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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"""Utility functions for fgcmcal.
23This file contains utility functions that are used by more than one task,
24and do not need to be part of a task.
25"""
27import numpy as np
28import os
29import re
30from deprecated.sphinx import deprecated
32from lsst.daf.base import PropertyList
33import lsst.daf.persistence as dafPersist
34import lsst.afw.cameraGeom as afwCameraGeom
35import lsst.afw.table as afwTable
36import lsst.afw.image as afwImage
37import lsst.afw.math as afwMath
38import lsst.geom as geom
39from lsst.obs.base import createInitialSkyWcs
40from lsst.obs.base import Instrument
42import fgcm
45FGCM_EXP_FIELD = 'VISIT'
46FGCM_CCD_FIELD = 'DETECTOR'
47FGCM_ILLEGAL_VALUE = -9999.0
50def makeConfigDict(config, log, camera, maxIter,
51 resetFitParameters, outputZeropoints,
52 lutFilterNames, tract=None):
53 """
54 Make the FGCM fit cycle configuration dict
56 Parameters
57 ----------
58 config: `lsst.fgcmcal.FgcmFitCycleConfig`
59 Configuration object
60 log: `lsst.log.Log`
61 LSST log object
62 camera: `lsst.afw.cameraGeom.Camera`
63 Camera from the butler
64 maxIter: `int`
65 Maximum number of iterations
66 resetFitParameters: `bool`
67 Reset fit parameters before fitting?
68 outputZeropoints: `bool`
69 Compute zeropoints for output?
70 lutFilterNames : array-like, `str`
71 Array of physical filter names in the LUT.
72 tract: `int`, optional
73 Tract number for extending the output file name for debugging.
74 Default is None.
76 Returns
77 -------
78 configDict: `dict`
79 Configuration dictionary for fgcm
80 """
81 # Extract the bands that are _not_ being fit for fgcm configuration
82 notFitBands = [b for b in config.bands if b not in config.fitBands]
84 # process the starColorCuts
85 starColorCutList = []
86 for ccut in config.starColorCuts:
87 parts = ccut.split(',')
88 starColorCutList.append([parts[0], parts[1], float(parts[2]), float(parts[3])])
90 # TODO: Having direct access to the mirror area from the camera would be
91 # useful. See DM-16489.
92 # Mirror area in cm**2
93 mirrorArea = np.pi*(camera.telescopeDiameter*100./2.)**2.
95 # Get approximate average camera gain:
96 gains = [amp.getGain() for detector in camera for amp in detector.getAmplifiers()]
97 cameraGain = float(np.median(gains))
99 # Cut down the filter map to those that are in the LUT
100 filterToBand = {filterName: config.physicalFilterMap[filterName] for
101 filterName in lutFilterNames}
103 if tract is None:
104 outfileBase = config.outfileBase
105 else:
106 outfileBase = '%s-%06d' % (config.outfileBase, tract)
108 # create a configuration dictionary for fgcmFitCycle
109 configDict = {'outfileBase': outfileBase,
110 'logger': log,
111 'exposureFile': None,
112 'obsFile': None,
113 'indexFile': None,
114 'lutFile': None,
115 'mirrorArea': mirrorArea,
116 'cameraGain': cameraGain,
117 'ccdStartIndex': camera[0].getId(),
118 'expField': FGCM_EXP_FIELD,
119 'ccdField': FGCM_CCD_FIELD,
120 'seeingField': 'DELTA_APER',
121 'fwhmField': 'PSFSIGMA',
122 'skyBrightnessField': 'SKYBACKGROUND',
123 'deepFlag': 'DEEPFLAG', # unused
124 'bands': list(config.bands),
125 'fitBands': list(config.fitBands),
126 'notFitBands': notFitBands,
127 'requiredBands': list(config.requiredBands),
128 'filterToBand': filterToBand,
129 'logLevel': 'INFO',
130 'nCore': config.nCore,
131 'nStarPerRun': config.nStarPerRun,
132 'nExpPerRun': config.nExpPerRun,
133 'reserveFraction': config.reserveFraction,
134 'freezeStdAtmosphere': config.freezeStdAtmosphere,
135 'precomputeSuperStarInitialCycle': config.precomputeSuperStarInitialCycle,
136 'superStarSubCCDDict': dict(config.superStarSubCcdDict),
137 'superStarSubCCDChebyshevOrder': config.superStarSubCcdChebyshevOrder,
138 'superStarSubCCDTriangular': config.superStarSubCcdTriangular,
139 'superStarSigmaClip': config.superStarSigmaClip,
140 'focalPlaneSigmaClip': config.focalPlaneSigmaClip,
141 'ccdGraySubCCDDict': dict(config.ccdGraySubCcdDict),
142 'ccdGraySubCCDChebyshevOrder': config.ccdGraySubCcdChebyshevOrder,
143 'ccdGraySubCCDTriangular': config.ccdGraySubCcdTriangular,
144 'ccdGrayFocalPlaneDict': dict(config.ccdGrayFocalPlaneDict),
145 'ccdGrayFocalPlaneChebyshevOrder': config.ccdGrayFocalPlaneChebyshevOrder,
146 'ccdGrayFocalPlaneFitMinCcd': config.ccdGrayFocalPlaneFitMinCcd,
147 'cycleNumber': config.cycleNumber,
148 'maxIter': maxIter,
149 'deltaMagBkgOffsetPercentile': config.deltaMagBkgOffsetPercentile,
150 'deltaMagBkgPerCcd': config.deltaMagBkgPerCcd,
151 'UTBoundary': config.utBoundary,
152 'washMJDs': config.washMjds,
153 'epochMJDs': config.epochMjds,
154 'coatingMJDs': config.coatingMjds,
155 'minObsPerBand': config.minObsPerBand,
156 'latitude': config.latitude,
157 'defaultCameraOrientation': config.defaultCameraOrientation,
158 'brightObsGrayMax': config.brightObsGrayMax,
159 'minStarPerCCD': config.minStarPerCcd,
160 'minCCDPerExp': config.minCcdPerExp,
161 'maxCCDGrayErr': config.maxCcdGrayErr,
162 'minStarPerExp': config.minStarPerExp,
163 'minExpPerNight': config.minExpPerNight,
164 'expGrayInitialCut': config.expGrayInitialCut,
165 'expGrayPhotometricCutDict': dict(config.expGrayPhotometricCutDict),
166 'expGrayHighCutDict': dict(config.expGrayHighCutDict),
167 'expGrayRecoverCut': config.expGrayRecoverCut,
168 'expVarGrayPhotometricCutDict': dict(config.expVarGrayPhotometricCutDict),
169 'expGrayErrRecoverCut': config.expGrayErrRecoverCut,
170 'refStarSnMin': config.refStarSnMin,
171 'refStarOutlierNSig': config.refStarOutlierNSig,
172 'applyRefStarColorCuts': config.applyRefStarColorCuts,
173 'illegalValue': FGCM_ILLEGAL_VALUE, # internally used by fgcm.
174 'starColorCuts': starColorCutList,
175 'aperCorrFitNBins': config.aperCorrFitNBins,
176 'aperCorrInputSlopeDict': dict(config.aperCorrInputSlopeDict),
177 'sedBoundaryTermDict': config.sedboundaryterms.toDict()['data'],
178 'sedTermDict': config.sedterms.toDict()['data'],
179 'colorSplitBands': list(config.colorSplitBands),
180 'sigFgcmMaxErr': config.sigFgcmMaxErr,
181 'sigFgcmMaxEGrayDict': dict(config.sigFgcmMaxEGrayDict),
182 'ccdGrayMaxStarErr': config.ccdGrayMaxStarErr,
183 'approxThroughputDict': dict(config.approxThroughputDict),
184 'sigmaCalRange': list(config.sigmaCalRange),
185 'sigmaCalFitPercentile': list(config.sigmaCalFitPercentile),
186 'sigmaCalPlotPercentile': list(config.sigmaCalPlotPercentile),
187 'sigma0Phot': config.sigma0Phot,
188 'mapLongitudeRef': config.mapLongitudeRef,
189 'mapNSide': config.mapNSide,
190 'varNSig': 100.0, # Turn off 'variable star selection' which doesn't work yet
191 'varMinBand': 2,
192 'useRetrievedPwv': False,
193 'useNightlyRetrievedPwv': False,
194 'pwvRetrievalSmoothBlock': 25,
195 'useQuadraticPwv': config.useQuadraticPwv,
196 'useRetrievedTauInit': False,
197 'tauRetrievalMinCCDPerNight': 500,
198 'modelMagErrors': config.modelMagErrors,
199 'instrumentParsPerBand': config.instrumentParsPerBand,
200 'instrumentSlopeMinDeltaT': config.instrumentSlopeMinDeltaT,
201 'fitMirrorChromaticity': config.fitMirrorChromaticity,
202 'useRepeatabilityForExpGrayCutsDict': dict(config.useRepeatabilityForExpGrayCutsDict),
203 'autoPhotometricCutNSig': config.autoPhotometricCutNSig,
204 'autoHighCutNSig': config.autoHighCutNSig,
205 'printOnly': False,
206 'quietMode': config.quietMode,
207 'randomSeed': config.randomSeed,
208 'outputStars': False,
209 'outputPath': os.path.abspath('.'),
210 'clobber': True,
211 'useSedLUT': False,
212 'resetParameters': resetFitParameters,
213 'doPlots': config.doPlots,
214 'outputFgcmcalZpts': True, # when outputting zpts, use fgcmcal format
215 'outputZeropoints': outputZeropoints}
217 return configDict
220def translateFgcmLut(lutCat, physicalFilterMap):
221 """
222 Translate the FGCM look-up-table into an fgcm-compatible object
224 Parameters
225 ----------
226 lutCat: `lsst.afw.table.BaseCatalog`
227 Catalog describing the FGCM look-up table
228 physicalFilterMap: `dict`
229 Physical filter to band mapping
231 Returns
232 -------
233 fgcmLut: `lsst.fgcm.FgcmLut`
234 Lookup table for FGCM
235 lutIndexVals: `numpy.ndarray`
236 Numpy array with LUT index information for FGCM
237 lutStd: `numpy.ndarray`
238 Numpy array with LUT standard throughput values for FGCM
240 Notes
241 -----
242 After running this code, it is wise to `del lutCat` to clear the memory.
243 """
245 # first we need the lutIndexVals
246 lutFilterNames = np.array(lutCat[0]['physicalFilters'].split(','), dtype='U')
247 lutStdFilterNames = np.array(lutCat[0]['stdPhysicalFilters'].split(','), dtype='U')
249 # Note that any discrepancies between config values will raise relevant
250 # exceptions in the FGCM code.
252 lutIndexVals = np.zeros(1, dtype=[('FILTERNAMES', lutFilterNames.dtype.str,
253 lutFilterNames.size),
254 ('STDFILTERNAMES', lutStdFilterNames.dtype.str,
255 lutStdFilterNames.size),
256 ('PMB', 'f8', lutCat[0]['pmb'].size),
257 ('PMBFACTOR', 'f8', lutCat[0]['pmbFactor'].size),
258 ('PMBELEVATION', 'f8'),
259 ('LAMBDANORM', 'f8'),
260 ('PWV', 'f8', lutCat[0]['pwv'].size),
261 ('O3', 'f8', lutCat[0]['o3'].size),
262 ('TAU', 'f8', lutCat[0]['tau'].size),
263 ('ALPHA', 'f8', lutCat[0]['alpha'].size),
264 ('ZENITH', 'f8', lutCat[0]['zenith'].size),
265 ('NCCD', 'i4')])
267 lutIndexVals['FILTERNAMES'][:] = lutFilterNames
268 lutIndexVals['STDFILTERNAMES'][:] = lutStdFilterNames
269 lutIndexVals['PMB'][:] = lutCat[0]['pmb']
270 lutIndexVals['PMBFACTOR'][:] = lutCat[0]['pmbFactor']
271 lutIndexVals['PMBELEVATION'] = lutCat[0]['pmbElevation']
272 lutIndexVals['LAMBDANORM'] = lutCat[0]['lambdaNorm']
273 lutIndexVals['PWV'][:] = lutCat[0]['pwv']
274 lutIndexVals['O3'][:] = lutCat[0]['o3']
275 lutIndexVals['TAU'][:] = lutCat[0]['tau']
276 lutIndexVals['ALPHA'][:] = lutCat[0]['alpha']
277 lutIndexVals['ZENITH'][:] = lutCat[0]['zenith']
278 lutIndexVals['NCCD'] = lutCat[0]['nCcd']
280 # now we need the Standard Values
281 lutStd = np.zeros(1, dtype=[('PMBSTD', 'f8'),
282 ('PWVSTD', 'f8'),
283 ('O3STD', 'f8'),
284 ('TAUSTD', 'f8'),
285 ('ALPHASTD', 'f8'),
286 ('ZENITHSTD', 'f8'),
287 ('LAMBDARANGE', 'f8', 2),
288 ('LAMBDASTEP', 'f8'),
289 ('LAMBDASTD', 'f8', lutFilterNames.size),
290 ('LAMBDASTDFILTER', 'f8', lutStdFilterNames.size),
291 ('I0STD', 'f8', lutFilterNames.size),
292 ('I1STD', 'f8', lutFilterNames.size),
293 ('I10STD', 'f8', lutFilterNames.size),
294 ('I2STD', 'f8', lutFilterNames.size),
295 ('LAMBDAB', 'f8', lutFilterNames.size),
296 ('ATMLAMBDA', 'f8', lutCat[0]['atmLambda'].size),
297 ('ATMSTDTRANS', 'f8', lutCat[0]['atmStdTrans'].size)])
298 lutStd['PMBSTD'] = lutCat[0]['pmbStd']
299 lutStd['PWVSTD'] = lutCat[0]['pwvStd']
300 lutStd['O3STD'] = lutCat[0]['o3Std']
301 lutStd['TAUSTD'] = lutCat[0]['tauStd']
302 lutStd['ALPHASTD'] = lutCat[0]['alphaStd']
303 lutStd['ZENITHSTD'] = lutCat[0]['zenithStd']
304 lutStd['LAMBDARANGE'][:] = lutCat[0]['lambdaRange'][:]
305 lutStd['LAMBDASTEP'] = lutCat[0]['lambdaStep']
306 lutStd['LAMBDASTD'][:] = lutCat[0]['lambdaStd']
307 lutStd['LAMBDASTDFILTER'][:] = lutCat[0]['lambdaStdFilter']
308 lutStd['I0STD'][:] = lutCat[0]['i0Std']
309 lutStd['I1STD'][:] = lutCat[0]['i1Std']
310 lutStd['I10STD'][:] = lutCat[0]['i10Std']
311 lutStd['I2STD'][:] = lutCat[0]['i2Std']
312 lutStd['LAMBDAB'][:] = lutCat[0]['lambdaB']
313 lutStd['ATMLAMBDA'][:] = lutCat[0]['atmLambda'][:]
314 lutStd['ATMSTDTRANS'][:] = lutCat[0]['atmStdTrans'][:]
316 lutTypes = [row['luttype'] for row in lutCat]
318 # And the flattened look-up-table
319 lutFlat = np.zeros(lutCat[0]['lut'].size, dtype=[('I0', 'f4'),
320 ('I1', 'f4')])
322 lutFlat['I0'][:] = lutCat[lutTypes.index('I0')]['lut'][:]
323 lutFlat['I1'][:] = lutCat[lutTypes.index('I1')]['lut'][:]
325 lutDerivFlat = np.zeros(lutCat[0]['lut'].size, dtype=[('D_LNPWV', 'f4'),
326 ('D_O3', 'f4'),
327 ('D_LNTAU', 'f4'),
328 ('D_ALPHA', 'f4'),
329 ('D_SECZENITH', 'f4'),
330 ('D_LNPWV_I1', 'f4'),
331 ('D_O3_I1', 'f4'),
332 ('D_LNTAU_I1', 'f4'),
333 ('D_ALPHA_I1', 'f4'),
334 ('D_SECZENITH_I1', 'f4')])
336 for name in lutDerivFlat.dtype.names:
337 lutDerivFlat[name][:] = lutCat[lutTypes.index(name)]['lut'][:]
339 # The fgcm.FgcmLUT() class copies all the LUT information into special
340 # shared memory objects that will not blow up the memory usage when used
341 # with python multiprocessing. Once all the numbers are copied, the
342 # references to the temporary objects (lutCat, lutFlat, lutDerivFlat)
343 # will fall out of scope and can be cleaned up by the garbage collector.
344 fgcmLut = fgcm.FgcmLUT(lutIndexVals, lutFlat, lutDerivFlat, lutStd,
345 filterToBand=physicalFilterMap)
347 return fgcmLut, lutIndexVals, lutStd
350def translateVisitCatalog(visitCat):
351 """
352 Translate the FGCM visit catalog to an fgcm-compatible object
354 Parameters
355 ----------
356 visitCat: `lsst.afw.table.BaseCatalog`
357 FGCM visitCat from `lsst.fgcmcal.FgcmBuildStarsTask`
359 Returns
360 -------
361 fgcmExpInfo: `numpy.ndarray`
362 Numpy array for visit information for FGCM
364 Notes
365 -----
366 After running this code, it is wise to `del visitCat` to clear the memory.
367 """
369 fgcmExpInfo = np.zeros(len(visitCat), dtype=[('VISIT', 'i8'),
370 ('MJD', 'f8'),
371 ('EXPTIME', 'f8'),
372 ('PSFSIGMA', 'f8'),
373 ('DELTA_APER', 'f8'),
374 ('SKYBACKGROUND', 'f8'),
375 ('DEEPFLAG', 'i2'),
376 ('TELHA', 'f8'),
377 ('TELRA', 'f8'),
378 ('TELDEC', 'f8'),
379 ('TELROT', 'f8'),
380 ('PMB', 'f8'),
381 ('FILTERNAME', 'a50')])
382 fgcmExpInfo['VISIT'][:] = visitCat['visit']
383 fgcmExpInfo['MJD'][:] = visitCat['mjd']
384 fgcmExpInfo['EXPTIME'][:] = visitCat['exptime']
385 fgcmExpInfo['DEEPFLAG'][:] = visitCat['deepFlag']
386 fgcmExpInfo['TELHA'][:] = visitCat['telha']
387 fgcmExpInfo['TELRA'][:] = visitCat['telra']
388 fgcmExpInfo['TELDEC'][:] = visitCat['teldec']
389 fgcmExpInfo['TELROT'][:] = visitCat['telrot']
390 fgcmExpInfo['PMB'][:] = visitCat['pmb']
391 fgcmExpInfo['PSFSIGMA'][:] = visitCat['psfSigma']
392 fgcmExpInfo['DELTA_APER'][:] = visitCat['deltaAper']
393 fgcmExpInfo['SKYBACKGROUND'][:] = visitCat['skyBackground']
394 # Note that we have to go through asAstropy() to get a string
395 # array out of an afwTable. This is faster than a row-by-row loop.
396 fgcmExpInfo['FILTERNAME'][:] = visitCat.asAstropy()['physicalFilter']
398 return fgcmExpInfo
401@deprecated(reason="This method is no longer used in fgcmcal. It will be removed after v23.",
402 version="v23.0", category=FutureWarning)
403def computeCcdOffsets(camera, defaultOrientation):
404 """
405 Compute the CCD offsets in ra/dec and x/y space
407 Parameters
408 ----------
409 camera: `lsst.afw.cameraGeom.Camera`
410 defaultOrientation: `float`
411 Default camera orientation (degrees)
413 Returns
414 -------
415 ccdOffsets: `numpy.ndarray`
416 Numpy array with ccd offset information for input to FGCM.
417 Angular units are degrees, and x/y units are pixels.
418 """
419 # TODO: DM-21215 will fully generalize to arbitrary camera orientations
421 # and we need to know the ccd offsets from the camera geometry
422 ccdOffsets = np.zeros(len(camera), dtype=[('CCDNUM', 'i4'),
423 ('DELTA_RA', 'f8'),
424 ('DELTA_DEC', 'f8'),
425 ('RA_SIZE', 'f8'),
426 ('DEC_SIZE', 'f8'),
427 ('X_SIZE', 'i4'),
428 ('Y_SIZE', 'i4')])
430 # Generate fake WCSs centered at 180/0 to avoid the RA=0/360 problem,
431 # since we are looking for relative positions
432 boresight = geom.SpherePoint(180.0*geom.degrees, 0.0*geom.degrees)
434 # TODO: DM-17597 will update testdata_jointcal so that the test data
435 # does not have nan as the boresight angle for HSC data. For the
436 # time being, there is this ungainly hack.
437 if camera.getName() == 'HSC' and np.isnan(defaultOrientation):
438 orientation = 270*geom.degrees
439 else:
440 orientation = defaultOrientation*geom.degrees
441 flipX = False
443 # Create a temporary visitInfo for input to createInitialSkyWcs
444 visitInfo = afwImage.VisitInfo(boresightRaDec=boresight,
445 boresightRotAngle=orientation,
446 rotType=afwImage.RotType.SKY)
448 for i, detector in enumerate(camera):
449 ccdOffsets['CCDNUM'][i] = detector.getId()
451 wcs = createInitialSkyWcs(visitInfo, detector, flipX)
453 detCenter = wcs.pixelToSky(detector.getCenter(afwCameraGeom.PIXELS))
454 ccdOffsets['DELTA_RA'][i] = (detCenter.getRa() - boresight.getRa()).asDegrees()
455 ccdOffsets['DELTA_DEC'][i] = (detCenter.getDec() - boresight.getDec()).asDegrees()
457 bbox = detector.getBBox()
459 detCorner1 = wcs.pixelToSky(geom.Point2D(bbox.getMin()))
460 detCorner2 = wcs.pixelToSky(geom.Point2D(bbox.getMax()))
462 ccdOffsets['RA_SIZE'][i] = np.abs((detCorner2.getRa() - detCorner1.getRa()).asDegrees())
463 ccdOffsets['DEC_SIZE'][i] = np.abs((detCorner2.getDec() - detCorner1.getDec()).asDegrees())
465 ccdOffsets['X_SIZE'][i] = bbox.getMaxX()
466 ccdOffsets['Y_SIZE'][i] = bbox.getMaxY()
468 return ccdOffsets
471def computeReferencePixelScale(camera):
472 """
473 Compute the median pixel scale in the camera
475 Returns
476 -------
477 pixelScale: `float`
478 Average pixel scale (arcsecond) over the camera
479 """
481 boresight = geom.SpherePoint(180.0*geom.degrees, 0.0*geom.degrees)
482 orientation = 0.0*geom.degrees
483 flipX = False
485 # Create a temporary visitInfo for input to createInitialSkyWcs
486 visitInfo = afwImage.VisitInfo(boresightRaDec=boresight,
487 boresightRotAngle=orientation,
488 rotType=afwImage.RotType.SKY)
490 pixelScales = np.zeros(len(camera))
491 for i, detector in enumerate(camera):
492 wcs = createInitialSkyWcs(visitInfo, detector, flipX)
493 pixelScales[i] = wcs.getPixelScale().asArcseconds()
495 ok, = np.where(pixelScales > 0.0)
496 return np.median(pixelScales[ok])
499def computeApproxPixelAreaFields(camera):
500 """
501 Compute the approximate pixel area bounded fields from the camera
502 geometry.
504 Parameters
505 ----------
506 camera: `lsst.afw.cameraGeom.Camera`
508 Returns
509 -------
510 approxPixelAreaFields: `dict`
511 Dictionary of approximate area fields, keyed with detector ID
512 """
514 areaScaling = 1. / computeReferencePixelScale(camera)**2.
516 # Generate fake WCSs centered at 180/0 to avoid the RA=0/360 problem,
517 # since we are looking for relative scales
518 boresight = geom.SpherePoint(180.0*geom.degrees, 0.0*geom.degrees)
520 flipX = False
521 # Create a temporary visitInfo for input to createInitialSkyWcs
522 # The orientation does not matter for the area computation
523 visitInfo = afwImage.VisitInfo(boresightRaDec=boresight,
524 boresightRotAngle=0.0*geom.degrees,
525 rotType=afwImage.RotType.SKY)
527 approxPixelAreaFields = {}
529 for i, detector in enumerate(camera):
530 key = detector.getId()
532 wcs = createInitialSkyWcs(visitInfo, detector, flipX)
533 bbox = detector.getBBox()
535 areaField = afwMath.PixelAreaBoundedField(bbox, wcs,
536 unit=geom.arcseconds, scaling=areaScaling)
537 approxAreaField = afwMath.ChebyshevBoundedField.approximate(areaField)
539 approxPixelAreaFields[key] = approxAreaField
541 return approxPixelAreaFields
544def makeZptSchema(superStarChebyshevSize, zptChebyshevSize):
545 """
546 Make the zeropoint schema
548 Parameters
549 ----------
550 superStarChebyshevSize: `int`
551 Length of the superstar chebyshev array
552 zptChebyshevSize: `int`
553 Length of the zeropoint chebyshev array
555 Returns
556 -------
557 zptSchema: `lsst.afw.table.schema`
558 """
560 zptSchema = afwTable.Schema()
562 zptSchema.addField('visit', type=np.int32, doc='Visit number')
563 zptSchema.addField('detector', type=np.int32, doc='Detector ID number')
564 zptSchema.addField('fgcmFlag', type=np.int32, doc=('FGCM flag value: '
565 '1: Photometric, used in fit; '
566 '2: Photometric, not used in fit; '
567 '4: Non-photometric, on partly photometric night; '
568 '8: Non-photometric, on non-photometric night; '
569 '16: No zeropoint could be determined; '
570 '32: Too few stars for reliable gray computation'))
571 zptSchema.addField('fgcmZpt', type=np.float64, doc='FGCM zeropoint (center of CCD)')
572 zptSchema.addField('fgcmZptErr', type=np.float64,
573 doc='Error on zeropoint, estimated from repeatability + number of obs')
574 zptSchema.addField('fgcmfZptChebXyMax', type='ArrayD', size=2,
575 doc='maximum x/maximum y to scale to apply chebyshev parameters')
576 zptSchema.addField('fgcmfZptCheb', type='ArrayD',
577 size=zptChebyshevSize,
578 doc='Chebyshev parameters (flattened) for zeropoint')
579 zptSchema.addField('fgcmfZptSstarCheb', type='ArrayD',
580 size=superStarChebyshevSize,
581 doc='Chebyshev parameters (flattened) for superStarFlat')
582 zptSchema.addField('fgcmI0', type=np.float64, doc='Integral of the passband')
583 zptSchema.addField('fgcmI10', type=np.float64, doc='Normalized chromatic integral')
584 zptSchema.addField('fgcmR0', type=np.float64,
585 doc='Retrieved i0 integral, estimated from stars (only for flag 1)')
586 zptSchema.addField('fgcmR10', type=np.float64,
587 doc='Retrieved i10 integral, estimated from stars (only for flag 1)')
588 zptSchema.addField('fgcmGry', type=np.float64,
589 doc='Estimated gray extinction relative to atmospheric solution; '
590 'only for fgcmFlag <= 4 (see fgcmFlag) ')
591 zptSchema.addField('fgcmDeltaChrom', type=np.float64,
592 doc='Mean chromatic correction for stars in this ccd; '
593 'only for fgcmFlag <= 4 (see fgcmFlag)')
594 zptSchema.addField('fgcmZptVar', type=np.float64, doc='Variance of zeropoint over ccd')
595 zptSchema.addField('fgcmTilings', type=np.float64,
596 doc='Number of photometric tilings used for solution for ccd')
597 zptSchema.addField('fgcmFpGry', type=np.float64,
598 doc='Average gray extinction over the full focal plane '
599 '(same for all ccds in a visit)')
600 zptSchema.addField('fgcmFpGryBlue', type=np.float64,
601 doc='Average gray extinction over the full focal plane '
602 'for 25% bluest stars')
603 zptSchema.addField('fgcmFpGryBlueErr', type=np.float64,
604 doc='Error on Average gray extinction over the full focal plane '
605 'for 25% bluest stars')
606 zptSchema.addField('fgcmFpGryRed', type=np.float64,
607 doc='Average gray extinction over the full focal plane '
608 'for 25% reddest stars')
609 zptSchema.addField('fgcmFpGryRedErr', type=np.float64,
610 doc='Error on Average gray extinction over the full focal plane '
611 'for 25% reddest stars')
612 zptSchema.addField('fgcmFpVar', type=np.float64,
613 doc='Variance of gray extinction over the full focal plane '
614 '(same for all ccds in a visit)')
615 zptSchema.addField('fgcmDust', type=np.float64,
616 doc='Gray dust extinction from the primary/corrector'
617 'at the time of the exposure')
618 zptSchema.addField('fgcmFlat', type=np.float64, doc='Superstarflat illumination correction')
619 zptSchema.addField('fgcmAperCorr', type=np.float64, doc='Aperture correction estimated by fgcm')
620 zptSchema.addField('fgcmDeltaMagBkg', type=np.float64,
621 doc=('Local background correction from brightest percentile '
622 '(value set by deltaMagBkgOffsetPercentile) calibration '
623 'stars.'))
624 zptSchema.addField('exptime', type=np.float32, doc='Exposure time')
625 zptSchema.addField('filtername', type=str, size=10, doc='Filter name')
627 return zptSchema
630def makeZptCat(zptSchema, zpStruct):
631 """
632 Make the zeropoint catalog for persistence
634 Parameters
635 ----------
636 zptSchema: `lsst.afw.table.Schema`
637 Zeropoint catalog schema
638 zpStruct: `numpy.ndarray`
639 Zeropoint structure from fgcm
641 Returns
642 -------
643 zptCat: `afwTable.BaseCatalog`
644 Zeropoint catalog for persistence
645 """
647 zptCat = afwTable.BaseCatalog(zptSchema)
648 zptCat.reserve(zpStruct.size)
650 for filterName in zpStruct['FILTERNAME']:
651 rec = zptCat.addNew()
652 rec['filtername'] = filterName.decode('utf-8')
654 zptCat['visit'][:] = zpStruct[FGCM_EXP_FIELD]
655 zptCat['detector'][:] = zpStruct[FGCM_CCD_FIELD]
656 zptCat['fgcmFlag'][:] = zpStruct['FGCM_FLAG']
657 zptCat['fgcmZpt'][:] = zpStruct['FGCM_ZPT']
658 zptCat['fgcmZptErr'][:] = zpStruct['FGCM_ZPTERR']
659 zptCat['fgcmfZptChebXyMax'][:, :] = zpStruct['FGCM_FZPT_XYMAX']
660 zptCat['fgcmfZptCheb'][:, :] = zpStruct['FGCM_FZPT_CHEB']
661 zptCat['fgcmfZptSstarCheb'][:, :] = zpStruct['FGCM_FZPT_SSTAR_CHEB']
662 zptCat['fgcmI0'][:] = zpStruct['FGCM_I0']
663 zptCat['fgcmI10'][:] = zpStruct['FGCM_I10']
664 zptCat['fgcmR0'][:] = zpStruct['FGCM_R0']
665 zptCat['fgcmR10'][:] = zpStruct['FGCM_R10']
666 zptCat['fgcmGry'][:] = zpStruct['FGCM_GRY']
667 zptCat['fgcmDeltaChrom'][:] = zpStruct['FGCM_DELTACHROM']
668 zptCat['fgcmZptVar'][:] = zpStruct['FGCM_ZPTVAR']
669 zptCat['fgcmTilings'][:] = zpStruct['FGCM_TILINGS']
670 zptCat['fgcmFpGry'][:] = zpStruct['FGCM_FPGRY']
671 zptCat['fgcmFpGryBlue'][:] = zpStruct['FGCM_FPGRY_CSPLIT'][:, 0]
672 zptCat['fgcmFpGryBlueErr'][:] = zpStruct['FGCM_FPGRY_CSPLITERR'][:, 0]
673 zptCat['fgcmFpGryRed'][:] = zpStruct['FGCM_FPGRY_CSPLIT'][:, 2]
674 zptCat['fgcmFpGryRedErr'][:] = zpStruct['FGCM_FPGRY_CSPLITERR'][:, 2]
675 zptCat['fgcmFpVar'][:] = zpStruct['FGCM_FPVAR']
676 zptCat['fgcmDust'][:] = zpStruct['FGCM_DUST']
677 zptCat['fgcmFlat'][:] = zpStruct['FGCM_FLAT']
678 zptCat['fgcmAperCorr'][:] = zpStruct['FGCM_APERCORR']
679 zptCat['fgcmDeltaMagBkg'][:] = zpStruct['FGCM_DELTAMAGBKG']
680 zptCat['exptime'][:] = zpStruct['EXPTIME']
682 return zptCat
685def makeAtmSchema():
686 """
687 Make the atmosphere schema
689 Returns
690 -------
691 atmSchema: `lsst.afw.table.Schema`
692 """
694 atmSchema = afwTable.Schema()
696 atmSchema.addField('visit', type=np.int32, doc='Visit number')
697 atmSchema.addField('pmb', type=np.float64, doc='Barometric pressure (mb)')
698 atmSchema.addField('pwv', type=np.float64, doc='Water vapor (mm)')
699 atmSchema.addField('tau', type=np.float64, doc='Aerosol optical depth')
700 atmSchema.addField('alpha', type=np.float64, doc='Aerosol slope')
701 atmSchema.addField('o3', type=np.float64, doc='Ozone (dobson)')
702 atmSchema.addField('secZenith', type=np.float64, doc='Secant(zenith) (~ airmass)')
703 atmSchema.addField('cTrans', type=np.float64, doc='Transmission correction factor')
704 atmSchema.addField('lamStd', type=np.float64, doc='Wavelength for transmission correction')
706 return atmSchema
709def makeAtmCat(atmSchema, atmStruct):
710 """
711 Make the atmosphere catalog for persistence
713 Parameters
714 ----------
715 atmSchema: `lsst.afw.table.Schema`
716 Atmosphere catalog schema
717 atmStruct: `numpy.ndarray`
718 Atmosphere structure from fgcm
720 Returns
721 -------
722 atmCat: `lsst.afw.table.BaseCatalog`
723 Atmosphere catalog for persistence
724 """
726 atmCat = afwTable.BaseCatalog(atmSchema)
727 atmCat.resize(atmStruct.size)
729 atmCat['visit'][:] = atmStruct['VISIT']
730 atmCat['pmb'][:] = atmStruct['PMB']
731 atmCat['pwv'][:] = atmStruct['PWV']
732 atmCat['tau'][:] = atmStruct['TAU']
733 atmCat['alpha'][:] = atmStruct['ALPHA']
734 atmCat['o3'][:] = atmStruct['O3']
735 atmCat['secZenith'][:] = atmStruct['SECZENITH']
736 atmCat['cTrans'][:] = atmStruct['CTRANS']
737 atmCat['lamStd'][:] = atmStruct['LAMSTD']
739 return atmCat
742def makeStdSchema(nBands):
743 """
744 Make the standard star schema
746 Parameters
747 ----------
748 nBands: `int`
749 Number of bands in standard star catalog
751 Returns
752 -------
753 stdSchema: `lsst.afw.table.Schema`
754 """
756 stdSchema = afwTable.SimpleTable.makeMinimalSchema()
757 stdSchema.addField('ngood', type='ArrayI', doc='Number of good observations',
758 size=nBands)
759 stdSchema.addField('ntotal', type='ArrayI', doc='Number of total observations',
760 size=nBands)
761 stdSchema.addField('mag_std_noabs', type='ArrayF',
762 doc='Standard magnitude (no absolute calibration)',
763 size=nBands)
764 stdSchema.addField('magErr_std', type='ArrayF',
765 doc='Standard magnitude error',
766 size=nBands)
767 stdSchema.addField('npsfcand', type='ArrayI',
768 doc='Number of observations flagged as psf candidates',
769 size=nBands)
771 return stdSchema
774def makeStdCat(stdSchema, stdStruct, goodBands):
775 """
776 Make the standard star catalog for persistence
778 Parameters
779 ----------
780 stdSchema: `lsst.afw.table.Schema`
781 Standard star catalog schema
782 stdStruct: `numpy.ndarray`
783 Standard star structure in FGCM format
784 goodBands: `list`
785 List of good band names used in stdStruct
787 Returns
788 -------
789 stdCat: `lsst.afw.table.BaseCatalog`
790 Standard star catalog for persistence
791 """
793 stdCat = afwTable.SimpleCatalog(stdSchema)
794 stdCat.resize(stdStruct.size)
796 stdCat['id'][:] = stdStruct['FGCM_ID']
797 stdCat['coord_ra'][:] = stdStruct['RA'] * geom.degrees
798 stdCat['coord_dec'][:] = stdStruct['DEC'] * geom.degrees
799 stdCat['ngood'][:, :] = stdStruct['NGOOD'][:, :]
800 stdCat['ntotal'][:, :] = stdStruct['NTOTAL'][:, :]
801 stdCat['mag_std_noabs'][:, :] = stdStruct['MAG_STD'][:, :]
802 stdCat['magErr_std'][:, :] = stdStruct['MAGERR_STD'][:, :]
803 stdCat['npsfcand'][:, :] = stdStruct['NPSFCAND'][:, :]
805 md = PropertyList()
806 md.set("BANDS", list(goodBands))
807 stdCat.setMetadata(md)
809 return stdCat
812def computeApertureRadiusFromDataRef(dataRef, fluxField):
813 """
814 Compute the radius associated with a CircularApertureFlux field or
815 associated slot.
817 Parameters
818 ----------
819 dataRef : `lsst.daf.persistence.ButlerDataRef` or
820 `lsst.daf.butler.DeferredDatasetHandle`
821 fluxField : `str`
822 CircularApertureFlux or associated slot.
824 Returns
825 -------
826 apertureRadius : `float`
827 Radius of the aperture field, in pixels.
829 Raises
830 ------
831 RuntimeError: Raised if flux field is not a CircularApertureFlux, ApFlux,
832 apFlux, or associated slot.
833 """
834 # TODO: Move this method to more general stack method in DM-25775
835 if isinstance(dataRef, dafPersist.ButlerDataRef):
836 # Gen2 dataRef
837 datasetType = dataRef.butlerSubset.datasetType
838 else:
839 # Gen3 dataRef
840 datasetType = dataRef.ref.datasetType.name
842 if datasetType == 'src':
843 schema = dataRef.get(datasetType='src_schema').schema
844 try:
845 fluxFieldName = schema[fluxField].asField().getName()
846 except LookupError:
847 raise RuntimeError("Could not find %s or associated slot in schema." % (fluxField))
848 # This may also raise a RuntimeError
849 apertureRadius = computeApertureRadiusFromName(fluxFieldName)
850 else:
851 # This is a sourceTable_visit
852 apertureRadius = computeApertureRadiusFromName(fluxField)
854 return apertureRadius
857def computeApertureRadiusFromName(fluxField):
858 """
859 Compute the radius associated with a CircularApertureFlux or ApFlux field.
861 Parameters
862 ----------
863 fluxField : `str`
864 CircularApertureFlux or ApFlux
866 Returns
867 -------
868 apertureRadius : `float`
869 Radius of the aperture field, in pixels.
871 Raises
872 ------
873 RuntimeError: Raised if flux field is not a CircularApertureFlux,
874 ApFlux, or apFlux.
875 """
876 # TODO: Move this method to more general stack method in DM-25775
877 m = re.search(r'(CircularApertureFlux|ApFlux|apFlux)_(\d+)_(\d+)_', fluxField)
879 if m is None:
880 raise RuntimeError(f"Flux field {fluxField} does not correspond to a CircularApertureFlux or ApFlux")
882 apertureRadius = float(m.groups()[1]) + float(m.groups()[2])/10.
884 return apertureRadius
887def extractReferenceMags(refStars, bands, filterMap):
888 """
889 Extract reference magnitudes from refStars for given bands and
890 associated filterMap.
892 Parameters
893 ----------
894 refStars : `lsst.afw.table.BaseCatalog`
895 FGCM reference star catalog
896 bands : `list`
897 List of bands for calibration
898 filterMap: `dict`
899 FGCM mapping of filter to band
901 Returns
902 -------
903 refMag : `np.ndarray`
904 nstar x nband array of reference magnitudes
905 refMagErr : `np.ndarray`
906 nstar x nband array of reference magnitude errors
907 """
908 # After DM-23331 fgcm reference catalogs have FILTERNAMES to prevent
909 # against index errors and allow more flexibility in fitting after
910 # the build stars step.
912 md = refStars.getMetadata()
913 if 'FILTERNAMES' in md:
914 filternames = md.getArray('FILTERNAMES')
916 # The reference catalog that fgcm wants has one entry per band
917 # in the config file
918 refMag = np.zeros((len(refStars), len(bands)),
919 dtype=refStars['refMag'].dtype) + 99.0
920 refMagErr = np.zeros_like(refMag) + 99.0
921 for i, filtername in enumerate(filternames):
922 # We are allowed to run the fit configured so that we do not
923 # use every column in the reference catalog.
924 try:
925 band = filterMap[filtername]
926 except KeyError:
927 continue
928 try:
929 ind = bands.index(band)
930 except ValueError:
931 continue
933 refMag[:, ind] = refStars['refMag'][:, i]
934 refMagErr[:, ind] = refStars['refMagErr'][:, i]
936 else:
937 # Continue to use old catalogs as before.
938 refMag = refStars['refMag'][:, :]
939 refMagErr = refStars['refMagErr'][:, :]
941 return refMag, refMagErr
944def lookupStaticCalibrations(datasetType, registry, quantumDataId, collections):
945 instrument = Instrument.fromName(quantumDataId["instrument"], registry)
946 unboundedCollection = instrument.makeUnboundedCalibrationRunName()
948 return registry.queryDatasets(datasetType,
949 dataId=quantumDataId,
950 collections=[unboundedCollection])