Coverage for python/lsst/ip/diffim/imagePsfMatch.py: 13%
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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/>.
22import numpy as np
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
43__all__ = ["ImagePsfMatchConfig", "ImagePsfMatchTask", "subtractAlgorithmRegistry"]
45sigma2fwhm = 2.*np.sqrt(2.*np.log(2.))
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 )
68 def setDefaults(self):
69 # High sigma detections only
70 self.selectDetection.reEstimateBackground = False
71 self.selectDetection.thresholdValue = 10.0
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
82class ImagePsfMatchTask(PsfMatchTask):
83 """Psf-match two MaskedImages or Exposures using the sources in the images.
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__
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).
100 Description
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.
114 Sigma clipping of KernelCandidates is performed as follows:
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
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.
140 Invoking the Task
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`.
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.
156 See each method's returned lsst.pipe.base.Struct for more details.
158 Debug variables
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:
164 .. code-block:: py
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
195 Note that if you want addional logging info, you may add to your scripts:
197 .. code-block:: py
199 import lsst.log.utils as logUtils
200 logUtils.traceSetAt("lsst.ip.diffim", 4)
202 Examples
203 --------
204 A complete example of using ImagePsfMatchTask
206 This code is imagePsfMatchTask.py in the examples directory, and can be run as e.g.
208 .. code-block:: none
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
215 Create a subclass of ImagePsfMatchTask that allows us to either match exposures, or subtract exposures:
217 .. code-block:: none
219 class MyImagePsfMatchTask(ImagePsfMatchTask):
221 def __init__(self, args, kwargs):
222 ImagePsfMatchTask.__init__(self, args, kwargs)
224 def run(self, templateExp, scienceExp, mode):
225 if mode == "matchExposures":
226 return self.matchExposures(templateExp, scienceExp)
227 elif mode == "subtractExposures":
228 return self.subtractExposures(templateExp, scienceExp)
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.
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:
238 .. code-block:: py
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)
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:
254 .. code-block:: py
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
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):
270 .. code-block:: py
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
291 Create and run the Task:
293 .. code-block:: py
295 # Create the Task
296 psfMatchTask = MyImagePsfMatchTask(config=config)
297 # Run the Task
298 result = psfMatchTask.run(templateExp, scienceExp, args.mode)
300 And finally provide some optional debugging displays:
302 .. code-block:: py
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 """
317 ConfigClass = ImagePsfMatchConfig
319 def __init__(self, *args, **kwargs):
320 """Create the ImagePsfMatchTask.
321 """
322 PsfMatchTask.__init__(self, *args, **kwargs)
323 self.kConfig = self.config.kernel.active
324 self._warper = afwMath.Warper.fromConfig(self.kConfig.warpingConfig)
325 # the background subtraction task uses a config from an unusual location,
326 # so cannot easily be constructed with makeSubtask
327 self.background = SubtractBackgroundTask(config=self.kConfig.afwBackgroundConfig, name="background",
328 parentTask=self)
329 self.selectSchema = afwTable.SourceTable.makeMinimalSchema()
330 self.selectAlgMetadata = dafBase.PropertyList()
331 self.makeSubtask("selectDetection", schema=self.selectSchema)
332 self.makeSubtask("selectMeasurement", schema=self.selectSchema, algMetadata=self.selectAlgMetadata)
334 def getFwhmPix(self, psf):
335 """Return the FWHM in pixels of a Psf.
336 """
337 sigPix = psf.computeShape().getDeterminantRadius()
338 return sigPix*sigma2fwhm
340 @timeMethod
341 def matchExposures(self, templateExposure, scienceExposure,
342 templateFwhmPix=None, scienceFwhmPix=None,
343 candidateList=None, doWarping=True, convolveTemplate=True):
344 """Warp and PSF-match an exposure to the reference.
346 Do the following, in order:
348 - Warp templateExposure to match scienceExposure,
349 if doWarping True and their WCSs do not already match
350 - Determine a PSF matching kernel and differential background model
351 that matches templateExposure to scienceExposure
352 - Convolve templateExposure by PSF matching kernel
354 Parameters
355 ----------
356 templateExposure : `lsst.afw.image.Exposure`
357 Exposure to warp and PSF-match to the reference masked image
358 scienceExposure : `lsst.afw.image.Exposure`
359 Exposure whose WCS and PSF are to be matched to
360 templateFwhmPix :`float`
361 FWHM (in pixels) of the Psf in the template image (image to convolve)
362 scienceFwhmPix : `float`
363 FWHM (in pixels) of the Psf in the science image
364 candidateList : `list`, optional
365 a list of footprints/maskedImages for kernel candidates;
366 if `None` then source detection is run.
368 - Currently supported: list of Footprints or measAlg.PsfCandidateF
370 doWarping : `bool`
371 what to do if ``templateExposure`` and ``scienceExposure`` WCSs do not match:
373 - if `True` then warp ``templateExposure`` to match ``scienceExposure``
374 - if `False` then raise an Exception
376 convolveTemplate : `bool`
377 Whether to convolve the template image or the science image:
379 - if `True`, ``templateExposure`` is warped if doWarping,
380 ``templateExposure`` is convolved
381 - if `False`, ``templateExposure`` is warped if doWarping,
382 ``scienceExposure`` is convolved
384 Returns
385 -------
386 results : `lsst.pipe.base.Struct`
387 An `lsst.pipe.base.Struct` containing these fields:
389 - ``matchedImage`` : the PSF-matched exposure =
390 Warped ``templateExposure`` convolved by psfMatchingKernel. This has:
392 - the same parent bbox, Wcs and PhotoCalib as scienceExposure
393 - the same filter as templateExposure
394 - no Psf (because the PSF-matching process does not compute one)
396 - ``psfMatchingKernel`` : the PSF matching kernel
397 - ``backgroundModel`` : differential background model
398 - ``kernelCellSet`` : SpatialCellSet used to solve for the PSF matching kernel
400 Raises
401 ------
402 RuntimeError
403 Raised if doWarping is False and ``templateExposure`` and
404 ``scienceExposure`` WCSs do not match
405 """
406 if not self._validateWcs(templateExposure, scienceExposure):
407 if doWarping:
408 self.log.info("Astrometrically registering template to science image")
409 templatePsf = templateExposure.getPsf()
410 # Warp PSF before overwriting exposure
411 xyTransform = afwGeom.makeWcsPairTransform(templateExposure.getWcs(),
412 scienceExposure.getWcs())
413 psfWarped = WarpedPsf(templatePsf, xyTransform)
414 templateExposure = self._warper.warpExposure(scienceExposure.getWcs(),
415 templateExposure,
416 destBBox=scienceExposure.getBBox())
417 templateExposure.setPsf(psfWarped)
418 else:
419 self.log.error("ERROR: Input images not registered")
420 raise RuntimeError("Input images not registered")
422 if templateFwhmPix is None:
423 if not templateExposure.hasPsf():
424 self.log.warning("No estimate of Psf FWHM for template image")
425 else:
426 templateFwhmPix = self.getFwhmPix(templateExposure.getPsf())
427 self.log.info("templateFwhmPix: %s", templateFwhmPix)
429 if scienceFwhmPix is None:
430 if not scienceExposure.hasPsf():
431 self.log.warning("No estimate of Psf FWHM for science image")
432 else:
433 scienceFwhmPix = self.getFwhmPix(scienceExposure.getPsf())
434 self.log.info("scienceFwhmPix: %s", scienceFwhmPix)
436 if convolveTemplate:
437 kernelSize = self.makeKernelBasisList(templateFwhmPix, scienceFwhmPix)[0].getWidth()
438 candidateList = self.makeCandidateList(
439 templateExposure, scienceExposure, kernelSize, candidateList)
440 results = self.matchMaskedImages(
441 templateExposure.getMaskedImage(), scienceExposure.getMaskedImage(), candidateList,
442 templateFwhmPix=templateFwhmPix, scienceFwhmPix=scienceFwhmPix)
443 else:
444 kernelSize = self.makeKernelBasisList(scienceFwhmPix, templateFwhmPix)[0].getWidth()
445 candidateList = self.makeCandidateList(
446 templateExposure, scienceExposure, kernelSize, candidateList)
447 results = self.matchMaskedImages(
448 scienceExposure.getMaskedImage(), templateExposure.getMaskedImage(), candidateList,
449 templateFwhmPix=scienceFwhmPix, scienceFwhmPix=templateFwhmPix)
451 psfMatchedExposure = afwImage.makeExposure(results.matchedImage, scienceExposure.getWcs())
452 psfMatchedExposure.setFilterLabel(templateExposure.getFilterLabel())
453 psfMatchedExposure.setPhotoCalib(scienceExposure.getPhotoCalib())
454 results.warpedExposure = templateExposure
455 results.matchedExposure = psfMatchedExposure
456 return results
458 @timeMethod
459 def matchMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList,
460 templateFwhmPix=None, scienceFwhmPix=None):
461 """PSF-match a MaskedImage (templateMaskedImage) to a reference MaskedImage (scienceMaskedImage).
463 Do the following, in order:
465 - Determine a PSF matching kernel and differential background model
466 that matches templateMaskedImage to scienceMaskedImage
467 - Convolve templateMaskedImage by the PSF matching kernel
469 Parameters
470 ----------
471 templateMaskedImage : `lsst.afw.image.MaskedImage`
472 masked image to PSF-match to the reference masked image;
473 must be warped to match the reference masked image
474 scienceMaskedImage : `lsst.afw.image.MaskedImage`
475 maskedImage whose PSF is to be matched to
476 templateFwhmPix : `float`
477 FWHM (in pixels) of the Psf in the template image (image to convolve)
478 scienceFwhmPix : `float`
479 FWHM (in pixels) of the Psf in the science image
480 candidateList : `list`, optional
481 A list of footprints/maskedImages for kernel candidates;
482 if `None` then source detection is run.
484 - Currently supported: list of Footprints or measAlg.PsfCandidateF
486 Returns
487 -------
488 result : `callable`
489 An `lsst.pipe.base.Struct` containing these fields:
491 - psfMatchedMaskedImage: the PSF-matched masked image =
492 ``templateMaskedImage`` convolved with psfMatchingKernel.
493 This has the same xy0, dimensions and wcs as ``scienceMaskedImage``.
494 - psfMatchingKernel: the PSF matching kernel
495 - backgroundModel: differential background model
496 - kernelCellSet: SpatialCellSet used to solve for the PSF matching kernel
498 Raises
499 ------
500 RuntimeError
501 Raised if input images have different dimensions
502 """
503 import lsstDebug
504 display = lsstDebug.Info(__name__).display
505 displayTemplate = lsstDebug.Info(__name__).displayTemplate
506 displaySciIm = lsstDebug.Info(__name__).displaySciIm
507 displaySpatialCells = lsstDebug.Info(__name__).displaySpatialCells
508 maskTransparency = lsstDebug.Info(__name__).maskTransparency
509 if not maskTransparency:
510 maskTransparency = 0
511 if display:
512 afwDisplay.setDefaultMaskTransparency(maskTransparency)
514 if not candidateList:
515 raise RuntimeError("Candidate list must be populated by makeCandidateList")
517 if not self._validateSize(templateMaskedImage, scienceMaskedImage):
518 self.log.error("ERROR: Input images different size")
519 raise RuntimeError("Input images different size")
521 if display and displayTemplate:
522 disp = afwDisplay.Display(frame=lsstDebug.frame)
523 disp.mtv(templateMaskedImage, title="Image to convolve")
524 lsstDebug.frame += 1
526 if display and displaySciIm:
527 disp = afwDisplay.Display(frame=lsstDebug.frame)
528 disp.mtv(scienceMaskedImage, title="Image to not convolve")
529 lsstDebug.frame += 1
531 kernelCellSet = self._buildCellSet(templateMaskedImage,
532 scienceMaskedImage,
533 candidateList)
535 if display and displaySpatialCells:
536 diffimUtils.showKernelSpatialCells(scienceMaskedImage, kernelCellSet,
537 symb="o", ctype=afwDisplay.CYAN, ctypeUnused=afwDisplay.YELLOW,
538 ctypeBad=afwDisplay.RED, size=4, frame=lsstDebug.frame,
539 title="Image to not convolve")
540 lsstDebug.frame += 1
542 if templateFwhmPix and scienceFwhmPix:
543 self.log.info("Matching Psf FWHM %.2f -> %.2f pix", templateFwhmPix, scienceFwhmPix)
545 if self.kConfig.useBicForKernelBasis:
546 tmpKernelCellSet = self._buildCellSet(templateMaskedImage,
547 scienceMaskedImage,
548 candidateList)
549 nbe = diffimTools.NbasisEvaluator(self.kConfig, templateFwhmPix, scienceFwhmPix)
550 bicDegrees = nbe(tmpKernelCellSet, self.log)
551 basisList = self.makeKernelBasisList(templateFwhmPix, scienceFwhmPix,
552 basisDegGauss=bicDegrees[0], metadata=self.metadata)
553 del tmpKernelCellSet
554 else:
555 basisList = self.makeKernelBasisList(templateFwhmPix, scienceFwhmPix,
556 metadata=self.metadata)
558 spatialSolution, psfMatchingKernel, backgroundModel = self._solve(kernelCellSet, basisList)
560 psfMatchedMaskedImage = afwImage.MaskedImageF(templateMaskedImage.getBBox())
561 convolutionControl = afwMath.ConvolutionControl()
562 convolutionControl.setDoNormalize(False)
563 afwMath.convolve(psfMatchedMaskedImage, templateMaskedImage, psfMatchingKernel, convolutionControl)
564 return pipeBase.Struct(
565 matchedImage=psfMatchedMaskedImage,
566 psfMatchingKernel=psfMatchingKernel,
567 backgroundModel=backgroundModel,
568 kernelCellSet=kernelCellSet,
569 )
571 @timeMethod
572 def subtractExposures(self, templateExposure, scienceExposure,
573 templateFwhmPix=None, scienceFwhmPix=None,
574 candidateList=None, doWarping=True, convolveTemplate=True):
575 """Register, Psf-match and subtract two Exposures.
577 Do the following, in order:
579 - Warp templateExposure to match scienceExposure, if their WCSs do not already match
580 - Determine a PSF matching kernel and differential background model
581 that matches templateExposure to scienceExposure
582 - PSF-match templateExposure to scienceExposure
583 - Compute subtracted exposure (see return values for equation).
585 Parameters
586 ----------
587 templateExposure : `lsst.afw.image.ExposureF`
588 Exposure to PSF-match to scienceExposure
589 scienceExposure : `lsst.afw.image.ExposureF`
590 Reference Exposure
591 templateFwhmPix : `float`
592 FWHM (in pixels) of the Psf in the template image (image to convolve)
593 scienceFwhmPix : `float`
594 FWHM (in pixels) of the Psf in the science image
595 candidateList : `list`, optional
596 A list of footprints/maskedImages for kernel candidates;
597 if `None` then source detection is run.
599 - Currently supported: list of Footprints or measAlg.PsfCandidateF
601 doWarping : `bool`
602 What to do if ``templateExposure``` and ``scienceExposure`` WCSs do
603 not match:
605 - if `True` then warp ``templateExposure`` to match ``scienceExposure``
606 - if `False` then raise an Exception
608 convolveTemplate : `bool`
609 Convolve the template image or the science image
611 - if `True`, ``templateExposure`` is warped if doWarping,
612 ``templateExposure`` is convolved
613 - if `False`, ``templateExposure`` is warped if doWarping,
614 ``scienceExposure is`` convolved
616 Returns
617 -------
618 result : `lsst.pipe.base.Struct`
619 An `lsst.pipe.base.Struct` containing these fields:
621 - ``subtractedExposure`` : subtracted Exposure
622 scienceExposure - (matchedImage + backgroundModel)
623 - ``matchedImage`` : ``templateExposure`` after warping to match
624 ``templateExposure`` (if doWarping true),
625 and convolving with psfMatchingKernel
626 - ``psfMatchingKernel`` : PSF matching kernel
627 - ``backgroundModel`` : differential background model
628 - ``kernelCellSet`` : SpatialCellSet used to determine PSF matching kernel
629 """
630 results = self.matchExposures(
631 templateExposure=templateExposure,
632 scienceExposure=scienceExposure,
633 templateFwhmPix=templateFwhmPix,
634 scienceFwhmPix=scienceFwhmPix,
635 candidateList=candidateList,
636 doWarping=doWarping,
637 convolveTemplate=convolveTemplate
638 )
639 # Always inherit WCS and photocalib from science exposure
640 subtractedExposure = afwImage.ExposureF(scienceExposure, deep=True)
641 # Note, the decorrelation afterburner re-calculates the variance plane
642 # from the variance planes of the original exposures.
643 # That recalculation code must be in accordance with the
644 # photometric level set here in ``subtractedMaskedImage``.
645 if convolveTemplate:
646 subtractedMaskedImage = subtractedExposure.maskedImage
647 subtractedMaskedImage -= results.matchedExposure.maskedImage
648 subtractedMaskedImage -= results.backgroundModel
649 else:
650 subtractedMaskedImage = subtractedExposure.maskedImage
651 subtractedMaskedImage[:, :] = results.warpedExposure.maskedImage
652 subtractedMaskedImage -= results.matchedExposure.maskedImage
653 subtractedMaskedImage -= results.backgroundModel
655 # Preserve polarity of differences
656 subtractedMaskedImage *= -1
658 # Place back on native photometric scale
659 subtractedMaskedImage /= results.psfMatchingKernel.computeImage(
660 afwImage.ImageD(results.psfMatchingKernel.getDimensions()), False)
661 # We matched to the warped template
662 subtractedExposure.setPsf(results.warpedExposure.getPsf())
664 import lsstDebug
665 display = lsstDebug.Info(__name__).display
666 displayDiffIm = lsstDebug.Info(__name__).displayDiffIm
667 maskTransparency = lsstDebug.Info(__name__).maskTransparency
668 if not maskTransparency:
669 maskTransparency = 0
670 if display:
671 afwDisplay.setDefaultMaskTransparency(maskTransparency)
672 if display and displayDiffIm:
673 disp = afwDisplay.Display(frame=lsstDebug.frame)
674 disp.mtv(templateExposure, title="Template")
675 lsstDebug.frame += 1
676 disp = afwDisplay.Display(frame=lsstDebug.frame)
677 disp.mtv(results.matchedExposure, title="Matched template")
678 lsstDebug.frame += 1
679 disp = afwDisplay.Display(frame=lsstDebug.frame)
680 disp.mtv(scienceExposure, title="Science Image")
681 lsstDebug.frame += 1
682 disp = afwDisplay.Display(frame=lsstDebug.frame)
683 disp.mtv(subtractedExposure, title="Difference Image")
684 lsstDebug.frame += 1
686 results.subtractedExposure = subtractedExposure
687 return results
689 @timeMethod
690 def subtractMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList,
691 templateFwhmPix=None, scienceFwhmPix=None):
692 """Psf-match and subtract two MaskedImages.
694 Do the following, in order:
696 - PSF-match templateMaskedImage to scienceMaskedImage
697 - Determine the differential background
698 - Return the difference: scienceMaskedImage
699 ((warped templateMaskedImage convolved with psfMatchingKernel) + backgroundModel)
701 Parameters
702 ----------
703 templateMaskedImage : `lsst.afw.image.MaskedImage`
704 MaskedImage to PSF-match to ``scienceMaskedImage``
705 scienceMaskedImage : `lsst.afw.image.MaskedImage`
706 Reference MaskedImage
707 templateFwhmPix : `float`
708 FWHM (in pixels) of the Psf in the template image (image to convolve)
709 scienceFwhmPix : `float`
710 FWHM (in pixels) of the Psf in the science image
711 candidateList : `list`, optional
712 A list of footprints/maskedImages for kernel candidates;
713 if `None` then source detection is run.
715 - Currently supported: list of Footprints or measAlg.PsfCandidateF
717 Returns
718 -------
719 results : `lsst.pipe.base.Struct`
720 An `lsst.pipe.base.Struct` containing these fields:
722 - ``subtractedMaskedImage`` : ``scienceMaskedImage`` - (matchedImage + backgroundModel)
723 - ``matchedImage`` : templateMaskedImage convolved with psfMatchingKernel
724 - `psfMatchingKernel`` : PSF matching kernel
725 - ``backgroundModel`` : differential background model
726 - ``kernelCellSet`` : SpatialCellSet used to determine PSF matching kernel
728 """
729 if not candidateList:
730 raise RuntimeError("Candidate list must be populated by makeCandidateList")
732 results = self.matchMaskedImages(
733 templateMaskedImage=templateMaskedImage,
734 scienceMaskedImage=scienceMaskedImage,
735 candidateList=candidateList,
736 templateFwhmPix=templateFwhmPix,
737 scienceFwhmPix=scienceFwhmPix,
738 )
740 subtractedMaskedImage = afwImage.MaskedImageF(scienceMaskedImage, True)
741 subtractedMaskedImage -= results.matchedImage
742 subtractedMaskedImage -= results.backgroundModel
743 results.subtractedMaskedImage = subtractedMaskedImage
745 import lsstDebug
746 display = lsstDebug.Info(__name__).display
747 displayDiffIm = lsstDebug.Info(__name__).displayDiffIm
748 maskTransparency = lsstDebug.Info(__name__).maskTransparency
749 if not maskTransparency:
750 maskTransparency = 0
751 if display:
752 afwDisplay.setDefaultMaskTransparency(maskTransparency)
753 if display and displayDiffIm:
754 disp = afwDisplay.Display(frame=lsstDebug.frame)
755 disp.mtv(subtractedMaskedImage, title="Subtracted masked image")
756 lsstDebug.frame += 1
758 return results
760 def getSelectSources(self, exposure, sigma=None, doSmooth=True, idFactory=None):
761 """Get sources to use for Psf-matching.
763 This method runs detection and measurement on an exposure.
764 The returned set of sources will be used as candidates for
765 Psf-matching.
767 Parameters
768 ----------
769 exposure : `lsst.afw.image.Exposure`
770 Exposure on which to run detection/measurement
771 sigma : `float`
772 Detection threshold
773 doSmooth : `bool`
774 Whether or not to smooth the Exposure with Psf before detection
775 idFactory :
776 Factory for the generation of Source ids
778 Returns
779 -------
780 selectSources :
781 source catalog containing candidates for the Psf-matching
782 """
783 if idFactory:
784 table = afwTable.SourceTable.make(self.selectSchema, idFactory)
785 else:
786 table = afwTable.SourceTable.make(self.selectSchema)
787 mi = exposure.getMaskedImage()
789 imArr = mi.getImage().getArray()
790 maskArr = mi.getMask().getArray()
791 miArr = np.ma.masked_array(imArr, mask=maskArr)
792 try:
793 fitBg = self.background.fitBackground(mi)
794 bkgd = fitBg.getImageF(self.background.config.algorithm,
795 self.background.config.undersampleStyle)
796 except Exception:
797 self.log.warning("Failed to get background model. Falling back to median background estimation")
798 bkgd = np.ma.median(miArr)
800 # Take off background for detection
801 mi -= bkgd
802 try:
803 table.setMetadata(self.selectAlgMetadata)
804 detRet = self.selectDetection.run(
805 table=table,
806 exposure=exposure,
807 sigma=sigma,
808 doSmooth=doSmooth
809 )
810 selectSources = detRet.sources
811 self.selectMeasurement.run(measCat=selectSources, exposure=exposure)
812 finally:
813 # Put back on the background in case it is needed down stream
814 mi += bkgd
815 del bkgd
816 return selectSources
818 def makeCandidateList(self, templateExposure, scienceExposure, kernelSize, candidateList=None):
819 """Make a list of acceptable KernelCandidates.
821 Accept or generate a list of candidate sources for
822 Psf-matching, and examine the Mask planes in both of the
823 images for indications of bad pixels
825 Parameters
826 ----------
827 templateExposure : `lsst.afw.image.Exposure`
828 Exposure that will be convolved
829 scienceExposure : `lsst.afw.image.Exposure`
830 Exposure that will be matched-to
831 kernelSize : `float`
832 Dimensions of the Psf-matching Kernel, used to grow detection footprints
833 candidateList : `list`, optional
834 List of Sources to examine. Elements must be of type afw.table.Source
835 or a type that wraps a Source and has a getSource() method, such as
836 meas.algorithms.PsfCandidateF.
838 Returns
839 -------
840 candidateList : `list` of `dict`
841 A list of dicts having a "source" and "footprint"
842 field for the Sources deemed to be appropriate for Psf
843 matching
844 """
845 if candidateList is None:
846 candidateList = self.getSelectSources(scienceExposure)
848 if len(candidateList) < 1:
849 raise RuntimeError("No candidates in candidateList")
851 listTypes = set(type(x) for x in candidateList)
852 if len(listTypes) > 1:
853 raise RuntimeError("Candidate list contains mixed types: %s" % [t for t in listTypes])
855 if not isinstance(candidateList[0], afwTable.SourceRecord):
856 try:
857 candidateList[0].getSource()
858 except Exception as e:
859 raise RuntimeError(f"Candidate List is of type: {type(candidateList[0])} "
860 "Can only make candidate list from list of afwTable.SourceRecords, "
861 f"measAlg.PsfCandidateF or other type with a getSource() method: {e}")
862 candidateList = [c.getSource() for c in candidateList]
864 candidateList = diffimTools.sourceToFootprintList(candidateList,
865 templateExposure, scienceExposure,
866 kernelSize,
867 self.kConfig.detectionConfig,
868 self.log)
869 if len(candidateList) == 0:
870 raise RuntimeError("Cannot find any objects suitable for KernelCandidacy")
872 return candidateList
874 def makeKernelBasisList(self, targetFwhmPix=None, referenceFwhmPix=None,
875 basisDegGauss=None, basisSigmaGauss=None, metadata=None):
876 """Wrapper to set log messages for
877 `lsst.ip.diffim.makeKernelBasisList`.
879 Parameters
880 ----------
881 targetFwhmPix : `float`, optional
882 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
883 Not used for delta function basis sets.
884 referenceFwhmPix : `float`, optional
885 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
886 Not used for delta function basis sets.
887 basisDegGauss : `list` of `int`, optional
888 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
889 Not used for delta function basis sets.
890 basisSigmaGauss : `list` of `int`, optional
891 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
892 Not used for delta function basis sets.
893 metadata : `lsst.daf.base.PropertySet`, optional
894 Passed on to `lsst.ip.diffim.generateAlardLuptonBasisList`.
895 Not used for delta function basis sets.
897 Returns
898 -------
899 basisList: `list` of `lsst.afw.math.kernel.FixedKernel`
900 List of basis kernels.
901 """
902 basisList = makeKernelBasisList(self.kConfig,
903 targetFwhmPix=targetFwhmPix,
904 referenceFwhmPix=referenceFwhmPix,
905 basisDegGauss=basisDegGauss,
906 basisSigmaGauss=basisSigmaGauss,
907 metadata=metadata)
908 if targetFwhmPix == referenceFwhmPix:
909 self.log.info("Target and reference psf fwhms are equal, falling back to config values")
910 elif referenceFwhmPix > targetFwhmPix:
911 self.log.info("Reference psf fwhm is the greater, normal convolution mode")
912 else:
913 self.log.info("Target psf fwhm is the greater, deconvolution mode")
915 return basisList
917 def _adaptCellSize(self, candidateList):
918 """NOT IMPLEMENTED YET.
919 """
920 return self.kConfig.sizeCellX, self.kConfig.sizeCellY
922 def _buildCellSet(self, templateMaskedImage, scienceMaskedImage, candidateList):
923 """Build a SpatialCellSet for use with the solve method.
925 Parameters
926 ----------
927 templateMaskedImage : `lsst.afw.image.MaskedImage`
928 MaskedImage to PSF-matched to scienceMaskedImage
929 scienceMaskedImage : `lsst.afw.image.MaskedImage`
930 Reference MaskedImage
931 candidateList : `list`
932 A list of footprints/maskedImages for kernel candidates;
934 - Currently supported: list of Footprints or measAlg.PsfCandidateF
936 Returns
937 -------
938 kernelCellSet : `lsst.afw.math.SpatialCellSet`
939 a SpatialCellSet for use with self._solve
940 """
941 if not candidateList:
942 raise RuntimeError("Candidate list must be populated by makeCandidateList")
944 sizeCellX, sizeCellY = self._adaptCellSize(candidateList)
946 # Object to store the KernelCandidates for spatial modeling
947 kernelCellSet = afwMath.SpatialCellSet(templateMaskedImage.getBBox(),
948 sizeCellX, sizeCellY)
950 ps = pexConfig.makePropertySet(self.kConfig)
951 # Place candidates within the spatial grid
952 for cand in candidateList:
953 if isinstance(cand, afwDetect.Footprint):
954 bbox = cand.getBBox()
955 else:
956 bbox = cand['footprint'].getBBox()
957 tmi = afwImage.MaskedImageF(templateMaskedImage, bbox)
958 smi = afwImage.MaskedImageF(scienceMaskedImage, bbox)
960 if not isinstance(cand, afwDetect.Footprint):
961 if 'source' in cand:
962 cand = cand['source']
963 xPos = cand.getCentroid()[0]
964 yPos = cand.getCentroid()[1]
965 cand = diffimLib.makeKernelCandidate(xPos, yPos, tmi, smi, ps)
967 self.log.debug("Candidate %d at %f, %f", cand.getId(), cand.getXCenter(), cand.getYCenter())
968 kernelCellSet.insertCandidate(cand)
970 return kernelCellSet
972 def _validateSize(self, templateMaskedImage, scienceMaskedImage):
973 """Return True if two image-like objects are the same size.
974 """
975 return templateMaskedImage.getDimensions() == scienceMaskedImage.getDimensions()
977 def _validateWcs(self, templateExposure, scienceExposure):
978 """Return True if the WCS of the two Exposures have the same origin and extent.
979 """
980 templateWcs = templateExposure.getWcs()
981 scienceWcs = scienceExposure.getWcs()
982 templateBBox = templateExposure.getBBox()
983 scienceBBox = scienceExposure.getBBox()
985 # LLC
986 templateOrigin = templateWcs.pixelToSky(geom.Point2D(templateBBox.getBegin()))
987 scienceOrigin = scienceWcs.pixelToSky(geom.Point2D(scienceBBox.getBegin()))
989 # URC
990 templateLimit = templateWcs.pixelToSky(geom.Point2D(templateBBox.getEnd()))
991 scienceLimit = scienceWcs.pixelToSky(geom.Point2D(scienceBBox.getEnd()))
993 self.log.info("Template Wcs : %f,%f -> %f,%f",
994 templateOrigin[0], templateOrigin[1],
995 templateLimit[0], templateLimit[1])
996 self.log.info("Science Wcs : %f,%f -> %f,%f",
997 scienceOrigin[0], scienceOrigin[1],
998 scienceLimit[0], scienceLimit[1])
1000 templateBBox = geom.Box2D(templateOrigin.getPosition(geom.degrees),
1001 templateLimit.getPosition(geom.degrees))
1002 scienceBBox = geom.Box2D(scienceOrigin.getPosition(geom.degrees),
1003 scienceLimit.getPosition(geom.degrees))
1004 if not (templateBBox.overlaps(scienceBBox)):
1005 raise RuntimeError("Input images do not overlap at all")
1007 if ((templateOrigin != scienceOrigin)
1008 or (templateLimit != scienceLimit)
1009 or (templateExposure.getDimensions() != scienceExposure.getDimensions())):
1010 return False
1011 return True
1014subtractAlgorithmRegistry = pexConfig.makeRegistry(
1015 doc="A registry of subtraction algorithms for use as a subtask in imageDifference",
1016)
1018subtractAlgorithmRegistry.register('al', ImagePsfMatchTask)