24 import lsst.utils
as utils
32 __all__ = [
"BaseSelectImagesTask",
"BaseExposureInfo",
"WcsSelectImagesTask",
"PsfWcsSelectImagesTask",
33 "DatabaseSelectImagesConfig",
"BestSeeingWcsSelectImagesTask",
"BestSeeingSelectVisitsTask",
34 "BestSeeingQuantileSelectVisitsTask"]
38 """Base configuration for subclasses of BaseSelectImagesTask that use a database"""
39 host = pexConfig.Field(
40 doc=
"Database server host name",
43 port = pexConfig.Field(
44 doc=
"Database server port",
47 database = pexConfig.Field(
48 doc=
"Name of database",
51 maxExposures = pexConfig.Field(
52 doc=
"maximum exposures to select; intended for debugging; ignored if None",
59 """Data about a selected exposure
63 """Create exposure information that can be used to generate data references
65 The object has the following fields:
66 - dataId: data ID of exposure (a dict)
67 - coordList: ICRS coordinates of the corners of the exposure (list of lsst.geom.SpherePoint)
68 plus any others items that are desired
70 super(BaseExposureInfo, self).
__init__(dataId=dataId, coordList=coordList)
74 """Base task for selecting images suitable for coaddition
76 ConfigClass = pexConfig.Config
77 _DefaultName =
"selectImages"
80 def run(self, coordList):
81 """Select images suitable for coaddition in a particular region
83 @param[in] coordList: list of coordinates defining region of interest; if None then select all images
84 subclasses may add additional keyword arguments, as required
86 @return a pipeBase Struct containing:
87 - exposureInfoList: a list of exposure information objects (subclasses of BaseExposureInfo),
88 which have at least the following fields:
89 - dataId: data ID dictionary
90 - coordList: ICRS coordinates of the corners of the exposure (list of lsst.geom.SpherePoint)
92 raise NotImplementedError()
94 def _runArgDictFromDataId(self, dataId):
95 """Extract keyword arguments for run (other than coordList) from a data ID
97 @return keyword arguments for run (other than coordList), as a dict
99 raise NotImplementedError()
101 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]):
102 """Run based on a data reference
104 This delegates to run() and _runArgDictFromDataId() to do the actual
105 selection. In the event that the selectDataList is non-empty, this will
106 be used to further restrict the selection, providing the user with
107 additional control over the selection.
109 @param[in] dataRef: data reference; must contain any extra keys needed by the subclass
110 @param[in] coordList: list of coordinates defining region of interest; if None, search the whole sky
111 @param[in] makeDataRefList: if True, return dataRefList
112 @param[in] selectDataList: List of SelectStruct with dataRefs to consider for selection
113 @return a pipeBase Struct containing:
114 - exposureInfoList: a list of objects derived from ExposureInfo
115 - dataRefList: a list of data references (None if makeDataRefList False)
118 exposureInfoList = self.
runrun(coordList, **runArgDict).exposureInfoList
120 if len(selectDataList) > 0
and len(exposureInfoList) > 0:
122 ccdKeys, ccdValues = _extractKeyValue(exposureInfoList)
123 inKeys, inValues = _extractKeyValue([s.dataRef
for s
in selectDataList], keys=ccdKeys)
124 inValues = set(inValues)
125 newExposureInfoList = []
126 for info, ccdVal
in zip(exposureInfoList, ccdValues):
127 if ccdVal
in inValues:
128 newExposureInfoList.append(info)
130 self.log.info(
"De-selecting exposure %s: not in selectDataList", info.dataId)
131 exposureInfoList = newExposureInfoList
134 butler = dataRef.butlerSubset.butler
135 dataRefList = [butler.dataRef(datasetType=
"calexp",
136 dataId=expInfo.dataId,
137 )
for expInfo
in exposureInfoList]
141 return pipeBase.Struct(
142 dataRefList=dataRefList,
143 exposureInfoList=exposureInfoList,
147 def _extractKeyValue(dataList, keys=None):
148 """Extract the keys and values from a list of dataIds
150 The input dataList is a list of objects that have 'dataId' members.
151 This allows it to be used for both a list of data references and a
154 assert len(dataList) > 0
156 keys = sorted(dataList[0].dataId.keys())
159 for data
in dataList:
160 thisKeys = set(data.dataId.keys())
161 if thisKeys != keySet:
162 raise RuntimeError(
"DataId keys inconsistent: %s vs %s" % (keySet, thisKeys))
163 values.append(tuple(data.dataId[k]
for k
in keys))
168 """A container for data to be passed to the WcsSelectImagesTask"""
171 super(SelectStruct, self).
__init__(dataRef=dataRef, wcs=wcs, bbox=bbox)
175 """Select images using their Wcs
177 We use the "convexHull" method of lsst.sphgeom.ConvexPolygon to define
178 polygons on the celestial sphere, and test the polygon of the
179 patch for overlap with the polygon of the image.
181 We use "convexHull" instead of generating a ConvexPolygon
182 directly because the standard for the inputs to ConvexPolygon
183 are pretty high and we don't want to be responsible for reaching them.
186 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]):
187 """Select images in the selectDataList that overlap the patch
189 This method is the old entry point for the Gen2 commandline tasks and drivers
190 Will be deprecated in v22.
192 @param dataRef: Data reference for coadd/tempExp (with tract, patch)
193 @param coordList: List of ICRS coordinates (lsst.geom.SpherePoint) specifying boundary of patch
194 @param makeDataRefList: Construct a list of data references?
195 @param selectDataList: List of SelectStruct, to consider for selection
198 exposureInfoList = []
200 patchVertices = [coord.getVector()
for coord
in coordList]
203 for data
in selectDataList:
204 dataRef = data.dataRef
208 imageCorners = self.
getValidImageCornersgetValidImageCorners(imageWcs, imageBox, patchPoly, dataId=
None)
210 dataRefList.append(dataRef)
213 return pipeBase.Struct(
214 dataRefList=dataRefList
if makeDataRefList
else None,
215 exposureInfoList=exposureInfoList,
218 def run(self, wcsList, bboxList, coordList, dataIds=None, **kwargs):
219 """Return indices of provided lists that meet the selection criteria
223 wcsList : `list` of `lsst.afw.geom.SkyWcs`
224 specifying the WCS's of the input ccds to be selected
225 bboxList : `list` of `lsst.geom.Box2I`
226 specifying the bounding boxes of the input ccds to be selected
227 coordList : `list` of `lsst.geom.SpherePoint`
228 ICRS coordinates specifying boundary of the patch.
232 result: `list` of `int`
233 of indices of selected ccds
236 dataIds = [
None] * len(wcsList)
237 patchVertices = [coord.getVector()
for coord
in coordList]
240 for i, (imageWcs, imageBox, dataId)
in enumerate(zip(wcsList, bboxList, dataIds)):
241 imageCorners = self.
getValidImageCornersgetValidImageCorners(imageWcs, imageBox, patchPoly, dataId)
247 "Return corners or None if bad"
249 imageCorners = [imageWcs.pixelToSky(pix)
for pix
in geom.Box2D(imageBox).getCorners()]
250 except (pexExceptions.DomainError, pexExceptions.RuntimeError)
as e:
252 self.log.debug(
"WCS error in testing calexp %s (%s): deselecting", dataId, e)
256 if imagePoly
is None:
257 self.log.debug(
"Unable to create polygon from image %s: deselecting", dataId)
260 if patchPoly.intersects(imagePoly):
262 self.log.info(
"Selecting calexp %s", dataId)
267 "Return median absolute deviation scaled to normally distributed data"
268 return 1.4826*np.median(np.abs(array - np.median(array)))
272 dimensions=(
"tract",
"patch",
"skymap",
"instrument",
"visit"),
273 defaultTemplates={
"coaddName":
"deep"}):
277 class PsfWcsSelectImagesConfig(pipeBase.PipelineTaskConfig,
278 pipelineConnections=PsfWcsSelectImagesConnections):
279 maxEllipResidual = pexConfig.Field(
280 doc=
"Maximum median ellipticity residual",
285 maxSizeScatter = pexConfig.Field(
286 doc=
"Maximum scatter in the size residuals",
290 maxScaledSizeScatter = pexConfig.Field(
291 doc=
"Maximum scatter in the size residuals, scaled by the median size",
296 starSelection = pexConfig.Field(
297 doc=
"select star with this field",
299 default=
'calib_psf_used',
300 deprecated=(
'This field has been moved to ComputeExposureSummaryStatsTask and '
301 'will be removed after v24.')
303 starShape = pexConfig.Field(
304 doc=
"name of star shape",
306 default=
'base_SdssShape',
307 deprecated=(
'This field has been moved to ComputeExposureSummaryStatsTask and '
308 'will be removed after v24.')
310 psfShape = pexConfig.Field(
311 doc=
"name of psf shape",
313 default=
'base_SdssShape_psf',
314 deprecated=(
'This field has been moved to ComputeExposureSummaryStatsTask and '
315 'will be removed after v24.')
320 """Select images using their Wcs and cuts on the PSF properties
322 The PSF quality criteria are based on the size and ellipticity residuals from the
323 adaptive second moments of the star and the PSF.
326 - the median of the ellipticty residuals
327 - the robust scatter of the size residuals (using the median absolute deviation)
328 - the robust scatter of the size residuals scaled by the square of
332 ConfigClass = PsfWcsSelectImagesConfig
333 _DefaultName =
"PsfWcsSelectImages"
335 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]):
336 """Select images in the selectDataList that overlap the patch and satisfy PSF quality critera.
338 This method is the old entry point for the Gen2 commandline tasks and drivers
339 Will be deprecated in v22.
341 @param dataRef: Data reference for coadd/tempExp (with tract, patch)
342 @param coordList: List of ICRS coordinates (lsst.geom.SpherePoint) specifying boundary of patch
343 @param makeDataRefList: Construct a list of data references?
344 @param selectDataList: List of SelectStruct, to consider for selection
346 result = super(PsfWcsSelectImagesTask, self).runDataRef(dataRef, coordList, makeDataRefList,
350 exposureInfoList = []
351 for dataRef, exposureInfo
in zip(result.dataRefList, result.exposureInfoList):
352 butler = dataRef.butlerSubset.butler
353 srcCatalog = butler.get(
'src', dataRef.dataId)
354 valid = self.isValidGen2(srcCatalog, dataRef.dataId)
358 dataRefList.append(dataRef)
359 exposureInfoList.append(exposureInfo)
361 return pipeBase.Struct(
362 dataRefList=dataRefList,
363 exposureInfoList=exposureInfoList,
366 def run(self, wcsList, bboxList, coordList, visitSummary, dataIds=None, **kwargs):
367 """Return indices of provided lists that meet the selection criteria
371 wcsList : `list` of `lsst.afw.geom.SkyWcs`
372 specifying the WCS's of the input ccds to be selected
373 bboxList : `list` of `lsst.geom.Box2I`
374 specifying the bounding boxes of the input ccds to be selected
375 coordList : `list` of `lsst.geom.SpherePoint`
376 ICRS coordinates specifying boundary of the patch.
377 visitSummary : `list` of `lsst.afw.table.ExposureCatalog`
378 containing the PSF shape information for the input ccds to be selected
382 goodPsf: `list` of `int`
383 of indices of selected ccds
385 goodWcs = super(PsfWcsSelectImagesTask, self).run(wcsList=wcsList, bboxList=bboxList,
386 coordList=coordList, dataIds=dataIds)
390 for i, dataId
in enumerate(dataIds):
393 if self.isValid(visitSummary, dataId[
'detector']):
398 def isValid(self, visitSummary, detectorId):
399 """Should this ccd be selected based on its PSF shape information.
403 visitSummary : `lsst.afw.table.ExposureCatalog`
412 row = visitSummary.find(detectorId)
415 self.log.warning(
"Removing visit %d detector %d because summary stats not available.",
416 row[
"visit"], detectorId)
419 medianE = np.sqrt(row[
"psfStarDeltaE1Median"]**2. + row[
"psfStarDeltaE2Median"]**2.)
420 scatterSize = row[
"psfStarDeltaSizeScatter"]
421 scaledScatterSize = row[
"psfStarScaledDeltaSizeScatter"]
424 if self.config.maxEllipResidual
and medianE > self.config.maxEllipResidual:
425 self.log.info(
"Removing visit %d detector %d because median e residual too large: %f vs %f",
426 row[
"visit"], detectorId, medianE, self.config.maxEllipResidual)
428 elif self.config.maxSizeScatter
and scatterSize > self.config.maxSizeScatter:
429 self.log.info(
"Removing visit %d detector %d because size scatter too large: %f vs %f",
430 row[
"visit"], detectorId, scatterSize, self.config.maxSizeScatter)
432 elif self.config.maxScaledSizeScatter
and scaledScatterSize > self.config.maxScaledSizeScatter:
433 self.log.info(
"Removing visit %d detector %d because scaled size scatter too large: %f vs %f",
434 row[
"visit"], detectorId, scaledScatterSize, self.config.maxScaledSizeScatter)
439 def isValidGen2(self, srcCatalog, dataId=None):
440 """Should this ccd be selected based on its PSF shape information.
442 This routine is only used in Gen2 processing, and can be
443 removed when Gen2 is retired.
447 srcCatalog : `lsst.afw.table.SourceCatalog`
448 dataId : `dict` of dataId keys, optional.
449 Used only for logging. Defaults to None.
456 mask = srcCatalog[self.config.starSelection]
458 starXX = srcCatalog[self.config.starShape+
'_xx'][mask]
459 starYY = srcCatalog[self.config.starShape+
'_yy'][mask]
460 starXY = srcCatalog[self.config.starShape+
'_xy'][mask]
461 psfXX = srcCatalog[self.config.psfShape+
'_xx'][mask]
462 psfYY = srcCatalog[self.config.psfShape+
'_yy'][mask]
463 psfXY = srcCatalog[self.config.psfShape+
'_xy'][mask]
465 starSize = np.power(starXX*starYY - starXY**2, 0.25)
466 starE1 = (starXX - starYY)/(starXX + starYY)
467 starE2 = 2*starXY/(starXX + starYY)
468 medianSize = np.median(starSize)
470 psfSize = np.power(psfXX*psfYY - psfXY**2, 0.25)
471 psfE1 = (psfXX - psfYY)/(psfXX + psfYY)
472 psfE2 = 2*psfXY/(psfXX + psfYY)
474 medianE1 = np.abs(np.median(starE1 - psfE1))
475 medianE2 = np.abs(np.median(starE2 - psfE2))
476 medianE = np.sqrt(medianE1**2 + medianE2**2)
478 scatterSize =
sigmaMad(starSize - psfSize)
479 scaledScatterSize = scatterSize/medianSize**2
482 if self.config.maxEllipResidual
and medianE > self.config.maxEllipResidual:
483 self.log.info(
"Removing visit %s because median e residual too large: %f vs %f",
484 dataId, medianE, self.config.maxEllipResidual)
486 elif self.config.maxSizeScatter
and scatterSize > self.config.maxSizeScatter:
487 self.log.info(
"Removing visit %s because size scatter is too large: %f vs %f",
488 dataId, scatterSize, self.config.maxSizeScatter)
490 elif self.config.maxScaledSizeScatter
and scaledScatterSize > self.config.maxScaledSizeScatter:
491 self.log.info(
"Removing visit %s because scaled size scatter is too large: %f vs %f",
492 dataId, scaledScatterSize, self.config.maxScaledSizeScatter)
498 class BestSeeingWcsSelectImageConfig(WcsSelectImagesTask.ConfigClass):
499 """Base configuration for BestSeeingSelectImagesTask.
501 nImagesMax = pexConfig.RangeField(
503 doc=
"Maximum number of images to select",
506 maxPsfFwhm = pexConfig.Field(
508 doc=
"Maximum PSF FWHM (in arcseconds) to select",
511 minPsfFwhm = pexConfig.Field(
513 doc=
"Minimum PSF FWHM (in arcseconds) to select",
519 """Select up to a maximum number of the best-seeing images using their Wcs.
521 ConfigClass = BestSeeingWcsSelectImageConfig
523 def runDataRef(self, dataRef, coordList, makeDataRefList=True,
524 selectDataList=None):
525 """Select the best-seeing images in the selectDataList that overlap the patch.
527 This method is the old entry point for the Gen2 commandline tasks and drivers
528 Will be deprecated in v22.
532 dataRef : `lsst.daf.persistence.ButlerDataRef`
533 Data reference for coadd/tempExp (with tract, patch)
534 coordList : `list` of `lsst.geom.SpherePoint`
535 List of ICRS sky coordinates specifying boundary of patch
536 makeDataRefList : `boolean`, optional
537 Construct a list of data references?
538 selectDataList : `list` of `SelectStruct`
539 List of SelectStruct, to consider for selection
543 result : `lsst.pipe.base.Struct`
544 Result struct with components:
545 - ``exposureList``: the selected exposures
546 (`list` of `lsst.pipe.tasks.selectImages.BaseExposureInfo`).
547 - ``dataRefList``: the optional data references corresponding to
548 each element of ``exposureList``
549 (`list` of `lsst.daf.persistence.ButlerDataRef`, or `None`).
553 exposureInfoList = []
555 if selectDataList
is None:
558 result = super().runDataRef(dataRef, coordList, makeDataRefList=
True, selectDataList=selectDataList)
560 for dataRef, exposureInfo
in zip(result.dataRefList, result.exposureInfoList):
561 cal = dataRef.get(
"calexp", immediate=
True)
564 pixToArcseconds = cal.getWcs().getPixelScale().asArcseconds()
565 psfSize = cal.getPsf().computeShape().getDeterminantRadius()*pixToArcseconds
566 sizeFwhm = psfSize * np.sqrt(8.*np.log(2.))
567 if self.config.maxPsfFwhm
and sizeFwhm > self.config.maxPsfFwhm:
569 if self.config.minPsfFwhm
and sizeFwhm < self.config.minPsfFwhm:
571 psfSizes.append(sizeFwhm)
572 dataRefList.append(dataRef)
573 exposureInfoList.append(exposureInfo)
575 if len(psfSizes) > self.config.nImagesMax:
576 sortedIndices = np.argsort(psfSizes)[:self.config.nImagesMax]
577 filteredDataRefList = [dataRefList[i]
for i
in sortedIndices]
578 filteredExposureInfoList = [exposureInfoList[i]
for i
in sortedIndices]
579 self.log.info(
"%d images selected with FWHM range of %f--%f arcseconds",
580 len(sortedIndices), psfSizes[sortedIndices[0]], psfSizes[sortedIndices[-1]])
583 if len(psfSizes) == 0:
584 self.log.warning(
"0 images selected.")
586 self.log.debug(
"%d images selected with FWHM range of %d--%d arcseconds",
587 len(psfSizes), psfSizes[0], psfSizes[-1])
588 filteredDataRefList = dataRefList
589 filteredExposureInfoList = exposureInfoList
591 return pipeBase.Struct(
592 dataRefList=filteredDataRefList
if makeDataRefList
else None,
593 exposureInfoList=filteredExposureInfoList,
597 class BestSeeingSelectVisitsConnections(pipeBase.PipelineTaskConnections,
598 dimensions=(
"tract",
"patch",
"skymap",
"band",
"instrument"),
599 defaultTemplates={
"coaddName":
"goodSeeing"}):
600 skyMap = pipeBase.connectionTypes.Input(
601 doc=
"Input definition of geometry/bbox and projection/wcs for coadded exposures",
602 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
603 storageClass=
"SkyMap",
604 dimensions=(
"skymap",),
606 visitSummaries = pipeBase.connectionTypes.Input(
607 doc=
"Per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask",
609 storageClass=
"ExposureCatalog",
610 dimensions=(
"instrument",
"visit",),
614 goodVisits = pipeBase.connectionTypes.Output(
615 doc=
"Selected visits to be coadded.",
616 name=
"{coaddName}Visits",
617 storageClass=
"StructuredDataDict",
618 dimensions=(
"instrument",
"tract",
"patch",
"skymap",
"band"),
622 class BestSeeingSelectVisitsConfig(pipeBase.PipelineTaskConfig,
623 pipelineConnections=BestSeeingSelectVisitsConnections):
624 nVisitsMax = pexConfig.RangeField(
626 doc=
"Maximum number of visits to select",
630 maxPsfFwhm = pexConfig.Field(
632 doc=
"Maximum PSF FWHM (in arcseconds) to select",
636 minPsfFwhm = pexConfig.Field(
638 doc=
"Minimum PSF FWHM (in arcseconds) to select",
642 doConfirmOverlap = pexConfig.Field(
644 doc=
"Do remove visits that do not actually overlap the patch?",
647 minMJD = pexConfig.Field(
649 doc=
"Minimum visit MJD to select",
653 maxMJD = pexConfig.Field(
655 doc=
"Maximum visit MJD to select",
661 class BestSeeingSelectVisitsTask(pipeBase.PipelineTask):
662 """Select up to a maximum number of the best-seeing visits
664 Don't exceed the FWHM range specified by configs min(max)PsfFwhm.
665 This Task is a port of the Gen2 image-selector used in the AP pipeline:
666 BestSeeingSelectImagesTask. This Task selects full visits based on the
667 average PSF of the entire visit.
669 ConfigClass = BestSeeingSelectVisitsConfig
670 _DefaultName =
'bestSeeingSelectVisits'
672 def runQuantum(self, butlerQC, inputRefs, outputRefs):
673 inputs = butlerQC.get(inputRefs)
674 quantumDataId = butlerQC.quantum.dataId
675 outputs = self.run(**inputs, dataId=quantumDataId)
676 butlerQC.put(outputs, outputRefs)
678 def run(self, visitSummaries, skyMap, dataId):
683 visitSummary : `list`
684 List of `lsst.pipe.base.connections.DeferredDatasetRef` of
685 visitSummary tables of type `lsst.afw.table.ExposureCatalog`
686 skyMap : `lsst.skyMap.SkyMap`
687 SkyMap for checking visits overlap patch
688 dataId : `dict` of dataId keys
689 For retrieving patch info for checking visits overlap patch
693 result : `lsst.pipe.base.Struct`
694 Result struct with components:
696 - `goodVisits`: `dict` with selected visit ids as keys,
697 so that it can be be saved as a StructuredDataDict.
698 StructuredDataList's are currently limited.
701 if self.config.doConfirmOverlap:
702 patchPolygon = self.makePatchPolygon(skyMap, dataId)
704 inputVisits = [visitSummary.ref.dataId[
'visit']
for visitSummary
in visitSummaries]
707 for visit, visitSummary
in zip(inputVisits, visitSummaries):
709 visitSummary = visitSummary.get()
712 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
714 pixToArcseconds = [vs.getWcs().getPixelScale(vs.getBBox().getCenter()).asArcseconds()
715 for vs
in visitSummary]
717 psfSigmas = np.array([vs[
'psfSigma']
for vs
in visitSummary])
718 fwhm = np.nanmean(psfSigmas * pixToArcseconds) * np.sqrt(8.*np.log(2.))
720 if self.config.maxPsfFwhm
and fwhm > self.config.maxPsfFwhm:
722 if self.config.minPsfFwhm
and fwhm < self.config.minPsfFwhm:
724 if self.config.minMJD
and mjd < self.config.minMJD:
725 self.log.debug(
'MJD %f earlier than %.2f; rejecting', mjd, self.config.minMJD)
727 if self.config.maxMJD
and mjd > self.config.maxMJD:
728 self.log.debug(
'MJD %f later than %.2f; rejecting', mjd, self.config.maxMJD)
730 if self.config.doConfirmOverlap
and not self.doesIntersectPolygon(visitSummary, patchPolygon):
733 fwhmSizes.append(fwhm)
736 sortedVisits = [ind
for (_, ind)
in sorted(zip(fwhmSizes, visits))]
737 output = sortedVisits[:self.config.nVisitsMax]
738 self.log.info(
"%d images selected with FWHM range of %d--%d arcseconds",
739 len(output), fwhmSizes[visits.index(output[0])], fwhmSizes[visits.index(output[-1])])
742 goodVisits = {key:
True for key
in output}
743 return pipeBase.Struct(goodVisits=goodVisits)
745 def makePatchPolygon(self, skyMap, dataId):
746 """Return True if sky polygon overlaps visit
750 skyMap : `lsst.afw.table.ExposureCatalog`
751 Exposure catalog with per-detector geometry
752 dataId : `dict` of dataId keys
753 For retrieving patch info
757 result :` lsst.sphgeom.ConvexPolygon.convexHull`
758 Polygon of patch's outer bbox
760 wcs = skyMap[dataId[
'tract']].getWcs()
761 bbox = skyMap[dataId[
'tract']][dataId[
'patch']].getOuterBBox()
766 def doesIntersectPolygon(self, visitSummary, polygon):
767 """Return True if sky polygon overlaps visit
771 visitSummary : `lsst.afw.table.ExposureCatalog`
772 Exposure catalog with per-detector geometry
773 polygon :` lsst.sphgeom.ConvexPolygon.convexHull`
774 Polygon to check overlap
778 doesIntersect: `bool`
779 Does the visit overlap the polygon
781 doesIntersect =
False
782 for detectorSummary
in visitSummary:
784 zip(detectorSummary[
'raCorners'], detectorSummary[
'decCorners'])]
786 if detectorPolygon.intersects(polygon):
792 class BestSeeingQuantileSelectVisitsConfig(pipeBase.PipelineTaskConfig,
793 pipelineConnections=BestSeeingSelectVisitsConnections):
794 qMin = pexConfig.RangeField(
795 doc=
"Lower bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
796 "and select those in the interquantile range (qMin, qMax). Set qMin to 0 for Best Seeing. "
797 "This config should be changed from zero only for exploratory diffIm testing.",
803 qMax = pexConfig.RangeField(
804 doc=
"Upper bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
805 "and select those in the interquantile range (qMin, qMax). Set qMax to 1 for Worst Seeing.",
811 nVisitsMin = pexConfig.Field(
812 doc=
"At least this number of visits selected and supercedes quantile. For example, if 10 visits "
813 "cover this patch, qMin=0.33, and nVisitsMin=5, the best 5 visits will be selected.",
817 doConfirmOverlap = pexConfig.Field(
819 doc=
"Do remove visits that do not actually overlap the patch?",
822 minMJD = pexConfig.Field(
824 doc=
"Minimum visit MJD to select",
828 maxMJD = pexConfig.Field(
830 doc=
"Maximum visit MJD to select",
836 class BestSeeingQuantileSelectVisitsTask(BestSeeingSelectVisitsTask):
837 """Select a quantile of the best-seeing visits
839 Selects the best (for example, third) full visits based on the average
840 PSF width in the entire visit. It can also be used for difference imaging
841 experiments that require templates with the worst seeing visits.
842 For example, selecting the worst third can be acheived by
843 changing the config parameters qMin to 0.66 and qMax to 1.
845 ConfigClass = BestSeeingQuantileSelectVisitsConfig
846 _DefaultName =
'bestSeeingQuantileSelectVisits'
848 @utils.inheritDoc(BestSeeingSelectVisitsTask)
849 def run(self, visitSummaries, skyMap, dataId):
850 if self.config.doConfirmOverlap:
851 patchPolygon = self.makePatchPolygon(skyMap, dataId)
852 visits = np.array([visitSummary.ref.dataId[
'visit']
for visitSummary
in visitSummaries])
853 radius = np.empty(len(visits))
854 intersects = np.full(len(visits),
True)
855 for i, visitSummary
in enumerate(visitSummaries):
857 visitSummary = visitSummary.get()
859 psfSigma = np.nanmedian([vs[
'psfSigma']
for vs
in visitSummary])
861 if self.config.doConfirmOverlap:
862 intersects[i] = self.doesIntersectPolygon(visitSummary, patchPolygon)
863 if self.config.minMJD
or self.config.maxMJD:
865 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
866 aboveMin = mjd > self.config.minMJD
if self.config.minMJD
else True
867 belowMax = mjd < self.config.maxMJD
if self.config.maxMJD
else True
868 intersects[i] = intersects[i]
and aboveMin
and belowMax
870 sortedVisits = [v
for rad, v
in sorted(zip(radius[intersects], visits[intersects]))]
871 lowerBound = min(int(np.round(self.config.qMin*len(visits[intersects]))),
872 max(0, len(visits[intersects]) - self.config.nVisitsMin))
873 upperBound = max(int(np.round(self.config.qMax*len(visits[intersects]))), self.config.nVisitsMin)
876 goodVisits = {int(visit):
True for visit
in sortedVisits[lowerBound:upperBound]}
877 return pipeBase.Struct(goodVisits=goodVisits)
def __init__(self, dataId, coordList)
def _runArgDictFromDataId(self, dataId)
def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[])
def __init__(self, dataRef, wcs, bbox)
def getValidImageCorners(self, imageWcs, imageBox, patchPoly, dataId=None)
def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[])
def run(self, wcsList, bboxList, coordList, dataIds=None, **kwargs)
static ConvexPolygon convexHull(std::vector< UnitVector3d > const &points)