Hide keyboard shortcuts

Hot-keys on this page

r m x p   toggle line displays

j k   next/prev highlighted chunk

0   (zero) top of page

1   (one) first highlighted chunk

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 

41 

42__all__ = ["ImagePsfMatchConfig", "ImagePsfMatchTask", "subtractAlgorithmRegistry"] 

43 

44sigma2fwhm = 2.*np.sqrt(2.*np.log(2.)) 

45 

46 

47class ImagePsfMatchConfig(pexConfig.Config): 

48 """Configuration for image-to-image Psf matching. 

49 """ 

50 kernel = pexConfig.ConfigChoiceField( 

51 doc="kernel type", 

52 typemap=dict( 

53 AL=PsfMatchConfigAL, 

54 DF=PsfMatchConfigDF 

55 ), 

56 default="AL", 

57 ) 

58 selectDetection = pexConfig.ConfigurableField( 

59 target=SourceDetectionTask, 

60 doc="Initial detections used to feed stars to kernel fitting", 

61 ) 

62 selectMeasurement = pexConfig.ConfigurableField( 

63 target=SingleFrameMeasurementTask, 

64 doc="Initial measurements used to feed stars to kernel fitting", 

65 ) 

66 

67 def setDefaults(self): 

68 # High sigma detections only 

69 self.selectDetection.reEstimateBackground = False 

70 self.selectDetection.thresholdValue = 10.0 

71 

72 # Minimal set of measurments for star selection 

73 self.selectMeasurement.algorithms.names.clear() 

74 self.selectMeasurement.algorithms.names = ('base_SdssCentroid', 'base_PsfFlux', 'base_PixelFlags', 

75 'base_SdssShape', 'base_GaussianFlux', 'base_SkyCoord') 

76 self.selectMeasurement.slots.modelFlux = None 

77 self.selectMeasurement.slots.apFlux = None 

78 self.selectMeasurement.slots.calibFlux = None 

79 

80 

81class ImagePsfMatchTask(PsfMatchTask): 

82 """Psf-match two MaskedImages or Exposures using the sources in the images. 

83 

84 Parameters 

85 ---------- 

86 args : 

87 Arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__ 

88 kwargs : 

89 Keyword arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__ 

90 

91 Notes 

92 ----- 

93 Upon initialization, the kernel configuration is defined by self.config.kernel.active. 

94 The task creates an lsst.afw.math.Warper from the subConfig self.config.kernel.active.warpingConfig. 

95 A schema for the selection and measurement of candidate lsst.ip.diffim.KernelCandidates is 

96 defined, and used to initize subTasks selectDetection (for candidate detection) and selectMeasurement 

97 (for candidate measurement). 

98 

99 Description 

100 

101 Build a Psf-matching kernel using two input images, either as MaskedImages (in which case they need 

102 to be astrometrically aligned) or Exposures (in which case astrometric alignment will happen by 

103 default but may be turned off). This requires a list of input Sources which may be provided 

104 by the calling Task; if not, the Task will perform a coarse source detection 

105 and selection for this purpose. Sources are vetted for signal-to-noise and masked pixels 

106 (in both the template and science image), and substamps around each acceptable 

107 source are extracted and used to create an instance of KernelCandidate. 

108 Each KernelCandidate is then placed within a lsst.afw.math.SpatialCellSet, which is used by an ensemble of 

109 lsst.afw.math.CandidateVisitor instances to build the Psf-matching kernel. These visitors include, in 

110 the order that they are called: BuildSingleKernelVisitor, KernelSumVisitor, BuildSpatialKernelVisitor, 

111 and AssessSpatialKernelVisitor. 

112 

113 Sigma clipping of KernelCandidates is performed as follows: 

114 

115 - BuildSingleKernelVisitor, using the substamp diffim residuals from the per-source kernel fit, 

116 if PsfMatchConfig.singleKernelClipping is True 

117 - KernelSumVisitor, using the mean and standard deviation of the kernel sum from all candidates, 

118 if PsfMatchConfig.kernelSumClipping is True 

119 - AssessSpatialKernelVisitor, using the substamp diffim ressiduals from the spatial kernel fit, 

120 if PsfMatchConfig.spatialKernelClipping is True 

121 

122 The actual solving for the kernel (and differential background model) happens in 

123 lsst.ip.diffim.PsfMatchTask._solve. This involves a loop over the SpatialCellSet that first builds the 

124 per-candidate matching kernel for the requested number of KernelCandidates per cell 

125 (PsfMatchConfig.nStarPerCell). The quality of this initial per-candidate difference image is examined, 

126 using moments of the pixel residuals in the difference image normalized by the square root of the variance 

127 (i.e. sigma); ideally this should follow a normal (0, 1) distribution, 

128 but the rejection thresholds are set 

129 by the config (PsfMatchConfig.candidateResidualMeanMax and PsfMatchConfig.candidateResidualStdMax). 

130 All candidates that pass this initial build are then examined en masse to find the 

131 mean/stdev of the kernel sums across all candidates. 

132 Objects that are significantly above or below the mean, 

133 typically due to variability or sources that are saturated in one image but not the other, 

134 are also rejected.This threshold is defined by PsfMatchConfig.maxKsumSigma. 

135 Finally, a spatial model is built using all currently-acceptable candidates, 

136 and the spatial model used to derive a second set of (spatial) residuals 

137 which are again used to reject bad candidates, using the same thresholds as above. 

138 

139 Invoking the Task 

140 

141 There is no run() method for this Task. Instead there are 4 methods that 

142 may be used to invoke the Psf-matching. These are 

143 `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchMaskedImages`, 

144 `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractMaskedImages`, 

145 `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchExposures`, and 

146 `~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractExposures`. 

147 

148 The methods that operate on lsst.afw.image.MaskedImage require that the images already be astrometrically 

149 aligned, and are the same shape. The methods that operate on lsst.afw.image.Exposure allow for the 

150 input images to be misregistered and potentially be different sizes; by default a 

151 lsst.afw.math.LanczosWarpingKernel is used to perform the astrometric alignment. The methods 

152 that "match" images return a Psf-matched image, while the methods that "subtract" images 

153 return a Psf-matched and template subtracted image. 

154 

155 See each method's returned lsst.pipe.base.Struct for more details. 

156 

157 Debug variables 

158 

159 The lsst.pipe.base.cmdLineTask.CmdLineTask command line task interface supports a 

160 flag -d/--debug to import debug.py from your PYTHONPATH. The relevant contents of debug.py 

161 for this Task include: 

162 

163 .. code-block:: py 

164 

165 import sys 

166 import lsstDebug 

167 def DebugInfo(name): 

168 di = lsstDebug.getInfo(name) 

169 if name == "lsst.ip.diffim.psfMatch": 

170 di.display = True # enable debug output 

171 di.maskTransparency = 80 # display mask transparency 

172 di.displayCandidates = True # show all the candidates and residuals 

173 di.displayKernelBasis = False # show kernel basis functions 

174 di.displayKernelMosaic = True # show kernel realized across the image 

175 di.plotKernelSpatialModel = False # show coefficients of spatial model 

176 di.showBadCandidates = True # show the bad candidates (red) along with good (green) 

177 elif name == "lsst.ip.diffim.imagePsfMatch": 

178 di.display = True # enable debug output 

179 di.maskTransparency = 30 # display mask transparency 

180 di.displayTemplate = True # show full (remapped) template 

181 di.displaySciIm = True # show science image to match to 

182 di.displaySpatialCells = True # show spatial cells 

183 di.displayDiffIm = True # show difference image 

184 di.showBadCandidates = True # show the bad candidates (red) along with good (green) 

185 elif name == "lsst.ip.diffim.diaCatalogSourceSelector": 

186 di.display = False # enable debug output 

187 di.maskTransparency = 30 # display mask transparency 

188 di.displayExposure = True # show exposure with candidates indicated 

189 di.pauseAtEnd = False # pause when done 

190 return di 

191 lsstDebug.Info = DebugInfo 

192 lsstDebug.frame = 1 

193 

194 Note that if you want addional logging info, you may add to your scripts: 

195 

196 .. code-block:: py 

197 

198 import lsst.log.utils as logUtils 

199 logUtils.traceSetAt("ip.diffim", 4) 

200 

201 Examples 

202 -------- 

203 A complete example of using ImagePsfMatchTask 

204 

205 This code is imagePsfMatchTask.py in the examples directory, and can be run as e.g. 

206 

207 .. code-block:: none 

208 

209 examples/imagePsfMatchTask.py --debug 

210 examples/imagePsfMatchTask.py --debug --mode="matchExposures" 

211 examples/imagePsfMatchTask.py --debug --template /path/to/templateExp.fits 

212 --science /path/to/scienceExp.fits 

213 

214 Create a subclass of ImagePsfMatchTask that allows us to either match exposures, or subtract exposures: 

215 

216 .. code-block:: none 

217 

218 class MyImagePsfMatchTask(ImagePsfMatchTask): 

219 

220 def __init__(self, args, kwargs): 

221 ImagePsfMatchTask.__init__(self, args, kwargs) 

222 

223 def run(self, templateExp, scienceExp, mode): 

224 if mode == "matchExposures": 

225 return self.matchExposures(templateExp, scienceExp) 

226 elif mode == "subtractExposures": 

227 return self.subtractExposures(templateExp, scienceExp) 

228 

229 And allow the user the freedom to either run the script in default mode, 

230 or point to their own images on disk. 

231 Note that these images must be readable as an lsst.afw.image.Exposure. 

232 

233 We have enabled some minor display debugging in this script via the --debug option. However, if you 

234 have an lsstDebug debug.py in your PYTHONPATH you will get additional debugging displays. The following 

235 block checks for this script: 

236 

237 .. code-block:: py 

238 

239 if args.debug: 

240 try: 

241 import debug 

242 # Since I am displaying 2 images here, set the starting frame number for the LSST debug LSST 

243 debug.lsstDebug.frame = 3 

244 except ImportError as e: 

245 print(e, file=sys.stderr) 

246 

247 Finally, we call a run method that we define below. 

248 First set up a Config and modify some of the parameters. 

249 E.g. use an "Alard-Lupton" sum-of-Gaussian basis, 

250 fit for a differential background, and use low order spatial 

251 variation in the kernel and background: 

252 

253 .. code-block:: py 

254 

255 def run(args): 

256 # 

257 # Create the Config and use sum of gaussian basis 

258 # 

259 config = ImagePsfMatchTask.ConfigClass() 

260 config.kernel.name = "AL" 

261 config.kernel.active.fitForBackground = True 

262 config.kernel.active.spatialKernelOrder = 1 

263 config.kernel.active.spatialBgOrder = 0 

264 

265 Make sure the images (if any) that were sent to the script exist on disk and are readable. If no images 

266 are sent, make some fake data up for the sake of this example script (have a look at the code if you want 

267 more details on generateFakeImages): 

268 

269 .. code-block:: py 

270 

271 # Run the requested method of the Task 

272 if args.template is not None and args.science is not None: 

273 if not os.path.isfile(args.template): 

274 raise Exception("Template image %s does not exist" % (args.template)) 

275 if not os.path.isfile(args.science): 

276 raise Exception("Science image %s does not exist" % (args.science)) 

277 try: 

278 templateExp = afwImage.ExposureF(args.template) 

279 except Exception as e: 

280 raise Exception("Cannot read template image %s" % (args.template)) 

281 try: 

282 scienceExp = afwImage.ExposureF(args.science) 

283 except Exception as e: 

284 raise Exception("Cannot read science image %s" % (args.science)) 

285 else: 

286 templateExp, scienceExp = generateFakeImages() 

287 config.kernel.active.sizeCellX = 128 

288 config.kernel.active.sizeCellY = 128 

289 

290 Create and run the Task: 

291 

292 .. code-block:: py 

293 

294 # Create the Task 

295 psfMatchTask = MyImagePsfMatchTask(config=config) 

296 # Run the Task 

297 result = psfMatchTask.run(templateExp, scienceExp, args.mode) 

298 

299 And finally provide some optional debugging displays: 

300 

301 .. code-block:: py 

302 

303 if args.debug: 

304 # See if the LSST debug has incremented the frame number; if not start with frame 3 

305 try: 

306 frame = debug.lsstDebug.frame + 1 

307 except Exception: 

308 frame = 3 

309 afwDisplay.Display(frame=frame).mtv(result.matchedExposure, 

310 title="Example script: Matched Template Image") 

311 if "subtractedExposure" in result.getDict(): 

312 afwDisplay.Display(frame=frame + 1).mtv(result.subtractedExposure, 

313 title="Example script: Subtracted Image") 

314 """ 

315 

316 ConfigClass = ImagePsfMatchConfig 

317 

318 def __init__(self, *args, **kwargs): 

319 """Create the ImagePsfMatchTask. 

320 """ 

321 PsfMatchTask.__init__(self, *args, **kwargs) 

322 self.kConfig = self.config.kernel.active 

323 self._warper = afwMath.Warper.fromConfig(self.kConfig.warpingConfig) 

324 # the background subtraction task uses a config from an unusual location, 

325 # so cannot easily be constructed with makeSubtask 

326 self.background = SubtractBackgroundTask(config=self.kConfig.afwBackgroundConfig, name="background", 

327 parentTask=self) 

328 self.selectSchema = afwTable.SourceTable.makeMinimalSchema() 

329 self.selectAlgMetadata = dafBase.PropertyList() 

330 self.makeSubtask("selectDetection", schema=self.selectSchema) 

331 self.makeSubtask("selectMeasurement", schema=self.selectSchema, algMetadata=self.selectAlgMetadata) 

332 

333 def getFwhmPix(self, psf): 

334 """Return the FWHM in pixels of a Psf. 

335 """ 

336 sigPix = psf.computeShape().getDeterminantRadius() 

337 return sigPix*sigma2fwhm 

338 

339 @pipeBase.timeMethod 

340 def matchExposures(self, templateExposure, scienceExposure, 

341 templateFwhmPix=None, scienceFwhmPix=None, 

342 candidateList=None, doWarping=True, convolveTemplate=True): 

343 """Warp and PSF-match an exposure to the reference. 

344 

345 Do the following, in order: 

346 

347 - Warp templateExposure to match scienceExposure, 

348 if doWarping True and their WCSs do not already match 

349 - Determine a PSF matching kernel and differential background model 

350 that matches templateExposure to scienceExposure 

351 - Convolve templateExposure by PSF matching kernel 

352 

353 Parameters 

354 ---------- 

355 templateExposure : `lsst.afw.image.Exposure` 

356 Exposure to warp and PSF-match to the reference masked image 

357 scienceExposure : `lsst.afw.image.Exposure` 

358 Exposure whose WCS and PSF are to be matched to 

359 templateFwhmPix :`float` 

360 FWHM (in pixels) of the Psf in the template image (image to convolve) 

361 scienceFwhmPix : `float` 

362 FWHM (in pixels) of the Psf in the science image 

363 candidateList : `list`, optional 

364 a list of footprints/maskedImages for kernel candidates; 

365 if `None` then source detection is run. 

366 

367 - Currently supported: list of Footprints or measAlg.PsfCandidateF 

368 

369 doWarping : `bool` 

370 what to do if ``templateExposure`` and ``scienceExposure`` WCSs do not match: 

371 

372 - if `True` then warp ``templateExposure`` to match ``scienceExposure`` 

373 - if `False` then raise an Exception 

374 

375 convolveTemplate : `bool` 

376 Whether to convolve the template image or the science image: 

377 

378 - if `True`, ``templateExposure`` is warped if doWarping, 

379 ``templateExposure`` is convolved 

380 - if `False`, ``templateExposure`` is warped if doWarping, 

381 ``scienceExposure`` is convolved 

382 

383 Returns 

384 ------- 

385 results : `lsst.pipe.base.Struct` 

386 An `lsst.pipe.base.Struct` containing these fields: 

387 

388 - ``matchedImage`` : the PSF-matched exposure = 

389 Warped ``templateExposure`` convolved by psfMatchingKernel. This has: 

390 

391 - the same parent bbox, Wcs and PhotoCalib as scienceExposure 

392 - the same filter as templateExposure 

393 - no Psf (because the PSF-matching process does not compute one) 

394 

395 - ``psfMatchingKernel`` : the PSF matching kernel 

396 - ``backgroundModel`` : differential background model 

397 - ``kernelCellSet`` : SpatialCellSet used to solve for the PSF matching kernel 

398 

399 Raises 

400 ------ 

401 RuntimeError 

402 Raised if doWarping is False and ``templateExposure`` and 

403 ``scienceExposure`` WCSs do not match 

404 """ 

405 if not self._validateWcs(templateExposure, scienceExposure): 

406 if doWarping: 

407 self.log.info("Astrometrically registering template to science image") 

408 templatePsf = templateExposure.getPsf() 

409 # Warp PSF before overwriting exposure 

410 xyTransform = afwGeom.makeWcsPairTransform(templateExposure.getWcs(), 

411 scienceExposure.getWcs()) 

412 psfWarped = WarpedPsf(templatePsf, xyTransform) 

413 templateExposure = self._warper.warpExposure(scienceExposure.getWcs(), 

414 templateExposure, 

415 destBBox=scienceExposure.getBBox()) 

416 templateExposure.setPsf(psfWarped) 

417 else: 

418 self.log.error("ERROR: Input images not registered") 

419 raise RuntimeError("Input images not registered") 

420 

421 if templateFwhmPix is None: 

422 if not templateExposure.hasPsf(): 

423 self.log.warn("No estimate of Psf FWHM for template image") 

424 else: 

425 templateFwhmPix = self.getFwhmPix(templateExposure.getPsf()) 

426 self.log.info("templateFwhmPix: {}".format(templateFwhmPix)) 

427 

428 if scienceFwhmPix is None: 

429 if not scienceExposure.hasPsf(): 

430 self.log.warn("No estimate of Psf FWHM for science image") 

431 else: 

432 scienceFwhmPix = self.getFwhmPix(scienceExposure.getPsf()) 

433 self.log.info("scienceFwhmPix: {}".format(scienceFwhmPix)) 

434 

435 if convolveTemplate: 

436 kernelSize = makeKernelBasisList(self.kConfig, templateFwhmPix, scienceFwhmPix)[0].getWidth() 

437 candidateList = self.makeCandidateList( 

438 templateExposure, scienceExposure, kernelSize, candidateList) 

439 results = self.matchMaskedImages( 

440 templateExposure.getMaskedImage(), scienceExposure.getMaskedImage(), candidateList, 

441 templateFwhmPix=templateFwhmPix, scienceFwhmPix=scienceFwhmPix) 

442 else: 

443 kernelSize = makeKernelBasisList(self.kConfig, scienceFwhmPix, templateFwhmPix)[0].getWidth() 

444 candidateList = self.makeCandidateList( 

445 templateExposure, scienceExposure, kernelSize, candidateList) 

446 results = self.matchMaskedImages( 

447 scienceExposure.getMaskedImage(), templateExposure.getMaskedImage(), candidateList, 

448 templateFwhmPix=scienceFwhmPix, scienceFwhmPix=templateFwhmPix) 

449 

450 psfMatchedExposure = afwImage.makeExposure(results.matchedImage, scienceExposure.getWcs()) 

451 psfMatchedExposure.setFilter(templateExposure.getFilter()) 

452 psfMatchedExposure.setPhotoCalib(scienceExposure.getPhotoCalib()) 

453 results.warpedExposure = templateExposure 

454 results.matchedExposure = psfMatchedExposure 

455 return results 

456 

457 @pipeBase.timeMethod 

458 def matchMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList, 

459 templateFwhmPix=None, scienceFwhmPix=None): 

460 """PSF-match a MaskedImage (templateMaskedImage) to a reference MaskedImage (scienceMaskedImage). 

461 

462 Do the following, in order: 

463 

464 - Determine a PSF matching kernel and differential background model 

465 that matches templateMaskedImage to scienceMaskedImage 

466 - Convolve templateMaskedImage by the PSF matching kernel 

467 

468 Parameters 

469 ---------- 

470 templateMaskedImage : `lsst.afw.image.MaskedImage` 

471 masked image to PSF-match to the reference masked image; 

472 must be warped to match the reference masked image 

473 scienceMaskedImage : `lsst.afw.image.MaskedImage` 

474 maskedImage whose PSF is to be matched to 

475 templateFwhmPix : `float` 

476 FWHM (in pixels) of the Psf in the template image (image to convolve) 

477 scienceFwhmPix : `float` 

478 FWHM (in pixels) of the Psf in the science image 

479 candidateList : `list`, optional 

480 A list of footprints/maskedImages for kernel candidates; 

481 if `None` then source detection is run. 

482 

483 - Currently supported: list of Footprints or measAlg.PsfCandidateF 

484 

485 Returns 

486 ------- 

487 result : `callable` 

488 An `lsst.pipe.base.Struct` containing these fields: 

489 

490 - psfMatchedMaskedImage: the PSF-matched masked image = 

491 ``templateMaskedImage`` convolved with psfMatchingKernel. 

492 This has the same xy0, dimensions and wcs as ``scienceMaskedImage``. 

493 - psfMatchingKernel: the PSF matching kernel 

494 - backgroundModel: differential background model 

495 - kernelCellSet: SpatialCellSet used to solve for the PSF matching kernel 

496 

497 Raises 

498 ------ 

499 RuntimeError 

500 Raised if input images have different dimensions 

501 """ 

502 import lsstDebug 

503 display = lsstDebug.Info(__name__).display 

504 displayTemplate = lsstDebug.Info(__name__).displayTemplate 

505 displaySciIm = lsstDebug.Info(__name__).displaySciIm 

506 displaySpatialCells = lsstDebug.Info(__name__).displaySpatialCells 

507 maskTransparency = lsstDebug.Info(__name__).maskTransparency 

508 if not maskTransparency: 

509 maskTransparency = 0 

510 if display: 

511 afwDisplay.setDefaultMaskTransparency(maskTransparency) 

512 

513 if not candidateList: 

514 raise RuntimeError("Candidate list must be populated by makeCandidateList") 

515 

516 if not self._validateSize(templateMaskedImage, scienceMaskedImage): 

517 self.log.error("ERROR: Input images different size") 

518 raise RuntimeError("Input images different size") 

519 

520 if display and displayTemplate: 

521 disp = afwDisplay.Display(frame=lsstDebug.frame) 

522 disp.mtv(templateMaskedImage, title="Image to convolve") 

523 lsstDebug.frame += 1 

524 

525 if display and displaySciIm: 

526 disp = afwDisplay.Display(frame=lsstDebug.frame) 

527 disp.mtv(scienceMaskedImage, title="Image to not convolve") 

528 lsstDebug.frame += 1 

529 

530 kernelCellSet = self._buildCellSet(templateMaskedImage, 

531 scienceMaskedImage, 

532 candidateList) 

533 

534 if display and displaySpatialCells: 

535 diffimUtils.showKernelSpatialCells(scienceMaskedImage, kernelCellSet, 

536 symb="o", ctype=afwDisplay.CYAN, ctypeUnused=afwDisplay.YELLOW, 

537 ctypeBad=afwDisplay.RED, size=4, frame=lsstDebug.frame, 

538 title="Image to not convolve") 

539 lsstDebug.frame += 1 

540 

541 if templateFwhmPix and scienceFwhmPix: 

542 self.log.info("Matching Psf FWHM %.2f -> %.2f pix", templateFwhmPix, scienceFwhmPix) 

543 

544 if self.kConfig.useBicForKernelBasis: 

545 tmpKernelCellSet = self._buildCellSet(templateMaskedImage, 

546 scienceMaskedImage, 

547 candidateList) 

548 nbe = diffimTools.NbasisEvaluator(self.kConfig, templateFwhmPix, scienceFwhmPix) 

549 bicDegrees = nbe(tmpKernelCellSet, self.log) 

550 basisList = makeKernelBasisList(self.kConfig, templateFwhmPix, scienceFwhmPix, 

551 alardDegGauss=bicDegrees[0], metadata=self.metadata) 

552 del tmpKernelCellSet 

553 else: 

554 basisList = makeKernelBasisList(self.kConfig, templateFwhmPix, scienceFwhmPix, 

555 metadata=self.metadata) 

556 

557 spatialSolution, psfMatchingKernel, backgroundModel = self._solve(kernelCellSet, basisList) 

558 

559 psfMatchedMaskedImage = afwImage.MaskedImageF(templateMaskedImage.getBBox()) 

560 doNormalize = False 

561 afwMath.convolve(psfMatchedMaskedImage, templateMaskedImage, psfMatchingKernel, doNormalize) 

562 return pipeBase.Struct( 

563 matchedImage=psfMatchedMaskedImage, 

564 psfMatchingKernel=psfMatchingKernel, 

565 backgroundModel=backgroundModel, 

566 kernelCellSet=kernelCellSet, 

567 ) 

568 

569 @pipeBase.timeMethod 

570 def subtractExposures(self, templateExposure, scienceExposure, 

571 templateFwhmPix=None, scienceFwhmPix=None, 

572 candidateList=None, doWarping=True, convolveTemplate=True): 

573 """Register, Psf-match and subtract two Exposures. 

574 

575 Do the following, in order: 

576 

577 - Warp templateExposure to match scienceExposure, if their WCSs do not already match 

578 - Determine a PSF matching kernel and differential background model 

579 that matches templateExposure to scienceExposure 

580 - PSF-match templateExposure to scienceExposure 

581 - Compute subtracted exposure (see return values for equation). 

582 

583 Parameters 

584 ---------- 

585 templateExposure : `lsst.afw.image.Exposure` 

586 Exposure to PSF-match to scienceExposure 

587 scienceExposure : `lsst.afw.image.Exposure` 

588 Reference Exposure 

589 templateFwhmPix : `float` 

590 FWHM (in pixels) of the Psf in the template image (image to convolve) 

591 scienceFwhmPix : `float` 

592 FWHM (in pixels) of the Psf in the science image 

593 candidateList : `list`, optional 

594 A list of footprints/maskedImages for kernel candidates; 

595 if `None` then source detection is run. 

596 

597 - Currently supported: list of Footprints or measAlg.PsfCandidateF 

598 

599 doWarping : `bool` 

600 What to do if ``templateExposure``` and ``scienceExposure`` WCSs do 

601 not match: 

602 

603 - if `True` then warp ``templateExposure`` to match ``scienceExposure`` 

604 - if `False` then raise an Exception 

605 

606 convolveTemplate : `bool` 

607 Convolve the template image or the science image 

608 

609 - if `True`, ``templateExposure`` is warped if doWarping, 

610 ``templateExposure`` is convolved 

611 - if `False`, ``templateExposure`` is warped if doWarping, 

612 ``scienceExposure is`` convolved 

613 

614 Returns 

615 ------- 

616 result : `lsst.pipe.base.Struct` 

617 An `lsst.pipe.base.Struct` containing these fields: 

618 

619 - ``subtractedExposure`` : subtracted Exposure 

620 scienceExposure - (matchedImage + backgroundModel) 

621 - ``matchedImage`` : ``templateExposure`` after warping to match 

622 ``templateExposure`` (if doWarping true), 

623 and convolving with psfMatchingKernel 

624 - ``psfMatchingKernel`` : PSF matching kernel 

625 - ``backgroundModel`` : differential background model 

626 - ``kernelCellSet`` : SpatialCellSet used to determine PSF matching kernel 

627 """ 

628 results = self.matchExposures( 

629 templateExposure=templateExposure, 

630 scienceExposure=scienceExposure, 

631 templateFwhmPix=templateFwhmPix, 

632 scienceFwhmPix=scienceFwhmPix, 

633 candidateList=candidateList, 

634 doWarping=doWarping, 

635 convolveTemplate=convolveTemplate 

636 ) 

637 

638 subtractedExposure = afwImage.ExposureF(scienceExposure, True) 

639 if convolveTemplate: 

640 subtractedMaskedImage = subtractedExposure.getMaskedImage() 

641 subtractedMaskedImage -= results.matchedExposure.getMaskedImage() 

642 subtractedMaskedImage -= results.backgroundModel 

643 else: 

644 subtractedExposure.setMaskedImage(results.warpedExposure.getMaskedImage()) 

645 subtractedMaskedImage = subtractedExposure.getMaskedImage() 

646 subtractedMaskedImage -= results.matchedExposure.getMaskedImage() 

647 subtractedMaskedImage -= results.backgroundModel 

648 

649 # Preserve polarity of differences 

650 subtractedMaskedImage *= -1 

651 

652 # Place back on native photometric scale 

653 subtractedMaskedImage /= results.psfMatchingKernel.computeImage( 

654 afwImage.ImageD(results.psfMatchingKernel.getDimensions()), False) 

655 

656 import lsstDebug 

657 display = lsstDebug.Info(__name__).display 

658 displayDiffIm = lsstDebug.Info(__name__).displayDiffIm 

659 maskTransparency = lsstDebug.Info(__name__).maskTransparency 

660 if not maskTransparency: 

661 maskTransparency = 0 

662 if display: 

663 afwDisplay.setDefaultMaskTransparency(maskTransparency) 

664 if display and displayDiffIm: 

665 disp = afwDisplay.Display(frame=lsstDebug.frame) 

666 disp.mtv(templateExposure, title="Template") 

667 lsstDebug.frame += 1 

668 disp = afwDisplay.Display(frame=lsstDebug.frame) 

669 disp.mtv(results.matchedExposure, title="Matched template") 

670 lsstDebug.frame += 1 

671 disp = afwDisplay.Display(frame=lsstDebug.frame) 

672 disp.mtv(scienceExposure, title="Science Image") 

673 lsstDebug.frame += 1 

674 disp = afwDisplay.Display(frame=lsstDebug.frame) 

675 disp.mtv(subtractedExposure, title="Difference Image") 

676 lsstDebug.frame += 1 

677 

678 results.subtractedExposure = subtractedExposure 

679 return results 

680 

681 @pipeBase.timeMethod 

682 def subtractMaskedImages(self, templateMaskedImage, scienceMaskedImage, candidateList, 

683 templateFwhmPix=None, scienceFwhmPix=None): 

684 """Psf-match and subtract two MaskedImages. 

685 

686 Do the following, in order: 

687 

688 - PSF-match templateMaskedImage to scienceMaskedImage 

689 - Determine the differential background 

690 - Return the difference: scienceMaskedImage 

691 ((warped templateMaskedImage convolved with psfMatchingKernel) + backgroundModel) 

692 

693 Parameters 

694 ---------- 

695 templateMaskedImage : `lsst.afw.image.MaskedImage` 

696 MaskedImage to PSF-match to ``scienceMaskedImage`` 

697 scienceMaskedImage : `lsst.afw.image.MaskedImage` 

698 Reference MaskedImage 

699 templateFwhmPix : `float` 

700 FWHM (in pixels) of the Psf in the template image (image to convolve) 

701 scienceFwhmPix : `float` 

702 FWHM (in pixels) of the Psf in the science image 

703 candidateList : `list`, optional 

704 A list of footprints/maskedImages for kernel candidates; 

705 if `None` then source detection is run. 

706 

707 - Currently supported: list of Footprints or measAlg.PsfCandidateF 

708 

709 Returns 

710 ------- 

711 results : `lsst.pipe.base.Struct` 

712 An `lsst.pipe.base.Struct` containing these fields: 

713 

714 - ``subtractedMaskedImage`` : ``scienceMaskedImage`` - (matchedImage + backgroundModel) 

715 - ``matchedImage`` : templateMaskedImage convolved with psfMatchingKernel 

716 - `psfMatchingKernel`` : PSF matching kernel 

717 - ``backgroundModel`` : differential background model 

718 - ``kernelCellSet`` : SpatialCellSet used to determine PSF matching kernel 

719 

720 """ 

721 if not candidateList: 

722 raise RuntimeError("Candidate list must be populated by makeCandidateList") 

723 

724 results = self.matchMaskedImages( 

725 templateMaskedImage=templateMaskedImage, 

726 scienceMaskedImage=scienceMaskedImage, 

727 candidateList=candidateList, 

728 templateFwhmPix=templateFwhmPix, 

729 scienceFwhmPix=scienceFwhmPix, 

730 ) 

731 

732 subtractedMaskedImage = afwImage.MaskedImageF(scienceMaskedImage, True) 

733 subtractedMaskedImage -= results.matchedImage 

734 subtractedMaskedImage -= results.backgroundModel 

735 results.subtractedMaskedImage = subtractedMaskedImage 

736 

737 import lsstDebug 

738 display = lsstDebug.Info(__name__).display 

739 displayDiffIm = lsstDebug.Info(__name__).displayDiffIm 

740 maskTransparency = lsstDebug.Info(__name__).maskTransparency 

741 if not maskTransparency: 

742 maskTransparency = 0 

743 if display: 

744 afwDisplay.setDefaultMaskTransparency(maskTransparency) 

745 if display and displayDiffIm: 

746 disp = afwDisplay.Display(frame=lsstDebug.frame) 

747 disp.mtv(subtractedMaskedImage, title="Subtracted masked image") 

748 lsstDebug.frame += 1 

749 

750 return results 

751 

752 def getSelectSources(self, exposure, sigma=None, doSmooth=True, idFactory=None): 

753 """Get sources to use for Psf-matching. 

754 

755 This method runs detection and measurement on an exposure. 

756 The returned set of sources will be used as candidates for 

757 Psf-matching. 

758 

759 Parameters 

760 ---------- 

761 exposure : `lsst.afw.image.Exposure` 

762 Exposure on which to run detection/measurement 

763 sigma : `float` 

764 Detection threshold 

765 doSmooth : `bool` 

766 Whether or not to smooth the Exposure with Psf before detection 

767 idFactory : 

768 Factory for the generation of Source ids 

769 

770 Returns 

771 ------- 

772 selectSources : 

773 source catalog containing candidates for the Psf-matching 

774 """ 

775 if idFactory: 

776 table = afwTable.SourceTable.make(self.selectSchema, idFactory) 

777 else: 

778 table = afwTable.SourceTable.make(self.selectSchema) 

779 mi = exposure.getMaskedImage() 

780 

781 imArr = mi.getImage().getArray() 

782 maskArr = mi.getMask().getArray() 

783 miArr = np.ma.masked_array(imArr, mask=maskArr) 

784 try: 

785 fitBg = self.background.fitBackground(mi) 

786 bkgd = fitBg.getImageF(self.background.config.algorithm, 

787 self.background.config.undersampleStyle) 

788 except Exception: 

789 self.log.warn("Failed to get background model. Falling back to median background estimation") 

790 bkgd = np.ma.extras.median(miArr) 

791 

792 # Take off background for detection 

793 mi -= bkgd 

794 try: 

795 table.setMetadata(self.selectAlgMetadata) 

796 detRet = self.selectDetection.run( 

797 table=table, 

798 exposure=exposure, 

799 sigma=sigma, 

800 doSmooth=doSmooth 

801 ) 

802 selectSources = detRet.sources 

803 self.selectMeasurement.run(measCat=selectSources, exposure=exposure) 

804 finally: 

805 # Put back on the background in case it is needed down stream 

806 mi += bkgd 

807 del bkgd 

808 return selectSources 

809 

810 def makeCandidateList(self, templateExposure, scienceExposure, kernelSize, candidateList=None): 

811 """Make a list of acceptable KernelCandidates. 

812 

813 Accept or generate a list of candidate sources for 

814 Psf-matching, and examine the Mask planes in both of the 

815 images for indications of bad pixels 

816 

817 Parameters 

818 ---------- 

819 templateExposure : `lsst.afw.image.Exposure` 

820 Exposure that will be convolved 

821 scienceExposure : `lsst.afw.image.Exposure` 

822 Exposure that will be matched-to 

823 kernelSize : `float` 

824 Dimensions of the Psf-matching Kernel, used to grow detection footprints 

825 candidateList : `list`, optional 

826 List of Sources to examine. Elements must be of type afw.table.Source 

827 or a type that wraps a Source and has a getSource() method, such as 

828 meas.algorithms.PsfCandidateF. 

829 

830 Returns 

831 ------- 

832 candidateList : `list` of `dict` 

833 A list of dicts having a "source" and "footprint" 

834 field for the Sources deemed to be appropriate for Psf 

835 matching 

836 """ 

837 if candidateList is None: 

838 candidateList = self.getSelectSources(scienceExposure) 

839 

840 if len(candidateList) < 1: 

841 raise RuntimeError("No candidates in candidateList") 

842 

843 listTypes = set(type(x) for x in candidateList) 

844 if len(listTypes) > 1: 

845 raise RuntimeError("Candidate list contains mixed types: %s" % [l for l in listTypes]) 

846 

847 if not isinstance(candidateList[0], afwTable.SourceRecord): 

848 try: 

849 candidateList[0].getSource() 

850 except Exception as e: 

851 raise RuntimeError(f"Candidate List is of type: {type(candidateList[0])} " 

852 "Can only make candidate list from list of afwTable.SourceRecords, " 

853 f"measAlg.PsfCandidateF or other type with a getSource() method: {e}") 

854 candidateList = [c.getSource() for c in candidateList] 

855 

856 candidateList = diffimTools.sourceToFootprintList(candidateList, 

857 templateExposure, scienceExposure, 

858 kernelSize, 

859 self.kConfig.detectionConfig, 

860 self.log) 

861 if len(candidateList) == 0: 

862 raise RuntimeError("Cannot find any objects suitable for KernelCandidacy") 

863 

864 return candidateList 

865 

866 def _adaptCellSize(self, candidateList): 

867 """NOT IMPLEMENTED YET. 

868 """ 

869 return self.kConfig.sizeCellX, self.kConfig.sizeCellY 

870 

871 def _buildCellSet(self, templateMaskedImage, scienceMaskedImage, candidateList): 

872 """Build a SpatialCellSet for use with the solve method. 

873 

874 Parameters 

875 ---------- 

876 templateMaskedImage : `lsst.afw.image.MaskedImage` 

877 MaskedImage to PSF-matched to scienceMaskedImage 

878 scienceMaskedImage : `lsst.afw.image.MaskedImage` 

879 Reference MaskedImage 

880 candidateList : `list` 

881 A list of footprints/maskedImages for kernel candidates; 

882 

883 - Currently supported: list of Footprints or measAlg.PsfCandidateF 

884 

885 Returns 

886 ------- 

887 kernelCellSet : `lsst.afw.math.SpatialCellSet` 

888 a SpatialCellSet for use with self._solve 

889 """ 

890 if not candidateList: 

891 raise RuntimeError("Candidate list must be populated by makeCandidateList") 

892 

893 sizeCellX, sizeCellY = self._adaptCellSize(candidateList) 

894 

895 # Object to store the KernelCandidates for spatial modeling 

896 kernelCellSet = afwMath.SpatialCellSet(templateMaskedImage.getBBox(), 

897 sizeCellX, sizeCellY) 

898 

899 ps = pexConfig.makePropertySet(self.kConfig) 

900 # Place candidates within the spatial grid 

901 for cand in candidateList: 

902 if isinstance(cand, afwDetect.Footprint): 

903 bbox = cand.getBBox() 

904 else: 

905 bbox = cand['footprint'].getBBox() 

906 tmi = afwImage.MaskedImageF(templateMaskedImage, bbox) 

907 smi = afwImage.MaskedImageF(scienceMaskedImage, bbox) 

908 

909 if not isinstance(cand, afwDetect.Footprint): 

910 if 'source' in cand: 

911 cand = cand['source'] 

912 xPos = cand.getCentroid()[0] 

913 yPos = cand.getCentroid()[1] 

914 cand = diffimLib.makeKernelCandidate(xPos, yPos, tmi, smi, ps) 

915 

916 self.log.debug("Candidate %d at %f, %f", cand.getId(), cand.getXCenter(), cand.getYCenter()) 

917 kernelCellSet.insertCandidate(cand) 

918 

919 return kernelCellSet 

920 

921 def _validateSize(self, templateMaskedImage, scienceMaskedImage): 

922 """Return True if two image-like objects are the same size. 

923 """ 

924 return templateMaskedImage.getDimensions() == scienceMaskedImage.getDimensions() 

925 

926 def _validateWcs(self, templateExposure, scienceExposure): 

927 """Return True if the WCS of the two Exposures have the same origin and extent. 

928 """ 

929 templateWcs = templateExposure.getWcs() 

930 scienceWcs = scienceExposure.getWcs() 

931 templateBBox = templateExposure.getBBox() 

932 scienceBBox = scienceExposure.getBBox() 

933 

934 # LLC 

935 templateOrigin = templateWcs.pixelToSky(geom.Point2D(templateBBox.getBegin())) 

936 scienceOrigin = scienceWcs.pixelToSky(geom.Point2D(scienceBBox.getBegin())) 

937 

938 # URC 

939 templateLimit = templateWcs.pixelToSky(geom.Point2D(templateBBox.getEnd())) 

940 scienceLimit = scienceWcs.pixelToSky(geom.Point2D(scienceBBox.getEnd())) 

941 

942 self.log.info("Template Wcs : %f,%f -> %f,%f", 

943 templateOrigin[0], templateOrigin[1], 

944 templateLimit[0], templateLimit[1]) 

945 self.log.info("Science Wcs : %f,%f -> %f,%f", 

946 scienceOrigin[0], scienceOrigin[1], 

947 scienceLimit[0], scienceLimit[1]) 

948 

949 templateBBox = geom.Box2D(templateOrigin.getPosition(geom.degrees), 

950 templateLimit.getPosition(geom.degrees)) 

951 scienceBBox = geom.Box2D(scienceOrigin.getPosition(geom.degrees), 

952 scienceLimit.getPosition(geom.degrees)) 

953 if not (templateBBox.overlaps(scienceBBox)): 

954 raise RuntimeError("Input images do not overlap at all") 

955 

956 if ((templateOrigin != scienceOrigin) 

957 or (templateLimit != scienceLimit) 

958 or (templateExposure.getDimensions() != scienceExposure.getDimensions())): 

959 return False 

960 return True 

961 

962 

963subtractAlgorithmRegistry = pexConfig.makeRegistry( 

964 doc="A registry of subtraction algorithms for use as a subtask in imageDifference", 

965) 

966 

967subtractAlgorithmRegistry.register('al', ImagePsfMatchTask)