Coverage for python/lsst/fgcmcal/utilities.py : 10%

Hot-keys on this page
r m x p toggle line displays
j k next/prev highlighted chunk
0 (zero) top of page
1 (one) first highlighted chunk
1# This file is part of fgcmcal.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
21"""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 re
30from lsst.daf.base import PropertyList
31import lsst.daf.persistence as dafPersist
32import lsst.afw.cameraGeom as afwCameraGeom
33import lsst.afw.table as afwTable
34import lsst.afw.image as afwImage
35import lsst.afw.math as afwMath
36import lsst.geom as geom
37from lsst.obs.base import createInitialSkyWcs
38from lsst.obs.base import Instrument
41import fgcm
44FGCM_EXP_FIELD = 'VISIT'
45FGCM_CCD_FIELD = 'DETECTOR'
48def makeConfigDict(config, log, camera, maxIter,
49 resetFitParameters, outputZeropoints,
50 lutFilterNames, tract=None):
51 """
52 Make the FGCM fit cycle configuration dict
54 Parameters
55 ----------
56 config: `lsst.fgcmcal.FgcmFitCycleConfig`
57 Configuration object
58 log: `lsst.log.Log`
59 LSST log object
60 camera: `lsst.afw.cameraGeom.Camera`
61 Camera from the butler
62 maxIter: `int`
63 Maximum number of iterations
64 resetFitParameters: `bool`
65 Reset fit parameters before fitting?
66 outputZeropoints: `bool`
67 Compute zeropoints for output?
68 lutFilterNames : array-like, `str`
69 Array of physical filter names in the LUT.
70 tract: `int`, optional
71 Tract number for extending the output file name for debugging.
72 Default is None.
74 Returns
75 -------
76 configDict: `dict`
77 Configuration dictionary for fgcm
78 """
79 # Extract the bands that are _not_ being fit for fgcm configuration
80 notFitBands = [b for b in config.bands if b not in config.fitBands]
82 # process the starColorCuts
83 starColorCutList = []
84 for ccut in config.starColorCuts:
85 parts = ccut.split(',')
86 starColorCutList.append([parts[0], parts[1], float(parts[2]), float(parts[3])])
88 # TODO: Having direct access to the mirror area from the camera would be
89 # useful. See DM-16489.
90 # Mirror area in cm**2
91 mirrorArea = np.pi*(camera.telescopeDiameter*100./2.)**2.
93 # Get approximate average camera gain:
94 gains = [amp.getGain() for detector in camera for amp in detector.getAmplifiers()]
95 cameraGain = float(np.median(gains))
97 # Cut down the filter map to those that are in the LUT
98 filterToBand = {filterName: config.physicalFilterMap[filterName] for
99 filterName in lutFilterNames}
101 if tract is None:
102 outfileBase = config.outfileBase
103 else:
104 outfileBase = '%s-%06d' % (config.outfileBase, tract)
106 # create a configuration dictionary for fgcmFitCycle
107 configDict = {'outfileBase': outfileBase,
108 'logger': log,
109 'exposureFile': None,
110 'obsFile': None,
111 'indexFile': None,
112 'lutFile': None,
113 'mirrorArea': mirrorArea,
114 'cameraGain': cameraGain,
115 'ccdStartIndex': camera[0].getId(),
116 'expField': FGCM_EXP_FIELD,
117 'ccdField': FGCM_CCD_FIELD,
118 'seeingField': 'DELTA_APER',
119 'fwhmField': 'PSFSIGMA',
120 'skyBrightnessField': 'SKYBACKGROUND',
121 'deepFlag': 'DEEPFLAG', # unused
122 'bands': list(config.bands),
123 'fitBands': list(config.fitBands),
124 'notFitBands': notFitBands,
125 'requiredBands': list(config.requiredBands),
126 'filterToBand': filterToBand,
127 'logLevel': 'INFO',
128 'nCore': config.nCore,
129 'nStarPerRun': config.nStarPerRun,
130 'nExpPerRun': config.nExpPerRun,
131 'reserveFraction': config.reserveFraction,
132 'freezeStdAtmosphere': config.freezeStdAtmosphere,
133 'precomputeSuperStarInitialCycle': config.precomputeSuperStarInitialCycle,
134 'superStarSubCCDDict': dict(config.superStarSubCcdDict),
135 'superStarSubCCDChebyshevOrder': config.superStarSubCcdChebyshevOrder,
136 'superStarSubCCDTriangular': config.superStarSubCcdTriangular,
137 'superStarSigmaClip': config.superStarSigmaClip,
138 'focalPlaneSigmaClip': config.focalPlaneSigmaClip,
139 'ccdGraySubCCDDict': dict(config.ccdGraySubCcdDict),
140 'ccdGraySubCCDChebyshevOrder': config.ccdGraySubCcdChebyshevOrder,
141 'ccdGraySubCCDTriangular': config.ccdGraySubCcdTriangular,
142 'ccdGrayFocalPlaneDict': dict(config.ccdGrayFocalPlaneDict),
143 'ccdGrayFocalPlaneChebyshevOrder': config.ccdGrayFocalPlaneChebyshevOrder,
144 'ccdGrayFocalPlaneFitMinCcd': config.ccdGrayFocalPlaneFitMinCcd,
145 'cycleNumber': config.cycleNumber,
146 'maxIter': maxIter,
147 'deltaMagBkgOffsetPercentile': config.deltaMagBkgOffsetPercentile,
148 'deltaMagBkgPerCcd': config.deltaMagBkgPerCcd,
149 'UTBoundary': config.utBoundary,
150 'washMJDs': config.washMjds,
151 'epochMJDs': config.epochMjds,
152 'coatingMJDs': config.coatingMjds,
153 'minObsPerBand': config.minObsPerBand,
154 'latitude': config.latitude,
155 'brightObsGrayMax': config.brightObsGrayMax,
156 'minStarPerCCD': config.minStarPerCcd,
157 'minCCDPerExp': config.minCcdPerExp,
158 'maxCCDGrayErr': config.maxCcdGrayErr,
159 'minStarPerExp': config.minStarPerExp,
160 'minExpPerNight': config.minExpPerNight,
161 'expGrayInitialCut': config.expGrayInitialCut,
162 'expGrayPhotometricCutDict': dict(config.expGrayPhotometricCutDict),
163 'expGrayHighCutDict': dict(config.expGrayHighCutDict),
164 'expGrayRecoverCut': config.expGrayRecoverCut,
165 'expVarGrayPhotometricCutDict': dict(config.expVarGrayPhotometricCutDict),
166 'expGrayErrRecoverCut': config.expGrayErrRecoverCut,
167 'refStarSnMin': config.refStarSnMin,
168 'refStarOutlierNSig': config.refStarOutlierNSig,
169 'applyRefStarColorCuts': config.applyRefStarColorCuts,
170 'illegalValue': -9999.0, # internally used by fgcm.
171 'starColorCuts': starColorCutList,
172 'aperCorrFitNBins': config.aperCorrFitNBins,
173 'aperCorrInputSlopeDict': dict(config.aperCorrInputSlopeDict),
174 'sedBoundaryTermDict': config.sedboundaryterms.toDict()['data'],
175 'sedTermDict': config.sedterms.toDict()['data'],
176 'colorSplitBands': list(config.colorSplitBands),
177 'sigFgcmMaxErr': config.sigFgcmMaxErr,
178 'sigFgcmMaxEGrayDict': dict(config.sigFgcmMaxEGrayDict),
179 'ccdGrayMaxStarErr': config.ccdGrayMaxStarErr,
180 'approxThroughputDict': dict(config.approxThroughputDict),
181 'sigmaCalRange': list(config.sigmaCalRange),
182 'sigmaCalFitPercentile': list(config.sigmaCalFitPercentile),
183 'sigmaCalPlotPercentile': list(config.sigmaCalPlotPercentile),
184 'sigma0Phot': config.sigma0Phot,
185 'mapLongitudeRef': config.mapLongitudeRef,
186 'mapNSide': config.mapNSide,
187 'varNSig': 100.0, # Turn off 'variable star selection' which doesn't work yet
188 'varMinBand': 2,
189 'useRetrievedPwv': False,
190 'useNightlyRetrievedPwv': False,
191 'pwvRetrievalSmoothBlock': 25,
192 'useQuadraticPwv': config.useQuadraticPwv,
193 'useRetrievedTauInit': False,
194 'tauRetrievalMinCCDPerNight': 500,
195 'modelMagErrors': config.modelMagErrors,
196 'instrumentParsPerBand': config.instrumentParsPerBand,
197 'instrumentSlopeMinDeltaT': config.instrumentSlopeMinDeltaT,
198 'fitMirrorChromaticity': config.fitMirrorChromaticity,
199 'useRepeatabilityForExpGrayCutsDict': dict(config.useRepeatabilityForExpGrayCutsDict),
200 'autoPhotometricCutNSig': config.autoPhotometricCutNSig,
201 'autoHighCutNSig': config.autoHighCutNSig,
202 'printOnly': False,
203 'quietMode': config.quietMode,
204 'randomSeed': config.randomSeed,
205 'outputStars': False,
206 'clobber': True,
207 'useSedLUT': False,
208 'resetParameters': resetFitParameters,
209 'outputFgcmcalZpts': True, # when outputting zpts, use fgcmcal format
210 'outputZeropoints': outputZeropoints}
212 return configDict
215def translateFgcmLut(lutCat, physicalFilterMap):
216 """
217 Translate the FGCM look-up-table into an fgcm-compatible object
219 Parameters
220 ----------
221 lutCat: `lsst.afw.table.BaseCatalog`
222 Catalog describing the FGCM look-up table
223 physicalFilterMap: `dict`
224 Physical filter to band mapping
226 Returns
227 -------
228 fgcmLut: `lsst.fgcm.FgcmLut`
229 Lookup table for FGCM
230 lutIndexVals: `numpy.ndarray`
231 Numpy array with LUT index information for FGCM
232 lutStd: `numpy.ndarray`
233 Numpy array with LUT standard throughput values for FGCM
235 Notes
236 -----
237 After running this code, it is wise to `del lutCat` to clear the memory.
238 """
240 # first we need the lutIndexVals
241 lutFilterNames = np.array(lutCat[0]['physicalFilters'].split(','), dtype='U')
242 lutStdFilterNames = np.array(lutCat[0]['stdPhysicalFilters'].split(','), dtype='U')
244 # Note that any discrepancies between config values will raise relevant
245 # exceptions in the FGCM code.
247 lutIndexVals = np.zeros(1, dtype=[('FILTERNAMES', lutFilterNames.dtype.str,
248 lutFilterNames.size),
249 ('STDFILTERNAMES', lutStdFilterNames.dtype.str,
250 lutStdFilterNames.size),
251 ('PMB', 'f8', lutCat[0]['pmb'].size),
252 ('PMBFACTOR', 'f8', lutCat[0]['pmbFactor'].size),
253 ('PMBELEVATION', 'f8'),
254 ('LAMBDANORM', 'f8'),
255 ('PWV', 'f8', lutCat[0]['pwv'].size),
256 ('O3', 'f8', lutCat[0]['o3'].size),
257 ('TAU', 'f8', lutCat[0]['tau'].size),
258 ('ALPHA', 'f8', lutCat[0]['alpha'].size),
259 ('ZENITH', 'f8', lutCat[0]['zenith'].size),
260 ('NCCD', 'i4')])
262 lutIndexVals['FILTERNAMES'][:] = lutFilterNames
263 lutIndexVals['STDFILTERNAMES'][:] = lutStdFilterNames
264 lutIndexVals['PMB'][:] = lutCat[0]['pmb']
265 lutIndexVals['PMBFACTOR'][:] = lutCat[0]['pmbFactor']
266 lutIndexVals['PMBELEVATION'] = lutCat[0]['pmbElevation']
267 lutIndexVals['LAMBDANORM'] = lutCat[0]['lambdaNorm']
268 lutIndexVals['PWV'][:] = lutCat[0]['pwv']
269 lutIndexVals['O3'][:] = lutCat[0]['o3']
270 lutIndexVals['TAU'][:] = lutCat[0]['tau']
271 lutIndexVals['ALPHA'][:] = lutCat[0]['alpha']
272 lutIndexVals['ZENITH'][:] = lutCat[0]['zenith']
273 lutIndexVals['NCCD'] = lutCat[0]['nCcd']
275 # now we need the Standard Values
276 lutStd = np.zeros(1, dtype=[('PMBSTD', 'f8'),
277 ('PWVSTD', 'f8'),
278 ('O3STD', 'f8'),
279 ('TAUSTD', 'f8'),
280 ('ALPHASTD', 'f8'),
281 ('ZENITHSTD', 'f8'),
282 ('LAMBDARANGE', 'f8', 2),
283 ('LAMBDASTEP', 'f8'),
284 ('LAMBDASTD', 'f8', lutFilterNames.size),
285 ('LAMBDASTDFILTER', 'f8', lutStdFilterNames.size),
286 ('I0STD', 'f8', lutFilterNames.size),
287 ('I1STD', 'f8', lutFilterNames.size),
288 ('I10STD', 'f8', lutFilterNames.size),
289 ('I2STD', 'f8', lutFilterNames.size),
290 ('LAMBDAB', 'f8', lutFilterNames.size),
291 ('ATMLAMBDA', 'f8', lutCat[0]['atmLambda'].size),
292 ('ATMSTDTRANS', 'f8', lutCat[0]['atmStdTrans'].size)])
293 lutStd['PMBSTD'] = lutCat[0]['pmbStd']
294 lutStd['PWVSTD'] = lutCat[0]['pwvStd']
295 lutStd['O3STD'] = lutCat[0]['o3Std']
296 lutStd['TAUSTD'] = lutCat[0]['tauStd']
297 lutStd['ALPHASTD'] = lutCat[0]['alphaStd']
298 lutStd['ZENITHSTD'] = lutCat[0]['zenithStd']
299 lutStd['LAMBDARANGE'][:] = lutCat[0]['lambdaRange'][:]
300 lutStd['LAMBDASTEP'] = lutCat[0]['lambdaStep']
301 lutStd['LAMBDASTD'][:] = lutCat[0]['lambdaStd']
302 lutStd['LAMBDASTDFILTER'][:] = lutCat[0]['lambdaStdFilter']
303 lutStd['I0STD'][:] = lutCat[0]['i0Std']
304 lutStd['I1STD'][:] = lutCat[0]['i1Std']
305 lutStd['I10STD'][:] = lutCat[0]['i10Std']
306 lutStd['I2STD'][:] = lutCat[0]['i2Std']
307 lutStd['LAMBDAB'][:] = lutCat[0]['lambdaB']
308 lutStd['ATMLAMBDA'][:] = lutCat[0]['atmLambda'][:]
309 lutStd['ATMSTDTRANS'][:] = lutCat[0]['atmStdTrans'][:]
311 lutTypes = [row['luttype'] for row in lutCat]
313 # And the flattened look-up-table
314 lutFlat = np.zeros(lutCat[0]['lut'].size, dtype=[('I0', 'f4'),
315 ('I1', 'f4')])
317 lutFlat['I0'][:] = lutCat[lutTypes.index('I0')]['lut'][:]
318 lutFlat['I1'][:] = lutCat[lutTypes.index('I1')]['lut'][:]
320 lutDerivFlat = np.zeros(lutCat[0]['lut'].size, dtype=[('D_LNPWV', 'f4'),
321 ('D_O3', 'f4'),
322 ('D_LNTAU', 'f4'),
323 ('D_ALPHA', 'f4'),
324 ('D_SECZENITH', 'f4'),
325 ('D_LNPWV_I1', 'f4'),
326 ('D_O3_I1', 'f4'),
327 ('D_LNTAU_I1', 'f4'),
328 ('D_ALPHA_I1', 'f4'),
329 ('D_SECZENITH_I1', 'f4')])
331 for name in lutDerivFlat.dtype.names:
332 lutDerivFlat[name][:] = lutCat[lutTypes.index(name)]['lut'][:]
334 # The fgcm.FgcmLUT() class copies all the LUT information into special
335 # shared memory objects that will not blow up the memory usage when used
336 # with python multiprocessing. Once all the numbers are copied, the
337 # references to the temporary objects (lutCat, lutFlat, lutDerivFlat)
338 # will fall out of scope and can be cleaned up by the garbage collector.
339 fgcmLut = fgcm.FgcmLUT(lutIndexVals, lutFlat, lutDerivFlat, lutStd,
340 filterToBand=physicalFilterMap)
342 return fgcmLut, lutIndexVals, lutStd
345def translateVisitCatalog(visitCat):
346 """
347 Translate the FGCM visit catalog to an fgcm-compatible object
349 Parameters
350 ----------
351 visitCat: `lsst.afw.table.BaseCatalog`
352 FGCM visitCat from `lsst.fgcmcal.FgcmBuildStarsTask`
354 Returns
355 -------
356 fgcmExpInfo: `numpy.ndarray`
357 Numpy array for visit information for FGCM
359 Notes
360 -----
361 After running this code, it is wise to `del visitCat` to clear the memory.
362 """
364 fgcmExpInfo = np.zeros(len(visitCat), dtype=[('VISIT', 'i8'),
365 ('MJD', 'f8'),
366 ('EXPTIME', 'f8'),
367 ('PSFSIGMA', 'f8'),
368 ('DELTA_APER', 'f8'),
369 ('SKYBACKGROUND', 'f8'),
370 ('DEEPFLAG', 'i2'),
371 ('TELHA', 'f8'),
372 ('TELRA', 'f8'),
373 ('TELDEC', 'f8'),
374 ('TELROT', 'f8'),
375 ('PMB', 'f8'),
376 ('FILTERNAME', 'a50')])
377 fgcmExpInfo['VISIT'][:] = visitCat['visit']
378 fgcmExpInfo['MJD'][:] = visitCat['mjd']
379 fgcmExpInfo['EXPTIME'][:] = visitCat['exptime']
380 fgcmExpInfo['DEEPFLAG'][:] = visitCat['deepFlag']
381 fgcmExpInfo['TELHA'][:] = visitCat['telha']
382 fgcmExpInfo['TELRA'][:] = visitCat['telra']
383 fgcmExpInfo['TELDEC'][:] = visitCat['teldec']
384 fgcmExpInfo['TELROT'][:] = visitCat['telrot']
385 fgcmExpInfo['PMB'][:] = visitCat['pmb']
386 fgcmExpInfo['PSFSIGMA'][:] = visitCat['psfSigma']
387 fgcmExpInfo['DELTA_APER'][:] = visitCat['deltaAper']
388 fgcmExpInfo['SKYBACKGROUND'][:] = visitCat['skyBackground']
389 # Note that we have to go through asAstropy() to get a string
390 # array out of an afwTable. This is faster than a row-by-row loop.
391 fgcmExpInfo['FILTERNAME'][:] = visitCat.asAstropy()['physicalFilter']
393 return fgcmExpInfo
396def computeCcdOffsets(camera, defaultOrientation):
397 """
398 Compute the CCD offsets in ra/dec and x/y space
400 Parameters
401 ----------
402 camera: `lsst.afw.cameraGeom.Camera`
403 defaultOrientation: `float`
404 Default camera orientation (degrees)
406 Returns
407 -------
408 ccdOffsets: `numpy.ndarray`
409 Numpy array with ccd offset information for input to FGCM.
410 Angular units are degrees, and x/y units are pixels.
411 """
412 # TODO: DM-21215 will fully generalize to arbitrary camera orientations
414 # and we need to know the ccd offsets from the camera geometry
415 ccdOffsets = np.zeros(len(camera), dtype=[('CCDNUM', 'i4'),
416 ('DELTA_RA', 'f8'),
417 ('DELTA_DEC', 'f8'),
418 ('RA_SIZE', 'f8'),
419 ('DEC_SIZE', 'f8'),
420 ('X_SIZE', 'i4'),
421 ('Y_SIZE', 'i4')])
423 # Generate fake WCSs centered at 180/0 to avoid the RA=0/360 problem,
424 # since we are looking for relative positions
425 boresight = geom.SpherePoint(180.0*geom.degrees, 0.0*geom.degrees)
427 # TODO: DM-17597 will update testdata_jointcal so that the test data
428 # does not have nan as the boresight angle for HSC data. For the
429 # time being, there is this ungainly hack.
430 if camera.getName() == 'HSC' and np.isnan(defaultOrientation):
431 orientation = 270*geom.degrees
432 else:
433 orientation = defaultOrientation*geom.degrees
434 flipX = False
436 # Create a temporary visitInfo for input to createInitialSkyWcs
437 visitInfo = afwImage.VisitInfo(boresightRaDec=boresight,
438 boresightRotAngle=orientation,
439 rotType=afwImage.RotType.SKY)
441 for i, detector in enumerate(camera):
442 ccdOffsets['CCDNUM'][i] = detector.getId()
444 wcs = createInitialSkyWcs(visitInfo, detector, flipX)
446 detCenter = wcs.pixelToSky(detector.getCenter(afwCameraGeom.PIXELS))
447 ccdOffsets['DELTA_RA'][i] = (detCenter.getRa() - boresight.getRa()).asDegrees()
448 ccdOffsets['DELTA_DEC'][i] = (detCenter.getDec() - boresight.getDec()).asDegrees()
450 bbox = detector.getBBox()
452 detCorner1 = wcs.pixelToSky(geom.Point2D(bbox.getMin()))
453 detCorner2 = wcs.pixelToSky(geom.Point2D(bbox.getMax()))
455 ccdOffsets['RA_SIZE'][i] = np.abs((detCorner2.getRa() - detCorner1.getRa()).asDegrees())
456 ccdOffsets['DEC_SIZE'][i] = np.abs((detCorner2.getDec() - detCorner1.getDec()).asDegrees())
458 ccdOffsets['X_SIZE'][i] = bbox.getMaxX()
459 ccdOffsets['Y_SIZE'][i] = bbox.getMaxY()
461 return ccdOffsets
464def computeReferencePixelScale(camera):
465 """
466 Compute the median pixel scale in the camera
468 Returns
469 -------
470 pixelScale: `float`
471 Average pixel scale (arcsecond) over the camera
472 """
474 boresight = geom.SpherePoint(180.0*geom.degrees, 0.0*geom.degrees)
475 orientation = 0.0*geom.degrees
476 flipX = False
478 # Create a temporary visitInfo for input to createInitialSkyWcs
479 visitInfo = afwImage.VisitInfo(boresightRaDec=boresight,
480 boresightRotAngle=orientation,
481 rotType=afwImage.RotType.SKY)
483 pixelScales = np.zeros(len(camera))
484 for i, detector in enumerate(camera):
485 wcs = createInitialSkyWcs(visitInfo, detector, flipX)
486 pixelScales[i] = wcs.getPixelScale().asArcseconds()
488 ok, = np.where(pixelScales > 0.0)
489 return np.median(pixelScales[ok])
492def computeApproxPixelAreaFields(camera):
493 """
494 Compute the approximate pixel area bounded fields from the camera
495 geometry.
497 Parameters
498 ----------
499 camera: `lsst.afw.cameraGeom.Camera`
501 Returns
502 -------
503 approxPixelAreaFields: `dict`
504 Dictionary of approximate area fields, keyed with detector ID
505 """
507 areaScaling = 1. / computeReferencePixelScale(camera)**2.
509 # Generate fake WCSs centered at 180/0 to avoid the RA=0/360 problem,
510 # since we are looking for relative scales
511 boresight = geom.SpherePoint(180.0*geom.degrees, 0.0*geom.degrees)
513 flipX = False
514 # Create a temporary visitInfo for input to createInitialSkyWcs
515 # The orientation does not matter for the area computation
516 visitInfo = afwImage.VisitInfo(boresightRaDec=boresight,
517 boresightRotAngle=0.0*geom.degrees,
518 rotType=afwImage.RotType.SKY)
520 approxPixelAreaFields = {}
522 for i, detector in enumerate(camera):
523 key = detector.getId()
525 wcs = createInitialSkyWcs(visitInfo, detector, flipX)
526 bbox = detector.getBBox()
528 areaField = afwMath.PixelAreaBoundedField(bbox, wcs,
529 unit=geom.arcseconds, scaling=areaScaling)
530 approxAreaField = afwMath.ChebyshevBoundedField.approximate(areaField)
532 approxPixelAreaFields[key] = approxAreaField
534 return approxPixelAreaFields
537def makeZptSchema(superStarChebyshevSize, zptChebyshevSize):
538 """
539 Make the zeropoint schema
541 Parameters
542 ----------
543 superStarChebyshevSize: `int`
544 Length of the superstar chebyshev array
545 zptChebyshevSize: `int`
546 Length of the zeropoint chebyshev array
548 Returns
549 -------
550 zptSchema: `lsst.afw.table.schema`
551 """
553 zptSchema = afwTable.Schema()
555 zptSchema.addField('visit', type=np.int32, doc='Visit number')
556 zptSchema.addField('detector', type=np.int32, doc='Detector ID number')
557 zptSchema.addField('fgcmFlag', type=np.int32, doc=('FGCM flag value: '
558 '1: Photometric, used in fit; '
559 '2: Photometric, not used in fit; '
560 '4: Non-photometric, on partly photometric night; '
561 '8: Non-photometric, on non-photometric night; '
562 '16: No zeropoint could be determined; '
563 '32: Too few stars for reliable gray computation'))
564 zptSchema.addField('fgcmZpt', type=np.float64, doc='FGCM zeropoint (center of CCD)')
565 zptSchema.addField('fgcmZptErr', type=np.float64,
566 doc='Error on zeropoint, estimated from repeatability + number of obs')
567 zptSchema.addField('fgcmfZptChebXyMax', type='ArrayD', size=2,
568 doc='maximum x/maximum y to scale to apply chebyshev parameters')
569 zptSchema.addField('fgcmfZptCheb', type='ArrayD',
570 size=zptChebyshevSize,
571 doc='Chebyshev parameters (flattened) for zeropoint')
572 zptSchema.addField('fgcmfZptSstarCheb', type='ArrayD',
573 size=superStarChebyshevSize,
574 doc='Chebyshev parameters (flattened) for superStarFlat')
575 zptSchema.addField('fgcmI0', type=np.float64, doc='Integral of the passband')
576 zptSchema.addField('fgcmI10', type=np.float64, doc='Normalized chromatic integral')
577 zptSchema.addField('fgcmR0', type=np.float64,
578 doc='Retrieved i0 integral, estimated from stars (only for flag 1)')
579 zptSchema.addField('fgcmR10', type=np.float64,
580 doc='Retrieved i10 integral, estimated from stars (only for flag 1)')
581 zptSchema.addField('fgcmGry', type=np.float64,
582 doc='Estimated gray extinction relative to atmospheric solution; '
583 'only for fgcmFlag <= 4 (see fgcmFlag) ')
584 zptSchema.addField('fgcmDeltaChrom', type=np.float64,
585 doc='Mean chromatic correction for stars in this ccd; '
586 'only for fgcmFlag <= 4 (see fgcmFlag)')
587 zptSchema.addField('fgcmZptVar', type=np.float64, doc='Variance of zeropoint over ccd')
588 zptSchema.addField('fgcmTilings', type=np.float64,
589 doc='Number of photometric tilings used for solution for ccd')
590 zptSchema.addField('fgcmFpGry', type=np.float64,
591 doc='Average gray extinction over the full focal plane '
592 '(same for all ccds in a visit)')
593 zptSchema.addField('fgcmFpGryBlue', type=np.float64,
594 doc='Average gray extinction over the full focal plane '
595 'for 25% bluest stars')
596 zptSchema.addField('fgcmFpGryBlueErr', type=np.float64,
597 doc='Error on Average gray extinction over the full focal plane '
598 'for 25% bluest stars')
599 zptSchema.addField('fgcmFpGryRed', type=np.float64,
600 doc='Average gray extinction over the full focal plane '
601 'for 25% reddest stars')
602 zptSchema.addField('fgcmFpGryRedErr', type=np.float64,
603 doc='Error on Average gray extinction over the full focal plane '
604 'for 25% reddest stars')
605 zptSchema.addField('fgcmFpVar', type=np.float64,
606 doc='Variance of gray extinction over the full focal plane '
607 '(same for all ccds in a visit)')
608 zptSchema.addField('fgcmDust', type=np.float64,
609 doc='Gray dust extinction from the primary/corrector'
610 'at the time of the exposure')
611 zptSchema.addField('fgcmFlat', type=np.float64, doc='Superstarflat illumination correction')
612 zptSchema.addField('fgcmAperCorr', type=np.float64, doc='Aperture correction estimated by fgcm')
613 zptSchema.addField('fgcmDeltaMagBkg', type=np.float64,
614 doc=('Local background correction from brightest percentile '
615 '(value set by deltaMagBkgOffsetPercentile) calibration '
616 'stars.'))
617 zptSchema.addField('exptime', type=np.float32, doc='Exposure time')
618 zptSchema.addField('filtername', type=str, size=10, doc='Filter name')
620 return zptSchema
623def makeZptCat(zptSchema, zpStruct):
624 """
625 Make the zeropoint catalog for persistence
627 Parameters
628 ----------
629 zptSchema: `lsst.afw.table.Schema`
630 Zeropoint catalog schema
631 zpStruct: `numpy.ndarray`
632 Zeropoint structure from fgcm
634 Returns
635 -------
636 zptCat: `afwTable.BaseCatalog`
637 Zeropoint catalog for persistence
638 """
640 zptCat = afwTable.BaseCatalog(zptSchema)
641 zptCat.reserve(zpStruct.size)
643 for filterName in zpStruct['FILTERNAME']:
644 rec = zptCat.addNew()
645 rec['filtername'] = filterName.decode('utf-8')
647 zptCat['visit'][:] = zpStruct[FGCM_EXP_FIELD]
648 zptCat['detector'][:] = zpStruct[FGCM_CCD_FIELD]
649 zptCat['fgcmFlag'][:] = zpStruct['FGCM_FLAG']
650 zptCat['fgcmZpt'][:] = zpStruct['FGCM_ZPT']
651 zptCat['fgcmZptErr'][:] = zpStruct['FGCM_ZPTERR']
652 zptCat['fgcmfZptChebXyMax'][:, :] = zpStruct['FGCM_FZPT_XYMAX']
653 zptCat['fgcmfZptCheb'][:, :] = zpStruct['FGCM_FZPT_CHEB']
654 zptCat['fgcmfZptSstarCheb'][:, :] = zpStruct['FGCM_FZPT_SSTAR_CHEB']
655 zptCat['fgcmI0'][:] = zpStruct['FGCM_I0']
656 zptCat['fgcmI10'][:] = zpStruct['FGCM_I10']
657 zptCat['fgcmR0'][:] = zpStruct['FGCM_R0']
658 zptCat['fgcmR10'][:] = zpStruct['FGCM_R10']
659 zptCat['fgcmGry'][:] = zpStruct['FGCM_GRY']
660 zptCat['fgcmDeltaChrom'][:] = zpStruct['FGCM_DELTACHROM']
661 zptCat['fgcmZptVar'][:] = zpStruct['FGCM_ZPTVAR']
662 zptCat['fgcmTilings'][:] = zpStruct['FGCM_TILINGS']
663 zptCat['fgcmFpGry'][:] = zpStruct['FGCM_FPGRY']
664 zptCat['fgcmFpGryBlue'][:] = zpStruct['FGCM_FPGRY_CSPLIT'][:, 0]
665 zptCat['fgcmFpGryBlueErr'][:] = zpStruct['FGCM_FPGRY_CSPLITERR'][:, 0]
666 zptCat['fgcmFpGryRed'][:] = zpStruct['FGCM_FPGRY_CSPLIT'][:, 2]
667 zptCat['fgcmFpGryRedErr'][:] = zpStruct['FGCM_FPGRY_CSPLITERR'][:, 2]
668 zptCat['fgcmFpVar'][:] = zpStruct['FGCM_FPVAR']
669 zptCat['fgcmDust'][:] = zpStruct['FGCM_DUST']
670 zptCat['fgcmFlat'][:] = zpStruct['FGCM_FLAT']
671 zptCat['fgcmAperCorr'][:] = zpStruct['FGCM_APERCORR']
672 zptCat['fgcmDeltaMagBkg'][:] = zpStruct['FGCM_DELTAMAGBKG']
673 zptCat['exptime'][:] = zpStruct['EXPTIME']
675 return zptCat
678def makeAtmSchema():
679 """
680 Make the atmosphere schema
682 Returns
683 -------
684 atmSchema: `lsst.afw.table.Schema`
685 """
687 atmSchema = afwTable.Schema()
689 atmSchema.addField('visit', type=np.int32, doc='Visit number')
690 atmSchema.addField('pmb', type=np.float64, doc='Barometric pressure (mb)')
691 atmSchema.addField('pwv', type=np.float64, doc='Water vapor (mm)')
692 atmSchema.addField('tau', type=np.float64, doc='Aerosol optical depth')
693 atmSchema.addField('alpha', type=np.float64, doc='Aerosol slope')
694 atmSchema.addField('o3', type=np.float64, doc='Ozone (dobson)')
695 atmSchema.addField('secZenith', type=np.float64, doc='Secant(zenith) (~ airmass)')
696 atmSchema.addField('cTrans', type=np.float64, doc='Transmission correction factor')
697 atmSchema.addField('lamStd', type=np.float64, doc='Wavelength for transmission correction')
699 return atmSchema
702def makeAtmCat(atmSchema, atmStruct):
703 """
704 Make the atmosphere catalog for persistence
706 Parameters
707 ----------
708 atmSchema: `lsst.afw.table.Schema`
709 Atmosphere catalog schema
710 atmStruct: `numpy.ndarray`
711 Atmosphere structure from fgcm
713 Returns
714 -------
715 atmCat: `lsst.afw.table.BaseCatalog`
716 Atmosphere catalog for persistence
717 """
719 atmCat = afwTable.BaseCatalog(atmSchema)
720 atmCat.resize(atmStruct.size)
722 atmCat['visit'][:] = atmStruct['VISIT']
723 atmCat['pmb'][:] = atmStruct['PMB']
724 atmCat['pwv'][:] = atmStruct['PWV']
725 atmCat['tau'][:] = atmStruct['TAU']
726 atmCat['alpha'][:] = atmStruct['ALPHA']
727 atmCat['o3'][:] = atmStruct['O3']
728 atmCat['secZenith'][:] = atmStruct['SECZENITH']
729 atmCat['cTrans'][:] = atmStruct['CTRANS']
730 atmCat['lamStd'][:] = atmStruct['LAMSTD']
732 return atmCat
735def makeStdSchema(nBands):
736 """
737 Make the standard star schema
739 Parameters
740 ----------
741 nBands: `int`
742 Number of bands in standard star catalog
744 Returns
745 -------
746 stdSchema: `lsst.afw.table.Schema`
747 """
749 stdSchema = afwTable.SimpleTable.makeMinimalSchema()
750 stdSchema.addField('ngood', type='ArrayI', doc='Number of good observations',
751 size=nBands)
752 stdSchema.addField('ntotal', type='ArrayI', doc='Number of total observations',
753 size=nBands)
754 stdSchema.addField('mag_std_noabs', type='ArrayF',
755 doc='Standard magnitude (no absolute calibration)',
756 size=nBands)
757 stdSchema.addField('magErr_std', type='ArrayF',
758 doc='Standard magnitude error',
759 size=nBands)
760 stdSchema.addField('npsfcand', type='ArrayI',
761 doc='Number of observations flagged as psf candidates',
762 size=nBands)
764 return stdSchema
767def makeStdCat(stdSchema, stdStruct, goodBands):
768 """
769 Make the standard star catalog for persistence
771 Parameters
772 ----------
773 stdSchema: `lsst.afw.table.Schema`
774 Standard star catalog schema
775 stdStruct: `numpy.ndarray`
776 Standard star structure in FGCM format
777 goodBands: `list`
778 List of good band names used in stdStruct
780 Returns
781 -------
782 stdCat: `lsst.afw.table.BaseCatalog`
783 Standard star catalog for persistence
784 """
786 stdCat = afwTable.SimpleCatalog(stdSchema)
787 stdCat.resize(stdStruct.size)
789 stdCat['id'][:] = stdStruct['FGCM_ID']
790 stdCat['coord_ra'][:] = stdStruct['RA'] * geom.degrees
791 stdCat['coord_dec'][:] = stdStruct['DEC'] * geom.degrees
792 stdCat['ngood'][:, :] = stdStruct['NGOOD'][:, :]
793 stdCat['ntotal'][:, :] = stdStruct['NTOTAL'][:, :]
794 stdCat['mag_std_noabs'][:, :] = stdStruct['MAG_STD'][:, :]
795 stdCat['magErr_std'][:, :] = stdStruct['MAGERR_STD'][:, :]
796 stdCat['npsfcand'][:, :] = stdStruct['NPSFCAND'][:, :]
798 md = PropertyList()
799 md.set("BANDS", list(goodBands))
800 stdCat.setMetadata(md)
802 return stdCat
805def computeApertureRadiusFromDataRef(dataRef, fluxField):
806 """
807 Compute the radius associated with a CircularApertureFlux field or
808 associated slot.
810 Parameters
811 ----------
812 dataRef : `lsst.daf.persistence.ButlerDataRef` or
813 `lsst.daf.butler.DeferredDatasetHandle`
814 fluxField : `str`
815 CircularApertureFlux or associated slot.
817 Returns
818 -------
819 apertureRadius : `float`
820 Radius of the aperture field, in pixels.
822 Raises
823 ------
824 RuntimeError: Raised if flux field is not a CircularApertureFlux, ApFlux,
825 or associated slot.
826 """
827 # TODO: Move this method to more general stack method in DM-25775
828 if isinstance(dataRef, dafPersist.ButlerDataRef):
829 # Gen2 dataRef
830 datasetType = dataRef.butlerSubset.datasetType
831 else:
832 # Gen3 dataRef
833 datasetType = dataRef.ref.datasetType.name
835 if datasetType == 'src':
836 schema = dataRef.get(datasetType='src_schema').schema
837 try:
838 fluxFieldName = schema[fluxField].asField().getName()
839 except LookupError:
840 raise RuntimeError("Could not find %s or associated slot in schema." % (fluxField))
841 # This may also raise a RuntimeError
842 apertureRadius = computeApertureRadiusFromName(fluxFieldName)
843 else:
844 # This is a sourceTable_visit
845 apertureRadius = computeApertureRadiusFromName(fluxField)
847 return apertureRadius
850def computeApertureRadiusFromName(fluxField):
851 """
852 Compute the radius associated with a CircularApertureFlux or ApFlux field.
854 Parameters
855 ----------
856 fluxField : `str`
857 CircularApertureFlux or ApFlux
859 Returns
860 -------
861 apertureRadius : `float`
862 Radius of the aperture field, in pixels.
864 Raises
865 ------
866 RuntimeError: Raised if flux field is not a CircularApertureFlux
867 or ApFlux.
868 """
869 # TODO: Move this method to more general stack method in DM-25775
870 m = re.search(r'(CircularApertureFlux|ApFlux)_(\d+)_(\d+)_', fluxField)
872 if m is None:
873 raise RuntimeError(f"Flux field {fluxField} does not correspond to a CircularApertureFlux or ApFlux")
875 apertureRadius = float(m.groups()[1]) + float(m.groups()[2])/10.
877 return apertureRadius
880def extractReferenceMags(refStars, bands, filterMap):
881 """
882 Extract reference magnitudes from refStars for given bands and
883 associated filterMap.
885 Parameters
886 ----------
887 refStars : `lsst.afw.table.BaseCatalog`
888 FGCM reference star catalog
889 bands : `list`
890 List of bands for calibration
891 filterMap: `dict`
892 FGCM mapping of filter to band
894 Returns
895 -------
896 refMag : `np.ndarray`
897 nstar x nband array of reference magnitudes
898 refMagErr : `np.ndarray`
899 nstar x nband array of reference magnitude errors
900 """
901 # After DM-23331 fgcm reference catalogs have FILTERNAMES to prevent
902 # against index errors and allow more flexibility in fitting after
903 # the build stars step.
905 md = refStars.getMetadata()
906 if 'FILTERNAMES' in md:
907 filternames = md.getArray('FILTERNAMES')
909 # The reference catalog that fgcm wants has one entry per band
910 # in the config file
911 refMag = np.zeros((len(refStars), len(bands)),
912 dtype=refStars['refMag'].dtype) + 99.0
913 refMagErr = np.zeros_like(refMag) + 99.0
914 for i, filtername in enumerate(filternames):
915 # We are allowed to run the fit configured so that we do not
916 # use every column in the reference catalog.
917 try:
918 band = filterMap[filtername]
919 except KeyError:
920 continue
921 try:
922 ind = bands.index(band)
923 except ValueError:
924 continue
926 refMag[:, ind] = refStars['refMag'][:, i]
927 refMagErr[:, ind] = refStars['refMagErr'][:, i]
929 else:
930 # Continue to use old catalogs as before.
931 refMag = refStars['refMag'][:, :]
932 refMagErr = refStars['refMagErr'][:, :]
934 return refMag, refMagErr
937def lookupStaticCalibrations(datasetType, registry, quantumDataId, collections):
938 instrument = Instrument.fromName(quantumDataId["instrument"], registry)
939 unboundedCollection = instrument.makeUnboundedCalibrationRunName()
941 return registry.queryDatasets(datasetType,
942 dataId=quantumDataId,
943 collections=[unboundedCollection])