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LSST Data Management Base Package
imagePsfMatch.py
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1# This file is part of ip_diffim.
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/>.
21
22import numpy as np
23
24import lsst.daf.base as dafBase
25import lsst.pex.config as pexConfig
26import lsst.afw.detection as afwDetect
27import lsst.afw.image as afwImage
28import lsst.afw.math as afwMath
29import lsst.afw.geom as afwGeom
30import lsst.afw.table as afwTable
31import lsst.geom as geom
32import lsst.pipe.base as pipeBase
33from lsst.meas.algorithms import SourceDetectionTask, SubtractBackgroundTask, WarpedPsf
34from lsst.meas.base import SingleFrameMeasurementTask
35from .makeKernelBasisList import makeKernelBasisList
36from .psfMatch import PsfMatchTask, PsfMatchConfigDF, PsfMatchConfigAL
37from . import utils as diffimUtils
38from . import diffimLib
39from . import diffimTools
40import lsst.afw.display as afwDisplay
41from lsst.utils.timer import timeMethod
42
43__all__ = ["ImagePsfMatchConfig", "ImagePsfMatchTask", "subtractAlgorithmRegistry"]
44
45sigma2fwhm = 2.*np.sqrt(2.*np.log(2.))
46
47
48class ImagePsfMatchConfig(pexConfig.Config):
49 """Configuration for image-to-image Psf matching.
50 """
51 kernel = pexConfig.ConfigChoiceField(
52 doc="kernel type",
53 typemap=dict(
54 AL=PsfMatchConfigAL,
55 DF=PsfMatchConfigDF
56 ),
57 default="AL",
58 )
59 selectDetection = pexConfig.ConfigurableField(
60 target=SourceDetectionTask,
61 doc="Initial detections used to feed stars to kernel fitting",
62 )
63 selectMeasurement = pexConfig.ConfigurableField(
64 target=SingleFrameMeasurementTask,
65 doc="Initial measurements used to feed stars to kernel fitting",
66 )
67
68 def setDefaults(self):
69 # High sigma detections only
70 self.selectDetectionselectDetection.reEstimateBackground = False
71 self.selectDetectionselectDetection.thresholdValue = 10.0
72
73 # Minimal set of measurments for star selection
74 self.selectMeasurementselectMeasurement.algorithms.names.clear()
75 self.selectMeasurementselectMeasurement.algorithms.names = ('base_SdssCentroid', 'base_PsfFlux', 'base_PixelFlags',
76 'base_SdssShape', 'base_GaussianFlux', 'base_SkyCoord')
77 self.selectMeasurementselectMeasurement.slots.modelFlux = None
78 self.selectMeasurementselectMeasurement.slots.apFlux = None
79 self.selectMeasurementselectMeasurement.slots.calibFlux = None
80
81
83 """Psf-match two MaskedImages or Exposures using the sources in the images.
84
85 Parameters
86 ----------
87 args :
88 Arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__
89 kwargs :
90 Keyword arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__
91
92 Notes
93 -----
94 Upon initialization, the kernel configuration is defined by self.config.kernel.active.
95 The task creates an lsst.afw.math.Warper from the subConfig self.config.kernel.active.warpingConfig.
96 A schema for the selection and measurement of candidate lsst.ip.diffim.KernelCandidates is
97 defined, and used to initize subTasks selectDetection (for candidate detection) and selectMeasurement
98 (for candidate measurement).
99
100 Description
101
102 Build a Psf-matching kernel using two input images, either as MaskedImages (in which case they need
103 to be astrometrically aligned) or Exposures (in which case astrometric alignment will happen by
104 default but may be turned off). This requires a list of input Sources which may be provided
105 by the calling Task; if not, the Task will perform a coarse source detection
106 and selection for this purpose. Sources are vetted for signal-to-noise and masked pixels
107 (in both the template and science image), and substamps around each acceptable
108 source are extracted and used to create an instance of KernelCandidate.
109 Each KernelCandidate is then placed within a lsst.afw.math.SpatialCellSet, which is used by an ensemble of
110 lsst.afw.math.CandidateVisitor instances to build the Psf-matching kernel. These visitors include, in
111 the order that they are called: BuildSingleKernelVisitor, KernelSumVisitor, BuildSpatialKernelVisitor,
112 and AssessSpatialKernelVisitor.
113
114 Sigma clipping of KernelCandidates is performed as follows:
115
116 - BuildSingleKernelVisitor, using the substamp diffim residuals from the per-source kernel fit,
117 if PsfMatchConfig.singleKernelClipping is True
118 - KernelSumVisitor, using the mean and standard deviation of the kernel sum from all candidates,
119 if PsfMatchConfig.kernelSumClipping is True
120 - AssessSpatialKernelVisitor, using the substamp diffim ressiduals from the spatial kernel fit,
121 if PsfMatchConfig.spatialKernelClipping is True
122
123 The actual solving for the kernel (and differential background model) happens in
124 lsst.ip.diffim.PsfMatchTask._solve. This involves a loop over the SpatialCellSet that first builds the
125 per-candidate matching kernel for the requested number of KernelCandidates per cell
126 (PsfMatchConfig.nStarPerCell). The quality of this initial per-candidate difference image is examined,
127 using moments of the pixel residuals in the difference image normalized by the square root of the variance
128 (i.e. sigma); ideally this should follow a normal (0, 1) distribution,
129 but the rejection thresholds are set
130 by the config (PsfMatchConfig.candidateResidualMeanMax and PsfMatchConfig.candidateResidualStdMax).
131 All candidates that pass this initial build are then examined en masse to find the
132 mean/stdev of the kernel sums across all candidates.
133 Objects that are significantly above or below the mean,
134 typically due to variability or sources that are saturated in one image but not the other,
135 are also rejected.This threshold is defined by PsfMatchConfig.maxKsumSigma.
136 Finally, a spatial model is built using all currently-acceptable candidates,
137 and the spatial model used to derive a second set of (spatial) residuals
138 which are again used to reject bad candidates, using the same thresholds as above.
139
140 Invoking the Task
141
142 There is no run() method for this Task. Instead there are 4 methods that
143 may be used to invoke the Psf-matching. These are
144 `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchMaskedImages`,
145 `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractMaskedImages`,
146 `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchExposures`, and
147 `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractExposures`.
148
149 The methods that operate on lsst.afw.image.MaskedImage require that the images already be astrometrically
150 aligned, and are the same shape. The methods that operate on lsst.afw.image.Exposure allow for the
151 input images to be misregistered and potentially be different sizes; by default a
152 lsst.afw.math.LanczosWarpingKernel is used to perform the astrometric alignment. The methods
153 that "match" images return a Psf-matched image, while the methods that "subtract" images
154 return a Psf-matched and template subtracted image.
155
156 See each method's returned lsst.pipe.base.Struct for more details.
157
158 Debug variables
159
160 The lsst.pipe.base.cmdLineTask.CmdLineTask command line task interface supports a
161 flag -d/--debug to import debug.py from your PYTHONPATH. The relevant contents of debug.py
162 for this Task include:
163
164 .. code-block:: py
165
166 import sys
167 import lsstDebug
168 def DebugInfo(name):
169 di = lsstDebug.getInfo(name)
170 if name == "lsst.ip.diffim.psfMatch":
171 di.display = True # enable debug output
172 di.maskTransparency = 80 # display mask transparency
173 di.displayCandidates = True # show all the candidates and residuals
174 di.displayKernelBasis = False # show kernel basis functions
175 di.displayKernelMosaic = True # show kernel realized across the image
176 di.plotKernelSpatialModel = False # show coefficients of spatial model
177 di.showBadCandidates = True # show the bad candidates (red) along with good (green)
178 elif name == "lsst.ip.diffim.imagePsfMatch":
179 di.display = True # enable debug output
180 di.maskTransparency = 30 # display mask transparency
181 di.displayTemplate = True # show full (remapped) template
182 di.displaySciIm = True # show science image to match to
183 di.displaySpatialCells = True # show spatial cells
184 di.displayDiffIm = True # show difference image
185 di.showBadCandidates = True # show the bad candidates (red) along with good (green)
186 elif name == "lsst.ip.diffim.diaCatalogSourceSelector":
187 di.display = False # enable debug output
188 di.maskTransparency = 30 # display mask transparency
189 di.displayExposure = True # show exposure with candidates indicated
190 di.pauseAtEnd = False # pause when done
191 return di
192 lsstDebug.Info = DebugInfo
193 lsstDebug.frame = 1
194
195 Note that if you want addional logging info, you may add to your scripts:
196
197 .. code-block:: py
198
199 import lsst.utils.logging as logUtils
200 logUtils.trace_set_at("lsst.ip.diffim", 4)
201
202 Examples
203 --------
204 A complete example of using ImagePsfMatchTask
205
206 This code is imagePsfMatchTask.py in the examples directory, and can be run as e.g.
207
208 .. code-block:: none
209
210 examples/imagePsfMatchTask.py --debug
211 examples/imagePsfMatchTask.py --debug --mode="matchExposures"
212 examples/imagePsfMatchTask.py --debug --template /path/to/templateExp.fits
213 --science /path/to/scienceExp.fits
214
215 Create a subclass of ImagePsfMatchTask that allows us to either match exposures, or subtract exposures:
216
217 .. code-block:: none
218
219 class MyImagePsfMatchTask(ImagePsfMatchTask):
220
221 def __init__(self, args, kwargs):
222 ImagePsfMatchTask.__init__(self, args, kwargs)
223
224 def run(self, templateExp, scienceExp, mode):
225 if mode == "matchExposures":
226 return self.matchExposuresmatchExposures(templateExp, scienceExp)
227 elif mode == "subtractExposures":
228 return self.subtractExposuressubtractExposures(templateExp, scienceExp)
229
230 And allow the user the freedom to either run the script in default mode,
231 or point to their own images on disk.
232 Note that these images must be readable as an lsst.afw.image.Exposure.
233
234 We have enabled some minor display debugging in this script via the --debug option. However, if you
235 have an lsstDebug debug.py in your PYTHONPATH you will get additional debugging displays. The following
236 block checks for this script:
237
238 .. code-block:: py
239
240 if args.debug:
241 try:
242 import debug
243 # Since I am displaying 2 images here, set the starting frame number for the LSST debug LSST
244 debug.lsstDebug.frame = 3
245 except ImportError as e:
246 print(e, file=sys.stderr)
247
248 Finally, we call a run method that we define below.
249 First set up a Config and modify some of the parameters.
250 E.g. use an "Alard-Lupton" sum-of-Gaussian basis,
251 fit for a differential background, and use low order spatial
252 variation in the kernel and background:
253
254 .. code-block:: py
255
256 def run(args):
257 #
258 # Create the Config and use sum of gaussian basis
259 #
260 config = ImagePsfMatchTask.ConfigClass()
261 config.kernel.name = "AL"
262 config.kernel.active.fitForBackground = True
263 config.kernel.active.spatialKernelOrder = 1
264 config.kernel.active.spatialBgOrder = 0
265
266 Make sure the images (if any) that were sent to the script exist on disk and are readable. If no images
267 are sent, make some fake data up for the sake of this example script (have a look at the code if you want
268 more details on generateFakeImages):
269
270 .. code-block:: py
271
272 # Run the requested method of the Task
273 if args.template is not None and args.science is not None:
274 if not os.path.isfile(args.template):
275 raise FileNotFoundError("Template image %s does not exist" % (args.template))
276 if not os.path.isfile(args.science):
277 raise FileNotFoundError("Science image %s does not exist" % (args.science))
278 try:
279 templateExp = afwImage.ExposureF(args.template)
280 except Exception as e:
281 raise RuntimeError("Cannot read template image %s" % (args.template))
282 try:
283 scienceExp = afwImage.ExposureF(args.science)
284 except Exception as e:
285 raise RuntimeError("Cannot read science image %s" % (args.science))
286 else:
287 templateExp, scienceExp = generateFakeImages()
288 config.kernel.active.sizeCellX = 128
289 config.kernel.active.sizeCellY = 128
290
291 Create and run the Task:
292
293 .. code-block:: py
294
295 # Create the Task
296 psfMatchTask = MyImagePsfMatchTask(config=config)
297 # Run the Task
298 result = psfMatchTask.run(templateExp, scienceExp, args.mode)
299
300 And finally provide some optional debugging displays:
301
302 .. code-block:: py
303
304 if args.debug:
305 # See if the LSST debug has incremented the frame number; if not start with frame 3
306 try:
307 frame = debug.lsstDebug.frame + 1
308 except Exception:
309 frame = 3
310 afwDisplay.Display(frame=frame).mtv(result.matchedExposure,
311 title="Example script: Matched Template Image")
312 if "subtractedExposure" in result.getDict():
313 afwDisplay.Display(frame=frame + 1).mtv(result.subtractedExposure,
314 title="Example script: Subtracted Image")
315 """
316
317 ConfigClass = ImagePsfMatchConfig
318
319 def __init__(self, *args, **kwargs):
320 """Create the ImagePsfMatchTask.
321 """
322 PsfMatchTask.__init__(self, *args, **kwargs)
323 self.kConfigkConfigkConfig = self.config.kernel.active
324 self._warper_warper = afwMath.Warper.fromConfig(self.kConfigkConfigkConfig.warpingConfig)
325 # the background subtraction task uses a config from an unusual location,
326 # so cannot easily be constructed with makeSubtask
327 self.backgroundbackground = SubtractBackgroundTask(config=self.kConfigkConfigkConfig.afwBackgroundConfig, name="background",
328 parentTask=self)
329 self.selectSchemaselectSchema = afwTable.SourceTable.makeMinimalSchema()
330 self.selectAlgMetadataselectAlgMetadata = dafBase.PropertyList()
331 self.makeSubtask("selectDetection", schema=self.selectSchemaselectSchema)
332 self.makeSubtask("selectMeasurement", schema=self.selectSchemaselectSchema, algMetadata=self.selectAlgMetadataselectAlgMetadata)
333
334 def getFwhmPix(self, psf, position=None):
335 """Return the FWHM in pixels of a Psf.
336 """
337 if position is None:
338 position = psf.getAveragePosition()
339 sigPix = psf.computeShape(position).getDeterminantRadius()
340 return sigPix*sigma2fwhm
341
342 @timeMethod
343 def matchExposures(self, templateExposure, scienceExposure,
344 templateFwhmPix=None, scienceFwhmPix=None,
345 candidateList=None, doWarping=True, convolveTemplate=True):
346 """Warp and PSF-match an exposure to the reference.
347
348 Do the following, in order:
349
350 - Warp templateExposure to match scienceExposure,
351 if doWarping True and their WCSs do not already match
352 - Determine a PSF matching kernel and differential background model
353 that matches templateExposure to scienceExposure
354 - Convolve templateExposure by PSF matching kernel
355
356 Parameters
357 ----------
358 templateExposure : `lsst.afw.image.Exposure`
359 Exposure to warp and PSF-match to the reference masked image
360 scienceExposure : `lsst.afw.image.Exposure`
361 Exposure whose WCS and PSF are to be matched to
362 templateFwhmPix :`float`
363 FWHM (in pixels) of the Psf in the template image (image to convolve)
364 scienceFwhmPix : `float`
365 FWHM (in pixels) of the Psf in the science image
366 candidateList : `list`, optional
367 a list of footprints/maskedImages for kernel candidates;
368 if `None` then source detection is run.
369
370 - Currently supported: list of Footprints or measAlg.PsfCandidateF
371
372 doWarping : `bool`
373 what to do if ``templateExposure`` and ``scienceExposure`` WCSs do not match:
374
375 - if `True` then warp ``templateExposure`` to match ``scienceExposure``
376 - if `False` then raise an Exception
377
378 convolveTemplate : `bool`
379 Whether to convolve the template image or the science image:
380
381 - if `True`, ``templateExposure`` is warped if doWarping,
382 ``templateExposure`` is convolved
383 - if `False`, ``templateExposure`` is warped if doWarping,
384 ``scienceExposure`` is convolved
385
386 Returns
387 -------
388 results : `lsst.pipe.base.Struct`
389 An `lsst.pipe.base.Struct` containing these fields:
390
391 - ``matchedImage`` : the PSF-matched exposure =
392 Warped ``templateExposure`` convolved by psfMatchingKernel. This has:
393
394 - the same parent bbox, Wcs and PhotoCalib as scienceExposure
395 - the same filter as templateExposure
396 - no Psf (because the PSF-matching process does not compute one)
397
398 - ``psfMatchingKernel`` : the PSF matching kernel
399 - ``backgroundModel`` : differential background model
400 - ``kernelCellSet`` : SpatialCellSet used to solve for the PSF matching kernel
401
402 Raises
403 ------
404 RuntimeError
405 Raised if doWarping is False and ``templateExposure`` and
406 ``scienceExposure`` WCSs do not match
407 """
408 if not self._validateWcs_validateWcs(templateExposure, scienceExposure):
409 if doWarping:
410 self.log.info("Astrometrically registering template to science image")
411 templatePsf = templateExposure.getPsf()
412 # Warp PSF before overwriting exposure
413 xyTransform = afwGeom.makeWcsPairTransform(templateExposure.getWcs(),
414 scienceExposure.getWcs())
415 psfWarped = WarpedPsf(templatePsf, xyTransform)
416 templateExposure = self._warper_warper.warpExposure(scienceExposure.getWcs(),
417 templateExposure,
418 destBBox=scienceExposure.getBBox())
419 templateExposure.setPsf(psfWarped)
420 else:
421 self.log.error("ERROR: Input images not registered")
422 raise RuntimeError("Input images not registered")
423
424 if templateFwhmPix is None:
425 if not templateExposure.hasPsf():
426 self.log.warning("No estimate of Psf FWHM for template image")
427 else:
428 templateFwhmPix = self.getFwhmPixgetFwhmPix(templateExposure.getPsf())
429 self.log.info("templateFwhmPix: %s", templateFwhmPix)
430
431 if scienceFwhmPix is None:
432 if not scienceExposure.hasPsf():
433 self.log.warning("No estimate of Psf FWHM for science image")
434 else:
435 scienceFwhmPix = self.getFwhmPixgetFwhmPix(scienceExposure.getPsf())
436 self.log.info("scienceFwhmPix: %s", scienceFwhmPix)
437
438 if convolveTemplate:
439 kernelSize = self.makeKernelBasisListmakeKernelBasisList(templateFwhmPix, scienceFwhmPix)[0].getWidth()
440 candidateList = self.makeCandidateListmakeCandidateList(
441 templateExposure, scienceExposure, kernelSize, candidateList)
442 results = self.matchMaskedImagesmatchMaskedImages(
443 templateExposure.getMaskedImage(), scienceExposure.getMaskedImage(), candidateList,
444 templateFwhmPix=templateFwhmPix, scienceFwhmPix=scienceFwhmPix)
445 else:
446 kernelSize = self.makeKernelBasisListmakeKernelBasisList(scienceFwhmPix, templateFwhmPix)[0].getWidth()
447 candidateList = self.makeCandidateListmakeCandidateList(
448 templateExposure, scienceExposure, kernelSize, candidateList)
449 results = self.matchMaskedImagesmatchMaskedImages(
450 scienceExposure.getMaskedImage(), templateExposure.getMaskedImage(), candidateList,
451 templateFwhmPix=scienceFwhmPix, scienceFwhmPix=templateFwhmPix)
452
453 psfMatchedExposure = afwImage.makeExposure(results.matchedImage, scienceExposure.getWcs())
454 psfMatchedExposure.setFilter(templateExposure.getFilter())
455 psfMatchedExposure.setPhotoCalib(scienceExposure.getPhotoCalib())
456 results.warpedExposure = templateExposure
457 results.matchedExposure = psfMatchedExposure
458 return results
459
460 @timeMethod
461 def matchMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList,
462 templateFwhmPix=None, scienceFwhmPix=None):
463 """PSF-match a MaskedImage (templateMaskedImage) to a reference MaskedImage (scienceMaskedImage).
464
465 Do the following, in order:
466
467 - Determine a PSF matching kernel and differential background model
468 that matches templateMaskedImage to scienceMaskedImage
469 - Convolve templateMaskedImage by the PSF matching kernel
470
471 Parameters
472 ----------
473 templateMaskedImage : `lsst.afw.image.MaskedImage`
474 masked image to PSF-match to the reference masked image;
475 must be warped to match the reference masked image
476 scienceMaskedImage : `lsst.afw.image.MaskedImage`
477 maskedImage whose PSF is to be matched to
478 templateFwhmPix : `float`
479 FWHM (in pixels) of the Psf in the template image (image to convolve)
480 scienceFwhmPix : `float`
481 FWHM (in pixels) of the Psf in the science image
482 candidateList : `list`, optional
483 A list of footprints/maskedImages for kernel candidates;
484 if `None` then source detection is run.
485
486 - Currently supported: list of Footprints or measAlg.PsfCandidateF
487
488 Returns
489 -------
490 result : `callable`
491 An `lsst.pipe.base.Struct` containing these fields:
492
493 - psfMatchedMaskedImage: the PSF-matched masked image =
494 ``templateMaskedImage`` convolved with psfMatchingKernel.
495 This has the same xy0, dimensions and wcs as ``scienceMaskedImage``.
496 - psfMatchingKernel: the PSF matching kernel
497 - backgroundModel: differential background model
498 - kernelCellSet: SpatialCellSet used to solve for the PSF matching kernel
499
500 Raises
501 ------
502 RuntimeError
503 Raised if input images have different dimensions
504 """
505 import lsstDebug
506 display = lsstDebug.Info(__name__).display
507 displayTemplate = lsstDebug.Info(__name__).displayTemplate
508 displaySciIm = lsstDebug.Info(__name__).displaySciIm
509 displaySpatialCells = lsstDebug.Info(__name__).displaySpatialCells
510 maskTransparency = lsstDebug.Info(__name__).maskTransparency
511 if not maskTransparency:
512 maskTransparency = 0
513 if display:
514 afwDisplay.setDefaultMaskTransparency(maskTransparency)
515
516 if not candidateList:
517 raise RuntimeError("Candidate list must be populated by makeCandidateList")
518
519 if not self._validateSize_validateSize(templateMaskedImage, scienceMaskedImage):
520 self.log.error("ERROR: Input images different size")
521 raise RuntimeError("Input images different size")
522
523 if display and displayTemplate:
524 disp = afwDisplay.Display(frame=lsstDebug.frame)
525 disp.mtv(templateMaskedImage, title="Image to convolve")
526 lsstDebug.frame += 1
527
528 if display and displaySciIm:
529 disp = afwDisplay.Display(frame=lsstDebug.frame)
530 disp.mtv(scienceMaskedImage, title="Image to not convolve")
531 lsstDebug.frame += 1
532
533 kernelCellSet = self._buildCellSet_buildCellSet_buildCellSet(templateMaskedImage,
534 scienceMaskedImage,
535 candidateList)
536
537 if display and displaySpatialCells:
538 diffimUtils.showKernelSpatialCells(scienceMaskedImage, kernelCellSet,
539 symb="o", ctype=afwDisplay.CYAN, ctypeUnused=afwDisplay.YELLOW,
540 ctypeBad=afwDisplay.RED, size=4, frame=lsstDebug.frame,
541 title="Image to not convolve")
542 lsstDebug.frame += 1
543
544 if templateFwhmPix and scienceFwhmPix:
545 self.log.info("Matching Psf FWHM %.2f -> %.2f pix", templateFwhmPix, scienceFwhmPix)
546
547 if self.kConfigkConfigkConfig.useBicForKernelBasis:
548 tmpKernelCellSet = self._buildCellSet_buildCellSet_buildCellSet(templateMaskedImage,
549 scienceMaskedImage,
550 candidateList)
551 nbe = diffimTools.NbasisEvaluator(self.kConfigkConfigkConfig, templateFwhmPix, scienceFwhmPix)
552 bicDegrees = nbe(tmpKernelCellSet, self.log)
553 basisList = self.makeKernelBasisListmakeKernelBasisList(templateFwhmPix, scienceFwhmPix,
554 basisDegGauss=bicDegrees[0], metadata=self.metadata)
555 del tmpKernelCellSet
556 else:
557 basisList = self.makeKernelBasisListmakeKernelBasisList(templateFwhmPix, scienceFwhmPix,
558 metadata=self.metadata)
559
560 spatialSolution, psfMatchingKernel, backgroundModel = self._solve_solve(kernelCellSet, basisList)
561
562 psfMatchedMaskedImage = afwImage.MaskedImageF(templateMaskedImage.getBBox())
563 convolutionControl = afwMath.ConvolutionControl()
564 convolutionControl.setDoNormalize(False)
565 afwMath.convolve(psfMatchedMaskedImage, templateMaskedImage, psfMatchingKernel, convolutionControl)
566 return pipeBase.Struct(
567 matchedImage=psfMatchedMaskedImage,
568 psfMatchingKernel=psfMatchingKernel,
569 backgroundModel=backgroundModel,
570 kernelCellSet=kernelCellSet,
571 )
572
573 @timeMethod
574 def subtractExposures(self, templateExposure, scienceExposure,
575 templateFwhmPix=None, scienceFwhmPix=None,
576 candidateList=None, doWarping=True, convolveTemplate=True):
577 """Register, Psf-match and subtract two Exposures.
578
579 Do the following, in order:
580
581 - Warp templateExposure to match scienceExposure, if their WCSs do not already match
582 - Determine a PSF matching kernel and differential background model
583 that matches templateExposure to scienceExposure
584 - PSF-match templateExposure to scienceExposure
585 - Compute subtracted exposure (see return values for equation).
586
587 Parameters
588 ----------
589 templateExposure : `lsst.afw.image.ExposureF`
590 Exposure to PSF-match to scienceExposure
591 scienceExposure : `lsst.afw.image.ExposureF`
592 Reference Exposure
593 templateFwhmPix : `float`
594 FWHM (in pixels) of the Psf in the template image (image to convolve)
595 scienceFwhmPix : `float`
596 FWHM (in pixels) of the Psf in the science image
597 candidateList : `list`, optional
598 A list of footprints/maskedImages for kernel candidates;
599 if `None` then source detection is run.
600
601 - Currently supported: list of Footprints or measAlg.PsfCandidateF
602
603 doWarping : `bool`
604 What to do if ``templateExposure``` and ``scienceExposure`` WCSs do
605 not match:
606
607 - if `True` then warp ``templateExposure`` to match ``scienceExposure``
608 - if `False` then raise an Exception
609
610 convolveTemplate : `bool`
611 Convolve the template image or the science image
612
613 - if `True`, ``templateExposure`` is warped if doWarping,
614 ``templateExposure`` is convolved
615 - if `False`, ``templateExposure`` is warped if doWarping,
616 ``scienceExposure is`` convolved
617
618 Returns
619 -------
620 result : `lsst.pipe.base.Struct`
621 An `lsst.pipe.base.Struct` containing these fields:
622
623 - ``subtractedExposure`` : subtracted Exposure
624 scienceExposure - (matchedImage + backgroundModel)
625 - ``matchedImage`` : ``templateExposure`` after warping to match
626 ``templateExposure`` (if doWarping true),
627 and convolving with psfMatchingKernel
628 - ``psfMatchingKernel`` : PSF matching kernel
629 - ``backgroundModel`` : differential background model
630 - ``kernelCellSet`` : SpatialCellSet used to determine PSF matching kernel
631 """
632 results = self.matchExposuresmatchExposures(
633 templateExposure=templateExposure,
634 scienceExposure=scienceExposure,
635 templateFwhmPix=templateFwhmPix,
636 scienceFwhmPix=scienceFwhmPix,
637 candidateList=candidateList,
638 doWarping=doWarping,
639 convolveTemplate=convolveTemplate
640 )
641 # Always inherit WCS and photocalib from science exposure
642 subtractedExposure = afwImage.ExposureF(scienceExposure, deep=True)
643 # Note, the decorrelation afterburner re-calculates the variance plane
644 # from the variance planes of the original exposures.
645 # That recalculation code must be in accordance with the
646 # photometric level set here in ``subtractedMaskedImage``.
647 if convolveTemplate:
648 subtractedMaskedImage = subtractedExposure.maskedImage
649 subtractedMaskedImage -= results.matchedExposure.maskedImage
650 subtractedMaskedImage -= results.backgroundModel
651 else:
652 subtractedMaskedImage = subtractedExposure.maskedImage
653 subtractedMaskedImage[:, :] = results.warpedExposure.maskedImage
654 subtractedMaskedImage -= results.matchedExposure.maskedImage
655 subtractedMaskedImage -= results.backgroundModel
656
657 # Preserve polarity of differences
658 subtractedMaskedImage *= -1
659
660 # Place back on native photometric scale
661 subtractedMaskedImage /= results.psfMatchingKernel.computeImage(
662 afwImage.ImageD(results.psfMatchingKernel.getDimensions()), False)
663 # We matched to the warped template
664 subtractedExposure.setPsf(results.warpedExposure.getPsf())
665
666 import lsstDebug
667 display = lsstDebug.Info(__name__).display
668 displayDiffIm = lsstDebug.Info(__name__).displayDiffIm
669 maskTransparency = lsstDebug.Info(__name__).maskTransparency
670 if not maskTransparency:
671 maskTransparency = 0
672 if display:
673 afwDisplay.setDefaultMaskTransparency(maskTransparency)
674 if display and displayDiffIm:
675 disp = afwDisplay.Display(frame=lsstDebug.frame)
676 disp.mtv(templateExposure, title="Template")
677 lsstDebug.frame += 1
678 disp = afwDisplay.Display(frame=lsstDebug.frame)
679 disp.mtv(results.matchedExposure, title="Matched template")
680 lsstDebug.frame += 1
681 disp = afwDisplay.Display(frame=lsstDebug.frame)
682 disp.mtv(scienceExposure, title="Science Image")
683 lsstDebug.frame += 1
684 disp = afwDisplay.Display(frame=lsstDebug.frame)
685 disp.mtv(subtractedExposure, title="Difference Image")
686 lsstDebug.frame += 1
687
688 results.subtractedExposure = subtractedExposure
689 return results
690
691 @timeMethod
692 def subtractMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList,
693 templateFwhmPix=None, scienceFwhmPix=None):
694 """Psf-match and subtract two MaskedImages.
695
696 Do the following, in order:
697
698 - PSF-match templateMaskedImage to scienceMaskedImage
699 - Determine the differential background
700 - Return the difference: scienceMaskedImage
701 ((warped templateMaskedImage convolved with psfMatchingKernel) + backgroundModel)
702
703 Parameters
704 ----------
705 templateMaskedImage : `lsst.afw.image.MaskedImage`
706 MaskedImage to PSF-match to ``scienceMaskedImage``
707 scienceMaskedImage : `lsst.afw.image.MaskedImage`
708 Reference MaskedImage
709 templateFwhmPix : `float`
710 FWHM (in pixels) of the Psf in the template image (image to convolve)
711 scienceFwhmPix : `float`
712 FWHM (in pixels) of the Psf in the science image
713 candidateList : `list`, optional
714 A list of footprints/maskedImages for kernel candidates;
715 if `None` then source detection is run.
716
717 - Currently supported: list of Footprints or measAlg.PsfCandidateF
718
719 Returns
720 -------
721 results : `lsst.pipe.base.Struct`
722 An `lsst.pipe.base.Struct` containing these fields:
723
724 - ``subtractedMaskedImage`` : ``scienceMaskedImage`` - (matchedImage + backgroundModel)
725 - ``matchedImage`` : templateMaskedImage convolved with psfMatchingKernel
726 - `psfMatchingKernel`` : PSF matching kernel
727 - ``backgroundModel`` : differential background model
728 - ``kernelCellSet`` : SpatialCellSet used to determine PSF matching kernel
729
730 """
731 if not candidateList:
732 raise RuntimeError("Candidate list must be populated by makeCandidateList")
733
734 results = self.matchMaskedImagesmatchMaskedImages(
735 templateMaskedImage=templateMaskedImage,
736 scienceMaskedImage=scienceMaskedImage,
737 candidateList=candidateList,
738 templateFwhmPix=templateFwhmPix,
739 scienceFwhmPix=scienceFwhmPix,
740 )
741
742 subtractedMaskedImage = afwImage.MaskedImageF(scienceMaskedImage, True)
743 subtractedMaskedImage -= results.matchedImage
744 subtractedMaskedImage -= results.backgroundModel
745 results.subtractedMaskedImage = subtractedMaskedImage
746
747 import lsstDebug
748 display = lsstDebug.Info(__name__).display
749 displayDiffIm = lsstDebug.Info(__name__).displayDiffIm
750 maskTransparency = lsstDebug.Info(__name__).maskTransparency
751 if not maskTransparency:
752 maskTransparency = 0
753 if display:
754 afwDisplay.setDefaultMaskTransparency(maskTransparency)
755 if display and displayDiffIm:
756 disp = afwDisplay.Display(frame=lsstDebug.frame)
757 disp.mtv(subtractedMaskedImage, title="Subtracted masked image")
758 lsstDebug.frame += 1
759
760 return results
761
762 def getSelectSources(self, exposure, sigma=None, doSmooth=True, idFactory=None):
763 """Get sources to use for Psf-matching.
764
765 This method runs detection and measurement on an exposure.
766 The returned set of sources will be used as candidates for
767 Psf-matching.
768
769 Parameters
770 ----------
771 exposure : `lsst.afw.image.Exposure`
772 Exposure on which to run detection/measurement
773 sigma : `float`
774 Detection threshold
775 doSmooth : `bool`
776 Whether or not to smooth the Exposure with Psf before detection
777 idFactory :
778 Factory for the generation of Source ids
779
780 Returns
781 -------
782 selectSources :
783 source catalog containing candidates for the Psf-matching
784 """
785 if idFactory:
786 table = afwTable.SourceTable.make(self.selectSchemaselectSchema, idFactory)
787 else:
788 table = afwTable.SourceTable.make(self.selectSchemaselectSchema)
789 mi = exposure.getMaskedImage()
790
791 imArr = mi.getImage().getArray()
792 maskArr = mi.getMask().getArray()
793 miArr = np.ma.masked_array(imArr, mask=maskArr)
794 try:
795 fitBg = self.backgroundbackground.fitBackground(mi)
796 bkgd = fitBg.getImageF(self.backgroundbackground.config.algorithm,
797 self.backgroundbackground.config.undersampleStyle)
798 except Exception:
799 self.log.warning("Failed to get background model. Falling back to median background estimation")
800 bkgd = np.ma.median(miArr)
801
802 # Take off background for detection
803 mi -= bkgd
804 try:
805 table.setMetadata(self.selectAlgMetadataselectAlgMetadata)
806 detRet = self.selectDetection.run(
807 table=table,
808 exposure=exposure,
809 sigma=sigma,
810 doSmooth=doSmooth
811 )
812 selectSources = detRet.sources
813 self.selectMeasurement.run(measCat=selectSources, exposure=exposure)
814 finally:
815 # Put back on the background in case it is needed down stream
816 mi += bkgd
817 del bkgd
818 return selectSources
819
820 def makeCandidateList(self, templateExposure, scienceExposure, kernelSize, candidateList=None):
821 """Make a list of acceptable KernelCandidates.
822
823 Accept or generate a list of candidate sources for
824 Psf-matching, and examine the Mask planes in both of the
825 images for indications of bad pixels
826
827 Parameters
828 ----------
829 templateExposure : `lsst.afw.image.Exposure`
830 Exposure that will be convolved
831 scienceExposure : `lsst.afw.image.Exposure`
832 Exposure that will be matched-to
833 kernelSize : `float`
834 Dimensions of the Psf-matching Kernel, used to grow detection footprints
835 candidateList : `list`, optional
836 List of Sources to examine. Elements must be of type afw.table.Source
837 or a type that wraps a Source and has a getSource() method, such as
839
840 Returns
841 -------
842 candidateList : `list` of `dict`
843 A list of dicts having a "source" and "footprint"
844 field for the Sources deemed to be appropriate for Psf
845 matching
846 """
847 if candidateList is None:
848 candidateList = self.getSelectSourcesgetSelectSources(scienceExposure)
849
850 if len(candidateList) < 1:
851 raise RuntimeError("No candidates in candidateList")
852
853 listTypes = set(type(x) for x in candidateList)
854 if len(listTypes) > 1:
855 raise RuntimeError("Candidate list contains mixed types: %s" % [t for t in listTypes])
856
857 if not isinstance(candidateList[0], afwTable.SourceRecord):
858 try:
859 candidateList[0].getSource()
860 except Exception as e:
861 raise RuntimeError(f"Candidate List is of type: {type(candidateList[0])} "
862 "Can only make candidate list from list of afwTable.SourceRecords, "
863 f"measAlg.PsfCandidateF or other type with a getSource() method: {e}")
864 candidateList = [c.getSource() for c in candidateList]
865
866 candidateList = diffimTools.sourceToFootprintList(candidateList,
867 templateExposure, scienceExposure,
868 kernelSize,
869 self.kConfigkConfigkConfig.detectionConfig,
870 self.log)
871 if len(candidateList) == 0:
872 raise RuntimeError("Cannot find any objects suitable for KernelCandidacy")
873
874 return candidateList
875
876 def makeKernelBasisList(self, targetFwhmPix=None, referenceFwhmPix=None,
877 basisDegGauss=None, basisSigmaGauss=None, metadata=None):
878 """Wrapper to set log messages for
880
881 Parameters
882 ----------
883 targetFwhmPix : `float`, optional
884 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
885 Not used for delta function basis sets.
886 referenceFwhmPix : `float`, optional
887 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
888 Not used for delta function basis sets.
889 basisDegGauss : `list` of `int`, optional
890 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
891 Not used for delta function basis sets.
892 basisSigmaGauss : `list` of `int`, optional
893 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
894 Not used for delta function basis sets.
895 metadata : `lsst.daf.base.PropertySet`, optional
896 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
897 Not used for delta function basis sets.
898
899 Returns
900 -------
901 basisList: `list` of `lsst.afw.math.kernel.FixedKernel`
902 List of basis kernels.
903 """
904 basisList = makeKernelBasisList(self.kConfigkConfigkConfig,
905 targetFwhmPix=targetFwhmPix,
906 referenceFwhmPix=referenceFwhmPix,
907 basisDegGauss=basisDegGauss,
908 basisSigmaGauss=basisSigmaGauss,
909 metadata=metadata)
910 if targetFwhmPix == referenceFwhmPix:
911 self.log.info("Target and reference psf fwhms are equal, falling back to config values")
912 elif referenceFwhmPix > targetFwhmPix:
913 self.log.info("Reference psf fwhm is the greater, normal convolution mode")
914 else:
915 self.log.info("Target psf fwhm is the greater, deconvolution mode")
916
917 return basisList
918
919 def _adaptCellSize(self, candidateList):
920 """NOT IMPLEMENTED YET.
921 """
922 return self.kConfigkConfigkConfig.sizeCellX, self.kConfigkConfigkConfig.sizeCellY
923
924 def _buildCellSet(self, templateMaskedImage, scienceMaskedImage, candidateList):
925 """Build a SpatialCellSet for use with the solve method.
926
927 Parameters
928 ----------
929 templateMaskedImage : `lsst.afw.image.MaskedImage`
930 MaskedImage to PSF-matched to scienceMaskedImage
931 scienceMaskedImage : `lsst.afw.image.MaskedImage`
932 Reference MaskedImage
933 candidateList : `list`
934 A list of footprints/maskedImages for kernel candidates;
935
936 - Currently supported: list of Footprints or measAlg.PsfCandidateF
937
938 Returns
939 -------
940 kernelCellSet : `lsst.afw.math.SpatialCellSet`
941 a SpatialCellSet for use with self._solve_solve
942 """
943 if not candidateList:
944 raise RuntimeError("Candidate list must be populated by makeCandidateList")
945
946 sizeCellX, sizeCellY = self._adaptCellSize_adaptCellSize(candidateList)
947
948 # Object to store the KernelCandidates for spatial modeling
949 kernelCellSet = afwMath.SpatialCellSet(templateMaskedImage.getBBox(),
950 sizeCellX, sizeCellY)
951
952 ps = pexConfig.makePropertySet(self.kConfigkConfigkConfig)
953 # Place candidates within the spatial grid
954 for cand in candidateList:
955 if isinstance(cand, afwDetect.Footprint):
956 bbox = cand.getBBox()
957 else:
958 bbox = cand['footprint'].getBBox()
959 tmi = afwImage.MaskedImageF(templateMaskedImage, bbox)
960 smi = afwImage.MaskedImageF(scienceMaskedImage, bbox)
961
962 if not isinstance(cand, afwDetect.Footprint):
963 if 'source' in cand:
964 cand = cand['source']
965 xPos = cand.getCentroid()[0]
966 yPos = cand.getCentroid()[1]
967 cand = diffimLib.makeKernelCandidate(xPos, yPos, tmi, smi, ps)
968
969 self.log.debug("Candidate %d at %f, %f", cand.getId(), cand.getXCenter(), cand.getYCenter())
970 kernelCellSet.insertCandidate(cand)
971
972 return kernelCellSet
973
974 def _validateSize(self, templateMaskedImage, scienceMaskedImage):
975 """Return True if two image-like objects are the same size.
976 """
977 return templateMaskedImage.getDimensions() == scienceMaskedImage.getDimensions()
978
979 def _validateWcs(self, templateExposure, scienceExposure):
980 """Return True if the WCS of the two Exposures have the same origin and extent.
981 """
982 templateWcs = templateExposure.getWcs()
983 scienceWcs = scienceExposure.getWcs()
984 templateBBox = templateExposure.getBBox()
985 scienceBBox = scienceExposure.getBBox()
986
987 # LLC
988 templateOrigin = templateWcs.pixelToSky(geom.Point2D(templateBBox.getBegin()))
989 scienceOrigin = scienceWcs.pixelToSky(geom.Point2D(scienceBBox.getBegin()))
990
991 # URC
992 templateLimit = templateWcs.pixelToSky(geom.Point2D(templateBBox.getEnd()))
993 scienceLimit = scienceWcs.pixelToSky(geom.Point2D(scienceBBox.getEnd()))
994
995 self.log.info("Template Wcs : %f,%f -> %f,%f",
996 templateOrigin[0], templateOrigin[1],
997 templateLimit[0], templateLimit[1])
998 self.log.info("Science Wcs : %f,%f -> %f,%f",
999 scienceOrigin[0], scienceOrigin[1],
1000 scienceLimit[0], scienceLimit[1])
1001
1002 templateBBox = geom.Box2D(templateOrigin.getPosition(geom.degrees),
1003 templateLimit.getPosition(geom.degrees))
1004 scienceBBox = geom.Box2D(scienceOrigin.getPosition(geom.degrees),
1005 scienceLimit.getPosition(geom.degrees))
1006 if not (templateBBox.overlaps(scienceBBox)):
1007 raise RuntimeError("Input images do not overlap at all")
1008
1009 if ((templateOrigin != scienceOrigin)
1010 or (templateLimit != scienceLimit)
1011 or (templateExposure.getDimensions() != scienceExposure.getDimensions())):
1012 return False
1013 return True
1014
1015
1016subtractAlgorithmRegistry = pexConfig.makeRegistry(
1017 doc="A registry of subtraction algorithms for use as a subtask in imageDifference",
1018)
1019
1020subtractAlgorithmRegistry.register('al', ImagePsfMatchTask)
table::Key< int > type
Definition: Detector.cc:163
Class to describe the properties of a detected object from an image.
Definition: Footprint.h:63
A class to contain the data, WCS, and other information needed to describe an image of the sky.
Definition: Exposure.h:72
A class to manipulate images, masks, and variance as a single object.
Definition: MaskedImage.h:73
Parameters to control convolution.
Definition: ConvolveImage.h:50
Lanczos warping: accurate but slow and can introduce ringing artifacts.
Definition: warpExposure.h:66
A collection of SpatialCells covering an entire image.
Definition: SpatialCell.h:383
Record class that contains measurements made on a single exposure.
Definition: Source.h:78
Class for storing ordered metadata with comments.
Definition: PropertyList.h:68
Class for storing generic metadata.
Definition: PropertySet.h:66
A floating-point coordinate rectangle geometry.
Definition: Box.h:413
def _validateWcs(self, templateExposure, scienceExposure)
def _buildCellSet(self, templateMaskedImage, scienceMaskedImage, candidateList)
def _validateSize(self, templateMaskedImage, scienceMaskedImage)
def subtractMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList, templateFwhmPix=None, scienceFwhmPix=None)
def matchMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList, templateFwhmPix=None, scienceFwhmPix=None)
def getSelectSources(self, exposure, sigma=None, doSmooth=True, idFactory=None)
def subtractExposures(self, templateExposure, scienceExposure, templateFwhmPix=None, scienceFwhmPix=None, candidateList=None, doWarping=True, convolveTemplate=True)
def matchExposures(self, templateExposure, scienceExposure, templateFwhmPix=None, scienceFwhmPix=None, candidateList=None, doWarping=True, convolveTemplate=True)
def makeKernelBasisList(self, targetFwhmPix=None, referenceFwhmPix=None, basisDegGauss=None, basisSigmaGauss=None, metadata=None)
def makeCandidateList(self, templateExposure, scienceExposure, kernelSize, candidateList=None)
def _solve(self, kernelCellSet, basisList, returnOnExcept=False)
Definition: psfMatch.py:881
A Psf class that maps an arbitrary Psf through a coordinate transformation.
Definition: WarpedPsf.h:52
daf::base::PropertySet * set
Definition: fits.cc:912
def mtv(data, frame=None, title="", wcs=None, *args, **kwargs)
Definition: ds9.py:92
std::shared_ptr< TransformPoint2ToPoint2 > makeWcsPairTransform(SkyWcs const &src, SkyWcs const &dst)
A Transform obtained by putting two SkyWcs objects "back to back".
Definition: SkyWcs.cc:146
Backwards-compatibility support for depersisting the old Calib (FluxMag0/FluxMag0Err) objects.
std::shared_ptr< Exposure< ImagePixelT, MaskPixelT, VariancePixelT > > makeExposure(MaskedImage< ImagePixelT, MaskPixelT, VariancePixelT > &mimage, std::shared_ptr< geom::SkyWcs const > wcs=std::shared_ptr< geom::SkyWcs const >())
A function to return an Exposure of the correct type (cf.
Definition: Exposure.h:454
void convolve(OutImageT &convolvedImage, InImageT const &inImage, KernelT const &kernel, ConvolutionControl const &convolutionControl=ConvolutionControl())
Convolve an Image or MaskedImage with a Kernel, setting pixels of an existing output image.
int warpExposure(DestExposureT &destExposure, SrcExposureT const &srcExposure, WarpingControl const &control, typename DestExposureT::MaskedImageT::SinglePixel padValue=lsst::afw::math::edgePixel< typename DestExposureT::MaskedImageT >(typename lsst::afw::image::detail::image_traits< typename DestExposureT::MaskedImageT >::image_category()))
Warp (remap) one exposure to another.
def run(self, coaddExposures, bbox, wcs, dataIds, **kwargs)
Definition: getTemplate.py:596
Fit spatial kernel using approximate fluxes for candidates, and solving a linear system of equations.