Coverage for python/lsst/pipe/tasks/selectImages.py: 24%
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1# This file is part of pipe_tasks.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
22__all__ = ["BaseSelectImagesTask", "BaseExposureInfo", "WcsSelectImagesTask", "PsfWcsSelectImagesTask",
23 "DatabaseSelectImagesConfig", "BestSeeingSelectVisitsTask",
24 "BestSeeingQuantileSelectVisitsTask"]
26import numpy as np
27import lsst.sphgeom
28import lsst.utils as utils
29import lsst.pex.config as pexConfig
30import lsst.pex.exceptions as pexExceptions
31import lsst.geom as geom
32import lsst.pipe.base as pipeBase
33from lsst.skymap import BaseSkyMap
34from lsst.daf.base import DateTime
35from lsst.utils.timer import timeMethod
38class DatabaseSelectImagesConfig(pexConfig.Config):
39 """Base configuration for subclasses of BaseSelectImagesTask that use a
40 database.
41 """
43 host = pexConfig.Field(
44 doc="Database server host name",
45 dtype=str,
46 )
47 port = pexConfig.Field(
48 doc="Database server port",
49 dtype=int,
50 )
51 database = pexConfig.Field(
52 doc="Name of database",
53 dtype=str,
54 )
55 maxExposures = pexConfig.Field(
56 doc="maximum exposures to select; intended for debugging; ignored if None",
57 dtype=int,
58 optional=True,
59 )
62class BaseExposureInfo(pipeBase.Struct):
63 """Data about a selected exposure.
65 Parameters
66 ----------
67 dataId : `dict`
68 Data ID keys of exposure.
69 coordList : `list` [`lsst.afw.geom.SpherePoint`]
70 ICRS coordinates of the corners of the exposure
71 plus any others items that are desired.
72 """
74 def __init__(self, dataId, coordList):
75 super(BaseExposureInfo, self).__init__(dataId=dataId, coordList=coordList)
78class BaseSelectImagesTask(pipeBase.Task):
79 """Base task for selecting images suitable for coaddition.
80 """
82 ConfigClass = pexConfig.Config
83 _DefaultName = "selectImages"
85 @timeMethod
86 def run(self, coordList):
87 """Select images suitable for coaddition in a particular region.
89 Parameters
90 ----------
91 coordList : `list` [`lsst.geom.SpherePoint`] or `None`
92 List of coordinates defining region of interest; if `None`, then
93 select all images subclasses may add additional keyword arguments,
94 as required.
96 Returns
97 -------
98 result : `pipeBase.Struct`
99 Results as a struct with attributes:
101 ``exposureInfoList``
102 A list of exposure information objects (subclasses of
103 BaseExposureInfo), which have at least the following fields:
104 - dataId: Data ID dictionary (`dict`).
105 - coordList: ICRS coordinates of the corners of the exposure.
106 (`list` [`lsst.geom.SpherePoint`])
107 """
108 raise NotImplementedError()
111def _extractKeyValue(dataList, keys=None):
112 """Extract the keys and values from a list of dataIds.
114 The input dataList is a list of objects that have 'dataId' members.
115 This allows it to be used for both a list of data references and a
116 list of ExposureInfo.
118 Parameters
119 ----------
120 dataList : `Unknown`
121 keys : `Unknown`
123 Returns
124 -------
125 keys : `Unknown`
126 values : `Unknown`
128 Raises
129 ------
130 RuntimeError
131 Raised if DataId keys are inconsistent.
132 """
133 assert len(dataList) > 0
134 if keys is None:
135 keys = sorted(dataList[0].dataId.keys())
136 keySet = set(keys)
137 values = list()
138 for data in dataList:
139 thisKeys = set(data.dataId.keys())
140 if thisKeys != keySet:
141 raise RuntimeError("DataId keys inconsistent: %s vs %s" % (keySet, thisKeys))
142 values.append(tuple(data.dataId[k] for k in keys))
143 return keys, values
146class SelectStruct(pipeBase.Struct):
147 """A container for data to be passed to the WcsSelectImagesTask.
149 Parameters
150 ----------
151 dataRef : `Unknown`
152 Data reference.
153 wcs : `lsst.afw.geom.SkyWcs`
154 Coordinate system definition (wcs).
155 bbox : `lsst.geom.box.Box2I`
156 Integer bounding box for image.
157 """
159 def __init__(self, dataRef, wcs, bbox):
160 super(SelectStruct, self).__init__(dataRef=dataRef, wcs=wcs, bbox=bbox)
163class WcsSelectImagesTask(BaseSelectImagesTask):
164 """Select images using their Wcs.
166 We use the "convexHull" method of lsst.sphgeom.ConvexPolygon to define
167 polygons on the celestial sphere, and test the polygon of the
168 patch for overlap with the polygon of the image.
170 We use "convexHull" instead of generating a ConvexPolygon
171 directly because the standard for the inputs to ConvexPolygon
172 are pretty high and we don't want to be responsible for reaching them.
173 """
175 def run(self, wcsList, bboxList, coordList, dataIds=None, **kwargs):
176 """Return indices of provided lists that meet the selection criteria.
178 Parameters
179 ----------
180 wcsList : `list` [`lsst.afw.geom.SkyWcs`]
181 Specifying the WCS's of the input ccds to be selected.
182 bboxList : `list` [`lsst.geom.Box2I`]
183 Specifying the bounding boxes of the input ccds to be selected.
184 coordList : `list` [`lsst.geom.SpherePoint`]
185 ICRS coordinates specifying boundary of the patch.
186 dataIds : iterable [`lsst.daf.butler.dataId`] or `None`, optional
187 An iterable object of dataIds which point to reference catalogs.
188 **kwargs
189 Additional keyword arguments.
191 Returns
192 -------
193 result : `list` [`int`]
194 The indices of selected ccds.
195 """
196 if dataIds is None:
197 dataIds = [None] * len(wcsList)
198 patchVertices = [coord.getVector() for coord in coordList]
199 patchPoly = lsst.sphgeom.ConvexPolygon.convexHull(patchVertices)
200 result = []
201 for i, (imageWcs, imageBox, dataId) in enumerate(zip(wcsList, bboxList, dataIds)):
202 if imageWcs is None:
203 self.log.info("De-selecting exposure %s: Exposure has no WCS.", dataId)
204 else:
205 imageCorners = self.getValidImageCorners(imageWcs, imageBox, patchPoly, dataId)
206 if imageCorners:
207 result.append(i)
208 return result
210 def getValidImageCorners(self, imageWcs, imageBox, patchPoly, dataId=None):
211 """Return corners or `None` if bad.
213 Parameters
214 ----------
215 imageWcs : `Unknown`
216 imageBox : `Unknown`
217 patchPoly : `Unknown`
218 dataId : `Unknown`
219 """
220 try:
221 imageCorners = [imageWcs.pixelToSky(pix) for pix in geom.Box2D(imageBox).getCorners()]
222 except (pexExceptions.DomainError, pexExceptions.RuntimeError) as e:
223 # Protecting ourselves from awful Wcs solutions in input images
224 self.log.debug("WCS error in testing calexp %s (%s): deselecting", dataId, e)
225 return None
227 imagePoly = lsst.sphgeom.ConvexPolygon.convexHull([coord.getVector() for coord in imageCorners])
228 if imagePoly is None:
229 self.log.debug("Unable to create polygon from image %s: deselecting", dataId)
230 return None
232 if patchPoly.intersects(imagePoly):
233 # "intersects" also covers "contains" or "is contained by"
234 self.log.info("Selecting calexp %s", dataId)
235 return imageCorners
237 return None
240class PsfWcsSelectImagesConnections(pipeBase.PipelineTaskConnections,
241 dimensions=("tract", "patch", "skymap", "instrument", "visit"),
242 defaultTemplates={"coaddName": "deep"}):
243 pass
246class PsfWcsSelectImagesConfig(pipeBase.PipelineTaskConfig,
247 pipelineConnections=PsfWcsSelectImagesConnections):
248 maxEllipResidual = pexConfig.Field(
249 doc="Maximum median ellipticity residual",
250 dtype=float,
251 default=0.007,
252 optional=True,
253 )
254 maxSizeScatter = pexConfig.Field(
255 doc="Maximum scatter in the size residuals",
256 dtype=float,
257 optional=True,
258 )
259 maxScaledSizeScatter = pexConfig.Field(
260 doc="Maximum scatter in the size residuals, scaled by the median size",
261 dtype=float,
262 default=0.009,
263 optional=True,
264 )
265 maxPsfTraceRadiusDelta = pexConfig.Field(
266 doc="Maximum delta (max - min) of model PSF trace radius values evaluated on a grid on "
267 "the unmasked detector pixels (pixel).",
268 dtype=float,
269 default=0.7,
270 optional=True,
271 )
274class PsfWcsSelectImagesTask(WcsSelectImagesTask):
275 """Select images using their Wcs and cuts on the PSF properties.
277 The PSF quality criteria are based on the size and ellipticity
278 residuals from the adaptive second moments of the star and the PSF.
280 The criteria are:
281 - the median of the ellipticty residuals.
282 - the robust scatter of the size residuals (using the median absolute
283 deviation).
284 - the robust scatter of the size residuals scaled by the square of
285 the median size.
286 """
288 ConfigClass = PsfWcsSelectImagesConfig
289 _DefaultName = "PsfWcsSelectImages"
291 def run(self, wcsList, bboxList, coordList, visitSummary, dataIds=None, **kwargs):
292 """Return indices of provided lists that meet the selection criteria.
294 Parameters
295 ----------
296 wcsList : `list` [`lsst.afw.geom.SkyWcs`]
297 Specifying the WCS's of the input ccds to be selected.
298 bboxList : `list` [`lsst.geom.Box2I`]
299 Specifying the bounding boxes of the input ccds to be selected.
300 coordList : `list` [`lsst.geom.SpherePoint`]
301 ICRS coordinates specifying boundary of the patch.
302 visitSummary : `list` [`lsst.afw.table.ExposureCatalog`]
303 containing the PSF shape information for the input ccds to be
304 selected.
305 dataIds : iterable [`lsst.daf.butler.dataId`] or `None`, optional
306 An iterable object of dataIds which point to reference catalogs.
307 **kwargs
308 Additional keyword arguments.
310 Returns
311 -------
312 goodPsf : `list` [`int`]
313 The indices of selected ccds.
314 """
315 goodWcs = super(PsfWcsSelectImagesTask, self).run(wcsList=wcsList, bboxList=bboxList,
316 coordList=coordList, dataIds=dataIds)
318 goodPsf = []
320 for i, dataId in enumerate(dataIds):
321 if i not in goodWcs:
322 continue
323 if self.isValid(visitSummary, dataId["detector"]):
324 goodPsf.append(i)
326 return goodPsf
328 def isValid(self, visitSummary, detectorId):
329 """Should this ccd be selected based on its PSF shape information.
331 Parameters
332 ----------
333 visitSummary : `lsst.afw.table.ExposureCatalog`
334 Exposure catalog with per-detector summary information.
335 detectorId : `int`
336 Detector identifier.
338 Returns
339 -------
340 valid : `bool`
341 True if selected.
342 """
343 row = visitSummary.find(detectorId)
344 if row is None:
345 # This is not listed, so it must be bad.
346 self.log.warning("Removing detector %d because summary stats not available.", detectorId)
347 return False
349 medianE = np.sqrt(row["psfStarDeltaE1Median"]**2. + row["psfStarDeltaE2Median"]**2.)
350 scatterSize = row["psfStarDeltaSizeScatter"]
351 scaledScatterSize = row["psfStarScaledDeltaSizeScatter"]
352 psfTraceRadiusDelta = row["psfTraceRadiusDelta"]
354 valid = True
355 if self.config.maxEllipResidual and not (medianE <= self.config.maxEllipResidual):
356 self.log.info("Removing visit %d detector %d because median e residual too large: %f vs %f",
357 row["visit"], detectorId, medianE, self.config.maxEllipResidual)
358 valid = False
359 elif self.config.maxSizeScatter and not (scatterSize <= self.config.maxSizeScatter):
360 self.log.info("Removing visit %d detector %d because size scatter too large: %f vs %f",
361 row["visit"], detectorId, scatterSize, self.config.maxSizeScatter)
362 valid = False
363 elif self.config.maxScaledSizeScatter and not (scaledScatterSize <= self.config.maxScaledSizeScatter):
364 self.log.info("Removing visit %d detector %d because scaled size scatter too large: %f vs %f",
365 row["visit"], detectorId, scaledScatterSize, self.config.maxScaledSizeScatter)
366 valid = False
367 elif (
368 self.config.maxPsfTraceRadiusDelta is not None
369 and not (psfTraceRadiusDelta <= self.config.maxPsfTraceRadiusDelta)
370 ):
371 self.log.info(
372 "Removing visit %d detector %d because max-min delta of model PSF trace radius values "
373 "across the unmasked detector pixels is not finite or too large: %.3f vs %.3f (pixels)",
374 row["visit"], detectorId, psfTraceRadiusDelta, self.config.maxPsfTraceRadiusDelta
375 )
376 valid = False
378 return valid
381class BestSeeingSelectVisitsConnections(pipeBase.PipelineTaskConnections,
382 dimensions=("tract", "patch", "skymap", "band", "instrument"),
383 defaultTemplates={"coaddName": "goodSeeing"}):
384 skyMap = pipeBase.connectionTypes.Input(
385 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures",
386 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
387 storageClass="SkyMap",
388 dimensions=("skymap",),
389 )
390 visitSummaries = pipeBase.connectionTypes.Input(
391 doc="Per-visit consolidated exposure metadata",
392 name="finalVisitSummary",
393 storageClass="ExposureCatalog",
394 dimensions=("instrument", "visit",),
395 multiple=True,
396 deferLoad=True
397 )
398 goodVisits = pipeBase.connectionTypes.Output(
399 doc="Selected visits to be coadded.",
400 name="{coaddName}Visits",
401 storageClass="StructuredDataDict",
402 dimensions=("instrument", "tract", "patch", "skymap", "band"),
403 )
406class BestSeeingSelectVisitsConfig(pipeBase.PipelineTaskConfig,
407 pipelineConnections=BestSeeingSelectVisitsConnections):
408 nVisitsMax = pexConfig.RangeField(
409 dtype=int,
410 doc="Maximum number of visits to select",
411 default=12,
412 min=0
413 )
414 maxPsfFwhm = pexConfig.Field(
415 dtype=float,
416 doc="Maximum PSF FWHM (in arcseconds) to select",
417 default=1.5,
418 optional=True
419 )
420 minPsfFwhm = pexConfig.Field(
421 dtype=float,
422 doc="Minimum PSF FWHM (in arcseconds) to select",
423 default=0.,
424 optional=True
425 )
426 doConfirmOverlap = pexConfig.Field(
427 dtype=bool,
428 doc="Do remove visits that do not actually overlap the patch?",
429 default=True,
430 )
431 minMJD = pexConfig.Field(
432 dtype=float,
433 doc="Minimum visit MJD to select",
434 default=None,
435 optional=True
436 )
437 maxMJD = pexConfig.Field(
438 dtype=float,
439 doc="Maximum visit MJD to select",
440 default=None,
441 optional=True
442 )
445class BestSeeingSelectVisitsTask(pipeBase.PipelineTask):
446 """Select up to a maximum number of the best-seeing visits.
448 Don't exceed the FWHM range specified by configs min(max)PsfFwhm.
449 This Task is a port of the Gen2 image-selector used in the AP pipeline:
450 BestSeeingSelectImagesTask. This Task selects full visits based on the
451 average PSF of the entire visit.
452 """
454 ConfigClass = BestSeeingSelectVisitsConfig
455 _DefaultName = 'bestSeeingSelectVisits'
457 def runQuantum(self, butlerQC, inputRefs, outputRefs):
458 inputs = butlerQC.get(inputRefs)
459 quantumDataId = butlerQC.quantum.dataId
460 outputs = self.run(**inputs, dataId=quantumDataId)
461 butlerQC.put(outputs, outputRefs)
463 def run(self, visitSummaries, skyMap, dataId):
464 """Run task.
466 Parameters
467 ----------
468 visitSummary : `list` [`lsst.pipe.base.connections.DeferredDatasetRef`]
469 List of `lsst.pipe.base.connections.DeferredDatasetRef` of
470 visitSummary tables of type `lsst.afw.table.ExposureCatalog`.
471 skyMap : `lsst.skyMap.SkyMap`
472 SkyMap for checking visits overlap patch.
473 dataId : `dict` of dataId keys
474 For retrieving patch info for checking visits overlap patch.
476 Returns
477 -------
478 result : `lsst.pipe.base.Struct`
479 Results as a struct with attributes:
481 ``goodVisits``
482 A `dict` with selected visit ids as keys,
483 so that it can be be saved as a StructuredDataDict.
484 StructuredDataList's are currently limited.
485 """
486 if self.config.doConfirmOverlap:
487 patchPolygon = self.makePatchPolygon(skyMap, dataId)
489 inputVisits = [visitSummary.ref.dataId['visit'] for visitSummary in visitSummaries]
490 fwhmSizes = []
491 visits = []
492 for visit, visitSummary in zip(inputVisits, visitSummaries):
493 # read in one-by-one and only once. There may be hundreds
494 visitSummary = visitSummary.get()
496 # mjd is guaranteed to be the same for every detector in the
497 # visitSummary.
498 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
500 pixToArcseconds = [vs.getWcs().getPixelScale(vs.getBBox().getCenter()).asArcseconds()
501 for vs in visitSummary if vs.getWcs()]
502 # psfSigma is PSF model determinant radius at chip center in pixels
503 psfSigmas = np.array([vs['psfSigma'] for vs in visitSummary if vs.getWcs()])
504 fwhm = np.nanmean(psfSigmas * pixToArcseconds) * np.sqrt(8.*np.log(2.))
506 if self.config.maxPsfFwhm and fwhm > self.config.maxPsfFwhm:
507 continue
508 if self.config.minPsfFwhm and fwhm < self.config.minPsfFwhm:
509 continue
510 if self.config.minMJD and mjd < self.config.minMJD:
511 self.log.debug('MJD %f earlier than %.2f; rejecting', mjd, self.config.minMJD)
512 continue
513 if self.config.maxMJD and mjd > self.config.maxMJD:
514 self.log.debug('MJD %f later than %.2f; rejecting', mjd, self.config.maxMJD)
515 continue
516 if self.config.doConfirmOverlap and not self.doesIntersectPolygon(visitSummary, patchPolygon):
517 continue
519 fwhmSizes.append(fwhm)
520 visits.append(visit)
522 sortedVisits = [ind for (_, ind) in sorted(zip(fwhmSizes, visits))]
523 output = sortedVisits[:self.config.nVisitsMax]
524 self.log.info("%d images selected with FWHM range of %d--%d arcseconds",
525 len(output), fwhmSizes[visits.index(output[0])], fwhmSizes[visits.index(output[-1])])
527 # In order to store as a StructuredDataDict, convert list to dict
528 goodVisits = {key: True for key in output}
529 return pipeBase.Struct(goodVisits=goodVisits)
531 def makePatchPolygon(self, skyMap, dataId):
532 """Return True if sky polygon overlaps visit.
534 Parameters
535 ----------
536 skyMap : `lsst.afw.table.ExposureCatalog`
537 Exposure catalog with per-detector geometry.
538 dataId : `dict` of dataId keys
539 For retrieving patch info.
541 Returns
542 -------
543 result : `lsst.sphgeom.ConvexPolygon.convexHull`
544 Polygon of patch's outer bbox.
545 """
546 wcs = skyMap[dataId['tract']].getWcs()
547 bbox = skyMap[dataId['tract']][dataId['patch']].getOuterBBox()
548 sphCorners = wcs.pixelToSky(lsst.geom.Box2D(bbox).getCorners())
549 result = lsst.sphgeom.ConvexPolygon.convexHull([coord.getVector() for coord in sphCorners])
550 return result
552 def doesIntersectPolygon(self, visitSummary, polygon):
553 """Return True if sky polygon overlaps visit.
555 Parameters
556 ----------
557 visitSummary : `lsst.afw.table.ExposureCatalog`
558 Exposure catalog with per-detector geometry.
559 polygon :` lsst.sphgeom.ConvexPolygon.convexHull`
560 Polygon to check overlap.
562 Returns
563 -------
564 doesIntersect : `bool`
565 True if the visit overlaps the polygon.
566 """
567 doesIntersect = False
568 for detectorSummary in visitSummary:
569 if (np.all(np.isfinite(detectorSummary['raCorners']))
570 and np.all(np.isfinite(detectorSummary['decCorners']))):
571 corners = [lsst.geom.SpherePoint(ra, decl, units=lsst.geom.degrees).getVector()
572 for (ra, decl) in
573 zip(detectorSummary['raCorners'], detectorSummary['decCorners'])]
574 detectorPolygon = lsst.sphgeom.ConvexPolygon.convexHull(corners)
575 if detectorPolygon.intersects(polygon):
576 doesIntersect = True
577 break
578 return doesIntersect
581class BestSeeingQuantileSelectVisitsConfig(pipeBase.PipelineTaskConfig,
582 pipelineConnections=BestSeeingSelectVisitsConnections):
583 qMin = pexConfig.RangeField(
584 doc="Lower bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
585 "and select those in the interquantile range (qMin, qMax). Set qMin to 0 for Best Seeing. "
586 "This config should be changed from zero only for exploratory diffIm testing.",
587 dtype=float,
588 default=0,
589 min=0,
590 max=1,
591 )
592 qMax = pexConfig.RangeField(
593 doc="Upper bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
594 "and select those in the interquantile range (qMin, qMax). Set qMax to 1 for Worst Seeing.",
595 dtype=float,
596 default=0.33,
597 min=0,
598 max=1,
599 )
600 nVisitsMin = pexConfig.Field(
601 doc="At least this number of visits selected and supercedes quantile. For example, if 10 visits "
602 "cover this patch, qMin=0.33, and nVisitsMin=5, the best 5 visits will be selected.",
603 dtype=int,
604 default=6,
605 )
606 doConfirmOverlap = pexConfig.Field(
607 dtype=bool,
608 doc="Do remove visits that do not actually overlap the patch?",
609 default=True,
610 )
611 minMJD = pexConfig.Field(
612 dtype=float,
613 doc="Minimum visit MJD to select",
614 default=None,
615 optional=True
616 )
617 maxMJD = pexConfig.Field(
618 dtype=float,
619 doc="Maximum visit MJD to select",
620 default=None,
621 optional=True
622 )
625class BestSeeingQuantileSelectVisitsTask(BestSeeingSelectVisitsTask):
626 """Select a quantile of the best-seeing visits.
628 Selects the best (for example, third) full visits based on the average
629 PSF width in the entire visit. It can also be used for difference imaging
630 experiments that require templates with the worst seeing visits.
631 For example, selecting the worst third can be acheived by
632 changing the config parameters qMin to 0.66 and qMax to 1.
633 """
634 ConfigClass = BestSeeingQuantileSelectVisitsConfig
635 _DefaultName = 'bestSeeingQuantileSelectVisits'
637 @utils.inheritDoc(BestSeeingSelectVisitsTask)
638 def run(self, visitSummaries, skyMap, dataId):
639 if self.config.doConfirmOverlap:
640 patchPolygon = self.makePatchPolygon(skyMap, dataId)
641 visits = np.array([visitSummary.ref.dataId['visit'] for visitSummary in visitSummaries])
642 radius = np.empty(len(visits))
643 intersects = np.full(len(visits), True)
644 for i, visitSummary in enumerate(visitSummaries):
645 # read in one-by-one and only once. There may be hundreds
646 visitSummary = visitSummary.get()
647 # psfSigma is PSF model determinant radius at chip center in pixels
648 psfSigma = np.nanmedian([vs['psfSigma'] for vs in visitSummary])
649 radius[i] = psfSigma
650 if self.config.doConfirmOverlap:
651 intersects[i] = self.doesIntersectPolygon(visitSummary, patchPolygon)
652 if self.config.minMJD or self.config.maxMJD:
653 # mjd is guaranteed to be the same for every detector in the
654 # visitSummary.
655 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
656 aboveMin = mjd > self.config.minMJD if self.config.minMJD else True
657 belowMax = mjd < self.config.maxMJD if self.config.maxMJD else True
658 intersects[i] = intersects[i] and aboveMin and belowMax
660 sortedVisits = [v for rad, v in sorted(zip(radius[intersects], visits[intersects]))]
661 lowerBound = min(int(np.round(self.config.qMin*len(visits[intersects]))),
662 max(0, len(visits[intersects]) - self.config.nVisitsMin))
663 upperBound = max(int(np.round(self.config.qMax*len(visits[intersects]))), self.config.nVisitsMin)
665 # In order to store as a StructuredDataDict, convert list to dict
666 goodVisits = {int(visit): True for visit in sortedVisits[lowerBound:upperBound]}
667 return pipeBase.Struct(goodVisits=goodVisits)