lsst.pipe.tasks ge01ce967e7+296d498e58
assembleCoadd.py
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22import os
23import copy
24import numpy
25import warnings
26import logging
27import lsst.pex.config as pexConfig
28import lsst.pex.exceptions as pexExceptions
29import lsst.geom as geom
30import lsst.afw.geom as afwGeom
31import lsst.afw.image as afwImage
32import lsst.afw.math as afwMath
33import lsst.afw.table as afwTable
34import lsst.afw.detection as afwDet
35import lsst.coadd.utils as coaddUtils
36import lsst.pipe.base as pipeBase
37import lsst.meas.algorithms as measAlg
38import lsstDebug
39import lsst.utils as utils
40from lsst.skymap import BaseSkyMap
41from .coaddBase import CoaddBaseTask, SelectDataIdContainer, makeSkyInfo, makeCoaddSuffix, reorderAndPadList
42from .interpImage import InterpImageTask
43from .scaleZeroPoint import ScaleZeroPointTask
44from .coaddHelpers import groupPatchExposures, getGroupDataRef
45from .scaleVariance import ScaleVarianceTask
46from .maskStreaks import MaskStreaksTask
47from .healSparseMapping import HealSparseInputMapTask
48from lsst.meas.algorithms import SourceDetectionTask, AccumulatorMeanStack
49from lsst.daf.butler import DeferredDatasetHandle
50from lsst.utils.timer import timeMethod
51
52__all__ = ["AssembleCoaddTask", "AssembleCoaddConnections", "AssembleCoaddConfig",
53 "SafeClipAssembleCoaddTask", "SafeClipAssembleCoaddConfig",
54 "CompareWarpAssembleCoaddTask", "CompareWarpAssembleCoaddConfig"]
55
56log = logging.getLogger(__name__)
57
58
59class AssembleCoaddConnections(pipeBase.PipelineTaskConnections,
60 dimensions=("tract", "patch", "band", "skymap"),
61 defaultTemplates={"inputCoaddName": "deep",
62 "outputCoaddName": "deep",
63 "warpType": "direct",
64 "warpTypeSuffix": ""}):
65
66 inputWarps = pipeBase.connectionTypes.Input(
67 doc=("Input list of warps to be assemebled i.e. stacked."
68 "WarpType (e.g. direct, psfMatched) is controlled by the warpType config parameter"),
69 name="{inputCoaddName}Coadd_{warpType}Warp",
70 storageClass="ExposureF",
71 dimensions=("tract", "patch", "skymap", "visit", "instrument"),
72 deferLoad=True,
73 multiple=True
74 )
75 skyMap = pipeBase.connectionTypes.Input(
76 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures",
77 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
78 storageClass="SkyMap",
79 dimensions=("skymap", ),
80 )
81 selectedVisits = pipeBase.connectionTypes.Input(
82 doc="Selected visits to be coadded.",
83 name="{outputCoaddName}Visits",
84 storageClass="StructuredDataDict",
85 dimensions=("instrument", "tract", "patch", "skymap", "band")
86 )
87 brightObjectMask = pipeBase.connectionTypes.PrerequisiteInput(
88 doc=("Input Bright Object Mask mask produced with external catalogs to be applied to the mask plane"
89 " BRIGHT_OBJECT."),
90 name="brightObjectMask",
91 storageClass="ObjectMaskCatalog",
92 dimensions=("tract", "patch", "skymap", "band"),
93 )
94 coaddExposure = pipeBase.connectionTypes.Output(
95 doc="Output coadded exposure, produced by stacking input warps",
96 name="{outputCoaddName}Coadd{warpTypeSuffix}",
97 storageClass="ExposureF",
98 dimensions=("tract", "patch", "skymap", "band"),
99 )
100 nImage = pipeBase.connectionTypes.Output(
101 doc="Output image of number of input images per pixel",
102 name="{outputCoaddName}Coadd_nImage",
103 storageClass="ImageU",
104 dimensions=("tract", "patch", "skymap", "band"),
105 )
106 inputMap = pipeBase.connectionTypes.Output(
107 doc="Output healsparse map of input images",
108 name="{outputCoaddName}Coadd_inputMap",
109 storageClass="HealSparseMap",
110 dimensions=("tract", "patch", "skymap", "band"),
111 )
112
113 def __init__(self, *, config=None):
114 super().__init__(config=config)
115
116 # Override the connection's name template with config to replicate Gen2 behavior
117 # This duplicates some of the logic in the base class, due to wanting Gen2 and
118 # Gen3 configs to stay in sync. This should be removed when gen2 is deprecated
119 templateValues = {name: getattr(config.connections, name) for name in self.defaultTemplates}
120 templateValues['warpType'] = config.warpType
121 templateValues['warpTypeSuffix'] = makeCoaddSuffix(config.warpType)
122 self._nameOverrides = {name: getattr(config.connections, name).format(**templateValues)
123 for name in self.allConnections}
124 self._typeNameToVarName = {v: k for k, v in self._nameOverrides.items()}
125 # End code to remove after deprecation
126
127 if not config.doMaskBrightObjects:
128 self.prerequisiteInputs.remove("brightObjectMask")
129
130 if not config.doSelectVisits:
131 self.inputs.remove("selectedVisits")
132
133 if not config.doNImage:
134 self.outputs.remove("nImage")
135
136 if not self.config.doInputMap:
137 self.outputs.remove("inputMap")
138
139
140class AssembleCoaddConfig(CoaddBaseTask.ConfigClass, pipeBase.PipelineTaskConfig,
141 pipelineConnections=AssembleCoaddConnections):
142 """Configuration parameters for the `AssembleCoaddTask`.
143
144 Notes
145 -----
146 The `doMaskBrightObjects` and `brightObjectMaskName` configuration options
147 only set the bitplane config.brightObjectMaskName. To make this useful you
148 *must* also configure the flags.pixel algorithm, for example by adding
149
150 .. code-block:: none
151
152 config.measurement.plugins["base_PixelFlags"].masksFpCenter.append("BRIGHT_OBJECT")
153 config.measurement.plugins["base_PixelFlags"].masksFpAnywhere.append("BRIGHT_OBJECT")
154
155 to your measureCoaddSources.py and forcedPhotCoadd.py config overrides.
156 """
157 warpType = pexConfig.Field(
158 doc="Warp name: one of 'direct' or 'psfMatched'",
159 dtype=str,
160 default="direct",
161 )
162 subregionSize = pexConfig.ListField(
163 dtype=int,
164 doc="Width, height of stack subregion size; "
165 "make small enough that a full stack of images will fit into memory at once.",
166 length=2,
167 default=(2000, 2000),
168 )
169 statistic = pexConfig.Field(
170 dtype=str,
171 doc="Main stacking statistic for aggregating over the epochs.",
172 default="MEANCLIP",
173 )
174 doOnlineForMean = pexConfig.Field(
175 dtype=bool,
176 doc="Perform online coaddition when statistic=\"MEAN\" to save memory?",
177 default=False,
178 )
179 doSigmaClip = pexConfig.Field(
180 dtype=bool,
181 doc="Perform sigma clipped outlier rejection with MEANCLIP statistic? (DEPRECATED)",
182 default=False,
183 )
184 sigmaClip = pexConfig.Field(
185 dtype=float,
186 doc="Sigma for outlier rejection; ignored if non-clipping statistic selected.",
187 default=3.0,
188 )
189 clipIter = pexConfig.Field(
190 dtype=int,
191 doc="Number of iterations of outlier rejection; ignored if non-clipping statistic selected.",
192 default=2,
193 )
194 calcErrorFromInputVariance = pexConfig.Field(
195 dtype=bool,
196 doc="Calculate coadd variance from input variance by stacking statistic."
197 "Passed to StatisticsControl.setCalcErrorFromInputVariance()",
198 default=True,
199 )
200 scaleZeroPoint = pexConfig.ConfigurableField(
201 target=ScaleZeroPointTask,
202 doc="Task to adjust the photometric zero point of the coadd temp exposures",
203 )
204 doInterp = pexConfig.Field(
205 doc="Interpolate over NaN pixels? Also extrapolate, if necessary, but the results are ugly.",
206 dtype=bool,
207 default=True,
208 )
209 interpImage = pexConfig.ConfigurableField(
210 target=InterpImageTask,
211 doc="Task to interpolate (and extrapolate) over NaN pixels",
212 )
213 doWrite = pexConfig.Field(
214 doc="Persist coadd?",
215 dtype=bool,
216 default=True,
217 )
218 doNImage = pexConfig.Field(
219 doc="Create image of number of contributing exposures for each pixel",
220 dtype=bool,
221 default=False,
222 )
223 doUsePsfMatchedPolygons = pexConfig.Field(
224 doc="Use ValidPolygons from shrunk Psf-Matched Calexps? Should be set to True by CompareWarp only.",
225 dtype=bool,
226 default=False,
227 )
228 maskPropagationThresholds = pexConfig.DictField(
229 keytype=str,
230 itemtype=float,
231 doc=("Threshold (in fractional weight) of rejection at which we propagate a mask plane to "
232 "the coadd; that is, we set the mask bit on the coadd if the fraction the rejected frames "
233 "would have contributed exceeds this value."),
234 default={"SAT": 0.1},
235 )
236 removeMaskPlanes = pexConfig.ListField(dtype=str, default=["NOT_DEBLENDED"],
237 doc="Mask planes to remove before coadding")
238 doMaskBrightObjects = pexConfig.Field(dtype=bool, default=False,
239 doc="Set mask and flag bits for bright objects?")
240 brightObjectMaskName = pexConfig.Field(dtype=str, default="BRIGHT_OBJECT",
241 doc="Name of mask bit used for bright objects")
242 coaddPsf = pexConfig.ConfigField(
243 doc="Configuration for CoaddPsf",
244 dtype=measAlg.CoaddPsfConfig,
245 )
246 doAttachTransmissionCurve = pexConfig.Field(
247 dtype=bool, default=False, optional=False,
248 doc=("Attach a piecewise TransmissionCurve for the coadd? "
249 "(requires all input Exposures to have TransmissionCurves).")
250 )
251 hasFakes = pexConfig.Field(
252 dtype=bool,
253 default=False,
254 doc="Should be set to True if fake sources have been inserted into the input data."
255 )
256 doSelectVisits = pexConfig.Field(
257 doc="Coadd only visits selected by a SelectVisitsTask",
258 dtype=bool,
259 default=False,
260 )
261 doInputMap = pexConfig.Field(
262 doc="Create a bitwise map of coadd inputs",
263 dtype=bool,
264 default=False,
265 )
266 inputMapper = pexConfig.ConfigurableField(
267 doc="Input map creation subtask.",
268 target=HealSparseInputMapTask,
269 )
270
271 def setDefaults(self):
272 super().setDefaults()
273 self.badMaskPlanes = ["NO_DATA", "BAD", "SAT", "EDGE"]
274
275 def validate(self):
276 super().validate()
277 if self.doPsfMatch:
278 # Backwards compatibility.
279 # Configs do not have loggers
280 log.warning("Config doPsfMatch deprecated. Setting warpType='psfMatched'")
281 self.warpType = 'psfMatched'
282 if self.doSigmaClip and self.statistic != "MEANCLIP":
283 log.warning('doSigmaClip deprecated. To replicate behavior, setting statistic to "MEANCLIP"')
284 self.statistic = "MEANCLIP"
285 if self.doInterp and self.statistic not in ['MEAN', 'MEDIAN', 'MEANCLIP', 'VARIANCE', 'VARIANCECLIP']:
286 raise ValueError("Must set doInterp=False for statistic=%s, which does not "
287 "compute and set a non-zero coadd variance estimate." % (self.statistic))
288
289 unstackableStats = ['NOTHING', 'ERROR', 'ORMASK']
290 if not hasattr(afwMath.Property, self.statistic) or self.statistic in unstackableStats:
291 stackableStats = [str(k) for k in afwMath.Property.__members__.keys()
292 if str(k) not in unstackableStats]
293 raise ValueError("statistic %s is not allowed. Please choose one of %s."
294 % (self.statistic, stackableStats))
295
296
297class AssembleCoaddTask(CoaddBaseTask, pipeBase.PipelineTask):
298 """Assemble a coadded image from a set of warps (coadded temporary exposures).
299
300 We want to assemble a coadded image from a set of Warps (also called
301 coadded temporary exposures or ``coaddTempExps``).
302 Each input Warp covers a patch on the sky and corresponds to a single
303 run/visit/exposure of the covered patch. We provide the task with a list
304 of Warps (``selectDataList``) from which it selects Warps that cover the
305 specified patch (pointed at by ``dataRef``).
306 Each Warp that goes into a coadd will typically have an independent
307 photometric zero-point. Therefore, we must scale each Warp to set it to
308 a common photometric zeropoint. WarpType may be one of 'direct' or
309 'psfMatched', and the boolean configs `config.makeDirect` and
310 `config.makePsfMatched` set which of the warp types will be coadded.
311 The coadd is computed as a mean with optional outlier rejection.
312 Criteria for outlier rejection are set in `AssembleCoaddConfig`.
313 Finally, Warps can have bad 'NaN' pixels which received no input from the
314 source calExps. We interpolate over these bad (NaN) pixels.
315
316 `AssembleCoaddTask` uses several sub-tasks. These are
317
318 - `ScaleZeroPointTask`
319 - create and use an ``imageScaler`` object to scale the photometric zeropoint for each Warp
320 - `InterpImageTask`
321 - interpolate across bad pixels (NaN) in the final coadd
322
323 You can retarget these subtasks if you wish.
324
325 Notes
326 -----
327 The `lsst.pipe.base.cmdLineTask.CmdLineTask` interface supports a
328 flag ``-d`` to import ``debug.py`` from your ``PYTHONPATH``; see
329 `baseDebug` for more about ``debug.py`` files. `AssembleCoaddTask` has
330 no debug variables of its own. Some of the subtasks may support debug
331 variables. See the documentation for the subtasks for further information.
332
333 Examples
334 --------
335 `AssembleCoaddTask` assembles a set of warped images into a coadded image.
336 The `AssembleCoaddTask` can be invoked by running ``assembleCoadd.py``
337 with the flag '--legacyCoadd'. Usage of assembleCoadd.py expects two
338 inputs: a data reference to the tract patch and filter to be coadded, and
339 a list of Warps to attempt to coadd. These are specified using ``--id`` and
340 ``--selectId``, respectively:
341
342 .. code-block:: none
343
344 --id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]
345 --selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]
346
347 Only the Warps that cover the specified tract and patch will be coadded.
348 A list of the available optional arguments can be obtained by calling
349 ``assembleCoadd.py`` with the ``--help`` command line argument:
350
351 .. code-block:: none
352
353 assembleCoadd.py --help
354
355 To demonstrate usage of the `AssembleCoaddTask` in the larger context of
356 multi-band processing, we will generate the HSC-I & -R band coadds from
357 HSC engineering test data provided in the ``ci_hsc`` package. To begin,
358 assuming that the lsst stack has been already set up, we must set up the
359 obs_subaru and ``ci_hsc`` packages. This defines the environment variable
360 ``$CI_HSC_DIR`` and points at the location of the package. The raw HSC
361 data live in the ``$CI_HSC_DIR/raw directory``. To begin assembling the
362 coadds, we must first
363
364 - processCcd
365 - process the individual ccds in $CI_HSC_RAW to produce calibrated exposures
366 - makeSkyMap
367 - create a skymap that covers the area of the sky present in the raw exposures
368 - makeCoaddTempExp
369 - warp the individual calibrated exposures to the tangent plane of the coadd
370
371 We can perform all of these steps by running
372
373 .. code-block:: none
374
375 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988
376
377 This will produce warped exposures for each visit. To coadd the warped
378 data, we call assembleCoadd.py as follows:
379
380 .. code-block:: none
381
382 assembleCoadd.py --legacyCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \
383 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \
384 --selectId visit=903986 ccd=100 --selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \
385 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \
386 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \
387 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \
388 --selectId visit=903988 ccd=24
389
390 that will process the HSC-I band data. The results are written in
391 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``.
392
393 You may also choose to run:
394
395 .. code-block:: none
396
397 scons warp-903334 warp-903336 warp-903338 warp-903342 warp-903344 warp-903346
398 assembleCoadd.py --legacyCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-R \
399 --selectId visit=903334 ccd=16 --selectId visit=903334 ccd=22 --selectId visit=903334 ccd=23 \
400 --selectId visit=903334 ccd=100 --selectId visit=903336 ccd=17 --selectId visit=903336 ccd=24 \
401 --selectId visit=903338 ccd=18 --selectId visit=903338 ccd=25 --selectId visit=903342 ccd=4 \
402 --selectId visit=903342 ccd=10 --selectId visit=903342 ccd=100 --selectId visit=903344 ccd=0 \
403 --selectId visit=903344 ccd=5 --selectId visit=903344 ccd=11 --selectId visit=903346 ccd=1 \
404 --selectId visit=903346 ccd=6 --selectId visit=903346 ccd=12
405
406 to generate the coadd for the HSC-R band if you are interested in
407 following multiBand Coadd processing as discussed in `pipeTasks_multiBand`
408 (but note that normally, one would use the `SafeClipAssembleCoaddTask`
409 rather than `AssembleCoaddTask` to make the coadd.
410 """
411 ConfigClass = AssembleCoaddConfig
412 _DefaultName = "assembleCoadd"
413
414 def __init__(self, *args, **kwargs):
415 # TODO: DM-17415 better way to handle previously allowed passed args e.g.`AssembleCoaddTask(config)`
416 if args:
417 argNames = ["config", "name", "parentTask", "log"]
418 kwargs.update({k: v for k, v in zip(argNames, args)})
419 warnings.warn("AssembleCoadd received positional args, and casting them as kwargs: %s. "
420 "PipelineTask will not take positional args" % argNames, FutureWarning)
421
422 super().__init__(**kwargs)
423 self.makeSubtask("interpImage")
424 self.makeSubtask("scaleZeroPoint")
425
426 if self.config.doMaskBrightObjects:
427 mask = afwImage.Mask()
428 try:
429 self.brightObjectBitmask = 1 << mask.addMaskPlane(self.config.brightObjectMaskName)
430 except pexExceptions.LsstCppException:
431 raise RuntimeError("Unable to define mask plane for bright objects; planes used are %s" %
432 mask.getMaskPlaneDict().keys())
433 del mask
434
435 if self.config.doInputMap:
436 self.makeSubtask("inputMapper")
437
438 self.warpType = self.config.warpType
439
440 @utils.inheritDoc(pipeBase.PipelineTask)
441 def runQuantum(self, butlerQC, inputRefs, outputRefs):
442 # Docstring to be formatted with info from PipelineTask.runQuantum
443 """
444 Notes
445 -----
446 Assemble a coadd from a set of Warps.
447
448 PipelineTask (Gen3) entry point to Coadd a set of Warps.
449 Analogous to `runDataRef`, it prepares all the data products to be
450 passed to `run`, and processes the results before returning a struct
451 of results to be written out. AssembleCoadd cannot fit all Warps in memory.
452 Therefore, its inputs are accessed subregion by subregion
453 by the Gen3 `DeferredDatasetHandle` that is analagous to the Gen2
454 `lsst.daf.persistence.ButlerDataRef`. Any updates to this method should
455 correspond to an update in `runDataRef` while both entry points
456 are used.
457 """
458 inputData = butlerQC.get(inputRefs)
459
460 # Construct skyInfo expected by run
461 # Do not remove skyMap from inputData in case makeSupplementaryDataGen3 needs it
462 skyMap = inputData["skyMap"]
463 outputDataId = butlerQC.quantum.dataId
464
465 inputData['skyInfo'] = makeSkyInfo(skyMap,
466 tractId=outputDataId['tract'],
467 patchId=outputDataId['patch'])
468
469 if self.config.doSelectVisits:
470 warpRefList = self.filterWarps(inputData['inputWarps'], inputData['selectedVisits'])
471 else:
472 warpRefList = inputData['inputWarps']
473
474 # Perform same middle steps as `runDataRef` does
475 inputs = self.prepareInputs(warpRefList)
476 self.log.info("Found %d %s", len(inputs.tempExpRefList),
477 self.getTempExpDatasetName(self.warpType))
478 if len(inputs.tempExpRefList) == 0:
479 raise pipeBase.NoWorkFound("No coadd temporary exposures found")
480
481 supplementaryData = self.makeSupplementaryDataGen3(butlerQC, inputRefs, outputRefs)
482 retStruct = self.run(inputData['skyInfo'], inputs.tempExpRefList, inputs.imageScalerList,
483 inputs.weightList, supplementaryData=supplementaryData)
484
485 inputData.setdefault('brightObjectMask', None)
486 self.processResults(retStruct.coaddExposure, inputData['brightObjectMask'], outputDataId)
487
488 if self.config.doWrite:
489 butlerQC.put(retStruct, outputRefs)
490 return retStruct
491
492 @timeMethod
493 def runDataRef(self, dataRef, selectDataList=None, warpRefList=None):
494 """Assemble a coadd from a set of Warps.
495
496 Pipebase.CmdlineTask entry point to Coadd a set of Warps.
497 Compute weights to be applied to each Warp and
498 find scalings to match the photometric zeropoint to a reference Warp.
499 Assemble the Warps using `run`. Interpolate over NaNs and
500 optionally write the coadd to disk. Return the coadded exposure.
501
502 Parameters
503 ----------
505 Data reference defining the patch for coaddition and the
506 reference Warp (if ``config.autoReference=False``).
507 Used to access the following data products:
508 - ``self.config.coaddName + "Coadd_skyMap"``
509 - ``self.config.coaddName + "Coadd_ + <warpType> + "Warp"`` (optionally)
510 - ``self.config.coaddName + "Coadd"``
511 selectDataList : `list`
512 List of data references to Calexps. Data to be coadded will be
513 selected from this list based on overlap with the patch defined
514 by dataRef, grouped by visit, and converted to a list of data
515 references to warps.
516 warpRefList : `list`
517 List of data references to Warps to be coadded.
518 Note: `warpRefList` is just the new name for `tempExpRefList`.
519
520 Returns
521 -------
522 retStruct : `lsst.pipe.base.Struct`
523 Result struct with components:
524
525 - ``coaddExposure``: coadded exposure (``Exposure``).
526 - ``nImage``: exposure count image (``Image``).
527 """
528 if selectDataList and warpRefList:
529 raise RuntimeError("runDataRef received both a selectDataList and warpRefList, "
530 "and which to use is ambiguous. Please pass only one.")
531
532 skyInfo = self.getSkyInfo(dataRef)
533 if warpRefList is None:
534 calExpRefList = self.selectExposures(dataRef, skyInfo, selectDataList=selectDataList)
535 if len(calExpRefList) == 0:
536 self.log.warning("No exposures to coadd")
537 return
538 self.log.info("Coadding %d exposures", len(calExpRefList))
539
540 warpRefList = self.getTempExpRefList(dataRef, calExpRefList)
541
542 inputData = self.prepareInputs(warpRefList)
543 self.log.info("Found %d %s", len(inputData.tempExpRefList),
544 self.getTempExpDatasetName(self.warpType))
545 if len(inputData.tempExpRefList) == 0:
546 self.log.warning("No coadd temporary exposures found")
547 return
548
549 supplementaryData = self.makeSupplementaryData(dataRef, warpRefList=inputData.tempExpRefList)
550
551 retStruct = self.run(skyInfo, inputData.tempExpRefList, inputData.imageScalerList,
552 inputData.weightList, supplementaryData=supplementaryData)
553
554 brightObjects = self.readBrightObjectMasks(dataRef) if self.config.doMaskBrightObjects else None
555 self.processResults(retStruct.coaddExposure, brightObjectMasks=brightObjects, dataId=dataRef.dataId)
556
557 if self.config.doWrite:
558 if self.getCoaddDatasetName(self.warpType) == "deepCoadd" and self.config.hasFakes:
559 coaddDatasetName = "fakes_" + self.getCoaddDatasetName(self.warpType)
560 else:
561 coaddDatasetName = self.getCoaddDatasetName(self.warpType)
562 self.log.info("Persisting %s", coaddDatasetName)
563 dataRef.put(retStruct.coaddExposure, coaddDatasetName)
564 if self.config.doNImage and retStruct.nImage is not None:
565 dataRef.put(retStruct.nImage, self.getCoaddDatasetName(self.warpType) + '_nImage')
566
567 return retStruct
568
569 def processResults(self, coaddExposure, brightObjectMasks=None, dataId=None):
570 """Interpolate over missing data and mask bright stars.
571
572 Parameters
573 ----------
574 coaddExposure : `lsst.afw.image.Exposure`
575 The coadded exposure to process.
576 dataRef : `lsst.daf.persistence.ButlerDataRef`
577 Butler data reference for supplementary data.
578 """
579 if self.config.doInterp:
580 self.interpImage.run(coaddExposure.getMaskedImage(), planeName="NO_DATA")
581 # The variance must be positive; work around for DM-3201.
582 varArray = coaddExposure.variance.array
583 with numpy.errstate(invalid="ignore"):
584 varArray[:] = numpy.where(varArray > 0, varArray, numpy.inf)
585
586 if self.config.doMaskBrightObjects:
587 self.setBrightObjectMasks(coaddExposure, brightObjectMasks, dataId)
588
589 def makeSupplementaryData(self, dataRef, selectDataList=None, warpRefList=None):
590 """Make additional inputs to run() specific to subclasses (Gen2)
591
592 Duplicates interface of `runDataRef` method
593 Available to be implemented by subclasses only if they need the
594 coadd dataRef for performing preliminary processing before
595 assembling the coadd.
596
597 Parameters
598 ----------
599 dataRef : `lsst.daf.persistence.ButlerDataRef`
600 Butler data reference for supplementary data.
601 selectDataList : `list` (optional)
602 Optional List of data references to Calexps.
603 warpRefList : `list` (optional)
604 Optional List of data references to Warps.
605 """
606 return pipeBase.Struct()
607
608 def makeSupplementaryDataGen3(self, butlerQC, inputRefs, outputRefs):
609 """Make additional inputs to run() specific to subclasses (Gen3)
610
611 Duplicates interface of `runQuantum` method.
612 Available to be implemented by subclasses only if they need the
613 coadd dataRef for performing preliminary processing before
614 assembling the coadd.
615
616 Parameters
617 ----------
618 butlerQC : `lsst.pipe.base.ButlerQuantumContext`
619 Gen3 Butler object for fetching additional data products before
620 running the Task specialized for quantum being processed
621 inputRefs : `lsst.pipe.base.InputQuantizedConnection`
622 Attributes are the names of the connections describing input dataset types.
623 Values are DatasetRefs that task consumes for corresponding dataset type.
624 DataIds are guaranteed to match data objects in ``inputData``.
625 outputRefs : `lsst.pipe.base.OutputQuantizedConnection`
626 Attributes are the names of the connections describing output dataset types.
627 Values are DatasetRefs that task is to produce
628 for corresponding dataset type.
629 """
630 return pipeBase.Struct()
631
632 def getTempExpRefList(self, patchRef, calExpRefList):
633 """Generate list data references corresponding to warped exposures
634 that lie within the patch to be coadded.
635
636 Parameters
637 ----------
638 patchRef : `dataRef`
639 Data reference for patch.
640 calExpRefList : `list`
641 List of data references for input calexps.
642
643 Returns
644 -------
645 tempExpRefList : `list`
646 List of Warp/CoaddTempExp data references.
647 """
648 butler = patchRef.getButler()
649 groupData = groupPatchExposures(patchRef, calExpRefList, self.getCoaddDatasetName(self.warpType),
650 self.getTempExpDatasetName(self.warpType))
651 tempExpRefList = [getGroupDataRef(butler, self.getTempExpDatasetName(self.warpType),
652 g, groupData.keys) for
653 g in groupData.groups.keys()]
654 return tempExpRefList
655
656 def prepareInputs(self, refList):
657 """Prepare the input warps for coaddition by measuring the weight for
658 each warp and the scaling for the photometric zero point.
659
660 Each Warp has its own photometric zeropoint and background variance.
661 Before coadding these Warps together, compute a scale factor to
662 normalize the photometric zeropoint and compute the weight for each Warp.
663
664 Parameters
665 ----------
666 refList : `list`
667 List of data references to tempExp
668
669 Returns
670 -------
671 result : `lsst.pipe.base.Struct`
672 Result struct with components:
673
674 - ``tempExprefList``: `list` of data references to tempExp.
675 - ``weightList``: `list` of weightings.
676 - ``imageScalerList``: `list` of image scalers.
677 """
678 statsCtrl = afwMath.StatisticsControl()
679 statsCtrl.setNumSigmaClip(self.config.sigmaClip)
680 statsCtrl.setNumIter(self.config.clipIter)
681 statsCtrl.setAndMask(self.getBadPixelMask())
682 statsCtrl.setNanSafe(True)
683 # compute tempExpRefList: a list of tempExpRef that actually exist
684 # and weightList: a list of the weight of the associated coadd tempExp
685 # and imageScalerList: a list of scale factors for the associated coadd tempExp
686 tempExpRefList = []
687 weightList = []
688 imageScalerList = []
689 tempExpName = self.getTempExpDatasetName(self.warpType)
690 for tempExpRef in refList:
691 # Gen3's DeferredDatasetHandles are guaranteed to exist and
692 # therefore have no datasetExists() method
693 if not isinstance(tempExpRef, DeferredDatasetHandle):
694 if not tempExpRef.datasetExists(tempExpName):
695 self.log.warning("Could not find %s %s; skipping it", tempExpName, tempExpRef.dataId)
696 continue
697
698 tempExp = tempExpRef.get(datasetType=tempExpName, immediate=True)
699 # Ignore any input warp that is empty of data
700 if numpy.isnan(tempExp.image.array).all():
701 continue
702 maskedImage = tempExp.getMaskedImage()
703 imageScaler = self.scaleZeroPoint.computeImageScaler(
704 exposure=tempExp,
705 dataRef=tempExpRef,
706 )
707 try:
708 imageScaler.scaleMaskedImage(maskedImage)
709 except Exception as e:
710 self.log.warning("Scaling failed for %s (skipping it): %s", tempExpRef.dataId, e)
711 continue
712 statObj = afwMath.makeStatistics(maskedImage.getVariance(), maskedImage.getMask(),
713 afwMath.MEANCLIP, statsCtrl)
714 meanVar, meanVarErr = statObj.getResult(afwMath.MEANCLIP)
715 weight = 1.0 / float(meanVar)
716 if not numpy.isfinite(weight):
717 self.log.warning("Non-finite weight for %s: skipping", tempExpRef.dataId)
718 continue
719 self.log.info("Weight of %s %s = %0.3f", tempExpName, tempExpRef.dataId, weight)
720
721 del maskedImage
722 del tempExp
723
724 tempExpRefList.append(tempExpRef)
725 weightList.append(weight)
726 imageScalerList.append(imageScaler)
727
728 return pipeBase.Struct(tempExpRefList=tempExpRefList, weightList=weightList,
729 imageScalerList=imageScalerList)
730
731 def prepareStats(self, mask=None):
732 """Prepare the statistics for coadding images.
733
734 Parameters
735 ----------
736 mask : `int`, optional
737 Bit mask value to exclude from coaddition.
738
739 Returns
740 -------
741 stats : `lsst.pipe.base.Struct`
742 Statistics structure with the following fields:
743
744 - ``statsCtrl``: Statistics control object for coadd
746 - ``statsFlags``: Statistic for coadd (`lsst.afw.math.Property`)
747 """
748 if mask is None:
749 mask = self.getBadPixelMask()
750 statsCtrl = afwMath.StatisticsControl()
751 statsCtrl.setNumSigmaClip(self.config.sigmaClip)
752 statsCtrl.setNumIter(self.config.clipIter)
753 statsCtrl.setAndMask(mask)
754 statsCtrl.setNanSafe(True)
755 statsCtrl.setWeighted(True)
756 statsCtrl.setCalcErrorFromInputVariance(self.config.calcErrorFromInputVariance)
757 for plane, threshold in self.config.maskPropagationThresholds.items():
758 bit = afwImage.Mask.getMaskPlane(plane)
759 statsCtrl.setMaskPropagationThreshold(bit, threshold)
760 statsFlags = afwMath.stringToStatisticsProperty(self.config.statistic)
761 return pipeBase.Struct(ctrl=statsCtrl, flags=statsFlags)
762
763 @timeMethod
764 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
765 altMaskList=None, mask=None, supplementaryData=None):
766 """Assemble a coadd from input warps
767
768 Assemble the coadd using the provided list of coaddTempExps. Since
769 the full coadd covers a patch (a large area), the assembly is
770 performed over small areas on the image at a time in order to
771 conserve memory usage. Iterate over subregions within the outer
772 bbox of the patch using `assembleSubregion` to stack the corresponding
773 subregions from the coaddTempExps with the statistic specified.
774 Set the edge bits the coadd mask based on the weight map.
775
776 Parameters
777 ----------
778 skyInfo : `lsst.pipe.base.Struct`
779 Struct with geometric information about the patch.
780 tempExpRefList : `list`
781 List of data references to Warps (previously called CoaddTempExps).
782 imageScalerList : `list`
783 List of image scalers.
784 weightList : `list`
785 List of weights
786 altMaskList : `list`, optional
787 List of alternate masks to use rather than those stored with
788 tempExp.
789 mask : `int`, optional
790 Bit mask value to exclude from coaddition.
791 supplementaryData : lsst.pipe.base.Struct, optional
792 Struct with additional data products needed to assemble coadd.
793 Only used by subclasses that implement `makeSupplementaryData`
794 and override `run`.
795
796 Returns
797 -------
798 result : `lsst.pipe.base.Struct`
799 Result struct with components:
800
801 - ``coaddExposure``: coadded exposure (``lsst.afw.image.Exposure``).
802 - ``nImage``: exposure count image (``lsst.afw.image.Image``), if requested.
803 - ``inputMap``: bit-wise map of inputs, if requested.
804 - ``warpRefList``: input list of refs to the warps (
805 ``lsst.daf.butler.DeferredDatasetHandle`` or
806 ``lsst.daf.persistence.ButlerDataRef``)
807 (unmodified)
808 - ``imageScalerList``: input list of image scalers (unmodified)
809 - ``weightList``: input list of weights (unmodified)
810 """
811 tempExpName = self.getTempExpDatasetName(self.warpType)
812 self.log.info("Assembling %s %s", len(tempExpRefList), tempExpName)
813 stats = self.prepareStats(mask=mask)
814
815 if altMaskList is None:
816 altMaskList = [None]*len(tempExpRefList)
817
818 coaddExposure = afwImage.ExposureF(skyInfo.bbox, skyInfo.wcs)
819 coaddExposure.setPhotoCalib(self.scaleZeroPoint.getPhotoCalib())
820 coaddExposure.getInfo().setCoaddInputs(self.inputRecorder.makeCoaddInputs())
821 self.assembleMetadata(coaddExposure, tempExpRefList, weightList)
822 coaddMaskedImage = coaddExposure.getMaskedImage()
823 subregionSizeArr = self.config.subregionSize
824 subregionSize = geom.Extent2I(subregionSizeArr[0], subregionSizeArr[1])
825 # if nImage is requested, create a zero one which can be passed to assembleSubregion
826 if self.config.doNImage:
827 nImage = afwImage.ImageU(skyInfo.bbox)
828 else:
829 nImage = None
830 # If inputMap is requested, create the initial version that can be masked in
831 # assembleSubregion.
832 if self.config.doInputMap:
833 self.inputMapper.build_ccd_input_map(skyInfo.bbox,
834 skyInfo.wcs,
835 coaddExposure.getInfo().getCoaddInputs().ccds)
836
837 if self.config.doOnlineForMean and self.config.statistic == "MEAN":
838 try:
839 self.assembleOnlineMeanCoadd(coaddExposure, tempExpRefList, imageScalerList,
840 weightList, altMaskList, stats.ctrl,
841 nImage=nImage)
842 except Exception as e:
843 self.log.exception("Cannot compute online coadd %s", e)
844 raise
845 else:
846 for subBBox in self._subBBoxIter(skyInfo.bbox, subregionSize):
847 try:
848 self.assembleSubregion(coaddExposure, subBBox, tempExpRefList, imageScalerList,
849 weightList, altMaskList, stats.flags, stats.ctrl,
850 nImage=nImage)
851 except Exception as e:
852 self.log.exception("Cannot compute coadd %s: %s", subBBox, e)
853 raise
854
855 # If inputMap is requested, we must finalize the map after the accumulation.
856 if self.config.doInputMap:
857 self.inputMapper.finalize_ccd_input_map_mask()
858 inputMap = self.inputMapper.ccd_input_map
859 else:
860 inputMap = None
861
862 self.setInexactPsf(coaddMaskedImage.getMask())
863 # Despite the name, the following doesn't really deal with "EDGE" pixels: it identifies
864 # pixels that didn't receive any unmasked inputs (as occurs around the edge of the field).
865 coaddUtils.setCoaddEdgeBits(coaddMaskedImage.getMask(), coaddMaskedImage.getVariance())
866 return pipeBase.Struct(coaddExposure=coaddExposure, nImage=nImage,
867 warpRefList=tempExpRefList, imageScalerList=imageScalerList,
868 weightList=weightList, inputMap=inputMap)
869
870 def assembleMetadata(self, coaddExposure, tempExpRefList, weightList):
871 """Set the metadata for the coadd.
872
873 This basic implementation sets the filter from the first input.
874
875 Parameters
876 ----------
877 coaddExposure : `lsst.afw.image.Exposure`
878 The target exposure for the coadd.
879 tempExpRefList : `list`
880 List of data references to tempExp.
881 weightList : `list`
882 List of weights.
883 """
884 assert len(tempExpRefList) == len(weightList), "Length mismatch"
885 tempExpName = self.getTempExpDatasetName(self.warpType)
886 # We load a single pixel of each coaddTempExp, because we just want to get at the metadata
887 # (and we need more than just the PropertySet that contains the header), which is not possible
888 # with the current butler (see #2777).
889 bbox = geom.Box2I(coaddExposure.getBBox().getMin(), geom.Extent2I(1, 1))
890
891 if isinstance(tempExpRefList[0], DeferredDatasetHandle):
892 # Gen 3 API
893 tempExpList = [tempExpRef.get(parameters={'bbox': bbox}) for tempExpRef in tempExpRefList]
894 else:
895 # Gen 2 API. Delete this when Gen 2 retired
896 tempExpList = [tempExpRef.get(tempExpName + "_sub", bbox=bbox, immediate=True)
897 for tempExpRef in tempExpRefList]
898 numCcds = sum(len(tempExp.getInfo().getCoaddInputs().ccds) for tempExp in tempExpList)
899
900 # Set the coadd FilterLabel to the band of the first input exposure:
901 # Coadds are calibrated, so the physical label is now meaningless.
902 coaddExposure.setFilterLabel(afwImage.FilterLabel(tempExpList[0].getFilterLabel().bandLabel))
903 coaddInputs = coaddExposure.getInfo().getCoaddInputs()
904 coaddInputs.ccds.reserve(numCcds)
905 coaddInputs.visits.reserve(len(tempExpList))
906
907 for tempExp, weight in zip(tempExpList, weightList):
908 self.inputRecorder.addVisitToCoadd(coaddInputs, tempExp, weight)
909
910 if self.config.doUsePsfMatchedPolygons:
911 self.shrinkValidPolygons(coaddInputs)
912
913 coaddInputs.visits.sort()
914 coaddInputs.ccds.sort()
915 if self.warpType == "psfMatched":
916 # The modelPsf BBox for a psfMatchedWarp/coaddTempExp was dynamically defined by
917 # ModelPsfMatchTask as the square box bounding its spatially-variable, pre-matched WarpedPsf.
918 # Likewise, set the PSF of a PSF-Matched Coadd to the modelPsf
919 # having the maximum width (sufficient because square)
920 modelPsfList = [tempExp.getPsf() for tempExp in tempExpList]
921 modelPsfWidthList = [modelPsf.computeBBox(modelPsf.getAveragePosition()).getWidth()
922 for modelPsf in modelPsfList]
923 psf = modelPsfList[modelPsfWidthList.index(max(modelPsfWidthList))]
924 else:
925 psf = measAlg.CoaddPsf(coaddInputs.ccds, coaddExposure.getWcs(),
926 self.config.coaddPsf.makeControl())
927 coaddExposure.setPsf(psf)
928 apCorrMap = measAlg.makeCoaddApCorrMap(coaddInputs.ccds, coaddExposure.getBBox(afwImage.PARENT),
929 coaddExposure.getWcs())
930 coaddExposure.getInfo().setApCorrMap(apCorrMap)
931 if self.config.doAttachTransmissionCurve:
932 transmissionCurve = measAlg.makeCoaddTransmissionCurve(coaddExposure.getWcs(), coaddInputs.ccds)
933 coaddExposure.getInfo().setTransmissionCurve(transmissionCurve)
934
935 def assembleSubregion(self, coaddExposure, bbox, tempExpRefList, imageScalerList, weightList,
936 altMaskList, statsFlags, statsCtrl, nImage=None):
937 """Assemble the coadd for a sub-region.
938
939 For each coaddTempExp, check for (and swap in) an alternative mask
940 if one is passed. Remove mask planes listed in
941 `config.removeMaskPlanes`. Finally, stack the actual exposures using
942 `lsst.afw.math.statisticsStack` with the statistic specified by
943 statsFlags. Typically, the statsFlag will be one of lsst.afw.math.MEAN for
944 a mean-stack or `lsst.afw.math.MEANCLIP` for outlier rejection using
945 an N-sigma clipped mean where N and iterations are specified by
946 statsCtrl. Assign the stacked subregion back to the coadd.
947
948 Parameters
949 ----------
950 coaddExposure : `lsst.afw.image.Exposure`
951 The target exposure for the coadd.
952 bbox : `lsst.geom.Box`
953 Sub-region to coadd.
954 tempExpRefList : `list`
955 List of data reference to tempExp.
956 imageScalerList : `list`
957 List of image scalers.
958 weightList : `list`
959 List of weights.
960 altMaskList : `list`
961 List of alternate masks to use rather than those stored with
962 tempExp, or None. Each element is dict with keys = mask plane
963 name to which to add the spans.
964 statsFlags : `lsst.afw.math.Property`
965 Property object for statistic for coadd.
967 Statistics control object for coadd.
968 nImage : `lsst.afw.image.ImageU`, optional
969 Keeps track of exposure count for each pixel.
970 """
971 self.log.debug("Computing coadd over %s", bbox)
972 tempExpName = self.getTempExpDatasetName(self.warpType)
973 coaddExposure.mask.addMaskPlane("REJECTED")
974 coaddExposure.mask.addMaskPlane("CLIPPED")
975 coaddExposure.mask.addMaskPlane("SENSOR_EDGE")
976 maskMap = self.setRejectedMaskMapping(statsCtrl)
977 clipped = afwImage.Mask.getPlaneBitMask("CLIPPED")
978 maskedImageList = []
979 if nImage is not None:
980 subNImage = afwImage.ImageU(bbox.getWidth(), bbox.getHeight())
981 for tempExpRef, imageScaler, altMask in zip(tempExpRefList, imageScalerList, altMaskList):
982
983 if isinstance(tempExpRef, DeferredDatasetHandle):
984 # Gen 3 API
985 exposure = tempExpRef.get(parameters={'bbox': bbox})
986 else:
987 # Gen 2 API. Delete this when Gen 2 retired
988 exposure = tempExpRef.get(tempExpName + "_sub", bbox=bbox)
989
990 maskedImage = exposure.getMaskedImage()
991 mask = maskedImage.getMask()
992 if altMask is not None:
993 self.applyAltMaskPlanes(mask, altMask)
994 imageScaler.scaleMaskedImage(maskedImage)
995
996 # Add 1 for each pixel which is not excluded by the exclude mask.
997 # In legacyCoadd, pixels may also be excluded by afwMath.statisticsStack.
998 if nImage is not None:
999 subNImage.getArray()[maskedImage.getMask().getArray() & statsCtrl.getAndMask() == 0] += 1
1000 if self.config.removeMaskPlanes:
1001 self.removeMaskPlanes(maskedImage)
1002 maskedImageList.append(maskedImage)
1003
1004 if self.config.doInputMap:
1005 visit = exposure.getInfo().getCoaddInputs().visits[0].getId()
1006 self.inputMapper.mask_warp_bbox(bbox, visit, mask, statsCtrl.getAndMask())
1007
1008 with self.timer("stack"):
1009 coaddSubregion = afwMath.statisticsStack(maskedImageList, statsFlags, statsCtrl, weightList,
1010 clipped, # also set output to CLIPPED if sigma-clipped
1011 maskMap)
1012 coaddExposure.maskedImage.assign(coaddSubregion, bbox)
1013 if nImage is not None:
1014 nImage.assign(subNImage, bbox)
1015
1016 def assembleOnlineMeanCoadd(self, coaddExposure, tempExpRefList, imageScalerList, weightList,
1017 altMaskList, statsCtrl, nImage=None):
1018 """Assemble the coadd using the "online" method.
1019
1020 This method takes a running sum of images and weights to save memory.
1021 It only works for MEAN statistics.
1022
1023 Parameters
1024 ----------
1025 coaddExposure : `lsst.afw.image.Exposure`
1026 The target exposure for the coadd.
1027 tempExpRefList : `list`
1028 List of data reference to tempExp.
1029 imageScalerList : `list`
1030 List of image scalers.
1031 weightList : `list`
1032 List of weights.
1033 altMaskList : `list`
1034 List of alternate masks to use rather than those stored with
1035 tempExp, or None. Each element is dict with keys = mask plane
1036 name to which to add the spans.
1038 Statistics control object for coadd
1039 nImage : `lsst.afw.image.ImageU`, optional
1040 Keeps track of exposure count for each pixel.
1041 """
1042 self.log.debug("Computing online coadd.")
1043 tempExpName = self.getTempExpDatasetName(self.warpType)
1044 coaddExposure.mask.addMaskPlane("REJECTED")
1045 coaddExposure.mask.addMaskPlane("CLIPPED")
1046 coaddExposure.mask.addMaskPlane("SENSOR_EDGE")
1047 maskMap = self.setRejectedMaskMapping(statsCtrl)
1048 thresholdDict = AccumulatorMeanStack.stats_ctrl_to_threshold_dict(statsCtrl)
1049
1050 bbox = coaddExposure.maskedImage.getBBox()
1051
1052 stacker = AccumulatorMeanStack(
1053 coaddExposure.image.array.shape,
1054 statsCtrl.getAndMask(),
1055 mask_threshold_dict=thresholdDict,
1056 mask_map=maskMap,
1057 no_good_pixels_mask=statsCtrl.getNoGoodPixelsMask(),
1058 calc_error_from_input_variance=self.config.calcErrorFromInputVariance,
1059 compute_n_image=(nImage is not None)
1060 )
1061
1062 for tempExpRef, imageScaler, altMask, weight in zip(tempExpRefList,
1063 imageScalerList,
1064 altMaskList,
1065 weightList):
1066 if isinstance(tempExpRef, DeferredDatasetHandle):
1067 # Gen 3 API
1068 exposure = tempExpRef.get()
1069 else:
1070 # Gen 2 API. Delete this when Gen 2 retired
1071 exposure = tempExpRef.get(tempExpName)
1072
1073 maskedImage = exposure.getMaskedImage()
1074 mask = maskedImage.getMask()
1075 if altMask is not None:
1076 self.applyAltMaskPlanes(mask, altMask)
1077 imageScaler.scaleMaskedImage(maskedImage)
1078 if self.config.removeMaskPlanes:
1079 self.removeMaskPlanes(maskedImage)
1080
1081 stacker.add_masked_image(maskedImage, weight=weight)
1082
1083 if self.config.doInputMap:
1084 visit = exposure.getInfo().getCoaddInputs().visits[0].getId()
1085 self.inputMapper.mask_warp_bbox(bbox, visit, mask, statsCtrl.getAndMask())
1086
1087 stacker.fill_stacked_masked_image(coaddExposure.maskedImage)
1088
1089 if nImage is not None:
1090 nImage.array[:, :] = stacker.n_image
1091
1092 def removeMaskPlanes(self, maskedImage):
1093 """Unset the mask of an image for mask planes specified in the config.
1094
1095 Parameters
1096 ----------
1097 maskedImage : `lsst.afw.image.MaskedImage`
1098 The masked image to be modified.
1099 """
1100 mask = maskedImage.getMask()
1101 for maskPlane in self.config.removeMaskPlanes:
1102 try:
1103 mask &= ~mask.getPlaneBitMask(maskPlane)
1104 except pexExceptions.InvalidParameterError:
1105 self.log.debug("Unable to remove mask plane %s: no mask plane with that name was found.",
1106 maskPlane)
1107
1108 @staticmethod
1109 def setRejectedMaskMapping(statsCtrl):
1110 """Map certain mask planes of the warps to new planes for the coadd.
1111
1112 If a pixel is rejected due to a mask value other than EDGE, NO_DATA,
1113 or CLIPPED, set it to REJECTED on the coadd.
1114 If a pixel is rejected due to EDGE, set the coadd pixel to SENSOR_EDGE.
1115 If a pixel is rejected due to CLIPPED, set the coadd pixel to CLIPPED.
1116
1117 Parameters
1118 ----------
1120 Statistics control object for coadd
1121
1122 Returns
1123 -------
1124 maskMap : `list` of `tuple` of `int`
1125 A list of mappings of mask planes of the warped exposures to
1126 mask planes of the coadd.
1127 """
1128 edge = afwImage.Mask.getPlaneBitMask("EDGE")
1129 noData = afwImage.Mask.getPlaneBitMask("NO_DATA")
1130 clipped = afwImage.Mask.getPlaneBitMask("CLIPPED")
1131 toReject = statsCtrl.getAndMask() & (~noData) & (~edge) & (~clipped)
1132 maskMap = [(toReject, afwImage.Mask.getPlaneBitMask("REJECTED")),
1133 (edge, afwImage.Mask.getPlaneBitMask("SENSOR_EDGE")),
1134 (clipped, clipped)]
1135 return maskMap
1136
1137 def applyAltMaskPlanes(self, mask, altMaskSpans):
1138 """Apply in place alt mask formatted as SpanSets to a mask.
1139
1140 Parameters
1141 ----------
1142 mask : `lsst.afw.image.Mask`
1143 Original mask.
1144 altMaskSpans : `dict`
1145 SpanSet lists to apply. Each element contains the new mask
1146 plane name (e.g. "CLIPPED and/or "NO_DATA") as the key,
1147 and list of SpanSets to apply to the mask.
1148
1149 Returns
1150 -------
1151 mask : `lsst.afw.image.Mask`
1152 Updated mask.
1153 """
1154 if self.config.doUsePsfMatchedPolygons:
1155 if ("NO_DATA" in altMaskSpans) and ("NO_DATA" in self.config.badMaskPlanes):
1156 # Clear away any other masks outside the validPolygons. These pixels are no longer
1157 # contributing to inexact PSFs, and will still be rejected because of NO_DATA
1158 # self.config.doUsePsfMatchedPolygons should be True only in CompareWarpAssemble
1159 # This mask-clearing step must only occur *before* applying the new masks below
1160 for spanSet in altMaskSpans['NO_DATA']:
1161 spanSet.clippedTo(mask.getBBox()).clearMask(mask, self.getBadPixelMask())
1162
1163 for plane, spanSetList in altMaskSpans.items():
1164 maskClipValue = mask.addMaskPlane(plane)
1165 for spanSet in spanSetList:
1166 spanSet.clippedTo(mask.getBBox()).setMask(mask, 2**maskClipValue)
1167 return mask
1168
1169 def shrinkValidPolygons(self, coaddInputs):
1170 """Shrink coaddInputs' ccds' ValidPolygons in place.
1171
1172 Either modify each ccd's validPolygon in place, or if CoaddInputs
1173 does not have a validPolygon, create one from its bbox.
1174
1175 Parameters
1176 ----------
1177 coaddInputs : `lsst.afw.image.coaddInputs`
1178 Original mask.
1179
1180 """
1181 for ccd in coaddInputs.ccds:
1182 polyOrig = ccd.getValidPolygon()
1183 validPolyBBox = polyOrig.getBBox() if polyOrig else ccd.getBBox()
1184 validPolyBBox.grow(-self.config.matchingKernelSize//2)
1185 if polyOrig:
1186 validPolygon = polyOrig.intersectionSingle(validPolyBBox)
1187 else:
1188 validPolygon = afwGeom.polygon.Polygon(geom.Box2D(validPolyBBox))
1189 ccd.setValidPolygon(validPolygon)
1190
1191 def readBrightObjectMasks(self, dataRef):
1192 """Retrieve the bright object masks.
1193
1194 Returns None on failure.
1195
1196 Parameters
1197 ----------
1199 A Butler dataRef.
1200
1201 Returns
1202 -------
1204 Bright object mask from the Butler object, or None if it cannot
1205 be retrieved.
1206 """
1207 try:
1208 return dataRef.get(datasetType="brightObjectMask", immediate=True)
1209 except Exception as e:
1210 self.log.warning("Unable to read brightObjectMask for %s: %s", dataRef.dataId, e)
1211 return None
1212
1213 def setBrightObjectMasks(self, exposure, brightObjectMasks, dataId=None):
1214 """Set the bright object masks.
1215
1216 Parameters
1217 ----------
1218 exposure : `lsst.afw.image.Exposure`
1219 Exposure under consideration.
1221 Data identifier dict for patch.
1222 brightObjectMasks : `lsst.afw.table`
1223 Table of bright objects to mask.
1224 """
1225
1226 if brightObjectMasks is None:
1227 self.log.warning("Unable to apply bright object mask: none supplied")
1228 return
1229 self.log.info("Applying %d bright object masks to %s", len(brightObjectMasks), dataId)
1230 mask = exposure.getMaskedImage().getMask()
1231 wcs = exposure.getWcs()
1232 plateScale = wcs.getPixelScale().asArcseconds()
1233
1234 for rec in brightObjectMasks:
1235 center = geom.PointI(wcs.skyToPixel(rec.getCoord()))
1236 if rec["type"] == "box":
1237 assert rec["angle"] == 0.0, ("Angle != 0 for mask object %s" % rec["id"])
1238 width = rec["width"].asArcseconds()/plateScale # convert to pixels
1239 height = rec["height"].asArcseconds()/plateScale # convert to pixels
1240
1241 halfSize = geom.ExtentI(0.5*width, 0.5*height)
1242 bbox = geom.Box2I(center - halfSize, center + halfSize)
1243
1244 bbox = geom.BoxI(geom.PointI(int(center[0] - 0.5*width), int(center[1] - 0.5*height)),
1245 geom.PointI(int(center[0] + 0.5*width), int(center[1] + 0.5*height)))
1246 spans = afwGeom.SpanSet(bbox)
1247 elif rec["type"] == "circle":
1248 radius = int(rec["radius"].asArcseconds()/plateScale) # convert to pixels
1249 spans = afwGeom.SpanSet.fromShape(radius, offset=center)
1250 else:
1251 self.log.warning("Unexpected region type %s at %s", rec["type"], center)
1252 continue
1253 spans.clippedTo(mask.getBBox()).setMask(mask, self.brightObjectBitmask)
1254
1255 def setInexactPsf(self, mask):
1256 """Set INEXACT_PSF mask plane.
1257
1258 If any of the input images isn't represented in the coadd (due to
1259 clipped pixels or chip gaps), the `CoaddPsf` will be inexact. Flag
1260 these pixels.
1261
1262 Parameters
1263 ----------
1264 mask : `lsst.afw.image.Mask`
1265 Coadded exposure's mask, modified in-place.
1266 """
1267 mask.addMaskPlane("INEXACT_PSF")
1268 inexactPsf = mask.getPlaneBitMask("INEXACT_PSF")
1269 sensorEdge = mask.getPlaneBitMask("SENSOR_EDGE") # chip edges (so PSF is discontinuous)
1270 clipped = mask.getPlaneBitMask("CLIPPED") # pixels clipped from coadd
1271 rejected = mask.getPlaneBitMask("REJECTED") # pixels rejected from coadd due to masks
1272 array = mask.getArray()
1273 selected = array & (sensorEdge | clipped | rejected) > 0
1274 array[selected] |= inexactPsf
1275
1276 @classmethod
1277 def _makeArgumentParser(cls):
1278 """Create an argument parser.
1279 """
1280 parser = pipeBase.ArgumentParser(name=cls._DefaultName)
1281 parser.add_id_argument("--id", cls.ConfigClass().coaddName + "Coadd_"
1282 + cls.ConfigClass().warpType + "Warp",
1283 help="data ID, e.g. --id tract=12345 patch=1,2",
1284 ContainerClass=AssembleCoaddDataIdContainer)
1285 parser.add_id_argument("--selectId", "calexp", help="data ID, e.g. --selectId visit=6789 ccd=0..9",
1286 ContainerClass=SelectDataIdContainer)
1287 return parser
1288
1289 @staticmethod
1290 def _subBBoxIter(bbox, subregionSize):
1291 """Iterate over subregions of a bbox.
1292
1293 Parameters
1294 ----------
1295 bbox : `lsst.geom.Box2I`
1296 Bounding box over which to iterate.
1297 subregionSize: `lsst.geom.Extent2I`
1298 Size of sub-bboxes.
1299
1300 Yields
1301 ------
1302 subBBox : `lsst.geom.Box2I`
1303 Next sub-bounding box of size ``subregionSize`` or smaller; each ``subBBox``
1304 is contained within ``bbox``, so it may be smaller than ``subregionSize`` at
1305 the edges of ``bbox``, but it will never be empty.
1306 """
1307 if bbox.isEmpty():
1308 raise RuntimeError("bbox %s is empty" % (bbox,))
1309 if subregionSize[0] < 1 or subregionSize[1] < 1:
1310 raise RuntimeError("subregionSize %s must be nonzero" % (subregionSize,))
1311
1312 for rowShift in range(0, bbox.getHeight(), subregionSize[1]):
1313 for colShift in range(0, bbox.getWidth(), subregionSize[0]):
1314 subBBox = geom.Box2I(bbox.getMin() + geom.Extent2I(colShift, rowShift), subregionSize)
1315 subBBox.clip(bbox)
1316 if subBBox.isEmpty():
1317 raise RuntimeError("Bug: empty bbox! bbox=%s, subregionSize=%s, "
1318 "colShift=%s, rowShift=%s" %
1319 (bbox, subregionSize, colShift, rowShift))
1320 yield subBBox
1321
1322 def filterWarps(self, inputs, goodVisits):
1323 """Return list of only inputRefs with visitId in goodVisits ordered by goodVisit
1324
1325 Parameters
1326 ----------
1327 inputs : list
1328 List of `lsst.pipe.base.connections.DeferredDatasetRef` with dataId containing visit
1329 goodVisit : `dict`
1330 Dictionary with good visitIds as the keys. Value ignored.
1331
1332 Returns:
1333 --------
1334 filteredInputs : `list`
1335 Filtered and sorted list of `lsst.pipe.base.connections.DeferredDatasetRef`
1336 """
1337 inputWarpDict = {inputRef.ref.dataId['visit']: inputRef for inputRef in inputs}
1338 filteredInputs = []
1339 for visit in goodVisits.keys():
1340 if visit in inputWarpDict:
1341 filteredInputs.append(inputWarpDict[visit])
1342 return filteredInputs
1343
1344
1345class AssembleCoaddDataIdContainer(pipeBase.DataIdContainer):
1346 """A version of `lsst.pipe.base.DataIdContainer` specialized for assembleCoadd.
1347 """
1348
1349 def makeDataRefList(self, namespace):
1350 """Make self.refList from self.idList.
1351
1352 Parameters
1353 ----------
1354 namespace
1355 Results of parsing command-line (with ``butler`` and ``log`` elements).
1356 """
1357 datasetType = namespace.config.coaddName + "Coadd"
1358 keysCoadd = namespace.butler.getKeys(datasetType=datasetType, level=self.level)
1359
1360 for dataId in self.idList:
1361 # tract and patch are required
1362 for key in keysCoadd:
1363 if key not in dataId:
1364 raise RuntimeError("--id must include " + key)
1365
1366 dataRef = namespace.butler.dataRef(
1367 datasetType=datasetType,
1368 dataId=dataId,
1369 )
1370 self.refList.append(dataRef)
1371
1372
1373def countMaskFromFootprint(mask, footprint, bitmask, ignoreMask):
1374 """Function to count the number of pixels with a specific mask in a
1375 footprint.
1376
1377 Find the intersection of mask & footprint. Count all pixels in the mask
1378 that are in the intersection that have bitmask set but do not have
1379 ignoreMask set. Return the count.
1380
1381 Parameters
1382 ----------
1383 mask : `lsst.afw.image.Mask`
1384 Mask to define intersection region by.
1385 footprint : `lsst.afw.detection.Footprint`
1386 Footprint to define the intersection region by.
1387 bitmask
1388 Specific mask that we wish to count the number of occurances of.
1389 ignoreMask
1390 Pixels to not consider.
1391
1392 Returns
1393 -------
1394 result : `int`
1395 Count of number of pixels in footprint with specified mask.
1396 """
1397 bbox = footprint.getBBox()
1398 bbox.clip(mask.getBBox(afwImage.PARENT))
1399 fp = afwImage.Mask(bbox)
1400 subMask = mask.Factory(mask, bbox, afwImage.PARENT)
1401 footprint.spans.setMask(fp, bitmask)
1402 return numpy.logical_and((subMask.getArray() & fp.getArray()) > 0,
1403 (subMask.getArray() & ignoreMask) == 0).sum()
1404
1405
1406class SafeClipAssembleCoaddConfig(AssembleCoaddConfig, pipelineConnections=AssembleCoaddConnections):
1407 """Configuration parameters for the SafeClipAssembleCoaddTask.
1408 """
1409 clipDetection = pexConfig.ConfigurableField(
1410 target=SourceDetectionTask,
1411 doc="Detect sources on difference between unclipped and clipped coadd")
1412 minClipFootOverlap = pexConfig.Field(
1413 doc="Minimum fractional overlap of clipped footprint with visit DETECTED to be clipped",
1414 dtype=float,
1415 default=0.6
1416 )
1417 minClipFootOverlapSingle = pexConfig.Field(
1418 doc="Minimum fractional overlap of clipped footprint with visit DETECTED to be "
1419 "clipped when only one visit overlaps",
1420 dtype=float,
1421 default=0.5
1422 )
1423 minClipFootOverlapDouble = pexConfig.Field(
1424 doc="Minimum fractional overlap of clipped footprints with visit DETECTED to be "
1425 "clipped when two visits overlap",
1426 dtype=float,
1427 default=0.45
1428 )
1429 maxClipFootOverlapDouble = pexConfig.Field(
1430 doc="Maximum fractional overlap of clipped footprints with visit DETECTED when "
1431 "considering two visits",
1432 dtype=float,
1433 default=0.15
1434 )
1435 minBigOverlap = pexConfig.Field(
1436 doc="Minimum number of pixels in footprint to use DETECTED mask from the single visits "
1437 "when labeling clipped footprints",
1438 dtype=int,
1439 default=100
1440 )
1441
1442 def setDefaults(self):
1443 """Set default values for clipDetection.
1444
1445 Notes
1446 -----
1447 The numeric values for these configuration parameters were
1448 empirically determined, future work may further refine them.
1449 """
1450 AssembleCoaddConfig.setDefaults(self)
1451 self.clipDetectionclipDetection.doTempLocalBackground = False
1452 self.clipDetectionclipDetection.reEstimateBackground = False
1453 self.clipDetectionclipDetection.returnOriginalFootprints = False
1454 self.clipDetectionclipDetection.thresholdPolarity = "both"
1455 self.clipDetectionclipDetection.thresholdValue = 2
1456 self.clipDetectionclipDetection.nSigmaToGrow = 2
1457 self.clipDetectionclipDetection.minPixels = 4
1458 self.clipDetectionclipDetection.isotropicGrow = True
1459 self.clipDetectionclipDetection.thresholdType = "pixel_stdev"
1460 self.sigmaClipsigmaClip = 1.5
1461 self.clipIterclipIter = 3
1462 self.statisticstatistic = "MEAN"
1463
1464 def validate(self):
1465 if self.doSigmaClipdoSigmaClip:
1466 log.warning("Additional Sigma-clipping not allowed in Safe-clipped Coadds. "
1467 "Ignoring doSigmaClip.")
1468 self.doSigmaClipdoSigmaClip = False
1469 if self.statisticstatistic != "MEAN":
1470 raise ValueError("Only MEAN statistic allowed for final stacking in SafeClipAssembleCoadd "
1471 "(%s chosen). Please set statistic to MEAN."
1472 % (self.statisticstatistic))
1473 AssembleCoaddTask.ConfigClass.validate(self)
1474
1475
1476class SafeClipAssembleCoaddTask(AssembleCoaddTask):
1477 """Assemble a coadded image from a set of coadded temporary exposures,
1478 being careful to clip & flag areas with potential artifacts.
1479
1480 In ``AssembleCoaddTask``, we compute the coadd as an clipped mean (i.e.,
1481 we clip outliers). The problem with doing this is that when computing the
1482 coadd PSF at a given location, individual visit PSFs from visits with
1483 outlier pixels contribute to the coadd PSF and cannot be treated correctly.
1484 In this task, we correct for this behavior by creating a new
1485 ``badMaskPlane`` 'CLIPPED'. We populate this plane on the input
1486 coaddTempExps and the final coadd where
1487
1488 i. difference imaging suggests that there is an outlier and
1489 ii. this outlier appears on only one or two images.
1490
1491 Such regions will not contribute to the final coadd. Furthermore, any
1492 routine to determine the coadd PSF can now be cognizant of clipped regions.
1493 Note that the algorithm implemented by this task is preliminary and works
1494 correctly for HSC data. Parameter modifications and or considerable
1495 redesigning of the algorithm is likley required for other surveys.
1496
1497 ``SafeClipAssembleCoaddTask`` uses a ``SourceDetectionTask``
1498 "clipDetection" subtask and also sub-classes ``AssembleCoaddTask``.
1499 You can retarget the ``SourceDetectionTask`` "clipDetection" subtask
1500 if you wish.
1501
1502 Notes
1503 -----
1504 The `lsst.pipe.base.cmdLineTask.CmdLineTask` interface supports a
1505 flag ``-d`` to import ``debug.py`` from your ``PYTHONPATH``;
1506 see `baseDebug` for more about ``debug.py`` files.
1507 `SafeClipAssembleCoaddTask` has no debug variables of its own.
1508 The ``SourceDetectionTask`` "clipDetection" subtasks may support debug
1509 variables. See the documetation for `SourceDetectionTask` "clipDetection"
1510 for further information.
1511
1512 Examples
1513 --------
1514 `SafeClipAssembleCoaddTask` assembles a set of warped ``coaddTempExp``
1515 images into a coadded image. The `SafeClipAssembleCoaddTask` is invoked by
1516 running assembleCoadd.py *without* the flag '--legacyCoadd'.
1517
1518 Usage of ``assembleCoadd.py`` expects a data reference to the tract patch
1519 and filter to be coadded (specified using
1520 '--id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]')
1521 along with a list of coaddTempExps to attempt to coadd (specified using
1522 '--selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]').
1523 Only the coaddTempExps that cover the specified tract and patch will be
1524 coadded. A list of the available optional arguments can be obtained by
1525 calling assembleCoadd.py with the --help command line argument:
1526
1527 .. code-block:: none
1528
1529 assembleCoadd.py --help
1530
1531 To demonstrate usage of the `SafeClipAssembleCoaddTask` in the larger
1532 context of multi-band processing, we will generate the HSC-I & -R band
1533 coadds from HSC engineering test data provided in the ci_hsc package.
1534 To begin, assuming that the lsst stack has been already set up, we must
1535 set up the obs_subaru and ci_hsc packages. This defines the environment
1536 variable $CI_HSC_DIR and points at the location of the package. The raw
1537 HSC data live in the ``$CI_HSC_DIR/raw`` directory. To begin assembling
1538 the coadds, we must first
1539
1540 - ``processCcd``
1541 process the individual ccds in $CI_HSC_RAW to produce calibrated exposures
1542 - ``makeSkyMap``
1543 create a skymap that covers the area of the sky present in the raw exposures
1544 - ``makeCoaddTempExp``
1545 warp the individual calibrated exposures to the tangent plane of the coadd</DD>
1546
1547 We can perform all of these steps by running
1548
1549 .. code-block:: none
1550
1551 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988
1552
1553 This will produce warped coaddTempExps for each visit. To coadd the
1554 warped data, we call ``assembleCoadd.py`` as follows:
1555
1556 .. code-block:: none
1557
1558 assembleCoadd.py $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \
1559 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \
1560 --selectId visit=903986 ccd=100--selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \
1561 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \
1562 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \
1563 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \
1564 --selectId visit=903988 ccd=24
1565
1566 This will process the HSC-I band data. The results are written in
1567 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``.
1568
1569 You may also choose to run:
1570
1571 .. code-block:: none
1572
1573 scons warp-903334 warp-903336 warp-903338 warp-903342 warp-903344 warp-903346 nnn
1574 assembleCoadd.py $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-R --selectId visit=903334 ccd=16 \
1575 --selectId visit=903334 ccd=22 --selectId visit=903334 ccd=23 --selectId visit=903334 ccd=100 \
1576 --selectId visit=903336 ccd=17 --selectId visit=903336 ccd=24 --selectId visit=903338 ccd=18 \
1577 --selectId visit=903338 ccd=25 --selectId visit=903342 ccd=4 --selectId visit=903342 ccd=10 \
1578 --selectId visit=903342 ccd=100 --selectId visit=903344 ccd=0 --selectId visit=903344 ccd=5 \
1579 --selectId visit=903344 ccd=11 --selectId visit=903346 ccd=1 --selectId visit=903346 ccd=6 \
1580 --selectId visit=903346 ccd=12
1581
1582 to generate the coadd for the HSC-R band if you are interested in following
1583 multiBand Coadd processing as discussed in ``pipeTasks_multiBand``.
1584 """
1585 ConfigClass = SafeClipAssembleCoaddConfig
1586 _DefaultName = "safeClipAssembleCoadd"
1587
1588 def __init__(self, *args, **kwargs):
1589 AssembleCoaddTask.__init__(self, *args, **kwargs)
1590 schema = afwTable.SourceTable.makeMinimalSchema()
1591 self.makeSubtask("clipDetection", schema=schema)
1592
1593 @utils.inheritDoc(AssembleCoaddTask)
1594 @timeMethod
1595 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, *args, **kwargs):
1596 """Assemble the coadd for a region.
1597
1598 Compute the difference of coadds created with and without outlier
1599 rejection to identify coadd pixels that have outlier values in some
1600 individual visits.
1601 Detect clipped regions on the difference image and mark these regions
1602 on the one or two individual coaddTempExps where they occur if there
1603 is significant overlap between the clipped region and a source. This
1604 leaves us with a set of footprints from the difference image that have
1605 been identified as having occured on just one or two individual visits.
1606 However, these footprints were generated from a difference image. It
1607 is conceivable for a large diffuse source to have become broken up
1608 into multiple footprints acrosss the coadd difference in this process.
1609 Determine the clipped region from all overlapping footprints from the
1610 detected sources in each visit - these are big footprints.
1611 Combine the small and big clipped footprints and mark them on a new
1612 bad mask plane.
1613 Generate the coadd using `AssembleCoaddTask.run` without outlier
1614 removal. Clipped footprints will no longer make it into the coadd
1615 because they are marked in the new bad mask plane.
1616
1617 Notes
1618 -----
1619 args and kwargs are passed but ignored in order to match the call
1620 signature expected by the parent task.
1621 """
1622 exp = self.buildDifferenceImagebuildDifferenceImage(skyInfo, tempExpRefList, imageScalerList, weightList)
1623 mask = exp.getMaskedImage().getMask()
1624 mask.addMaskPlane("CLIPPED")
1625
1626 result = self.detectClipdetectClip(exp, tempExpRefList)
1627
1628 self.log.info('Found %d clipped objects', len(result.clipFootprints))
1629
1630 maskClipValue = mask.getPlaneBitMask("CLIPPED")
1631 maskDetValue = mask.getPlaneBitMask("DETECTED") | mask.getPlaneBitMask("DETECTED_NEGATIVE")
1632 # Append big footprints from individual Warps to result.clipSpans
1633 bigFootprints = self.detectClipBigdetectClipBig(result.clipSpans, result.clipFootprints, result.clipIndices,
1634 result.detectionFootprints, maskClipValue, maskDetValue,
1635 exp.getBBox())
1636 # Create mask of the current clipped footprints
1637 maskClip = mask.Factory(mask.getBBox(afwImage.PARENT))
1638 afwDet.setMaskFromFootprintList(maskClip, result.clipFootprints, maskClipValue)
1639
1640 maskClipBig = maskClip.Factory(mask.getBBox(afwImage.PARENT))
1641 afwDet.setMaskFromFootprintList(maskClipBig, bigFootprints, maskClipValue)
1642 maskClip |= maskClipBig
1643
1644 # Assemble coadd from base class, but ignoring CLIPPED pixels
1645 badMaskPlanes = self.config.badMaskPlanes[:]
1646 badMaskPlanes.append("CLIPPED")
1647 badPixelMask = afwImage.Mask.getPlaneBitMask(badMaskPlanes)
1648 return AssembleCoaddTask.run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
1649 result.clipSpans, mask=badPixelMask)
1650
1651 def buildDifferenceImage(self, skyInfo, tempExpRefList, imageScalerList, weightList):
1652 """Return an exposure that contains the difference between unclipped
1653 and clipped coadds.
1654
1655 Generate a difference image between clipped and unclipped coadds.
1656 Compute the difference image by subtracting an outlier-clipped coadd
1657 from an outlier-unclipped coadd. Return the difference image.
1658
1659 Parameters
1660 ----------
1661 skyInfo : `lsst.pipe.base.Struct`
1662 Patch geometry information, from getSkyInfo
1663 tempExpRefList : `list`
1664 List of data reference to tempExp
1665 imageScalerList : `list`
1666 List of image scalers
1667 weightList : `list`
1668 List of weights
1669
1670 Returns
1671 -------
1673 Difference image of unclipped and clipped coadd wrapped in an Exposure
1674 """
1675 config = AssembleCoaddConfig()
1676 # getattr necessary because subtasks do not survive Config.toDict()
1677 # exclude connections because the class of self.config.connections is not
1678 # the same as AssembleCoaddConfig.connections, and the connections are not
1679 # needed to run this task anyway.
1680 configIntersection = {k: getattr(self.config, k)
1681 for k, v in self.config.toDict().items()
1682 if (k in config.keys() and k != "connections")}
1683 configIntersection['doInputMap'] = False
1684 configIntersection['doNImage'] = False
1685 config.update(**configIntersection)
1686
1687 # statistic MEAN copied from self.config.statistic, but for clarity explicitly assign
1688 config.statistic = 'MEAN'
1689 task = AssembleCoaddTask(config=config)
1690 coaddMean = task.run(skyInfo, tempExpRefList, imageScalerList, weightList).coaddExposure
1691
1692 config.statistic = 'MEANCLIP'
1693 task = AssembleCoaddTask(config=config)
1694 coaddClip = task.run(skyInfo, tempExpRefList, imageScalerList, weightList).coaddExposure
1695
1696 coaddDiff = coaddMean.getMaskedImage().Factory(coaddMean.getMaskedImage())
1697 coaddDiff -= coaddClip.getMaskedImage()
1698 exp = afwImage.ExposureF(coaddDiff)
1699 exp.setPsf(coaddMean.getPsf())
1700 return exp
1701
1702 def detectClip(self, exp, tempExpRefList):
1703 """Detect clipped regions on an exposure and set the mask on the
1704 individual tempExp masks.
1705
1706 Detect footprints in the difference image after smoothing the
1707 difference image with a Gaussian kernal. Identify footprints that
1708 overlap with one or two input ``coaddTempExps`` by comparing the
1709 computed overlap fraction to thresholds set in the config. A different
1710 threshold is applied depending on the number of overlapping visits
1711 (restricted to one or two). If the overlap exceeds the thresholds,
1712 the footprint is considered "CLIPPED" and is marked as such on the
1713 coaddTempExp. Return a struct with the clipped footprints, the indices
1714 of the ``coaddTempExps`` that end up overlapping with the clipped
1715 footprints, and a list of new masks for the ``coaddTempExps``.
1716
1717 Parameters
1718 ----------
1720 Exposure to run detection on.
1721 tempExpRefList : `list`
1722 List of data reference to tempExp.
1723
1724 Returns
1725 -------
1726 result : `lsst.pipe.base.Struct`
1727 Result struct with components:
1728
1729 - ``clipFootprints``: list of clipped footprints.
1730 - ``clipIndices``: indices for each ``clippedFootprint`` in
1731 ``tempExpRefList``.
1732 - ``clipSpans``: List of dictionaries containing spanSet lists
1733 to clip. Each element contains the new maskplane name
1734 ("CLIPPED") as the key and list of ``SpanSets`` as the value.
1735 - ``detectionFootprints``: List of DETECTED/DETECTED_NEGATIVE plane
1736 compressed into footprints.
1737 """
1738 mask = exp.getMaskedImage().getMask()
1739 maskDetValue = mask.getPlaneBitMask("DETECTED") | mask.getPlaneBitMask("DETECTED_NEGATIVE")
1740 fpSet = self.clipDetection.detectFootprints(exp, doSmooth=True, clearMask=True)
1741 # Merge positive and negative together footprints together
1742 fpSet.positive.merge(fpSet.negative)
1743 footprints = fpSet.positive
1744 self.log.info('Found %d potential clipped objects', len(footprints.getFootprints()))
1745 ignoreMask = self.getBadPixelMask()
1746
1747 clipFootprints = []
1748 clipIndices = []
1749 artifactSpanSets = [{'CLIPPED': list()} for _ in tempExpRefList]
1750
1751 # for use by detectClipBig
1752 visitDetectionFootprints = []
1753
1754 dims = [len(tempExpRefList), len(footprints.getFootprints())]
1755 overlapDetArr = numpy.zeros(dims, dtype=numpy.uint16)
1756 ignoreArr = numpy.zeros(dims, dtype=numpy.uint16)
1757
1758 # Loop over masks once and extract/store only relevant overlap metrics and detection footprints
1759 for i, warpRef in enumerate(tempExpRefList):
1760 tmpExpMask = warpRef.get(datasetType=self.getTempExpDatasetName(self.warpType),
1761 immediate=True).getMaskedImage().getMask()
1762 maskVisitDet = tmpExpMask.Factory(tmpExpMask, tmpExpMask.getBBox(afwImage.PARENT),
1763 afwImage.PARENT, True)
1764 maskVisitDet &= maskDetValue
1765 visitFootprints = afwDet.FootprintSet(maskVisitDet, afwDet.Threshold(1))
1766 visitDetectionFootprints.append(visitFootprints)
1767
1768 for j, footprint in enumerate(footprints.getFootprints()):
1769 ignoreArr[i, j] = countMaskFromFootprint(tmpExpMask, footprint, ignoreMask, 0x0)
1770 overlapDetArr[i, j] = countMaskFromFootprint(tmpExpMask, footprint, maskDetValue, ignoreMask)
1771
1772 # build a list of clipped spans for each visit
1773 for j, footprint in enumerate(footprints.getFootprints()):
1774 nPixel = footprint.getArea()
1775 overlap = [] # hold the overlap with each visit
1776 indexList = [] # index of visit in global list
1777 for i in range(len(tempExpRefList)):
1778 ignore = ignoreArr[i, j]
1779 overlapDet = overlapDetArr[i, j]
1780 totPixel = nPixel - ignore
1781
1782 # If we have more bad pixels than detection skip
1783 if ignore > overlapDet or totPixel <= 0.5*nPixel or overlapDet == 0:
1784 continue
1785 overlap.append(overlapDet/float(totPixel))
1786 indexList.append(i)
1787
1788 overlap = numpy.array(overlap)
1789 if not len(overlap):
1790 continue
1791
1792 keep = False # Should this footprint be marked as clipped?
1793 keepIndex = [] # Which tempExps does the clipped footprint belong to
1794
1795 # If footprint only has one overlap use a lower threshold
1796 if len(overlap) == 1:
1797 if overlap[0] > self.config.minClipFootOverlapSingle:
1798 keep = True
1799 keepIndex = [0]
1800 else:
1801 # This is the general case where only visit should be clipped
1802 clipIndex = numpy.where(overlap > self.config.minClipFootOverlap)[0]
1803 if len(clipIndex) == 1:
1804 keep = True
1805 keepIndex = [clipIndex[0]]
1806
1807 # Test if there are clipped objects that overlap two different visits
1808 clipIndex = numpy.where(overlap > self.config.minClipFootOverlapDouble)[0]
1809 if len(clipIndex) == 2 and len(overlap) > 3:
1810 clipIndexComp = numpy.where(overlap <= self.config.minClipFootOverlapDouble)[0]
1811 if numpy.max(overlap[clipIndexComp]) <= self.config.maxClipFootOverlapDouble:
1812 keep = True
1813 keepIndex = clipIndex
1814
1815 if not keep:
1816 continue
1817
1818 for index in keepIndex:
1819 globalIndex = indexList[index]
1820 artifactSpanSets[globalIndex]['CLIPPED'].append(footprint.spans)
1821
1822 clipIndices.append(numpy.array(indexList)[keepIndex])
1823 clipFootprints.append(footprint)
1824
1825 return pipeBase.Struct(clipFootprints=clipFootprints, clipIndices=clipIndices,
1826 clipSpans=artifactSpanSets, detectionFootprints=visitDetectionFootprints)
1827
1828 def detectClipBig(self, clipList, clipFootprints, clipIndices, detectionFootprints,
1829 maskClipValue, maskDetValue, coaddBBox):
1830 """Return individual warp footprints for large artifacts and append
1831 them to ``clipList`` in place.
1832
1833 Identify big footprints composed of many sources in the coadd
1834 difference that may have originated in a large diffuse source in the
1835 coadd. We do this by indentifying all clipped footprints that overlap
1836 significantly with each source in all the coaddTempExps.
1837
1838 Parameters
1839 ----------
1840 clipList : `list`
1841 List of alt mask SpanSets with clipping information. Modified.
1842 clipFootprints : `list`
1843 List of clipped footprints.
1844 clipIndices : `list`
1845 List of which entries in tempExpClipList each footprint belongs to.
1846 maskClipValue
1847 Mask value of clipped pixels.
1848 maskDetValue
1849 Mask value of detected pixels.
1850 coaddBBox : `lsst.geom.Box`
1851 BBox of the coadd and warps.
1852
1853 Returns
1854 -------
1855 bigFootprintsCoadd : `list`
1856 List of big footprints
1857 """
1858 bigFootprintsCoadd = []
1859 ignoreMask = self.getBadPixelMask()
1860 for index, (clippedSpans, visitFootprints) in enumerate(zip(clipList, detectionFootprints)):
1861 maskVisitDet = afwImage.MaskX(coaddBBox, 0x0)
1862 for footprint in visitFootprints.getFootprints():
1863 footprint.spans.setMask(maskVisitDet, maskDetValue)
1864
1865 # build a mask of clipped footprints that are in this visit
1866 clippedFootprintsVisit = []
1867 for foot, clipIndex in zip(clipFootprints, clipIndices):
1868 if index not in clipIndex:
1869 continue
1870 clippedFootprintsVisit.append(foot)
1871 maskVisitClip = maskVisitDet.Factory(maskVisitDet.getBBox(afwImage.PARENT))
1872 afwDet.setMaskFromFootprintList(maskVisitClip, clippedFootprintsVisit, maskClipValue)
1873
1874 bigFootprintsVisit = []
1875 for foot in visitFootprints.getFootprints():
1876 if foot.getArea() < self.config.minBigOverlap:
1877 continue
1878 nCount = countMaskFromFootprint(maskVisitClip, foot, maskClipValue, ignoreMask)
1879 if nCount > self.config.minBigOverlap:
1880 bigFootprintsVisit.append(foot)
1881 bigFootprintsCoadd.append(foot)
1882
1883 for footprint in bigFootprintsVisit:
1884 clippedSpans["CLIPPED"].append(footprint.spans)
1885
1886 return bigFootprintsCoadd
1887
1888
1890 psfMatchedWarps = pipeBase.connectionTypes.Input(
1891 doc=("PSF-Matched Warps are required by CompareWarp regardless of the coadd type requested. "
1892 "Only PSF-Matched Warps make sense for image subtraction. "
1893 "Therefore, they must be an additional declared input."),
1894 name="{inputCoaddName}Coadd_psfMatchedWarp",
1895 storageClass="ExposureF",
1896 dimensions=("tract", "patch", "skymap", "visit"),
1897 deferLoad=True,
1898 multiple=True
1899 )
1900 templateCoadd = pipeBase.connectionTypes.Output(
1901 doc=("Model of the static sky, used to find temporal artifacts. Typically a PSF-Matched, "
1902 "sigma-clipped coadd. Written if and only if assembleStaticSkyModel.doWrite=True"),
1903 name="{outputCoaddName}CoaddPsfMatched",
1904 storageClass="ExposureF",
1905 dimensions=("tract", "patch", "skymap", "band"),
1906 )
1907
1908 def __init__(self, *, config=None):
1909 super().__init__(config=config)
1910 if not config.assembleStaticSkyModel.doWrite:
1911 self.outputs.remove("templateCoadd")
1912 config.validate()
1913
1914
1915class CompareWarpAssembleCoaddConfig(AssembleCoaddConfig,
1916 pipelineConnections=CompareWarpAssembleCoaddConnections):
1917 assembleStaticSkyModel = pexConfig.ConfigurableField(
1918 target=AssembleCoaddTask,
1919 doc="Task to assemble an artifact-free, PSF-matched Coadd to serve as a"
1920 " naive/first-iteration model of the static sky.",
1921 )
1922 detect = pexConfig.ConfigurableField(
1923 target=SourceDetectionTask,
1924 doc="Detect outlier sources on difference between each psfMatched warp and static sky model"
1925 )
1926 detectTemplate = pexConfig.ConfigurableField(
1927 target=SourceDetectionTask,
1928 doc="Detect sources on static sky model. Only used if doPreserveContainedBySource is True"
1929 )
1930 maskStreaks = pexConfig.ConfigurableField(
1931 target=MaskStreaksTask,
1932 doc="Detect streaks on difference between each psfMatched warp and static sky model. Only used if "
1933 "doFilterMorphological is True. Adds a mask plane to an exposure, with the mask plane name set by"
1934 "streakMaskName"
1935 )
1936 streakMaskName = pexConfig.Field(
1937 dtype=str,
1938 default="STREAK",
1939 doc="Name of mask bit used for streaks"
1940 )
1941 maxNumEpochs = pexConfig.Field(
1942 doc="Charactistic maximum local number of epochs/visits in which an artifact candidate can appear "
1943 "and still be masked. The effective maxNumEpochs is a broken linear function of local "
1944 "number of epochs (N): min(maxFractionEpochsLow*N, maxNumEpochs + maxFractionEpochsHigh*N). "
1945 "For each footprint detected on the image difference between the psfMatched warp and static sky "
1946 "model, if a significant fraction of pixels (defined by spatialThreshold) are residuals in more "
1947 "than the computed effective maxNumEpochs, the artifact candidate is deemed persistant rather "
1948 "than transient and not masked.",
1949 dtype=int,
1950 default=2
1951 )
1952 maxFractionEpochsLow = pexConfig.RangeField(
1953 doc="Fraction of local number of epochs (N) to use as effective maxNumEpochs for low N. "
1954 "Effective maxNumEpochs = "
1955 "min(maxFractionEpochsLow * N, maxNumEpochs + maxFractionEpochsHigh * N)",
1956 dtype=float,
1957 default=0.4,
1958 min=0., max=1.,
1959 )
1960 maxFractionEpochsHigh = pexConfig.RangeField(
1961 doc="Fraction of local number of epochs (N) to use as effective maxNumEpochs for high N. "
1962 "Effective maxNumEpochs = "
1963 "min(maxFractionEpochsLow * N, maxNumEpochs + maxFractionEpochsHigh * N)",
1964 dtype=float,
1965 default=0.03,
1966 min=0., max=1.,
1967 )
1968 spatialThreshold = pexConfig.RangeField(
1969 doc="Unitless fraction of pixels defining how much of the outlier region has to meet the "
1970 "temporal criteria. If 0, clip all. If 1, clip none.",
1971 dtype=float,
1972 default=0.5,
1973 min=0., max=1.,
1974 inclusiveMin=True, inclusiveMax=True
1975 )
1976 doScaleWarpVariance = pexConfig.Field(
1977 doc="Rescale Warp variance plane using empirical noise?",
1978 dtype=bool,
1979 default=True,
1980 )
1981 scaleWarpVariance = pexConfig.ConfigurableField(
1982 target=ScaleVarianceTask,
1983 doc="Rescale variance on warps",
1984 )
1985 doPreserveContainedBySource = pexConfig.Field(
1986 doc="Rescue artifacts from clipping that completely lie within a footprint detected"
1987 "on the PsfMatched Template Coadd. Replicates a behavior of SafeClip.",
1988 dtype=bool,
1989 default=True,
1990 )
1991 doPrefilterArtifacts = pexConfig.Field(
1992 doc="Ignore artifact candidates that are mostly covered by the bad pixel mask, "
1993 "because they will be excluded anyway. This prevents them from contributing "
1994 "to the outlier epoch count image and potentially being labeled as persistant."
1995 "'Mostly' is defined by the config 'prefilterArtifactsRatio'.",
1996 dtype=bool,
1997 default=True
1998 )
1999 prefilterArtifactsMaskPlanes = pexConfig.ListField(
2000 doc="Prefilter artifact candidates that are mostly covered by these bad mask planes.",
2001 dtype=str,
2002 default=('NO_DATA', 'BAD', 'SAT', 'SUSPECT'),
2003 )
2004 prefilterArtifactsRatio = pexConfig.Field(
2005 doc="Prefilter artifact candidates with less than this fraction overlapping good pixels",
2006 dtype=float,
2007 default=0.05
2008 )
2009 doFilterMorphological = pexConfig.Field(
2010 doc="Filter artifact candidates based on morphological criteria, i.g. those that appear to "
2011 "be streaks.",
2012 dtype=bool,
2013 default=False
2014 )
2015
2016 def setDefaults(self):
2017 AssembleCoaddConfig.setDefaults(self)
2018 self.statisticstatistic = 'MEAN'
2019 self.doUsePsfMatchedPolygonsdoUsePsfMatchedPolygons = True
2020
2021 # Real EDGE removed by psfMatched NO_DATA border half the width of the matching kernel
2022 # CompareWarp applies psfMatched EDGE pixels to directWarps before assembling
2023 if "EDGE" in self.badMaskPlanes:
2024 self.badMaskPlanes.remove('EDGE')
2025 self.removeMaskPlanes.append('EDGE')
2026 self.assembleStaticSkyModelassembleStaticSkyModel.badMaskPlanes = ["NO_DATA", ]
2027 self.assembleStaticSkyModelassembleStaticSkyModel.warpType = 'psfMatched'
2028 self.assembleStaticSkyModelassembleStaticSkyModel.connections.warpType = 'psfMatched'
2029 self.assembleStaticSkyModelassembleStaticSkyModel.statistic = 'MEANCLIP'
2030 self.assembleStaticSkyModelassembleStaticSkyModel.sigmaClip = 2.5
2031 self.assembleStaticSkyModelassembleStaticSkyModel.clipIter = 3
2032 self.assembleStaticSkyModelassembleStaticSkyModel.calcErrorFromInputVariance = False
2033 self.assembleStaticSkyModelassembleStaticSkyModel.doWrite = False
2034 self.detectdetect.doTempLocalBackground = False
2035 self.detectdetect.reEstimateBackground = False
2036 self.detectdetect.returnOriginalFootprints = False
2037 self.detectdetect.thresholdPolarity = "both"
2038 self.detectdetect.thresholdValue = 5
2039 self.detectdetect.minPixels = 4
2040 self.detectdetect.isotropicGrow = True
2041 self.detectdetect.thresholdType = "pixel_stdev"
2042 self.detectdetect.nSigmaToGrow = 0.4
2043 # The default nSigmaToGrow for SourceDetectionTask is already 2.4,
2044 # Explicitly restating because ratio with detect.nSigmaToGrow matters
2045 self.detectTemplatedetectTemplate.nSigmaToGrow = 2.4
2046 self.detectTemplatedetectTemplate.doTempLocalBackground = False
2047 self.detectTemplatedetectTemplate.reEstimateBackground = False
2048 self.detectTemplatedetectTemplate.returnOriginalFootprints = False
2049
2050 def validate(self):
2051 super().validate()
2052 if self.assembleStaticSkyModelassembleStaticSkyModel.doNImage:
2053 raise ValueError("No dataset type exists for a PSF-Matched Template N Image."
2054 "Please set assembleStaticSkyModel.doNImage=False")
2055
2056 if self.assembleStaticSkyModelassembleStaticSkyModel.doWrite and (self.warpTypewarpType == self.assembleStaticSkyModelassembleStaticSkyModel.warpType):
2057 raise ValueError("warpType (%s) == assembleStaticSkyModel.warpType (%s) and will compete for "
2058 "the same dataset name. Please set assembleStaticSkyModel.doWrite to False "
2059 "or warpType to 'direct'. assembleStaticSkyModel.warpType should ways be "
2060 "'PsfMatched'" % (self.warpTypewarpType, self.assembleStaticSkyModelassembleStaticSkyModel.warpType))
2061
2062
2063class CompareWarpAssembleCoaddTask(AssembleCoaddTask):
2064 """Assemble a compareWarp coadded image from a set of warps
2065 by masking artifacts detected by comparing PSF-matched warps.
2066
2067 In ``AssembleCoaddTask``, we compute the coadd as an clipped mean (i.e.,
2068 we clip outliers). The problem with doing this is that when computing the
2069 coadd PSF at a given location, individual visit PSFs from visits with
2070 outlier pixels contribute to the coadd PSF and cannot be treated correctly.
2071 In this task, we correct for this behavior by creating a new badMaskPlane
2072 'CLIPPED' which marks pixels in the individual warps suspected to contain
2073 an artifact. We populate this plane on the input warps by comparing
2074 PSF-matched warps with a PSF-matched median coadd which serves as a
2075 model of the static sky. Any group of pixels that deviates from the
2076 PSF-matched template coadd by more than config.detect.threshold sigma,
2077 is an artifact candidate. The candidates are then filtered to remove
2078 variable sources and sources that are difficult to subtract such as
2079 bright stars. This filter is configured using the config parameters
2080 ``temporalThreshold`` and ``spatialThreshold``. The temporalThreshold is
2081 the maximum fraction of epochs that the deviation can appear in and still
2082 be considered an artifact. The spatialThreshold is the maximum fraction of
2083 pixels in the footprint of the deviation that appear in other epochs
2084 (where other epochs is defined by the temporalThreshold). If the deviant
2085 region meets this criteria of having a significant percentage of pixels
2086 that deviate in only a few epochs, these pixels have the 'CLIPPED' bit
2087 set in the mask. These regions will not contribute to the final coadd.
2088 Furthermore, any routine to determine the coadd PSF can now be cognizant
2089 of clipped regions. Note that the algorithm implemented by this task is
2090 preliminary and works correctly for HSC data. Parameter modifications and
2091 or considerable redesigning of the algorithm is likley required for other
2092 surveys.
2093
2094 ``CompareWarpAssembleCoaddTask`` sub-classes
2095 ``AssembleCoaddTask`` and instantiates ``AssembleCoaddTask``
2096 as a subtask to generate the TemplateCoadd (the model of the static sky).
2097
2098 Notes
2099 -----
2100 The `lsst.pipe.base.cmdLineTask.CmdLineTask` interface supports a
2101 flag ``-d`` to import ``debug.py`` from your ``PYTHONPATH``; see
2102 ``baseDebug`` for more about ``debug.py`` files.
2103
2104 This task supports the following debug variables:
2105
2106 - ``saveCountIm``
2107 If True then save the Epoch Count Image as a fits file in the `figPath`
2108 - ``figPath``
2109 Path to save the debug fits images and figures
2110
2111 For example, put something like:
2112
2113 .. code-block:: python
2114
2115 import lsstDebug
2116 def DebugInfo(name):
2117 di = lsstDebug.getInfo(name)
2118 if name == "lsst.pipe.tasks.assembleCoadd":
2119 di.saveCountIm = True
2120 di.figPath = "/desired/path/to/debugging/output/images"
2121 return di
2122 lsstDebug.Info = DebugInfo
2123
2124 into your ``debug.py`` file and run ``assemebleCoadd.py`` with the
2125 ``--debug`` flag. Some subtasks may have their own debug variables;
2126 see individual Task documentation.
2127
2128 Examples
2129 --------
2130 ``CompareWarpAssembleCoaddTask`` assembles a set of warped images into a
2131 coadded image. The ``CompareWarpAssembleCoaddTask`` is invoked by running
2132 ``assembleCoadd.py`` with the flag ``--compareWarpCoadd``.
2133 Usage of ``assembleCoadd.py`` expects a data reference to the tract patch
2134 and filter to be coadded (specified using
2135 '--id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]')
2136 along with a list of coaddTempExps to attempt to coadd (specified using
2137 '--selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]').
2138 Only the warps that cover the specified tract and patch will be coadded.
2139 A list of the available optional arguments can be obtained by calling
2140 ``assembleCoadd.py`` with the ``--help`` command line argument:
2141
2142 .. code-block:: none
2143
2144 assembleCoadd.py --help
2145
2146 To demonstrate usage of the ``CompareWarpAssembleCoaddTask`` in the larger
2147 context of multi-band processing, we will generate the HSC-I & -R band
2148 oadds from HSC engineering test data provided in the ``ci_hsc`` package.
2149 To begin, assuming that the lsst stack has been already set up, we must
2150 set up the ``obs_subaru`` and ``ci_hsc`` packages.
2151 This defines the environment variable ``$CI_HSC_DIR`` and points at the
2152 location of the package. The raw HSC data live in the ``$CI_HSC_DIR/raw``
2153 directory. To begin assembling the coadds, we must first
2154
2155 - processCcd
2156 process the individual ccds in $CI_HSC_RAW to produce calibrated exposures
2157 - makeSkyMap
2158 create a skymap that covers the area of the sky present in the raw exposures
2159 - makeCoaddTempExp
2160 warp the individual calibrated exposures to the tangent plane of the coadd
2161
2162 We can perform all of these steps by running
2163
2164 .. code-block:: none
2165
2166 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988
2167
2168 This will produce warped ``coaddTempExps`` for each visit. To coadd the
2169 warped data, we call ``assembleCoadd.py`` as follows:
2170
2171 .. code-block:: none
2172
2173 assembleCoadd.py --compareWarpCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \
2174 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \
2175 --selectId visit=903986 ccd=100 --selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \
2176 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \
2177 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \
2178 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \
2179 --selectId visit=903988 ccd=24
2180
2181 This will process the HSC-I band data. The results are written in
2182 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``.
2183 """
2184 ConfigClass = CompareWarpAssembleCoaddConfig
2185 _DefaultName = "compareWarpAssembleCoadd"
2186
2187 def __init__(self, *args, **kwargs):
2188 AssembleCoaddTask.__init__(self, *args, **kwargs)
2189 self.makeSubtask("assembleStaticSkyModel")
2190 detectionSchema = afwTable.SourceTable.makeMinimalSchema()
2191 self.makeSubtask("detect", schema=detectionSchema)
2192 if self.config.doPreserveContainedBySource:
2193 self.makeSubtask("detectTemplate", schema=afwTable.SourceTable.makeMinimalSchema())
2194 if self.config.doScaleWarpVariance:
2195 self.makeSubtask("scaleWarpVariance")
2196 if self.config.doFilterMorphological:
2197 self.makeSubtask("maskStreaks")
2198
2199 @utils.inheritDoc(AssembleCoaddTask)
2200 def makeSupplementaryDataGen3(self, butlerQC, inputRefs, outputRefs):
2201 """
2202 Generate a templateCoadd to use as a naive model of static sky to
2203 subtract from PSF-Matched warps.
2204
2205 Returns
2206 -------
2207 result : `lsst.pipe.base.Struct`
2208 Result struct with components:
2209
2210 - ``templateCoadd`` : coadded exposure (``lsst.afw.image.Exposure``)
2211 - ``nImage`` : N Image (``lsst.afw.image.Image``)
2212 """
2213 # Ensure that psfMatchedWarps are used as input warps for template generation
2214 staticSkyModelInputRefs = copy.deepcopy(inputRefs)
2215 staticSkyModelInputRefs.inputWarps = inputRefs.psfMatchedWarps
2216
2217 # Because subtasks don't have connections we have to make one.
2218 # The main task's `templateCoadd` is the subtask's `coaddExposure`
2219 staticSkyModelOutputRefs = copy.deepcopy(outputRefs)
2220 if self.config.assembleStaticSkyModel.doWrite:
2221 staticSkyModelOutputRefs.coaddExposure = staticSkyModelOutputRefs.templateCoadd
2222 # Remove template coadd from both subtask's and main tasks outputs,
2223 # because it is handled by the subtask as `coaddExposure`
2224 del outputRefs.templateCoadd
2225 del staticSkyModelOutputRefs.templateCoadd
2226
2227 # A PSF-Matched nImage does not exist as a dataset type
2228 if 'nImage' in staticSkyModelOutputRefs.keys():
2229 del staticSkyModelOutputRefs.nImage
2230
2231 templateCoadd = self.assembleStaticSkyModel.runQuantum(butlerQC, staticSkyModelInputRefs,
2232 staticSkyModelOutputRefs)
2233 if templateCoadd is None:
2234 raise RuntimeError(self._noTemplateMessage_noTemplateMessage(self.assembleStaticSkyModel.warpType))
2235
2236 return pipeBase.Struct(templateCoadd=templateCoadd.coaddExposure,
2237 nImage=templateCoadd.nImage,
2238 warpRefList=templateCoadd.warpRefList,
2239 imageScalerList=templateCoadd.imageScalerList,
2240 weightList=templateCoadd.weightList)
2241
2242 @utils.inheritDoc(AssembleCoaddTask)
2243 def makeSupplementaryData(self, dataRef, selectDataList=None, warpRefList=None):
2244 """
2245 Generate a templateCoadd to use as a naive model of static sky to
2246 subtract from PSF-Matched warps.
2247
2248 Returns
2249 -------
2250 result : `lsst.pipe.base.Struct`
2251 Result struct with components:
2252
2253 - ``templateCoadd``: coadded exposure (``lsst.afw.image.Exposure``)
2254 - ``nImage``: N Image (``lsst.afw.image.Image``)
2255 """
2256 templateCoadd = self.assembleStaticSkyModel.runDataRef(dataRef, selectDataList, warpRefList)
2257 if templateCoadd is None:
2258 raise RuntimeError(self._noTemplateMessage_noTemplateMessage(self.assembleStaticSkyModel.warpType))
2259
2260 return pipeBase.Struct(templateCoadd=templateCoadd.coaddExposure,
2261 nImage=templateCoadd.nImage,
2262 warpRefList=templateCoadd.warpRefList,
2263 imageScalerList=templateCoadd.imageScalerList,
2264 weightList=templateCoadd.weightList)
2265
2266 def _noTemplateMessage(self, warpType):
2267 warpName = (warpType[0].upper() + warpType[1:])
2268 message = """No %(warpName)s warps were found to build the template coadd which is
2269 required to run CompareWarpAssembleCoaddTask. To continue assembling this type of coadd,
2270 first either rerun makeCoaddTempExp with config.make%(warpName)s=True or
2271 coaddDriver with config.makeCoadTempExp.make%(warpName)s=True, before assembleCoadd.
2272
2273 Alternatively, to use another algorithm with existing warps, retarget the CoaddDriverConfig to
2274 another algorithm like:
2275
2276 from lsst.pipe.tasks.assembleCoadd import SafeClipAssembleCoaddTask
2277 config.assemble.retarget(SafeClipAssembleCoaddTask)
2278 """ % {"warpName": warpName}
2279 return message
2280
2281 @utils.inheritDoc(AssembleCoaddTask)
2282 @timeMethod
2283 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
2284 supplementaryData, *args, **kwargs):
2285 """Assemble the coadd.
2286
2287 Find artifacts and apply them to the warps' masks creating a list of
2288 alternative masks with a new "CLIPPED" plane and updated "NO_DATA"
2289 plane. Then pass these alternative masks to the base class's `run`
2290 method.
2291
2292 The input parameters ``supplementaryData`` is a `lsst.pipe.base.Struct`
2293 that must contain a ``templateCoadd`` that serves as the
2294 model of the static sky.
2295 """
2296
2297 # Check and match the order of the supplementaryData
2298 # (PSF-matched) inputs to the order of the direct inputs,
2299 # so that the artifact mask is applied to the right warp
2300 dataIds = [ref.dataId for ref in tempExpRefList]
2301 psfMatchedDataIds = [ref.dataId for ref in supplementaryData.warpRefList]
2302
2303 if dataIds != psfMatchedDataIds:
2304 self.log.info("Reordering and or/padding PSF-matched visit input list")
2305 supplementaryData.warpRefList = reorderAndPadList(supplementaryData.warpRefList,
2306 psfMatchedDataIds, dataIds)
2307 supplementaryData.imageScalerList = reorderAndPadList(supplementaryData.imageScalerList,
2308 psfMatchedDataIds, dataIds)
2309
2310 # Use PSF-Matched Warps (and corresponding scalers) and coadd to find artifacts
2311 spanSetMaskList = self.findArtifactsfindArtifacts(supplementaryData.templateCoadd,
2312 supplementaryData.warpRefList,
2313 supplementaryData.imageScalerList)
2314
2315 badMaskPlanes = self.config.badMaskPlanes[:]
2316 badMaskPlanes.append("CLIPPED")
2317 badPixelMask = afwImage.Mask.getPlaneBitMask(badMaskPlanes)
2318
2319 result = AssembleCoaddTask.run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
2320 spanSetMaskList, mask=badPixelMask)
2321
2322 # Propagate PSF-matched EDGE pixels to coadd SENSOR_EDGE and INEXACT_PSF
2323 # Psf-Matching moves the real edge inwards
2324 self.applyAltEdgeMaskapplyAltEdgeMask(result.coaddExposure.maskedImage.mask, spanSetMaskList)
2325 return result
2326
2327 def applyAltEdgeMask(self, mask, altMaskList):
2328 """Propagate alt EDGE mask to SENSOR_EDGE AND INEXACT_PSF planes.
2329
2330 Parameters
2331 ----------
2332 mask : `lsst.afw.image.Mask`
2333 Original mask.
2334 altMaskList : `list`
2335 List of Dicts containing ``spanSet`` lists.
2336 Each element contains the new mask plane name (e.g. "CLIPPED
2337 and/or "NO_DATA") as the key, and list of ``SpanSets`` to apply to
2338 the mask.
2339 """
2340 maskValue = mask.getPlaneBitMask(["SENSOR_EDGE", "INEXACT_PSF"])
2341 for visitMask in altMaskList:
2342 if "EDGE" in visitMask:
2343 for spanSet in visitMask['EDGE']:
2344 spanSet.clippedTo(mask.getBBox()).setMask(mask, maskValue)
2345
2346 def findArtifacts(self, templateCoadd, tempExpRefList, imageScalerList):
2347 """Find artifacts.
2348
2349 Loop through warps twice. The first loop builds a map with the count
2350 of how many epochs each pixel deviates from the templateCoadd by more
2351 than ``config.chiThreshold`` sigma. The second loop takes each
2352 difference image and filters the artifacts detected in each using
2353 count map to filter out variable sources and sources that are
2354 difficult to subtract cleanly.
2355
2356 Parameters
2357 ----------
2358 templateCoadd : `lsst.afw.image.Exposure`
2359 Exposure to serve as model of static sky.
2360 tempExpRefList : `list`
2361 List of data references to warps.
2362 imageScalerList : `list`
2363 List of image scalers.
2364
2365 Returns
2366 -------
2367 altMasks : `list`
2368 List of dicts containing information about CLIPPED
2369 (i.e., artifacts), NO_DATA, and EDGE pixels.
2370 """
2371
2372 self.log.debug("Generating Count Image, and mask lists.")
2373 coaddBBox = templateCoadd.getBBox()
2374 slateIm = afwImage.ImageU(coaddBBox)
2375 epochCountImage = afwImage.ImageU(coaddBBox)
2376 nImage = afwImage.ImageU(coaddBBox)
2377 spanSetArtifactList = []
2378 spanSetNoDataMaskList = []
2379 spanSetEdgeList = []
2380 spanSetBadMorphoList = []
2381 badPixelMask = self.getBadPixelMask()
2382
2383 # mask of the warp diffs should = that of only the warp
2384 templateCoadd.mask.clearAllMaskPlanes()
2385
2386 if self.config.doPreserveContainedBySource:
2387 templateFootprints = self.detectTemplate.detectFootprints(templateCoadd)
2388 else:
2389 templateFootprints = None
2390
2391 for warpRef, imageScaler in zip(tempExpRefList, imageScalerList):
2392 warpDiffExp = self._readAndComputeWarpDiff_readAndComputeWarpDiff(warpRef, imageScaler, templateCoadd)
2393 if warpDiffExp is not None:
2394 # This nImage only approximates the final nImage because it uses the PSF-matched mask
2395 nImage.array += (numpy.isfinite(warpDiffExp.image.array)
2396 * ((warpDiffExp.mask.array & badPixelMask) == 0)).astype(numpy.uint16)
2397 fpSet = self.detect.detectFootprints(warpDiffExp, doSmooth=False, clearMask=True)
2398 fpSet.positive.merge(fpSet.negative)
2399 footprints = fpSet.positive
2400 slateIm.set(0)
2401 spanSetList = [footprint.spans for footprint in footprints.getFootprints()]
2402
2403 # Remove artifacts due to defects before they contribute to the epochCountImage
2404 if self.config.doPrefilterArtifacts:
2405 spanSetList = self.prefilterArtifactsprefilterArtifacts(spanSetList, warpDiffExp)
2406
2407 # Clear mask before adding prefiltered spanSets
2408 self.detect.clearMask(warpDiffExp.mask)
2409 for spans in spanSetList:
2410 spans.setImage(slateIm, 1, doClip=True)
2411 spans.setMask(warpDiffExp.mask, warpDiffExp.mask.getPlaneBitMask("DETECTED"))
2412 epochCountImage += slateIm
2413
2414 if self.config.doFilterMorphological:
2415 maskName = self.config.streakMaskName
2416 _ = self.maskStreaks.run(warpDiffExp)
2417 streakMask = warpDiffExp.mask
2418 spanSetStreak = afwGeom.SpanSet.fromMask(streakMask,
2419 streakMask.getPlaneBitMask(maskName)).split()
2420
2421 # PSF-Matched warps have less available area (~the matching kernel) because the calexps
2422 # undergo a second convolution. Pixels with data in the direct warp
2423 # but not in the PSF-matched warp will not have their artifacts detected.
2424 # NaNs from the PSF-matched warp therefore must be masked in the direct warp
2425 nans = numpy.where(numpy.isnan(warpDiffExp.maskedImage.image.array), 1, 0)
2426 nansMask = afwImage.makeMaskFromArray(nans.astype(afwImage.MaskPixel))
2427 nansMask.setXY0(warpDiffExp.getXY0())
2428 edgeMask = warpDiffExp.mask
2429 spanSetEdgeMask = afwGeom.SpanSet.fromMask(edgeMask,
2430 edgeMask.getPlaneBitMask("EDGE")).split()
2431 else:
2432 # If the directWarp has <1% coverage, the psfMatchedWarp can have 0% and not exist
2433 # In this case, mask the whole epoch
2434 nansMask = afwImage.MaskX(coaddBBox, 1)
2435 spanSetList = []
2436 spanSetEdgeMask = []
2437 spanSetStreak = []
2438
2439 spanSetNoDataMask = afwGeom.SpanSet.fromMask(nansMask).split()
2440
2441 spanSetNoDataMaskList.append(spanSetNoDataMask)
2442 spanSetArtifactList.append(spanSetList)
2443 spanSetEdgeList.append(spanSetEdgeMask)
2444 if self.config.doFilterMorphological:
2445 spanSetBadMorphoList.append(spanSetStreak)
2446
2447 if lsstDebug.Info(__name__).saveCountIm:
2448 path = self._dataRef2DebugPath_dataRef2DebugPath("epochCountIm", tempExpRefList[0], coaddLevel=True)
2449 epochCountImage.writeFits(path)
2450
2451 for i, spanSetList in enumerate(spanSetArtifactList):
2452 if spanSetList:
2453 filteredSpanSetList = self.filterArtifactsfilterArtifacts(spanSetList, epochCountImage, nImage,
2454 templateFootprints)
2455 spanSetArtifactList[i] = filteredSpanSetList
2456 if self.config.doFilterMorphological:
2457 spanSetArtifactList[i] += spanSetBadMorphoList[i]
2458
2459 altMasks = []
2460 for artifacts, noData, edge in zip(spanSetArtifactList, spanSetNoDataMaskList, spanSetEdgeList):
2461 altMasks.append({'CLIPPED': artifacts,
2462 'NO_DATA': noData,
2463 'EDGE': edge})
2464 return altMasks
2465
2466 def prefilterArtifacts(self, spanSetList, exp):
2467 """Remove artifact candidates covered by bad mask plane.
2468
2469 Any future editing of the candidate list that does not depend on
2470 temporal information should go in this method.
2471
2472 Parameters
2473 ----------
2474 spanSetList : `list`
2475 List of SpanSets representing artifact candidates.
2477 Exposure containing mask planes used to prefilter.
2478
2479 Returns
2480 -------
2481 returnSpanSetList : `list`
2482 List of SpanSets with artifacts.
2483 """
2484 badPixelMask = exp.mask.getPlaneBitMask(self.config.prefilterArtifactsMaskPlanes)
2485 goodArr = (exp.mask.array & badPixelMask) == 0
2486 returnSpanSetList = []
2487 bbox = exp.getBBox()
2488 x0, y0 = exp.getXY0()
2489 for i, span in enumerate(spanSetList):
2490 y, x = span.clippedTo(bbox).indices()
2491 yIndexLocal = numpy.array(y) - y0
2492 xIndexLocal = numpy.array(x) - x0
2493 goodRatio = numpy.count_nonzero(goodArr[yIndexLocal, xIndexLocal])/span.getArea()
2494 if goodRatio > self.config.prefilterArtifactsRatio:
2495 returnSpanSetList.append(span)
2496 return returnSpanSetList
2497
2498 def filterArtifacts(self, spanSetList, epochCountImage, nImage, footprintsToExclude=None):
2499 """Filter artifact candidates.
2500
2501 Parameters
2502 ----------
2503 spanSetList : `list`
2504 List of SpanSets representing artifact candidates.
2505 epochCountImage : `lsst.afw.image.Image`
2506 Image of accumulated number of warpDiff detections.
2507 nImage : `lsst.afw.image.Image`
2508 Image of the accumulated number of total epochs contributing.
2509
2510 Returns
2511 -------
2512 maskSpanSetList : `list`
2513 List of SpanSets with artifacts.
2514 """
2515
2516 maskSpanSetList = []
2517 x0, y0 = epochCountImage.getXY0()
2518 for i, span in enumerate(spanSetList):
2519 y, x = span.indices()
2520 yIdxLocal = [y1 - y0 for y1 in y]
2521 xIdxLocal = [x1 - x0 for x1 in x]
2522 outlierN = epochCountImage.array[yIdxLocal, xIdxLocal]
2523 totalN = nImage.array[yIdxLocal, xIdxLocal]
2524
2525 # effectiveMaxNumEpochs is broken line (fraction of N) with characteristic config.maxNumEpochs
2526 effMaxNumEpochsHighN = (self.config.maxNumEpochs
2527 + self.config.maxFractionEpochsHigh*numpy.mean(totalN))
2528 effMaxNumEpochsLowN = self.config.maxFractionEpochsLow * numpy.mean(totalN)
2529 effectiveMaxNumEpochs = int(min(effMaxNumEpochsLowN, effMaxNumEpochsHighN))
2530 nPixelsBelowThreshold = numpy.count_nonzero((outlierN > 0)
2531 & (outlierN <= effectiveMaxNumEpochs))
2532 percentBelowThreshold = nPixelsBelowThreshold / len(outlierN)
2533 if percentBelowThreshold > self.config.spatialThreshold:
2534 maskSpanSetList.append(span)
2535
2536 if self.config.doPreserveContainedBySource and footprintsToExclude is not None:
2537 # If a candidate is contained by a footprint on the template coadd, do not clip
2538 filteredMaskSpanSetList = []
2539 for span in maskSpanSetList:
2540 doKeep = True
2541 for footprint in footprintsToExclude.positive.getFootprints():
2542 if footprint.spans.contains(span):
2543 doKeep = False
2544 break
2545 if doKeep:
2546 filteredMaskSpanSetList.append(span)
2547 maskSpanSetList = filteredMaskSpanSetList
2548
2549 return maskSpanSetList
2550
2551 def _readAndComputeWarpDiff(self, warpRef, imageScaler, templateCoadd):
2552 """Fetch a warp from the butler and return a warpDiff.
2553
2554 Parameters
2555 ----------
2557 Butler dataRef for the warp.
2559 An image scaler object.
2560 templateCoadd : `lsst.afw.image.Exposure`
2561 Exposure to be substracted from the scaled warp.
2562
2563 Returns
2564 -------
2566 Exposure of the image difference between the warp and template.
2567 """
2568
2569 # If the PSF-Matched warp did not exist for this direct warp
2570 # None is holding its place to maintain order in Gen 3
2571 if warpRef is None:
2572 return None
2573 # Warp comparison must use PSF-Matched Warps regardless of requested coadd warp type
2574 warpName = self.getTempExpDatasetName('psfMatched')
2575 if not isinstance(warpRef, DeferredDatasetHandle):
2576 if not warpRef.datasetExists(warpName):
2577 self.log.warning("Could not find %s %s; skipping it", warpName, warpRef.dataId)
2578 return None
2579 warp = warpRef.get(datasetType=warpName, immediate=True)
2580 # direct image scaler OK for PSF-matched Warp
2581 imageScaler.scaleMaskedImage(warp.getMaskedImage())
2582 mi = warp.getMaskedImage()
2583 if self.config.doScaleWarpVariance:
2584 try:
2585 self.scaleWarpVariance.run(mi)
2586 except Exception as exc:
2587 self.log.warning("Unable to rescale variance of warp (%s); leaving it as-is", exc)
2588 mi -= templateCoadd.getMaskedImage()
2589 return warp
2590
2591 def _dataRef2DebugPath(self, prefix, warpRef, coaddLevel=False):
2592 """Return a path to which to write debugging output.
2593
2594 Creates a hyphen-delimited string of dataId values for simple filenames.
2595
2596 Parameters
2597 ----------
2598 prefix : `str`
2599 Prefix for filename.
2601 Butler dataRef to make the path from.
2602 coaddLevel : `bool`, optional.
2603 If True, include only coadd-level keys (e.g., 'tract', 'patch',
2604 'filter', but no 'visit').
2605
2606 Returns
2607 -------
2608 result : `str`
2609 Path for debugging output.
2610 """
2611 if coaddLevel:
2612 keys = warpRef.getButler().getKeys(self.getCoaddDatasetName(self.warpType))
2613 else:
2614 keys = warpRef.dataId.keys()
2615 keyList = sorted(keys, reverse=True)
2616 directory = lsstDebug.Info(__name__).figPath if lsstDebug.Info(__name__).figPath else "."
2617 filename = "%s-%s.fits" % (prefix, '-'.join([str(warpRef.dataId[k]) for k in keyList]))
2618 return os.path.join(directory, filename)
def makeSupplementaryDataGen3(self, butlerQC, inputRefs, outputRefs)
def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, supplementaryData, *args, **kwargs)
def findArtifacts(self, templateCoadd, tempExpRefList, imageScalerList)
def _readAndComputeWarpDiff(self, warpRef, imageScaler, templateCoadd)
def _dataRef2DebugPath(self, prefix, warpRef, coaddLevel=False)
def makeSupplementaryData(self, dataRef, selectDataList=None, warpRefList=None)
def filterArtifacts(self, spanSetList, epochCountImage, nImage, footprintsToExclude=None)
def buildDifferenceImage(self, skyInfo, tempExpRefList, imageScalerList, weightList)
def detectClipBig(self, clipList, clipFootprints, clipIndices, detectionFootprints, maskClipValue, maskDetValue, coaddBBox)
def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, *args, **kwargs)
Base class for coaddition.
Definition: coaddBase.py:141
def prepareStats(self, mask=None)
def readBrightObjectMasks(self, dataRef)
def makeSupplementaryData(self, dataRef, selectDataList=None, warpRefList=None)
def countMaskFromFootprint(mask, footprint, bitmask, ignoreMask)
def assembleOnlineMeanCoadd(self, coaddExposure, tempExpRefList, imageScalerList, weightList, altMaskList, statsCtrl, nImage=None)
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 filterWarps(self, inputs, goodVisits)
def prepareInputs(self, refList)
def assembleSubregion(self, coaddExposure, bbox, tempExpRefList, imageScalerList, weightList, altMaskList, statsFlags, statsCtrl, nImage=None)
def processResults(self, coaddExposure, brightObjectMasks=None, dataId=None)
def reorderAndPadList(inputList, inputKeys, outputKeys, padWith=None)
Definition: coaddBase.py:362
def makeCoaddSuffix(warpType="direct")
Definition: coaddBase.py:346
def makeSkyInfo(skyMap, tractId, patchId)
Definition: coaddBase.py:289
def getGroupDataRef(butler, datasetType, groupTuple, keys)
Definition: coaddHelpers.py:99
def groupPatchExposures(patchDataRef, calexpDataRefList, coaddDatasetType="deepCoadd", tempExpDatasetType="deepCoadd_directWarp")
Definition: coaddHelpers.py:60