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331 statements  

1# 

2# LSST Data Management System 

3# Copyright 2008, 2009, 2010 LSST Corporation. 

4# 

5# This product includes software developed by the 

6# LSST Project (http://www.lsst.org/). 

7# 

8# This program is free software: you can redistribute it and/or modify 

9# it under the terms of the GNU General Public License as published by 

10# the Free Software Foundation, either version 3 of the License, or 

11# (at your option) any later version. 

12# 

13# This program is distributed in the hope that it will be useful, 

14# but WITHOUT ANY WARRANTY; without even the implied warranty of 

15# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

16# GNU General Public License for more details. 

17# 

18# You should have received a copy of the LSST License Statement and 

19# the GNU General Public License along with this program. If not, 

20# see <http://www.lsstcorp.org/LegalNotices/>. 

21# 

22import numpy as np 

23import lsst.sphgeom 

24import lsst.utils as utils 

25import lsst.pex.config as pexConfig 

26import lsst.pex.exceptions as pexExceptions 

27import lsst.geom as geom 

28import lsst.pipe.base as pipeBase 

29from lsst.skymap import BaseSkyMap 

30from lsst.daf.base import DateTime 

31from lsst.utils.timer import timeMethod 

32 

33__all__ = ["BaseSelectImagesTask", "BaseExposureInfo", "WcsSelectImagesTask", "PsfWcsSelectImagesTask", 

34 "DatabaseSelectImagesConfig", "BestSeeingWcsSelectImagesTask", "BestSeeingSelectVisitsTask", 

35 "BestSeeingQuantileSelectVisitsTask"] 

36 

37 

38class DatabaseSelectImagesConfig(pexConfig.Config): 

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

40 host = pexConfig.Field( 

41 doc="Database server host name", 

42 dtype=str, 

43 ) 

44 port = pexConfig.Field( 

45 doc="Database server port", 

46 dtype=int, 

47 ) 

48 database = pexConfig.Field( 

49 doc="Name of database", 

50 dtype=str, 

51 ) 

52 maxExposures = pexConfig.Field( 

53 doc="maximum exposures to select; intended for debugging; ignored if None", 

54 dtype=int, 

55 optional=True, 

56 ) 

57 

58 

59class BaseExposureInfo(pipeBase.Struct): 

60 """Data about a selected exposure 

61 """ 

62 

63 def __init__(self, dataId, coordList): 

64 """Create exposure information that can be used to generate data references 

65 

66 The object has the following fields: 

67 - dataId: data ID of exposure (a dict) 

68 - coordList: ICRS coordinates of the corners of the exposure (list of lsst.geom.SpherePoint) 

69 plus any others items that are desired 

70 """ 

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

72 

73 

74class BaseSelectImagesTask(pipeBase.Task): 

75 """Base task for selecting images suitable for coaddition 

76 """ 

77 ConfigClass = pexConfig.Config 

78 _DefaultName = "selectImages" 

79 

80 @timeMethod 

81 def run(self, coordList): 

82 """Select images suitable for coaddition in a particular region 

83 

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 

86 

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 

91 - coordList: ICRS coordinates of the corners of the exposure (list of lsst.geom.SpherePoint) 

92 """ 

93 raise NotImplementedError() 

94 

95 def _runArgDictFromDataId(self, dataId): 

96 """Extract keyword arguments for run (other than coordList) from a data ID 

97 

98 @return keyword arguments for run (other than coordList), as a dict 

99 """ 

100 raise NotImplementedError() 

101 

102 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]): 

103 """Run based on a data reference 

104 

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. 

109 

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) 

117 """ 

118 runArgDict = self._runArgDictFromDataId(dataRef.dataId) 

119 exposureInfoList = self.run(coordList, **runArgDict).exposureInfoList 

120 

121 if len(selectDataList) > 0 and len(exposureInfoList) > 0: 

122 # Restrict the exposure selection further 

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) 

130 else: 

131 self.log.info("De-selecting exposure %s: not in selectDataList", info.dataId) 

132 exposureInfoList = newExposureInfoList 

133 

134 if makeDataRefList: 

135 butler = dataRef.butlerSubset.butler 

136 dataRefList = [butler.dataRef(datasetType="calexp", 

137 dataId=expInfo.dataId, 

138 ) for expInfo in exposureInfoList] 

139 else: 

140 dataRefList = None 

141 

142 return pipeBase.Struct( 

143 dataRefList=dataRefList, 

144 exposureInfoList=exposureInfoList, 

145 ) 

146 

147 

148def _extractKeyValue(dataList, keys=None): 

149 """Extract the keys and values from a list of dataIds 

150 

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 

153 list of ExposureInfo 

154 """ 

155 assert len(dataList) > 0 

156 if keys is None: 

157 keys = sorted(dataList[0].dataId.keys()) 

158 keySet = set(keys) 

159 values = list() 

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)) 

165 return keys, values 

166 

167 

168class SelectStruct(pipeBase.Struct): 

169 """A container for data to be passed to the WcsSelectImagesTask""" 

170 

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

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

173 

174 

175class WcsSelectImagesTask(BaseSelectImagesTask): 

176 """Select images using their Wcs 

177 

178 We use the "convexHull" method of lsst.sphgeom.ConvexPolygon to define 

179 polygons on the celestial sphere, and test the polygon of the 

180 patch for overlap with the polygon of the image. 

181 

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. 

185 """ 

186 

187 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]): 

188 """Select images in the selectDataList that overlap the patch 

189 

190 This method is the old entry point for the Gen2 commandline tasks and drivers 

191 Will be deprecated in v22. 

192 

193 @param dataRef: Data reference for coadd/tempExp (with tract, patch) 

194 @param coordList: List of ICRS coordinates (lsst.geom.SpherePoint) specifying boundary of patch 

195 @param makeDataRefList: Construct a list of data references? 

196 @param selectDataList: List of SelectStruct, to consider for selection 

197 """ 

198 dataRefList = [] 

199 exposureInfoList = [] 

200 

201 patchVertices = [coord.getVector() for coord in coordList] 

202 patchPoly = lsst.sphgeom.ConvexPolygon.convexHull(patchVertices) 

203 

204 for data in selectDataList: 

205 dataRef = data.dataRef 

206 imageWcs = data.wcs 

207 imageBox = data.bbox 

208 

209 imageCorners = self.getValidImageCorners(imageWcs, imageBox, patchPoly, dataId=None) 

210 if imageCorners: 

211 dataRefList.append(dataRef) 

212 exposureInfoList.append(BaseExposureInfo(dataRef.dataId, imageCorners)) 

213 

214 return pipeBase.Struct( 

215 dataRefList=dataRefList if makeDataRefList else None, 

216 exposureInfoList=exposureInfoList, 

217 ) 

218 

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

220 """Return indices of provided lists that meet the selection criteria 

221 

222 Parameters: 

223 ----------- 

224 wcsList : `list` of `lsst.afw.geom.SkyWcs` 

225 specifying the WCS's of the input ccds to be selected 

226 bboxList : `list` of `lsst.geom.Box2I` 

227 specifying the bounding boxes of the input ccds to be selected 

228 coordList : `list` of `lsst.geom.SpherePoint` 

229 ICRS coordinates specifying boundary of the patch. 

230 

231 Returns: 

232 -------- 

233 result: `list` of `int` 

234 of indices of selected ccds 

235 """ 

236 if dataIds is None: 

237 dataIds = [None] * len(wcsList) 

238 patchVertices = [coord.getVector() for coord in coordList] 

239 patchPoly = lsst.sphgeom.ConvexPolygon.convexHull(patchVertices) 

240 result = [] 

241 for i, (imageWcs, imageBox, dataId) in enumerate(zip(wcsList, bboxList, dataIds)): 

242 imageCorners = self.getValidImageCorners(imageWcs, imageBox, patchPoly, dataId) 

243 if imageCorners: 

244 result.append(i) 

245 return result 

246 

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

248 "Return corners or None if bad" 

249 try: 

250 imageCorners = [imageWcs.pixelToSky(pix) for pix in geom.Box2D(imageBox).getCorners()] 

251 except (pexExceptions.DomainError, pexExceptions.RuntimeError) as e: 

252 # Protecting ourselves from awful Wcs solutions in input images 

253 self.log.debug("WCS error in testing calexp %s (%s): deselecting", dataId, e) 

254 return 

255 

256 imagePoly = lsst.sphgeom.ConvexPolygon.convexHull([coord.getVector() for coord in imageCorners]) 

257 if imagePoly is None: 

258 self.log.debug("Unable to create polygon from image %s: deselecting", dataId) 

259 return 

260 

261 if patchPoly.intersects(imagePoly): 

262 # "intersects" also covers "contains" or "is contained by" 

263 self.log.info("Selecting calexp %s", dataId) 

264 return imageCorners 

265 

266 

267def sigmaMad(array): 

268 "Return median absolute deviation scaled to normally distributed data" 

269 return 1.4826*np.median(np.abs(array - np.median(array))) 

270 

271 

272class PsfWcsSelectImagesConnections(pipeBase.PipelineTaskConnections, 

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

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

275 pass 

276 

277 

278class PsfWcsSelectImagesConfig(pipeBase.PipelineTaskConfig, 

279 pipelineConnections=PsfWcsSelectImagesConnections): 

280 maxEllipResidual = pexConfig.Field( 

281 doc="Maximum median ellipticity residual", 

282 dtype=float, 

283 default=0.007, 

284 optional=True, 

285 ) 

286 maxSizeScatter = pexConfig.Field( 

287 doc="Maximum scatter in the size residuals", 

288 dtype=float, 

289 optional=True, 

290 ) 

291 maxScaledSizeScatter = pexConfig.Field( 

292 doc="Maximum scatter in the size residuals, scaled by the median size", 

293 dtype=float, 

294 default=0.009, 

295 optional=True, 

296 ) 

297 starSelection = pexConfig.Field( 

298 doc="select star with this field", 

299 dtype=str, 

300 default='calib_psf_used', 

301 deprecated=('This field has been moved to ComputeExposureSummaryStatsTask and ' 

302 'will be removed after v24.') 

303 ) 

304 starShape = pexConfig.Field( 

305 doc="name of star shape", 

306 dtype=str, 

307 default='base_SdssShape', 

308 deprecated=('This field has been moved to ComputeExposureSummaryStatsTask and ' 

309 'will be removed after v24.') 

310 ) 

311 psfShape = pexConfig.Field( 

312 doc="name of psf shape", 

313 dtype=str, 

314 default='base_SdssShape_psf', 

315 deprecated=('This field has been moved to ComputeExposureSummaryStatsTask and ' 

316 'will be removed after v24.') 

317 ) 

318 doLegacyStarSelectionComputation = pexConfig.Field( 

319 doc="Perform the legacy star selection computations (for backwards compatibility)", 

320 dtype=bool, 

321 default=False, 

322 deprecated=("This field is here for backwards compatibility and will be " 

323 "removed after v24.") 

324 ) 

325 

326 

327class PsfWcsSelectImagesTask(WcsSelectImagesTask): 

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

329 

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. 

332 

333 The criteria are: 

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 

337 the median size 

338 """ 

339 

340 ConfigClass = PsfWcsSelectImagesConfig 

341 _DefaultName = "PsfWcsSelectImages" 

342 

343 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]): 

344 """Select images in the selectDataList that overlap the patch and satisfy PSF quality critera. 

345 

346 This method is the old entry point for the Gen2 commandline tasks and drivers 

347 Will be deprecated in v22. 

348 

349 @param dataRef: Data reference for coadd/tempExp (with tract, patch) 

350 @param coordList: List of ICRS coordinates (lsst.geom.SpherePoint) specifying boundary of patch 

351 @param makeDataRefList: Construct a list of data references? 

352 @param selectDataList: List of SelectStruct, to consider for selection 

353 """ 

354 result = super(PsfWcsSelectImagesTask, self).runDataRef(dataRef, coordList, makeDataRefList, 

355 selectDataList) 

356 

357 dataRefList = [] 

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) 

363 if valid is False: 

364 continue 

365 

366 dataRefList.append(dataRef) 

367 exposureInfoList.append(exposureInfo) 

368 

369 return pipeBase.Struct( 

370 dataRefList=dataRefList, 

371 exposureInfoList=exposureInfoList, 

372 ) 

373 

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

375 """Return indices of provided lists that meet the selection criteria 

376 

377 Parameters: 

378 ----------- 

379 wcsList : `list` of `lsst.afw.geom.SkyWcs` 

380 specifying the WCS's of the input ccds to be selected 

381 bboxList : `list` of `lsst.geom.Box2I` 

382 specifying the bounding boxes of the input ccds to be selected 

383 coordList : `list` of `lsst.geom.SpherePoint` 

384 ICRS coordinates specifying boundary of the patch. 

385 visitSummary : `list` of `lsst.afw.table.ExposureCatalog` 

386 containing the PSF shape information for the input ccds to be selected. 

387 srcList : `list` of `lsst.afw.table.SourceCatalog`, optional 

388 containing the PSF shape information for the input ccds to be selected. 

389 This is only used if ``config.doLegacyStarSelectionComputation`` is 

390 True. 

391 

392 Returns: 

393 -------- 

394 goodPsf: `list` of `int` 

395 of indices of selected ccds 

396 """ 

397 goodWcs = super(PsfWcsSelectImagesTask, self).run(wcsList=wcsList, bboxList=bboxList, 

398 coordList=coordList, dataIds=dataIds) 

399 

400 goodPsf = [] 

401 

402 if not self.config.doLegacyStarSelectionComputation: 

403 # Check for old inputs, and give a helpful error message if so. 

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.") 

408 

409 for i, dataId in enumerate(dataIds): 

410 if i not in goodWcs: 

411 continue 

412 if self.isValid(visitSummary, dataId["detector"]): 

413 goodPsf.append(i) 

414 else: 

415 if dataIds is None: 

416 dataIds = [None] * len(srcList) 

417 for i, (srcCatalog, dataId) in enumerate(zip(srcList, dataIds)): 

418 if i not in goodWcs: 

419 continue 

420 if self.isValidLegacy(srcCatalog, dataId): 

421 goodPsf.append(i) 

422 

423 return goodPsf 

424 

425 def isValid(self, visitSummary, detectorId): 

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

427 

428 Parameters 

429 ---------- 

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

431 detectorId : `int` 

432 Detector identifier. 

433 

434 Returns 

435 ------- 

436 valid : `bool` 

437 True if selected. 

438 """ 

439 row = visitSummary.find(detectorId) 

440 if row is None: 

441 # This is not listed, so it must be bad. 

442 self.log.warning("Removing detector %d because summary stats not available.", detectorId) 

443 return False 

444 

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

446 scatterSize = row["psfStarDeltaSizeScatter"] 

447 scaledScatterSize = row["psfStarScaledDeltaSizeScatter"] 

448 

449 valid = True 

450 if self.config.maxEllipResidual and medianE > self.config.maxEllipResidual: 

451 self.log.info("Removing visit %d detector %d because median e residual too large: %f vs %f", 

452 row["visit"], detectorId, medianE, self.config.maxEllipResidual) 

453 valid = False 

454 elif self.config.maxSizeScatter and scatterSize > self.config.maxSizeScatter: 

455 self.log.info("Removing visit %d detector %d because size scatter too large: %f vs %f", 

456 row["visit"], detectorId, scatterSize, self.config.maxSizeScatter) 

457 valid = False 

458 elif self.config.maxScaledSizeScatter and scaledScatterSize > self.config.maxScaledSizeScatter: 

459 self.log.info("Removing visit %d detector %d because scaled size scatter too large: %f vs %f", 

460 row["visit"], detectorId, scaledScatterSize, self.config.maxScaledSizeScatter) 

461 valid = False 

462 

463 return valid 

464 

465 def isValidLegacy(self, srcCatalog, dataId=None): 

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

467 

468 This routine is only used in legacy processing (gen2 and 

469 backwards compatible old calexps) and should be removed after v24. 

470 

471 Parameters 

472 ---------- 

473 srcCatalog : `lsst.afw.table.SourceCatalog` 

474 dataId : `dict` of dataId keys, optional. 

475 Used only for logging. Defaults to None. 

476 

477 Returns 

478 ------- 

479 valid : `bool` 

480 True if selected. 

481 """ 

482 mask = srcCatalog[self.config.starSelection] 

483 

484 starXX = srcCatalog[self.config.starShape+'_xx'][mask] 

485 starYY = srcCatalog[self.config.starShape+'_yy'][mask] 

486 starXY = srcCatalog[self.config.starShape+'_xy'][mask] 

487 psfXX = srcCatalog[self.config.psfShape+'_xx'][mask] 

488 psfYY = srcCatalog[self.config.psfShape+'_yy'][mask] 

489 psfXY = srcCatalog[self.config.psfShape+'_xy'][mask] 

490 

491 starSize = np.power(starXX*starYY - starXY**2, 0.25) 

492 starE1 = (starXX - starYY)/(starXX + starYY) 

493 starE2 = 2*starXY/(starXX + starYY) 

494 medianSize = np.median(starSize) 

495 

496 psfSize = np.power(psfXX*psfYY - psfXY**2, 0.25) 

497 psfE1 = (psfXX - psfYY)/(psfXX + psfYY) 

498 psfE2 = 2*psfXY/(psfXX + psfYY) 

499 

500 medianE1 = np.abs(np.median(starE1 - psfE1)) 

501 medianE2 = np.abs(np.median(starE2 - psfE2)) 

502 medianE = np.sqrt(medianE1**2 + medianE2**2) 

503 

504 scatterSize = sigmaMad(starSize - psfSize) 

505 scaledScatterSize = scatterSize/medianSize**2 

506 

507 valid = True 

508 if self.config.maxEllipResidual and medianE > self.config.maxEllipResidual: 

509 self.log.info("Removing visit %s because median e residual too large: %f vs %f", 

510 dataId, medianE, self.config.maxEllipResidual) 

511 valid = False 

512 elif self.config.maxSizeScatter and scatterSize > self.config.maxSizeScatter: 

513 self.log.info("Removing visit %s because size scatter is too large: %f vs %f", 

514 dataId, scatterSize, self.config.maxSizeScatter) 

515 valid = False 

516 elif self.config.maxScaledSizeScatter and scaledScatterSize > self.config.maxScaledSizeScatter: 

517 self.log.info("Removing visit %s because scaled size scatter is too large: %f vs %f", 

518 dataId, scaledScatterSize, self.config.maxScaledSizeScatter) 

519 valid = False 

520 

521 return valid 

522 

523 

524class BestSeeingWcsSelectImageConfig(WcsSelectImagesTask.ConfigClass): 

525 """Base configuration for BestSeeingSelectImagesTask. 

526 """ 

527 nImagesMax = pexConfig.RangeField( 

528 dtype=int, 

529 doc="Maximum number of images to select", 

530 default=5, 

531 min=0) 

532 maxPsfFwhm = pexConfig.Field( 

533 dtype=float, 

534 doc="Maximum PSF FWHM (in arcseconds) to select", 

535 default=1.5, 

536 optional=True) 

537 minPsfFwhm = pexConfig.Field( 

538 dtype=float, 

539 doc="Minimum PSF FWHM (in arcseconds) to select", 

540 default=0., 

541 optional=True) 

542 

543 

544class BestSeeingWcsSelectImagesTask(WcsSelectImagesTask): 

545 """Select up to a maximum number of the best-seeing images using their Wcs. 

546 """ 

547 ConfigClass = BestSeeingWcsSelectImageConfig 

548 

549 def runDataRef(self, dataRef, coordList, makeDataRefList=True, 

550 selectDataList=None): 

551 """Select the best-seeing images in the selectDataList that overlap the patch. 

552 

553 This method is the old entry point for the Gen2 commandline tasks and drivers 

554 Will be deprecated in v22. 

555 

556 Parameters 

557 ---------- 

558 dataRef : `lsst.daf.persistence.ButlerDataRef` 

559 Data reference for coadd/tempExp (with tract, patch) 

560 coordList : `list` of `lsst.geom.SpherePoint` 

561 List of ICRS sky coordinates specifying boundary of patch 

562 makeDataRefList : `boolean`, optional 

563 Construct a list of data references? 

564 selectDataList : `list` of `SelectStruct` 

565 List of SelectStruct, to consider for selection 

566 

567 Returns 

568 ------- 

569 result : `lsst.pipe.base.Struct` 

570 Result struct with components: 

571 - ``exposureList``: the selected exposures 

572 (`list` of `lsst.pipe.tasks.selectImages.BaseExposureInfo`). 

573 - ``dataRefList``: the optional data references corresponding to 

574 each element of ``exposureList`` 

575 (`list` of `lsst.daf.persistence.ButlerDataRef`, or `None`). 

576 """ 

577 psfSizes = [] 

578 dataRefList = [] 

579 exposureInfoList = [] 

580 

581 if selectDataList is None: 

582 selectDataList = [] 

583 

584 result = super().runDataRef(dataRef, coordList, makeDataRefList=True, selectDataList=selectDataList) 

585 

586 for dataRef, exposureInfo in zip(result.dataRefList, result.exposureInfoList): 

587 cal = dataRef.get("calexp", immediate=True) 

588 

589 # if min/max PSF values are defined, remove images out of bounds 

590 pixToArcseconds = cal.getWcs().getPixelScale().asArcseconds() 

591 # Just need a rough estimate; average positions are fine 

592 psfAvgPos = cal.getPsf().getAveragePosition() 

593 psfSize = cal.getPsf().computeShape(psfAvgPos).getDeterminantRadius()*pixToArcseconds 

594 sizeFwhm = psfSize * np.sqrt(8.*np.log(2.)) 

595 if self.config.maxPsfFwhm and sizeFwhm > self.config.maxPsfFwhm: 

596 continue 

597 if self.config.minPsfFwhm and sizeFwhm < self.config.minPsfFwhm: 

598 continue 

599 psfSizes.append(sizeFwhm) 

600 dataRefList.append(dataRef) 

601 exposureInfoList.append(exposureInfo) 

602 

603 if len(psfSizes) > self.config.nImagesMax: 

604 sortedIndices = np.argsort(psfSizes)[:self.config.nImagesMax] 

605 filteredDataRefList = [dataRefList[i] for i in sortedIndices] 

606 filteredExposureInfoList = [exposureInfoList[i] for i in sortedIndices] 

607 self.log.info("%d images selected with FWHM range of %f--%f arcseconds", 

608 len(sortedIndices), psfSizes[sortedIndices[0]], psfSizes[sortedIndices[-1]]) 

609 

610 else: 

611 if len(psfSizes) == 0: 

612 self.log.warning("0 images selected.") 

613 else: 

614 self.log.debug("%d images selected with FWHM range of %d--%d arcseconds", 

615 len(psfSizes), psfSizes[0], psfSizes[-1]) 

616 filteredDataRefList = dataRefList 

617 filteredExposureInfoList = exposureInfoList 

618 

619 return pipeBase.Struct( 

620 dataRefList=filteredDataRefList if makeDataRefList else None, 

621 exposureInfoList=filteredExposureInfoList, 

622 ) 

623 

624 

625class BestSeeingSelectVisitsConnections(pipeBase.PipelineTaskConnections, 

626 dimensions=("tract", "patch", "skymap", "band", "instrument"), 

627 defaultTemplates={"coaddName": "goodSeeing"}): 

628 skyMap = pipeBase.connectionTypes.Input( 

629 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures", 

630 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME, 

631 storageClass="SkyMap", 

632 dimensions=("skymap",), 

633 ) 

634 visitSummaries = pipeBase.connectionTypes.Input( 

635 doc="Per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask", 

636 name="visitSummary", 

637 storageClass="ExposureCatalog", 

638 dimensions=("instrument", "visit",), 

639 multiple=True, 

640 deferLoad=True 

641 ) 

642 goodVisits = pipeBase.connectionTypes.Output( 

643 doc="Selected visits to be coadded.", 

644 name="{coaddName}Visits", 

645 storageClass="StructuredDataDict", 

646 dimensions=("instrument", "tract", "patch", "skymap", "band"), 

647 ) 

648 

649 

650class BestSeeingSelectVisitsConfig(pipeBase.PipelineTaskConfig, 

651 pipelineConnections=BestSeeingSelectVisitsConnections): 

652 nVisitsMax = pexConfig.RangeField( 

653 dtype=int, 

654 doc="Maximum number of visits to select", 

655 default=12, 

656 min=0 

657 ) 

658 maxPsfFwhm = pexConfig.Field( 

659 dtype=float, 

660 doc="Maximum PSF FWHM (in arcseconds) to select", 

661 default=1.5, 

662 optional=True 

663 ) 

664 minPsfFwhm = pexConfig.Field( 

665 dtype=float, 

666 doc="Minimum PSF FWHM (in arcseconds) to select", 

667 default=0., 

668 optional=True 

669 ) 

670 doConfirmOverlap = pexConfig.Field( 

671 dtype=bool, 

672 doc="Do remove visits that do not actually overlap the patch?", 

673 default=True, 

674 ) 

675 minMJD = pexConfig.Field( 

676 dtype=float, 

677 doc="Minimum visit MJD to select", 

678 default=None, 

679 optional=True 

680 ) 

681 maxMJD = pexConfig.Field( 

682 dtype=float, 

683 doc="Maximum visit MJD to select", 

684 default=None, 

685 optional=True 

686 ) 

687 

688 

689class BestSeeingSelectVisitsTask(pipeBase.PipelineTask): 

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

691 

692 Don't exceed the FWHM range specified by configs min(max)PsfFwhm. 

693 This Task is a port of the Gen2 image-selector used in the AP pipeline: 

694 BestSeeingSelectImagesTask. This Task selects full visits based on the 

695 average PSF of the entire visit. 

696 """ 

697 ConfigClass = BestSeeingSelectVisitsConfig 

698 _DefaultName = 'bestSeeingSelectVisits' 

699 

700 def runQuantum(self, butlerQC, inputRefs, outputRefs): 

701 inputs = butlerQC.get(inputRefs) 

702 quantumDataId = butlerQC.quantum.dataId 

703 outputs = self.run(**inputs, dataId=quantumDataId) 

704 butlerQC.put(outputs, outputRefs) 

705 

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

707 """Run task 

708 

709 Parameters: 

710 ----------- 

711 visitSummary : `list` 

712 List of `lsst.pipe.base.connections.DeferredDatasetRef` of 

713 visitSummary tables of type `lsst.afw.table.ExposureCatalog` 

714 skyMap : `lsst.skyMap.SkyMap` 

715 SkyMap for checking visits overlap patch 

716 dataId : `dict` of dataId keys 

717 For retrieving patch info for checking visits overlap patch 

718 

719 Returns 

720 ------- 

721 result : `lsst.pipe.base.Struct` 

722 Result struct with components: 

723 

724 - `goodVisits`: `dict` with selected visit ids as keys, 

725 so that it can be be saved as a StructuredDataDict. 

726 StructuredDataList's are currently limited. 

727 """ 

728 

729 if self.config.doConfirmOverlap: 

730 patchPolygon = self.makePatchPolygon(skyMap, dataId) 

731 

732 inputVisits = [visitSummary.ref.dataId['visit'] for visitSummary in visitSummaries] 

733 fwhmSizes = [] 

734 visits = [] 

735 for visit, visitSummary in zip(inputVisits, visitSummaries): 

736 # read in one-by-one and only once. There may be hundreds 

737 visitSummary = visitSummary.get() 

738 

739 # mjd is guaranteed to be the same for every detector in the visitSummary. 

740 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD) 

741 

742 pixToArcseconds = [vs.getWcs().getPixelScale(vs.getBBox().getCenter()).asArcseconds() 

743 for vs in visitSummary] 

744 # psfSigma is PSF model determinant radius at chip center in pixels 

745 psfSigmas = np.array([vs['psfSigma'] for vs in visitSummary]) 

746 fwhm = np.nanmean(psfSigmas * pixToArcseconds) * np.sqrt(8.*np.log(2.)) 

747 

748 if self.config.maxPsfFwhm and fwhm > self.config.maxPsfFwhm: 

749 continue 

750 if self.config.minPsfFwhm and fwhm < self.config.minPsfFwhm: 

751 continue 

752 if self.config.minMJD and mjd < self.config.minMJD: 

753 self.log.debug('MJD %f earlier than %.2f; rejecting', mjd, self.config.minMJD) 

754 continue 

755 if self.config.maxMJD and mjd > self.config.maxMJD: 

756 self.log.debug('MJD %f later than %.2f; rejecting', mjd, self.config.maxMJD) 

757 continue 

758 if self.config.doConfirmOverlap and not self.doesIntersectPolygon(visitSummary, patchPolygon): 

759 continue 

760 

761 fwhmSizes.append(fwhm) 

762 visits.append(visit) 

763 

764 sortedVisits = [ind for (_, ind) in sorted(zip(fwhmSizes, visits))] 

765 output = sortedVisits[:self.config.nVisitsMax] 

766 self.log.info("%d images selected with FWHM range of %d--%d arcseconds", 

767 len(output), fwhmSizes[visits.index(output[0])], fwhmSizes[visits.index(output[-1])]) 

768 

769 # In order to store as a StructuredDataDict, convert list to dict 

770 goodVisits = {key: True for key in output} 

771 return pipeBase.Struct(goodVisits=goodVisits) 

772 

773 def makePatchPolygon(self, skyMap, dataId): 

774 """Return True if sky polygon overlaps visit 

775 

776 Parameters: 

777 ----------- 

778 skyMap : `lsst.afw.table.ExposureCatalog` 

779 Exposure catalog with per-detector geometry 

780 dataId : `dict` of dataId keys 

781 For retrieving patch info 

782 

783 Returns: 

784 -------- 

785 result :` lsst.sphgeom.ConvexPolygon.convexHull` 

786 Polygon of patch's outer bbox 

787 """ 

788 wcs = skyMap[dataId['tract']].getWcs() 

789 bbox = skyMap[dataId['tract']][dataId['patch']].getOuterBBox() 

790 sphCorners = wcs.pixelToSky(lsst.geom.Box2D(bbox).getCorners()) 

791 result = lsst.sphgeom.ConvexPolygon.convexHull([coord.getVector() for coord in sphCorners]) 

792 return result 

793 

794 def doesIntersectPolygon(self, visitSummary, polygon): 

795 """Return True if sky polygon overlaps visit 

796 

797 Parameters: 

798 ----------- 

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

800 Exposure catalog with per-detector geometry 

801 polygon :` lsst.sphgeom.ConvexPolygon.convexHull` 

802 Polygon to check overlap 

803 

804 Returns: 

805 -------- 

806 doesIntersect: `bool` 

807 Does the visit overlap the polygon 

808 """ 

809 doesIntersect = False 

810 for detectorSummary in visitSummary: 

811 corners = [lsst.geom.SpherePoint(ra, decl, units=lsst.geom.degrees).getVector() for (ra, decl) in 

812 zip(detectorSummary['raCorners'], detectorSummary['decCorners'])] 

813 detectorPolygon = lsst.sphgeom.ConvexPolygon.convexHull(corners) 

814 if detectorPolygon.intersects(polygon): 

815 doesIntersect = True 

816 break 

817 return doesIntersect 

818 

819 

820class BestSeeingQuantileSelectVisitsConfig(pipeBase.PipelineTaskConfig, 

821 pipelineConnections=BestSeeingSelectVisitsConnections): 

822 qMin = pexConfig.RangeField( 

823 doc="Lower bound of quantile range to select. Sorts visits by seeing from narrow to wide, " 

824 "and select those in the interquantile range (qMin, qMax). Set qMin to 0 for Best Seeing. " 

825 "This config should be changed from zero only for exploratory diffIm testing.", 

826 dtype=float, 

827 default=0, 

828 min=0, 

829 max=1, 

830 ) 

831 qMax = pexConfig.RangeField( 

832 doc="Upper bound of quantile range to select. Sorts visits by seeing from narrow to wide, " 

833 "and select those in the interquantile range (qMin, qMax). Set qMax to 1 for Worst Seeing.", 

834 dtype=float, 

835 default=0.33, 

836 min=0, 

837 max=1, 

838 ) 

839 nVisitsMin = pexConfig.Field( 

840 doc="At least this number of visits selected and supercedes quantile. For example, if 10 visits " 

841 "cover this patch, qMin=0.33, and nVisitsMin=5, the best 5 visits will be selected.", 

842 dtype=int, 

843 default=6, 

844 ) 

845 doConfirmOverlap = pexConfig.Field( 

846 dtype=bool, 

847 doc="Do remove visits that do not actually overlap the patch?", 

848 default=True, 

849 ) 

850 minMJD = pexConfig.Field( 

851 dtype=float, 

852 doc="Minimum visit MJD to select", 

853 default=None, 

854 optional=True 

855 ) 

856 maxMJD = pexConfig.Field( 

857 dtype=float, 

858 doc="Maximum visit MJD to select", 

859 default=None, 

860 optional=True 

861 ) 

862 

863 

864class BestSeeingQuantileSelectVisitsTask(BestSeeingSelectVisitsTask): 

865 """Select a quantile of the best-seeing visits 

866 

867 Selects the best (for example, third) full visits based on the average 

868 PSF width in the entire visit. It can also be used for difference imaging 

869 experiments that require templates with the worst seeing visits. 

870 For example, selecting the worst third can be acheived by 

871 changing the config parameters qMin to 0.66 and qMax to 1. 

872 """ 

873 ConfigClass = BestSeeingQuantileSelectVisitsConfig 

874 _DefaultName = 'bestSeeingQuantileSelectVisits' 

875 

876 @utils.inheritDoc(BestSeeingSelectVisitsTask) 

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

878 if self.config.doConfirmOverlap: 

879 patchPolygon = self.makePatchPolygon(skyMap, dataId) 

880 visits = np.array([visitSummary.ref.dataId['visit'] for visitSummary in visitSummaries]) 

881 radius = np.empty(len(visits)) 

882 intersects = np.full(len(visits), True) 

883 for i, visitSummary in enumerate(visitSummaries): 

884 # read in one-by-one and only once. There may be hundreds 

885 visitSummary = visitSummary.get() 

886 # psfSigma is PSF model determinant radius at chip center in pixels 

887 psfSigma = np.nanmedian([vs['psfSigma'] for vs in visitSummary]) 

888 radius[i] = psfSigma 

889 if self.config.doConfirmOverlap: 

890 intersects[i] = self.doesIntersectPolygon(visitSummary, patchPolygon) 

891 if self.config.minMJD or self.config.maxMJD: 

892 # mjd is guaranteed to be the same for every detector in the visitSummary. 

893 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD) 

894 aboveMin = mjd > self.config.minMJD if self.config.minMJD else True 

895 belowMax = mjd < self.config.maxMJD if self.config.maxMJD else True 

896 intersects[i] = intersects[i] and aboveMin and belowMax 

897 

898 sortedVisits = [v for rad, v in sorted(zip(radius[intersects], visits[intersects]))] 

899 lowerBound = min(int(np.round(self.config.qMin*len(visits[intersects]))), 

900 max(0, len(visits[intersects]) - self.config.nVisitsMin)) 

901 upperBound = max(int(np.round(self.config.qMax*len(visits[intersects]))), self.config.nVisitsMin) 

902 

903 # In order to store as a StructuredDataDict, convert list to dict 

904 goodVisits = {int(visit): True for visit in sortedVisits[lowerBound:upperBound]} 

905 return pipeBase.Struct(goodVisits=goodVisits)