Coverage for python/lsst/pipe/tasks/assembleCoadd.py: 15%

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

891 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# 

22import os 

23import copy 

24import numpy 

25import warnings 

26import logging 

27import lsst.pex.config as pexConfig 

28import lsst.pex.exceptions as pexExceptions 

29import lsst.geom as geom 

30import lsst.afw.geom as afwGeom 

31import lsst.afw.image as afwImage 

32import lsst.afw.math as afwMath 

33import lsst.afw.table as afwTable 

34import lsst.afw.detection as afwDet 

35import lsst.coadd.utils as coaddUtils 

36import lsst.pipe.base as pipeBase 

37import lsst.meas.algorithms as measAlg 

38import lsstDebug 

39import lsst.utils as utils 

40from lsst.skymap import BaseSkyMap 

41from .coaddBase import CoaddBaseTask, SelectDataIdContainer, makeSkyInfo, makeCoaddSuffix, reorderAndPadList 

42from .interpImage import InterpImageTask 

43from .scaleZeroPoint import ScaleZeroPointTask 

44from .coaddHelpers import groupPatchExposures, getGroupDataRef 

45from .scaleVariance import ScaleVarianceTask 

46from .maskStreaks import MaskStreaksTask 

47from .healSparseMapping import HealSparseInputMapTask 

48from lsst.meas.algorithms import SourceDetectionTask, AccumulatorMeanStack 

49from lsst.daf.butler import DeferredDatasetHandle 

50from lsst.utils.timer import timeMethod 

51 

52__all__ = ["AssembleCoaddTask", "AssembleCoaddConnections", "AssembleCoaddConfig", 

53 "SafeClipAssembleCoaddTask", "SafeClipAssembleCoaddConfig", 

54 "CompareWarpAssembleCoaddTask", "CompareWarpAssembleCoaddConfig"] 

55 

56log = logging.getLogger(__name__) 

57 

58 

59class AssembleCoaddConnections(pipeBase.PipelineTaskConnections, 

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

61 defaultTemplates={"inputCoaddName": "deep", 

62 "outputCoaddName": "deep", 

63 "warpType": "direct", 

64 "warpTypeSuffix": ""}): 

65 

66 inputWarps = pipeBase.connectionTypes.Input( 

67 doc=("Input list of warps to be assemebled i.e. stacked." 

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

69 name="{inputCoaddName}Coadd_{warpType}Warp", 

70 storageClass="ExposureF", 

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

72 deferLoad=True, 

73 multiple=True 

74 ) 

75 skyMap = pipeBase.connectionTypes.Input( 

76 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures", 

77 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME, 

78 storageClass="SkyMap", 

79 dimensions=("skymap", ), 

80 ) 

81 selectedVisits = pipeBase.connectionTypes.Input( 

82 doc="Selected visits to be coadded.", 

83 name="{outputCoaddName}Visits", 

84 storageClass="StructuredDataDict", 

85 dimensions=("instrument", "tract", "patch", "skymap", "band") 

86 ) 

87 brightObjectMask = pipeBase.connectionTypes.PrerequisiteInput( 

88 doc=("Input Bright Object Mask mask produced with external catalogs to be applied to the mask plane" 

89 " BRIGHT_OBJECT."), 

90 name="brightObjectMask", 

91 storageClass="ObjectMaskCatalog", 

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

93 ) 

94 coaddExposure = pipeBase.connectionTypes.Output( 

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

96 name="{outputCoaddName}Coadd{warpTypeSuffix}", 

97 storageClass="ExposureF", 

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

99 ) 

100 nImage = pipeBase.connectionTypes.Output( 

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

102 name="{outputCoaddName}Coadd_nImage", 

103 storageClass="ImageU", 

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

105 ) 

106 inputMap = pipeBase.connectionTypes.Output( 

107 doc="Output healsparse map of input images", 

108 name="{outputCoaddName}Coadd_inputMap", 

109 storageClass="HealSparseMap", 

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

111 ) 

112 

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

114 super().__init__(config=config) 

115 

116 # Override the connection's name template with config to replicate Gen2 behavior 

117 # This duplicates some of the logic in the base class, due to wanting Gen2 and 

118 # Gen3 configs to stay in sync. This should be removed when gen2 is deprecated 

119 templateValues = {name: getattr(config.connections, name) for name in self.defaultTemplates} 

120 templateValues['warpType'] = config.warpType 

121 templateValues['warpTypeSuffix'] = makeCoaddSuffix(config.warpType) 

122 self._nameOverrides = {name: getattr(config.connections, name).format(**templateValues) 

123 for name in self.allConnections} 

124 self._typeNameToVarName = {v: k for k, v in self._nameOverrides.items()} 

125 # End code to remove after deprecation 

126 

127 if not config.doMaskBrightObjects: 

128 self.prerequisiteInputs.remove("brightObjectMask") 

129 

130 if not config.doSelectVisits: 

131 self.inputs.remove("selectedVisits") 

132 

133 if not config.doNImage: 

134 self.outputs.remove("nImage") 

135 

136 if not self.config.doInputMap: 

137 self.outputs.remove("inputMap") 

138 

139 

140class AssembleCoaddConfig(CoaddBaseTask.ConfigClass, pipeBase.PipelineTaskConfig, 

141 pipelineConnections=AssembleCoaddConnections): 

142 """Configuration parameters for the `AssembleCoaddTask`. 

143 

144 Notes 

145 ----- 

146 The `doMaskBrightObjects` and `brightObjectMaskName` configuration options 

147 only set the bitplane config.brightObjectMaskName. To make this useful you 

148 *must* also configure the flags.pixel algorithm, for example by adding 

149 

150 .. code-block:: none 

151 

152 config.measurement.plugins["base_PixelFlags"].masksFpCenter.append("BRIGHT_OBJECT") 

153 config.measurement.plugins["base_PixelFlags"].masksFpAnywhere.append("BRIGHT_OBJECT") 

154 

155 to your measureCoaddSources.py and forcedPhotCoadd.py config overrides. 

156 """ 

157 warpType = pexConfig.Field( 

158 doc="Warp name: one of 'direct' or 'psfMatched'", 

159 dtype=str, 

160 default="direct", 

161 ) 

162 subregionSize = pexConfig.ListField( 

163 dtype=int, 

164 doc="Width, height of stack subregion size; " 

165 "make small enough that a full stack of images will fit into memory at once.", 

166 length=2, 

167 default=(2000, 2000), 

168 ) 

169 statistic = pexConfig.Field( 

170 dtype=str, 

171 doc="Main stacking statistic for aggregating over the epochs.", 

172 default="MEANCLIP", 

173 ) 

174 doOnlineForMean = pexConfig.Field( 

175 dtype=bool, 

176 doc="Perform online coaddition when statistic=\"MEAN\" to save memory?", 

177 default=False, 

178 ) 

179 doSigmaClip = pexConfig.Field( 

180 dtype=bool, 

181 doc="Perform sigma clipped outlier rejection with MEANCLIP statistic? (DEPRECATED)", 

182 default=False, 

183 ) 

184 sigmaClip = pexConfig.Field( 

185 dtype=float, 

186 doc="Sigma for outlier rejection; ignored if non-clipping statistic selected.", 

187 default=3.0, 

188 ) 

189 clipIter = pexConfig.Field( 

190 dtype=int, 

191 doc="Number of iterations of outlier rejection; ignored if non-clipping statistic selected.", 

192 default=2, 

193 ) 

194 calcErrorFromInputVariance = pexConfig.Field( 

195 dtype=bool, 

196 doc="Calculate coadd variance from input variance by stacking statistic." 

197 "Passed to StatisticsControl.setCalcErrorFromInputVariance()", 

198 default=True, 

199 ) 

200 scaleZeroPoint = pexConfig.ConfigurableField( 

201 target=ScaleZeroPointTask, 

202 doc="Task to adjust the photometric zero point of the coadd temp exposures", 

203 ) 

204 doInterp = pexConfig.Field( 

205 doc="Interpolate over NaN pixels? Also extrapolate, if necessary, but the results are ugly.", 

206 dtype=bool, 

207 default=True, 

208 ) 

209 interpImage = pexConfig.ConfigurableField( 

210 target=InterpImageTask, 

211 doc="Task to interpolate (and extrapolate) over NaN pixels", 

212 ) 

213 doWrite = pexConfig.Field( 

214 doc="Persist coadd?", 

215 dtype=bool, 

216 default=True, 

217 ) 

218 doNImage = pexConfig.Field( 

219 doc="Create image of number of contributing exposures for each pixel", 

220 dtype=bool, 

221 default=False, 

222 ) 

223 doUsePsfMatchedPolygons = pexConfig.Field( 

224 doc="Use ValidPolygons from shrunk Psf-Matched Calexps? Should be set to True by CompareWarp only.", 

225 dtype=bool, 

226 default=False, 

227 ) 

228 maskPropagationThresholds = pexConfig.DictField( 

229 keytype=str, 

230 itemtype=float, 

231 doc=("Threshold (in fractional weight) of rejection at which we propagate a mask plane to " 

232 "the coadd; that is, we set the mask bit on the coadd if the fraction the rejected frames " 

233 "would have contributed exceeds this value."), 

234 default={"SAT": 0.1}, 

235 ) 

236 removeMaskPlanes = pexConfig.ListField(dtype=str, default=["NOT_DEBLENDED"], 

237 doc="Mask planes to remove before coadding") 

238 doMaskBrightObjects = pexConfig.Field(dtype=bool, default=False, 

239 doc="Set mask and flag bits for bright objects?") 

240 brightObjectMaskName = pexConfig.Field(dtype=str, default="BRIGHT_OBJECT", 

241 doc="Name of mask bit used for bright objects") 

242 coaddPsf = pexConfig.ConfigField( 

243 doc="Configuration for CoaddPsf", 

244 dtype=measAlg.CoaddPsfConfig, 

245 ) 

246 doAttachTransmissionCurve = pexConfig.Field( 

247 dtype=bool, default=False, optional=False, 

248 doc=("Attach a piecewise TransmissionCurve for the coadd? " 

249 "(requires all input Exposures to have TransmissionCurves).") 

250 ) 

251 hasFakes = pexConfig.Field( 

252 dtype=bool, 

253 default=False, 

254 doc="Should be set to True if fake sources have been inserted into the input data." 

255 ) 

256 doSelectVisits = pexConfig.Field( 

257 doc="Coadd only visits selected by a SelectVisitsTask", 

258 dtype=bool, 

259 default=False, 

260 ) 

261 doInputMap = pexConfig.Field( 

262 doc="Create a bitwise map of coadd inputs", 

263 dtype=bool, 

264 default=False, 

265 ) 

266 inputMapper = pexConfig.ConfigurableField( 

267 doc="Input map creation subtask.", 

268 target=HealSparseInputMapTask, 

269 ) 

270 

271 def setDefaults(self): 

272 super().setDefaults() 

273 self.badMaskPlanes = ["NO_DATA", "BAD", "SAT", "EDGE"] 

274 

275 def validate(self): 

276 super().validate() 

277 if self.doPsfMatch: 

278 # Backwards compatibility. 

279 # Configs do not have loggers 

280 log.warning("Config doPsfMatch deprecated. Setting warpType='psfMatched'") 

281 self.warpType = 'psfMatched' 

282 if self.doSigmaClip and self.statistic != "MEANCLIP": 

283 log.warning('doSigmaClip deprecated. To replicate behavior, setting statistic to "MEANCLIP"') 

284 self.statistic = "MEANCLIP" 

285 if self.doInterp and self.statistic not in ['MEAN', 'MEDIAN', 'MEANCLIP', 'VARIANCE', 'VARIANCECLIP']: 

286 raise ValueError("Must set doInterp=False for statistic=%s, which does not " 

287 "compute and set a non-zero coadd variance estimate." % (self.statistic)) 

288 

289 unstackableStats = ['NOTHING', 'ERROR', 'ORMASK'] 

290 if not hasattr(afwMath.Property, self.statistic) or self.statistic in unstackableStats: 

291 stackableStats = [str(k) for k in afwMath.Property.__members__.keys() 

292 if str(k) not in unstackableStats] 

293 raise ValueError("statistic %s is not allowed. Please choose one of %s." 

294 % (self.statistic, stackableStats)) 

295 

296 

297class AssembleCoaddTask(CoaddBaseTask, pipeBase.PipelineTask): 

298 """Assemble a coadded image from a set of warps (coadded temporary exposures). 

299 

300 We want to assemble a coadded image from a set of Warps (also called 

301 coadded temporary exposures or ``coaddTempExps``). 

302 Each input Warp covers a patch on the sky and corresponds to a single 

303 run/visit/exposure of the covered patch. We provide the task with a list 

304 of Warps (``selectDataList``) from which it selects Warps that cover the 

305 specified patch (pointed at by ``dataRef``). 

306 Each Warp that goes into a coadd will typically have an independent 

307 photometric zero-point. Therefore, we must scale each Warp to set it to 

308 a common photometric zeropoint. WarpType may be one of 'direct' or 

309 'psfMatched', and the boolean configs `config.makeDirect` and 

310 `config.makePsfMatched` set which of the warp types will be coadded. 

311 The coadd is computed as a mean with optional outlier rejection. 

312 Criteria for outlier rejection are set in `AssembleCoaddConfig`. 

313 Finally, Warps can have bad 'NaN' pixels which received no input from the 

314 source calExps. We interpolate over these bad (NaN) pixels. 

315 

316 `AssembleCoaddTask` uses several sub-tasks. These are 

317 

318 - `ScaleZeroPointTask` 

319 - create and use an ``imageScaler`` object to scale the photometric zeropoint for each Warp 

320 - `InterpImageTask` 

321 - interpolate across bad pixels (NaN) in the final coadd 

322 

323 You can retarget these subtasks if you wish. 

324 

325 Notes 

326 ----- 

327 The `lsst.pipe.base.cmdLineTask.CmdLineTask` interface supports a 

328 flag ``-d`` to import ``debug.py`` from your ``PYTHONPATH``; see 

329 `baseDebug` for more about ``debug.py`` files. `AssembleCoaddTask` has 

330 no debug variables of its own. Some of the subtasks may support debug 

331 variables. See the documentation for the subtasks for further information. 

332 

333 Examples 

334 -------- 

335 `AssembleCoaddTask` assembles a set of warped images into a coadded image. 

336 The `AssembleCoaddTask` can be invoked by running ``assembleCoadd.py`` 

337 with the flag '--legacyCoadd'. Usage of assembleCoadd.py expects two 

338 inputs: a data reference to the tract patch and filter to be coadded, and 

339 a list of Warps to attempt to coadd. These are specified using ``--id`` and 

340 ``--selectId``, respectively: 

341 

342 .. code-block:: none 

343 

344 --id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]] 

345 --selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]] 

346 

347 Only the Warps that cover the specified tract and patch will be coadded. 

348 A list of the available optional arguments can be obtained by calling 

349 ``assembleCoadd.py`` with the ``--help`` command line argument: 

350 

351 .. code-block:: none 

352 

353 assembleCoadd.py --help 

354 

355 To demonstrate usage of the `AssembleCoaddTask` in the larger context of 

356 multi-band processing, we will generate the HSC-I & -R band coadds from 

357 HSC engineering test data provided in the ``ci_hsc`` package. To begin, 

358 assuming that the lsst stack has been already set up, we must set up the 

359 obs_subaru and ``ci_hsc`` packages. This defines the environment variable 

360 ``$CI_HSC_DIR`` and points at the location of the package. The raw HSC 

361 data live in the ``$CI_HSC_DIR/raw directory``. To begin assembling the 

362 coadds, we must first 

363 

364 - processCcd 

365 - process the individual ccds in $CI_HSC_RAW to produce calibrated exposures 

366 - makeSkyMap 

367 - create a skymap that covers the area of the sky present in the raw exposures 

368 - makeCoaddTempExp 

369 - warp the individual calibrated exposures to the tangent plane of the coadd 

370 

371 We can perform all of these steps by running 

372 

373 .. code-block:: none 

374 

375 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988 

376 

377 This will produce warped exposures for each visit. To coadd the warped 

378 data, we call assembleCoadd.py as follows: 

379 

380 .. code-block:: none 

381 

382 assembleCoadd.py --legacyCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \ 

383 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \ 

384 --selectId visit=903986 ccd=100 --selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \ 

385 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \ 

386 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \ 

387 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \ 

388 --selectId visit=903988 ccd=24 

389 

390 that will process the HSC-I band data. The results are written in 

391 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``. 

392 

393 You may also choose to run: 

394 

395 .. code-block:: none 

396 

397 scons warp-903334 warp-903336 warp-903338 warp-903342 warp-903344 warp-903346 

398 assembleCoadd.py --legacyCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-R \ 

399 --selectId visit=903334 ccd=16 --selectId visit=903334 ccd=22 --selectId visit=903334 ccd=23 \ 

400 --selectId visit=903334 ccd=100 --selectId visit=903336 ccd=17 --selectId visit=903336 ccd=24 \ 

401 --selectId visit=903338 ccd=18 --selectId visit=903338 ccd=25 --selectId visit=903342 ccd=4 \ 

402 --selectId visit=903342 ccd=10 --selectId visit=903342 ccd=100 --selectId visit=903344 ccd=0 \ 

403 --selectId visit=903344 ccd=5 --selectId visit=903344 ccd=11 --selectId visit=903346 ccd=1 \ 

404 --selectId visit=903346 ccd=6 --selectId visit=903346 ccd=12 

405 

406 to generate the coadd for the HSC-R band if you are interested in 

407 following multiBand Coadd processing as discussed in `pipeTasks_multiBand` 

408 (but note that normally, one would use the `SafeClipAssembleCoaddTask` 

409 rather than `AssembleCoaddTask` to make the coadd. 

410 """ 

411 ConfigClass = AssembleCoaddConfig 

412 _DefaultName = "assembleCoadd" 

413 

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

415 # TODO: DM-17415 better way to handle previously allowed passed args e.g.`AssembleCoaddTask(config)` 

416 if args: 

417 argNames = ["config", "name", "parentTask", "log"] 

418 kwargs.update({k: v for k, v in zip(argNames, args)}) 

419 warnings.warn("AssembleCoadd received positional args, and casting them as kwargs: %s. " 

420 "PipelineTask will not take positional args" % argNames, FutureWarning) 

421 

422 super().__init__(**kwargs) 

423 self.makeSubtask("interpImage") 

424 self.makeSubtask("scaleZeroPoint") 

425 

426 if self.config.doMaskBrightObjects: 

427 mask = afwImage.Mask() 

428 try: 

429 self.brightObjectBitmask = 1 << mask.addMaskPlane(self.config.brightObjectMaskName) 

430 except pexExceptions.LsstCppException: 

431 raise RuntimeError("Unable to define mask plane for bright objects; planes used are %s" % 

432 mask.getMaskPlaneDict().keys()) 

433 del mask 

434 

435 if self.config.doInputMap: 

436 self.makeSubtask("inputMapper") 

437 

438 self.warpType = self.config.warpType 

439 

440 @utils.inheritDoc(pipeBase.PipelineTask) 

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

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

443 """ 

444 Notes 

445 ----- 

446 Assemble a coadd from a set of Warps. 

447 

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

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

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

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

452 Therefore, its inputs are accessed subregion by subregion 

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

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

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

456 are used. 

457 """ 

458 inputData = butlerQC.get(inputRefs) 

459 

460 # Construct skyInfo expected by run 

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

462 skyMap = inputData["skyMap"] 

463 outputDataId = butlerQC.quantum.dataId 

464 

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

466 tractId=outputDataId['tract'], 

467 patchId=outputDataId['patch']) 

468 

469 if self.config.doSelectVisits: 

470 warpRefList = self.filterWarps(inputData['inputWarps'], inputData['selectedVisits']) 

471 else: 

472 warpRefList = inputData['inputWarps'] 

473 

474 # Perform same middle steps as `runDataRef` does 

475 inputs = self.prepareInputs(warpRefList) 

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

477 self.getTempExpDatasetName(self.warpType)) 

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

479 raise pipeBase.NoWorkFound("No coadd temporary exposures found") 

480 

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

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

483 inputs.weightList, supplementaryData=supplementaryData) 

484 

485 inputData.setdefault('brightObjectMask', None) 

486 self.processResults(retStruct.coaddExposure, inputData['brightObjectMask'], outputDataId) 

487 

488 if self.config.doWrite: 

489 butlerQC.put(retStruct, outputRefs) 

490 return retStruct 

491 

492 @timeMethod 

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

494 """Assemble a coadd from a set of Warps. 

495 

496 Pipebase.CmdlineTask entry point to Coadd a set of Warps. 

497 Compute weights to be applied to each Warp and 

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

499 Assemble the Warps using `run`. Interpolate over NaNs and 

500 optionally write the coadd to disk. Return the coadded exposure. 

501 

502 Parameters 

503 ---------- 

504 dataRef : `lsst.daf.persistence.butlerSubset.ButlerDataRef` 

505 Data reference defining the patch for coaddition and the 

506 reference Warp (if ``config.autoReference=False``). 

507 Used to access the following data products: 

508 - ``self.config.coaddName + "Coadd_skyMap"`` 

509 - ``self.config.coaddName + "Coadd_ + <warpType> + "Warp"`` (optionally) 

510 - ``self.config.coaddName + "Coadd"`` 

511 selectDataList : `list` 

512 List of data references to Calexps. Data to be coadded will be 

513 selected from this list based on overlap with the patch defined 

514 by dataRef, grouped by visit, and converted to a list of data 

515 references to warps. 

516 warpRefList : `list` 

517 List of data references to Warps to be coadded. 

518 Note: `warpRefList` is just the new name for `tempExpRefList`. 

519 

520 Returns 

521 ------- 

522 retStruct : `lsst.pipe.base.Struct` 

523 Result struct with components: 

524 

525 - ``coaddExposure``: coadded exposure (``Exposure``). 

526 - ``nImage``: exposure count image (``Image``). 

527 """ 

528 if selectDataList and warpRefList: 

529 raise RuntimeError("runDataRef received both a selectDataList and warpRefList, " 

530 "and which to use is ambiguous. Please pass only one.") 

531 

532 skyInfo = self.getSkyInfo(dataRef) 

533 if warpRefList is None: 

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

535 if len(calExpRefList) == 0: 

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

537 return 

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

539 

540 warpRefList = self.getTempExpRefList(dataRef, calExpRefList) 

541 

542 inputData = self.prepareInputs(warpRefList) 

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

544 self.getTempExpDatasetName(self.warpType)) 

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

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

547 return 

548 

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

550 

551 retStruct = self.run(skyInfo, inputData.tempExpRefList, inputData.imageScalerList, 

552 inputData.weightList, supplementaryData=supplementaryData) 

553 

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

555 self.processResults(retStruct.coaddExposure, brightObjectMasks=brightObjects, dataId=dataRef.dataId) 

556 

557 if self.config.doWrite: 

558 if self.getCoaddDatasetName(self.warpType) == "deepCoadd" and self.config.hasFakes: 

559 coaddDatasetName = "fakes_" + self.getCoaddDatasetName(self.warpType) 

560 else: 

561 coaddDatasetName = self.getCoaddDatasetName(self.warpType) 

562 self.log.info("Persisting %s", coaddDatasetName) 

563 dataRef.put(retStruct.coaddExposure, coaddDatasetName) 

564 if self.config.doNImage and retStruct.nImage is not None: 

565 dataRef.put(retStruct.nImage, self.getCoaddDatasetName(self.warpType) + '_nImage') 

566 

567 return retStruct 

568 

569 def processResults(self, coaddExposure, brightObjectMasks=None, dataId=None): 

570 """Interpolate over missing data and mask bright stars. 

571 

572 Parameters 

573 ---------- 

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

575 The coadded exposure to process. 

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

577 Butler data reference for supplementary data. 

578 """ 

579 if self.config.doInterp: 

580 self.interpImage.run(coaddExposure.getMaskedImage(), planeName="NO_DATA") 

581 # The variance must be positive; work around for DM-3201. 

582 varArray = coaddExposure.variance.array 

583 with numpy.errstate(invalid="ignore"): 

584 varArray[:] = numpy.where(varArray > 0, varArray, numpy.inf) 

585 

586 if self.config.doMaskBrightObjects: 

587 self.setBrightObjectMasks(coaddExposure, brightObjectMasks, dataId) 

588 

589 def makeSupplementaryData(self, dataRef, selectDataList=None, warpRefList=None): 

590 """Make additional inputs to run() specific to subclasses (Gen2) 

591 

592 Duplicates interface of `runDataRef` method 

593 Available to be implemented by subclasses only if they need the 

594 coadd dataRef for performing preliminary processing before 

595 assembling the coadd. 

596 

597 Parameters 

598 ---------- 

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

600 Butler data reference for supplementary data. 

601 selectDataList : `list` (optional) 

602 Optional List of data references to Calexps. 

603 warpRefList : `list` (optional) 

604 Optional List of data references to Warps. 

605 """ 

606 return pipeBase.Struct() 

607 

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

609 """Make additional inputs to run() specific to subclasses (Gen3) 

610 

611 Duplicates interface of `runQuantum` method. 

612 Available to be implemented by subclasses only if they need the 

613 coadd dataRef for performing preliminary processing before 

614 assembling the coadd. 

615 

616 Parameters 

617 ---------- 

618 butlerQC : `lsst.pipe.base.ButlerQuantumContext` 

619 Gen3 Butler object for fetching additional data products before 

620 running the Task specialized for quantum being processed 

621 inputRefs : `lsst.pipe.base.InputQuantizedConnection` 

622 Attributes are the names of the connections describing input dataset types. 

623 Values are DatasetRefs that task consumes for corresponding dataset type. 

624 DataIds are guaranteed to match data objects in ``inputData``. 

625 outputRefs : `lsst.pipe.base.OutputQuantizedConnection` 

626 Attributes are the names of the connections describing output dataset types. 

627 Values are DatasetRefs that task is to produce 

628 for corresponding dataset type. 

629 """ 

630 return pipeBase.Struct() 

631 

632 def getTempExpRefList(self, patchRef, calExpRefList): 

633 """Generate list data references corresponding to warped exposures 

634 that lie within the patch to be coadded. 

635 

636 Parameters 

637 ---------- 

638 patchRef : `dataRef` 

639 Data reference for patch. 

640 calExpRefList : `list` 

641 List of data references for input calexps. 

642 

643 Returns 

644 ------- 

645 tempExpRefList : `list` 

646 List of Warp/CoaddTempExp data references. 

647 """ 

648 butler = patchRef.getButler() 

649 groupData = groupPatchExposures(patchRef, calExpRefList, self.getCoaddDatasetName(self.warpType), 

650 self.getTempExpDatasetName(self.warpType)) 

651 tempExpRefList = [getGroupDataRef(butler, self.getTempExpDatasetName(self.warpType), 

652 g, groupData.keys) for 

653 g in groupData.groups.keys()] 

654 return tempExpRefList 

655 

656 def prepareInputs(self, refList): 

657 """Prepare the input warps for coaddition by measuring the weight for 

658 each warp and the scaling for the photometric zero point. 

659 

660 Each Warp has its own photometric zeropoint and background variance. 

661 Before coadding these Warps together, compute a scale factor to 

662 normalize the photometric zeropoint and compute the weight for each Warp. 

663 

664 Parameters 

665 ---------- 

666 refList : `list` 

667 List of data references to tempExp 

668 

669 Returns 

670 ------- 

671 result : `lsst.pipe.base.Struct` 

672 Result struct with components: 

673 

674 - ``tempExprefList``: `list` of data references to tempExp. 

675 - ``weightList``: `list` of weightings. 

676 - ``imageScalerList``: `list` of image scalers. 

677 """ 

678 statsCtrl = afwMath.StatisticsControl() 

679 statsCtrl.setNumSigmaClip(self.config.sigmaClip) 

680 statsCtrl.setNumIter(self.config.clipIter) 

681 statsCtrl.setAndMask(self.getBadPixelMask()) 

682 statsCtrl.setNanSafe(True) 

683 # compute tempExpRefList: a list of tempExpRef that actually exist 

684 # and weightList: a list of the weight of the associated coadd tempExp 

685 # and imageScalerList: a list of scale factors for the associated coadd tempExp 

686 tempExpRefList = [] 

687 weightList = [] 

688 imageScalerList = [] 

689 tempExpName = self.getTempExpDatasetName(self.warpType) 

690 for tempExpRef in refList: 

691 # Gen3's DeferredDatasetHandles are guaranteed to exist and 

692 # therefore have no datasetExists() method 

693 if not isinstance(tempExpRef, DeferredDatasetHandle): 

694 if not tempExpRef.datasetExists(tempExpName): 

695 self.log.warning("Could not find %s %s; skipping it", tempExpName, tempExpRef.dataId) 

696 continue 

697 

698 tempExp = tempExpRef.get(datasetType=tempExpName, immediate=True) 

699 # Ignore any input warp that is empty of data 

700 if numpy.isnan(tempExp.image.array).all(): 

701 continue 

702 maskedImage = tempExp.getMaskedImage() 

703 imageScaler = self.scaleZeroPoint.computeImageScaler( 

704 exposure=tempExp, 

705 dataRef=tempExpRef, 

706 ) 

707 try: 

708 imageScaler.scaleMaskedImage(maskedImage) 

709 except Exception as e: 

710 self.log.warning("Scaling failed for %s (skipping it): %s", tempExpRef.dataId, e) 

711 continue 

712 statObj = afwMath.makeStatistics(maskedImage.getVariance(), maskedImage.getMask(), 

713 afwMath.MEANCLIP, statsCtrl) 

714 meanVar, meanVarErr = statObj.getResult(afwMath.MEANCLIP) 

715 weight = 1.0 / float(meanVar) 

716 if not numpy.isfinite(weight): 

717 self.log.warning("Non-finite weight for %s: skipping", tempExpRef.dataId) 

718 continue 

719 self.log.info("Weight of %s %s = %0.3f", tempExpName, tempExpRef.dataId, weight) 

720 

721 del maskedImage 

722 del tempExp 

723 

724 tempExpRefList.append(tempExpRef) 

725 weightList.append(weight) 

726 imageScalerList.append(imageScaler) 

727 

728 return pipeBase.Struct(tempExpRefList=tempExpRefList, weightList=weightList, 

729 imageScalerList=imageScalerList) 

730 

731 def prepareStats(self, mask=None): 

732 """Prepare the statistics for coadding images. 

733 

734 Parameters 

735 ---------- 

736 mask : `int`, optional 

737 Bit mask value to exclude from coaddition. 

738 

739 Returns 

740 ------- 

741 stats : `lsst.pipe.base.Struct` 

742 Statistics structure with the following fields: 

743 

744 - ``statsCtrl``: Statistics control object for coadd 

745 (`lsst.afw.math.StatisticsControl`) 

746 - ``statsFlags``: Statistic for coadd (`lsst.afw.math.Property`) 

747 """ 

748 if mask is None: 

749 mask = self.getBadPixelMask() 

750 statsCtrl = afwMath.StatisticsControl() 

751 statsCtrl.setNumSigmaClip(self.config.sigmaClip) 

752 statsCtrl.setNumIter(self.config.clipIter) 

753 statsCtrl.setAndMask(mask) 

754 statsCtrl.setNanSafe(True) 

755 statsCtrl.setWeighted(True) 

756 statsCtrl.setCalcErrorFromInputVariance(self.config.calcErrorFromInputVariance) 

757 for plane, threshold in self.config.maskPropagationThresholds.items(): 

758 bit = afwImage.Mask.getMaskPlane(plane) 

759 statsCtrl.setMaskPropagationThreshold(bit, threshold) 

760 statsFlags = afwMath.stringToStatisticsProperty(self.config.statistic) 

761 return pipeBase.Struct(ctrl=statsCtrl, flags=statsFlags) 

762 

763 @timeMethod 

764 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, 

765 altMaskList=None, mask=None, supplementaryData=None): 

766 """Assemble a coadd from input warps 

767 

768 Assemble the coadd using the provided list of coaddTempExps. Since 

769 the full coadd covers a patch (a large area), the assembly is 

770 performed over small areas on the image at a time in order to 

771 conserve memory usage. Iterate over subregions within the outer 

772 bbox of the patch using `assembleSubregion` to stack the corresponding 

773 subregions from the coaddTempExps with the statistic specified. 

774 Set the edge bits the coadd mask based on the weight map. 

775 

776 Parameters 

777 ---------- 

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

779 Struct with geometric information about the patch. 

780 tempExpRefList : `list` 

781 List of data references to Warps (previously called CoaddTempExps). 

782 imageScalerList : `list` 

783 List of image scalers. 

784 weightList : `list` 

785 List of weights 

786 altMaskList : `list`, optional 

787 List of alternate masks to use rather than those stored with 

788 tempExp. 

789 mask : `int`, optional 

790 Bit mask value to exclude from coaddition. 

791 supplementaryData : lsst.pipe.base.Struct, optional 

792 Struct with additional data products needed to assemble coadd. 

793 Only used by subclasses that implement `makeSupplementaryData` 

794 and override `run`. 

795 

796 Returns 

797 ------- 

798 result : `lsst.pipe.base.Struct` 

799 Result struct with components: 

800 

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

802 - ``nImage``: exposure count image (``lsst.afw.image.Image``), if requested. 

803 - ``inputMap``: bit-wise map of inputs, if requested. 

804 - ``warpRefList``: input list of refs to the warps ( 

805 ``lsst.daf.butler.DeferredDatasetHandle`` or 

806 ``lsst.daf.persistence.ButlerDataRef``) 

807 (unmodified) 

808 - ``imageScalerList``: input list of image scalers (unmodified) 

809 - ``weightList``: input list of weights (unmodified) 

810 """ 

811 tempExpName = self.getTempExpDatasetName(self.warpType) 

812 self.log.info("Assembling %s %s", len(tempExpRefList), tempExpName) 

813 stats = self.prepareStats(mask=mask) 

814 

815 if altMaskList is None: 

816 altMaskList = [None]*len(tempExpRefList) 

817 

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

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

820 coaddExposure.getInfo().setCoaddInputs(self.inputRecorder.makeCoaddInputs()) 

821 self.assembleMetadata(coaddExposure, tempExpRefList, weightList) 

822 coaddMaskedImage = coaddExposure.getMaskedImage() 

823 subregionSizeArr = self.config.subregionSize 

824 subregionSize = geom.Extent2I(subregionSizeArr[0], subregionSizeArr[1]) 

825 # if nImage is requested, create a zero one which can be passed to assembleSubregion 

826 if self.config.doNImage: 

827 nImage = afwImage.ImageU(skyInfo.bbox) 

828 else: 

829 nImage = None 

830 # If inputMap is requested, create the initial version that can be masked in 

831 # assembleSubregion. 

832 if self.config.doInputMap: 

833 self.inputMapper.build_ccd_input_map(skyInfo.bbox, 

834 skyInfo.wcs, 

835 coaddExposure.getInfo().getCoaddInputs().ccds) 

836 

837 if self.config.doOnlineForMean and self.config.statistic == "MEAN": 

838 try: 

839 self.assembleOnlineMeanCoadd(coaddExposure, tempExpRefList, imageScalerList, 

840 weightList, altMaskList, stats.ctrl, 

841 nImage=nImage) 

842 except Exception as e: 

843 self.log.fatal("Cannot compute online coadd %s", e) 

844 else: 

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

846 try: 

847 self.assembleSubregion(coaddExposure, subBBox, tempExpRefList, imageScalerList, 

848 weightList, altMaskList, stats.flags, stats.ctrl, 

849 nImage=nImage) 

850 except Exception as e: 

851 self.log.fatal("Cannot compute coadd %s: %s", subBBox, e) 

852 

853 # If inputMap is requested, we must finalize the map after the accumulation. 

854 if self.config.doInputMap: 

855 self.inputMapper.finalize_ccd_input_map_mask() 

856 inputMap = self.inputMapper.ccd_input_map 

857 else: 

858 inputMap = None 

859 

860 self.setInexactPsf(coaddMaskedImage.getMask()) 

861 # Despite the name, the following doesn't really deal with "EDGE" pixels: it identifies 

862 # pixels that didn't receive any unmasked inputs (as occurs around the edge of the field). 

863 coaddUtils.setCoaddEdgeBits(coaddMaskedImage.getMask(), coaddMaskedImage.getVariance()) 

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

865 warpRefList=tempExpRefList, imageScalerList=imageScalerList, 

866 weightList=weightList, inputMap=inputMap) 

867 

868 def assembleMetadata(self, coaddExposure, tempExpRefList, weightList): 

869 """Set the metadata for the coadd. 

870 

871 This basic implementation sets the filter from the first input. 

872 

873 Parameters 

874 ---------- 

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

876 The target exposure for the coadd. 

877 tempExpRefList : `list` 

878 List of data references to tempExp. 

879 weightList : `list` 

880 List of weights. 

881 """ 

882 assert len(tempExpRefList) == len(weightList), "Length mismatch" 

883 tempExpName = self.getTempExpDatasetName(self.warpType) 

884 # We load a single pixel of each coaddTempExp, because we just want to get at the metadata 

885 # (and we need more than just the PropertySet that contains the header), which is not possible 

886 # with the current butler (see #2777). 

887 bbox = geom.Box2I(coaddExposure.getBBox().getMin(), geom.Extent2I(1, 1)) 

888 

889 if isinstance(tempExpRefList[0], DeferredDatasetHandle): 

890 # Gen 3 API 

891 tempExpList = [tempExpRef.get(parameters={'bbox': bbox}) for tempExpRef in tempExpRefList] 

892 else: 

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

894 tempExpList = [tempExpRef.get(tempExpName + "_sub", bbox=bbox, immediate=True) 

895 for tempExpRef in tempExpRefList] 

896 numCcds = sum(len(tempExp.getInfo().getCoaddInputs().ccds) for tempExp in tempExpList) 

897 

898 # Set the coadd FilterLabel to the band of the first input exposure: 

899 # Coadds are calibrated, so the physical label is now meaningless. 

900 coaddExposure.setFilterLabel(afwImage.FilterLabel(tempExpList[0].getFilterLabel().bandLabel)) 

901 coaddInputs = coaddExposure.getInfo().getCoaddInputs() 

902 coaddInputs.ccds.reserve(numCcds) 

903 coaddInputs.visits.reserve(len(tempExpList)) 

904 

905 for tempExp, weight in zip(tempExpList, weightList): 

906 self.inputRecorder.addVisitToCoadd(coaddInputs, tempExp, weight) 

907 

908 if self.config.doUsePsfMatchedPolygons: 

909 self.shrinkValidPolygons(coaddInputs) 

910 

911 coaddInputs.visits.sort() 

912 coaddInputs.ccds.sort() 

913 if self.warpType == "psfMatched": 

914 # The modelPsf BBox for a psfMatchedWarp/coaddTempExp was dynamically defined by 

915 # ModelPsfMatchTask as the square box bounding its spatially-variable, pre-matched WarpedPsf. 

916 # Likewise, set the PSF of a PSF-Matched Coadd to the modelPsf 

917 # having the maximum width (sufficient because square) 

918 modelPsfList = [tempExp.getPsf() for tempExp in tempExpList] 

919 modelPsfWidthList = [modelPsf.computeBBox(modelPsf.getAveragePosition()).getWidth() 

920 for modelPsf in modelPsfList] 

921 psf = modelPsfList[modelPsfWidthList.index(max(modelPsfWidthList))] 

922 else: 

923 psf = measAlg.CoaddPsf(coaddInputs.ccds, coaddExposure.getWcs(), 

924 self.config.coaddPsf.makeControl()) 

925 coaddExposure.setPsf(psf) 

926 apCorrMap = measAlg.makeCoaddApCorrMap(coaddInputs.ccds, coaddExposure.getBBox(afwImage.PARENT), 

927 coaddExposure.getWcs()) 

928 coaddExposure.getInfo().setApCorrMap(apCorrMap) 

929 if self.config.doAttachTransmissionCurve: 

930 transmissionCurve = measAlg.makeCoaddTransmissionCurve(coaddExposure.getWcs(), coaddInputs.ccds) 

931 coaddExposure.getInfo().setTransmissionCurve(transmissionCurve) 

932 

933 def assembleSubregion(self, coaddExposure, bbox, tempExpRefList, imageScalerList, weightList, 

934 altMaskList, statsFlags, statsCtrl, nImage=None): 

935 """Assemble the coadd for a sub-region. 

936 

937 For each coaddTempExp, check for (and swap in) an alternative mask 

938 if one is passed. Remove mask planes listed in 

939 `config.removeMaskPlanes`. Finally, stack the actual exposures using 

940 `lsst.afw.math.statisticsStack` with the statistic specified by 

941 statsFlags. Typically, the statsFlag will be one of lsst.afw.math.MEAN for 

942 a mean-stack or `lsst.afw.math.MEANCLIP` for outlier rejection using 

943 an N-sigma clipped mean where N and iterations are specified by 

944 statsCtrl. Assign the stacked subregion back to the coadd. 

945 

946 Parameters 

947 ---------- 

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

949 The target exposure for the coadd. 

950 bbox : `lsst.geom.Box` 

951 Sub-region to coadd. 

952 tempExpRefList : `list` 

953 List of data reference to tempExp. 

954 imageScalerList : `list` 

955 List of image scalers. 

956 weightList : `list` 

957 List of weights. 

958 altMaskList : `list` 

959 List of alternate masks to use rather than those stored with 

960 tempExp, or None. Each element is dict with keys = mask plane 

961 name to which to add the spans. 

962 statsFlags : `lsst.afw.math.Property` 

963 Property object for statistic for coadd. 

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

965 Statistics control object for coadd. 

966 nImage : `lsst.afw.image.ImageU`, optional 

967 Keeps track of exposure count for each pixel. 

968 """ 

969 self.log.debug("Computing coadd over %s", bbox) 

970 tempExpName = self.getTempExpDatasetName(self.warpType) 

971 coaddExposure.mask.addMaskPlane("REJECTED") 

972 coaddExposure.mask.addMaskPlane("CLIPPED") 

973 coaddExposure.mask.addMaskPlane("SENSOR_EDGE") 

974 maskMap = self.setRejectedMaskMapping(statsCtrl) 

975 clipped = afwImage.Mask.getPlaneBitMask("CLIPPED") 

976 maskedImageList = [] 

977 if nImage is not None: 

978 subNImage = afwImage.ImageU(bbox.getWidth(), bbox.getHeight()) 

979 for tempExpRef, imageScaler, altMask in zip(tempExpRefList, imageScalerList, altMaskList): 

980 

981 if isinstance(tempExpRef, DeferredDatasetHandle): 

982 # Gen 3 API 

983 exposure = tempExpRef.get(parameters={'bbox': bbox}) 

984 else: 

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

986 exposure = tempExpRef.get(tempExpName + "_sub", bbox=bbox) 

987 

988 maskedImage = exposure.getMaskedImage() 

989 mask = maskedImage.getMask() 

990 if altMask is not None: 

991 self.applyAltMaskPlanes(mask, altMask) 

992 imageScaler.scaleMaskedImage(maskedImage) 

993 

994 # Add 1 for each pixel which is not excluded by the exclude mask. 

995 # In legacyCoadd, pixels may also be excluded by afwMath.statisticsStack. 

996 if nImage is not None: 

997 subNImage.getArray()[maskedImage.getMask().getArray() & statsCtrl.getAndMask() == 0] += 1 

998 if self.config.removeMaskPlanes: 

999 self.removeMaskPlanes(maskedImage) 

1000 maskedImageList.append(maskedImage) 

1001 

1002 if self.config.doInputMap: 

1003 visit = exposure.getInfo().getCoaddInputs().visits[0].getId() 

1004 self.inputMapper.mask_warp_bbox(bbox, visit, mask, statsCtrl.getAndMask()) 

1005 

1006 with self.timer("stack"): 

1007 coaddSubregion = afwMath.statisticsStack(maskedImageList, statsFlags, statsCtrl, weightList, 

1008 clipped, # also set output to CLIPPED if sigma-clipped 

1009 maskMap) 

1010 coaddExposure.maskedImage.assign(coaddSubregion, bbox) 

1011 if nImage is not None: 

1012 nImage.assign(subNImage, bbox) 

1013 

1014 def assembleOnlineMeanCoadd(self, coaddExposure, tempExpRefList, imageScalerList, weightList, 

1015 altMaskList, statsCtrl, nImage=None): 

1016 """Assemble the coadd using the "online" method. 

1017 

1018 This method takes a running sum of images and weights to save memory. 

1019 It only works for MEAN statistics. 

1020 

1021 Parameters 

1022 ---------- 

1023 coaddExposure : `lsst.afw.image.Exposure` 

1024 The target exposure for the coadd. 

1025 tempExpRefList : `list` 

1026 List of data reference to tempExp. 

1027 imageScalerList : `list` 

1028 List of image scalers. 

1029 weightList : `list` 

1030 List of weights. 

1031 altMaskList : `list` 

1032 List of alternate masks to use rather than those stored with 

1033 tempExp, or None. Each element is dict with keys = mask plane 

1034 name to which to add the spans. 

1035 statsCtrl : `lsst.afw.math.StatisticsControl` 

1036 Statistics control object for coadd 

1037 nImage : `lsst.afw.image.ImageU`, optional 

1038 Keeps track of exposure count for each pixel. 

1039 """ 

1040 self.log.debug("Computing online coadd.") 

1041 tempExpName = self.getTempExpDatasetName(self.warpType) 

1042 coaddExposure.mask.addMaskPlane("REJECTED") 

1043 coaddExposure.mask.addMaskPlane("CLIPPED") 

1044 coaddExposure.mask.addMaskPlane("SENSOR_EDGE") 

1045 maskMap = self.setRejectedMaskMapping(statsCtrl) 

1046 thresholdDict = AccumulatorMeanStack.stats_ctrl_to_threshold_dict(statsCtrl) 

1047 

1048 bbox = coaddExposure.maskedImage.getBBox() 

1049 

1050 stacker = AccumulatorMeanStack( 

1051 coaddExposure.image.array.shape, 

1052 statsCtrl.getAndMask(), 

1053 mask_threshold_dict=thresholdDict, 

1054 mask_map=maskMap, 

1055 no_good_pixels_mask=statsCtrl.getNoGoodPixelsMask(), 

1056 calc_error_from_input_variance=self.config.calcErrorFromInputVariance, 

1057 compute_n_image=(nImage is not None) 

1058 ) 

1059 

1060 for tempExpRef, imageScaler, altMask, weight in zip(tempExpRefList, 

1061 imageScalerList, 

1062 altMaskList, 

1063 weightList): 

1064 if isinstance(tempExpRef, DeferredDatasetHandle): 

1065 # Gen 3 API 

1066 exposure = tempExpRef.get() 

1067 else: 

1068 # Gen 2 API. Delete this when Gen 2 retired 

1069 exposure = tempExpRef.get(tempExpName) 

1070 

1071 maskedImage = exposure.getMaskedImage() 

1072 mask = maskedImage.getMask() 

1073 if altMask is not None: 

1074 self.applyAltMaskPlanes(mask, altMask) 

1075 imageScaler.scaleMaskedImage(maskedImage) 

1076 if self.config.removeMaskPlanes: 

1077 self.removeMaskPlanes(maskedImage) 

1078 

1079 stacker.add_masked_image(maskedImage, weight=weight) 

1080 

1081 if self.config.doInputMap: 

1082 visit = exposure.getInfo().getCoaddInputs().visits[0].getId() 

1083 self.inputMapper.mask_warp_bbox(bbox, visit, mask, statsCtrl.getAndMask()) 

1084 

1085 stacker.fill_stacked_masked_image(coaddExposure.maskedImage) 

1086 

1087 if nImage is not None: 

1088 nImage.array[:, :] = stacker.n_image 

1089 

1090 def removeMaskPlanes(self, maskedImage): 

1091 """Unset the mask of an image for mask planes specified in the config. 

1092 

1093 Parameters 

1094 ---------- 

1095 maskedImage : `lsst.afw.image.MaskedImage` 

1096 The masked image to be modified. 

1097 """ 

1098 mask = maskedImage.getMask() 

1099 for maskPlane in self.config.removeMaskPlanes: 

1100 try: 

1101 mask &= ~mask.getPlaneBitMask(maskPlane) 

1102 except pexExceptions.InvalidParameterError: 

1103 self.log.debug("Unable to remove mask plane %s: no mask plane with that name was found.", 

1104 maskPlane) 

1105 

1106 @staticmethod 

1107 def setRejectedMaskMapping(statsCtrl): 

1108 """Map certain mask planes of the warps to new planes for the coadd. 

1109 

1110 If a pixel is rejected due to a mask value other than EDGE, NO_DATA, 

1111 or CLIPPED, set it to REJECTED on the coadd. 

1112 If a pixel is rejected due to EDGE, set the coadd pixel to SENSOR_EDGE. 

1113 If a pixel is rejected due to CLIPPED, set the coadd pixel to CLIPPED. 

1114 

1115 Parameters 

1116 ---------- 

1117 statsCtrl : `lsst.afw.math.StatisticsControl` 

1118 Statistics control object for coadd 

1119 

1120 Returns 

1121 ------- 

1122 maskMap : `list` of `tuple` of `int` 

1123 A list of mappings of mask planes of the warped exposures to 

1124 mask planes of the coadd. 

1125 """ 

1126 edge = afwImage.Mask.getPlaneBitMask("EDGE") 

1127 noData = afwImage.Mask.getPlaneBitMask("NO_DATA") 

1128 clipped = afwImage.Mask.getPlaneBitMask("CLIPPED") 

1129 toReject = statsCtrl.getAndMask() & (~noData) & (~edge) & (~clipped) 

1130 maskMap = [(toReject, afwImage.Mask.getPlaneBitMask("REJECTED")), 

1131 (edge, afwImage.Mask.getPlaneBitMask("SENSOR_EDGE")), 

1132 (clipped, clipped)] 

1133 return maskMap 

1134 

1135 def applyAltMaskPlanes(self, mask, altMaskSpans): 

1136 """Apply in place alt mask formatted as SpanSets to a mask. 

1137 

1138 Parameters 

1139 ---------- 

1140 mask : `lsst.afw.image.Mask` 

1141 Original mask. 

1142 altMaskSpans : `dict` 

1143 SpanSet lists to apply. Each element contains the new mask 

1144 plane name (e.g. "CLIPPED and/or "NO_DATA") as the key, 

1145 and list of SpanSets to apply to the mask. 

1146 

1147 Returns 

1148 ------- 

1149 mask : `lsst.afw.image.Mask` 

1150 Updated mask. 

1151 """ 

1152 if self.config.doUsePsfMatchedPolygons: 

1153 if ("NO_DATA" in altMaskSpans) and ("NO_DATA" in self.config.badMaskPlanes): 

1154 # Clear away any other masks outside the validPolygons. These pixels are no longer 

1155 # contributing to inexact PSFs, and will still be rejected because of NO_DATA 

1156 # self.config.doUsePsfMatchedPolygons should be True only in CompareWarpAssemble 

1157 # This mask-clearing step must only occur *before* applying the new masks below 

1158 for spanSet in altMaskSpans['NO_DATA']: 

1159 spanSet.clippedTo(mask.getBBox()).clearMask(mask, self.getBadPixelMask()) 

1160 

1161 for plane, spanSetList in altMaskSpans.items(): 

1162 maskClipValue = mask.addMaskPlane(plane) 

1163 for spanSet in spanSetList: 

1164 spanSet.clippedTo(mask.getBBox()).setMask(mask, 2**maskClipValue) 

1165 return mask 

1166 

1167 def shrinkValidPolygons(self, coaddInputs): 

1168 """Shrink coaddInputs' ccds' ValidPolygons in place. 

1169 

1170 Either modify each ccd's validPolygon in place, or if CoaddInputs 

1171 does not have a validPolygon, create one from its bbox. 

1172 

1173 Parameters 

1174 ---------- 

1175 coaddInputs : `lsst.afw.image.coaddInputs` 

1176 Original mask. 

1177 

1178 """ 

1179 for ccd in coaddInputs.ccds: 

1180 polyOrig = ccd.getValidPolygon() 

1181 validPolyBBox = polyOrig.getBBox() if polyOrig else ccd.getBBox() 

1182 validPolyBBox.grow(-self.config.matchingKernelSize//2) 

1183 if polyOrig: 

1184 validPolygon = polyOrig.intersectionSingle(validPolyBBox) 

1185 else: 

1186 validPolygon = afwGeom.polygon.Polygon(geom.Box2D(validPolyBBox)) 

1187 ccd.setValidPolygon(validPolygon) 

1188 

1189 def readBrightObjectMasks(self, dataRef): 

1190 """Retrieve the bright object masks. 

1191 

1192 Returns None on failure. 

1193 

1194 Parameters 

1195 ---------- 

1196 dataRef : `lsst.daf.persistence.butlerSubset.ButlerDataRef` 

1197 A Butler dataRef. 

1198 

1199 Returns 

1200 ------- 

1201 result : `lsst.daf.persistence.butlerSubset.ButlerDataRef` 

1202 Bright object mask from the Butler object, or None if it cannot 

1203 be retrieved. 

1204 """ 

1205 try: 

1206 return dataRef.get(datasetType="brightObjectMask", immediate=True) 

1207 except Exception as e: 

1208 self.log.warning("Unable to read brightObjectMask for %s: %s", dataRef.dataId, e) 

1209 return None 

1210 

1211 def setBrightObjectMasks(self, exposure, brightObjectMasks, dataId=None): 

1212 """Set the bright object masks. 

1213 

1214 Parameters 

1215 ---------- 

1216 exposure : `lsst.afw.image.Exposure` 

1217 Exposure under consideration. 

1218 dataId : `lsst.daf.persistence.dataId` 

1219 Data identifier dict for patch. 

1220 brightObjectMasks : `lsst.afw.table` 

1221 Table of bright objects to mask. 

1222 """ 

1223 

1224 if brightObjectMasks is None: 

1225 self.log.warning("Unable to apply bright object mask: none supplied") 

1226 return 

1227 self.log.info("Applying %d bright object masks to %s", len(brightObjectMasks), dataId) 

1228 mask = exposure.getMaskedImage().getMask() 

1229 wcs = exposure.getWcs() 

1230 plateScale = wcs.getPixelScale().asArcseconds() 

1231 

1232 for rec in brightObjectMasks: 

1233 center = geom.PointI(wcs.skyToPixel(rec.getCoord())) 

1234 if rec["type"] == "box": 

1235 assert rec["angle"] == 0.0, ("Angle != 0 for mask object %s" % rec["id"]) 

1236 width = rec["width"].asArcseconds()/plateScale # convert to pixels 

1237 height = rec["height"].asArcseconds()/plateScale # convert to pixels 

1238 

1239 halfSize = geom.ExtentI(0.5*width, 0.5*height) 

1240 bbox = geom.Box2I(center - halfSize, center + halfSize) 

1241 

1242 bbox = geom.BoxI(geom.PointI(int(center[0] - 0.5*width), int(center[1] - 0.5*height)), 

1243 geom.PointI(int(center[0] + 0.5*width), int(center[1] + 0.5*height))) 

1244 spans = afwGeom.SpanSet(bbox) 

1245 elif rec["type"] == "circle": 

1246 radius = int(rec["radius"].asArcseconds()/plateScale) # convert to pixels 

1247 spans = afwGeom.SpanSet.fromShape(radius, offset=center) 

1248 else: 

1249 self.log.warning("Unexpected region type %s at %s", rec["type"], center) 

1250 continue 

1251 spans.clippedTo(mask.getBBox()).setMask(mask, self.brightObjectBitmask) 

1252 

1253 def setInexactPsf(self, mask): 

1254 """Set INEXACT_PSF mask plane. 

1255 

1256 If any of the input images isn't represented in the coadd (due to 

1257 clipped pixels or chip gaps), the `CoaddPsf` will be inexact. Flag 

1258 these pixels. 

1259 

1260 Parameters 

1261 ---------- 

1262 mask : `lsst.afw.image.Mask` 

1263 Coadded exposure's mask, modified in-place. 

1264 """ 

1265 mask.addMaskPlane("INEXACT_PSF") 

1266 inexactPsf = mask.getPlaneBitMask("INEXACT_PSF") 

1267 sensorEdge = mask.getPlaneBitMask("SENSOR_EDGE") # chip edges (so PSF is discontinuous) 

1268 clipped = mask.getPlaneBitMask("CLIPPED") # pixels clipped from coadd 

1269 rejected = mask.getPlaneBitMask("REJECTED") # pixels rejected from coadd due to masks 

1270 array = mask.getArray() 

1271 selected = array & (sensorEdge | clipped | rejected) > 0 

1272 array[selected] |= inexactPsf 

1273 

1274 @classmethod 

1275 def _makeArgumentParser(cls): 

1276 """Create an argument parser. 

1277 """ 

1278 parser = pipeBase.ArgumentParser(name=cls._DefaultName) 

1279 parser.add_id_argument("--id", cls.ConfigClass().coaddName + "Coadd_" 

1280 + cls.ConfigClass().warpType + "Warp", 

1281 help="data ID, e.g. --id tract=12345 patch=1,2", 

1282 ContainerClass=AssembleCoaddDataIdContainer) 

1283 parser.add_id_argument("--selectId", "calexp", help="data ID, e.g. --selectId visit=6789 ccd=0..9", 

1284 ContainerClass=SelectDataIdContainer) 

1285 return parser 

1286 

1287 @staticmethod 

1288 def _subBBoxIter(bbox, subregionSize): 

1289 """Iterate over subregions of a bbox. 

1290 

1291 Parameters 

1292 ---------- 

1293 bbox : `lsst.geom.Box2I` 

1294 Bounding box over which to iterate. 

1295 subregionSize: `lsst.geom.Extent2I` 

1296 Size of sub-bboxes. 

1297 

1298 Yields 

1299 ------ 

1300 subBBox : `lsst.geom.Box2I` 

1301 Next sub-bounding box of size ``subregionSize`` or smaller; each ``subBBox`` 

1302 is contained within ``bbox``, so it may be smaller than ``subregionSize`` at 

1303 the edges of ``bbox``, but it will never be empty. 

1304 """ 

1305 if bbox.isEmpty(): 

1306 raise RuntimeError("bbox %s is empty" % (bbox,)) 

1307 if subregionSize[0] < 1 or subregionSize[1] < 1: 

1308 raise RuntimeError("subregionSize %s must be nonzero" % (subregionSize,)) 

1309 

1310 for rowShift in range(0, bbox.getHeight(), subregionSize[1]): 

1311 for colShift in range(0, bbox.getWidth(), subregionSize[0]): 

1312 subBBox = geom.Box2I(bbox.getMin() + geom.Extent2I(colShift, rowShift), subregionSize) 

1313 subBBox.clip(bbox) 

1314 if subBBox.isEmpty(): 

1315 raise RuntimeError("Bug: empty bbox! bbox=%s, subregionSize=%s, " 

1316 "colShift=%s, rowShift=%s" % 

1317 (bbox, subregionSize, colShift, rowShift)) 

1318 yield subBBox 

1319 

1320 def filterWarps(self, inputs, goodVisits): 

1321 """Return list of only inputRefs with visitId in goodVisits ordered by goodVisit 

1322 

1323 Parameters 

1324 ---------- 

1325 inputs : list 

1326 List of `lsst.pipe.base.connections.DeferredDatasetRef` with dataId containing visit 

1327 goodVisit : `dict` 

1328 Dictionary with good visitIds as the keys. Value ignored. 

1329 

1330 Returns: 

1331 -------- 

1332 filteredInputs : `list` 

1333 Filtered and sorted list of `lsst.pipe.base.connections.DeferredDatasetRef` 

1334 """ 

1335 inputWarpDict = {inputRef.ref.dataId['visit']: inputRef for inputRef in inputs} 

1336 filteredInputs = [] 

1337 for visit in goodVisits.keys(): 

1338 if visit in inputWarpDict: 

1339 filteredInputs.append(inputWarpDict[visit]) 

1340 return filteredInputs 

1341 

1342 

1343class AssembleCoaddDataIdContainer(pipeBase.DataIdContainer): 

1344 """A version of `lsst.pipe.base.DataIdContainer` specialized for assembleCoadd. 

1345 """ 

1346 

1347 def makeDataRefList(self, namespace): 

1348 """Make self.refList from self.idList. 

1349 

1350 Parameters 

1351 ---------- 

1352 namespace 

1353 Results of parsing command-line (with ``butler`` and ``log`` elements). 

1354 """ 

1355 datasetType = namespace.config.coaddName + "Coadd" 

1356 keysCoadd = namespace.butler.getKeys(datasetType=datasetType, level=self.level) 

1357 

1358 for dataId in self.idList: 

1359 # tract and patch are required 

1360 for key in keysCoadd: 

1361 if key not in dataId: 

1362 raise RuntimeError("--id must include " + key) 

1363 

1364 dataRef = namespace.butler.dataRef( 

1365 datasetType=datasetType, 

1366 dataId=dataId, 

1367 ) 

1368 self.refList.append(dataRef) 

1369 

1370 

1371def countMaskFromFootprint(mask, footprint, bitmask, ignoreMask): 

1372 """Function to count the number of pixels with a specific mask in a 

1373 footprint. 

1374 

1375 Find the intersection of mask & footprint. Count all pixels in the mask 

1376 that are in the intersection that have bitmask set but do not have 

1377 ignoreMask set. Return the count. 

1378 

1379 Parameters 

1380 ---------- 

1381 mask : `lsst.afw.image.Mask` 

1382 Mask to define intersection region by. 

1383 footprint : `lsst.afw.detection.Footprint` 

1384 Footprint to define the intersection region by. 

1385 bitmask 

1386 Specific mask that we wish to count the number of occurances of. 

1387 ignoreMask 

1388 Pixels to not consider. 

1389 

1390 Returns 

1391 ------- 

1392 result : `int` 

1393 Count of number of pixels in footprint with specified mask. 

1394 """ 

1395 bbox = footprint.getBBox() 

1396 bbox.clip(mask.getBBox(afwImage.PARENT)) 

1397 fp = afwImage.Mask(bbox) 

1398 subMask = mask.Factory(mask, bbox, afwImage.PARENT) 

1399 footprint.spans.setMask(fp, bitmask) 

1400 return numpy.logical_and((subMask.getArray() & fp.getArray()) > 0, 

1401 (subMask.getArray() & ignoreMask) == 0).sum() 

1402 

1403 

1404class SafeClipAssembleCoaddConfig(AssembleCoaddConfig, pipelineConnections=AssembleCoaddConnections): 

1405 """Configuration parameters for the SafeClipAssembleCoaddTask. 

1406 """ 

1407 clipDetection = pexConfig.ConfigurableField( 

1408 target=SourceDetectionTask, 

1409 doc="Detect sources on difference between unclipped and clipped coadd") 

1410 minClipFootOverlap = pexConfig.Field( 

1411 doc="Minimum fractional overlap of clipped footprint with visit DETECTED to be clipped", 

1412 dtype=float, 

1413 default=0.6 

1414 ) 

1415 minClipFootOverlapSingle = pexConfig.Field( 

1416 doc="Minimum fractional overlap of clipped footprint with visit DETECTED to be " 

1417 "clipped when only one visit overlaps", 

1418 dtype=float, 

1419 default=0.5 

1420 ) 

1421 minClipFootOverlapDouble = pexConfig.Field( 

1422 doc="Minimum fractional overlap of clipped footprints with visit DETECTED to be " 

1423 "clipped when two visits overlap", 

1424 dtype=float, 

1425 default=0.45 

1426 ) 

1427 maxClipFootOverlapDouble = pexConfig.Field( 

1428 doc="Maximum fractional overlap of clipped footprints with visit DETECTED when " 

1429 "considering two visits", 

1430 dtype=float, 

1431 default=0.15 

1432 ) 

1433 minBigOverlap = pexConfig.Field( 

1434 doc="Minimum number of pixels in footprint to use DETECTED mask from the single visits " 

1435 "when labeling clipped footprints", 

1436 dtype=int, 

1437 default=100 

1438 ) 

1439 

1440 def setDefaults(self): 

1441 """Set default values for clipDetection. 

1442 

1443 Notes 

1444 ----- 

1445 The numeric values for these configuration parameters were 

1446 empirically determined, future work may further refine them. 

1447 """ 

1448 AssembleCoaddConfig.setDefaults(self) 

1449 self.clipDetection.doTempLocalBackground = False 

1450 self.clipDetection.reEstimateBackground = False 

1451 self.clipDetection.returnOriginalFootprints = False 

1452 self.clipDetection.thresholdPolarity = "both" 

1453 self.clipDetection.thresholdValue = 2 

1454 self.clipDetection.nSigmaToGrow = 2 

1455 self.clipDetection.minPixels = 4 

1456 self.clipDetection.isotropicGrow = True 

1457 self.clipDetection.thresholdType = "pixel_stdev" 

1458 self.sigmaClip = 1.5 

1459 self.clipIter = 3 

1460 self.statistic = "MEAN" 

1461 

1462 def validate(self): 

1463 if self.doSigmaClip: 

1464 log.warning("Additional Sigma-clipping not allowed in Safe-clipped Coadds. " 

1465 "Ignoring doSigmaClip.") 

1466 self.doSigmaClip = False 

1467 if self.statistic != "MEAN": 

1468 raise ValueError("Only MEAN statistic allowed for final stacking in SafeClipAssembleCoadd " 

1469 "(%s chosen). Please set statistic to MEAN." 

1470 % (self.statistic)) 

1471 AssembleCoaddTask.ConfigClass.validate(self) 

1472 

1473 

1474class SafeClipAssembleCoaddTask(AssembleCoaddTask): 

1475 """Assemble a coadded image from a set of coadded temporary exposures, 

1476 being careful to clip & flag areas with potential artifacts. 

1477 

1478 In ``AssembleCoaddTask``, we compute the coadd as an clipped mean (i.e., 

1479 we clip outliers). The problem with doing this is that when computing the 

1480 coadd PSF at a given location, individual visit PSFs from visits with 

1481 outlier pixels contribute to the coadd PSF and cannot be treated correctly. 

1482 In this task, we correct for this behavior by creating a new 

1483 ``badMaskPlane`` 'CLIPPED'. We populate this plane on the input 

1484 coaddTempExps and the final coadd where 

1485 

1486 i. difference imaging suggests that there is an outlier and 

1487 ii. this outlier appears on only one or two images. 

1488 

1489 Such regions will not contribute to the final coadd. Furthermore, any 

1490 routine to determine the coadd PSF can now be cognizant of clipped regions. 

1491 Note that the algorithm implemented by this task is preliminary and works 

1492 correctly for HSC data. Parameter modifications and or considerable 

1493 redesigning of the algorithm is likley required for other surveys. 

1494 

1495 ``SafeClipAssembleCoaddTask`` uses a ``SourceDetectionTask`` 

1496 "clipDetection" subtask and also sub-classes ``AssembleCoaddTask``. 

1497 You can retarget the ``SourceDetectionTask`` "clipDetection" subtask 

1498 if you wish. 

1499 

1500 Notes 

1501 ----- 

1502 The `lsst.pipe.base.cmdLineTask.CmdLineTask` interface supports a 

1503 flag ``-d`` to import ``debug.py`` from your ``PYTHONPATH``; 

1504 see `baseDebug` for more about ``debug.py`` files. 

1505 `SafeClipAssembleCoaddTask` has no debug variables of its own. 

1506 The ``SourceDetectionTask`` "clipDetection" subtasks may support debug 

1507 variables. See the documetation for `SourceDetectionTask` "clipDetection" 

1508 for further information. 

1509 

1510 Examples 

1511 -------- 

1512 `SafeClipAssembleCoaddTask` assembles a set of warped ``coaddTempExp`` 

1513 images into a coadded image. The `SafeClipAssembleCoaddTask` is invoked by 

1514 running assembleCoadd.py *without* the flag '--legacyCoadd'. 

1515 

1516 Usage of ``assembleCoadd.py`` expects a data reference to the tract patch 

1517 and filter to be coadded (specified using 

1518 '--id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]') 

1519 along with a list of coaddTempExps to attempt to coadd (specified using 

1520 '--selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]'). 

1521 Only the coaddTempExps that cover the specified tract and patch will be 

1522 coadded. A list of the available optional arguments can be obtained by 

1523 calling assembleCoadd.py with the --help command line argument: 

1524 

1525 .. code-block:: none 

1526 

1527 assembleCoadd.py --help 

1528 

1529 To demonstrate usage of the `SafeClipAssembleCoaddTask` in the larger 

1530 context of multi-band processing, we will generate the HSC-I & -R band 

1531 coadds from HSC engineering test data provided in the ci_hsc package. 

1532 To begin, assuming that the lsst stack has been already set up, we must 

1533 set up the obs_subaru and ci_hsc packages. This defines the environment 

1534 variable $CI_HSC_DIR and points at the location of the package. The raw 

1535 HSC data live in the ``$CI_HSC_DIR/raw`` directory. To begin assembling 

1536 the coadds, we must first 

1537 

1538 - ``processCcd`` 

1539 process the individual ccds in $CI_HSC_RAW to produce calibrated exposures 

1540 - ``makeSkyMap`` 

1541 create a skymap that covers the area of the sky present in the raw exposures 

1542 - ``makeCoaddTempExp`` 

1543 warp the individual calibrated exposures to the tangent plane of the coadd</DD> 

1544 

1545 We can perform all of these steps by running 

1546 

1547 .. code-block:: none 

1548 

1549 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988 

1550 

1551 This will produce warped coaddTempExps for each visit. To coadd the 

1552 warped data, we call ``assembleCoadd.py`` as follows: 

1553 

1554 .. code-block:: none 

1555 

1556 assembleCoadd.py $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \ 

1557 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \ 

1558 --selectId visit=903986 ccd=100--selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \ 

1559 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \ 

1560 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \ 

1561 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \ 

1562 --selectId visit=903988 ccd=24 

1563 

1564 This will process the HSC-I band data. The results are written in 

1565 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``. 

1566 

1567 You may also choose to run: 

1568 

1569 .. code-block:: none 

1570 

1571 scons warp-903334 warp-903336 warp-903338 warp-903342 warp-903344 warp-903346 nnn 

1572 assembleCoadd.py $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-R --selectId visit=903334 ccd=16 \ 

1573 --selectId visit=903334 ccd=22 --selectId visit=903334 ccd=23 --selectId visit=903334 ccd=100 \ 

1574 --selectId visit=903336 ccd=17 --selectId visit=903336 ccd=24 --selectId visit=903338 ccd=18 \ 

1575 --selectId visit=903338 ccd=25 --selectId visit=903342 ccd=4 --selectId visit=903342 ccd=10 \ 

1576 --selectId visit=903342 ccd=100 --selectId visit=903344 ccd=0 --selectId visit=903344 ccd=5 \ 

1577 --selectId visit=903344 ccd=11 --selectId visit=903346 ccd=1 --selectId visit=903346 ccd=6 \ 

1578 --selectId visit=903346 ccd=12 

1579 

1580 to generate the coadd for the HSC-R band if you are interested in following 

1581 multiBand Coadd processing as discussed in ``pipeTasks_multiBand``. 

1582 """ 

1583 ConfigClass = SafeClipAssembleCoaddConfig 

1584 _DefaultName = "safeClipAssembleCoadd" 

1585 

1586 def __init__(self, *args, **kwargs): 

1587 AssembleCoaddTask.__init__(self, *args, **kwargs) 

1588 schema = afwTable.SourceTable.makeMinimalSchema() 

1589 self.makeSubtask("clipDetection", schema=schema) 

1590 

1591 @utils.inheritDoc(AssembleCoaddTask) 

1592 @timeMethod 

1593 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, *args, **kwargs): 

1594 """Assemble the coadd for a region. 

1595 

1596 Compute the difference of coadds created with and without outlier 

1597 rejection to identify coadd pixels that have outlier values in some 

1598 individual visits. 

1599 Detect clipped regions on the difference image and mark these regions 

1600 on the one or two individual coaddTempExps where they occur if there 

1601 is significant overlap between the clipped region and a source. This 

1602 leaves us with a set of footprints from the difference image that have 

1603 been identified as having occured on just one or two individual visits. 

1604 However, these footprints were generated from a difference image. It 

1605 is conceivable for a large diffuse source to have become broken up 

1606 into multiple footprints acrosss the coadd difference in this process. 

1607 Determine the clipped region from all overlapping footprints from the 

1608 detected sources in each visit - these are big footprints. 

1609 Combine the small and big clipped footprints and mark them on a new 

1610 bad mask plane. 

1611 Generate the coadd using `AssembleCoaddTask.run` without outlier 

1612 removal. Clipped footprints will no longer make it into the coadd 

1613 because they are marked in the new bad mask plane. 

1614 

1615 Notes 

1616 ----- 

1617 args and kwargs are passed but ignored in order to match the call 

1618 signature expected by the parent task. 

1619 """ 

1620 exp = self.buildDifferenceImage(skyInfo, tempExpRefList, imageScalerList, weightList) 

1621 mask = exp.getMaskedImage().getMask() 

1622 mask.addMaskPlane("CLIPPED") 

1623 

1624 result = self.detectClip(exp, tempExpRefList) 

1625 

1626 self.log.info('Found %d clipped objects', len(result.clipFootprints)) 

1627 

1628 maskClipValue = mask.getPlaneBitMask("CLIPPED") 

1629 maskDetValue = mask.getPlaneBitMask("DETECTED") | mask.getPlaneBitMask("DETECTED_NEGATIVE") 

1630 # Append big footprints from individual Warps to result.clipSpans 

1631 bigFootprints = self.detectClipBig(result.clipSpans, result.clipFootprints, result.clipIndices, 

1632 result.detectionFootprints, maskClipValue, maskDetValue, 

1633 exp.getBBox()) 

1634 # Create mask of the current clipped footprints 

1635 maskClip = mask.Factory(mask.getBBox(afwImage.PARENT)) 

1636 afwDet.setMaskFromFootprintList(maskClip, result.clipFootprints, maskClipValue) 

1637 

1638 maskClipBig = maskClip.Factory(mask.getBBox(afwImage.PARENT)) 

1639 afwDet.setMaskFromFootprintList(maskClipBig, bigFootprints, maskClipValue) 

1640 maskClip |= maskClipBig 

1641 

1642 # Assemble coadd from base class, but ignoring CLIPPED pixels 

1643 badMaskPlanes = self.config.badMaskPlanes[:] 

1644 badMaskPlanes.append("CLIPPED") 

1645 badPixelMask = afwImage.Mask.getPlaneBitMask(badMaskPlanes) 

1646 return AssembleCoaddTask.run(self, skyInfo, tempExpRefList, imageScalerList, weightList, 

1647 result.clipSpans, mask=badPixelMask) 

1648 

1649 def buildDifferenceImage(self, skyInfo, tempExpRefList, imageScalerList, weightList): 

1650 """Return an exposure that contains the difference between unclipped 

1651 and clipped coadds. 

1652 

1653 Generate a difference image between clipped and unclipped coadds. 

1654 Compute the difference image by subtracting an outlier-clipped coadd 

1655 from an outlier-unclipped coadd. Return the difference image. 

1656 

1657 Parameters 

1658 ---------- 

1659 skyInfo : `lsst.pipe.base.Struct` 

1660 Patch geometry information, from getSkyInfo 

1661 tempExpRefList : `list` 

1662 List of data reference to tempExp 

1663 imageScalerList : `list` 

1664 List of image scalers 

1665 weightList : `list` 

1666 List of weights 

1667 

1668 Returns 

1669 ------- 

1670 exp : `lsst.afw.image.Exposure` 

1671 Difference image of unclipped and clipped coadd wrapped in an Exposure 

1672 """ 

1673 config = AssembleCoaddConfig() 

1674 # getattr necessary because subtasks do not survive Config.toDict() 

1675 # exclude connections because the class of self.config.connections is not 

1676 # the same as AssembleCoaddConfig.connections, and the connections are not 

1677 # needed to run this task anyway. 

1678 configIntersection = {k: getattr(self.config, k) 

1679 for k, v in self.config.toDict().items() 

1680 if (k in config.keys() and k != "connections")} 

1681 configIntersection['doInputMap'] = False 

1682 configIntersection['doNImage'] = False 

1683 config.update(**configIntersection) 

1684 

1685 # statistic MEAN copied from self.config.statistic, but for clarity explicitly assign 

1686 config.statistic = 'MEAN' 

1687 task = AssembleCoaddTask(config=config) 

1688 coaddMean = task.run(skyInfo, tempExpRefList, imageScalerList, weightList).coaddExposure 

1689 

1690 config.statistic = 'MEANCLIP' 

1691 task = AssembleCoaddTask(config=config) 

1692 coaddClip = task.run(skyInfo, tempExpRefList, imageScalerList, weightList).coaddExposure 

1693 

1694 coaddDiff = coaddMean.getMaskedImage().Factory(coaddMean.getMaskedImage()) 

1695 coaddDiff -= coaddClip.getMaskedImage() 

1696 exp = afwImage.ExposureF(coaddDiff) 

1697 exp.setPsf(coaddMean.getPsf()) 

1698 return exp 

1699 

1700 def detectClip(self, exp, tempExpRefList): 

1701 """Detect clipped regions on an exposure and set the mask on the 

1702 individual tempExp masks. 

1703 

1704 Detect footprints in the difference image after smoothing the 

1705 difference image with a Gaussian kernal. Identify footprints that 

1706 overlap with one or two input ``coaddTempExps`` by comparing the 

1707 computed overlap fraction to thresholds set in the config. A different 

1708 threshold is applied depending on the number of overlapping visits 

1709 (restricted to one or two). If the overlap exceeds the thresholds, 

1710 the footprint is considered "CLIPPED" and is marked as such on the 

1711 coaddTempExp. Return a struct with the clipped footprints, the indices 

1712 of the ``coaddTempExps`` that end up overlapping with the clipped 

1713 footprints, and a list of new masks for the ``coaddTempExps``. 

1714 

1715 Parameters 

1716 ---------- 

1717 exp : `lsst.afw.image.Exposure` 

1718 Exposure to run detection on. 

1719 tempExpRefList : `list` 

1720 List of data reference to tempExp. 

1721 

1722 Returns 

1723 ------- 

1724 result : `lsst.pipe.base.Struct` 

1725 Result struct with components: 

1726 

1727 - ``clipFootprints``: list of clipped footprints. 

1728 - ``clipIndices``: indices for each ``clippedFootprint`` in 

1729 ``tempExpRefList``. 

1730 - ``clipSpans``: List of dictionaries containing spanSet lists 

1731 to clip. Each element contains the new maskplane name 

1732 ("CLIPPED") as the key and list of ``SpanSets`` as the value. 

1733 - ``detectionFootprints``: List of DETECTED/DETECTED_NEGATIVE plane 

1734 compressed into footprints. 

1735 """ 

1736 mask = exp.getMaskedImage().getMask() 

1737 maskDetValue = mask.getPlaneBitMask("DETECTED") | mask.getPlaneBitMask("DETECTED_NEGATIVE") 

1738 fpSet = self.clipDetection.detectFootprints(exp, doSmooth=True, clearMask=True) 

1739 # Merge positive and negative together footprints together 

1740 fpSet.positive.merge(fpSet.negative) 

1741 footprints = fpSet.positive 

1742 self.log.info('Found %d potential clipped objects', len(footprints.getFootprints())) 

1743 ignoreMask = self.getBadPixelMask() 

1744 

1745 clipFootprints = [] 

1746 clipIndices = [] 

1747 artifactSpanSets = [{'CLIPPED': list()} for _ in tempExpRefList] 

1748 

1749 # for use by detectClipBig 

1750 visitDetectionFootprints = [] 

1751 

1752 dims = [len(tempExpRefList), len(footprints.getFootprints())] 

1753 overlapDetArr = numpy.zeros(dims, dtype=numpy.uint16) 

1754 ignoreArr = numpy.zeros(dims, dtype=numpy.uint16) 

1755 

1756 # Loop over masks once and extract/store only relevant overlap metrics and detection footprints 

1757 for i, warpRef in enumerate(tempExpRefList): 

1758 tmpExpMask = warpRef.get(datasetType=self.getTempExpDatasetName(self.warpType), 

1759 immediate=True).getMaskedImage().getMask() 

1760 maskVisitDet = tmpExpMask.Factory(tmpExpMask, tmpExpMask.getBBox(afwImage.PARENT), 

1761 afwImage.PARENT, True) 

1762 maskVisitDet &= maskDetValue 

1763 visitFootprints = afwDet.FootprintSet(maskVisitDet, afwDet.Threshold(1)) 

1764 visitDetectionFootprints.append(visitFootprints) 

1765 

1766 for j, footprint in enumerate(footprints.getFootprints()): 

1767 ignoreArr[i, j] = countMaskFromFootprint(tmpExpMask, footprint, ignoreMask, 0x0) 

1768 overlapDetArr[i, j] = countMaskFromFootprint(tmpExpMask, footprint, maskDetValue, ignoreMask) 

1769 

1770 # build a list of clipped spans for each visit 

1771 for j, footprint in enumerate(footprints.getFootprints()): 

1772 nPixel = footprint.getArea() 

1773 overlap = [] # hold the overlap with each visit 

1774 indexList = [] # index of visit in global list 

1775 for i in range(len(tempExpRefList)): 

1776 ignore = ignoreArr[i, j] 

1777 overlapDet = overlapDetArr[i, j] 

1778 totPixel = nPixel - ignore 

1779 

1780 # If we have more bad pixels than detection skip 

1781 if ignore > overlapDet or totPixel <= 0.5*nPixel or overlapDet == 0: 

1782 continue 

1783 overlap.append(overlapDet/float(totPixel)) 

1784 indexList.append(i) 

1785 

1786 overlap = numpy.array(overlap) 

1787 if not len(overlap): 

1788 continue 

1789 

1790 keep = False # Should this footprint be marked as clipped? 

1791 keepIndex = [] # Which tempExps does the clipped footprint belong to 

1792 

1793 # If footprint only has one overlap use a lower threshold 

1794 if len(overlap) == 1: 

1795 if overlap[0] > self.config.minClipFootOverlapSingle: 

1796 keep = True 

1797 keepIndex = [0] 

1798 else: 

1799 # This is the general case where only visit should be clipped 

1800 clipIndex = numpy.where(overlap > self.config.minClipFootOverlap)[0] 

1801 if len(clipIndex) == 1: 

1802 keep = True 

1803 keepIndex = [clipIndex[0]] 

1804 

1805 # Test if there are clipped objects that overlap two different visits 

1806 clipIndex = numpy.where(overlap > self.config.minClipFootOverlapDouble)[0] 

1807 if len(clipIndex) == 2 and len(overlap) > 3: 

1808 clipIndexComp = numpy.where(overlap <= self.config.minClipFootOverlapDouble)[0] 

1809 if numpy.max(overlap[clipIndexComp]) <= self.config.maxClipFootOverlapDouble: 

1810 keep = True 

1811 keepIndex = clipIndex 

1812 

1813 if not keep: 

1814 continue 

1815 

1816 for index in keepIndex: 

1817 globalIndex = indexList[index] 

1818 artifactSpanSets[globalIndex]['CLIPPED'].append(footprint.spans) 

1819 

1820 clipIndices.append(numpy.array(indexList)[keepIndex]) 

1821 clipFootprints.append(footprint) 

1822 

1823 return pipeBase.Struct(clipFootprints=clipFootprints, clipIndices=clipIndices, 

1824 clipSpans=artifactSpanSets, detectionFootprints=visitDetectionFootprints) 

1825 

1826 def detectClipBig(self, clipList, clipFootprints, clipIndices, detectionFootprints, 

1827 maskClipValue, maskDetValue, coaddBBox): 

1828 """Return individual warp footprints for large artifacts and append 

1829 them to ``clipList`` in place. 

1830 

1831 Identify big footprints composed of many sources in the coadd 

1832 difference that may have originated in a large diffuse source in the 

1833 coadd. We do this by indentifying all clipped footprints that overlap 

1834 significantly with each source in all the coaddTempExps. 

1835 

1836 Parameters 

1837 ---------- 

1838 clipList : `list` 

1839 List of alt mask SpanSets with clipping information. Modified. 

1840 clipFootprints : `list` 

1841 List of clipped footprints. 

1842 clipIndices : `list` 

1843 List of which entries in tempExpClipList each footprint belongs to. 

1844 maskClipValue 

1845 Mask value of clipped pixels. 

1846 maskDetValue 

1847 Mask value of detected pixels. 

1848 coaddBBox : `lsst.geom.Box` 

1849 BBox of the coadd and warps. 

1850 

1851 Returns 

1852 ------- 

1853 bigFootprintsCoadd : `list` 

1854 List of big footprints 

1855 """ 

1856 bigFootprintsCoadd = [] 

1857 ignoreMask = self.getBadPixelMask() 

1858 for index, (clippedSpans, visitFootprints) in enumerate(zip(clipList, detectionFootprints)): 

1859 maskVisitDet = afwImage.MaskX(coaddBBox, 0x0) 

1860 for footprint in visitFootprints.getFootprints(): 

1861 footprint.spans.setMask(maskVisitDet, maskDetValue) 

1862 

1863 # build a mask of clipped footprints that are in this visit 

1864 clippedFootprintsVisit = [] 

1865 for foot, clipIndex in zip(clipFootprints, clipIndices): 

1866 if index not in clipIndex: 

1867 continue 

1868 clippedFootprintsVisit.append(foot) 

1869 maskVisitClip = maskVisitDet.Factory(maskVisitDet.getBBox(afwImage.PARENT)) 

1870 afwDet.setMaskFromFootprintList(maskVisitClip, clippedFootprintsVisit, maskClipValue) 

1871 

1872 bigFootprintsVisit = [] 

1873 for foot in visitFootprints.getFootprints(): 

1874 if foot.getArea() < self.config.minBigOverlap: 

1875 continue 

1876 nCount = countMaskFromFootprint(maskVisitClip, foot, maskClipValue, ignoreMask) 

1877 if nCount > self.config.minBigOverlap: 

1878 bigFootprintsVisit.append(foot) 

1879 bigFootprintsCoadd.append(foot) 

1880 

1881 for footprint in bigFootprintsVisit: 

1882 clippedSpans["CLIPPED"].append(footprint.spans) 

1883 

1884 return bigFootprintsCoadd 

1885 

1886 

1887class CompareWarpAssembleCoaddConnections(AssembleCoaddConnections): 

1888 psfMatchedWarps = pipeBase.connectionTypes.Input( 

1889 doc=("PSF-Matched Warps are required by CompareWarp regardless of the coadd type requested. " 

1890 "Only PSF-Matched Warps make sense for image subtraction. " 

1891 "Therefore, they must be an additional declared input."), 

1892 name="{inputCoaddName}Coadd_psfMatchedWarp", 

1893 storageClass="ExposureF", 

1894 dimensions=("tract", "patch", "skymap", "visit"), 

1895 deferLoad=True, 

1896 multiple=True 

1897 ) 

1898 templateCoadd = pipeBase.connectionTypes.Output( 

1899 doc=("Model of the static sky, used to find temporal artifacts. Typically a PSF-Matched, " 

1900 "sigma-clipped coadd. Written if and only if assembleStaticSkyModel.doWrite=True"), 

1901 name="{outputCoaddName}CoaddPsfMatched", 

1902 storageClass="ExposureF", 

1903 dimensions=("tract", "patch", "skymap", "band"), 

1904 ) 

1905 

1906 def __init__(self, *, config=None): 

1907 super().__init__(config=config) 

1908 if not config.assembleStaticSkyModel.doWrite: 

1909 self.outputs.remove("templateCoadd") 

1910 config.validate() 

1911 

1912 

1913class CompareWarpAssembleCoaddConfig(AssembleCoaddConfig, 

1914 pipelineConnections=CompareWarpAssembleCoaddConnections): 

1915 assembleStaticSkyModel = pexConfig.ConfigurableField( 

1916 target=AssembleCoaddTask, 

1917 doc="Task to assemble an artifact-free, PSF-matched Coadd to serve as a" 

1918 " naive/first-iteration model of the static sky.", 

1919 ) 

1920 detect = pexConfig.ConfigurableField( 

1921 target=SourceDetectionTask, 

1922 doc="Detect outlier sources on difference between each psfMatched warp and static sky model" 

1923 ) 

1924 detectTemplate = pexConfig.ConfigurableField( 

1925 target=SourceDetectionTask, 

1926 doc="Detect sources on static sky model. Only used if doPreserveContainedBySource is True" 

1927 ) 

1928 maskStreaks = pexConfig.ConfigurableField( 

1929 target=MaskStreaksTask, 

1930 doc="Detect streaks on difference between each psfMatched warp and static sky model. Only used if " 

1931 "doFilterMorphological is True. Adds a mask plane to an exposure, with the mask plane name set by" 

1932 "streakMaskName" 

1933 ) 

1934 streakMaskName = pexConfig.Field( 

1935 dtype=str, 

1936 default="STREAK", 

1937 doc="Name of mask bit used for streaks" 

1938 ) 

1939 maxNumEpochs = pexConfig.Field( 

1940 doc="Charactistic maximum local number of epochs/visits in which an artifact candidate can appear " 

1941 "and still be masked. The effective maxNumEpochs is a broken linear function of local " 

1942 "number of epochs (N): min(maxFractionEpochsLow*N, maxNumEpochs + maxFractionEpochsHigh*N). " 

1943 "For each footprint detected on the image difference between the psfMatched warp and static sky " 

1944 "model, if a significant fraction of pixels (defined by spatialThreshold) are residuals in more " 

1945 "than the computed effective maxNumEpochs, the artifact candidate is deemed persistant rather " 

1946 "than transient and not masked.", 

1947 dtype=int, 

1948 default=2 

1949 ) 

1950 maxFractionEpochsLow = pexConfig.RangeField( 

1951 doc="Fraction of local number of epochs (N) to use as effective maxNumEpochs for low N. " 

1952 "Effective maxNumEpochs = " 

1953 "min(maxFractionEpochsLow * N, maxNumEpochs + maxFractionEpochsHigh * N)", 

1954 dtype=float, 

1955 default=0.4, 

1956 min=0., max=1., 

1957 ) 

1958 maxFractionEpochsHigh = pexConfig.RangeField( 

1959 doc="Fraction of local number of epochs (N) to use as effective maxNumEpochs for high N. " 

1960 "Effective maxNumEpochs = " 

1961 "min(maxFractionEpochsLow * N, maxNumEpochs + maxFractionEpochsHigh * N)", 

1962 dtype=float, 

1963 default=0.03, 

1964 min=0., max=1., 

1965 ) 

1966 spatialThreshold = pexConfig.RangeField( 

1967 doc="Unitless fraction of pixels defining how much of the outlier region has to meet the " 

1968 "temporal criteria. If 0, clip all. If 1, clip none.", 

1969 dtype=float, 

1970 default=0.5, 

1971 min=0., max=1., 

1972 inclusiveMin=True, inclusiveMax=True 

1973 ) 

1974 doScaleWarpVariance = pexConfig.Field( 

1975 doc="Rescale Warp variance plane using empirical noise?", 

1976 dtype=bool, 

1977 default=True, 

1978 ) 

1979 scaleWarpVariance = pexConfig.ConfigurableField( 

1980 target=ScaleVarianceTask, 

1981 doc="Rescale variance on warps", 

1982 ) 

1983 doPreserveContainedBySource = pexConfig.Field( 

1984 doc="Rescue artifacts from clipping that completely lie within a footprint detected" 

1985 "on the PsfMatched Template Coadd. Replicates a behavior of SafeClip.", 

1986 dtype=bool, 

1987 default=True, 

1988 ) 

1989 doPrefilterArtifacts = pexConfig.Field( 

1990 doc="Ignore artifact candidates that are mostly covered by the bad pixel mask, " 

1991 "because they will be excluded anyway. This prevents them from contributing " 

1992 "to the outlier epoch count image and potentially being labeled as persistant." 

1993 "'Mostly' is defined by the config 'prefilterArtifactsRatio'.", 

1994 dtype=bool, 

1995 default=True 

1996 ) 

1997 prefilterArtifactsMaskPlanes = pexConfig.ListField( 

1998 doc="Prefilter artifact candidates that are mostly covered by these bad mask planes.", 

1999 dtype=str, 

2000 default=('NO_DATA', 'BAD', 'SAT', 'SUSPECT'), 

2001 ) 

2002 prefilterArtifactsRatio = pexConfig.Field( 

2003 doc="Prefilter artifact candidates with less than this fraction overlapping good pixels", 

2004 dtype=float, 

2005 default=0.05 

2006 ) 

2007 doFilterMorphological = pexConfig.Field( 

2008 doc="Filter artifact candidates based on morphological criteria, i.g. those that appear to " 

2009 "be streaks.", 

2010 dtype=bool, 

2011 default=False 

2012 ) 

2013 

2014 def setDefaults(self): 

2015 AssembleCoaddConfig.setDefaults(self) 

2016 self.statistic = 'MEAN' 

2017 self.doUsePsfMatchedPolygons = True 

2018 

2019 # Real EDGE removed by psfMatched NO_DATA border half the width of the matching kernel 

2020 # CompareWarp applies psfMatched EDGE pixels to directWarps before assembling 

2021 if "EDGE" in self.badMaskPlanes: 

2022 self.badMaskPlanes.remove('EDGE') 

2023 self.removeMaskPlanes.append('EDGE') 

2024 self.assembleStaticSkyModel.badMaskPlanes = ["NO_DATA", ] 

2025 self.assembleStaticSkyModel.warpType = 'psfMatched' 

2026 self.assembleStaticSkyModel.connections.warpType = 'psfMatched' 

2027 self.assembleStaticSkyModel.statistic = 'MEANCLIP' 

2028 self.assembleStaticSkyModel.sigmaClip = 2.5 

2029 self.assembleStaticSkyModel.clipIter = 3 

2030 self.assembleStaticSkyModel.calcErrorFromInputVariance = False 

2031 self.assembleStaticSkyModel.doWrite = False 

2032 self.detect.doTempLocalBackground = False 

2033 self.detect.reEstimateBackground = False 

2034 self.detect.returnOriginalFootprints = False 

2035 self.detect.thresholdPolarity = "both" 

2036 self.detect.thresholdValue = 5 

2037 self.detect.minPixels = 4 

2038 self.detect.isotropicGrow = True 

2039 self.detect.thresholdType = "pixel_stdev" 

2040 self.detect.nSigmaToGrow = 0.4 

2041 # The default nSigmaToGrow for SourceDetectionTask is already 2.4, 

2042 # Explicitly restating because ratio with detect.nSigmaToGrow matters 

2043 self.detectTemplate.nSigmaToGrow = 2.4 

2044 self.detectTemplate.doTempLocalBackground = False 

2045 self.detectTemplate.reEstimateBackground = False 

2046 self.detectTemplate.returnOriginalFootprints = False 

2047 

2048 def validate(self): 

2049 super().validate() 

2050 if self.assembleStaticSkyModel.doNImage: 

2051 raise ValueError("No dataset type exists for a PSF-Matched Template N Image." 

2052 "Please set assembleStaticSkyModel.doNImage=False") 

2053 

2054 if self.assembleStaticSkyModel.doWrite and (self.warpType == self.assembleStaticSkyModel.warpType): 

2055 raise ValueError("warpType (%s) == assembleStaticSkyModel.warpType (%s) and will compete for " 

2056 "the same dataset name. Please set assembleStaticSkyModel.doWrite to False " 

2057 "or warpType to 'direct'. assembleStaticSkyModel.warpType should ways be " 

2058 "'PsfMatched'" % (self.warpType, self.assembleStaticSkyModel.warpType)) 

2059 

2060 

2061class CompareWarpAssembleCoaddTask(AssembleCoaddTask): 

2062 """Assemble a compareWarp coadded image from a set of warps 

2063 by masking artifacts detected by comparing PSF-matched warps. 

2064 

2065 In ``AssembleCoaddTask``, we compute the coadd as an clipped mean (i.e., 

2066 we clip outliers). The problem with doing this is that when computing the 

2067 coadd PSF at a given location, individual visit PSFs from visits with 

2068 outlier pixels contribute to the coadd PSF and cannot be treated correctly. 

2069 In this task, we correct for this behavior by creating a new badMaskPlane 

2070 'CLIPPED' which marks pixels in the individual warps suspected to contain 

2071 an artifact. We populate this plane on the input warps by comparing 

2072 PSF-matched warps with a PSF-matched median coadd which serves as a 

2073 model of the static sky. Any group of pixels that deviates from the 

2074 PSF-matched template coadd by more than config.detect.threshold sigma, 

2075 is an artifact candidate. The candidates are then filtered to remove 

2076 variable sources and sources that are difficult to subtract such as 

2077 bright stars. This filter is configured using the config parameters 

2078 ``temporalThreshold`` and ``spatialThreshold``. The temporalThreshold is 

2079 the maximum fraction of epochs that the deviation can appear in and still 

2080 be considered an artifact. The spatialThreshold is the maximum fraction of 

2081 pixels in the footprint of the deviation that appear in other epochs 

2082 (where other epochs is defined by the temporalThreshold). If the deviant 

2083 region meets this criteria of having a significant percentage of pixels 

2084 that deviate in only a few epochs, these pixels have the 'CLIPPED' bit 

2085 set in the mask. These regions will not contribute to the final coadd. 

2086 Furthermore, any routine to determine the coadd PSF can now be cognizant 

2087 of clipped regions. Note that the algorithm implemented by this task is 

2088 preliminary and works correctly for HSC data. Parameter modifications and 

2089 or considerable redesigning of the algorithm is likley required for other 

2090 surveys. 

2091 

2092 ``CompareWarpAssembleCoaddTask`` sub-classes 

2093 ``AssembleCoaddTask`` and instantiates ``AssembleCoaddTask`` 

2094 as a subtask to generate the TemplateCoadd (the model of the static sky). 

2095 

2096 Notes 

2097 ----- 

2098 The `lsst.pipe.base.cmdLineTask.CmdLineTask` interface supports a 

2099 flag ``-d`` to import ``debug.py`` from your ``PYTHONPATH``; see 

2100 ``baseDebug`` for more about ``debug.py`` files. 

2101 

2102 This task supports the following debug variables: 

2103 

2104 - ``saveCountIm`` 

2105 If True then save the Epoch Count Image as a fits file in the `figPath` 

2106 - ``figPath`` 

2107 Path to save the debug fits images and figures 

2108 

2109 For example, put something like: 

2110 

2111 .. code-block:: python 

2112 

2113 import lsstDebug 

2114 def DebugInfo(name): 

2115 di = lsstDebug.getInfo(name) 

2116 if name == "lsst.pipe.tasks.assembleCoadd": 

2117 di.saveCountIm = True 

2118 di.figPath = "/desired/path/to/debugging/output/images" 

2119 return di 

2120 lsstDebug.Info = DebugInfo 

2121 

2122 into your ``debug.py`` file and run ``assemebleCoadd.py`` with the 

2123 ``--debug`` flag. Some subtasks may have their own debug variables; 

2124 see individual Task documentation. 

2125 

2126 Examples 

2127 -------- 

2128 ``CompareWarpAssembleCoaddTask`` assembles a set of warped images into a 

2129 coadded image. The ``CompareWarpAssembleCoaddTask`` is invoked by running 

2130 ``assembleCoadd.py`` with the flag ``--compareWarpCoadd``. 

2131 Usage of ``assembleCoadd.py`` expects a data reference to the tract patch 

2132 and filter to be coadded (specified using 

2133 '--id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]') 

2134 along with a list of coaddTempExps to attempt to coadd (specified using 

2135 '--selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]'). 

2136 Only the warps that cover the specified tract and patch will be coadded. 

2137 A list of the available optional arguments can be obtained by calling 

2138 ``assembleCoadd.py`` with the ``--help`` command line argument: 

2139 

2140 .. code-block:: none 

2141 

2142 assembleCoadd.py --help 

2143 

2144 To demonstrate usage of the ``CompareWarpAssembleCoaddTask`` in the larger 

2145 context of multi-band processing, we will generate the HSC-I & -R band 

2146 oadds from HSC engineering test data provided in the ``ci_hsc`` package. 

2147 To begin, assuming that the lsst stack has been already set up, we must 

2148 set up the ``obs_subaru`` and ``ci_hsc`` packages. 

2149 This defines the environment variable ``$CI_HSC_DIR`` and points at the 

2150 location of the package. The raw HSC data live in the ``$CI_HSC_DIR/raw`` 

2151 directory. To begin assembling the coadds, we must first 

2152 

2153 - processCcd 

2154 process the individual ccds in $CI_HSC_RAW to produce calibrated exposures 

2155 - makeSkyMap 

2156 create a skymap that covers the area of the sky present in the raw exposures 

2157 - makeCoaddTempExp 

2158 warp the individual calibrated exposures to the tangent plane of the coadd 

2159 

2160 We can perform all of these steps by running 

2161 

2162 .. code-block:: none 

2163 

2164 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988 

2165 

2166 This will produce warped ``coaddTempExps`` for each visit. To coadd the 

2167 warped data, we call ``assembleCoadd.py`` as follows: 

2168 

2169 .. code-block:: none 

2170 

2171 assembleCoadd.py --compareWarpCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \ 

2172 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \ 

2173 --selectId visit=903986 ccd=100 --selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \ 

2174 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \ 

2175 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \ 

2176 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \ 

2177 --selectId visit=903988 ccd=24 

2178 

2179 This will process the HSC-I band data. The results are written in 

2180 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``. 

2181 """ 

2182 ConfigClass = CompareWarpAssembleCoaddConfig 

2183 _DefaultName = "compareWarpAssembleCoadd" 

2184 

2185 def __init__(self, *args, **kwargs): 

2186 AssembleCoaddTask.__init__(self, *args, **kwargs) 

2187 self.makeSubtask("assembleStaticSkyModel") 

2188 detectionSchema = afwTable.SourceTable.makeMinimalSchema() 

2189 self.makeSubtask("detect", schema=detectionSchema) 

2190 if self.config.doPreserveContainedBySource: 

2191 self.makeSubtask("detectTemplate", schema=afwTable.SourceTable.makeMinimalSchema()) 

2192 if self.config.doScaleWarpVariance: 

2193 self.makeSubtask("scaleWarpVariance") 

2194 if self.config.doFilterMorphological: 

2195 self.makeSubtask("maskStreaks") 

2196 

2197 @utils.inheritDoc(AssembleCoaddTask) 

2198 def makeSupplementaryDataGen3(self, butlerQC, inputRefs, outputRefs): 

2199 """ 

2200 Generate a templateCoadd to use as a naive model of static sky to 

2201 subtract from PSF-Matched warps. 

2202 

2203 Returns 

2204 ------- 

2205 result : `lsst.pipe.base.Struct` 

2206 Result struct with components: 

2207 

2208 - ``templateCoadd`` : coadded exposure (``lsst.afw.image.Exposure``) 

2209 - ``nImage`` : N Image (``lsst.afw.image.Image``) 

2210 """ 

2211 # Ensure that psfMatchedWarps are used as input warps for template generation 

2212 staticSkyModelInputRefs = copy.deepcopy(inputRefs) 

2213 staticSkyModelInputRefs.inputWarps = inputRefs.psfMatchedWarps 

2214 

2215 # Because subtasks don't have connections we have to make one. 

2216 # The main task's `templateCoadd` is the subtask's `coaddExposure` 

2217 staticSkyModelOutputRefs = copy.deepcopy(outputRefs) 

2218 if self.config.assembleStaticSkyModel.doWrite: 

2219 staticSkyModelOutputRefs.coaddExposure = staticSkyModelOutputRefs.templateCoadd 

2220 # Remove template coadd from both subtask's and main tasks outputs, 

2221 # because it is handled by the subtask as `coaddExposure` 

2222 del outputRefs.templateCoadd 

2223 del staticSkyModelOutputRefs.templateCoadd 

2224 

2225 # A PSF-Matched nImage does not exist as a dataset type 

2226 if 'nImage' in staticSkyModelOutputRefs.keys(): 

2227 del staticSkyModelOutputRefs.nImage 

2228 

2229 templateCoadd = self.assembleStaticSkyModel.runQuantum(butlerQC, staticSkyModelInputRefs, 

2230 staticSkyModelOutputRefs) 

2231 if templateCoadd is None: 

2232 raise RuntimeError(self._noTemplateMessage(self.assembleStaticSkyModel.warpType)) 

2233 

2234 return pipeBase.Struct(templateCoadd=templateCoadd.coaddExposure, 

2235 nImage=templateCoadd.nImage, 

2236 warpRefList=templateCoadd.warpRefList, 

2237 imageScalerList=templateCoadd.imageScalerList, 

2238 weightList=templateCoadd.weightList) 

2239 

2240 @utils.inheritDoc(AssembleCoaddTask) 

2241 def makeSupplementaryData(self, dataRef, selectDataList=None, warpRefList=None): 

2242 """ 

2243 Generate a templateCoadd to use as a naive model of static sky to 

2244 subtract from PSF-Matched warps. 

2245 

2246 Returns 

2247 ------- 

2248 result : `lsst.pipe.base.Struct` 

2249 Result struct with components: 

2250 

2251 - ``templateCoadd``: coadded exposure (``lsst.afw.image.Exposure``) 

2252 - ``nImage``: N Image (``lsst.afw.image.Image``) 

2253 """ 

2254 templateCoadd = self.assembleStaticSkyModel.runDataRef(dataRef, selectDataList, warpRefList) 

2255 if templateCoadd is None: 

2256 raise RuntimeError(self._noTemplateMessage(self.assembleStaticSkyModel.warpType)) 

2257 

2258 return pipeBase.Struct(templateCoadd=templateCoadd.coaddExposure, 

2259 nImage=templateCoadd.nImage, 

2260 warpRefList=templateCoadd.warpRefList, 

2261 imageScalerList=templateCoadd.imageScalerList, 

2262 weightList=templateCoadd.weightList) 

2263 

2264 def _noTemplateMessage(self, warpType): 

2265 warpName = (warpType[0].upper() + warpType[1:]) 

2266 message = """No %(warpName)s warps were found to build the template coadd which is 

2267 required to run CompareWarpAssembleCoaddTask. To continue assembling this type of coadd, 

2268 first either rerun makeCoaddTempExp with config.make%(warpName)s=True or 

2269 coaddDriver with config.makeCoadTempExp.make%(warpName)s=True, before assembleCoadd. 

2270 

2271 Alternatively, to use another algorithm with existing warps, retarget the CoaddDriverConfig to 

2272 another algorithm like: 

2273 

2274 from lsst.pipe.tasks.assembleCoadd import SafeClipAssembleCoaddTask 

2275 config.assemble.retarget(SafeClipAssembleCoaddTask) 

2276 """ % {"warpName": warpName} 

2277 return message 

2278 

2279 @utils.inheritDoc(AssembleCoaddTask) 

2280 @timeMethod 

2281 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, 

2282 supplementaryData, *args, **kwargs): 

2283 """Assemble the coadd. 

2284 

2285 Find artifacts and apply them to the warps' masks creating a list of 

2286 alternative masks with a new "CLIPPED" plane and updated "NO_DATA" 

2287 plane. Then pass these alternative masks to the base class's `run` 

2288 method. 

2289 

2290 The input parameters ``supplementaryData`` is a `lsst.pipe.base.Struct` 

2291 that must contain a ``templateCoadd`` that serves as the 

2292 model of the static sky. 

2293 """ 

2294 

2295 # Check and match the order of the supplementaryData 

2296 # (PSF-matched) inputs to the order of the direct inputs, 

2297 # so that the artifact mask is applied to the right warp 

2298 dataIds = [ref.dataId for ref in tempExpRefList] 

2299 psfMatchedDataIds = [ref.dataId for ref in supplementaryData.warpRefList] 

2300 

2301 if dataIds != psfMatchedDataIds: 

2302 self.log.info("Reordering and or/padding PSF-matched visit input list") 

2303 supplementaryData.warpRefList = reorderAndPadList(supplementaryData.warpRefList, 

2304 psfMatchedDataIds, dataIds) 

2305 supplementaryData.imageScalerList = reorderAndPadList(supplementaryData.imageScalerList, 

2306 psfMatchedDataIds, dataIds) 

2307 

2308 # Use PSF-Matched Warps (and corresponding scalers) and coadd to find artifacts 

2309 spanSetMaskList = self.findArtifacts(supplementaryData.templateCoadd, 

2310 supplementaryData.warpRefList, 

2311 supplementaryData.imageScalerList) 

2312 

2313 badMaskPlanes = self.config.badMaskPlanes[:] 

2314 badMaskPlanes.append("CLIPPED") 

2315 badPixelMask = afwImage.Mask.getPlaneBitMask(badMaskPlanes) 

2316 

2317 result = AssembleCoaddTask.run(self, skyInfo, tempExpRefList, imageScalerList, weightList, 

2318 spanSetMaskList, mask=badPixelMask) 

2319 

2320 # Propagate PSF-matched EDGE pixels to coadd SENSOR_EDGE and INEXACT_PSF 

2321 # Psf-Matching moves the real edge inwards 

2322 self.applyAltEdgeMask(result.coaddExposure.maskedImage.mask, spanSetMaskList) 

2323 return result 

2324 

2325 def applyAltEdgeMask(self, mask, altMaskList): 

2326 """Propagate alt EDGE mask to SENSOR_EDGE AND INEXACT_PSF planes. 

2327 

2328 Parameters 

2329 ---------- 

2330 mask : `lsst.afw.image.Mask` 

2331 Original mask. 

2332 altMaskList : `list` 

2333 List of Dicts containing ``spanSet`` lists. 

2334 Each element contains the new mask plane name (e.g. "CLIPPED 

2335 and/or "NO_DATA") as the key, and list of ``SpanSets`` to apply to 

2336 the mask. 

2337 """ 

2338 maskValue = mask.getPlaneBitMask(["SENSOR_EDGE", "INEXACT_PSF"]) 

2339 for visitMask in altMaskList: 

2340 if "EDGE" in visitMask: 

2341 for spanSet in visitMask['EDGE']: 

2342 spanSet.clippedTo(mask.getBBox()).setMask(mask, maskValue) 

2343 

2344 def findArtifacts(self, templateCoadd, tempExpRefList, imageScalerList): 

2345 """Find artifacts. 

2346 

2347 Loop through warps twice. The first loop builds a map with the count 

2348 of how many epochs each pixel deviates from the templateCoadd by more 

2349 than ``config.chiThreshold`` sigma. The second loop takes each 

2350 difference image and filters the artifacts detected in each using 

2351 count map to filter out variable sources and sources that are 

2352 difficult to subtract cleanly. 

2353 

2354 Parameters 

2355 ---------- 

2356 templateCoadd : `lsst.afw.image.Exposure` 

2357 Exposure to serve as model of static sky. 

2358 tempExpRefList : `list` 

2359 List of data references to warps. 

2360 imageScalerList : `list` 

2361 List of image scalers. 

2362 

2363 Returns 

2364 ------- 

2365 altMasks : `list` 

2366 List of dicts containing information about CLIPPED 

2367 (i.e., artifacts), NO_DATA, and EDGE pixels. 

2368 """ 

2369 

2370 self.log.debug("Generating Count Image, and mask lists.") 

2371 coaddBBox = templateCoadd.getBBox() 

2372 slateIm = afwImage.ImageU(coaddBBox) 

2373 epochCountImage = afwImage.ImageU(coaddBBox) 

2374 nImage = afwImage.ImageU(coaddBBox) 

2375 spanSetArtifactList = [] 

2376 spanSetNoDataMaskList = [] 

2377 spanSetEdgeList = [] 

2378 spanSetBadMorphoList = [] 

2379 badPixelMask = self.getBadPixelMask() 

2380 

2381 # mask of the warp diffs should = that of only the warp 

2382 templateCoadd.mask.clearAllMaskPlanes() 

2383 

2384 if self.config.doPreserveContainedBySource: 

2385 templateFootprints = self.detectTemplate.detectFootprints(templateCoadd) 

2386 else: 

2387 templateFootprints = None 

2388 

2389 for warpRef, imageScaler in zip(tempExpRefList, imageScalerList): 

2390 warpDiffExp = self._readAndComputeWarpDiff(warpRef, imageScaler, templateCoadd) 

2391 if warpDiffExp is not None: 

2392 # This nImage only approximates the final nImage because it uses the PSF-matched mask 

2393 nImage.array += (numpy.isfinite(warpDiffExp.image.array) 

2394 * ((warpDiffExp.mask.array & badPixelMask) == 0)).astype(numpy.uint16) 

2395 fpSet = self.detect.detectFootprints(warpDiffExp, doSmooth=False, clearMask=True) 

2396 fpSet.positive.merge(fpSet.negative) 

2397 footprints = fpSet.positive 

2398 slateIm.set(0) 

2399 spanSetList = [footprint.spans for footprint in footprints.getFootprints()] 

2400 

2401 # Remove artifacts due to defects before they contribute to the epochCountImage 

2402 if self.config.doPrefilterArtifacts: 

2403 spanSetList = self.prefilterArtifacts(spanSetList, warpDiffExp) 

2404 

2405 # Clear mask before adding prefiltered spanSets 

2406 self.detect.clearMask(warpDiffExp.mask) 

2407 for spans in spanSetList: 

2408 spans.setImage(slateIm, 1, doClip=True) 

2409 spans.setMask(warpDiffExp.mask, warpDiffExp.mask.getPlaneBitMask("DETECTED")) 

2410 epochCountImage += slateIm 

2411 

2412 if self.config.doFilterMorphological: 

2413 maskName = self.config.streakMaskName 

2414 _ = self.maskStreaks.run(warpDiffExp) 

2415 streakMask = warpDiffExp.mask 

2416 spanSetStreak = afwGeom.SpanSet.fromMask(streakMask, 

2417 streakMask.getPlaneBitMask(maskName)).split() 

2418 

2419 # PSF-Matched warps have less available area (~the matching kernel) because the calexps 

2420 # undergo a second convolution. Pixels with data in the direct warp 

2421 # but not in the PSF-matched warp will not have their artifacts detected. 

2422 # NaNs from the PSF-matched warp therefore must be masked in the direct warp 

2423 nans = numpy.where(numpy.isnan(warpDiffExp.maskedImage.image.array), 1, 0) 

2424 nansMask = afwImage.makeMaskFromArray(nans.astype(afwImage.MaskPixel)) 

2425 nansMask.setXY0(warpDiffExp.getXY0()) 

2426 edgeMask = warpDiffExp.mask 

2427 spanSetEdgeMask = afwGeom.SpanSet.fromMask(edgeMask, 

2428 edgeMask.getPlaneBitMask("EDGE")).split() 

2429 else: 

2430 # If the directWarp has <1% coverage, the psfMatchedWarp can have 0% and not exist 

2431 # In this case, mask the whole epoch 

2432 nansMask = afwImage.MaskX(coaddBBox, 1) 

2433 spanSetList = [] 

2434 spanSetEdgeMask = [] 

2435 spanSetStreak = [] 

2436 

2437 spanSetNoDataMask = afwGeom.SpanSet.fromMask(nansMask).split() 

2438 

2439 spanSetNoDataMaskList.append(spanSetNoDataMask) 

2440 spanSetArtifactList.append(spanSetList) 

2441 spanSetEdgeList.append(spanSetEdgeMask) 

2442 if self.config.doFilterMorphological: 

2443 spanSetBadMorphoList.append(spanSetStreak) 

2444 

2445 if lsstDebug.Info(__name__).saveCountIm: 

2446 path = self._dataRef2DebugPath("epochCountIm", tempExpRefList[0], coaddLevel=True) 

2447 epochCountImage.writeFits(path) 

2448 

2449 for i, spanSetList in enumerate(spanSetArtifactList): 

2450 if spanSetList: 

2451 filteredSpanSetList = self.filterArtifacts(spanSetList, epochCountImage, nImage, 

2452 templateFootprints) 

2453 spanSetArtifactList[i] = filteredSpanSetList 

2454 if self.config.doFilterMorphological: 

2455 spanSetArtifactList[i] += spanSetBadMorphoList[i] 

2456 

2457 altMasks = [] 

2458 for artifacts, noData, edge in zip(spanSetArtifactList, spanSetNoDataMaskList, spanSetEdgeList): 

2459 altMasks.append({'CLIPPED': artifacts, 

2460 'NO_DATA': noData, 

2461 'EDGE': edge}) 

2462 return altMasks 

2463 

2464 def prefilterArtifacts(self, spanSetList, exp): 

2465 """Remove artifact candidates covered by bad mask plane. 

2466 

2467 Any future editing of the candidate list that does not depend on 

2468 temporal information should go in this method. 

2469 

2470 Parameters 

2471 ---------- 

2472 spanSetList : `list` 

2473 List of SpanSets representing artifact candidates. 

2474 exp : `lsst.afw.image.Exposure` 

2475 Exposure containing mask planes used to prefilter. 

2476 

2477 Returns 

2478 ------- 

2479 returnSpanSetList : `list` 

2480 List of SpanSets with artifacts. 

2481 """ 

2482 badPixelMask = exp.mask.getPlaneBitMask(self.config.prefilterArtifactsMaskPlanes) 

2483 goodArr = (exp.mask.array & badPixelMask) == 0 

2484 returnSpanSetList = [] 

2485 bbox = exp.getBBox() 

2486 x0, y0 = exp.getXY0() 

2487 for i, span in enumerate(spanSetList): 

2488 y, x = span.clippedTo(bbox).indices() 

2489 yIndexLocal = numpy.array(y) - y0 

2490 xIndexLocal = numpy.array(x) - x0 

2491 goodRatio = numpy.count_nonzero(goodArr[yIndexLocal, xIndexLocal])/span.getArea() 

2492 if goodRatio > self.config.prefilterArtifactsRatio: 

2493 returnSpanSetList.append(span) 

2494 return returnSpanSetList 

2495 

2496 def filterArtifacts(self, spanSetList, epochCountImage, nImage, footprintsToExclude=None): 

2497 """Filter artifact candidates. 

2498 

2499 Parameters 

2500 ---------- 

2501 spanSetList : `list` 

2502 List of SpanSets representing artifact candidates. 

2503 epochCountImage : `lsst.afw.image.Image` 

2504 Image of accumulated number of warpDiff detections. 

2505 nImage : `lsst.afw.image.Image` 

2506 Image of the accumulated number of total epochs contributing. 

2507 

2508 Returns 

2509 ------- 

2510 maskSpanSetList : `list` 

2511 List of SpanSets with artifacts. 

2512 """ 

2513 

2514 maskSpanSetList = [] 

2515 x0, y0 = epochCountImage.getXY0() 

2516 for i, span in enumerate(spanSetList): 

2517 y, x = span.indices() 

2518 yIdxLocal = [y1 - y0 for y1 in y] 

2519 xIdxLocal = [x1 - x0 for x1 in x] 

2520 outlierN = epochCountImage.array[yIdxLocal, xIdxLocal] 

2521 totalN = nImage.array[yIdxLocal, xIdxLocal] 

2522 

2523 # effectiveMaxNumEpochs is broken line (fraction of N) with characteristic config.maxNumEpochs 

2524 effMaxNumEpochsHighN = (self.config.maxNumEpochs 

2525 + self.config.maxFractionEpochsHigh*numpy.mean(totalN)) 

2526 effMaxNumEpochsLowN = self.config.maxFractionEpochsLow * numpy.mean(totalN) 

2527 effectiveMaxNumEpochs = int(min(effMaxNumEpochsLowN, effMaxNumEpochsHighN)) 

2528 nPixelsBelowThreshold = numpy.count_nonzero((outlierN > 0) 

2529 & (outlierN <= effectiveMaxNumEpochs)) 

2530 percentBelowThreshold = nPixelsBelowThreshold / len(outlierN) 

2531 if percentBelowThreshold > self.config.spatialThreshold: 

2532 maskSpanSetList.append(span) 

2533 

2534 if self.config.doPreserveContainedBySource and footprintsToExclude is not None: 

2535 # If a candidate is contained by a footprint on the template coadd, do not clip 

2536 filteredMaskSpanSetList = [] 

2537 for span in maskSpanSetList: 

2538 doKeep = True 

2539 for footprint in footprintsToExclude.positive.getFootprints(): 

2540 if footprint.spans.contains(span): 

2541 doKeep = False 

2542 break 

2543 if doKeep: 

2544 filteredMaskSpanSetList.append(span) 

2545 maskSpanSetList = filteredMaskSpanSetList 

2546 

2547 return maskSpanSetList 

2548 

2549 def _readAndComputeWarpDiff(self, warpRef, imageScaler, templateCoadd): 

2550 """Fetch a warp from the butler and return a warpDiff. 

2551 

2552 Parameters 

2553 ---------- 

2554 warpRef : `lsst.daf.persistence.butlerSubset.ButlerDataRef` 

2555 Butler dataRef for the warp. 

2556 imageScaler : `lsst.pipe.tasks.scaleZeroPoint.ImageScaler` 

2557 An image scaler object. 

2558 templateCoadd : `lsst.afw.image.Exposure` 

2559 Exposure to be substracted from the scaled warp. 

2560 

2561 Returns 

2562 ------- 

2563 warp : `lsst.afw.image.Exposure` 

2564 Exposure of the image difference between the warp and template. 

2565 """ 

2566 

2567 # If the PSF-Matched warp did not exist for this direct warp 

2568 # None is holding its place to maintain order in Gen 3 

2569 if warpRef is None: 

2570 return None 

2571 # Warp comparison must use PSF-Matched Warps regardless of requested coadd warp type 

2572 warpName = self.getTempExpDatasetName('psfMatched') 

2573 if not isinstance(warpRef, DeferredDatasetHandle): 

2574 if not warpRef.datasetExists(warpName): 

2575 self.log.warning("Could not find %s %s; skipping it", warpName, warpRef.dataId) 

2576 return None 

2577 warp = warpRef.get(datasetType=warpName, immediate=True) 

2578 # direct image scaler OK for PSF-matched Warp 

2579 imageScaler.scaleMaskedImage(warp.getMaskedImage()) 

2580 mi = warp.getMaskedImage() 

2581 if self.config.doScaleWarpVariance: 

2582 try: 

2583 self.scaleWarpVariance.run(mi) 

2584 except Exception as exc: 

2585 self.log.warning("Unable to rescale variance of warp (%s); leaving it as-is", exc) 

2586 mi -= templateCoadd.getMaskedImage() 

2587 return warp 

2588 

2589 def _dataRef2DebugPath(self, prefix, warpRef, coaddLevel=False): 

2590 """Return a path to which to write debugging output. 

2591 

2592 Creates a hyphen-delimited string of dataId values for simple filenames. 

2593 

2594 Parameters 

2595 ---------- 

2596 prefix : `str` 

2597 Prefix for filename. 

2598 warpRef : `lsst.daf.persistence.butlerSubset.ButlerDataRef` 

2599 Butler dataRef to make the path from. 

2600 coaddLevel : `bool`, optional. 

2601 If True, include only coadd-level keys (e.g., 'tract', 'patch', 

2602 'filter', but no 'visit'). 

2603 

2604 Returns 

2605 ------- 

2606 result : `str` 

2607 Path for debugging output. 

2608 """ 

2609 if coaddLevel: 

2610 keys = warpRef.getButler().getKeys(self.getCoaddDatasetName(self.warpType)) 

2611 else: 

2612 keys = warpRef.dataId.keys() 

2613 keyList = sorted(keys, reverse=True) 

2614 directory = lsstDebug.Info(__name__).figPath if lsstDebug.Info(__name__).figPath else "." 

2615 filename = "%s-%s.fits" % (prefix, '-'.join([str(warpRef.dataId[k]) for k in keyList])) 

2616 return os.path.join(directory, filename)