lsst.ip.diffim gef7b216a29+f391811cb4
Loading...
Searching...
No Matches
imagePsfMatch.py
Go to the documentation of this file.
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.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 def getFwhmPix(self, psf, position=None):
326 """Return the FWHM in pixels of a Psf.
327 """
328 if position is None:
329 position = psf.getAveragePosition()
330 sigPix = psf.computeShape(position).getDeterminantRadius()
331 return sigPix*sigma2fwhm
332
333 @timeMethod
334 def matchExposures(self, templateExposure, scienceExposure,
335 templateFwhmPix=None, scienceFwhmPix=None,
336 candidateList=None, doWarping=True, convolveTemplate=True):
337 """Warp and PSF-match an exposure to the reference.
338
339 Do the following, in order:
340
341 - Warp templateExposure to match scienceExposure,
342 if doWarping True and their WCSs do not already match
343 - Determine a PSF matching kernel and differential background model
344 that matches templateExposure to scienceExposure
345 - Convolve templateExposure by PSF matching kernel
346
347 Parameters
348 ----------
349 templateExposure : `lsst.afw.image.Exposure`
350 Exposure to warp and PSF-match to the reference masked image
351 scienceExposure : `lsst.afw.image.Exposure`
352 Exposure whose WCS and PSF are to be matched to
353 templateFwhmPix :`float`
354 FWHM (in pixels) of the Psf in the template image (image to convolve)
355 scienceFwhmPix : `float`
356 FWHM (in pixels) of the Psf in the science image
357 candidateList : `list`, optional
358 a list of footprints/maskedImages for kernel candidates;
359 if `None` then source detection is run.
360
361 - Currently supported: list of Footprints or measAlg.PsfCandidateF
362
363 doWarping : `bool`
364 what to do if ``templateExposure`` and ``scienceExposure`` WCSs do not match:
365
366 - if `True` then warp ``templateExposure`` to match ``scienceExposure``
367 - if `False` then raise an Exception
368
369 convolveTemplate : `bool`
370 Whether to convolve the template image or the science image:
371
372 - if `True`, ``templateExposure`` is warped if doWarping,
373 ``templateExposure`` is convolved
374 - if `False`, ``templateExposure`` is warped if doWarping,
375 ``scienceExposure`` is convolved
376
377 Returns
378 -------
379 results : `lsst.pipe.base.Struct`
380 An `lsst.pipe.base.Struct` containing these fields:
381
382 - ``matchedImage`` : the PSF-matched exposure =
383 Warped ``templateExposure`` convolved by psfMatchingKernel. This has:
384
385 - the same parent bbox, Wcs and PhotoCalib as scienceExposure
386 - the same filter as templateExposure
387 - no Psf (because the PSF-matching process does not compute one)
388
389 - ``psfMatchingKernel`` : the PSF matching kernel
390 - ``backgroundModel`` : differential background model
391 - ``kernelCellSet`` : SpatialCellSet used to solve for the PSF matching kernel
392
393 Raises
394 ------
395 RuntimeError
396 Raised if doWarping is False and ``templateExposure`` and
397 ``scienceExposure`` WCSs do not match
398 """
399 if not self._validateWcs(templateExposure, scienceExposure):
400 if doWarping:
401 self.log.info("Astrometrically registering template to science image")
402 templatePsf = templateExposure.getPsf()
403 # Warp PSF before overwriting exposure
404 xyTransform = afwGeom.makeWcsPairTransform(templateExposure.getWcs(),
405 scienceExposure.getWcs())
406 psfWarped = WarpedPsf(templatePsf, xyTransform)
407 templateExposure = self._warper.warpExposure(scienceExposure.getWcs(),
408 templateExposure,
409 destBBox=scienceExposure.getBBox())
410 templateExposure.setPsf(psfWarped)
411 else:
412 self.log.error("ERROR: Input images not registered")
413 raise RuntimeError("Input images not registered")
414
415 if templateFwhmPix is None:
416 if not templateExposure.hasPsf():
417 self.log.warning("No estimate of Psf FWHM for template image")
418 else:
419 templateFwhmPix = self.getFwhmPix(templateExposure.getPsf())
420 self.log.info("templateFwhmPix: %s", templateFwhmPix)
421
422 if scienceFwhmPix is None:
423 if not scienceExposure.hasPsf():
424 self.log.warning("No estimate of Psf FWHM for science image")
425 else:
426 scienceFwhmPix = self.getFwhmPix(scienceExposure.getPsf())
427 self.log.info("scienceFwhmPix: %s", scienceFwhmPix)
428
429 if convolveTemplate:
430 kernelSize = self.makeKernelBasisList(templateFwhmPix, scienceFwhmPix)[0].getWidth()
431 candidateList = self.makeCandidateList(
432 templateExposure, scienceExposure, kernelSize, candidateList)
433 results = self.matchMaskedImages(
434 templateExposure.getMaskedImage(), scienceExposure.getMaskedImage(), candidateList,
435 templateFwhmPix=templateFwhmPix, scienceFwhmPix=scienceFwhmPix)
436 else:
437 kernelSize = self.makeKernelBasisList(scienceFwhmPix, templateFwhmPix)[0].getWidth()
438 candidateList = self.makeCandidateList(
439 templateExposure, scienceExposure, kernelSize, candidateList)
440 results = self.matchMaskedImages(
441 scienceExposure.getMaskedImage(), templateExposure.getMaskedImage(), candidateList,
442 templateFwhmPix=scienceFwhmPix, scienceFwhmPix=templateFwhmPix)
443
444 psfMatchedExposure = afwImage.makeExposure(results.matchedImage, scienceExposure.getWcs())
445 psfMatchedExposure.setFilter(templateExposure.getFilter())
446 psfMatchedExposure.setPhotoCalib(scienceExposure.getPhotoCalib())
447 results.warpedExposure = templateExposure
448 results.matchedExposure = psfMatchedExposure
449 return results
450
451 @timeMethod
452 def matchMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList,
453 templateFwhmPix=None, scienceFwhmPix=None):
454 """PSF-match a MaskedImage (templateMaskedImage) to a reference MaskedImage (scienceMaskedImage).
455
456 Do the following, in order:
457
458 - Determine a PSF matching kernel and differential background model
459 that matches templateMaskedImage to scienceMaskedImage
460 - Convolve templateMaskedImage by the PSF matching kernel
461
462 Parameters
463 ----------
464 templateMaskedImage : `lsst.afw.image.MaskedImage`
465 masked image to PSF-match to the reference masked image;
466 must be warped to match the reference masked image
467 scienceMaskedImage : `lsst.afw.image.MaskedImage`
468 maskedImage whose PSF is to be matched to
469 templateFwhmPix : `float`
470 FWHM (in pixels) of the Psf in the template image (image to convolve)
471 scienceFwhmPix : `float`
472 FWHM (in pixels) of the Psf in the science image
473 candidateList : `list`, optional
474 A list of footprints/maskedImages for kernel candidates;
475 if `None` then source detection is run.
476
477 - Currently supported: list of Footprints or measAlg.PsfCandidateF
478
479 Returns
480 -------
481 result : `callable`
482 An `lsst.pipe.base.Struct` containing these fields:
483
484 - psfMatchedMaskedImage: the PSF-matched masked image =
485 ``templateMaskedImage`` convolved with psfMatchingKernel.
486 This has the same xy0, dimensions and wcs as ``scienceMaskedImage``.
487 - psfMatchingKernel: the PSF matching kernel
488 - backgroundModel: differential background model
489 - kernelCellSet: SpatialCellSet used to solve for the PSF matching kernel
490
491 Raises
492 ------
493 RuntimeError
494 Raised if input images have different dimensions
495 """
496 import lsstDebug
497 display = lsstDebug.Info(__name__).display
498 displayTemplate = lsstDebug.Info(__name__).displayTemplate
499 displaySciIm = lsstDebug.Info(__name__).displaySciIm
500 displaySpatialCells = lsstDebug.Info(__name__).displaySpatialCells
501 maskTransparency = lsstDebug.Info(__name__).maskTransparency
502 if not maskTransparency:
503 maskTransparency = 0
504 if display:
505 afwDisplay.setDefaultMaskTransparency(maskTransparency)
506
507 if not candidateList:
508 raise RuntimeError("Candidate list must be populated by makeCandidateList")
509
510 if not self._validateSize(templateMaskedImage, scienceMaskedImage):
511 self.log.error("ERROR: Input images different size")
512 raise RuntimeError("Input images different size")
513
514 if display and displayTemplate:
515 disp = afwDisplay.Display(frame=lsstDebug.frame)
516 disp.mtv(templateMaskedImage, title="Image to convolve")
517 lsstDebug.frame += 1
518
519 if display and displaySciIm:
520 disp = afwDisplay.Display(frame=lsstDebug.frame)
521 disp.mtv(scienceMaskedImage, title="Image to not convolve")
522 lsstDebug.frame += 1
523
524 kernelCellSet = self._buildCellSet_buildCellSet(templateMaskedImage,
525 scienceMaskedImage,
526 candidateList)
527
528 if display and displaySpatialCells:
529 diffimUtils.showKernelSpatialCells(scienceMaskedImage, kernelCellSet,
530 symb="o", ctype=afwDisplay.CYAN, ctypeUnused=afwDisplay.YELLOW,
531 ctypeBad=afwDisplay.RED, size=4, frame=lsstDebug.frame,
532 title="Image to not convolve")
533 lsstDebug.frame += 1
534
535 if templateFwhmPix and scienceFwhmPix:
536 self.log.info("Matching Psf FWHM %.2f -> %.2f pix", templateFwhmPix, scienceFwhmPix)
537
538 if self.kConfigkConfig.useBicForKernelBasis:
539 tmpKernelCellSet = self._buildCellSet_buildCellSet(templateMaskedImage,
540 scienceMaskedImage,
541 candidateList)
542 nbe = diffimTools.NbasisEvaluator(self.kConfigkConfig, templateFwhmPix, scienceFwhmPix)
543 bicDegrees = nbe(tmpKernelCellSet, self.log)
544 basisList = self.makeKernelBasisList(templateFwhmPix, scienceFwhmPix,
545 basisDegGauss=bicDegrees[0], metadata=self.metadata)
546 del tmpKernelCellSet
547 else:
548 basisList = self.makeKernelBasisList(templateFwhmPix, scienceFwhmPix,
549 metadata=self.metadata)
550
551 spatialSolution, psfMatchingKernel, backgroundModel = self._solve(kernelCellSet, basisList)
552
553 psfMatchedMaskedImage = afwImage.MaskedImageF(templateMaskedImage.getBBox())
554 convolutionControl = afwMath.ConvolutionControl()
555 convolutionControl.setDoNormalize(False)
556 afwMath.convolve(psfMatchedMaskedImage, templateMaskedImage, psfMatchingKernel, convolutionControl)
557 return pipeBase.Struct(
558 matchedImage=psfMatchedMaskedImage,
559 psfMatchingKernel=psfMatchingKernel,
560 backgroundModel=backgroundModel,
561 kernelCellSet=kernelCellSet,
562 )
563
564 @timeMethod
565 def subtractExposures(self, templateExposure, scienceExposure,
566 templateFwhmPix=None, scienceFwhmPix=None,
567 candidateList=None, doWarping=True, convolveTemplate=True):
568 """Register, Psf-match and subtract two Exposures.
569
570 Do the following, in order:
571
572 - Warp templateExposure to match scienceExposure, if their WCSs do not already match
573 - Determine a PSF matching kernel and differential background model
574 that matches templateExposure to scienceExposure
575 - PSF-match templateExposure to scienceExposure
576 - Compute subtracted exposure (see return values for equation).
577
578 Parameters
579 ----------
580 templateExposure : `lsst.afw.image.ExposureF`
581 Exposure to PSF-match to scienceExposure
582 scienceExposure : `lsst.afw.image.ExposureF`
583 Reference Exposure
584 templateFwhmPix : `float`
585 FWHM (in pixels) of the Psf in the template image (image to convolve)
586 scienceFwhmPix : `float`
587 FWHM (in pixels) of the Psf in the science image
588 candidateList : `list`, optional
589 A list of footprints/maskedImages for kernel candidates;
590 if `None` then source detection is run.
591
592 - Currently supported: list of Footprints or measAlg.PsfCandidateF
593
594 doWarping : `bool`
595 What to do if ``templateExposure``` and ``scienceExposure`` WCSs do
596 not match:
597
598 - if `True` then warp ``templateExposure`` to match ``scienceExposure``
599 - if `False` then raise an Exception
600
601 convolveTemplate : `bool`
602 Convolve the template image or the science image
603
604 - if `True`, ``templateExposure`` is warped if doWarping,
605 ``templateExposure`` is convolved
606 - if `False`, ``templateExposure`` is warped if doWarping,
607 ``scienceExposure is`` convolved
608
609 Returns
610 -------
611 result : `lsst.pipe.base.Struct`
612 An `lsst.pipe.base.Struct` containing these fields:
613
614 - ``subtractedExposure`` : subtracted Exposure
615 scienceExposure - (matchedImage + backgroundModel)
616 - ``matchedImage`` : ``templateExposure`` after warping to match
617 ``templateExposure`` (if doWarping true),
618 and convolving with psfMatchingKernel
619 - ``psfMatchingKernel`` : PSF matching kernel
620 - ``backgroundModel`` : differential background model
621 - ``kernelCellSet`` : SpatialCellSet used to determine PSF matching kernel
622 """
623 results = self.matchExposures(
624 templateExposure=templateExposure,
625 scienceExposure=scienceExposure,
626 templateFwhmPix=templateFwhmPix,
627 scienceFwhmPix=scienceFwhmPix,
628 candidateList=candidateList,
629 doWarping=doWarping,
630 convolveTemplate=convolveTemplate
631 )
632 # Always inherit WCS and photocalib from science exposure
633 subtractedExposure = afwImage.ExposureF(scienceExposure, deep=True)
634 # Note, the decorrelation afterburner re-calculates the variance plane
635 # from the variance planes of the original exposures.
636 # That recalculation code must be in accordance with the
637 # photometric level set here in ``subtractedMaskedImage``.
638 if convolveTemplate:
639 subtractedMaskedImage = subtractedExposure.maskedImage
640 subtractedMaskedImage -= results.matchedExposure.maskedImage
641 subtractedMaskedImage -= results.backgroundModel
642 else:
643 subtractedMaskedImage = subtractedExposure.maskedImage
644 subtractedMaskedImage[:, :] = results.warpedExposure.maskedImage
645 subtractedMaskedImage -= results.matchedExposure.maskedImage
646 subtractedMaskedImage -= results.backgroundModel
647
648 # Preserve polarity of differences
649 subtractedMaskedImage *= -1
650
651 # Place back on native photometric scale
652 subtractedMaskedImage /= results.psfMatchingKernel.computeImage(
653 afwImage.ImageD(results.psfMatchingKernel.getDimensions()), False)
654 # We matched to the warped template
655 subtractedExposure.setPsf(results.warpedExposure.getPsf())
656
657 import lsstDebug
658 display = lsstDebug.Info(__name__).display
659 displayDiffIm = lsstDebug.Info(__name__).displayDiffIm
660 maskTransparency = lsstDebug.Info(__name__).maskTransparency
661 if not maskTransparency:
662 maskTransparency = 0
663 if display:
664 afwDisplay.setDefaultMaskTransparency(maskTransparency)
665 if display and displayDiffIm:
666 disp = afwDisplay.Display(frame=lsstDebug.frame)
667 disp.mtv(templateExposure, title="Template")
668 lsstDebug.frame += 1
669 disp = afwDisplay.Display(frame=lsstDebug.frame)
670 disp.mtv(results.matchedExposure, title="Matched template")
671 lsstDebug.frame += 1
672 disp = afwDisplay.Display(frame=lsstDebug.frame)
673 disp.mtv(scienceExposure, title="Science Image")
674 lsstDebug.frame += 1
675 disp = afwDisplay.Display(frame=lsstDebug.frame)
676 disp.mtv(subtractedExposure, title="Difference Image")
677 lsstDebug.frame += 1
678
679 results.subtractedExposure = subtractedExposure
680 return results
681
682 @timeMethod
683 def subtractMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList,
684 templateFwhmPix=None, scienceFwhmPix=None):
685 """Psf-match and subtract two MaskedImages.
686
687 Do the following, in order:
688
689 - PSF-match templateMaskedImage to scienceMaskedImage
690 - Determine the differential background
691 - Return the difference: scienceMaskedImage
692 ((warped templateMaskedImage convolved with psfMatchingKernel) + backgroundModel)
693
694 Parameters
695 ----------
696 templateMaskedImage : `lsst.afw.image.MaskedImage`
697 MaskedImage to PSF-match to ``scienceMaskedImage``
698 scienceMaskedImage : `lsst.afw.image.MaskedImage`
699 Reference MaskedImage
700 templateFwhmPix : `float`
701 FWHM (in pixels) of the Psf in the template image (image to convolve)
702 scienceFwhmPix : `float`
703 FWHM (in pixels) of the Psf in the science image
704 candidateList : `list`, optional
705 A list of footprints/maskedImages for kernel candidates;
706 if `None` then source detection is run.
707
708 - Currently supported: list of Footprints or measAlg.PsfCandidateF
709
710 Returns
711 -------
712 results : `lsst.pipe.base.Struct`
713 An `lsst.pipe.base.Struct` containing these fields:
714
715 - ``subtractedMaskedImage`` : ``scienceMaskedImage`` - (matchedImage + backgroundModel)
716 - ``matchedImage`` : templateMaskedImage convolved with psfMatchingKernel
717 - `psfMatchingKernel`` : PSF matching kernel
718 - ``backgroundModel`` : differential background model
719 - ``kernelCellSet`` : SpatialCellSet used to determine PSF matching kernel
720
721 """
722 if not candidateList:
723 raise RuntimeError("Candidate list must be populated by makeCandidateList")
724
725 results = self.matchMaskedImages(
726 templateMaskedImage=templateMaskedImage,
727 scienceMaskedImage=scienceMaskedImage,
728 candidateList=candidateList,
729 templateFwhmPix=templateFwhmPix,
730 scienceFwhmPix=scienceFwhmPix,
731 )
732
733 subtractedMaskedImage = afwImage.MaskedImageF(scienceMaskedImage, True)
734 subtractedMaskedImage -= results.matchedImage
735 subtractedMaskedImage -= results.backgroundModel
736 results.subtractedMaskedImage = subtractedMaskedImage
737
738 import lsstDebug
739 display = lsstDebug.Info(__name__).display
740 displayDiffIm = lsstDebug.Info(__name__).displayDiffIm
741 maskTransparency = lsstDebug.Info(__name__).maskTransparency
742 if not maskTransparency:
743 maskTransparency = 0
744 if display:
745 afwDisplay.setDefaultMaskTransparency(maskTransparency)
746 if display and displayDiffIm:
747 disp = afwDisplay.Display(frame=lsstDebug.frame)
748 disp.mtv(subtractedMaskedImage, title="Subtracted masked image")
749 lsstDebug.frame += 1
750
751 return results
752
753 def getSelectSources(self, exposure, sigma=None, doSmooth=True, idFactory=None):
754 """Get sources to use for Psf-matching.
755
756 This method runs detection and measurement on an exposure.
757 The returned set of sources will be used as candidates for
758 Psf-matching.
759
760 Parameters
761 ----------
762 exposure : `lsst.afw.image.Exposure`
763 Exposure on which to run detection/measurement
764 sigma : `float`
765 Detection threshold
766 doSmooth : `bool`
767 Whether or not to smooth the Exposure with Psf before detection
768 idFactory :
769 Factory for the generation of Source ids
770
771 Returns
772 -------
773 selectSources :
774 source catalog containing candidates for the Psf-matching
775 """
776 if idFactory:
777 table = afwTable.SourceTable.make(self.selectSchema, idFactory)
778 else:
779 table = afwTable.SourceTable.make(self.selectSchema)
780 mi = exposure.getMaskedImage()
781
782 imArr = mi.getImage().getArray()
783 maskArr = mi.getMask().getArray()
784 miArr = np.ma.masked_array(imArr, mask=maskArr)
785 try:
786 fitBg = self.background.fitBackground(mi)
787 bkgd = fitBg.getImageF(self.background.config.algorithm,
788 self.background.config.undersampleStyle)
789 except Exception:
790 self.log.warning("Failed to get background model. Falling back to median background estimation")
791 bkgd = np.ma.median(miArr)
792
793 # Take off background for detection
794 mi -= bkgd
795 try:
796 table.setMetadata(self.selectAlgMetadata)
797 detRet = self.selectDetection.run(
798 table=table,
799 exposure=exposure,
800 sigma=sigma,
801 doSmooth=doSmooth
802 )
803 selectSources = detRet.sources
804 self.selectMeasurement.run(measCat=selectSources, exposure=exposure)
805 finally:
806 # Put back on the background in case it is needed down stream
807 mi += bkgd
808 del bkgd
809 return selectSources
810
811 def makeCandidateList(self, templateExposure, scienceExposure, kernelSize, candidateList=None):
812 """Make a list of acceptable KernelCandidates.
813
814 Accept or generate a list of candidate sources for
815 Psf-matching, and examine the Mask planes in both of the
816 images for indications of bad pixels
817
818 Parameters
819 ----------
820 templateExposure : `lsst.afw.image.Exposure`
821 Exposure that will be convolved
822 scienceExposure : `lsst.afw.image.Exposure`
823 Exposure that will be matched-to
824 kernelSize : `float`
825 Dimensions of the Psf-matching Kernel, used to grow detection footprints
826 candidateList : `list`, optional
827 List of Sources to examine. Elements must be of type afw.table.Source
828 or a type that wraps a Source and has a getSource() method, such as
829 meas.algorithms.PsfCandidateF.
830
831 Returns
832 -------
833 candidateList : `list` of `dict`
834 A list of dicts having a "source" and "footprint"
835 field for the Sources deemed to be appropriate for Psf
836 matching
837 """
838 if candidateList is None:
839 candidateList = self.getSelectSources(scienceExposure)
840
841 if len(candidateList) < 1:
842 raise RuntimeError("No candidates in candidateList")
843
844 listTypes = set(type(x) for x in candidateList)
845 if len(listTypes) > 1:
846 raise RuntimeError("Candidate list contains mixed types: %s" % [t for t in listTypes])
847
848 if not isinstance(candidateList[0], afwTable.SourceRecord):
849 try:
850 candidateList[0].getSource()
851 except Exception as e:
852 raise RuntimeError(f"Candidate List is of type: {type(candidateList[0])} "
853 "Can only make candidate list from list of afwTable.SourceRecords, "
854 f"measAlg.PsfCandidateF or other type with a getSource() method: {e}")
855 candidateList = [c.getSource() for c in candidateList]
856
857 candidateList = diffimTools.sourceToFootprintList(candidateList,
858 templateExposure, scienceExposure,
859 kernelSize,
860 self.kConfigkConfig.detectionConfig,
861 self.log)
862 if len(candidateList) == 0:
863 raise RuntimeError("Cannot find any objects suitable for KernelCandidacy")
864
865 return candidateList
866
867 def makeKernelBasisList(self, targetFwhmPix=None, referenceFwhmPix=None,
868 basisDegGauss=None, basisSigmaGauss=None, metadata=None):
869 """Wrapper to set log messages for
871
872 Parameters
873 ----------
874 targetFwhmPix : `float`, optional
875 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
876 Not used for delta function basis sets.
877 referenceFwhmPix : `float`, optional
878 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
879 Not used for delta function basis sets.
880 basisDegGauss : `list` of `int`, optional
881 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
882 Not used for delta function basis sets.
883 basisSigmaGauss : `list` of `int`, optional
884 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
885 Not used for delta function basis sets.
886 metadata : `lsst.daf.base.PropertySet`, optional
887 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
888 Not used for delta function basis sets.
889
890 Returns
891 -------
892 basisList: `list` of `lsst.afw.math.kernel.FixedKernel`
893 List of basis kernels.
894 """
895 basisList = makeKernelBasisList(self.kConfigkConfig,
896 targetFwhmPix=targetFwhmPix,
897 referenceFwhmPix=referenceFwhmPix,
898 basisDegGauss=basisDegGauss,
899 basisSigmaGauss=basisSigmaGauss,
900 metadata=metadata)
901 if targetFwhmPix == referenceFwhmPix:
902 self.log.info("Target and reference psf fwhms are equal, falling back to config values")
903 elif referenceFwhmPix > targetFwhmPix:
904 self.log.info("Reference psf fwhm is the greater, normal convolution mode")
905 else:
906 self.log.info("Target psf fwhm is the greater, deconvolution mode")
907
908 return basisList
909
910 def _adaptCellSize(self, candidateList):
911 """NOT IMPLEMENTED YET.
912 """
913 return self.kConfigkConfig.sizeCellX, self.kConfigkConfig.sizeCellY
914
915 def _buildCellSet(self, templateMaskedImage, scienceMaskedImage, candidateList):
916 """Build a SpatialCellSet for use with the solve method.
917
918 Parameters
919 ----------
920 templateMaskedImage : `lsst.afw.image.MaskedImage`
921 MaskedImage to PSF-matched to scienceMaskedImage
922 scienceMaskedImage : `lsst.afw.image.MaskedImage`
923 Reference MaskedImage
924 candidateList : `list`
925 A list of footprints/maskedImages for kernel candidates;
926
927 - Currently supported: list of Footprints or measAlg.PsfCandidateF
928
929 Returns
930 -------
931 kernelCellSet : `lsst.afw.math.SpatialCellSet`
932 a SpatialCellSet for use with self._solve
933 """
934 if not candidateList:
935 raise RuntimeError("Candidate list must be populated by makeCandidateList")
936
937 sizeCellX, sizeCellY = self._adaptCellSize(candidateList)
938
939 # Object to store the KernelCandidates for spatial modeling
940 kernelCellSet = afwMath.SpatialCellSet(templateMaskedImage.getBBox(),
941 sizeCellX, sizeCellY)
942
943 ps = pexConfig.makePropertySet(self.kConfigkConfig)
944 # Place candidates within the spatial grid
945 for cand in candidateList:
946 if isinstance(cand, afwDetect.Footprint):
947 bbox = cand.getBBox()
948 else:
949 bbox = cand['footprint'].getBBox()
950 tmi = afwImage.MaskedImageF(templateMaskedImage, bbox)
951 smi = afwImage.MaskedImageF(scienceMaskedImage, bbox)
952
953 if not isinstance(cand, afwDetect.Footprint):
954 if 'source' in cand:
955 cand = cand['source']
956 xPos = cand.getCentroid()[0]
957 yPos = cand.getCentroid()[1]
958 cand = diffimLib.makeKernelCandidate(xPos, yPos, tmi, smi, ps)
959
960 self.log.debug("Candidate %d at %f, %f", cand.getId(), cand.getXCenter(), cand.getYCenter())
961 kernelCellSet.insertCandidate(cand)
962
963 return kernelCellSet
964
965 def _validateSize(self, templateMaskedImage, scienceMaskedImage):
966 """Return True if two image-like objects are the same size.
967 """
968 return templateMaskedImage.getDimensions() == scienceMaskedImage.getDimensions()
969
970 def _validateWcs(self, templateExposure, scienceExposure):
971 """Return True if the WCS of the two Exposures have the same origin and extent.
972 """
973 templateWcs = templateExposure.getWcs()
974 scienceWcs = scienceExposure.getWcs()
975 templateBBox = templateExposure.getBBox()
976 scienceBBox = scienceExposure.getBBox()
977
978 # LLC
979 templateOrigin = templateWcs.pixelToSky(geom.Point2D(templateBBox.getBegin()))
980 scienceOrigin = scienceWcs.pixelToSky(geom.Point2D(scienceBBox.getBegin()))
981
982 # URC
983 templateLimit = templateWcs.pixelToSky(geom.Point2D(templateBBox.getEnd()))
984 scienceLimit = scienceWcs.pixelToSky(geom.Point2D(scienceBBox.getEnd()))
985
986 self.log.info("Template Wcs : %f,%f -> %f,%f",
987 templateOrigin[0], templateOrigin[1],
988 templateLimit[0], templateLimit[1])
989 self.log.info("Science Wcs : %f,%f -> %f,%f",
990 scienceOrigin[0], scienceOrigin[1],
991 scienceLimit[0], scienceLimit[1])
992
993 templateBBox = geom.Box2D(templateOrigin.getPosition(geom.degrees),
994 templateLimit.getPosition(geom.degrees))
995 scienceBBox = geom.Box2D(scienceOrigin.getPosition(geom.degrees),
996 scienceLimit.getPosition(geom.degrees))
997 if not (templateBBox.overlaps(scienceBBox)):
998 raise RuntimeError("Input images do not overlap at all")
999
1000 if ((templateOrigin != scienceOrigin)
1001 or (templateLimit != scienceLimit)
1002 or (templateExposure.getDimensions() != scienceExposure.getDimensions())):
1003 return False
1004 return True
1005
1006
1007subtractAlgorithmRegistry = pexConfig.makeRegistry(
1008 doc="A registry of subtraction algorithms for use as a subtask in imageDifference",
1009)
1010
1011subtractAlgorithmRegistry.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