Coverage for python/lsst/pipe/tasks/dcrAssembleCoadd.py: 13%

Shortcuts 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

454 statements  

1# This file is part of pipe_tasks. 

2# 

3# LSST Data Management System 

4# This product includes software developed by the 

5# LSST Project (http://www.lsst.org/). 

6# See COPYRIGHT file at the top of the source tree. 

7# 

8# This program is free software: you can redistribute it and/or modify 

9# it under the terms of the GNU General Public License as published by 

10# the Free Software Foundation, either version 3 of the License, or 

11# (at your option) any later version. 

12# 

13# This program is distributed in the hope that it will be useful, 

14# but WITHOUT ANY WARRANTY; without even the implied warranty of 

15# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

16# GNU General Public License for more details. 

17# 

18# You should have received a copy of the LSST License Statement and 

19# the GNU General Public License along with this program. If not, 

20# see <https://www.lsstcorp.org/LegalNotices/>. 

21# 

22 

23from math import ceil 

24import numpy as np 

25from scipy import ndimage 

26import lsst.geom as geom 

27import lsst.afw.image as afwImage 

28import lsst.afw.table as afwTable 

29import lsst.coadd.utils as coaddUtils 

30from lsst.daf.butler import DeferredDatasetHandle 

31from lsst.ip.diffim.dcrModel import applyDcr, calculateDcr, DcrModel 

32import lsst.meas.algorithms as measAlg 

33from lsst.meas.base import SingleFrameMeasurementTask 

34import lsst.pex.config as pexConfig 

35import lsst.pipe.base as pipeBase 

36import lsst.utils as utils 

37from lsst.utils.timer import timeMethod 

38from .assembleCoadd import (AssembleCoaddConnections, 

39 AssembleCoaddTask, 

40 CompareWarpAssembleCoaddConfig, 

41 CompareWarpAssembleCoaddTask) 

42from .coaddBase import makeSkyInfo 

43from .measurePsf import MeasurePsfTask 

44 

45__all__ = ["DcrAssembleCoaddConnections", "DcrAssembleCoaddTask", "DcrAssembleCoaddConfig"] 

46 

47 

48class DcrAssembleCoaddConnections(AssembleCoaddConnections, 

49 dimensions=("tract", "patch", "band", "skymap"), 

50 defaultTemplates={"inputWarpName": "deep", 

51 "inputCoaddName": "deep", 

52 "outputCoaddName": "dcr", 

53 "warpType": "direct", 

54 "warpTypeSuffix": "", 

55 "fakesType": ""}): 

56 inputWarps = pipeBase.connectionTypes.Input( 

57 doc=("Input list of warps to be assembled i.e. stacked." 

58 "Note that this will often be different than the inputCoaddName." 

59 "WarpType (e.g. direct, psfMatched) is controlled by the warpType config parameter"), 

60 name="{inputWarpName}Coadd_{warpType}Warp", 

61 storageClass="ExposureF", 

62 dimensions=("tract", "patch", "skymap", "visit", "instrument"), 

63 deferLoad=True, 

64 multiple=True 

65 ) 

66 templateExposure = pipeBase.connectionTypes.Input( 

67 doc="Input coadded exposure, produced by previous call to AssembleCoadd", 

68 name="{fakesType}{inputCoaddName}Coadd{warpTypeSuffix}", 

69 storageClass="ExposureF", 

70 dimensions=("tract", "patch", "skymap", "band"), 

71 ) 

72 dcrCoadds = pipeBase.connectionTypes.Output( 

73 doc="Output coadded exposure, produced by stacking input warps", 

74 name="{fakesType}{outputCoaddName}Coadd{warpTypeSuffix}", 

75 storageClass="ExposureF", 

76 dimensions=("tract", "patch", "skymap", "band", "subfilter"), 

77 multiple=True, 

78 ) 

79 dcrNImages = pipeBase.connectionTypes.Output( 

80 doc="Output image of number of input images per pixel", 

81 name="{outputCoaddName}Coadd_nImage", 

82 storageClass="ImageU", 

83 dimensions=("tract", "patch", "skymap", "band", "subfilter"), 

84 multiple=True, 

85 ) 

86 

87 def __init__(self, *, config=None): 

88 super().__init__(config=config) 

89 if not config.doWrite: 

90 self.outputs.remove("dcrCoadds") 

91 if not config.doNImage: 

92 self.outputs.remove("dcrNImages") 

93 # Remove outputs inherited from ``AssembleCoaddConnections`` that are not used 

94 self.outputs.remove("coaddExposure") 

95 self.outputs.remove("nImage") 

96 

97 

98class DcrAssembleCoaddConfig(CompareWarpAssembleCoaddConfig, 

99 pipelineConnections=DcrAssembleCoaddConnections): 

100 dcrNumSubfilters = pexConfig.Field( 

101 dtype=int, 

102 doc="Number of sub-filters to forward model chromatic effects to fit the supplied exposures.", 

103 default=3, 

104 ) 

105 maxNumIter = pexConfig.Field( 

106 dtype=int, 

107 optional=True, 

108 doc="Maximum number of iterations of forward modeling.", 

109 default=None, 

110 ) 

111 minNumIter = pexConfig.Field( 

112 dtype=int, 

113 optional=True, 

114 doc="Minimum number of iterations of forward modeling.", 

115 default=None, 

116 ) 

117 convergenceThreshold = pexConfig.Field( 

118 dtype=float, 

119 doc="Target relative change in convergence between iterations of forward modeling.", 

120 default=0.001, 

121 ) 

122 useConvergence = pexConfig.Field( 

123 dtype=bool, 

124 doc="Use convergence test as a forward modeling end condition?" 

125 "If not set, skips calculating convergence and runs for ``maxNumIter`` iterations", 

126 default=True, 

127 ) 

128 baseGain = pexConfig.Field( 

129 dtype=float, 

130 optional=True, 

131 doc="Relative weight to give the new solution vs. the last solution when updating the model." 

132 "A value of 1.0 gives equal weight to both solutions." 

133 "Small values imply slower convergence of the solution, but can " 

134 "help prevent overshooting and failures in the fit." 

135 "If ``baseGain`` is None, a conservative gain " 

136 "will be calculated from the number of subfilters. ", 

137 default=None, 

138 ) 

139 useProgressiveGain = pexConfig.Field( 

140 dtype=bool, 

141 doc="Use a gain that slowly increases above ``baseGain`` to accelerate convergence? " 

142 "When calculating the next gain, we use up to 5 previous gains and convergence values." 

143 "Can be set to False to force the model to change at the rate of ``baseGain``. ", 

144 default=True, 

145 ) 

146 doAirmassWeight = pexConfig.Field( 

147 dtype=bool, 

148 doc="Weight exposures by airmass? Useful if there are relatively few high-airmass observations.", 

149 default=False, 

150 ) 

151 modelWeightsWidth = pexConfig.Field( 

152 dtype=float, 

153 doc="Width of the region around detected sources to include in the DcrModel.", 

154 default=3, 

155 ) 

156 useModelWeights = pexConfig.Field( 

157 dtype=bool, 

158 doc="Width of the region around detected sources to include in the DcrModel.", 

159 default=True, 

160 ) 

161 splitSubfilters = pexConfig.Field( 

162 dtype=bool, 

163 doc="Calculate DCR for two evenly-spaced wavelengths in each subfilter." 

164 "Instead of at the midpoint", 

165 default=True, 

166 ) 

167 splitThreshold = pexConfig.Field( 

168 dtype=float, 

169 doc="Minimum DCR difference within a subfilter to use ``splitSubfilters``, in pixels." 

170 "Set to 0 to always split the subfilters.", 

171 default=0.1, 

172 ) 

173 regularizeModelIterations = pexConfig.Field( 

174 dtype=float, 

175 doc="Maximum relative change of the model allowed between iterations." 

176 "Set to zero to disable.", 

177 default=2., 

178 ) 

179 regularizeModelFrequency = pexConfig.Field( 

180 dtype=float, 

181 doc="Maximum relative change of the model allowed between subfilters." 

182 "Set to zero to disable.", 

183 default=4., 

184 ) 

185 convergenceMaskPlanes = pexConfig.ListField( 

186 dtype=str, 

187 default=["DETECTED"], 

188 doc="Mask planes to use to calculate convergence." 

189 ) 

190 regularizationWidth = pexConfig.Field( 

191 dtype=int, 

192 default=2, 

193 doc="Minimum radius of a region to include in regularization, in pixels." 

194 ) 

195 imageInterpOrder = pexConfig.Field( 

196 dtype=int, 

197 doc="The order of the spline interpolation used to shift the image plane.", 

198 default=3, 

199 ) 

200 accelerateModel = pexConfig.Field( 

201 dtype=float, 

202 doc="Factor to amplify the differences between model planes by to speed convergence.", 

203 default=3, 

204 ) 

205 doCalculatePsf = pexConfig.Field( 

206 dtype=bool, 

207 doc="Set to detect stars and recalculate the PSF from the final coadd." 

208 "Otherwise the PSF is estimated from a selection of the best input exposures", 

209 default=False, 

210 ) 

211 detectPsfSources = pexConfig.ConfigurableField( 

212 target=measAlg.SourceDetectionTask, 

213 doc="Task to detect sources for PSF measurement, if ``doCalculatePsf`` is set.", 

214 ) 

215 measurePsfSources = pexConfig.ConfigurableField( 

216 target=SingleFrameMeasurementTask, 

217 doc="Task to measure sources for PSF measurement, if ``doCalculatePsf`` is set." 

218 ) 

219 measurePsf = pexConfig.ConfigurableField( 

220 target=MeasurePsfTask, 

221 doc="Task to measure the PSF of the coadd, if ``doCalculatePsf`` is set.", 

222 ) 

223 effectiveWavelength = pexConfig.Field( 

224 doc="Effective wavelength of the filter, in nm." 

225 "Required if transmission curves aren't used." 

226 "Support for using transmission curves is to be added in DM-13668.", 

227 dtype=float, 

228 ) 

229 bandwidth = pexConfig.Field( 

230 doc="Bandwidth of the physical filter, in nm." 

231 "Required if transmission curves aren't used." 

232 "Support for using transmission curves is to be added in DM-13668.", 

233 dtype=float, 

234 ) 

235 

236 def setDefaults(self): 

237 CompareWarpAssembleCoaddConfig.setDefaults(self) 

238 self.assembleStaticSkyModel.retarget(CompareWarpAssembleCoaddTask) 

239 self.doNImage = True 

240 self.assembleStaticSkyModel.warpType = self.warpType 

241 # The deepCoadd and nImage files will be overwritten by this Task, so don't write them the first time 

242 self.assembleStaticSkyModel.doNImage = False 

243 self.assembleStaticSkyModel.doWrite = False 

244 self.detectPsfSources.returnOriginalFootprints = False 

245 self.detectPsfSources.thresholdPolarity = "positive" 

246 # Only use bright sources for PSF measurement 

247 self.detectPsfSources.thresholdValue = 50 

248 self.detectPsfSources.nSigmaToGrow = 2 

249 # A valid star for PSF measurement should at least fill 5x5 pixels 

250 self.detectPsfSources.minPixels = 25 

251 # Use the variance plane to calculate signal to noise 

252 self.detectPsfSources.thresholdType = "pixel_stdev" 

253 # The signal to noise limit is good enough, while the flux limit is set 

254 # in dimensionless units and may not be appropriate for all data sets. 

255 self.measurePsf.starSelector["objectSize"].doFluxLimit = False 

256 

257 

258class DcrAssembleCoaddTask(CompareWarpAssembleCoaddTask): 

259 """Assemble DCR coadded images from a set of warps. 

260 

261 Attributes 

262 ---------- 

263 bufferSize : `int` 

264 The number of pixels to grow each subregion by to allow for DCR. 

265 

266 Notes 

267 ----- 

268 As with AssembleCoaddTask, we want to assemble a coadded image from a set of 

269 Warps (also called coadded temporary exposures), including the effects of 

270 Differential Chromatic Refraction (DCR). 

271 For full details of the mathematics and algorithm, please see 

272 DMTN-037: DCR-matched template generation (https://dmtn-037.lsst.io). 

273 

274 This Task produces a DCR-corrected deepCoadd, as well as a dcrCoadd for 

275 each subfilter used in the iterative calculation. 

276 It begins by dividing the bandpass-defining filter into N equal bandwidth 

277 "subfilters", and divides the flux in each pixel from an initial coadd 

278 equally into each as a "dcrModel". Because the airmass and parallactic 

279 angle of each individual exposure is known, we can calculate the shift 

280 relative to the center of the band in each subfilter due to DCR. For each 

281 exposure we apply this shift as a linear transformation to the dcrModels 

282 and stack the results to produce a DCR-matched exposure. The matched 

283 exposures are subtracted from the input exposures to produce a set of 

284 residual images, and these residuals are reverse shifted for each 

285 exposures' subfilters and stacked. The shifted and stacked residuals are 

286 added to the dcrModels to produce a new estimate of the flux in each pixel 

287 within each subfilter. The dcrModels are solved for iteratively, which 

288 continues until the solution from a new iteration improves by less than 

289 a set percentage, or a maximum number of iterations is reached. 

290 Two forms of regularization are employed to reduce unphysical results. 

291 First, the new solution is averaged with the solution from the previous 

292 iteration, which mitigates oscillating solutions where the model 

293 overshoots with alternating very high and low values. 

294 Second, a common degeneracy when the data have a limited range of airmass or 

295 parallactic angle values is for one subfilter to be fit with very low or 

296 negative values, while another subfilter is fit with very high values. This 

297 typically appears in the form of holes next to sources in one subfilter, 

298 and corresponding extended wings in another. Because each subfilter has 

299 a narrow bandwidth we assume that physical sources that are above the noise 

300 level will not vary in flux by more than a factor of `frequencyClampFactor` 

301 between subfilters, and pixels that have flux deviations larger than that 

302 factor will have the excess flux distributed evenly among all subfilters. 

303 If `splitSubfilters` is set, then each subfilter will be further sub- 

304 divided during the forward modeling step (only). This approximates using 

305 a higher number of subfilters that may be necessary for high airmass 

306 observations, but does not increase the number of free parameters in the 

307 fit. This is needed when there are high airmass observations which would 

308 otherwise have significant DCR even within a subfilter. Because calculating 

309 the shifted images takes most of the time, splitting the subfilters is 

310 turned off by way of the `splitThreshold` option for low-airmass 

311 observations that do not suffer from DCR within a subfilter. 

312 """ 

313 

314 ConfigClass = DcrAssembleCoaddConfig 

315 _DefaultName = "dcrAssembleCoadd" 

316 

317 def __init__(self, *args, **kwargs): 

318 super().__init__(*args, **kwargs) 

319 if self.config.doCalculatePsf: 

320 self.schema = afwTable.SourceTable.makeMinimalSchema() 

321 self.makeSubtask("detectPsfSources", schema=self.schema) 

322 self.makeSubtask("measurePsfSources", schema=self.schema) 

323 self.makeSubtask("measurePsf", schema=self.schema) 

324 

325 @utils.inheritDoc(pipeBase.PipelineTask) 

326 def runQuantum(self, butlerQC, inputRefs, outputRefs): 

327 # Docstring to be formatted with info from PipelineTask.runQuantum 

328 """ 

329 Notes 

330 ----- 

331 Assemble a coadd from a set of Warps. 

332 

333 PipelineTask (Gen3) entry point to Coadd a set of Warps. 

334 Analogous to `runDataRef`, it prepares all the data products to be 

335 passed to `run`, and processes the results before returning a struct 

336 of results to be written out. AssembleCoadd cannot fit all Warps in memory. 

337 Therefore, its inputs are accessed subregion by subregion 

338 by the Gen3 `DeferredDatasetHandle` that is analagous to the Gen2 

339 `lsst.daf.persistence.ButlerDataRef`. Any updates to this method should 

340 correspond to an update in `runDataRef` while both entry points 

341 are used. 

342 """ 

343 inputData = butlerQC.get(inputRefs) 

344 

345 # Construct skyInfo expected by run 

346 # Do not remove skyMap from inputData in case makeSupplementaryDataGen3 needs it 

347 skyMap = inputData["skyMap"] 

348 outputDataId = butlerQC.quantum.dataId 

349 

350 inputData['skyInfo'] = makeSkyInfo(skyMap, 

351 tractId=outputDataId['tract'], 

352 patchId=outputDataId['patch']) 

353 

354 # Construct list of input Deferred Datasets 

355 # These quack a bit like like Gen2 DataRefs 

356 warpRefList = inputData['inputWarps'] 

357 # Perform same middle steps as `runDataRef` does 

358 inputs = self.prepareInputs(warpRefList) 

359 self.log.info("Found %d %s", len(inputs.tempExpRefList), 

360 self.getTempExpDatasetName(self.warpType)) 

361 if len(inputs.tempExpRefList) == 0: 

362 self.log.warning("No coadd temporary exposures found") 

363 return 

364 

365 supplementaryData = self.makeSupplementaryDataGen3(butlerQC, inputRefs, outputRefs) 

366 retStruct = self.run(inputData['skyInfo'], inputs.tempExpRefList, inputs.imageScalerList, 

367 inputs.weightList, supplementaryData=supplementaryData) 

368 

369 inputData.setdefault('brightObjectMask', None) 

370 for subfilter in range(self.config.dcrNumSubfilters): 

371 # Use the PSF of the stacked dcrModel, and do not recalculate the PSF for each subfilter 

372 retStruct.dcrCoadds[subfilter].setPsf(retStruct.coaddExposure.getPsf()) 

373 self.processResults(retStruct.dcrCoadds[subfilter], inputData['brightObjectMask'], outputDataId) 

374 

375 if self.config.doWrite: 

376 butlerQC.put(retStruct, outputRefs) 

377 return retStruct 

378 

379 @timeMethod 

380 def runDataRef(self, dataRef, selectDataList=None, warpRefList=None): 

381 """Assemble a coadd from a set of warps. 

382 

383 Coadd a set of Warps. Compute weights to be applied to each Warp and 

384 find scalings to match the photometric zeropoint to a reference Warp. 

385 Assemble the Warps using run method. 

386 Forward model chromatic effects across multiple subfilters, 

387 and subtract from the input Warps to build sets of residuals. 

388 Use the residuals to construct a new ``DcrModel`` for each subfilter, 

389 and iterate until the model converges. 

390 Interpolate over NaNs and optionally write the coadd to disk. 

391 Return the coadded exposure. 

392 

393 Parameters 

394 ---------- 

395 dataRef : `lsst.daf.persistence.ButlerDataRef` 

396 Data reference defining the patch for coaddition and the 

397 reference Warp 

398 selectDataList : `list` of `lsst.daf.persistence.ButlerDataRef` 

399 List of data references to warps. Data to be coadded will be 

400 selected from this list based on overlap with the patch defined by 

401 the data reference. 

402 

403 Returns 

404 ------- 

405 results : `lsst.pipe.base.Struct` 

406 The Struct contains the following fields: 

407 

408 - ``coaddExposure``: coadded exposure (`lsst.afw.image.Exposure`) 

409 - ``nImage``: exposure count image (`lsst.afw.image.ImageU`) 

410 - ``dcrCoadds``: `list` of coadded exposures for each subfilter 

411 - ``dcrNImages``: `list` of exposure count images for each subfilter 

412 """ 

413 if (selectDataList is None and warpRefList is None) or (selectDataList and warpRefList): 

414 raise RuntimeError("runDataRef must be supplied either a selectDataList or warpRefList") 

415 

416 skyInfo = self.getSkyInfo(dataRef) 

417 if warpRefList is None: 

418 calExpRefList = self.selectExposures(dataRef, skyInfo, selectDataList=selectDataList) 

419 if len(calExpRefList) == 0: 

420 self.log.warning("No exposures to coadd") 

421 return 

422 self.log.info("Coadding %d exposures", len(calExpRefList)) 

423 

424 warpRefList = self.getTempExpRefList(dataRef, calExpRefList) 

425 

426 inputData = self.prepareInputs(warpRefList) 

427 self.log.info("Found %d %s", len(inputData.tempExpRefList), 

428 self.getTempExpDatasetName(self.warpType)) 

429 if len(inputData.tempExpRefList) == 0: 

430 self.log.warning("No coadd temporary exposures found") 

431 return 

432 

433 supplementaryData = self.makeSupplementaryData(dataRef, warpRefList=inputData.tempExpRefList) 

434 

435 results = self.run(skyInfo, inputData.tempExpRefList, inputData.imageScalerList, 

436 inputData.weightList, supplementaryData=supplementaryData) 

437 if results is None: 

438 self.log.warning("Could not construct DcrModel for patch %s: no data to coadd.", 

439 skyInfo.patchInfo.getIndex()) 

440 return 

441 

442 if self.config.doCalculatePsf: 

443 self.measureCoaddPsf(results.coaddExposure) 

444 brightObjects = self.readBrightObjectMasks(dataRef) if self.config.doMaskBrightObjects else None 

445 for subfilter in range(self.config.dcrNumSubfilters): 

446 # Use the PSF of the stacked dcrModel, and do not recalculate the PSF for each subfilter 

447 results.dcrCoadds[subfilter].setPsf(results.coaddExposure.getPsf()) 

448 self.processResults(results.dcrCoadds[subfilter], 

449 brightObjectMasks=brightObjects, dataId=dataRef.dataId) 

450 if self.config.doWrite: 

451 self.log.info("Persisting dcrCoadd") 

452 dataRef.put(results.dcrCoadds[subfilter], "dcrCoadd", subfilter=subfilter, 

453 numSubfilters=self.config.dcrNumSubfilters) 

454 if self.config.doNImage and results.dcrNImages is not None: 

455 dataRef.put(results.dcrNImages[subfilter], "dcrCoadd_nImage", subfilter=subfilter, 

456 numSubfilters=self.config.dcrNumSubfilters) 

457 

458 return results 

459 

460 @utils.inheritDoc(AssembleCoaddTask) 

461 def makeSupplementaryDataGen3(self, butlerQC, inputRefs, outputRefs): 

462 """Load the previously-generated template coadd. 

463 

464 This can be removed entirely once we no longer support the Gen 2 butler. 

465 

466 Returns 

467 ------- 

468 templateCoadd : `lsst.pipe.base.Struct` 

469 Result struct with components: 

470 

471 - ``templateCoadd``: coadded exposure (`lsst.afw.image.ExposureF`) 

472 """ 

473 templateCoadd = butlerQC.get(inputRefs.templateExposure) 

474 

475 return pipeBase.Struct(templateCoadd=templateCoadd) 

476 

477 def measureCoaddPsf(self, coaddExposure): 

478 """Detect sources on the coadd exposure and measure the final PSF. 

479 

480 Parameters 

481 ---------- 

482 coaddExposure : `lsst.afw.image.Exposure` 

483 The final coadded exposure. 

484 """ 

485 table = afwTable.SourceTable.make(self.schema) 

486 detResults = self.detectPsfSources.run(table, coaddExposure, clearMask=False) 

487 coaddSources = detResults.sources 

488 self.measurePsfSources.run( 

489 measCat=coaddSources, 

490 exposure=coaddExposure 

491 ) 

492 # Measure the PSF on the stacked subfilter coadds if possible. 

493 # We should already have a decent estimate of the coadd PSF, however, 

494 # so in case of any errors simply log them as a warning and use the 

495 # default PSF. 

496 try: 

497 psfResults = self.measurePsf.run(coaddExposure, coaddSources) 

498 except Exception as e: 

499 self.log.warning("Unable to calculate PSF, using default coadd PSF: %s", e) 

500 else: 

501 coaddExposure.setPsf(psfResults.psf) 

502 

503 def prepareDcrInputs(self, templateCoadd, warpRefList, weightList): 

504 """Prepare the DCR coadd by iterating through the visitInfo of the input warps. 

505 

506 Sets the property ``bufferSize``. 

507 

508 Parameters 

509 ---------- 

510 templateCoadd : `lsst.afw.image.ExposureF` 

511 The initial coadd exposure before accounting for DCR. 

512 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 

513 `lsst.daf.persistence.ButlerDataRef` 

514 The data references to the input warped exposures. 

515 weightList : `list` of `float` 

516 The weight to give each input exposure in the coadd 

517 Will be modified in place if ``doAirmassWeight`` is set. 

518 

519 Returns 

520 ------- 

521 dcrModels : `lsst.pipe.tasks.DcrModel` 

522 Best fit model of the true sky after correcting chromatic effects. 

523 

524 Raises 

525 ------ 

526 NotImplementedError 

527 If ``lambdaMin`` is missing from the Mapper class of the obs package being used. 

528 """ 

529 sigma2fwhm = 2.*np.sqrt(2.*np.log(2.)) 

530 filterLabel = templateCoadd.getFilterLabel() 

531 tempExpName = self.getTempExpDatasetName(self.warpType) 

532 dcrShifts = [] 

533 airmassDict = {} 

534 angleDict = {} 

535 psfSizeDict = {} 

536 for visitNum, warpExpRef in enumerate(warpRefList): 

537 if isinstance(warpExpRef, DeferredDatasetHandle): 

538 # Gen 3 API 

539 visitInfo = warpExpRef.get(component="visitInfo") 

540 psf = warpExpRef.get(component="psf") 

541 else: 

542 # Gen 2 API. Delete this when Gen 2 retired 

543 visitInfo = warpExpRef.get(tempExpName + "_visitInfo") 

544 psf = warpExpRef.get(tempExpName).getPsf() 

545 visit = warpExpRef.dataId["visit"] 

546 psfSize = psf.computeShape().getDeterminantRadius()*sigma2fwhm 

547 airmass = visitInfo.getBoresightAirmass() 

548 parallacticAngle = visitInfo.getBoresightParAngle().asDegrees() 

549 airmassDict[visit] = airmass 

550 angleDict[visit] = parallacticAngle 

551 psfSizeDict[visit] = psfSize 

552 if self.config.doAirmassWeight: 

553 weightList[visitNum] *= airmass 

554 dcrShifts.append(np.max(np.abs(calculateDcr(visitInfo, templateCoadd.getWcs(), 

555 self.config.effectiveWavelength, 

556 self.config.bandwidth, 

557 self.config.dcrNumSubfilters)))) 

558 self.log.info("Selected airmasses:\n%s", airmassDict) 

559 self.log.info("Selected parallactic angles:\n%s", angleDict) 

560 self.log.info("Selected PSF sizes:\n%s", psfSizeDict) 

561 self.bufferSize = int(np.ceil(np.max(dcrShifts)) + 1) 

562 try: 

563 psf = self.selectCoaddPsf(templateCoadd, warpRefList) 

564 except Exception as e: 

565 self.log.warning("Unable to calculate restricted PSF, using default coadd PSF: %s", e) 

566 else: 

567 psf = templateCoadd.getPsf() 

568 dcrModels = DcrModel.fromImage(templateCoadd.maskedImage, 

569 self.config.dcrNumSubfilters, 

570 effectiveWavelength=self.config.effectiveWavelength, 

571 bandwidth=self.config.bandwidth, 

572 filterLabel=filterLabel, 

573 psf=psf) 

574 return dcrModels 

575 

576 @timeMethod 

577 def run(self, skyInfo, warpRefList, imageScalerList, weightList, 

578 supplementaryData=None): 

579 """Assemble the coadd. 

580 

581 Requires additional inputs Struct ``supplementaryData`` to contain a 

582 ``templateCoadd`` that serves as the model of the static sky. 

583 

584 Find artifacts and apply them to the warps' masks creating a list of 

585 alternative masks with a new "CLIPPED" plane and updated "NO_DATA" plane 

586 Then pass these alternative masks to the base class's assemble method. 

587 

588 Divide the ``templateCoadd`` evenly between each subfilter of a 

589 ``DcrModel`` as the starting best estimate of the true wavelength- 

590 dependent sky. Forward model the ``DcrModel`` using the known 

591 chromatic effects in each subfilter and calculate a convergence metric 

592 based on how well the modeled template matches the input warps. If 

593 the convergence has not yet reached the desired threshold, then shift 

594 and stack the residual images to build a new ``DcrModel``. Apply 

595 conditioning to prevent oscillating solutions between iterations or 

596 between subfilters. 

597 

598 Once the ``DcrModel`` reaches convergence or the maximum number of 

599 iterations has been reached, fill the metadata for each subfilter 

600 image and make them proper ``coaddExposure``s. 

601 

602 Parameters 

603 ---------- 

604 skyInfo : `lsst.pipe.base.Struct` 

605 Patch geometry information, from getSkyInfo 

606 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 

607 `lsst.daf.persistence.ButlerDataRef` 

608 The data references to the input warped exposures. 

609 imageScalerList : `list` of `lsst.pipe.task.ImageScaler` 

610 The image scalars correct for the zero point of the exposures. 

611 weightList : `list` of `float` 

612 The weight to give each input exposure in the coadd 

613 supplementaryData : `lsst.pipe.base.Struct` 

614 Result struct returned by ``makeSupplementaryData`` with components: 

615 

616 - ``templateCoadd``: coadded exposure (`lsst.afw.image.Exposure`) 

617 

618 Returns 

619 ------- 

620 result : `lsst.pipe.base.Struct` 

621 Result struct with components: 

622 

623 - ``coaddExposure``: coadded exposure (`lsst.afw.image.Exposure`) 

624 - ``nImage``: exposure count image (`lsst.afw.image.ImageU`) 

625 - ``dcrCoadds``: `list` of coadded exposures for each subfilter 

626 - ``dcrNImages``: `list` of exposure count images for each subfilter 

627 """ 

628 minNumIter = self.config.minNumIter or self.config.dcrNumSubfilters 

629 maxNumIter = self.config.maxNumIter or self.config.dcrNumSubfilters*3 

630 templateCoadd = supplementaryData.templateCoadd 

631 baseMask = templateCoadd.mask.clone() 

632 # The variance plane is for each subfilter 

633 # and should be proportionately lower than the full-band image 

634 baseVariance = templateCoadd.variance.clone() 

635 baseVariance /= self.config.dcrNumSubfilters 

636 spanSetMaskList = self.findArtifacts(templateCoadd, warpRefList, imageScalerList) 

637 # Note that the mask gets cleared in ``findArtifacts``, but we want to preserve the mask. 

638 templateCoadd.setMask(baseMask) 

639 badMaskPlanes = self.config.badMaskPlanes[:] 

640 # Note that is important that we do not add "CLIPPED" to ``badMaskPlanes`` 

641 # This is because pixels in observations that are significantly affect by DCR 

642 # are likely to have many pixels that are both "DETECTED" and "CLIPPED", 

643 # but those are necessary to constrain the DCR model. 

644 badPixelMask = templateCoadd.mask.getPlaneBitMask(badMaskPlanes) 

645 

646 stats = self.prepareStats(mask=badPixelMask) 

647 dcrModels = self.prepareDcrInputs(templateCoadd, warpRefList, weightList) 

648 if self.config.doNImage: 

649 dcrNImages, dcrWeights = self.calculateNImage(dcrModels, skyInfo.bbox, warpRefList, 

650 spanSetMaskList, stats.ctrl) 

651 nImage = afwImage.ImageU(skyInfo.bbox) 

652 # Note that this nImage will be a factor of dcrNumSubfilters higher than 

653 # the nImage returned by assembleCoadd for most pixels. This is because each 

654 # subfilter may have a different nImage, and fractional values are not allowed. 

655 for dcrNImage in dcrNImages: 

656 nImage += dcrNImage 

657 else: 

658 dcrNImages = None 

659 

660 subregionSize = geom.Extent2I(*self.config.subregionSize) 

661 nSubregions = (ceil(skyInfo.bbox.getHeight()/subregionSize[1]) 

662 * ceil(skyInfo.bbox.getWidth()/subregionSize[0])) 

663 subIter = 0 

664 for subBBox in self._subBBoxIter(skyInfo.bbox, subregionSize): 

665 modelIter = 0 

666 subIter += 1 

667 self.log.info("Computing coadd over patch %s subregion %s of %s: %s", 

668 skyInfo.patchInfo.getIndex(), subIter, nSubregions, subBBox) 

669 dcrBBox = geom.Box2I(subBBox) 

670 dcrBBox.grow(self.bufferSize) 

671 dcrBBox.clip(dcrModels.bbox) 

672 modelWeights = self.calculateModelWeights(dcrModels, dcrBBox) 

673 subExposures = self.loadSubExposures(dcrBBox, stats.ctrl, warpRefList, 

674 imageScalerList, spanSetMaskList) 

675 convergenceMetric = self.calculateConvergence(dcrModels, subExposures, subBBox, 

676 warpRefList, weightList, stats.ctrl) 

677 self.log.info("Initial convergence : %s", convergenceMetric) 

678 convergenceList = [convergenceMetric] 

679 gainList = [] 

680 convergenceCheck = 1. 

681 refImage = templateCoadd.image 

682 while (convergenceCheck > self.config.convergenceThreshold or modelIter <= minNumIter): 

683 gain = self.calculateGain(convergenceList, gainList) 

684 self.dcrAssembleSubregion(dcrModels, subExposures, subBBox, dcrBBox, warpRefList, 

685 stats.ctrl, convergenceMetric, gain, 

686 modelWeights, refImage, dcrWeights) 

687 if self.config.useConvergence: 

688 convergenceMetric = self.calculateConvergence(dcrModels, subExposures, subBBox, 

689 warpRefList, weightList, stats.ctrl) 

690 if convergenceMetric == 0: 

691 self.log.warning("Coadd patch %s subregion %s had convergence metric of 0.0 which is " 

692 "most likely due to there being no valid data in the region.", 

693 skyInfo.patchInfo.getIndex(), subIter) 

694 break 

695 convergenceCheck = (convergenceList[-1] - convergenceMetric)/convergenceMetric 

696 if (convergenceCheck < 0) & (modelIter > minNumIter): 

697 self.log.warning("Coadd patch %s subregion %s diverged before reaching maximum " 

698 "iterations or desired convergence improvement of %s." 

699 " Divergence: %s", 

700 skyInfo.patchInfo.getIndex(), subIter, 

701 self.config.convergenceThreshold, convergenceCheck) 

702 break 

703 convergenceList.append(convergenceMetric) 

704 if modelIter > maxNumIter: 

705 if self.config.useConvergence: 

706 self.log.warning("Coadd patch %s subregion %s reached maximum iterations " 

707 "before reaching desired convergence improvement of %s." 

708 " Final convergence improvement: %s", 

709 skyInfo.patchInfo.getIndex(), subIter, 

710 self.config.convergenceThreshold, convergenceCheck) 

711 break 

712 

713 if self.config.useConvergence: 

714 self.log.info("Iteration %s with convergence metric %s, %.4f%% improvement (gain: %.2f)", 

715 modelIter, convergenceMetric, 100.*convergenceCheck, gain) 

716 modelIter += 1 

717 else: 

718 if self.config.useConvergence: 

719 self.log.info("Coadd patch %s subregion %s finished with " 

720 "convergence metric %s after %s iterations", 

721 skyInfo.patchInfo.getIndex(), subIter, convergenceMetric, modelIter) 

722 else: 

723 self.log.info("Coadd patch %s subregion %s finished after %s iterations", 

724 skyInfo.patchInfo.getIndex(), subIter, modelIter) 

725 if self.config.useConvergence and convergenceMetric > 0: 

726 self.log.info("Final convergence improvement was %.4f%% overall", 

727 100*(convergenceList[0] - convergenceMetric)/convergenceMetric) 

728 

729 dcrCoadds = self.fillCoadd(dcrModels, skyInfo, warpRefList, weightList, 

730 calibration=self.scaleZeroPoint.getPhotoCalib(), 

731 coaddInputs=templateCoadd.getInfo().getCoaddInputs(), 

732 mask=baseMask, 

733 variance=baseVariance) 

734 coaddExposure = self.stackCoadd(dcrCoadds) 

735 return pipeBase.Struct(coaddExposure=coaddExposure, nImage=nImage, 

736 dcrCoadds=dcrCoadds, dcrNImages=dcrNImages) 

737 

738 def calculateNImage(self, dcrModels, bbox, warpRefList, spanSetMaskList, statsCtrl): 

739 """Calculate the number of exposures contributing to each subfilter. 

740 

741 Parameters 

742 ---------- 

743 dcrModels : `lsst.pipe.tasks.DcrModel` 

744 Best fit model of the true sky after correcting chromatic effects. 

745 bbox : `lsst.geom.box.Box2I` 

746 Bounding box of the patch to coadd. 

747 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 

748 `lsst.daf.persistence.ButlerDataRef` 

749 The data references to the input warped exposures. 

750 spanSetMaskList : `list` of `dict` containing spanSet lists, or None 

751 Each element of the `dict` contains the new mask plane name 

752 (e.g. "CLIPPED and/or "NO_DATA") as the key, 

753 and the list of SpanSets to apply to the mask. 

754 statsCtrl : `lsst.afw.math.StatisticsControl` 

755 Statistics control object for coadd 

756 

757 Returns 

758 ------- 

759 dcrNImages : `list` of `lsst.afw.image.ImageU` 

760 List of exposure count images for each subfilter 

761 dcrWeights : `list` of `lsst.afw.image.ImageF` 

762 Per-pixel weights for each subfilter. 

763 Equal to 1/(number of unmasked images contributing to each pixel). 

764 """ 

765 dcrNImages = [afwImage.ImageU(bbox) for subfilter in range(self.config.dcrNumSubfilters)] 

766 dcrWeights = [afwImage.ImageF(bbox) for subfilter in range(self.config.dcrNumSubfilters)] 

767 tempExpName = self.getTempExpDatasetName(self.warpType) 

768 for warpExpRef, altMaskSpans in zip(warpRefList, spanSetMaskList): 

769 if isinstance(warpExpRef, DeferredDatasetHandle): 

770 # Gen 3 API 

771 exposure = warpExpRef.get(parameters={'bbox': bbox}) 

772 else: 

773 # Gen 2 API. Delete this when Gen 2 retired 

774 exposure = warpExpRef.get(tempExpName + "_sub", bbox=bbox) 

775 visitInfo = exposure.getInfo().getVisitInfo() 

776 wcs = exposure.getInfo().getWcs() 

777 mask = exposure.mask 

778 if altMaskSpans is not None: 

779 self.applyAltMaskPlanes(mask, altMaskSpans) 

780 weightImage = np.zeros_like(exposure.image.array) 

781 weightImage[(mask.array & statsCtrl.getAndMask()) == 0] = 1. 

782 # The weights must be shifted in exactly the same way as the residuals, 

783 # because they will be used as the denominator in the weighted average of residuals. 

784 weightsGenerator = self.dcrResiduals(weightImage, visitInfo, wcs, 

785 dcrModels.effectiveWavelength, dcrModels.bandwidth) 

786 for shiftedWeights, dcrNImage, dcrWeight in zip(weightsGenerator, dcrNImages, dcrWeights): 

787 dcrNImage.array += np.rint(shiftedWeights).astype(dcrNImage.array.dtype) 

788 dcrWeight.array += shiftedWeights 

789 # Exclude any pixels that don't have at least one exposure contributing in all subfilters 

790 weightsThreshold = 1. 

791 goodPix = dcrWeights[0].array > weightsThreshold 

792 for weights in dcrWeights[1:]: 

793 goodPix = (weights.array > weightsThreshold) & goodPix 

794 for subfilter in range(self.config.dcrNumSubfilters): 

795 dcrWeights[subfilter].array[goodPix] = 1./dcrWeights[subfilter].array[goodPix] 

796 dcrWeights[subfilter].array[~goodPix] = 0. 

797 dcrNImages[subfilter].array[~goodPix] = 0 

798 return (dcrNImages, dcrWeights) 

799 

800 def dcrAssembleSubregion(self, dcrModels, subExposures, bbox, dcrBBox, warpRefList, 

801 statsCtrl, convergenceMetric, 

802 gain, modelWeights, refImage, dcrWeights): 

803 """Assemble the DCR coadd for a sub-region. 

804 

805 Build a DCR-matched template for each input exposure, then shift the 

806 residuals according to the DCR in each subfilter. 

807 Stack the shifted residuals and apply them as a correction to the 

808 solution from the previous iteration. 

809 Restrict the new model solutions from varying by more than a factor of 

810 `modelClampFactor` from the last solution, and additionally restrict the 

811 individual subfilter models from varying by more than a factor of 

812 `frequencyClampFactor` from their average. 

813 Finally, mitigate potentially oscillating solutions by averaging the new 

814 solution with the solution from the previous iteration, weighted by 

815 their convergence metric. 

816 

817 Parameters 

818 ---------- 

819 dcrModels : `lsst.pipe.tasks.DcrModel` 

820 Best fit model of the true sky after correcting chromatic effects. 

821 subExposures : `dict` of `lsst.afw.image.ExposureF` 

822 The pre-loaded exposures for the current subregion. 

823 bbox : `lsst.geom.box.Box2I` 

824 Bounding box of the subregion to coadd. 

825 dcrBBox : `lsst.geom.box.Box2I` 

826 Sub-region of the coadd which includes a buffer to allow for DCR. 

827 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 

828 `lsst.daf.persistence.ButlerDataRef` 

829 The data references to the input warped exposures. 

830 statsCtrl : `lsst.afw.math.StatisticsControl` 

831 Statistics control object for coadd 

832 convergenceMetric : `float` 

833 Quality of fit metric for the matched templates of the input images. 

834 gain : `float`, optional 

835 Relative weight to give the new solution when updating the model. 

836 modelWeights : `numpy.ndarray` or `float` 

837 A 2D array of weight values that tapers smoothly to zero away from detected sources. 

838 Set to a placeholder value of 1.0 if ``self.config.useModelWeights`` is False. 

839 refImage : `lsst.afw.image.Image` 

840 A reference image used to supply the default pixel values. 

841 dcrWeights : `list` of `lsst.afw.image.Image` 

842 Per-pixel weights for each subfilter. 

843 Equal to 1/(number of unmasked images contributing to each pixel). 

844 """ 

845 residualGeneratorList = [] 

846 

847 for warpExpRef in warpRefList: 

848 visit = warpExpRef.dataId["visit"] 

849 exposure = subExposures[visit] 

850 visitInfo = exposure.getInfo().getVisitInfo() 

851 wcs = exposure.getInfo().getWcs() 

852 templateImage = dcrModels.buildMatchedTemplate(exposure=exposure, 

853 order=self.config.imageInterpOrder, 

854 splitSubfilters=self.config.splitSubfilters, 

855 splitThreshold=self.config.splitThreshold, 

856 amplifyModel=self.config.accelerateModel) 

857 residual = exposure.image.array - templateImage.array 

858 # Note that the variance plane here is used to store weights, not the actual variance 

859 residual *= exposure.variance.array 

860 # The residuals are stored as a list of generators. 

861 # This allows the residual for a given subfilter and exposure to be created 

862 # on the fly, instead of needing to store them all in memory. 

863 residualGeneratorList.append(self.dcrResiduals(residual, visitInfo, wcs, 

864 dcrModels.effectiveWavelength, 

865 dcrModels.bandwidth)) 

866 

867 dcrSubModelOut = self.newModelFromResidual(dcrModels, residualGeneratorList, dcrBBox, statsCtrl, 

868 gain=gain, 

869 modelWeights=modelWeights, 

870 refImage=refImage, 

871 dcrWeights=dcrWeights) 

872 dcrModels.assign(dcrSubModelOut, bbox) 

873 

874 def dcrResiduals(self, residual, visitInfo, wcs, effectiveWavelength, bandwidth): 

875 """Prepare a residual image for stacking in each subfilter by applying the reverse DCR shifts. 

876 

877 Parameters 

878 ---------- 

879 residual : `numpy.ndarray` 

880 The residual masked image for one exposure, 

881 after subtracting the matched template 

882 visitInfo : `lsst.afw.image.VisitInfo` 

883 Metadata for the exposure. 

884 wcs : `lsst.afw.geom.SkyWcs` 

885 Coordinate system definition (wcs) for the exposure. 

886 

887 Yields 

888 ------ 

889 residualImage : `numpy.ndarray` 

890 The residual image for the next subfilter, shifted for DCR. 

891 """ 

892 # Pre-calculate the spline-filtered residual image, so that step can be 

893 # skipped in the shift calculation in `applyDcr`. 

894 filteredResidual = ndimage.spline_filter(residual, order=self.config.imageInterpOrder) 

895 # Note that `splitSubfilters` is always turned off in the reverse direction. 

896 # This option introduces additional blurring if applied to the residuals. 

897 dcrShift = calculateDcr(visitInfo, wcs, effectiveWavelength, bandwidth, self.config.dcrNumSubfilters, 

898 splitSubfilters=False) 

899 for dcr in dcrShift: 

900 yield applyDcr(filteredResidual, dcr, useInverse=True, splitSubfilters=False, 

901 doPrefilter=False, order=self.config.imageInterpOrder) 

902 

903 def newModelFromResidual(self, dcrModels, residualGeneratorList, dcrBBox, statsCtrl, 

904 gain, modelWeights, refImage, dcrWeights): 

905 """Calculate a new DcrModel from a set of image residuals. 

906 

907 Parameters 

908 ---------- 

909 dcrModels : `lsst.pipe.tasks.DcrModel` 

910 Current model of the true sky after correcting chromatic effects. 

911 residualGeneratorList : `generator` of `numpy.ndarray` 

912 The residual image for the next subfilter, shifted for DCR. 

913 dcrBBox : `lsst.geom.box.Box2I` 

914 Sub-region of the coadd which includes a buffer to allow for DCR. 

915 statsCtrl : `lsst.afw.math.StatisticsControl` 

916 Statistics control object for coadd 

917 gain : `float` 

918 Relative weight to give the new solution when updating the model. 

919 modelWeights : `numpy.ndarray` or `float` 

920 A 2D array of weight values that tapers smoothly to zero away from detected sources. 

921 Set to a placeholder value of 1.0 if ``self.config.useModelWeights`` is False. 

922 refImage : `lsst.afw.image.Image` 

923 A reference image used to supply the default pixel values. 

924 dcrWeights : `list` of `lsst.afw.image.Image` 

925 Per-pixel weights for each subfilter. 

926 Equal to 1/(number of unmasked images contributing to each pixel). 

927 

928 Returns 

929 ------- 

930 dcrModel : `lsst.pipe.tasks.DcrModel` 

931 New model of the true sky after correcting chromatic effects. 

932 """ 

933 newModelImages = [] 

934 for subfilter, model in enumerate(dcrModels): 

935 residualsList = [next(residualGenerator) for residualGenerator in residualGeneratorList] 

936 residual = np.sum(residualsList, axis=0) 

937 residual *= dcrWeights[subfilter][dcrBBox].array 

938 # `MaskedImage`s only support in-place addition, so rename for readability 

939 newModel = model[dcrBBox].clone() 

940 newModel.array += residual 

941 # Catch any invalid values 

942 badPixels = ~np.isfinite(newModel.array) 

943 newModel.array[badPixels] = model[dcrBBox].array[badPixels] 

944 if self.config.regularizeModelIterations > 0: 

945 dcrModels.regularizeModelIter(subfilter, newModel, dcrBBox, 

946 self.config.regularizeModelIterations, 

947 self.config.regularizationWidth) 

948 newModelImages.append(newModel) 

949 if self.config.regularizeModelFrequency > 0: 

950 dcrModels.regularizeModelFreq(newModelImages, dcrBBox, statsCtrl, 

951 self.config.regularizeModelFrequency, 

952 self.config.regularizationWidth) 

953 dcrModels.conditionDcrModel(newModelImages, dcrBBox, gain=gain) 

954 self.applyModelWeights(newModelImages, refImage[dcrBBox], modelWeights) 

955 return DcrModel(newModelImages, dcrModels.filter, dcrModels.effectiveWavelength, 

956 dcrModels.bandwidth, dcrModels.psf, 

957 dcrModels.mask, dcrModels.variance) 

958 

959 def calculateConvergence(self, dcrModels, subExposures, bbox, warpRefList, weightList, statsCtrl): 

960 """Calculate a quality of fit metric for the matched templates. 

961 

962 Parameters 

963 ---------- 

964 dcrModels : `lsst.pipe.tasks.DcrModel` 

965 Best fit model of the true sky after correcting chromatic effects. 

966 subExposures : `dict` of `lsst.afw.image.ExposureF` 

967 The pre-loaded exposures for the current subregion. 

968 bbox : `lsst.geom.box.Box2I` 

969 Sub-region to coadd 

970 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 

971 `lsst.daf.persistence.ButlerDataRef` 

972 The data references to the input warped exposures. 

973 weightList : `list` of `float` 

974 The weight to give each input exposure in the coadd 

975 statsCtrl : `lsst.afw.math.StatisticsControl` 

976 Statistics control object for coadd 

977 

978 Returns 

979 ------- 

980 convergenceMetric : `float` 

981 Quality of fit metric for all input exposures, within the sub-region 

982 """ 

983 significanceImage = np.abs(dcrModels.getReferenceImage(bbox)) 

984 nSigma = 3. 

985 significanceImage += nSigma*dcrModels.calculateNoiseCutoff(dcrModels[1], statsCtrl, 

986 bufferSize=self.bufferSize) 

987 if np.max(significanceImage) == 0: 

988 significanceImage += 1. 

989 weight = 0 

990 metric = 0. 

991 metricList = {} 

992 for warpExpRef, expWeight in zip(warpRefList, weightList): 

993 visit = warpExpRef.dataId["visit"] 

994 exposure = subExposures[visit][bbox] 

995 singleMetric = self.calculateSingleConvergence(dcrModels, exposure, significanceImage, statsCtrl) 

996 metric += singleMetric 

997 metricList[visit] = singleMetric 

998 weight += 1. 

999 self.log.info("Individual metrics:\n%s", metricList) 

1000 return 1.0 if weight == 0.0 else metric/weight 

1001 

1002 def calculateSingleConvergence(self, dcrModels, exposure, significanceImage, statsCtrl): 

1003 """Calculate a quality of fit metric for a single matched template. 

1004 

1005 Parameters 

1006 ---------- 

1007 dcrModels : `lsst.pipe.tasks.DcrModel` 

1008 Best fit model of the true sky after correcting chromatic effects. 

1009 exposure : `lsst.afw.image.ExposureF` 

1010 The input warped exposure to evaluate. 

1011 significanceImage : `numpy.ndarray` 

1012 Array of weights for each pixel corresponding to its significance 

1013 for the convergence calculation. 

1014 statsCtrl : `lsst.afw.math.StatisticsControl` 

1015 Statistics control object for coadd 

1016 

1017 Returns 

1018 ------- 

1019 convergenceMetric : `float` 

1020 Quality of fit metric for one exposure, within the sub-region. 

1021 """ 

1022 convergeMask = exposure.mask.getPlaneBitMask(self.config.convergenceMaskPlanes) 

1023 templateImage = dcrModels.buildMatchedTemplate(exposure=exposure, 

1024 order=self.config.imageInterpOrder, 

1025 splitSubfilters=self.config.splitSubfilters, 

1026 splitThreshold=self.config.splitThreshold, 

1027 amplifyModel=self.config.accelerateModel) 

1028 diffVals = np.abs(exposure.image.array - templateImage.array)*significanceImage 

1029 refVals = np.abs(exposure.image.array + templateImage.array)*significanceImage/2. 

1030 

1031 finitePixels = np.isfinite(diffVals) 

1032 goodMaskPixels = (exposure.mask.array & statsCtrl.getAndMask()) == 0 

1033 convergeMaskPixels = exposure.mask.array & convergeMask > 0 

1034 usePixels = finitePixels & goodMaskPixels & convergeMaskPixels 

1035 if np.sum(usePixels) == 0: 

1036 metric = 0. 

1037 else: 

1038 diffUse = diffVals[usePixels] 

1039 refUse = refVals[usePixels] 

1040 metric = np.sum(diffUse/np.median(diffUse))/np.sum(refUse/np.median(diffUse)) 

1041 return metric 

1042 

1043 def stackCoadd(self, dcrCoadds): 

1044 """Add a list of sub-band coadds together. 

1045 

1046 Parameters 

1047 ---------- 

1048 dcrCoadds : `list` of `lsst.afw.image.ExposureF` 

1049 A list of coadd exposures, each exposure containing 

1050 the model for one subfilter. 

1051 

1052 Returns 

1053 ------- 

1054 coaddExposure : `lsst.afw.image.ExposureF` 

1055 A single coadd exposure that is the sum of the sub-bands. 

1056 """ 

1057 coaddExposure = dcrCoadds[0].clone() 

1058 for coadd in dcrCoadds[1:]: 

1059 coaddExposure.maskedImage += coadd.maskedImage 

1060 return coaddExposure 

1061 

1062 def fillCoadd(self, dcrModels, skyInfo, warpRefList, weightList, calibration=None, coaddInputs=None, 

1063 mask=None, variance=None): 

1064 """Create a list of coadd exposures from a list of masked images. 

1065 

1066 Parameters 

1067 ---------- 

1068 dcrModels : `lsst.pipe.tasks.DcrModel` 

1069 Best fit model of the true sky after correcting chromatic effects. 

1070 skyInfo : `lsst.pipe.base.Struct` 

1071 Patch geometry information, from getSkyInfo 

1072 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 

1073 `lsst.daf.persistence.ButlerDataRef` 

1074 The data references to the input warped exposures. 

1075 weightList : `list` of `float` 

1076 The weight to give each input exposure in the coadd 

1077 calibration : `lsst.afw.Image.PhotoCalib`, optional 

1078 Scale factor to set the photometric calibration of an exposure. 

1079 coaddInputs : `lsst.afw.Image.CoaddInputs`, optional 

1080 A record of the observations that are included in the coadd. 

1081 mask : `lsst.afw.image.Mask`, optional 

1082 Optional mask to override the values in the final coadd. 

1083 variance : `lsst.afw.image.Image`, optional 

1084 Optional variance plane to override the values in the final coadd. 

1085 

1086 Returns 

1087 ------- 

1088 dcrCoadds : `list` of `lsst.afw.image.ExposureF` 

1089 A list of coadd exposures, each exposure containing 

1090 the model for one subfilter. 

1091 """ 

1092 dcrCoadds = [] 

1093 refModel = dcrModels.getReferenceImage() 

1094 for model in dcrModels: 

1095 if self.config.accelerateModel > 1: 

1096 model.array = (model.array - refModel)*self.config.accelerateModel + refModel 

1097 coaddExposure = afwImage.ExposureF(skyInfo.bbox, skyInfo.wcs) 

1098 if calibration is not None: 

1099 coaddExposure.setPhotoCalib(calibration) 

1100 if coaddInputs is not None: 

1101 coaddExposure.getInfo().setCoaddInputs(coaddInputs) 

1102 # Set the metadata for the coadd, including PSF and aperture corrections. 

1103 self.assembleMetadata(coaddExposure, warpRefList, weightList) 

1104 # Overwrite the PSF 

1105 coaddExposure.setPsf(dcrModels.psf) 

1106 coaddUtils.setCoaddEdgeBits(dcrModels.mask[skyInfo.bbox], dcrModels.variance[skyInfo.bbox]) 

1107 maskedImage = afwImage.MaskedImageF(dcrModels.bbox) 

1108 maskedImage.image = model 

1109 maskedImage.mask = dcrModels.mask 

1110 maskedImage.variance = dcrModels.variance 

1111 coaddExposure.setMaskedImage(maskedImage[skyInfo.bbox]) 

1112 coaddExposure.setPhotoCalib(self.scaleZeroPoint.getPhotoCalib()) 

1113 if mask is not None: 

1114 coaddExposure.setMask(mask) 

1115 if variance is not None: 

1116 coaddExposure.setVariance(variance) 

1117 dcrCoadds.append(coaddExposure) 

1118 return dcrCoadds 

1119 

1120 def calculateGain(self, convergenceList, gainList): 

1121 """Calculate the gain to use for the current iteration. 

1122 

1123 After calculating a new DcrModel, each value is averaged with the 

1124 value in the corresponding pixel from the previous iteration. This 

1125 reduces oscillating solutions that iterative techniques are plagued by, 

1126 and speeds convergence. By far the biggest changes to the model 

1127 happen in the first couple iterations, so we can also use a more 

1128 aggressive gain later when the model is changing slowly. 

1129 

1130 Parameters 

1131 ---------- 

1132 convergenceList : `list` of `float` 

1133 The quality of fit metric from each previous iteration. 

1134 gainList : `list` of `float` 

1135 The gains used in each previous iteration: appended with the new 

1136 gain value. 

1137 Gains are numbers between ``self.config.baseGain`` and 1. 

1138 

1139 Returns 

1140 ------- 

1141 gain : `float` 

1142 Relative weight to give the new solution when updating the model. 

1143 A value of 1.0 gives equal weight to both solutions. 

1144 

1145 Raises 

1146 ------ 

1147 ValueError 

1148 If ``len(convergenceList) != len(gainList)+1``. 

1149 """ 

1150 nIter = len(convergenceList) 

1151 if nIter != len(gainList) + 1: 

1152 raise ValueError("convergenceList (%d) must be one element longer than gainList (%d)." 

1153 % (len(convergenceList), len(gainList))) 

1154 

1155 if self.config.baseGain is None: 

1156 # If ``baseGain`` is not set, calculate it from the number of DCR subfilters 

1157 # The more subfilters being modeled, the lower the gain should be. 

1158 baseGain = 1./(self.config.dcrNumSubfilters - 1) 

1159 else: 

1160 baseGain = self.config.baseGain 

1161 

1162 if self.config.useProgressiveGain and nIter > 2: 

1163 # To calculate the best gain to use, compare the past gains that have been used 

1164 # with the resulting convergences to estimate the best gain to use. 

1165 # Algorithmically, this is a Kalman filter. 

1166 # If forward modeling proceeds perfectly, the convergence metric should 

1167 # asymptotically approach a final value. 

1168 # We can estimate that value from the measured changes in convergence 

1169 # weighted by the gains used in each previous iteration. 

1170 estFinalConv = [((1 + gainList[i])*convergenceList[i + 1] - convergenceList[i])/gainList[i] 

1171 for i in range(nIter - 1)] 

1172 # The convergence metric is strictly positive, so if the estimated final convergence is 

1173 # less than zero, force it to zero. 

1174 estFinalConv = np.array(estFinalConv) 

1175 estFinalConv[estFinalConv < 0] = 0 

1176 # Because the estimate may slowly change over time, only use the most recent measurements. 

1177 estFinalConv = np.median(estFinalConv[max(nIter - 5, 0):]) 

1178 lastGain = gainList[-1] 

1179 lastConv = convergenceList[-2] 

1180 newConv = convergenceList[-1] 

1181 # The predicted convergence is the value we would get if the new model calculated 

1182 # in the previous iteration was perfect. Recall that the updated model that is 

1183 # actually used is the gain-weighted average of the new and old model, 

1184 # so the convergence would be similarly weighted. 

1185 predictedConv = (estFinalConv*lastGain + lastConv)/(1. + lastGain) 

1186 # If the measured and predicted convergence are very close, that indicates 

1187 # that our forward model is accurate and we can use a more aggressive gain 

1188 # If the measured convergence is significantly worse (or better!) than predicted, 

1189 # that indicates that the model is not converging as expected and 

1190 # we should use a more conservative gain. 

1191 delta = (predictedConv - newConv)/((lastConv - estFinalConv)/(1 + lastGain)) 

1192 newGain = 1 - abs(delta) 

1193 # Average the gains to prevent oscillating solutions. 

1194 newGain = (newGain + lastGain)/2. 

1195 gain = max(baseGain, newGain) 

1196 else: 

1197 gain = baseGain 

1198 gainList.append(gain) 

1199 return gain 

1200 

1201 def calculateModelWeights(self, dcrModels, dcrBBox): 

1202 """Build an array that smoothly tapers to 0 away from detected sources. 

1203 

1204 Parameters 

1205 ---------- 

1206 dcrModels : `lsst.pipe.tasks.DcrModel` 

1207 Best fit model of the true sky after correcting chromatic effects. 

1208 dcrBBox : `lsst.geom.box.Box2I` 

1209 Sub-region of the coadd which includes a buffer to allow for DCR. 

1210 

1211 Returns 

1212 ------- 

1213 weights : `numpy.ndarray` or `float` 

1214 A 2D array of weight values that tapers smoothly to zero away from detected sources. 

1215 Set to a placeholder value of 1.0 if ``self.config.useModelWeights`` is False. 

1216 

1217 Raises 

1218 ------ 

1219 ValueError 

1220 If ``useModelWeights`` is set and ``modelWeightsWidth`` is negative. 

1221 """ 

1222 if not self.config.useModelWeights: 

1223 return 1.0 

1224 if self.config.modelWeightsWidth < 0: 

1225 raise ValueError("modelWeightsWidth must not be negative if useModelWeights is set") 

1226 convergeMask = dcrModels.mask.getPlaneBitMask(self.config.convergenceMaskPlanes) 

1227 convergeMaskPixels = dcrModels.mask[dcrBBox].array & convergeMask > 0 

1228 weights = np.zeros_like(dcrModels[0][dcrBBox].array) 

1229 weights[convergeMaskPixels] = 1. 

1230 weights = ndimage.filters.gaussian_filter(weights, self.config.modelWeightsWidth) 

1231 weights /= np.max(weights) 

1232 return weights 

1233 

1234 def applyModelWeights(self, modelImages, refImage, modelWeights): 

1235 """Smoothly replace model pixel values with those from a 

1236 reference at locations away from detected sources. 

1237 

1238 Parameters 

1239 ---------- 

1240 modelImages : `list` of `lsst.afw.image.Image` 

1241 The new DCR model images from the current iteration. 

1242 The values will be modified in place. 

1243 refImage : `lsst.afw.image.MaskedImage` 

1244 A reference image used to supply the default pixel values. 

1245 modelWeights : `numpy.ndarray` or `float` 

1246 A 2D array of weight values that tapers smoothly to zero away from detected sources. 

1247 Set to a placeholder value of 1.0 if ``self.config.useModelWeights`` is False. 

1248 """ 

1249 if self.config.useModelWeights: 

1250 for model in modelImages: 

1251 model.array *= modelWeights 

1252 model.array += refImage.array*(1. - modelWeights)/self.config.dcrNumSubfilters 

1253 

1254 def loadSubExposures(self, bbox, statsCtrl, warpRefList, imageScalerList, spanSetMaskList): 

1255 """Pre-load sub-regions of a list of exposures. 

1256 

1257 Parameters 

1258 ---------- 

1259 bbox : `lsst.geom.box.Box2I` 

1260 Sub-region to coadd 

1261 statsCtrl : `lsst.afw.math.StatisticsControl` 

1262 Statistics control object for coadd 

1263 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 

1264 `lsst.daf.persistence.ButlerDataRef` 

1265 The data references to the input warped exposures. 

1266 imageScalerList : `list` of `lsst.pipe.task.ImageScaler` 

1267 The image scalars correct for the zero point of the exposures. 

1268 spanSetMaskList : `list` of `dict` containing spanSet lists, or None 

1269 Each element is dict with keys = mask plane name to add the spans to 

1270 

1271 Returns 

1272 ------- 

1273 subExposures : `dict` 

1274 The `dict` keys are the visit IDs, 

1275 and the values are `lsst.afw.image.ExposureF` 

1276 The pre-loaded exposures for the current subregion. 

1277 The variance plane contains weights, and not the variance 

1278 """ 

1279 tempExpName = self.getTempExpDatasetName(self.warpType) 

1280 zipIterables = zip(warpRefList, imageScalerList, spanSetMaskList) 

1281 subExposures = {} 

1282 for warpExpRef, imageScaler, altMaskSpans in zipIterables: 

1283 if isinstance(warpExpRef, DeferredDatasetHandle): 

1284 exposure = warpExpRef.get(parameters={'bbox': bbox}) 

1285 else: 

1286 exposure = warpExpRef.get(tempExpName + "_sub", bbox=bbox) 

1287 visit = warpExpRef.dataId["visit"] 

1288 if altMaskSpans is not None: 

1289 self.applyAltMaskPlanes(exposure.mask, altMaskSpans) 

1290 imageScaler.scaleMaskedImage(exposure.maskedImage) 

1291 # Note that the variance plane here is used to store weights, not the actual variance 

1292 exposure.variance.array[:, :] = 0. 

1293 # Set the weight of unmasked pixels to 1. 

1294 exposure.variance.array[(exposure.mask.array & statsCtrl.getAndMask()) == 0] = 1. 

1295 # Set the image value of masked pixels to zero. 

1296 # This eliminates needing the mask plane when stacking images in ``newModelFromResidual`` 

1297 exposure.image.array[(exposure.mask.array & statsCtrl.getAndMask()) > 0] = 0. 

1298 subExposures[visit] = exposure 

1299 return subExposures 

1300 

1301 def selectCoaddPsf(self, templateCoadd, warpRefList): 

1302 """Compute the PSF of the coadd from the exposures with the best seeing. 

1303 

1304 Parameters 

1305 ---------- 

1306 templateCoadd : `lsst.afw.image.ExposureF` 

1307 The initial coadd exposure before accounting for DCR. 

1308 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 

1309 `lsst.daf.persistence.ButlerDataRef` 

1310 The data references to the input warped exposures. 

1311 

1312 Returns 

1313 ------- 

1314 psf : `lsst.meas.algorithms.CoaddPsf` 

1315 The average PSF of the input exposures with the best seeing. 

1316 """ 

1317 sigma2fwhm = 2.*np.sqrt(2.*np.log(2.)) 

1318 tempExpName = self.getTempExpDatasetName(self.warpType) 

1319 # Note: ``ccds`` is a `lsst.afw.table.ExposureCatalog` with one entry per ccd and per visit 

1320 # If there are multiple ccds, it will have that many times more elements than ``warpExpRef`` 

1321 ccds = templateCoadd.getInfo().getCoaddInputs().ccds 

1322 psfRefSize = templateCoadd.getPsf().computeShape().getDeterminantRadius()*sigma2fwhm 

1323 psfSizes = np.zeros(len(ccds)) 

1324 ccdVisits = np.array(ccds["visit"]) 

1325 for warpExpRef in warpRefList: 

1326 if isinstance(warpExpRef, DeferredDatasetHandle): 

1327 # Gen 3 API 

1328 psf = warpExpRef.get(component="psf") 

1329 else: 

1330 # Gen 2 API. Delete this when Gen 2 retired 

1331 psf = warpExpRef.get(tempExpName).getPsf() 

1332 visit = warpExpRef.dataId["visit"] 

1333 psfSize = psf.computeShape().getDeterminantRadius()*sigma2fwhm 

1334 psfSizes[ccdVisits == visit] = psfSize 

1335 # Note that the input PSFs include DCR, which should be absent from the DcrCoadd 

1336 # The selected PSFs are those that have a FWHM less than or equal to the smaller 

1337 # of the mean or median FWHM of the input exposures. 

1338 sizeThreshold = min(np.median(psfSizes), psfRefSize) 

1339 goodPsfs = psfSizes <= sizeThreshold 

1340 psf = measAlg.CoaddPsf(ccds[goodPsfs], templateCoadd.getWcs(), 

1341 self.config.coaddPsf.makeControl()) 

1342 return psf