lsst.ip.diffim ga1d1d01525+68ee36fe98
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imagePsfMatch.py
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1# This file is part of ip_diffim.
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3# Developed for the LSST Data Management System.
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5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
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8#
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14# This program is distributed in the hope that it will be useful,
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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.selectDetection.reEstimateBackground = False
71 self.selectDetection.thresholdValue = 10.0
72
73 # Minimal set of measurments for star selection
74 self.selectMeasurement.algorithms.names.clear()
75 self.selectMeasurement.algorithms.names = ('base_SdssCentroid', 'base_PsfFlux', 'base_PixelFlags',
76 'base_SdssShape', 'base_GaussianFlux', 'base_SkyCoord')
77 self.selectMeasurement.slots.modelFlux = None
78 self.selectMeasurement.slots.apFlux = None
79 self.selectMeasurement.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 ``pipetask`` command line interface supports a
161 flag --debug to import @b 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 Create a subclass of ImagePsfMatchTask that allows us to either match exposures, or subtract exposures:
207
208 .. code-block:: none
209
210 class MyImagePsfMatchTask(ImagePsfMatchTask):
211
212 def __init__(self, args, kwargs):
213 ImagePsfMatchTask.__init__(self, args, kwargs)
214
215 def run(self, templateExp, scienceExp, mode):
216 if mode == "matchExposures":
217 return self.matchExposures(templateExp, scienceExp)
218 elif mode == "subtractExposures":
219 return self.subtractExposures(templateExp, scienceExp)
220
221 And allow the user the freedom to either run the script in default mode,
222 or point to their own images on disk.
223 Note that these images must be readable as an lsst.afw.image.Exposure.
224
225 We have enabled some minor display debugging in this script via the --debug option. However, if you
226 have an lsstDebug debug.py in your PYTHONPATH you will get additional debugging displays. The following
227 block checks for this script:
228
229 .. code-block:: py
230
231 if args.debug:
232 try:
233 import debug
234 # Since I am displaying 2 images here, set the starting frame number for the LSST debug LSST
235 debug.lsstDebug.frame = 3
236 except ImportError as e:
237 print(e, file=sys.stderr)
238
239 Finally, we call a run method that we define below.
240 First set up a Config and modify some of the parameters.
241 E.g. use an "Alard-Lupton" sum-of-Gaussian basis,
242 fit for a differential background, and use low order spatial
243 variation in the kernel and background:
244
245 .. code-block:: py
246
247 def run(args):
248 #
249 # Create the Config and use sum of gaussian basis
250 #
251 config = ImagePsfMatchTask.ConfigClass()
252 config.kernel.name = "AL"
253 config.kernel.active.fitForBackground = True
254 config.kernel.active.spatialKernelOrder = 1
255 config.kernel.active.spatialBgOrder = 0
256
257 Make sure the images (if any) that were sent to the script exist on disk and are readable. If no images
258 are sent, make some fake data up for the sake of this example script (have a look at the code if you want
259 more details on generateFakeImages):
260
261 .. code-block:: py
262
263 # Run the requested method of the Task
264 if args.template is not None and args.science is not None:
265 if not os.path.isfile(args.template):
266 raise FileNotFoundError("Template image %s does not exist" % (args.template))
267 if not os.path.isfile(args.science):
268 raise FileNotFoundError("Science image %s does not exist" % (args.science))
269 try:
270 templateExp = afwImage.ExposureF(args.template)
271 except Exception as e:
272 raise RuntimeError("Cannot read template image %s" % (args.template))
273 try:
274 scienceExp = afwImage.ExposureF(args.science)
275 except Exception as e:
276 raise RuntimeError("Cannot read science image %s" % (args.science))
277 else:
278 templateExp, scienceExp = generateFakeImages()
279 config.kernel.active.sizeCellX = 128
280 config.kernel.active.sizeCellY = 128
281
282 Create and run the Task:
283
284 .. code-block:: py
285
286 # Create the Task
287 psfMatchTask = MyImagePsfMatchTask(config=config)
288 # Run the Task
289 result = psfMatchTask.run(templateExp, scienceExp, args.mode)
290
291 And finally provide some optional debugging displays:
292
293 .. code-block:: py
294
295 if args.debug:
296 # See if the LSST debug has incremented the frame number; if not start with frame 3
297 try:
298 frame = debug.lsstDebug.frame + 1
299 except Exception:
300 frame = 3
301 afwDisplay.Display(frame=frame).mtv(result.matchedExposure,
302 title="Example script: Matched Template Image")
303 if "subtractedExposure" in result.getDict():
304 afwDisplay.Display(frame=frame + 1).mtv(result.subtractedExposure,
305 title="Example script: Subtracted Image")
306 """
307
308 ConfigClass = ImagePsfMatchConfig
309
310 def __init__(self, *args, **kwargs):
311 """Create the ImagePsfMatchTask.
312 """
313 PsfMatchTask.__init__(self, *args, **kwargs)
314 self.kConfigkConfig = self.config.kernel.active
315 self._warper = afwMath.Warper.fromConfig(self.kConfigkConfig.warpingConfig)
316 # the background subtraction task uses a config from an unusual location,
317 # so cannot easily be constructed with makeSubtask
318 self.background = SubtractBackgroundTask(config=self.kConfigkConfig.afwBackgroundConfig, name="background",
319 parentTask=self)
320 self.selectSchema = afwTable.SourceTable.makeMinimalSchema()
322 self.makeSubtask("selectDetection", schema=self.selectSchema)
323 self.makeSubtask("selectMeasurement", schema=self.selectSchema, algMetadata=self.selectAlgMetadata)
324
325 @timeMethod
326 def matchExposures(self, templateExposure, scienceExposure,
327 templateFwhmPix=None, scienceFwhmPix=None,
328 candidateList=None, doWarping=True, convolveTemplate=True):
329 """Warp and PSF-match an exposure to the reference.
330
331 Do the following, in order:
332
333 - Warp templateExposure to match scienceExposure,
334 if doWarping True and their WCSs do not already match
335 - Determine a PSF matching kernel and differential background model
336 that matches templateExposure to scienceExposure
337 - Convolve templateExposure by PSF matching kernel
338
339 Parameters
340 ----------
341 templateExposure : `lsst.afw.image.Exposure`
342 Exposure to warp and PSF-match to the reference masked image
343 scienceExposure : `lsst.afw.image.Exposure`
344 Exposure whose WCS and PSF are to be matched to
345 templateFwhmPix :`float`
346 FWHM (in pixels) of the Psf in the template image (image to convolve)
347 scienceFwhmPix : `float`
348 FWHM (in pixels) of the Psf in the science image
349 candidateList : `list`, optional
350 a list of footprints/maskedImages for kernel candidates;
351 if `None` then source detection is run.
352
353 - Currently supported: list of Footprints or measAlg.PsfCandidateF
354
355 doWarping : `bool`
356 what to do if ``templateExposure`` and ``scienceExposure`` WCSs do not match:
357
358 - if `True` then warp ``templateExposure`` to match ``scienceExposure``
359 - if `False` then raise an Exception
360
361 convolveTemplate : `bool`
362 Whether to convolve the template image or the science image:
363
364 - if `True`, ``templateExposure`` is warped if doWarping,
365 ``templateExposure`` is convolved
366 - if `False`, ``templateExposure`` is warped if doWarping,
367 ``scienceExposure`` is convolved
368
369 Returns
370 -------
371 results : `lsst.pipe.base.Struct`
372 An `lsst.pipe.base.Struct` containing these fields:
373
374 - ``matchedImage`` : the PSF-matched exposure =
375 Warped ``templateExposure`` convolved by psfMatchingKernel. This has:
376
377 - the same parent bbox, Wcs and PhotoCalib as scienceExposure
378 - the same filter as templateExposure
379 - no Psf (because the PSF-matching process does not compute one)
380
381 - ``psfMatchingKernel`` : the PSF matching kernel
382 - ``backgroundModel`` : differential background model
383 - ``kernelCellSet`` : SpatialCellSet used to solve for the PSF matching kernel
384
385 Raises
386 ------
387 RuntimeError
388 Raised if doWarping is False and ``templateExposure`` and
389 ``scienceExposure`` WCSs do not match
390 """
391 if not self._validateWcs(templateExposure, scienceExposure):
392 if doWarping:
393 self.log.info("Astrometrically registering template to science image")
394 templatePsf = templateExposure.getPsf()
395 # Warp PSF before overwriting exposure
396 xyTransform = afwGeom.makeWcsPairTransform(templateExposure.getWcs(),
397 scienceExposure.getWcs())
398 psfWarped = WarpedPsf(templatePsf, xyTransform)
399 templateExposure = self._warper.warpExposure(scienceExposure.getWcs(),
400 templateExposure,
401 destBBox=scienceExposure.getBBox())
402 templateExposure.setPsf(psfWarped)
403 else:
404 self.log.error("ERROR: Input images not registered")
405 raise RuntimeError("Input images not registered")
406
407 if templateFwhmPix is None:
408 if not templateExposure.hasPsf():
409 self.log.warning("No estimate of Psf FWHM for template image")
410 else:
411 templateFwhmPix = diffimUtils.getPsfFwhm(templateExposure.psf)
412 self.log.info("templateFwhmPix: %s", templateFwhmPix)
413
414 if scienceFwhmPix is None:
415 if not scienceExposure.hasPsf():
416 self.log.warning("No estimate of Psf FWHM for science image")
417 else:
418 scienceFwhmPix = diffimUtils.getPsfFwhm(scienceExposure.psf)
419 self.log.info("scienceFwhmPix: %s", scienceFwhmPix)
420
421 if convolveTemplate:
422 kernelSize = self.makeKernelBasisList(templateFwhmPix, scienceFwhmPix)[0].getWidth()
423 candidateList = self.makeCandidateList(
424 templateExposure, scienceExposure, kernelSize, candidateList)
425 results = self.matchMaskedImages(
426 templateExposure.getMaskedImage(), scienceExposure.getMaskedImage(), candidateList,
427 templateFwhmPix=templateFwhmPix, scienceFwhmPix=scienceFwhmPix)
428 else:
429 kernelSize = self.makeKernelBasisList(scienceFwhmPix, templateFwhmPix)[0].getWidth()
430 candidateList = self.makeCandidateList(
431 templateExposure, scienceExposure, kernelSize, candidateList)
432 results = self.matchMaskedImages(
433 scienceExposure.getMaskedImage(), templateExposure.getMaskedImage(), candidateList,
434 templateFwhmPix=scienceFwhmPix, scienceFwhmPix=templateFwhmPix)
435
436 psfMatchedExposure = afwImage.makeExposure(results.matchedImage, scienceExposure.getWcs())
437 psfMatchedExposure.setFilter(templateExposure.getFilter())
438 psfMatchedExposure.setPhotoCalib(scienceExposure.getPhotoCalib())
439 results.warpedExposure = templateExposure
440 results.matchedExposure = psfMatchedExposure
441 return results
442
443 @timeMethod
444 def matchMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList,
445 templateFwhmPix=None, scienceFwhmPix=None):
446 """PSF-match a MaskedImage (templateMaskedImage) to a reference MaskedImage (scienceMaskedImage).
447
448 Do the following, in order:
449
450 - Determine a PSF matching kernel and differential background model
451 that matches templateMaskedImage to scienceMaskedImage
452 - Convolve templateMaskedImage by the PSF matching kernel
453
454 Parameters
455 ----------
456 templateMaskedImage : `lsst.afw.image.MaskedImage`
457 masked image to PSF-match to the reference masked image;
458 must be warped to match the reference masked image
459 scienceMaskedImage : `lsst.afw.image.MaskedImage`
460 maskedImage whose PSF is to be matched to
461 templateFwhmPix : `float`
462 FWHM (in pixels) of the Psf in the template image (image to convolve)
463 scienceFwhmPix : `float`
464 FWHM (in pixels) of the Psf in the science image
465 candidateList : `list`, optional
466 A list of footprints/maskedImages for kernel candidates;
467 if `None` then source detection is run.
468
469 - Currently supported: list of Footprints or measAlg.PsfCandidateF
470
471 Returns
472 -------
473 result : `callable`
474 An `lsst.pipe.base.Struct` containing these fields:
475
476 - psfMatchedMaskedImage: the PSF-matched masked image =
477 ``templateMaskedImage`` convolved with psfMatchingKernel.
478 This has the same xy0, dimensions and wcs as ``scienceMaskedImage``.
479 - psfMatchingKernel: the PSF matching kernel
480 - backgroundModel: differential background model
481 - kernelCellSet: SpatialCellSet used to solve for the PSF matching kernel
482
483 Raises
484 ------
485 RuntimeError
486 Raised if input images have different dimensions
487 """
488 import lsstDebug
489 display = lsstDebug.Info(__name__).display
490 displayTemplate = lsstDebug.Info(__name__).displayTemplate
491 displaySciIm = lsstDebug.Info(__name__).displaySciIm
492 displaySpatialCells = lsstDebug.Info(__name__).displaySpatialCells
493 maskTransparency = lsstDebug.Info(__name__).maskTransparency
494 if not maskTransparency:
495 maskTransparency = 0
496 if display:
497 afwDisplay.setDefaultMaskTransparency(maskTransparency)
498
499 if not candidateList:
500 raise RuntimeError("Candidate list must be populated by makeCandidateList")
501
502 if not self._validateSize(templateMaskedImage, scienceMaskedImage):
503 self.log.error("ERROR: Input images different size")
504 raise RuntimeError("Input images different size")
505
506 if display and displayTemplate:
507 disp = afwDisplay.Display(frame=lsstDebug.frame)
508 disp.mtv(templateMaskedImage, title="Image to convolve")
509 lsstDebug.frame += 1
510
511 if display and displaySciIm:
512 disp = afwDisplay.Display(frame=lsstDebug.frame)
513 disp.mtv(scienceMaskedImage, title="Image to not convolve")
514 lsstDebug.frame += 1
515
516 kernelCellSet = self._buildCellSet_buildCellSet(templateMaskedImage,
517 scienceMaskedImage,
518 candidateList)
519
520 if display and displaySpatialCells:
521 diffimUtils.showKernelSpatialCells(scienceMaskedImage, kernelCellSet,
522 symb="o", ctype=afwDisplay.CYAN, ctypeUnused=afwDisplay.YELLOW,
523 ctypeBad=afwDisplay.RED, size=4, frame=lsstDebug.frame,
524 title="Image to not convolve")
525 lsstDebug.frame += 1
526
527 if templateFwhmPix and scienceFwhmPix:
528 self.log.info("Matching Psf FWHM %.2f -> %.2f pix", templateFwhmPix, scienceFwhmPix)
529
530 if self.kConfigkConfig.useBicForKernelBasis:
531 tmpKernelCellSet = self._buildCellSet_buildCellSet(templateMaskedImage,
532 scienceMaskedImage,
533 candidateList)
534 nbe = diffimTools.NbasisEvaluator(self.kConfigkConfig, templateFwhmPix, scienceFwhmPix)
535 bicDegrees = nbe(tmpKernelCellSet, self.log)
536 basisList = self.makeKernelBasisList(templateFwhmPix, scienceFwhmPix,
537 basisDegGauss=bicDegrees[0], metadata=self.metadata)
538 del tmpKernelCellSet
539 else:
540 basisList = self.makeKernelBasisList(templateFwhmPix, scienceFwhmPix,
541 metadata=self.metadata)
542
543 spatialSolution, psfMatchingKernel, backgroundModel = self._solve(kernelCellSet, basisList)
544
545 psfMatchedMaskedImage = afwImage.MaskedImageF(templateMaskedImage.getBBox())
546 convolutionControl = afwMath.ConvolutionControl()
547 convolutionControl.setDoNormalize(False)
548 afwMath.convolve(psfMatchedMaskedImage, templateMaskedImage, psfMatchingKernel, convolutionControl)
549 return pipeBase.Struct(
550 matchedImage=psfMatchedMaskedImage,
551 psfMatchingKernel=psfMatchingKernel,
552 backgroundModel=backgroundModel,
553 kernelCellSet=kernelCellSet,
554 )
555
556 @timeMethod
557 def subtractExposures(self, templateExposure, scienceExposure,
558 templateFwhmPix=None, scienceFwhmPix=None,
559 candidateList=None, doWarping=True, convolveTemplate=True):
560 """Register, Psf-match and subtract two Exposures.
561
562 Do the following, in order:
563
564 - Warp templateExposure to match scienceExposure, if their WCSs do not already match
565 - Determine a PSF matching kernel and differential background model
566 that matches templateExposure to scienceExposure
567 - PSF-match templateExposure to scienceExposure
568 - Compute subtracted exposure (see return values for equation).
569
570 Parameters
571 ----------
572 templateExposure : `lsst.afw.image.ExposureF`
573 Exposure to PSF-match to scienceExposure
574 scienceExposure : `lsst.afw.image.ExposureF`
575 Reference Exposure
576 templateFwhmPix : `float`
577 FWHM (in pixels) of the Psf in the template image (image to convolve)
578 scienceFwhmPix : `float`
579 FWHM (in pixels) of the Psf in the science image
580 candidateList : `list`, optional
581 A list of footprints/maskedImages for kernel candidates;
582 if `None` then source detection is run.
583
584 - Currently supported: list of Footprints or measAlg.PsfCandidateF
585
586 doWarping : `bool`
587 What to do if ``templateExposure``` and ``scienceExposure`` WCSs do
588 not match:
589
590 - if `True` then warp ``templateExposure`` to match ``scienceExposure``
591 - if `False` then raise an Exception
592
593 convolveTemplate : `bool`
594 Convolve the template image or the science image
595
596 - if `True`, ``templateExposure`` is warped if doWarping,
597 ``templateExposure`` is convolved
598 - if `False`, ``templateExposure`` is warped if doWarping,
599 ``scienceExposure is`` convolved
600
601 Returns
602 -------
603 result : `lsst.pipe.base.Struct`
604 An `lsst.pipe.base.Struct` containing these fields:
605
606 - ``subtractedExposure`` : subtracted Exposure
607 scienceExposure - (matchedImage + backgroundModel)
608 - ``matchedImage`` : ``templateExposure`` after warping to match
609 ``templateExposure`` (if doWarping true),
610 and convolving with psfMatchingKernel
611 - ``psfMatchingKernel`` : PSF matching kernel
612 - ``backgroundModel`` : differential background model
613 - ``kernelCellSet`` : SpatialCellSet used to determine PSF matching kernel
614 """
615 results = self.matchExposures(
616 templateExposure=templateExposure,
617 scienceExposure=scienceExposure,
618 templateFwhmPix=templateFwhmPix,
619 scienceFwhmPix=scienceFwhmPix,
620 candidateList=candidateList,
621 doWarping=doWarping,
622 convolveTemplate=convolveTemplate
623 )
624 # Always inherit WCS and photocalib from science exposure
625 subtractedExposure = afwImage.ExposureF(scienceExposure, deep=True)
626 # Note, the decorrelation afterburner re-calculates the variance plane
627 # from the variance planes of the original exposures.
628 # That recalculation code must be in accordance with the
629 # photometric level set here in ``subtractedMaskedImage``.
630 if convolveTemplate:
631 subtractedMaskedImage = subtractedExposure.maskedImage
632 subtractedMaskedImage -= results.matchedExposure.maskedImage
633 subtractedMaskedImage -= results.backgroundModel
634 else:
635 subtractedMaskedImage = subtractedExposure.maskedImage
636 subtractedMaskedImage[:, :] = results.warpedExposure.maskedImage
637 subtractedMaskedImage -= results.matchedExposure.maskedImage
638 subtractedMaskedImage -= results.backgroundModel
639
640 # Preserve polarity of differences
641 subtractedMaskedImage *= -1
642
643 # Place back on native photometric scale
644 subtractedMaskedImage /= results.psfMatchingKernel.computeImage(
645 afwImage.ImageD(results.psfMatchingKernel.getDimensions()), False)
646 # We matched to the warped template
647 subtractedExposure.setPsf(results.warpedExposure.getPsf())
648
649 import lsstDebug
650 display = lsstDebug.Info(__name__).display
651 displayDiffIm = lsstDebug.Info(__name__).displayDiffIm
652 maskTransparency = lsstDebug.Info(__name__).maskTransparency
653 if not maskTransparency:
654 maskTransparency = 0
655 if display:
656 afwDisplay.setDefaultMaskTransparency(maskTransparency)
657 if display and displayDiffIm:
658 disp = afwDisplay.Display(frame=lsstDebug.frame)
659 disp.mtv(templateExposure, title="Template")
660 lsstDebug.frame += 1
661 disp = afwDisplay.Display(frame=lsstDebug.frame)
662 disp.mtv(results.matchedExposure, title="Matched template")
663 lsstDebug.frame += 1
664 disp = afwDisplay.Display(frame=lsstDebug.frame)
665 disp.mtv(scienceExposure, title="Science Image")
666 lsstDebug.frame += 1
667 disp = afwDisplay.Display(frame=lsstDebug.frame)
668 disp.mtv(subtractedExposure, title="Difference Image")
669 lsstDebug.frame += 1
670
671 results.subtractedExposure = subtractedExposure
672 return results
673
674 @timeMethod
675 def subtractMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList,
676 templateFwhmPix=None, scienceFwhmPix=None):
677 """Psf-match and subtract two MaskedImages.
678
679 Do the following, in order:
680
681 - PSF-match templateMaskedImage to scienceMaskedImage
682 - Determine the differential background
683 - Return the difference: scienceMaskedImage
684 ((warped templateMaskedImage convolved with psfMatchingKernel) + backgroundModel)
685
686 Parameters
687 ----------
688 templateMaskedImage : `lsst.afw.image.MaskedImage`
689 MaskedImage to PSF-match to ``scienceMaskedImage``
690 scienceMaskedImage : `lsst.afw.image.MaskedImage`
691 Reference MaskedImage
692 templateFwhmPix : `float`
693 FWHM (in pixels) of the Psf in the template image (image to convolve)
694 scienceFwhmPix : `float`
695 FWHM (in pixels) of the Psf in the science image
696 candidateList : `list`, optional
697 A list of footprints/maskedImages for kernel candidates;
698 if `None` then source detection is run.
699
700 - Currently supported: list of Footprints or measAlg.PsfCandidateF
701
702 Returns
703 -------
704 results : `lsst.pipe.base.Struct`
705 An `lsst.pipe.base.Struct` containing these fields:
706
707 - ``subtractedMaskedImage`` : ``scienceMaskedImage`` - (matchedImage + backgroundModel)
708 - ``matchedImage`` : templateMaskedImage convolved with psfMatchingKernel
709 - `psfMatchingKernel`` : PSF matching kernel
710 - ``backgroundModel`` : differential background model
711 - ``kernelCellSet`` : SpatialCellSet used to determine PSF matching kernel
712
713 """
714 if not candidateList:
715 raise RuntimeError("Candidate list must be populated by makeCandidateList")
716
717 results = self.matchMaskedImages(
718 templateMaskedImage=templateMaskedImage,
719 scienceMaskedImage=scienceMaskedImage,
720 candidateList=candidateList,
721 templateFwhmPix=templateFwhmPix,
722 scienceFwhmPix=scienceFwhmPix,
723 )
724
725 subtractedMaskedImage = afwImage.MaskedImageF(scienceMaskedImage, True)
726 subtractedMaskedImage -= results.matchedImage
727 subtractedMaskedImage -= results.backgroundModel
728 results.subtractedMaskedImage = subtractedMaskedImage
729
730 import lsstDebug
731 display = lsstDebug.Info(__name__).display
732 displayDiffIm = lsstDebug.Info(__name__).displayDiffIm
733 maskTransparency = lsstDebug.Info(__name__).maskTransparency
734 if not maskTransparency:
735 maskTransparency = 0
736 if display:
737 afwDisplay.setDefaultMaskTransparency(maskTransparency)
738 if display and displayDiffIm:
739 disp = afwDisplay.Display(frame=lsstDebug.frame)
740 disp.mtv(subtractedMaskedImage, title="Subtracted masked image")
741 lsstDebug.frame += 1
742
743 return results
744
745 def getSelectSources(self, exposure, sigma=None, doSmooth=True, idFactory=None):
746 """Get sources to use for Psf-matching.
747
748 This method runs detection and measurement on an exposure.
749 The returned set of sources will be used as candidates for
750 Psf-matching.
751
752 Parameters
753 ----------
754 exposure : `lsst.afw.image.Exposure`
755 Exposure on which to run detection/measurement
756 sigma : `float`
757 Detection threshold
758 doSmooth : `bool`
759 Whether or not to smooth the Exposure with Psf before detection
760 idFactory :
761 Factory for the generation of Source ids
762
763 Returns
764 -------
765 selectSources :
766 source catalog containing candidates for the Psf-matching
767 """
768 if idFactory:
769 table = afwTable.SourceTable.make(self.selectSchema, idFactory)
770 else:
771 table = afwTable.SourceTable.make(self.selectSchema)
772 mi = exposure.getMaskedImage()
773
774 imArr = mi.getImage().getArray()
775 maskArr = mi.getMask().getArray()
776 miArr = np.ma.masked_array(imArr, mask=maskArr)
777 try:
778 fitBg = self.background.fitBackground(mi)
779 bkgd = fitBg.getImageF(self.background.config.algorithm,
780 self.background.config.undersampleStyle)
781 except Exception:
782 self.log.warning("Failed to get background model. Falling back to median background estimation")
783 bkgd = np.ma.median(miArr)
784
785 # Take off background for detection
786 mi -= bkgd
787 try:
788 table.setMetadata(self.selectAlgMetadata)
789 detRet = self.selectDetection.run(
790 table=table,
791 exposure=exposure,
792 sigma=sigma,
793 doSmooth=doSmooth
794 )
795 selectSources = detRet.sources
796 self.selectMeasurement.run(measCat=selectSources, exposure=exposure)
797 finally:
798 # Put back on the background in case it is needed down stream
799 mi += bkgd
800 del bkgd
801 return selectSources
802
803 def makeCandidateList(self, templateExposure, scienceExposure, kernelSize, candidateList=None):
804 """Make a list of acceptable KernelCandidates.
805
806 Accept or generate a list of candidate sources for
807 Psf-matching, and examine the Mask planes in both of the
808 images for indications of bad pixels
809
810 Parameters
811 ----------
812 templateExposure : `lsst.afw.image.Exposure`
813 Exposure that will be convolved
814 scienceExposure : `lsst.afw.image.Exposure`
815 Exposure that will be matched-to
816 kernelSize : `float`
817 Dimensions of the Psf-matching Kernel, used to grow detection footprints
818 candidateList : `list`, optional
819 List of Sources to examine. Elements must be of type afw.table.Source
820 or a type that wraps a Source and has a getSource() method, such as
821 meas.algorithms.PsfCandidateF.
822
823 Returns
824 -------
825 candidateList : `list` of `dict`
826 A list of dicts having a "source" and "footprint"
827 field for the Sources deemed to be appropriate for Psf
828 matching
829 """
830 if candidateList is None:
831 candidateList = self.getSelectSources(scienceExposure)
832
833 if len(candidateList) < 1:
834 raise RuntimeError("No candidates in candidateList")
835
836 listTypes = set(type(x) for x in candidateList)
837 if len(listTypes) > 1:
838 raise RuntimeError("Candidate list contains mixed types: %s" % [t for t in listTypes])
839
840 if not isinstance(candidateList[0], afwTable.SourceRecord):
841 try:
842 candidateList[0].getSource()
843 except Exception as e:
844 raise RuntimeError(f"Candidate List is of type: {type(candidateList[0])} "
845 "Can only make candidate list from list of afwTable.SourceRecords, "
846 f"measAlg.PsfCandidateF or other type with a getSource() method: {e}")
847 candidateList = [c.getSource() for c in candidateList]
848
849 candidateList = diffimTools.sourceToFootprintList(candidateList,
850 templateExposure, scienceExposure,
851 kernelSize,
852 self.kConfigkConfig.detectionConfig,
853 self.log)
854 if len(candidateList) == 0:
855 raise RuntimeError("Cannot find any objects suitable for KernelCandidacy")
856
857 return candidateList
858
859 def makeKernelBasisList(self, targetFwhmPix=None, referenceFwhmPix=None,
860 basisDegGauss=None, basisSigmaGauss=None, metadata=None):
861 """Wrapper to set log messages for
863
864 Parameters
865 ----------
866 targetFwhmPix : `float`, optional
867 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
868 Not used for delta function basis sets.
869 referenceFwhmPix : `float`, optional
870 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
871 Not used for delta function basis sets.
872 basisDegGauss : `list` of `int`, optional
873 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
874 Not used for delta function basis sets.
875 basisSigmaGauss : `list` of `int`, optional
876 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
877 Not used for delta function basis sets.
878 metadata : `lsst.daf.base.PropertySet`, optional
879 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
880 Not used for delta function basis sets.
881
882 Returns
883 -------
884 basisList: `list` of `lsst.afw.math.kernel.FixedKernel`
885 List of basis kernels.
886 """
887 basisList = makeKernelBasisList(self.kConfigkConfig,
888 targetFwhmPix=targetFwhmPix,
889 referenceFwhmPix=referenceFwhmPix,
890 basisDegGauss=basisDegGauss,
891 basisSigmaGauss=basisSigmaGauss,
892 metadata=metadata)
893 if targetFwhmPix == referenceFwhmPix:
894 self.log.info("Target and reference psf fwhms are equal, falling back to config values")
895 elif referenceFwhmPix > targetFwhmPix:
896 self.log.info("Reference psf fwhm is the greater, normal convolution mode")
897 else:
898 self.log.info("Target psf fwhm is the greater, deconvolution mode")
899
900 return basisList
901
902 def _adaptCellSize(self, candidateList):
903 """NOT IMPLEMENTED YET.
904 """
905 return self.kConfigkConfig.sizeCellX, self.kConfigkConfig.sizeCellY
906
907 def _buildCellSet(self, templateMaskedImage, scienceMaskedImage, candidateList):
908 """Build a SpatialCellSet for use with the solve method.
909
910 Parameters
911 ----------
912 templateMaskedImage : `lsst.afw.image.MaskedImage`
913 MaskedImage to PSF-matched to scienceMaskedImage
914 scienceMaskedImage : `lsst.afw.image.MaskedImage`
915 Reference MaskedImage
916 candidateList : `list`
917 A list of footprints/maskedImages for kernel candidates;
918
919 - Currently supported: list of Footprints or measAlg.PsfCandidateF
920
921 Returns
922 -------
923 kernelCellSet : `lsst.afw.math.SpatialCellSet`
924 a SpatialCellSet for use with self._solve
925 """
926 if not candidateList:
927 raise RuntimeError("Candidate list must be populated by makeCandidateList")
928
929 sizeCellX, sizeCellY = self._adaptCellSize(candidateList)
930
931 # Object to store the KernelCandidates for spatial modeling
932 kernelCellSet = afwMath.SpatialCellSet(templateMaskedImage.getBBox(),
933 sizeCellX, sizeCellY)
934
935 ps = pexConfig.makePropertySet(self.kConfigkConfig)
936 # Place candidates within the spatial grid
937 for cand in candidateList:
938 if isinstance(cand, afwDetect.Footprint):
939 bbox = cand.getBBox()
940 else:
941 bbox = cand['footprint'].getBBox()
942 tmi = afwImage.MaskedImageF(templateMaskedImage, bbox)
943 smi = afwImage.MaskedImageF(scienceMaskedImage, bbox)
944
945 if not isinstance(cand, afwDetect.Footprint):
946 if 'source' in cand:
947 cand = cand['source']
948 xPos = cand.getCentroid()[0]
949 yPos = cand.getCentroid()[1]
950 cand = diffimLib.makeKernelCandidate(xPos, yPos, tmi, smi, ps)
951
952 self.log.debug("Candidate %d at %f, %f", cand.getId(), cand.getXCenter(), cand.getYCenter())
953 kernelCellSet.insertCandidate(cand)
954
955 return kernelCellSet
956
957 def _validateSize(self, templateMaskedImage, scienceMaskedImage):
958 """Return True if two image-like objects are the same size.
959 """
960 return templateMaskedImage.getDimensions() == scienceMaskedImage.getDimensions()
961
962 def _validateWcs(self, templateExposure, scienceExposure):
963 """Return True if the WCS of the two Exposures have the same origin and extent.
964 """
965 templateWcs = templateExposure.getWcs()
966 scienceWcs = scienceExposure.getWcs()
967 templateBBox = templateExposure.getBBox()
968 scienceBBox = scienceExposure.getBBox()
969
970 # LLC
971 templateOrigin = templateWcs.pixelToSky(geom.Point2D(templateBBox.getBegin()))
972 scienceOrigin = scienceWcs.pixelToSky(geom.Point2D(scienceBBox.getBegin()))
973
974 # URC
975 templateLimit = templateWcs.pixelToSky(geom.Point2D(templateBBox.getEnd()))
976 scienceLimit = scienceWcs.pixelToSky(geom.Point2D(scienceBBox.getEnd()))
977
978 self.log.info("Template Wcs : %f,%f -> %f,%f",
979 templateOrigin[0], templateOrigin[1],
980 templateLimit[0], templateLimit[1])
981 self.log.info("Science Wcs : %f,%f -> %f,%f",
982 scienceOrigin[0], scienceOrigin[1],
983 scienceLimit[0], scienceLimit[1])
984
985 templateBBox = geom.Box2D(templateOrigin.getPosition(geom.degrees),
986 templateLimit.getPosition(geom.degrees))
987 scienceBBox = geom.Box2D(scienceOrigin.getPosition(geom.degrees),
988 scienceLimit.getPosition(geom.degrees))
989 if not (templateBBox.overlaps(scienceBBox)):
990 raise RuntimeError("Input images do not overlap at all")
991
992 if ((templateOrigin != scienceOrigin)
993 or (templateLimit != scienceLimit)
994 or (templateExposure.getDimensions() != scienceExposure.getDimensions())):
995 return False
996 return True
997
998
999subtractAlgorithmRegistry = pexConfig.makeRegistry(
1000 doc="A registry of subtraction algorithms for use as a subtask in imageDifference",
1001)
1002
1003subtractAlgorithmRegistry.register('al', ImagePsfMatchTask)
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
std::shared_ptr< TransformPoint2ToPoint2 > makeWcsPairTransform(SkyWcs const &src, SkyWcs const &dst)
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 >())
void convolve(OutImageT &convolvedImage, InImageT const &inImage, KernelT const &kernel, ConvolutionControl const &convolutionControl=ConvolutionControl())
def run(self, coaddExposures, bbox, wcs, dataIds, **kwargs)
Definition: getTemplate.py:494