Coverage for python/lsst/pipe/tasks/selectImages.py: 31%
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1# This file is part of pipe_tasks.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
22__all__ = ["BaseSelectImagesTask", "BaseExposureInfo", "WcsSelectImagesTask", "PsfWcsSelectImagesTask",
23 "DatabaseSelectImagesConfig", "BestSeeingSelectVisitsTask",
24 "BestSeeingQuantileSelectVisitsTask"]
26import numpy as np
27import lsst.sphgeom
28import lsst.utils as utils
29import lsst.pex.config as pexConfig
30import lsst.pex.exceptions as pexExceptions
31import lsst.geom as geom
32import lsst.pipe.base as pipeBase
33from lsst.skymap import BaseSkyMap
34from lsst.daf.base import DateTime
35from lsst.utils.timer import timeMethod
38class DatabaseSelectImagesConfig(pexConfig.Config):
39 """Base configuration for subclasses of BaseSelectImagesTask that use a database."""
41 host = pexConfig.Field(
42 doc="Database server host name",
43 dtype=str,
44 )
45 port = pexConfig.Field(
46 doc="Database server port",
47 dtype=int,
48 )
49 database = pexConfig.Field(
50 doc="Name of database",
51 dtype=str,
52 )
53 maxExposures = pexConfig.Field(
54 doc="maximum exposures to select; intended for debugging; ignored if None",
55 dtype=int,
56 optional=True,
57 )
60class BaseExposureInfo(pipeBase.Struct):
61 """Data about a selected exposure.
63 Parameters
64 ----------
65 dataId : `dict` of dataId keys
66 Data ID of exposure.
67 coordList : `list` of `lsst.afw.geom.SpherePoint`
68 ICRS coordinates of the corners of the exposure
69 plus any others items that are desired.
70 """
72 def __init__(self, dataId, coordList):
73 super(BaseExposureInfo, self).__init__(dataId=dataId, coordList=coordList)
76class BaseSelectImagesTask(pipeBase.Task):
77 """Base task for selecting images suitable for coaddition.
78 """
80 ConfigClass = pexConfig.Config
81 _DefaultName = "selectImages"
83 @timeMethod
84 def run(self, coordList):
85 """Select images suitable for coaddition in a particular region.
87 Parameters
88 ----------
89 coordList : `list` of `lsst.geom.SpherePoint` or `None`
90 List of coordinates defining region of interest; if None then select all images
91 subclasses may add additional keyword arguments, as required.
93 Returns
94 -------
95 result : `pipeBase.Struct`
96 Results as a struct with attributes:
98 ``exposureInfoList``
99 A list of exposure information objects (subclasses of BaseExposureInfo),
100 which have at least the following fields:
101 - dataId: Data ID dictionary (`dict`).
102 - coordList: ICRS coordinates of the corners of the exposure.
103 (`list` of `lsst.geom.SpherePoint`)
104 """
105 raise NotImplementedError()
108def _extractKeyValue(dataList, keys=None):
109 """Extract the keys and values from a list of dataIds.
111 The input dataList is a list of objects that have 'dataId' members.
112 This allows it to be used for both a list of data references and a
113 list of ExposureInfo.
115 Parameters
116 ----------
117 dataList : `Unknown`
118 keys : `Unknown`
120 Returns
121 -------
122 keys : `Unknown`
123 values : `Unknown`
125 Raises
126 ------
127 RuntimeError
128 Raised if DataId keys are inconsistent.
129 """
130 assert len(dataList) > 0
131 if keys is None:
132 keys = sorted(dataList[0].dataId.keys())
133 keySet = set(keys)
134 values = list()
135 for data in dataList:
136 thisKeys = set(data.dataId.keys())
137 if thisKeys != keySet:
138 raise RuntimeError("DataId keys inconsistent: %s vs %s" % (keySet, thisKeys))
139 values.append(tuple(data.dataId[k] for k in keys))
140 return keys, values
143class SelectStruct(pipeBase.Struct):
144 """A container for data to be passed to the WcsSelectImagesTask.
146 Parameters
147 ----------
148 dataRef : `Unknown`
149 Data reference.
150 wcs : `lsst.afw.geom.SkyWcs`
151 Coordinate system definition (wcs).
152 bbox : `lsst.geom.box.Box2I`
153 Integer bounding box for image.
154 """
156 def __init__(self, dataRef, wcs, bbox):
157 super(SelectStruct, self).__init__(dataRef=dataRef, wcs=wcs, bbox=bbox)
160class WcsSelectImagesTask(BaseSelectImagesTask):
161 """Select images using their Wcs.
163 We use the "convexHull" method of lsst.sphgeom.ConvexPolygon to define
164 polygons on the celestial sphere, and test the polygon of the
165 patch for overlap with the polygon of the image.
167 We use "convexHull" instead of generating a ConvexPolygon
168 directly because the standard for the inputs to ConvexPolygon
169 are pretty high and we don't want to be responsible for reaching them.
170 """
172 def run(self, wcsList, bboxList, coordList, dataIds=None, **kwargs):
173 """Return indices of provided lists that meet the selection criteria.
175 Parameters
176 ----------
177 wcsList : `list` of `lsst.afw.geom.SkyWcs`
178 Specifying the WCS's of the input ccds to be selected.
179 bboxList : `list` of `lsst.geom.Box2I`
180 Specifying the bounding boxes of the input ccds to be selected.
181 coordList : `list` of `lsst.geom.SpherePoint`
182 ICRS coordinates specifying boundary of the patch.
183 dataIds : iterable of `lsst.daf.butler.dataId` or `None`, optional
184 An iterable object of dataIds which point to reference catalogs.
185 **kwargs
186 Additional keyword arguments.
188 Returns
189 -------
190 result : `list` of `int`
191 The indices of selected ccds.
192 """
193 if dataIds is None:
194 dataIds = [None] * len(wcsList)
195 patchVertices = [coord.getVector() for coord in coordList]
196 patchPoly = lsst.sphgeom.ConvexPolygon.convexHull(patchVertices)
197 result = []
198 for i, (imageWcs, imageBox, dataId) in enumerate(zip(wcsList, bboxList, dataIds)):
199 imageCorners = self.getValidImageCorners(imageWcs, imageBox, patchPoly, dataId)
200 if imageCorners:
201 result.append(i)
202 return result
204 def getValidImageCorners(self, imageWcs, imageBox, patchPoly, dataId=None):
205 """Return corners or `None` if bad.
207 Parameters
208 ----------
209 imageWcs : `Unknown`
210 imageBox : `Unknown`
211 patchPoly : `Unknown`
212 dataId : `Unknown`
213 """
214 try:
215 imageCorners = [imageWcs.pixelToSky(pix) for pix in geom.Box2D(imageBox).getCorners()]
216 except (pexExceptions.DomainError, pexExceptions.RuntimeError) as e:
217 # Protecting ourselves from awful Wcs solutions in input images
218 self.log.debug("WCS error in testing calexp %s (%s): deselecting", dataId, e)
219 return
221 imagePoly = lsst.sphgeom.ConvexPolygon.convexHull([coord.getVector() for coord in imageCorners])
222 if imagePoly is None:
223 self.log.debug("Unable to create polygon from image %s: deselecting", dataId)
224 return
226 if patchPoly.intersects(imagePoly):
227 # "intersects" also covers "contains" or "is contained by"
228 self.log.info("Selecting calexp %s", dataId)
229 return imageCorners
232class PsfWcsSelectImagesConnections(pipeBase.PipelineTaskConnections,
233 dimensions=("tract", "patch", "skymap", "instrument", "visit"),
234 defaultTemplates={"coaddName": "deep"}):
235 pass
238class PsfWcsSelectImagesConfig(pipeBase.PipelineTaskConfig,
239 pipelineConnections=PsfWcsSelectImagesConnections):
240 maxEllipResidual = pexConfig.Field(
241 doc="Maximum median ellipticity residual",
242 dtype=float,
243 default=0.007,
244 optional=True,
245 )
246 maxSizeScatter = pexConfig.Field(
247 doc="Maximum scatter in the size residuals",
248 dtype=float,
249 optional=True,
250 )
251 maxScaledSizeScatter = pexConfig.Field(
252 doc="Maximum scatter in the size residuals, scaled by the median size",
253 dtype=float,
254 default=0.009,
255 optional=True,
256 )
259class PsfWcsSelectImagesTask(WcsSelectImagesTask):
260 """Select images using their Wcs and cuts on the PSF properties.
262 The PSF quality criteria are based on the size and ellipticity residuals from the
263 adaptive second moments of the star and the PSF.
265 The criteria are:
266 - the median of the ellipticty residuals.
267 - the robust scatter of the size residuals (using the median absolute deviation).
268 - the robust scatter of the size residuals scaled by the square of
269 the median size.
270 """
272 ConfigClass = PsfWcsSelectImagesConfig
273 _DefaultName = "PsfWcsSelectImages"
275 def run(self, wcsList, bboxList, coordList, visitSummary, dataIds=None, **kwargs):
276 """Return indices of provided lists that meet the selection criteria.
278 Parameters
279 ----------
280 wcsList : `list` of `lsst.afw.geom.SkyWcs`
281 Specifying the WCS's of the input ccds to be selected.
282 bboxList : `list` of `lsst.geom.Box2I`
283 Specifying the bounding boxes of the input ccds to be selected.
284 coordList : `list` of `lsst.geom.SpherePoint`
285 ICRS coordinates specifying boundary of the patch.
286 visitSummary : `list` of `lsst.afw.table.ExposureCatalog`
287 containing the PSF shape information for the input ccds to be selected.
288 dataIds : iterable of `lsst.daf.butler.dataId` or `None`, optional
289 An iterable object of dataIds which point to reference catalogs.
290 **kwargs
291 Additional keyword arguments.
293 Returns
294 -------
295 goodPsf: `list` of `int`
296 The indices of selected ccds.
297 """
298 goodWcs = super(PsfWcsSelectImagesTask, self).run(wcsList=wcsList, bboxList=bboxList,
299 coordList=coordList, dataIds=dataIds)
301 goodPsf = []
303 for i, dataId in enumerate(dataIds):
304 if i not in goodWcs:
305 continue
306 if self.isValid(visitSummary, dataId["detector"]):
307 goodPsf.append(i)
309 return goodPsf
311 def isValid(self, visitSummary, detectorId):
312 """Should this ccd be selected based on its PSF shape information.
314 Parameters
315 ----------
316 visitSummary : `lsst.afw.table.ExposureCatalog`
317 Exposure catalog with per-detector summary information.
318 detectorId : `int`
319 Detector identifier.
321 Returns
322 -------
323 valid : `bool`
324 True if selected.
325 """
326 row = visitSummary.find(detectorId)
327 if row is None:
328 # This is not listed, so it must be bad.
329 self.log.warning("Removing detector %d because summary stats not available.", detectorId)
330 return False
332 medianE = np.sqrt(row["psfStarDeltaE1Median"]**2. + row["psfStarDeltaE2Median"]**2.)
333 scatterSize = row["psfStarDeltaSizeScatter"]
334 scaledScatterSize = row["psfStarScaledDeltaSizeScatter"]
336 valid = True
337 if self.config.maxEllipResidual and medianE > self.config.maxEllipResidual:
338 self.log.info("Removing visit %d detector %d because median e residual too large: %f vs %f",
339 row["visit"], detectorId, medianE, self.config.maxEllipResidual)
340 valid = False
341 elif self.config.maxSizeScatter and scatterSize > self.config.maxSizeScatter:
342 self.log.info("Removing visit %d detector %d because size scatter too large: %f vs %f",
343 row["visit"], detectorId, scatterSize, self.config.maxSizeScatter)
344 valid = False
345 elif self.config.maxScaledSizeScatter and scaledScatterSize > self.config.maxScaledSizeScatter:
346 self.log.info("Removing visit %d detector %d because scaled size scatter too large: %f vs %f",
347 row["visit"], detectorId, scaledScatterSize, self.config.maxScaledSizeScatter)
348 valid = False
350 return valid
353class BestSeeingSelectVisitsConnections(pipeBase.PipelineTaskConnections,
354 dimensions=("tract", "patch", "skymap", "band", "instrument"),
355 defaultTemplates={"coaddName": "goodSeeing"}):
356 skyMap = pipeBase.connectionTypes.Input(
357 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures",
358 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
359 storageClass="SkyMap",
360 dimensions=("skymap",),
361 )
362 visitSummaries = pipeBase.connectionTypes.Input(
363 doc="Per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask",
364 name="visitSummary",
365 storageClass="ExposureCatalog",
366 dimensions=("instrument", "visit",),
367 multiple=True,
368 deferLoad=True
369 )
370 goodVisits = pipeBase.connectionTypes.Output(
371 doc="Selected visits to be coadded.",
372 name="{coaddName}Visits",
373 storageClass="StructuredDataDict",
374 dimensions=("instrument", "tract", "patch", "skymap", "band"),
375 )
378class BestSeeingSelectVisitsConfig(pipeBase.PipelineTaskConfig,
379 pipelineConnections=BestSeeingSelectVisitsConnections):
380 nVisitsMax = pexConfig.RangeField(
381 dtype=int,
382 doc="Maximum number of visits to select",
383 default=12,
384 min=0
385 )
386 maxPsfFwhm = pexConfig.Field(
387 dtype=float,
388 doc="Maximum PSF FWHM (in arcseconds) to select",
389 default=1.5,
390 optional=True
391 )
392 minPsfFwhm = pexConfig.Field(
393 dtype=float,
394 doc="Minimum PSF FWHM (in arcseconds) to select",
395 default=0.,
396 optional=True
397 )
398 doConfirmOverlap = pexConfig.Field(
399 dtype=bool,
400 doc="Do remove visits that do not actually overlap the patch?",
401 default=True,
402 )
403 minMJD = pexConfig.Field(
404 dtype=float,
405 doc="Minimum visit MJD to select",
406 default=None,
407 optional=True
408 )
409 maxMJD = pexConfig.Field(
410 dtype=float,
411 doc="Maximum visit MJD to select",
412 default=None,
413 optional=True
414 )
417class BestSeeingSelectVisitsTask(pipeBase.PipelineTask):
418 """Select up to a maximum number of the best-seeing visits.
420 Don't exceed the FWHM range specified by configs min(max)PsfFwhm.
421 This Task is a port of the Gen2 image-selector used in the AP pipeline:
422 BestSeeingSelectImagesTask. This Task selects full visits based on the
423 average PSF of the entire visit.
424 """
426 ConfigClass = BestSeeingSelectVisitsConfig
427 _DefaultName = 'bestSeeingSelectVisits'
429 def runQuantum(self, butlerQC, inputRefs, outputRefs):
430 inputs = butlerQC.get(inputRefs)
431 quantumDataId = butlerQC.quantum.dataId
432 outputs = self.run(**inputs, dataId=quantumDataId)
433 butlerQC.put(outputs, outputRefs)
435 def run(self, visitSummaries, skyMap, dataId):
436 """Run task.
438 Parameters
439 ----------
440 visitSummary : `list` of `lsst.pipe.base.connections.DeferredDatasetRef`
441 List of `lsst.pipe.base.connections.DeferredDatasetRef` of
442 visitSummary tables of type `lsst.afw.table.ExposureCatalog`.
443 skyMap : `lsst.skyMap.SkyMap`
444 SkyMap for checking visits overlap patch.
445 dataId : `dict` of dataId keys
446 For retrieving patch info for checking visits overlap patch.
448 Returns
449 -------
450 result : `lsst.pipe.base.Struct`
451 Results as a struct with attributes:
453 ``goodVisits``
454 A `dict` with selected visit ids as keys,
455 so that it can be be saved as a StructuredDataDict.
456 StructuredDataList's are currently limited.
457 """
458 if self.config.doConfirmOverlap:
459 patchPolygon = self.makePatchPolygon(skyMap, dataId)
461 inputVisits = [visitSummary.ref.dataId['visit'] for visitSummary in visitSummaries]
462 fwhmSizes = []
463 visits = []
464 for visit, visitSummary in zip(inputVisits, visitSummaries):
465 # read in one-by-one and only once. There may be hundreds
466 visitSummary = visitSummary.get()
468 # mjd is guaranteed to be the same for every detector in the visitSummary.
469 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
471 pixToArcseconds = [vs.getWcs().getPixelScale(vs.getBBox().getCenter()).asArcseconds()
472 for vs in visitSummary]
473 # psfSigma is PSF model determinant radius at chip center in pixels
474 psfSigmas = np.array([vs['psfSigma'] for vs in visitSummary])
475 fwhm = np.nanmean(psfSigmas * pixToArcseconds) * np.sqrt(8.*np.log(2.))
477 if self.config.maxPsfFwhm and fwhm > self.config.maxPsfFwhm:
478 continue
479 if self.config.minPsfFwhm and fwhm < self.config.minPsfFwhm:
480 continue
481 if self.config.minMJD and mjd < self.config.minMJD:
482 self.log.debug('MJD %f earlier than %.2f; rejecting', mjd, self.config.minMJD)
483 continue
484 if self.config.maxMJD and mjd > self.config.maxMJD:
485 self.log.debug('MJD %f later than %.2f; rejecting', mjd, self.config.maxMJD)
486 continue
487 if self.config.doConfirmOverlap and not self.doesIntersectPolygon(visitSummary, patchPolygon):
488 continue
490 fwhmSizes.append(fwhm)
491 visits.append(visit)
493 sortedVisits = [ind for (_, ind) in sorted(zip(fwhmSizes, visits))]
494 output = sortedVisits[:self.config.nVisitsMax]
495 self.log.info("%d images selected with FWHM range of %d--%d arcseconds",
496 len(output), fwhmSizes[visits.index(output[0])], fwhmSizes[visits.index(output[-1])])
498 # In order to store as a StructuredDataDict, convert list to dict
499 goodVisits = {key: True for key in output}
500 return pipeBase.Struct(goodVisits=goodVisits)
502 def makePatchPolygon(self, skyMap, dataId):
503 """Return True if sky polygon overlaps visit.
505 Parameters
506 ----------
507 skyMap : `lsst.afw.table.ExposureCatalog`
508 Exposure catalog with per-detector geometry.
509 dataId : `dict` of dataId keys
510 For retrieving patch info.
512 Returns
513 -------
514 result : `lsst.sphgeom.ConvexPolygon.convexHull`
515 Polygon of patch's outer bbox.
516 """
517 wcs = skyMap[dataId['tract']].getWcs()
518 bbox = skyMap[dataId['tract']][dataId['patch']].getOuterBBox()
519 sphCorners = wcs.pixelToSky(lsst.geom.Box2D(bbox).getCorners())
520 result = lsst.sphgeom.ConvexPolygon.convexHull([coord.getVector() for coord in sphCorners])
521 return result
523 def doesIntersectPolygon(self, visitSummary, polygon):
524 """Return True if sky polygon overlaps visit.
526 Parameters
527 ----------
528 visitSummary : `lsst.afw.table.ExposureCatalog`
529 Exposure catalog with per-detector geometry.
530 polygon :` lsst.sphgeom.ConvexPolygon.convexHull`
531 Polygon to check overlap.
533 Returns
534 -------
535 doesIntersect : `bool`
536 True if the visit overlaps the polygon.
537 """
538 doesIntersect = False
539 for detectorSummary in visitSummary:
540 corners = [lsst.geom.SpherePoint(ra, decl, units=lsst.geom.degrees).getVector() for (ra, decl) in
541 zip(detectorSummary['raCorners'], detectorSummary['decCorners'])]
542 detectorPolygon = lsst.sphgeom.ConvexPolygon.convexHull(corners)
543 if detectorPolygon.intersects(polygon):
544 doesIntersect = True
545 break
546 return doesIntersect
549class BestSeeingQuantileSelectVisitsConfig(pipeBase.PipelineTaskConfig,
550 pipelineConnections=BestSeeingSelectVisitsConnections):
551 qMin = pexConfig.RangeField(
552 doc="Lower bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
553 "and select those in the interquantile range (qMin, qMax). Set qMin to 0 for Best Seeing. "
554 "This config should be changed from zero only for exploratory diffIm testing.",
555 dtype=float,
556 default=0,
557 min=0,
558 max=1,
559 )
560 qMax = pexConfig.RangeField(
561 doc="Upper bound of quantile range to select. Sorts visits by seeing from narrow to wide, "
562 "and select those in the interquantile range (qMin, qMax). Set qMax to 1 for Worst Seeing.",
563 dtype=float,
564 default=0.33,
565 min=0,
566 max=1,
567 )
568 nVisitsMin = pexConfig.Field(
569 doc="At least this number of visits selected and supercedes quantile. For example, if 10 visits "
570 "cover this patch, qMin=0.33, and nVisitsMin=5, the best 5 visits will be selected.",
571 dtype=int,
572 default=6,
573 )
574 doConfirmOverlap = pexConfig.Field(
575 dtype=bool,
576 doc="Do remove visits that do not actually overlap the patch?",
577 default=True,
578 )
579 minMJD = pexConfig.Field(
580 dtype=float,
581 doc="Minimum visit MJD to select",
582 default=None,
583 optional=True
584 )
585 maxMJD = pexConfig.Field(
586 dtype=float,
587 doc="Maximum visit MJD to select",
588 default=None,
589 optional=True
590 )
593class BestSeeingQuantileSelectVisitsTask(BestSeeingSelectVisitsTask):
594 """Select a quantile of the best-seeing visits.
596 Selects the best (for example, third) full visits based on the average
597 PSF width in the entire visit. It can also be used for difference imaging
598 experiments that require templates with the worst seeing visits.
599 For example, selecting the worst third can be acheived by
600 changing the config parameters qMin to 0.66 and qMax to 1.
601 """
602 ConfigClass = BestSeeingQuantileSelectVisitsConfig
603 _DefaultName = 'bestSeeingQuantileSelectVisits'
605 @utils.inheritDoc(BestSeeingSelectVisitsTask)
606 def run(self, visitSummaries, skyMap, dataId):
607 if self.config.doConfirmOverlap:
608 patchPolygon = self.makePatchPolygon(skyMap, dataId)
609 visits = np.array([visitSummary.ref.dataId['visit'] for visitSummary in visitSummaries])
610 radius = np.empty(len(visits))
611 intersects = np.full(len(visits), True)
612 for i, visitSummary in enumerate(visitSummaries):
613 # read in one-by-one and only once. There may be hundreds
614 visitSummary = visitSummary.get()
615 # psfSigma is PSF model determinant radius at chip center in pixels
616 psfSigma = np.nanmedian([vs['psfSigma'] for vs in visitSummary])
617 radius[i] = psfSigma
618 if self.config.doConfirmOverlap:
619 intersects[i] = self.doesIntersectPolygon(visitSummary, patchPolygon)
620 if self.config.minMJD or self.config.maxMJD:
621 # mjd is guaranteed to be the same for every detector in the visitSummary.
622 mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)
623 aboveMin = mjd > self.config.minMJD if self.config.minMJD else True
624 belowMax = mjd < self.config.maxMJD if self.config.maxMJD else True
625 intersects[i] = intersects[i] and aboveMin and belowMax
627 sortedVisits = [v for rad, v in sorted(zip(radius[intersects], visits[intersects]))]
628 lowerBound = min(int(np.round(self.config.qMin*len(visits[intersects]))),
629 max(0, len(visits[intersects]) - self.config.nVisitsMin))
630 upperBound = max(int(np.round(self.config.qMax*len(visits[intersects]))), self.config.nVisitsMin)
632 # In order to store as a StructuredDataDict, convert list to dict
633 goodVisits = {int(visit): True for visit in sortedVisits[lowerBound:upperBound]}
634 return pipeBase.Struct(goodVisits=goodVisits)