25 from scipy
import ndimage
30 from lsst.daf.butler
import DeferredDatasetHandle
34 import lsst.pex.config
as pexConfig
37 from .assembleCoadd
import (AssembleCoaddTask,
38 CompareWarpAssembleCoaddConfig,
39 CompareWarpAssembleCoaddTask)
40 from .coaddBase
import makeSkyInfo
41 from .measurePsf
import MeasurePsfTask
43 __all__ = [
"DcrAssembleCoaddConnections",
"DcrAssembleCoaddTask",
"DcrAssembleCoaddConfig"]
47 dimensions=(
"tract",
"patch",
"abstract_filter",
"skymap"),
48 defaultTemplates={
"inputCoaddName":
"deep",
49 "outputCoaddName":
"dcr",
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"),
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", ),
68 brightObjectMask = pipeBase.connectionTypes.PrerequisiteInput(
69 doc=(
"Input Bright Object Mask mask produced with external catalogs to be applied to the mask plane" 71 name=
"brightObjectMask",
72 storageClass=
"ObjectMaskCatalog",
73 dimensions=(
"tract",
"patch",
"skymap",
"abstract_filter"),
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"),
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"),
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"),
96 def __init__(self, *, config=None):
97 super().__init__(config=config)
98 if not config.doWrite:
99 self.outputs.remove(
"dcrCoadds")
102 class DcrAssembleCoaddConfig(CompareWarpAssembleCoaddConfig,
103 pipelineConnections=DcrAssembleCoaddConnections):
104 dcrNumSubfilters = pexConfig.Field(
106 doc=
"Number of sub-filters to forward model chromatic effects to fit the supplied exposures.",
109 maxNumIter = pexConfig.Field(
112 doc=
"Maximum number of iterations of forward modeling.",
115 minNumIter = pexConfig.Field(
118 doc=
"Minimum number of iterations of forward modeling.",
121 convergenceThreshold = pexConfig.Field(
123 doc=
"Target relative change in convergence between iterations of forward modeling.",
126 useConvergence = pexConfig.Field(
128 doc=
"Use convergence test as a forward modeling end condition?" 129 "If not set, skips calculating convergence and runs for ``maxNumIter`` iterations",
132 baseGain = pexConfig.Field(
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. ",
143 useProgressiveGain = pexConfig.Field(
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``. ",
150 doAirmassWeight = pexConfig.Field(
152 doc=
"Weight exposures by airmass? Useful if there are relatively few high-airmass observations.",
155 modelWeightsWidth = pexConfig.Field(
157 doc=
"Width of the region around detected sources to include in the DcrModel.",
160 useModelWeights = pexConfig.Field(
162 doc=
"Width of the region around detected sources to include in the DcrModel.",
165 splitSubfilters = pexConfig.Field(
167 doc=
"Calculate DCR for two evenly-spaced wavelengths in each subfilter." 168 "Instead of at the midpoint",
171 splitThreshold = pexConfig.Field(
173 doc=
"Minimum DCR difference within a subfilter to use ``splitSubfilters``, in pixels." 174 "Set to 0 to always split the subfilters.",
177 regularizeModelIterations = pexConfig.Field(
179 doc=
"Maximum relative change of the model allowed between iterations." 180 "Set to zero to disable.",
183 regularizeModelFrequency = pexConfig.Field(
185 doc=
"Maximum relative change of the model allowed between subfilters." 186 "Set to zero to disable.",
189 convergenceMaskPlanes = pexConfig.ListField(
191 default=[
"DETECTED"],
192 doc=
"Mask planes to use to calculate convergence." 194 regularizationWidth = pexConfig.Field(
197 doc=
"Minimum radius of a region to include in regularization, in pixels." 199 imageInterpOrder = pexConfig.Field(
201 doc=
"The order of the spline interpolation used to shift the image plane.",
204 accelerateModel = pexConfig.Field(
206 doc=
"Factor to amplify the differences between model planes by to speed convergence.",
209 doCalculatePsf = pexConfig.Field(
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",
215 detectPsfSources = pexConfig.ConfigurableField(
216 target=measAlg.SourceDetectionTask,
217 doc=
"Task to detect sources for PSF measurement, if ``doCalculatePsf`` is set.",
219 measurePsfSources = pexConfig.ConfigurableField(
220 target=SingleFrameMeasurementTask,
221 doc=
"Task to measure sources for PSF measurement, if ``doCalculatePsf`` is set." 223 measurePsf = pexConfig.ConfigurableField(
224 target=MeasurePsfTask,
225 doc=
"Task to measure the PSF of the coadd, if ``doCalculatePsf`` is set.",
228 def setDefaults(self):
229 CompareWarpAssembleCoaddConfig.setDefaults(self)
230 self.assembleStaticSkyModel.retarget(CompareWarpAssembleCoaddTask)
232 self.warpType =
"direct" 233 self.assembleStaticSkyModel.warpType = self.warpType
235 self.assembleStaticSkyModel.doNImage =
False 236 self.assembleStaticSkyModel.doWrite =
False 237 self.detectPsfSources.returnOriginalFootprints =
False 238 self.detectPsfSources.thresholdPolarity =
"positive" 240 self.detectPsfSources.thresholdValue = 50
241 self.detectPsfSources.nSigmaToGrow = 2
243 self.detectPsfSources.minPixels = 25
245 self.detectPsfSources.thresholdType =
"pixel_stdev" 248 self.measurePsf.starSelector[
"objectSize"].doFluxLimit =
False 251 class DcrAssembleCoaddTask(CompareWarpAssembleCoaddTask):
252 """Assemble DCR coadded images from a set of warps. 257 The number of pixels to grow each subregion by to allow for DCR. 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. 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):
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 336 inputData = butlerQC.get(inputRefs)
340 skyMap = inputData[
"skyMap"]
341 outputDataId = butlerQC.quantum.dataId
344 tractId=outputDataId[
'tract'],
345 patchId=outputDataId[
'patch'])
349 warpRefList = inputData[
'inputWarps']
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")
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):
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)
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. 388 dataRef : `lsst.daf.persistence.ButlerDataRef` 389 Data reference defining the patch for coaddition and the 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 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 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)
412 skyInfo = self.getSkyInfo(dataRef)
413 self.log.warn(
"Could not construct DcrModel for patch %s: no data to coadd.",
414 skyInfo.patchInfo.getIndex())
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):
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)
434 @utils.inheritDoc(AssembleCoaddTask)
436 """Load the previously-generated template coadd. 438 This can be removed entirely once we no longer support the Gen 2 butler. 442 templateCoadd : `lsst.pipe.base.Struct` 443 Result struct with components: 445 - ``templateCoadd``: coadded exposure (`lsst.afw.image.ExposureF`) 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. 456 coaddExposure : `lsst.afw.image.Exposure` 457 The final coadded exposure. 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
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)
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``. 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. 495 dcrModels : `lsst.pipe.tasks.DcrModel` 496 Best fit model of the true sky after correcting chromatic effects. 501 If ``lambdaMin`` is missing from the Mapper class of the obs package being used. 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)
514 for visitNum, warpExpRef
in enumerate(warpRefList):
515 if isinstance(warpExpRef, DeferredDatasetHandle):
517 visitInfo = warpExpRef.get(component=
"visitInfo")
518 psf = warpExpRef.get(component=
"psf")
519 visit = warpExpRef.datasetRefOrType.dataId[
"visit"]
522 visitInfo = warpExpRef.get(tempExpName +
"_visitInfo")
523 psf = warpExpRef.get(tempExpName).getPsf()
524 visit = warpExpRef.dataId[
"visit"]
525 psfSize = psf.computeShape().getDeterminantRadius()*sigma2fwhm
526 airmass = visitInfo.getBoresightAirmass()
527 parallacticAngle = visitInfo.getBoresightParAngle().asDegrees()
528 airmassDict[visit] = airmass
529 angleDict[visit] = parallacticAngle
530 psfSizeDict[visit] = psfSize
531 if self.config.doAirmassWeight:
532 weightList[visitNum] *= airmass
533 dcrShifts.append(np.max(np.abs(calculateDcr(visitInfo, templateCoadd.getWcs(),
534 filterInfo, self.config.dcrNumSubfilters))))
535 self.log.info(
"Selected airmasses:\n%s", airmassDict)
536 self.log.info(
"Selected parallactic angles:\n%s", angleDict)
537 self.log.info(
"Selected PSF sizes:\n%s", psfSizeDict)
538 self.bufferSize = int(np.ceil(np.max(dcrShifts)) + 1)
539 psf = self.selectCoaddPsf(templateCoadd, warpRefList)
540 dcrModels = DcrModel.fromImage(templateCoadd.maskedImage,
541 self.config.dcrNumSubfilters,
542 filterInfo=filterInfo,
546 def run(self, skyInfo, warpRefList, imageScalerList, weightList,
547 supplementaryData=None):
548 """Assemble the coadd. 550 Requires additional inputs Struct ``supplementaryData`` to contain a 551 ``templateCoadd`` that serves as the model of the static sky. 553 Find artifacts and apply them to the warps' masks creating a list of 554 alternative masks with a new "CLIPPED" plane and updated "NO_DATA" plane 555 Then pass these alternative masks to the base class's assemble method. 557 Divide the ``templateCoadd`` evenly between each subfilter of a 558 ``DcrModel`` as the starting best estimate of the true wavelength- 559 dependent sky. Forward model the ``DcrModel`` using the known 560 chromatic effects in each subfilter and calculate a convergence metric 561 based on how well the modeled template matches the input warps. If 562 the convergence has not yet reached the desired threshold, then shift 563 and stack the residual images to build a new ``DcrModel``. Apply 564 conditioning to prevent oscillating solutions between iterations or 567 Once the ``DcrModel`` reaches convergence or the maximum number of 568 iterations has been reached, fill the metadata for each subfilter 569 image and make them proper ``coaddExposure``s. 573 skyInfo : `lsst.pipe.base.Struct` 574 Patch geometry information, from getSkyInfo 575 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 576 `lsst.daf.persistence.ButlerDataRef` 577 The data references to the input warped exposures. 578 imageScalerList : `list` of `lsst.pipe.task.ImageScaler` 579 The image scalars correct for the zero point of the exposures. 580 weightList : `list` of `float` 581 The weight to give each input exposure in the coadd 582 supplementaryData : `lsst.pipe.base.Struct` 583 Result struct returned by ``makeSupplementaryData`` with components: 585 - ``templateCoadd``: coadded exposure (`lsst.afw.image.Exposure`) 589 result : `lsst.pipe.base.Struct` 590 Result struct with components: 592 - ``coaddExposure``: coadded exposure (`lsst.afw.image.Exposure`) 593 - ``nImage``: exposure count image (`lsst.afw.image.ImageU`) 594 - ``dcrCoadds``: `list` of coadded exposures for each subfilter 595 - ``dcrNImages``: `list` of exposure count images for each subfilter 597 minNumIter = self.config.minNumIter
or self.config.dcrNumSubfilters
598 maxNumIter = self.config.maxNumIter
or self.config.dcrNumSubfilters*3
599 templateCoadd = supplementaryData.templateCoadd
600 baseMask = templateCoadd.mask.clone()
603 baseVariance = templateCoadd.variance.clone()
604 baseVariance /= self.config.dcrNumSubfilters
605 spanSetMaskList = self.findArtifacts(templateCoadd, warpRefList, imageScalerList)
607 templateCoadd.setMask(baseMask)
608 badMaskPlanes = self.config.badMaskPlanes[:]
613 badPixelMask = templateCoadd.mask.getPlaneBitMask(badMaskPlanes)
615 stats = self.prepareStats(mask=badPixelMask)
616 dcrModels = self.prepareDcrInputs(templateCoadd, warpRefList, weightList)
617 if self.config.doNImage:
618 dcrNImages, dcrWeights = self.calculateNImage(dcrModels, skyInfo.bbox, warpRefList,
619 spanSetMaskList, stats.ctrl)
620 nImage = afwImage.ImageU(skyInfo.bbox)
624 for dcrNImage
in dcrNImages:
630 nSubregions = (ceil(skyInfo.bbox.getHeight()/subregionSize[1]) *
631 ceil(skyInfo.bbox.getWidth()/subregionSize[0]))
633 for subBBox
in self._subBBoxIter(skyInfo.bbox, subregionSize):
636 self.log.info(
"Computing coadd over patch %s subregion %s of %s: %s",
637 skyInfo.patchInfo.getIndex(), subIter, nSubregions, subBBox)
639 dcrBBox.grow(self.bufferSize)
640 dcrBBox.clip(dcrModels.bbox)
641 modelWeights = self.calculateModelWeights(dcrModels, dcrBBox)
642 subExposures = self.loadSubExposures(dcrBBox, stats.ctrl, warpRefList,
643 imageScalerList, spanSetMaskList)
644 convergenceMetric = self.calculateConvergence(dcrModels, subExposures, subBBox,
645 warpRefList, weightList, stats.ctrl)
646 self.log.info(
"Initial convergence : %s", convergenceMetric)
647 convergenceList = [convergenceMetric]
649 convergenceCheck = 1.
650 refImage = templateCoadd.image
651 while (convergenceCheck > self.config.convergenceThreshold
or modelIter <= minNumIter):
652 gain = self.calculateGain(convergenceList, gainList)
653 self.dcrAssembleSubregion(dcrModels, subExposures, subBBox, dcrBBox, warpRefList,
654 stats.ctrl, convergenceMetric, gain,
655 modelWeights, refImage, dcrWeights)
656 if self.config.useConvergence:
657 convergenceMetric = self.calculateConvergence(dcrModels, subExposures, subBBox,
658 warpRefList, weightList, stats.ctrl)
659 if convergenceMetric == 0:
660 self.log.warn(
"Coadd patch %s subregion %s had convergence metric of 0.0 which is " 661 "most likely due to there being no valid data in the region.",
662 skyInfo.patchInfo.getIndex(), subIter)
664 convergenceCheck = (convergenceList[-1] - convergenceMetric)/convergenceMetric
665 if (convergenceCheck < 0) & (modelIter > minNumIter):
666 self.log.warn(
"Coadd patch %s subregion %s diverged before reaching maximum " 667 "iterations or desired convergence improvement of %s." 669 skyInfo.patchInfo.getIndex(), subIter,
670 self.config.convergenceThreshold, convergenceCheck)
672 convergenceList.append(convergenceMetric)
673 if modelIter > maxNumIter:
674 if self.config.useConvergence:
675 self.log.warn(
"Coadd patch %s subregion %s reached maximum iterations " 676 "before reaching desired convergence improvement of %s." 677 " Final convergence improvement: %s",
678 skyInfo.patchInfo.getIndex(), subIter,
679 self.config.convergenceThreshold, convergenceCheck)
682 if self.config.useConvergence:
683 self.log.info(
"Iteration %s with convergence metric %s, %.4f%% improvement (gain: %.2f)",
684 modelIter, convergenceMetric, 100.*convergenceCheck, gain)
687 if self.config.useConvergence:
688 self.log.info(
"Coadd patch %s subregion %s finished with " 689 "convergence metric %s after %s iterations",
690 skyInfo.patchInfo.getIndex(), subIter, convergenceMetric, modelIter)
692 self.log.info(
"Coadd patch %s subregion %s finished after %s iterations",
693 skyInfo.patchInfo.getIndex(), subIter, modelIter)
694 if self.config.useConvergence
and convergenceMetric > 0:
695 self.log.info(
"Final convergence improvement was %.4f%% overall",
696 100*(convergenceList[0] - convergenceMetric)/convergenceMetric)
698 dcrCoadds = self.fillCoadd(dcrModels, skyInfo, warpRefList, weightList,
699 calibration=self.scaleZeroPoint.getPhotoCalib(),
700 coaddInputs=templateCoadd.getInfo().getCoaddInputs(),
702 variance=baseVariance)
703 coaddExposure = self.stackCoadd(dcrCoadds)
704 return pipeBase.Struct(coaddExposure=coaddExposure, nImage=nImage,
705 dcrCoadds=dcrCoadds, dcrNImages=dcrNImages)
707 def calculateNImage(self, dcrModels, bbox, warpRefList, spanSetMaskList, statsCtrl):
708 """Calculate the number of exposures contributing to each subfilter. 712 dcrModels : `lsst.pipe.tasks.DcrModel` 713 Best fit model of the true sky after correcting chromatic effects. 714 bbox : `lsst.geom.box.Box2I` 715 Bounding box of the patch to coadd. 716 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 717 `lsst.daf.persistence.ButlerDataRef` 718 The data references to the input warped exposures. 719 spanSetMaskList : `list` of `dict` containing spanSet lists, or None 720 Each element of the `dict` contains the new mask plane name 721 (e.g. "CLIPPED and/or "NO_DATA") as the key, 722 and the list of SpanSets to apply to the mask. 723 statsCtrl : `lsst.afw.math.StatisticsControl` 724 Statistics control object for coadd 728 dcrNImages : `list` of `lsst.afw.image.ImageU` 729 List of exposure count images for each subfilter 730 dcrWeights : `list` of `lsst.afw.image.ImageF` 731 Per-pixel weights for each subfilter. 732 Equal to 1/(number of unmasked images contributing to each pixel). 734 dcrNImages = [afwImage.ImageU(bbox)
for subfilter
in range(self.config.dcrNumSubfilters)]
735 dcrWeights = [afwImage.ImageF(bbox)
for subfilter
in range(self.config.dcrNumSubfilters)]
736 tempExpName = self.getTempExpDatasetName(self.warpType)
737 for warpExpRef, altMaskSpans
in zip(warpRefList, spanSetMaskList):
738 if isinstance(warpExpRef, DeferredDatasetHandle):
740 exposure = warpExpRef.get(parameters={
'bbox': bbox})
743 exposure = warpExpRef.get(tempExpName +
"_sub", bbox=bbox)
744 visitInfo = exposure.getInfo().getVisitInfo()
745 wcs = exposure.getInfo().getWcs()
747 if altMaskSpans
is not None:
748 self.applyAltMaskPlanes(mask, altMaskSpans)
749 weightImage = np.zeros_like(exposure.image.array)
750 weightImage[(mask.array & statsCtrl.getAndMask()) == 0] = 1.
753 weightsGenerator = self.dcrResiduals(weightImage, visitInfo, wcs, dcrModels.filter)
754 for shiftedWeights, dcrNImage, dcrWeight
in zip(weightsGenerator, dcrNImages, dcrWeights):
755 dcrNImage.array += np.rint(shiftedWeights).astype(dcrNImage.array.dtype)
756 dcrWeight.array += shiftedWeights
758 weightsThreshold = 1.
759 goodPix = dcrWeights[0].array > weightsThreshold
760 for weights
in dcrWeights[1:]:
761 goodPix = (weights.array > weightsThreshold) & goodPix
762 for subfilter
in range(self.config.dcrNumSubfilters):
763 dcrWeights[subfilter].array[goodPix] = 1./dcrWeights[subfilter].array[goodPix]
764 dcrWeights[subfilter].array[~goodPix] = 0.
765 dcrNImages[subfilter].array[~goodPix] = 0
766 return (dcrNImages, dcrWeights)
769 statsCtrl, convergenceMetric,
770 gain, modelWeights, refImage, dcrWeights):
771 """Assemble the DCR coadd for a sub-region. 773 Build a DCR-matched template for each input exposure, then shift the 774 residuals according to the DCR in each subfilter. 775 Stack the shifted residuals and apply them as a correction to the 776 solution from the previous iteration. 777 Restrict the new model solutions from varying by more than a factor of 778 `modelClampFactor` from the last solution, and additionally restrict the 779 individual subfilter models from varying by more than a factor of 780 `frequencyClampFactor` from their average. 781 Finally, mitigate potentially oscillating solutions by averaging the new 782 solution with the solution from the previous iteration, weighted by 783 their convergence metric. 787 dcrModels : `lsst.pipe.tasks.DcrModel` 788 Best fit model of the true sky after correcting chromatic effects. 789 subExposures : `dict` of `lsst.afw.image.ExposureF` 790 The pre-loaded exposures for the current subregion. 791 bbox : `lsst.geom.box.Box2I` 792 Bounding box of the subregion to coadd. 793 dcrBBox : `lsst.geom.box.Box2I` 794 Sub-region of the coadd which includes a buffer to allow for DCR. 795 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 796 `lsst.daf.persistence.ButlerDataRef` 797 The data references to the input warped exposures. 798 statsCtrl : `lsst.afw.math.StatisticsControl` 799 Statistics control object for coadd 800 convergenceMetric : `float` 801 Quality of fit metric for the matched templates of the input images. 802 gain : `float`, optional 803 Relative weight to give the new solution when updating the model. 804 modelWeights : `numpy.ndarray` or `float` 805 A 2D array of weight values that tapers smoothly to zero away from detected sources. 806 Set to a placeholder value of 1.0 if ``self.config.useModelWeights`` is False. 807 refImage : `lsst.afw.image.Image` 808 A reference image used to supply the default pixel values. 809 dcrWeights : `list` of `lsst.afw.image.Image` 810 Per-pixel weights for each subfilter. 811 Equal to 1/(number of unmasked images contributing to each pixel). 813 residualGeneratorList = []
815 for warpExpRef
in warpRefList:
816 if isinstance(warpExpRef, DeferredDatasetHandle):
817 visit = warpExpRef.datasetRefOrType.dataId[
"visit"]
819 visit = warpExpRef.dataId[
"visit"]
820 exposure = subExposures[visit]
821 visitInfo = exposure.getInfo().getVisitInfo()
822 wcs = exposure.getInfo().getWcs()
823 templateImage = dcrModels.buildMatchedTemplate(exposure=exposure,
824 order=self.config.imageInterpOrder,
825 splitSubfilters=self.config.splitSubfilters,
826 splitThreshold=self.config.splitThreshold,
827 amplifyModel=self.config.accelerateModel)
828 residual = exposure.image.array - templateImage.array
830 residual *= exposure.variance.array
834 residualGeneratorList.append(self.dcrResiduals(residual, visitInfo, wcs, dcrModels.filter))
836 dcrSubModelOut = self.newModelFromResidual(dcrModels, residualGeneratorList, dcrBBox, statsCtrl,
838 modelWeights=modelWeights,
840 dcrWeights=dcrWeights)
841 dcrModels.assign(dcrSubModelOut, bbox)
844 """Prepare a residual image for stacking in each subfilter by applying the reverse DCR shifts. 848 residual : `numpy.ndarray` 849 The residual masked image for one exposure, 850 after subtracting the matched template 851 visitInfo : `lsst.afw.image.VisitInfo` 852 Metadata for the exposure. 853 wcs : `lsst.afw.geom.SkyWcs` 854 Coordinate system definition (wcs) for the exposure. 855 filterInfo : `lsst.afw.image.Filter` 856 The filter definition, set in the current instruments' obs package. 857 Required for any calculation of DCR, including making matched templates. 861 residualImage : `numpy.ndarray` 862 The residual image for the next subfilter, shifted for DCR. 866 filteredResidual = ndimage.spline_filter(residual, order=self.config.imageInterpOrder)
869 dcrShift = calculateDcr(visitInfo, wcs, filterInfo, self.config.dcrNumSubfilters,
870 splitSubfilters=
False)
872 yield applyDcr(filteredResidual, dcr, useInverse=
True, splitSubfilters=
False,
873 doPrefilter=
False, order=self.config.imageInterpOrder)
876 gain, modelWeights, refImage, dcrWeights):
877 """Calculate a new DcrModel from a set of image residuals. 881 dcrModels : `lsst.pipe.tasks.DcrModel` 882 Current model of the true sky after correcting chromatic effects. 883 residualGeneratorList : `generator` of `numpy.ndarray` 884 The residual image for the next subfilter, shifted for DCR. 885 dcrBBox : `lsst.geom.box.Box2I` 886 Sub-region of the coadd which includes a buffer to allow for DCR. 887 statsCtrl : `lsst.afw.math.StatisticsControl` 888 Statistics control object for coadd 890 Relative weight to give the new solution when updating the model. 891 modelWeights : `numpy.ndarray` or `float` 892 A 2D array of weight values that tapers smoothly to zero away from detected sources. 893 Set to a placeholder value of 1.0 if ``self.config.useModelWeights`` is False. 894 refImage : `lsst.afw.image.Image` 895 A reference image used to supply the default pixel values. 896 dcrWeights : `list` of `lsst.afw.image.Image` 897 Per-pixel weights for each subfilter. 898 Equal to 1/(number of unmasked images contributing to each pixel). 902 dcrModel : `lsst.pipe.tasks.DcrModel` 903 New model of the true sky after correcting chromatic effects. 906 for subfilter, model
in enumerate(dcrModels):
907 residualsList = [next(residualGenerator)
for residualGenerator
in residualGeneratorList]
908 residual = np.sum(residualsList, axis=0)
909 residual *= dcrWeights[subfilter][dcrBBox].array
911 newModel = model[dcrBBox].clone()
912 newModel.array += residual
914 badPixels = ~np.isfinite(newModel.array)
915 newModel.array[badPixels] = model[dcrBBox].array[badPixels]
916 if self.config.regularizeModelIterations > 0:
917 dcrModels.regularizeModelIter(subfilter, newModel, dcrBBox,
918 self.config.regularizeModelIterations,
919 self.config.regularizationWidth)
920 newModelImages.append(newModel)
921 if self.config.regularizeModelFrequency > 0:
922 dcrModels.regularizeModelFreq(newModelImages, dcrBBox, statsCtrl,
923 self.config.regularizeModelFrequency,
924 self.config.regularizationWidth)
925 dcrModels.conditionDcrModel(newModelImages, dcrBBox, gain=gain)
926 self.applyModelWeights(newModelImages, refImage[dcrBBox], modelWeights)
927 return DcrModel(newModelImages, dcrModels.filter, dcrModels.psf,
928 dcrModels.mask, dcrModels.variance)
931 """Calculate a quality of fit metric for the matched templates. 935 dcrModels : `lsst.pipe.tasks.DcrModel` 936 Best fit model of the true sky after correcting chromatic effects. 937 subExposures : `dict` of `lsst.afw.image.ExposureF` 938 The pre-loaded exposures for the current subregion. 939 bbox : `lsst.geom.box.Box2I` 941 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 942 `lsst.daf.persistence.ButlerDataRef` 943 The data references to the input warped exposures. 944 weightList : `list` of `float` 945 The weight to give each input exposure in the coadd 946 statsCtrl : `lsst.afw.math.StatisticsControl` 947 Statistics control object for coadd 951 convergenceMetric : `float` 952 Quality of fit metric for all input exposures, within the sub-region 954 significanceImage = np.abs(dcrModels.getReferenceImage(bbox))
956 significanceImage += nSigma*dcrModels.calculateNoiseCutoff(dcrModels[1], statsCtrl,
957 bufferSize=self.bufferSize)
958 if np.max(significanceImage) == 0:
959 significanceImage += 1.
963 for warpExpRef, expWeight
in zip(warpRefList, weightList):
964 if isinstance(warpExpRef, DeferredDatasetHandle):
965 visit = warpExpRef.datasetRefOrType.dataId[
"visit"]
967 visit = warpExpRef.dataId[
"visit"]
968 exposure = subExposures[visit][bbox]
969 singleMetric = self.calculateSingleConvergence(dcrModels, exposure, significanceImage, statsCtrl)
970 metric += singleMetric
971 metricList[visit] = singleMetric
973 self.log.info(
"Individual metrics:\n%s", metricList)
974 return 1.0
if weight == 0.0
else metric/weight
977 """Calculate a quality of fit metric for a single matched template. 981 dcrModels : `lsst.pipe.tasks.DcrModel` 982 Best fit model of the true sky after correcting chromatic effects. 983 exposure : `lsst.afw.image.ExposureF` 984 The input warped exposure to evaluate. 985 significanceImage : `numpy.ndarray` 986 Array of weights for each pixel corresponding to its significance 987 for the convergence calculation. 988 statsCtrl : `lsst.afw.math.StatisticsControl` 989 Statistics control object for coadd 993 convergenceMetric : `float` 994 Quality of fit metric for one exposure, within the sub-region. 996 convergeMask = exposure.mask.getPlaneBitMask(self.config.convergenceMaskPlanes)
997 templateImage = dcrModels.buildMatchedTemplate(exposure=exposure,
998 order=self.config.imageInterpOrder,
999 splitSubfilters=self.config.splitSubfilters,
1000 splitThreshold=self.config.splitThreshold,
1001 amplifyModel=self.config.accelerateModel)
1002 diffVals = np.abs(exposure.image.array - templateImage.array)*significanceImage
1003 refVals = np.abs(exposure.image.array + templateImage.array)*significanceImage/2.
1005 finitePixels = np.isfinite(diffVals)
1006 goodMaskPixels = (exposure.mask.array & statsCtrl.getAndMask()) == 0
1007 convergeMaskPixels = exposure.mask.array & convergeMask > 0
1008 usePixels = finitePixels & goodMaskPixels & convergeMaskPixels
1009 if np.sum(usePixels) == 0:
1012 diffUse = diffVals[usePixels]
1013 refUse = refVals[usePixels]
1014 metric = np.sum(diffUse/np.median(diffUse))/np.sum(refUse/np.median(diffUse))
1018 """Add a list of sub-band coadds together. 1022 dcrCoadds : `list` of `lsst.afw.image.ExposureF` 1023 A list of coadd exposures, each exposure containing 1024 the model for one subfilter. 1028 coaddExposure : `lsst.afw.image.ExposureF` 1029 A single coadd exposure that is the sum of the sub-bands. 1031 coaddExposure = dcrCoadds[0].clone()
1032 for coadd
in dcrCoadds[1:]:
1033 coaddExposure.maskedImage += coadd.maskedImage
1034 return coaddExposure
1036 def fillCoadd(self, dcrModels, skyInfo, warpRefList, weightList, calibration=None, coaddInputs=None,
1037 mask=None, variance=None):
1038 """Create a list of coadd exposures from a list of masked images. 1042 dcrModels : `lsst.pipe.tasks.DcrModel` 1043 Best fit model of the true sky after correcting chromatic effects. 1044 skyInfo : `lsst.pipe.base.Struct` 1045 Patch geometry information, from getSkyInfo 1046 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 1047 `lsst.daf.persistence.ButlerDataRef` 1048 The data references to the input warped exposures. 1049 weightList : `list` of `float` 1050 The weight to give each input exposure in the coadd 1051 calibration : `lsst.afw.Image.PhotoCalib`, optional 1052 Scale factor to set the photometric calibration of an exposure. 1053 coaddInputs : `lsst.afw.Image.CoaddInputs`, optional 1054 A record of the observations that are included in the coadd. 1055 mask : `lsst.afw.image.Mask`, optional 1056 Optional mask to override the values in the final coadd. 1057 variance : `lsst.afw.image.Image`, optional 1058 Optional variance plane to override the values in the final coadd. 1062 dcrCoadds : `list` of `lsst.afw.image.ExposureF` 1063 A list of coadd exposures, each exposure containing 1064 the model for one subfilter. 1067 refModel = dcrModels.getReferenceImage()
1068 for model
in dcrModels:
1069 if self.config.accelerateModel > 1:
1070 model.array = (model.array - refModel)*self.config.accelerateModel + refModel
1071 coaddExposure = afwImage.ExposureF(skyInfo.bbox, skyInfo.wcs)
1072 if calibration
is not None:
1073 coaddExposure.setPhotoCalib(calibration)
1074 if coaddInputs
is not None:
1075 coaddExposure.getInfo().setCoaddInputs(coaddInputs)
1077 self.assembleMetadata(coaddExposure, warpRefList, weightList)
1079 coaddExposure.setPsf(dcrModels.psf)
1080 coaddUtils.setCoaddEdgeBits(dcrModels.mask[skyInfo.bbox], dcrModels.variance[skyInfo.bbox])
1081 maskedImage = afwImage.MaskedImageF(dcrModels.bbox)
1082 maskedImage.image = model
1083 maskedImage.mask = dcrModels.mask
1084 maskedImage.variance = dcrModels.variance
1085 coaddExposure.setMaskedImage(maskedImage[skyInfo.bbox])
1086 coaddExposure.setPhotoCalib(self.scaleZeroPoint.getPhotoCalib())
1087 if mask
is not None:
1088 coaddExposure.setMask(mask)
1089 if variance
is not None:
1090 coaddExposure.setVariance(variance)
1091 dcrCoadds.append(coaddExposure)
1095 """Calculate the gain to use for the current iteration. 1097 After calculating a new DcrModel, each value is averaged with the 1098 value in the corresponding pixel from the previous iteration. This 1099 reduces oscillating solutions that iterative techniques are plagued by, 1100 and speeds convergence. By far the biggest changes to the model 1101 happen in the first couple iterations, so we can also use a more 1102 aggressive gain later when the model is changing slowly. 1106 convergenceList : `list` of `float` 1107 The quality of fit metric from each previous iteration. 1108 gainList : `list` of `float` 1109 The gains used in each previous iteration: appended with the new 1111 Gains are numbers between ``self.config.baseGain`` and 1. 1116 Relative weight to give the new solution when updating the model. 1117 A value of 1.0 gives equal weight to both solutions. 1122 If ``len(convergenceList) != len(gainList)+1``. 1124 nIter = len(convergenceList)
1125 if nIter != len(gainList) + 1:
1126 raise ValueError(
"convergenceList (%d) must be one element longer than gainList (%d)." 1127 % (len(convergenceList), len(gainList)))
1129 if self.config.baseGain
is None:
1132 baseGain = 1./(self.config.dcrNumSubfilters - 1)
1134 baseGain = self.config.baseGain
1136 if self.config.useProgressiveGain
and nIter > 2:
1144 estFinalConv = [((1 + gainList[i])*convergenceList[i + 1] - convergenceList[i])/gainList[i]
1145 for i
in range(nIter - 1)]
1148 estFinalConv = np.array(estFinalConv)
1149 estFinalConv[estFinalConv < 0] = 0
1151 estFinalConv = np.median(estFinalConv[max(nIter - 5, 0):])
1152 lastGain = gainList[-1]
1153 lastConv = convergenceList[-2]
1154 newConv = convergenceList[-1]
1159 predictedConv = (estFinalConv*lastGain + lastConv)/(1. + lastGain)
1165 delta = (predictedConv - newConv)/((lastConv - estFinalConv)/(1 + lastGain))
1166 newGain = 1 - abs(delta)
1168 newGain = (newGain + lastGain)/2.
1169 gain = max(baseGain, newGain)
1172 gainList.append(gain)
1176 """Build an array that smoothly tapers to 0 away from detected sources. 1180 dcrModels : `lsst.pipe.tasks.DcrModel` 1181 Best fit model of the true sky after correcting chromatic effects. 1182 dcrBBox : `lsst.geom.box.Box2I` 1183 Sub-region of the coadd which includes a buffer to allow for DCR. 1187 weights : `numpy.ndarray` or `float` 1188 A 2D array of weight values that tapers smoothly to zero away from detected sources. 1189 Set to a placeholder value of 1.0 if ``self.config.useModelWeights`` is False. 1194 If ``useModelWeights`` is set and ``modelWeightsWidth`` is negative. 1196 if not self.config.useModelWeights:
1198 if self.config.modelWeightsWidth < 0:
1199 raise ValueError(
"modelWeightsWidth must not be negative if useModelWeights is set")
1200 convergeMask = dcrModels.mask.getPlaneBitMask(self.config.convergenceMaskPlanes)
1201 convergeMaskPixels = dcrModels.mask[dcrBBox].array & convergeMask > 0
1202 weights = np.zeros_like(dcrModels[0][dcrBBox].array)
1203 weights[convergeMaskPixels] = 1.
1204 weights = ndimage.filters.gaussian_filter(weights, self.config.modelWeightsWidth)
1205 weights /= np.max(weights)
1209 """Smoothly replace model pixel values with those from a 1210 reference at locations away from detected sources. 1214 modelImages : `list` of `lsst.afw.image.Image` 1215 The new DCR model images from the current iteration. 1216 The values will be modified in place. 1217 refImage : `lsst.afw.image.MaskedImage` 1218 A reference image used to supply the default pixel values. 1219 modelWeights : `numpy.ndarray` or `float` 1220 A 2D array of weight values that tapers smoothly to zero away from detected sources. 1221 Set to a placeholder value of 1.0 if ``self.config.useModelWeights`` is False. 1223 if self.config.useModelWeights:
1224 for model
in modelImages:
1225 model.array *= modelWeights
1226 model.array += refImage.array*(1. - modelWeights)/self.config.dcrNumSubfilters
1229 """Pre-load sub-regions of a list of exposures. 1233 bbox : `lsst.geom.box.Box2I` 1235 statsCtrl : `lsst.afw.math.StatisticsControl` 1236 Statistics control object for coadd 1237 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 1238 `lsst.daf.persistence.ButlerDataRef` 1239 The data references to the input warped exposures. 1240 imageScalerList : `list` of `lsst.pipe.task.ImageScaler` 1241 The image scalars correct for the zero point of the exposures. 1242 spanSetMaskList : `list` of `dict` containing spanSet lists, or None 1243 Each element is dict with keys = mask plane name to add the spans to 1247 subExposures : `dict` 1248 The `dict` keys are the visit IDs, 1249 and the values are `lsst.afw.image.ExposureF` 1250 The pre-loaded exposures for the current subregion. 1251 The variance plane contains weights, and not the variance 1253 tempExpName = self.getTempExpDatasetName(self.warpType)
1254 zipIterables = zip(warpRefList, imageScalerList, spanSetMaskList)
1256 for warpExpRef, imageScaler, altMaskSpans
in zipIterables:
1257 if isinstance(warpExpRef, DeferredDatasetHandle):
1258 visit = warpExpRef.datasetRefOrType.dataId[
"visit"]
1259 exposure = warpExpRef.get(parameters={
'bbox': bbox})
1261 visit = warpExpRef.dataId[
"visit"]
1262 exposure = warpExpRef.get(tempExpName +
"_sub", bbox=bbox)
1263 if altMaskSpans
is not None:
1264 self.applyAltMaskPlanes(exposure.mask, altMaskSpans)
1265 imageScaler.scaleMaskedImage(exposure.maskedImage)
1267 exposure.variance.array[:, :] = 0.
1269 exposure.variance.array[(exposure.mask.array & statsCtrl.getAndMask()) == 0] = 1.
1272 exposure.image.array[(exposure.mask.array & statsCtrl.getAndMask()) > 0] = 0.
1273 subExposures[visit] = exposure
1277 """Compute the PSF of the coadd from the exposures with the best seeing. 1281 templateCoadd : `lsst.afw.image.ExposureF` 1282 The initial coadd exposure before accounting for DCR. 1283 warpRefList : `list` of `lsst.daf.butler.DeferredDatasetHandle` or 1284 `lsst.daf.persistence.ButlerDataRef` 1285 The data references to the input warped exposures. 1289 psf : `lsst.meas.algorithms.CoaddPsf` 1290 The average PSF of the input exposures with the best seeing. 1292 sigma2fwhm = 2.*np.sqrt(2.*np.log(2.))
1293 tempExpName = self.getTempExpDatasetName(self.warpType)
1296 ccds = templateCoadd.getInfo().getCoaddInputs().ccds
1297 psfRefSize = templateCoadd.getPsf().computeShape().getDeterminantRadius()*sigma2fwhm
1298 psfSizes = np.zeros(len(ccds))
1299 ccdVisits = np.array(ccds[
"visit"])
1300 for warpExpRef
in warpRefList:
1301 if isinstance(warpExpRef, DeferredDatasetHandle):
1303 psf = warpExpRef.get(component=
"psf")
1304 visit = warpExpRef.datasetRefOrType.dataId[
"visit"]
1307 psf = warpExpRef.get(tempExpName).getPsf()
1308 visit = warpExpRef.dataId[
"visit"]
1309 psfSize = psf.computeShape().getDeterminantRadius()*sigma2fwhm
1310 psfSizes[ccdVisits == visit] = psfSize
1314 sizeThreshold = min(np.median(psfSizes), psfRefSize)
1315 goodPsfs = psfSizes <= sizeThreshold
1316 psf = measAlg.CoaddPsf(ccds[goodPsfs], templateCoadd.getWcs(),
1317 self.config.coaddPsf.makeControl())
def selectCoaddPsf(self, templateCoadd, warpRefList)
def dcrResiduals(self, residual, visitInfo, wcs, filterInfo)
def stackCoadd(self, dcrCoadds)
def calculateSingleConvergence(self, dcrModels, exposure, significanceImage, statsCtrl)
def loadSubExposures(self, bbox, statsCtrl, warpRefList, imageScalerList, spanSetMaskList)
def dcrAssembleSubregion(self, dcrModels, subExposures, bbox, dcrBBox, warpRefList, statsCtrl, convergenceMetric, gain, modelWeights, refImage, dcrWeights)
def makeSupplementaryDataGen3(self, butlerQC, inputRefs, outputRefs)
def calculateGain(self, convergenceList, gainList)
def makeSkyInfo(skyMap, tractId, patchId)
def calculateModelWeights(self, dcrModels, dcrBBox)
def newModelFromResidual(self, dcrModels, residualGeneratorList, dcrBBox, statsCtrl, gain, modelWeights, refImage, dcrWeights)
def applyModelWeights(self, modelImages, refImage, modelWeights)
def calculateConvergence(self, dcrModels, subExposures, bbox, warpRefList, weightList, statsCtrl)
def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, altMaskList=None, mask=None, supplementaryData=None)
def fillCoadd(self, dcrModels, skyInfo, warpRefList, weightList, calibration=None, coaddInputs=None, mask=None, variance=None)