25from scipy
import ndimage
30from lsst.daf.butler
import DeferredDatasetHandle
36import lsst.utils
as utils
37from lsst.utils.timer
import timeMethod
38from .assembleCoadd
import (AssembleCoaddConnections,
40 CompareWarpAssembleCoaddConfig,
41 CompareWarpAssembleCoaddTask)
42from .coaddBase
import makeSkyInfo
43from .measurePsf
import MeasurePsfTask
45__all__ = [
"DcrAssembleCoaddConnections",
"DcrAssembleCoaddTask",
"DcrAssembleCoaddConfig"]
49 dimensions=(
"tract",
"patch",
"band",
"skymap"),
50 defaultTemplates={
"inputWarpName":
"deep",
51 "inputCoaddName":
"deep",
52 "outputCoaddName":
"dcr",
56 inputWarps = pipeBase.connectionTypes.Input(
57 doc=(
"Input list of warps to be assembled i.e. stacked."
58 "Note that this will often be different than the inputCoaddName."
59 "WarpType (e.g. direct, psfMatched) is controlled by the warpType config parameter"),
60 name=
"{inputWarpName}Coadd_{warpType}Warp",
61 storageClass=
"ExposureF",
62 dimensions=(
"tract",
"patch",
"skymap",
"visit",
"instrument"),
66 templateExposure = pipeBase.connectionTypes.Input(
67 doc=
"Input coadded exposure, produced by previous call to AssembleCoadd",
68 name=
"{fakesType}{inputCoaddName}Coadd{warpTypeSuffix}",
69 storageClass=
"ExposureF",
70 dimensions=(
"tract",
"patch",
"skymap",
"band"),
72 dcrCoadds = pipeBase.connectionTypes.Output(
73 doc=
"Output coadded exposure, produced by stacking input warps",
74 name=
"{fakesType}{outputCoaddName}Coadd{warpTypeSuffix}",
75 storageClass=
"ExposureF",
76 dimensions=(
"tract",
"patch",
"skymap",
"band",
"subfilter"),
79 dcrNImages = pipeBase.connectionTypes.Output(
80 doc=
"Output image of number of input images per pixel",
81 name=
"{outputCoaddName}Coadd_nImage",
82 storageClass=
"ImageU",
83 dimensions=(
"tract",
"patch",
"skymap",
"band",
"subfilter"),
87 def __init__(self, *, config=None):
88 super().__init__(config=config)
89 if not config.doWrite:
90 self.outputs.remove(
"dcrCoadds")
91 if not config.doNImage:
92 self.outputs.remove(
"dcrNImages")
94 self.outputs.remove(
"coaddExposure")
95 self.outputs.remove(
"nImage")
99 pipelineConnections=DcrAssembleCoaddConnections):
100 dcrNumSubfilters = pexConfig.Field(
102 doc=
"Number of sub-filters to forward model chromatic effects to fit the supplied exposures.",
105 maxNumIter = pexConfig.Field(
108 doc=
"Maximum number of iterations of forward modeling.",
111 minNumIter = pexConfig.Field(
114 doc=
"Minimum number of iterations of forward modeling.",
117 convergenceThreshold = pexConfig.Field(
119 doc=
"Target relative change in convergence between iterations of forward modeling.",
122 useConvergence = pexConfig.Field(
124 doc=
"Use convergence test as a forward modeling end condition?"
125 "If not set, skips calculating convergence and runs for ``maxNumIter`` iterations",
128 baseGain = pexConfig.Field(
131 doc=
"Relative weight to give the new solution vs. the last solution when updating the model."
132 "A value of 1.0 gives equal weight to both solutions."
133 "Small values imply slower convergence of the solution, but can "
134 "help prevent overshooting and failures in the fit."
135 "If ``baseGain`` is None, a conservative gain "
136 "will be calculated from the number of subfilters. ",
139 useProgressiveGain = pexConfig.Field(
141 doc=
"Use a gain that slowly increases above ``baseGain`` to accelerate convergence? "
142 "When calculating the next gain, we use up to 5 previous gains and convergence values."
143 "Can be set to False to force the model to change at the rate of ``baseGain``. ",
146 doAirmassWeight = pexConfig.Field(
148 doc=
"Weight exposures by airmass? Useful if there are relatively few high-airmass observations.",
151 modelWeightsWidth = pexConfig.Field(
153 doc=
"Width of the region around detected sources to include in the DcrModel.",
156 useModelWeights = pexConfig.Field(
158 doc=
"Width of the region around detected sources to include in the DcrModel.",
161 splitSubfilters = pexConfig.Field(
163 doc=
"Calculate DCR for two evenly-spaced wavelengths in each subfilter."
164 "Instead of at the midpoint",
167 splitThreshold = pexConfig.Field(
169 doc=
"Minimum DCR difference within a subfilter to use ``splitSubfilters``, in pixels."
170 "Set to 0 to always split the subfilters.",
173 regularizeModelIterations = pexConfig.Field(
175 doc=
"Maximum relative change of the model allowed between iterations."
176 "Set to zero to disable.",
179 regularizeModelFrequency = pexConfig.Field(
181 doc=
"Maximum relative change of the model allowed between subfilters."
182 "Set to zero to disable.",
185 convergenceMaskPlanes = pexConfig.ListField(
187 default=[
"DETECTED"],
188 doc=
"Mask planes to use to calculate convergence."
190 regularizationWidth = pexConfig.Field(
193 doc=
"Minimum radius of a region to include in regularization, in pixels."
195 imageInterpOrder = pexConfig.Field(
197 doc=
"The order of the spline interpolation used to shift the image plane.",
200 accelerateModel = pexConfig.Field(
202 doc=
"Factor to amplify the differences between model planes by to speed convergence.",
205 doCalculatePsf = pexConfig.Field(
207 doc=
"Set to detect stars and recalculate the PSF from the final coadd."
208 "Otherwise the PSF is estimated from a selection of the best input exposures",
211 detectPsfSources = pexConfig.ConfigurableField(
212 target=measAlg.SourceDetectionTask,
213 doc=
"Task to detect sources for PSF measurement, if ``doCalculatePsf`` is set.",
215 measurePsfSources = pexConfig.ConfigurableField(
216 target=SingleFrameMeasurementTask,
217 doc=
"Task to measure sources for PSF measurement, if ``doCalculatePsf`` is set."
219 measurePsf = pexConfig.ConfigurableField(
220 target=MeasurePsfTask,
221 doc=
"Task to measure the PSF of the coadd, if ``doCalculatePsf`` is set.",
223 effectiveWavelength = pexConfig.Field(
224 doc=
"Effective wavelength of the filter, in nm."
225 "Required if transmission curves aren't used."
226 "Support for using transmission curves is to be added in DM-13668.",
229 bandwidth = pexConfig.Field(
230 doc=
"Bandwidth of the physical filter, in nm."
231 "Required if transmission curves aren't used."
232 "Support for using transmission curves is to be added in DM-13668.",
236 def setDefaults(self):
237 CompareWarpAssembleCoaddConfig.setDefaults(self)
238 self.assembleStaticSkyModel.retarget(CompareWarpAssembleCoaddTask)
240 self.assembleStaticSkyModel.warpType = self.warpType
242 self.assembleStaticSkyModel.doNImage =
False
243 self.assembleStaticSkyModel.doWrite =
False
244 self.detectPsfSources.returnOriginalFootprints =
False
245 self.detectPsfSources.thresholdPolarity =
"positive"
247 self.detectPsfSources.thresholdValue = 50
248 self.detectPsfSources.nSigmaToGrow = 2
250 self.detectPsfSources.minPixels = 25
252 self.detectPsfSources.thresholdType =
"pixel_stdev"
255 self.measurePsf.starSelector[
"objectSize"].doFluxLimit =
False
259 """Assemble DCR coadded images from a set of warps.
264 The number of pixels to grow each subregion by to allow for DCR.
268 As
with AssembleCoaddTask, we want to assemble a coadded image
from a set of
269 Warps (also called coadded temporary exposures), including the effects of
270 Differential Chromatic Refraction (DCR).
271 For full details of the mathematics
and algorithm, please see
272 DMTN-037: DCR-matched template generation (https://dmtn-037.lsst.io).
274 This Task produces a DCR-corrected deepCoadd,
as well
as a dcrCoadd
for
275 each subfilter used
in the iterative calculation.
276 It begins by dividing the bandpass-defining filter into N equal bandwidth
277 "subfilters",
and divides the flux
in each pixel
from an initial coadd
278 equally into each
as a
"dcrModel". Because the airmass
and parallactic
279 angle of each individual exposure
is known, we can calculate the shift
280 relative to the center of the band
in each subfilter due to DCR. For each
281 exposure we apply this shift
as a linear transformation to the dcrModels
282 and stack the results to produce a DCR-matched exposure. The matched
283 exposures are subtracted
from the input exposures to produce a set of
284 residual images,
and these residuals are reverse shifted
for each
285 exposures
' subfilters and stacked. The shifted and stacked residuals are
286 added to the dcrModels to produce a new estimate of the flux in each pixel
287 within each subfilter. The dcrModels are solved
for iteratively, which
288 continues until the solution
from a new iteration improves by less than
289 a set percentage,
or a maximum number of iterations
is reached.
290 Two forms of regularization are employed to reduce unphysical results.
291 First, the new solution
is averaged
with the solution
from the previous
292 iteration, which mitigates oscillating solutions where the model
293 overshoots
with alternating very high
and low values.
294 Second, a common degeneracy when the data have a limited range of airmass
or
295 parallactic angle values
is for one subfilter to be fit
with very low
or
296 negative values,
while another subfilter
is fit
with very high values. This
297 typically appears
in the form of holes next to sources
in one subfilter,
298 and corresponding extended wings
in another. Because each subfilter has
299 a narrow bandwidth we assume that physical sources that are above the noise
300 level will
not vary
in flux by more than a factor of `frequencyClampFactor`
301 between subfilters,
and pixels that have flux deviations larger than that
302 factor will have the excess flux distributed evenly among all subfilters.
303 If `splitSubfilters`
is set, then each subfilter will be further sub-
304 divided during the forward modeling step (only). This approximates using
305 a higher number of subfilters that may be necessary
for high airmass
306 observations, but does
not increase the number of free parameters
in the
307 fit. This
is needed when there are high airmass observations which would
308 otherwise have significant DCR even within a subfilter. Because calculating
309 the shifted images takes most of the time, splitting the subfilters
is
310 turned off by way of the `splitThreshold` option
for low-airmass
311 observations that do
not suffer
from DCR within a subfilter.
314 ConfigClass = DcrAssembleCoaddConfig
315 _DefaultName = "dcrAssembleCoadd"
317 def __init__(self, *args, **kwargs):
318 super().__init__(*args, **kwargs)
319 if self.config.doCalculatePsf:
320 self.schema = afwTable.SourceTable.makeMinimalSchema()
321 self.makeSubtask(
"detectPsfSources", schema=self.schema)
322 self.makeSubtask(
"measurePsfSources", schema=self.schema)
323 self.makeSubtask(
"measurePsf", schema=self.schema)
325 @utils.inheritDoc(pipeBase.PipelineTask)
326 def runQuantum(self, butlerQC, inputRefs, outputRefs):
331 Assemble a coadd from a set of Warps.
333 PipelineTask (Gen3) entry point to Coadd a set of Warps.
334 Analogous to `runDataRef`, it prepares all the data products to be
335 passed to `run`,
and processes the results before returning a struct
336 of results to be written out. AssembleCoadd cannot fit all Warps
in memory.
337 Therefore, its inputs are accessed subregion by subregion
338 by the Gen3 `DeferredDatasetHandle` that
is analagous to the Gen2
339 `lsst.daf.persistence.ButlerDataRef`. Any updates to this method should
340 correspond to an update
in `runDataRef`
while both entry points
343 inputData = butlerQC.get(inputRefs)
347 skyMap = inputData[
"skyMap"]
348 outputDataId = butlerQC.quantum.dataId
351 tractId=outputDataId[
'tract'],
352 patchId=outputDataId[
'patch'])
356 warpRefList = inputData[
'inputWarps']
358 inputs = self.prepareInputs(warpRefList)
359 self.log.info(
"Found %d %s", len(inputs.tempExpRefList),
360 self.getTempExpDatasetName(self.warpType))
361 if len(inputs.tempExpRefList) == 0:
362 self.log.warning(
"No coadd temporary exposures found")
365 supplementaryData = self.makeSupplementaryDataGen3(butlerQC, inputRefs, outputRefs)
366 retStruct = self.run(inputData[
'skyInfo'], inputs.tempExpRefList, inputs.imageScalerList,
367 inputs.weightList, supplementaryData=supplementaryData)
369 inputData.setdefault(
'brightObjectMask',
None)
370 for subfilter
in range(self.config.dcrNumSubfilters):
372 retStruct.dcrCoadds[subfilter].setPsf(retStruct.coaddExposure.getPsf())
373 self.processResults(retStruct.dcrCoadds[subfilter], inputData[
'brightObjectMask'], outputDataId)
375 if self.config.doWrite:
376 butlerQC.put(retStruct, outputRefs)
380 def runDataRef(self, dataRef, selectDataList=None, warpRefList=None):
381 """Assemble a coadd from a set of warps.
383 Coadd a set of Warps. Compute weights to be applied to each Warp and
384 find scalings to match the photometric zeropoint to a reference Warp.
385 Assemble the Warps using run method.
386 Forward model chromatic effects across multiple subfilters,
387 and subtract
from the input Warps to build sets of residuals.
388 Use the residuals to construct a new ``DcrModel``
for each subfilter,
389 and iterate until the model converges.
390 Interpolate over NaNs
and optionally write the coadd to disk.
391 Return the coadded exposure.
395 dataRef : `lsst.daf.persistence.ButlerDataRef`
396 Data reference defining the patch
for coaddition
and the
398 selectDataList : `list` of `lsst.daf.persistence.ButlerDataRef`
399 List of data references to warps. Data to be coadded will be
400 selected
from this list based on overlap
with the patch defined by
405 results : `lsst.pipe.base.Struct`
406 The Struct contains the following fields:
409 - ``nImage``: exposure count image (`lsst.afw.image.ImageU`)
410 - ``dcrCoadds``: `list` of coadded exposures
for each subfilter
411 - ``dcrNImages``: `list` of exposure count images
for each subfilter
413 if (selectDataList
is None and warpRefList
is None)
or (selectDataList
and warpRefList):
414 raise RuntimeError(
"runDataRef must be supplied either a selectDataList or warpRefList")
416 skyInfo = self.getSkyInfo(dataRef)
417 if warpRefList
is None:
418 calExpRefList = self.selectExposures(dataRef, skyInfo, selectDataList=selectDataList)
419 if len(calExpRefList) == 0:
420 self.log.warning(
"No exposures to coadd")
422 self.log.info(
"Coadding %d exposures", len(calExpRefList))
424 warpRefList = self.getTempExpRefList(dataRef, calExpRefList)
426 inputData = self.prepareInputs(warpRefList)
427 self.log.info(
"Found %d %s", len(inputData.tempExpRefList),
428 self.getTempExpDatasetName(self.warpType))
429 if len(inputData.tempExpRefList) == 0:
430 self.log.warning(
"No coadd temporary exposures found")
433 supplementaryData = self.makeSupplementaryData(dataRef, warpRefList=inputData.tempExpRefList)
435 results = self.run(skyInfo, inputData.tempExpRefList, inputData.imageScalerList,
436 inputData.weightList, supplementaryData=supplementaryData)
438 self.log.warning(
"Could not construct DcrModel for patch %s: no data to coadd.",
439 skyInfo.patchInfo.getIndex())
442 if self.config.doCalculatePsf:
443 self.measureCoaddPsf(results.coaddExposure)
444 brightObjects = self.readBrightObjectMasks(dataRef)
if self.config.doMaskBrightObjects
else None
445 for subfilter
in range(self.config.dcrNumSubfilters):
447 results.dcrCoadds[subfilter].setPsf(results.coaddExposure.getPsf())
448 self.processResults(results.dcrCoadds[subfilter],
449 brightObjectMasks=brightObjects, dataId=dataRef.dataId)
450 if self.config.doWrite:
451 self.log.info(
"Persisting dcrCoadd")
452 dataRef.put(results.dcrCoadds[subfilter],
"dcrCoadd", subfilter=subfilter,
453 numSubfilters=self.config.dcrNumSubfilters)
454 if self.config.doNImage
and results.dcrNImages
is not None:
455 dataRef.put(results.dcrNImages[subfilter],
"dcrCoadd_nImage", subfilter=subfilter,
456 numSubfilters=self.config.dcrNumSubfilters)
460 @utils.inheritDoc(AssembleCoaddTask)
462 """Load the previously-generated template coadd.
464 This can be removed entirely once we no longer support the Gen 2 butler.
468 templateCoadd : `lsst.pipe.base.Struct`
469 Result struct with components:
471 - ``templateCoadd``: coadded exposure (`lsst.afw.image.ExposureF`)
473 templateCoadd = butlerQC.get(inputRefs.templateExposure)
475 return pipeBase.Struct(templateCoadd=templateCoadd)
477 def measureCoaddPsf(self, coaddExposure):
478 """Detect sources on the coadd exposure and measure the final PSF.
483 The final coadded exposure.
485 table = afwTable.SourceTable.make(self.schema)
486 detResults = self.detectPsfSources.run(table, coaddExposure, clearMask=False)
487 coaddSources = detResults.sources
488 self.measurePsfSources.run(
489 measCat=coaddSources,
490 exposure=coaddExposure
497 psfResults = self.measurePsf.run(coaddExposure, coaddSources)
498 except Exception
as e:
499 self.log.warning(
"Unable to calculate PSF, using default coadd PSF: %s", e)
501 coaddExposure.setPsf(psfResults.psf)
503 def prepareDcrInputs(self, templateCoadd, warpRefList, weightList):
504 """Prepare the DCR coadd by iterating through the visitInfo of the input warps.
506 Sets the property ``bufferSize``.
510 templateCoadd : `lsst.afw.image.ExposureF`
511 The initial coadd exposure before accounting for DCR.
512 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle`
or
513 `lsst.daf.persistence.ButlerDataRef`
514 The data references to the input warped exposures.
515 weightList : `list` of `float`
516 The weight to give each input exposure
in the coadd
517 Will be modified
in place
if ``doAirmassWeight``
is set.
521 dcrModels : `lsst.pipe.tasks.DcrModel`
522 Best fit model of the true sky after correcting chromatic effects.
527 If ``lambdaMin``
is missing
from the Mapper
class of the obs package being used.
529 sigma2fwhm = 2.*np.sqrt(2.*np.log(2.))
530 filterLabel = templateCoadd.getFilterLabel()
531 tempExpName = self.getTempExpDatasetName(self.warpType)
536 for visitNum, warpExpRef
in enumerate(warpRefList):
537 if isinstance(warpExpRef, DeferredDatasetHandle):
539 visitInfo = warpExpRef.get(component=
"visitInfo")
540 psf = warpExpRef.get(component=
"psf")
543 visitInfo = warpExpRef.get(tempExpName +
"_visitInfo")
544 psf = warpExpRef.get(tempExpName).getPsf()
545 visit = warpExpRef.dataId[
"visit"]
547 psfAvgPos = psf.getAveragePosition()
548 psfSize = psf.computeShape(psfAvgPos).getDeterminantRadius()*sigma2fwhm
549 airmass = visitInfo.getBoresightAirmass()
550 parallacticAngle = visitInfo.getBoresightParAngle().asDegrees()
551 airmassDict[visit] = airmass
552 angleDict[visit] = parallacticAngle
553 psfSizeDict[visit] = psfSize
554 if self.config.doAirmassWeight:
555 weightList[visitNum] *= airmass
556 dcrShifts.append(np.max(np.abs(calculateDcr(visitInfo, templateCoadd.getWcs(),
557 self.config.effectiveWavelength,
558 self.config.bandwidth,
559 self.config.dcrNumSubfilters))))
560 self.log.info(
"Selected airmasses:\n%s", airmassDict)
561 self.log.info(
"Selected parallactic angles:\n%s", angleDict)
562 self.log.info(
"Selected PSF sizes:\n%s", psfSizeDict)
563 self.bufferSize = int(np.ceil(np.max(dcrShifts)) + 1)
565 psf = self.selectCoaddPsf(templateCoadd, warpRefList)
566 except Exception
as e:
567 self.log.warning(
"Unable to calculate restricted PSF, using default coadd PSF: %s", e)
569 psf = templateCoadd.getPsf()
570 dcrModels = DcrModel.fromImage(templateCoadd.maskedImage,
571 self.config.dcrNumSubfilters,
572 effectiveWavelength=self.config.effectiveWavelength,
573 bandwidth=self.config.bandwidth,
574 filterLabel=filterLabel,
579 def run(self, skyInfo, warpRefList, imageScalerList, weightList,
580 supplementaryData=None):
581 """Assemble the coadd.
583 Requires additional inputs Struct ``supplementaryData`` to contain a
584 ``templateCoadd`` that serves as the model of the static sky.
586 Find artifacts
and apply them to the warps
' masks creating a list of
587 alternative masks with a new
"CLIPPED" plane
and updated
"NO_DATA" plane
588 Then
pass these alternative masks to the base
class's assemble method.
590 Divide the ``templateCoadd`` evenly between each subfilter of a
591 ``DcrModel`` as the starting best estimate of the true wavelength-
592 dependent sky. Forward model the ``DcrModel`` using the known
593 chromatic effects
in each subfilter
and calculate a convergence metric
594 based on how well the modeled template matches the input warps. If
595 the convergence has
not yet reached the desired threshold, then shift
596 and stack the residual images to build a new ``DcrModel``. Apply
597 conditioning to prevent oscillating solutions between iterations
or
600 Once the ``DcrModel`` reaches convergence
or the maximum number of
601 iterations has been reached, fill the metadata
for each subfilter
602 image
and make them proper ``coaddExposure``s.
606 skyInfo : `lsst.pipe.base.Struct`
607 Patch geometry information,
from getSkyInfo
608 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle`
or
609 `lsst.daf.persistence.ButlerDataRef`
610 The data references to the input warped exposures.
611 imageScalerList : `list` of `lsst.pipe.task.ImageScaler`
612 The image scalars correct
for the zero point of the exposures.
613 weightList : `list` of `float`
614 The weight to give each input exposure
in the coadd
615 supplementaryData : `lsst.pipe.base.Struct`
616 Result struct returned by ``makeSupplementaryData``
with components:
622 result : `lsst.pipe.base.Struct`
623 Result struct
with components:
626 - ``nImage``: exposure count image (`lsst.afw.image.ImageU`)
627 - ``dcrCoadds``: `list` of coadded exposures
for each subfilter
628 - ``dcrNImages``: `list` of exposure count images
for each subfilter
630 minNumIter = self.config.minNumIter or self.config.dcrNumSubfilters
631 maxNumIter = self.config.maxNumIter
or self.config.dcrNumSubfilters*3
632 templateCoadd = supplementaryData.templateCoadd
633 baseMask = templateCoadd.mask.clone()
636 baseVariance = templateCoadd.variance.clone()
637 baseVariance /= self.config.dcrNumSubfilters
638 spanSetMaskList = self.findArtifacts(templateCoadd, warpRefList, imageScalerList)
640 templateCoadd.setMask(baseMask)
641 badMaskPlanes = self.config.badMaskPlanes[:]
646 badPixelMask = templateCoadd.mask.getPlaneBitMask(badMaskPlanes)
648 stats = self.prepareStats(mask=badPixelMask)
649 dcrModels = self.prepareDcrInputs(templateCoadd, warpRefList, weightList)
650 if self.config.doNImage:
651 dcrNImages, dcrWeights = self.calculateNImage(dcrModels, skyInfo.bbox, warpRefList,
652 spanSetMaskList, stats.ctrl)
653 nImage = afwImage.ImageU(skyInfo.bbox)
657 for dcrNImage
in dcrNImages:
663 nSubregions = (ceil(skyInfo.bbox.getHeight()/subregionSize[1])
664 * ceil(skyInfo.bbox.getWidth()/subregionSize[0]))
666 for subBBox
in self._subBBoxIter(skyInfo.bbox, subregionSize):
669 self.log.info(
"Computing coadd over patch %s subregion %s of %s: %s",
670 skyInfo.patchInfo.getIndex(), subIter, nSubregions, subBBox)
672 dcrBBox.grow(self.bufferSize)
673 dcrBBox.clip(dcrModels.bbox)
674 modelWeights = self.calculateModelWeights(dcrModels, dcrBBox)
675 subExposures = self.loadSubExposures(dcrBBox, stats.ctrl, warpRefList,
676 imageScalerList, spanSetMaskList)
677 convergenceMetric = self.calculateConvergence(dcrModels, subExposures, subBBox,
678 warpRefList, weightList, stats.ctrl)
679 self.log.info(
"Initial convergence : %s", convergenceMetric)
680 convergenceList = [convergenceMetric]
682 convergenceCheck = 1.
683 refImage = templateCoadd.image
684 while (convergenceCheck > self.config.convergenceThreshold
or modelIter <= minNumIter):
685 gain = self.calculateGain(convergenceList, gainList)
686 self.dcrAssembleSubregion(dcrModels, subExposures, subBBox, dcrBBox, warpRefList,
687 stats.ctrl, convergenceMetric, gain,
688 modelWeights, refImage, dcrWeights)
689 if self.config.useConvergence:
690 convergenceMetric = self.calculateConvergence(dcrModels, subExposures, subBBox,
691 warpRefList, weightList, stats.ctrl)
692 if convergenceMetric == 0:
693 self.log.warning(
"Coadd patch %s subregion %s had convergence metric of 0.0 which is "
694 "most likely due to there being no valid data in the region.",
695 skyInfo.patchInfo.getIndex(), subIter)
697 convergenceCheck = (convergenceList[-1] - convergenceMetric)/convergenceMetric
698 if (convergenceCheck < 0) & (modelIter > minNumIter):
699 self.log.warning(
"Coadd patch %s subregion %s diverged before reaching maximum "
700 "iterations or desired convergence improvement of %s."
702 skyInfo.patchInfo.getIndex(), subIter,
703 self.config.convergenceThreshold, convergenceCheck)
705 convergenceList.append(convergenceMetric)
706 if modelIter > maxNumIter:
707 if self.config.useConvergence:
708 self.log.warning(
"Coadd patch %s subregion %s reached maximum iterations "
709 "before reaching desired convergence improvement of %s."
710 " Final convergence improvement: %s",
711 skyInfo.patchInfo.getIndex(), subIter,
712 self.config.convergenceThreshold, convergenceCheck)
715 if self.config.useConvergence:
716 self.log.info(
"Iteration %s with convergence metric %s, %.4f%% improvement (gain: %.2f)",
717 modelIter, convergenceMetric, 100.*convergenceCheck, gain)
720 if self.config.useConvergence:
721 self.log.info(
"Coadd patch %s subregion %s finished with "
722 "convergence metric %s after %s iterations",
723 skyInfo.patchInfo.getIndex(), subIter, convergenceMetric, modelIter)
725 self.log.info(
"Coadd patch %s subregion %s finished after %s iterations",
726 skyInfo.patchInfo.getIndex(), subIter, modelIter)
727 if self.config.useConvergence
and convergenceMetric > 0:
728 self.log.info(
"Final convergence improvement was %.4f%% overall",
729 100*(convergenceList[0] - convergenceMetric)/convergenceMetric)
731 dcrCoadds = self.fillCoadd(dcrModels, skyInfo, warpRefList, weightList,
732 calibration=self.scaleZeroPoint.getPhotoCalib(),
733 coaddInputs=templateCoadd.getInfo().getCoaddInputs(),
735 variance=baseVariance)
736 coaddExposure = self.stackCoadd(dcrCoadds)
737 return pipeBase.Struct(coaddExposure=coaddExposure, nImage=nImage,
738 dcrCoadds=dcrCoadds, dcrNImages=dcrNImages)
740 def calculateNImage(self, dcrModels, bbox, warpRefList, spanSetMaskList, statsCtrl):
741 """Calculate the number of exposures contributing to each subfilter.
745 dcrModels : `lsst.pipe.tasks.DcrModel`
746 Best fit model of the true sky after correcting chromatic effects.
747 bbox : `lsst.geom.box.Box2I`
748 Bounding box of the patch to coadd.
749 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or
750 `lsst.daf.persistence.ButlerDataRef`
751 The data references to the input warped exposures.
752 spanSetMaskList : `list` of `dict` containing spanSet lists,
or None
753 Each element of the `dict` contains the new mask plane name
754 (e.g.
"CLIPPED and/or "NO_DATA
") as the key,
755 and the list of SpanSets to apply to the mask.
757 Statistics control object
for coadd
761 dcrNImages : `list` of `lsst.afw.image.ImageU`
762 List of exposure count images
for each subfilter
763 dcrWeights : `list` of `lsst.afw.image.ImageF`
764 Per-pixel weights
for each subfilter.
765 Equal to 1/(number of unmasked images contributing to each pixel).
767 dcrNImages = [afwImage.ImageU(bbox) for subfilter
in range(self.config.dcrNumSubfilters)]
768 dcrWeights = [afwImage.ImageF(bbox)
for subfilter
in range(self.config.dcrNumSubfilters)]
769 tempExpName = self.getTempExpDatasetName(self.warpType)
770 for warpExpRef, altMaskSpans
in zip(warpRefList, spanSetMaskList):
771 if isinstance(warpExpRef, DeferredDatasetHandle):
773 exposure = warpExpRef.get(parameters={
'bbox': bbox})
776 exposure = warpExpRef.get(tempExpName +
"_sub", bbox=bbox)
777 visitInfo = exposure.getInfo().getVisitInfo()
778 wcs = exposure.getInfo().getWcs()
780 if altMaskSpans
is not None:
781 self.applyAltMaskPlanes(mask, altMaskSpans)
782 weightImage = np.zeros_like(exposure.image.array)
783 weightImage[(mask.array & statsCtrl.getAndMask()) == 0] = 1.
786 weightsGenerator = self.dcrResiduals(weightImage, visitInfo, wcs,
787 dcrModels.effectiveWavelength, dcrModels.bandwidth)
788 for shiftedWeights, dcrNImage, dcrWeight
in zip(weightsGenerator, dcrNImages, dcrWeights):
789 dcrNImage.array += np.rint(shiftedWeights).astype(dcrNImage.array.dtype)
790 dcrWeight.array += shiftedWeights
792 weightsThreshold = 1.
793 goodPix = dcrWeights[0].array > weightsThreshold
794 for weights
in dcrWeights[1:]:
795 goodPix = (weights.array > weightsThreshold) & goodPix
796 for subfilter
in range(self.config.dcrNumSubfilters):
797 dcrWeights[subfilter].array[goodPix] = 1./dcrWeights[subfilter].array[goodPix]
798 dcrWeights[subfilter].array[~goodPix] = 0.
799 dcrNImages[subfilter].array[~goodPix] = 0
800 return (dcrNImages, dcrWeights)
803 statsCtrl, convergenceMetric,
804 gain, modelWeights, refImage, dcrWeights):
805 """Assemble the DCR coadd for a sub-region.
807 Build a DCR-matched template for each input exposure, then shift the
808 residuals according to the DCR
in each subfilter.
809 Stack the shifted residuals
and apply them
as a correction to the
810 solution
from the previous iteration.
811 Restrict the new model solutions
from varying by more than a factor of
812 `modelClampFactor`
from the last solution,
and additionally restrict the
813 individual subfilter models
from varying by more than a factor of
814 `frequencyClampFactor`
from their average.
815 Finally, mitigate potentially oscillating solutions by averaging the new
816 solution
with the solution
from the previous iteration, weighted by
817 their convergence metric.
821 dcrModels : `lsst.pipe.tasks.DcrModel`
822 Best fit model of the true sky after correcting chromatic effects.
823 subExposures : `dict` of `lsst.afw.image.ExposureF`
824 The pre-loaded exposures
for the current subregion.
825 bbox : `lsst.geom.box.Box2I`
826 Bounding box of the subregion to coadd.
827 dcrBBox : `lsst.geom.box.Box2I`
828 Sub-region of the coadd which includes a buffer to allow
for DCR.
829 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle`
or
830 `lsst.daf.persistence.ButlerDataRef`
831 The data references to the input warped exposures.
833 Statistics control object
for coadd
834 convergenceMetric : `float`
835 Quality of fit metric
for the matched templates of the input images.
836 gain : `float`, optional
837 Relative weight to give the new solution when updating the model.
838 modelWeights : `numpy.ndarray`
or `float`
839 A 2D array of weight values that tapers smoothly to zero away
from detected sources.
840 Set to a placeholder value of 1.0
if ``self.config.useModelWeights``
is False.
842 A reference image used to supply the default pixel values.
844 Per-pixel weights
for each subfilter.
845 Equal to 1/(number of unmasked images contributing to each pixel).
847 residualGeneratorList = []
849 for warpExpRef
in warpRefList:
850 visit = warpExpRef.dataId[
"visit"]
851 exposure = subExposures[visit]
852 visitInfo = exposure.getInfo().getVisitInfo()
853 wcs = exposure.getInfo().getWcs()
854 templateImage = dcrModels.buildMatchedTemplate(exposure=exposure,
855 order=self.config.imageInterpOrder,
856 splitSubfilters=self.config.splitSubfilters,
857 splitThreshold=self.config.splitThreshold,
858 amplifyModel=self.config.accelerateModel)
859 residual = exposure.image.array - templateImage.array
861 residual *= exposure.variance.array
865 residualGeneratorList.append(self.dcrResiduals(residual, visitInfo, wcs,
866 dcrModels.effectiveWavelength,
867 dcrModels.bandwidth))
869 dcrSubModelOut = self.newModelFromResidual(dcrModels, residualGeneratorList, dcrBBox, statsCtrl,
871 modelWeights=modelWeights,
873 dcrWeights=dcrWeights)
874 dcrModels.assign(dcrSubModelOut, bbox)
876 def dcrResiduals(self, residual, visitInfo, wcs, effectiveWavelength, bandwidth):
877 """Prepare a residual image for stacking in each subfilter by applying the reverse DCR shifts.
881 residual : `numpy.ndarray`
882 The residual masked image for one exposure,
883 after subtracting the matched template
885 Metadata
for the exposure.
887 Coordinate system definition (wcs)
for the exposure.
891 residualImage : `numpy.ndarray`
892 The residual image
for the next subfilter, shifted
for DCR.
896 filteredResidual = ndimage.spline_filter(residual, order=self.config.imageInterpOrder)
899 dcrShift = calculateDcr(visitInfo, wcs, effectiveWavelength, bandwidth, self.config.dcrNumSubfilters,
900 splitSubfilters=
False)
902 yield applyDcr(filteredResidual, dcr, useInverse=
True, splitSubfilters=
False,
903 doPrefilter=
False, order=self.config.imageInterpOrder)
906 gain, modelWeights, refImage, dcrWeights):
907 """Calculate a new DcrModel from a set of image residuals.
911 dcrModels : `lsst.pipe.tasks.DcrModel`
912 Current model of the true sky after correcting chromatic effects.
913 residualGeneratorList : `generator` of `numpy.ndarray`
914 The residual image for the next subfilter, shifted
for DCR.
915 dcrBBox : `lsst.geom.box.Box2I`
916 Sub-region of the coadd which includes a buffer to allow
for DCR.
918 Statistics control object
for coadd
920 Relative weight to give the new solution when updating the model.
921 modelWeights : `numpy.ndarray`
or `float`
922 A 2D array of weight values that tapers smoothly to zero away
from detected sources.
923 Set to a placeholder value of 1.0
if ``self.config.useModelWeights``
is False.
925 A reference image used to supply the default pixel values.
927 Per-pixel weights
for each subfilter.
928 Equal to 1/(number of unmasked images contributing to each pixel).
932 dcrModel : `lsst.pipe.tasks.DcrModel`
933 New model of the true sky after correcting chromatic effects.
936 for subfilter, model
in enumerate(dcrModels):
937 residualsList = [next(residualGenerator)
for residualGenerator
in residualGeneratorList]
938 residual = np.sum(residualsList, axis=0)
939 residual *= dcrWeights[subfilter][dcrBBox].array
941 newModel = model[dcrBBox].clone()
942 newModel.array += residual
944 badPixels = ~np.isfinite(newModel.array)
945 newModel.array[badPixels] = model[dcrBBox].array[badPixels]
946 if self.config.regularizeModelIterations > 0:
947 dcrModels.regularizeModelIter(subfilter, newModel, dcrBBox,
948 self.config.regularizeModelIterations,
949 self.config.regularizationWidth)
950 newModelImages.append(newModel)
951 if self.config.regularizeModelFrequency > 0:
952 dcrModels.regularizeModelFreq(newModelImages, dcrBBox, statsCtrl,
953 self.config.regularizeModelFrequency,
954 self.config.regularizationWidth)
955 dcrModels.conditionDcrModel(newModelImages, dcrBBox, gain=gain)
956 self.applyModelWeights(newModelImages, refImage[dcrBBox], modelWeights)
957 return DcrModel(newModelImages, dcrModels.filter, dcrModels.effectiveWavelength,
958 dcrModels.bandwidth, dcrModels.psf,
959 dcrModels.mask, dcrModels.variance)
962 """Calculate a quality of fit metric for the matched templates.
966 dcrModels : `lsst.pipe.tasks.DcrModel`
967 Best fit model of the true sky after correcting chromatic effects.
968 subExposures : `dict` of `lsst.afw.image.ExposureF`
969 The pre-loaded exposures for the current subregion.
970 bbox : `lsst.geom.box.Box2I`
972 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle`
or
973 `lsst.daf.persistence.ButlerDataRef`
974 The data references to the input warped exposures.
975 weightList : `list` of `float`
976 The weight to give each input exposure
in the coadd
978 Statistics control object
for coadd
982 convergenceMetric : `float`
983 Quality of fit metric
for all input exposures, within the sub-region
985 significanceImage = np.abs(dcrModels.getReferenceImage(bbox))
987 significanceImage += nSigma*dcrModels.calculateNoiseCutoff(dcrModels[1], statsCtrl,
988 bufferSize=self.bufferSize)
989 if np.max(significanceImage) == 0:
990 significanceImage += 1.
994 for warpExpRef, expWeight
in zip(warpRefList, weightList):
995 visit = warpExpRef.dataId[
"visit"]
996 exposure = subExposures[visit][bbox]
997 singleMetric = self.calculateSingleConvergence(dcrModels, exposure, significanceImage, statsCtrl)
998 metric += singleMetric
999 metricList[visit] = singleMetric
1001 self.log.info(
"Individual metrics:\n%s", metricList)
1002 return 1.0
if weight == 0.0
else metric/weight
1005 """Calculate a quality of fit metric for a single matched template.
1009 dcrModels : `lsst.pipe.tasks.DcrModel`
1010 Best fit model of the true sky after correcting chromatic effects.
1011 exposure : `lsst.afw.image.ExposureF`
1012 The input warped exposure to evaluate.
1013 significanceImage : `numpy.ndarray`
1014 Array of weights for each pixel corresponding to its significance
1015 for the convergence calculation.
1017 Statistics control object
for coadd
1021 convergenceMetric : `float`
1022 Quality of fit metric
for one exposure, within the sub-region.
1024 convergeMask = exposure.mask.getPlaneBitMask(self.config.convergenceMaskPlanes)
1025 templateImage = dcrModels.buildMatchedTemplate(exposure=exposure,
1026 order=self.config.imageInterpOrder,
1027 splitSubfilters=self.config.splitSubfilters,
1028 splitThreshold=self.config.splitThreshold,
1029 amplifyModel=self.config.accelerateModel)
1030 diffVals = np.abs(exposure.image.array - templateImage.array)*significanceImage
1031 refVals = np.abs(exposure.image.array + templateImage.array)*significanceImage/2.
1033 finitePixels = np.isfinite(diffVals)
1034 goodMaskPixels = (exposure.mask.array & statsCtrl.getAndMask()) == 0
1035 convergeMaskPixels = exposure.mask.array & convergeMask > 0
1036 usePixels = finitePixels & goodMaskPixels & convergeMaskPixels
1037 if np.sum(usePixels) == 0:
1040 diffUse = diffVals[usePixels]
1041 refUse = refVals[usePixels]
1042 metric = np.sum(diffUse/np.median(diffUse))/np.sum(refUse/np.median(diffUse))
1046 """Add a list of sub-band coadds together.
1050 dcrCoadds : `list` of `lsst.afw.image.ExposureF`
1051 A list of coadd exposures, each exposure containing
1052 the model for one subfilter.
1056 coaddExposure : `lsst.afw.image.ExposureF`
1057 A single coadd exposure that
is the sum of the sub-bands.
1059 coaddExposure = dcrCoadds[0].clone()
1060 for coadd
in dcrCoadds[1:]:
1061 coaddExposure.maskedImage += coadd.maskedImage
1062 return coaddExposure
1064 def fillCoadd(self, dcrModels, skyInfo, warpRefList, weightList, calibration=None, coaddInputs=None,
1065 mask=None, variance=None):
1066 """Create a list of coadd exposures from a list of masked images.
1070 dcrModels : `lsst.pipe.tasks.DcrModel`
1071 Best fit model of the true sky after correcting chromatic effects.
1072 skyInfo : `lsst.pipe.base.Struct`
1073 Patch geometry information, from getSkyInfo
1074 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle`
or
1075 `lsst.daf.persistence.ButlerDataRef`
1076 The data references to the input warped exposures.
1077 weightList : `list` of `float`
1078 The weight to give each input exposure
in the coadd
1079 calibration : `lsst.afw.Image.PhotoCalib`, optional
1080 Scale factor to set the photometric calibration of an exposure.
1081 coaddInputs : `lsst.afw.Image.CoaddInputs`, optional
1082 A record of the observations that are included
in the coadd.
1084 Optional mask to override the values
in the final coadd.
1086 Optional variance plane to override the values
in the final coadd.
1090 dcrCoadds : `list` of `lsst.afw.image.ExposureF`
1091 A list of coadd exposures, each exposure containing
1092 the model
for one subfilter.
1095 refModel = dcrModels.getReferenceImage()
1096 for model
in dcrModels:
1097 if self.config.accelerateModel > 1:
1098 model.array = (model.array - refModel)*self.config.accelerateModel + refModel
1099 coaddExposure = afwImage.ExposureF(skyInfo.bbox, skyInfo.wcs)
1100 if calibration
is not None:
1101 coaddExposure.setPhotoCalib(calibration)
1102 if coaddInputs
is not None:
1103 coaddExposure.getInfo().setCoaddInputs(coaddInputs)
1105 self.assembleMetadata(coaddExposure, warpRefList, weightList)
1107 coaddExposure.setPsf(dcrModels.psf)
1108 coaddUtils.setCoaddEdgeBits(dcrModels.mask[skyInfo.bbox], dcrModels.variance[skyInfo.bbox])
1109 maskedImage = afwImage.MaskedImageF(dcrModels.bbox)
1110 maskedImage.image = model
1111 maskedImage.mask = dcrModels.mask
1112 maskedImage.variance = dcrModels.variance
1113 coaddExposure.setMaskedImage(maskedImage[skyInfo.bbox])
1114 coaddExposure.setPhotoCalib(self.scaleZeroPoint.getPhotoCalib())
1115 if mask
is not None:
1116 coaddExposure.setMask(mask)
1117 if variance
is not None:
1118 coaddExposure.setVariance(variance)
1119 dcrCoadds.append(coaddExposure)
1123 """Calculate the gain to use for the current iteration.
1125 After calculating a new DcrModel, each value is averaged
with the
1126 value
in the corresponding pixel
from the previous iteration. This
1127 reduces oscillating solutions that iterative techniques are plagued by,
1128 and speeds convergence. By far the biggest changes to the model
1129 happen
in the first couple iterations, so we can also use a more
1130 aggressive gain later when the model
is changing slowly.
1134 convergenceList : `list` of `float`
1135 The quality of fit metric
from each previous iteration.
1136 gainList : `list` of `float`
1137 The gains used
in each previous iteration: appended
with the new
1139 Gains are numbers between ``self.config.baseGain``
and 1.
1144 Relative weight to give the new solution when updating the model.
1145 A value of 1.0 gives equal weight to both solutions.
1150 If ``len(convergenceList) != len(gainList)+1``.
1152 nIter = len(convergenceList)
1153 if nIter != len(gainList) + 1:
1154 raise ValueError(
"convergenceList (%d) must be one element longer than gainList (%d)."
1155 % (len(convergenceList), len(gainList)))
1157 if self.config.baseGain
is None:
1160 baseGain = 1./(self.config.dcrNumSubfilters - 1)
1162 baseGain = self.config.baseGain
1164 if self.config.useProgressiveGain
and nIter > 2:
1172 estFinalConv = [((1 + gainList[i])*convergenceList[i + 1] - convergenceList[i])/gainList[i]
1173 for i
in range(nIter - 1)]
1176 estFinalConv = np.array(estFinalConv)
1177 estFinalConv[estFinalConv < 0] = 0
1179 estFinalConv = np.median(estFinalConv[max(nIter - 5, 0):])
1180 lastGain = gainList[-1]
1181 lastConv = convergenceList[-2]
1182 newConv = convergenceList[-1]
1187 predictedConv = (estFinalConv*lastGain + lastConv)/(1. + lastGain)
1193 delta = (predictedConv - newConv)/((lastConv - estFinalConv)/(1 + lastGain))
1194 newGain = 1 - abs(delta)
1196 newGain = (newGain + lastGain)/2.
1197 gain = max(baseGain, newGain)
1200 gainList.append(gain)
1204 """Build an array that smoothly tapers to 0 away from detected sources.
1208 dcrModels : `lsst.pipe.tasks.DcrModel`
1209 Best fit model of the true sky after correcting chromatic effects.
1210 dcrBBox : `lsst.geom.box.Box2I`
1211 Sub-region of the coadd which includes a buffer to allow for DCR.
1215 weights : `numpy.ndarray`
or `float`
1216 A 2D array of weight values that tapers smoothly to zero away
from detected sources.
1217 Set to a placeholder value of 1.0
if ``self.config.useModelWeights``
is False.
1222 If ``useModelWeights``
is set
and ``modelWeightsWidth``
is negative.
1224 if not self.config.useModelWeights:
1226 if self.config.modelWeightsWidth < 0:
1227 raise ValueError(
"modelWeightsWidth must not be negative if useModelWeights is set")
1228 convergeMask = dcrModels.mask.getPlaneBitMask(self.config.convergenceMaskPlanes)
1229 convergeMaskPixels = dcrModels.mask[dcrBBox].array & convergeMask > 0
1230 weights = np.zeros_like(dcrModels[0][dcrBBox].array)
1231 weights[convergeMaskPixels] = 1.
1232 weights = ndimage.filters.gaussian_filter(weights, self.config.modelWeightsWidth)
1233 weights /= np.max(weights)
1237 """Smoothly replace model pixel values with those from a
1238 reference at locations away from detected sources.
1243 The new DCR model images
from the current iteration.
1244 The values will be modified
in place.
1246 A reference image used to supply the default pixel values.
1247 modelWeights : `numpy.ndarray`
or `float`
1248 A 2D array of weight values that tapers smoothly to zero away
from detected sources.
1249 Set to a placeholder value of 1.0
if ``self.config.useModelWeights``
is False.
1251 if self.config.useModelWeights:
1252 for model
in modelImages:
1253 model.array *= modelWeights
1254 model.array += refImage.array*(1. - modelWeights)/self.config.dcrNumSubfilters
1257 """Pre-load sub-regions of a list of exposures.
1261 bbox : `lsst.geom.box.Box2I`
1264 Statistics control object for coadd
1265 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle`
or
1266 `lsst.daf.persistence.ButlerDataRef`
1267 The data references to the input warped exposures.
1268 imageScalerList : `list` of `lsst.pipe.task.ImageScaler`
1269 The image scalars correct
for the zero point of the exposures.
1270 spanSetMaskList : `list` of `dict` containing spanSet lists,
or None
1271 Each element
is dict
with keys = mask plane name to add the spans to
1275 subExposures : `dict`
1276 The `dict` keys are the visit IDs,
1277 and the values are `lsst.afw.image.ExposureF`
1278 The pre-loaded exposures
for the current subregion.
1279 The variance plane contains weights,
and not the variance
1281 tempExpName = self.getTempExpDatasetName(self.warpType)
1282 zipIterables = zip(warpRefList, imageScalerList, spanSetMaskList)
1284 for warpExpRef, imageScaler, altMaskSpans
in zipIterables:
1285 if isinstance(warpExpRef, DeferredDatasetHandle):
1286 exposure = warpExpRef.get(parameters={
'bbox': bbox})
1288 exposure = warpExpRef.get(tempExpName +
"_sub", bbox=bbox)
1289 visit = warpExpRef.dataId[
"visit"]
1290 if altMaskSpans
is not None:
1291 self.applyAltMaskPlanes(exposure.mask, altMaskSpans)
1292 imageScaler.scaleMaskedImage(exposure.maskedImage)
1294 exposure.variance.array[:, :] = 0.
1296 exposure.variance.array[(exposure.mask.array & statsCtrl.getAndMask()) == 0] = 1.
1299 exposure.image.array[(exposure.mask.array & statsCtrl.getAndMask()) > 0] = 0.
1300 subExposures[visit] = exposure
1304 """Compute the PSF of the coadd from the exposures with the best seeing.
1308 templateCoadd : `lsst.afw.image.ExposureF`
1309 The initial coadd exposure before accounting for DCR.
1310 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle`
or
1311 `lsst.daf.persistence.ButlerDataRef`
1312 The data references to the input warped exposures.
1317 The average PSF of the input exposures
with the best seeing.
1319 sigma2fwhm = 2.*np.sqrt(2.*np.log(2.))
1320 tempExpName = self.getTempExpDatasetName(self.warpType)
1323 ccds = templateCoadd.getInfo().getCoaddInputs().ccds
1324 templatePsf = templateCoadd.getPsf()
1326 templateAvgPos = templatePsf.getAveragePosition()
1327 psfRefSize = templatePsf.computeShape(templateAvgPos).getDeterminantRadius()*sigma2fwhm
1328 psfSizes = np.zeros(len(ccds))
1329 ccdVisits = np.array(ccds[
"visit"])
1330 for warpExpRef
in warpRefList:
1331 if isinstance(warpExpRef, DeferredDatasetHandle):
1333 psf = warpExpRef.get(component=
"psf")
1336 psf = warpExpRef.get(tempExpName).getPsf()
1337 visit = warpExpRef.dataId[
"visit"]
1338 psfAvgPos = psf.getAveragePosition()
1339 psfSize = psf.computeShape(psfAvgPos).getDeterminantRadius()*sigma2fwhm
1340 psfSizes[ccdVisits == visit] = psfSize
1344 sizeThreshold = min(np.median(psfSizes), psfRefSize)
1345 goodPsfs = psfSizes <= sizeThreshold
1346 psf = measAlg.CoaddPsf(ccds[goodPsfs], templateCoadd.getWcs(),
1347 self.config.coaddPsf.makeControl())
def makeSupplementaryDataGen3(self, butlerQC, inputRefs, outputRefs)
def makeSkyInfo(skyMap, tractId, patchId)
def loadSubExposures(self, bbox, statsCtrl, warpRefList, imageScalerList, spanSetMaskList)
def fillCoadd(self, dcrModels, skyInfo, warpRefList, weightList, calibration=None, coaddInputs=None, mask=None, variance=None)
def applyModelWeights(self, modelImages, refImage, modelWeights)
def calculateSingleConvergence(self, dcrModels, exposure, significanceImage, statsCtrl)
def stackCoadd(self, dcrCoadds)
def calculateConvergence(self, dcrModels, subExposures, bbox, warpRefList, weightList, statsCtrl)
def dcrAssembleSubregion(self, dcrModels, subExposures, bbox, dcrBBox, warpRefList, statsCtrl, convergenceMetric, gain, modelWeights, refImage, dcrWeights)
def calculateGain(self, convergenceList, gainList)
def calculateModelWeights(self, dcrModels, dcrBBox)
def newModelFromResidual(self, dcrModels, residualGeneratorList, dcrBBox, statsCtrl, gain, modelWeights, refImage, dcrWeights)
def selectCoaddPsf(self, templateCoadd, warpRefList)
def dcrResiduals(self, residual, visitInfo, wcs, effectiveWavelength, bandwidth)