24import 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)
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
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,
147def _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
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)
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
224 specifying the WCS's of the input ccds to be selected
226 specifying the bounding boxes of the input ccds to be selected
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"}):
277class 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.')
317 doLegacyStarSelectionComputation = pexConfig.Field(
318 doc=
"Perform the legacy star selection computations (for backwards compatibility)",
321 deprecated=(
"This field is here for backwards compatibility and will be "
322 "removed after v24.")
327 """Select images using their Wcs and cuts on the PSF properties
329 The PSF quality criteria are based on the size and ellipticity residuals
from the
330 adaptive second moments of the star
and the PSF.
333 - the median of the ellipticty residuals
334 - the robust scatter of the size residuals (using the median absolute deviation)
335 - the robust scatter of the size residuals scaled by the square of
339 ConfigClass = PsfWcsSelectImagesConfig
340 _DefaultName = "PsfWcsSelectImages"
342 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]):
343 """Select images in the selectDataList that overlap the patch and satisfy PSF quality critera.
345 This method is the old entry point
for the Gen2 commandline tasks
and drivers
346 Will be deprecated
in v22.
348 @param dataRef: Data reference
for coadd/tempExp (
with tract, patch)
350 @param makeDataRefList: Construct a list of data references?
351 @param selectDataList: List of SelectStruct, to consider
for selection
353 result = super(PsfWcsSelectImagesTask, self).runDataRef(dataRef, coordList, makeDataRefList,
357 exposureInfoList = []
358 for dataRef, exposureInfo
in zip(result.dataRefList, result.exposureInfoList):
359 butler = dataRef.butlerSubset.butler
360 srcCatalog = butler.get(
'src', dataRef.dataId)
361 valid = self.isValidLegacy(srcCatalog, dataRef.dataId)
365 dataRefList.append(dataRef)
366 exposureInfoList.append(exposureInfo)
368 return pipeBase.Struct(
369 dataRefList=dataRefList,
370 exposureInfoList=exposureInfoList,
373 def run(self, wcsList, bboxList, coordList, visitSummary, dataIds=None, srcList=None, **kwargs):
374 """Return indices of provided lists that meet the selection criteria
379 specifying the WCS's of the input ccds to be selected
381 specifying the bounding boxes of the input ccds to be selected
383 ICRS coordinates specifying boundary of the patch.
385 containing the PSF shape information for the input ccds to be selected.
387 containing the PSF shape information
for the input ccds to be selected.
388 This
is only used
if ``config.doLegacyStarSelectionComputation``
is
393 goodPsf: `list` of `int`
394 of indices of selected ccds
396 goodWcs = super(PsfWcsSelectImagesTask, self).run(wcsList=wcsList, bboxList=bboxList,
397 coordList=coordList, dataIds=dataIds)
401 if not self.config.doLegacyStarSelectionComputation:
403 if 'nPsfStar' not in visitSummary[0].schema.getNames():
404 raise RuntimeError(
"Old calexps detected. "
405 "Please set config.doLegacyStarSelectionComputation=True for "
406 "backwards compatibility.")
408 for i, dataId
in enumerate(dataIds):
411 if self.isValid(visitSummary, dataId[
"detector"]):
415 dataIds = [
None] * len(srcList)
416 for i, (srcCatalog, dataId)
in enumerate(zip(srcList, dataIds)):
419 if self.isValidLegacy(srcCatalog, dataId):
424 def isValid(self, visitSummary, detectorId):
425 """Should this ccd be selected based on its PSF shape information.
438 row = visitSummary.find(detectorId)
441 self.log.warning(
"Removing detector %d because summary stats not available.", detectorId)
444 medianE = np.sqrt(row[
"psfStarDeltaE1Median"]**2. + row[
"psfStarDeltaE2Median"]**2.)
445 scatterSize = row[
"psfStarDeltaSizeScatter"]
446 scaledScatterSize = row[
"psfStarScaledDeltaSizeScatter"]
449 if self.config.maxEllipResidual
and medianE > self.config.maxEllipResidual:
450 self.log.info(
"Removing visit %d detector %d because median e residual too large: %f vs %f",
451 row[
"visit"], detectorId, medianE, self.config.maxEllipResidual)
453 elif self.config.maxSizeScatter
and scatterSize > self.config.maxSizeScatter:
454 self.log.info(
"Removing visit %d detector %d because size scatter too large: %f vs %f",
455 row[
"visit"], detectorId, scatterSize, self.config.maxSizeScatter)
457 elif self.config.maxScaledSizeScatter
and scaledScatterSize > self.config.maxScaledSizeScatter:
458 self.log.info(
"Removing visit %d detector %d because scaled size scatter too large: %f vs %f",
459 row[
"visit"], detectorId, scaledScatterSize, self.config.maxScaledSizeScatter)
464 def isValidLegacy(self, srcCatalog, dataId=None):
465 """Should this ccd be selected based on its PSF shape information.
467 This routine is only used
in legacy processing (gen2
and
468 backwards compatible old calexps)
and should be removed after v24.
473 dataId : `dict` of dataId keys, optional.
474 Used only
for logging. Defaults to
None.
481 mask = srcCatalog[self.config.starSelection]
483 starXX = srcCatalog[self.config.starShape+'_xx'][mask]
484 starYY = srcCatalog[self.config.starShape+
'_yy'][mask]
485 starXY = srcCatalog[self.config.starShape+
'_xy'][mask]
486 psfXX = srcCatalog[self.config.psfShape+
'_xx'][mask]
487 psfYY = srcCatalog[self.config.psfShape+
'_yy'][mask]
488 psfXY = srcCatalog[self.config.psfShape+
'_xy'][mask]
490 starSize = np.power(starXX*starYY - starXY**2, 0.25)
491 starE1 = (starXX - starYY)/(starXX + starYY)
492 starE2 = 2*starXY/(starXX + starYY)
493 medianSize = np.median(starSize)
495 psfSize = np.power(psfXX*psfYY - psfXY**2, 0.25)
496 psfE1 = (psfXX - psfYY)/(psfXX + psfYY)
497 psfE2 = 2*psfXY/(psfXX + psfYY)
499 medianE1 = np.abs(np.median(starE1 - psfE1))
500 medianE2 = np.abs(np.median(starE2 - psfE2))
501 medianE = np.sqrt(medianE1**2 + medianE2**2)
503 scatterSize =
sigmaMad(starSize - psfSize)
504 scaledScatterSize = scatterSize/medianSize**2
507 if self.config.maxEllipResidual
and medianE > self.config.maxEllipResidual:
508 self.log.info(
"Removing visit %s because median e residual too large: %f vs %f",
509 dataId, medianE, self.config.maxEllipResidual)
511 elif self.config.maxSizeScatter
and scatterSize > self.config.maxSizeScatter:
512 self.log.info(
"Removing visit %s because size scatter is too large: %f vs %f",
513 dataId, scatterSize, self.config.maxSizeScatter)
515 elif self.config.maxScaledSizeScatter
and scaledScatterSize > self.config.maxScaledSizeScatter:
516 self.log.info(
"Removing visit %s because scaled size scatter is too large: %f vs %f",
517 dataId, scaledScatterSize, self.config.maxScaledSizeScatter)
523class BestSeeingWcsSelectImageConfig(WcsSelectImagesTask.ConfigClass):
524 """Base configuration for BestSeeingSelectImagesTask.
526 nImagesMax = pexConfig.RangeField(
528 doc="Maximum number of images to select",
531 maxPsfFwhm = pexConfig.Field(
533 doc=
"Maximum PSF FWHM (in arcseconds) to select",
536 minPsfFwhm = pexConfig.Field(
538 doc=
"Minimum PSF FWHM (in arcseconds) to select",
544 """Select up to a maximum number of the best-seeing images using their Wcs.
546 ConfigClass = BestSeeingWcsSelectImageConfig
548 def runDataRef(self, dataRef, coordList, makeDataRefList=True,
549 selectDataList=None):
550 """Select the best-seeing images in the selectDataList that overlap the patch.
552 This method is the old entry point
for the Gen2 commandline tasks
and drivers
553 Will be deprecated
in v22.
557 dataRef : `lsst.daf.persistence.ButlerDataRef`
558 Data reference
for coadd/tempExp (
with tract, patch)
560 List of ICRS sky coordinates specifying boundary of patch
561 makeDataRefList : `boolean`, optional
562 Construct a list of data references?
563 selectDataList : `list` of `SelectStruct`
564 List of SelectStruct, to consider
for selection
568 result : `lsst.pipe.base.Struct`
569 Result struct
with components:
570 - ``exposureList``: the selected exposures
572 - ``dataRefList``: the optional data references corresponding to
573 each element of ``exposureList``
574 (`list` of `lsst.daf.persistence.ButlerDataRef`,
or `
None`).
578 exposureInfoList = []
580 if selectDataList
is None:
583 result = super().runDataRef(dataRef, coordList, makeDataRefList=
True, selectDataList=selectDataList)
585 for dataRef, exposureInfo
in zip(result.dataRefList, result.exposureInfoList):
586 cal = dataRef.get(
"calexp", immediate=
True)
589 pixToArcseconds = cal.getWcs().getPixelScale().asArcseconds()
590 psfSize = cal.getPsf().computeShape().getDeterminantRadius()*pixToArcseconds
591 sizeFwhm = psfSize * np.sqrt(8.*np.log(2.))
592 if self.config.maxPsfFwhm
and sizeFwhm > self.config.maxPsfFwhm:
594 if self.config.minPsfFwhm
and sizeFwhm < self.config.minPsfFwhm:
596 psfSizes.append(sizeFwhm)
597 dataRefList.append(dataRef)
598 exposureInfoList.append(exposureInfo)
600 if len(psfSizes) > self.config.nImagesMax:
601 sortedIndices = np.argsort(psfSizes)[:self.config.nImagesMax]
602 filteredDataRefList = [dataRefList[i]
for i
in sortedIndices]
603 filteredExposureInfoList = [exposureInfoList[i]
for i
in sortedIndices]
604 self.log.info(
"%d images selected with FWHM range of %f--%f arcseconds",
605 len(sortedIndices), psfSizes[sortedIndices[0]], psfSizes[sortedIndices[-1]])
608 if len(psfSizes) == 0:
609 self.log.warning(
"0 images selected.")
611 self.log.debug(
"%d images selected with FWHM range of %d--%d arcseconds",
612 len(psfSizes), psfSizes[0], psfSizes[-1])
613 filteredDataRefList = dataRefList
614 filteredExposureInfoList = exposureInfoList
616 return pipeBase.Struct(
617 dataRefList=filteredDataRefList
if makeDataRefList
else None,
618 exposureInfoList=filteredExposureInfoList,
622class BestSeeingSelectVisitsConnections(pipeBase.PipelineTaskConnections,
623 dimensions=(
"tract",
"patch",
"skymap",
"band",
"instrument"),
624 defaultTemplates={
"coaddName":
"goodSeeing"}):
625 skyMap = pipeBase.connectionTypes.Input(
626 doc=
"Input definition of geometry/bbox and projection/wcs for coadded exposures",
627 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
628 storageClass=
"SkyMap",
629 dimensions=(
"skymap",),
631 visitSummaries = pipeBase.connectionTypes.Input(
632 doc=
"Per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask",
634 storageClass=
"ExposureCatalog",
635 dimensions=(
"instrument",
"visit",),
639 goodVisits = pipeBase.connectionTypes.Output(
640 doc=
"Selected visits to be coadded.",
641 name=
"{coaddName}Visits",
642 storageClass=
"StructuredDataDict",
643 dimensions=(
"instrument",
"tract",
"patch",
"skymap",
"band"),
647class BestSeeingSelectVisitsConfig(pipeBase.PipelineTaskConfig,
648 pipelineConnections=BestSeeingSelectVisitsConnections):
649 nVisitsMax = pexConfig.RangeField(
651 doc=
"Maximum number of visits to select",
655 maxPsfFwhm = pexConfig.Field(
657 doc=
"Maximum PSF FWHM (in arcseconds) to select",
661 minPsfFwhm = pexConfig.Field(
663 doc=
"Minimum PSF FWHM (in arcseconds) to select",
667 doConfirmOverlap = pexConfig.Field(
669 doc=
"Do remove visits that do not actually overlap the patch?",
672 minMJD = pexConfig.Field(
674 doc=
"Minimum visit MJD to select",
678 maxMJD = pexConfig.Field(
680 doc=
"Maximum visit MJD to select",
686class BestSeeingSelectVisitsTask(pipeBase.PipelineTask):
687 """Select up to a maximum number of the best-seeing visits
689 Don't exceed the FWHM range specified by configs min(max)PsfFwhm.
690 This Task is a port of the Gen2 image-selector used
in the AP pipeline:
691 BestSeeingSelectImagesTask. This Task selects full visits based on the
692 average PSF of the entire visit.
694 ConfigClass = BestSeeingSelectVisitsConfig
695 _DefaultName = 'bestSeeingSelectVisits'
697 def runQuantum(self, butlerQC, inputRefs, outputRefs):
698 inputs = butlerQC.get(inputRefs)
699 quantumDataId = butlerQC.quantum.dataId
700 outputs = self.run(**inputs, dataId=quantumDataId)
701 butlerQC.put(outputs, outputRefs)
703 def run(self, visitSummaries, skyMap, dataId):
708 visitSummary : `list`
709 List of `lsst.pipe.base.connections.DeferredDatasetRef` of
711 skyMap : `lsst.skyMap.SkyMap`
712 SkyMap for checking visits overlap patch
713 dataId : `dict` of dataId keys
714 For retrieving patch info
for checking visits overlap patch
718 result : `lsst.pipe.base.Struct`
719 Result struct
with components:
721 - `goodVisits`: `dict`
with selected visit ids
as keys,
722 so that it can be be saved
as a StructuredDataDict.
723 StructuredDataList
's are currently limited.
726 if self.config.doConfirmOverlap:
727 patchPolygon = self.makePatchPolygon(skyMap, dataId)
729 inputVisits = [visitSummary.ref.dataId[
'visit']
for visitSummary
in visitSummaries]
732 for visit, visitSummary
in zip(inputVisits, visitSummaries):
734 visitSummary = visitSummary.get()
737 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
739 pixToArcseconds = [vs.getWcs().getPixelScale(vs.getBBox().getCenter()).asArcseconds()
740 for vs
in visitSummary]
742 psfSigmas = np.array([vs[
'psfSigma']
for vs
in visitSummary])
743 fwhm = np.nanmean(psfSigmas * pixToArcseconds) * np.sqrt(8.*np.log(2.))
745 if self.config.maxPsfFwhm
and fwhm > self.config.maxPsfFwhm:
747 if self.config.minPsfFwhm
and fwhm < self.config.minPsfFwhm:
749 if self.config.minMJD
and mjd < self.config.minMJD:
750 self.log.debug(
'MJD %f earlier than %.2f; rejecting', mjd, self.config.minMJD)
752 if self.config.maxMJD
and mjd > self.config.maxMJD:
753 self.log.debug(
'MJD %f later than %.2f; rejecting', mjd, self.config.maxMJD)
755 if self.config.doConfirmOverlap
and not self.doesIntersectPolygon(visitSummary, patchPolygon):
758 fwhmSizes.append(fwhm)
761 sortedVisits = [ind
for (_, ind)
in sorted(zip(fwhmSizes, visits))]
762 output = sortedVisits[:self.config.nVisitsMax]
763 self.log.info(
"%d images selected with FWHM range of %d--%d arcseconds",
764 len(output), fwhmSizes[visits.index(output[0])], fwhmSizes[visits.index(output[-1])])
767 goodVisits = {key:
True for key
in output}
768 return pipeBase.Struct(goodVisits=goodVisits)
770 def makePatchPolygon(self, skyMap, dataId):
771 """Return True if sky polygon overlaps visit
776 Exposure catalog with per-detector geometry
777 dataId : `dict` of dataId keys
778 For retrieving patch info
782 result :` lsst.sphgeom.ConvexPolygon.convexHull`
783 Polygon of patch
's outer bbox
785 wcs = skyMap[dataId['tract']].getWcs()
786 bbox = skyMap[dataId[
'tract']][dataId[
'patch']].getOuterBBox()
791 def doesIntersectPolygon(self, visitSummary, polygon):
792 """Return True if sky polygon overlaps visit
797 Exposure catalog with per-detector geometry
798 polygon :` lsst.sphgeom.ConvexPolygon.convexHull`
799 Polygon to check overlap
803 doesIntersect: `bool`
804 Does the visit overlap the polygon
806 doesIntersect = False
807 for detectorSummary
in visitSummary:
809 zip(detectorSummary[
'raCorners'], detectorSummary[
'decCorners'])]
811 if detectorPolygon.intersects(polygon):
817class BestSeeingQuantileSelectVisitsConfig(pipeBase.PipelineTaskConfig,
818 pipelineConnections=BestSeeingSelectVisitsConnections):
819 qMin = pexConfig.RangeField(
820 doc=
"Lower bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
821 "and select those in the interquantile range (qMin, qMax). Set qMin to 0 for Best Seeing. "
822 "This config should be changed from zero only for exploratory diffIm testing.",
828 qMax = pexConfig.RangeField(
829 doc=
"Upper bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
830 "and select those in the interquantile range (qMin, qMax). Set qMax to 1 for Worst Seeing.",
836 nVisitsMin = pexConfig.Field(
837 doc=
"At least this number of visits selected and supercedes quantile. For example, if 10 visits "
838 "cover this patch, qMin=0.33, and nVisitsMin=5, the best 5 visits will be selected.",
842 doConfirmOverlap = pexConfig.Field(
844 doc=
"Do remove visits that do not actually overlap the patch?",
847 minMJD = pexConfig.Field(
849 doc=
"Minimum visit MJD to select",
853 maxMJD = pexConfig.Field(
855 doc=
"Maximum visit MJD to select",
861class BestSeeingQuantileSelectVisitsTask(BestSeeingSelectVisitsTask):
862 """Select a quantile of the best-seeing visits
864 Selects the best (for example, third) full visits based on the average
865 PSF width
in the entire visit. It can also be used
for difference imaging
866 experiments that require templates
with the worst seeing visits.
867 For example, selecting the worst third can be acheived by
868 changing the config parameters qMin to 0.66
and qMax to 1.
870 ConfigClass = BestSeeingQuantileSelectVisitsConfig
871 _DefaultName = 'bestSeeingQuantileSelectVisits'
873 @utils.inheritDoc(BestSeeingSelectVisitsTask)
874 def run(self, visitSummaries, skyMap, dataId):
875 if self.config.doConfirmOverlap:
876 patchPolygon = self.makePatchPolygon(skyMap, dataId)
877 visits = np.array([visitSummary.ref.dataId[
'visit']
for visitSummary
in visitSummaries])
878 radius = np.empty(len(visits))
879 intersects = np.full(len(visits),
True)
880 for i, visitSummary
in enumerate(visitSummaries):
882 visitSummary = visitSummary.get()
884 psfSigma = np.nanmedian([vs[
'psfSigma']
for vs
in visitSummary])
886 if self.config.doConfirmOverlap:
887 intersects[i] = self.doesIntersectPolygon(visitSummary, patchPolygon)
888 if self.config.minMJD
or self.config.maxMJD:
890 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
891 aboveMin = mjd > self.config.minMJD
if self.config.minMJD
else True
892 belowMax = mjd < self.config.maxMJD
if self.config.maxMJD
else True
893 intersects[i] = intersects[i]
and aboveMin
and belowMax
895 sortedVisits = [v
for rad, v
in sorted(zip(radius[intersects], visits[intersects]))]
896 lowerBound = min(int(np.round(self.config.qMin*len(visits[intersects]))),
897 max(0, len(visits[intersects]) - self.config.nVisitsMin))
898 upperBound = max(int(np.round(self.config.qMax*len(visits[intersects]))), self.config.nVisitsMin)
901 goodVisits = {int(visit):
True for visit
in sortedVisits[lowerBound:upperBound]}
902 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)