24import lsst.utils
as utils
31from lsst.utils.timer
import timeMethod
33__all__ = [
"BaseSelectImagesTask",
"BaseExposureInfo",
"WcsSelectImagesTask",
"PsfWcsSelectImagesTask",
34 "DatabaseSelectImagesConfig",
"BestSeeingWcsSelectImagesTask",
"BestSeeingSelectVisitsTask",
35 "BestSeeingQuantileSelectVisitsTask"]
39 """Base configuration for subclasses of BaseSelectImagesTask that use a database"""
40 host = pexConfig.Field(
41 doc=
"Database server host name",
44 port = pexConfig.Field(
45 doc=
"Database server port",
48 database = pexConfig.Field(
49 doc=
"Name of database",
52 maxExposures = pexConfig.Field(
53 doc=
"maximum exposures to select; intended for debugging; ignored if None",
60 """Data about a selected exposure
64 """Create exposure information that can be used to generate data references
66 The object has the following fields:
67 - dataId: data ID of exposure (a dict)
69 plus any others items that are desired
71 super(BaseExposureInfo, self).__init__(dataId=dataId, coordList=coordList)
75 """Base task for selecting images suitable for coaddition
77 ConfigClass = pexConfig.Config
78 _DefaultName = "selectImages"
81 def run(self, coordList):
82 """Select images suitable for coaddition in a particular region
84 @param[
in] coordList: list of coordinates defining region of interest;
if None then select all images
85 subclasses may add additional keyword arguments,
as required
87 @return a pipeBase Struct containing:
88 - exposureInfoList: a list of exposure information objects (subclasses of BaseExposureInfo),
89 which have at least the following fields:
90 - dataId: data ID dictionary
93 raise NotImplementedError()
95 def _runArgDictFromDataId(self, dataId):
96 """Extract keyword arguments for run (other than coordList) from a data ID
98 @return keyword arguments
for run (other than coordList),
as a dict
100 raise NotImplementedError()
102 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]):
103 """Run based on a data reference
105 This delegates to run() and _runArgDictFromDataId() to do the actual
106 selection. In the event that the selectDataList
is non-empty, this will
107 be used to further restrict the selection, providing the user
with
108 additional control over the selection.
110 @param[
in] dataRef: data reference; must contain any extra keys needed by the subclass
111 @param[
in] coordList: list of coordinates defining region of interest;
if None, search the whole sky
112 @param[
in] makeDataRefList:
if True,
return dataRefList
113 @param[
in] selectDataList: List of SelectStruct
with dataRefs to consider
for selection
114 @return a pipeBase Struct containing:
115 - exposureInfoList: a list of objects derived
from ExposureInfo
116 - dataRefList: a list of data references (
None if makeDataRefList
False)
119 exposureInfoList = self.runrun(coordList, **runArgDict).exposureInfoList
121 if len(selectDataList) > 0
and len(exposureInfoList) > 0:
123 ccdKeys, ccdValues = _extractKeyValue(exposureInfoList)
124 inKeys, inValues = _extractKeyValue([s.dataRef
for s
in selectDataList], keys=ccdKeys)
125 inValues = set(inValues)
126 newExposureInfoList = []
127 for info, ccdVal
in zip(exposureInfoList, ccdValues):
128 if ccdVal
in inValues:
129 newExposureInfoList.append(info)
131 self.log.info(
"De-selecting exposure %s: not in selectDataList", info.dataId)
132 exposureInfoList = newExposureInfoList
135 butler = dataRef.butlerSubset.butler
136 dataRefList = [butler.dataRef(datasetType=
"calexp",
137 dataId=expInfo.dataId,
138 )
for expInfo
in exposureInfoList]
142 return pipeBase.Struct(
143 dataRefList=dataRefList,
144 exposureInfoList=exposureInfoList,
148def _extractKeyValue(dataList, keys=None):
149 """Extract the keys and values from a list of dataIds
151 The input dataList is a list of objects that have
'dataId' members.
152 This allows it to be used
for both a list of data references
and a
155 assert len(dataList) > 0
157 keys = sorted(dataList[0].dataId.keys())
160 for data
in dataList:
161 thisKeys = set(data.dataId.keys())
162 if thisKeys != keySet:
163 raise RuntimeError(
"DataId keys inconsistent: %s vs %s" % (keySet, thisKeys))
164 values.append(tuple(data.dataId[k]
for k
in keys))
169 """A container for data to be passed to the WcsSelectImagesTask"""
172 super(SelectStruct, self).
__init__(dataRef=dataRef, wcs=wcs, bbox=bbox)
176 """Select images using their Wcs
179 polygons on the celestial sphere,
and test the polygon of the
180 patch
for overlap
with the polygon of the image.
182 We use
"convexHull" instead of generating a ConvexPolygon
183 directly because the standard
for the inputs to ConvexPolygon
184 are pretty high
and we don
't want to be responsible for reaching them.
187 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]):
188 """Select images in the selectDataList that overlap the patch
190 This method is the old entry point
for the Gen2 commandline tasks
and drivers
191 Will be deprecated
in v22.
193 @param dataRef: Data reference
for coadd/tempExp (
with tract, patch)
195 @param makeDataRefList: Construct a list of data references?
196 @param selectDataList: List of SelectStruct, to consider
for selection
199 exposureInfoList = []
201 patchVertices = [coord.getVector() for coord
in coordList]
204 for data
in selectDataList:
205 dataRef = data.dataRef
209 imageCorners = self.
getValidImageCornersgetValidImageCorners(imageWcs, imageBox, patchPoly, dataId=
None)
211 dataRefList.append(dataRef)
214 return pipeBase.Struct(
215 dataRefList=dataRefList
if makeDataRefList
else None,
216 exposureInfoList=exposureInfoList,
219 def run(self, wcsList, bboxList, coordList, dataIds=None, **kwargs):
220 """Return indices of provided lists that meet the selection criteria
225 specifying the WCS's of the input ccds to be selected
227 specifying the bounding boxes of the input ccds to be selected
229 ICRS coordinates specifying boundary of the patch.
233 result: `list` of `int`
234 of indices of selected ccds
237 dataIds = [
None] * len(wcsList)
238 patchVertices = [coord.getVector()
for coord
in coordList]
241 for i, (imageWcs, imageBox, dataId)
in enumerate(zip(wcsList, bboxList, dataIds)):
242 imageCorners = self.
getValidImageCornersgetValidImageCorners(imageWcs, imageBox, patchPoly, dataId)
248 "Return corners or None if bad"
250 imageCorners = [imageWcs.pixelToSky(pix)
for pix
in geom.Box2D(imageBox).getCorners()]
251 except (pexExceptions.DomainError, pexExceptions.RuntimeError)
as e:
253 self.log.debug(
"WCS error in testing calexp %s (%s): deselecting", dataId, e)
257 if imagePoly
is None:
258 self.log.debug(
"Unable to create polygon from image %s: deselecting", dataId)
261 if patchPoly.intersects(imagePoly):
263 self.log.info(
"Selecting calexp %s", dataId)
268 "Return median absolute deviation scaled to normally distributed data"
269 return 1.4826*np.median(np.abs(array - np.median(array)))
273 dimensions=(
"tract",
"patch",
"skymap",
"instrument",
"visit"),
274 defaultTemplates={
"coaddName":
"deep"}):
278class PsfWcsSelectImagesConfig(pipeBase.PipelineTaskConfig,
279 pipelineConnections=PsfWcsSelectImagesConnections):
280 maxEllipResidual = pexConfig.Field(
281 doc=
"Maximum median ellipticity residual",
286 maxSizeScatter = pexConfig.Field(
287 doc=
"Maximum scatter in the size residuals",
291 maxScaledSizeScatter = pexConfig.Field(
292 doc=
"Maximum scatter in the size residuals, scaled by the median size",
297 starSelection = pexConfig.Field(
298 doc=
"select star with this field",
300 default=
'calib_psf_used',
301 deprecated=(
'This field has been moved to ComputeExposureSummaryStatsTask and '
302 'will be removed after v24.')
304 starShape = pexConfig.Field(
305 doc=
"name of star shape",
307 default=
'base_SdssShape',
308 deprecated=(
'This field has been moved to ComputeExposureSummaryStatsTask and '
309 'will be removed after v24.')
311 psfShape = pexConfig.Field(
312 doc=
"name of psf shape",
314 default=
'base_SdssShape_psf',
315 deprecated=(
'This field has been moved to ComputeExposureSummaryStatsTask and '
316 'will be removed after v24.')
318 doLegacyStarSelectionComputation = pexConfig.Field(
319 doc=
"Perform the legacy star selection computations (for backwards compatibility)",
322 deprecated=(
"This field is here for backwards compatibility and will be "
323 "removed after v24.")
328 """Select images using their Wcs and cuts on the PSF properties
330 The PSF quality criteria are based on the size and ellipticity residuals
from the
331 adaptive second moments of the star
and the PSF.
334 - the median of the ellipticty residuals
335 - the robust scatter of the size residuals (using the median absolute deviation)
336 - the robust scatter of the size residuals scaled by the square of
340 ConfigClass = PsfWcsSelectImagesConfig
341 _DefaultName = "PsfWcsSelectImages"
343 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]):
344 """Select images in the selectDataList that overlap the patch and satisfy PSF quality critera.
346 This method is the old entry point
for the Gen2 commandline tasks
and drivers
347 Will be deprecated
in v22.
349 @param dataRef: Data reference
for coadd/tempExp (
with tract, patch)
351 @param makeDataRefList: Construct a list of data references?
352 @param selectDataList: List of SelectStruct, to consider
for selection
354 result = super(PsfWcsSelectImagesTask, self).runDataRef(dataRef, coordList, makeDataRefList,
358 exposureInfoList = []
359 for dataRef, exposureInfo
in zip(result.dataRefList, result.exposureInfoList):
360 butler = dataRef.butlerSubset.butler
361 srcCatalog = butler.get(
'src', dataRef.dataId)
362 valid = self.isValidLegacy(srcCatalog, dataRef.dataId)
366 dataRefList.append(dataRef)
367 exposureInfoList.append(exposureInfo)
369 return pipeBase.Struct(
370 dataRefList=dataRefList,
371 exposureInfoList=exposureInfoList,
374 def run(self, wcsList, bboxList, coordList, visitSummary, dataIds=None, srcList=None, **kwargs):
375 """Return indices of provided lists that meet the selection criteria
380 specifying the WCS's of the input ccds to be selected
382 specifying the bounding boxes of the input ccds to be selected
384 ICRS coordinates specifying boundary of the patch.
386 containing the PSF shape information for the input ccds to be selected.
388 containing the PSF shape information
for the input ccds to be selected.
389 This
is only used
if ``config.doLegacyStarSelectionComputation``
is
394 goodPsf: `list` of `int`
395 of indices of selected ccds
397 goodWcs = super(PsfWcsSelectImagesTask, self).run(wcsList=wcsList, bboxList=bboxList,
398 coordList=coordList, dataIds=dataIds)
402 if not self.config.doLegacyStarSelectionComputation:
404 if 'nPsfStar' not in visitSummary[0].schema.getNames():
405 raise RuntimeError(
"Old calexps detected. "
406 "Please set config.doLegacyStarSelectionComputation=True for "
407 "backwards compatibility.")
409 for i, dataId
in enumerate(dataIds):
412 if self.isValid(visitSummary, dataId[
"detector"]):
416 dataIds = [
None] * len(srcList)
417 for i, (srcCatalog, dataId)
in enumerate(zip(srcList, dataIds)):
420 if self.isValidLegacy(srcCatalog, dataId):
425 def isValid(self, visitSummary, detectorId):
426 """Should this ccd be selected based on its PSF shape information.
439 row = visitSummary.find(detectorId)
442 self.log.warning(
"Removing visit %d detector %d because summary stats not available.",
443 row[
"visit"], detectorId)
446 medianE = np.sqrt(row[
"psfStarDeltaE1Median"]**2. + row[
"psfStarDeltaE2Median"]**2.)
447 scatterSize = row[
"psfStarDeltaSizeScatter"]
448 scaledScatterSize = row[
"psfStarScaledDeltaSizeScatter"]
451 if self.config.maxEllipResidual
and medianE > self.config.maxEllipResidual:
452 self.log.info(
"Removing visit %d detector %d because median e residual too large: %f vs %f",
453 row[
"visit"], detectorId, medianE, self.config.maxEllipResidual)
455 elif self.config.maxSizeScatter
and scatterSize > self.config.maxSizeScatter:
456 self.log.info(
"Removing visit %d detector %d because size scatter too large: %f vs %f",
457 row[
"visit"], detectorId, scatterSize, self.config.maxSizeScatter)
459 elif self.config.maxScaledSizeScatter
and scaledScatterSize > self.config.maxScaledSizeScatter:
460 self.log.info(
"Removing visit %d detector %d because scaled size scatter too large: %f vs %f",
461 row[
"visit"], detectorId, scaledScatterSize, self.config.maxScaledSizeScatter)
466 def isValidLegacy(self, srcCatalog, dataId=None):
467 """Should this ccd be selected based on its PSF shape information.
469 This routine is only used
in legacy processing (gen2
and
470 backwards compatible old calexps)
and should be removed after v24.
475 dataId : `dict` of dataId keys, optional.
476 Used only
for logging. Defaults to
None.
483 mask = srcCatalog[self.config.starSelection]
485 starXX = srcCatalog[self.config.starShape+'_xx'][mask]
486 starYY = srcCatalog[self.config.starShape+
'_yy'][mask]
487 starXY = srcCatalog[self.config.starShape+
'_xy'][mask]
488 psfXX = srcCatalog[self.config.psfShape+
'_xx'][mask]
489 psfYY = srcCatalog[self.config.psfShape+
'_yy'][mask]
490 psfXY = srcCatalog[self.config.psfShape+
'_xy'][mask]
492 starSize = np.power(starXX*starYY - starXY**2, 0.25)
493 starE1 = (starXX - starYY)/(starXX + starYY)
494 starE2 = 2*starXY/(starXX + starYY)
495 medianSize = np.median(starSize)
497 psfSize = np.power(psfXX*psfYY - psfXY**2, 0.25)
498 psfE1 = (psfXX - psfYY)/(psfXX + psfYY)
499 psfE2 = 2*psfXY/(psfXX + psfYY)
501 medianE1 = np.abs(np.median(starE1 - psfE1))
502 medianE2 = np.abs(np.median(starE2 - psfE2))
503 medianE = np.sqrt(medianE1**2 + medianE2**2)
505 scatterSize =
sigmaMad(starSize - psfSize)
506 scaledScatterSize = scatterSize/medianSize**2
509 if self.config.maxEllipResidual
and medianE > self.config.maxEllipResidual:
510 self.log.info(
"Removing visit %s because median e residual too large: %f vs %f",
511 dataId, medianE, self.config.maxEllipResidual)
513 elif self.config.maxSizeScatter
and scatterSize > self.config.maxSizeScatter:
514 self.log.info(
"Removing visit %s because size scatter is too large: %f vs %f",
515 dataId, scatterSize, self.config.maxSizeScatter)
517 elif self.config.maxScaledSizeScatter
and scaledScatterSize > self.config.maxScaledSizeScatter:
518 self.log.info(
"Removing visit %s because scaled size scatter is too large: %f vs %f",
519 dataId, scaledScatterSize, self.config.maxScaledSizeScatter)
525class BestSeeingWcsSelectImageConfig(WcsSelectImagesTask.ConfigClass):
526 """Base configuration for BestSeeingSelectImagesTask.
528 nImagesMax = pexConfig.RangeField(
530 doc="Maximum number of images to select",
533 maxPsfFwhm = pexConfig.Field(
535 doc=
"Maximum PSF FWHM (in arcseconds) to select",
538 minPsfFwhm = pexConfig.Field(
540 doc=
"Minimum PSF FWHM (in arcseconds) to select",
546 """Select up to a maximum number of the best-seeing images using their Wcs.
548 ConfigClass = BestSeeingWcsSelectImageConfig
550 def runDataRef(self, dataRef, coordList, makeDataRefList=True,
551 selectDataList=None):
552 """Select the best-seeing images in the selectDataList that overlap the patch.
554 This method is the old entry point
for the Gen2 commandline tasks
and drivers
555 Will be deprecated
in v22.
559 dataRef : `lsst.daf.persistence.ButlerDataRef`
560 Data reference
for coadd/tempExp (
with tract, patch)
562 List of ICRS sky coordinates specifying boundary of patch
563 makeDataRefList : `boolean`, optional
564 Construct a list of data references?
565 selectDataList : `list` of `SelectStruct`
566 List of SelectStruct, to consider
for selection
570 result : `lsst.pipe.base.Struct`
571 Result struct
with components:
572 - ``exposureList``: the selected exposures
574 - ``dataRefList``: the optional data references corresponding to
575 each element of ``exposureList``
576 (`list` of `lsst.daf.persistence.ButlerDataRef`,
or `
None`).
580 exposureInfoList = []
582 if selectDataList
is None:
585 result = super().runDataRef(dataRef, coordList, makeDataRefList=
True, selectDataList=selectDataList)
587 for dataRef, exposureInfo
in zip(result.dataRefList, result.exposureInfoList):
588 cal = dataRef.get(
"calexp", immediate=
True)
591 pixToArcseconds = cal.getWcs().getPixelScale().asArcseconds()
592 psfSize = cal.getPsf().computeShape().getDeterminantRadius()*pixToArcseconds
593 sizeFwhm = psfSize * np.sqrt(8.*np.log(2.))
594 if self.config.maxPsfFwhm
and sizeFwhm > self.config.maxPsfFwhm:
596 if self.config.minPsfFwhm
and sizeFwhm < self.config.minPsfFwhm:
598 psfSizes.append(sizeFwhm)
599 dataRefList.append(dataRef)
600 exposureInfoList.append(exposureInfo)
602 if len(psfSizes) > self.config.nImagesMax:
603 sortedIndices = np.argsort(psfSizes)[:self.config.nImagesMax]
604 filteredDataRefList = [dataRefList[i]
for i
in sortedIndices]
605 filteredExposureInfoList = [exposureInfoList[i]
for i
in sortedIndices]
606 self.log.info(
"%d images selected with FWHM range of %f--%f arcseconds",
607 len(sortedIndices), psfSizes[sortedIndices[0]], psfSizes[sortedIndices[-1]])
610 if len(psfSizes) == 0:
611 self.log.warning(
"0 images selected.")
613 self.log.debug(
"%d images selected with FWHM range of %d--%d arcseconds",
614 len(psfSizes), psfSizes[0], psfSizes[-1])
615 filteredDataRefList = dataRefList
616 filteredExposureInfoList = exposureInfoList
618 return pipeBase.Struct(
619 dataRefList=filteredDataRefList
if makeDataRefList
else None,
620 exposureInfoList=filteredExposureInfoList,
624class BestSeeingSelectVisitsConnections(pipeBase.PipelineTaskConnections,
625 dimensions=(
"tract",
"patch",
"skymap",
"band",
"instrument"),
626 defaultTemplates={
"coaddName":
"goodSeeing"}):
627 skyMap = pipeBase.connectionTypes.Input(
628 doc=
"Input definition of geometry/bbox and projection/wcs for coadded exposures",
629 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
630 storageClass=
"SkyMap",
631 dimensions=(
"skymap",),
633 visitSummaries = pipeBase.connectionTypes.Input(
634 doc=
"Per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask",
636 storageClass=
"ExposureCatalog",
637 dimensions=(
"instrument",
"visit",),
641 goodVisits = pipeBase.connectionTypes.Output(
642 doc=
"Selected visits to be coadded.",
643 name=
"{coaddName}Visits",
644 storageClass=
"StructuredDataDict",
645 dimensions=(
"instrument",
"tract",
"patch",
"skymap",
"band"),
649class BestSeeingSelectVisitsConfig(pipeBase.PipelineTaskConfig,
650 pipelineConnections=BestSeeingSelectVisitsConnections):
651 nVisitsMax = pexConfig.RangeField(
653 doc=
"Maximum number of visits to select",
657 maxPsfFwhm = pexConfig.Field(
659 doc=
"Maximum PSF FWHM (in arcseconds) to select",
663 minPsfFwhm = pexConfig.Field(
665 doc=
"Minimum PSF FWHM (in arcseconds) to select",
669 doConfirmOverlap = pexConfig.Field(
671 doc=
"Do remove visits that do not actually overlap the patch?",
674 minMJD = pexConfig.Field(
676 doc=
"Minimum visit MJD to select",
680 maxMJD = pexConfig.Field(
682 doc=
"Maximum visit MJD to select",
688class BestSeeingSelectVisitsTask(pipeBase.PipelineTask):
689 """Select up to a maximum number of the best-seeing visits
691 Don't exceed the FWHM range specified by configs min(max)PsfFwhm.
692 This Task is a port of the Gen2 image-selector used
in the AP pipeline:
693 BestSeeingSelectImagesTask. This Task selects full visits based on the
694 average PSF of the entire visit.
696 ConfigClass = BestSeeingSelectVisitsConfig
697 _DefaultName = 'bestSeeingSelectVisits'
699 def runQuantum(self, butlerQC, inputRefs, outputRefs):
700 inputs = butlerQC.get(inputRefs)
701 quantumDataId = butlerQC.quantum.dataId
702 outputs = self.run(**inputs, dataId=quantumDataId)
703 butlerQC.put(outputs, outputRefs)
705 def run(self, visitSummaries, skyMap, dataId):
710 visitSummary : `list`
711 List of `lsst.pipe.base.connections.DeferredDatasetRef` of
713 skyMap : `lsst.skyMap.SkyMap`
714 SkyMap for checking visits overlap patch
715 dataId : `dict` of dataId keys
716 For retrieving patch info
for checking visits overlap patch
720 result : `lsst.pipe.base.Struct`
721 Result struct
with components:
723 - `goodVisits`: `dict`
with selected visit ids
as keys,
724 so that it can be be saved
as a StructuredDataDict.
725 StructuredDataList
's are currently limited.
728 if self.config.doConfirmOverlap:
729 patchPolygon = self.makePatchPolygon(skyMap, dataId)
731 inputVisits = [visitSummary.ref.dataId[
'visit']
for visitSummary
in visitSummaries]
734 for visit, visitSummary
in zip(inputVisits, visitSummaries):
736 visitSummary = visitSummary.get()
739 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
741 pixToArcseconds = [vs.getWcs().getPixelScale(vs.getBBox().getCenter()).asArcseconds()
742 for vs
in visitSummary]
744 psfSigmas = np.array([vs[
'psfSigma']
for vs
in visitSummary])
745 fwhm = np.nanmean(psfSigmas * pixToArcseconds) * np.sqrt(8.*np.log(2.))
747 if self.config.maxPsfFwhm
and fwhm > self.config.maxPsfFwhm:
749 if self.config.minPsfFwhm
and fwhm < self.config.minPsfFwhm:
751 if self.config.minMJD
and mjd < self.config.minMJD:
752 self.log.debug(
'MJD %f earlier than %.2f; rejecting', mjd, self.config.minMJD)
754 if self.config.maxMJD
and mjd > self.config.maxMJD:
755 self.log.debug(
'MJD %f later than %.2f; rejecting', mjd, self.config.maxMJD)
757 if self.config.doConfirmOverlap
and not self.doesIntersectPolygon(visitSummary, patchPolygon):
760 fwhmSizes.append(fwhm)
763 sortedVisits = [ind
for (_, ind)
in sorted(zip(fwhmSizes, visits))]
764 output = sortedVisits[:self.config.nVisitsMax]
765 self.log.info(
"%d images selected with FWHM range of %d--%d arcseconds",
766 len(output), fwhmSizes[visits.index(output[0])], fwhmSizes[visits.index(output[-1])])
769 goodVisits = {key:
True for key
in output}
770 return pipeBase.Struct(goodVisits=goodVisits)
772 def makePatchPolygon(self, skyMap, dataId):
773 """Return True if sky polygon overlaps visit
778 Exposure catalog with per-detector geometry
779 dataId : `dict` of dataId keys
780 For retrieving patch info
784 result :` lsst.sphgeom.ConvexPolygon.convexHull`
785 Polygon of patch
's outer bbox
787 wcs = skyMap[dataId['tract']].getWcs()
788 bbox = skyMap[dataId[
'tract']][dataId[
'patch']].getOuterBBox()
793 def doesIntersectPolygon(self, visitSummary, polygon):
794 """Return True if sky polygon overlaps visit
799 Exposure catalog with per-detector geometry
800 polygon :` lsst.sphgeom.ConvexPolygon.convexHull`
801 Polygon to check overlap
805 doesIntersect: `bool`
806 Does the visit overlap the polygon
808 doesIntersect = False
809 for detectorSummary
in visitSummary:
811 zip(detectorSummary[
'raCorners'], detectorSummary[
'decCorners'])]
813 if detectorPolygon.intersects(polygon):
819class BestSeeingQuantileSelectVisitsConfig(pipeBase.PipelineTaskConfig,
820 pipelineConnections=BestSeeingSelectVisitsConnections):
821 qMin = pexConfig.RangeField(
822 doc=
"Lower bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
823 "and select those in the interquantile range (qMin, qMax). Set qMin to 0 for Best Seeing. "
824 "This config should be changed from zero only for exploratory diffIm testing.",
830 qMax = pexConfig.RangeField(
831 doc=
"Upper bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
832 "and select those in the interquantile range (qMin, qMax). Set qMax to 1 for Worst Seeing.",
838 nVisitsMin = pexConfig.Field(
839 doc=
"At least this number of visits selected and supercedes quantile. For example, if 10 visits "
840 "cover this patch, qMin=0.33, and nVisitsMin=5, the best 5 visits will be selected.",
844 doConfirmOverlap = pexConfig.Field(
846 doc=
"Do remove visits that do not actually overlap the patch?",
849 minMJD = pexConfig.Field(
851 doc=
"Minimum visit MJD to select",
855 maxMJD = pexConfig.Field(
857 doc=
"Maximum visit MJD to select",
863class BestSeeingQuantileSelectVisitsTask(BestSeeingSelectVisitsTask):
864 """Select a quantile of the best-seeing visits
866 Selects the best (for example, third) full visits based on the average
867 PSF width
in the entire visit. It can also be used
for difference imaging
868 experiments that require templates
with the worst seeing visits.
869 For example, selecting the worst third can be acheived by
870 changing the config parameters qMin to 0.66
and qMax to 1.
872 ConfigClass = BestSeeingQuantileSelectVisitsConfig
873 _DefaultName = 'bestSeeingQuantileSelectVisits'
875 @utils.inheritDoc(BestSeeingSelectVisitsTask)
876 def run(self, visitSummaries, skyMap, dataId):
877 if self.config.doConfirmOverlap:
878 patchPolygon = self.makePatchPolygon(skyMap, dataId)
879 visits = np.array([visitSummary.ref.dataId[
'visit']
for visitSummary
in visitSummaries])
880 radius = np.empty(len(visits))
881 intersects = np.full(len(visits),
True)
882 for i, visitSummary
in enumerate(visitSummaries):
884 visitSummary = visitSummary.get()
886 psfSigma = np.nanmedian([vs[
'psfSigma']
for vs
in visitSummary])
888 if self.config.doConfirmOverlap:
889 intersects[i] = self.doesIntersectPolygon(visitSummary, patchPolygon)
890 if self.config.minMJD
or self.config.maxMJD:
892 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
893 aboveMin = mjd > self.config.minMJD
if self.config.minMJD
else True
894 belowMax = mjd < self.config.maxMJD
if self.config.maxMJD
else True
895 intersects[i] = intersects[i]
and aboveMin
and belowMax
897 sortedVisits = [v
for rad, v
in sorted(zip(radius[intersects], visits[intersects]))]
898 lowerBound = min(int(np.round(self.config.qMin*len(visits[intersects]))),
899 max(0, len(visits[intersects]) - self.config.nVisitsMin))
900 upperBound = max(int(np.round(self.config.qMax*len(visits[intersects]))), self.config.nVisitsMin)
903 goodVisits = {int(visit):
True for visit
in sortedVisits[lowerBound:upperBound]}
904 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)