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1# This file is part of pipe_tasks.
2#
3# LSST Data Management System
4# This product includes software developed by the
5# LSST Project (http://www.lsst.org/).
6# See COPYRIGHT file at the top of the source tree.
7#
8# This program is free software: you can redistribute it and/or modify
9# it under the terms of the GNU General Public License as published by
10# the Free Software Foundation, either version 3 of the License, or
11# (at your option) any later version.
12#
13# This program is distributed in the hope that it will be useful,
14# but WITHOUT ANY WARRANTY; without even the implied warranty of
15# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
16# GNU General Public License for more details.
17#
18# You should have received a copy of the LSST License Statement and
19# the GNU General Public License along with this program. If not,
20# see <https://www.lsstcorp.org/LegalNotices/>.
21#
22import os
23import copy
24import numpy
25import warnings
26import lsst.pex.config as pexConfig
27import lsst.pex.exceptions as pexExceptions
28import lsst.geom as geom
29import lsst.afw.geom as afwGeom
30import lsst.afw.image as afwImage
31import lsst.afw.math as afwMath
32import lsst.afw.table as afwTable
33import lsst.afw.detection as afwDet
34import lsst.coadd.utils as coaddUtils
35import lsst.pipe.base as pipeBase
36import lsst.meas.algorithms as measAlg
37import lsst.log as log
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
49from lsst.daf.butler import DeferredDatasetHandle
51__all__ = ["AssembleCoaddTask", "AssembleCoaddConnections", "AssembleCoaddConfig",
52 "SafeClipAssembleCoaddTask", "SafeClipAssembleCoaddConfig",
53 "CompareWarpAssembleCoaddTask", "CompareWarpAssembleCoaddConfig"]
56class AssembleCoaddConnections(pipeBase.PipelineTaskConnections,
57 dimensions=("tract", "patch", "band", "skymap"),
58 defaultTemplates={"inputCoaddName": "deep",
59 "outputCoaddName": "deep",
60 "warpType": "direct",
61 "warpTypeSuffix": "",
62 "fakesType": ""}):
63 inputWarps = pipeBase.connectionTypes.Input(
64 doc=("Input list of warps to be assemebled i.e. stacked."
65 "WarpType (e.g. direct, psfMatched) is controlled by the warpType config parameter"),
66 name="{inputCoaddName}Coadd_{warpType}Warp",
67 storageClass="ExposureF",
68 dimensions=("tract", "patch", "skymap", "visit", "instrument"),
69 deferLoad=True,
70 multiple=True
71 )
72 skyMap = pipeBase.connectionTypes.Input(
73 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures",
74 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
75 storageClass="SkyMap",
76 dimensions=("skymap", ),
77 )
78 selectedVisits = pipeBase.connectionTypes.Input(
79 doc="Selected visits to be coadded.",
80 name="{outputCoaddName}Visits",
81 storageClass="StructuredDataDict",
82 dimensions=("instrument", "tract", "patch", "skymap", "band")
83 )
84 brightObjectMask = pipeBase.connectionTypes.PrerequisiteInput(
85 doc=("Input Bright Object Mask mask produced with external catalogs to be applied to the mask plane"
86 " BRIGHT_OBJECT."),
87 name="brightObjectMask",
88 storageClass="ObjectMaskCatalog",
89 dimensions=("tract", "patch", "skymap", "band"),
90 )
91 coaddExposure = pipeBase.connectionTypes.Output(
92 doc="Output coadded exposure, produced by stacking input warps",
93 name="{fakesType}{outputCoaddName}Coadd{warpTypeSuffix}",
94 storageClass="ExposureF",
95 dimensions=("tract", "patch", "skymap", "band"),
96 )
97 nImage = pipeBase.connectionTypes.Output(
98 doc="Output image of number of input images per pixel",
99 name="{outputCoaddName}Coadd_nImage",
100 storageClass="ImageU",
101 dimensions=("tract", "patch", "skymap", "band"),
102 )
103 inputMap = pipeBase.connectionTypes.Output(
104 doc="Output healsparse map of input images",
105 name="{outputCoaddName}Coadd_inputMap",
106 storageClass="HealSparseMap",
107 dimensions=("tract", "patch", "skymap", "band"),
108 )
110 def __init__(self, *, config=None):
111 super().__init__(config=config)
113 # Override the connection's name template with config to replicate Gen2 behavior
114 # This duplicates some of the logic in the base class, due to wanting Gen2 and
115 # Gen3 configs to stay in sync. This should be removed when gen2 is deprecated
116 templateValues = {name: getattr(config.connections, name) for name in self.defaultTemplates}
117 templateValues['warpType'] = config.warpType
118 templateValues['warpTypeSuffix'] = makeCoaddSuffix(config.warpType)
119 if config.hasFakes:
120 templateValues['fakesType'] = "_fakes"
121 self._nameOverrides = {name: getattr(config.connections, name).format(**templateValues)
122 for name in self.allConnections}
123 self._typeNameToVarName = {v: k for k, v in self._nameOverrides.items()}
124 # End code to remove after deprecation
126 if not config.doMaskBrightObjects:
127 self.prerequisiteInputs.remove("brightObjectMask")
129 if not config.doSelectVisits:
130 self.inputs.remove("selectedVisits")
132 if not config.doNImage:
133 self.outputs.remove("nImage")
135 if not self.config.doInputMap:
136 self.outputs.remove("inputMap")
139class AssembleCoaddConfig(CoaddBaseTask.ConfigClass, pipeBase.PipelineTaskConfig,
140 pipelineConnections=AssembleCoaddConnections):
141 """Configuration parameters for the `AssembleCoaddTask`.
143 Notes
144 -----
145 The `doMaskBrightObjects` and `brightObjectMaskName` configuration options
146 only set the bitplane config.brightObjectMaskName. To make this useful you
147 *must* also configure the flags.pixel algorithm, for example by adding
149 .. code-block:: none
151 config.measurement.plugins["base_PixelFlags"].masksFpCenter.append("BRIGHT_OBJECT")
152 config.measurement.plugins["base_PixelFlags"].masksFpAnywhere.append("BRIGHT_OBJECT")
154 to your measureCoaddSources.py and forcedPhotCoadd.py config overrides.
155 """
156 warpType = pexConfig.Field(
157 doc="Warp name: one of 'direct' or 'psfMatched'",
158 dtype=str,
159 default="direct",
160 )
161 subregionSize = pexConfig.ListField(
162 dtype=int,
163 doc="Width, height of stack subregion size; "
164 "make small enough that a full stack of images will fit into memory at once.",
165 length=2,
166 default=(2000, 2000),
167 )
168 statistic = pexConfig.Field(
169 dtype=str,
170 doc="Main stacking statistic for aggregating over the epochs.",
171 default="MEANCLIP",
172 )
173 doSigmaClip = pexConfig.Field(
174 dtype=bool,
175 doc="Perform sigma clipped outlier rejection with MEANCLIP statistic? (DEPRECATED)",
176 default=False,
177 )
178 sigmaClip = pexConfig.Field(
179 dtype=float,
180 doc="Sigma for outlier rejection; ignored if non-clipping statistic selected.",
181 default=3.0,
182 )
183 clipIter = pexConfig.Field(
184 dtype=int,
185 doc="Number of iterations of outlier rejection; ignored if non-clipping statistic selected.",
186 default=2,
187 )
188 calcErrorFromInputVariance = pexConfig.Field(
189 dtype=bool,
190 doc="Calculate coadd variance from input variance by stacking statistic."
191 "Passed to StatisticsControl.setCalcErrorFromInputVariance()",
192 default=True,
193 )
194 scaleZeroPoint = pexConfig.ConfigurableField(
195 target=ScaleZeroPointTask,
196 doc="Task to adjust the photometric zero point of the coadd temp exposures",
197 )
198 doInterp = pexConfig.Field(
199 doc="Interpolate over NaN pixels? Also extrapolate, if necessary, but the results are ugly.",
200 dtype=bool,
201 default=True,
202 )
203 interpImage = pexConfig.ConfigurableField(
204 target=InterpImageTask,
205 doc="Task to interpolate (and extrapolate) over NaN pixels",
206 )
207 doWrite = pexConfig.Field(
208 doc="Persist coadd?",
209 dtype=bool,
210 default=True,
211 )
212 doNImage = pexConfig.Field(
213 doc="Create image of number of contributing exposures for each pixel",
214 dtype=bool,
215 default=False,
216 )
217 doUsePsfMatchedPolygons = pexConfig.Field(
218 doc="Use ValidPolygons from shrunk Psf-Matched Calexps? Should be set to True by CompareWarp only.",
219 dtype=bool,
220 default=False,
221 )
222 maskPropagationThresholds = pexConfig.DictField(
223 keytype=str,
224 itemtype=float,
225 doc=("Threshold (in fractional weight) of rejection at which we propagate a mask plane to "
226 "the coadd; that is, we set the mask bit on the coadd if the fraction the rejected frames "
227 "would have contributed exceeds this value."),
228 default={"SAT": 0.1},
229 )
230 removeMaskPlanes = pexConfig.ListField(dtype=str, default=["NOT_DEBLENDED"],
231 doc="Mask planes to remove before coadding")
232 doMaskBrightObjects = pexConfig.Field(dtype=bool, default=False,
233 doc="Set mask and flag bits for bright objects?")
234 brightObjectMaskName = pexConfig.Field(dtype=str, default="BRIGHT_OBJECT",
235 doc="Name of mask bit used for bright objects")
236 coaddPsf = pexConfig.ConfigField(
237 doc="Configuration for CoaddPsf",
238 dtype=measAlg.CoaddPsfConfig,
239 )
240 doAttachTransmissionCurve = pexConfig.Field(
241 dtype=bool, default=False, optional=False,
242 doc=("Attach a piecewise TransmissionCurve for the coadd? "
243 "(requires all input Exposures to have TransmissionCurves).")
244 )
245 hasFakes = pexConfig.Field(
246 dtype=bool,
247 default=False,
248 doc="Should be set to True if fake sources have been inserted into the input data."
249 )
250 doSelectVisits = pexConfig.Field(
251 doc="Coadd only visits selected by a SelectVisitsTask",
252 dtype=bool,
253 default=False,
254 )
255 doInputMap = pexConfig.Field(
256 doc="Create a bitwise map of coadd inputs",
257 dtype=bool,
258 default=False,
259 )
260 inputMapper = pexConfig.ConfigurableField(
261 doc="Input map creation subtask.",
262 target=HealSparseInputMapTask,
263 )
265 def setDefaults(self):
266 super().setDefaults()
267 self.badMaskPlanes = ["NO_DATA", "BAD", "SAT", "EDGE"]
269 def validate(self):
270 super().validate()
271 if self.doPsfMatch:
272 # Backwards compatibility.
273 # Configs do not have loggers
274 log.warn("Config doPsfMatch deprecated. Setting warpType='psfMatched'")
275 self.warpType = 'psfMatched'
276 if self.doSigmaClip and self.statistic != "MEANCLIP":
277 log.warn('doSigmaClip deprecated. To replicate behavior, setting statistic to "MEANCLIP"')
278 self.statistic = "MEANCLIP"
279 if self.doInterp and self.statistic not in ['MEAN', 'MEDIAN', 'MEANCLIP', 'VARIANCE', 'VARIANCECLIP']:
280 raise ValueError("Must set doInterp=False for statistic=%s, which does not "
281 "compute and set a non-zero coadd variance estimate." % (self.statistic))
283 unstackableStats = ['NOTHING', 'ERROR', 'ORMASK']
284 if not hasattr(afwMath.Property, self.statistic) or self.statistic in unstackableStats:
285 stackableStats = [str(k) for k in afwMath.Property.__members__.keys()
286 if str(k) not in unstackableStats]
287 raise ValueError("statistic %s is not allowed. Please choose one of %s."
288 % (self.statistic, stackableStats))
291class AssembleCoaddTask(CoaddBaseTask, pipeBase.PipelineTask):
292 """Assemble a coadded image from a set of warps (coadded temporary exposures).
294 We want to assemble a coadded image from a set of Warps (also called
295 coadded temporary exposures or ``coaddTempExps``).
296 Each input Warp covers a patch on the sky and corresponds to a single
297 run/visit/exposure of the covered patch. We provide the task with a list
298 of Warps (``selectDataList``) from which it selects Warps that cover the
299 specified patch (pointed at by ``dataRef``).
300 Each Warp that goes into a coadd will typically have an independent
301 photometric zero-point. Therefore, we must scale each Warp to set it to
302 a common photometric zeropoint. WarpType may be one of 'direct' or
303 'psfMatched', and the boolean configs `config.makeDirect` and
304 `config.makePsfMatched` set which of the warp types will be coadded.
305 The coadd is computed as a mean with optional outlier rejection.
306 Criteria for outlier rejection are set in `AssembleCoaddConfig`.
307 Finally, Warps can have bad 'NaN' pixels which received no input from the
308 source calExps. We interpolate over these bad (NaN) pixels.
310 `AssembleCoaddTask` uses several sub-tasks. These are
312 - `ScaleZeroPointTask`
313 - create and use an ``imageScaler`` object to scale the photometric zeropoint for each Warp
314 - `InterpImageTask`
315 - interpolate across bad pixels (NaN) in the final coadd
317 You can retarget these subtasks if you wish.
319 Notes
320 -----
321 The `lsst.pipe.base.cmdLineTask.CmdLineTask` interface supports a
322 flag ``-d`` to import ``debug.py`` from your ``PYTHONPATH``; see
323 `baseDebug` for more about ``debug.py`` files. `AssembleCoaddTask` has
324 no debug variables of its own. Some of the subtasks may support debug
325 variables. See the documentation for the subtasks for further information.
327 Examples
328 --------
329 `AssembleCoaddTask` assembles a set of warped images into a coadded image.
330 The `AssembleCoaddTask` can be invoked by running ``assembleCoadd.py``
331 with the flag '--legacyCoadd'. Usage of assembleCoadd.py expects two
332 inputs: a data reference to the tract patch and filter to be coadded, and
333 a list of Warps to attempt to coadd. These are specified using ``--id`` and
334 ``--selectId``, respectively:
336 .. code-block:: none
338 --id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]
339 --selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]
341 Only the Warps that cover the specified tract and patch will be coadded.
342 A list of the available optional arguments can be obtained by calling
343 ``assembleCoadd.py`` with the ``--help`` command line argument:
345 .. code-block:: none
347 assembleCoadd.py --help
349 To demonstrate usage of the `AssembleCoaddTask` in the larger context of
350 multi-band processing, we will generate the HSC-I & -R band coadds from
351 HSC engineering test data provided in the ``ci_hsc`` package. To begin,
352 assuming that the lsst stack has been already set up, we must set up the
353 obs_subaru and ``ci_hsc`` packages. This defines the environment variable
354 ``$CI_HSC_DIR`` and points at the location of the package. The raw HSC
355 data live in the ``$CI_HSC_DIR/raw directory``. To begin assembling the
356 coadds, we must first
358 - processCcd
359 - process the individual ccds in $CI_HSC_RAW to produce calibrated exposures
360 - makeSkyMap
361 - create a skymap that covers the area of the sky present in the raw exposures
362 - makeCoaddTempExp
363 - warp the individual calibrated exposures to the tangent plane of the coadd
365 We can perform all of these steps by running
367 .. code-block:: none
369 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988
371 This will produce warped exposures for each visit. To coadd the warped
372 data, we call assembleCoadd.py as follows:
374 .. code-block:: none
376 assembleCoadd.py --legacyCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \
377 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \
378 --selectId visit=903986 ccd=100 --selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \
379 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \
380 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \
381 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \
382 --selectId visit=903988 ccd=24
384 that will process the HSC-I band data. The results are written in
385 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``.
387 You may also choose to run:
389 .. code-block:: none
391 scons warp-903334 warp-903336 warp-903338 warp-903342 warp-903344 warp-903346
392 assembleCoadd.py --legacyCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-R \
393 --selectId visit=903334 ccd=16 --selectId visit=903334 ccd=22 --selectId visit=903334 ccd=23 \
394 --selectId visit=903334 ccd=100 --selectId visit=903336 ccd=17 --selectId visit=903336 ccd=24 \
395 --selectId visit=903338 ccd=18 --selectId visit=903338 ccd=25 --selectId visit=903342 ccd=4 \
396 --selectId visit=903342 ccd=10 --selectId visit=903342 ccd=100 --selectId visit=903344 ccd=0 \
397 --selectId visit=903344 ccd=5 --selectId visit=903344 ccd=11 --selectId visit=903346 ccd=1 \
398 --selectId visit=903346 ccd=6 --selectId visit=903346 ccd=12
400 to generate the coadd for the HSC-R band if you are interested in
401 following multiBand Coadd processing as discussed in `pipeTasks_multiBand`
402 (but note that normally, one would use the `SafeClipAssembleCoaddTask`
403 rather than `AssembleCoaddTask` to make the coadd.
404 """
405 ConfigClass = AssembleCoaddConfig
406 _DefaultName = "assembleCoadd"
408 def __init__(self, *args, **kwargs):
409 # TODO: DM-17415 better way to handle previously allowed passed args e.g.`AssembleCoaddTask(config)`
410 if args:
411 argNames = ["config", "name", "parentTask", "log"]
412 kwargs.update({k: v for k, v in zip(argNames, args)})
413 warnings.warn("AssembleCoadd received positional args, and casting them as kwargs: %s. "
414 "PipelineTask will not take positional args" % argNames, FutureWarning)
416 super().__init__(**kwargs)
417 self.makeSubtask("interpImage")
418 self.makeSubtask("scaleZeroPoint")
420 if self.config.doMaskBrightObjects:
421 mask = afwImage.Mask()
422 try:
423 self.brightObjectBitmask = 1 << mask.addMaskPlane(self.config.brightObjectMaskName)
424 except pexExceptions.LsstCppException:
425 raise RuntimeError("Unable to define mask plane for bright objects; planes used are %s" %
426 mask.getMaskPlaneDict().keys())
427 del mask
429 if self.config.doInputMap:
430 self.makeSubtask("inputMapper")
432 self.warpType = self.config.warpType
434 @utils.inheritDoc(pipeBase.PipelineTask)
435 def runQuantum(self, butlerQC, inputRefs, outputRefs):
436 # Docstring to be formatted with info from PipelineTask.runQuantum
437 """
438 Notes
439 -----
440 Assemble a coadd from a set of Warps.
442 PipelineTask (Gen3) entry point to Coadd a set of Warps.
443 Analogous to `runDataRef`, it prepares all the data products to be
444 passed to `run`, and processes the results before returning a struct
445 of results to be written out. AssembleCoadd cannot fit all Warps in memory.
446 Therefore, its inputs are accessed subregion by subregion
447 by the Gen3 `DeferredDatasetHandle` that is analagous to the Gen2
448 `lsst.daf.persistence.ButlerDataRef`. Any updates to this method should
449 correspond to an update in `runDataRef` while both entry points
450 are used.
451 """
452 inputData = butlerQC.get(inputRefs)
454 # Construct skyInfo expected by run
455 # Do not remove skyMap from inputData in case makeSupplementaryDataGen3 needs it
456 skyMap = inputData["skyMap"]
457 outputDataId = butlerQC.quantum.dataId
459 inputData['skyInfo'] = makeSkyInfo(skyMap,
460 tractId=outputDataId['tract'],
461 patchId=outputDataId['patch'])
463 if self.config.doSelectVisits:
464 warpRefList = self.filterWarps(inputData['inputWarps'], inputData['selectedVisits'])
465 else:
466 warpRefList = inputData['inputWarps']
468 # Perform same middle steps as `runDataRef` does
469 inputs = self.prepareInputs(warpRefList)
470 self.log.info("Found %d %s", len(inputs.tempExpRefList),
471 self.getTempExpDatasetName(self.warpType))
472 if len(inputs.tempExpRefList) == 0:
473 self.log.warn("No coadd temporary exposures found")
474 return
476 supplementaryData = self.makeSupplementaryDataGen3(butlerQC, inputRefs, outputRefs)
477 retStruct = self.run(inputData['skyInfo'], inputs.tempExpRefList, inputs.imageScalerList,
478 inputs.weightList, supplementaryData=supplementaryData)
480 inputData.setdefault('brightObjectMask', None)
481 self.processResults(retStruct.coaddExposure, inputData['brightObjectMask'], outputDataId)
483 if self.config.doWrite:
484 butlerQC.put(retStruct, outputRefs)
485 return retStruct
487 @pipeBase.timeMethod
488 def runDataRef(self, dataRef, selectDataList=None, warpRefList=None):
489 """Assemble a coadd from a set of Warps.
491 Pipebase.CmdlineTask entry point to Coadd a set of Warps.
492 Compute weights to be applied to each Warp and
493 find scalings to match the photometric zeropoint to a reference Warp.
494 Assemble the Warps using `run`. Interpolate over NaNs and
495 optionally write the coadd to disk. Return the coadded exposure.
497 Parameters
498 ----------
499 dataRef : `lsst.daf.persistence.butlerSubset.ButlerDataRef`
500 Data reference defining the patch for coaddition and the
501 reference Warp (if ``config.autoReference=False``).
502 Used to access the following data products:
503 - ``self.config.coaddName + "Coadd_skyMap"``
504 - ``self.config.coaddName + "Coadd_ + <warpType> + "Warp"`` (optionally)
505 - ``self.config.coaddName + "Coadd"``
506 selectDataList : `list`
507 List of data references to Calexps. Data to be coadded will be
508 selected from this list based on overlap with the patch defined
509 by dataRef, grouped by visit, and converted to a list of data
510 references to warps.
511 warpRefList : `list`
512 List of data references to Warps to be coadded.
513 Note: `warpRefList` is just the new name for `tempExpRefList`.
515 Returns
516 -------
517 retStruct : `lsst.pipe.base.Struct`
518 Result struct with components:
520 - ``coaddExposure``: coadded exposure (``Exposure``).
521 - ``nImage``: exposure count image (``Image``).
522 """
523 if selectDataList and warpRefList:
524 raise RuntimeError("runDataRef received both a selectDataList and warpRefList, "
525 "and which to use is ambiguous. Please pass only one.")
527 skyInfo = self.getSkyInfo(dataRef)
528 if warpRefList is None:
529 calExpRefList = self.selectExposures(dataRef, skyInfo, selectDataList=selectDataList)
530 if len(calExpRefList) == 0:
531 self.log.warn("No exposures to coadd")
532 return
533 self.log.info("Coadding %d exposures", len(calExpRefList))
535 warpRefList = self.getTempExpRefList(dataRef, calExpRefList)
537 inputData = self.prepareInputs(warpRefList)
538 self.log.info("Found %d %s", len(inputData.tempExpRefList),
539 self.getTempExpDatasetName(self.warpType))
540 if len(inputData.tempExpRefList) == 0:
541 self.log.warn("No coadd temporary exposures found")
542 return
544 supplementaryData = self.makeSupplementaryData(dataRef, warpRefList=inputData.tempExpRefList)
546 retStruct = self.run(skyInfo, inputData.tempExpRefList, inputData.imageScalerList,
547 inputData.weightList, supplementaryData=supplementaryData)
549 brightObjects = self.readBrightObjectMasks(dataRef) if self.config.doMaskBrightObjects else None
550 self.processResults(retStruct.coaddExposure, brightObjectMasks=brightObjects, dataId=dataRef.dataId)
552 if self.config.doWrite:
553 if self.getCoaddDatasetName(self.warpType) == "deepCoadd" and self.config.hasFakes:
554 coaddDatasetName = "fakes_" + self.getCoaddDatasetName(self.warpType)
555 else:
556 coaddDatasetName = self.getCoaddDatasetName(self.warpType)
557 self.log.info("Persisting %s" % coaddDatasetName)
558 dataRef.put(retStruct.coaddExposure, coaddDatasetName)
559 if self.config.doNImage and retStruct.nImage is not None:
560 dataRef.put(retStruct.nImage, self.getCoaddDatasetName(self.warpType) + '_nImage')
562 return retStruct
564 def processResults(self, coaddExposure, brightObjectMasks=None, dataId=None):
565 """Interpolate over missing data and mask bright stars.
567 Parameters
568 ----------
569 coaddExposure : `lsst.afw.image.Exposure`
570 The coadded exposure to process.
571 dataRef : `lsst.daf.persistence.ButlerDataRef`
572 Butler data reference for supplementary data.
573 """
574 if self.config.doInterp:
575 self.interpImage.run(coaddExposure.getMaskedImage(), planeName="NO_DATA")
576 # The variance must be positive; work around for DM-3201.
577 varArray = coaddExposure.variance.array
578 with numpy.errstate(invalid="ignore"):
579 varArray[:] = numpy.where(varArray > 0, varArray, numpy.inf)
581 if self.config.doMaskBrightObjects:
582 self.setBrightObjectMasks(coaddExposure, brightObjectMasks, dataId)
584 def makeSupplementaryData(self, dataRef, selectDataList=None, warpRefList=None):
585 """Make additional inputs to run() specific to subclasses (Gen2)
587 Duplicates interface of `runDataRef` method
588 Available to be implemented by subclasses only if they need the
589 coadd dataRef for performing preliminary processing before
590 assembling the coadd.
592 Parameters
593 ----------
594 dataRef : `lsst.daf.persistence.ButlerDataRef`
595 Butler data reference for supplementary data.
596 selectDataList : `list` (optional)
597 Optional List of data references to Calexps.
598 warpRefList : `list` (optional)
599 Optional List of data references to Warps.
600 """
601 return pipeBase.Struct()
603 def makeSupplementaryDataGen3(self, butlerQC, inputRefs, outputRefs):
604 """Make additional inputs to run() specific to subclasses (Gen3)
606 Duplicates interface of `runQuantum` method.
607 Available to be implemented by subclasses only if they need the
608 coadd dataRef for performing preliminary processing before
609 assembling the coadd.
611 Parameters
612 ----------
613 butlerQC : `lsst.pipe.base.ButlerQuantumContext`
614 Gen3 Butler object for fetching additional data products before
615 running the Task specialized for quantum being processed
616 inputRefs : `lsst.pipe.base.InputQuantizedConnection`
617 Attributes are the names of the connections describing input dataset types.
618 Values are DatasetRefs that task consumes for corresponding dataset type.
619 DataIds are guaranteed to match data objects in ``inputData``.
620 outputRefs : `lsst.pipe.base.OutputQuantizedConnection`
621 Attributes are the names of the connections describing output dataset types.
622 Values are DatasetRefs that task is to produce
623 for corresponding dataset type.
624 """
625 return pipeBase.Struct()
627 def getTempExpRefList(self, patchRef, calExpRefList):
628 """Generate list data references corresponding to warped exposures
629 that lie within the patch to be coadded.
631 Parameters
632 ----------
633 patchRef : `dataRef`
634 Data reference for patch.
635 calExpRefList : `list`
636 List of data references for input calexps.
638 Returns
639 -------
640 tempExpRefList : `list`
641 List of Warp/CoaddTempExp data references.
642 """
643 butler = patchRef.getButler()
644 groupData = groupPatchExposures(patchRef, calExpRefList, self.getCoaddDatasetName(self.warpType),
645 self.getTempExpDatasetName(self.warpType))
646 tempExpRefList = [getGroupDataRef(butler, self.getTempExpDatasetName(self.warpType),
647 g, groupData.keys) for
648 g in groupData.groups.keys()]
649 return tempExpRefList
651 def prepareInputs(self, refList):
652 """Prepare the input warps for coaddition by measuring the weight for
653 each warp and the scaling for the photometric zero point.
655 Each Warp has its own photometric zeropoint and background variance.
656 Before coadding these Warps together, compute a scale factor to
657 normalize the photometric zeropoint and compute the weight for each Warp.
659 Parameters
660 ----------
661 refList : `list`
662 List of data references to tempExp
664 Returns
665 -------
666 result : `lsst.pipe.base.Struct`
667 Result struct with components:
669 - ``tempExprefList``: `list` of data references to tempExp.
670 - ``weightList``: `list` of weightings.
671 - ``imageScalerList``: `list` of image scalers.
672 """
673 statsCtrl = afwMath.StatisticsControl()
674 statsCtrl.setNumSigmaClip(self.config.sigmaClip)
675 statsCtrl.setNumIter(self.config.clipIter)
676 statsCtrl.setAndMask(self.getBadPixelMask())
677 statsCtrl.setNanSafe(True)
678 # compute tempExpRefList: a list of tempExpRef that actually exist
679 # and weightList: a list of the weight of the associated coadd tempExp
680 # and imageScalerList: a list of scale factors for the associated coadd tempExp
681 tempExpRefList = []
682 weightList = []
683 imageScalerList = []
684 tempExpName = self.getTempExpDatasetName(self.warpType)
685 for tempExpRef in refList:
686 # Gen3's DeferredDatasetHandles are guaranteed to exist and
687 # therefore have no datasetExists() method
688 if not isinstance(tempExpRef, DeferredDatasetHandle):
689 if not tempExpRef.datasetExists(tempExpName):
690 self.log.warn("Could not find %s %s; skipping it", tempExpName, tempExpRef.dataId)
691 continue
693 tempExp = tempExpRef.get(datasetType=tempExpName, immediate=True)
694 # Ignore any input warp that is empty of data
695 if numpy.isnan(tempExp.image.array).all():
696 continue
697 maskedImage = tempExp.getMaskedImage()
698 imageScaler = self.scaleZeroPoint.computeImageScaler(
699 exposure=tempExp,
700 dataRef=tempExpRef,
701 )
702 try:
703 imageScaler.scaleMaskedImage(maskedImage)
704 except Exception as e:
705 self.log.warn("Scaling failed for %s (skipping it): %s", tempExpRef.dataId, e)
706 continue
707 statObj = afwMath.makeStatistics(maskedImage.getVariance(), maskedImage.getMask(),
708 afwMath.MEANCLIP, statsCtrl)
709 meanVar, meanVarErr = statObj.getResult(afwMath.MEANCLIP)
710 weight = 1.0 / float(meanVar)
711 if not numpy.isfinite(weight):
712 self.log.warn("Non-finite weight for %s: skipping", tempExpRef.dataId)
713 continue
714 self.log.info("Weight of %s %s = %0.3f", tempExpName, tempExpRef.dataId, weight)
716 del maskedImage
717 del tempExp
719 tempExpRefList.append(tempExpRef)
720 weightList.append(weight)
721 imageScalerList.append(imageScaler)
723 return pipeBase.Struct(tempExpRefList=tempExpRefList, weightList=weightList,
724 imageScalerList=imageScalerList)
726 def prepareStats(self, mask=None):
727 """Prepare the statistics for coadding images.
729 Parameters
730 ----------
731 mask : `int`, optional
732 Bit mask value to exclude from coaddition.
734 Returns
735 -------
736 stats : `lsst.pipe.base.Struct`
737 Statistics structure with the following fields:
739 - ``statsCtrl``: Statistics control object for coadd
740 (`lsst.afw.math.StatisticsControl`)
741 - ``statsFlags``: Statistic for coadd (`lsst.afw.math.Property`)
742 """
743 if mask is None:
744 mask = self.getBadPixelMask()
745 statsCtrl = afwMath.StatisticsControl()
746 statsCtrl.setNumSigmaClip(self.config.sigmaClip)
747 statsCtrl.setNumIter(self.config.clipIter)
748 statsCtrl.setAndMask(mask)
749 statsCtrl.setNanSafe(True)
750 statsCtrl.setWeighted(True)
751 statsCtrl.setCalcErrorFromInputVariance(self.config.calcErrorFromInputVariance)
752 for plane, threshold in self.config.maskPropagationThresholds.items():
753 bit = afwImage.Mask.getMaskPlane(plane)
754 statsCtrl.setMaskPropagationThreshold(bit, threshold)
755 statsFlags = afwMath.stringToStatisticsProperty(self.config.statistic)
756 return pipeBase.Struct(ctrl=statsCtrl, flags=statsFlags)
758 @pipeBase.timeMethod
759 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
760 altMaskList=None, mask=None, supplementaryData=None):
761 """Assemble a coadd from input warps
763 Assemble the coadd using the provided list of coaddTempExps. Since
764 the full coadd covers a patch (a large area), the assembly is
765 performed over small areas on the image at a time in order to
766 conserve memory usage. Iterate over subregions within the outer
767 bbox of the patch using `assembleSubregion` to stack the corresponding
768 subregions from the coaddTempExps with the statistic specified.
769 Set the edge bits the coadd mask based on the weight map.
771 Parameters
772 ----------
773 skyInfo : `lsst.pipe.base.Struct`
774 Struct with geometric information about the patch.
775 tempExpRefList : `list`
776 List of data references to Warps (previously called CoaddTempExps).
777 imageScalerList : `list`
778 List of image scalers.
779 weightList : `list`
780 List of weights
781 altMaskList : `list`, optional
782 List of alternate masks to use rather than those stored with
783 tempExp.
784 mask : `int`, optional
785 Bit mask value to exclude from coaddition.
786 supplementaryData : lsst.pipe.base.Struct, optional
787 Struct with additional data products needed to assemble coadd.
788 Only used by subclasses that implement `makeSupplementaryData`
789 and override `run`.
791 Returns
792 -------
793 result : `lsst.pipe.base.Struct`
794 Result struct with components:
796 - ``coaddExposure``: coadded exposure (``lsst.afw.image.Exposure``).
797 - ``nImage``: exposure count image (``lsst.afw.image.Image``), if requested.
798 - ``inputMap``: bit-wise map of inputs, if requested.
799 - ``warpRefList``: input list of refs to the warps (
800 ``lsst.daf.butler.DeferredDatasetHandle`` or
801 ``lsst.daf.persistence.ButlerDataRef``)
802 (unmodified)
803 - ``imageScalerList``: input list of image scalers (unmodified)
804 - ``weightList``: input list of weights (unmodified)
805 """
806 tempExpName = self.getTempExpDatasetName(self.warpType)
807 self.log.info("Assembling %s %s", len(tempExpRefList), tempExpName)
808 stats = self.prepareStats(mask=mask)
810 if altMaskList is None:
811 altMaskList = [None]*len(tempExpRefList)
813 coaddExposure = afwImage.ExposureF(skyInfo.bbox, skyInfo.wcs)
814 coaddExposure.setPhotoCalib(self.scaleZeroPoint.getPhotoCalib())
815 coaddExposure.getInfo().setCoaddInputs(self.inputRecorder.makeCoaddInputs())
816 self.assembleMetadata(coaddExposure, tempExpRefList, weightList)
817 coaddMaskedImage = coaddExposure.getMaskedImage()
818 subregionSizeArr = self.config.subregionSize
819 subregionSize = geom.Extent2I(subregionSizeArr[0], subregionSizeArr[1])
820 # if nImage is requested, create a zero one which can be passed to assembleSubregion
821 if self.config.doNImage:
822 nImage = afwImage.ImageU(skyInfo.bbox)
823 else:
824 nImage = None
825 # If inputMap is requested, create the initial version that can be masked in
826 # assembleSubregion.
827 if self.config.doInputMap:
828 self.inputMapper.build_ccd_input_map(skyInfo.bbox,
829 skyInfo.wcs,
830 coaddExposure.getInfo().getCoaddInputs().ccds)
832 for subBBox in self._subBBoxIter(skyInfo.bbox, subregionSize):
833 try:
834 self.assembleSubregion(coaddExposure, subBBox, tempExpRefList, imageScalerList,
835 weightList, altMaskList, stats.flags, stats.ctrl,
836 nImage=nImage)
837 except Exception as e:
838 self.log.fatal("Cannot compute coadd %s: %s", subBBox, e)
840 # If inputMap is requested, we must finalize the map after the accumulation.
841 if self.config.doInputMap:
842 self.inputMapper.finalize_ccd_input_map_mask()
843 inputMap = self.inputMapper.ccd_input_map
844 else:
845 inputMap = None
847 self.setInexactPsf(coaddMaskedImage.getMask())
848 # Despite the name, the following doesn't really deal with "EDGE" pixels: it identifies
849 # pixels that didn't receive any unmasked inputs (as occurs around the edge of the field).
850 coaddUtils.setCoaddEdgeBits(coaddMaskedImage.getMask(), coaddMaskedImage.getVariance())
851 return pipeBase.Struct(coaddExposure=coaddExposure, nImage=nImage,
852 warpRefList=tempExpRefList, imageScalerList=imageScalerList,
853 weightList=weightList, inputMap=inputMap)
855 def assembleMetadata(self, coaddExposure, tempExpRefList, weightList):
856 """Set the metadata for the coadd.
858 This basic implementation sets the filter from the first input.
860 Parameters
861 ----------
862 coaddExposure : `lsst.afw.image.Exposure`
863 The target exposure for the coadd.
864 tempExpRefList : `list`
865 List of data references to tempExp.
866 weightList : `list`
867 List of weights.
868 """
869 assert len(tempExpRefList) == len(weightList), "Length mismatch"
870 tempExpName = self.getTempExpDatasetName(self.warpType)
871 # We load a single pixel of each coaddTempExp, because we just want to get at the metadata
872 # (and we need more than just the PropertySet that contains the header), which is not possible
873 # with the current butler (see #2777).
874 bbox = geom.Box2I(coaddExposure.getBBox().getMin(), geom.Extent2I(1, 1))
876 if isinstance(tempExpRefList[0], DeferredDatasetHandle):
877 # Gen 3 API
878 tempExpList = [tempExpRef.get(parameters={'bbox': bbox}) for tempExpRef in tempExpRefList]
879 else:
880 # Gen 2 API. Delete this when Gen 2 retired
881 tempExpList = [tempExpRef.get(tempExpName + "_sub", bbox=bbox, immediate=True)
882 for tempExpRef in tempExpRefList]
883 numCcds = sum(len(tempExp.getInfo().getCoaddInputs().ccds) for tempExp in tempExpList)
885 # Set the coadd FilterLabel to the band of the first input exposure:
886 # Coadds are calibrated, so the physical label is now meaningless.
887 coaddExposure.setFilterLabel(afwImage.FilterLabel(tempExpList[0].getFilterLabel().bandLabel))
888 coaddInputs = coaddExposure.getInfo().getCoaddInputs()
889 coaddInputs.ccds.reserve(numCcds)
890 coaddInputs.visits.reserve(len(tempExpList))
892 for tempExp, weight in zip(tempExpList, weightList):
893 self.inputRecorder.addVisitToCoadd(coaddInputs, tempExp, weight)
895 if self.config.doUsePsfMatchedPolygons:
896 self.shrinkValidPolygons(coaddInputs)
898 coaddInputs.visits.sort()
899 if self.warpType == "psfMatched":
900 # The modelPsf BBox for a psfMatchedWarp/coaddTempExp was dynamically defined by
901 # ModelPsfMatchTask as the square box bounding its spatially-variable, pre-matched WarpedPsf.
902 # Likewise, set the PSF of a PSF-Matched Coadd to the modelPsf
903 # having the maximum width (sufficient because square)
904 modelPsfList = [tempExp.getPsf() for tempExp in tempExpList]
905 modelPsfWidthList = [modelPsf.computeBBox().getWidth() for modelPsf in modelPsfList]
906 psf = modelPsfList[modelPsfWidthList.index(max(modelPsfWidthList))]
907 else:
908 psf = measAlg.CoaddPsf(coaddInputs.ccds, coaddExposure.getWcs(),
909 self.config.coaddPsf.makeControl())
910 coaddExposure.setPsf(psf)
911 apCorrMap = measAlg.makeCoaddApCorrMap(coaddInputs.ccds, coaddExposure.getBBox(afwImage.PARENT),
912 coaddExposure.getWcs())
913 coaddExposure.getInfo().setApCorrMap(apCorrMap)
914 if self.config.doAttachTransmissionCurve:
915 transmissionCurve = measAlg.makeCoaddTransmissionCurve(coaddExposure.getWcs(), coaddInputs.ccds)
916 coaddExposure.getInfo().setTransmissionCurve(transmissionCurve)
918 def assembleSubregion(self, coaddExposure, bbox, tempExpRefList, imageScalerList, weightList,
919 altMaskList, statsFlags, statsCtrl, nImage=None):
920 """Assemble the coadd for a sub-region.
922 For each coaddTempExp, check for (and swap in) an alternative mask
923 if one is passed. Remove mask planes listed in
924 `config.removeMaskPlanes`. Finally, stack the actual exposures using
925 `lsst.afw.math.statisticsStack` with the statistic specified by
926 statsFlags. Typically, the statsFlag will be one of lsst.afw.math.MEAN for
927 a mean-stack or `lsst.afw.math.MEANCLIP` for outlier rejection using
928 an N-sigma clipped mean where N and iterations are specified by
929 statsCtrl. Assign the stacked subregion back to the coadd.
931 Parameters
932 ----------
933 coaddExposure : `lsst.afw.image.Exposure`
934 The target exposure for the coadd.
935 bbox : `lsst.geom.Box`
936 Sub-region to coadd.
937 tempExpRefList : `list`
938 List of data reference to tempExp.
939 imageScalerList : `list`
940 List of image scalers.
941 weightList : `list`
942 List of weights.
943 altMaskList : `list`
944 List of alternate masks to use rather than those stored with
945 tempExp, or None. Each element is dict with keys = mask plane
946 name to which to add the spans.
947 statsFlags : `lsst.afw.math.Property`
948 Property object for statistic for coadd.
949 statsCtrl : `lsst.afw.math.StatisticsControl`
950 Statistics control object for coadd.
951 nImage : `lsst.afw.image.ImageU`, optional
952 Keeps track of exposure count for each pixel.
953 """
954 self.log.debug("Computing coadd over %s", bbox)
955 tempExpName = self.getTempExpDatasetName(self.warpType)
956 coaddExposure.mask.addMaskPlane("REJECTED")
957 coaddExposure.mask.addMaskPlane("CLIPPED")
958 coaddExposure.mask.addMaskPlane("SENSOR_EDGE")
959 maskMap = self.setRejectedMaskMapping(statsCtrl)
960 clipped = afwImage.Mask.getPlaneBitMask("CLIPPED")
961 maskedImageList = []
962 if nImage is not None:
963 subNImage = afwImage.ImageU(bbox.getWidth(), bbox.getHeight())
964 for tempExpRef, imageScaler, altMask in zip(tempExpRefList, imageScalerList, altMaskList):
966 if isinstance(tempExpRef, DeferredDatasetHandle):
967 # Gen 3 API
968 exposure = tempExpRef.get(parameters={'bbox': bbox})
969 else:
970 # Gen 2 API. Delete this when Gen 2 retired
971 exposure = tempExpRef.get(tempExpName + "_sub", bbox=bbox)
973 maskedImage = exposure.getMaskedImage()
974 mask = maskedImage.getMask()
975 if altMask is not None:
976 self.applyAltMaskPlanes(mask, altMask)
977 imageScaler.scaleMaskedImage(maskedImage)
979 # Add 1 for each pixel which is not excluded by the exclude mask.
980 # In legacyCoadd, pixels may also be excluded by afwMath.statisticsStack.
981 if nImage is not None:
982 subNImage.getArray()[maskedImage.getMask().getArray() & statsCtrl.getAndMask() == 0] += 1
983 if self.config.removeMaskPlanes:
984 self.removeMaskPlanes(maskedImage)
985 maskedImageList.append(maskedImage)
987 if self.config.doInputMap:
988 visit = exposure.getInfo().getCoaddInputs().visits[0].getId()
989 self.inputMapper.mask_warp_bbox(bbox, visit, mask, statsCtrl.getAndMask())
991 with self.timer("stack"):
992 coaddSubregion = afwMath.statisticsStack(maskedImageList, statsFlags, statsCtrl, weightList,
993 clipped, # also set output to CLIPPED if sigma-clipped
994 maskMap)
995 coaddExposure.maskedImage.assign(coaddSubregion, bbox)
996 if nImage is not None:
997 nImage.assign(subNImage, bbox)
999 def removeMaskPlanes(self, maskedImage):
1000 """Unset the mask of an image for mask planes specified in the config.
1002 Parameters
1003 ----------
1004 maskedImage : `lsst.afw.image.MaskedImage`
1005 The masked image to be modified.
1006 """
1007 mask = maskedImage.getMask()
1008 for maskPlane in self.config.removeMaskPlanes:
1009 try:
1010 mask &= ~mask.getPlaneBitMask(maskPlane)
1011 except pexExceptions.InvalidParameterError:
1012 self.log.debug("Unable to remove mask plane %s: no mask plane with that name was found.",
1013 maskPlane)
1015 @staticmethod
1016 def setRejectedMaskMapping(statsCtrl):
1017 """Map certain mask planes of the warps to new planes for the coadd.
1019 If a pixel is rejected due to a mask value other than EDGE, NO_DATA,
1020 or CLIPPED, set it to REJECTED on the coadd.
1021 If a pixel is rejected due to EDGE, set the coadd pixel to SENSOR_EDGE.
1022 If a pixel is rejected due to CLIPPED, set the coadd pixel to CLIPPED.
1024 Parameters
1025 ----------
1026 statsCtrl : `lsst.afw.math.StatisticsControl`
1027 Statistics control object for coadd
1029 Returns
1030 -------
1031 maskMap : `list` of `tuple` of `int`
1032 A list of mappings of mask planes of the warped exposures to
1033 mask planes of the coadd.
1034 """
1035 edge = afwImage.Mask.getPlaneBitMask("EDGE")
1036 noData = afwImage.Mask.getPlaneBitMask("NO_DATA")
1037 clipped = afwImage.Mask.getPlaneBitMask("CLIPPED")
1038 toReject = statsCtrl.getAndMask() & (~noData) & (~edge) & (~clipped)
1039 maskMap = [(toReject, afwImage.Mask.getPlaneBitMask("REJECTED")),
1040 (edge, afwImage.Mask.getPlaneBitMask("SENSOR_EDGE")),
1041 (clipped, clipped)]
1042 return maskMap
1044 def applyAltMaskPlanes(self, mask, altMaskSpans):
1045 """Apply in place alt mask formatted as SpanSets to a mask.
1047 Parameters
1048 ----------
1049 mask : `lsst.afw.image.Mask`
1050 Original mask.
1051 altMaskSpans : `dict`
1052 SpanSet lists to apply. Each element contains the new mask
1053 plane name (e.g. "CLIPPED and/or "NO_DATA") as the key,
1054 and list of SpanSets to apply to the mask.
1056 Returns
1057 -------
1058 mask : `lsst.afw.image.Mask`
1059 Updated mask.
1060 """
1061 if self.config.doUsePsfMatchedPolygons:
1062 if ("NO_DATA" in altMaskSpans) and ("NO_DATA" in self.config.badMaskPlanes):
1063 # Clear away any other masks outside the validPolygons. These pixels are no longer
1064 # contributing to inexact PSFs, and will still be rejected because of NO_DATA
1065 # self.config.doUsePsfMatchedPolygons should be True only in CompareWarpAssemble
1066 # This mask-clearing step must only occur *before* applying the new masks below
1067 for spanSet in altMaskSpans['NO_DATA']:
1068 spanSet.clippedTo(mask.getBBox()).clearMask(mask, self.getBadPixelMask())
1070 for plane, spanSetList in altMaskSpans.items():
1071 maskClipValue = mask.addMaskPlane(plane)
1072 for spanSet in spanSetList:
1073 spanSet.clippedTo(mask.getBBox()).setMask(mask, 2**maskClipValue)
1074 return mask
1076 def shrinkValidPolygons(self, coaddInputs):
1077 """Shrink coaddInputs' ccds' ValidPolygons in place.
1079 Either modify each ccd's validPolygon in place, or if CoaddInputs
1080 does not have a validPolygon, create one from its bbox.
1082 Parameters
1083 ----------
1084 coaddInputs : `lsst.afw.image.coaddInputs`
1085 Original mask.
1087 """
1088 for ccd in coaddInputs.ccds:
1089 polyOrig = ccd.getValidPolygon()
1090 validPolyBBox = polyOrig.getBBox() if polyOrig else ccd.getBBox()
1091 validPolyBBox.grow(-self.config.matchingKernelSize//2)
1092 if polyOrig:
1093 validPolygon = polyOrig.intersectionSingle(validPolyBBox)
1094 else:
1095 validPolygon = afwGeom.polygon.Polygon(geom.Box2D(validPolyBBox))
1096 ccd.setValidPolygon(validPolygon)
1098 def readBrightObjectMasks(self, dataRef):
1099 """Retrieve the bright object masks.
1101 Returns None on failure.
1103 Parameters
1104 ----------
1105 dataRef : `lsst.daf.persistence.butlerSubset.ButlerDataRef`
1106 A Butler dataRef.
1108 Returns
1109 -------
1110 result : `lsst.daf.persistence.butlerSubset.ButlerDataRef`
1111 Bright object mask from the Butler object, or None if it cannot
1112 be retrieved.
1113 """
1114 try:
1115 return dataRef.get(datasetType="brightObjectMask", immediate=True)
1116 except Exception as e:
1117 self.log.warn("Unable to read brightObjectMask for %s: %s", dataRef.dataId, e)
1118 return None
1120 def setBrightObjectMasks(self, exposure, brightObjectMasks, dataId=None):
1121 """Set the bright object masks.
1123 Parameters
1124 ----------
1125 exposure : `lsst.afw.image.Exposure`
1126 Exposure under consideration.
1127 dataId : `lsst.daf.persistence.dataId`
1128 Data identifier dict for patch.
1129 brightObjectMasks : `lsst.afw.table`
1130 Table of bright objects to mask.
1131 """
1133 if brightObjectMasks is None:
1134 self.log.warn("Unable to apply bright object mask: none supplied")
1135 return
1136 self.log.info("Applying %d bright object masks to %s", len(brightObjectMasks), dataId)
1137 mask = exposure.getMaskedImage().getMask()
1138 wcs = exposure.getWcs()
1139 plateScale = wcs.getPixelScale().asArcseconds()
1141 for rec in brightObjectMasks:
1142 center = geom.PointI(wcs.skyToPixel(rec.getCoord()))
1143 if rec["type"] == "box":
1144 assert rec["angle"] == 0.0, ("Angle != 0 for mask object %s" % rec["id"])
1145 width = rec["width"].asArcseconds()/plateScale # convert to pixels
1146 height = rec["height"].asArcseconds()/plateScale # convert to pixels
1148 halfSize = geom.ExtentI(0.5*width, 0.5*height)
1149 bbox = geom.Box2I(center - halfSize, center + halfSize)
1151 bbox = geom.BoxI(geom.PointI(int(center[0] - 0.5*width), int(center[1] - 0.5*height)),
1152 geom.PointI(int(center[0] + 0.5*width), int(center[1] + 0.5*height)))
1153 spans = afwGeom.SpanSet(bbox)
1154 elif rec["type"] == "circle":
1155 radius = int(rec["radius"].asArcseconds()/plateScale) # convert to pixels
1156 spans = afwGeom.SpanSet.fromShape(radius, offset=center)
1157 else:
1158 self.log.warn("Unexpected region type %s at %s" % rec["type"], center)
1159 continue
1160 spans.clippedTo(mask.getBBox()).setMask(mask, self.brightObjectBitmask)
1162 def setInexactPsf(self, mask):
1163 """Set INEXACT_PSF mask plane.
1165 If any of the input images isn't represented in the coadd (due to
1166 clipped pixels or chip gaps), the `CoaddPsf` will be inexact. Flag
1167 these pixels.
1169 Parameters
1170 ----------
1171 mask : `lsst.afw.image.Mask`
1172 Coadded exposure's mask, modified in-place.
1173 """
1174 mask.addMaskPlane("INEXACT_PSF")
1175 inexactPsf = mask.getPlaneBitMask("INEXACT_PSF")
1176 sensorEdge = mask.getPlaneBitMask("SENSOR_EDGE") # chip edges (so PSF is discontinuous)
1177 clipped = mask.getPlaneBitMask("CLIPPED") # pixels clipped from coadd
1178 rejected = mask.getPlaneBitMask("REJECTED") # pixels rejected from coadd due to masks
1179 array = mask.getArray()
1180 selected = array & (sensorEdge | clipped | rejected) > 0
1181 array[selected] |= inexactPsf
1183 @classmethod
1184 def _makeArgumentParser(cls):
1185 """Create an argument parser.
1186 """
1187 parser = pipeBase.ArgumentParser(name=cls._DefaultName)
1188 parser.add_id_argument("--id", cls.ConfigClass().coaddName + "Coadd_"
1189 + cls.ConfigClass().warpType + "Warp",
1190 help="data ID, e.g. --id tract=12345 patch=1,2",
1191 ContainerClass=AssembleCoaddDataIdContainer)
1192 parser.add_id_argument("--selectId", "calexp", help="data ID, e.g. --selectId visit=6789 ccd=0..9",
1193 ContainerClass=SelectDataIdContainer)
1194 return parser
1196 @staticmethod
1197 def _subBBoxIter(bbox, subregionSize):
1198 """Iterate over subregions of a bbox.
1200 Parameters
1201 ----------
1202 bbox : `lsst.geom.Box2I`
1203 Bounding box over which to iterate.
1204 subregionSize: `lsst.geom.Extent2I`
1205 Size of sub-bboxes.
1207 Yields
1208 ------
1209 subBBox : `lsst.geom.Box2I`
1210 Next sub-bounding box of size ``subregionSize`` or smaller; each ``subBBox``
1211 is contained within ``bbox``, so it may be smaller than ``subregionSize`` at
1212 the edges of ``bbox``, but it will never be empty.
1213 """
1214 if bbox.isEmpty():
1215 raise RuntimeError("bbox %s is empty" % (bbox,))
1216 if subregionSize[0] < 1 or subregionSize[1] < 1:
1217 raise RuntimeError("subregionSize %s must be nonzero" % (subregionSize,))
1219 for rowShift in range(0, bbox.getHeight(), subregionSize[1]):
1220 for colShift in range(0, bbox.getWidth(), subregionSize[0]):
1221 subBBox = geom.Box2I(bbox.getMin() + geom.Extent2I(colShift, rowShift), subregionSize)
1222 subBBox.clip(bbox)
1223 if subBBox.isEmpty():
1224 raise RuntimeError("Bug: empty bbox! bbox=%s, subregionSize=%s, "
1225 "colShift=%s, rowShift=%s" %
1226 (bbox, subregionSize, colShift, rowShift))
1227 yield subBBox
1229 def filterWarps(self, inputs, goodVisits):
1230 """Return list of only inputRefs with visitId in goodVisits ordered by goodVisit
1232 Parameters
1233 ----------
1234 inputs : list
1235 List of `lsst.pipe.base.connections.DeferredDatasetRef` with dataId containing visit
1236 goodVisit : `dict`
1237 Dictionary with good visitIds as the keys. Value ignored.
1239 Returns:
1240 --------
1241 filteredInputs : `list`
1242 Filtered and sorted list of `lsst.pipe.base.connections.DeferredDatasetRef`
1243 """
1244 inputWarpDict = {inputRef.ref.dataId['visit']: inputRef for inputRef in inputs}
1245 filteredInputs = []
1246 for visit in goodVisits.keys():
1247 filteredInputs.append(inputWarpDict[visit])
1248 return filteredInputs
1251class AssembleCoaddDataIdContainer(pipeBase.DataIdContainer):
1252 """A version of `lsst.pipe.base.DataIdContainer` specialized for assembleCoadd.
1253 """
1255 def makeDataRefList(self, namespace):
1256 """Make self.refList from self.idList.
1258 Parameters
1259 ----------
1260 namespace
1261 Results of parsing command-line (with ``butler`` and ``log`` elements).
1262 """
1263 datasetType = namespace.config.coaddName + "Coadd"
1264 keysCoadd = namespace.butler.getKeys(datasetType=datasetType, level=self.level)
1266 for dataId in self.idList:
1267 # tract and patch are required
1268 for key in keysCoadd:
1269 if key not in dataId:
1270 raise RuntimeError("--id must include " + key)
1272 dataRef = namespace.butler.dataRef(
1273 datasetType=datasetType,
1274 dataId=dataId,
1275 )
1276 self.refList.append(dataRef)
1279def countMaskFromFootprint(mask, footprint, bitmask, ignoreMask):
1280 """Function to count the number of pixels with a specific mask in a
1281 footprint.
1283 Find the intersection of mask & footprint. Count all pixels in the mask
1284 that are in the intersection that have bitmask set but do not have
1285 ignoreMask set. Return the count.
1287 Parameters
1288 ----------
1289 mask : `lsst.afw.image.Mask`
1290 Mask to define intersection region by.
1291 footprint : `lsst.afw.detection.Footprint`
1292 Footprint to define the intersection region by.
1293 bitmask
1294 Specific mask that we wish to count the number of occurances of.
1295 ignoreMask
1296 Pixels to not consider.
1298 Returns
1299 -------
1300 result : `int`
1301 Count of number of pixels in footprint with specified mask.
1302 """
1303 bbox = footprint.getBBox()
1304 bbox.clip(mask.getBBox(afwImage.PARENT))
1305 fp = afwImage.Mask(bbox)
1306 subMask = mask.Factory(mask, bbox, afwImage.PARENT)
1307 footprint.spans.setMask(fp, bitmask)
1308 return numpy.logical_and((subMask.getArray() & fp.getArray()) > 0,
1309 (subMask.getArray() & ignoreMask) == 0).sum()
1312class SafeClipAssembleCoaddConfig(AssembleCoaddConfig, pipelineConnections=AssembleCoaddConnections):
1313 """Configuration parameters for the SafeClipAssembleCoaddTask.
1314 """
1315 clipDetection = pexConfig.ConfigurableField(
1316 target=SourceDetectionTask,
1317 doc="Detect sources on difference between unclipped and clipped coadd")
1318 minClipFootOverlap = pexConfig.Field(
1319 doc="Minimum fractional overlap of clipped footprint with visit DETECTED to be clipped",
1320 dtype=float,
1321 default=0.6
1322 )
1323 minClipFootOverlapSingle = pexConfig.Field(
1324 doc="Minimum fractional overlap of clipped footprint with visit DETECTED to be "
1325 "clipped when only one visit overlaps",
1326 dtype=float,
1327 default=0.5
1328 )
1329 minClipFootOverlapDouble = pexConfig.Field(
1330 doc="Minimum fractional overlap of clipped footprints with visit DETECTED to be "
1331 "clipped when two visits overlap",
1332 dtype=float,
1333 default=0.45
1334 )
1335 maxClipFootOverlapDouble = pexConfig.Field(
1336 doc="Maximum fractional overlap of clipped footprints with visit DETECTED when "
1337 "considering two visits",
1338 dtype=float,
1339 default=0.15
1340 )
1341 minBigOverlap = pexConfig.Field(
1342 doc="Minimum number of pixels in footprint to use DETECTED mask from the single visits "
1343 "when labeling clipped footprints",
1344 dtype=int,
1345 default=100
1346 )
1348 def setDefaults(self):
1349 """Set default values for clipDetection.
1351 Notes
1352 -----
1353 The numeric values for these configuration parameters were
1354 empirically determined, future work may further refine them.
1355 """
1356 AssembleCoaddConfig.setDefaults(self)
1357 self.clipDetection.doTempLocalBackground = False
1358 self.clipDetection.reEstimateBackground = False
1359 self.clipDetection.returnOriginalFootprints = False
1360 self.clipDetection.thresholdPolarity = "both"
1361 self.clipDetection.thresholdValue = 2
1362 self.clipDetection.nSigmaToGrow = 2
1363 self.clipDetection.minPixels = 4
1364 self.clipDetection.isotropicGrow = True
1365 self.clipDetection.thresholdType = "pixel_stdev"
1366 self.sigmaClip = 1.5
1367 self.clipIter = 3
1368 self.statistic = "MEAN"
1370 def validate(self):
1371 if self.doSigmaClip:
1372 log.warn("Additional Sigma-clipping not allowed in Safe-clipped Coadds. "
1373 "Ignoring doSigmaClip.")
1374 self.doSigmaClip = False
1375 if self.statistic != "MEAN":
1376 raise ValueError("Only MEAN statistic allowed for final stacking in SafeClipAssembleCoadd "
1377 "(%s chosen). Please set statistic to MEAN."
1378 % (self.statistic))
1379 AssembleCoaddTask.ConfigClass.validate(self)
1382class SafeClipAssembleCoaddTask(AssembleCoaddTask):
1383 """Assemble a coadded image from a set of coadded temporary exposures,
1384 being careful to clip & flag areas with potential artifacts.
1386 In ``AssembleCoaddTask``, we compute the coadd as an clipped mean (i.e.,
1387 we clip outliers). The problem with doing this is that when computing the
1388 coadd PSF at a given location, individual visit PSFs from visits with
1389 outlier pixels contribute to the coadd PSF and cannot be treated correctly.
1390 In this task, we correct for this behavior by creating a new
1391 ``badMaskPlane`` 'CLIPPED'. We populate this plane on the input
1392 coaddTempExps and the final coadd where
1394 i. difference imaging suggests that there is an outlier and
1395 ii. this outlier appears on only one or two images.
1397 Such regions will not contribute to the final coadd. Furthermore, any
1398 routine to determine the coadd PSF can now be cognizant of clipped regions.
1399 Note that the algorithm implemented by this task is preliminary and works
1400 correctly for HSC data. Parameter modifications and or considerable
1401 redesigning of the algorithm is likley required for other surveys.
1403 ``SafeClipAssembleCoaddTask`` uses a ``SourceDetectionTask``
1404 "clipDetection" subtask and also sub-classes ``AssembleCoaddTask``.
1405 You can retarget the ``SourceDetectionTask`` "clipDetection" subtask
1406 if you wish.
1408 Notes
1409 -----
1410 The `lsst.pipe.base.cmdLineTask.CmdLineTask` interface supports a
1411 flag ``-d`` to import ``debug.py`` from your ``PYTHONPATH``;
1412 see `baseDebug` for more about ``debug.py`` files.
1413 `SafeClipAssembleCoaddTask` has no debug variables of its own.
1414 The ``SourceDetectionTask`` "clipDetection" subtasks may support debug
1415 variables. See the documetation for `SourceDetectionTask` "clipDetection"
1416 for further information.
1418 Examples
1419 --------
1420 `SafeClipAssembleCoaddTask` assembles a set of warped ``coaddTempExp``
1421 images into a coadded image. The `SafeClipAssembleCoaddTask` is invoked by
1422 running assembleCoadd.py *without* the flag '--legacyCoadd'.
1424 Usage of ``assembleCoadd.py`` expects a data reference to the tract patch
1425 and filter to be coadded (specified using
1426 '--id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]')
1427 along with a list of coaddTempExps to attempt to coadd (specified using
1428 '--selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]').
1429 Only the coaddTempExps that cover the specified tract and patch will be
1430 coadded. A list of the available optional arguments can be obtained by
1431 calling assembleCoadd.py with the --help command line argument:
1433 .. code-block:: none
1435 assembleCoadd.py --help
1437 To demonstrate usage of the `SafeClipAssembleCoaddTask` in the larger
1438 context of multi-band processing, we will generate the HSC-I & -R band
1439 coadds from HSC engineering test data provided in the ci_hsc package.
1440 To begin, assuming that the lsst stack has been already set up, we must
1441 set up the obs_subaru and ci_hsc packages. This defines the environment
1442 variable $CI_HSC_DIR and points at the location of the package. The raw
1443 HSC data live in the ``$CI_HSC_DIR/raw`` directory. To begin assembling
1444 the coadds, we must first
1446 - ``processCcd``
1447 process the individual ccds in $CI_HSC_RAW to produce calibrated exposures
1448 - ``makeSkyMap``
1449 create a skymap that covers the area of the sky present in the raw exposures
1450 - ``makeCoaddTempExp``
1451 warp the individual calibrated exposures to the tangent plane of the coadd</DD>
1453 We can perform all of these steps by running
1455 .. code-block:: none
1457 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988
1459 This will produce warped coaddTempExps for each visit. To coadd the
1460 warped data, we call ``assembleCoadd.py`` as follows:
1462 .. code-block:: none
1464 assembleCoadd.py $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \
1465 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \
1466 --selectId visit=903986 ccd=100--selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \
1467 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \
1468 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \
1469 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \
1470 --selectId visit=903988 ccd=24
1472 This will process the HSC-I band data. The results are written in
1473 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``.
1475 You may also choose to run:
1477 .. code-block:: none
1479 scons warp-903334 warp-903336 warp-903338 warp-903342 warp-903344 warp-903346 nnn
1480 assembleCoadd.py $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-R --selectId visit=903334 ccd=16 \
1481 --selectId visit=903334 ccd=22 --selectId visit=903334 ccd=23 --selectId visit=903334 ccd=100 \
1482 --selectId visit=903336 ccd=17 --selectId visit=903336 ccd=24 --selectId visit=903338 ccd=18 \
1483 --selectId visit=903338 ccd=25 --selectId visit=903342 ccd=4 --selectId visit=903342 ccd=10 \
1484 --selectId visit=903342 ccd=100 --selectId visit=903344 ccd=0 --selectId visit=903344 ccd=5 \
1485 --selectId visit=903344 ccd=11 --selectId visit=903346 ccd=1 --selectId visit=903346 ccd=6 \
1486 --selectId visit=903346 ccd=12
1488 to generate the coadd for the HSC-R band if you are interested in following
1489 multiBand Coadd processing as discussed in ``pipeTasks_multiBand``.
1490 """
1491 ConfigClass = SafeClipAssembleCoaddConfig
1492 _DefaultName = "safeClipAssembleCoadd"
1494 def __init__(self, *args, **kwargs):
1495 AssembleCoaddTask.__init__(self, *args, **kwargs)
1496 schema = afwTable.SourceTable.makeMinimalSchema()
1497 self.makeSubtask("clipDetection", schema=schema)
1499 @utils.inheritDoc(AssembleCoaddTask)
1500 @pipeBase.timeMethod
1501 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, *args, **kwargs):
1502 """Assemble the coadd for a region.
1504 Compute the difference of coadds created with and without outlier
1505 rejection to identify coadd pixels that have outlier values in some
1506 individual visits.
1507 Detect clipped regions on the difference image and mark these regions
1508 on the one or two individual coaddTempExps where they occur if there
1509 is significant overlap between the clipped region and a source. This
1510 leaves us with a set of footprints from the difference image that have
1511 been identified as having occured on just one or two individual visits.
1512 However, these footprints were generated from a difference image. It
1513 is conceivable for a large diffuse source to have become broken up
1514 into multiple footprints acrosss the coadd difference in this process.
1515 Determine the clipped region from all overlapping footprints from the
1516 detected sources in each visit - these are big footprints.
1517 Combine the small and big clipped footprints and mark them on a new
1518 bad mask plane.
1519 Generate the coadd using `AssembleCoaddTask.run` without outlier
1520 removal. Clipped footprints will no longer make it into the coadd
1521 because they are marked in the new bad mask plane.
1523 Notes
1524 -----
1525 args and kwargs are passed but ignored in order to match the call
1526 signature expected by the parent task.
1527 """
1528 exp = self.buildDifferenceImage(skyInfo, tempExpRefList, imageScalerList, weightList)
1529 mask = exp.getMaskedImage().getMask()
1530 mask.addMaskPlane("CLIPPED")
1532 result = self.detectClip(exp, tempExpRefList)
1534 self.log.info('Found %d clipped objects', len(result.clipFootprints))
1536 maskClipValue = mask.getPlaneBitMask("CLIPPED")
1537 maskDetValue = mask.getPlaneBitMask("DETECTED") | mask.getPlaneBitMask("DETECTED_NEGATIVE")
1538 # Append big footprints from individual Warps to result.clipSpans
1539 bigFootprints = self.detectClipBig(result.clipSpans, result.clipFootprints, result.clipIndices,
1540 result.detectionFootprints, maskClipValue, maskDetValue,
1541 exp.getBBox())
1542 # Create mask of the current clipped footprints
1543 maskClip = mask.Factory(mask.getBBox(afwImage.PARENT))
1544 afwDet.setMaskFromFootprintList(maskClip, result.clipFootprints, maskClipValue)
1546 maskClipBig = maskClip.Factory(mask.getBBox(afwImage.PARENT))
1547 afwDet.setMaskFromFootprintList(maskClipBig, bigFootprints, maskClipValue)
1548 maskClip |= maskClipBig
1550 # Assemble coadd from base class, but ignoring CLIPPED pixels
1551 badMaskPlanes = self.config.badMaskPlanes[:]
1552 badMaskPlanes.append("CLIPPED")
1553 badPixelMask = afwImage.Mask.getPlaneBitMask(badMaskPlanes)
1554 return AssembleCoaddTask.run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
1555 result.clipSpans, mask=badPixelMask)
1557 def buildDifferenceImage(self, skyInfo, tempExpRefList, imageScalerList, weightList):
1558 """Return an exposure that contains the difference between unclipped
1559 and clipped coadds.
1561 Generate a difference image between clipped and unclipped coadds.
1562 Compute the difference image by subtracting an outlier-clipped coadd
1563 from an outlier-unclipped coadd. Return the difference image.
1565 Parameters
1566 ----------
1567 skyInfo : `lsst.pipe.base.Struct`
1568 Patch geometry information, from getSkyInfo
1569 tempExpRefList : `list`
1570 List of data reference to tempExp
1571 imageScalerList : `list`
1572 List of image scalers
1573 weightList : `list`
1574 List of weights
1576 Returns
1577 -------
1578 exp : `lsst.afw.image.Exposure`
1579 Difference image of unclipped and clipped coadd wrapped in an Exposure
1580 """
1581 config = AssembleCoaddConfig()
1582 # getattr necessary because subtasks do not survive Config.toDict()
1583 # exclude connections because the class of self.config.connections is not
1584 # the same as AssembleCoaddConfig.connections, and the connections are not
1585 # needed to run this task anyway.
1586 configIntersection = {k: getattr(self.config, k)
1587 for k, v in self.config.toDict().items()
1588 if (k in config.keys() and k != "connections")}
1589 configIntersection['doInputMap'] = False
1590 configIntersection['doNImage'] = False
1591 config.update(**configIntersection)
1593 # statistic MEAN copied from self.config.statistic, but for clarity explicitly assign
1594 config.statistic = 'MEAN'
1595 task = AssembleCoaddTask(config=config)
1596 coaddMean = task.run(skyInfo, tempExpRefList, imageScalerList, weightList).coaddExposure
1598 config.statistic = 'MEANCLIP'
1599 task = AssembleCoaddTask(config=config)
1600 coaddClip = task.run(skyInfo, tempExpRefList, imageScalerList, weightList).coaddExposure
1602 coaddDiff = coaddMean.getMaskedImage().Factory(coaddMean.getMaskedImage())
1603 coaddDiff -= coaddClip.getMaskedImage()
1604 exp = afwImage.ExposureF(coaddDiff)
1605 exp.setPsf(coaddMean.getPsf())
1606 return exp
1608 def detectClip(self, exp, tempExpRefList):
1609 """Detect clipped regions on an exposure and set the mask on the
1610 individual tempExp masks.
1612 Detect footprints in the difference image after smoothing the
1613 difference image with a Gaussian kernal. Identify footprints that
1614 overlap with one or two input ``coaddTempExps`` by comparing the
1615 computed overlap fraction to thresholds set in the config. A different
1616 threshold is applied depending on the number of overlapping visits
1617 (restricted to one or two). If the overlap exceeds the thresholds,
1618 the footprint is considered "CLIPPED" and is marked as such on the
1619 coaddTempExp. Return a struct with the clipped footprints, the indices
1620 of the ``coaddTempExps`` that end up overlapping with the clipped
1621 footprints, and a list of new masks for the ``coaddTempExps``.
1623 Parameters
1624 ----------
1625 exp : `lsst.afw.image.Exposure`
1626 Exposure to run detection on.
1627 tempExpRefList : `list`
1628 List of data reference to tempExp.
1630 Returns
1631 -------
1632 result : `lsst.pipe.base.Struct`
1633 Result struct with components:
1635 - ``clipFootprints``: list of clipped footprints.
1636 - ``clipIndices``: indices for each ``clippedFootprint`` in
1637 ``tempExpRefList``.
1638 - ``clipSpans``: List of dictionaries containing spanSet lists
1639 to clip. Each element contains the new maskplane name
1640 ("CLIPPED") as the key and list of ``SpanSets`` as the value.
1641 - ``detectionFootprints``: List of DETECTED/DETECTED_NEGATIVE plane
1642 compressed into footprints.
1643 """
1644 mask = exp.getMaskedImage().getMask()
1645 maskDetValue = mask.getPlaneBitMask("DETECTED") | mask.getPlaneBitMask("DETECTED_NEGATIVE")
1646 fpSet = self.clipDetection.detectFootprints(exp, doSmooth=True, clearMask=True)
1647 # Merge positive and negative together footprints together
1648 fpSet.positive.merge(fpSet.negative)
1649 footprints = fpSet.positive
1650 self.log.info('Found %d potential clipped objects', len(footprints.getFootprints()))
1651 ignoreMask = self.getBadPixelMask()
1653 clipFootprints = []
1654 clipIndices = []
1655 artifactSpanSets = [{'CLIPPED': list()} for _ in tempExpRefList]
1657 # for use by detectClipBig
1658 visitDetectionFootprints = []
1660 dims = [len(tempExpRefList), len(footprints.getFootprints())]
1661 overlapDetArr = numpy.zeros(dims, dtype=numpy.uint16)
1662 ignoreArr = numpy.zeros(dims, dtype=numpy.uint16)
1664 # Loop over masks once and extract/store only relevant overlap metrics and detection footprints
1665 for i, warpRef in enumerate(tempExpRefList):
1666 tmpExpMask = warpRef.get(datasetType=self.getTempExpDatasetName(self.warpType),
1667 immediate=True).getMaskedImage().getMask()
1668 maskVisitDet = tmpExpMask.Factory(tmpExpMask, tmpExpMask.getBBox(afwImage.PARENT),
1669 afwImage.PARENT, True)
1670 maskVisitDet &= maskDetValue
1671 visitFootprints = afwDet.FootprintSet(maskVisitDet, afwDet.Threshold(1))
1672 visitDetectionFootprints.append(visitFootprints)
1674 for j, footprint in enumerate(footprints.getFootprints()):
1675 ignoreArr[i, j] = countMaskFromFootprint(tmpExpMask, footprint, ignoreMask, 0x0)
1676 overlapDetArr[i, j] = countMaskFromFootprint(tmpExpMask, footprint, maskDetValue, ignoreMask)
1678 # build a list of clipped spans for each visit
1679 for j, footprint in enumerate(footprints.getFootprints()):
1680 nPixel = footprint.getArea()
1681 overlap = [] # hold the overlap with each visit
1682 indexList = [] # index of visit in global list
1683 for i in range(len(tempExpRefList)):
1684 ignore = ignoreArr[i, j]
1685 overlapDet = overlapDetArr[i, j]
1686 totPixel = nPixel - ignore
1688 # If we have more bad pixels than detection skip
1689 if ignore > overlapDet or totPixel <= 0.5*nPixel or overlapDet == 0:
1690 continue
1691 overlap.append(overlapDet/float(totPixel))
1692 indexList.append(i)
1694 overlap = numpy.array(overlap)
1695 if not len(overlap):
1696 continue
1698 keep = False # Should this footprint be marked as clipped?
1699 keepIndex = [] # Which tempExps does the clipped footprint belong to
1701 # If footprint only has one overlap use a lower threshold
1702 if len(overlap) == 1:
1703 if overlap[0] > self.config.minClipFootOverlapSingle:
1704 keep = True
1705 keepIndex = [0]
1706 else:
1707 # This is the general case where only visit should be clipped
1708 clipIndex = numpy.where(overlap > self.config.minClipFootOverlap)[0]
1709 if len(clipIndex) == 1:
1710 keep = True
1711 keepIndex = [clipIndex[0]]
1713 # Test if there are clipped objects that overlap two different visits
1714 clipIndex = numpy.where(overlap > self.config.minClipFootOverlapDouble)[0]
1715 if len(clipIndex) == 2 and len(overlap) > 3:
1716 clipIndexComp = numpy.where(overlap <= self.config.minClipFootOverlapDouble)[0]
1717 if numpy.max(overlap[clipIndexComp]) <= self.config.maxClipFootOverlapDouble:
1718 keep = True
1719 keepIndex = clipIndex
1721 if not keep:
1722 continue
1724 for index in keepIndex:
1725 globalIndex = indexList[index]
1726 artifactSpanSets[globalIndex]['CLIPPED'].append(footprint.spans)
1728 clipIndices.append(numpy.array(indexList)[keepIndex])
1729 clipFootprints.append(footprint)
1731 return pipeBase.Struct(clipFootprints=clipFootprints, clipIndices=clipIndices,
1732 clipSpans=artifactSpanSets, detectionFootprints=visitDetectionFootprints)
1734 def detectClipBig(self, clipList, clipFootprints, clipIndices, detectionFootprints,
1735 maskClipValue, maskDetValue, coaddBBox):
1736 """Return individual warp footprints for large artifacts and append
1737 them to ``clipList`` in place.
1739 Identify big footprints composed of many sources in the coadd
1740 difference that may have originated in a large diffuse source in the
1741 coadd. We do this by indentifying all clipped footprints that overlap
1742 significantly with each source in all the coaddTempExps.
1744 Parameters
1745 ----------
1746 clipList : `list`
1747 List of alt mask SpanSets with clipping information. Modified.
1748 clipFootprints : `list`
1749 List of clipped footprints.
1750 clipIndices : `list`
1751 List of which entries in tempExpClipList each footprint belongs to.
1752 maskClipValue
1753 Mask value of clipped pixels.
1754 maskDetValue
1755 Mask value of detected pixels.
1756 coaddBBox : `lsst.geom.Box`
1757 BBox of the coadd and warps.
1759 Returns
1760 -------
1761 bigFootprintsCoadd : `list`
1762 List of big footprints
1763 """
1764 bigFootprintsCoadd = []
1765 ignoreMask = self.getBadPixelMask()
1766 for index, (clippedSpans, visitFootprints) in enumerate(zip(clipList, detectionFootprints)):
1767 maskVisitDet = afwImage.MaskX(coaddBBox, 0x0)
1768 for footprint in visitFootprints.getFootprints():
1769 footprint.spans.setMask(maskVisitDet, maskDetValue)
1771 # build a mask of clipped footprints that are in this visit
1772 clippedFootprintsVisit = []
1773 for foot, clipIndex in zip(clipFootprints, clipIndices):
1774 if index not in clipIndex:
1775 continue
1776 clippedFootprintsVisit.append(foot)
1777 maskVisitClip = maskVisitDet.Factory(maskVisitDet.getBBox(afwImage.PARENT))
1778 afwDet.setMaskFromFootprintList(maskVisitClip, clippedFootprintsVisit, maskClipValue)
1780 bigFootprintsVisit = []
1781 for foot in visitFootprints.getFootprints():
1782 if foot.getArea() < self.config.minBigOverlap:
1783 continue
1784 nCount = countMaskFromFootprint(maskVisitClip, foot, maskClipValue, ignoreMask)
1785 if nCount > self.config.minBigOverlap:
1786 bigFootprintsVisit.append(foot)
1787 bigFootprintsCoadd.append(foot)
1789 for footprint in bigFootprintsVisit:
1790 clippedSpans["CLIPPED"].append(footprint.spans)
1792 return bigFootprintsCoadd
1795class CompareWarpAssembleCoaddConnections(AssembleCoaddConnections):
1796 psfMatchedWarps = pipeBase.connectionTypes.Input(
1797 doc=("PSF-Matched Warps are required by CompareWarp regardless of the coadd type requested. "
1798 "Only PSF-Matched Warps make sense for image subtraction. "
1799 "Therefore, they must be an additional declared input."),
1800 name="{inputCoaddName}Coadd_psfMatchedWarp",
1801 storageClass="ExposureF",
1802 dimensions=("tract", "patch", "skymap", "visit"),
1803 deferLoad=True,
1804 multiple=True
1805 )
1806 templateCoadd = pipeBase.connectionTypes.Output(
1807 doc=("Model of the static sky, used to find temporal artifacts. Typically a PSF-Matched, "
1808 "sigma-clipped coadd. Written if and only if assembleStaticSkyModel.doWrite=True"),
1809 name="{fakesType}{outputCoaddName}CoaddPsfMatched",
1810 storageClass="ExposureF",
1811 dimensions=("tract", "patch", "skymap", "band"),
1812 )
1814 def __init__(self, *, config=None):
1815 super().__init__(config=config)
1816 if not config.assembleStaticSkyModel.doWrite:
1817 self.outputs.remove("templateCoadd")
1818 config.validate()
1821class CompareWarpAssembleCoaddConfig(AssembleCoaddConfig,
1822 pipelineConnections=CompareWarpAssembleCoaddConnections):
1823 assembleStaticSkyModel = pexConfig.ConfigurableField(
1824 target=AssembleCoaddTask,
1825 doc="Task to assemble an artifact-free, PSF-matched Coadd to serve as a"
1826 " naive/first-iteration model of the static sky.",
1827 )
1828 detect = pexConfig.ConfigurableField(
1829 target=SourceDetectionTask,
1830 doc="Detect outlier sources on difference between each psfMatched warp and static sky model"
1831 )
1832 detectTemplate = pexConfig.ConfigurableField(
1833 target=SourceDetectionTask,
1834 doc="Detect sources on static sky model. Only used if doPreserveContainedBySource is True"
1835 )
1836 maskStreaks = pexConfig.ConfigurableField(
1837 target=MaskStreaksTask,
1838 doc="Detect streaks on difference between each psfMatched warp and static sky model. Only used if "
1839 "doFilterMorphological is True. Adds a mask plane to an exposure, with the mask plane name set by"
1840 "streakMaskName"
1841 )
1842 streakMaskName = pexConfig.Field(
1843 dtype=str,
1844 default="STREAK",
1845 doc="Name of mask bit used for streaks"
1846 )
1847 maxNumEpochs = pexConfig.Field(
1848 doc="Charactistic maximum local number of epochs/visits in which an artifact candidate can appear "
1849 "and still be masked. The effective maxNumEpochs is a broken linear function of local "
1850 "number of epochs (N): min(maxFractionEpochsLow*N, maxNumEpochs + maxFractionEpochsHigh*N). "
1851 "For each footprint detected on the image difference between the psfMatched warp and static sky "
1852 "model, if a significant fraction of pixels (defined by spatialThreshold) are residuals in more "
1853 "than the computed effective maxNumEpochs, the artifact candidate is deemed persistant rather "
1854 "than transient and not masked.",
1855 dtype=int,
1856 default=2
1857 )
1858 maxFractionEpochsLow = pexConfig.RangeField(
1859 doc="Fraction of local number of epochs (N) to use as effective maxNumEpochs for low N. "
1860 "Effective maxNumEpochs = "
1861 "min(maxFractionEpochsLow * N, maxNumEpochs + maxFractionEpochsHigh * N)",
1862 dtype=float,
1863 default=0.4,
1864 min=0., max=1.,
1865 )
1866 maxFractionEpochsHigh = pexConfig.RangeField(
1867 doc="Fraction of local number of epochs (N) to use as effective maxNumEpochs for high N. "
1868 "Effective maxNumEpochs = "
1869 "min(maxFractionEpochsLow * N, maxNumEpochs + maxFractionEpochsHigh * N)",
1870 dtype=float,
1871 default=0.03,
1872 min=0., max=1.,
1873 )
1874 spatialThreshold = pexConfig.RangeField(
1875 doc="Unitless fraction of pixels defining how much of the outlier region has to meet the "
1876 "temporal criteria. If 0, clip all. If 1, clip none.",
1877 dtype=float,
1878 default=0.5,
1879 min=0., max=1.,
1880 inclusiveMin=True, inclusiveMax=True
1881 )
1882 doScaleWarpVariance = pexConfig.Field(
1883 doc="Rescale Warp variance plane using empirical noise?",
1884 dtype=bool,
1885 default=True,
1886 )
1887 scaleWarpVariance = pexConfig.ConfigurableField(
1888 target=ScaleVarianceTask,
1889 doc="Rescale variance on warps",
1890 )
1891 doPreserveContainedBySource = pexConfig.Field(
1892 doc="Rescue artifacts from clipping that completely lie within a footprint detected"
1893 "on the PsfMatched Template Coadd. Replicates a behavior of SafeClip.",
1894 dtype=bool,
1895 default=True,
1896 )
1897 doPrefilterArtifacts = pexConfig.Field(
1898 doc="Ignore artifact candidates that are mostly covered by the bad pixel mask, "
1899 "because they will be excluded anyway. This prevents them from contributing "
1900 "to the outlier epoch count image and potentially being labeled as persistant."
1901 "'Mostly' is defined by the config 'prefilterArtifactsRatio'.",
1902 dtype=bool,
1903 default=True
1904 )
1905 prefilterArtifactsMaskPlanes = pexConfig.ListField(
1906 doc="Prefilter artifact candidates that are mostly covered by these bad mask planes.",
1907 dtype=str,
1908 default=('NO_DATA', 'BAD', 'SAT', 'SUSPECT'),
1909 )
1910 prefilterArtifactsRatio = pexConfig.Field(
1911 doc="Prefilter artifact candidates with less than this fraction overlapping good pixels",
1912 dtype=float,
1913 default=0.05
1914 )
1915 doFilterMorphological = pexConfig.Field(
1916 doc="Filter artifact candidates based on morphological criteria, i.g. those that appear to "
1917 "be streaks.",
1918 dtype=bool,
1919 default=False
1920 )
1922 def setDefaults(self):
1923 AssembleCoaddConfig.setDefaults(self)
1924 self.statistic = 'MEAN'
1925 self.doUsePsfMatchedPolygons = True
1927 # Real EDGE removed by psfMatched NO_DATA border half the width of the matching kernel
1928 # CompareWarp applies psfMatched EDGE pixels to directWarps before assembling
1929 if "EDGE" in self.badMaskPlanes:
1930 self.badMaskPlanes.remove('EDGE')
1931 self.removeMaskPlanes.append('EDGE')
1932 self.assembleStaticSkyModel.badMaskPlanes = ["NO_DATA", ]
1933 self.assembleStaticSkyModel.warpType = 'psfMatched'
1934 self.assembleStaticSkyModel.connections.warpType = 'psfMatched'
1935 self.assembleStaticSkyModel.statistic = 'MEANCLIP'
1936 self.assembleStaticSkyModel.sigmaClip = 2.5
1937 self.assembleStaticSkyModel.clipIter = 3
1938 self.assembleStaticSkyModel.calcErrorFromInputVariance = False
1939 self.assembleStaticSkyModel.doWrite = False
1940 self.detect.doTempLocalBackground = False
1941 self.detect.reEstimateBackground = False
1942 self.detect.returnOriginalFootprints = False
1943 self.detect.thresholdPolarity = "both"
1944 self.detect.thresholdValue = 5
1945 self.detect.minPixels = 4
1946 self.detect.isotropicGrow = True
1947 self.detect.thresholdType = "pixel_stdev"
1948 self.detect.nSigmaToGrow = 0.4
1949 # The default nSigmaToGrow for SourceDetectionTask is already 2.4,
1950 # Explicitly restating because ratio with detect.nSigmaToGrow matters
1951 self.detectTemplate.nSigmaToGrow = 2.4
1952 self.detectTemplate.doTempLocalBackground = False
1953 self.detectTemplate.reEstimateBackground = False
1954 self.detectTemplate.returnOriginalFootprints = False
1956 def validate(self):
1957 super().validate()
1958 if self.assembleStaticSkyModel.doNImage:
1959 raise ValueError("No dataset type exists for a PSF-Matched Template N Image."
1960 "Please set assembleStaticSkyModel.doNImage=False")
1962 if self.assembleStaticSkyModel.doWrite and (self.warpType == self.assembleStaticSkyModel.warpType):
1963 raise ValueError("warpType (%s) == assembleStaticSkyModel.warpType (%s) and will compete for "
1964 "the same dataset name. Please set assembleStaticSkyModel.doWrite to False "
1965 "or warpType to 'direct'. assembleStaticSkyModel.warpType should ways be "
1966 "'PsfMatched'" % (self.warpType, self.assembleStaticSkyModel.warpType))
1969class CompareWarpAssembleCoaddTask(AssembleCoaddTask):
1970 """Assemble a compareWarp coadded image from a set of warps
1971 by masking artifacts detected by comparing PSF-matched warps.
1973 In ``AssembleCoaddTask``, we compute the coadd as an clipped mean (i.e.,
1974 we clip outliers). The problem with doing this is that when computing the
1975 coadd PSF at a given location, individual visit PSFs from visits with
1976 outlier pixels contribute to the coadd PSF and cannot be treated correctly.
1977 In this task, we correct for this behavior by creating a new badMaskPlane
1978 'CLIPPED' which marks pixels in the individual warps suspected to contain
1979 an artifact. We populate this plane on the input warps by comparing
1980 PSF-matched warps with a PSF-matched median coadd which serves as a
1981 model of the static sky. Any group of pixels that deviates from the
1982 PSF-matched template coadd by more than config.detect.threshold sigma,
1983 is an artifact candidate. The candidates are then filtered to remove
1984 variable sources and sources that are difficult to subtract such as
1985 bright stars. This filter is configured using the config parameters
1986 ``temporalThreshold`` and ``spatialThreshold``. The temporalThreshold is
1987 the maximum fraction of epochs that the deviation can appear in and still
1988 be considered an artifact. The spatialThreshold is the maximum fraction of
1989 pixels in the footprint of the deviation that appear in other epochs
1990 (where other epochs is defined by the temporalThreshold). If the deviant
1991 region meets this criteria of having a significant percentage of pixels
1992 that deviate in only a few epochs, these pixels have the 'CLIPPED' bit
1993 set in the mask. These regions will not contribute to the final coadd.
1994 Furthermore, any routine to determine the coadd PSF can now be cognizant
1995 of clipped regions. Note that the algorithm implemented by this task is
1996 preliminary and works correctly for HSC data. Parameter modifications and
1997 or considerable redesigning of the algorithm is likley required for other
1998 surveys.
2000 ``CompareWarpAssembleCoaddTask`` sub-classes
2001 ``AssembleCoaddTask`` and instantiates ``AssembleCoaddTask``
2002 as a subtask to generate the TemplateCoadd (the model of the static sky).
2004 Notes
2005 -----
2006 The `lsst.pipe.base.cmdLineTask.CmdLineTask` interface supports a
2007 flag ``-d`` to import ``debug.py`` from your ``PYTHONPATH``; see
2008 ``baseDebug`` for more about ``debug.py`` files.
2010 This task supports the following debug variables:
2012 - ``saveCountIm``
2013 If True then save the Epoch Count Image as a fits file in the `figPath`
2014 - ``figPath``
2015 Path to save the debug fits images and figures
2017 For example, put something like:
2019 .. code-block:: python
2021 import lsstDebug
2022 def DebugInfo(name):
2023 di = lsstDebug.getInfo(name)
2024 if name == "lsst.pipe.tasks.assembleCoadd":
2025 di.saveCountIm = True
2026 di.figPath = "/desired/path/to/debugging/output/images"
2027 return di
2028 lsstDebug.Info = DebugInfo
2030 into your ``debug.py`` file and run ``assemebleCoadd.py`` with the
2031 ``--debug`` flag. Some subtasks may have their own debug variables;
2032 see individual Task documentation.
2034 Examples
2035 --------
2036 ``CompareWarpAssembleCoaddTask`` assembles a set of warped images into a
2037 coadded image. The ``CompareWarpAssembleCoaddTask`` is invoked by running
2038 ``assembleCoadd.py`` with the flag ``--compareWarpCoadd``.
2039 Usage of ``assembleCoadd.py`` expects a data reference to the tract patch
2040 and filter to be coadded (specified using
2041 '--id = [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]')
2042 along with a list of coaddTempExps to attempt to coadd (specified using
2043 '--selectId [KEY=VALUE1[^VALUE2[^VALUE3...] [KEY=VALUE1[^VALUE2[^VALUE3...] ...]]').
2044 Only the warps that cover the specified tract and patch will be coadded.
2045 A list of the available optional arguments can be obtained by calling
2046 ``assembleCoadd.py`` with the ``--help`` command line argument:
2048 .. code-block:: none
2050 assembleCoadd.py --help
2052 To demonstrate usage of the ``CompareWarpAssembleCoaddTask`` in the larger
2053 context of multi-band processing, we will generate the HSC-I & -R band
2054 oadds from HSC engineering test data provided in the ``ci_hsc`` package.
2055 To begin, assuming that the lsst stack has been already set up, we must
2056 set up the ``obs_subaru`` and ``ci_hsc`` packages.
2057 This defines the environment variable ``$CI_HSC_DIR`` and points at the
2058 location of the package. The raw HSC data live in the ``$CI_HSC_DIR/raw``
2059 directory. To begin assembling the coadds, we must first
2061 - processCcd
2062 process the individual ccds in $CI_HSC_RAW to produce calibrated exposures
2063 - makeSkyMap
2064 create a skymap that covers the area of the sky present in the raw exposures
2065 - makeCoaddTempExp
2066 warp the individual calibrated exposures to the tangent plane of the coadd
2068 We can perform all of these steps by running
2070 .. code-block:: none
2072 $CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988
2074 This will produce warped ``coaddTempExps`` for each visit. To coadd the
2075 warped data, we call ``assembleCoadd.py`` as follows:
2077 .. code-block:: none
2079 assembleCoadd.py --compareWarpCoadd $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I \
2080 --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23 \
2081 --selectId visit=903986 ccd=100 --selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6 \
2082 --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25 \
2083 --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100 \
2084 --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23 \
2085 --selectId visit=903988 ccd=24
2087 This will process the HSC-I band data. The results are written in
2088 ``$CI_HSC_DIR/DATA/deepCoadd-results/HSC-I``.
2089 """
2090 ConfigClass = CompareWarpAssembleCoaddConfig
2091 _DefaultName = "compareWarpAssembleCoadd"
2093 def __init__(self, *args, **kwargs):
2094 AssembleCoaddTask.__init__(self, *args, **kwargs)
2095 self.makeSubtask("assembleStaticSkyModel")
2096 detectionSchema = afwTable.SourceTable.makeMinimalSchema()
2097 self.makeSubtask("detect", schema=detectionSchema)
2098 if self.config.doPreserveContainedBySource:
2099 self.makeSubtask("detectTemplate", schema=afwTable.SourceTable.makeMinimalSchema())
2100 if self.config.doScaleWarpVariance:
2101 self.makeSubtask("scaleWarpVariance")
2102 if self.config.doFilterMorphological:
2103 self.makeSubtask("maskStreaks")
2105 @utils.inheritDoc(AssembleCoaddTask)
2106 def makeSupplementaryDataGen3(self, butlerQC, inputRefs, outputRefs):
2107 """
2108 Generate a templateCoadd to use as a naive model of static sky to
2109 subtract from PSF-Matched warps.
2111 Returns
2112 -------
2113 result : `lsst.pipe.base.Struct`
2114 Result struct with components:
2116 - ``templateCoadd`` : coadded exposure (``lsst.afw.image.Exposure``)
2117 - ``nImage`` : N Image (``lsst.afw.image.Image``)
2118 """
2119 # Ensure that psfMatchedWarps are used as input warps for template generation
2120 staticSkyModelInputRefs = copy.deepcopy(inputRefs)
2121 staticSkyModelInputRefs.inputWarps = inputRefs.psfMatchedWarps
2123 # Because subtasks don't have connections we have to make one.
2124 # The main task's `templateCoadd` is the subtask's `coaddExposure`
2125 staticSkyModelOutputRefs = copy.deepcopy(outputRefs)
2126 if self.config.assembleStaticSkyModel.doWrite:
2127 staticSkyModelOutputRefs.coaddExposure = staticSkyModelOutputRefs.templateCoadd
2128 # Remove template coadd from both subtask's and main tasks outputs,
2129 # because it is handled by the subtask as `coaddExposure`
2130 del outputRefs.templateCoadd
2131 del staticSkyModelOutputRefs.templateCoadd
2133 # A PSF-Matched nImage does not exist as a dataset type
2134 if 'nImage' in staticSkyModelOutputRefs.keys():
2135 del staticSkyModelOutputRefs.nImage
2137 templateCoadd = self.assembleStaticSkyModel.runQuantum(butlerQC, staticSkyModelInputRefs,
2138 staticSkyModelOutputRefs)
2139 if templateCoadd is None:
2140 raise RuntimeError(self._noTemplateMessage(self.assembleStaticSkyModel.warpType))
2142 return pipeBase.Struct(templateCoadd=templateCoadd.coaddExposure,
2143 nImage=templateCoadd.nImage,
2144 warpRefList=templateCoadd.warpRefList,
2145 imageScalerList=templateCoadd.imageScalerList,
2146 weightList=templateCoadd.weightList)
2148 @utils.inheritDoc(AssembleCoaddTask)
2149 def makeSupplementaryData(self, dataRef, selectDataList=None, warpRefList=None):
2150 """
2151 Generate a templateCoadd to use as a naive model of static sky to
2152 subtract from PSF-Matched warps.
2154 Returns
2155 -------
2156 result : `lsst.pipe.base.Struct`
2157 Result struct with components:
2159 - ``templateCoadd``: coadded exposure (``lsst.afw.image.Exposure``)
2160 - ``nImage``: N Image (``lsst.afw.image.Image``)
2161 """
2162 templateCoadd = self.assembleStaticSkyModel.runDataRef(dataRef, selectDataList, warpRefList)
2163 if templateCoadd is None:
2164 raise RuntimeError(self._noTemplateMessage(self.assembleStaticSkyModel.warpType))
2166 return pipeBase.Struct(templateCoadd=templateCoadd.coaddExposure,
2167 nImage=templateCoadd.nImage,
2168 warpRefList=templateCoadd.warpRefList,
2169 imageScalerList=templateCoadd.imageScalerList,
2170 weightList=templateCoadd.weightList)
2172 def _noTemplateMessage(self, warpType):
2173 warpName = (warpType[0].upper() + warpType[1:])
2174 message = """No %(warpName)s warps were found to build the template coadd which is
2175 required to run CompareWarpAssembleCoaddTask. To continue assembling this type of coadd,
2176 first either rerun makeCoaddTempExp with config.make%(warpName)s=True or
2177 coaddDriver with config.makeCoadTempExp.make%(warpName)s=True, before assembleCoadd.
2179 Alternatively, to use another algorithm with existing warps, retarget the CoaddDriverConfig to
2180 another algorithm like:
2182 from lsst.pipe.tasks.assembleCoadd import SafeClipAssembleCoaddTask
2183 config.assemble.retarget(SafeClipAssembleCoaddTask)
2184 """ % {"warpName": warpName}
2185 return message
2187 @utils.inheritDoc(AssembleCoaddTask)
2188 @pipeBase.timeMethod
2189 def run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
2190 supplementaryData, *args, **kwargs):
2191 """Assemble the coadd.
2193 Find artifacts and apply them to the warps' masks creating a list of
2194 alternative masks with a new "CLIPPED" plane and updated "NO_DATA"
2195 plane. Then pass these alternative masks to the base class's `run`
2196 method.
2198 The input parameters ``supplementaryData`` is a `lsst.pipe.base.Struct`
2199 that must contain a ``templateCoadd`` that serves as the
2200 model of the static sky.
2201 """
2203 # Check and match the order of the supplementaryData
2204 # (PSF-matched) inputs to the order of the direct inputs,
2205 # so that the artifact mask is applied to the right warp
2206 dataIds = [ref.dataId for ref in tempExpRefList]
2207 psfMatchedDataIds = [ref.dataId for ref in supplementaryData.warpRefList]
2209 if dataIds != psfMatchedDataIds:
2210 self.log.info("Reordering and or/padding PSF-matched visit input list")
2211 supplementaryData.warpRefList = reorderAndPadList(supplementaryData.warpRefList,
2212 psfMatchedDataIds, dataIds)
2213 supplementaryData.imageScalerList = reorderAndPadList(supplementaryData.imageScalerList,
2214 psfMatchedDataIds, dataIds)
2216 # Use PSF-Matched Warps (and corresponding scalers) and coadd to find artifacts
2217 spanSetMaskList = self.findArtifacts(supplementaryData.templateCoadd,
2218 supplementaryData.warpRefList,
2219 supplementaryData.imageScalerList)
2221 badMaskPlanes = self.config.badMaskPlanes[:]
2222 badMaskPlanes.append("CLIPPED")
2223 badPixelMask = afwImage.Mask.getPlaneBitMask(badMaskPlanes)
2225 result = AssembleCoaddTask.run(self, skyInfo, tempExpRefList, imageScalerList, weightList,
2226 spanSetMaskList, mask=badPixelMask)
2228 # Propagate PSF-matched EDGE pixels to coadd SENSOR_EDGE and INEXACT_PSF
2229 # Psf-Matching moves the real edge inwards
2230 self.applyAltEdgeMask(result.coaddExposure.maskedImage.mask, spanSetMaskList)
2231 return result
2233 def applyAltEdgeMask(self, mask, altMaskList):
2234 """Propagate alt EDGE mask to SENSOR_EDGE AND INEXACT_PSF planes.
2236 Parameters
2237 ----------
2238 mask : `lsst.afw.image.Mask`
2239 Original mask.
2240 altMaskList : `list`
2241 List of Dicts containing ``spanSet`` lists.
2242 Each element contains the new mask plane name (e.g. "CLIPPED
2243 and/or "NO_DATA") as the key, and list of ``SpanSets`` to apply to
2244 the mask.
2245 """
2246 maskValue = mask.getPlaneBitMask(["SENSOR_EDGE", "INEXACT_PSF"])
2247 for visitMask in altMaskList:
2248 if "EDGE" in visitMask:
2249 for spanSet in visitMask['EDGE']:
2250 spanSet.clippedTo(mask.getBBox()).setMask(mask, maskValue)
2252 def findArtifacts(self, templateCoadd, tempExpRefList, imageScalerList):
2253 """Find artifacts.
2255 Loop through warps twice. The first loop builds a map with the count
2256 of how many epochs each pixel deviates from the templateCoadd by more
2257 than ``config.chiThreshold`` sigma. The second loop takes each
2258 difference image and filters the artifacts detected in each using
2259 count map to filter out variable sources and sources that are
2260 difficult to subtract cleanly.
2262 Parameters
2263 ----------
2264 templateCoadd : `lsst.afw.image.Exposure`
2265 Exposure to serve as model of static sky.
2266 tempExpRefList : `list`
2267 List of data references to warps.
2268 imageScalerList : `list`
2269 List of image scalers.
2271 Returns
2272 -------
2273 altMasks : `list`
2274 List of dicts containing information about CLIPPED
2275 (i.e., artifacts), NO_DATA, and EDGE pixels.
2276 """
2278 self.log.debug("Generating Count Image, and mask lists.")
2279 coaddBBox = templateCoadd.getBBox()
2280 slateIm = afwImage.ImageU(coaddBBox)
2281 epochCountImage = afwImage.ImageU(coaddBBox)
2282 nImage = afwImage.ImageU(coaddBBox)
2283 spanSetArtifactList = []
2284 spanSetNoDataMaskList = []
2285 spanSetEdgeList = []
2286 spanSetBadMorphoList = []
2287 badPixelMask = self.getBadPixelMask()
2289 # mask of the warp diffs should = that of only the warp
2290 templateCoadd.mask.clearAllMaskPlanes()
2292 if self.config.doPreserveContainedBySource:
2293 templateFootprints = self.detectTemplate.detectFootprints(templateCoadd)
2294 else:
2295 templateFootprints = None
2297 for warpRef, imageScaler in zip(tempExpRefList, imageScalerList):
2298 warpDiffExp = self._readAndComputeWarpDiff(warpRef, imageScaler, templateCoadd)
2299 if warpDiffExp is not None:
2300 # This nImage only approximates the final nImage because it uses the PSF-matched mask
2301 nImage.array += (numpy.isfinite(warpDiffExp.image.array)
2302 * ((warpDiffExp.mask.array & badPixelMask) == 0)).astype(numpy.uint16)
2303 fpSet = self.detect.detectFootprints(warpDiffExp, doSmooth=False, clearMask=True)
2304 fpSet.positive.merge(fpSet.negative)
2305 footprints = fpSet.positive
2306 slateIm.set(0)
2307 spanSetList = [footprint.spans for footprint in footprints.getFootprints()]
2309 # Remove artifacts due to defects before they contribute to the epochCountImage
2310 if self.config.doPrefilterArtifacts:
2311 spanSetList = self.prefilterArtifacts(spanSetList, warpDiffExp)
2313 # Clear mask before adding prefiltered spanSets
2314 self.detect.clearMask(warpDiffExp.mask)
2315 for spans in spanSetList:
2316 spans.setImage(slateIm, 1, doClip=True)
2317 spans.setMask(warpDiffExp.mask, warpDiffExp.mask.getPlaneBitMask("DETECTED"))
2318 epochCountImage += slateIm
2320 if self.config.doFilterMorphological:
2321 maskName = self.config.streakMaskName
2322 _ = self.maskStreaks.run(warpDiffExp)
2323 streakMask = warpDiffExp.mask
2324 spanSetStreak = afwGeom.SpanSet.fromMask(streakMask,
2325 streakMask.getPlaneBitMask(maskName)).split()
2327 # PSF-Matched warps have less available area (~the matching kernel) because the calexps
2328 # undergo a second convolution. Pixels with data in the direct warp
2329 # but not in the PSF-matched warp will not have their artifacts detected.
2330 # NaNs from the PSF-matched warp therefore must be masked in the direct warp
2331 nans = numpy.where(numpy.isnan(warpDiffExp.maskedImage.image.array), 1, 0)
2332 nansMask = afwImage.makeMaskFromArray(nans.astype(afwImage.MaskPixel))
2333 nansMask.setXY0(warpDiffExp.getXY0())
2334 edgeMask = warpDiffExp.mask
2335 spanSetEdgeMask = afwGeom.SpanSet.fromMask(edgeMask,
2336 edgeMask.getPlaneBitMask("EDGE")).split()
2337 else:
2338 # If the directWarp has <1% coverage, the psfMatchedWarp can have 0% and not exist
2339 # In this case, mask the whole epoch
2340 nansMask = afwImage.MaskX(coaddBBox, 1)
2341 spanSetList = []
2342 spanSetEdgeMask = []
2343 spanSetStreak = []
2345 spanSetNoDataMask = afwGeom.SpanSet.fromMask(nansMask).split()
2347 spanSetNoDataMaskList.append(spanSetNoDataMask)
2348 spanSetArtifactList.append(spanSetList)
2349 spanSetEdgeList.append(spanSetEdgeMask)
2350 if self.config.doFilterMorphological:
2351 spanSetBadMorphoList.append(spanSetStreak)
2353 if lsstDebug.Info(__name__).saveCountIm:
2354 path = self._dataRef2DebugPath("epochCountIm", tempExpRefList[0], coaddLevel=True)
2355 epochCountImage.writeFits(path)
2357 for i, spanSetList in enumerate(spanSetArtifactList):
2358 if spanSetList:
2359 filteredSpanSetList = self.filterArtifacts(spanSetList, epochCountImage, nImage,
2360 templateFootprints)
2361 spanSetArtifactList[i] = filteredSpanSetList
2362 if self.config.doFilterMorphological:
2363 spanSetArtifactList[i] += spanSetBadMorphoList[i]
2365 altMasks = []
2366 for artifacts, noData, edge in zip(spanSetArtifactList, spanSetNoDataMaskList, spanSetEdgeList):
2367 altMasks.append({'CLIPPED': artifacts,
2368 'NO_DATA': noData,
2369 'EDGE': edge})
2370 return altMasks
2372 def prefilterArtifacts(self, spanSetList, exp):
2373 """Remove artifact candidates covered by bad mask plane.
2375 Any future editing of the candidate list that does not depend on
2376 temporal information should go in this method.
2378 Parameters
2379 ----------
2380 spanSetList : `list`
2381 List of SpanSets representing artifact candidates.
2382 exp : `lsst.afw.image.Exposure`
2383 Exposure containing mask planes used to prefilter.
2385 Returns
2386 -------
2387 returnSpanSetList : `list`
2388 List of SpanSets with artifacts.
2389 """
2390 badPixelMask = exp.mask.getPlaneBitMask(self.config.prefilterArtifactsMaskPlanes)
2391 goodArr = (exp.mask.array & badPixelMask) == 0
2392 returnSpanSetList = []
2393 bbox = exp.getBBox()
2394 x0, y0 = exp.getXY0()
2395 for i, span in enumerate(spanSetList):
2396 y, x = span.clippedTo(bbox).indices()
2397 yIndexLocal = numpy.array(y) - y0
2398 xIndexLocal = numpy.array(x) - x0
2399 goodRatio = numpy.count_nonzero(goodArr[yIndexLocal, xIndexLocal])/span.getArea()
2400 if goodRatio > self.config.prefilterArtifactsRatio:
2401 returnSpanSetList.append(span)
2402 return returnSpanSetList
2404 def filterArtifacts(self, spanSetList, epochCountImage, nImage, footprintsToExclude=None):
2405 """Filter artifact candidates.
2407 Parameters
2408 ----------
2409 spanSetList : `list`
2410 List of SpanSets representing artifact candidates.
2411 epochCountImage : `lsst.afw.image.Image`
2412 Image of accumulated number of warpDiff detections.
2413 nImage : `lsst.afw.image.Image`
2414 Image of the accumulated number of total epochs contributing.
2416 Returns
2417 -------
2418 maskSpanSetList : `list`
2419 List of SpanSets with artifacts.
2420 """
2422 maskSpanSetList = []
2423 x0, y0 = epochCountImage.getXY0()
2424 for i, span in enumerate(spanSetList):
2425 y, x = span.indices()
2426 yIdxLocal = [y1 - y0 for y1 in y]
2427 xIdxLocal = [x1 - x0 for x1 in x]
2428 outlierN = epochCountImage.array[yIdxLocal, xIdxLocal]
2429 totalN = nImage.array[yIdxLocal, xIdxLocal]
2431 # effectiveMaxNumEpochs is broken line (fraction of N) with characteristic config.maxNumEpochs
2432 effMaxNumEpochsHighN = (self.config.maxNumEpochs
2433 + self.config.maxFractionEpochsHigh*numpy.mean(totalN))
2434 effMaxNumEpochsLowN = self.config.maxFractionEpochsLow * numpy.mean(totalN)
2435 effectiveMaxNumEpochs = int(min(effMaxNumEpochsLowN, effMaxNumEpochsHighN))
2436 nPixelsBelowThreshold = numpy.count_nonzero((outlierN > 0)
2437 & (outlierN <= effectiveMaxNumEpochs))
2438 percentBelowThreshold = nPixelsBelowThreshold / len(outlierN)
2439 if percentBelowThreshold > self.config.spatialThreshold:
2440 maskSpanSetList.append(span)
2442 if self.config.doPreserveContainedBySource and footprintsToExclude is not None:
2443 # If a candidate is contained by a footprint on the template coadd, do not clip
2444 filteredMaskSpanSetList = []
2445 for span in maskSpanSetList:
2446 doKeep = True
2447 for footprint in footprintsToExclude.positive.getFootprints():
2448 if footprint.spans.contains(span):
2449 doKeep = False
2450 break
2451 if doKeep:
2452 filteredMaskSpanSetList.append(span)
2453 maskSpanSetList = filteredMaskSpanSetList
2455 return maskSpanSetList
2457 def _readAndComputeWarpDiff(self, warpRef, imageScaler, templateCoadd):
2458 """Fetch a warp from the butler and return a warpDiff.
2460 Parameters
2461 ----------
2462 warpRef : `lsst.daf.persistence.butlerSubset.ButlerDataRef`
2463 Butler dataRef for the warp.
2464 imageScaler : `lsst.pipe.tasks.scaleZeroPoint.ImageScaler`
2465 An image scaler object.
2466 templateCoadd : `lsst.afw.image.Exposure`
2467 Exposure to be substracted from the scaled warp.
2469 Returns
2470 -------
2471 warp : `lsst.afw.image.Exposure`
2472 Exposure of the image difference between the warp and template.
2473 """
2475 # If the PSF-Matched warp did not exist for this direct warp
2476 # None is holding its place to maintain order in Gen 3
2477 if warpRef is None:
2478 return None
2479 # Warp comparison must use PSF-Matched Warps regardless of requested coadd warp type
2480 warpName = self.getTempExpDatasetName('psfMatched')
2481 if not isinstance(warpRef, DeferredDatasetHandle):
2482 if not warpRef.datasetExists(warpName):
2483 self.log.warn("Could not find %s %s; skipping it", warpName, warpRef.dataId)
2484 return None
2485 warp = warpRef.get(datasetType=warpName, immediate=True)
2486 # direct image scaler OK for PSF-matched Warp
2487 imageScaler.scaleMaskedImage(warp.getMaskedImage())
2488 mi = warp.getMaskedImage()
2489 if self.config.doScaleWarpVariance:
2490 try:
2491 self.scaleWarpVariance.run(mi)
2492 except Exception as exc:
2493 self.log.warn("Unable to rescale variance of warp (%s); leaving it as-is" % (exc,))
2494 mi -= templateCoadd.getMaskedImage()
2495 return warp
2497 def _dataRef2DebugPath(self, prefix, warpRef, coaddLevel=False):
2498 """Return a path to which to write debugging output.
2500 Creates a hyphen-delimited string of dataId values for simple filenames.
2502 Parameters
2503 ----------
2504 prefix : `str`
2505 Prefix for filename.
2506 warpRef : `lsst.daf.persistence.butlerSubset.ButlerDataRef`
2507 Butler dataRef to make the path from.
2508 coaddLevel : `bool`, optional.
2509 If True, include only coadd-level keys (e.g., 'tract', 'patch',
2510 'filter', but no 'visit').
2512 Returns
2513 -------
2514 result : `str`
2515 Path for debugging output.
2516 """
2517 if coaddLevel:
2518 keys = warpRef.getButler().getKeys(self.getCoaddDatasetName(self.warpType))
2519 else:
2520 keys = warpRef.dataId.keys()
2521 keyList = sorted(keys, reverse=True)
2522 directory = lsstDebug.Info(__name__).figPath if lsstDebug.Info(__name__).figPath else "."
2523 filename = "%s-%s.fits" % (prefix, '-'.join([str(warpRef.dataId[k]) for k in keyList]))
2524 return os.path.join(directory, filename)