Coverage for python/lsst/pipe/tasks/assembleCoadd.py: 16%
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5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
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22__all__ = ["AssembleCoaddTask", "AssembleCoaddConnections", "AssembleCoaddConfig",
23 "CompareWarpAssembleCoaddTask", "CompareWarpAssembleCoaddConfig"]
25import copy
26import numpy
27import warnings
28import logging
29import lsst.pex.config as pexConfig
30import lsst.pex.exceptions as pexExceptions
31import lsst.geom as geom
32import lsst.afw.geom as afwGeom
33import lsst.afw.image as afwImage
34import lsst.afw.math as afwMath
35import lsst.afw.table as afwTable
36import lsst.coadd.utils as coaddUtils
37import lsst.pipe.base as pipeBase
38import lsst.meas.algorithms as measAlg
39import lsstDebug
40import lsst.utils as utils
41from lsst.skymap import BaseSkyMap
42from .coaddBase import CoaddBaseTask, makeSkyInfo, reorderAndPadList
43from .interpImage import InterpImageTask
44from .scaleZeroPoint import ScaleZeroPointTask
45from .maskStreaks import MaskStreaksTask
46from .healSparseMapping import HealSparseInputMapTask
47from lsst.meas.algorithms import SourceDetectionTask, AccumulatorMeanStack, ScaleVarianceTask
48from lsst.utils.timer import timeMethod
49from deprecated.sphinx import deprecated
51log = logging.getLogger(__name__)
54class AssembleCoaddConnections(pipeBase.PipelineTaskConnections,
55 dimensions=("tract", "patch", "band", "skymap"),
56 defaultTemplates={"inputCoaddName": "deep",
57 "outputCoaddName": "deep",
58 "warpType": "direct",
59 "warpTypeSuffix": ""}):
61 inputWarps = pipeBase.connectionTypes.Input(
62 doc=("Input list of warps to be assemebled i.e. stacked."
63 "WarpType (e.g. direct, psfMatched) is controlled by the warpType config parameter"),
64 name="{inputCoaddName}Coadd_{warpType}Warp",
65 storageClass="ExposureF",
66 dimensions=("tract", "patch", "skymap", "visit", "instrument"),
67 deferLoad=True,
68 multiple=True
69 )
70 skyMap = pipeBase.connectionTypes.Input(
71 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures",
72 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
73 storageClass="SkyMap",
74 dimensions=("skymap", ),
75 )
76 selectedVisits = pipeBase.connectionTypes.Input(
77 doc="Selected visits to be coadded.",
78 name="{outputCoaddName}Visits",
79 storageClass="StructuredDataDict",
80 dimensions=("instrument", "tract", "patch", "skymap", "band")
81 )
82 brightObjectMask = pipeBase.connectionTypes.PrerequisiteInput(
83 doc=("Input Bright Object Mask mask produced with external catalogs to be applied to the mask plane"
84 " BRIGHT_OBJECT."),
85 name="brightObjectMask",
86 storageClass="ObjectMaskCatalog",
87 dimensions=("tract", "patch", "skymap", "band"),
88 minimum=0,
89 )
90 coaddExposure = pipeBase.connectionTypes.Output(
91 doc="Output coadded exposure, produced by stacking input warps",
92 name="{outputCoaddName}Coadd{warpTypeSuffix}",
93 storageClass="ExposureF",
94 dimensions=("tract", "patch", "skymap", "band"),
95 )
96 nImage = pipeBase.connectionTypes.Output(
97 doc="Output image of number of input images per pixel",
98 name="{outputCoaddName}Coadd_nImage",
99 storageClass="ImageU",
100 dimensions=("tract", "patch", "skymap", "band"),
101 )
102 inputMap = pipeBase.connectionTypes.Output(
103 doc="Output healsparse map of input images",
104 name="{outputCoaddName}Coadd_inputMap",
105 storageClass="HealSparseMap",
106 dimensions=("tract", "patch", "skymap", "band"),
107 )
109 def __init__(self, *, config=None):
110 super().__init__(config=config)
112 if not config.doMaskBrightObjects:
113 self.prerequisiteInputs.remove("brightObjectMask")
115 if not config.doSelectVisits:
116 self.inputs.remove("selectedVisits")
118 if not config.doNImage:
119 self.outputs.remove("nImage")
121 if not self.config.doInputMap:
122 self.outputs.remove("inputMap")
125class AssembleCoaddConfig(CoaddBaseTask.ConfigClass, pipeBase.PipelineTaskConfig,
126 pipelineConnections=AssembleCoaddConnections):
127 warpType = pexConfig.Field(
128 doc="Warp name: one of 'direct' or 'psfMatched'",
129 dtype=str,
130 default="direct",
131 )
132 subregionSize = pexConfig.ListField(
133 dtype=int,
134 doc="Width, height of stack subregion size; "
135 "make small enough that a full stack of images will fit into memory at once.",
136 length=2,
137 default=(2000, 2000),
138 )
139 statistic = pexConfig.Field(
140 dtype=str,
141 doc="Main stacking statistic for aggregating over the epochs.",
142 default="MEANCLIP",
143 )
144 doOnlineForMean = pexConfig.Field(
145 dtype=bool,
146 doc="Perform online coaddition when statistic=\"MEAN\" to save memory?",
147 default=False,
148 )
149 doSigmaClip = pexConfig.Field(
150 dtype=bool,
151 doc="Perform sigma clipped outlier rejection with MEANCLIP statistic? (DEPRECATED)",
152 default=False,
153 )
154 sigmaClip = pexConfig.Field(
155 dtype=float,
156 doc="Sigma for outlier rejection; ignored if non-clipping statistic selected.",
157 default=3.0,
158 )
159 clipIter = pexConfig.Field(
160 dtype=int,
161 doc="Number of iterations of outlier rejection; ignored if non-clipping statistic selected.",
162 default=2,
163 )
164 calcErrorFromInputVariance = pexConfig.Field(
165 dtype=bool,
166 doc="Calculate coadd variance from input variance by stacking statistic."
167 "Passed to StatisticsControl.setCalcErrorFromInputVariance()",
168 default=True,
169 )
170 scaleZeroPoint = pexConfig.ConfigurableField(
171 target=ScaleZeroPointTask,
172 doc="Task to adjust the photometric zero point of the coadd temp exposures",
173 )
174 doInterp = pexConfig.Field(
175 doc="Interpolate over NaN pixels? Also extrapolate, if necessary, but the results are ugly.",
176 dtype=bool,
177 default=True,
178 )
179 interpImage = pexConfig.ConfigurableField(
180 target=InterpImageTask,
181 doc="Task to interpolate (and extrapolate) over NaN pixels",
182 )
183 doWrite = pexConfig.Field(
184 doc="Persist coadd?",
185 dtype=bool,
186 default=True,
187 )
188 doNImage = pexConfig.Field(
189 doc="Create image of number of contributing exposures for each pixel",
190 dtype=bool,
191 default=False,
192 )
193 doUsePsfMatchedPolygons = pexConfig.Field(
194 doc="Use ValidPolygons from shrunk Psf-Matched Calexps? Should be set to True by CompareWarp only.",
195 dtype=bool,
196 default=False,
197 )
198 maskPropagationThresholds = pexConfig.DictField(
199 keytype=str,
200 itemtype=float,
201 doc=("Threshold (in fractional weight) of rejection at which we propagate a mask plane to "
202 "the coadd; that is, we set the mask bit on the coadd if the fraction the rejected frames "
203 "would have contributed exceeds this value."),
204 default={"SAT": 0.1},
205 )
206 removeMaskPlanes = pexConfig.ListField(dtype=str, default=["NOT_DEBLENDED"],
207 doc="Mask planes to remove before coadding")
208 doMaskBrightObjects = pexConfig.Field(dtype=bool, default=False,
209 doc="Set mask and flag bits for bright objects?")
210 brightObjectMaskName = pexConfig.Field(dtype=str, default="BRIGHT_OBJECT",
211 doc="Name of mask bit used for bright objects")
212 coaddPsf = pexConfig.ConfigField(
213 doc="Configuration for CoaddPsf",
214 dtype=measAlg.CoaddPsfConfig,
215 )
216 doAttachTransmissionCurve = pexConfig.Field(
217 dtype=bool, default=False, optional=False,
218 doc=("Attach a piecewise TransmissionCurve for the coadd? "
219 "(requires all input Exposures to have TransmissionCurves).")
220 )
221 hasFakes = pexConfig.Field(
222 dtype=bool,
223 default=False,
224 doc="Should be set to True if fake sources have been inserted into the input data."
225 )
226 doSelectVisits = pexConfig.Field(
227 doc="Coadd only visits selected by a SelectVisitsTask",
228 dtype=bool,
229 default=False,
230 )
231 doInputMap = pexConfig.Field(
232 doc="Create a bitwise map of coadd inputs",
233 dtype=bool,
234 default=False,
235 )
236 inputMapper = pexConfig.ConfigurableField(
237 doc="Input map creation subtask.",
238 target=HealSparseInputMapTask,
239 )
241 def setDefaults(self):
242 super().setDefaults()
243 self.badMaskPlanes = ["NO_DATA", "BAD", "SAT", "EDGE"]
245 def validate(self):
246 super().validate()
247 if self.doPsfMatch: # TODO: Remove this in DM-39841
248 # Backwards compatibility.
249 # Configs do not have loggers
250 log.warning("Config doPsfMatch deprecated. Setting warpType='psfMatched'")
251 self.warpType = 'psfMatched'
252 if self.doSigmaClip and self.statistic != "MEANCLIP":
253 log.warning('doSigmaClip deprecated. To replicate behavior, setting statistic to "MEANCLIP"')
254 self.statistic = "MEANCLIP"
255 if self.doInterp and self.statistic not in ['MEAN', 'MEDIAN', 'MEANCLIP', 'VARIANCE', 'VARIANCECLIP']:
256 raise ValueError("Must set doInterp=False for statistic=%s, which does not "
257 "compute and set a non-zero coadd variance estimate." % (self.statistic))
259 unstackableStats = ['NOTHING', 'ERROR', 'ORMASK']
260 if not hasattr(afwMath.Property, self.statistic) or self.statistic in unstackableStats:
261 stackableStats = [str(k) for k in afwMath.Property.__members__.keys()
262 if str(k) not in unstackableStats]
263 raise ValueError("statistic %s is not allowed. Please choose one of %s."
264 % (self.statistic, stackableStats))
267class AssembleCoaddTask(CoaddBaseTask, pipeBase.PipelineTask):
268 """Assemble a coadded image from a set of warps.
270 Each Warp that goes into a coadd will typically have an independent
271 photometric zero-point. Therefore, we must scale each Warp to set it to
272 a common photometric zeropoint. WarpType may be one of 'direct' or
273 'psfMatched', and the boolean configs `config.makeDirect` and
274 `config.makePsfMatched` set which of the warp types will be coadded.
275 The coadd is computed as a mean with optional outlier rejection.
276 Criteria for outlier rejection are set in `AssembleCoaddConfig`.
277 Finally, Warps can have bad 'NaN' pixels which received no input from the
278 source calExps. We interpolate over these bad (NaN) pixels.
280 `AssembleCoaddTask` uses several sub-tasks. These are
282 - `~lsst.pipe.tasks.ScaleZeroPointTask`
283 - create and use an ``imageScaler`` object to scale the photometric zeropoint for each Warp
284 - `~lsst.pipe.tasks.InterpImageTask`
285 - interpolate across bad pixels (NaN) in the final coadd
287 You can retarget these subtasks if you wish.
289 Raises
290 ------
291 RuntimeError
292 Raised if unable to define mask plane for bright objects.
294 Notes
295 -----
296 Debugging:
297 `AssembleCoaddTask` has no debug variables of its own. Some of the
298 subtasks may support `~lsst.base.lsstDebug` variables. See the
299 documentation for the subtasks for further information.
301 Examples
302 --------
303 `AssembleCoaddTask` assembles a set of warped images into a coadded image.
304 The `AssembleCoaddTask` can be invoked by running ``assembleCoadd.py``
305 with the flag '--legacyCoadd'. Usage of assembleCoadd.py expects two
306 inputs: a data reference to the tract patch and filter to be coadded, and
307 a list of Warps to attempt to coadd. These are specified using ``--id`` and
308 ``--selectId``, respectively:
310 .. code-block:: none
312 --id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]
313 --selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]
315 Only the Warps that cover the specified tract and patch will be coadded.
316 A list of the available optional arguments can be obtained by calling
317 ``assembleCoadd.py`` with the ``--help`` command line argument:
319 .. code-block:: none
321 assembleCoadd.py --help
323 To demonstrate usage of the `AssembleCoaddTask` in the larger context of
324 multi-band processing, we will generate the HSC-I & -R band coadds from
325 HSC engineering test data provided in the ``ci_hsc`` package. To begin,
326 assuming that the lsst stack has been already set up, we must set up the
327 obs_subaru and ``ci_hsc`` packages. This defines the environment variable
328 ``$CI_HSC_DIR`` and points at the location of the package. The raw HSC
329 data live in the ``$CI_HSC_DIR/raw directory``. To begin assembling the
330 coadds, we must first run:
332 - processCcd
333 - process the individual ccds in $CI_HSC_RAW to produce calibrated exposures
334 - makeSkyMap
335 - create a skymap that covers the area of the sky present in the raw exposures
336 - makeCoaddTempExp
337 - warp the individual calibrated exposures to the tangent plane of the coadd
339 We can perform all of these steps by running
341 .. code-block:: none
343 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988
345 This will produce warped exposures for each visit. To coadd the warped
346 data, we call assembleCoadd.py as follows:
348 .. code-block:: none
350 assembleCoadd.py --legacyCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \
351 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \
352 --selectId visit=903986 ccd=100 --selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \
353 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \
354 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \
355 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \
356 --selectId visit=903988 ccd=24
358 that will process the HSC-I band data. The results are written in
359 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``.
361 You may also choose to run:
363 .. code-block:: none
365 scons warp-903334 warp-903336 warp-903338 warp-903342 warp-903344 warp-903346
366 assembleCoadd.py --legacyCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-R \
367 --selectId visit=903334 ccd=16 --selectId visit=903334 ccd=22 --selectId visit=903334 ccd=23 \
368 --selectId visit=903334 ccd=100 --selectId visit=903336 ccd=17 --selectId visit=903336 ccd=24 \
369 --selectId visit=903338 ccd=18 --selectId visit=903338 ccd=25 --selectId visit=903342 ccd=4 \
370 --selectId visit=903342 ccd=10 --selectId visit=903342 ccd=100 --selectId visit=903344 ccd=0 \
371 --selectId visit=903344 ccd=5 --selectId visit=903344 ccd=11 --selectId visit=903346 ccd=1 \
372 --selectId visit=903346 ccd=6 --selectId visit=903346 ccd=12
374 to generate the coadd for the HSC-R band if you are interested in
375 following multiBand Coadd processing as discussed in `pipeTasks_multiBand`
376 (but note that normally, one would use the `SafeClipAssembleCoaddTask`
377 rather than `AssembleCoaddTask` to make the coadd.
378 """
380 ConfigClass = AssembleCoaddConfig
381 _DefaultName = "assembleCoadd"
383 def __init__(self, *args, **kwargs):
384 # TODO: DM-17415 better way to handle previously allowed passed args e.g.`AssembleCoaddTask(config)`
385 if args:
386 argNames = ["config", "name", "parentTask", "log"]
387 kwargs.update({k: v for k, v in zip(argNames, args)})
388 warnings.warn("AssembleCoadd received positional args, and casting them as kwargs: %s. "
389 "PipelineTask will not take positional args" % argNames, FutureWarning,
390 stacklevel=2)
392 super().__init__(**kwargs)
393 self.makeSubtask("interpImage")
394 self.makeSubtask("scaleZeroPoint")
396 if self.config.doMaskBrightObjects:
397 mask = afwImage.Mask()
398 try:
399 self.brightObjectBitmask = 1 << mask.addMaskPlane(self.config.brightObjectMaskName)
400 except pexExceptions.LsstCppException:
401 raise RuntimeError("Unable to define mask plane for bright objects; planes used are %s" %
402 mask.getMaskPlaneDict().keys())
403 del mask
405 if self.config.doInputMap:
406 self.makeSubtask("inputMapper")
408 self.warpType = self.config.warpType
410 def runQuantum(self, butlerQC, inputRefs, outputRefs):
411 inputData = butlerQC.get(inputRefs)
413 # Construct skyInfo expected by run
414 # Do not remove skyMap from inputData in case _makeSupplementaryData needs it
415 skyMap = inputData["skyMap"]
416 outputDataId = butlerQC.quantum.dataId
418 inputData['skyInfo'] = makeSkyInfo(skyMap,
419 tractId=outputDataId['tract'],
420 patchId=outputDataId['patch'])
422 if self.config.doSelectVisits:
423 warpRefList = self.filterWarps(inputData['inputWarps'], inputData['selectedVisits'])
424 else:
425 warpRefList = inputData['inputWarps']
427 inputs = self.prepareInputs(warpRefList)
428 self.log.info("Found %d %s", len(inputs.tempExpRefList),
429 self.getTempExpDatasetName(self.warpType))
430 if len(inputs.tempExpRefList) == 0:
431 raise pipeBase.NoWorkFound("No coadd temporary exposures found")
433 supplementaryData = self._makeSupplementaryData(butlerQC, inputRefs, outputRefs)
434 retStruct = self.run(inputData['skyInfo'], inputs.tempExpRefList, inputs.imageScalerList,
435 inputs.weightList, supplementaryData=supplementaryData)
437 inputData.setdefault('brightObjectMask', None)
438 if self.config.doMaskBrightObjects and inputData["brightObjectMask"] is None:
439 log.warning("doMaskBrightObjects is set to True, but brightObjectMask not loaded")
440 self.processResults(retStruct.coaddExposure, inputData['brightObjectMask'], outputDataId)
442 if self.config.doWrite:
443 butlerQC.put(retStruct, outputRefs)
444 return retStruct
446 def processResults(self, coaddExposure, brightObjectMasks=None, dataId=None):
447 """Interpolate over missing data and mask bright stars.
449 Parameters
450 ----------
451 coaddExposure : `lsst.afw.image.Exposure`
452 The coadded exposure to process.
453 brightObjectMasks : `lsst.afw.table` or `None`, optional
454 Table of bright objects to mask.
455 dataId : `lsst.daf.butler.DataId` or `None`, optional
456 Data identification.
457 """
458 if self.config.doInterp:
459 self.interpImage.run(coaddExposure.getMaskedImage(), planeName="NO_DATA")
460 # The variance must be positive; work around for DM-3201.
461 varArray = coaddExposure.variance.array
462 with numpy.errstate(invalid="ignore"):
463 varArray[:] = numpy.where(varArray > 0, varArray, numpy.inf)
465 if self.config.doMaskBrightObjects:
466 self.setBrightObjectMasks(coaddExposure, brightObjectMasks, dataId)
468 def _makeSupplementaryData(self, butlerQC, inputRefs, outputRefs):
469 """Make additional inputs to run() specific to subclasses (Gen3).
471 Duplicates interface of `runQuantum` method.
472 Available to be implemented by subclasses only if they need the
473 coadd dataRef for performing preliminary processing before
474 assembling the coadd.
476 Parameters
477 ----------
478 butlerQC : `~lsst.pipe.base.ButlerQuantumContext`
479 Gen3 Butler object for fetching additional data products before
480 running the Task specialized for quantum being processed.
481 inputRefs : `~lsst.pipe.base.InputQuantizedConnection`
482 Attributes are the names of the connections describing input dataset types.
483 Values are DatasetRefs that task consumes for corresponding dataset type.
484 DataIds are guaranteed to match data objects in ``inputData``.
485 outputRefs : `~lsst.pipe.base.OutputQuantizedConnection`
486 Attributes are the names of the connections describing output dataset types.
487 Values are DatasetRefs that task is to produce
488 for corresponding dataset type.
489 """
490 return pipeBase.Struct()
492 @deprecated(
493 reason="makeSupplementaryDataGen3 is deprecated in favor of _makeSupplementaryData",
494 version="v25.0",
495 category=FutureWarning
496 )
497 def makeSupplementaryDataGen3(self, butlerQC, inputRefs, outputRefs):
498 return self._makeSupplementaryData(butlerQC, inputRefs, outputRefs)
500 def prepareInputs(self, refList):
501 """Prepare the input warps for coaddition by measuring the weight for
502 each warp and the scaling for the photometric zero point.
504 Each Warp has its own photometric zeropoint and background variance.
505 Before coadding these Warps together, compute a scale factor to
506 normalize the photometric zeropoint and compute the weight for each Warp.
508 Parameters
509 ----------
510 refList : `list`
511 List of data references to tempExp.
513 Returns
514 -------
515 result : `~lsst.pipe.base.Struct`
516 Results as a struct with attributes:
518 ``tempExprefList``
519 `list` of data references to tempExp.
520 ``weightList``
521 `list` of weightings.
522 ``imageScalerList``
523 `list` of image scalers.
524 """
525 statsCtrl = afwMath.StatisticsControl()
526 statsCtrl.setNumSigmaClip(self.config.sigmaClip)
527 statsCtrl.setNumIter(self.config.clipIter)
528 statsCtrl.setAndMask(self.getBadPixelMask())
529 statsCtrl.setNanSafe(True)
530 # compute tempExpRefList: a list of tempExpRef that actually exist
531 # and weightList: a list of the weight of the associated coadd tempExp
532 # and imageScalerList: a list of scale factors for the associated coadd tempExp
533 tempExpRefList = []
534 weightList = []
535 imageScalerList = []
536 tempExpName = self.getTempExpDatasetName(self.warpType)
537 for tempExpRef in refList:
538 tempExp = tempExpRef.get()
539 # Ignore any input warp that is empty of data
540 if numpy.isnan(tempExp.image.array).all():
541 continue
542 maskedImage = tempExp.getMaskedImage()
543 imageScaler = self.scaleZeroPoint.computeImageScaler(
544 exposure=tempExp,
545 dataRef=tempExpRef, # FIXME
546 )
547 try:
548 imageScaler.scaleMaskedImage(maskedImage)
549 except Exception as e:
550 self.log.warning("Scaling failed for %s (skipping it): %s", tempExpRef.dataId, e)
551 continue
552 statObj = afwMath.makeStatistics(maskedImage.getVariance(), maskedImage.getMask(),
553 afwMath.MEANCLIP, statsCtrl)
554 meanVar, meanVarErr = statObj.getResult(afwMath.MEANCLIP)
555 weight = 1.0 / float(meanVar)
556 if not numpy.isfinite(weight):
557 self.log.warning("Non-finite weight for %s: skipping", tempExpRef.dataId)
558 continue
559 self.log.info("Weight of %s %s = %0.3f", tempExpName, tempExpRef.dataId, weight)
561 del maskedImage
562 del tempExp
564 tempExpRefList.append(tempExpRef)
565 weightList.append(weight)
566 imageScalerList.append(imageScaler)
568 return pipeBase.Struct(tempExpRefList=tempExpRefList, weightList=weightList,
569 imageScalerList=imageScalerList)
571 def prepareStats(self, mask=None):
572 """Prepare the statistics for coadding images.
574 Parameters
575 ----------
576 mask : `int`, optional
577 Bit mask value to exclude from coaddition.
579 Returns
580 -------
581 stats : `~lsst.pipe.base.Struct`
582 Statistics as a struct with attributes:
584 ``statsCtrl``
585 Statistics control object for coadd (`~lsst.afw.math.StatisticsControl`).
586 ``statsFlags``
587 Statistic for coadd (`~lsst.afw.math.Property`).
588 """
589 if mask is None:
590 mask = self.getBadPixelMask()
591 statsCtrl = afwMath.StatisticsControl()
592 statsCtrl.setNumSigmaClip(self.config.sigmaClip)
593 statsCtrl.setNumIter(self.config.clipIter)
594 statsCtrl.setAndMask(mask)
595 statsCtrl.setNanSafe(True)
596 statsCtrl.setWeighted(True)
597 statsCtrl.setCalcErrorFromInputVariance(self.config.calcErrorFromInputVariance)
598 for plane, threshold in self.config.maskPropagationThresholds.items():
599 bit = afwImage.Mask.getMaskPlane(plane)
600 statsCtrl.setMaskPropagationThreshold(bit, threshold)
601 statsFlags = afwMath.stringToStatisticsProperty(self.config.statistic)
602 return pipeBase.Struct(ctrl=statsCtrl, flags=statsFlags)
604 @timeMethod
605 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
606 altMaskList=None, mask=None, supplementaryData=None):
607 """Assemble a coadd from input warps.
609 Assemble the coadd using the provided list of coaddTempExps. Since
610 the full coadd covers a patch (a large area), the assembly is
611 performed over small areas on the image at a time in order to
612 conserve memory usage. Iterate over subregions within the outer
613 bbox of the patch using `assembleSubregion` to stack the corresponding
614 subregions from the coaddTempExps with the statistic specified.
615 Set the edge bits the coadd mask based on the weight map.
617 Parameters
618 ----------
619 skyInfo : `~lsst.pipe.base.Struct`
620 Struct with geometric information about the patch.
621 tempExpRefList : `list`
622 List of data references to Warps (previously called CoaddTempExps).
623 imageScalerList : `list`
624 List of image scalers.
625 weightList : `list`
626 List of weights.
627 altMaskList : `list`, optional
628 List of alternate masks to use rather than those stored with
629 tempExp.
630 mask : `int`, optional
631 Bit mask value to exclude from coaddition.
632 supplementaryData : `~lsst.pipe.base.Struct`, optional
633 Struct with additional data products needed to assemble coadd.
634 Only used by subclasses that implement ``_makeSupplementaryData``
635 and override `run`.
637 Returns
638 -------
639 result : `~lsst.pipe.base.Struct`
640 Results as a struct with attributes:
642 ``coaddExposure``
643 Coadded exposure (``lsst.afw.image.Exposure``).
644 ``nImage``
645 Exposure count image (``lsst.afw.image.Image``), if requested.
646 ``inputMap``
647 Bit-wise map of inputs, if requested.
648 ``warpRefList``
649 Input list of refs to the warps (``lsst.daf.butler.DeferredDatasetHandle``)
650 (unmodified).
651 ``imageScalerList``
652 Input list of image scalers (`list`) (unmodified).
653 ``weightList``
654 Input list of weights (`list`) (unmodified).
656 Raises
657 ------
658 lsst.pipe.base.NoWorkFound
659 Raised if no data references are provided.
660 """
661 tempExpName = self.getTempExpDatasetName(self.warpType)
662 self.log.info("Assembling %s %s", len(tempExpRefList), tempExpName)
663 if not tempExpRefList:
664 raise pipeBase.NoWorkFound("No exposures provided for co-addition.")
666 stats = self.prepareStats(mask=mask)
668 if altMaskList is None:
669 altMaskList = [None]*len(tempExpRefList)
671 coaddExposure = afwImage.ExposureF(skyInfo.bbox, skyInfo.wcs)
672 coaddExposure.setPhotoCalib(self.scaleZeroPoint.getPhotoCalib())
673 coaddExposure.getInfo().setCoaddInputs(self.inputRecorder.makeCoaddInputs())
674 self.assembleMetadata(coaddExposure, tempExpRefList, weightList)
675 coaddMaskedImage = coaddExposure.getMaskedImage()
676 subregionSizeArr = self.config.subregionSize
677 subregionSize = geom.Extent2I(subregionSizeArr[0], subregionSizeArr[1])
678 # if nImage is requested, create a zero one which can be passed to assembleSubregion
679 if self.config.doNImage:
680 nImage = afwImage.ImageU(skyInfo.bbox)
681 else:
682 nImage = None
683 # If inputMap is requested, create the initial version that can be masked in
684 # assembleSubregion.
685 if self.config.doInputMap:
686 self.inputMapper.build_ccd_input_map(skyInfo.bbox,
687 skyInfo.wcs,
688 coaddExposure.getInfo().getCoaddInputs().ccds)
690 if self.config.doOnlineForMean and self.config.statistic == "MEAN":
691 try:
692 self.assembleOnlineMeanCoadd(coaddExposure, tempExpRefList, imageScalerList,
693 weightList, altMaskList, stats.ctrl,
694 nImage=nImage)
695 except Exception as e:
696 self.log.exception("Cannot compute online coadd %s", e)
697 raise
698 else:
699 for subBBox in self._subBBoxIter(skyInfo.bbox, subregionSize):
700 try:
701 self.assembleSubregion(coaddExposure, subBBox, tempExpRefList, imageScalerList,
702 weightList, altMaskList, stats.flags, stats.ctrl,
703 nImage=nImage)
704 except Exception as e:
705 self.log.exception("Cannot compute coadd %s: %s", subBBox, e)
706 raise
708 # If inputMap is requested, we must finalize the map after the accumulation.
709 if self.config.doInputMap:
710 self.inputMapper.finalize_ccd_input_map_mask()
711 inputMap = self.inputMapper.ccd_input_map
712 else:
713 inputMap = None
715 self.setInexactPsf(coaddMaskedImage.getMask())
716 # Despite the name, the following doesn't really deal with "EDGE" pixels: it identifies
717 # pixels that didn't receive any unmasked inputs (as occurs around the edge of the field).
718 coaddUtils.setCoaddEdgeBits(coaddMaskedImage.getMask(), coaddMaskedImage.getVariance())
719 return pipeBase.Struct(coaddExposure=coaddExposure, nImage=nImage,
720 warpRefList=tempExpRefList, imageScalerList=imageScalerList,
721 weightList=weightList, inputMap=inputMap)
723 def assembleMetadata(self, coaddExposure, tempExpRefList, weightList):
724 """Set the metadata for the coadd.
726 This basic implementation sets the filter from the first input.
728 Parameters
729 ----------
730 coaddExposure : `lsst.afw.image.Exposure`
731 The target exposure for the coadd.
732 tempExpRefList : `list`
733 List of data references to tempExp.
734 weightList : `list`
735 List of weights.
737 Raises
738 ------
739 AssertionError
740 Raised if there is a length mismatch.
741 """
742 assert len(tempExpRefList) == len(weightList), "Length mismatch"
744 # We load a single pixel of each coaddTempExp, because we just want to get at the metadata
745 # (and we need more than just the PropertySet that contains the header), which is not possible
746 # with the current butler (see #2777).
747 bbox = geom.Box2I(coaddExposure.getBBox().getMin(), geom.Extent2I(1, 1))
749 tempExpList = [tempExpRef.get(parameters={'bbox': bbox}) for tempExpRef in tempExpRefList]
751 numCcds = sum(len(tempExp.getInfo().getCoaddInputs().ccds) for tempExp in tempExpList)
753 # Set the coadd FilterLabel to the band of the first input exposure:
754 # Coadds are calibrated, so the physical label is now meaningless.
755 coaddExposure.setFilter(afwImage.FilterLabel(tempExpList[0].getFilter().bandLabel))
756 coaddInputs = coaddExposure.getInfo().getCoaddInputs()
757 coaddInputs.ccds.reserve(numCcds)
758 coaddInputs.visits.reserve(len(tempExpList))
760 for tempExp, weight in zip(tempExpList, weightList):
761 self.inputRecorder.addVisitToCoadd(coaddInputs, tempExp, weight)
763 if self.config.doUsePsfMatchedPolygons:
764 self.shrinkValidPolygons(coaddInputs)
766 coaddInputs.visits.sort()
767 coaddInputs.ccds.sort()
768 if self.warpType == "psfMatched":
769 # The modelPsf BBox for a psfMatchedWarp/coaddTempExp was dynamically defined by
770 # ModelPsfMatchTask as the square box bounding its spatially-variable, pre-matched WarpedPsf.
771 # Likewise, set the PSF of a PSF-Matched Coadd to the modelPsf
772 # having the maximum width (sufficient because square)
773 modelPsfList = [tempExp.getPsf() for tempExp in tempExpList]
774 modelPsfWidthList = [modelPsf.computeBBox(modelPsf.getAveragePosition()).getWidth()
775 for modelPsf in modelPsfList]
776 psf = modelPsfList[modelPsfWidthList.index(max(modelPsfWidthList))]
777 else:
778 psf = measAlg.CoaddPsf(coaddInputs.ccds, coaddExposure.getWcs(),
779 self.config.coaddPsf.makeControl())
780 coaddExposure.setPsf(psf)
781 apCorrMap = measAlg.makeCoaddApCorrMap(coaddInputs.ccds, coaddExposure.getBBox(afwImage.PARENT),
782 coaddExposure.getWcs())
783 coaddExposure.getInfo().setApCorrMap(apCorrMap)
784 if self.config.doAttachTransmissionCurve:
785 transmissionCurve = measAlg.makeCoaddTransmissionCurve(coaddExposure.getWcs(), coaddInputs.ccds)
786 coaddExposure.getInfo().setTransmissionCurve(transmissionCurve)
788 def assembleSubregion(self, coaddExposure, bbox, tempExpRefList, imageScalerList, weightList,
789 altMaskList, statsFlags, statsCtrl, nImage=None):
790 """Assemble the coadd for a sub-region.
792 For each coaddTempExp, check for (and swap in) an alternative mask
793 if one is passed. Remove mask planes listed in
794 `config.removeMaskPlanes`. Finally, stack the actual exposures using
795 `lsst.afw.math.statisticsStack` with the statistic specified by
796 statsFlags. Typically, the statsFlag will be one of lsst.afw.math.MEAN for
797 a mean-stack or `lsst.afw.math.MEANCLIP` for outlier rejection using
798 an N-sigma clipped mean where N and iterations are specified by
799 statsCtrl. Assign the stacked subregion back to the coadd.
801 Parameters
802 ----------
803 coaddExposure : `lsst.afw.image.Exposure`
804 The target exposure for the coadd.
805 bbox : `lsst.geom.Box`
806 Sub-region to coadd.
807 tempExpRefList : `list`
808 List of data reference to tempExp.
809 imageScalerList : `list`
810 List of image scalers.
811 weightList : `list`
812 List of weights.
813 altMaskList : `list`
814 List of alternate masks to use rather than those stored with
815 tempExp, or None. Each element is dict with keys = mask plane
816 name to which to add the spans.
817 statsFlags : `lsst.afw.math.Property`
818 Property object for statistic for coadd.
819 statsCtrl : `lsst.afw.math.StatisticsControl`
820 Statistics control object for coadd.
821 nImage : `lsst.afw.image.ImageU`, optional
822 Keeps track of exposure count for each pixel.
823 """
824 self.log.debug("Computing coadd over %s", bbox)
826 coaddExposure.mask.addMaskPlane("REJECTED")
827 coaddExposure.mask.addMaskPlane("CLIPPED")
828 coaddExposure.mask.addMaskPlane("SENSOR_EDGE")
829 maskMap = self.setRejectedMaskMapping(statsCtrl)
830 clipped = afwImage.Mask.getPlaneBitMask("CLIPPED")
831 maskedImageList = []
832 if nImage is not None:
833 subNImage = afwImage.ImageU(bbox.getWidth(), bbox.getHeight())
834 for tempExpRef, imageScaler, altMask in zip(tempExpRefList, imageScalerList, altMaskList):
836 exposure = tempExpRef.get(parameters={'bbox': bbox})
838 maskedImage = exposure.getMaskedImage()
839 mask = maskedImage.getMask()
840 if altMask is not None:
841 self.applyAltMaskPlanes(mask, altMask)
842 imageScaler.scaleMaskedImage(maskedImage)
844 # Add 1 for each pixel which is not excluded by the exclude mask.
845 # In legacyCoadd, pixels may also be excluded by afwMath.statisticsStack.
846 if nImage is not None:
847 subNImage.getArray()[maskedImage.getMask().getArray() & statsCtrl.getAndMask() == 0] += 1
848 if self.config.removeMaskPlanes:
849 self.removeMaskPlanes(maskedImage)
850 maskedImageList.append(maskedImage)
852 if self.config.doInputMap:
853 visit = exposure.getInfo().getCoaddInputs().visits[0].getId()
854 self.inputMapper.mask_warp_bbox(bbox, visit, mask, statsCtrl.getAndMask())
856 with self.timer("stack"):
857 coaddSubregion = afwMath.statisticsStack(maskedImageList, statsFlags, statsCtrl, weightList,
858 clipped, # also set output to CLIPPED if sigma-clipped
859 maskMap)
860 coaddExposure.maskedImage.assign(coaddSubregion, bbox)
861 if nImage is not None:
862 nImage.assign(subNImage, bbox)
864 def assembleOnlineMeanCoadd(self, coaddExposure, tempExpRefList, imageScalerList, weightList,
865 altMaskList, statsCtrl, nImage=None):
866 """Assemble the coadd using the "online" method.
868 This method takes a running sum of images and weights to save memory.
869 It only works for MEAN statistics.
871 Parameters
872 ----------
873 coaddExposure : `lsst.afw.image.Exposure`
874 The target exposure for the coadd.
875 tempExpRefList : `list`
876 List of data reference to tempExp.
877 imageScalerList : `list`
878 List of image scalers.
879 weightList : `list`
880 List of weights.
881 altMaskList : `list`
882 List of alternate masks to use rather than those stored with
883 tempExp, or None. Each element is dict with keys = mask plane
884 name to which to add the spans.
885 statsCtrl : `lsst.afw.math.StatisticsControl`
886 Statistics control object for coadd.
887 nImage : `lsst.afw.image.ImageU`, optional
888 Keeps track of exposure count for each pixel.
889 """
890 self.log.debug("Computing online coadd.")
892 coaddExposure.mask.addMaskPlane("REJECTED")
893 coaddExposure.mask.addMaskPlane("CLIPPED")
894 coaddExposure.mask.addMaskPlane("SENSOR_EDGE")
895 maskMap = self.setRejectedMaskMapping(statsCtrl)
896 thresholdDict = AccumulatorMeanStack.stats_ctrl_to_threshold_dict(statsCtrl)
898 bbox = coaddExposure.maskedImage.getBBox()
900 stacker = AccumulatorMeanStack(
901 coaddExposure.image.array.shape,
902 statsCtrl.getAndMask(),
903 mask_threshold_dict=thresholdDict,
904 mask_map=maskMap,
905 no_good_pixels_mask=statsCtrl.getNoGoodPixelsMask(),
906 calc_error_from_input_variance=self.config.calcErrorFromInputVariance,
907 compute_n_image=(nImage is not None)
908 )
910 for tempExpRef, imageScaler, altMask, weight in zip(tempExpRefList,
911 imageScalerList,
912 altMaskList,
913 weightList):
914 exposure = tempExpRef.get()
915 maskedImage = exposure.getMaskedImage()
916 mask = maskedImage.getMask()
917 if altMask is not None:
918 self.applyAltMaskPlanes(mask, altMask)
919 imageScaler.scaleMaskedImage(maskedImage)
920 if self.config.removeMaskPlanes:
921 self.removeMaskPlanes(maskedImage)
923 stacker.add_masked_image(maskedImage, weight=weight)
925 if self.config.doInputMap:
926 visit = exposure.getInfo().getCoaddInputs().visits[0].getId()
927 self.inputMapper.mask_warp_bbox(bbox, visit, mask, statsCtrl.getAndMask())
929 stacker.fill_stacked_masked_image(coaddExposure.maskedImage)
931 if nImage is not None:
932 nImage.array[:, :] = stacker.n_image
934 def removeMaskPlanes(self, maskedImage):
935 """Unset the mask of an image for mask planes specified in the config.
937 Parameters
938 ----------
939 maskedImage : `lsst.afw.image.MaskedImage`
940 The masked image to be modified.
942 Raises
943 ------
944 InvalidParameterError
945 Raised if no mask plane with that name was found.
946 """
947 mask = maskedImage.getMask()
948 for maskPlane in self.config.removeMaskPlanes:
949 try:
950 mask &= ~mask.getPlaneBitMask(maskPlane)
951 except pexExceptions.InvalidParameterError:
952 self.log.debug("Unable to remove mask plane %s: no mask plane with that name was found.",
953 maskPlane)
955 @staticmethod
956 def setRejectedMaskMapping(statsCtrl):
957 """Map certain mask planes of the warps to new planes for the coadd.
959 If a pixel is rejected due to a mask value other than EDGE, NO_DATA,
960 or CLIPPED, set it to REJECTED on the coadd.
961 If a pixel is rejected due to EDGE, set the coadd pixel to SENSOR_EDGE.
962 If a pixel is rejected due to CLIPPED, set the coadd pixel to CLIPPED.
964 Parameters
965 ----------
966 statsCtrl : `lsst.afw.math.StatisticsControl`
967 Statistics control object for coadd.
969 Returns
970 -------
971 maskMap : `list` of `tuple` of `int`
972 A list of mappings of mask planes of the warped exposures to
973 mask planes of the coadd.
974 """
975 edge = afwImage.Mask.getPlaneBitMask("EDGE")
976 noData = afwImage.Mask.getPlaneBitMask("NO_DATA")
977 clipped = afwImage.Mask.getPlaneBitMask("CLIPPED")
978 toReject = statsCtrl.getAndMask() & (~noData) & (~edge) & (~clipped)
979 maskMap = [(toReject, afwImage.Mask.getPlaneBitMask("REJECTED")),
980 (edge, afwImage.Mask.getPlaneBitMask("SENSOR_EDGE")),
981 (clipped, clipped)]
982 return maskMap
984 def applyAltMaskPlanes(self, mask, altMaskSpans):
985 """Apply in place alt mask formatted as SpanSets to a mask.
987 Parameters
988 ----------
989 mask : `lsst.afw.image.Mask`
990 Original mask.
991 altMaskSpans : `dict`
992 SpanSet lists to apply. Each element contains the new mask
993 plane name (e.g. "CLIPPED and/or "NO_DATA") as the key,
994 and list of SpanSets to apply to the mask.
996 Returns
997 -------
998 mask : `lsst.afw.image.Mask`
999 Updated mask.
1000 """
1001 if self.config.doUsePsfMatchedPolygons:
1002 if ("NO_DATA" in altMaskSpans) and ("NO_DATA" in self.config.badMaskPlanes):
1003 # Clear away any other masks outside the validPolygons. These pixels are no longer
1004 # contributing to inexact PSFs, and will still be rejected because of NO_DATA
1005 # self.config.doUsePsfMatchedPolygons should be True only in CompareWarpAssemble
1006 # This mask-clearing step must only occur *before* applying the new masks below
1007 for spanSet in altMaskSpans['NO_DATA']:
1008 spanSet.clippedTo(mask.getBBox()).clearMask(mask, self.getBadPixelMask())
1010 for plane, spanSetList in altMaskSpans.items():
1011 maskClipValue = mask.addMaskPlane(plane)
1012 for spanSet in spanSetList:
1013 spanSet.clippedTo(mask.getBBox()).setMask(mask, 2**maskClipValue)
1014 return mask
1016 def shrinkValidPolygons(self, coaddInputs):
1017 """Shrink coaddInputs' ccds' ValidPolygons in place.
1019 Either modify each ccd's validPolygon in place, or if CoaddInputs
1020 does not have a validPolygon, create one from its bbox.
1022 Parameters
1023 ----------
1024 coaddInputs : `lsst.afw.image.coaddInputs`
1025 Original mask.
1026 """
1027 for ccd in coaddInputs.ccds:
1028 polyOrig = ccd.getValidPolygon()
1029 validPolyBBox = polyOrig.getBBox() if polyOrig else ccd.getBBox()
1030 validPolyBBox.grow(-self.config.matchingKernelSize//2)
1031 if polyOrig:
1032 validPolygon = polyOrig.intersectionSingle(validPolyBBox)
1033 else:
1034 validPolygon = afwGeom.polygon.Polygon(geom.Box2D(validPolyBBox))
1035 ccd.setValidPolygon(validPolygon)
1037 def setBrightObjectMasks(self, exposure, brightObjectMasks, dataId=None):
1038 """Set the bright object masks.
1040 Parameters
1041 ----------
1042 exposure : `lsst.afw.image.Exposure`
1043 Exposure under consideration.
1044 brightObjectMasks : `lsst.afw.table`
1045 Table of bright objects to mask.
1046 dataId : `lsst.daf.butler.DataId`, optional
1047 Data identifier dict for patch.
1048 """
1049 if brightObjectMasks is None:
1050 self.log.warning("Unable to apply bright object mask: none supplied")
1051 return
1052 self.log.info("Applying %d bright object masks to %s", len(brightObjectMasks), dataId)
1053 mask = exposure.getMaskedImage().getMask()
1054 wcs = exposure.getWcs()
1055 plateScale = wcs.getPixelScale().asArcseconds()
1057 for rec in brightObjectMasks:
1058 center = geom.PointI(wcs.skyToPixel(rec.getCoord()))
1059 if rec["type"] == "box":
1060 assert rec["angle"] == 0.0, ("Angle != 0 for mask object %s" % rec["id"])
1061 width = rec["width"].asArcseconds()/plateScale # convert to pixels
1062 height = rec["height"].asArcseconds()/plateScale # convert to pixels
1064 halfSize = geom.ExtentI(0.5*width, 0.5*height)
1065 bbox = geom.Box2I(center - halfSize, center + halfSize)
1067 bbox = geom.BoxI(geom.PointI(int(center[0] - 0.5*width), int(center[1] - 0.5*height)),
1068 geom.PointI(int(center[0] + 0.5*width), int(center[1] + 0.5*height)))
1069 spans = afwGeom.SpanSet(bbox)
1070 elif rec["type"] == "circle":
1071 radius = int(rec["radius"].asArcseconds()/plateScale) # convert to pixels
1072 spans = afwGeom.SpanSet.fromShape(radius, offset=center)
1073 else:
1074 self.log.warning("Unexpected region type %s at %s", rec["type"], center)
1075 continue
1076 spans.clippedTo(mask.getBBox()).setMask(mask, self.brightObjectBitmask)
1078 def setInexactPsf(self, mask):
1079 """Set INEXACT_PSF mask plane.
1081 If any of the input images isn't represented in the coadd (due to
1082 clipped pixels or chip gaps), the `CoaddPsf` will be inexact. Flag
1083 these pixels.
1085 Parameters
1086 ----------
1087 mask : `lsst.afw.image.Mask`
1088 Coadded exposure's mask, modified in-place.
1089 """
1090 mask.addMaskPlane("INEXACT_PSF")
1091 inexactPsf = mask.getPlaneBitMask("INEXACT_PSF")
1092 sensorEdge = mask.getPlaneBitMask("SENSOR_EDGE") # chip edges (so PSF is discontinuous)
1093 clipped = mask.getPlaneBitMask("CLIPPED") # pixels clipped from coadd
1094 rejected = mask.getPlaneBitMask("REJECTED") # pixels rejected from coadd due to masks
1095 array = mask.getArray()
1096 selected = array & (sensorEdge | clipped | rejected) > 0
1097 array[selected] |= inexactPsf
1099 @staticmethod
1100 def _subBBoxIter(bbox, subregionSize):
1101 """Iterate over subregions of a bbox.
1103 Parameters
1104 ----------
1105 bbox : `lsst.geom.Box2I`
1106 Bounding box over which to iterate.
1107 subregionSize : `lsst.geom.Extent2I`
1108 Size of sub-bboxes.
1110 Yields
1111 ------
1112 subBBox : `lsst.geom.Box2I`
1113 Next sub-bounding box of size ``subregionSize`` or smaller; each ``subBBox``
1114 is contained within ``bbox``, so it may be smaller than ``subregionSize`` at
1115 the edges of ``bbox``, but it will never be empty.
1117 Raises
1118 ------
1119 RuntimeError
1120 Raised if any of the following occur:
1121 - The given bbox is empty.
1122 - The subregionSize is 0.
1123 """
1124 if bbox.isEmpty():
1125 raise RuntimeError("bbox %s is empty" % (bbox,))
1126 if subregionSize[0] < 1 or subregionSize[1] < 1:
1127 raise RuntimeError("subregionSize %s must be nonzero" % (subregionSize,))
1129 for rowShift in range(0, bbox.getHeight(), subregionSize[1]):
1130 for colShift in range(0, bbox.getWidth(), subregionSize[0]):
1131 subBBox = geom.Box2I(bbox.getMin() + geom.Extent2I(colShift, rowShift), subregionSize)
1132 subBBox.clip(bbox)
1133 if subBBox.isEmpty():
1134 raise RuntimeError("Bug: empty bbox! bbox=%s, subregionSize=%s, "
1135 "colShift=%s, rowShift=%s" %
1136 (bbox, subregionSize, colShift, rowShift))
1137 yield subBBox
1139 def filterWarps(self, inputs, goodVisits):
1140 """Return list of only inputRefs with visitId in goodVisits ordered by goodVisit.
1142 Parameters
1143 ----------
1144 inputs : `list` of `~lsst.pipe.base.connections.DeferredDatasetRef`
1145 List of `lsst.pipe.base.connections.DeferredDatasetRef` with dataId containing visit.
1146 goodVisit : `dict`
1147 Dictionary with good visitIds as the keys. Value ignored.
1149 Returns
1150 -------
1151 filteredInputs : `list` of `~lsst.pipe.base.connections.DeferredDatasetRef`
1152 Filtered and sorted list of inputRefs with visitId in goodVisits ordered by goodVisit.
1153 """
1154 inputWarpDict = {inputRef.ref.dataId['visit']: inputRef for inputRef in inputs}
1155 filteredInputs = []
1156 for visit in goodVisits.keys():
1157 if visit in inputWarpDict:
1158 filteredInputs.append(inputWarpDict[visit])
1159 return filteredInputs
1162def countMaskFromFootprint(mask, footprint, bitmask, ignoreMask):
1163 """Function to count the number of pixels with a specific mask in a
1164 footprint.
1166 Find the intersection of mask & footprint. Count all pixels in the mask
1167 that are in the intersection that have bitmask set but do not have
1168 ignoreMask set. Return the count.
1170 Parameters
1171 ----------
1172 mask : `lsst.afw.image.Mask`
1173 Mask to define intersection region by.
1174 footprint : `lsst.afw.detection.Footprint`
1175 Footprint to define the intersection region by.
1176 bitmask : `Unknown`
1177 Specific mask that we wish to count the number of occurances of.
1178 ignoreMask : `Unknown`
1179 Pixels to not consider.
1181 Returns
1182 -------
1183 result : `int`
1184 Number of pixels in footprint with specified mask.
1185 """
1186 bbox = footprint.getBBox()
1187 bbox.clip(mask.getBBox(afwImage.PARENT))
1188 fp = afwImage.Mask(bbox)
1189 subMask = mask.Factory(mask, bbox, afwImage.PARENT)
1190 footprint.spans.setMask(fp, bitmask)
1191 return numpy.logical_and((subMask.getArray() & fp.getArray()) > 0,
1192 (subMask.getArray() & ignoreMask) == 0).sum()
1195class CompareWarpAssembleCoaddConnections(AssembleCoaddConnections):
1196 psfMatchedWarps = pipeBase.connectionTypes.Input(
1197 doc=("PSF-Matched Warps are required by CompareWarp regardless of the coadd type requested. "
1198 "Only PSF-Matched Warps make sense for image subtraction. "
1199 "Therefore, they must be an additional declared input."),
1200 name="{inputCoaddName}Coadd_psfMatchedWarp",
1201 storageClass="ExposureF",
1202 dimensions=("tract", "patch", "skymap", "visit"),
1203 deferLoad=True,
1204 multiple=True
1205 )
1206 templateCoadd = pipeBase.connectionTypes.Output(
1207 doc=("Model of the static sky, used to find temporal artifacts. Typically a PSF-Matched, "
1208 "sigma-clipped coadd. Written if and only if assembleStaticSkyModel.doWrite=True"),
1209 name="{outputCoaddName}CoaddPsfMatched",
1210 storageClass="ExposureF",
1211 dimensions=("tract", "patch", "skymap", "band"),
1212 )
1214 def __init__(self, *, config=None):
1215 super().__init__(config=config)
1216 if not config.assembleStaticSkyModel.doWrite:
1217 self.outputs.remove("templateCoadd")
1218 config.validate()
1221class CompareWarpAssembleCoaddConfig(AssembleCoaddConfig,
1222 pipelineConnections=CompareWarpAssembleCoaddConnections):
1223 assembleStaticSkyModel = pexConfig.ConfigurableField(
1224 target=AssembleCoaddTask,
1225 doc="Task to assemble an artifact-free, PSF-matched Coadd to serve as a"
1226 " naive/first-iteration model of the static sky.",
1227 )
1228 detect = pexConfig.ConfigurableField(
1229 target=SourceDetectionTask,
1230 doc="Detect outlier sources on difference between each psfMatched warp and static sky model"
1231 )
1232 detectTemplate = pexConfig.ConfigurableField(
1233 target=SourceDetectionTask,
1234 doc="Detect sources on static sky model. Only used if doPreserveContainedBySource is True"
1235 )
1236 maskStreaks = pexConfig.ConfigurableField(
1237 target=MaskStreaksTask,
1238 doc="Detect streaks on difference between each psfMatched warp and static sky model. Only used if "
1239 "doFilterMorphological is True. Adds a mask plane to an exposure, with the mask plane name set by"
1240 "streakMaskName"
1241 )
1242 streakMaskName = pexConfig.Field(
1243 dtype=str,
1244 default="STREAK",
1245 doc="Name of mask bit used for streaks"
1246 )
1247 maxNumEpochs = pexConfig.Field(
1248 doc="Charactistic maximum local number of epochs/visits in which an artifact candidate can appear "
1249 "and still be masked. The effective maxNumEpochs is a broken linear function of local "
1250 "number of epochs (N): min(maxFractionEpochsLow*N, maxNumEpochs + maxFractionEpochsHigh*N). "
1251 "For each footprint detected on the image difference between the psfMatched warp and static sky "
1252 "model, if a significant fraction of pixels (defined by spatialThreshold) are residuals in more "
1253 "than the computed effective maxNumEpochs, the artifact candidate is deemed persistant rather "
1254 "than transient and not masked.",
1255 dtype=int,
1256 default=2
1257 )
1258 maxFractionEpochsLow = pexConfig.RangeField(
1259 doc="Fraction of local number of epochs (N) to use as effective maxNumEpochs for low N. "
1260 "Effective maxNumEpochs = "
1261 "min(maxFractionEpochsLow * N, maxNumEpochs + maxFractionEpochsHigh * N)",
1262 dtype=float,
1263 default=0.4,
1264 min=0., max=1.,
1265 )
1266 maxFractionEpochsHigh = pexConfig.RangeField(
1267 doc="Fraction of local number of epochs (N) to use as effective maxNumEpochs for high N. "
1268 "Effective maxNumEpochs = "
1269 "min(maxFractionEpochsLow * N, maxNumEpochs + maxFractionEpochsHigh * N)",
1270 dtype=float,
1271 default=0.03,
1272 min=0., max=1.,
1273 )
1274 spatialThreshold = pexConfig.RangeField(
1275 doc="Unitless fraction of pixels defining how much of the outlier region has to meet the "
1276 "temporal criteria. If 0, clip all. If 1, clip none.",
1277 dtype=float,
1278 default=0.5,
1279 min=0., max=1.,
1280 inclusiveMin=True, inclusiveMax=True
1281 )
1282 doScaleWarpVariance = pexConfig.Field(
1283 doc="Rescale Warp variance plane using empirical noise?",
1284 dtype=bool,
1285 default=True,
1286 )
1287 scaleWarpVariance = pexConfig.ConfigurableField(
1288 target=ScaleVarianceTask,
1289 doc="Rescale variance on warps",
1290 )
1291 doPreserveContainedBySource = pexConfig.Field(
1292 doc="Rescue artifacts from clipping that completely lie within a footprint detected"
1293 "on the PsfMatched Template Coadd. Replicates a behavior of SafeClip.",
1294 dtype=bool,
1295 default=True,
1296 )
1297 doPrefilterArtifacts = pexConfig.Field(
1298 doc="Ignore artifact candidates that are mostly covered by the bad pixel mask, "
1299 "because they will be excluded anyway. This prevents them from contributing "
1300 "to the outlier epoch count image and potentially being labeled as persistant."
1301 "'Mostly' is defined by the config 'prefilterArtifactsRatio'.",
1302 dtype=bool,
1303 default=True
1304 )
1305 prefilterArtifactsMaskPlanes = pexConfig.ListField(
1306 doc="Prefilter artifact candidates that are mostly covered by these bad mask planes.",
1307 dtype=str,
1308 default=('NO_DATA', 'BAD', 'SAT', 'SUSPECT'),
1309 )
1310 prefilterArtifactsRatio = pexConfig.Field(
1311 doc="Prefilter artifact candidates with less than this fraction overlapping good pixels",
1312 dtype=float,
1313 default=0.05
1314 )
1315 doFilterMorphological = pexConfig.Field(
1316 doc="Filter artifact candidates based on morphological criteria, i.g. those that appear to "
1317 "be streaks.",
1318 dtype=bool,
1319 default=False
1320 )
1321 growStreakFp = pexConfig.Field(
1322 doc="Grow streak footprints by this number multiplied by the PSF width",
1323 dtype=float,
1324 default=5
1325 )
1327 def setDefaults(self):
1328 AssembleCoaddConfig.setDefaults(self)
1329 self.statistic = 'MEAN'
1330 self.doUsePsfMatchedPolygons = True
1332 # Real EDGE removed by psfMatched NO_DATA border half the width of the matching kernel
1333 # CompareWarp applies psfMatched EDGE pixels to directWarps before assembling
1334 if "EDGE" in self.badMaskPlanes:
1335 self.badMaskPlanes.remove('EDGE')
1336 self.removeMaskPlanes.append('EDGE')
1337 self.assembleStaticSkyModel.badMaskPlanes = ["NO_DATA", ]
1338 self.assembleStaticSkyModel.warpType = 'psfMatched'
1339 self.assembleStaticSkyModel.connections.warpType = 'psfMatched'
1340 self.assembleStaticSkyModel.statistic = 'MEANCLIP'
1341 self.assembleStaticSkyModel.sigmaClip = 2.5
1342 self.assembleStaticSkyModel.clipIter = 3
1343 self.assembleStaticSkyModel.calcErrorFromInputVariance = False
1344 self.assembleStaticSkyModel.doWrite = False
1345 self.detect.doTempLocalBackground = False
1346 self.detect.reEstimateBackground = False
1347 self.detect.returnOriginalFootprints = False
1348 self.detect.thresholdPolarity = "both"
1349 self.detect.thresholdValue = 5
1350 self.detect.minPixels = 4
1351 self.detect.isotropicGrow = True
1352 self.detect.thresholdType = "pixel_stdev"
1353 self.detect.nSigmaToGrow = 0.4
1354 # The default nSigmaToGrow for SourceDetectionTask is already 2.4,
1355 # Explicitly restating because ratio with detect.nSigmaToGrow matters
1356 self.detectTemplate.nSigmaToGrow = 2.4
1357 self.detectTemplate.doTempLocalBackground = False
1358 self.detectTemplate.reEstimateBackground = False
1359 self.detectTemplate.returnOriginalFootprints = False
1361 def validate(self):
1362 super().validate()
1363 if self.assembleStaticSkyModel.doNImage:
1364 raise ValueError("No dataset type exists for a PSF-Matched Template N Image."
1365 "Please set assembleStaticSkyModel.doNImage=False")
1367 if self.assembleStaticSkyModel.doWrite and (self.warpType == self.assembleStaticSkyModel.warpType):
1368 raise ValueError("warpType (%s) == assembleStaticSkyModel.warpType (%s) and will compete for "
1369 "the same dataset name. Please set assembleStaticSkyModel.doWrite to False "
1370 "or warpType to 'direct'. assembleStaticSkyModel.warpType should ways be "
1371 "'PsfMatched'" % (self.warpType, self.assembleStaticSkyModel.warpType))
1374class CompareWarpAssembleCoaddTask(AssembleCoaddTask):
1375 """Assemble a compareWarp coadded image from a set of warps
1376 by masking artifacts detected by comparing PSF-matched warps.
1378 In ``AssembleCoaddTask``, we compute the coadd as an clipped mean (i.e.,
1379 we clip outliers). The problem with doing this is that when computing the
1380 coadd PSF at a given location, individual visit PSFs from visits with
1381 outlier pixels contribute to the coadd PSF and cannot be treated correctly.
1382 In this task, we correct for this behavior by creating a new badMaskPlane
1383 'CLIPPED' which marks pixels in the individual warps suspected to contain
1384 an artifact. We populate this plane on the input warps by comparing
1385 PSF-matched warps with a PSF-matched median coadd which serves as a
1386 model of the static sky. Any group of pixels that deviates from the
1387 PSF-matched template coadd by more than config.detect.threshold sigma,
1388 is an artifact candidate. The candidates are then filtered to remove
1389 variable sources and sources that are difficult to subtract such as
1390 bright stars. This filter is configured using the config parameters
1391 ``temporalThreshold`` and ``spatialThreshold``. The temporalThreshold is
1392 the maximum fraction of epochs that the deviation can appear in and still
1393 be considered an artifact. The spatialThreshold is the maximum fraction of
1394 pixels in the footprint of the deviation that appear in other epochs
1395 (where other epochs is defined by the temporalThreshold). If the deviant
1396 region meets this criteria of having a significant percentage of pixels
1397 that deviate in only a few epochs, these pixels have the 'CLIPPED' bit
1398 set in the mask. These regions will not contribute to the final coadd.
1399 Furthermore, any routine to determine the coadd PSF can now be cognizant
1400 of clipped regions. Note that the algorithm implemented by this task is
1401 preliminary and works correctly for HSC data. Parameter modifications and
1402 or considerable redesigning of the algorithm is likley required for other
1403 surveys.
1405 ``CompareWarpAssembleCoaddTask`` sub-classes
1406 ``AssembleCoaddTask`` and instantiates ``AssembleCoaddTask``
1407 as a subtask to generate the TemplateCoadd (the model of the static sky).
1409 Notes
1410 -----
1411 Debugging:
1412 This task supports the following debug variables:
1413 - ``saveCountIm``
1414 If True then save the Epoch Count Image as a fits file in the `figPath`
1415 - ``figPath``
1416 Path to save the debug fits images and figures
1417 """
1419 ConfigClass = CompareWarpAssembleCoaddConfig
1420 _DefaultName = "compareWarpAssembleCoadd"
1422 def __init__(self, *args, **kwargs):
1423 AssembleCoaddTask.__init__(self, *args, **kwargs)
1424 self.makeSubtask("assembleStaticSkyModel")
1425 detectionSchema = afwTable.SourceTable.makeMinimalSchema()
1426 self.makeSubtask("detect", schema=detectionSchema)
1427 if self.config.doPreserveContainedBySource:
1428 self.makeSubtask("detectTemplate", schema=afwTable.SourceTable.makeMinimalSchema())
1429 if self.config.doScaleWarpVariance:
1430 self.makeSubtask("scaleWarpVariance")
1431 if self.config.doFilterMorphological:
1432 self.makeSubtask("maskStreaks")
1434 @utils.inheritDoc(AssembleCoaddTask)
1435 def _makeSupplementaryData(self, butlerQC, inputRefs, outputRefs):
1436 """Generate a templateCoadd to use as a naive model of static sky to
1437 subtract from PSF-Matched warps.
1439 Returns
1440 -------
1441 result : `~lsst.pipe.base.Struct`
1442 Results as a struct with attributes:
1444 ``templateCoadd``
1445 Coadded exposure (`lsst.afw.image.Exposure`).
1446 ``nImage``
1447 Keeps track of exposure count for each pixel (`lsst.afw.image.ImageU`).
1449 Raises
1450 ------
1451 RuntimeError
1452 Raised if ``templateCoadd`` is `None`.
1453 """
1454 # Ensure that psfMatchedWarps are used as input warps for template generation
1455 staticSkyModelInputRefs = copy.deepcopy(inputRefs)
1456 staticSkyModelInputRefs.inputWarps = inputRefs.psfMatchedWarps
1458 # Because subtasks don't have connections we have to make one.
1459 # The main task's `templateCoadd` is the subtask's `coaddExposure`
1460 staticSkyModelOutputRefs = copy.deepcopy(outputRefs)
1461 if self.config.assembleStaticSkyModel.doWrite:
1462 staticSkyModelOutputRefs.coaddExposure = staticSkyModelOutputRefs.templateCoadd
1463 # Remove template coadd from both subtask's and main tasks outputs,
1464 # because it is handled by the subtask as `coaddExposure`
1465 del outputRefs.templateCoadd
1466 del staticSkyModelOutputRefs.templateCoadd
1468 # A PSF-Matched nImage does not exist as a dataset type
1469 if 'nImage' in staticSkyModelOutputRefs.keys():
1470 del staticSkyModelOutputRefs.nImage
1472 templateCoadd = self.assembleStaticSkyModel.runQuantum(butlerQC, staticSkyModelInputRefs,
1473 staticSkyModelOutputRefs)
1474 if templateCoadd is None:
1475 raise RuntimeError(self._noTemplateMessage(self.assembleStaticSkyModel.warpType))
1477 return pipeBase.Struct(templateCoadd=templateCoadd.coaddExposure,
1478 nImage=templateCoadd.nImage,
1479 warpRefList=templateCoadd.warpRefList,
1480 imageScalerList=templateCoadd.imageScalerList,
1481 weightList=templateCoadd.weightList)
1483 def _noTemplateMessage(self, warpType):
1484 warpName = (warpType[0].upper() + warpType[1:])
1485 message = """No %(warpName)s warps were found to build the template coadd which is
1486 required to run CompareWarpAssembleCoaddTask. To continue assembling this type of coadd,
1487 first either rerun makeCoaddTempExp with config.make%(warpName)s=True or
1488 coaddDriver with config.makeCoadTempExp.make%(warpName)s=True, before assembleCoadd.
1490 Alternatively, to use another algorithm with existing warps, retarget the CoaddDriverConfig to
1491 another algorithm like:
1493 from lsst.pipe.tasks.assembleCoadd import SafeClipAssembleCoaddTask
1494 config.assemble.retarget(SafeClipAssembleCoaddTask)
1495 """ % {"warpName": warpName}
1496 return message
1498 @utils.inheritDoc(AssembleCoaddTask)
1499 @timeMethod
1500 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
1501 supplementaryData):
1502 """Notes
1503 -----
1504 Assemble the coadd.
1506 Find artifacts and apply them to the warps' masks creating a list of
1507 alternative masks with a new "CLIPPED" plane and updated "NO_DATA"
1508 plane. Then pass these alternative masks to the base class's ``run``
1509 method.
1510 """
1511 # Check and match the order of the supplementaryData
1512 # (PSF-matched) inputs to the order of the direct inputs,
1513 # so that the artifact mask is applied to the right warp
1514 dataIds = [ref.dataId for ref in tempExpRefList]
1515 psfMatchedDataIds = [ref.dataId for ref in supplementaryData.warpRefList]
1517 if dataIds != psfMatchedDataIds:
1518 self.log.info("Reordering and or/padding PSF-matched visit input list")
1519 supplementaryData.warpRefList = reorderAndPadList(supplementaryData.warpRefList,
1520 psfMatchedDataIds, dataIds)
1521 supplementaryData.imageScalerList = reorderAndPadList(supplementaryData.imageScalerList,
1522 psfMatchedDataIds, dataIds)
1524 # Use PSF-Matched Warps (and corresponding scalers) and coadd to find artifacts
1525 spanSetMaskList = self.findArtifacts(supplementaryData.templateCoadd,
1526 supplementaryData.warpRefList,
1527 supplementaryData.imageScalerList)
1529 badMaskPlanes = self.config.badMaskPlanes[:]
1530 badMaskPlanes.append("CLIPPED")
1531 badPixelMask = afwImage.Mask.getPlaneBitMask(badMaskPlanes)
1533 result = AssembleCoaddTask.run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
1534 spanSetMaskList, mask=badPixelMask)
1536 # Propagate PSF-matched EDGE pixels to coadd SENSOR_EDGE and INEXACT_PSF
1537 # Psf-Matching moves the real edge inwards
1538 self.applyAltEdgeMask(result.coaddExposure.maskedImage.mask, spanSetMaskList)
1539 return result
1541 def applyAltEdgeMask(self, mask, altMaskList):
1542 """Propagate alt EDGE mask to SENSOR_EDGE AND INEXACT_PSF planes.
1544 Parameters
1545 ----------
1546 mask : `lsst.afw.image.Mask`
1547 Original mask.
1548 altMaskList : `list` of `dict`
1549 List of Dicts containing ``spanSet`` lists.
1550 Each element contains the new mask plane name (e.g. "CLIPPED
1551 and/or "NO_DATA") as the key, and list of ``SpanSets`` to apply to
1552 the mask.
1553 """
1554 maskValue = mask.getPlaneBitMask(["SENSOR_EDGE", "INEXACT_PSF"])
1555 for visitMask in altMaskList:
1556 if "EDGE" in visitMask:
1557 for spanSet in visitMask['EDGE']:
1558 spanSet.clippedTo(mask.getBBox()).setMask(mask, maskValue)
1560 def findArtifacts(self, templateCoadd, tempExpRefList, imageScalerList):
1561 """Find artifacts.
1563 Loop through warps twice. The first loop builds a map with the count
1564 of how many epochs each pixel deviates from the templateCoadd by more
1565 than ``config.chiThreshold`` sigma. The second loop takes each
1566 difference image and filters the artifacts detected in each using
1567 count map to filter out variable sources and sources that are
1568 difficult to subtract cleanly.
1570 Parameters
1571 ----------
1572 templateCoadd : `lsst.afw.image.Exposure`
1573 Exposure to serve as model of static sky.
1574 tempExpRefList : `list`
1575 List of data references to warps.
1576 imageScalerList : `list`
1577 List of image scalers.
1579 Returns
1580 -------
1581 altMasks : `list` of `dict`
1582 List of dicts containing information about CLIPPED
1583 (i.e., artifacts), NO_DATA, and EDGE pixels.
1584 """
1585 self.log.debug("Generating Count Image, and mask lists.")
1586 coaddBBox = templateCoadd.getBBox()
1587 slateIm = afwImage.ImageU(coaddBBox)
1588 epochCountImage = afwImage.ImageU(coaddBBox)
1589 nImage = afwImage.ImageU(coaddBBox)
1590 spanSetArtifactList = []
1591 spanSetNoDataMaskList = []
1592 spanSetEdgeList = []
1593 spanSetBadMorphoList = []
1594 badPixelMask = self.getBadPixelMask()
1596 # mask of the warp diffs should = that of only the warp
1597 templateCoadd.mask.clearAllMaskPlanes()
1599 if self.config.doPreserveContainedBySource:
1600 templateFootprints = self.detectTemplate.detectFootprints(templateCoadd)
1601 else:
1602 templateFootprints = None
1604 for warpRef, imageScaler in zip(tempExpRefList, imageScalerList):
1605 warpDiffExp = self._readAndComputeWarpDiff(warpRef, imageScaler, templateCoadd)
1606 if warpDiffExp is not None:
1607 # This nImage only approximates the final nImage because it uses the PSF-matched mask
1608 nImage.array += (numpy.isfinite(warpDiffExp.image.array)
1609 * ((warpDiffExp.mask.array & badPixelMask) == 0)).astype(numpy.uint16)
1610 fpSet = self.detect.detectFootprints(warpDiffExp, doSmooth=False, clearMask=True)
1611 fpSet.positive.merge(fpSet.negative)
1612 footprints = fpSet.positive
1613 slateIm.set(0)
1614 spanSetList = [footprint.spans for footprint in footprints.getFootprints()]
1616 # Remove artifacts due to defects before they contribute to the epochCountImage
1617 if self.config.doPrefilterArtifacts:
1618 spanSetList = self.prefilterArtifacts(spanSetList, warpDiffExp)
1620 # Clear mask before adding prefiltered spanSets
1621 self.detect.clearMask(warpDiffExp.mask)
1622 for spans in spanSetList:
1623 spans.setImage(slateIm, 1, doClip=True)
1624 spans.setMask(warpDiffExp.mask, warpDiffExp.mask.getPlaneBitMask("DETECTED"))
1625 epochCountImage += slateIm
1627 if self.config.doFilterMorphological:
1628 maskName = self.config.streakMaskName
1629 _ = self.maskStreaks.run(warpDiffExp)
1630 streakMask = warpDiffExp.mask
1631 spanSetStreak = afwGeom.SpanSet.fromMask(streakMask,
1632 streakMask.getPlaneBitMask(maskName)).split()
1633 # Pad the streaks to account for low-surface brightness wings
1634 psf = warpDiffExp.getPsf()
1635 for s, sset in enumerate(spanSetStreak):
1636 psfShape = psf.computeShape(sset.computeCentroid())
1637 dilation = self.config.growStreakFp * psfShape.getDeterminantRadius()
1638 sset_dilated = sset.dilated(int(dilation))
1639 spanSetStreak[s] = sset_dilated
1641 # PSF-Matched warps have less available area (~the matching kernel) because the calexps
1642 # undergo a second convolution. Pixels with data in the direct warp
1643 # but not in the PSF-matched warp will not have their artifacts detected.
1644 # NaNs from the PSF-matched warp therefore must be masked in the direct warp
1645 nans = numpy.where(numpy.isnan(warpDiffExp.maskedImage.image.array), 1, 0)
1646 nansMask = afwImage.makeMaskFromArray(nans.astype(afwImage.MaskPixel))
1647 nansMask.setXY0(warpDiffExp.getXY0())
1648 edgeMask = warpDiffExp.mask
1649 spanSetEdgeMask = afwGeom.SpanSet.fromMask(edgeMask,
1650 edgeMask.getPlaneBitMask("EDGE")).split()
1651 else:
1652 # If the directWarp has <1% coverage, the psfMatchedWarp can have 0% and not exist
1653 # In this case, mask the whole epoch
1654 nansMask = afwImage.MaskX(coaddBBox, 1)
1655 spanSetList = []
1656 spanSetEdgeMask = []
1657 spanSetStreak = []
1659 spanSetNoDataMask = afwGeom.SpanSet.fromMask(nansMask).split()
1661 spanSetNoDataMaskList.append(spanSetNoDataMask)
1662 spanSetArtifactList.append(spanSetList)
1663 spanSetEdgeList.append(spanSetEdgeMask)
1664 if self.config.doFilterMorphological:
1665 spanSetBadMorphoList.append(spanSetStreak)
1667 if lsstDebug.Info(__name__).saveCountIm:
1668 path = self._dataRef2DebugPath("epochCountIm", tempExpRefList[0], coaddLevel=True)
1669 epochCountImage.writeFits(path)
1671 for i, spanSetList in enumerate(spanSetArtifactList):
1672 if spanSetList:
1673 filteredSpanSetList = self.filterArtifacts(spanSetList, epochCountImage, nImage,
1674 templateFootprints)
1675 spanSetArtifactList[i] = filteredSpanSetList
1676 if self.config.doFilterMorphological:
1677 spanSetArtifactList[i] += spanSetBadMorphoList[i]
1679 altMasks = []
1680 for artifacts, noData, edge in zip(spanSetArtifactList, spanSetNoDataMaskList, spanSetEdgeList):
1681 altMasks.append({'CLIPPED': artifacts,
1682 'NO_DATA': noData,
1683 'EDGE': edge})
1684 return altMasks
1686 def prefilterArtifacts(self, spanSetList, exp):
1687 """Remove artifact candidates covered by bad mask plane.
1689 Any future editing of the candidate list that does not depend on
1690 temporal information should go in this method.
1692 Parameters
1693 ----------
1694 spanSetList : `list` of `lsst.afw.geom.SpanSet`
1695 List of SpanSets representing artifact candidates.
1696 exp : `lsst.afw.image.Exposure`
1697 Exposure containing mask planes used to prefilter.
1699 Returns
1700 -------
1701 returnSpanSetList : `list` of `lsst.afw.geom.SpanSet`
1702 List of SpanSets with artifacts.
1703 """
1704 badPixelMask = exp.mask.getPlaneBitMask(self.config.prefilterArtifactsMaskPlanes)
1705 goodArr = (exp.mask.array & badPixelMask) == 0
1706 returnSpanSetList = []
1707 bbox = exp.getBBox()
1708 x0, y0 = exp.getXY0()
1709 for i, span in enumerate(spanSetList):
1710 y, x = span.clippedTo(bbox).indices()
1711 yIndexLocal = numpy.array(y) - y0
1712 xIndexLocal = numpy.array(x) - x0
1713 goodRatio = numpy.count_nonzero(goodArr[yIndexLocal, xIndexLocal])/span.getArea()
1714 if goodRatio > self.config.prefilterArtifactsRatio:
1715 returnSpanSetList.append(span)
1716 return returnSpanSetList
1718 def filterArtifacts(self, spanSetList, epochCountImage, nImage, footprintsToExclude=None):
1719 """Filter artifact candidates.
1721 Parameters
1722 ----------
1723 spanSetList : `list` of `lsst.afw.geom.SpanSet`
1724 List of SpanSets representing artifact candidates.
1725 epochCountImage : `lsst.afw.image.Image`
1726 Image of accumulated number of warpDiff detections.
1727 nImage : `lsst.afw.image.ImageU`
1728 Image of the accumulated number of total epochs contributing.
1730 Returns
1731 -------
1732 maskSpanSetList : `list`
1733 List of SpanSets with artifacts.
1734 """
1735 maskSpanSetList = []
1736 x0, y0 = epochCountImage.getXY0()
1737 for i, span in enumerate(spanSetList):
1738 y, x = span.indices()
1739 yIdxLocal = [y1 - y0 for y1 in y]
1740 xIdxLocal = [x1 - x0 for x1 in x]
1741 outlierN = epochCountImage.array[yIdxLocal, xIdxLocal]
1742 totalN = nImage.array[yIdxLocal, xIdxLocal]
1744 # effectiveMaxNumEpochs is broken line (fraction of N) with characteristic config.maxNumEpochs
1745 effMaxNumEpochsHighN = (self.config.maxNumEpochs
1746 + self.config.maxFractionEpochsHigh*numpy.mean(totalN))
1747 effMaxNumEpochsLowN = self.config.maxFractionEpochsLow * numpy.mean(totalN)
1748 effectiveMaxNumEpochs = int(min(effMaxNumEpochsLowN, effMaxNumEpochsHighN))
1749 nPixelsBelowThreshold = numpy.count_nonzero((outlierN > 0)
1750 & (outlierN <= effectiveMaxNumEpochs))
1751 percentBelowThreshold = nPixelsBelowThreshold / len(outlierN)
1752 if percentBelowThreshold > self.config.spatialThreshold:
1753 maskSpanSetList.append(span)
1755 if self.config.doPreserveContainedBySource and footprintsToExclude is not None:
1756 # If a candidate is contained by a footprint on the template coadd, do not clip
1757 filteredMaskSpanSetList = []
1758 for span in maskSpanSetList:
1759 doKeep = True
1760 for footprint in footprintsToExclude.positive.getFootprints():
1761 if footprint.spans.contains(span):
1762 doKeep = False
1763 break
1764 if doKeep:
1765 filteredMaskSpanSetList.append(span)
1766 maskSpanSetList = filteredMaskSpanSetList
1768 return maskSpanSetList
1770 def _readAndComputeWarpDiff(self, warpRef, imageScaler, templateCoadd):
1771 """Fetch a warp from the butler and return a warpDiff.
1773 Parameters
1774 ----------
1775 warpRef : `lsst.daf.butler.DeferredDatasetHandle`
1776 Handle for the warp.
1777 imageScaler : `lsst.pipe.tasks.scaleZeroPoint.ImageScaler`
1778 An image scaler object.
1779 templateCoadd : `lsst.afw.image.Exposure`
1780 Exposure to be substracted from the scaled warp.
1782 Returns
1783 -------
1784 warp : `lsst.afw.image.Exposure`
1785 Exposure of the image difference between the warp and template.
1786 """
1787 # If the PSF-Matched warp did not exist for this direct warp
1788 # None is holding its place to maintain order in Gen 3
1789 if warpRef is None:
1790 return None
1792 warp = warpRef.get()
1793 # direct image scaler OK for PSF-matched Warp
1794 imageScaler.scaleMaskedImage(warp.getMaskedImage())
1795 mi = warp.getMaskedImage()
1796 if self.config.doScaleWarpVariance:
1797 try:
1798 self.scaleWarpVariance.run(mi)
1799 except Exception as exc:
1800 self.log.warning("Unable to rescale variance of warp (%s); leaving it as-is", exc)
1801 mi -= templateCoadd.getMaskedImage()
1802 return warp