Coverage for python/lsst/pipe/tasks/selectImages.py: 24%
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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
33__all__ = ["BaseSelectImagesTask", "BaseExposureInfo", "WcsSelectImagesTask", "PsfWcsSelectImagesTask",
34 "DatabaseSelectImagesConfig", "BestSeeingWcsSelectImagesTask", "BestSeeingSelectVisitsTask",
35 "BestSeeingQuantileSelectVisitsTask"]
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 )
59class BaseExposureInfo(pipeBase.Struct):
60 """Data about a selected exposure
61 """
63 def __init__(self, dataId, coordList):
64 """Create exposure information that can be used to generate data references
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)
74class BaseSelectImagesTask(pipeBase.Task):
75 """Base task for selecting images suitable for coaddition
76 """
77 ConfigClass = pexConfig.Config
78 _DefaultName = "selectImages"
80 @timeMethod
81 def run(self, coordList):
82 """Select images suitable for coaddition in a particular region
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
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()
95 def _runArgDictFromDataId(self, dataId):
96 """Extract keyword arguments for run (other than coordList) from a data ID
98 @return keyword arguments for run (other than coordList), as a dict
99 """
100 raise NotImplementedError()
102 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]):
103 """Run based on a data reference
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.
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
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
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
142 return pipeBase.Struct(
143 dataRefList=dataRefList,
144 exposureInfoList=exposureInfoList,
145 )
148def _extractKeyValue(dataList, keys=None):
149 """Extract the keys and values from a list of dataIds
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
168class SelectStruct(pipeBase.Struct):
169 """A container for data to be passed to the WcsSelectImagesTask"""
171 def __init__(self, dataRef, wcs, bbox):
172 super(SelectStruct, self).__init__(dataRef=dataRef, wcs=wcs, bbox=bbox)
175class WcsSelectImagesTask(BaseSelectImagesTask):
176 """Select images using their Wcs
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.
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 """
187 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]):
188 """Select images in the selectDataList that overlap the patch
190 This method is the old entry point for the Gen2 commandline tasks and drivers
191 Will be deprecated in v22.
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 = []
201 patchVertices = [coord.getVector() for coord in coordList]
202 patchPoly = lsst.sphgeom.ConvexPolygon.convexHull(patchVertices)
204 for data in selectDataList:
205 dataRef = data.dataRef
206 imageWcs = data.wcs
207 imageBox = data.bbox
209 imageCorners = self.getValidImageCorners(imageWcs, imageBox, patchPoly, dataId=None)
210 if imageCorners:
211 dataRefList.append(dataRef)
212 exposureInfoList.append(BaseExposureInfo(dataRef.dataId, imageCorners))
214 return pipeBase.Struct(
215 dataRefList=dataRefList if makeDataRefList else None,
216 exposureInfoList=exposureInfoList,
217 )
219 def run(self, wcsList, bboxList, coordList, dataIds=None, **kwargs):
220 """Return indices of provided lists that meet the selection criteria
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.
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
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
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
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
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)))
272class PsfWcsSelectImagesConnections(pipeBase.PipelineTaskConnections,
273 dimensions=("tract", "patch", "skymap", "instrument", "visit"),
274 defaultTemplates={"coaddName": "deep"}):
275 pass
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 )
320class PsfWcsSelectImagesTask(WcsSelectImagesTask):
321 """Select images using their Wcs and cuts on the PSF properties
323 The PSF quality criteria are based on the size and ellipticity residuals from the
324 adaptive second moments of the star and the PSF.
326 The criteria are:
327 - the median of the ellipticty residuals
328 - the robust scatter of the size residuals (using the median absolute deviation)
329 - the robust scatter of the size residuals scaled by the square of
330 the median size
331 """
333 ConfigClass = PsfWcsSelectImagesConfig
334 _DefaultName = "PsfWcsSelectImages"
336 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]):
337 """Select images in the selectDataList that overlap the patch and satisfy PSF quality critera.
339 This method is the old entry point for the Gen2 commandline tasks and drivers
340 Will be deprecated in v22.
342 @param dataRef: Data reference for coadd/tempExp (with tract, patch)
343 @param coordList: List of ICRS coordinates (lsst.geom.SpherePoint) specifying boundary of patch
344 @param makeDataRefList: Construct a list of data references?
345 @param selectDataList: List of SelectStruct, to consider for selection
346 """
347 result = super(PsfWcsSelectImagesTask, self).runDataRef(dataRef, coordList, makeDataRefList,
348 selectDataList)
350 dataRefList = []
351 exposureInfoList = []
352 for dataRef, exposureInfo in zip(result.dataRefList, result.exposureInfoList):
353 butler = dataRef.butlerSubset.butler
354 srcCatalog = butler.get('src', dataRef.dataId)
355 valid = self.isValidGen2(srcCatalog, dataRef.dataId)
356 if valid is False:
357 continue
359 dataRefList.append(dataRef)
360 exposureInfoList.append(exposureInfo)
362 return pipeBase.Struct(
363 dataRefList=dataRefList,
364 exposureInfoList=exposureInfoList,
365 )
367 def run(self, wcsList, bboxList, coordList, visitSummary, dataIds=None, **kwargs):
368 """Return indices of provided lists that meet the selection criteria
370 Parameters:
371 -----------
372 wcsList : `list` of `lsst.afw.geom.SkyWcs`
373 specifying the WCS's of the input ccds to be selected
374 bboxList : `list` of `lsst.geom.Box2I`
375 specifying the bounding boxes of the input ccds to be selected
376 coordList : `list` of `lsst.geom.SpherePoint`
377 ICRS coordinates specifying boundary of the patch.
378 visitSummary : `list` of `lsst.afw.table.ExposureCatalog`
379 containing the PSF shape information for the input ccds to be selected
381 Returns:
382 --------
383 goodPsf: `list` of `int`
384 of indices of selected ccds
385 """
386 goodWcs = super(PsfWcsSelectImagesTask, self).run(wcsList=wcsList, bboxList=bboxList,
387 coordList=coordList, dataIds=dataIds)
389 goodPsf = []
391 for i, dataId in enumerate(dataIds):
392 if i not in goodWcs:
393 continue
394 if self.isValid(visitSummary, dataId['detector']):
395 goodPsf.append(i)
397 return goodPsf
399 def isValid(self, visitSummary, detectorId):
400 """Should this ccd be selected based on its PSF shape information.
402 Parameters
403 ----------
404 visitSummary : `lsst.afw.table.ExposureCatalog`
405 detectorId : `int`
406 Detector identifier.
408 Returns
409 -------
410 valid : `bool`
411 True if selected.
412 """
413 row = visitSummary.find(detectorId)
414 if row is None:
415 # This is not listed, so it must be bad.
416 self.log.warning("Removing visit %d detector %d because summary stats not available.",
417 row["visit"], detectorId)
418 return False
420 medianE = np.sqrt(row["psfStarDeltaE1Median"]**2. + row["psfStarDeltaE2Median"]**2.)
421 scatterSize = row["psfStarDeltaSizeScatter"]
422 scaledScatterSize = row["psfStarScaledDeltaSizeScatter"]
424 valid = True
425 if self.config.maxEllipResidual and medianE > self.config.maxEllipResidual:
426 self.log.info("Removing visit %d detector %d because median e residual too large: %f vs %f",
427 row["visit"], detectorId, medianE, self.config.maxEllipResidual)
428 valid = False
429 elif self.config.maxSizeScatter and scatterSize > self.config.maxSizeScatter:
430 self.log.info("Removing visit %d detector %d because size scatter too large: %f vs %f",
431 row["visit"], detectorId, scatterSize, self.config.maxSizeScatter)
432 valid = False
433 elif self.config.maxScaledSizeScatter and scaledScatterSize > self.config.maxScaledSizeScatter:
434 self.log.info("Removing visit %d detector %d because scaled size scatter too large: %f vs %f",
435 row["visit"], detectorId, scaledScatterSize, self.config.maxScaledSizeScatter)
436 valid = False
438 return valid
440 def isValidGen2(self, srcCatalog, dataId=None):
441 """Should this ccd be selected based on its PSF shape information.
443 This routine is only used in Gen2 processing, and can be
444 removed when Gen2 is retired.
446 Parameters
447 ----------
448 srcCatalog : `lsst.afw.table.SourceCatalog`
449 dataId : `dict` of dataId keys, optional.
450 Used only for logging. Defaults to None.
452 Returns
453 -------
454 valid : `bool`
455 True if selected.
456 """
457 mask = srcCatalog[self.config.starSelection]
459 starXX = srcCatalog[self.config.starShape+'_xx'][mask]
460 starYY = srcCatalog[self.config.starShape+'_yy'][mask]
461 starXY = srcCatalog[self.config.starShape+'_xy'][mask]
462 psfXX = srcCatalog[self.config.psfShape+'_xx'][mask]
463 psfYY = srcCatalog[self.config.psfShape+'_yy'][mask]
464 psfXY = srcCatalog[self.config.psfShape+'_xy'][mask]
466 starSize = np.power(starXX*starYY - starXY**2, 0.25)
467 starE1 = (starXX - starYY)/(starXX + starYY)
468 starE2 = 2*starXY/(starXX + starYY)
469 medianSize = np.median(starSize)
471 psfSize = np.power(psfXX*psfYY - psfXY**2, 0.25)
472 psfE1 = (psfXX - psfYY)/(psfXX + psfYY)
473 psfE2 = 2*psfXY/(psfXX + psfYY)
475 medianE1 = np.abs(np.median(starE1 - psfE1))
476 medianE2 = np.abs(np.median(starE2 - psfE2))
477 medianE = np.sqrt(medianE1**2 + medianE2**2)
479 scatterSize = sigmaMad(starSize - psfSize)
480 scaledScatterSize = scatterSize/medianSize**2
482 valid = True
483 if self.config.maxEllipResidual and medianE > self.config.maxEllipResidual:
484 self.log.info("Removing visit %s because median e residual too large: %f vs %f",
485 dataId, medianE, self.config.maxEllipResidual)
486 valid = False
487 elif self.config.maxSizeScatter and scatterSize > self.config.maxSizeScatter:
488 self.log.info("Removing visit %s because size scatter is too large: %f vs %f",
489 dataId, scatterSize, self.config.maxSizeScatter)
490 valid = False
491 elif self.config.maxScaledSizeScatter and scaledScatterSize > self.config.maxScaledSizeScatter:
492 self.log.info("Removing visit %s because scaled size scatter is too large: %f vs %f",
493 dataId, scaledScatterSize, self.config.maxScaledSizeScatter)
494 valid = False
496 return valid
499class BestSeeingWcsSelectImageConfig(WcsSelectImagesTask.ConfigClass):
500 """Base configuration for BestSeeingSelectImagesTask.
501 """
502 nImagesMax = pexConfig.RangeField(
503 dtype=int,
504 doc="Maximum number of images to select",
505 default=5,
506 min=0)
507 maxPsfFwhm = pexConfig.Field(
508 dtype=float,
509 doc="Maximum PSF FWHM (in arcseconds) to select",
510 default=1.5,
511 optional=True)
512 minPsfFwhm = pexConfig.Field(
513 dtype=float,
514 doc="Minimum PSF FWHM (in arcseconds) to select",
515 default=0.,
516 optional=True)
519class BestSeeingWcsSelectImagesTask(WcsSelectImagesTask):
520 """Select up to a maximum number of the best-seeing images using their Wcs.
521 """
522 ConfigClass = BestSeeingWcsSelectImageConfig
524 def runDataRef(self, dataRef, coordList, makeDataRefList=True,
525 selectDataList=None):
526 """Select the best-seeing images in the selectDataList that overlap the patch.
528 This method is the old entry point for the Gen2 commandline tasks and drivers
529 Will be deprecated in v22.
531 Parameters
532 ----------
533 dataRef : `lsst.daf.persistence.ButlerDataRef`
534 Data reference for coadd/tempExp (with tract, patch)
535 coordList : `list` of `lsst.geom.SpherePoint`
536 List of ICRS sky coordinates specifying boundary of patch
537 makeDataRefList : `boolean`, optional
538 Construct a list of data references?
539 selectDataList : `list` of `SelectStruct`
540 List of SelectStruct, to consider for selection
542 Returns
543 -------
544 result : `lsst.pipe.base.Struct`
545 Result struct with components:
546 - ``exposureList``: the selected exposures
547 (`list` of `lsst.pipe.tasks.selectImages.BaseExposureInfo`).
548 - ``dataRefList``: the optional data references corresponding to
549 each element of ``exposureList``
550 (`list` of `lsst.daf.persistence.ButlerDataRef`, or `None`).
551 """
552 psfSizes = []
553 dataRefList = []
554 exposureInfoList = []
556 if selectDataList is None:
557 selectDataList = []
559 result = super().runDataRef(dataRef, coordList, makeDataRefList=True, selectDataList=selectDataList)
561 for dataRef, exposureInfo in zip(result.dataRefList, result.exposureInfoList):
562 cal = dataRef.get("calexp", immediate=True)
564 # if min/max PSF values are defined, remove images out of bounds
565 pixToArcseconds = cal.getWcs().getPixelScale().asArcseconds()
566 psfSize = cal.getPsf().computeShape().getDeterminantRadius()*pixToArcseconds
567 sizeFwhm = psfSize * np.sqrt(8.*np.log(2.))
568 if self.config.maxPsfFwhm and sizeFwhm > self.config.maxPsfFwhm:
569 continue
570 if self.config.minPsfFwhm and sizeFwhm < self.config.minPsfFwhm:
571 continue
572 psfSizes.append(sizeFwhm)
573 dataRefList.append(dataRef)
574 exposureInfoList.append(exposureInfo)
576 if len(psfSizes) > self.config.nImagesMax:
577 sortedIndices = np.argsort(psfSizes)[:self.config.nImagesMax]
578 filteredDataRefList = [dataRefList[i] for i in sortedIndices]
579 filteredExposureInfoList = [exposureInfoList[i] for i in sortedIndices]
580 self.log.info("%d images selected with FWHM range of %f--%f arcseconds",
581 len(sortedIndices), psfSizes[sortedIndices[0]], psfSizes[sortedIndices[-1]])
583 else:
584 if len(psfSizes) == 0:
585 self.log.warning("0 images selected.")
586 else:
587 self.log.debug("%d images selected with FWHM range of %d--%d arcseconds",
588 len(psfSizes), psfSizes[0], psfSizes[-1])
589 filteredDataRefList = dataRefList
590 filteredExposureInfoList = exposureInfoList
592 return pipeBase.Struct(
593 dataRefList=filteredDataRefList if makeDataRefList else None,
594 exposureInfoList=filteredExposureInfoList,
595 )
598class BestSeeingSelectVisitsConnections(pipeBase.PipelineTaskConnections,
599 dimensions=("tract", "patch", "skymap", "band", "instrument"),
600 defaultTemplates={"coaddName": "goodSeeing"}):
601 skyMap = pipeBase.connectionTypes.Input(
602 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures",
603 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
604 storageClass="SkyMap",
605 dimensions=("skymap",),
606 )
607 visitSummaries = pipeBase.connectionTypes.Input(
608 doc="Per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask",
609 name="visitSummary",
610 storageClass="ExposureCatalog",
611 dimensions=("instrument", "visit",),
612 multiple=True,
613 deferLoad=True
614 )
615 goodVisits = pipeBase.connectionTypes.Output(
616 doc="Selected visits to be coadded.",
617 name="{coaddName}Visits",
618 storageClass="StructuredDataDict",
619 dimensions=("instrument", "tract", "patch", "skymap", "band"),
620 )
623class BestSeeingSelectVisitsConfig(pipeBase.PipelineTaskConfig,
624 pipelineConnections=BestSeeingSelectVisitsConnections):
625 nVisitsMax = pexConfig.RangeField(
626 dtype=int,
627 doc="Maximum number of visits to select",
628 default=12,
629 min=0
630 )
631 maxPsfFwhm = pexConfig.Field(
632 dtype=float,
633 doc="Maximum PSF FWHM (in arcseconds) to select",
634 default=1.5,
635 optional=True
636 )
637 minPsfFwhm = pexConfig.Field(
638 dtype=float,
639 doc="Minimum PSF FWHM (in arcseconds) to select",
640 default=0.,
641 optional=True
642 )
643 doConfirmOverlap = pexConfig.Field(
644 dtype=bool,
645 doc="Do remove visits that do not actually overlap the patch?",
646 default=True,
647 )
648 minMJD = pexConfig.Field(
649 dtype=float,
650 doc="Minimum visit MJD to select",
651 default=None,
652 optional=True
653 )
654 maxMJD = pexConfig.Field(
655 dtype=float,
656 doc="Maximum visit MJD to select",
657 default=None,
658 optional=True
659 )
662class BestSeeingSelectVisitsTask(pipeBase.PipelineTask):
663 """Select up to a maximum number of the best-seeing visits
665 Don't exceed the FWHM range specified by configs min(max)PsfFwhm.
666 This Task is a port of the Gen2 image-selector used in the AP pipeline:
667 BestSeeingSelectImagesTask. This Task selects full visits based on the
668 average PSF of the entire visit.
669 """
670 ConfigClass = BestSeeingSelectVisitsConfig
671 _DefaultName = 'bestSeeingSelectVisits'
673 def runQuantum(self, butlerQC, inputRefs, outputRefs):
674 inputs = butlerQC.get(inputRefs)
675 quantumDataId = butlerQC.quantum.dataId
676 outputs = self.run(**inputs, dataId=quantumDataId)
677 butlerQC.put(outputs, outputRefs)
679 def run(self, visitSummaries, skyMap, dataId):
680 """Run task
682 Parameters:
683 -----------
684 visitSummary : `list`
685 List of `lsst.pipe.base.connections.DeferredDatasetRef` of
686 visitSummary tables of type `lsst.afw.table.ExposureCatalog`
687 skyMap : `lsst.skyMap.SkyMap`
688 SkyMap for checking visits overlap patch
689 dataId : `dict` of dataId keys
690 For retrieving patch info for checking visits overlap patch
692 Returns
693 -------
694 result : `lsst.pipe.base.Struct`
695 Result struct with components:
697 - `goodVisits`: `dict` with selected visit ids as keys,
698 so that it can be be saved as a StructuredDataDict.
699 StructuredDataList's are currently limited.
700 """
702 if self.config.doConfirmOverlap:
703 patchPolygon = self.makePatchPolygon(skyMap, dataId)
705 inputVisits = [visitSummary.ref.dataId['visit'] for visitSummary in visitSummaries]
706 fwhmSizes = []
707 visits = []
708 for visit, visitSummary in zip(inputVisits, visitSummaries):
709 # read in one-by-one and only once. There may be hundreds
710 visitSummary = visitSummary.get()
712 # mjd is guaranteed to be the same for every detector in the visitSummary.
713 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
715 pixToArcseconds = [vs.getWcs().getPixelScale(vs.getBBox().getCenter()).asArcseconds()
716 for vs in visitSummary]
717 # psfSigma is PSF model determinant radius at chip center in pixels
718 psfSigmas = np.array([vs['psfSigma'] for vs in visitSummary])
719 fwhm = np.nanmean(psfSigmas * pixToArcseconds) * np.sqrt(8.*np.log(2.))
721 if self.config.maxPsfFwhm and fwhm > self.config.maxPsfFwhm:
722 continue
723 if self.config.minPsfFwhm and fwhm < self.config.minPsfFwhm:
724 continue
725 if self.config.minMJD and mjd < self.config.minMJD:
726 self.log.debug('MJD %f earlier than %.2f; rejecting', mjd, self.config.minMJD)
727 continue
728 if self.config.maxMJD and mjd > self.config.maxMJD:
729 self.log.debug('MJD %f later than %.2f; rejecting', mjd, self.config.maxMJD)
730 continue
731 if self.config.doConfirmOverlap and not self.doesIntersectPolygon(visitSummary, patchPolygon):
732 continue
734 fwhmSizes.append(fwhm)
735 visits.append(visit)
737 sortedVisits = [ind for (_, ind) in sorted(zip(fwhmSizes, visits))]
738 output = sortedVisits[:self.config.nVisitsMax]
739 self.log.info("%d images selected with FWHM range of %d--%d arcseconds",
740 len(output), fwhmSizes[visits.index(output[0])], fwhmSizes[visits.index(output[-1])])
742 # In order to store as a StructuredDataDict, convert list to dict
743 goodVisits = {key: True for key in output}
744 return pipeBase.Struct(goodVisits=goodVisits)
746 def makePatchPolygon(self, skyMap, dataId):
747 """Return True if sky polygon overlaps visit
749 Parameters:
750 -----------
751 skyMap : `lsst.afw.table.ExposureCatalog`
752 Exposure catalog with per-detector geometry
753 dataId : `dict` of dataId keys
754 For retrieving patch info
756 Returns:
757 --------
758 result :` lsst.sphgeom.ConvexPolygon.convexHull`
759 Polygon of patch's outer bbox
760 """
761 wcs = skyMap[dataId['tract']].getWcs()
762 bbox = skyMap[dataId['tract']][dataId['patch']].getOuterBBox()
763 sphCorners = wcs.pixelToSky(lsst.geom.Box2D(bbox).getCorners())
764 result = lsst.sphgeom.ConvexPolygon.convexHull([coord.getVector() for coord in sphCorners])
765 return result
767 def doesIntersectPolygon(self, visitSummary, polygon):
768 """Return True if sky polygon overlaps visit
770 Parameters:
771 -----------
772 visitSummary : `lsst.afw.table.ExposureCatalog`
773 Exposure catalog with per-detector geometry
774 polygon :` lsst.sphgeom.ConvexPolygon.convexHull`
775 Polygon to check overlap
777 Returns:
778 --------
779 doesIntersect: `bool`
780 Does the visit overlap the polygon
781 """
782 doesIntersect = False
783 for detectorSummary in visitSummary:
784 corners = [lsst.geom.SpherePoint(ra, decl, units=lsst.geom.degrees).getVector() for (ra, decl) in
785 zip(detectorSummary['raCorners'], detectorSummary['decCorners'])]
786 detectorPolygon = lsst.sphgeom.ConvexPolygon.convexHull(corners)
787 if detectorPolygon.intersects(polygon):
788 doesIntersect = True
789 break
790 return doesIntersect
793class BestSeeingQuantileSelectVisitsConfig(pipeBase.PipelineTaskConfig,
794 pipelineConnections=BestSeeingSelectVisitsConnections):
795 qMin = pexConfig.RangeField(
796 doc="Lower bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
797 "and select those in the interquantile range (qMin, qMax). Set qMin to 0 for Best Seeing. "
798 "This config should be changed from zero only for exploratory diffIm testing.",
799 dtype=float,
800 default=0,
801 min=0,
802 max=1,
803 )
804 qMax = pexConfig.RangeField(
805 doc="Upper bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
806 "and select those in the interquantile range (qMin, qMax). Set qMax to 1 for Worst Seeing.",
807 dtype=float,
808 default=0.33,
809 min=0,
810 max=1,
811 )
812 nVisitsMin = pexConfig.Field(
813 doc="At least this number of visits selected and supercedes quantile. For example, if 10 visits "
814 "cover this patch, qMin=0.33, and nVisitsMin=5, the best 5 visits will be selected.",
815 dtype=int,
816 default=6,
817 )
818 doConfirmOverlap = pexConfig.Field(
819 dtype=bool,
820 doc="Do remove visits that do not actually overlap the patch?",
821 default=True,
822 )
823 minMJD = pexConfig.Field(
824 dtype=float,
825 doc="Minimum visit MJD to select",
826 default=None,
827 optional=True
828 )
829 maxMJD = pexConfig.Field(
830 dtype=float,
831 doc="Maximum visit MJD to select",
832 default=None,
833 optional=True
834 )
837class BestSeeingQuantileSelectVisitsTask(BestSeeingSelectVisitsTask):
838 """Select a quantile of the best-seeing visits
840 Selects the best (for example, third) full visits based on the average
841 PSF width in the entire visit. It can also be used for difference imaging
842 experiments that require templates with the worst seeing visits.
843 For example, selecting the worst third can be acheived by
844 changing the config parameters qMin to 0.66 and qMax to 1.
845 """
846 ConfigClass = BestSeeingQuantileSelectVisitsConfig
847 _DefaultName = 'bestSeeingQuantileSelectVisits'
849 @utils.inheritDoc(BestSeeingSelectVisitsTask)
850 def run(self, visitSummaries, skyMap, dataId):
851 if self.config.doConfirmOverlap:
852 patchPolygon = self.makePatchPolygon(skyMap, dataId)
853 visits = np.array([visitSummary.ref.dataId['visit'] for visitSummary in visitSummaries])
854 radius = np.empty(len(visits))
855 intersects = np.full(len(visits), True)
856 for i, visitSummary in enumerate(visitSummaries):
857 # read in one-by-one and only once. There may be hundreds
858 visitSummary = visitSummary.get()
859 # psfSigma is PSF model determinant radius at chip center in pixels
860 psfSigma = np.nanmedian([vs['psfSigma'] for vs in visitSummary])
861 radius[i] = psfSigma
862 if self.config.doConfirmOverlap:
863 intersects[i] = self.doesIntersectPolygon(visitSummary, patchPolygon)
864 if self.config.minMJD or self.config.maxMJD:
865 # mjd is guaranteed to be the same for every detector in the visitSummary.
866 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
867 aboveMin = mjd > self.config.minMJD if self.config.minMJD else True
868 belowMax = mjd < self.config.maxMJD if self.config.maxMJD else True
869 intersects[i] = intersects[i] and aboveMin and belowMax
871 sortedVisits = [v for rad, v in sorted(zip(radius[intersects], visits[intersects]))]
872 lowerBound = min(int(np.round(self.config.qMin*len(visits[intersects]))),
873 max(0, len(visits[intersects]) - self.config.nVisitsMin))
874 upperBound = max(int(np.round(self.config.qMax*len(visits[intersects]))), self.config.nVisitsMin)
876 # In order to store as a StructuredDataDict, convert list to dict
877 goodVisits = {int(visit): True for visit in sortedVisits[lowerBound:upperBound]}
878 return pipeBase.Struct(goodVisits=goodVisits)