Coverage for python/lsst/pipe/tasks/selectImages.py : 20%

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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 )
302 starShape = pexConfig.Field(
303 doc="name of star shape",
304 dtype=str,
305 default='base_SdssShape'
306 )
307 psfShape = pexConfig.Field(
308 doc="name of psf shape",
309 dtype=str,
310 default='base_SdssShape_psf'
311 )
314class PsfWcsSelectImagesTask(WcsSelectImagesTask):
315 """Select images using their Wcs and cuts on the PSF properties
317 The PSF quality criteria are based on the size and ellipticity residuals from the
318 adaptive second moments of the star and the PSF.
320 The criteria are:
321 - the median of the ellipticty residuals
322 - the robust scatter of the size residuals (using the median absolute deviation)
323 - the robust scatter of the size residuals scaled by the square of
324 the median size
325 """
327 ConfigClass = PsfWcsSelectImagesConfig
328 _DefaultName = "PsfWcsSelectImages"
330 def runDataRef(self, dataRef, coordList, makeDataRefList=True, selectDataList=[]):
331 """Select images in the selectDataList that overlap the patch and satisfy PSF quality critera.
333 This method is the old entry point for the Gen2 commandline tasks and drivers
334 Will be deprecated in v22.
336 @param dataRef: Data reference for coadd/tempExp (with tract, patch)
337 @param coordList: List of ICRS coordinates (lsst.geom.SpherePoint) specifying boundary of patch
338 @param makeDataRefList: Construct a list of data references?
339 @param selectDataList: List of SelectStruct, to consider for selection
340 """
341 result = super(PsfWcsSelectImagesTask, self).runDataRef(dataRef, coordList, makeDataRefList,
342 selectDataList)
344 dataRefList = []
345 exposureInfoList = []
346 for dataRef, exposureInfo in zip(result.dataRefList, result.exposureInfoList):
347 butler = dataRef.butlerSubset.butler
348 srcCatalog = butler.get('src', dataRef.dataId)
349 valid = self.isValid(srcCatalog, dataRef.dataId)
350 if valid is False:
351 continue
353 dataRefList.append(dataRef)
354 exposureInfoList.append(exposureInfo)
356 return pipeBase.Struct(
357 dataRefList=dataRefList,
358 exposureInfoList=exposureInfoList,
359 )
361 def run(self, wcsList, bboxList, coordList, srcList, dataIds=None, **kwargs):
362 """Return indices of provided lists that meet the selection criteria
364 Parameters:
365 -----------
366 wcsList : `list` of `lsst.afw.geom.SkyWcs`
367 specifying the WCS's of the input ccds to be selected
368 bboxList : `list` of `lsst.geom.Box2I`
369 specifying the bounding boxes of the input ccds to be selected
370 coordList : `list` of `lsst.geom.SpherePoint`
371 ICRS coordinates specifying boundary of the patch.
372 srcList : `list` of `lsst.afw.table.SourceCatalog`
373 containing the PSF shape information for the input ccds to be selected
375 Returns:
376 --------
377 goodPsf: `list` of `int`
378 of indices of selected ccds
379 """
380 goodWcs = super(PsfWcsSelectImagesTask, self).run(wcsList=wcsList, bboxList=bboxList,
381 coordList=coordList, dataIds=dataIds)
383 goodPsf = []
384 if dataIds is None:
385 dataIds = [None] * len(srcList)
386 for i, (srcCatalog, dataId) in enumerate(zip(srcList, dataIds)):
387 if i not in goodWcs:
388 continue
389 if self.isValid(srcCatalog, dataId):
390 goodPsf.append(i)
392 return goodPsf
394 def isValid(self, srcCatalog, dataId=None):
395 """Should this ccd be selected based on its PSF shape information
397 Parameters
398 ----------
399 srcCatalog : `lsst.afw.table.SourceCatalog`
400 dataId : `dict` of dataId keys, optional.
401 Used only for logging. Defaults to None.
403 Returns
404 -------
405 valid : `bool`
406 True if selected.
407 """
408 mask = srcCatalog[self.config.starSelection]
410 starXX = srcCatalog[self.config.starShape+'_xx'][mask]
411 starYY = srcCatalog[self.config.starShape+'_yy'][mask]
412 starXY = srcCatalog[self.config.starShape+'_xy'][mask]
413 psfXX = srcCatalog[self.config.psfShape+'_xx'][mask]
414 psfYY = srcCatalog[self.config.psfShape+'_yy'][mask]
415 psfXY = srcCatalog[self.config.psfShape+'_xy'][mask]
417 starSize = np.power(starXX*starYY - starXY**2, 0.25)
418 starE1 = (starXX - starYY)/(starXX + starYY)
419 starE2 = 2*starXY/(starXX + starYY)
420 medianSize = np.median(starSize)
422 psfSize = np.power(psfXX*psfYY - psfXY**2, 0.25)
423 psfE1 = (psfXX - psfYY)/(psfXX + psfYY)
424 psfE2 = 2*psfXY/(psfXX + psfYY)
426 medianE1 = np.abs(np.median(starE1 - psfE1))
427 medianE2 = np.abs(np.median(starE2 - psfE2))
428 medianE = np.sqrt(medianE1**2 + medianE2**2)
430 scatterSize = sigmaMad(starSize - psfSize)
431 scaledScatterSize = scatterSize/medianSize**2
433 valid = True
434 if self.config.maxEllipResidual and medianE > self.config.maxEllipResidual:
435 self.log.info("Removing visit %s because median e residual too large: %f vs %f",
436 dataId, medianE, self.config.maxEllipResidual)
437 valid = False
438 elif self.config.maxSizeScatter and scatterSize > self.config.maxSizeScatter:
439 self.log.info("Removing visit %s because size scatter is too large: %f vs %f",
440 dataId, scatterSize, self.config.maxSizeScatter)
441 valid = False
442 elif self.config.maxScaledSizeScatter and scaledScatterSize > self.config.maxScaledSizeScatter:
443 self.log.info("Removing visit %s because scaled size scatter is too large: %f vs %f",
444 dataId, scaledScatterSize, self.config.maxScaledSizeScatter)
445 valid = False
447 return valid
450class BestSeeingWcsSelectImageConfig(WcsSelectImagesTask.ConfigClass):
451 """Base configuration for BestSeeingSelectImagesTask.
452 """
453 nImagesMax = pexConfig.RangeField(
454 dtype=int,
455 doc="Maximum number of images to select",
456 default=5,
457 min=0)
458 maxPsfFwhm = pexConfig.Field(
459 dtype=float,
460 doc="Maximum PSF FWHM (in arcseconds) to select",
461 default=1.5,
462 optional=True)
463 minPsfFwhm = pexConfig.Field(
464 dtype=float,
465 doc="Minimum PSF FWHM (in arcseconds) to select",
466 default=0.,
467 optional=True)
470class BestSeeingWcsSelectImagesTask(WcsSelectImagesTask):
471 """Select up to a maximum number of the best-seeing images using their Wcs.
472 """
473 ConfigClass = BestSeeingWcsSelectImageConfig
475 def runDataRef(self, dataRef, coordList, makeDataRefList=True,
476 selectDataList=None):
477 """Select the best-seeing images in the selectDataList that overlap the patch.
479 This method is the old entry point for the Gen2 commandline tasks and drivers
480 Will be deprecated in v22.
482 Parameters
483 ----------
484 dataRef : `lsst.daf.persistence.ButlerDataRef`
485 Data reference for coadd/tempExp (with tract, patch)
486 coordList : `list` of `lsst.geom.SpherePoint`
487 List of ICRS sky coordinates specifying boundary of patch
488 makeDataRefList : `boolean`, optional
489 Construct a list of data references?
490 selectDataList : `list` of `SelectStruct`
491 List of SelectStruct, to consider for selection
493 Returns
494 -------
495 result : `lsst.pipe.base.Struct`
496 Result struct with components:
497 - ``exposureList``: the selected exposures
498 (`list` of `lsst.pipe.tasks.selectImages.BaseExposureInfo`).
499 - ``dataRefList``: the optional data references corresponding to
500 each element of ``exposureList``
501 (`list` of `lsst.daf.persistence.ButlerDataRef`, or `None`).
502 """
503 psfSizes = []
504 dataRefList = []
505 exposureInfoList = []
507 if selectDataList is None:
508 selectDataList = []
510 result = super().runDataRef(dataRef, coordList, makeDataRefList=True, selectDataList=selectDataList)
512 for dataRef, exposureInfo in zip(result.dataRefList, result.exposureInfoList):
513 cal = dataRef.get("calexp", immediate=True)
515 # if min/max PSF values are defined, remove images out of bounds
516 pixToArcseconds = cal.getWcs().getPixelScale().asArcseconds()
517 psfSize = cal.getPsf().computeShape().getDeterminantRadius()*pixToArcseconds
518 sizeFwhm = psfSize * np.sqrt(8.*np.log(2.))
519 if self.config.maxPsfFwhm and sizeFwhm > self.config.maxPsfFwhm:
520 continue
521 if self.config.minPsfFwhm and sizeFwhm < self.config.minPsfFwhm:
522 continue
523 psfSizes.append(sizeFwhm)
524 dataRefList.append(dataRef)
525 exposureInfoList.append(exposureInfo)
527 if len(psfSizes) > self.config.nImagesMax:
528 sortedIndices = np.argsort(psfSizes)[:self.config.nImagesMax]
529 filteredDataRefList = [dataRefList[i] for i in sortedIndices]
530 filteredExposureInfoList = [exposureInfoList[i] for i in sortedIndices]
531 self.log.info("%d images selected with FWHM range of %f--%f arcseconds",
532 len(sortedIndices), psfSizes[sortedIndices[0]], psfSizes[sortedIndices[-1]])
534 else:
535 if len(psfSizes) == 0:
536 self.log.warning("0 images selected.")
537 else:
538 self.log.debug("%d images selected with FWHM range of %d--%d arcseconds",
539 len(psfSizes), psfSizes[0], psfSizes[-1])
540 filteredDataRefList = dataRefList
541 filteredExposureInfoList = exposureInfoList
543 return pipeBase.Struct(
544 dataRefList=filteredDataRefList if makeDataRefList else None,
545 exposureInfoList=filteredExposureInfoList,
546 )
549class BestSeeingSelectVisitsConnections(pipeBase.PipelineTaskConnections,
550 dimensions=("tract", "patch", "skymap", "band", "instrument"),
551 defaultTemplates={"coaddName": "goodSeeing"}):
552 skyMap = pipeBase.connectionTypes.Input(
553 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures",
554 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
555 storageClass="SkyMap",
556 dimensions=("skymap",),
557 )
558 visitSummaries = pipeBase.connectionTypes.Input(
559 doc="Per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask",
560 name="visitSummary",
561 storageClass="ExposureCatalog",
562 dimensions=("instrument", "visit",),
563 multiple=True,
564 deferLoad=True
565 )
566 goodVisits = pipeBase.connectionTypes.Output(
567 doc="Selected visits to be coadded.",
568 name="{coaddName}Visits",
569 storageClass="StructuredDataDict",
570 dimensions=("instrument", "tract", "patch", "skymap", "band"),
571 )
574class BestSeeingSelectVisitsConfig(pipeBase.PipelineTaskConfig,
575 pipelineConnections=BestSeeingSelectVisitsConnections):
576 nVisitsMax = pexConfig.RangeField(
577 dtype=int,
578 doc="Maximum number of visits to select",
579 default=12,
580 min=0
581 )
582 maxPsfFwhm = pexConfig.Field(
583 dtype=float,
584 doc="Maximum PSF FWHM (in arcseconds) to select",
585 default=1.5,
586 optional=True
587 )
588 minPsfFwhm = pexConfig.Field(
589 dtype=float,
590 doc="Minimum PSF FWHM (in arcseconds) to select",
591 default=0.,
592 optional=True
593 )
594 doConfirmOverlap = pexConfig.Field(
595 dtype=bool,
596 doc="Do remove visits that do not actually overlap the patch?",
597 default=True,
598 )
599 minMJD = pexConfig.Field(
600 dtype=float,
601 doc="Minimum visit MJD to select",
602 default=None,
603 optional=True
604 )
605 maxMJD = pexConfig.Field(
606 dtype=float,
607 doc="Maximum visit MJD to select",
608 default=None,
609 optional=True
610 )
613class BestSeeingSelectVisitsTask(pipeBase.PipelineTask):
614 """Select up to a maximum number of the best-seeing visits
616 Don't exceed the FWHM range specified by configs min(max)PsfFwhm.
617 This Task is a port of the Gen2 image-selector used in the AP pipeline:
618 BestSeeingSelectImagesTask. This Task selects full visits based on the
619 average PSF of the entire visit.
620 """
621 ConfigClass = BestSeeingSelectVisitsConfig
622 _DefaultName = 'bestSeeingSelectVisits'
624 def runQuantum(self, butlerQC, inputRefs, outputRefs):
625 inputs = butlerQC.get(inputRefs)
626 quantumDataId = butlerQC.quantum.dataId
627 outputs = self.run(**inputs, dataId=quantumDataId)
628 butlerQC.put(outputs, outputRefs)
630 def run(self, visitSummaries, skyMap, dataId):
631 """Run task
633 Parameters:
634 -----------
635 visitSummary : `list`
636 List of `lsst.pipe.base.connections.DeferredDatasetRef` of
637 visitSummary tables of type `lsst.afw.table.ExposureCatalog`
638 skyMap : `lsst.skyMap.SkyMap`
639 SkyMap for checking visits overlap patch
640 dataId : `dict` of dataId keys
641 For retrieving patch info for checking visits overlap patch
643 Returns
644 -------
645 result : `lsst.pipe.base.Struct`
646 Result struct with components:
648 - `goodVisits`: `dict` with selected visit ids as keys,
649 so that it can be be saved as a StructuredDataDict.
650 StructuredDataList's are currently limited.
651 """
653 if self.config.doConfirmOverlap:
654 patchPolygon = self.makePatchPolygon(skyMap, dataId)
656 inputVisits = [visitSummary.ref.dataId['visit'] for visitSummary in visitSummaries]
657 fwhmSizes = []
658 visits = []
659 for visit, visitSummary in zip(inputVisits, visitSummaries):
660 # read in one-by-one and only once. There may be hundreds
661 visitSummary = visitSummary.get()
663 # mjd is guaranteed to be the same for every detector in the visitSummary.
664 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
666 pixToArcseconds = [vs.getWcs().getPixelScale(vs.getBBox().getCenter()).asArcseconds()
667 for vs in visitSummary]
668 # psfSigma is PSF model determinant radius at chip center in pixels
669 psfSigmas = np.array([vs['psfSigma'] for vs in visitSummary])
670 fwhm = np.nanmean(psfSigmas * pixToArcseconds) * np.sqrt(8.*np.log(2.))
672 if self.config.maxPsfFwhm and fwhm > self.config.maxPsfFwhm:
673 continue
674 if self.config.minPsfFwhm and fwhm < self.config.minPsfFwhm:
675 continue
676 if self.config.minMJD and mjd < self.config.minMJD:
677 self.log.debug('MJD %f earlier than %.2f; rejecting', mjd, self.config.minMJD)
678 continue
679 if self.config.maxMJD and mjd > self.config.maxMJD:
680 self.log.debug('MJD %f later than %.2f; rejecting', mjd, self.config.maxMJD)
681 continue
682 if self.config.doConfirmOverlap and not self.doesIntersectPolygon(visitSummary, patchPolygon):
683 continue
685 fwhmSizes.append(fwhm)
686 visits.append(visit)
688 sortedVisits = [ind for (_, ind) in sorted(zip(fwhmSizes, visits))]
689 output = sortedVisits[:self.config.nVisitsMax]
690 self.log.info("%d images selected with FWHM range of %d--%d arcseconds",
691 len(output), fwhmSizes[visits.index(output[0])], fwhmSizes[visits.index(output[-1])])
693 # In order to store as a StructuredDataDict, convert list to dict
694 goodVisits = {key: True for key in output}
695 return pipeBase.Struct(goodVisits=goodVisits)
697 def makePatchPolygon(self, skyMap, dataId):
698 """Return True if sky polygon overlaps visit
700 Parameters:
701 -----------
702 skyMap : `lsst.afw.table.ExposureCatalog`
703 Exposure catalog with per-detector geometry
704 dataId : `dict` of dataId keys
705 For retrieving patch info
707 Returns:
708 --------
709 result :` lsst.sphgeom.ConvexPolygon.convexHull`
710 Polygon of patch's outer bbox
711 """
712 wcs = skyMap[dataId['tract']].getWcs()
713 bbox = skyMap[dataId['tract']][dataId['patch']].getOuterBBox()
714 sphCorners = wcs.pixelToSky(lsst.geom.Box2D(bbox).getCorners())
715 result = lsst.sphgeom.ConvexPolygon.convexHull([coord.getVector() for coord in sphCorners])
716 return result
718 def doesIntersectPolygon(self, visitSummary, polygon):
719 """Return True if sky polygon overlaps visit
721 Parameters:
722 -----------
723 visitSummary : `lsst.afw.table.ExposureCatalog`
724 Exposure catalog with per-detector geometry
725 polygon :` lsst.sphgeom.ConvexPolygon.convexHull`
726 Polygon to check overlap
728 Returns:
729 --------
730 doesIntersect: `bool`
731 Does the visit overlap the polygon
732 """
733 doesIntersect = False
734 for detectorSummary in visitSummary:
735 corners = [lsst.geom.SpherePoint(ra, decl, units=lsst.geom.degrees).getVector() for (ra, decl) in
736 zip(detectorSummary['raCorners'], detectorSummary['decCorners'])]
737 detectorPolygon = lsst.sphgeom.ConvexPolygon.convexHull(corners)
738 if detectorPolygon.intersects(polygon):
739 doesIntersect = True
740 break
741 return doesIntersect
744class BestSeeingQuantileSelectVisitsConfig(pipeBase.PipelineTaskConfig,
745 pipelineConnections=BestSeeingSelectVisitsConnections):
746 qMin = pexConfig.RangeField(
747 doc="Lower bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
748 "and select those in the interquantile range (qMin, qMax). Set qMin to 0 for Best Seeing. "
749 "This config should be changed from zero only for exploratory diffIm testing.",
750 dtype=float,
751 default=0,
752 min=0,
753 max=1,
754 )
755 qMax = pexConfig.RangeField(
756 doc="Upper bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
757 "and select those in the interquantile range (qMin, qMax). Set qMax to 1 for Worst Seeing.",
758 dtype=float,
759 default=0.33,
760 min=0,
761 max=1,
762 )
763 nVisitsMin = pexConfig.Field(
764 doc="At least this number of visits selected and supercedes quantile. For example, if 10 visits "
765 "cover this patch, qMin=0.33, and nVisitsMin=5, the best 5 visits will be selected.",
766 dtype=int,
767 default=6,
768 )
769 doConfirmOverlap = pexConfig.Field(
770 dtype=bool,
771 doc="Do remove visits that do not actually overlap the patch?",
772 default=True,
773 )
774 minMJD = pexConfig.Field(
775 dtype=float,
776 doc="Minimum visit MJD to select",
777 default=None,
778 optional=True
779 )
780 maxMJD = pexConfig.Field(
781 dtype=float,
782 doc="Maximum visit MJD to select",
783 default=None,
784 optional=True
785 )
788class BestSeeingQuantileSelectVisitsTask(BestSeeingSelectVisitsTask):
789 """Select a quantile of the best-seeing visits
791 Selects the best (for example, third) full visits based on the average
792 PSF width in the entire visit. It can also be used for difference imaging
793 experiments that require templates with the worst seeing visits.
794 For example, selecting the worst third can be acheived by
795 changing the config parameters qMin to 0.66 and qMax to 1.
796 """
797 ConfigClass = BestSeeingQuantileSelectVisitsConfig
798 _DefaultName = 'bestSeeingQuantileSelectVisits'
800 @utils.inheritDoc(BestSeeingSelectVisitsTask)
801 def run(self, visitSummaries, skyMap, dataId):
802 if self.config.doConfirmOverlap:
803 patchPolygon = self.makePatchPolygon(skyMap, dataId)
804 visits = np.array([visitSummary.ref.dataId['visit'] for visitSummary in visitSummaries])
805 radius = np.empty(len(visits))
806 intersects = np.full(len(visits), True)
807 for i, visitSummary in enumerate(visitSummaries):
808 # read in one-by-one and only once. There may be hundreds
809 visitSummary = visitSummary.get()
810 # psfSigma is PSF model determinant radius at chip center in pixels
811 psfSigma = np.nanmedian([vs['psfSigma'] for vs in visitSummary])
812 radius[i] = psfSigma
813 if self.config.doConfirmOverlap:
814 intersects[i] = self.doesIntersectPolygon(visitSummary, patchPolygon)
815 if self.config.minMJD or self.config.maxMJD:
816 # mjd is guaranteed to be the same for every detector in the visitSummary.
817 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
818 aboveMin = mjd > self.config.minMJD if self.config.minMJD else True
819 belowMax = mjd < self.config.maxMJD if self.config.maxMJD else True
820 intersects[i] = intersects[i] and aboveMin and belowMax
822 sortedVisits = [v for rad, v in sorted(zip(radius[intersects], visits[intersects]))]
823 lowerBound = min(int(np.round(self.config.qMin*len(visits[intersects]))),
824 max(0, len(visits[intersects]) - self.config.nVisitsMin))
825 upperBound = max(int(np.round(self.config.qMax*len(visits[intersects]))), self.config.nVisitsMin)
827 # In order to store as a StructuredDataDict, convert list to dict
828 goodVisits = {int(visit): True for visit in sortedVisits[lowerBound:upperBound]}
829 return pipeBase.Struct(goodVisits=goodVisits)