lsst.pipe.tasks 21.0.0-173-gc56afc4e+90051df921
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.fatal("Cannot compute online coadd %s", e)
844 else:
845 for subBBox in self._subBBoxIter(skyInfo.bbox, subregionSize):
846 try:
847 self.assembleSubregion(coaddExposure, subBBox, tempExpRefList, imageScalerList,
848 weightList, altMaskList, stats.flags, stats.ctrl,
849 nImage=nImage)
850 except Exception as e:
851 self.log.fatal("Cannot compute coadd %s: %s", subBBox, e)
852
853 # If inputMap is requested, we must finalize the map after the accumulation.
854 if self.config.doInputMap:
855 self.inputMapper.finalize_ccd_input_map_mask()
856 inputMap = self.inputMapper.ccd_input_map
857 else:
858 inputMap = None
859
860 self.setInexactPsf(coaddMaskedImage.getMask())
861 # Despite the name, the following doesn't really deal with "EDGE" pixels: it identifies
862 # pixels that didn't receive any unmasked inputs (as occurs around the edge of the field).
863 coaddUtils.setCoaddEdgeBits(coaddMaskedImage.getMask(), coaddMaskedImage.getVariance())
864 return pipeBase.Struct(coaddExposure=coaddExposure, nImage=nImage,
865 warpRefList=tempExpRefList, imageScalerList=imageScalerList,
866 weightList=weightList, inputMap=inputMap)
867
868 def assembleMetadata(self, coaddExposure, tempExpRefList, weightList):
869 """Set the metadata for the coadd.
870
871 This basic implementation sets the filter from the first input.
872
873 Parameters
874 ----------
875 coaddExposure : `lsst.afw.image.Exposure`
876 The target exposure for the coadd.
877 tempExpRefList : `list`
878 List of data references to tempExp.
879 weightList : `list`
880 List of weights.
881 """
882 assert len(tempExpRefList) == len(weightList), "Length mismatch"
883 tempExpName = self.getTempExpDatasetName(self.warpType)
884 # We load a single pixel of each coaddTempExp, because we just want to get at the metadata
885 # (and we need more than just the PropertySet that contains the header), which is not possible
886 # with the current butler (see #2777).
887 bbox = geom.Box2I(coaddExposure.getBBox().getMin(), geom.Extent2I(1, 1))
888
889 if isinstance(tempExpRefList[0], DeferredDatasetHandle):
890 # Gen 3 API
891 tempExpList = [tempExpRef.get(parameters={'bbox': bbox}) for tempExpRef in tempExpRefList]
892 else:
893 # Gen 2 API. Delete this when Gen 2 retired
894 tempExpList = [tempExpRef.get(tempExpName + "_sub", bbox=bbox, immediate=True)
895 for tempExpRef in tempExpRefList]
896 numCcds = sum(len(tempExp.getInfo().getCoaddInputs().ccds) for tempExp in tempExpList)
897
898 # Set the coadd FilterLabel to the band of the first input exposure:
899 # Coadds are calibrated, so the physical label is now meaningless.
900 coaddExposure.setFilterLabel(afwImage.FilterLabel(tempExpList[0].getFilterLabel().bandLabel))
901 coaddInputs = coaddExposure.getInfo().getCoaddInputs()
902 coaddInputs.ccds.reserve(numCcds)
903 coaddInputs.visits.reserve(len(tempExpList))
904
905 for tempExp, weight in zip(tempExpList, weightList):
906 self.inputRecorder.addVisitToCoadd(coaddInputs, tempExp, weight)
907
908 if self.config.doUsePsfMatchedPolygons:
909 self.shrinkValidPolygons(coaddInputs)
910
911 coaddInputs.visits.sort()
912 if self.warpType == "psfMatched":
913 # The modelPsf BBox for a psfMatchedWarp/coaddTempExp was dynamically defined by
914 # ModelPsfMatchTask as the square box bounding its spatially-variable, pre-matched WarpedPsf.
915 # Likewise, set the PSF of a PSF-Matched Coadd to the modelPsf
916 # having the maximum width (sufficient because square)
917 modelPsfList = [tempExp.getPsf() for tempExp in tempExpList]
918 modelPsfWidthList = [modelPsf.computeBBox().getWidth() for modelPsf in modelPsfList]
919 psf = modelPsfList[modelPsfWidthList.index(max(modelPsfWidthList))]
920 else:
921 psf = measAlg.CoaddPsf(coaddInputs.ccds, coaddExposure.getWcs(),
922 self.config.coaddPsf.makeControl())
923 coaddExposure.setPsf(psf)
924 apCorrMap = measAlg.makeCoaddApCorrMap(coaddInputs.ccds, coaddExposure.getBBox(afwImage.PARENT),
925 coaddExposure.getWcs())
926 coaddExposure.getInfo().setApCorrMap(apCorrMap)
927 if self.config.doAttachTransmissionCurve:
928 transmissionCurve = measAlg.makeCoaddTransmissionCurve(coaddExposure.getWcs(), coaddInputs.ccds)
929 coaddExposure.getInfo().setTransmissionCurve(transmissionCurve)
930
931 def assembleSubregion(self, coaddExposure, bbox, tempExpRefList, imageScalerList, weightList,
932 altMaskList, statsFlags, statsCtrl, nImage=None):
933 """Assemble the coadd for a sub-region.
934
935 For each coaddTempExp, check for (and swap in) an alternative mask
936 if one is passed. Remove mask planes listed in
937 `config.removeMaskPlanes`. Finally, stack the actual exposures using
938 `lsst.afw.math.statisticsStack` with the statistic specified by
939 statsFlags. Typically, the statsFlag will be one of lsst.afw.math.MEAN for
940 a mean-stack or `lsst.afw.math.MEANCLIP` for outlier rejection using
941 an N-sigma clipped mean where N and iterations are specified by
942 statsCtrl. Assign the stacked subregion back to the coadd.
943
944 Parameters
945 ----------
946 coaddExposure : `lsst.afw.image.Exposure`
947 The target exposure for the coadd.
948 bbox : `lsst.geom.Box`
949 Sub-region to coadd.
950 tempExpRefList : `list`
951 List of data reference to tempExp.
952 imageScalerList : `list`
953 List of image scalers.
954 weightList : `list`
955 List of weights.
956 altMaskList : `list`
957 List of alternate masks to use rather than those stored with
958 tempExp, or None. Each element is dict with keys = mask plane
959 name to which to add the spans.
960 statsFlags : `lsst.afw.math.Property`
961 Property object for statistic for coadd.
963 Statistics control object for coadd.
964 nImage : `lsst.afw.image.ImageU`, optional
965 Keeps track of exposure count for each pixel.
966 """
967 self.log.debug("Computing coadd over %s", bbox)
968 tempExpName = self.getTempExpDatasetName(self.warpType)
969 coaddExposure.mask.addMaskPlane("REJECTED")
970 coaddExposure.mask.addMaskPlane("CLIPPED")
971 coaddExposure.mask.addMaskPlane("SENSOR_EDGE")
972 maskMap = self.setRejectedMaskMapping(statsCtrl)
973 clipped = afwImage.Mask.getPlaneBitMask("CLIPPED")
974 maskedImageList = []
975 if nImage is not None:
976 subNImage = afwImage.ImageU(bbox.getWidth(), bbox.getHeight())
977 for tempExpRef, imageScaler, altMask in zip(tempExpRefList, imageScalerList, altMaskList):
978
979 if isinstance(tempExpRef, DeferredDatasetHandle):
980 # Gen 3 API
981 exposure = tempExpRef.get(parameters={'bbox': bbox})
982 else:
983 # Gen 2 API. Delete this when Gen 2 retired
984 exposure = tempExpRef.get(tempExpName + "_sub", bbox=bbox)
985
986 maskedImage = exposure.getMaskedImage()
987 mask = maskedImage.getMask()
988 if altMask is not None:
989 self.applyAltMaskPlanes(mask, altMask)
990 imageScaler.scaleMaskedImage(maskedImage)
991
992 # Add 1 for each pixel which is not excluded by the exclude mask.
993 # In legacyCoadd, pixels may also be excluded by afwMath.statisticsStack.
994 if nImage is not None:
995 subNImage.getArray()[maskedImage.getMask().getArray() & statsCtrl.getAndMask() == 0] += 1
996 if self.config.removeMaskPlanes:
997 self.removeMaskPlanes(maskedImage)
998 maskedImageList.append(maskedImage)
999
1000 if self.config.doInputMap:
1001 visit = exposure.getInfo().getCoaddInputs().visits[0].getId()
1002 self.inputMapper.mask_warp_bbox(bbox, visit, mask, statsCtrl.getAndMask())
1003
1004 with self.timer("stack"):
1005 coaddSubregion = afwMath.statisticsStack(maskedImageList, statsFlags, statsCtrl, weightList,
1006 clipped, # also set output to CLIPPED if sigma-clipped
1007 maskMap)
1008 coaddExposure.maskedImage.assign(coaddSubregion, bbox)
1009 if nImage is not None:
1010 nImage.assign(subNImage, bbox)
1011
1012 def assembleOnlineMeanCoadd(self, coaddExposure, tempExpRefList, imageScalerList, weightList,
1013 altMaskList, statsCtrl, nImage=None):
1014 """Assemble the coadd using the "online" method.
1015
1016 This method takes a running sum of images and weights to save memory.
1017 It only works for MEAN statistics.
1018
1019 Parameters
1020 ----------
1021 coaddExposure : `lsst.afw.image.Exposure`
1022 The target exposure for the coadd.
1023 tempExpRefList : `list`
1024 List of data reference to tempExp.
1025 imageScalerList : `list`
1026 List of image scalers.
1027 weightList : `list`
1028 List of weights.
1029 altMaskList : `list`
1030 List of alternate masks to use rather than those stored with
1031 tempExp, or None. Each element is dict with keys = mask plane
1032 name to which to add the spans.
1034 Statistics control object for coadd
1035 nImage : `lsst.afw.image.ImageU`, optional
1036 Keeps track of exposure count for each pixel.
1037 """
1038 self.log.debug("Computing online coadd.")
1039 tempExpName = self.getTempExpDatasetName(self.warpType)
1040 coaddExposure.mask.addMaskPlane("REJECTED")
1041 coaddExposure.mask.addMaskPlane("CLIPPED")
1042 coaddExposure.mask.addMaskPlane("SENSOR_EDGE")
1043 maskMap = self.setRejectedMaskMapping(statsCtrl)
1044 thresholdDict = AccumulatorMeanStack.stats_ctrl_to_threshold_dict(statsCtrl)
1045
1046 bbox = coaddExposure.maskedImage.getBBox()
1047
1048 stacker = AccumulatorMeanStack(
1049 coaddExposure.image.array.shape,
1050 statsCtrl.getAndMask(),
1051 mask_threshold_dict=thresholdDict,
1052 mask_map=maskMap,
1053 no_good_pixels_mask=statsCtrl.getNoGoodPixelsMask(),
1054 calc_error_from_input_variance=self.config.calcErrorFromInputVariance,
1055 compute_n_image=(nImage is not None)
1056 )
1057
1058 for tempExpRef, imageScaler, altMask, weight in zip(tempExpRefList,
1059 imageScalerList,
1060 altMaskList,
1061 weightList):
1062 if isinstance(tempExpRef, DeferredDatasetHandle):
1063 # Gen 3 API
1064 exposure = tempExpRef.get()
1065 else:
1066 # Gen 2 API. Delete this when Gen 2 retired
1067 exposure = tempExpRef.get(tempExpName)
1068
1069 maskedImage = exposure.getMaskedImage()
1070 mask = maskedImage.getMask()
1071 if altMask is not None:
1072 self.applyAltMaskPlanes(mask, altMask)
1073 imageScaler.scaleMaskedImage(maskedImage)
1074 if self.config.removeMaskPlanes:
1075 self.removeMaskPlanes(maskedImage)
1076
1077 stacker.add_masked_image(maskedImage, weight=weight)
1078
1079 if self.config.doInputMap:
1080 visit = exposure.getInfo().getCoaddInputs().visits[0].getId()
1081 self.inputMapper.mask_warp_bbox(bbox, visit, mask, statsCtrl.getAndMask())
1082
1083 stacker.fill_stacked_masked_image(coaddExposure.maskedImage)
1084
1085 if nImage is not None:
1086 nImage.array[:, :] = stacker.n_image
1087
1088 def removeMaskPlanes(self, maskedImage):
1089 """Unset the mask of an image for mask planes specified in the config.
1090
1091 Parameters
1092 ----------
1093 maskedImage : `lsst.afw.image.MaskedImage`
1094 The masked image to be modified.
1095 """
1096 mask = maskedImage.getMask()
1097 for maskPlane in self.config.removeMaskPlanes:
1098 try:
1099 mask &= ~mask.getPlaneBitMask(maskPlane)
1100 except pexExceptions.InvalidParameterError:
1101 self.log.debug("Unable to remove mask plane %s: no mask plane with that name was found.",
1102 maskPlane)
1103
1104 @staticmethod
1105 def setRejectedMaskMapping(statsCtrl):
1106 """Map certain mask planes of the warps to new planes for the coadd.
1107
1108 If a pixel is rejected due to a mask value other than EDGE, NO_DATA,
1109 or CLIPPED, set it to REJECTED on the coadd.
1110 If a pixel is rejected due to EDGE, set the coadd pixel to SENSOR_EDGE.
1111 If a pixel is rejected due to CLIPPED, set the coadd pixel to CLIPPED.
1112
1113 Parameters
1114 ----------
1116 Statistics control object for coadd
1117
1118 Returns
1119 -------
1120 maskMap : `list` of `tuple` of `int`
1121 A list of mappings of mask planes of the warped exposures to
1122 mask planes of the coadd.
1123 """
1124 edge = afwImage.Mask.getPlaneBitMask("EDGE")
1125 noData = afwImage.Mask.getPlaneBitMask("NO_DATA")
1126 clipped = afwImage.Mask.getPlaneBitMask("CLIPPED")
1127 toReject = statsCtrl.getAndMask() & (~noData) & (~edge) & (~clipped)
1128 maskMap = [(toReject, afwImage.Mask.getPlaneBitMask("REJECTED")),
1129 (edge, afwImage.Mask.getPlaneBitMask("SENSOR_EDGE")),
1130 (clipped, clipped)]
1131 return maskMap
1132
1133 def applyAltMaskPlanes(self, mask, altMaskSpans):
1134 """Apply in place alt mask formatted as SpanSets to a mask.
1135
1136 Parameters
1137 ----------
1138 mask : `lsst.afw.image.Mask`
1139 Original mask.
1140 altMaskSpans : `dict`
1141 SpanSet lists to apply. Each element contains the new mask
1142 plane name (e.g. "CLIPPED and/or "NO_DATA") as the key,
1143 and list of SpanSets to apply to the mask.
1144
1145 Returns
1146 -------
1147 mask : `lsst.afw.image.Mask`
1148 Updated mask.
1149 """
1150 if self.config.doUsePsfMatchedPolygons:
1151 if ("NO_DATA" in altMaskSpans) and ("NO_DATA" in self.config.badMaskPlanes):
1152 # Clear away any other masks outside the validPolygons. These pixels are no longer
1153 # contributing to inexact PSFs, and will still be rejected because of NO_DATA
1154 # self.config.doUsePsfMatchedPolygons should be True only in CompareWarpAssemble
1155 # This mask-clearing step must only occur *before* applying the new masks below
1156 for spanSet in altMaskSpans['NO_DATA']:
1157 spanSet.clippedTo(mask.getBBox()).clearMask(mask, self.getBadPixelMask())
1158
1159 for plane, spanSetList in altMaskSpans.items():
1160 maskClipValue = mask.addMaskPlane(plane)
1161 for spanSet in spanSetList:
1162 spanSet.clippedTo(mask.getBBox()).setMask(mask, 2**maskClipValue)
1163 return mask
1164
1165 def shrinkValidPolygons(self, coaddInputs):
1166 """Shrink coaddInputs' ccds' ValidPolygons in place.
1167
1168 Either modify each ccd's validPolygon in place, or if CoaddInputs
1169 does not have a validPolygon, create one from its bbox.
1170
1171 Parameters
1172 ----------
1173 coaddInputs : `lsst.afw.image.coaddInputs`
1174 Original mask.
1175
1176 """
1177 for ccd in coaddInputs.ccds:
1178 polyOrig = ccd.getValidPolygon()
1179 validPolyBBox = polyOrig.getBBox() if polyOrig else ccd.getBBox()
1180 validPolyBBox.grow(-self.config.matchingKernelSize//2)
1181 if polyOrig:
1182 validPolygon = polyOrig.intersectionSingle(validPolyBBox)
1183 else:
1184 validPolygon = afwGeom.polygon.Polygon(geom.Box2D(validPolyBBox))
1185 ccd.setValidPolygon(validPolygon)
1186
1187 def readBrightObjectMasks(self, dataRef):
1188 """Retrieve the bright object masks.
1189
1190 Returns None on failure.
1191
1192 Parameters
1193 ----------
1195 A Butler dataRef.
1196
1197 Returns
1198 -------
1200 Bright object mask from the Butler object, or None if it cannot
1201 be retrieved.
1202 """
1203 try:
1204 return dataRef.get(datasetType="brightObjectMask", immediate=True)
1205 except Exception as e:
1206 self.log.warning("Unable to read brightObjectMask for %s: %s", dataRef.dataId, e)
1207 return None
1208
1209 def setBrightObjectMasks(self, exposure, brightObjectMasks, dataId=None):
1210 """Set the bright object masks.
1211
1212 Parameters
1213 ----------
1214 exposure : `lsst.afw.image.Exposure`
1215 Exposure under consideration.
1217 Data identifier dict for patch.
1218 brightObjectMasks : `lsst.afw.table`
1219 Table of bright objects to mask.
1220 """
1221
1222 if brightObjectMasks is None:
1223 self.log.warning("Unable to apply bright object mask: none supplied")
1224 return
1225 self.log.info("Applying %d bright object masks to %s", len(brightObjectMasks), dataId)
1226 mask = exposure.getMaskedImage().getMask()
1227 wcs = exposure.getWcs()
1228 plateScale = wcs.getPixelScale().asArcseconds()
1229
1230 for rec in brightObjectMasks:
1231 center = geom.PointI(wcs.skyToPixel(rec.getCoord()))
1232 if rec["type"] == "box":
1233 assert rec["angle"] == 0.0, ("Angle != 0 for mask object %s" % rec["id"])
1234 width = rec["width"].asArcseconds()/plateScale # convert to pixels
1235 height = rec["height"].asArcseconds()/plateScale # convert to pixels
1236
1237 halfSize = geom.ExtentI(0.5*width, 0.5*height)
1238 bbox = geom.Box2I(center - halfSize, center + halfSize)
1239
1240 bbox = geom.BoxI(geom.PointI(int(center[0] - 0.5*width), int(center[1] - 0.5*height)),
1241 geom.PointI(int(center[0] + 0.5*width), int(center[1] + 0.5*height)))
1242 spans = afwGeom.SpanSet(bbox)
1243 elif rec["type"] == "circle":
1244 radius = int(rec["radius"].asArcseconds()/plateScale) # convert to pixels
1245 spans = afwGeom.SpanSet.fromShape(radius, offset=center)
1246 else:
1247 self.log.warning("Unexpected region type %s at %s", rec["type"], center)
1248 continue
1249 spans.clippedTo(mask.getBBox()).setMask(mask, self.brightObjectBitmask)
1250
1251 def setInexactPsf(self, mask):
1252 """Set INEXACT_PSF mask plane.
1253
1254 If any of the input images isn't represented in the coadd (due to
1255 clipped pixels or chip gaps), the `CoaddPsf` will be inexact. Flag
1256 these pixels.
1257
1258 Parameters
1259 ----------
1260 mask : `lsst.afw.image.Mask`
1261 Coadded exposure's mask, modified in-place.
1262 """
1263 mask.addMaskPlane("INEXACT_PSF")
1264 inexactPsf = mask.getPlaneBitMask("INEXACT_PSF")
1265 sensorEdge = mask.getPlaneBitMask("SENSOR_EDGE") # chip edges (so PSF is discontinuous)
1266 clipped = mask.getPlaneBitMask("CLIPPED") # pixels clipped from coadd
1267 rejected = mask.getPlaneBitMask("REJECTED") # pixels rejected from coadd due to masks
1268 array = mask.getArray()
1269 selected = array & (sensorEdge | clipped | rejected) > 0
1270 array[selected] |= inexactPsf
1271
1272 @classmethod
1273 def _makeArgumentParser(cls):
1274 """Create an argument parser.
1275 """
1276 parser = pipeBase.ArgumentParser(name=cls._DefaultName)
1277 parser.add_id_argument("--id", cls.ConfigClass().coaddName + "Coadd_"
1278 + cls.ConfigClass().warpType + "Warp",
1279 help="data ID, e.g. --id tract=12345 patch=1,2",
1280 ContainerClass=AssembleCoaddDataIdContainer)
1281 parser.add_id_argument("--selectId", "calexp", help="data ID, e.g. --selectId visit=6789 ccd=0..9",
1282 ContainerClass=SelectDataIdContainer)
1283 return parser
1284
1285 @staticmethod
1286 def _subBBoxIter(bbox, subregionSize):
1287 """Iterate over subregions of a bbox.
1288
1289 Parameters
1290 ----------
1291 bbox : `lsst.geom.Box2I`
1292 Bounding box over which to iterate.
1293 subregionSize: `lsst.geom.Extent2I`
1294 Size of sub-bboxes.
1295
1296 Yields
1297 ------
1298 subBBox : `lsst.geom.Box2I`
1299 Next sub-bounding box of size ``subregionSize`` or smaller; each ``subBBox``
1300 is contained within ``bbox``, so it may be smaller than ``subregionSize`` at
1301 the edges of ``bbox``, but it will never be empty.
1302 """
1303 if bbox.isEmpty():
1304 raise RuntimeError("bbox %s is empty" % (bbox,))
1305 if subregionSize[0] < 1 or subregionSize[1] < 1:
1306 raise RuntimeError("subregionSize %s must be nonzero" % (subregionSize,))
1307
1308 for rowShift in range(0, bbox.getHeight(), subregionSize[1]):
1309 for colShift in range(0, bbox.getWidth(), subregionSize[0]):
1310 subBBox = geom.Box2I(bbox.getMin() + geom.Extent2I(colShift, rowShift), subregionSize)
1311 subBBox.clip(bbox)
1312 if subBBox.isEmpty():
1313 raise RuntimeError("Bug: empty bbox! bbox=%s, subregionSize=%s, "
1314 "colShift=%s, rowShift=%s" %
1315 (bbox, subregionSize, colShift, rowShift))
1316 yield subBBox
1317
1318 def filterWarps(self, inputs, goodVisits):
1319 """Return list of only inputRefs with visitId in goodVisits ordered by goodVisit
1320
1321 Parameters
1322 ----------
1323 inputs : list
1324 List of `lsst.pipe.base.connections.DeferredDatasetRef` with dataId containing visit
1325 goodVisit : `dict`
1326 Dictionary with good visitIds as the keys. Value ignored.
1327
1328 Returns:
1329 --------
1330 filteredInputs : `list`
1331 Filtered and sorted list of `lsst.pipe.base.connections.DeferredDatasetRef`
1332 """
1333 inputWarpDict = {inputRef.ref.dataId['visit']: inputRef for inputRef in inputs}
1334 filteredInputs = []
1335 for visit in goodVisits.keys():
1336 if visit in inputWarpDict:
1337 filteredInputs.append(inputWarpDict[visit])
1338 return filteredInputs
1339
1340
1341class AssembleCoaddDataIdContainer(pipeBase.DataIdContainer):
1342 """A version of `lsst.pipe.base.DataIdContainer` specialized for assembleCoadd.
1343 """
1344
1345 def makeDataRefList(self, namespace):
1346 """Make self.refList from self.idList.
1347
1348 Parameters
1349 ----------
1350 namespace
1351 Results of parsing command-line (with ``butler`` and ``log`` elements).
1352 """
1353 datasetType = namespace.config.coaddName + "Coadd"
1354 keysCoadd = namespace.butler.getKeys(datasetType=datasetType, level=self.level)
1355
1356 for dataId in self.idList:
1357 # tract and patch are required
1358 for key in keysCoadd:
1359 if key not in dataId:
1360 raise RuntimeError("--id must include " + key)
1361
1362 dataRef = namespace.butler.dataRef(
1363 datasetType=datasetType,
1364 dataId=dataId,
1365 )
1366 self.refList.append(dataRef)
1367
1368
1369def countMaskFromFootprint(mask, footprint, bitmask, ignoreMask):
1370 """Function to count the number of pixels with a specific mask in a
1371 footprint.
1372
1373 Find the intersection of mask & footprint. Count all pixels in the mask
1374 that are in the intersection that have bitmask set but do not have
1375 ignoreMask set. Return the count.
1376
1377 Parameters
1378 ----------
1379 mask : `lsst.afw.image.Mask`
1380 Mask to define intersection region by.
1381 footprint : `lsst.afw.detection.Footprint`
1382 Footprint to define the intersection region by.
1383 bitmask
1384 Specific mask that we wish to count the number of occurances of.
1385 ignoreMask
1386 Pixels to not consider.
1387
1388 Returns
1389 -------
1390 result : `int`
1391 Count of number of pixels in footprint with specified mask.
1392 """
1393 bbox = footprint.getBBox()
1394 bbox.clip(mask.getBBox(afwImage.PARENT))
1395 fp = afwImage.Mask(bbox)
1396 subMask = mask.Factory(mask, bbox, afwImage.PARENT)
1397 footprint.spans.setMask(fp, bitmask)
1398 return numpy.logical_and((subMask.getArray() & fp.getArray()) > 0,
1399 (subMask.getArray() & ignoreMask) == 0).sum()
1400
1401
1402class SafeClipAssembleCoaddConfig(AssembleCoaddConfig, pipelineConnections=AssembleCoaddConnections):
1403 """Configuration parameters for the SafeClipAssembleCoaddTask.
1404 """
1405 clipDetection = pexConfig.ConfigurableField(
1406 target=SourceDetectionTask,
1407 doc="Detect sources on difference between unclipped and clipped coadd")
1408 minClipFootOverlap = pexConfig.Field(
1409 doc="Minimum fractional overlap of clipped footprint with visit DETECTED to be clipped",
1410 dtype=float,
1411 default=0.6
1412 )
1413 minClipFootOverlapSingle = pexConfig.Field(
1414 doc="Minimum fractional overlap of clipped footprint with visit DETECTED to be "
1415 "clipped when only one visit overlaps",
1416 dtype=float,
1417 default=0.5
1418 )
1419 minClipFootOverlapDouble = pexConfig.Field(
1420 doc="Minimum fractional overlap of clipped footprints with visit DETECTED to be "
1421 "clipped when two visits overlap",
1422 dtype=float,
1423 default=0.45
1424 )
1425 maxClipFootOverlapDouble = pexConfig.Field(
1426 doc="Maximum fractional overlap of clipped footprints with visit DETECTED when "
1427 "considering two visits",
1428 dtype=float,
1429 default=0.15
1430 )
1431 minBigOverlap = pexConfig.Field(
1432 doc="Minimum number of pixels in footprint to use DETECTED mask from the single visits "
1433 "when labeling clipped footprints",
1434 dtype=int,
1435 default=100
1436 )
1437
1438 def setDefaults(self):
1439 """Set default values for clipDetection.
1440
1441 Notes
1442 -----
1443 The numeric values for these configuration parameters were
1444 empirically determined, future work may further refine them.
1445 """
1446 AssembleCoaddConfig.setDefaults(self)
1447 self.clipDetectionclipDetection.doTempLocalBackground = False
1448 self.clipDetectionclipDetection.reEstimateBackground = False
1449 self.clipDetectionclipDetection.returnOriginalFootprints = False
1450 self.clipDetectionclipDetection.thresholdPolarity = "both"
1451 self.clipDetectionclipDetection.thresholdValue = 2
1452 self.clipDetectionclipDetection.nSigmaToGrow = 2
1453 self.clipDetectionclipDetection.minPixels = 4
1454 self.clipDetectionclipDetection.isotropicGrow = True
1455 self.clipDetectionclipDetection.thresholdType = "pixel_stdev"
1456 self.sigmaClipsigmaClip = 1.5
1457 self.clipIterclipIter = 3
1458 self.statisticstatistic = "MEAN"
1459
1460 def validate(self):
1461 if self.doSigmaClipdoSigmaClip:
1462 log.warning("Additional Sigma-clipping not allowed in Safe-clipped Coadds. "
1463 "Ignoring doSigmaClip.")
1464 self.doSigmaClipdoSigmaClip = False
1465 if self.statisticstatistic != "MEAN":
1466 raise ValueError("Only MEAN statistic allowed for final stacking in SafeClipAssembleCoadd "
1467 "(%s chosen). Please set statistic to MEAN."
1468 % (self.statisticstatistic))
1469 AssembleCoaddTask.ConfigClass.validate(self)
1470
1471
1472class SafeClipAssembleCoaddTask(AssembleCoaddTask):
1473 """Assemble a coadded image from a set of coadded temporary exposures,
1474 being careful to clip & flag areas with potential artifacts.
1475
1476 In ``AssembleCoaddTask``, we compute the coadd as an clipped mean (i.e.,
1477 we clip outliers). The problem with doing this is that when computing the
1478 coadd PSF at a given location, individual visit PSFs from visits with
1479 outlier pixels contribute to the coadd PSF and cannot be treated correctly.
1480 In this task, we correct for this behavior by creating a new
1481 ``badMaskPlane`` 'CLIPPED'. We populate this plane on the input
1482 coaddTempExps and the final coadd where
1483
1484 i. difference imaging suggests that there is an outlier and
1485 ii. this outlier appears on only one or two images.
1486
1487 Such regions will not contribute to the final coadd. Furthermore, any
1488 routine to determine the coadd PSF can now be cognizant of clipped regions.
1489 Note that the algorithm implemented by this task is preliminary and works
1490 correctly for HSC data. Parameter modifications and or considerable
1491 redesigning of the algorithm is likley required for other surveys.
1492
1493 ``SafeClipAssembleCoaddTask`` uses a ``SourceDetectionTask``
1494 "clipDetection" subtask and also sub-classes ``AssembleCoaddTask``.
1495 You can retarget the ``SourceDetectionTask`` "clipDetection" subtask
1496 if you wish.
1497
1498 Notes
1499 -----
1500 The `lsst.pipe.base.cmdLineTask.CmdLineTask` interface supports a
1501 flag ``-d`` to import ``debug.py`` from your ``PYTHONPATH``;
1502 see `baseDebug` for more about ``debug.py`` files.
1503 `SafeClipAssembleCoaddTask` has no debug variables of its own.
1504 The ``SourceDetectionTask`` "clipDetection" subtasks may support debug
1505 variables. See the documetation for `SourceDetectionTask` "clipDetection"
1506 for further information.
1507
1508 Examples
1509 --------
1510 `SafeClipAssembleCoaddTask` assembles a set of warped ``coaddTempExp``
1511 images into a coadded image. The `SafeClipAssembleCoaddTask` is invoked by
1512 running assembleCoadd.py *without* the flag '--legacyCoadd'.
1513
1514 Usage of ``assembleCoadd.py`` expects a data reference to the tract patch
1515 and filter to be coadded (specified using
1516 '--id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]')
1517 along with a list of coaddTempExps to attempt to coadd (specified using
1518 '--selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]').
1519 Only the coaddTempExps that cover the specified tract and patch will be
1520 coadded. A list of the available optional arguments can be obtained by
1521 calling assembleCoadd.py with the --help command line argument:
1522
1523 .. code-block:: none
1524
1525 assembleCoadd.py --help
1526
1527 To demonstrate usage of the `SafeClipAssembleCoaddTask` in the larger
1528 context of multi-band processing, we will generate the HSC-I & -R band
1529 coadds from HSC engineering test data provided in the ci_hsc package.
1530 To begin, assuming that the lsst stack has been already set up, we must
1531 set up the obs_subaru and ci_hsc packages. This defines the environment
1532 variable $CI_HSC_DIR and points at the location of the package. The raw
1533 HSC data live in the ``$CI_HSC_DIR/raw`` directory. To begin assembling
1534 the coadds, we must first
1535
1536 - ``processCcd``
1537 process the individual ccds in $CI_HSC_RAW to produce calibrated exposures
1538 - ``makeSkyMap``
1539 create a skymap that covers the area of the sky present in the raw exposures
1540 - ``makeCoaddTempExp``
1541 warp the individual calibrated exposures to the tangent plane of the coadd</DD>
1542
1543 We can perform all of these steps by running
1544
1545 .. code-block:: none
1546
1547 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988
1548
1549 This will produce warped coaddTempExps for each visit. To coadd the
1550 warped data, we call ``assembleCoadd.py`` as follows:
1551
1552 .. code-block:: none
1553
1554 assembleCoadd.py $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \
1555 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \
1556 --selectId visit=903986 ccd=100--selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \
1557 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \
1558 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \
1559 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \
1560 --selectId visit=903988 ccd=24
1561
1562 This will process the HSC-I band data. The results are written in
1563 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``.
1564
1565 You may also choose to run:
1566
1567 .. code-block:: none
1568
1569 scons warp-903334 warp-903336 warp-903338 warp-903342 warp-903344 warp-903346 nnn
1570 assembleCoadd.py $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-R --selectId visit=903334 ccd=16 \
1571 --selectId visit=903334 ccd=22 --selectId visit=903334 ccd=23 --selectId visit=903334 ccd=100 \
1572 --selectId visit=903336 ccd=17 --selectId visit=903336 ccd=24 --selectId visit=903338 ccd=18 \
1573 --selectId visit=903338 ccd=25 --selectId visit=903342 ccd=4 --selectId visit=903342 ccd=10 \
1574 --selectId visit=903342 ccd=100 --selectId visit=903344 ccd=0 --selectId visit=903344 ccd=5 \
1575 --selectId visit=903344 ccd=11 --selectId visit=903346 ccd=1 --selectId visit=903346 ccd=6 \
1576 --selectId visit=903346 ccd=12
1577
1578 to generate the coadd for the HSC-R band if you are interested in following
1579 multiBand Coadd processing as discussed in ``pipeTasks_multiBand``.
1580 """
1581 ConfigClass = SafeClipAssembleCoaddConfig
1582 _DefaultName = "safeClipAssembleCoadd"
1583
1584 def __init__(self, *args, **kwargs):
1585 AssembleCoaddTask.__init__(self, *args, **kwargs)
1586 schema = afwTable.SourceTable.makeMinimalSchema()
1587 self.makeSubtask("clipDetection", schema=schema)
1588
1589 @utils.inheritDoc(AssembleCoaddTask)
1590 @timeMethod
1591 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, *args, **kwargs):
1592 """Assemble the coadd for a region.
1593
1594 Compute the difference of coadds created with and without outlier
1595 rejection to identify coadd pixels that have outlier values in some
1596 individual visits.
1597 Detect clipped regions on the difference image and mark these regions
1598 on the one or two individual coaddTempExps where they occur if there
1599 is significant overlap between the clipped region and a source. This
1600 leaves us with a set of footprints from the difference image that have
1601 been identified as having occured on just one or two individual visits.
1602 However, these footprints were generated from a difference image. It
1603 is conceivable for a large diffuse source to have become broken up
1604 into multiple footprints acrosss the coadd difference in this process.
1605 Determine the clipped region from all overlapping footprints from the
1606 detected sources in each visit - these are big footprints.
1607 Combine the small and big clipped footprints and mark them on a new
1608 bad mask plane.
1609 Generate the coadd using `AssembleCoaddTask.run` without outlier
1610 removal. Clipped footprints will no longer make it into the coadd
1611 because they are marked in the new bad mask plane.
1612
1613 Notes
1614 -----
1615 args and kwargs are passed but ignored in order to match the call
1616 signature expected by the parent task.
1617 """
1618 exp = self.buildDifferenceImagebuildDifferenceImage(skyInfo, tempExpRefList, imageScalerList, weightList)
1619 mask = exp.getMaskedImage().getMask()
1620 mask.addMaskPlane("CLIPPED")
1621
1622 result = self.detectClipdetectClip(exp, tempExpRefList)
1623
1624 self.log.info('Found %d clipped objects', len(result.clipFootprints))
1625
1626 maskClipValue = mask.getPlaneBitMask("CLIPPED")
1627 maskDetValue = mask.getPlaneBitMask("DETECTED") | mask.getPlaneBitMask("DETECTED_NEGATIVE")
1628 # Append big footprints from individual Warps to result.clipSpans
1629 bigFootprints = self.detectClipBigdetectClipBig(result.clipSpans, result.clipFootprints, result.clipIndices,
1630 result.detectionFootprints, maskClipValue, maskDetValue,
1631 exp.getBBox())
1632 # Create mask of the current clipped footprints
1633 maskClip = mask.Factory(mask.getBBox(afwImage.PARENT))
1634 afwDet.setMaskFromFootprintList(maskClip, result.clipFootprints, maskClipValue)
1635
1636 maskClipBig = maskClip.Factory(mask.getBBox(afwImage.PARENT))
1637 afwDet.setMaskFromFootprintList(maskClipBig, bigFootprints, maskClipValue)
1638 maskClip |= maskClipBig
1639
1640 # Assemble coadd from base class, but ignoring CLIPPED pixels
1641 badMaskPlanes = self.config.badMaskPlanes[:]
1642 badMaskPlanes.append("CLIPPED")
1643 badPixelMask = afwImage.Mask.getPlaneBitMask(badMaskPlanes)
1644 return AssembleCoaddTask.run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
1645 result.clipSpans, mask=badPixelMask)
1646
1647 def buildDifferenceImage(self, skyInfo, tempExpRefList, imageScalerList, weightList):
1648 """Return an exposure that contains the difference between unclipped
1649 and clipped coadds.
1650
1651 Generate a difference image between clipped and unclipped coadds.
1652 Compute the difference image by subtracting an outlier-clipped coadd
1653 from an outlier-unclipped coadd. Return the difference image.
1654
1655 Parameters
1656 ----------
1657 skyInfo : `lsst.pipe.base.Struct`
1658 Patch geometry information, from getSkyInfo
1659 tempExpRefList : `list`
1660 List of data reference to tempExp
1661 imageScalerList : `list`
1662 List of image scalers
1663 weightList : `list`
1664 List of weights
1665
1666 Returns
1667 -------
1669 Difference image of unclipped and clipped coadd wrapped in an Exposure
1670 """
1671 config = AssembleCoaddConfig()
1672 # getattr necessary because subtasks do not survive Config.toDict()
1673 # exclude connections because the class of self.config.connections is not
1674 # the same as AssembleCoaddConfig.connections, and the connections are not
1675 # needed to run this task anyway.
1676 configIntersection = {k: getattr(self.config, k)
1677 for k, v in self.config.toDict().items()
1678 if (k in config.keys() and k != "connections")}
1679 configIntersection['doInputMap'] = False
1680 configIntersection['doNImage'] = False
1681 config.update(**configIntersection)
1682
1683 # statistic MEAN copied from self.config.statistic, but for clarity explicitly assign
1684 config.statistic = 'MEAN'
1685 task = AssembleCoaddTask(config=config)
1686 coaddMean = task.run(skyInfo, tempExpRefList, imageScalerList, weightList).coaddExposure
1687
1688 config.statistic = 'MEANCLIP'
1689 task = AssembleCoaddTask(config=config)
1690 coaddClip = task.run(skyInfo, tempExpRefList, imageScalerList, weightList).coaddExposure
1691
1692 coaddDiff = coaddMean.getMaskedImage().Factory(coaddMean.getMaskedImage())
1693 coaddDiff -= coaddClip.getMaskedImage()
1694 exp = afwImage.ExposureF(coaddDiff)
1695 exp.setPsf(coaddMean.getPsf())
1696 return exp
1697
1698 def detectClip(self, exp, tempExpRefList):
1699 """Detect clipped regions on an exposure and set the mask on the
1700 individual tempExp masks.
1701
1702 Detect footprints in the difference image after smoothing the
1703 difference image with a Gaussian kernal. Identify footprints that
1704 overlap with one or two input ``coaddTempExps`` by comparing the
1705 computed overlap fraction to thresholds set in the config. A different
1706 threshold is applied depending on the number of overlapping visits
1707 (restricted to one or two). If the overlap exceeds the thresholds,
1708 the footprint is considered "CLIPPED" and is marked as such on the
1709 coaddTempExp. Return a struct with the clipped footprints, the indices
1710 of the ``coaddTempExps`` that end up overlapping with the clipped
1711 footprints, and a list of new masks for the ``coaddTempExps``.
1712
1713 Parameters
1714 ----------
1716 Exposure to run detection on.
1717 tempExpRefList : `list`
1718 List of data reference to tempExp.
1719
1720 Returns
1721 -------
1722 result : `lsst.pipe.base.Struct`
1723 Result struct with components:
1724
1725 - ``clipFootprints``: list of clipped footprints.
1726 - ``clipIndices``: indices for each ``clippedFootprint`` in
1727 ``tempExpRefList``.
1728 - ``clipSpans``: List of dictionaries containing spanSet lists
1729 to clip. Each element contains the new maskplane name
1730 ("CLIPPED") as the key and list of ``SpanSets`` as the value.
1731 - ``detectionFootprints``: List of DETECTED/DETECTED_NEGATIVE plane
1732 compressed into footprints.
1733 """
1734 mask = exp.getMaskedImage().getMask()
1735 maskDetValue = mask.getPlaneBitMask("DETECTED") | mask.getPlaneBitMask("DETECTED_NEGATIVE")
1736 fpSet = self.clipDetection.detectFootprints(exp, doSmooth=True, clearMask=True)
1737 # Merge positive and negative together footprints together
1738 fpSet.positive.merge(fpSet.negative)
1739 footprints = fpSet.positive
1740 self.log.info('Found %d potential clipped objects', len(footprints.getFootprints()))
1741 ignoreMask = self.getBadPixelMask()
1742
1743 clipFootprints = []
1744 clipIndices = []
1745 artifactSpanSets = [{'CLIPPED': list()} for _ in tempExpRefList]
1746
1747 # for use by detectClipBig
1748 visitDetectionFootprints = []
1749
1750 dims = [len(tempExpRefList), len(footprints.getFootprints())]
1751 overlapDetArr = numpy.zeros(dims, dtype=numpy.uint16)
1752 ignoreArr = numpy.zeros(dims, dtype=numpy.uint16)
1753
1754 # Loop over masks once and extract/store only relevant overlap metrics and detection footprints
1755 for i, warpRef in enumerate(tempExpRefList):
1756 tmpExpMask = warpRef.get(datasetType=self.getTempExpDatasetName(self.warpType),
1757 immediate=True).getMaskedImage().getMask()
1758 maskVisitDet = tmpExpMask.Factory(tmpExpMask, tmpExpMask.getBBox(afwImage.PARENT),
1759 afwImage.PARENT, True)
1760 maskVisitDet &= maskDetValue
1761 visitFootprints = afwDet.FootprintSet(maskVisitDet, afwDet.Threshold(1))
1762 visitDetectionFootprints.append(visitFootprints)
1763
1764 for j, footprint in enumerate(footprints.getFootprints()):
1765 ignoreArr[i, j] = countMaskFromFootprint(tmpExpMask, footprint, ignoreMask, 0x0)
1766 overlapDetArr[i, j] = countMaskFromFootprint(tmpExpMask, footprint, maskDetValue, ignoreMask)
1767
1768 # build a list of clipped spans for each visit
1769 for j, footprint in enumerate(footprints.getFootprints()):
1770 nPixel = footprint.getArea()
1771 overlap = [] # hold the overlap with each visit
1772 indexList = [] # index of visit in global list
1773 for i in range(len(tempExpRefList)):
1774 ignore = ignoreArr[i, j]
1775 overlapDet = overlapDetArr[i, j]
1776 totPixel = nPixel - ignore
1777
1778 # If we have more bad pixels than detection skip
1779 if ignore > overlapDet or totPixel <= 0.5*nPixel or overlapDet == 0:
1780 continue
1781 overlap.append(overlapDet/float(totPixel))
1782 indexList.append(i)
1783
1784 overlap = numpy.array(overlap)
1785 if not len(overlap):
1786 continue
1787
1788 keep = False # Should this footprint be marked as clipped?
1789 keepIndex = [] # Which tempExps does the clipped footprint belong to
1790
1791 # If footprint only has one overlap use a lower threshold
1792 if len(overlap) == 1:
1793 if overlap[0] > self.config.minClipFootOverlapSingle:
1794 keep = True
1795 keepIndex = [0]
1796 else:
1797 # This is the general case where only visit should be clipped
1798 clipIndex = numpy.where(overlap > self.config.minClipFootOverlap)[0]
1799 if len(clipIndex) == 1:
1800 keep = True
1801 keepIndex = [clipIndex[0]]
1802
1803 # Test if there are clipped objects that overlap two different visits
1804 clipIndex = numpy.where(overlap > self.config.minClipFootOverlapDouble)[0]
1805 if len(clipIndex) == 2 and len(overlap) > 3:
1806 clipIndexComp = numpy.where(overlap <= self.config.minClipFootOverlapDouble)[0]
1807 if numpy.max(overlap[clipIndexComp]) <= self.config.maxClipFootOverlapDouble:
1808 keep = True
1809 keepIndex = clipIndex
1810
1811 if not keep:
1812 continue
1813
1814 for index in keepIndex:
1815 globalIndex = indexList[index]
1816 artifactSpanSets[globalIndex]['CLIPPED'].append(footprint.spans)
1817
1818 clipIndices.append(numpy.array(indexList)[keepIndex])
1819 clipFootprints.append(footprint)
1820
1821 return pipeBase.Struct(clipFootprints=clipFootprints, clipIndices=clipIndices,
1822 clipSpans=artifactSpanSets, detectionFootprints=visitDetectionFootprints)
1823
1824 def detectClipBig(self, clipList, clipFootprints, clipIndices, detectionFootprints,
1825 maskClipValue, maskDetValue, coaddBBox):
1826 """Return individual warp footprints for large artifacts and append
1827 them to ``clipList`` in place.
1828
1829 Identify big footprints composed of many sources in the coadd
1830 difference that may have originated in a large diffuse source in the
1831 coadd. We do this by indentifying all clipped footprints that overlap
1832 significantly with each source in all the coaddTempExps.
1833
1834 Parameters
1835 ----------
1836 clipList : `list`
1837 List of alt mask SpanSets with clipping information. Modified.
1838 clipFootprints : `list`
1839 List of clipped footprints.
1840 clipIndices : `list`
1841 List of which entries in tempExpClipList each footprint belongs to.
1842 maskClipValue
1843 Mask value of clipped pixels.
1844 maskDetValue
1845 Mask value of detected pixels.
1846 coaddBBox : `lsst.geom.Box`
1847 BBox of the coadd and warps.
1848
1849 Returns
1850 -------
1851 bigFootprintsCoadd : `list`
1852 List of big footprints
1853 """
1854 bigFootprintsCoadd = []
1855 ignoreMask = self.getBadPixelMask()
1856 for index, (clippedSpans, visitFootprints) in enumerate(zip(clipList, detectionFootprints)):
1857 maskVisitDet = afwImage.MaskX(coaddBBox, 0x0)
1858 for footprint in visitFootprints.getFootprints():
1859 footprint.spans.setMask(maskVisitDet, maskDetValue)
1860
1861 # build a mask of clipped footprints that are in this visit
1862 clippedFootprintsVisit = []
1863 for foot, clipIndex in zip(clipFootprints, clipIndices):
1864 if index not in clipIndex:
1865 continue
1866 clippedFootprintsVisit.append(foot)
1867 maskVisitClip = maskVisitDet.Factory(maskVisitDet.getBBox(afwImage.PARENT))
1868 afwDet.setMaskFromFootprintList(maskVisitClip, clippedFootprintsVisit, maskClipValue)
1869
1870 bigFootprintsVisit = []
1871 for foot in visitFootprints.getFootprints():
1872 if foot.getArea() < self.config.minBigOverlap:
1873 continue
1874 nCount = countMaskFromFootprint(maskVisitClip, foot, maskClipValue, ignoreMask)
1875 if nCount > self.config.minBigOverlap:
1876 bigFootprintsVisit.append(foot)
1877 bigFootprintsCoadd.append(foot)
1878
1879 for footprint in bigFootprintsVisit:
1880 clippedSpans["CLIPPED"].append(footprint.spans)
1881
1882 return bigFootprintsCoadd
1883
1884
1886 psfMatchedWarps = pipeBase.connectionTypes.Input(
1887 doc=("PSF-Matched Warps are required by CompareWarp regardless of the coadd type requested. "
1888 "Only PSF-Matched Warps make sense for image subtraction. "
1889 "Therefore, they must be an additional declared input."),
1890 name="{inputCoaddName}Coadd_psfMatchedWarp",
1891 storageClass="ExposureF",
1892 dimensions=("tract", "patch", "skymap", "visit"),
1893 deferLoad=True,
1894 multiple=True
1895 )
1896 templateCoadd = pipeBase.connectionTypes.Output(
1897 doc=("Model of the static sky, used to find temporal artifacts. Typically a PSF-Matched, "
1898 "sigma-clipped coadd. Written if and only if assembleStaticSkyModel.doWrite=True"),
1899 name="{outputCoaddName}CoaddPsfMatched",
1900 storageClass="ExposureF",
1901 dimensions=("tract", "patch", "skymap", "band"),
1902 )
1903
1904 def __init__(self, *, config=None):
1905 super().__init__(config=config)
1906 if not config.assembleStaticSkyModel.doWrite:
1907 self.outputs.remove("templateCoadd")
1908 config.validate()
1909
1910
1911class CompareWarpAssembleCoaddConfig(AssembleCoaddConfig,
1912 pipelineConnections=CompareWarpAssembleCoaddConnections):
1913 assembleStaticSkyModel = pexConfig.ConfigurableField(
1914 target=AssembleCoaddTask,
1915 doc="Task to assemble an artifact-free, PSF-matched Coadd to serve as a"
1916 " naive/first-iteration model of the static sky.",
1917 )
1918 detect = pexConfig.ConfigurableField(
1919 target=SourceDetectionTask,
1920 doc="Detect outlier sources on difference between each psfMatched warp and static sky model"
1921 )
1922 detectTemplate = pexConfig.ConfigurableField(
1923 target=SourceDetectionTask,
1924 doc="Detect sources on static sky model. Only used if doPreserveContainedBySource is True"
1925 )
1926 maskStreaks = pexConfig.ConfigurableField(
1927 target=MaskStreaksTask,
1928 doc="Detect streaks on difference between each psfMatched warp and static sky model. Only used if "
1929 "doFilterMorphological is True. Adds a mask plane to an exposure, with the mask plane name set by"
1930 "streakMaskName"
1931 )
1932 streakMaskName = pexConfig.Field(
1933 dtype=str,
1934 default="STREAK",
1935 doc="Name of mask bit used for streaks"
1936 )
1937 maxNumEpochs = pexConfig.Field(
1938 doc="Charactistic maximum local number of epochs/visits in which an artifact candidate can appear "
1939 "and still be masked. The effective maxNumEpochs is a broken linear function of local "
1940 "number of epochs (N): min(maxFractionEpochsLow*N, maxNumEpochs + maxFractionEpochsHigh*N). "
1941 "For each footprint detected on the image difference between the psfMatched warp and static sky "
1942 "model, if a significant fraction of pixels (defined by spatialThreshold) are residuals in more "
1943 "than the computed effective maxNumEpochs, the artifact candidate is deemed persistant rather "
1944 "than transient and not masked.",
1945 dtype=int,
1946 default=2
1947 )
1948 maxFractionEpochsLow = pexConfig.RangeField(
1949 doc="Fraction of local number of epochs (N) to use as effective maxNumEpochs for low N. "
1950 "Effective maxNumEpochs = "
1951 "min(maxFractionEpochsLow * N, maxNumEpochs + maxFractionEpochsHigh * N)",
1952 dtype=float,
1953 default=0.4,
1954 min=0., max=1.,
1955 )
1956 maxFractionEpochsHigh = pexConfig.RangeField(
1957 doc="Fraction of local number of epochs (N) to use as effective maxNumEpochs for high N. "
1958 "Effective maxNumEpochs = "
1959 "min(maxFractionEpochsLow * N, maxNumEpochs + maxFractionEpochsHigh * N)",
1960 dtype=float,
1961 default=0.03,
1962 min=0., max=1.,
1963 )
1964 spatialThreshold = pexConfig.RangeField(
1965 doc="Unitless fraction of pixels defining how much of the outlier region has to meet the "
1966 "temporal criteria. If 0, clip all. If 1, clip none.",
1967 dtype=float,
1968 default=0.5,
1969 min=0., max=1.,
1970 inclusiveMin=True, inclusiveMax=True
1971 )
1972 doScaleWarpVariance = pexConfig.Field(
1973 doc="Rescale Warp variance plane using empirical noise?",
1974 dtype=bool,
1975 default=True,
1976 )
1977 scaleWarpVariance = pexConfig.ConfigurableField(
1978 target=ScaleVarianceTask,
1979 doc="Rescale variance on warps",
1980 )
1981 doPreserveContainedBySource = pexConfig.Field(
1982 doc="Rescue artifacts from clipping that completely lie within a footprint detected"
1983 "on the PsfMatched Template Coadd. Replicates a behavior of SafeClip.",
1984 dtype=bool,
1985 default=True,
1986 )
1987 doPrefilterArtifacts = pexConfig.Field(
1988 doc="Ignore artifact candidates that are mostly covered by the bad pixel mask, "
1989 "because they will be excluded anyway. This prevents them from contributing "
1990 "to the outlier epoch count image and potentially being labeled as persistant."
1991 "'Mostly' is defined by the config 'prefilterArtifactsRatio'.",
1992 dtype=bool,
1993 default=True
1994 )
1995 prefilterArtifactsMaskPlanes = pexConfig.ListField(
1996 doc="Prefilter artifact candidates that are mostly covered by these bad mask planes.",
1997 dtype=str,
1998 default=('NO_DATA', 'BAD', 'SAT', 'SUSPECT'),
1999 )
2000 prefilterArtifactsRatio = pexConfig.Field(
2001 doc="Prefilter artifact candidates with less than this fraction overlapping good pixels",
2002 dtype=float,
2003 default=0.05
2004 )
2005 doFilterMorphological = pexConfig.Field(
2006 doc="Filter artifact candidates based on morphological criteria, i.g. those that appear to "
2007 "be streaks.",
2008 dtype=bool,
2009 default=False
2010 )
2011
2012 def setDefaults(self):
2013 AssembleCoaddConfig.setDefaults(self)
2014 self.statisticstatistic = 'MEAN'
2015 self.doUsePsfMatchedPolygonsdoUsePsfMatchedPolygons = True
2016
2017 # Real EDGE removed by psfMatched NO_DATA border half the width of the matching kernel
2018 # CompareWarp applies psfMatched EDGE pixels to directWarps before assembling
2019 if "EDGE" in self.badMaskPlanes:
2020 self.badMaskPlanes.remove('EDGE')
2021 self.removeMaskPlanes.append('EDGE')
2022 self.assembleStaticSkyModelassembleStaticSkyModel.badMaskPlanes = ["NO_DATA", ]
2023 self.assembleStaticSkyModelassembleStaticSkyModel.warpType = 'psfMatched'
2024 self.assembleStaticSkyModelassembleStaticSkyModel.connections.warpType = 'psfMatched'
2025 self.assembleStaticSkyModelassembleStaticSkyModel.statistic = 'MEANCLIP'
2026 self.assembleStaticSkyModelassembleStaticSkyModel.sigmaClip = 2.5
2027 self.assembleStaticSkyModelassembleStaticSkyModel.clipIter = 3
2028 self.assembleStaticSkyModelassembleStaticSkyModel.calcErrorFromInputVariance = False
2029 self.assembleStaticSkyModelassembleStaticSkyModel.doWrite = False
2030 self.detectdetect.doTempLocalBackground = False
2031 self.detectdetect.reEstimateBackground = False
2032 self.detectdetect.returnOriginalFootprints = False
2033 self.detectdetect.thresholdPolarity = "both"
2034 self.detectdetect.thresholdValue = 5
2035 self.detectdetect.minPixels = 4
2036 self.detectdetect.isotropicGrow = True
2037 self.detectdetect.thresholdType = "pixel_stdev"
2038 self.detectdetect.nSigmaToGrow = 0.4
2039 # The default nSigmaToGrow for SourceDetectionTask is already 2.4,
2040 # Explicitly restating because ratio with detect.nSigmaToGrow matters
2041 self.detectTemplatedetectTemplate.nSigmaToGrow = 2.4
2042 self.detectTemplatedetectTemplate.doTempLocalBackground = False
2043 self.detectTemplatedetectTemplate.reEstimateBackground = False
2044 self.detectTemplatedetectTemplate.returnOriginalFootprints = False
2045
2046 def validate(self):
2047 super().validate()
2048 if self.assembleStaticSkyModelassembleStaticSkyModel.doNImage:
2049 raise ValueError("No dataset type exists for a PSF-Matched Template N Image."
2050 "Please set assembleStaticSkyModel.doNImage=False")
2051
2052 if self.assembleStaticSkyModelassembleStaticSkyModel.doWrite and (self.warpTypewarpType == self.assembleStaticSkyModelassembleStaticSkyModel.warpType):
2053 raise ValueError("warpType (%s) == assembleStaticSkyModel.warpType (%s) and will compete for "
2054 "the same dataset name. Please set assembleStaticSkyModel.doWrite to False "
2055 "or warpType to 'direct'. assembleStaticSkyModel.warpType should ways be "
2056 "'PsfMatched'" % (self.warpTypewarpType, self.assembleStaticSkyModelassembleStaticSkyModel.warpType))
2057
2058
2059class CompareWarpAssembleCoaddTask(AssembleCoaddTask):
2060 """Assemble a compareWarp coadded image from a set of warps
2061 by masking artifacts detected by comparing PSF-matched warps.
2062
2063 In ``AssembleCoaddTask``, we compute the coadd as an clipped mean (i.e.,
2064 we clip outliers). The problem with doing this is that when computing the
2065 coadd PSF at a given location, individual visit PSFs from visits with
2066 outlier pixels contribute to the coadd PSF and cannot be treated correctly.
2067 In this task, we correct for this behavior by creating a new badMaskPlane
2068 'CLIPPED' which marks pixels in the individual warps suspected to contain
2069 an artifact. We populate this plane on the input warps by comparing
2070 PSF-matched warps with a PSF-matched median coadd which serves as a
2071 model of the static sky. Any group of pixels that deviates from the
2072 PSF-matched template coadd by more than config.detect.threshold sigma,
2073 is an artifact candidate. The candidates are then filtered to remove
2074 variable sources and sources that are difficult to subtract such as
2075 bright stars. This filter is configured using the config parameters
2076 ``temporalThreshold`` and ``spatialThreshold``. The temporalThreshold is
2077 the maximum fraction of epochs that the deviation can appear in and still
2078 be considered an artifact. The spatialThreshold is the maximum fraction of
2079 pixels in the footprint of the deviation that appear in other epochs
2080 (where other epochs is defined by the temporalThreshold). If the deviant
2081 region meets this criteria of having a significant percentage of pixels
2082 that deviate in only a few epochs, these pixels have the 'CLIPPED' bit
2083 set in the mask. These regions will not contribute to the final coadd.
2084 Furthermore, any routine to determine the coadd PSF can now be cognizant
2085 of clipped regions. Note that the algorithm implemented by this task is
2086 preliminary and works correctly for HSC data. Parameter modifications and
2087 or considerable redesigning of the algorithm is likley required for other
2088 surveys.
2089
2090 ``CompareWarpAssembleCoaddTask`` sub-classes
2091 ``AssembleCoaddTask`` and instantiates ``AssembleCoaddTask``
2092 as a subtask to generate the TemplateCoadd (the model of the static sky).
2093
2094 Notes
2095 -----
2096 The `lsst.pipe.base.cmdLineTask.CmdLineTask` interface supports a
2097 flag ``-d`` to import ``debug.py`` from your ``PYTHONPATH``; see
2098 ``baseDebug`` for more about ``debug.py`` files.
2099
2100 This task supports the following debug variables:
2101
2102 - ``saveCountIm``
2103 If True then save the Epoch Count Image as a fits file in the `figPath`
2104 - ``figPath``
2105 Path to save the debug fits images and figures
2106
2107 For example, put something like:
2108
2109 .. code-block:: python
2110
2111 import lsstDebug
2112 def DebugInfo(name):
2113 di = lsstDebug.getInfo(name)
2114 if name == "lsst.pipe.tasks.assembleCoadd":
2115 di.saveCountIm = True
2116 di.figPath = "/desired/path/to/debugging/output/images"
2117 return di
2118 lsstDebug.Info = DebugInfo
2119
2120 into your ``debug.py`` file and run ``assemebleCoadd.py`` with the
2121 ``--debug`` flag. Some subtasks may have their own debug variables;
2122 see individual Task documentation.
2123
2124 Examples
2125 --------
2126 ``CompareWarpAssembleCoaddTask`` assembles a set of warped images into a
2127 coadded image. The ``CompareWarpAssembleCoaddTask`` is invoked by running
2128 ``assembleCoadd.py`` with the flag ``--compareWarpCoadd``.
2129 Usage of ``assembleCoadd.py`` expects a data reference to the tract patch
2130 and filter to be coadded (specified using
2131 '--id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]')
2132 along with a list of coaddTempExps to attempt to coadd (specified using
2133 '--selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]').
2134 Only the warps that cover the specified tract and patch will be coadded.
2135 A list of the available optional arguments can be obtained by calling
2136 ``assembleCoadd.py`` with the ``--help`` command line argument:
2137
2138 .. code-block:: none
2139
2140 assembleCoadd.py --help
2141
2142 To demonstrate usage of the ``CompareWarpAssembleCoaddTask`` in the larger
2143 context of multi-band processing, we will generate the HSC-I & -R band
2144 oadds from HSC engineering test data provided in the ``ci_hsc`` package.
2145 To begin, assuming that the lsst stack has been already set up, we must
2146 set up the ``obs_subaru`` and ``ci_hsc`` packages.
2147 This defines the environment variable ``$CI_HSC_DIR`` and points at the
2148 location of the package. The raw HSC data live in the ``$CI_HSC_DIR/raw``
2149 directory. To begin assembling the coadds, we must first
2150
2151 - processCcd
2152 process the individual ccds in $CI_HSC_RAW to produce calibrated exposures
2153 - makeSkyMap
2154 create a skymap that covers the area of the sky present in the raw exposures
2155 - makeCoaddTempExp
2156 warp the individual calibrated exposures to the tangent plane of the coadd
2157
2158 We can perform all of these steps by running
2159
2160 .. code-block:: none
2161
2162 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988
2163
2164 This will produce warped ``coaddTempExps`` for each visit. To coadd the
2165 warped data, we call ``assembleCoadd.py`` as follows:
2166
2167 .. code-block:: none
2168
2169 assembleCoadd.py --compareWarpCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \
2170 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \
2171 --selectId visit=903986 ccd=100 --selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \
2172 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \
2173 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \
2174 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \
2175 --selectId visit=903988 ccd=24
2176
2177 This will process the HSC-I band data. The results are written in
2178 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``.
2179 """
2180 ConfigClass = CompareWarpAssembleCoaddConfig
2181 _DefaultName = "compareWarpAssembleCoadd"
2182
2183 def __init__(self, *args, **kwargs):
2184 AssembleCoaddTask.__init__(self, *args, **kwargs)
2185 self.makeSubtask("assembleStaticSkyModel")
2186 detectionSchema = afwTable.SourceTable.makeMinimalSchema()
2187 self.makeSubtask("detect", schema=detectionSchema)
2188 if self.config.doPreserveContainedBySource:
2189 self.makeSubtask("detectTemplate", schema=afwTable.SourceTable.makeMinimalSchema())
2190 if self.config.doScaleWarpVariance:
2191 self.makeSubtask("scaleWarpVariance")
2192 if self.config.doFilterMorphological:
2193 self.makeSubtask("maskStreaks")
2194
2195 @utils.inheritDoc(AssembleCoaddTask)
2196 def makeSupplementaryDataGen3(self, butlerQC, inputRefs, outputRefs):
2197 """
2198 Generate a templateCoadd to use as a naive model of static sky to
2199 subtract from PSF-Matched warps.
2200
2201 Returns
2202 -------
2203 result : `lsst.pipe.base.Struct`
2204 Result struct with components:
2205
2206 - ``templateCoadd`` : coadded exposure (``lsst.afw.image.Exposure``)
2207 - ``nImage`` : N Image (``lsst.afw.image.Image``)
2208 """
2209 # Ensure that psfMatchedWarps are used as input warps for template generation
2210 staticSkyModelInputRefs = copy.deepcopy(inputRefs)
2211 staticSkyModelInputRefs.inputWarps = inputRefs.psfMatchedWarps
2212
2213 # Because subtasks don't have connections we have to make one.
2214 # The main task's `templateCoadd` is the subtask's `coaddExposure`
2215 staticSkyModelOutputRefs = copy.deepcopy(outputRefs)
2216 if self.config.assembleStaticSkyModel.doWrite:
2217 staticSkyModelOutputRefs.coaddExposure = staticSkyModelOutputRefs.templateCoadd
2218 # Remove template coadd from both subtask's and main tasks outputs,
2219 # because it is handled by the subtask as `coaddExposure`
2220 del outputRefs.templateCoadd
2221 del staticSkyModelOutputRefs.templateCoadd
2222
2223 # A PSF-Matched nImage does not exist as a dataset type
2224 if 'nImage' in staticSkyModelOutputRefs.keys():
2225 del staticSkyModelOutputRefs.nImage
2226
2227 templateCoadd = self.assembleStaticSkyModel.runQuantum(butlerQC, staticSkyModelInputRefs,
2228 staticSkyModelOutputRefs)
2229 if templateCoadd is None:
2230 raise RuntimeError(self._noTemplateMessage_noTemplateMessage(self.assembleStaticSkyModel.warpType))
2231
2232 return pipeBase.Struct(templateCoadd=templateCoadd.coaddExposure,
2233 nImage=templateCoadd.nImage,
2234 warpRefList=templateCoadd.warpRefList,
2235 imageScalerList=templateCoadd.imageScalerList,
2236 weightList=templateCoadd.weightList)
2237
2238 @utils.inheritDoc(AssembleCoaddTask)
2239 def makeSupplementaryData(self, dataRef, selectDataList=None, warpRefList=None):
2240 """
2241 Generate a templateCoadd to use as a naive model of static sky to
2242 subtract from PSF-Matched warps.
2243
2244 Returns
2245 -------
2246 result : `lsst.pipe.base.Struct`
2247 Result struct with components:
2248
2249 - ``templateCoadd``: coadded exposure (``lsst.afw.image.Exposure``)
2250 - ``nImage``: N Image (``lsst.afw.image.Image``)
2251 """
2252 templateCoadd = self.assembleStaticSkyModel.runDataRef(dataRef, selectDataList, warpRefList)
2253 if templateCoadd is None:
2254 raise RuntimeError(self._noTemplateMessage_noTemplateMessage(self.assembleStaticSkyModel.warpType))
2255
2256 return pipeBase.Struct(templateCoadd=templateCoadd.coaddExposure,
2257 nImage=templateCoadd.nImage,
2258 warpRefList=templateCoadd.warpRefList,
2259 imageScalerList=templateCoadd.imageScalerList,
2260 weightList=templateCoadd.weightList)
2261
2262 def _noTemplateMessage(self, warpType):
2263 warpName = (warpType[0].upper() + warpType[1:])
2264 message = """No %(warpName)s warps were found to build the template coadd which is
2265 required to run CompareWarpAssembleCoaddTask. To continue assembling this type of coadd,
2266 first either rerun makeCoaddTempExp with config.make%(warpName)s=True or
2267 coaddDriver with config.makeCoadTempExp.make%(warpName)s=True, before assembleCoadd.
2268
2269 Alternatively, to use another algorithm with existing warps, retarget the CoaddDriverConfig to
2270 another algorithm like:
2271
2272 from lsst.pipe.tasks.assembleCoadd import SafeClipAssembleCoaddTask
2273 config.assemble.retarget(SafeClipAssembleCoaddTask)
2274 """ % {"warpName": warpName}
2275 return message
2276
2277 @utils.inheritDoc(AssembleCoaddTask)
2278 @timeMethod
2279 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
2280 supplementaryData, *args, **kwargs):
2281 """Assemble the coadd.
2282
2283 Find artifacts and apply them to the warps' masks creating a list of
2284 alternative masks with a new "CLIPPED" plane and updated "NO_DATA"
2285 plane. Then pass these alternative masks to the base class's `run`
2286 method.
2287
2288 The input parameters ``supplementaryData`` is a `lsst.pipe.base.Struct`
2289 that must contain a ``templateCoadd`` that serves as the
2290 model of the static sky.
2291 """
2292
2293 # Check and match the order of the supplementaryData
2294 # (PSF-matched) inputs to the order of the direct inputs,
2295 # so that the artifact mask is applied to the right warp
2296 dataIds = [ref.dataId for ref in tempExpRefList]
2297 psfMatchedDataIds = [ref.dataId for ref in supplementaryData.warpRefList]
2298
2299 if dataIds != psfMatchedDataIds:
2300 self.log.info("Reordering and or/padding PSF-matched visit input list")
2301 supplementaryData.warpRefList = reorderAndPadList(supplementaryData.warpRefList,
2302 psfMatchedDataIds, dataIds)
2303 supplementaryData.imageScalerList = reorderAndPadList(supplementaryData.imageScalerList,
2304 psfMatchedDataIds, dataIds)
2305
2306 # Use PSF-Matched Warps (and corresponding scalers) and coadd to find artifacts
2307 spanSetMaskList = self.findArtifactsfindArtifacts(supplementaryData.templateCoadd,
2308 supplementaryData.warpRefList,
2309 supplementaryData.imageScalerList)
2310
2311 badMaskPlanes = self.config.badMaskPlanes[:]
2312 badMaskPlanes.append("CLIPPED")
2313 badPixelMask = afwImage.Mask.getPlaneBitMask(badMaskPlanes)
2314
2315 result = AssembleCoaddTask.run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
2316 spanSetMaskList, mask=badPixelMask)
2317
2318 # Propagate PSF-matched EDGE pixels to coadd SENSOR_EDGE and INEXACT_PSF
2319 # Psf-Matching moves the real edge inwards
2320 self.applyAltEdgeMaskapplyAltEdgeMask(result.coaddExposure.maskedImage.mask, spanSetMaskList)
2321 return result
2322
2323 def applyAltEdgeMask(self, mask, altMaskList):
2324 """Propagate alt EDGE mask to SENSOR_EDGE AND INEXACT_PSF planes.
2325
2326 Parameters
2327 ----------
2328 mask : `lsst.afw.image.Mask`
2329 Original mask.
2330 altMaskList : `list`
2331 List of Dicts containing ``spanSet`` lists.
2332 Each element contains the new mask plane name (e.g. "CLIPPED
2333 and/or "NO_DATA") as the key, and list of ``SpanSets`` to apply to
2334 the mask.
2335 """
2336 maskValue = mask.getPlaneBitMask(["SENSOR_EDGE", "INEXACT_PSF"])
2337 for visitMask in altMaskList:
2338 if "EDGE" in visitMask:
2339 for spanSet in visitMask['EDGE']:
2340 spanSet.clippedTo(mask.getBBox()).setMask(mask, maskValue)
2341
2342 def findArtifacts(self, templateCoadd, tempExpRefList, imageScalerList):
2343 """Find artifacts.
2344
2345 Loop through warps twice. The first loop builds a map with the count
2346 of how many epochs each pixel deviates from the templateCoadd by more
2347 than ``config.chiThreshold`` sigma. The second loop takes each
2348 difference image and filters the artifacts detected in each using
2349 count map to filter out variable sources and sources that are
2350 difficult to subtract cleanly.
2351
2352 Parameters
2353 ----------
2354 templateCoadd : `lsst.afw.image.Exposure`
2355 Exposure to serve as model of static sky.
2356 tempExpRefList : `list`
2357 List of data references to warps.
2358 imageScalerList : `list`
2359 List of image scalers.
2360
2361 Returns
2362 -------
2363 altMasks : `list`
2364 List of dicts containing information about CLIPPED
2365 (i.e., artifacts), NO_DATA, and EDGE pixels.
2366 """
2367
2368 self.log.debug("Generating Count Image, and mask lists.")
2369 coaddBBox = templateCoadd.getBBox()
2370 slateIm = afwImage.ImageU(coaddBBox)
2371 epochCountImage = afwImage.ImageU(coaddBBox)
2372 nImage = afwImage.ImageU(coaddBBox)
2373 spanSetArtifactList = []
2374 spanSetNoDataMaskList = []
2375 spanSetEdgeList = []
2376 spanSetBadMorphoList = []
2377 badPixelMask = self.getBadPixelMask()
2378
2379 # mask of the warp diffs should = that of only the warp
2380 templateCoadd.mask.clearAllMaskPlanes()
2381
2382 if self.config.doPreserveContainedBySource:
2383 templateFootprints = self.detectTemplate.detectFootprints(templateCoadd)
2384 else:
2385 templateFootprints = None
2386
2387 for warpRef, imageScaler in zip(tempExpRefList, imageScalerList):
2388 warpDiffExp = self._readAndComputeWarpDiff_readAndComputeWarpDiff(warpRef, imageScaler, templateCoadd)
2389 if warpDiffExp is not None:
2390 # This nImage only approximates the final nImage because it uses the PSF-matched mask
2391 nImage.array += (numpy.isfinite(warpDiffExp.image.array)
2392 * ((warpDiffExp.mask.array & badPixelMask) == 0)).astype(numpy.uint16)
2393 fpSet = self.detect.detectFootprints(warpDiffExp, doSmooth=False, clearMask=True)
2394 fpSet.positive.merge(fpSet.negative)
2395 footprints = fpSet.positive
2396 slateIm.set(0)
2397 spanSetList = [footprint.spans for footprint in footprints.getFootprints()]
2398
2399 # Remove artifacts due to defects before they contribute to the epochCountImage
2400 if self.config.doPrefilterArtifacts:
2401 spanSetList = self.prefilterArtifactsprefilterArtifacts(spanSetList, warpDiffExp)
2402
2403 # Clear mask before adding prefiltered spanSets
2404 self.detect.clearMask(warpDiffExp.mask)
2405 for spans in spanSetList:
2406 spans.setImage(slateIm, 1, doClip=True)
2407 spans.setMask(warpDiffExp.mask, warpDiffExp.mask.getPlaneBitMask("DETECTED"))
2408 epochCountImage += slateIm
2409
2410 if self.config.doFilterMorphological:
2411 maskName = self.config.streakMaskName
2412 _ = self.maskStreaks.run(warpDiffExp)
2413 streakMask = warpDiffExp.mask
2414 spanSetStreak = afwGeom.SpanSet.fromMask(streakMask,
2415 streakMask.getPlaneBitMask(maskName)).split()
2416
2417 # PSF-Matched warps have less available area (~the matching kernel) because the calexps
2418 # undergo a second convolution. Pixels with data in the direct warp
2419 # but not in the PSF-matched warp will not have their artifacts detected.
2420 # NaNs from the PSF-matched warp therefore must be masked in the direct warp
2421 nans = numpy.where(numpy.isnan(warpDiffExp.maskedImage.image.array), 1, 0)
2422 nansMask = afwImage.makeMaskFromArray(nans.astype(afwImage.MaskPixel))
2423 nansMask.setXY0(warpDiffExp.getXY0())
2424 edgeMask = warpDiffExp.mask
2425 spanSetEdgeMask = afwGeom.SpanSet.fromMask(edgeMask,
2426 edgeMask.getPlaneBitMask("EDGE")).split()
2427 else:
2428 # If the directWarp has <1% coverage, the psfMatchedWarp can have 0% and not exist
2429 # In this case, mask the whole epoch
2430 nansMask = afwImage.MaskX(coaddBBox, 1)
2431 spanSetList = []
2432 spanSetEdgeMask = []
2433 spanSetStreak = []
2434
2435 spanSetNoDataMask = afwGeom.SpanSet.fromMask(nansMask).split()
2436
2437 spanSetNoDataMaskList.append(spanSetNoDataMask)
2438 spanSetArtifactList.append(spanSetList)
2439 spanSetEdgeList.append(spanSetEdgeMask)
2440 if self.config.doFilterMorphological:
2441 spanSetBadMorphoList.append(spanSetStreak)
2442
2443 if lsstDebug.Info(__name__).saveCountIm:
2444 path = self._dataRef2DebugPath_dataRef2DebugPath("epochCountIm", tempExpRefList[0], coaddLevel=True)
2445 epochCountImage.writeFits(path)
2446
2447 for i, spanSetList in enumerate(spanSetArtifactList):
2448 if spanSetList:
2449 filteredSpanSetList = self.filterArtifactsfilterArtifacts(spanSetList, epochCountImage, nImage,
2450 templateFootprints)
2451 spanSetArtifactList[i] = filteredSpanSetList
2452 if self.config.doFilterMorphological:
2453 spanSetArtifactList[i] += spanSetBadMorphoList[i]
2454
2455 altMasks = []
2456 for artifacts, noData, edge in zip(spanSetArtifactList, spanSetNoDataMaskList, spanSetEdgeList):
2457 altMasks.append({'CLIPPED': artifacts,
2458 'NO_DATA': noData,
2459 'EDGE': edge})
2460 return altMasks
2461
2462 def prefilterArtifacts(self, spanSetList, exp):
2463 """Remove artifact candidates covered by bad mask plane.
2464
2465 Any future editing of the candidate list that does not depend on
2466 temporal information should go in this method.
2467
2468 Parameters
2469 ----------
2470 spanSetList : `list`
2471 List of SpanSets representing artifact candidates.
2473 Exposure containing mask planes used to prefilter.
2474
2475 Returns
2476 -------
2477 returnSpanSetList : `list`
2478 List of SpanSets with artifacts.
2479 """
2480 badPixelMask = exp.mask.getPlaneBitMask(self.config.prefilterArtifactsMaskPlanes)
2481 goodArr = (exp.mask.array & badPixelMask) == 0
2482 returnSpanSetList = []
2483 bbox = exp.getBBox()
2484 x0, y0 = exp.getXY0()
2485 for i, span in enumerate(spanSetList):
2486 y, x = span.clippedTo(bbox).indices()
2487 yIndexLocal = numpy.array(y) - y0
2488 xIndexLocal = numpy.array(x) - x0
2489 goodRatio = numpy.count_nonzero(goodArr[yIndexLocal, xIndexLocal])/span.getArea()
2490 if goodRatio > self.config.prefilterArtifactsRatio:
2491 returnSpanSetList.append(span)
2492 return returnSpanSetList
2493
2494 def filterArtifacts(self, spanSetList, epochCountImage, nImage, footprintsToExclude=None):
2495 """Filter artifact candidates.
2496
2497 Parameters
2498 ----------
2499 spanSetList : `list`
2500 List of SpanSets representing artifact candidates.
2501 epochCountImage : `lsst.afw.image.Image`
2502 Image of accumulated number of warpDiff detections.
2503 nImage : `lsst.afw.image.Image`
2504 Image of the accumulated number of total epochs contributing.
2505
2506 Returns
2507 -------
2508 maskSpanSetList : `list`
2509 List of SpanSets with artifacts.
2510 """
2511
2512 maskSpanSetList = []
2513 x0, y0 = epochCountImage.getXY0()
2514 for i, span in enumerate(spanSetList):
2515 y, x = span.indices()
2516 yIdxLocal = [y1 - y0 for y1 in y]
2517 xIdxLocal = [x1 - x0 for x1 in x]
2518 outlierN = epochCountImage.array[yIdxLocal, xIdxLocal]
2519 totalN = nImage.array[yIdxLocal, xIdxLocal]
2520
2521 # effectiveMaxNumEpochs is broken line (fraction of N) with characteristic config.maxNumEpochs
2522 effMaxNumEpochsHighN = (self.config.maxNumEpochs
2523 + self.config.maxFractionEpochsHigh*numpy.mean(totalN))
2524 effMaxNumEpochsLowN = self.config.maxFractionEpochsLow * numpy.mean(totalN)
2525 effectiveMaxNumEpochs = int(min(effMaxNumEpochsLowN, effMaxNumEpochsHighN))
2526 nPixelsBelowThreshold = numpy.count_nonzero((outlierN > 0)
2527 & (outlierN <= effectiveMaxNumEpochs))
2528 percentBelowThreshold = nPixelsBelowThreshold / len(outlierN)
2529 if percentBelowThreshold > self.config.spatialThreshold:
2530 maskSpanSetList.append(span)
2531
2532 if self.config.doPreserveContainedBySource and footprintsToExclude is not None:
2533 # If a candidate is contained by a footprint on the template coadd, do not clip
2534 filteredMaskSpanSetList = []
2535 for span in maskSpanSetList:
2536 doKeep = True
2537 for footprint in footprintsToExclude.positive.getFootprints():
2538 if footprint.spans.contains(span):
2539 doKeep = False
2540 break
2541 if doKeep:
2542 filteredMaskSpanSetList.append(span)
2543 maskSpanSetList = filteredMaskSpanSetList
2544
2545 return maskSpanSetList
2546
2547 def _readAndComputeWarpDiff(self, warpRef, imageScaler, templateCoadd):
2548 """Fetch a warp from the butler and return a warpDiff.
2549
2550 Parameters
2551 ----------
2553 Butler dataRef for the warp.
2555 An image scaler object.
2556 templateCoadd : `lsst.afw.image.Exposure`
2557 Exposure to be substracted from the scaled warp.
2558
2559 Returns
2560 -------
2562 Exposure of the image difference between the warp and template.
2563 """
2564
2565 # If the PSF-Matched warp did not exist for this direct warp
2566 # None is holding its place to maintain order in Gen 3
2567 if warpRef is None:
2568 return None
2569 # Warp comparison must use PSF-Matched Warps regardless of requested coadd warp type
2570 warpName = self.getTempExpDatasetName('psfMatched')
2571 if not isinstance(warpRef, DeferredDatasetHandle):
2572 if not warpRef.datasetExists(warpName):
2573 self.log.warning("Could not find %s %s; skipping it", warpName, warpRef.dataId)
2574 return None
2575 warp = warpRef.get(datasetType=warpName, immediate=True)
2576 # direct image scaler OK for PSF-matched Warp
2577 imageScaler.scaleMaskedImage(warp.getMaskedImage())
2578 mi = warp.getMaskedImage()
2579 if self.config.doScaleWarpVariance:
2580 try:
2581 self.scaleWarpVariance.run(mi)
2582 except Exception as exc:
2583 self.log.warning("Unable to rescale variance of warp (%s); leaving it as-is", exc)
2584 mi -= templateCoadd.getMaskedImage()
2585 return warp
2586
2587 def _dataRef2DebugPath(self, prefix, warpRef, coaddLevel=False):
2588 """Return a path to which to write debugging output.
2589
2590 Creates a hyphen-delimited string of dataId values for simple filenames.
2591
2592 Parameters
2593 ----------
2594 prefix : `str`
2595 Prefix for filename.
2597 Butler dataRef to make the path from.
2598 coaddLevel : `bool`, optional.
2599 If True, include only coadd-level keys (e.g., 'tract', 'patch',
2600 'filter', but no 'visit').
2601
2602 Returns
2603 -------
2604 result : `str`
2605 Path for debugging output.
2606 """
2607 if coaddLevel:
2608 keys = warpRef.getButler().getKeys(self.getCoaddDatasetName(self.warpType))
2609 else:
2610 keys = warpRef.dataId.keys()
2611 keyList = sorted(keys, reverse=True)
2612 directory = lsstDebug.Info(__name__).figPath if lsstDebug.Info(__name__).figPath else "."
2613 filename = "%s-%s.fits" % (prefix, '-'.join([str(warpRef.dataId[k]) for k in keyList]))
2614 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