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/>. 

21 

22__all__ = ["BaseSelectImagesTask", "BaseExposureInfo", "WcsSelectImagesTask", "PsfWcsSelectImagesTask", 

23 "DatabaseSelectImagesConfig", "BestSeeingSelectVisitsTask", 

24 "BestSeeingQuantileSelectVisitsTask"] 

25 

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 

36 

37 

38class DatabaseSelectImagesConfig(pexConfig.Config): 

39 """Base configuration for subclasses of BaseSelectImagesTask that use a 

40 database. 

41 """ 

42 

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 ) 

60 

61 

62class BaseExposureInfo(pipeBase.Struct): 

63 """Data about a selected exposure. 

64 

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 """ 

73 

74 def __init__(self, dataId, coordList): 

75 super(BaseExposureInfo, self).__init__(dataId=dataId, coordList=coordList) 

76 

77 

78class BaseSelectImagesTask(pipeBase.Task): 

79 """Base task for selecting images suitable for coaddition. 

80 """ 

81 

82 ConfigClass = pexConfig.Config 

83 _DefaultName = "selectImages" 

84 

85 @timeMethod 

86 def run(self, coordList): 

87 """Select images suitable for coaddition in a particular region. 

88 

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. 

95 

96 Returns 

97 ------- 

98 result : `pipeBase.Struct` 

99 Results as a struct with attributes: 

100 

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() 

109 

110 

111def _extractKeyValue(dataList, keys=None): 

112 """Extract the keys and values from a list of dataIds. 

113 

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. 

117 

118 Parameters 

119 ---------- 

120 dataList : `Unknown` 

121 keys : `Unknown` 

122 

123 Returns 

124 ------- 

125 keys : `Unknown` 

126 values : `Unknown` 

127 

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 

144 

145 

146class SelectStruct(pipeBase.Struct): 

147 """A container for data to be passed to the WcsSelectImagesTask. 

148 

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 """ 

158 

159 def __init__(self, dataRef, wcs, bbox): 

160 super(SelectStruct, self).__init__(dataRef=dataRef, wcs=wcs, bbox=bbox) 

161 

162 

163class WcsSelectImagesTask(BaseSelectImagesTask): 

164 """Select images using their Wcs. 

165 

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. 

169 

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 """ 

174 

175 def run(self, wcsList, bboxList, coordList, dataIds=None, **kwargs): 

176 """Return indices of provided lists that meet the selection criteria. 

177 

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. 

190 

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 

209 

210 def getValidImageCorners(self, imageWcs, imageBox, patchPoly, dataId=None): 

211 """Return corners or `None` if bad. 

212 

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 

226 

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 

231 

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 

236 

237 return None 

238 

239 

240class PsfWcsSelectImagesConnections(pipeBase.PipelineTaskConnections, 

241 dimensions=("tract", "patch", "skymap", "instrument", "visit"), 

242 defaultTemplates={"coaddName": "deep"}): 

243 pass 

244 

245 

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 ) 

272 

273 

274class PsfWcsSelectImagesTask(WcsSelectImagesTask): 

275 """Select images using their Wcs and cuts on the PSF properties. 

276 

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. 

279 

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 """ 

287 

288 ConfigClass = PsfWcsSelectImagesConfig 

289 _DefaultName = "PsfWcsSelectImages" 

290 

291 def run(self, wcsList, bboxList, coordList, visitSummary, dataIds=None, **kwargs): 

292 """Return indices of provided lists that meet the selection criteria. 

293 

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. 

309 

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) 

317 

318 goodPsf = [] 

319 

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) 

325 

326 return goodPsf 

327 

328 def isValid(self, visitSummary, detectorId): 

329 """Should this ccd be selected based on its PSF shape information. 

330 

331 Parameters 

332 ---------- 

333 visitSummary : `lsst.afw.table.ExposureCatalog` 

334 Exposure catalog with per-detector summary information. 

335 detectorId : `int` 

336 Detector identifier. 

337 

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 

348 

349 medianE = np.sqrt(row["psfStarDeltaE1Median"]**2. + row["psfStarDeltaE2Median"]**2.) 

350 scatterSize = row["psfStarDeltaSizeScatter"] 

351 scaledScatterSize = row["psfStarScaledDeltaSizeScatter"] 

352 psfTraceRadiusDelta = row["psfTraceRadiusDelta"] 

353 

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 

377 

378 return valid 

379 

380 

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 ) 

404 

405 

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 ) 

443 

444 

445class BestSeeingSelectVisitsTask(pipeBase.PipelineTask): 

446 """Select up to a maximum number of the best-seeing visits. 

447 

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 """ 

453 

454 ConfigClass = BestSeeingSelectVisitsConfig 

455 _DefaultName = 'bestSeeingSelectVisits' 

456 

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) 

462 

463 def run(self, visitSummaries, skyMap, dataId): 

464 """Run task. 

465 

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. 

475 

476 Returns 

477 ------- 

478 result : `lsst.pipe.base.Struct` 

479 Results as a struct with attributes: 

480 

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) 

488 

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() 

495 

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) 

499 

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.)) 

505 

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 

518 

519 fwhmSizes.append(fwhm) 

520 visits.append(visit) 

521 

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])]) 

526 

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) 

530 

531 def makePatchPolygon(self, skyMap, dataId): 

532 """Return True if sky polygon overlaps visit. 

533 

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. 

540 

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 

551 

552 def doesIntersectPolygon(self, visitSummary, polygon): 

553 """Return True if sky polygon overlaps visit. 

554 

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. 

561 

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 

579 

580 

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 ) 

623 

624 

625class BestSeeingQuantileSelectVisitsTask(BestSeeingSelectVisitsTask): 

626 """Select a quantile of the best-seeing visits. 

627 

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' 

636 

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 

659 

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) 

664 

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)