
637 """
638 Make a schema mapper for fgcm sources
640 Parameters
641 ----------
642 sourceSchema: `afwTable.Schema`
643 Default source schema from the butler
645 Returns
646 -------
647 sourceMapper: `afwTable.schemaMapper`
648 Mapper to the FGCM source schema
649 """
651 # create a mapper to the preferred output
652 sourceMapper = afwTable.SchemaMapper(sourceSchema)
654 # map to ra/dec
655 sourceMapper.addMapping(sourceSchema['coord_ra'].asKey(), 'ra')
656 sourceMapper.addMapping(sourceSchema['coord_dec'].asKey(), 'dec')
657 sourceMapper.addMapping(sourceSchema['slot_Centroid_x'].asKey(), 'x')
658 sourceMapper.addMapping(sourceSchema['slot_Centroid_y'].asKey(), 'y')
659 # Add the mapping if the field exists in the input catalog.
660 # If the field does not exist, simply add it (set to False).
661 # This field is not required for calibration, but is useful
662 # to collate if available.
663 try:
664 sourceMapper.addMapping(sourceSchema[self.config.psfCandidateName].asKey(),
665 'psf_candidate')
666 except LookupError:
667 sourceMapper.editOutputSchema().addField(
668 "psf_candidate", type='Flag',
669 doc=("Flag set if the source was a candidate for PSF determination, "
670 "as determined by the star selector."))
672 # and add the fields we want
673 sourceMapper.editOutputSchema().addField(
674 "visit", type=np.int32, doc="Visit number")
675 sourceMapper.editOutputSchema().addField(
676 "ccd", type=np.int32, doc="CCD number")
677 sourceMapper.editOutputSchema().addField(
678 "instMag", type=np.float32, doc="Instrumental magnitude")
679 sourceMapper.editOutputSchema().addField(
680 "instMagErr", type=np.float32, doc="Instrumental magnitude error")
681 sourceMapper.editOutputSchema().addField(
682 "jacobian", type=np.float32, doc="Relative pixel scale from wcs jacobian")
683 sourceMapper.editOutputSchema().addField(
684 "deltaMagBkg", type=np.float32, doc="Change in magnitude due to local background offset")
686 return sourceMapper
688 def fgcmMatchStars(self, visitCat, obsCat, lutDataRef=None):
689 """
690 Use FGCM code to match observations into unique stars.
692 Parameters
693 ----------
694 visitCat: `afw.table.BaseCatalog`
695 Catalog with visit data for fgcm
696 obsCat: `afw.table.BaseCatalog`
697 Full catalog of star observations for fgcm
698 lutDataRef: `lsst.daf.persistence.ButlerDataRef` or
699 `lsst.daf.butler.DeferredDatasetHandle`, optional
700 Data reference to fgcm look-up table (used if matching reference stars).
702 Returns
703 -------
704 fgcmStarIdCat: `afw.table.BaseCatalog`
705 Catalog of unique star identifiers and index keys
706 fgcmStarIndicesCat: `afwTable.BaseCatalog`
707 Catalog of unique star indices
708 fgcmRefCat: `afw.table.BaseCatalog`
709 Catalog of matched reference stars.
710 Will be None if `config.doReferenceMatches` is False.
711 """
712 # get filter names into a numpy array...
713 # This is the type that is expected by the fgcm code
714 visitFilterNames = np.zeros(len(visitCat), dtype='a30')
715 for i in range(len(visitCat)):
716 visitFilterNames[i] = visitCat[i]['physicalFilter']
718 # match to put filterNames with observations
719 visitIndex = np.searchsorted(visitCat['visit'],
720 obsCat['visit'])
722 obsFilterNames = visitFilterNames[visitIndex]
724 if self.config.doReferenceMatches:
725 # Get the reference filter names, using the LUT
726 lutCat = lutDataRef.get()
728 stdFilterDict = {filterName: stdFilter for (filterName, stdFilter) in
729 zip(lutCat[0]['physicalFilters'].split(','),
730 lutCat[0]['stdPhysicalFilters'].split(','))}
731 stdLambdaDict = {stdFilter: stdLambda for (stdFilter, stdLambda) in
732 zip(lutCat[0]['stdPhysicalFilters'].split(','),
733 lutCat[0]['lambdaStdFilter'])}
735 del lutCat
737 referenceFilterNames = self._getReferenceFilterNames(visitCat,
738 stdFilterDict,
739 stdLambdaDict)
740 self.log.info("Using the following reference filters: %s" %
741 (', '.join(referenceFilterNames)))
743 else:
744 # This should be an empty list
745 referenceFilterNames = []
747 # make the fgcm starConfig dict
748 starConfig = {'logger': self.log,
749 'filterToBand': self.config.physicalFilterMap,
750 'requiredBands': self.config.requiredBands,
751 'minPerBand': self.config.minPerBand,
752 'matchRadius': self.config.matchRadius,
753 'isolationRadius': self.config.isolationRadius,
754 'matchNSide': self.config.matchNside,
755 'coarseNSide': self.config.coarseNside,
756 'densNSide': self.config.densityCutNside,
757 'densMaxPerPixel': self.config.densityCutMaxPerPixel,
758 'primaryBands': self.config.primaryBands,
759 'referenceFilterNames': referenceFilterNames}
761 # initialize the FgcmMakeStars object
762 fgcmMakeStars = fgcm.FgcmMakeStars(starConfig)
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)
776 # and match all the stars
777 fgcmMakeStars.makeMatchedStars(obsCat['ra'] * conv,
778 obsCat['dec'] * conv,
779 obsFilterNames)
781 if self.config.doReferenceMatches:
782 fgcmMakeStars.makeReferenceMatches(self.fgcmLoadReferenceCatalog)
784 # now persist
786 objSchema = self._makeFgcmObjSchema()
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()
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']
801 obsSchema = self._makeFgcmObsSchema()
803 fgcmStarIndicesCat = afwTable.BaseCatalog(obsSchema)
804 fgcmStarIndicesCat.reserve(fgcmMakeStars.obsIndexCat.size)
805 for i in range(fgcmMakeStars.obsIndexCat.size):
806 fgcmStarIndicesCat.addNew()
808 fgcmStarIndicesCat['obsIndex'][:] = fgcmMakeStars.obsIndexCat['obsindex']
810 if self.config.doReferenceMatches:
811 refSchema = self._makeFgcmRefSchema(len(referenceFilterNames))
813 fgcmRefCat = afwTable.BaseCatalog(refSchema)
814 fgcmRefCat.reserve(fgcmMakeStars.referenceCat.size)
816 for i in range(fgcmMakeStars.referenceCat.size):
817 fgcmRefCat.addNew()
819 fgcmRefCat['fgcm_id'][:] = fgcmMakeStars.referenceCat['fgcm_id']
820 fgcmRefCat['refMag'][:, :] = fgcmMakeStars.referenceCat['refMag']
821 fgcmRefCat['refMagErr'][:, :] = fgcmMakeStars.referenceCat['refMagErr']
823 md = PropertyList()
824 md.set("REFSTARS_FORMAT_VERSION", REFSTARS_FORMAT_VERSION)
825 md.set("FILTERNAMES", referenceFilterNames)
826 fgcmRefCat.setMetadata(md)
828 else:
829 fgcmRefCat = None
831 return fgcmStarIdCat, fgcmStarIndicesCat, fgcmRefCat
833 def _makeFgcmVisitSchema(self, nCcd):
834 """
835 Make a schema for an fgcmVisitCatalog
837 Parameters
838 ----------
839 nCcd: `int`
840 Number of CCDs in the camera
842 Returns
843 -------
844 schema: `afwTable.Schema`
845 """
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.")
867 return schema
869 def _makeFgcmObjSchema(self):
870 """
871 Make a schema for the objIndexCat from fgcmMakeStars
873 Returns
874 -------
875 schema: `afwTable.Schema`
876 """
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')
887 return objSchema
889 def _makeFgcmObsSchema(self):
890 """
891 Make a schema for the obsIndexCat from fgcmMakeStars
893 Returns
894 -------
895 schema: `afwTable.Schema`
896 """
898 obsSchema = afwTable.Schema()
899 obsSchema.addField('obsIndex', type=np.int32, doc='Index in observation table')
901 return obsSchema
903 def _makeFgcmRefSchema(self, nReferenceBands):
904 """
905 Make a schema for the referenceCat from fgcmMakeStars
907 Parameters
908 ----------
909 nReferenceBands: `int`
910 Number of reference bands
912 Returns
913 -------
914 schema: `afwTable.Schema`
915 """
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)
924 return refSchema
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.
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
940 Returns
941 -------
942 referenceFilterNames: `list`
943 Wavelength-ordered list of reference filter names
944 """
946 # Find the unique list of filter names in visitCat
947 filterNames = np.unique(visitCat.asAstropy()['physicalFilter'])
949 # Find the unique list of "standard" filters
950 stdFilterNames = {stdFilterDict[filterName] for filterName in filterNames}
952 # And sort these by wavelength
953 referenceFilterNames = sorted(stdFilterNames, key=stdLambdaDict.get)
955 return referenceFilterNames