41 from .coaddBase
import CoaddBaseTask, SelectDataIdContainer, makeSkyInfo, makeCoaddSuffix
42 from .interpImage
import InterpImageTask
43 from .scaleZeroPoint
import ScaleZeroPointTask
44 from .coaddHelpers
import groupPatchExposures, getGroupDataRef
45 from .scaleVariance
import ScaleVarianceTask
46 from .maskStreaks
import MaskStreaksTask
48 from lsst.daf.butler
import DeferredDatasetHandle
50 __all__ = [
"AssembleCoaddTask",
"AssembleCoaddConnections",
"AssembleCoaddConfig",
51 "SafeClipAssembleCoaddTask",
"SafeClipAssembleCoaddConfig",
52 "CompareWarpAssembleCoaddTask",
"CompareWarpAssembleCoaddConfig"]
56 dimensions=(
"tract",
"patch",
"band",
"skymap"),
57 defaultTemplates={
"inputCoaddName":
"deep",
58 "outputCoaddName":
"deep",
62 inputWarps = pipeBase.connectionTypes.Input(
63 doc=(
"Input list of warps to be assemebled i.e. stacked."
64 "WarpType (e.g. direct, psfMatched) is controlled by the warpType config parameter"),
65 name=
"{inputCoaddName}Coadd_{warpType}Warp",
66 storageClass=
"ExposureF",
67 dimensions=(
"tract",
"patch",
"skymap",
"visit",
"instrument"),
71 skyMap = pipeBase.connectionTypes.Input(
72 doc=
"Input definition of geometry/bbox and projection/wcs for coadded exposures",
73 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
74 storageClass=
"SkyMap",
75 dimensions=(
"skymap", ),
77 brightObjectMask = pipeBase.connectionTypes.PrerequisiteInput(
78 doc=(
"Input Bright Object Mask mask produced with external catalogs to be applied to the mask plane"
80 name=
"brightObjectMask",
81 storageClass=
"ObjectMaskCatalog",
82 dimensions=(
"tract",
"patch",
"skymap",
"band"),
84 coaddExposure = pipeBase.connectionTypes.Output(
85 doc=
"Output coadded exposure, produced by stacking input warps",
86 name=
"{fakesType}{outputCoaddName}Coadd{warpTypeSuffix}",
87 storageClass=
"ExposureF",
88 dimensions=(
"tract",
"patch",
"skymap",
"band"),
90 nImage = pipeBase.connectionTypes.Output(
91 doc=
"Output image of number of input images per pixel",
92 name=
"{outputCoaddName}Coadd_nImage",
93 storageClass=
"ImageU",
94 dimensions=(
"tract",
"patch",
"skymap",
"band"),
97 def __init__(self, *, config=None):
98 super().__init__(config=config)
103 templateValues = {name: getattr(config.connections, name)
for name
in self.defaultTemplates}
104 templateValues[
'warpType'] = config.warpType
107 templateValues[
'fakesType'] =
"_fakes"
108 self._nameOverrides = {name: getattr(config.connections, name).format(**templateValues)
109 for name
in self.allConnections}
110 self._typeNameToVarName = {v: k
for k, v
in self._nameOverrides.items()}
113 if not config.doMaskBrightObjects:
114 self.prerequisiteInputs.remove(
"brightObjectMask")
116 if not config.doNImage:
117 self.outputs.remove(
"nImage")
120 class AssembleCoaddConfig(CoaddBaseTask.ConfigClass, pipeBase.PipelineTaskConfig,
121 pipelineConnections=AssembleCoaddConnections):
122 """Configuration parameters for the `AssembleCoaddTask`.
126 The `doMaskBrightObjects` and `brightObjectMaskName` configuration options
127 only set the bitplane config.brightObjectMaskName. To make this useful you
128 *must* also configure the flags.pixel algorithm, for example by adding
132 config.measurement.plugins["base_PixelFlags"].masksFpCenter.append("BRIGHT_OBJECT")
133 config.measurement.plugins["base_PixelFlags"].masksFpAnywhere.append("BRIGHT_OBJECT")
135 to your measureCoaddSources.py and forcedPhotCoadd.py config overrides.
137 warpType = pexConfig.Field(
138 doc=
"Warp name: one of 'direct' or 'psfMatched'",
142 subregionSize = pexConfig.ListField(
144 doc=
"Width, height of stack subregion size; "
145 "make small enough that a full stack of images will fit into memory at once.",
147 default=(2000, 2000),
149 statistic = pexConfig.Field(
151 doc=
"Main stacking statistic for aggregating over the epochs.",
154 doSigmaClip = pexConfig.Field(
156 doc=
"Perform sigma clipped outlier rejection with MEANCLIP statistic? (DEPRECATED)",
159 sigmaClip = pexConfig.Field(
161 doc=
"Sigma for outlier rejection; ignored if non-clipping statistic selected.",
164 clipIter = pexConfig.Field(
166 doc=
"Number of iterations of outlier rejection; ignored if non-clipping statistic selected.",
169 calcErrorFromInputVariance = pexConfig.Field(
171 doc=
"Calculate coadd variance from input variance by stacking statistic."
172 "Passed to StatisticsControl.setCalcErrorFromInputVariance()",
175 scaleZeroPoint = pexConfig.ConfigurableField(
176 target=ScaleZeroPointTask,
177 doc=
"Task to adjust the photometric zero point of the coadd temp exposures",
179 doInterp = pexConfig.Field(
180 doc=
"Interpolate over NaN pixels? Also extrapolate, if necessary, but the results are ugly.",
184 interpImage = pexConfig.ConfigurableField(
185 target=InterpImageTask,
186 doc=
"Task to interpolate (and extrapolate) over NaN pixels",
188 doWrite = pexConfig.Field(
189 doc=
"Persist coadd?",
193 doNImage = pexConfig.Field(
194 doc=
"Create image of number of contributing exposures for each pixel",
198 doUsePsfMatchedPolygons = pexConfig.Field(
199 doc=
"Use ValidPolygons from shrunk Psf-Matched Calexps? Should be set to True by CompareWarp only.",
203 maskPropagationThresholds = pexConfig.DictField(
206 doc=(
"Threshold (in fractional weight) of rejection at which we propagate a mask plane to "
207 "the coadd; that is, we set the mask bit on the coadd if the fraction the rejected frames "
208 "would have contributed exceeds this value."),
209 default={
"SAT": 0.1},
211 removeMaskPlanes = pexConfig.ListField(dtype=str, default=[
"NOT_DEBLENDED"],
212 doc=
"Mask planes to remove before coadding")
213 doMaskBrightObjects = pexConfig.Field(dtype=bool, default=
False,
214 doc=
"Set mask and flag bits for bright objects?")
215 brightObjectMaskName = pexConfig.Field(dtype=str, default=
"BRIGHT_OBJECT",
216 doc=
"Name of mask bit used for bright objects")
217 coaddPsf = pexConfig.ConfigField(
218 doc=
"Configuration for CoaddPsf",
219 dtype=measAlg.CoaddPsfConfig,
221 doAttachTransmissionCurve = pexConfig.Field(
222 dtype=bool, default=
False, optional=
False,
223 doc=(
"Attach a piecewise TransmissionCurve for the coadd? "
224 "(requires all input Exposures to have TransmissionCurves).")
226 hasFakes = pexConfig.Field(
229 doc=
"Should be set to True if fake sources have been inserted into the input data."
232 def setDefaults(self):
233 super().setDefaults()
234 self.badMaskPlanes = [
"NO_DATA",
"BAD",
"SAT",
"EDGE"]
241 log.warn(
"Config doPsfMatch deprecated. Setting warpType='psfMatched'")
242 self.warpType =
'psfMatched'
243 if self.doSigmaClip
and self.statistic !=
"MEANCLIP":
244 log.warn(
'doSigmaClip deprecated. To replicate behavior, setting statistic to "MEANCLIP"')
245 self.statistic =
"MEANCLIP"
246 if self.doInterp
and self.statistic
not in [
'MEAN',
'MEDIAN',
'MEANCLIP',
'VARIANCE',
'VARIANCECLIP']:
247 raise ValueError(
"Must set doInterp=False for statistic=%s, which does not "
248 "compute and set a non-zero coadd variance estimate." % (self.statistic))
250 unstackableStats = [
'NOTHING',
'ERROR',
'ORMASK']
251 if not hasattr(afwMath.Property, self.statistic)
or self.statistic
in unstackableStats:
252 stackableStats = [str(k)
for k
in afwMath.Property.__members__.keys()
253 if str(k)
not in unstackableStats]
254 raise ValueError(
"statistic %s is not allowed. Please choose one of %s."
255 % (self.statistic, stackableStats))
258 class AssembleCoaddTask(
CoaddBaseTask, pipeBase.PipelineTask):
259 """Assemble a coadded image from a set of warps (coadded temporary exposures).
261 We want to assemble a coadded image from a set of Warps (also called
262 coadded temporary exposures or ``coaddTempExps``).
263 Each input Warp covers a patch on the sky and corresponds to a single
264 run/visit/exposure of the covered patch. We provide the task with a list
265 of Warps (``selectDataList``) from which it selects Warps that cover the
266 specified patch (pointed at by ``dataRef``).
267 Each Warp that goes into a coadd will typically have an independent
268 photometric zero-point. Therefore, we must scale each Warp to set it to
269 a common photometric zeropoint. WarpType may be one of 'direct' or
270 'psfMatched', and the boolean configs `config.makeDirect` and
271 `config.makePsfMatched` set which of the warp types will be coadded.
272 The coadd is computed as a mean with optional outlier rejection.
273 Criteria for outlier rejection are set in `AssembleCoaddConfig`.
274 Finally, Warps can have bad 'NaN' pixels which received no input from the
275 source calExps. We interpolate over these bad (NaN) pixels.
277 `AssembleCoaddTask` uses several sub-tasks. These are
279 - `ScaleZeroPointTask`
280 - create and use an ``imageScaler`` object to scale the photometric zeropoint for each Warp
282 - interpolate across bad pixels (NaN) in the final coadd
284 You can retarget these subtasks if you wish.
288 The `lsst.pipe.base.cmdLineTask.CmdLineTask` interface supports a
289 flag ``-d`` to import ``debug.py`` from your ``PYTHONPATH``; see
290 `baseDebug` for more about ``debug.py`` files. `AssembleCoaddTask` has
291 no debug variables of its own. Some of the subtasks may support debug
292 variables. See the documentation for the subtasks for further information.
296 `AssembleCoaddTask` assembles a set of warped images into a coadded image.
297 The `AssembleCoaddTask` can be invoked by running ``assembleCoadd.py``
298 with the flag '--legacyCoadd'. Usage of assembleCoadd.py expects two
299 inputs: a data reference to the tract patch and filter to be coadded, and
300 a list of Warps to attempt to coadd. These are specified using ``--id`` and
301 ``--selectId``, respectively:
305 --id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]
306 --selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]
308 Only the Warps that cover the specified tract and patch will be coadded.
309 A list of the available optional arguments can be obtained by calling
310 ``assembleCoadd.py`` with the ``--help`` command line argument:
314 assembleCoadd.py --help
316 To demonstrate usage of the `AssembleCoaddTask` in the larger context of
317 multi-band processing, we will generate the HSC-I & -R band coadds from
318 HSC engineering test data provided in the ``ci_hsc`` package. To begin,
319 assuming that the lsst stack has been already set up, we must set up the
320 obs_subaru and ``ci_hsc`` packages. This defines the environment variable
321 ``$CI_HSC_DIR`` and points at the location of the package. The raw HSC
322 data live in the ``$CI_HSC_DIR/raw directory``. To begin assembling the
323 coadds, we must first
326 - process the individual ccds in $CI_HSC_RAW to produce calibrated exposures
328 - create a skymap that covers the area of the sky present in the raw exposures
330 - warp the individual calibrated exposures to the tangent plane of the coadd
332 We can perform all of these steps by running
336 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988
338 This will produce warped exposures for each visit. To coadd the warped
339 data, we call assembleCoadd.py as follows:
343 assembleCoadd.py --legacyCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \
344 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \
345 --selectId visit=903986 ccd=100 --selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \
346 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \
347 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \
348 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \
349 --selectId visit=903988 ccd=24
351 that will process the HSC-I band data. The results are written in
352 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``.
354 You may also choose to run:
358 scons warp-903334 warp-903336 warp-903338 warp-903342 warp-903344 warp-903346
359 assembleCoadd.py --legacyCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-R \
360 --selectId visit=903334 ccd=16 --selectId visit=903334 ccd=22 --selectId visit=903334 ccd=23 \
361 --selectId visit=903334 ccd=100 --selectId visit=903336 ccd=17 --selectId visit=903336 ccd=24 \
362 --selectId visit=903338 ccd=18 --selectId visit=903338 ccd=25 --selectId visit=903342 ccd=4 \
363 --selectId visit=903342 ccd=10 --selectId visit=903342 ccd=100 --selectId visit=903344 ccd=0 \
364 --selectId visit=903344 ccd=5 --selectId visit=903344 ccd=11 --selectId visit=903346 ccd=1 \
365 --selectId visit=903346 ccd=6 --selectId visit=903346 ccd=12
367 to generate the coadd for the HSC-R band if you are interested in
368 following multiBand Coadd processing as discussed in `pipeTasks_multiBand`
369 (but note that normally, one would use the `SafeClipAssembleCoaddTask`
370 rather than `AssembleCoaddTask` to make the coadd.
372 ConfigClass = AssembleCoaddConfig
373 _DefaultName =
"assembleCoadd"
375 def __init__(self, *args, **kwargs):
378 argNames = [
"config",
"name",
"parentTask",
"log"]
379 kwargs.update({k: v
for k, v
in zip(argNames, args)})
380 warnings.warn(
"AssembleCoadd received positional args, and casting them as kwargs: %s. "
381 "PipelineTask will not take positional args" % argNames, FutureWarning)
383 super().__init__(**kwargs)
384 self.makeSubtask(
"interpImage")
385 self.makeSubtask(
"scaleZeroPoint")
387 if self.config.doMaskBrightObjects:
388 mask = afwImage.Mask()
390 self.brightObjectBitmask = 1 << mask.addMaskPlane(self.config.brightObjectMaskName)
391 except pexExceptions.LsstCppException:
392 raise RuntimeError(
"Unable to define mask plane for bright objects; planes used are %s" %
393 mask.getMaskPlaneDict().keys())
396 self.warpType = self.config.warpType
398 @utils.inheritDoc(pipeBase.PipelineTask)
399 def runQuantum(self, butlerQC, inputRefs, outputRefs):
404 Assemble a coadd from a set of Warps.
406 PipelineTask (Gen3) entry point to Coadd a set of Warps.
407 Analogous to `runDataRef`, it prepares all the data products to be
408 passed to `run`, and processes the results before returning a struct
409 of results to be written out. AssembleCoadd cannot fit all Warps in memory.
410 Therefore, its inputs are accessed subregion by subregion
411 by the Gen3 `DeferredDatasetHandle` that is analagous to the Gen2
412 `lsst.daf.persistence.ButlerDataRef`. Any updates to this method should
413 correspond to an update in `runDataRef` while both entry points
416 inputData = butlerQC.get(inputRefs)
420 skyMap = inputData[
"skyMap"]
421 outputDataId = butlerQC.quantum.dataId
424 tractId=outputDataId[
'tract'],
425 patchId=outputDataId[
'patch'])
429 warpRefList = inputData[
'inputWarps']
431 inputs = self.prepareInputs(warpRefList)
432 self.log.info(
"Found %d %s", len(inputs.tempExpRefList),
433 self.getTempExpDatasetName(self.warpType))
434 if len(inputs.tempExpRefList) == 0:
435 self.log.warn(
"No coadd temporary exposures found")
438 supplementaryData = self.makeSupplementaryDataGen3(butlerQC, inputRefs, outputRefs)
439 retStruct = self.run(inputData[
'skyInfo'], inputs.tempExpRefList, inputs.imageScalerList,
440 inputs.weightList, supplementaryData=supplementaryData)
442 inputData.setdefault(
'brightObjectMask',
None)
443 self.processResults(retStruct.coaddExposure, inputData[
'brightObjectMask'], outputDataId)
445 if self.config.doWrite:
446 butlerQC.put(retStruct, outputRefs)
450 def runDataRef(self, dataRef, selectDataList=None, warpRefList=None):
451 """Assemble a coadd from a set of Warps.
453 Pipebase.CmdlineTask entry point to Coadd a set of Warps.
454 Compute weights to be applied to each Warp and
455 find scalings to match the photometric zeropoint to a reference Warp.
456 Assemble the Warps using `run`. Interpolate over NaNs and
457 optionally write the coadd to disk. Return the coadded exposure.
461 dataRef : `lsst.daf.persistence.butlerSubset.ButlerDataRef`
462 Data reference defining the patch for coaddition and the
463 reference Warp (if ``config.autoReference=False``).
464 Used to access the following data products:
465 - ``self.config.coaddName + "Coadd_skyMap"``
466 - ``self.config.coaddName + "Coadd_ + <warpType> + "Warp"`` (optionally)
467 - ``self.config.coaddName + "Coadd"``
468 selectDataList : `list`
469 List of data references to Calexps. Data to be coadded will be
470 selected from this list based on overlap with the patch defined
471 by dataRef, grouped by visit, and converted to a list of data
474 List of data references to Warps to be coadded.
475 Note: `warpRefList` is just the new name for `tempExpRefList`.
479 retStruct : `lsst.pipe.base.Struct`
480 Result struct with components:
482 - ``coaddExposure``: coadded exposure (``Exposure``).
483 - ``nImage``: exposure count image (``Image``).
485 if selectDataList
and warpRefList:
486 raise RuntimeError(
"runDataRef received both a selectDataList and warpRefList, "
487 "and which to use is ambiguous. Please pass only one.")
489 skyInfo = self.getSkyInfo(dataRef)
490 if warpRefList
is None:
491 calExpRefList = self.selectExposures(dataRef, skyInfo, selectDataList=selectDataList)
492 if len(calExpRefList) == 0:
493 self.log.warn(
"No exposures to coadd")
495 self.log.info(
"Coadding %d exposures", len(calExpRefList))
497 warpRefList = self.getTempExpRefList(dataRef, calExpRefList)
499 inputData = self.prepareInputs(warpRefList)
500 self.log.info(
"Found %d %s", len(inputData.tempExpRefList),
501 self.getTempExpDatasetName(self.warpType))
502 if len(inputData.tempExpRefList) == 0:
503 self.log.warn(
"No coadd temporary exposures found")
506 supplementaryData = self.makeSupplementaryData(dataRef, warpRefList=inputData.tempExpRefList)
508 retStruct = self.run(skyInfo, inputData.tempExpRefList, inputData.imageScalerList,
509 inputData.weightList, supplementaryData=supplementaryData)
511 brightObjects = self.readBrightObjectMasks(dataRef)
if self.config.doMaskBrightObjects
else None
512 self.processResults(retStruct.coaddExposure, brightObjectMasks=brightObjects, dataId=dataRef.dataId)
514 if self.config.doWrite:
515 if self.getCoaddDatasetName(self.warpType) ==
"deepCoadd" and self.config.hasFakes:
516 coaddDatasetName =
"fakes_" + self.getCoaddDatasetName(self.warpType)
518 coaddDatasetName = self.getCoaddDatasetName(self.warpType)
519 self.log.info(
"Persisting %s" % coaddDatasetName)
520 dataRef.put(retStruct.coaddExposure, coaddDatasetName)
521 if self.config.doNImage
and retStruct.nImage
is not None:
522 dataRef.put(retStruct.nImage, self.getCoaddDatasetName(self.warpType) +
'_nImage')
527 """Interpolate over missing data and mask bright stars.
531 coaddExposure : `lsst.afw.image.Exposure`
532 The coadded exposure to process.
533 dataRef : `lsst.daf.persistence.ButlerDataRef`
534 Butler data reference for supplementary data.
536 if self.config.doInterp:
537 self.interpImage.
run(coaddExposure.getMaskedImage(), planeName=
"NO_DATA")
539 varArray = coaddExposure.variance.array
540 with numpy.errstate(invalid=
"ignore"):
541 varArray[:] = numpy.where(varArray > 0, varArray, numpy.inf)
543 if self.config.doMaskBrightObjects:
544 self.setBrightObjectMasks(coaddExposure, brightObjectMasks, dataId)
547 """Make additional inputs to run() specific to subclasses (Gen2)
549 Duplicates interface of `runDataRef` method
550 Available to be implemented by subclasses only if they need the
551 coadd dataRef for performing preliminary processing before
552 assembling the coadd.
556 dataRef : `lsst.daf.persistence.ButlerDataRef`
557 Butler data reference for supplementary data.
558 selectDataList : `list` (optional)
559 Optional List of data references to Calexps.
560 warpRefList : `list` (optional)
561 Optional List of data references to Warps.
563 return pipeBase.Struct()
566 """Make additional inputs to run() specific to subclasses (Gen3)
568 Duplicates interface of `runQuantum` method.
569 Available to be implemented by subclasses only if they need the
570 coadd dataRef for performing preliminary processing before
571 assembling the coadd.
575 butlerQC : `lsst.pipe.base.ButlerQuantumContext`
576 Gen3 Butler object for fetching additional data products before
577 running the Task specialized for quantum being processed
578 inputRefs : `lsst.pipe.base.InputQuantizedConnection`
579 Attributes are the names of the connections describing input dataset types.
580 Values are DatasetRefs that task consumes for corresponding dataset type.
581 DataIds are guaranteed to match data objects in ``inputData``.
582 outputRefs : `lsst.pipe.base.OutputQuantizedConnection`
583 Attributes are the names of the connections describing output dataset types.
584 Values are DatasetRefs that task is to produce
585 for corresponding dataset type.
587 return pipeBase.Struct()
590 """Generate list data references corresponding to warped exposures
591 that lie within the patch to be coadded.
596 Data reference for patch.
597 calExpRefList : `list`
598 List of data references for input calexps.
602 tempExpRefList : `list`
603 List of Warp/CoaddTempExp data references.
605 butler = patchRef.getButler()
606 groupData =
groupPatchExposures(patchRef, calExpRefList, self.getCoaddDatasetName(self.warpType),
607 self.getTempExpDatasetName(self.warpType))
608 tempExpRefList = [
getGroupDataRef(butler, self.getTempExpDatasetName(self.warpType),
609 g, groupData.keys)
for
610 g
in groupData.groups.keys()]
611 return tempExpRefList
614 """Prepare the input warps for coaddition by measuring the weight for
615 each warp and the scaling for the photometric zero point.
617 Each Warp has its own photometric zeropoint and background variance.
618 Before coadding these Warps together, compute a scale factor to
619 normalize the photometric zeropoint and compute the weight for each Warp.
624 List of data references to tempExp
628 result : `lsst.pipe.base.Struct`
629 Result struct with components:
631 - ``tempExprefList``: `list` of data references to tempExp.
632 - ``weightList``: `list` of weightings.
633 - ``imageScalerList``: `list` of image scalers.
635 statsCtrl = afwMath.StatisticsControl()
636 statsCtrl.setNumSigmaClip(self.config.sigmaClip)
637 statsCtrl.setNumIter(self.config.clipIter)
638 statsCtrl.setAndMask(self.getBadPixelMask())
639 statsCtrl.setNanSafe(
True)
646 tempExpName = self.getTempExpDatasetName(self.warpType)
647 for tempExpRef
in refList:
650 if not isinstance(tempExpRef, DeferredDatasetHandle):
651 if not tempExpRef.datasetExists(tempExpName):
652 self.log.warn(
"Could not find %s %s; skipping it", tempExpName, tempExpRef.dataId)
655 tempExp = tempExpRef.get(datasetType=tempExpName, immediate=
True)
657 if numpy.isnan(tempExp.image.array).all():
659 maskedImage = tempExp.getMaskedImage()
660 imageScaler = self.scaleZeroPoint.computeImageScaler(
665 imageScaler.scaleMaskedImage(maskedImage)
666 except Exception
as e:
667 self.log.warn(
"Scaling failed for %s (skipping it): %s", tempExpRef.dataId, e)
669 statObj = afwMath.makeStatistics(maskedImage.getVariance(), maskedImage.getMask(),
670 afwMath.MEANCLIP, statsCtrl)
671 meanVar, meanVarErr = statObj.getResult(afwMath.MEANCLIP)
672 weight = 1.0 / float(meanVar)
673 if not numpy.isfinite(weight):
674 self.log.warn(
"Non-finite weight for %s: skipping", tempExpRef.dataId)
676 self.log.info(
"Weight of %s %s = %0.3f", tempExpName, tempExpRef.dataId, weight)
681 tempExpRefList.append(tempExpRef)
682 weightList.append(weight)
683 imageScalerList.append(imageScaler)
685 return pipeBase.Struct(tempExpRefList=tempExpRefList, weightList=weightList,
686 imageScalerList=imageScalerList)
689 """Prepare the statistics for coadding images.
693 mask : `int`, optional
694 Bit mask value to exclude from coaddition.
698 stats : `lsst.pipe.base.Struct`
699 Statistics structure with the following fields:
701 - ``statsCtrl``: Statistics control object for coadd
702 (`lsst.afw.math.StatisticsControl`)
703 - ``statsFlags``: Statistic for coadd (`lsst.afw.math.Property`)
706 mask = self.getBadPixelMask()
707 statsCtrl = afwMath.StatisticsControl()
708 statsCtrl.setNumSigmaClip(self.config.sigmaClip)
709 statsCtrl.setNumIter(self.config.clipIter)
710 statsCtrl.setAndMask(mask)
711 statsCtrl.setNanSafe(
True)
712 statsCtrl.setWeighted(
True)
713 statsCtrl.setCalcErrorFromInputVariance(self.config.calcErrorFromInputVariance)
714 for plane, threshold
in self.config.maskPropagationThresholds.items():
715 bit = afwImage.Mask.getMaskPlane(plane)
716 statsCtrl.setMaskPropagationThreshold(bit, threshold)
717 statsFlags = afwMath.stringToStatisticsProperty(self.config.statistic)
718 return pipeBase.Struct(ctrl=statsCtrl, flags=statsFlags)
721 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
722 altMaskList=None, mask=None, supplementaryData=None):
723 """Assemble a coadd from input warps
725 Assemble the coadd using the provided list of coaddTempExps. Since
726 the full coadd covers a patch (a large area), the assembly is
727 performed over small areas on the image at a time in order to
728 conserve memory usage. Iterate over subregions within the outer
729 bbox of the patch using `assembleSubregion` to stack the corresponding
730 subregions from the coaddTempExps with the statistic specified.
731 Set the edge bits the coadd mask based on the weight map.
735 skyInfo : `lsst.pipe.base.Struct`
736 Struct with geometric information about the patch.
737 tempExpRefList : `list`
738 List of data references to Warps (previously called CoaddTempExps).
739 imageScalerList : `list`
740 List of image scalers.
743 altMaskList : `list`, optional
744 List of alternate masks to use rather than those stored with
746 mask : `int`, optional
747 Bit mask value to exclude from coaddition.
748 supplementaryData : lsst.pipe.base.Struct, optional
749 Struct with additional data products needed to assemble coadd.
750 Only used by subclasses that implement `makeSupplementaryData`
755 result : `lsst.pipe.base.Struct`
756 Result struct with components:
758 - ``coaddExposure``: coadded exposure (``lsst.afw.image.Exposure``).
759 - ``nImage``: exposure count image (``lsst.afw.image.Image``), if requested.
760 - ``warpRefList``: input list of refs to the warps (
761 ``lsst.daf.butler.DeferredDatasetHandle`` or
762 ``lsst.daf.persistence.ButlerDataRef``)
764 - ``imageScalerList``: input list of image scalers (unmodified)
765 - ``weightList``: input list of weights (unmodified)
767 tempExpName = self.getTempExpDatasetName(self.warpType)
768 self.log.info(
"Assembling %s %s", len(tempExpRefList), tempExpName)
769 stats = self.prepareStats(mask=mask)
771 if altMaskList
is None:
772 altMaskList = [
None]*len(tempExpRefList)
774 coaddExposure = afwImage.ExposureF(skyInfo.bbox, skyInfo.wcs)
775 coaddExposure.setPhotoCalib(self.scaleZeroPoint.getPhotoCalib())
776 coaddExposure.getInfo().setCoaddInputs(self.inputRecorder.makeCoaddInputs())
777 self.assembleMetadata(coaddExposure, tempExpRefList, weightList)
778 coaddMaskedImage = coaddExposure.getMaskedImage()
779 subregionSizeArr = self.config.subregionSize
780 subregionSize =
geom.Extent2I(subregionSizeArr[0], subregionSizeArr[1])
782 if self.config.doNImage:
783 nImage = afwImage.ImageU(skyInfo.bbox)
786 for subBBox
in self._subBBoxIter(skyInfo.bbox, subregionSize):
788 self.assembleSubregion(coaddExposure, subBBox, tempExpRefList, imageScalerList,
789 weightList, altMaskList, stats.flags, stats.ctrl,
791 except Exception
as e:
792 self.log.fatal(
"Cannot compute coadd %s: %s", subBBox, e)
794 self.setInexactPsf(coaddMaskedImage.getMask())
797 coaddUtils.setCoaddEdgeBits(coaddMaskedImage.getMask(), coaddMaskedImage.getVariance())
798 return pipeBase.Struct(coaddExposure=coaddExposure, nImage=nImage,
799 warpRefList=tempExpRefList, imageScalerList=imageScalerList,
800 weightList=weightList)
803 """Set the metadata for the coadd.
805 This basic implementation sets the filter from the first input.
809 coaddExposure : `lsst.afw.image.Exposure`
810 The target exposure for the coadd.
811 tempExpRefList : `list`
812 List of data references to tempExp.
816 assert len(tempExpRefList) == len(weightList),
"Length mismatch"
817 tempExpName = self.getTempExpDatasetName(self.warpType)
823 if isinstance(tempExpRefList[0], DeferredDatasetHandle):
825 tempExpList = [tempExpRef.get(parameters={
'bbox': bbox})
for tempExpRef
in tempExpRefList]
828 tempExpList = [tempExpRef.get(tempExpName +
"_sub", bbox=bbox, immediate=
True)
829 for tempExpRef
in tempExpRefList]
830 numCcds = sum(len(tempExp.getInfo().getCoaddInputs().ccds)
for tempExp
in tempExpList)
832 coaddExposure.setFilter(tempExpList[0].getFilter())
833 coaddInputs = coaddExposure.getInfo().getCoaddInputs()
834 coaddInputs.ccds.reserve(numCcds)
835 coaddInputs.visits.reserve(len(tempExpList))
837 for tempExp, weight
in zip(tempExpList, weightList):
838 self.inputRecorder.addVisitToCoadd(coaddInputs, tempExp, weight)
840 if self.config.doUsePsfMatchedPolygons:
841 self.shrinkValidPolygons(coaddInputs)
843 coaddInputs.visits.sort()
844 if self.warpType ==
"psfMatched":
849 modelPsfList = [tempExp.getPsf()
for tempExp
in tempExpList]
850 modelPsfWidthList = [modelPsf.computeBBox().getWidth()
for modelPsf
in modelPsfList]
851 psf = modelPsfList[modelPsfWidthList.index(max(modelPsfWidthList))]
853 psf = measAlg.CoaddPsf(coaddInputs.ccds, coaddExposure.getWcs(),
854 self.config.coaddPsf.makeControl())
855 coaddExposure.setPsf(psf)
856 apCorrMap = measAlg.makeCoaddApCorrMap(coaddInputs.ccds, coaddExposure.getBBox(afwImage.PARENT),
857 coaddExposure.getWcs())
858 coaddExposure.getInfo().setApCorrMap(apCorrMap)
859 if self.config.doAttachTransmissionCurve:
860 transmissionCurve = measAlg.makeCoaddTransmissionCurve(coaddExposure.getWcs(), coaddInputs.ccds)
861 coaddExposure.getInfo().setTransmissionCurve(transmissionCurve)
864 altMaskList, statsFlags, statsCtrl, nImage=None):
865 """Assemble the coadd for a sub-region.
867 For each coaddTempExp, check for (and swap in) an alternative mask
868 if one is passed. Remove mask planes listed in
869 `config.removeMaskPlanes`. Finally, stack the actual exposures using
870 `lsst.afw.math.statisticsStack` with the statistic specified by
871 statsFlags. Typically, the statsFlag will be one of lsst.afw.math.MEAN for
872 a mean-stack or `lsst.afw.math.MEANCLIP` for outlier rejection using
873 an N-sigma clipped mean where N and iterations are specified by
874 statsCtrl. Assign the stacked subregion back to the coadd.
878 coaddExposure : `lsst.afw.image.Exposure`
879 The target exposure for the coadd.
880 bbox : `lsst.geom.Box`
882 tempExpRefList : `list`
883 List of data reference to tempExp.
884 imageScalerList : `list`
885 List of image scalers.
889 List of alternate masks to use rather than those stored with
890 tempExp, or None. Each element is dict with keys = mask plane
891 name to which to add the spans.
892 statsFlags : `lsst.afw.math.Property`
893 Property object for statistic for coadd.
894 statsCtrl : `lsst.afw.math.StatisticsControl`
895 Statistics control object for coadd.
896 nImage : `lsst.afw.image.ImageU`, optional
897 Keeps track of exposure count for each pixel.
899 self.log.debug(
"Computing coadd over %s", bbox)
900 tempExpName = self.getTempExpDatasetName(self.warpType)
901 coaddExposure.mask.addMaskPlane(
"REJECTED")
902 coaddExposure.mask.addMaskPlane(
"CLIPPED")
903 coaddExposure.mask.addMaskPlane(
"SENSOR_EDGE")
904 maskMap = self.setRejectedMaskMapping(statsCtrl)
905 clipped = afwImage.Mask.getPlaneBitMask(
"CLIPPED")
907 if nImage
is not None:
908 subNImage = afwImage.ImageU(bbox.getWidth(), bbox.getHeight())
909 for tempExpRef, imageScaler, altMask
in zip(tempExpRefList, imageScalerList, altMaskList):
911 if isinstance(tempExpRef, DeferredDatasetHandle):
913 exposure = tempExpRef.get(parameters={
'bbox': bbox})
916 exposure = tempExpRef.get(tempExpName +
"_sub", bbox=bbox)
918 maskedImage = exposure.getMaskedImage()
919 mask = maskedImage.getMask()
920 if altMask
is not None:
921 self.applyAltMaskPlanes(mask, altMask)
922 imageScaler.scaleMaskedImage(maskedImage)
926 if nImage
is not None:
927 subNImage.getArray()[maskedImage.getMask().getArray() & statsCtrl.getAndMask() == 0] += 1
928 if self.config.removeMaskPlanes:
929 self.removeMaskPlanes(maskedImage)
930 maskedImageList.append(maskedImage)
932 with self.timer(
"stack"):
933 coaddSubregion = afwMath.statisticsStack(maskedImageList, statsFlags, statsCtrl, weightList,
936 coaddExposure.maskedImage.assign(coaddSubregion, bbox)
937 if nImage
is not None:
938 nImage.assign(subNImage, bbox)
941 """Unset the mask of an image for mask planes specified in the config.
945 maskedImage : `lsst.afw.image.MaskedImage`
946 The masked image to be modified.
948 mask = maskedImage.getMask()
949 for maskPlane
in self.config.removeMaskPlanes:
951 mask &= ~mask.getPlaneBitMask(maskPlane)
952 except pexExceptions.InvalidParameterError:
953 self.log.debug(
"Unable to remove mask plane %s: no mask plane with that name was found.",
957 def setRejectedMaskMapping(statsCtrl):
958 """Map certain mask planes of the warps to new planes for the coadd.
960 If a pixel is rejected due to a mask value other than EDGE, NO_DATA,
961 or CLIPPED, set it to REJECTED on the coadd.
962 If a pixel is rejected due to EDGE, set the coadd pixel to SENSOR_EDGE.
963 If a pixel is rejected due to CLIPPED, set the coadd pixel to CLIPPED.
967 statsCtrl : `lsst.afw.math.StatisticsControl`
968 Statistics control object for coadd
972 maskMap : `list` of `tuple` of `int`
973 A list of mappings of mask planes of the warped exposures to
974 mask planes of the coadd.
976 edge = afwImage.Mask.getPlaneBitMask(
"EDGE")
977 noData = afwImage.Mask.getPlaneBitMask(
"NO_DATA")
978 clipped = afwImage.Mask.getPlaneBitMask(
"CLIPPED")
979 toReject = statsCtrl.getAndMask() & (~noData) & (~edge) & (~clipped)
980 maskMap = [(toReject, afwImage.Mask.getPlaneBitMask(
"REJECTED")),
981 (edge, afwImage.Mask.getPlaneBitMask(
"SENSOR_EDGE")),
986 """Apply in place alt mask formatted as SpanSets to a mask.
990 mask : `lsst.afw.image.Mask`
992 altMaskSpans : `dict`
993 SpanSet lists to apply. Each element contains the new mask
994 plane name (e.g. "CLIPPED and/or "NO_DATA") as the key,
995 and list of SpanSets to apply to the mask.
999 mask : `lsst.afw.image.Mask`
1002 if self.config.doUsePsfMatchedPolygons:
1003 if (
"NO_DATA" in altMaskSpans)
and (
"NO_DATA" in self.config.badMaskPlanes):
1008 for spanSet
in altMaskSpans[
'NO_DATA']:
1009 spanSet.clippedTo(mask.getBBox()).clearMask(mask, self.getBadPixelMask())
1011 for plane, spanSetList
in altMaskSpans.items():
1012 maskClipValue = mask.addMaskPlane(plane)
1013 for spanSet
in spanSetList:
1014 spanSet.clippedTo(mask.getBBox()).setMask(mask, 2**maskClipValue)
1018 """Shrink coaddInputs' ccds' ValidPolygons in place.
1020 Either modify each ccd's validPolygon in place, or if CoaddInputs
1021 does not have a validPolygon, create one from its bbox.
1025 coaddInputs : `lsst.afw.image.coaddInputs`
1029 for ccd
in coaddInputs.ccds:
1030 polyOrig = ccd.getValidPolygon()
1031 validPolyBBox = polyOrig.getBBox()
if polyOrig
else ccd.getBBox()
1032 validPolyBBox.grow(-self.config.matchingKernelSize//2)
1034 validPolygon = polyOrig.intersectionSingle(validPolyBBox)
1036 validPolygon = afwGeom.polygon.Polygon(
geom.Box2D(validPolyBBox))
1037 ccd.setValidPolygon(validPolygon)
1040 """Retrieve the bright object masks.
1042 Returns None on failure.
1046 dataRef : `lsst.daf.persistence.butlerSubset.ButlerDataRef`
1051 result : `lsst.daf.persistence.butlerSubset.ButlerDataRef`
1052 Bright object mask from the Butler object, or None if it cannot
1056 return dataRef.get(datasetType=
"brightObjectMask", immediate=
True)
1057 except Exception
as e:
1058 self.log.warn(
"Unable to read brightObjectMask for %s: %s", dataRef.dataId, e)
1062 """Set the bright object masks.
1066 exposure : `lsst.afw.image.Exposure`
1067 Exposure under consideration.
1068 dataId : `lsst.daf.persistence.dataId`
1069 Data identifier dict for patch.
1070 brightObjectMasks : `lsst.afw.table`
1071 Table of bright objects to mask.
1074 if brightObjectMasks
is None:
1075 self.log.warn(
"Unable to apply bright object mask: none supplied")
1077 self.log.info(
"Applying %d bright object masks to %s", len(brightObjectMasks), dataId)
1078 mask = exposure.getMaskedImage().getMask()
1079 wcs = exposure.getWcs()
1080 plateScale = wcs.getPixelScale().asArcseconds()
1082 for rec
in brightObjectMasks:
1083 center =
geom.PointI(wcs.skyToPixel(rec.getCoord()))
1084 if rec[
"type"] ==
"box":
1085 assert rec[
"angle"] == 0.0, (
"Angle != 0 for mask object %s" % rec[
"id"])
1086 width = rec[
"width"].asArcseconds()/plateScale
1087 height = rec[
"height"].asArcseconds()/plateScale
1090 bbox =
geom.Box2I(center - halfSize, center + halfSize)
1093 geom.PointI(int(center[0] + 0.5*width), int(center[1] + 0.5*height)))
1094 spans = afwGeom.SpanSet(bbox)
1095 elif rec[
"type"] ==
"circle":
1096 radius = int(rec[
"radius"].asArcseconds()/plateScale)
1097 spans = afwGeom.SpanSet.fromShape(radius, offset=center)
1099 self.log.warn(
"Unexpected region type %s at %s" % rec[
"type"], center)
1101 spans.clippedTo(mask.getBBox()).setMask(mask, self.brightObjectBitmask)
1104 """Set INEXACT_PSF mask plane.
1106 If any of the input images isn't represented in the coadd (due to
1107 clipped pixels or chip gaps), the `CoaddPsf` will be inexact. Flag
1112 mask : `lsst.afw.image.Mask`
1113 Coadded exposure's mask, modified in-place.
1115 mask.addMaskPlane(
"INEXACT_PSF")
1116 inexactPsf = mask.getPlaneBitMask(
"INEXACT_PSF")
1117 sensorEdge = mask.getPlaneBitMask(
"SENSOR_EDGE")
1118 clipped = mask.getPlaneBitMask(
"CLIPPED")
1119 rejected = mask.getPlaneBitMask(
"REJECTED")
1120 array = mask.getArray()
1121 selected = array & (sensorEdge | clipped | rejected) > 0
1122 array[selected] |= inexactPsf
1125 def _makeArgumentParser(cls):
1126 """Create an argument parser.
1128 parser = pipeBase.ArgumentParser(name=cls._DefaultName)
1129 parser.add_id_argument(
"--id", cls.ConfigClass().coaddName +
"Coadd_"
1130 + cls.ConfigClass().warpType +
"Warp",
1131 help=
"data ID, e.g. --id tract=12345 patch=1,2",
1132 ContainerClass=AssembleCoaddDataIdContainer)
1133 parser.add_id_argument(
"--selectId",
"calexp", help=
"data ID, e.g. --selectId visit=6789 ccd=0..9",
1134 ContainerClass=SelectDataIdContainer)
1138 def _subBBoxIter(bbox, subregionSize):
1139 """Iterate over subregions of a bbox.
1143 bbox : `lsst.geom.Box2I`
1144 Bounding box over which to iterate.
1145 subregionSize: `lsst.geom.Extent2I`
1150 subBBox : `lsst.geom.Box2I`
1151 Next sub-bounding box of size ``subregionSize`` or smaller; each ``subBBox``
1152 is contained within ``bbox``, so it may be smaller than ``subregionSize`` at
1153 the edges of ``bbox``, but it will never be empty.
1156 raise RuntimeError(
"bbox %s is empty" % (bbox,))
1157 if subregionSize[0] < 1
or subregionSize[1] < 1:
1158 raise RuntimeError(
"subregionSize %s must be nonzero" % (subregionSize,))
1160 for rowShift
in range(0, bbox.getHeight(), subregionSize[1]):
1161 for colShift
in range(0, bbox.getWidth(), subregionSize[0]):
1164 if subBBox.isEmpty():
1165 raise RuntimeError(
"Bug: empty bbox! bbox=%s, subregionSize=%s, "
1166 "colShift=%s, rowShift=%s" %
1167 (bbox, subregionSize, colShift, rowShift))
1172 """A version of `lsst.pipe.base.DataIdContainer` specialized for assembleCoadd.
1176 """Make self.refList from self.idList.
1181 Results of parsing command-line (with ``butler`` and ``log`` elements).
1183 datasetType = namespace.config.coaddName +
"Coadd"
1184 keysCoadd = namespace.butler.getKeys(datasetType=datasetType, level=self.level)
1186 for dataId
in self.idList:
1188 for key
in keysCoadd:
1189 if key
not in dataId:
1190 raise RuntimeError(
"--id must include " + key)
1192 dataRef = namespace.butler.dataRef(
1193 datasetType=datasetType,
1196 self.refList.append(dataRef)
1200 """Function to count the number of pixels with a specific mask in a
1203 Find the intersection of mask & footprint. Count all pixels in the mask
1204 that are in the intersection that have bitmask set but do not have
1205 ignoreMask set. Return the count.
1209 mask : `lsst.afw.image.Mask`
1210 Mask to define intersection region by.
1211 footprint : `lsst.afw.detection.Footprint`
1212 Footprint to define the intersection region by.
1214 Specific mask that we wish to count the number of occurances of.
1216 Pixels to not consider.
1221 Count of number of pixels in footprint with specified mask.
1223 bbox = footprint.getBBox()
1224 bbox.clip(mask.getBBox(afwImage.PARENT))
1225 fp = afwImage.Mask(bbox)
1226 subMask = mask.Factory(mask, bbox, afwImage.PARENT)
1227 footprint.spans.setMask(fp, bitmask)
1228 return numpy.logical_and((subMask.getArray() & fp.getArray()) > 0,
1229 (subMask.getArray() & ignoreMask) == 0).sum()
1233 """Configuration parameters for the SafeClipAssembleCoaddTask.
1235 assembleMeanCoadd = pexConfig.ConfigurableField(
1236 target=AssembleCoaddTask,
1237 doc=
"Task to assemble an initial Coadd using the MEAN statistic.",
1239 assembleMeanClipCoadd = pexConfig.ConfigurableField(
1240 target=AssembleCoaddTask,
1241 doc=
"Task to assemble an initial Coadd using the MEANCLIP statistic.",
1243 clipDetection = pexConfig.ConfigurableField(
1244 target=SourceDetectionTask,
1245 doc=
"Detect sources on difference between unclipped and clipped coadd")
1246 minClipFootOverlap = pexConfig.Field(
1247 doc=
"Minimum fractional overlap of clipped footprint with visit DETECTED to be clipped",
1251 minClipFootOverlapSingle = pexConfig.Field(
1252 doc=
"Minimum fractional overlap of clipped footprint with visit DETECTED to be "
1253 "clipped when only one visit overlaps",
1257 minClipFootOverlapDouble = pexConfig.Field(
1258 doc=
"Minimum fractional overlap of clipped footprints with visit DETECTED to be "
1259 "clipped when two visits overlap",
1263 maxClipFootOverlapDouble = pexConfig.Field(
1264 doc=
"Maximum fractional overlap of clipped footprints with visit DETECTED when "
1265 "considering two visits",
1269 minBigOverlap = pexConfig.Field(
1270 doc=
"Minimum number of pixels in footprint to use DETECTED mask from the single visits "
1271 "when labeling clipped footprints",
1277 """Set default values for clipDetection.
1281 The numeric values for these configuration parameters were
1282 empirically determined, future work may further refine them.
1284 AssembleCoaddConfig.setDefaults(self)
1285 self.
clipDetectionclipDetection.doTempLocalBackground =
False
1286 self.
clipDetectionclipDetection.reEstimateBackground =
False
1287 self.
clipDetectionclipDetection.returnOriginalFootprints =
False
1293 self.
clipDetectionclipDetection.thresholdType =
"pixel_stdev"
1304 log.warn(
"Additional Sigma-clipping not allowed in Safe-clipped Coadds. "
1305 "Ignoring doSigmaClip.")
1308 raise ValueError(
"Only MEAN statistic allowed for final stacking in SafeClipAssembleCoadd "
1309 "(%s chosen). Please set statistic to MEAN."
1311 AssembleCoaddTask.ConfigClass.validate(self)
1315 """Assemble a coadded image from a set of coadded temporary exposures,
1316 being careful to clip & flag areas with potential artifacts.
1318 In ``AssembleCoaddTask``, we compute the coadd as an clipped mean (i.e.,
1319 we clip outliers). The problem with doing this is that when computing the
1320 coadd PSF at a given location, individual visit PSFs from visits with
1321 outlier pixels contribute to the coadd PSF and cannot be treated correctly.
1322 In this task, we correct for this behavior by creating a new
1323 ``badMaskPlane`` 'CLIPPED'. We populate this plane on the input
1324 coaddTempExps and the final coadd where
1326 i. difference imaging suggests that there is an outlier and
1327 ii. this outlier appears on only one or two images.
1329 Such regions will not contribute to the final coadd. Furthermore, any
1330 routine to determine the coadd PSF can now be cognizant of clipped regions.
1331 Note that the algorithm implemented by this task is preliminary and works
1332 correctly for HSC data. Parameter modifications and or considerable
1333 redesigning of the algorithm is likley required for other surveys.
1335 ``SafeClipAssembleCoaddTask`` uses a ``SourceDetectionTask``
1336 "clipDetection" subtask and also sub-classes ``AssembleCoaddTask``.
1337 You can retarget the ``SourceDetectionTask`` "clipDetection" subtask
1342 The `lsst.pipe.base.cmdLineTask.CmdLineTask` interface supports a
1343 flag ``-d`` to import ``debug.py`` from your ``PYTHONPATH``;
1344 see `baseDebug` for more about ``debug.py`` files.
1345 `SafeClipAssembleCoaddTask` has no debug variables of its own.
1346 The ``SourceDetectionTask`` "clipDetection" subtasks may support debug
1347 variables. See the documetation for `SourceDetectionTask` "clipDetection"
1348 for further information.
1352 `SafeClipAssembleCoaddTask` assembles a set of warped ``coaddTempExp``
1353 images into a coadded image. The `SafeClipAssembleCoaddTask` is invoked by
1354 running assembleCoadd.py *without* the flag '--legacyCoadd'.
1356 Usage of ``assembleCoadd.py`` expects a data reference to the tract patch
1357 and filter to be coadded (specified using
1358 '--id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]')
1359 along with a list of coaddTempExps to attempt to coadd (specified using
1360 '--selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]').
1361 Only the coaddTempExps that cover the specified tract and patch will be
1362 coadded. A list of the available optional arguments can be obtained by
1363 calling assembleCoadd.py with the --help command line argument:
1365 .. code-block:: none
1367 assembleCoadd.py --help
1369 To demonstrate usage of the `SafeClipAssembleCoaddTask` in the larger
1370 context of multi-band processing, we will generate the HSC-I & -R band
1371 coadds from HSC engineering test data provided in the ci_hsc package.
1372 To begin, assuming that the lsst stack has been already set up, we must
1373 set up the obs_subaru and ci_hsc packages. This defines the environment
1374 variable $CI_HSC_DIR and points at the location of the package. The raw
1375 HSC data live in the ``$CI_HSC_DIR/raw`` directory. To begin assembling
1376 the coadds, we must first
1379 process the individual ccds in $CI_HSC_RAW to produce calibrated exposures
1381 create a skymap that covers the area of the sky present in the raw exposures
1382 - ``makeCoaddTempExp``
1383 warp the individual calibrated exposures to the tangent plane of the coadd</DD>
1385 We can perform all of these steps by running
1387 .. code-block:: none
1389 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988
1391 This will produce warped coaddTempExps for each visit. To coadd the
1392 warped data, we call ``assembleCoadd.py`` as follows:
1394 .. code-block:: none
1396 assembleCoadd.py $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \
1397 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \
1398 --selectId visit=903986 ccd=100--selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \
1399 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \
1400 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \
1401 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \
1402 --selectId visit=903988 ccd=24
1404 This will process the HSC-I band data. The results are written in
1405 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``.
1407 You may also choose to run:
1409 .. code-block:: none
1411 scons warp-903334 warp-903336 warp-903338 warp-903342 warp-903344 warp-903346 nnn
1412 assembleCoadd.py $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-R --selectId visit=903334 ccd=16 \
1413 --selectId visit=903334 ccd=22 --selectId visit=903334 ccd=23 --selectId visit=903334 ccd=100 \
1414 --selectId visit=903336 ccd=17 --selectId visit=903336 ccd=24 --selectId visit=903338 ccd=18 \
1415 --selectId visit=903338 ccd=25 --selectId visit=903342 ccd=4 --selectId visit=903342 ccd=10 \
1416 --selectId visit=903342 ccd=100 --selectId visit=903344 ccd=0 --selectId visit=903344 ccd=5 \
1417 --selectId visit=903344 ccd=11 --selectId visit=903346 ccd=1 --selectId visit=903346 ccd=6 \
1418 --selectId visit=903346 ccd=12
1420 to generate the coadd for the HSC-R band if you are interested in following
1421 multiBand Coadd processing as discussed in ``pipeTasks_multiBand``.
1423 ConfigClass = SafeClipAssembleCoaddConfig
1424 _DefaultName =
"safeClipAssembleCoadd"
1427 AssembleCoaddTask.__init__(self, *args, **kwargs)
1428 schema = afwTable.SourceTable.makeMinimalSchema()
1429 self.makeSubtask(
"clipDetection", schema=schema)
1430 self.makeSubtask(
"assembleMeanClipCoadd")
1431 self.makeSubtask(
"assembleMeanCoadd")
1433 @utils.inheritDoc(AssembleCoaddTask)
1434 @pipeBase.timeMethod
1435 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, *args, **kwargs):
1436 """Assemble the coadd for a region.
1438 Compute the difference of coadds created with and without outlier
1439 rejection to identify coadd pixels that have outlier values in some
1441 Detect clipped regions on the difference image and mark these regions
1442 on the one or two individual coaddTempExps where they occur if there
1443 is significant overlap between the clipped region and a source. This
1444 leaves us with a set of footprints from the difference image that have
1445 been identified as having occured on just one or two individual visits.
1446 However, these footprints were generated from a difference image. It
1447 is conceivable for a large diffuse source to have become broken up
1448 into multiple footprints acrosss the coadd difference in this process.
1449 Determine the clipped region from all overlapping footprints from the
1450 detected sources in each visit - these are big footprints.
1451 Combine the small and big clipped footprints and mark them on a new
1453 Generate the coadd using `AssembleCoaddTask.run` without outlier
1454 removal. Clipped footprints will no longer make it into the coadd
1455 because they are marked in the new bad mask plane.
1459 args and kwargs are passed but ignored in order to match the call
1460 signature expected by the parent task.
1462 exp = self.
buildDifferenceImagebuildDifferenceImage(skyInfo, tempExpRefList, imageScalerList, weightList)
1463 mask = exp.getMaskedImage().getMask()
1464 mask.addMaskPlane(
"CLIPPED")
1466 result = self.
detectClipdetectClip(exp, tempExpRefList)
1468 self.log.info(
'Found %d clipped objects', len(result.clipFootprints))
1470 maskClipValue = mask.getPlaneBitMask(
"CLIPPED")
1471 maskDetValue = mask.getPlaneBitMask(
"DETECTED") | mask.getPlaneBitMask(
"DETECTED_NEGATIVE")
1473 bigFootprints = self.
detectClipBigdetectClipBig(result.clipSpans, result.clipFootprints, result.clipIndices,
1474 result.detectionFootprints, maskClipValue, maskDetValue,
1477 maskClip = mask.Factory(mask.getBBox(afwImage.PARENT))
1478 afwDet.setMaskFromFootprintList(maskClip, result.clipFootprints, maskClipValue)
1480 maskClipBig = maskClip.Factory(mask.getBBox(afwImage.PARENT))
1481 afwDet.setMaskFromFootprintList(maskClipBig, bigFootprints, maskClipValue)
1482 maskClip |= maskClipBig
1485 badMaskPlanes = self.config.badMaskPlanes[:]
1486 badMaskPlanes.append(
"CLIPPED")
1487 badPixelMask = afwImage.Mask.getPlaneBitMask(badMaskPlanes)
1488 return AssembleCoaddTask.run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
1489 result.clipSpans, mask=badPixelMask)
1492 """Return an exposure that contains the difference between unclipped
1495 Generate a difference image between clipped and unclipped coadds.
1496 Compute the difference image by subtracting an outlier-clipped coadd
1497 from an outlier-unclipped coadd. Return the difference image.
1501 skyInfo : `lsst.pipe.base.Struct`
1502 Patch geometry information, from getSkyInfo
1503 tempExpRefList : `list`
1504 List of data reference to tempExp
1505 imageScalerList : `list`
1506 List of image scalers
1512 exp : `lsst.afw.image.Exposure`
1513 Difference image of unclipped and clipped coadd wrapped in an Exposure
1515 coaddMean = self.assembleMeanCoadd.
run(skyInfo, tempExpRefList,
1516 imageScalerList, weightList).coaddExposure
1518 coaddClip = self.assembleMeanClipCoadd.
run(skyInfo, tempExpRefList,
1519 imageScalerList, weightList).coaddExposure
1521 coaddDiff = coaddMean.getMaskedImage().Factory(coaddMean.getMaskedImage())
1522 coaddDiff -= coaddClip.getMaskedImage()
1523 exp = afwImage.ExposureF(coaddDiff)
1524 exp.setPsf(coaddMean.getPsf())
1528 """Detect clipped regions on an exposure and set the mask on the
1529 individual tempExp masks.
1531 Detect footprints in the difference image after smoothing the
1532 difference image with a Gaussian kernal. Identify footprints that
1533 overlap with one or two input ``coaddTempExps`` by comparing the
1534 computed overlap fraction to thresholds set in the config. A different
1535 threshold is applied depending on the number of overlapping visits
1536 (restricted to one or two). If the overlap exceeds the thresholds,
1537 the footprint is considered "CLIPPED" and is marked as such on the
1538 coaddTempExp. Return a struct with the clipped footprints, the indices
1539 of the ``coaddTempExps`` that end up overlapping with the clipped
1540 footprints, and a list of new masks for the ``coaddTempExps``.
1544 exp : `lsst.afw.image.Exposure`
1545 Exposure to run detection on.
1546 tempExpRefList : `list`
1547 List of data reference to tempExp.
1551 result : `lsst.pipe.base.Struct`
1552 Result struct with components:
1554 - ``clipFootprints``: list of clipped footprints.
1555 - ``clipIndices``: indices for each ``clippedFootprint`` in
1557 - ``clipSpans``: List of dictionaries containing spanSet lists
1558 to clip. Each element contains the new maskplane name
1559 ("CLIPPED") as the key and list of ``SpanSets`` as the value.
1560 - ``detectionFootprints``: List of DETECTED/DETECTED_NEGATIVE plane
1561 compressed into footprints.
1563 mask = exp.getMaskedImage().getMask()
1564 maskDetValue = mask.getPlaneBitMask(
"DETECTED") | mask.getPlaneBitMask(
"DETECTED_NEGATIVE")
1565 fpSet = self.clipDetection.detectFootprints(exp, doSmooth=
True, clearMask=
True)
1567 fpSet.positive.merge(fpSet.negative)
1568 footprints = fpSet.positive
1569 self.log.info(
'Found %d potential clipped objects', len(footprints.getFootprints()))
1570 ignoreMask = self.getBadPixelMask()
1574 artifactSpanSets = [{
'CLIPPED': list()}
for _
in tempExpRefList]
1577 visitDetectionFootprints = []
1579 dims = [len(tempExpRefList), len(footprints.getFootprints())]
1580 overlapDetArr = numpy.zeros(dims, dtype=numpy.uint16)
1581 ignoreArr = numpy.zeros(dims, dtype=numpy.uint16)
1584 for i, warpRef
in enumerate(tempExpRefList):
1585 tmpExpMask = warpRef.get(datasetType=self.getTempExpDatasetName(self.warpType),
1586 immediate=
True).getMaskedImage().getMask()
1587 maskVisitDet = tmpExpMask.Factory(tmpExpMask, tmpExpMask.getBBox(afwImage.PARENT),
1588 afwImage.PARENT,
True)
1589 maskVisitDet &= maskDetValue
1590 visitFootprints = afwDet.FootprintSet(maskVisitDet, afwDet.Threshold(1))
1591 visitDetectionFootprints.append(visitFootprints)
1593 for j, footprint
in enumerate(footprints.getFootprints()):
1598 for j, footprint
in enumerate(footprints.getFootprints()):
1599 nPixel = footprint.getArea()
1602 for i
in range(len(tempExpRefList)):
1603 ignore = ignoreArr[i, j]
1604 overlapDet = overlapDetArr[i, j]
1605 totPixel = nPixel - ignore
1608 if ignore > overlapDet
or totPixel <= 0.5*nPixel
or overlapDet == 0:
1610 overlap.append(overlapDet/float(totPixel))
1613 overlap = numpy.array(overlap)
1614 if not len(overlap):
1621 if len(overlap) == 1:
1622 if overlap[0] > self.config.minClipFootOverlapSingle:
1627 clipIndex = numpy.where(overlap > self.config.minClipFootOverlap)[0]
1628 if len(clipIndex) == 1:
1630 keepIndex = [clipIndex[0]]
1633 clipIndex = numpy.where(overlap > self.config.minClipFootOverlapDouble)[0]
1634 if len(clipIndex) == 2
and len(overlap) > 3:
1635 clipIndexComp = numpy.where(overlap <= self.config.minClipFootOverlapDouble)[0]
1636 if numpy.max(overlap[clipIndexComp]) <= self.config.maxClipFootOverlapDouble:
1638 keepIndex = clipIndex
1643 for index
in keepIndex:
1644 globalIndex = indexList[index]
1645 artifactSpanSets[globalIndex][
'CLIPPED'].append(footprint.spans)
1647 clipIndices.append(numpy.array(indexList)[keepIndex])
1648 clipFootprints.append(footprint)
1650 return pipeBase.Struct(clipFootprints=clipFootprints, clipIndices=clipIndices,
1651 clipSpans=artifactSpanSets, detectionFootprints=visitDetectionFootprints)
1653 def detectClipBig(self, clipList, clipFootprints, clipIndices, detectionFootprints,
1654 maskClipValue, maskDetValue, coaddBBox):
1655 """Return individual warp footprints for large artifacts and append
1656 them to ``clipList`` in place.
1658 Identify big footprints composed of many sources in the coadd
1659 difference that may have originated in a large diffuse source in the
1660 coadd. We do this by indentifying all clipped footprints that overlap
1661 significantly with each source in all the coaddTempExps.
1666 List of alt mask SpanSets with clipping information. Modified.
1667 clipFootprints : `list`
1668 List of clipped footprints.
1669 clipIndices : `list`
1670 List of which entries in tempExpClipList each footprint belongs to.
1672 Mask value of clipped pixels.
1674 Mask value of detected pixels.
1675 coaddBBox : `lsst.geom.Box`
1676 BBox of the coadd and warps.
1680 bigFootprintsCoadd : `list`
1681 List of big footprints
1683 bigFootprintsCoadd = []
1684 ignoreMask = self.getBadPixelMask()
1685 for index, (clippedSpans, visitFootprints)
in enumerate(zip(clipList, detectionFootprints)):
1686 maskVisitDet = afwImage.MaskX(coaddBBox, 0x0)
1687 for footprint
in visitFootprints.getFootprints():
1688 footprint.spans.setMask(maskVisitDet, maskDetValue)
1691 clippedFootprintsVisit = []
1692 for foot, clipIndex
in zip(clipFootprints, clipIndices):
1693 if index
not in clipIndex:
1695 clippedFootprintsVisit.append(foot)
1696 maskVisitClip = maskVisitDet.Factory(maskVisitDet.getBBox(afwImage.PARENT))
1697 afwDet.setMaskFromFootprintList(maskVisitClip, clippedFootprintsVisit, maskClipValue)
1699 bigFootprintsVisit = []
1700 for foot
in visitFootprints.getFootprints():
1701 if foot.getArea() < self.config.minBigOverlap:
1704 if nCount > self.config.minBigOverlap:
1705 bigFootprintsVisit.append(foot)
1706 bigFootprintsCoadd.append(foot)
1708 for footprint
in bigFootprintsVisit:
1709 clippedSpans[
"CLIPPED"].append(footprint.spans)
1711 return bigFootprintsCoadd
1715 psfMatchedWarps = pipeBase.connectionTypes.Input(
1716 doc=(
"PSF-Matched Warps are required by CompareWarp regardless of the coadd type requested. "
1717 "Only PSF-Matched Warps make sense for image subtraction. "
1718 "Therefore, they must be an additional declared input."),
1719 name=
"{inputCoaddName}Coadd_psfMatchedWarp",
1720 storageClass=
"ExposureF",
1721 dimensions=(
"tract",
"patch",
"skymap",
"visit"),
1725 templateCoadd = pipeBase.connectionTypes.Output(
1726 doc=(
"Model of the static sky, used to find temporal artifacts. Typically a PSF-Matched, "
1727 "sigma-clipped coadd. Written if and only if assembleStaticSkyModel.doWrite=True"),
1728 name=
"{fakesType}{outputCoaddName}CoaddPsfMatched",
1729 storageClass=
"ExposureF",
1730 dimensions=(
"tract",
"patch",
"skymap",
"band"),
1735 if not config.assembleStaticSkyModel.doWrite:
1736 self.outputs.remove(
"templateCoadd")
1741 pipelineConnections=CompareWarpAssembleCoaddConnections):
1742 assembleStaticSkyModel = pexConfig.ConfigurableField(
1743 target=AssembleCoaddTask,
1744 doc=
"Task to assemble an artifact-free, PSF-matched Coadd to serve as a"
1745 " naive/first-iteration model of the static sky.",
1747 detect = pexConfig.ConfigurableField(
1748 target=SourceDetectionTask,
1749 doc=
"Detect outlier sources on difference between each psfMatched warp and static sky model"
1751 detectTemplate = pexConfig.ConfigurableField(
1752 target=SourceDetectionTask,
1753 doc=
"Detect sources on static sky model. Only used if doPreserveContainedBySource is True"
1755 maskStreaks = pexConfig.ConfigurableField(
1756 target=MaskStreaksTask,
1757 doc=
"Detect streaks on difference between each psfMatched warp and static sky model. Only used if "
1758 "doFilterMorphological is True. Adds a mask plane to an exposure, with the mask plane name set by"
1761 streakMaskName = pexConfig.Field(
1764 doc=
"Name of mask bit used for streaks"
1766 maxNumEpochs = pexConfig.Field(
1767 doc=
"Charactistic maximum local number of epochs/visits in which an artifact candidate can appear "
1768 "and still be masked. The effective maxNumEpochs is a broken linear function of local "
1769 "number of epochs (N): min(maxFractionEpochsLow*N, maxNumEpochs + maxFractionEpochsHigh*N). "
1770 "For each footprint detected on the image difference between the psfMatched warp and static sky "
1771 "model, if a significant fraction of pixels (defined by spatialThreshold) are residuals in more "
1772 "than the computed effective maxNumEpochs, the artifact candidate is deemed persistant rather "
1773 "than transient and not masked.",
1777 maxFractionEpochsLow = pexConfig.RangeField(
1778 doc=
"Fraction of local number of epochs (N) to use as effective maxNumEpochs for low N. "
1779 "Effective maxNumEpochs = "
1780 "min(maxFractionEpochsLow * N, maxNumEpochs + maxFractionEpochsHigh * N)",
1785 maxFractionEpochsHigh = pexConfig.RangeField(
1786 doc=
"Fraction of local number of epochs (N) to use as effective maxNumEpochs for high N. "
1787 "Effective maxNumEpochs = "
1788 "min(maxFractionEpochsLow * N, maxNumEpochs + maxFractionEpochsHigh * N)",
1793 spatialThreshold = pexConfig.RangeField(
1794 doc=
"Unitless fraction of pixels defining how much of the outlier region has to meet the "
1795 "temporal criteria. If 0, clip all. If 1, clip none.",
1799 inclusiveMin=
True, inclusiveMax=
True
1801 doScaleWarpVariance = pexConfig.Field(
1802 doc=
"Rescale Warp variance plane using empirical noise?",
1806 scaleWarpVariance = pexConfig.ConfigurableField(
1807 target=ScaleVarianceTask,
1808 doc=
"Rescale variance on warps",
1810 doPreserveContainedBySource = pexConfig.Field(
1811 doc=
"Rescue artifacts from clipping that completely lie within a footprint detected"
1812 "on the PsfMatched Template Coadd. Replicates a behavior of SafeClip.",
1816 doPrefilterArtifacts = pexConfig.Field(
1817 doc=
"Ignore artifact candidates that are mostly covered by the bad pixel mask, "
1818 "because they will be excluded anyway. This prevents them from contributing "
1819 "to the outlier epoch count image and potentially being labeled as persistant."
1820 "'Mostly' is defined by the config 'prefilterArtifactsRatio'.",
1824 prefilterArtifactsMaskPlanes = pexConfig.ListField(
1825 doc=
"Prefilter artifact candidates that are mostly covered by these bad mask planes.",
1827 default=(
'NO_DATA',
'BAD',
'SAT',
'SUSPECT'),
1829 prefilterArtifactsRatio = pexConfig.Field(
1830 doc=
"Prefilter artifact candidates with less than this fraction overlapping good pixels",
1834 doFilterMorphological = pexConfig.Field(
1835 doc=
"Filter artifact candidates based on morphological criteria, i.g. those that appear to "
1842 AssembleCoaddConfig.setDefaults(self)
1848 if "EDGE" in self.badMaskPlanes:
1849 self.badMaskPlanes.remove(
'EDGE')
1850 self.removeMaskPlanes.append(
'EDGE')
1859 self.
detectdetect.doTempLocalBackground =
False
1860 self.
detectdetect.reEstimateBackground =
False
1861 self.
detectdetect.returnOriginalFootprints =
False
1862 self.
detectdetect.thresholdPolarity =
"both"
1863 self.
detectdetect.thresholdValue = 5
1864 self.
detectdetect.minPixels = 4
1865 self.
detectdetect.isotropicGrow =
True
1866 self.
detectdetect.thresholdType =
"pixel_stdev"
1867 self.
detectdetect.nSigmaToGrow = 0.4
1873 self.
detectTemplatedetectTemplate.returnOriginalFootprints =
False
1878 raise ValueError(
"No dataset type exists for a PSF-Matched Template N Image."
1879 "Please set assembleStaticSkyModel.doNImage=False")
1882 raise ValueError(
"warpType (%s) == assembleStaticSkyModel.warpType (%s) and will compete for "
1883 "the same dataset name. Please set assembleStaticSkyModel.doWrite to False "
1884 "or warpType to 'direct'. assembleStaticSkyModel.warpType should ways be "
1889 """Assemble a compareWarp coadded image from a set of warps
1890 by masking artifacts detected by comparing PSF-matched warps.
1892 In ``AssembleCoaddTask``, we compute the coadd as an clipped mean (i.e.,
1893 we clip outliers). The problem with doing this is that when computing the
1894 coadd PSF at a given location, individual visit PSFs from visits with
1895 outlier pixels contribute to the coadd PSF and cannot be treated correctly.
1896 In this task, we correct for this behavior by creating a new badMaskPlane
1897 'CLIPPED' which marks pixels in the individual warps suspected to contain
1898 an artifact. We populate this plane on the input warps by comparing
1899 PSF-matched warps with a PSF-matched median coadd which serves as a
1900 model of the static sky. Any group of pixels that deviates from the
1901 PSF-matched template coadd by more than config.detect.threshold sigma,
1902 is an artifact candidate. The candidates are then filtered to remove
1903 variable sources and sources that are difficult to subtract such as
1904 bright stars. This filter is configured using the config parameters
1905 ``temporalThreshold`` and ``spatialThreshold``. The temporalThreshold is
1906 the maximum fraction of epochs that the deviation can appear in and still
1907 be considered an artifact. The spatialThreshold is the maximum fraction of
1908 pixels in the footprint of the deviation that appear in other epochs
1909 (where other epochs is defined by the temporalThreshold). If the deviant
1910 region meets this criteria of having a significant percentage of pixels
1911 that deviate in only a few epochs, these pixels have the 'CLIPPED' bit
1912 set in the mask. These regions will not contribute to the final coadd.
1913 Furthermore, any routine to determine the coadd PSF can now be cognizant
1914 of clipped regions. Note that the algorithm implemented by this task is
1915 preliminary and works correctly for HSC data. Parameter modifications and
1916 or considerable redesigning of the algorithm is likley required for other
1919 ``CompareWarpAssembleCoaddTask`` sub-classes
1920 ``AssembleCoaddTask`` and instantiates ``AssembleCoaddTask``
1921 as a subtask to generate the TemplateCoadd (the model of the static sky).
1925 The `lsst.pipe.base.cmdLineTask.CmdLineTask` interface supports a
1926 flag ``-d`` to import ``debug.py`` from your ``PYTHONPATH``; see
1927 ``baseDebug`` for more about ``debug.py`` files.
1929 This task supports the following debug variables:
1932 If True then save the Epoch Count Image as a fits file in the `figPath`
1934 Path to save the debug fits images and figures
1936 For example, put something like:
1938 .. code-block:: python
1941 def DebugInfo(name):
1942 di = lsstDebug.getInfo(name)
1943 if name == "lsst.pipe.tasks.assembleCoadd":
1944 di.saveCountIm = True
1945 di.figPath = "/desired/path/to/debugging/output/images"
1947 lsstDebug.Info = DebugInfo
1949 into your ``debug.py`` file and run ``assemebleCoadd.py`` with the
1950 ``--debug`` flag. Some subtasks may have their own debug variables;
1951 see individual Task documentation.
1955 ``CompareWarpAssembleCoaddTask`` assembles a set of warped images into a
1956 coadded image. The ``CompareWarpAssembleCoaddTask`` is invoked by running
1957 ``assembleCoadd.py`` with the flag ``--compareWarpCoadd``.
1958 Usage of ``assembleCoadd.py`` expects a data reference to the tract patch
1959 and filter to be coadded (specified using
1960 '--id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]')
1961 along with a list of coaddTempExps to attempt to coadd (specified using
1962 '--selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]').
1963 Only the warps that cover the specified tract and patch will be coadded.
1964 A list of the available optional arguments can be obtained by calling
1965 ``assembleCoadd.py`` with the ``--help`` command line argument:
1967 .. code-block:: none
1969 assembleCoadd.py --help
1971 To demonstrate usage of the ``CompareWarpAssembleCoaddTask`` in the larger
1972 context of multi-band processing, we will generate the HSC-I & -R band
1973 oadds from HSC engineering test data provided in the ``ci_hsc`` package.
1974 To begin, assuming that the lsst stack has been already set up, we must
1975 set up the ``obs_subaru`` and ``ci_hsc`` packages.
1976 This defines the environment variable ``$CI_HSC_DIR`` and points at the
1977 location of the package. The raw HSC data live in the ``$CI_HSC_DIR/raw``
1978 directory. To begin assembling the coadds, we must first
1981 process the individual ccds in $CI_HSC_RAW to produce calibrated exposures
1983 create a skymap that covers the area of the sky present in the raw exposures
1985 warp the individual calibrated exposures to the tangent plane of the coadd
1987 We can perform all of these steps by running
1989 .. code-block:: none
1991 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988
1993 This will produce warped ``coaddTempExps`` for each visit. To coadd the
1994 warped data, we call ``assembleCoadd.py`` as follows:
1996 .. code-block:: none
1998 assembleCoadd.py --compareWarpCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \
1999 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \
2000 --selectId visit=903986 ccd=100 --selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \
2001 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \
2002 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \
2003 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \
2004 --selectId visit=903988 ccd=24
2006 This will process the HSC-I band data. The results are written in
2007 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``.
2009 ConfigClass = CompareWarpAssembleCoaddConfig
2010 _DefaultName =
"compareWarpAssembleCoadd"
2013 AssembleCoaddTask.__init__(self, *args, **kwargs)
2014 self.makeSubtask(
"assembleStaticSkyModel")
2015 detectionSchema = afwTable.SourceTable.makeMinimalSchema()
2016 self.makeSubtask(
"detect", schema=detectionSchema)
2017 if self.config.doPreserveContainedBySource:
2018 self.makeSubtask(
"detectTemplate", schema=afwTable.SourceTable.makeMinimalSchema())
2019 if self.config.doScaleWarpVariance:
2020 self.makeSubtask(
"scaleWarpVariance")
2021 if self.config.doFilterMorphological:
2022 self.makeSubtask(
"maskStreaks")
2024 @utils.inheritDoc(AssembleCoaddTask)
2027 Generate a templateCoadd to use as a naive model of static sky to
2028 subtract from PSF-Matched warps.
2032 result : `lsst.pipe.base.Struct`
2033 Result struct with components:
2035 - ``templateCoadd`` : coadded exposure (``lsst.afw.image.Exposure``)
2036 - ``nImage`` : N Image (``lsst.afw.image.Image``)
2039 staticSkyModelInputRefs = copy.deepcopy(inputRefs)
2040 staticSkyModelInputRefs.inputWarps = inputRefs.psfMatchedWarps
2044 staticSkyModelOutputRefs = copy.deepcopy(outputRefs)
2045 if self.config.assembleStaticSkyModel.doWrite:
2046 staticSkyModelOutputRefs.coaddExposure = staticSkyModelOutputRefs.templateCoadd
2049 del outputRefs.templateCoadd
2050 del staticSkyModelOutputRefs.templateCoadd
2053 if 'nImage' in staticSkyModelOutputRefs.keys():
2054 del staticSkyModelOutputRefs.nImage
2056 templateCoadd = self.assembleStaticSkyModel.runQuantum(butlerQC, staticSkyModelInputRefs,
2057 staticSkyModelOutputRefs)
2058 if templateCoadd
is None:
2059 raise RuntimeError(self.
_noTemplateMessage_noTemplateMessage(self.assembleStaticSkyModel.warpType))
2061 return pipeBase.Struct(templateCoadd=templateCoadd.coaddExposure,
2062 nImage=templateCoadd.nImage,
2063 warpRefList=templateCoadd.warpRefList,
2064 imageScalerList=templateCoadd.imageScalerList,
2065 weightList=templateCoadd.weightList)
2067 @utils.inheritDoc(AssembleCoaddTask)
2070 Generate a templateCoadd to use as a naive model of static sky to
2071 subtract from PSF-Matched warps.
2075 result : `lsst.pipe.base.Struct`
2076 Result struct with components:
2078 - ``templateCoadd``: coadded exposure (``lsst.afw.image.Exposure``)
2079 - ``nImage``: N Image (``lsst.afw.image.Image``)
2081 templateCoadd = self.assembleStaticSkyModel.runDataRef(dataRef, selectDataList, warpRefList)
2082 if templateCoadd
is None:
2083 raise RuntimeError(self.
_noTemplateMessage_noTemplateMessage(self.assembleStaticSkyModel.warpType))
2085 return pipeBase.Struct(templateCoadd=templateCoadd.coaddExposure,
2086 nImage=templateCoadd.nImage,
2087 warpRefList=templateCoadd.warpRefList,
2088 imageScalerList=templateCoadd.imageScalerList,
2089 weightList=templateCoadd.weightList)
2091 def _noTemplateMessage(self, warpType):
2092 warpName = (warpType[0].upper() + warpType[1:])
2093 message =
"""No %(warpName)s warps were found to build the template coadd which is
2094 required to run CompareWarpAssembleCoaddTask. To continue assembling this type of coadd,
2095 first either rerun makeCoaddTempExp with config.make%(warpName)s=True or
2096 coaddDriver with config.makeCoadTempExp.make%(warpName)s=True, before assembleCoadd.
2098 Alternatively, to use another algorithm with existing warps, retarget the CoaddDriverConfig to
2099 another algorithm like:
2101 from lsst.pipe.tasks.assembleCoadd import SafeClipAssembleCoaddTask
2102 config.assemble.retarget(SafeClipAssembleCoaddTask)
2103 """ % {
"warpName": warpName}
2106 @utils.inheritDoc(AssembleCoaddTask)
2107 @pipeBase.timeMethod
2108 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
2109 supplementaryData, *args, **kwargs):
2110 """Assemble the coadd.
2112 Find artifacts and apply them to the warps' masks creating a list of
2113 alternative masks with a new "CLIPPED" plane and updated "NO_DATA"
2114 plane. Then pass these alternative masks to the base class's `run`
2117 The input parameters ``supplementaryData`` is a `lsst.pipe.base.Struct`
2118 that must contain a ``templateCoadd`` that serves as the
2119 model of the static sky.
2125 dataIds = [ref.dataId
for ref
in tempExpRefList]
2126 psfMatchedDataIds = [ref.dataId
for ref
in supplementaryData.warpRefList]
2128 if dataIds != psfMatchedDataIds:
2129 self.log.info(
"Reordering and or/padding PSF-matched visit input list")
2130 supplementaryData.warpRefList =
reorderAndPadList(supplementaryData.warpRefList,
2131 psfMatchedDataIds, dataIds)
2132 supplementaryData.imageScalerList =
reorderAndPadList(supplementaryData.imageScalerList,
2133 psfMatchedDataIds, dataIds)
2136 spanSetMaskList = self.
findArtifactsfindArtifacts(supplementaryData.templateCoadd,
2137 supplementaryData.warpRefList,
2138 supplementaryData.imageScalerList)
2140 badMaskPlanes = self.config.badMaskPlanes[:]
2141 badMaskPlanes.append(
"CLIPPED")
2142 badPixelMask = afwImage.Mask.getPlaneBitMask(badMaskPlanes)
2144 result = AssembleCoaddTask.run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
2145 spanSetMaskList, mask=badPixelMask)
2149 self.
applyAltEdgeMaskapplyAltEdgeMask(result.coaddExposure.maskedImage.mask, spanSetMaskList)
2153 """Propagate alt EDGE mask to SENSOR_EDGE AND INEXACT_PSF planes.
2157 mask : `lsst.afw.image.Mask`
2159 altMaskList : `list`
2160 List of Dicts containing ``spanSet`` lists.
2161 Each element contains the new mask plane name (e.g. "CLIPPED
2162 and/or "NO_DATA") as the key, and list of ``SpanSets`` to apply to
2165 maskValue = mask.getPlaneBitMask([
"SENSOR_EDGE",
"INEXACT_PSF"])
2166 for visitMask
in altMaskList:
2167 if "EDGE" in visitMask:
2168 for spanSet
in visitMask[
'EDGE']:
2169 spanSet.clippedTo(mask.getBBox()).setMask(mask, maskValue)
2174 Loop through warps twice. The first loop builds a map with the count
2175 of how many epochs each pixel deviates from the templateCoadd by more
2176 than ``config.chiThreshold`` sigma. The second loop takes each
2177 difference image and filters the artifacts detected in each using
2178 count map to filter out variable sources and sources that are
2179 difficult to subtract cleanly.
2183 templateCoadd : `lsst.afw.image.Exposure`
2184 Exposure to serve as model of static sky.
2185 tempExpRefList : `list`
2186 List of data references to warps.
2187 imageScalerList : `list`
2188 List of image scalers.
2193 List of dicts containing information about CLIPPED
2194 (i.e., artifacts), NO_DATA, and EDGE pixels.
2197 self.log.debug(
"Generating Count Image, and mask lists.")
2198 coaddBBox = templateCoadd.getBBox()
2199 slateIm = afwImage.ImageU(coaddBBox)
2200 epochCountImage = afwImage.ImageU(coaddBBox)
2201 nImage = afwImage.ImageU(coaddBBox)
2202 spanSetArtifactList = []
2203 spanSetNoDataMaskList = []
2204 spanSetEdgeList = []
2205 spanSetBadMorphoList = []
2206 badPixelMask = self.getBadPixelMask()
2209 templateCoadd.mask.clearAllMaskPlanes()
2211 if self.config.doPreserveContainedBySource:
2212 templateFootprints = self.detectTemplate.detectFootprints(templateCoadd)
2214 templateFootprints =
None
2216 for warpRef, imageScaler
in zip(tempExpRefList, imageScalerList):
2218 if warpDiffExp
is not None:
2220 nImage.array += (numpy.isfinite(warpDiffExp.image.array)
2221 * ((warpDiffExp.mask.array & badPixelMask) == 0)).astype(numpy.uint16)
2222 fpSet = self.detect.detectFootprints(warpDiffExp, doSmooth=
False, clearMask=
True)
2223 fpSet.positive.merge(fpSet.negative)
2224 footprints = fpSet.positive
2226 spanSetList = [footprint.spans
for footprint
in footprints.getFootprints()]
2229 if self.config.doPrefilterArtifacts:
2233 self.detect.clearMask(warpDiffExp.mask)
2234 for spans
in spanSetList:
2235 spans.setImage(slateIm, 1, doClip=
True)
2236 spans.setMask(warpDiffExp.mask, warpDiffExp.mask.getPlaneBitMask(
"DETECTED"))
2237 epochCountImage += slateIm
2239 if self.config.doFilterMorphological:
2240 maskName = self.config.streakMaskName
2241 _ = self.maskStreaks.
run(warpDiffExp)
2242 streakMask = warpDiffExp.mask
2243 spanSetStreak = afwGeom.SpanSet.fromMask(streakMask,
2244 streakMask.getPlaneBitMask(maskName)).split()
2250 nans = numpy.where(numpy.isnan(warpDiffExp.maskedImage.image.array), 1, 0)
2251 nansMask = afwImage.makeMaskFromArray(nans.astype(afwImage.MaskPixel))
2252 nansMask.setXY0(warpDiffExp.getXY0())
2253 edgeMask = warpDiffExp.mask
2254 spanSetEdgeMask = afwGeom.SpanSet.fromMask(edgeMask,
2255 edgeMask.getPlaneBitMask(
"EDGE")).split()
2259 nansMask = afwImage.MaskX(coaddBBox, 1)
2261 spanSetEdgeMask = []
2264 spanSetNoDataMask = afwGeom.SpanSet.fromMask(nansMask).split()
2266 spanSetNoDataMaskList.append(spanSetNoDataMask)
2267 spanSetArtifactList.append(spanSetList)
2268 spanSetEdgeList.append(spanSetEdgeMask)
2269 if self.config.doFilterMorphological:
2270 spanSetBadMorphoList.append(spanSetStreak)
2273 path = self.
_dataRef2DebugPath_dataRef2DebugPath(
"epochCountIm", tempExpRefList[0], coaddLevel=
True)
2274 epochCountImage.writeFits(path)
2276 for i, spanSetList
in enumerate(spanSetArtifactList):
2278 filteredSpanSetList = self.
filterArtifactsfilterArtifacts(spanSetList, epochCountImage, nImage,
2280 spanSetArtifactList[i] = filteredSpanSetList
2281 if self.config.doFilterMorphological:
2282 spanSetArtifactList[i] += spanSetBadMorphoList[i]
2285 for artifacts, noData, edge
in zip(spanSetArtifactList, spanSetNoDataMaskList, spanSetEdgeList):
2286 altMasks.append({
'CLIPPED': artifacts,
2292 """Remove artifact candidates covered by bad mask plane.
2294 Any future editing of the candidate list that does not depend on
2295 temporal information should go in this method.
2299 spanSetList : `list`
2300 List of SpanSets representing artifact candidates.
2301 exp : `lsst.afw.image.Exposure`
2302 Exposure containing mask planes used to prefilter.
2306 returnSpanSetList : `list`
2307 List of SpanSets with artifacts.
2309 badPixelMask = exp.mask.getPlaneBitMask(self.config.prefilterArtifactsMaskPlanes)
2310 goodArr = (exp.mask.array & badPixelMask) == 0
2311 returnSpanSetList = []
2312 bbox = exp.getBBox()
2313 x0, y0 = exp.getXY0()
2314 for i, span
in enumerate(spanSetList):
2315 y, x = span.clippedTo(bbox).indices()
2316 yIndexLocal = numpy.array(y) - y0
2317 xIndexLocal = numpy.array(x) - x0
2318 goodRatio = numpy.count_nonzero(goodArr[yIndexLocal, xIndexLocal])/span.getArea()
2319 if goodRatio > self.config.prefilterArtifactsRatio:
2320 returnSpanSetList.append(span)
2321 return returnSpanSetList
2323 def filterArtifacts(self, spanSetList, epochCountImage, nImage, footprintsToExclude=None):
2324 """Filter artifact candidates.
2328 spanSetList : `list`
2329 List of SpanSets representing artifact candidates.
2330 epochCountImage : `lsst.afw.image.Image`
2331 Image of accumulated number of warpDiff detections.
2332 nImage : `lsst.afw.image.Image`
2333 Image of the accumulated number of total epochs contributing.
2337 maskSpanSetList : `list`
2338 List of SpanSets with artifacts.
2341 maskSpanSetList = []
2342 x0, y0 = epochCountImage.getXY0()
2343 for i, span
in enumerate(spanSetList):
2344 y, x = span.indices()
2345 yIdxLocal = [y1 - y0
for y1
in y]
2346 xIdxLocal = [x1 - x0
for x1
in x]
2347 outlierN = epochCountImage.array[yIdxLocal, xIdxLocal]
2348 totalN = nImage.array[yIdxLocal, xIdxLocal]
2351 effMaxNumEpochsHighN = (self.config.maxNumEpochs
2352 + self.config.maxFractionEpochsHigh*numpy.mean(totalN))
2353 effMaxNumEpochsLowN = self.config.maxFractionEpochsLow * numpy.mean(totalN)
2354 effectiveMaxNumEpochs = int(min(effMaxNumEpochsLowN, effMaxNumEpochsHighN))
2355 nPixelsBelowThreshold = numpy.count_nonzero((outlierN > 0)
2356 & (outlierN <= effectiveMaxNumEpochs))
2357 percentBelowThreshold = nPixelsBelowThreshold / len(outlierN)
2358 if percentBelowThreshold > self.config.spatialThreshold:
2359 maskSpanSetList.append(span)
2361 if self.config.doPreserveContainedBySource
and footprintsToExclude
is not None:
2363 filteredMaskSpanSetList = []
2364 for span
in maskSpanSetList:
2366 for footprint
in footprintsToExclude.positive.getFootprints():
2367 if footprint.spans.contains(span):
2371 filteredMaskSpanSetList.append(span)
2372 maskSpanSetList = filteredMaskSpanSetList
2374 return maskSpanSetList
2376 def _readAndComputeWarpDiff(self, warpRef, imageScaler, templateCoadd):
2377 """Fetch a warp from the butler and return a warpDiff.
2381 warpRef : `lsst.daf.persistence.butlerSubset.ButlerDataRef`
2382 Butler dataRef for the warp.
2383 imageScaler : `lsst.pipe.tasks.scaleZeroPoint.ImageScaler`
2384 An image scaler object.
2385 templateCoadd : `lsst.afw.image.Exposure`
2386 Exposure to be substracted from the scaled warp.
2390 warp : `lsst.afw.image.Exposure`
2391 Exposure of the image difference between the warp and template.
2399 warpName = self.getTempExpDatasetName(
'psfMatched')
2400 if not isinstance(warpRef, DeferredDatasetHandle):
2401 if not warpRef.datasetExists(warpName):
2402 self.log.warn(
"Could not find %s %s; skipping it", warpName, warpRef.dataId)
2404 warp = warpRef.get(datasetType=warpName, immediate=
True)
2406 imageScaler.scaleMaskedImage(warp.getMaskedImage())
2407 mi = warp.getMaskedImage()
2408 if self.config.doScaleWarpVariance:
2410 self.scaleWarpVariance.
run(mi)
2411 except Exception
as exc:
2412 self.log.warn(
"Unable to rescale variance of warp (%s); leaving it as-is" % (exc,))
2413 mi -= templateCoadd.getMaskedImage()
2416 def _dataRef2DebugPath(self, prefix, warpRef, coaddLevel=False):
2417 """Return a path to which to write debugging output.
2419 Creates a hyphen-delimited string of dataId values for simple filenames.
2424 Prefix for filename.
2425 warpRef : `lsst.daf.persistence.butlerSubset.ButlerDataRef`
2426 Butler dataRef to make the path from.
2427 coaddLevel : `bool`, optional.
2428 If True, include only coadd-level keys (e.g., 'tract', 'patch',
2429 'filter', but no 'visit').
2434 Path for debugging output.
2437 keys = warpRef.getButler().getKeys(self.getCoaddDatasetName(self.warpType))
2439 keys = warpRef.dataId.keys()
2440 keyList = sorted(keys, reverse=
True)
2442 filename =
"%s-%s.fits" % (prefix,
'-'.join([str(warpRef.dataId[k])
for k
in keyList]))
2443 return os.path.join(directory, filename)
2447 """Match the order of one list to another, padding if necessary
2452 List to be reordered and padded. Elements can be any type.
2453 inputKeys : iterable
2454 Iterable of values to be compared with outputKeys.
2455 Length must match `inputList`
2456 outputKeys : iterable
2457 Iterable of values to be compared with inputKeys.
2459 Any value to be inserted where inputKey not in outputKeys
2464 Copy of inputList reordered per outputKeys and padded with `padWith`
2465 so that the length matches length of outputKeys.
2468 for d
in outputKeys:
2470 outputList.append(inputList[inputKeys.index(d)])
2472 outputList.append(padWith)
def makeDataRefList(self, namespace)
def __init__(self, *config=None)
def makeSupplementaryDataGen3(self, butlerQC, inputRefs, outputRefs)
def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, supplementaryData, *args, **kwargs)
def prefilterArtifacts(self, spanSetList, exp)
def findArtifacts(self, templateCoadd, tempExpRefList, imageScalerList)
def _readAndComputeWarpDiff(self, warpRef, imageScaler, templateCoadd)
def _dataRef2DebugPath(self, prefix, warpRef, coaddLevel=False)
def applyAltEdgeMask(self, mask, altMaskList)
def makeSupplementaryData(self, dataRef, selectDataList=None, warpRefList=None)
def _noTemplateMessage(self, warpType)
def filterArtifacts(self, spanSetList, epochCountImage, nImage, footprintsToExclude=None)
def __init__(self, *args, **kwargs)
def buildDifferenceImage(self, skyInfo, tempExpRefList, imageScalerList, weightList)
def detectClipBig(self, clipList, clipFootprints, clipIndices, detectionFootprints, maskClipValue, maskDetValue, coaddBBox)
def __init__(self, *args, **kwargs)
def detectClip(self, exp, tempExpRefList)
def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, *args, **kwargs)
Base class for coaddition.
def prepareStats(self, mask=None)
def readBrightObjectMasks(self, dataRef)
def makeSupplementaryData(self, dataRef, selectDataList=None, warpRefList=None)
def countMaskFromFootprint(mask, footprint, bitmask, ignoreMask)
def makeSupplementaryDataGen3(self, butlerQC, inputRefs, outputRefs)
def applyAltMaskPlanes(self, mask, altMaskSpans)
def shrinkValidPolygons(self, coaddInputs)
def setBrightObjectMasks(self, exposure, brightObjectMasks, dataId=None)
def getTempExpRefList(self, patchRef, calExpRefList)
def assembleMetadata(self, coaddExposure, tempExpRefList, weightList)
def removeMaskPlanes(self, maskedImage)
def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, altMaskList=None, mask=None, supplementaryData=None)
def reorderAndPadList(inputList, inputKeys, outputKeys, padWith=None)
def prepareInputs(self, refList)
def assembleSubregion(self, coaddExposure, bbox, tempExpRefList, imageScalerList, weightList, altMaskList, statsFlags, statsCtrl, nImage=None)
def setInexactPsf(self, mask)
def processResults(self, coaddExposure, brightObjectMasks=None, dataId=None)
def makeCoaddSuffix(warpType="direct")
def makeSkyInfo(skyMap, tractId, patchId)
def getGroupDataRef(butler, datasetType, groupTuple, keys)
def groupPatchExposures(patchDataRef, calexpDataRefList, coaddDatasetType="deepCoadd", tempExpDatasetType="deepCoadd_directWarp")