Coverage for python/lsst/fgcmcal/fgcmOutputProducts.py: 17%
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8# See the COPYRIGHT file at the top-level directory of this distribution
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10#
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23"""Make the final fgcmcal output products.
25This task takes the final output from fgcmFitCycle and produces the following
26outputs for use in the DM stack: the FGCM standard stars in a reference
27catalog format; the model atmospheres in "transmission_atmosphere_fgcm"
28format; and the zeropoints in "fgcm_photoCalib" format. Optionally, the
29task can transfer the 'absolute' calibration from a reference catalog
30to put the fgcm standard stars in units of Jansky. This is accomplished
31by matching stars in a sample of healpix pixels, and applying the median
32offset per band.
33"""
34import copy
36import numpy as np
37import hpgeom as hpg
38import esutil
39from astropy import units
41import lsst.daf.base as dafBase
42import lsst.pex.config as pexConfig
43import lsst.pipe.base as pipeBase
44from lsst.pipe.base import connectionTypes
45from lsst.afw.image import TransmissionCurve
46from lsst.meas.algorithms import LoadIndexedReferenceObjectsTask
47from lsst.meas.algorithms import ReferenceObjectLoader, LoadReferenceObjectsConfig
48from lsst.pipe.tasks.photoCal import PhotoCalTask
49import lsst.geom
50import lsst.afw.image as afwImage
51import lsst.afw.math as afwMath
52import lsst.afw.table as afwTable
53from lsst.meas.algorithms import DatasetConfig
54from lsst.meas.algorithms.ingestIndexReferenceTask import addRefCatMetadata
56from .utilities import computeApproxPixelAreaFields
57from .utilities import lookupStaticCalibrations
58from .utilities import FGCM_ILLEGAL_VALUE
60import fgcm
62__all__ = ['FgcmOutputProductsConfig', 'FgcmOutputProductsTask']
65class FgcmOutputProductsConnections(pipeBase.PipelineTaskConnections,
66 dimensions=("instrument",),
67 defaultTemplates={"cycleNumber": "0"}):
68 camera = connectionTypes.PrerequisiteInput(
69 doc="Camera instrument",
70 name="camera",
71 storageClass="Camera",
72 dimensions=("instrument",),
73 lookupFunction=lookupStaticCalibrations,
74 isCalibration=True,
75 )
77 fgcmLookUpTable = connectionTypes.PrerequisiteInput(
78 doc=("Atmosphere + instrument look-up-table for FGCM throughput and "
79 "chromatic corrections."),
80 name="fgcmLookUpTable",
81 storageClass="Catalog",
82 dimensions=("instrument",),
83 deferLoad=True,
84 )
86 fgcmVisitCatalog = connectionTypes.Input(
87 doc="Catalog of visit information for fgcm",
88 name="fgcmVisitCatalog",
89 storageClass="Catalog",
90 dimensions=("instrument",),
91 deferLoad=True,
92 )
94 fgcmStandardStars = connectionTypes.Input(
95 doc="Catalog of standard star data from fgcm fit",
96 name="fgcmStandardStars{cycleNumber}",
97 storageClass="SimpleCatalog",
98 dimensions=("instrument",),
99 deferLoad=True,
100 )
102 fgcmZeropoints = connectionTypes.Input(
103 doc="Catalog of zeropoints from fgcm fit",
104 name="fgcmZeropoints{cycleNumber}",
105 storageClass="Catalog",
106 dimensions=("instrument",),
107 deferLoad=True,
108 )
110 fgcmAtmosphereParameters = connectionTypes.Input(
111 doc="Catalog of atmosphere parameters from fgcm fit",
112 name="fgcmAtmosphereParameters{cycleNumber}",
113 storageClass="Catalog",
114 dimensions=("instrument",),
115 deferLoad=True,
116 )
118 refCat = connectionTypes.PrerequisiteInput(
119 doc="Reference catalog to use for photometric calibration",
120 name="cal_ref_cat",
121 storageClass="SimpleCatalog",
122 dimensions=("skypix",),
123 deferLoad=True,
124 multiple=True,
125 )
127 fgcmPhotoCalib = connectionTypes.Output(
128 doc=("Per-visit photometric calibrations derived from fgcm calibration. "
129 "These catalogs use detector id for the id and are sorted for "
130 "fast lookups of a detector."),
131 name="fgcmPhotoCalibCatalog",
132 storageClass="ExposureCatalog",
133 dimensions=("instrument", "visit",),
134 multiple=True,
135 )
137 fgcmTransmissionAtmosphere = connectionTypes.Output(
138 doc="Per-visit atmosphere transmission files produced from fgcm calibration",
139 name="transmission_atmosphere_fgcm",
140 storageClass="TransmissionCurve",
141 dimensions=("instrument",
142 "visit",),
143 multiple=True,
144 )
146 fgcmOffsets = connectionTypes.Output(
147 doc="Per-band offsets computed from doReferenceCalibration",
148 name="fgcmReferenceCalibrationOffsets",
149 storageClass="Catalog",
150 dimensions=("instrument",),
151 multiple=False,
152 )
154 def __init__(self, *, config=None):
155 super().__init__(config=config)
157 if str(int(config.connections.cycleNumber)) != config.connections.cycleNumber:
158 raise ValueError("cycleNumber must be of integer format")
160 if not config.doReferenceCalibration:
161 self.prerequisiteInputs.remove("refCat")
162 if not config.doAtmosphereOutput:
163 self.inputs.remove("fgcmAtmosphereParameters")
164 if not config.doZeropointOutput:
165 self.inputs.remove("fgcmZeropoints")
166 if not config.doReferenceCalibration:
167 self.outputs.remove("fgcmOffsets")
170class FgcmOutputProductsConfig(pipeBase.PipelineTaskConfig,
171 pipelineConnections=FgcmOutputProductsConnections):
172 """Config for FgcmOutputProductsTask"""
174 cycleNumber = pexConfig.Field(
175 doc="Final fit cycle from FGCM fit",
176 dtype=int,
177 default=None,
178 )
179 physicalFilterMap = pexConfig.DictField(
180 doc="Mapping from 'physicalFilter' to band.",
181 keytype=str,
182 itemtype=str,
183 default={},
184 )
185 # The following fields refer to calibrating from a reference
186 # catalog, but in the future this might need to be expanded
187 doReferenceCalibration = pexConfig.Field(
188 doc=("Transfer 'absolute' calibration from reference catalog? "
189 "This afterburner step is unnecessary if reference stars "
190 "were used in the full fit in FgcmFitCycleTask."),
191 dtype=bool,
192 default=False,
193 )
194 doRefcatOutput = pexConfig.Field(
195 doc="Output standard stars in reference catalog format",
196 dtype=bool,
197 default=False,
198 deprecated="doRefcatOutput is no longer supported; this config will be removed after v24"
199 )
200 doAtmosphereOutput = pexConfig.Field(
201 doc="Output atmospheres in transmission_atmosphere_fgcm format",
202 dtype=bool,
203 default=True,
204 )
205 doZeropointOutput = pexConfig.Field(
206 doc="Output zeropoints in fgcm_photoCalib format",
207 dtype=bool,
208 default=True,
209 )
210 doComposeWcsJacobian = pexConfig.Field(
211 doc="Compose Jacobian of WCS with fgcm calibration for output photoCalib?",
212 dtype=bool,
213 default=True,
214 )
215 doApplyMeanChromaticCorrection = pexConfig.Field(
216 doc="Apply the mean chromatic correction to the zeropoints?",
217 dtype=bool,
218 default=True,
219 )
220 refObjLoader = pexConfig.ConfigurableField(
221 target=LoadIndexedReferenceObjectsTask,
222 doc="reference object loader for 'absolute' photometric calibration",
223 deprecated="refObjLoader is deprecated, and will be removed after v24",
224 )
225 photoCal = pexConfig.ConfigurableField(
226 target=PhotoCalTask,
227 doc="task to perform 'absolute' calibration",
228 )
229 referencePixelizationNside = pexConfig.Field(
230 doc="Healpix nside to pixelize catalog to compare to reference catalog",
231 dtype=int,
232 default=64,
233 )
234 referencePixelizationMinStars = pexConfig.Field(
235 doc=("Minimum number of stars per healpix pixel to select for comparison"
236 "to the specified reference catalog"),
237 dtype=int,
238 default=200,
239 )
240 referenceMinMatch = pexConfig.Field(
241 doc="Minimum number of stars matched to reference catalog to be used in statistics",
242 dtype=int,
243 default=50,
244 )
245 referencePixelizationNPixels = pexConfig.Field(
246 doc=("Number of healpix pixels to sample to do comparison. "
247 "Doing too many will take a long time and not yield any more "
248 "precise results because the final number is the median offset "
249 "(per band) from the set of pixels."),
250 dtype=int,
251 default=100,
252 )
253 datasetConfig = pexConfig.ConfigField(
254 dtype=DatasetConfig,
255 doc="Configuration for writing/reading ingested catalog",
256 deprecated="The datasetConfig was only used for gen2; this config will be removed after v24.",
257 )
259 def setDefaults(self):
260 pexConfig.Config.setDefaults(self)
262 # In order to transfer the "absolute" calibration from a reference
263 # catalog to the relatively calibrated FGCM standard stars (one number
264 # per band), we use the PhotoCalTask to match stars in a sample of healpix
265 # pixels. These basic settings ensure that only well-measured, good stars
266 # from the source and reference catalogs are used for the matching.
268 # applyColorTerms needs to be False if doReferenceCalibration is False,
269 # as is the new default after DM-16702
270 self.photoCal.applyColorTerms = False
271 self.photoCal.fluxField = 'instFlux'
272 self.photoCal.magErrFloor = 0.003
273 self.photoCal.match.referenceSelection.doSignalToNoise = True
274 self.photoCal.match.referenceSelection.signalToNoise.minimum = 10.0
275 self.photoCal.match.sourceSelection.doSignalToNoise = True
276 self.photoCal.match.sourceSelection.signalToNoise.minimum = 10.0
277 self.photoCal.match.sourceSelection.signalToNoise.fluxField = 'instFlux'
278 self.photoCal.match.sourceSelection.signalToNoise.errField = 'instFluxErr'
279 self.photoCal.match.sourceSelection.doFlags = True
280 self.photoCal.match.sourceSelection.flags.good = []
281 self.photoCal.match.sourceSelection.flags.bad = ['flag_badStar']
282 self.photoCal.match.sourceSelection.doUnresolved = False
284 def validate(self):
285 super().validate()
287 # Force the connections to conform with cycleNumber
288 self.connections.cycleNumber = str(self.cycleNumber)
291class FgcmOutputProductsTask(pipeBase.PipelineTask):
292 """
293 Output products from FGCM global calibration.
294 """
296 ConfigClass = FgcmOutputProductsConfig
297 _DefaultName = "fgcmOutputProducts"
299 def __init__(self, **kwargs):
300 super().__init__(**kwargs)
302 def runQuantum(self, butlerQC, inputRefs, outputRefs):
303 handleDict = {}
304 handleDict['camera'] = butlerQC.get(inputRefs.camera)
305 handleDict['fgcmLookUpTable'] = butlerQC.get(inputRefs.fgcmLookUpTable)
306 handleDict['fgcmVisitCatalog'] = butlerQC.get(inputRefs.fgcmVisitCatalog)
307 handleDict['fgcmStandardStars'] = butlerQC.get(inputRefs.fgcmStandardStars)
309 if self.config.doZeropointOutput:
310 handleDict['fgcmZeropoints'] = butlerQC.get(inputRefs.fgcmZeropoints)
311 photoCalibRefDict = {photoCalibRef.dataId.byName()['visit']:
312 photoCalibRef for photoCalibRef in outputRefs.fgcmPhotoCalib}
314 if self.config.doAtmosphereOutput:
315 handleDict['fgcmAtmosphereParameters'] = butlerQC.get(inputRefs.fgcmAtmosphereParameters)
316 atmRefDict = {atmRef.dataId.byName()['visit']: atmRef for
317 atmRef in outputRefs.fgcmTransmissionAtmosphere}
319 if self.config.doReferenceCalibration:
320 refConfig = LoadReferenceObjectsConfig()
321 self.refObjLoader = ReferenceObjectLoader(dataIds=[ref.datasetRef.dataId
322 for ref in inputRefs.refCat],
323 refCats=butlerQC.get(inputRefs.refCat),
324 log=self.log,
325 config=refConfig)
326 else:
327 self.refObjLoader = None
329 struct = self.run(handleDict, self.config.physicalFilterMap)
331 # Output the photoCalib exposure catalogs
332 if struct.photoCalibCatalogs is not None:
333 self.log.info("Outputting photoCalib catalogs.")
334 for visit, expCatalog in struct.photoCalibCatalogs:
335 butlerQC.put(expCatalog, photoCalibRefDict[visit])
336 self.log.info("Done outputting photoCalib catalogs.")
338 # Output the atmospheres
339 if struct.atmospheres is not None:
340 self.log.info("Outputting atmosphere transmission files.")
341 for visit, atm in struct.atmospheres:
342 butlerQC.put(atm, atmRefDict[visit])
343 self.log.info("Done outputting atmosphere files.")
345 if self.config.doReferenceCalibration:
346 # Turn offset into simple catalog for persistence if necessary
347 schema = afwTable.Schema()
348 schema.addField('offset', type=np.float64,
349 doc="Post-process calibration offset (mag)")
350 offsetCat = afwTable.BaseCatalog(schema)
351 offsetCat.resize(len(struct.offsets))
352 offsetCat['offset'][:] = struct.offsets
354 butlerQC.put(offsetCat, outputRefs.fgcmOffsets)
356 return
358 def run(self, handleDict, physicalFilterMap):
359 """Run the output products task.
361 Parameters
362 ----------
363 handleDict : `dict`
364 All handles are `lsst.daf.butler.DeferredDatasetHandle`
365 handle dictionary with keys:
367 ``"camera"``
368 Camera object (`lsst.afw.cameraGeom.Camera`)
369 ``"fgcmLookUpTable"``
370 handle for the FGCM look-up table.
371 ``"fgcmVisitCatalog"``
372 handle for visit summary catalog.
373 ``"fgcmStandardStars"``
374 handle for the output standard star catalog.
375 ``"fgcmZeropoints"``
376 handle for the zeropoint data catalog.
377 ``"fgcmAtmosphereParameters"``
378 handle for the atmosphere parameter catalog.
379 ``"fgcmBuildStarsTableConfig"``
380 Config for `lsst.fgcmcal.fgcmBuildStarsTableTask`.
381 physicalFilterMap : `dict`
382 Dictionary of mappings from physical filter to FGCM band.
384 Returns
385 -------
386 retStruct : `lsst.pipe.base.Struct`
387 Output structure with keys:
389 offsets : `np.ndarray`
390 Final reference offsets, per band.
391 atmospheres : `generator` [(`int`, `lsst.afw.image.TransmissionCurve`)]
392 Generator that returns (visit, transmissionCurve) tuples.
393 photoCalibCatalogs : `generator` [(`int`, `lsst.afw.table.ExposureCatalog`)]
394 Generator that returns (visit, exposureCatalog) tuples.
395 """
396 stdCat = handleDict['fgcmStandardStars'].get()
397 md = stdCat.getMetadata()
398 bands = md.getArray('BANDS')
400 if self.config.doReferenceCalibration:
401 lutCat = handleDict['fgcmLookUpTable'].get()
402 offsets = self._computeReferenceOffsets(stdCat, lutCat, physicalFilterMap, bands)
403 else:
404 offsets = np.zeros(len(bands))
406 del stdCat
408 if self.config.doZeropointOutput:
409 zptCat = handleDict['fgcmZeropoints'].get()
410 visitCat = handleDict['fgcmVisitCatalog'].get()
412 pcgen = self._outputZeropoints(handleDict['camera'], zptCat, visitCat, offsets, bands,
413 physicalFilterMap)
414 else:
415 pcgen = None
417 if self.config.doAtmosphereOutput:
418 atmCat = handleDict['fgcmAtmosphereParameters'].get()
419 atmgen = self._outputAtmospheres(handleDict, atmCat)
420 else:
421 atmgen = None
423 retStruct = pipeBase.Struct(offsets=offsets,
424 atmospheres=atmgen)
425 retStruct.photoCalibCatalogs = pcgen
427 return retStruct
429 def generateTractOutputProducts(self, handleDict, tract,
430 visitCat, zptCat, atmCat, stdCat,
431 fgcmBuildStarsConfig):
432 """
433 Generate the output products for a given tract, as specified in the config.
435 This method is here to have an alternate entry-point for
436 FgcmCalibrateTract.
438 Parameters
439 ----------
440 handleDict : `dict`
441 All handles are `lsst.daf.butler.DeferredDatasetHandle`
442 handle dictionary with keys:
444 ``"camera"``
445 Camera object (`lsst.afw.cameraGeom.Camera`)
446 ``"fgcmLookUpTable"``
447 handle for the FGCM look-up table.
448 tract : `int`
449 Tract number
450 visitCat : `lsst.afw.table.BaseCatalog`
451 FGCM visitCat from `FgcmBuildStarsTask`
452 zptCat : `lsst.afw.table.BaseCatalog`
453 FGCM zeropoint catalog from `FgcmFitCycleTask`
454 atmCat : `lsst.afw.table.BaseCatalog`
455 FGCM atmosphere parameter catalog from `FgcmFitCycleTask`
456 stdCat : `lsst.afw.table.SimpleCatalog`
457 FGCM standard star catalog from `FgcmFitCycleTask`
458 fgcmBuildStarsConfig : `lsst.fgcmcal.FgcmBuildStarsConfig`
459 Configuration object from `FgcmBuildStarsTask`
461 Returns
462 -------
463 retStruct : `lsst.pipe.base.Struct`
464 Output structure with keys:
466 offsets : `np.ndarray`
467 Final reference offsets, per band.
468 atmospheres : `generator` [(`int`, `lsst.afw.image.TransmissionCurve`)]
469 Generator that returns (visit, transmissionCurve) tuples.
470 photoCalibCatalogs : `generator` [(`int`, `lsst.afw.table.ExposureCatalog`)]
471 Generator that returns (visit, exposureCatalog) tuples.
472 """
473 physicalFilterMap = fgcmBuildStarsConfig.physicalFilterMap
475 md = stdCat.getMetadata()
476 bands = md.getArray('BANDS')
478 if self.config.doComposeWcsJacobian and not fgcmBuildStarsConfig.doApplyWcsJacobian:
479 raise RuntimeError("Cannot compose the WCS jacobian if it hasn't been applied "
480 "in fgcmBuildStarsTask.")
482 if not self.config.doComposeWcsJacobian and fgcmBuildStarsConfig.doApplyWcsJacobian:
483 self.log.warning("Jacobian was applied in build-stars but doComposeWcsJacobian is not set.")
485 if self.config.doReferenceCalibration:
486 lutCat = handleDict['fgcmLookUpTable'].get()
487 offsets = self._computeReferenceOffsets(stdCat, lutCat, bands, physicalFilterMap)
488 else:
489 offsets = np.zeros(len(bands))
491 if self.config.doZeropointOutput:
492 pcgen = self._outputZeropoints(handleDict['camera'], zptCat, visitCat, offsets, bands,
493 physicalFilterMap)
494 else:
495 pcgen = None
497 if self.config.doAtmosphereOutput:
498 atmgen = self._outputAtmospheres(handleDict, atmCat)
499 else:
500 atmgen = None
502 retStruct = pipeBase.Struct(offsets=offsets,
503 atmospheres=atmgen)
504 retStruct.photoCalibCatalogs = pcgen
506 return retStruct
508 def _computeReferenceOffsets(self, stdCat, lutCat, physicalFilterMap, bands):
509 """
510 Compute offsets relative to a reference catalog.
512 This method splits the star catalog into healpix pixels
513 and computes the calibration transfer for a sample of
514 these pixels to approximate the 'absolute' calibration
515 values (on for each band) to apply to transfer the
516 absolute scale.
518 Parameters
519 ----------
520 stdCat : `lsst.afw.table.SimpleCatalog`
521 FGCM standard stars
522 lutCat : `lsst.afw.table.SimpleCatalog`
523 FGCM Look-up table
524 physicalFilterMap : `dict`
525 Dictionary of mappings from physical filter to FGCM band.
526 bands : `list` [`str`]
527 List of band names from FGCM output
528 Returns
529 -------
530 offsets : `numpy.array` of floats
531 Per band zeropoint offsets
532 """
534 # Only use stars that are observed in all the bands that were actually used
535 # This will ensure that we use the same healpix pixels for the absolute
536 # calibration of each band.
537 minObs = stdCat['ngood'].min(axis=1)
539 goodStars = (minObs >= 1)
540 stdCat = stdCat[goodStars]
542 self.log.info("Found %d stars with at least 1 good observation in each band" %
543 (len(stdCat)))
545 # Associate each band with the appropriate physicalFilter and make
546 # filterLabels
547 filterLabels = []
549 lutPhysicalFilters = lutCat[0]['physicalFilters'].split(',')
550 lutStdPhysicalFilters = lutCat[0]['stdPhysicalFilters'].split(',')
551 physicalFilterMapBands = list(physicalFilterMap.values())
552 physicalFilterMapFilters = list(physicalFilterMap.keys())
553 for band in bands:
554 # Find a physical filter associated from the band by doing
555 # a reverse lookup on the physicalFilterMap dict
556 physicalFilterMapIndex = physicalFilterMapBands.index(band)
557 physicalFilter = physicalFilterMapFilters[physicalFilterMapIndex]
558 # Find the appropriate fgcm standard physicalFilter
559 lutPhysicalFilterIndex = lutPhysicalFilters.index(physicalFilter)
560 stdPhysicalFilter = lutStdPhysicalFilters[lutPhysicalFilterIndex]
561 filterLabels.append(afwImage.FilterLabel(band=band,
562 physical=stdPhysicalFilter))
564 # We have to make a table for each pixel with flux/fluxErr
565 # This is a temporary table generated for input to the photoCal task.
566 # These fluxes are not instFlux (they are top-of-the-atmosphere approximate and
567 # have had chromatic corrections applied to get to the standard system
568 # specified by the atmosphere/instrumental parameters), nor are they
569 # in Jansky (since they don't have a proper absolute calibration: the overall
570 # zeropoint is estimated from the telescope size, etc.)
571 sourceMapper = afwTable.SchemaMapper(stdCat.schema)
572 sourceMapper.addMinimalSchema(afwTable.SimpleTable.makeMinimalSchema())
573 sourceMapper.editOutputSchema().addField('instFlux', type=np.float64,
574 doc="instrumental flux (counts)")
575 sourceMapper.editOutputSchema().addField('instFluxErr', type=np.float64,
576 doc="instrumental flux error (counts)")
577 badStarKey = sourceMapper.editOutputSchema().addField('flag_badStar',
578 type='Flag',
579 doc="bad flag")
581 # Split up the stars
582 # Note that there is an assumption here that the ra/dec coords stored
583 # on-disk are in radians, and therefore that starObs['coord_ra'] /
584 # starObs['coord_dec'] return radians when used as an array of numpy float64s.
585 ipring = hpg.angle_to_pixel(
586 self.config.referencePixelizationNside,
587 stdCat['coord_ra'],
588 stdCat['coord_dec'],
589 degrees=False,
590 )
591 h, rev = esutil.stat.histogram(ipring, rev=True)
593 gdpix, = np.where(h >= self.config.referencePixelizationMinStars)
595 self.log.info("Found %d pixels (nside=%d) with at least %d good stars" %
596 (gdpix.size,
597 self.config.referencePixelizationNside,
598 self.config.referencePixelizationMinStars))
600 if gdpix.size < self.config.referencePixelizationNPixels:
601 self.log.warning("Found fewer good pixels (%d) than preferred in configuration (%d)" %
602 (gdpix.size, self.config.referencePixelizationNPixels))
603 else:
604 # Sample out the pixels we want to use
605 gdpix = np.random.choice(gdpix, size=self.config.referencePixelizationNPixels, replace=False)
607 results = np.zeros(gdpix.size, dtype=[('hpix', 'i4'),
608 ('nstar', 'i4', len(bands)),
609 ('nmatch', 'i4', len(bands)),
610 ('zp', 'f4', len(bands)),
611 ('zpErr', 'f4', len(bands))])
612 results['hpix'] = ipring[rev[rev[gdpix]]]
614 # We need a boolean index to deal with catalogs...
615 selected = np.zeros(len(stdCat), dtype=bool)
617 refFluxFields = [None]*len(bands)
619 for p_index, pix in enumerate(gdpix):
620 i1a = rev[rev[pix]: rev[pix + 1]]
622 # the stdCat afwTable can only be indexed with boolean arrays,
623 # and not numpy index arrays (see DM-16497). This little trick
624 # converts the index array into a boolean array
625 selected[:] = False
626 selected[i1a] = True
628 for b_index, filterLabel in enumerate(filterLabels):
629 struct = self._computeOffsetOneBand(sourceMapper, badStarKey, b_index,
630 filterLabel, stdCat,
631 selected, refFluxFields)
632 results['nstar'][p_index, b_index] = len(i1a)
633 results['nmatch'][p_index, b_index] = len(struct.arrays.refMag)
634 results['zp'][p_index, b_index] = struct.zp
635 results['zpErr'][p_index, b_index] = struct.sigma
637 # And compute the summary statistics
638 offsets = np.zeros(len(bands))
640 for b_index, band in enumerate(bands):
641 # make configurable
642 ok, = np.where(results['nmatch'][:, b_index] >= self.config.referenceMinMatch)
643 offsets[b_index] = np.median(results['zp'][ok, b_index])
644 # use median absolute deviation to estimate Normal sigma
645 # see https://en.wikipedia.org/wiki/Median_absolute_deviation
646 madSigma = 1.4826*np.median(np.abs(results['zp'][ok, b_index] - offsets[b_index]))
647 self.log.info("Reference catalog offset for %s band: %.12f +/- %.12f",
648 band, offsets[b_index], madSigma)
650 return offsets
652 def _computeOffsetOneBand(self, sourceMapper, badStarKey,
653 b_index, filterLabel, stdCat, selected, refFluxFields):
654 """
655 Compute the zeropoint offset between the fgcm stdCat and the reference
656 stars for one pixel in one band
658 Parameters
659 ----------
660 sourceMapper : `lsst.afw.table.SchemaMapper`
661 Mapper to go from stdCat to calibratable catalog
662 badStarKey : `lsst.afw.table.Key`
663 Key for the field with bad stars
664 b_index : `int`
665 Index of the band in the star catalog
666 filterLabel : `lsst.afw.image.FilterLabel`
667 filterLabel with band and physical filter
668 stdCat : `lsst.afw.table.SimpleCatalog`
669 FGCM standard stars
670 selected : `numpy.array(dtype=bool)`
671 Boolean array of which stars are in the pixel
672 refFluxFields : `list`
673 List of names of flux fields for reference catalog
674 """
676 sourceCat = afwTable.SimpleCatalog(sourceMapper.getOutputSchema())
677 sourceCat.reserve(selected.sum())
678 sourceCat.extend(stdCat[selected], mapper=sourceMapper)
679 sourceCat['instFlux'] = 10.**(stdCat['mag_std_noabs'][selected, b_index]/(-2.5))
680 sourceCat['instFluxErr'] = (np.log(10.)/2.5)*(stdCat['magErr_std'][selected, b_index]
681 * sourceCat['instFlux'])
682 # Make sure we only use stars that have valid measurements
683 # (This is perhaps redundant with requirements above that the
684 # stars be observed in all bands, but it can't hurt)
685 badStar = (stdCat['mag_std_noabs'][selected, b_index] > 90.0)
686 for rec in sourceCat[badStar]:
687 rec.set(badStarKey, True)
689 exposure = afwImage.ExposureF()
690 exposure.setFilter(filterLabel)
692 if refFluxFields[b_index] is None:
693 # Need to find the flux field in the reference catalog
694 # to work around limitations of DirectMatch in PhotoCal
695 ctr = stdCat[0].getCoord()
696 rad = 0.05*lsst.geom.degrees
697 refDataTest = self.refObjLoader.loadSkyCircle(ctr, rad, filterLabel.bandLabel)
698 refFluxFields[b_index] = refDataTest.fluxField
700 # Make a copy of the config so that we can modify it
701 calConfig = copy.copy(self.config.photoCal.value)
702 calConfig.match.referenceSelection.signalToNoise.fluxField = refFluxFields[b_index]
703 calConfig.match.referenceSelection.signalToNoise.errField = refFluxFields[b_index] + 'Err'
704 calTask = self.config.photoCal.target(refObjLoader=self.refObjLoader,
705 config=calConfig,
706 schema=sourceCat.getSchema())
708 struct = calTask.run(exposure, sourceCat)
710 return struct
712 def _formatCatalog(self, fgcmStarCat, offsets, bands):
713 """
714 Turn an FGCM-formatted star catalog, applying zeropoint offsets.
716 Parameters
717 ----------
718 fgcmStarCat : `lsst.afw.Table.SimpleCatalog`
719 SimpleCatalog as output by fgcmcal
720 offsets : `list` with len(self.bands) entries
721 Zeropoint offsets to apply
722 bands : `list` [`str`]
723 List of band names from FGCM output
725 Returns
726 -------
727 formattedCat: `lsst.afw.table.SimpleCatalog`
728 SimpleCatalog suitable for using as a reference catalog
729 """
731 sourceMapper = afwTable.SchemaMapper(fgcmStarCat.schema)
732 minSchema = LoadIndexedReferenceObjectsTask.makeMinimalSchema(bands,
733 addCentroid=False,
734 addIsResolved=True,
735 coordErrDim=0)
736 sourceMapper.addMinimalSchema(minSchema)
737 for band in bands:
738 sourceMapper.editOutputSchema().addField('%s_nGood' % (band), type=np.int32)
739 sourceMapper.editOutputSchema().addField('%s_nTotal' % (band), type=np.int32)
740 sourceMapper.editOutputSchema().addField('%s_nPsfCandidate' % (band), type=np.int32)
742 formattedCat = afwTable.SimpleCatalog(sourceMapper.getOutputSchema())
743 formattedCat.reserve(len(fgcmStarCat))
744 formattedCat.extend(fgcmStarCat, mapper=sourceMapper)
746 # Note that we don't have to set `resolved` because the default is False
748 for b, band in enumerate(bands):
749 mag = fgcmStarCat['mag_std_noabs'][:, b].astype(np.float64) + offsets[b]
750 # We want fluxes in nJy from calibrated AB magnitudes
751 # (after applying offset). Updated after RFC-549 and RFC-575.
752 flux = (mag*units.ABmag).to_value(units.nJy)
753 fluxErr = (np.log(10.)/2.5)*flux*fgcmStarCat['magErr_std'][:, b].astype(np.float64)
755 formattedCat['%s_flux' % (band)][:] = flux
756 formattedCat['%s_fluxErr' % (band)][:] = fluxErr
757 formattedCat['%s_nGood' % (band)][:] = fgcmStarCat['ngood'][:, b]
758 formattedCat['%s_nTotal' % (band)][:] = fgcmStarCat['ntotal'][:, b]
759 formattedCat['%s_nPsfCandidate' % (band)][:] = fgcmStarCat['npsfcand'][:, b]
761 addRefCatMetadata(formattedCat)
763 return formattedCat
765 def _outputZeropoints(self, camera, zptCat, visitCat, offsets, bands,
766 physicalFilterMap, tract=None):
767 """Output the zeropoints in fgcm_photoCalib format.
769 Parameters
770 ----------
771 camera : `lsst.afw.cameraGeom.Camera`
772 Camera from the butler.
773 zptCat : `lsst.afw.table.BaseCatalog`
774 FGCM zeropoint catalog from `FgcmFitCycleTask`.
775 visitCat : `lsst.afw.table.BaseCatalog`
776 FGCM visitCat from `FgcmBuildStarsTask`.
777 offsets : `numpy.array`
778 Float array of absolute calibration offsets, one for each filter.
779 bands : `list` [`str`]
780 List of band names from FGCM output.
781 physicalFilterMap : `dict`
782 Dictionary of mappings from physical filter to FGCM band.
783 tract: `int`, optional
784 Tract number to output. Default is None (global calibration)
786 Returns
787 -------
788 photoCalibCatalogs : `generator` [(`int`, `lsst.afw.table.ExposureCatalog`)]
789 Generator that returns (visit, exposureCatalog) tuples.
790 """
791 # Select visit/ccds where we have a calibration
792 # This includes ccds where we were able to interpolate from neighboring
793 # ccds.
794 cannot_compute = fgcm.fgcmUtilities.zpFlagDict['CANNOT_COMPUTE_ZEROPOINT']
795 selected = (((zptCat['fgcmFlag'] & cannot_compute) == 0)
796 & (zptCat['fgcmZptVar'] > 0.0)
797 & (zptCat['fgcmZpt'] > FGCM_ILLEGAL_VALUE))
799 # Log warnings for any visit which has no valid zeropoints
800 badVisits = np.unique(zptCat['visit'][~selected])
801 goodVisits = np.unique(zptCat['visit'][selected])
802 allBadVisits = badVisits[~np.isin(badVisits, goodVisits)]
803 for allBadVisit in allBadVisits:
804 self.log.warning(f'No suitable photoCalib for visit {allBadVisit}')
806 # Get a mapping from filtername to the offsets
807 offsetMapping = {}
808 for f in physicalFilterMap:
809 # Not every filter in the map will necesarily have a band.
810 if physicalFilterMap[f] in bands:
811 offsetMapping[f] = offsets[bands.index(physicalFilterMap[f])]
813 # Get a mapping from "ccd" to the ccd index used for the scaling
814 ccdMapping = {}
815 for ccdIndex, detector in enumerate(camera):
816 ccdMapping[detector.getId()] = ccdIndex
818 # And a mapping to get the flat-field scaling values
819 scalingMapping = {}
820 for rec in visitCat:
821 scalingMapping[rec['visit']] = rec['scaling']
823 if self.config.doComposeWcsJacobian:
824 approxPixelAreaFields = computeApproxPixelAreaFields(camera)
826 # The zptCat is sorted by visit, which is useful
827 lastVisit = -1
828 zptVisitCatalog = None
830 metadata = dafBase.PropertyList()
831 metadata.add("COMMENT", "Catalog id is detector id, sorted.")
832 metadata.add("COMMENT", "Only detectors with data have entries.")
834 for rec in zptCat[selected]:
835 # Retrieve overall scaling
836 scaling = scalingMapping[rec['visit']][ccdMapping[rec['detector']]]
838 # The postCalibrationOffset describe any zeropoint offsets
839 # to apply after the fgcm calibration. The first part comes
840 # from the reference catalog match (used in testing). The
841 # second part comes from the mean chromatic correction
842 # (if configured).
843 postCalibrationOffset = offsetMapping[rec['filtername']]
844 if self.config.doApplyMeanChromaticCorrection:
845 postCalibrationOffset += rec['fgcmDeltaChrom']
847 fgcmSuperStarField = self._getChebyshevBoundedField(rec['fgcmfZptSstarCheb'],
848 rec['fgcmfZptChebXyMax'])
849 # Convert from FGCM AB to nJy
850 fgcmZptField = self._getChebyshevBoundedField((rec['fgcmfZptCheb']*units.AB).to_value(units.nJy),
851 rec['fgcmfZptChebXyMax'],
852 offset=postCalibrationOffset,
853 scaling=scaling)
855 if self.config.doComposeWcsJacobian:
857 fgcmField = afwMath.ProductBoundedField([approxPixelAreaFields[rec['detector']],
858 fgcmSuperStarField,
859 fgcmZptField])
860 else:
861 # The photoCalib is just the product of the fgcmSuperStarField and the
862 # fgcmZptField
863 fgcmField = afwMath.ProductBoundedField([fgcmSuperStarField, fgcmZptField])
865 # The "mean" calibration will be set to the center of the ccd for reference
866 calibCenter = fgcmField.evaluate(fgcmField.getBBox().getCenter())
867 calibErr = (np.log(10.0)/2.5)*calibCenter*np.sqrt(rec['fgcmZptVar'])
868 photoCalib = afwImage.PhotoCalib(calibrationMean=calibCenter,
869 calibrationErr=calibErr,
870 calibration=fgcmField,
871 isConstant=False)
873 # Return full per-visit exposure catalogs
874 if rec['visit'] != lastVisit:
875 # This is a new visit. If the last visit was not -1, yield
876 # the ExposureCatalog
877 if lastVisit > -1:
878 # ensure that the detectors are in sorted order, for fast lookups
879 zptVisitCatalog.sort()
880 yield (int(lastVisit), zptVisitCatalog)
881 else:
882 # We need to create a new schema
883 zptExpCatSchema = afwTable.ExposureTable.makeMinimalSchema()
884 zptExpCatSchema.addField('visit', type='L', doc='Visit number')
886 # And start a new one
887 zptVisitCatalog = afwTable.ExposureCatalog(zptExpCatSchema)
888 zptVisitCatalog.setMetadata(metadata)
890 lastVisit = int(rec['visit'])
892 catRecord = zptVisitCatalog.addNew()
893 catRecord['id'] = int(rec['detector'])
894 catRecord['visit'] = rec['visit']
895 catRecord.setPhotoCalib(photoCalib)
897 # Final output of last exposure catalog
898 # ensure that the detectors are in sorted order, for fast lookups
899 zptVisitCatalog.sort()
900 yield (int(lastVisit), zptVisitCatalog)
902 def _getChebyshevBoundedField(self, coefficients, xyMax, offset=0.0, scaling=1.0):
903 """
904 Make a ChebyshevBoundedField from fgcm coefficients, with optional offset
905 and scaling.
907 Parameters
908 ----------
909 coefficients: `numpy.array`
910 Flattened array of chebyshev coefficients
911 xyMax: `list` of length 2
912 Maximum x and y of the chebyshev bounding box
913 offset: `float`, optional
914 Absolute calibration offset. Default is 0.0
915 scaling: `float`, optional
916 Flat scaling value from fgcmBuildStars. Default is 1.0
918 Returns
919 -------
920 boundedField: `lsst.afw.math.ChebyshevBoundedField`
921 """
923 orderPlus1 = int(np.sqrt(coefficients.size))
924 pars = np.zeros((orderPlus1, orderPlus1))
926 bbox = lsst.geom.Box2I(lsst.geom.Point2I(0.0, 0.0),
927 lsst.geom.Point2I(*xyMax))
929 pars[:, :] = (coefficients.reshape(orderPlus1, orderPlus1)
930 * (10.**(offset/-2.5))*scaling)
932 boundedField = afwMath.ChebyshevBoundedField(bbox, pars)
934 return boundedField
936 def _outputAtmospheres(self, handleDict, atmCat):
937 """
938 Output the atmospheres.
940 Parameters
941 ----------
942 handleDict : `dict`
943 All data handles are `lsst.daf.butler.DeferredDatasetHandle`
944 The handleDict has the follownig keys:
946 ``"fgcmLookUpTable"``
947 handle for the FGCM look-up table.
948 atmCat : `lsst.afw.table.BaseCatalog`
949 FGCM atmosphere parameter catalog from fgcmFitCycleTask.
951 Returns
952 -------
953 atmospheres : `generator` [(`int`, `lsst.afw.image.TransmissionCurve`)]
954 Generator that returns (visit, transmissionCurve) tuples.
955 """
956 # First, we need to grab the look-up table and key info
957 lutCat = handleDict['fgcmLookUpTable'].get()
959 atmosphereTableName = lutCat[0]['tablename']
960 elevation = lutCat[0]['elevation']
961 atmLambda = lutCat[0]['atmLambda']
962 lutCat = None
964 # Make the atmosphere table if possible
965 try:
966 atmTable = fgcm.FgcmAtmosphereTable.initWithTableName(atmosphereTableName)
967 atmTable.loadTable()
968 except IOError:
969 atmTable = None
971 if atmTable is None:
972 # Try to use MODTRAN instead
973 try:
974 modGen = fgcm.ModtranGenerator(elevation)
975 lambdaRange = np.array([atmLambda[0], atmLambda[-1]])/10.
976 lambdaStep = (atmLambda[1] - atmLambda[0])/10.
977 except (ValueError, IOError) as e:
978 raise RuntimeError("FGCM look-up-table generated with modtran, "
979 "but modtran not configured to run.") from e
981 zenith = np.degrees(np.arccos(1./atmCat['secZenith']))
983 for i, visit in enumerate(atmCat['visit']):
984 if atmTable is not None:
985 # Interpolate the atmosphere table
986 atmVals = atmTable.interpolateAtmosphere(pmb=atmCat[i]['pmb'],
987 pwv=atmCat[i]['pwv'],
988 o3=atmCat[i]['o3'],
989 tau=atmCat[i]['tau'],
990 alpha=atmCat[i]['alpha'],
991 zenith=zenith[i],
992 ctranslamstd=[atmCat[i]['cTrans'],
993 atmCat[i]['lamStd']])
994 else:
995 # Run modtran
996 modAtm = modGen(pmb=atmCat[i]['pmb'],
997 pwv=atmCat[i]['pwv'],
998 o3=atmCat[i]['o3'],
999 tau=atmCat[i]['tau'],
1000 alpha=atmCat[i]['alpha'],
1001 zenith=zenith[i],
1002 lambdaRange=lambdaRange,
1003 lambdaStep=lambdaStep,
1004 ctranslamstd=[atmCat[i]['cTrans'],
1005 atmCat[i]['lamStd']])
1006 atmVals = modAtm['COMBINED']
1008 # Now need to create something to persist...
1009 curve = TransmissionCurve.makeSpatiallyConstant(throughput=atmVals,
1010 wavelengths=atmLambda,
1011 throughputAtMin=atmVals[0],
1012 throughputAtMax=atmVals[-1])
1014 yield (int(visit), curve)