Coverage for python / lsst / fgcmcal / fgcmBuildStarsBase.py: 19%
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2#
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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
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21"""Base class for BuildStars using src tables or sourceTable_visit tables.
22"""
24import abc
26import numpy as np
28import lsst.pex.config as pexConfig
29import lsst.pipe.base as pipeBase
30import lsst.afw.table as afwTable
31from lsst.daf.base import PropertyList
32from lsst.daf.base.dateTime import DateTime
33from lsst.meas.algorithms.sourceSelector import sourceSelectorRegistry
35from .fgcmLoadReferenceCatalog import FgcmLoadReferenceCatalogTask
36from .utilities import computeReferencePixelScale, countDetectors
38import fgcm
40REFSTARS_FORMAT_VERSION = 1
42__all__ = ['FgcmBuildStarsConfigBase', 'FgcmBuildStarsBaseTask']
45class FgcmBuildStarsConfigBase(pexConfig.Config):
46 """Base config for FgcmBuildStars tasks"""
48 instFluxField = pexConfig.Field(
49 doc=("Faull name of the source instFlux field to use, including 'instFlux'. "
50 "The associated flag will be implicitly included in badFlags"),
51 dtype=str,
52 default='slot_CalibFlux_instFlux',
53 )
54 minPerBand = pexConfig.Field(
55 doc="Minimum observations per band",
56 dtype=int,
57 default=2,
58 )
59 matchRadius = pexConfig.Field(
60 doc="Match radius (arcseconds)",
61 dtype=float,
62 default=1.0,
63 )
64 isolationRadius = pexConfig.Field(
65 doc="Isolation radius (arcseconds)",
66 dtype=float,
67 default=2.0,
68 )
69 densityCutNside = pexConfig.Field(
70 doc="Density cut healpix nside",
71 dtype=int,
72 default=128,
73 )
74 densityCutMaxPerPixel = pexConfig.Field(
75 doc="Density cut number of stars per pixel",
76 dtype=int,
77 default=1000,
78 )
79 randomSeed = pexConfig.Field(
80 doc="Random seed for high density down-sampling.",
81 dtype=int,
82 default=None,
83 optional=True,
84 )
85 matchNside = pexConfig.Field(
86 doc="Healpix Nside for matching",
87 dtype=int,
88 default=4096,
89 )
90 coarseNside = pexConfig.Field(
91 doc="Healpix coarse Nside for partitioning matches",
92 dtype=int,
93 default=8,
94 )
95 physicalFilterMap = pexConfig.DictField(
96 doc="Mapping from 'physicalFilter' to band.",
97 keytype=str,
98 itemtype=str,
99 default={},
100 )
101 requiredBands = pexConfig.ListField(
102 doc="Bands required for each star",
103 dtype=str,
104 default=(),
105 )
106 primaryBands = pexConfig.ListField(
107 doc=("Bands for 'primary' star matches. "
108 "A star must be observed in one of these bands to be considered "
109 "as a calibration star."),
110 dtype=str,
111 default=None
112 )
113 doApplyWcsJacobian = pexConfig.Field(
114 doc="Apply the jacobian of the WCS to the star observations prior to fit?",
115 dtype=bool,
116 default=True
117 )
118 doModelErrorsWithBackground = pexConfig.Field(
119 doc="Model flux errors with background term?",
120 dtype=bool,
121 default=True
122 )
123 psfCandidateName = pexConfig.Field(
124 doc="Name of field with psf candidate flag for propagation",
125 dtype=str,
126 default="calib_psf_candidate"
127 )
128 doSubtractLocalBackground = pexConfig.Field(
129 doc=("Subtract the local background before performing calibration? "
130 "This is only supported for circular aperture calibration fluxes."),
131 dtype=bool,
132 default=False
133 )
134 localBackgroundFluxField = pexConfig.Field(
135 doc="Full name of the local background instFlux field to use.",
136 dtype=str,
137 default='base_LocalBackground_instFlux'
138 )
139 sourceSelector = sourceSelectorRegistry.makeField(
140 doc="How to select sources",
141 default="science"
142 )
143 apertureInnerInstFluxField = pexConfig.Field(
144 doc=("Full name of instFlux field that contains inner aperture "
145 "flux for aperture correction proxy"),
146 dtype=str,
147 default='base_CircularApertureFlux_12_0_instFlux'
148 )
149 apertureOuterInstFluxField = pexConfig.Field(
150 doc=("Full name of instFlux field that contains outer aperture "
151 "flux for aperture correction proxy"),
152 dtype=str,
153 default='base_CircularApertureFlux_17_0_instFlux'
154 )
155 doReferenceMatches = pexConfig.Field(
156 doc="Match reference catalog as additional constraint on calibration",
157 dtype=bool,
158 default=True,
159 )
160 fgcmLoadReferenceCatalog = pexConfig.ConfigurableField(
161 target=FgcmLoadReferenceCatalogTask,
162 doc="FGCM reference object loader",
163 )
164 nVisitsPerCheckpoint = pexConfig.Field(
165 doc="Number of visits read between checkpoints",
166 dtype=int,
167 default=500,
168 )
170 def setDefaults(self):
171 sourceSelector = self.sourceSelector["science"]
172 sourceSelector.setDefaults()
174 sourceSelector.doFlags = True
175 sourceSelector.doUnresolved = True
176 sourceSelector.doSignalToNoise = True
177 sourceSelector.doIsolated = True
178 sourceSelector.doRequireFiniteRaDec = True
180 sourceSelector.signalToNoise.minimum = 10.0
181 sourceSelector.signalToNoise.maximum = 1000.0
183 # FGCM operates on unresolved sources, and this setting is
184 # appropriate for the current base_ClassificationExtendedness
185 sourceSelector.unresolved.maximum = 0.5
188class FgcmBuildStarsBaseTask(pipeBase.PipelineTask, abc.ABC):
189 """
190 Base task to build stars for FGCM global calibration
191 """
192 def __init__(self, initInputs=None, **kwargs):
193 super().__init__(**kwargs)
195 self.makeSubtask("sourceSelector")
196 # Only log warning and fatal errors from the sourceSelector
197 self.sourceSelector.log.setLevel(self.sourceSelector.log.WARN)
199 def fgcmMakeAllStarObservations(self, groupedHandles, visitCat,
200 sourceSchema,
201 camera,
202 calibFluxApertureRadius=None):
203 """
204 Compile all good star observations from visits in visitCat.
206 Parameters
207 ----------
208 groupedHandles : `dict` [`list` [`lsst.daf.butler.DeferredDatasetHandle`]]
209 Dataset handles, grouped by visit.
210 visitCat : `afw.table.BaseCatalog`
211 Catalog with visit data for FGCM
212 sourceSchema : `lsst.afw.table.Schema`
213 Schema for the input src catalogs.
214 camera : `lsst.afw.cameraGeom.Camera`
215 calibFluxApertureRadius : `float`, optional
216 Aperture radius for calibration flux.
217 inStarObsCat : `afw.table.BaseCatalog`
218 Input observation catalog. If this is incomplete, observations
219 will be appended from when it was cut off.
221 Returns
222 -------
223 fgcmStarObservations : `afw.table.BaseCatalog`
224 Full catalog of good observations.
226 Raises
227 ------
228 RuntimeError: Raised if doSubtractLocalBackground is True and
229 calibFluxApertureRadius is not set.
230 """
231 raise NotImplementedError("fgcmMakeAllStarObservations not implemented.")
233 def fgcmMakeVisitCatalog(self, camera, groupedHandles, useScienceDetectors=False):
234 """
235 Make a visit catalog with all the keys from each visit
237 Parameters
238 ----------
239 camera : `lsst.afw.cameraGeom.Camera`
240 Camera from the butler
241 groupedHandles : `dict` [`list` [`lsst.daf.butler.DeferredDatasetHandle`]]
242 Dataset handles, grouped by visit.
243 useScienceDetectors : `bool`, optional
244 Limit to science detectors?
246 Returns
247 -------
248 visitCat: `afw.table.BaseCatalog`
249 """
251 self.log.info("Assembling visitCatalog from %d visits", len(groupedHandles))
253 nCcd = countDetectors(camera, useScienceDetectors)
255 schema = self._makeFgcmVisitSchema(nCcd)
257 visitCat = afwTable.BaseCatalog(schema)
258 visitCat.reserve(len(groupedHandles))
259 visitCat.resize(len(groupedHandles))
261 visitCat['visit'] = list(groupedHandles.keys())
262 visitCat['used'] = 0
263 visitCat['sources_read'] = False
265 defaultPixelScale = computeReferencePixelScale(camera, useScienceDetectors=useScienceDetectors)
267 # No matter what, fill the catalog. This will check if it was
268 # already read.
269 self._fillVisitCatalog(visitCat, groupedHandles, defaultPixelScale)
271 return visitCat
273 def _fillVisitCatalog(self, visitCat, groupedHandles, defaultPixelScale):
274 """
275 Fill the visit catalog with visit metadata
277 Parameters
278 ----------
279 visitCat : `afw.table.BaseCatalog`
280 Visit catalog. See _makeFgcmVisitSchema() for schema definition.
281 groupedHandles : `dict` [`list` [`lsst.daf.butler.DeferredDatasetHandle`]]
282 Dataset handles, grouped by visit.
283 defaultPixelScale : `float`
284 Default pixel scale to use if not in visit summary (arcsecond/pixel).
285 """
287 # Guarantee that these are sorted.
288 for i, visit in enumerate(sorted(groupedHandles)):
289 if (i % self.config.nVisitsPerCheckpoint) == 0:
290 self.log.info("Retrieving metadata for visit %d (%d/%d)", visit, i, len(groupedHandles))
292 handle = groupedHandles[visit][0]
293 summary = handle.get()
295 summaryRow = summary.find(self.config.referenceCCD)
296 if summaryRow is None:
297 # Take the first available ccd if reference isn't available
298 summaryRow = summary[0]
300 visitInfo = summaryRow.getVisitInfo()
301 physicalFilter = summaryRow['physical_filter']
302 # Compute the median psf sigma and fwhm if possible.
303 if 'pixelScale' in summary.schema:
304 # This is not available in the older test summaries
305 pixelScales = summary['pixelScale']
306 else:
307 pixelScales = np.full(len(summary['psfSigma']), defaultPixelScale)
308 psfSigmas = summary['psfSigma']
309 goodSigma, = np.where((np.nan_to_num(psfSigmas) > 0) & (np.nan_to_num(pixelScales) > 0))
310 if goodSigma.size > 2:
311 psfSigma = np.median(psfSigmas[goodSigma])
312 psfFwhm = np.median(psfSigmas[goodSigma] * pixelScales[goodSigma]) * np.sqrt(8.*np.log(2.))
313 elif goodSigma.size > 0:
314 psfSigma = psfSigmas[goodSigma[0]]
315 psfFwhm = psfSigmas[goodSigma[0]] * pixelScales[goodSigma[0]] * np.sqrt(8.)*np.log(2.)
316 else:
317 self.log.warning("Could not find any good summary psfSigma for visit %d", visit)
318 psfSigma = 0.0
319 psfFwhm = 0.0
320 # Compute median background if possible
321 goodBackground, = np.where(np.nan_to_num(summary['skyBg']) > 0.0)
322 if goodBackground.size > 2:
323 skyBackground = np.median(summary['skyBg'][goodBackground])
324 elif goodBackground.size > 0:
325 skyBackground = summary['skyBg'][goodBackground[0]]
326 else:
327 self.log.warning('Could not find any good summary skyBg for visit %d', visit)
328 skyBackground = -1.0
330 rec = visitCat[i]
331 rec['visit'] = visit
332 rec['physicalFilter'] = physicalFilter
333 # TODO DM-26991: Use the wcs to refine the focal-plane center.
334 radec = visitInfo.getBoresightRaDec()
335 rec['telra'] = radec.getRa().asDegrees()
336 rec['teldec'] = radec.getDec().asDegrees()
337 rec['telha'] = visitInfo.getBoresightHourAngle().asDegrees()
338 rec['telrot'] = visitInfo.getBoresightRotAngle().asDegrees()
339 rec['mjd'] = visitInfo.getDate().get(system=DateTime.MJD)
340 rec['exptime'] = visitInfo.getExposureTime()
341 # convert from Pa to millibar
342 # Note that I don't know if this unit will need to be per-camera config
343 rec['pmb'] = visitInfo.getWeather().getAirPressure() / 100
344 # Flag to signify if this is a "deep" field. Not currently used
345 rec['deepFlag'] = 0
346 # Relative flat scaling (1.0 means no relative scaling)
347 rec['scaling'][:] = 1.0
348 # Median delta aperture, to be measured from stars
349 rec['deltaAper'] = -9999.0
350 rec['deltaAperDetector'][:] = -9999.0
351 rec['psfSigma'] = psfSigma
352 rec['psfFwhm'] = psfFwhm
353 rec['skyBackground'] = skyBackground
354 rec['used'] = 1
356 def _makeSourceMapper(self, sourceSchema):
357 """
358 Make a schema mapper for fgcm sources
360 Parameters
361 ----------
362 sourceSchema: `afwTable.Schema`
363 Default source schema from the butler
365 Returns
366 -------
367 sourceMapper: `afwTable.schemaMapper`
368 Mapper to the FGCM source schema
369 """
371 # create a mapper to the preferred output
372 sourceMapper = afwTable.SchemaMapper(sourceSchema)
374 # map to ra/dec
375 sourceMapper.addMapping(sourceSchema['coord_ra'].asKey(), 'ra')
376 sourceMapper.addMapping(sourceSchema['coord_dec'].asKey(), 'dec')
377 sourceMapper.addMapping(sourceSchema['slot_Centroid_x'].asKey(), 'x')
378 sourceMapper.addMapping(sourceSchema['slot_Centroid_y'].asKey(), 'y')
379 # Add the mapping if the field exists in the input catalog.
380 # If the field does not exist, simply add it (set to False).
381 # This field is not required for calibration, but is useful
382 # to collate if available.
383 try:
384 sourceMapper.addMapping(sourceSchema[self.config.psfCandidateName].asKey(),
385 'psf_candidate')
386 except LookupError:
387 sourceMapper.editOutputSchema().addField(
388 "psf_candidate", type='Flag',
389 doc=("Flag set if the source was a candidate for PSF determination, "
390 "as determined by the star selector."))
392 # and add the fields we want
393 sourceMapper.editOutputSchema().addField(
394 "visit", type=np.int64, doc="Visit number")
395 sourceMapper.editOutputSchema().addField(
396 "ccd", type=np.int32, doc="CCD number")
397 sourceMapper.editOutputSchema().addField(
398 "instMag", type=np.float32, doc="Instrumental magnitude")
399 sourceMapper.editOutputSchema().addField(
400 "instMagErr", type=np.float32, doc="Instrumental magnitude error")
401 sourceMapper.editOutputSchema().addField(
402 "jacobian", type=np.float32, doc="Relative pixel scale from wcs jacobian")
403 sourceMapper.editOutputSchema().addField(
404 "deltaMagBkg", type=np.float32, doc="Change in magnitude due to local background offset")
405 sourceMapper.editOutputSchema().addField(
406 "deltaMagAper", type=np.float32, doc="Change in magnitude from larger to smaller aperture")
408 return sourceMapper
410 def fgcmMatchStars(self, visitCat, obsCat, lutHandle=None):
411 """
412 Use FGCM code to match observations into unique stars.
414 Parameters
415 ----------
416 visitCat: `afw.table.BaseCatalog`
417 Catalog with visit data for fgcm
418 obsCat: `afw.table.BaseCatalog`
419 Full catalog of star observations for fgcm
420 lutHandle: `lsst.daf.butler.DeferredDatasetHandle`, optional
421 Data reference to fgcm look-up table (used if matching reference stars).
423 Returns
424 -------
425 fgcmStarIdCat: `afw.table.BaseCatalog`
426 Catalog of unique star identifiers and index keys
427 fgcmStarIndicesCat: `afwTable.BaseCatalog`
428 Catalog of unique star indices
429 fgcmRefCat: `afw.table.BaseCatalog`
430 Catalog of matched reference stars.
431 Will be None if `config.doReferenceMatches` is False.
432 """
433 # get filter names into a numpy array...
434 # This is the type that is expected by the fgcm code
435 visitFilterNames = np.zeros(len(visitCat), dtype='S30')
436 for i in range(len(visitCat)):
437 visitFilterNames[i] = visitCat[i]['physicalFilter']
439 # match to put filterNames with observations
440 visitIndex = np.searchsorted(visitCat['visit'],
441 obsCat['visit'])
443 obsFilterNames = visitFilterNames[visitIndex]
445 if self.config.doReferenceMatches:
446 # Get the reference filter names, using the LUT
447 lutCat = lutHandle.get()
449 stdFilterDict = {filterName: stdFilter for (filterName, stdFilter) in
450 zip(lutCat[0]['physicalFilters'].split(','),
451 lutCat[0]['stdPhysicalFilters'].split(','))}
452 stdLambdaDict = {stdFilter: stdLambda for (stdFilter, stdLambda) in
453 zip(lutCat[0]['stdPhysicalFilters'].split(','),
454 lutCat[0]['lambdaStdFilter'])}
456 del lutCat
458 referenceFilterNames = self._getReferenceFilterNames(visitCat,
459 stdFilterDict,
460 stdLambdaDict)
461 self.log.info("Using the following reference filters: %s" %
462 (', '.join(referenceFilterNames)))
464 else:
465 # This should be an empty list
466 referenceFilterNames = []
468 # make the fgcm starConfig dict
469 starConfig = {'logger': self.log,
470 'useHtm': True,
471 'filterToBand': self.config.physicalFilterMap,
472 'requiredBands': self.config.requiredBands,
473 'minPerBand': self.config.minPerBand,
474 'matchRadius': self.config.matchRadius,
475 'isolationRadius': self.config.isolationRadius,
476 'matchNSide': self.config.matchNside,
477 'coarseNSide': self.config.coarseNside,
478 'densNSide': self.config.densityCutNside,
479 'densMaxPerPixel': self.config.densityCutMaxPerPixel,
480 'randomSeed': self.config.randomSeed,
481 'primaryBands': self.config.primaryBands,
482 'referenceFilterNames': referenceFilterNames}
484 # initialize the FgcmMakeStars object
485 fgcmMakeStars = fgcm.FgcmMakeStars(starConfig)
487 # make the primary stars
488 # note that the ra/dec native Angle format is radians
489 # We determine the conversion from the native units (typically
490 # radians) to degrees for the first observation. This allows us
491 # to treate ra/dec as numpy arrays rather than Angles, which would
492 # be approximately 600x slower.
493 conv = obsCat[0]['ra'].asDegrees() / float(obsCat[0]['ra'])
494 fgcmMakeStars.makePrimaryStars(obsCat['ra'] * conv,
495 obsCat['dec'] * conv,
496 filterNameArray=obsFilterNames,
497 bandSelected=False)
499 # and match all the stars
500 fgcmMakeStars.makeMatchedStars(obsCat['ra'] * conv,
501 obsCat['dec'] * conv,
502 obsFilterNames)
504 if self.config.doReferenceMatches:
505 fgcmMakeStars.makeReferenceMatches(self.fgcmLoadReferenceCatalog)
507 # now persist
509 objSchema = self._makeFgcmObjSchema()
511 # make catalog and records
512 fgcmStarIdCat = afwTable.BaseCatalog(objSchema)
513 fgcmStarIdCat.reserve(fgcmMakeStars.objIndexCat.size)
514 for i in range(fgcmMakeStars.objIndexCat.size):
515 fgcmStarIdCat.addNew()
517 # fill the catalog
518 fgcmStarIdCat['fgcm_id'][:] = fgcmMakeStars.objIndexCat['fgcm_id']
519 fgcmStarIdCat['ra'][:] = fgcmMakeStars.objIndexCat['ra']
520 fgcmStarIdCat['dec'][:] = fgcmMakeStars.objIndexCat['dec']
521 fgcmStarIdCat['obsArrIndex'][:] = fgcmMakeStars.objIndexCat['obsarrindex']
522 fgcmStarIdCat['nObs'][:] = fgcmMakeStars.objIndexCat['nobs']
524 obsSchema = self._makeFgcmObsSchema()
526 fgcmStarIndicesCat = afwTable.BaseCatalog(obsSchema)
527 fgcmStarIndicesCat.reserve(fgcmMakeStars.obsIndexCat.size)
528 for i in range(fgcmMakeStars.obsIndexCat.size):
529 fgcmStarIndicesCat.addNew()
531 fgcmStarIndicesCat['obsIndex'][:] = fgcmMakeStars.obsIndexCat['obsindex']
533 if self.config.doReferenceMatches:
534 refSchema = self._makeFgcmRefSchema(len(referenceFilterNames))
536 fgcmRefCat = afwTable.BaseCatalog(refSchema)
537 fgcmRefCat.reserve(fgcmMakeStars.referenceCat.size)
539 for i in range(fgcmMakeStars.referenceCat.size):
540 fgcmRefCat.addNew()
542 fgcmRefCat['fgcm_id'][:] = fgcmMakeStars.referenceCat['fgcm_id']
543 fgcmRefCat['refMag'][:, :] = fgcmMakeStars.referenceCat['refMag']
544 fgcmRefCat['refMagErr'][:, :] = fgcmMakeStars.referenceCat['refMagErr']
546 md = PropertyList()
547 md.set("REFSTARS_FORMAT_VERSION", REFSTARS_FORMAT_VERSION)
548 md.set("FILTERNAMES", referenceFilterNames)
549 fgcmRefCat.setMetadata(md)
551 else:
552 fgcmRefCat = None
554 return fgcmStarIdCat, fgcmStarIndicesCat, fgcmRefCat
556 def _makeFgcmVisitSchema(self, nCcd):
557 """
558 Make a schema for an fgcmVisitCatalog
560 Parameters
561 ----------
562 nCcd: `int`
563 Number of CCDs in the camera
565 Returns
566 -------
567 schema: `afwTable.Schema`
568 """
570 schema = afwTable.Schema()
571 schema.addField('visit', type=np.int64, doc="Visit number")
572 schema.addField('physicalFilter', type=str, size=30, doc="Physical filter")
573 schema.addField('telra', type=np.float64, doc="Pointing RA (deg)")
574 schema.addField('teldec', type=np.float64, doc="Pointing Dec (deg)")
575 schema.addField('telha', type=np.float64, doc="Pointing Hour Angle (deg)")
576 schema.addField('telrot', type=np.float64, doc="Camera rotation (deg)")
577 schema.addField('mjd', type=np.float64, doc="MJD of visit")
578 schema.addField('exptime', type=np.float32, doc="Exposure time")
579 schema.addField('pmb', type=np.float32, doc="Pressure (millibar)")
580 schema.addField('psfSigma', type=np.float32, doc="PSF sigma (median); pixels")
581 schema.addField('psfFwhm', type=np.float32, doc="PSF FWHM (median); arcseconds")
582 schema.addField('deltaAper', type=np.float32, doc="Delta-aperture")
583 schema.addField('deltaAperDetector', type='ArrayF', doc='Delta-aperture per detector', size=nCcd)
584 schema.addField('skyBackground', type=np.float32, doc="Sky background (ADU) (reference CCD)")
585 # the following field is not used yet
586 schema.addField('deepFlag', type=np.int32, doc="Deep observation")
587 schema.addField('scaling', type='ArrayD', doc="Scaling applied due to flat adjustment",
588 size=nCcd)
589 schema.addField('used', type=np.int32, doc="This visit has been ingested.")
590 schema.addField('sources_read', type='Flag', doc="This visit had sources read.")
592 return schema
594 def _makeFgcmObjSchema(self):
595 """
596 Make a schema for the objIndexCat from fgcmMakeStars
598 Returns
599 -------
600 schema: `afwTable.Schema`
601 """
603 objSchema = afwTable.Schema()
604 objSchema.addField('fgcm_id', type=np.int32, doc='FGCM Unique ID')
605 # Will investigate making these angles...
606 objSchema.addField('ra', type=np.float64, doc='Mean object RA (deg)')
607 objSchema.addField('dec', type=np.float64, doc='Mean object Dec (deg)')
608 objSchema.addField('obsArrIndex', type=np.int32,
609 doc='Index in obsIndexTable for first observation')
610 objSchema.addField('nObs', type=np.int32, doc='Total number of observations')
612 return objSchema
614 def _makeFgcmObsSchema(self):
615 """
616 Make a schema for the obsIndexCat from fgcmMakeStars
618 Returns
619 -------
620 schema: `afwTable.Schema`
621 """
623 obsSchema = afwTable.Schema()
624 obsSchema.addField('obsIndex', type=np.int32, doc='Index in observation table')
626 return obsSchema
628 def _makeFgcmRefSchema(self, nReferenceBands):
629 """
630 Make a schema for the referenceCat from fgcmMakeStars
632 Parameters
633 ----------
634 nReferenceBands: `int`
635 Number of reference bands
637 Returns
638 -------
639 schema: `afwTable.Schema`
640 """
642 refSchema = afwTable.Schema()
643 refSchema.addField('fgcm_id', type=np.int32, doc='FGCM Unique ID')
644 refSchema.addField('refMag', type='ArrayF', doc='Reference magnitude array (AB)',
645 size=nReferenceBands)
646 refSchema.addField('refMagErr', type='ArrayF', doc='Reference magnitude error array',
647 size=nReferenceBands)
649 return refSchema
651 def _getReferenceFilterNames(self, visitCat, stdFilterDict, stdLambdaDict):
652 """
653 Get the reference filter names, in wavelength order, from the visitCat and
654 information from the look-up-table.
656 Parameters
657 ----------
658 visitCat: `afw.table.BaseCatalog`
659 Catalog with visit data for FGCM
660 stdFilterDict: `dict`
661 Mapping of filterName to stdFilterName from LUT
662 stdLambdaDict: `dict`
663 Mapping of stdFilterName to stdLambda from LUT
665 Returns
666 -------
667 referenceFilterNames: `list`
668 Wavelength-ordered list of reference filter names
669 """
671 # Find the unique list of filter names in visitCat
672 filterNames = np.unique(visitCat.asAstropy()['physicalFilter'])
674 # Find the unique list of "standard" filters
675 stdFilterNames = {stdFilterDict[filterName] for filterName in filterNames}
677 # And sort these by wavelength
678 referenceFilterNames = sorted(stdFilterNames, key=stdLambdaDict.get)
680 return referenceFilterNames