Coverage for python/lsst/pipe/tasks/dcrAssembleCoadd.py : 13%

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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
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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#
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21#
23from math import ceil
24import numpy as np
25from scipy import ndimage
26import lsst.geom as geom
27import lsst.afw.image as afwImage
28import lsst.afw.table as afwTable
29import lsst.coadd.utils as coaddUtils
30from lsst.daf.butler import DeferredDatasetHandle
31from lsst.ip.diffim.dcrModel import applyDcr, calculateDcr, DcrModel
32import lsst.meas.algorithms as measAlg
33from lsst.meas.base import SingleFrameMeasurementTask
34import lsst.pex.config as pexConfig
35import lsst.pipe.base as pipeBase
36import lsst.utils as utils
37from .assembleCoadd import (AssembleCoaddTask,
38 CompareWarpAssembleCoaddConfig,
39 CompareWarpAssembleCoaddTask)
40from .coaddBase import makeSkyInfo
41from .measurePsf import MeasurePsfTask
43__all__ = ["DcrAssembleCoaddConnections", "DcrAssembleCoaddTask", "DcrAssembleCoaddConfig"]
46class DcrAssembleCoaddConnections(pipeBase.PipelineTaskConnections,
47 dimensions=("tract", "patch", "abstract_filter", "skymap"),
48 defaultTemplates={"inputCoaddName": "deep",
49 "outputCoaddName": "dcr",
50 "warpType": "direct",
51 "warpTypeSuffix": "",
52 "fakesType": ""}):
53 inputWarps = pipeBase.connectionTypes.Input(
54 doc=("Input list of warps to be assembled i.e. stacked."
55 "WarpType (e.g. direct, psfMatched) is controlled by the warpType config parameter"),
56 name="{inputCoaddName}Coadd_{warpType}Warp",
57 storageClass="ExposureF",
58 dimensions=("tract", "patch", "skymap", "visit", "instrument"),
59 deferLoad=True,
60 multiple=True
61 )
62 skyMap = pipeBase.connectionTypes.Input(
63 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures",
64 name="{inputCoaddName}Coadd_skyMap",
65 storageClass="SkyMap",
66 dimensions=("skymap", ),
67 )
68 brightObjectMask = pipeBase.connectionTypes.PrerequisiteInput(
69 doc=("Input Bright Object Mask mask produced with external catalogs to be applied to the mask plane"
70 " BRIGHT_OBJECT."),
71 name="brightObjectMask",
72 storageClass="ObjectMaskCatalog",
73 dimensions=("tract", "patch", "skymap", "abstract_filter"),
74 )
75 templateExposure = pipeBase.connectionTypes.Input(
76 doc="Input coadded exposure, produced by previous call to AssembleCoadd",
77 name="{fakesType}{inputCoaddName}Coadd{warpTypeSuffix}",
78 storageClass="ExposureF",
79 dimensions=("tract", "patch", "skymap", "abstract_filter"),
80 )
81 dcrCoadds = pipeBase.connectionTypes.Output(
82 doc="Output coadded exposure, produced by stacking input warps",
83 name="{fakesType}{outputCoaddName}Coadd{warpTypeSuffix}",
84 storageClass="ExposureF",
85 dimensions=("tract", "patch", "skymap", "abstract_filter", "subfilter"),
86 multiple=True,
87 )
88 dcrNImages = pipeBase.connectionTypes.Output(
89 doc="Output image of number of input images per pixel",
90 name="{outputCoaddName}Coadd_nImage",
91 storageClass="ImageU",
92 dimensions=("tract", "patch", "skymap", "abstract_filter", "subfilter"),
93 multiple=True,
94 )
96 def __init__(self, *, config=None):
97 super().__init__(config=config)
98 if not config.doWrite:
99 self.outputs.remove("dcrCoadds")
102class DcrAssembleCoaddConfig(CompareWarpAssembleCoaddConfig,
103 pipelineConnections=DcrAssembleCoaddConnections):
104 dcrNumSubfilters = pexConfig.Field(
105 dtype=int,
106 doc="Number of sub-filters to forward model chromatic effects to fit the supplied exposures.",
107 default=3,
108 )
109 maxNumIter = pexConfig.Field(
110 dtype=int,
111 optional=True,
112 doc="Maximum number of iterations of forward modeling.",
113 default=None,
114 )
115 minNumIter = pexConfig.Field(
116 dtype=int,
117 optional=True,
118 doc="Minimum number of iterations of forward modeling.",
119 default=None,
120 )
121 convergenceThreshold = pexConfig.Field(
122 dtype=float,
123 doc="Target relative change in convergence between iterations of forward modeling.",
124 default=0.001,
125 )
126 useConvergence = pexConfig.Field(
127 dtype=bool,
128 doc="Use convergence test as a forward modeling end condition?"
129 "If not set, skips calculating convergence and runs for ``maxNumIter`` iterations",
130 default=True,
131 )
132 baseGain = pexConfig.Field(
133 dtype=float,
134 optional=True,
135 doc="Relative weight to give the new solution vs. the last solution when updating the model."
136 "A value of 1.0 gives equal weight to both solutions."
137 "Small values imply slower convergence of the solution, but can "
138 "help prevent overshooting and failures in the fit."
139 "If ``baseGain`` is None, a conservative gain "
140 "will be calculated from the number of subfilters. ",
141 default=None,
142 )
143 useProgressiveGain = pexConfig.Field(
144 dtype=bool,
145 doc="Use a gain that slowly increases above ``baseGain`` to accelerate convergence? "
146 "When calculating the next gain, we use up to 5 previous gains and convergence values."
147 "Can be set to False to force the model to change at the rate of ``baseGain``. ",
148 default=True,
149 )
150 doAirmassWeight = pexConfig.Field(
151 dtype=bool,
152 doc="Weight exposures by airmass? Useful if there are relatively few high-airmass observations.",
153 default=False,
154 )
155 modelWeightsWidth = pexConfig.Field(
156 dtype=float,
157 doc="Width of the region around detected sources to include in the DcrModel.",
158 default=3,
159 )
160 useModelWeights = pexConfig.Field(
161 dtype=bool,
162 doc="Width of the region around detected sources to include in the DcrModel.",
163 default=True,
164 )
165 splitSubfilters = pexConfig.Field(
166 dtype=bool,
167 doc="Calculate DCR for two evenly-spaced wavelengths in each subfilter."
168 "Instead of at the midpoint",
169 default=True,
170 )
171 splitThreshold = pexConfig.Field(
172 dtype=float,
173 doc="Minimum DCR difference within a subfilter to use ``splitSubfilters``, in pixels."
174 "Set to 0 to always split the subfilters.",
175 default=0.1,
176 )
177 regularizeModelIterations = pexConfig.Field(
178 dtype=float,
179 doc="Maximum relative change of the model allowed between iterations."
180 "Set to zero to disable.",
181 default=2.,
182 )
183 regularizeModelFrequency = pexConfig.Field(
184 dtype=float,
185 doc="Maximum relative change of the model allowed between subfilters."
186 "Set to zero to disable.",
187 default=4.,
188 )
189 convergenceMaskPlanes = pexConfig.ListField(
190 dtype=str,
191 default=["DETECTED"],
192 doc="Mask planes to use to calculate convergence."
193 )
194 regularizationWidth = pexConfig.Field(
195 dtype=int,
196 default=2,
197 doc="Minimum radius of a region to include in regularization, in pixels."
198 )
199 imageInterpOrder = pexConfig.Field(
200 dtype=int,
201 doc="The order of the spline interpolation used to shift the image plane.",
202 default=3,
203 )
204 accelerateModel = pexConfig.Field(
205 dtype=float,
206 doc="Factor to amplify the differences between model planes by to speed convergence.",
207 default=3,
208 )
209 doCalculatePsf = pexConfig.Field(
210 dtype=bool,
211 doc="Set to detect stars and recalculate the PSF from the final coadd."
212 "Otherwise the PSF is estimated from a selection of the best input exposures",
213 default=False,
214 )
215 detectPsfSources = pexConfig.ConfigurableField(
216 target=measAlg.SourceDetectionTask,
217 doc="Task to detect sources for PSF measurement, if ``doCalculatePsf`` is set.",
218 )
219 measurePsfSources = pexConfig.ConfigurableField(
220 target=SingleFrameMeasurementTask,
221 doc="Task to measure sources for PSF measurement, if ``doCalculatePsf`` is set."
222 )
223 measurePsf = pexConfig.ConfigurableField(
224 target=MeasurePsfTask,
225 doc="Task to measure the PSF of the coadd, if ``doCalculatePsf`` is set.",
226 )
228 def setDefaults(self):
229 CompareWarpAssembleCoaddConfig.setDefaults(self)
230 self.assembleStaticSkyModel.retarget(CompareWarpAssembleCoaddTask)
231 self.doNImage = True
232 self.warpType = "direct"
233 self.assembleStaticSkyModel.warpType = self.warpType
234 # The deepCoadd and nImage files will be overwritten by this Task, so don't write them the first time
235 self.assembleStaticSkyModel.doNImage = False
236 self.assembleStaticSkyModel.doWrite = False
237 self.detectPsfSources.returnOriginalFootprints = False
238 self.detectPsfSources.thresholdPolarity = "positive"
239 # Only use bright sources for PSF measurement
240 self.detectPsfSources.thresholdValue = 50
241 self.detectPsfSources.nSigmaToGrow = 2
242 # A valid star for PSF measurement should at least fill 5x5 pixels
243 self.detectPsfSources.minPixels = 25
244 # Use the variance plane to calculate signal to noise
245 self.detectPsfSources.thresholdType = "pixel_stdev"
246 # The signal to noise limit is good enough, while the flux limit is set
247 # in dimensionless units and may not be appropriate for all data sets.
248 self.measurePsf.starSelector["objectSize"].doFluxLimit = False
251class DcrAssembleCoaddTask(CompareWarpAssembleCoaddTask):
252 """Assemble DCR coadded images from a set of warps.
254 Attributes
255 ----------
256 bufferSize : `int`
257 The number of pixels to grow each subregion by to allow for DCR.
259 Notes
260 -----
261 As with AssembleCoaddTask, we want to assemble a coadded image from a set of
262 Warps (also called coadded temporary exposures), including the effects of
263 Differential Chromatic Refraction (DCR).
264 For full details of the mathematics and algorithm, please see
265 DMTN-037: DCR-matched template generation (https://dmtn-037.lsst.io).
267 This Task produces a DCR-corrected deepCoadd, as well as a dcrCoadd for
268 each subfilter used in the iterative calculation.
269 It begins by dividing the bandpass-defining filter into N equal bandwidth
270 "subfilters", and divides the flux in each pixel from an initial coadd
271 equally into each as a "dcrModel". Because the airmass and parallactic
272 angle of each individual exposure is known, we can calculate the shift
273 relative to the center of the band in each subfilter due to DCR. For each
274 exposure we apply this shift as a linear transformation to the dcrModels
275 and stack the results to produce a DCR-matched exposure. The matched
276 exposures are subtracted from the input exposures to produce a set of
277 residual images, and these residuals are reverse shifted for each
278 exposures' subfilters and stacked. The shifted and stacked residuals are
279 added to the dcrModels to produce a new estimate of the flux in each pixel
280 within each subfilter. The dcrModels are solved for iteratively, which
281 continues until the solution from a new iteration improves by less than
282 a set percentage, or a maximum number of iterations is reached.
283 Two forms of regularization are employed to reduce unphysical results.
284 First, the new solution is averaged with the solution from the previous
285 iteration, which mitigates oscillating solutions where the model
286 overshoots with alternating very high and low values.
287 Second, a common degeneracy when the data have a limited range of airmass or
288 parallactic angle values is for one subfilter to be fit with very low or
289 negative values, while another subfilter is fit with very high values. This
290 typically appears in the form of holes next to sources in one subfilter,
291 and corresponding extended wings in another. Because each subfilter has
292 a narrow bandwidth we assume that physical sources that are above the noise
293 level will not vary in flux by more than a factor of `frequencyClampFactor`
294 between subfilters, and pixels that have flux deviations larger than that
295 factor will have the excess flux distributed evenly among all subfilters.
296 If `splitSubfilters` is set, then each subfilter will be further sub-
297 divided during the forward modeling step (only). This approximates using
298 a higher number of subfilters that may be necessary for high airmass
299 observations, but does not increase the number of free parameters in the
300 fit. This is needed when there are high airmass observations which would
301 otherwise have significant DCR even within a subfilter. Because calculating
302 the shifted images takes most of the time, splitting the subfilters is
303 turned off by way of the `splitThreshold` option for low-airmass
304 observations that do not suffer from DCR within a subfilter.
305 """
307 ConfigClass = DcrAssembleCoaddConfig
308 _DefaultName = "dcrAssembleCoadd"
310 def __init__(self, *args, **kwargs):
311 super().__init__(*args, **kwargs)
312 if self.config.doCalculatePsf:
313 self.schema = afwTable.SourceTable.makeMinimalSchema()
314 self.makeSubtask("detectPsfSources", schema=self.schema)
315 self.makeSubtask("measurePsfSources", schema=self.schema)
316 self.makeSubtask("measurePsf", schema=self.schema)
318 @utils.inheritDoc(pipeBase.PipelineTask)
319 def runQuantum(self, butlerQC, inputRefs, outputRefs):
320 # Docstring to be formatted with info from PipelineTask.runQuantum
321 """
322 Notes
323 -----
324 Assemble a coadd from a set of Warps.
326 PipelineTask (Gen3) entry point to Coadd a set of Warps.
327 Analogous to `runDataRef`, it prepares all the data products to be
328 passed to `run`, and processes the results before returning a struct
329 of results to be written out. AssembleCoadd cannot fit all Warps in memory.
330 Therefore, its inputs are accessed subregion by subregion
331 by the Gen3 `DeferredDatasetHandle` that is analagous to the Gen2
332 `lsst.daf.persistence.ButlerDataRef`. Any updates to this method should
333 correspond to an update in `runDataRef` while both entry points
334 are used.
335 """
336 inputData = butlerQC.get(inputRefs)
338 # Construct skyInfo expected by run
339 # Do not remove skyMap from inputData in case makeSupplementaryDataGen3 needs it
340 skyMap = inputData["skyMap"]
341 outputDataId = butlerQC.quantum.dataId
343 inputData['skyInfo'] = makeSkyInfo(skyMap,
344 tractId=outputDataId['tract'],
345 patchId=outputDataId['patch'])
347 # Construct list of input Deferred Datasets
348 # These quack a bit like like Gen2 DataRefs
349 warpRefList = inputData['inputWarps']
350 # Perform same middle steps as `runDataRef` does
351 inputs = self.prepareInputs(warpRefList)
352 self.log.info("Found %d %s", len(inputs.tempExpRefList),
353 self.getTempExpDatasetName(self.warpType))
354 if len(inputs.tempExpRefList) == 0:
355 self.log.warn("No coadd temporary exposures found")
356 return
358 supplementaryData = self.makeSupplementaryDataGen3(butlerQC, inputRefs, outputRefs)
359 retStruct = self.run(inputData['skyInfo'], inputs.tempExpRefList, inputs.imageScalerList,
360 inputs.weightList, supplementaryData=supplementaryData)
362 inputData.setdefault('brightObjectMask', None)
363 for subfilter in range(self.config.dcrNumSubfilters):
364 # Use the PSF of the stacked dcrModel, and do not recalculate the PSF for each subfilter
365 retStruct.dcrCoadds[subfilter].setPsf(retStruct.coaddExposure.getPsf())
366 self.processResults(retStruct.dcrCoadds[subfilter], inputData['brightObjectMask'], outputDataId)
368 if self.config.doWrite:
369 butlerQC.put(retStruct, outputRefs)
370 return retStruct
372 @pipeBase.timeMethod
373 def runDataRef(self, dataRef, selectDataList=None, warpRefList=None):
374 """Assemble a coadd from a set of warps.
376 Coadd a set of Warps. Compute weights to be applied to each Warp and
377 find scalings to match the photometric zeropoint to a reference Warp.
378 Assemble the Warps using run method.
379 Forward model chromatic effects across multiple subfilters,
380 and subtract from the input Warps to build sets of residuals.
381 Use the residuals to construct a new ``DcrModel`` for each subfilter,
382 and iterate until the model converges.
383 Interpolate over NaNs and optionally write the coadd to disk.
384 Return the coadded exposure.
386 Parameters
387 ----------
388 dataRef : `lsst.daf.persistence.ButlerDataRef`
389 Data reference defining the patch for coaddition and the
390 reference Warp
391 selectDataList : `list` of `lsst.daf.persistence.ButlerDataRef`
392 List of data references to warps. Data to be coadded will be
393 selected from this list based on overlap with the patch defined by
394 the data reference.
396 Returns
397 -------
398 results : `lsst.pipe.base.Struct`
399 The Struct contains the following fields:
401 - ``coaddExposure``: coadded exposure (`lsst.afw.image.Exposure`)
402 - ``nImage``: exposure count image (`lsst.afw.image.ImageU`)
403 - ``dcrCoadds``: `list` of coadded exposures for each subfilter
404 - ``dcrNImages``: `list` of exposure count images for each subfilter
405 """
406 if (selectDataList is None and warpRefList is None) or (selectDataList and warpRefList):
407 raise RuntimeError("runDataRef must be supplied either a selectDataList or warpRefList")
409 results = AssembleCoaddTask.runDataRef(self, dataRef, selectDataList=selectDataList,
410 warpRefList=warpRefList)
411 if results is None:
412 skyInfo = self.getSkyInfo(dataRef)
413 self.log.warn("Could not construct DcrModel for patch %s: no data to coadd.",
414 skyInfo.patchInfo.getIndex())
415 return
416 if self.config.doCalculatePsf:
417 self.measureCoaddPsf(results.coaddExposure)
418 brightObjects = self.readBrightObjectMasks(dataRef) if self.config.doMaskBrightObjects else None
419 for subfilter in range(self.config.dcrNumSubfilters):
420 # Use the PSF of the stacked dcrModel, and do not recalculate the PSF for each subfilter
421 results.dcrCoadds[subfilter].setPsf(results.coaddExposure.getPsf())
422 self.processResults(results.dcrCoadds[subfilter],
423 brightObjectMasks=brightObjects, dataId=dataRef.dataId)
424 if self.config.doWrite:
425 self.log.info("Persisting dcrCoadd")
426 dataRef.put(results.dcrCoadds[subfilter], "dcrCoadd", subfilter=subfilter,
427 numSubfilters=self.config.dcrNumSubfilters)
428 if self.config.doNImage and results.dcrNImages is not None:
429 dataRef.put(results.dcrNImages[subfilter], "dcrCoadd_nImage", subfilter=subfilter,
430 numSubfilters=self.config.dcrNumSubfilters)
432 return results
434 @utils.inheritDoc(AssembleCoaddTask)
435 def makeSupplementaryDataGen3(self, butlerQC, inputRefs, outputRefs):
436 """Load the previously-generated template coadd.
438 This can be removed entirely once we no longer support the Gen 2 butler.
440 Returns
441 -------
442 templateCoadd : `lsst.pipe.base.Struct`
443 Result struct with components:
445 - ``templateCoadd``: coadded exposure (`lsst.afw.image.ExposureF`)
446 """
447 templateCoadd = butlerQC.get(inputRefs.templateExposure)
449 return pipeBase.Struct(templateCoadd=templateCoadd)
451 def measureCoaddPsf(self, coaddExposure):
452 """Detect sources on the coadd exposure and measure the final PSF.
454 Parameters
455 ----------
456 coaddExposure : `lsst.afw.image.Exposure`
457 The final coadded exposure.
458 """
459 table = afwTable.SourceTable.make(self.schema)
460 detResults = self.detectPsfSources.run(table, coaddExposure, clearMask=False)
461 coaddSources = detResults.sources
462 self.measurePsfSources.run(
463 measCat=coaddSources,
464 exposure=coaddExposure
465 )
466 # Measure the PSF on the stacked subfilter coadds if possible.
467 # We should already have a decent estimate of the coadd PSF, however,
468 # so in case of any errors simply log them as a warning and use the
469 # default PSF.
470 try:
471 psfResults = self.measurePsf.run(coaddExposure, coaddSources)
472 except Exception as e:
473 self.log.warn("Unable to calculate PSF, using default coadd PSF: %s" % e)
474 else:
475 coaddExposure.setPsf(psfResults.psf)
477 def prepareDcrInputs(self, templateCoadd, warpRefList, weightList):
478 """Prepare the DCR coadd by iterating through the visitInfo of the input warps.
480 Sets the property ``bufferSize``.
482 Parameters
483 ----------
484 templateCoadd : `lsst.afw.image.ExposureF`
485 The initial coadd exposure before accounting for DCR.
486 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or
487 `lsst.daf.persistence.ButlerDataRef`
488 The data references to the input warped exposures.
489 weightList : `list` of `float`
490 The weight to give each input exposure in the coadd
491 Will be modified in place if ``doAirmassWeight`` is set.
493 Returns
494 -------
495 dcrModels : `lsst.pipe.tasks.DcrModel`
496 Best fit model of the true sky after correcting chromatic effects.
498 Raises
499 ------
500 NotImplementedError
501 If ``lambdaMin`` is missing from the Mapper class of the obs package being used.
502 """
503 sigma2fwhm = 2.*np.sqrt(2.*np.log(2.))
504 filterInfo = templateCoadd.getFilter()
505 if np.isnan(filterInfo.getFilterProperty().getLambdaMin()):
506 raise NotImplementedError("No minimum/maximum wavelength information found"
507 " in the filter definition! Please add lambdaMin and lambdaMax"
508 " to the Mapper class in your obs package.")
509 tempExpName = self.getTempExpDatasetName(self.warpType)
510 dcrShifts = []
511 airmassDict = {}
512 angleDict = {}
513 psfSizeDict = {}
514 for visitNum, warpExpRef in enumerate(warpRefList):
515 if isinstance(warpExpRef, DeferredDatasetHandle):
516 # Gen 3 API
517 visitInfo = warpExpRef.get(component="visitInfo")
518 psf = warpExpRef.get(component="psf")
519 else:
520 # Gen 2 API. Delete this when Gen 2 retired
521 visitInfo = warpExpRef.get(tempExpName + "_visitInfo")
522 psf = warpExpRef.get(tempExpName).getPsf()
523 visit = warpExpRef.dataId["visit"]
524 psfSize = psf.computeShape().getDeterminantRadius()*sigma2fwhm
525 airmass = visitInfo.getBoresightAirmass()
526 parallacticAngle = visitInfo.getBoresightParAngle().asDegrees()
527 airmassDict[visit] = airmass
528 angleDict[visit] = parallacticAngle
529 psfSizeDict[visit] = psfSize
530 if self.config.doAirmassWeight:
531 weightList[visitNum] *= airmass
532 dcrShifts.append(np.max(np.abs(calculateDcr(visitInfo, templateCoadd.getWcs(),
533 filterInfo, self.config.dcrNumSubfilters))))
534 self.log.info("Selected airmasses:\n%s", airmassDict)
535 self.log.info("Selected parallactic angles:\n%s", angleDict)
536 self.log.info("Selected PSF sizes:\n%s", psfSizeDict)
537 self.bufferSize = int(np.ceil(np.max(dcrShifts)) + 1)
538 psf = self.selectCoaddPsf(templateCoadd, warpRefList)
539 dcrModels = DcrModel.fromImage(templateCoadd.maskedImage,
540 self.config.dcrNumSubfilters,
541 filterInfo=filterInfo,
542 psf=psf)
543 return dcrModels
545 def run(self, skyInfo, warpRefList, imageScalerList, weightList,
546 supplementaryData=None):
547 """Assemble the coadd.
549 Requires additional inputs Struct ``supplementaryData`` to contain a
550 ``templateCoadd`` that serves as the model of the static sky.
552 Find artifacts and apply them to the warps' masks creating a list of
553 alternative masks with a new "CLIPPED" plane and updated "NO_DATA" plane
554 Then pass these alternative masks to the base class's assemble method.
556 Divide the ``templateCoadd`` evenly between each subfilter of a
557 ``DcrModel`` as the starting best estimate of the true wavelength-
558 dependent sky. Forward model the ``DcrModel`` using the known
559 chromatic effects in each subfilter and calculate a convergence metric
560 based on how well the modeled template matches the input warps. If
561 the convergence has not yet reached the desired threshold, then shift
562 and stack the residual images to build a new ``DcrModel``. Apply
563 conditioning to prevent oscillating solutions between iterations or
564 between subfilters.
566 Once the ``DcrModel`` reaches convergence or the maximum number of
567 iterations has been reached, fill the metadata for each subfilter
568 image and make them proper ``coaddExposure``s.
570 Parameters
571 ----------
572 skyInfo : `lsst.pipe.base.Struct`
573 Patch geometry information, from getSkyInfo
574 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or
575 `lsst.daf.persistence.ButlerDataRef`
576 The data references to the input warped exposures.
577 imageScalerList : `list` of `lsst.pipe.task.ImageScaler`
578 The image scalars correct for the zero point of the exposures.
579 weightList : `list` of `float`
580 The weight to give each input exposure in the coadd
581 supplementaryData : `lsst.pipe.base.Struct`
582 Result struct returned by ``makeSupplementaryData`` with components:
584 - ``templateCoadd``: coadded exposure (`lsst.afw.image.Exposure`)
586 Returns
587 -------
588 result : `lsst.pipe.base.Struct`
589 Result struct with components:
591 - ``coaddExposure``: coadded exposure (`lsst.afw.image.Exposure`)
592 - ``nImage``: exposure count image (`lsst.afw.image.ImageU`)
593 - ``dcrCoadds``: `list` of coadded exposures for each subfilter
594 - ``dcrNImages``: `list` of exposure count images for each subfilter
595 """
596 minNumIter = self.config.minNumIter or self.config.dcrNumSubfilters
597 maxNumIter = self.config.maxNumIter or self.config.dcrNumSubfilters*3
598 templateCoadd = supplementaryData.templateCoadd
599 baseMask = templateCoadd.mask.clone()
600 # The variance plane is for each subfilter
601 # and should be proportionately lower than the full-band image
602 baseVariance = templateCoadd.variance.clone()
603 baseVariance /= self.config.dcrNumSubfilters
604 spanSetMaskList = self.findArtifacts(templateCoadd, warpRefList, imageScalerList)
605 # Note that the mask gets cleared in ``findArtifacts``, but we want to preserve the mask.
606 templateCoadd.setMask(baseMask)
607 badMaskPlanes = self.config.badMaskPlanes[:]
608 # Note that is important that we do not add "CLIPPED" to ``badMaskPlanes``
609 # This is because pixels in observations that are significantly affect by DCR
610 # are likely to have many pixels that are both "DETECTED" and "CLIPPED",
611 # but those are necessary to constrain the DCR model.
612 badPixelMask = templateCoadd.mask.getPlaneBitMask(badMaskPlanes)
614 stats = self.prepareStats(mask=badPixelMask)
615 dcrModels = self.prepareDcrInputs(templateCoadd, warpRefList, weightList)
616 if self.config.doNImage:
617 dcrNImages, dcrWeights = self.calculateNImage(dcrModels, skyInfo.bbox, warpRefList,
618 spanSetMaskList, stats.ctrl)
619 nImage = afwImage.ImageU(skyInfo.bbox)
620 # Note that this nImage will be a factor of dcrNumSubfilters higher than
621 # the nImage returned by assembleCoadd for most pixels. This is because each
622 # subfilter may have a different nImage, and fractional values are not allowed.
623 for dcrNImage in dcrNImages:
624 nImage += dcrNImage
625 else:
626 dcrNImages = None
628 subregionSize = geom.Extent2I(*self.config.subregionSize)
629 nSubregions = (ceil(skyInfo.bbox.getHeight()/subregionSize[1]) *
630 ceil(skyInfo.bbox.getWidth()/subregionSize[0]))
631 subIter = 0
632 for subBBox in self._subBBoxIter(skyInfo.bbox, subregionSize):
633 modelIter = 0
634 subIter += 1
635 self.log.info("Computing coadd over patch %s subregion %s of %s: %s",
636 skyInfo.patchInfo.getIndex(), subIter, nSubregions, subBBox)
637 dcrBBox = geom.Box2I(subBBox)
638 dcrBBox.grow(self.bufferSize)
639 dcrBBox.clip(dcrModels.bbox)
640 modelWeights = self.calculateModelWeights(dcrModels, dcrBBox)
641 subExposures = self.loadSubExposures(dcrBBox, stats.ctrl, warpRefList,
642 imageScalerList, spanSetMaskList)
643 convergenceMetric = self.calculateConvergence(dcrModels, subExposures, subBBox,
644 warpRefList, weightList, stats.ctrl)
645 self.log.info("Initial convergence : %s", convergenceMetric)
646 convergenceList = [convergenceMetric]
647 gainList = []
648 convergenceCheck = 1.
649 refImage = templateCoadd.image
650 while (convergenceCheck > self.config.convergenceThreshold or modelIter <= minNumIter):
651 gain = self.calculateGain(convergenceList, gainList)
652 self.dcrAssembleSubregion(dcrModels, subExposures, subBBox, dcrBBox, warpRefList,
653 stats.ctrl, convergenceMetric, gain,
654 modelWeights, refImage, dcrWeights)
655 if self.config.useConvergence:
656 convergenceMetric = self.calculateConvergence(dcrModels, subExposures, subBBox,
657 warpRefList, weightList, stats.ctrl)
658 if convergenceMetric == 0:
659 self.log.warn("Coadd patch %s subregion %s had convergence metric of 0.0 which is "
660 "most likely due to there being no valid data in the region.",
661 skyInfo.patchInfo.getIndex(), subIter)
662 break
663 convergenceCheck = (convergenceList[-1] - convergenceMetric)/convergenceMetric
664 if (convergenceCheck < 0) & (modelIter > minNumIter):
665 self.log.warn("Coadd patch %s subregion %s diverged before reaching maximum "
666 "iterations or desired convergence improvement of %s."
667 " Divergence: %s",
668 skyInfo.patchInfo.getIndex(), subIter,
669 self.config.convergenceThreshold, convergenceCheck)
670 break
671 convergenceList.append(convergenceMetric)
672 if modelIter > maxNumIter:
673 if self.config.useConvergence:
674 self.log.warn("Coadd patch %s subregion %s reached maximum iterations "
675 "before reaching desired convergence improvement of %s."
676 " Final convergence improvement: %s",
677 skyInfo.patchInfo.getIndex(), subIter,
678 self.config.convergenceThreshold, convergenceCheck)
679 break
681 if self.config.useConvergence:
682 self.log.info("Iteration %s with convergence metric %s, %.4f%% improvement (gain: %.2f)",
683 modelIter, convergenceMetric, 100.*convergenceCheck, gain)
684 modelIter += 1
685 else:
686 if self.config.useConvergence:
687 self.log.info("Coadd patch %s subregion %s finished with "
688 "convergence metric %s after %s iterations",
689 skyInfo.patchInfo.getIndex(), subIter, convergenceMetric, modelIter)
690 else:
691 self.log.info("Coadd patch %s subregion %s finished after %s iterations",
692 skyInfo.patchInfo.getIndex(), subIter, modelIter)
693 if self.config.useConvergence and convergenceMetric > 0:
694 self.log.info("Final convergence improvement was %.4f%% overall",
695 100*(convergenceList[0] - convergenceMetric)/convergenceMetric)
697 dcrCoadds = self.fillCoadd(dcrModels, skyInfo, warpRefList, weightList,
698 calibration=self.scaleZeroPoint.getPhotoCalib(),
699 coaddInputs=templateCoadd.getInfo().getCoaddInputs(),
700 mask=baseMask,
701 variance=baseVariance)
702 coaddExposure = self.stackCoadd(dcrCoadds)
703 return pipeBase.Struct(coaddExposure=coaddExposure, nImage=nImage,
704 dcrCoadds=dcrCoadds, dcrNImages=dcrNImages)
706 def calculateNImage(self, dcrModels, bbox, warpRefList, spanSetMaskList, statsCtrl):
707 """Calculate the number of exposures contributing to each subfilter.
709 Parameters
710 ----------
711 dcrModels : `lsst.pipe.tasks.DcrModel`
712 Best fit model of the true sky after correcting chromatic effects.
713 bbox : `lsst.geom.box.Box2I`
714 Bounding box of the patch to coadd.
715 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or
716 `lsst.daf.persistence.ButlerDataRef`
717 The data references to the input warped exposures.
718 spanSetMaskList : `list` of `dict` containing spanSet lists, or None
719 Each element of the `dict` contains the new mask plane name
720 (e.g. "CLIPPED and/or "NO_DATA") as the key,
721 and the list of SpanSets to apply to the mask.
722 statsCtrl : `lsst.afw.math.StatisticsControl`
723 Statistics control object for coadd
725 Returns
726 -------
727 dcrNImages : `list` of `lsst.afw.image.ImageU`
728 List of exposure count images for each subfilter
729 dcrWeights : `list` of `lsst.afw.image.ImageF`
730 Per-pixel weights for each subfilter.
731 Equal to 1/(number of unmasked images contributing to each pixel).
732 """
733 dcrNImages = [afwImage.ImageU(bbox) for subfilter in range(self.config.dcrNumSubfilters)]
734 dcrWeights = [afwImage.ImageF(bbox) for subfilter in range(self.config.dcrNumSubfilters)]
735 tempExpName = self.getTempExpDatasetName(self.warpType)
736 for warpExpRef, altMaskSpans in zip(warpRefList, spanSetMaskList):
737 if isinstance(warpExpRef, DeferredDatasetHandle):
738 # Gen 3 API
739 exposure = warpExpRef.get(parameters={'bbox': bbox})
740 else:
741 # Gen 2 API. Delete this when Gen 2 retired
742 exposure = warpExpRef.get(tempExpName + "_sub", bbox=bbox)
743 visitInfo = exposure.getInfo().getVisitInfo()
744 wcs = exposure.getInfo().getWcs()
745 mask = exposure.mask
746 if altMaskSpans is not None:
747 self.applyAltMaskPlanes(mask, altMaskSpans)
748 weightImage = np.zeros_like(exposure.image.array)
749 weightImage[(mask.array & statsCtrl.getAndMask()) == 0] = 1.
750 # The weights must be shifted in exactly the same way as the residuals,
751 # because they will be used as the denominator in the weighted average of residuals.
752 weightsGenerator = self.dcrResiduals(weightImage, visitInfo, wcs, dcrModels.filter)
753 for shiftedWeights, dcrNImage, dcrWeight in zip(weightsGenerator, dcrNImages, dcrWeights):
754 dcrNImage.array += np.rint(shiftedWeights).astype(dcrNImage.array.dtype)
755 dcrWeight.array += shiftedWeights
756 # Exclude any pixels that don't have at least one exposure contributing in all subfilters
757 weightsThreshold = 1.
758 goodPix = dcrWeights[0].array > weightsThreshold
759 for weights in dcrWeights[1:]:
760 goodPix = (weights.array > weightsThreshold) & goodPix
761 for subfilter in range(self.config.dcrNumSubfilters):
762 dcrWeights[subfilter].array[goodPix] = 1./dcrWeights[subfilter].array[goodPix]
763 dcrWeights[subfilter].array[~goodPix] = 0.
764 dcrNImages[subfilter].array[~goodPix] = 0
765 return (dcrNImages, dcrWeights)
767 def dcrAssembleSubregion(self, dcrModels, subExposures, bbox, dcrBBox, warpRefList,
768 statsCtrl, convergenceMetric,
769 gain, modelWeights, refImage, dcrWeights):
770 """Assemble the DCR coadd for a sub-region.
772 Build a DCR-matched template for each input exposure, then shift the
773 residuals according to the DCR in each subfilter.
774 Stack the shifted residuals and apply them as a correction to the
775 solution from the previous iteration.
776 Restrict the new model solutions from varying by more than a factor of
777 `modelClampFactor` from the last solution, and additionally restrict the
778 individual subfilter models from varying by more than a factor of
779 `frequencyClampFactor` from their average.
780 Finally, mitigate potentially oscillating solutions by averaging the new
781 solution with the solution from the previous iteration, weighted by
782 their convergence metric.
784 Parameters
785 ----------
786 dcrModels : `lsst.pipe.tasks.DcrModel`
787 Best fit model of the true sky after correcting chromatic effects.
788 subExposures : `dict` of `lsst.afw.image.ExposureF`
789 The pre-loaded exposures for the current subregion.
790 bbox : `lsst.geom.box.Box2I`
791 Bounding box of the subregion to coadd.
792 dcrBBox : `lsst.geom.box.Box2I`
793 Sub-region of the coadd which includes a buffer to allow for DCR.
794 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or
795 `lsst.daf.persistence.ButlerDataRef`
796 The data references to the input warped exposures.
797 statsCtrl : `lsst.afw.math.StatisticsControl`
798 Statistics control object for coadd
799 convergenceMetric : `float`
800 Quality of fit metric for the matched templates of the input images.
801 gain : `float`, optional
802 Relative weight to give the new solution when updating the model.
803 modelWeights : `numpy.ndarray` or `float`
804 A 2D array of weight values that tapers smoothly to zero away from detected sources.
805 Set to a placeholder value of 1.0 if ``self.config.useModelWeights`` is False.
806 refImage : `lsst.afw.image.Image`
807 A reference image used to supply the default pixel values.
808 dcrWeights : `list` of `lsst.afw.image.Image`
809 Per-pixel weights for each subfilter.
810 Equal to 1/(number of unmasked images contributing to each pixel).
811 """
812 residualGeneratorList = []
814 for warpExpRef in warpRefList:
815 visit = warpExpRef.dataId["visit"]
816 exposure = subExposures[visit]
817 visitInfo = exposure.getInfo().getVisitInfo()
818 wcs = exposure.getInfo().getWcs()
819 templateImage = dcrModels.buildMatchedTemplate(exposure=exposure,
820 order=self.config.imageInterpOrder,
821 splitSubfilters=self.config.splitSubfilters,
822 splitThreshold=self.config.splitThreshold,
823 amplifyModel=self.config.accelerateModel)
824 residual = exposure.image.array - templateImage.array
825 # Note that the variance plane here is used to store weights, not the actual variance
826 residual *= exposure.variance.array
827 # The residuals are stored as a list of generators.
828 # This allows the residual for a given subfilter and exposure to be created
829 # on the fly, instead of needing to store them all in memory.
830 residualGeneratorList.append(self.dcrResiduals(residual, visitInfo, wcs, dcrModels.filter))
832 dcrSubModelOut = self.newModelFromResidual(dcrModels, residualGeneratorList, dcrBBox, statsCtrl,
833 gain=gain,
834 modelWeights=modelWeights,
835 refImage=refImage,
836 dcrWeights=dcrWeights)
837 dcrModels.assign(dcrSubModelOut, bbox)
839 def dcrResiduals(self, residual, visitInfo, wcs, filterInfo):
840 """Prepare a residual image for stacking in each subfilter by applying the reverse DCR shifts.
842 Parameters
843 ----------
844 residual : `numpy.ndarray`
845 The residual masked image for one exposure,
846 after subtracting the matched template
847 visitInfo : `lsst.afw.image.VisitInfo`
848 Metadata for the exposure.
849 wcs : `lsst.afw.geom.SkyWcs`
850 Coordinate system definition (wcs) for the exposure.
851 filterInfo : `lsst.afw.image.Filter`
852 The filter definition, set in the current instruments' obs package.
853 Required for any calculation of DCR, including making matched templates.
855 Yields
856 ------
857 residualImage : `numpy.ndarray`
858 The residual image for the next subfilter, shifted for DCR.
859 """
860 # Pre-calculate the spline-filtered residual image, so that step can be
861 # skipped in the shift calculation in `applyDcr`.
862 filteredResidual = ndimage.spline_filter(residual, order=self.config.imageInterpOrder)
863 # Note that `splitSubfilters` is always turned off in the reverse direction.
864 # This option introduces additional blurring if applied to the residuals.
865 dcrShift = calculateDcr(visitInfo, wcs, filterInfo, self.config.dcrNumSubfilters,
866 splitSubfilters=False)
867 for dcr in dcrShift:
868 yield applyDcr(filteredResidual, dcr, useInverse=True, splitSubfilters=False,
869 doPrefilter=False, order=self.config.imageInterpOrder)
871 def newModelFromResidual(self, dcrModels, residualGeneratorList, dcrBBox, statsCtrl,
872 gain, modelWeights, refImage, dcrWeights):
873 """Calculate a new DcrModel from a set of image residuals.
875 Parameters
876 ----------
877 dcrModels : `lsst.pipe.tasks.DcrModel`
878 Current model of the true sky after correcting chromatic effects.
879 residualGeneratorList : `generator` of `numpy.ndarray`
880 The residual image for the next subfilter, shifted for DCR.
881 dcrBBox : `lsst.geom.box.Box2I`
882 Sub-region of the coadd which includes a buffer to allow for DCR.
883 statsCtrl : `lsst.afw.math.StatisticsControl`
884 Statistics control object for coadd
885 gain : `float`
886 Relative weight to give the new solution when updating the model.
887 modelWeights : `numpy.ndarray` or `float`
888 A 2D array of weight values that tapers smoothly to zero away from detected sources.
889 Set to a placeholder value of 1.0 if ``self.config.useModelWeights`` is False.
890 refImage : `lsst.afw.image.Image`
891 A reference image used to supply the default pixel values.
892 dcrWeights : `list` of `lsst.afw.image.Image`
893 Per-pixel weights for each subfilter.
894 Equal to 1/(number of unmasked images contributing to each pixel).
896 Returns
897 -------
898 dcrModel : `lsst.pipe.tasks.DcrModel`
899 New model of the true sky after correcting chromatic effects.
900 """
901 newModelImages = []
902 for subfilter, model in enumerate(dcrModels):
903 residualsList = [next(residualGenerator) for residualGenerator in residualGeneratorList]
904 residual = np.sum(residualsList, axis=0)
905 residual *= dcrWeights[subfilter][dcrBBox].array
906 # `MaskedImage`s only support in-place addition, so rename for readability
907 newModel = model[dcrBBox].clone()
908 newModel.array += residual
909 # Catch any invalid values
910 badPixels = ~np.isfinite(newModel.array)
911 newModel.array[badPixels] = model[dcrBBox].array[badPixels]
912 if self.config.regularizeModelIterations > 0:
913 dcrModels.regularizeModelIter(subfilter, newModel, dcrBBox,
914 self.config.regularizeModelIterations,
915 self.config.regularizationWidth)
916 newModelImages.append(newModel)
917 if self.config.regularizeModelFrequency > 0:
918 dcrModels.regularizeModelFreq(newModelImages, dcrBBox, statsCtrl,
919 self.config.regularizeModelFrequency,
920 self.config.regularizationWidth)
921 dcrModels.conditionDcrModel(newModelImages, dcrBBox, gain=gain)
922 self.applyModelWeights(newModelImages, refImage[dcrBBox], modelWeights)
923 return DcrModel(newModelImages, dcrModels.filter, dcrModels.psf,
924 dcrModels.mask, dcrModels.variance)
926 def calculateConvergence(self, dcrModels, subExposures, bbox, warpRefList, weightList, statsCtrl):
927 """Calculate a quality of fit metric for the matched templates.
929 Parameters
930 ----------
931 dcrModels : `lsst.pipe.tasks.DcrModel`
932 Best fit model of the true sky after correcting chromatic effects.
933 subExposures : `dict` of `lsst.afw.image.ExposureF`
934 The pre-loaded exposures for the current subregion.
935 bbox : `lsst.geom.box.Box2I`
936 Sub-region to coadd
937 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or
938 `lsst.daf.persistence.ButlerDataRef`
939 The data references to the input warped exposures.
940 weightList : `list` of `float`
941 The weight to give each input exposure in the coadd
942 statsCtrl : `lsst.afw.math.StatisticsControl`
943 Statistics control object for coadd
945 Returns
946 -------
947 convergenceMetric : `float`
948 Quality of fit metric for all input exposures, within the sub-region
949 """
950 significanceImage = np.abs(dcrModels.getReferenceImage(bbox))
951 nSigma = 3.
952 significanceImage += nSigma*dcrModels.calculateNoiseCutoff(dcrModels[1], statsCtrl,
953 bufferSize=self.bufferSize)
954 if np.max(significanceImage) == 0:
955 significanceImage += 1.
956 weight = 0
957 metric = 0.
958 metricList = {}
959 for warpExpRef, expWeight in zip(warpRefList, weightList):
960 visit = warpExpRef.dataId["visit"]
961 exposure = subExposures[visit][bbox]
962 singleMetric = self.calculateSingleConvergence(dcrModels, exposure, significanceImage, statsCtrl)
963 metric += singleMetric
964 metricList[visit] = singleMetric
965 weight += 1.
966 self.log.info("Individual metrics:\n%s", metricList)
967 return 1.0 if weight == 0.0 else metric/weight
969 def calculateSingleConvergence(self, dcrModels, exposure, significanceImage, statsCtrl):
970 """Calculate a quality of fit metric for a single matched template.
972 Parameters
973 ----------
974 dcrModels : `lsst.pipe.tasks.DcrModel`
975 Best fit model of the true sky after correcting chromatic effects.
976 exposure : `lsst.afw.image.ExposureF`
977 The input warped exposure to evaluate.
978 significanceImage : `numpy.ndarray`
979 Array of weights for each pixel corresponding to its significance
980 for the convergence calculation.
981 statsCtrl : `lsst.afw.math.StatisticsControl`
982 Statistics control object for coadd
984 Returns
985 -------
986 convergenceMetric : `float`
987 Quality of fit metric for one exposure, within the sub-region.
988 """
989 convergeMask = exposure.mask.getPlaneBitMask(self.config.convergenceMaskPlanes)
990 templateImage = dcrModels.buildMatchedTemplate(exposure=exposure,
991 order=self.config.imageInterpOrder,
992 splitSubfilters=self.config.splitSubfilters,
993 splitThreshold=self.config.splitThreshold,
994 amplifyModel=self.config.accelerateModel)
995 diffVals = np.abs(exposure.image.array - templateImage.array)*significanceImage
996 refVals = np.abs(exposure.image.array + templateImage.array)*significanceImage/2.
998 finitePixels = np.isfinite(diffVals)
999 goodMaskPixels = (exposure.mask.array & statsCtrl.getAndMask()) == 0
1000 convergeMaskPixels = exposure.mask.array & convergeMask > 0
1001 usePixels = finitePixels & goodMaskPixels & convergeMaskPixels
1002 if np.sum(usePixels) == 0:
1003 metric = 0.
1004 else:
1005 diffUse = diffVals[usePixels]
1006 refUse = refVals[usePixels]
1007 metric = np.sum(diffUse/np.median(diffUse))/np.sum(refUse/np.median(diffUse))
1008 return metric
1010 def stackCoadd(self, dcrCoadds):
1011 """Add a list of sub-band coadds together.
1013 Parameters
1014 ----------
1015 dcrCoadds : `list` of `lsst.afw.image.ExposureF`
1016 A list of coadd exposures, each exposure containing
1017 the model for one subfilter.
1019 Returns
1020 -------
1021 coaddExposure : `lsst.afw.image.ExposureF`
1022 A single coadd exposure that is the sum of the sub-bands.
1023 """
1024 coaddExposure = dcrCoadds[0].clone()
1025 for coadd in dcrCoadds[1:]:
1026 coaddExposure.maskedImage += coadd.maskedImage
1027 return coaddExposure
1029 def fillCoadd(self, dcrModels, skyInfo, warpRefList, weightList, calibration=None, coaddInputs=None,
1030 mask=None, variance=None):
1031 """Create a list of coadd exposures from a list of masked images.
1033 Parameters
1034 ----------
1035 dcrModels : `lsst.pipe.tasks.DcrModel`
1036 Best fit model of the true sky after correcting chromatic effects.
1037 skyInfo : `lsst.pipe.base.Struct`
1038 Patch geometry information, from getSkyInfo
1039 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or
1040 `lsst.daf.persistence.ButlerDataRef`
1041 The data references to the input warped exposures.
1042 weightList : `list` of `float`
1043 The weight to give each input exposure in the coadd
1044 calibration : `lsst.afw.Image.PhotoCalib`, optional
1045 Scale factor to set the photometric calibration of an exposure.
1046 coaddInputs : `lsst.afw.Image.CoaddInputs`, optional
1047 A record of the observations that are included in the coadd.
1048 mask : `lsst.afw.image.Mask`, optional
1049 Optional mask to override the values in the final coadd.
1050 variance : `lsst.afw.image.Image`, optional
1051 Optional variance plane to override the values in the final coadd.
1053 Returns
1054 -------
1055 dcrCoadds : `list` of `lsst.afw.image.ExposureF`
1056 A list of coadd exposures, each exposure containing
1057 the model for one subfilter.
1058 """
1059 dcrCoadds = []
1060 refModel = dcrModels.getReferenceImage()
1061 for model in dcrModels:
1062 if self.config.accelerateModel > 1:
1063 model.array = (model.array - refModel)*self.config.accelerateModel + refModel
1064 coaddExposure = afwImage.ExposureF(skyInfo.bbox, skyInfo.wcs)
1065 if calibration is not None:
1066 coaddExposure.setPhotoCalib(calibration)
1067 if coaddInputs is not None:
1068 coaddExposure.getInfo().setCoaddInputs(coaddInputs)
1069 # Set the metadata for the coadd, including PSF and aperture corrections.
1070 self.assembleMetadata(coaddExposure, warpRefList, weightList)
1071 # Overwrite the PSF
1072 coaddExposure.setPsf(dcrModels.psf)
1073 coaddUtils.setCoaddEdgeBits(dcrModels.mask[skyInfo.bbox], dcrModels.variance[skyInfo.bbox])
1074 maskedImage = afwImage.MaskedImageF(dcrModels.bbox)
1075 maskedImage.image = model
1076 maskedImage.mask = dcrModels.mask
1077 maskedImage.variance = dcrModels.variance
1078 coaddExposure.setMaskedImage(maskedImage[skyInfo.bbox])
1079 coaddExposure.setPhotoCalib(self.scaleZeroPoint.getPhotoCalib())
1080 if mask is not None:
1081 coaddExposure.setMask(mask)
1082 if variance is not None:
1083 coaddExposure.setVariance(variance)
1084 dcrCoadds.append(coaddExposure)
1085 return dcrCoadds
1087 def calculateGain(self, convergenceList, gainList):
1088 """Calculate the gain to use for the current iteration.
1090 After calculating a new DcrModel, each value is averaged with the
1091 value in the corresponding pixel from the previous iteration. This
1092 reduces oscillating solutions that iterative techniques are plagued by,
1093 and speeds convergence. By far the biggest changes to the model
1094 happen in the first couple iterations, so we can also use a more
1095 aggressive gain later when the model is changing slowly.
1097 Parameters
1098 ----------
1099 convergenceList : `list` of `float`
1100 The quality of fit metric from each previous iteration.
1101 gainList : `list` of `float`
1102 The gains used in each previous iteration: appended with the new
1103 gain value.
1104 Gains are numbers between ``self.config.baseGain`` and 1.
1106 Returns
1107 -------
1108 gain : `float`
1109 Relative weight to give the new solution when updating the model.
1110 A value of 1.0 gives equal weight to both solutions.
1112 Raises
1113 ------
1114 ValueError
1115 If ``len(convergenceList) != len(gainList)+1``.
1116 """
1117 nIter = len(convergenceList)
1118 if nIter != len(gainList) + 1:
1119 raise ValueError("convergenceList (%d) must be one element longer than gainList (%d)."
1120 % (len(convergenceList), len(gainList)))
1122 if self.config.baseGain is None:
1123 # If ``baseGain`` is not set, calculate it from the number of DCR subfilters
1124 # The more subfilters being modeled, the lower the gain should be.
1125 baseGain = 1./(self.config.dcrNumSubfilters - 1)
1126 else:
1127 baseGain = self.config.baseGain
1129 if self.config.useProgressiveGain and nIter > 2:
1130 # To calculate the best gain to use, compare the past gains that have been used
1131 # with the resulting convergences to estimate the best gain to use.
1132 # Algorithmically, this is a Kalman filter.
1133 # If forward modeling proceeds perfectly, the convergence metric should
1134 # asymptotically approach a final value.
1135 # We can estimate that value from the measured changes in convergence
1136 # weighted by the gains used in each previous iteration.
1137 estFinalConv = [((1 + gainList[i])*convergenceList[i + 1] - convergenceList[i])/gainList[i]
1138 for i in range(nIter - 1)]
1139 # The convergence metric is strictly positive, so if the estimated final convergence is
1140 # less than zero, force it to zero.
1141 estFinalConv = np.array(estFinalConv)
1142 estFinalConv[estFinalConv < 0] = 0
1143 # Because the estimate may slowly change over time, only use the most recent measurements.
1144 estFinalConv = np.median(estFinalConv[max(nIter - 5, 0):])
1145 lastGain = gainList[-1]
1146 lastConv = convergenceList[-2]
1147 newConv = convergenceList[-1]
1148 # The predicted convergence is the value we would get if the new model calculated
1149 # in the previous iteration was perfect. Recall that the updated model that is
1150 # actually used is the gain-weighted average of the new and old model,
1151 # so the convergence would be similarly weighted.
1152 predictedConv = (estFinalConv*lastGain + lastConv)/(1. + lastGain)
1153 # If the measured and predicted convergence are very close, that indicates
1154 # that our forward model is accurate and we can use a more aggressive gain
1155 # If the measured convergence is significantly worse (or better!) than predicted,
1156 # that indicates that the model is not converging as expected and
1157 # we should use a more conservative gain.
1158 delta = (predictedConv - newConv)/((lastConv - estFinalConv)/(1 + lastGain))
1159 newGain = 1 - abs(delta)
1160 # Average the gains to prevent oscillating solutions.
1161 newGain = (newGain + lastGain)/2.
1162 gain = max(baseGain, newGain)
1163 else:
1164 gain = baseGain
1165 gainList.append(gain)
1166 return gain
1168 def calculateModelWeights(self, dcrModels, dcrBBox):
1169 """Build an array that smoothly tapers to 0 away from detected sources.
1171 Parameters
1172 ----------
1173 dcrModels : `lsst.pipe.tasks.DcrModel`
1174 Best fit model of the true sky after correcting chromatic effects.
1175 dcrBBox : `lsst.geom.box.Box2I`
1176 Sub-region of the coadd which includes a buffer to allow for DCR.
1178 Returns
1179 -------
1180 weights : `numpy.ndarray` or `float`
1181 A 2D array of weight values that tapers smoothly to zero away from detected sources.
1182 Set to a placeholder value of 1.0 if ``self.config.useModelWeights`` is False.
1184 Raises
1185 ------
1186 ValueError
1187 If ``useModelWeights`` is set and ``modelWeightsWidth`` is negative.
1188 """
1189 if not self.config.useModelWeights:
1190 return 1.0
1191 if self.config.modelWeightsWidth < 0:
1192 raise ValueError("modelWeightsWidth must not be negative if useModelWeights is set")
1193 convergeMask = dcrModels.mask.getPlaneBitMask(self.config.convergenceMaskPlanes)
1194 convergeMaskPixels = dcrModels.mask[dcrBBox].array & convergeMask > 0
1195 weights = np.zeros_like(dcrModels[0][dcrBBox].array)
1196 weights[convergeMaskPixels] = 1.
1197 weights = ndimage.filters.gaussian_filter(weights, self.config.modelWeightsWidth)
1198 weights /= np.max(weights)
1199 return weights
1201 def applyModelWeights(self, modelImages, refImage, modelWeights):
1202 """Smoothly replace model pixel values with those from a
1203 reference at locations away from detected sources.
1205 Parameters
1206 ----------
1207 modelImages : `list` of `lsst.afw.image.Image`
1208 The new DCR model images from the current iteration.
1209 The values will be modified in place.
1210 refImage : `lsst.afw.image.MaskedImage`
1211 A reference image used to supply the default pixel values.
1212 modelWeights : `numpy.ndarray` or `float`
1213 A 2D array of weight values that tapers smoothly to zero away from detected sources.
1214 Set to a placeholder value of 1.0 if ``self.config.useModelWeights`` is False.
1215 """
1216 if self.config.useModelWeights:
1217 for model in modelImages:
1218 model.array *= modelWeights
1219 model.array += refImage.array*(1. - modelWeights)/self.config.dcrNumSubfilters
1221 def loadSubExposures(self, bbox, statsCtrl, warpRefList, imageScalerList, spanSetMaskList):
1222 """Pre-load sub-regions of a list of exposures.
1224 Parameters
1225 ----------
1226 bbox : `lsst.geom.box.Box2I`
1227 Sub-region to coadd
1228 statsCtrl : `lsst.afw.math.StatisticsControl`
1229 Statistics control object for coadd
1230 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or
1231 `lsst.daf.persistence.ButlerDataRef`
1232 The data references to the input warped exposures.
1233 imageScalerList : `list` of `lsst.pipe.task.ImageScaler`
1234 The image scalars correct for the zero point of the exposures.
1235 spanSetMaskList : `list` of `dict` containing spanSet lists, or None
1236 Each element is dict with keys = mask plane name to add the spans to
1238 Returns
1239 -------
1240 subExposures : `dict`
1241 The `dict` keys are the visit IDs,
1242 and the values are `lsst.afw.image.ExposureF`
1243 The pre-loaded exposures for the current subregion.
1244 The variance plane contains weights, and not the variance
1245 """
1246 tempExpName = self.getTempExpDatasetName(self.warpType)
1247 zipIterables = zip(warpRefList, imageScalerList, spanSetMaskList)
1248 subExposures = {}
1249 for warpExpRef, imageScaler, altMaskSpans in zipIterables:
1250 if isinstance(warpExpRef, DeferredDatasetHandle):
1251 exposure = warpExpRef.get(parameters={'bbox': bbox})
1252 else:
1253 exposure = warpExpRef.get(tempExpName + "_sub", bbox=bbox)
1254 visit = warpExpRef.dataId["visit"]
1255 if altMaskSpans is not None:
1256 self.applyAltMaskPlanes(exposure.mask, altMaskSpans)
1257 imageScaler.scaleMaskedImage(exposure.maskedImage)
1258 # Note that the variance plane here is used to store weights, not the actual variance
1259 exposure.variance.array[:, :] = 0.
1260 # Set the weight of unmasked pixels to 1.
1261 exposure.variance.array[(exposure.mask.array & statsCtrl.getAndMask()) == 0] = 1.
1262 # Set the image value of masked pixels to zero.
1263 # This eliminates needing the mask plane when stacking images in ``newModelFromResidual``
1264 exposure.image.array[(exposure.mask.array & statsCtrl.getAndMask()) > 0] = 0.
1265 subExposures[visit] = exposure
1266 return subExposures
1268 def selectCoaddPsf(self, templateCoadd, warpRefList):
1269 """Compute the PSF of the coadd from the exposures with the best seeing.
1271 Parameters
1272 ----------
1273 templateCoadd : `lsst.afw.image.ExposureF`
1274 The initial coadd exposure before accounting for DCR.
1275 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or
1276 `lsst.daf.persistence.ButlerDataRef`
1277 The data references to the input warped exposures.
1279 Returns
1280 -------
1281 psf : `lsst.meas.algorithms.CoaddPsf`
1282 The average PSF of the input exposures with the best seeing.
1283 """
1284 sigma2fwhm = 2.*np.sqrt(2.*np.log(2.))
1285 tempExpName = self.getTempExpDatasetName(self.warpType)
1286 # Note: ``ccds`` is a `lsst.afw.table.ExposureCatalog` with one entry per ccd and per visit
1287 # If there are multiple ccds, it will have that many times more elements than ``warpExpRef``
1288 ccds = templateCoadd.getInfo().getCoaddInputs().ccds
1289 psfRefSize = templateCoadd.getPsf().computeShape().getDeterminantRadius()*sigma2fwhm
1290 psfSizes = np.zeros(len(ccds))
1291 ccdVisits = np.array(ccds["visit"])
1292 for warpExpRef in warpRefList:
1293 if isinstance(warpExpRef, DeferredDatasetHandle):
1294 # Gen 3 API
1295 psf = warpExpRef.get(component="psf")
1296 else:
1297 # Gen 2 API. Delete this when Gen 2 retired
1298 psf = warpExpRef.get(tempExpName).getPsf()
1299 visit = warpExpRef.dataId["visit"]
1300 psfSize = psf.computeShape().getDeterminantRadius()*sigma2fwhm
1301 psfSizes[ccdVisits == visit] = psfSize
1302 # Note that the input PSFs include DCR, which should be absent from the DcrCoadd
1303 # The selected PSFs are those that have a FWHM less than or equal to the smaller
1304 # of the mean or median FWHM of the input exposures.
1305 sizeThreshold = min(np.median(psfSizes), psfRefSize)
1306 goodPsfs = psfSizes <= sizeThreshold
1307 psf = measAlg.CoaddPsf(ccds[goodPsfs], templateCoadd.getWcs(),
1308 self.config.coaddPsf.makeControl())
1309 return psf