lsst.pipe.tasks g3d3fc359a7+8335dcbd4d
Loading...
Searching...
No Matches
selectImages.py
Go to the documentation of this file.
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", "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
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 maxPsfTraceRadiusDelta = pexConfig.Field(
326 doc="Maximum delta (max - min) of model PSF trace radius values evaluated on a grid on "
327 "the unmasked detector pixels (pixel).",
328 dtype=float,
329 default=0.7,
330 optional=True,
331 )
332
333
334class PsfWcsSelectImagesTask(WcsSelectImagesTask):
335 """Select images using their Wcs and cuts on the PSF properties
336
337 The PSF quality criteria are based on the size and ellipticity residuals from the
338 adaptive second moments of the star and the PSF.
339
340 The criteria are:
341 - the median of the ellipticty residuals
342 - the robust scatter of the size residuals (using the median absolute deviation)
343 - the robust scatter of the size residuals scaled by the square of
344 the median size
345 """
346
347 ConfigClass = PsfWcsSelectImagesConfig
348 _DefaultName = "PsfWcsSelectImages"
349
350 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]):
351 """Select images in the selectDataList that overlap the patch and satisfy PSF quality critera.
352
353 This method is the old entry point for the Gen2 commandline tasks and drivers
354 Will be deprecated in v22.
355
356 @param dataRef: Data reference for coadd/tempExp (with tract, patch)
357 @param coordList: List of ICRS coordinates (lsst.geom.SpherePoint) specifying boundary of patch
358 @param makeDataRefList: Construct a list of data references?
359 @param selectDataList: List of SelectStruct, to consider for selection
360 """
361 result = super(PsfWcsSelectImagesTask, self).runDataRef(dataRef, coordList, makeDataRefList,
362 selectDataList)
363
364 dataRefList = []
365 exposureInfoList = []
366 for dataRef, exposureInfo in zip(result.dataRefList, result.exposureInfoList):
367 butler = dataRef.butlerSubset.butler
368 srcCatalog = butler.get('src', dataRef.dataId)
369 valid = self.isValidLegacy(srcCatalog, dataRef.dataId)
370 if valid is False:
371 continue
372
373 dataRefList.append(dataRef)
374 exposureInfoList.append(exposureInfo)
375
376 return pipeBase.Struct(
377 dataRefList=dataRefList,
378 exposureInfoList=exposureInfoList,
379 )
380
381 def run(self, wcsList, bboxList, coordList, visitSummary, dataIds=None, srcList=None, **kwargs):
382 """Return indices of provided lists that meet the selection criteria
383
384 Parameters:
385 -----------
386 wcsList : `list` of `lsst.afw.geom.SkyWcs`
387 specifying the WCS's of the input ccds to be selected
388 bboxList : `list` of `lsst.geom.Box2I`
389 specifying the bounding boxes of the input ccds to be selected
390 coordList : `list` of `lsst.geom.SpherePoint`
391 ICRS coordinates specifying boundary of the patch.
392 visitSummary : `list` of `lsst.afw.table.ExposureCatalog`
393 containing the PSF shape information for the input ccds to be selected.
394 srcList : `list` of `lsst.afw.table.SourceCatalog`, optional
395 containing the PSF shape information for the input ccds to be selected.
396 This is only used if ``config.doLegacyStarSelectionComputation`` is
397 True.
398
399 Returns:
400 --------
401 goodPsf: `list` of `int`
402 of indices of selected ccds
403 """
404 goodWcs = super(PsfWcsSelectImagesTask, self).run(wcsList=wcsList, bboxList=bboxList,
405 coordList=coordList, dataIds=dataIds)
406
407 goodPsf = []
408
409 if not self.config.doLegacyStarSelectionComputation:
410 # Check for old inputs, and give a helpful error message if so.
411 if 'nPsfStar' not in visitSummary[0].schema.getNames():
412 raise RuntimeError("Old calexps detected. "
413 "Please set config.doLegacyStarSelectionComputation=True for "
414 "backwards compatibility.")
415
416 for i, dataId in enumerate(dataIds):
417 if i not in goodWcs:
418 continue
419 if self.isValid(visitSummary, dataId["detector"]):
420 goodPsf.append(i)
421 else:
422 if dataIds is None:
423 dataIds = [None] * len(srcList)
424 for i, (srcCatalog, dataId) in enumerate(zip(srcList, dataIds)):
425 if i not in goodWcs:
426 continue
427 if self.isValidLegacy(srcCatalog, dataId):
428 goodPsf.append(i)
429
430 return goodPsf
431
432 def isValid(self, visitSummary, detectorId):
433 """Should this ccd be selected based on its PSF shape information.
434
435 Parameters
436 ----------
437 visitSummary : `lsst.afw.table.ExposureCatalog`
438 detectorId : `int`
439 Detector identifier.
440
441 Returns
442 -------
443 valid : `bool`
444 True if selected.
445 """
446 row = visitSummary.find(detectorId)
447 if row is None:
448 # This is not listed, so it must be bad.
449 self.log.warning("Removing detector %d because summary stats not available.", detectorId)
450 return False
451
452 medianE = np.sqrt(row["psfStarDeltaE1Median"]**2. + row["psfStarDeltaE2Median"]**2.)
453 scatterSize = row["psfStarDeltaSizeScatter"]
454 scaledScatterSize = row["psfStarScaledDeltaSizeScatter"]
455 psfTraceRadiusDelta = row["psfTraceRadiusDelta"]
456
457 valid = True
458 if self.config.maxEllipResidual and not (medianE <= self.config.maxEllipResidual):
459 self.log.info("Removing visit %d detector %d because median e residual too large: %f vs %f",
460 row["visit"], detectorId, medianE, self.config.maxEllipResidual)
461 valid = False
462 elif self.config.maxSizeScatter and not (scatterSize <= self.config.maxSizeScatter):
463 self.log.info("Removing visit %d detector %d because size scatter too large: %f vs %f",
464 row["visit"], detectorId, scatterSize, self.config.maxSizeScatter)
465 valid = False
466 elif self.config.maxScaledSizeScatter and not (scaledScatterSize <= self.config.maxScaledSizeScatter):
467 self.log.info("Removing visit %d detector %d because scaled size scatter too large: %f vs %f",
468 row["visit"], detectorId, scaledScatterSize, self.config.maxScaledSizeScatter)
469 valid = False
470 elif (
471 self.config.maxPsfTraceRadiusDelta is not None
472 and not (psfTraceRadiusDelta <= self.config.maxPsfTraceRadiusDelta)
473 ):
474 self.log.info(
475 "Removing visit %d detector %d because max-min delta of model PSF trace radius values "
476 "across the unmasked detector pixels is not finite or too large: %.3f vs %.3f (pixels)",
477 row["visit"], detectorId, psfTraceRadiusDelta, self.config.maxPsfTraceRadiusDelta
478 )
479 valid = False
480
481 return valid
482
483 def isValidLegacy(self, srcCatalog, dataId=None):
484 """Should this ccd be selected based on its PSF shape information.
485
486 This routine is only used in legacy processing (gen2 and
487 backwards compatible old calexps) and should be removed after v24.
488
489 Parameters
490 ----------
491 srcCatalog : `lsst.afw.table.SourceCatalog`
492 dataId : `dict` of dataId keys, optional.
493 Used only for logging. Defaults to None.
494
495 Returns
496 -------
497 valid : `bool`
498 True if selected.
499 """
500 mask = srcCatalog[self.config.starSelection]
501
502 starXX = srcCatalog[self.config.starShape+'_xx'][mask]
503 starYY = srcCatalog[self.config.starShape+'_yy'][mask]
504 starXY = srcCatalog[self.config.starShape+'_xy'][mask]
505 psfXX = srcCatalog[self.config.psfShape+'_xx'][mask]
506 psfYY = srcCatalog[self.config.psfShape+'_yy'][mask]
507 psfXY = srcCatalog[self.config.psfShape+'_xy'][mask]
508
509 starSize = np.power(starXX*starYY - starXY**2, 0.25)
510 starE1 = (starXX - starYY)/(starXX + starYY)
511 starE2 = 2*starXY/(starXX + starYY)
512 medianSize = np.median(starSize)
513
514 psfSize = np.power(psfXX*psfYY - psfXY**2, 0.25)
515 psfE1 = (psfXX - psfYY)/(psfXX + psfYY)
516 psfE2 = 2*psfXY/(psfXX + psfYY)
517
518 medianE1 = np.abs(np.median(starE1 - psfE1))
519 medianE2 = np.abs(np.median(starE2 - psfE2))
520 medianE = np.sqrt(medianE1**2 + medianE2**2)
521
522 scatterSize = sigmaMad(starSize - psfSize)
523 scaledScatterSize = scatterSize/medianSize**2
524
525 valid = True
526 if self.config.maxEllipResidual and medianE > self.config.maxEllipResidual:
527 self.log.info("Removing visit %s because median e residual too large: %f vs %f",
528 dataId, medianE, self.config.maxEllipResidual)
529 valid = False
530 elif self.config.maxSizeScatter and scatterSize > self.config.maxSizeScatter:
531 self.log.info("Removing visit %s because size scatter is too large: %f vs %f",
532 dataId, scatterSize, self.config.maxSizeScatter)
533 valid = False
534 elif self.config.maxScaledSizeScatter and scaledScatterSize > self.config.maxScaledSizeScatter:
535 self.log.info("Removing visit %s because scaled size scatter is too large: %f vs %f",
536 dataId, scaledScatterSize, self.config.maxScaledSizeScatter)
537 valid = False
538
539 return valid
540
541
542class BestSeeingSelectVisitsConnections(pipeBase.PipelineTaskConnections,
543 dimensions=("tract", "patch", "skymap", "band", "instrument"),
544 defaultTemplates={"coaddName": "goodSeeing"}):
545 skyMap = pipeBase.connectionTypes.Input(
546 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures",
547 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
548 storageClass="SkyMap",
549 dimensions=("skymap",),
550 )
551 visitSummaries = pipeBase.connectionTypes.Input(
552 doc="Per-visit consolidated exposure metadata",
553 name="finalVisitSummary",
554 storageClass="ExposureCatalog",
555 dimensions=("instrument", "visit",),
556 multiple=True,
557 deferLoad=True
558 )
559 goodVisits = pipeBase.connectionTypes.Output(
560 doc="Selected visits to be coadded.",
561 name="{coaddName}Visits",
562 storageClass="StructuredDataDict",
563 dimensions=("instrument", "tract", "patch", "skymap", "band"),
564 )
565
566
567class BestSeeingSelectVisitsConfig(pipeBase.PipelineTaskConfig,
568 pipelineConnections=BestSeeingSelectVisitsConnections):
569 nVisitsMax = pexConfig.RangeField(
570 dtype=int,
571 doc="Maximum number of visits to select",
572 default=12,
573 min=0
574 )
575 maxPsfFwhm = pexConfig.Field(
576 dtype=float,
577 doc="Maximum PSF FWHM (in arcseconds) to select",
578 default=1.5,
579 optional=True
580 )
581 minPsfFwhm = pexConfig.Field(
582 dtype=float,
583 doc="Minimum PSF FWHM (in arcseconds) to select",
584 default=0.,
585 optional=True
586 )
587 doConfirmOverlap = pexConfig.Field(
588 dtype=bool,
589 doc="Do remove visits that do not actually overlap the patch?",
590 default=True,
591 )
592 minMJD = pexConfig.Field(
593 dtype=float,
594 doc="Minimum visit MJD to select",
595 default=None,
596 optional=True
597 )
598 maxMJD = pexConfig.Field(
599 dtype=float,
600 doc="Maximum visit MJD to select",
601 default=None,
602 optional=True
603 )
604
605
606class BestSeeingSelectVisitsTask(pipeBase.PipelineTask):
607 """Select up to a maximum number of the best-seeing visits
608
609 Don't exceed the FWHM range specified by configs min(max)PsfFwhm.
610 This Task is a port of the Gen2 image-selector used in the AP pipeline:
611 BestSeeingSelectImagesTask. This Task selects full visits based on the
612 average PSF of the entire visit.
613 """
614 ConfigClass = BestSeeingSelectVisitsConfig
615 _DefaultName = 'bestSeeingSelectVisits'
616
617 def runQuantum(self, butlerQC, inputRefs, outputRefs):
618 inputs = butlerQC.get(inputRefs)
619 quantumDataId = butlerQC.quantum.dataId
620 outputs = self.run(**inputs, dataId=quantumDataId)
621 butlerQC.put(outputs, outputRefs)
622
623 def run(self, visitSummaries, skyMap, dataId):
624 """Run task
625
626 Parameters:
627 -----------
628 visitSummary : `list`
629 List of `lsst.pipe.base.connections.DeferredDatasetRef` of
630 visitSummary tables of type `lsst.afw.table.ExposureCatalog`
631 skyMap : `lsst.skyMap.SkyMap`
632 SkyMap for checking visits overlap patch
633 dataId : `dict` of dataId keys
634 For retrieving patch info for checking visits overlap patch
635
636 Returns
637 -------
638 result : `lsst.pipe.base.Struct`
639 Result struct with components:
640
641 - `goodVisits`: `dict` with selected visit ids as keys,
642 so that it can be be saved as a StructuredDataDict.
643 StructuredDataList's are currently limited.
644 """
645
646 if self.config.doConfirmOverlap:
647 patchPolygon = self.makePatchPolygon(skyMap, dataId)
648
649 inputVisits = [visitSummary.ref.dataId['visit'] for visitSummary in visitSummaries]
650 fwhmSizes = []
651 visits = []
652 for visit, visitSummary in zip(inputVisits, visitSummaries):
653 # read in one-by-one and only once. There may be hundreds
654 visitSummary = visitSummary.get()
655
656 # mjd is guaranteed to be the same for every detector in the visitSummary.
657 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
658
659 pixToArcseconds = [vs.getWcs().getPixelScale(vs.getBBox().getCenter()).asArcseconds()
660 for vs in visitSummary]
661 # psfSigma is PSF model determinant radius at chip center in pixels
662 psfSigmas = np.array([vs['psfSigma'] for vs in visitSummary])
663 fwhm = np.nanmean(psfSigmas * pixToArcseconds) * np.sqrt(8.*np.log(2.))
664
665 if self.config.maxPsfFwhm and fwhm > self.config.maxPsfFwhm:
666 continue
667 if self.config.minPsfFwhm and fwhm < self.config.minPsfFwhm:
668 continue
669 if self.config.minMJD and mjd < self.config.minMJD:
670 self.log.debug('MJD %f earlier than %.2f; rejecting', mjd, self.config.minMJD)
671 continue
672 if self.config.maxMJD and mjd > self.config.maxMJD:
673 self.log.debug('MJD %f later than %.2f; rejecting', mjd, self.config.maxMJD)
674 continue
675 if self.config.doConfirmOverlap and not self.doesIntersectPolygon(visitSummary, patchPolygon):
676 continue
677
678 fwhmSizes.append(fwhm)
679 visits.append(visit)
680
681 sortedVisits = [ind for (_, ind) in sorted(zip(fwhmSizes, visits))]
682 output = sortedVisits[:self.config.nVisitsMax]
683 self.log.info("%d images selected with FWHM range of %d--%d arcseconds",
684 len(output), fwhmSizes[visits.index(output[0])], fwhmSizes[visits.index(output[-1])])
685
686 # In order to store as a StructuredDataDict, convert list to dict
687 goodVisits = {key: True for key in output}
688 return pipeBase.Struct(goodVisits=goodVisits)
689
690 def makePatchPolygon(self, skyMap, dataId):
691 """Return True if sky polygon overlaps visit
692
693 Parameters:
694 -----------
696 Exposure catalog with per-detector geometry
697 dataId : `dict` of dataId keys
698 For retrieving patch info
699
700 Returns:
701 --------
702 result :` lsst.sphgeom.ConvexPolygon.convexHull`
703 Polygon of patch's outer bbox
704 """
705 wcs = skyMap[dataId['tract']].getWcs()
706 bbox = skyMap[dataId['tract']][dataId['patch']].getOuterBBox()
707 sphCorners = wcs.pixelToSky(lsst.geom.Box2D(bbox).getCorners())
708 result = lsst.sphgeom.ConvexPolygon.convexHull([coord.getVector() for coord in sphCorners])
709 return result
710
711 def doesIntersectPolygon(self, visitSummary, polygon):
712 """Return True if sky polygon overlaps visit
713
714 Parameters:
715 -----------
716 visitSummary : `lsst.afw.table.ExposureCatalog`
717 Exposure catalog with per-detector geometry
718 polygon :` lsst.sphgeom.ConvexPolygon.convexHull`
719 Polygon to check overlap
720
721 Returns:
722 --------
723 doesIntersect: `bool`
724 Does the visit overlap the polygon
725 """
726 doesIntersect = False
727 for detectorSummary in visitSummary:
728 corners = [lsst.geom.SpherePoint(ra, decl, units=lsst.geom.degrees).getVector() for (ra, decl) in
729 zip(detectorSummary['raCorners'], detectorSummary['decCorners'])]
730 detectorPolygon = lsst.sphgeom.ConvexPolygon.convexHull(corners)
731 if detectorPolygon.intersects(polygon):
732 doesIntersect = True
733 break
734 return doesIntersect
735
736
737class BestSeeingQuantileSelectVisitsConfig(pipeBase.PipelineTaskConfig,
738 pipelineConnections=BestSeeingSelectVisitsConnections):
739 qMin = pexConfig.RangeField(
740 doc="Lower bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
741 "and select those in the interquantile range (qMin, qMax). Set qMin to 0 for Best Seeing. "
742 "This config should be changed from zero only for exploratory diffIm testing.",
743 dtype=float,
744 default=0,
745 min=0,
746 max=1,
747 )
748 qMax = pexConfig.RangeField(
749 doc="Upper bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
750 "and select those in the interquantile range (qMin, qMax). Set qMax to 1 for Worst Seeing.",
751 dtype=float,
752 default=0.33,
753 min=0,
754 max=1,
755 )
756 nVisitsMin = pexConfig.Field(
757 doc="At least this number of visits selected and supercedes quantile. For example, if 10 visits "
758 "cover this patch, qMin=0.33, and nVisitsMin=5, the best 5 visits will be selected.",
759 dtype=int,
760 default=6,
761 )
762 doConfirmOverlap = pexConfig.Field(
763 dtype=bool,
764 doc="Do remove visits that do not actually overlap the patch?",
765 default=True,
766 )
767 minMJD = pexConfig.Field(
768 dtype=float,
769 doc="Minimum visit MJD to select",
770 default=None,
771 optional=True
772 )
773 maxMJD = pexConfig.Field(
774 dtype=float,
775 doc="Maximum visit MJD to select",
776 default=None,
777 optional=True
778 )
779
780
781class BestSeeingQuantileSelectVisitsTask(BestSeeingSelectVisitsTask):
782 """Select a quantile of the best-seeing visits
783
784 Selects the best (for example, third) full visits based on the average
785 PSF width in the entire visit. It can also be used for difference imaging
786 experiments that require templates with the worst seeing visits.
787 For example, selecting the worst third can be acheived by
788 changing the config parameters qMin to 0.66 and qMax to 1.
789 """
790 ConfigClass = BestSeeingQuantileSelectVisitsConfig
791 _DefaultName = 'bestSeeingQuantileSelectVisits'
792
793 @utils.inheritDoc(BestSeeingSelectVisitsTask)
794 def run(self, visitSummaries, skyMap, dataId):
795 if self.config.doConfirmOverlap:
796 patchPolygon = self.makePatchPolygon(skyMap, dataId)
797 visits = np.array([visitSummary.ref.dataId['visit'] for visitSummary in visitSummaries])
798 radius = np.empty(len(visits))
799 intersects = np.full(len(visits), True)
800 for i, visitSummary in enumerate(visitSummaries):
801 # read in one-by-one and only once. There may be hundreds
802 visitSummary = visitSummary.get()
803 # psfSigma is PSF model determinant radius at chip center in pixels
804 psfSigma = np.nanmedian([vs['psfSigma'] for vs in visitSummary])
805 radius[i] = psfSigma
806 if self.config.doConfirmOverlap:
807 intersects[i] = self.doesIntersectPolygon(visitSummary, patchPolygon)
808 if self.config.minMJD or self.config.maxMJD:
809 # mjd is guaranteed to be the same for every detector in the visitSummary.
810 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
811 aboveMin = mjd > self.config.minMJD if self.config.minMJD else True
812 belowMax = mjd < self.config.maxMJD if self.config.maxMJD else True
813 intersects[i] = intersects[i] and aboveMin and belowMax
814
815 sortedVisits = [v for rad, v in sorted(zip(radius[intersects], visits[intersects]))]
816 lowerBound = min(int(np.round(self.config.qMin*len(visits[intersects]))),
817 max(0, len(visits[intersects]) - self.config.nVisitsMin))
818 upperBound = max(int(np.round(self.config.qMax*len(visits[intersects]))), self.config.nVisitsMin)
819
820 # In order to store as a StructuredDataDict, convert list to dict
821 goodVisits = {int(visit): True for visit in sortedVisits[lowerBound:upperBound]}
822 return pipeBase.Struct(goodVisits=goodVisits)
def __init__(self, dataId, coordList)
Definition: selectImages.py:63
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)