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1# This file is part of ip_diffim. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (https://www.lsst.org). 

6# See the COPYRIGHT file at the top-level directory of this distribution 

7# for details of code ownership. 

8# 

9# This program is free software: you can redistribute it and/or modify 

10# it under the terms of the GNU General Public License as published by 

11# the Free Software Foundation, either version 3 of the License, or 

12# (at your option) any later version. 

13# 

14# This program is distributed in the hope that it will be useful, 

15# but WITHOUT ANY WARRANTY; without even the implied warranty of 

16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

17# GNU General Public License for more details. 

18# 

19# You should have received a copy of the GNU General Public License 

20# along with this program. If not, see <https://www.gnu.org/licenses/>. 

21 

22__all__ = ["DetectionConfig", "PsfMatchConfig", "PsfMatchConfigAL", "PsfMatchConfigDF", "PsfMatchTask"] 

23 

24import time 

25 

26import numpy as np 

27 

28import lsst.afw.image as afwImage 

29import lsst.pex.config as pexConfig 

30import lsst.afw.math as afwMath 

31import lsst.afw.display as afwDisplay 

32import lsst.log as log 

33import lsst.pipe.base as pipeBase 

34from lsst.meas.algorithms import SubtractBackgroundConfig 

35from . import utils as diutils 

36from . import diffimLib 

37 

38 

39class DetectionConfig(pexConfig.Config): 

40 """Configuration for detecting sources on images for building a 

41 PSF-matching kernel 

42 

43 Configuration for turning detected lsst.afw.detection.FootPrints into an 

44 acceptable (unmasked, high signal-to-noise, not too large or not too small) 

45 list of `lsst.ip.diffim.KernelSources` that are used to build the 

46 Psf-matching kernel""" 

47 

48 detThreshold = pexConfig.Field( 48 ↛ exitline 48 didn't jump to the function exit

49 dtype=float, 

50 doc="Value of footprint detection threshold", 

51 default=10.0, 

52 check=lambda x: x >= 3.0 

53 ) 

54 detThresholdType = pexConfig.ChoiceField( 

55 dtype=str, 

56 doc="Type of detection threshold", 

57 default="pixel_stdev", 

58 allowed={ 

59 "value": "Use counts as the detection threshold type", 

60 "stdev": "Use standard deviation of image plane", 

61 "variance": "Use variance of image plane", 

62 "pixel_stdev": "Use stdev derived from variance plane" 

63 } 

64 ) 

65 detOnTemplate = pexConfig.Field( 

66 dtype=bool, 

67 doc="""If true run detection on the template (image to convolve); 

68 if false run detection on the science image""", 

69 default=True 

70 ) 

71 badMaskPlanes = pexConfig.ListField( 

72 dtype=str, 

73 doc="""Mask planes that lead to an invalid detection. 

74 Options: NO_DATA EDGE SAT BAD CR INTRP""", 

75 default=("NO_DATA", "EDGE", "SAT") 

76 ) 

77 fpNpixMin = pexConfig.Field( 77 ↛ exitline 77 didn't jump to the function exit

78 dtype=int, 

79 doc="Minimum number of pixels in an acceptable Footprint", 

80 default=5, 

81 check=lambda x: x >= 5 

82 ) 

83 fpNpixMax = pexConfig.Field( 83 ↛ exitline 83 didn't jump to the function exit

84 dtype=int, 

85 doc="""Maximum number of pixels in an acceptable Footprint; 

86 too big and the subsequent convolutions become unwieldy""", 

87 default=500, 

88 check=lambda x: x <= 500 

89 ) 

90 fpGrowKernelScaling = pexConfig.Field( 90 ↛ exitline 90 didn't jump to the function exit

91 dtype=float, 

92 doc="""If config.scaleByFwhm, grow the footprint based on 

93 the final kernelSize. Each footprint will be 

94 2*fpGrowKernelScaling*kernelSize x 

95 2*fpGrowKernelScaling*kernelSize. With the value 

96 of 1.0, the remaining pixels in each KernelCandiate 

97 after convolution by the basis functions will be 

98 equal to the kernel size itself.""", 

99 default=1.0, 

100 check=lambda x: x >= 1.0 

101 ) 

102 fpGrowPix = pexConfig.Field( 102 ↛ exitline 102 didn't jump to the function exit

103 dtype=int, 

104 doc="""Growing radius (in pixels) for each raw detection 

105 footprint. The smaller the faster; however the 

106 kernel sum does not converge if the stamp is too 

107 small; and the kernel is not constrained at all if 

108 the stamp is the size of the kernel. The grown stamp 

109 is 2 * fpGrowPix pixels larger in each dimension. 

110 This is overridden by fpGrowKernelScaling if scaleByFwhm""", 

111 default=30, 

112 check=lambda x: x >= 10 

113 ) 

114 scaleByFwhm = pexConfig.Field( 

115 dtype=bool, 

116 doc="Scale fpGrowPix by input Fwhm?", 

117 default=True, 

118 ) 

119 

120 

121class PsfMatchConfig(pexConfig.Config): 

122 """Base configuration for Psf-matching 

123 

124 The base configuration of the Psf-matching kernel, and of the warping, detection, 

125 and background modeling subTasks.""" 

126 

127 warpingConfig = pexConfig.ConfigField("Config for warping exposures to a common alignment", 

128 afwMath.WarperConfig) 

129 detectionConfig = pexConfig.ConfigField("Controlling the detection of sources for kernel building", 

130 DetectionConfig) 

131 afwBackgroundConfig = pexConfig.ConfigField("Controlling the Afw background fitting", 

132 SubtractBackgroundConfig) 

133 

134 useAfwBackground = pexConfig.Field( 

135 dtype=bool, 

136 doc="Use afw background subtraction instead of ip_diffim", 

137 default=False, 

138 ) 

139 fitForBackground = pexConfig.Field( 

140 dtype=bool, 

141 doc="Include terms (including kernel cross terms) for background in ip_diffim", 

142 default=False, 

143 ) 

144 kernelBasisSet = pexConfig.ChoiceField( 

145 dtype=str, 

146 doc="Type of basis set for PSF matching kernel.", 

147 default="alard-lupton", 

148 allowed={ 

149 "alard-lupton": """Alard-Lupton sum-of-gaussians basis set, 

150 * The first term has no spatial variation 

151 * The kernel sum is conserved 

152 * You may want to turn off 'usePcaForSpatialKernel'""", 

153 "delta-function": """Delta-function kernel basis set, 

154 * You may enable the option useRegularization 

155 * You should seriously consider usePcaForSpatialKernel, which will also 

156 enable kernel sum conservation for the delta function kernels""" 

157 } 

158 ) 

159 kernelSize = pexConfig.Field( 

160 dtype=int, 

161 doc="""Number of rows/columns in the convolution kernel; should be odd-valued. 

162 Modified by kernelSizeFwhmScaling if scaleByFwhm = true""", 

163 default=21, 

164 ) 

165 scaleByFwhm = pexConfig.Field( 

166 dtype=bool, 

167 doc="Scale kernelSize, alardGaussians by input Fwhm", 

168 default=True, 

169 ) 

170 kernelSizeFwhmScaling = pexConfig.Field( 170 ↛ exitline 170 didn't jump to the function exit

171 dtype=float, 

172 doc="Multiplier of the largest AL Gaussian basis sigma to get the kernel bbox (pixel) size.", 

173 default=6.0, 

174 check=lambda x: x >= 1.0 

175 ) 

176 kernelSizeMin = pexConfig.Field( 

177 dtype=int, 

178 doc="Minimum kernel bbox (pixel) size.", 

179 default=21, 

180 ) 

181 kernelSizeMax = pexConfig.Field( 

182 dtype=int, 

183 doc="Maximum kernel bbox (pixel) size.", 

184 default=35, 

185 ) 

186 spatialModelType = pexConfig.ChoiceField( 

187 dtype=str, 

188 doc="Type of spatial functions for kernel and background", 

189 default="chebyshev1", 

190 allowed={ 

191 "chebyshev1": "Chebyshev polynomial of the first kind", 

192 "polynomial": "Standard x,y polynomial", 

193 } 

194 ) 

195 spatialKernelOrder = pexConfig.Field( 195 ↛ exitline 195 didn't jump to the function exit

196 dtype=int, 

197 doc="Spatial order of convolution kernel variation", 

198 default=2, 

199 check=lambda x: x >= 0 

200 ) 

201 spatialBgOrder = pexConfig.Field( 201 ↛ exitline 201 didn't jump to the function exit

202 dtype=int, 

203 doc="Spatial order of differential background variation", 

204 default=1, 

205 check=lambda x: x >= 0 

206 ) 

207 sizeCellX = pexConfig.Field( 207 ↛ exitline 207 didn't jump to the function exit

208 dtype=int, 

209 doc="Size (rows) in pixels of each SpatialCell for spatial modeling", 

210 default=128, 

211 check=lambda x: x >= 32 

212 ) 

213 sizeCellY = pexConfig.Field( 213 ↛ exitline 213 didn't jump to the function exit

214 dtype=int, 

215 doc="Size (columns) in pixels of each SpatialCell for spatial modeling", 

216 default=128, 

217 check=lambda x: x >= 32 

218 ) 

219 nStarPerCell = pexConfig.Field( 219 ↛ exitline 219 didn't jump to the function exit

220 dtype=int, 

221 doc="Number of KernelCandidates in each SpatialCell to use in the spatial fitting", 

222 default=3, 

223 check=lambda x: x >= 1 

224 ) 

225 maxSpatialIterations = pexConfig.Field( 225 ↛ exitline 225 didn't jump to the function exit

226 dtype=int, 

227 doc="Maximum number of iterations for rejecting bad KernelCandidates in spatial fitting", 

228 default=3, 

229 check=lambda x: x >= 1 and x <= 5 

230 ) 

231 usePcaForSpatialKernel = pexConfig.Field( 

232 dtype=bool, 

233 doc="""Use Pca to reduce the dimensionality of the kernel basis sets. 

234 This is particularly useful for delta-function kernels. 

235 Functionally, after all Cells have their raw kernels determined, we run 

236 a Pca on these Kernels, re-fit the Cells using the eigenKernels and then 

237 fit those for spatial variation using the same technique as for Alard-Lupton kernels. 

238 If this option is used, the first term will have no spatial variation and the 

239 kernel sum will be conserved.""", 

240 default=False, 

241 ) 

242 subtractMeanForPca = pexConfig.Field( 

243 dtype=bool, 

244 doc="Subtract off the mean feature before doing the Pca", 

245 default=True, 

246 ) 

247 numPrincipalComponents = pexConfig.Field( 247 ↛ exitline 247 didn't jump to the function exit

248 dtype=int, 

249 doc="""Number of principal components to use for Pca basis, including the 

250 mean kernel if requested.""", 

251 default=5, 

252 check=lambda x: x >= 3 

253 ) 

254 singleKernelClipping = pexConfig.Field( 

255 dtype=bool, 

256 doc="Do sigma clipping on each raw kernel candidate", 

257 default=True, 

258 ) 

259 kernelSumClipping = pexConfig.Field( 

260 dtype=bool, 

261 doc="Do sigma clipping on the ensemble of kernel sums", 

262 default=True, 

263 ) 

264 spatialKernelClipping = pexConfig.Field( 

265 dtype=bool, 

266 doc="Do sigma clipping after building the spatial model", 

267 default=True, 

268 ) 

269 checkConditionNumber = pexConfig.Field( 

270 dtype=bool, 

271 doc="""Test for maximum condition number when inverting a kernel matrix. 

272 Anything above maxConditionNumber is not used and the candidate is set as BAD. 

273 Also used to truncate inverse matrix in estimateBiasedRisk. However, 

274 if you are doing any deconvolution you will want to turn this off, or use 

275 a large maxConditionNumber""", 

276 default=False, 

277 ) 

278 badMaskPlanes = pexConfig.ListField( 

279 dtype=str, 

280 doc="""Mask planes to ignore when calculating diffim statistics 

281 Options: NO_DATA EDGE SAT BAD CR INTRP""", 

282 default=("NO_DATA", "EDGE", "SAT") 

283 ) 

284 candidateResidualMeanMax = pexConfig.Field( 284 ↛ exitline 284 didn't jump to the function exit

285 dtype=float, 

286 doc="""Rejects KernelCandidates yielding bad difference image quality. 

287 Used by BuildSingleKernelVisitor, AssessSpatialKernelVisitor. 

288 Represents average over pixels of (image/sqrt(variance)).""", 

289 default=0.25, 

290 check=lambda x: x >= 0.0 

291 ) 

292 candidateResidualStdMax = pexConfig.Field( 292 ↛ exitline 292 didn't jump to the function exit

293 dtype=float, 

294 doc="""Rejects KernelCandidates yielding bad difference image quality. 

295 Used by BuildSingleKernelVisitor, AssessSpatialKernelVisitor. 

296 Represents stddev over pixels of (image/sqrt(variance)).""", 

297 default=1.50, 

298 check=lambda x: x >= 0.0 

299 ) 

300 useCoreStats = pexConfig.Field( 

301 dtype=bool, 

302 doc="""Use the core of the footprint for the quality statistics, instead of the entire footprint. 

303 WARNING: if there is deconvolution we probably will need to turn this off""", 

304 default=False, 

305 ) 

306 candidateCoreRadius = pexConfig.Field( 306 ↛ exitline 306 didn't jump to the function exit

307 dtype=int, 

308 doc="""Radius for calculation of stats in 'core' of KernelCandidate diffim. 

309 Total number of pixels used will be (2*radius)**2. 

310 This is used both for 'core' diffim quality as well as ranking of 

311 KernelCandidates by their total flux in this core""", 

312 default=3, 

313 check=lambda x: x >= 1 

314 ) 

315 maxKsumSigma = pexConfig.Field( 315 ↛ exitline 315 didn't jump to the function exit

316 dtype=float, 

317 doc="""Maximum allowed sigma for outliers from kernel sum distribution. 

318 Used to reject variable objects from the kernel model""", 

319 default=3.0, 

320 check=lambda x: x >= 0.0 

321 ) 

322 maxConditionNumber = pexConfig.Field( 322 ↛ exitline 322 didn't jump to the function exit

323 dtype=float, 

324 doc="Maximum condition number for a well conditioned matrix", 

325 default=5.0e7, 

326 check=lambda x: x >= 0.0 

327 ) 

328 conditionNumberType = pexConfig.ChoiceField( 

329 dtype=str, 

330 doc="Use singular values (SVD) or eigen values (EIGENVALUE) to determine condition number", 

331 default="EIGENVALUE", 

332 allowed={ 

333 "SVD": "Use singular values", 

334 "EIGENVALUE": "Use eigen values (faster)", 

335 } 

336 ) 

337 maxSpatialConditionNumber = pexConfig.Field( 337 ↛ exitline 337 didn't jump to the function exit

338 dtype=float, 

339 doc="Maximum condition number for a well conditioned spatial matrix", 

340 default=1.0e10, 

341 check=lambda x: x >= 0.0 

342 ) 

343 iterateSingleKernel = pexConfig.Field( 

344 dtype=bool, 

345 doc="""Remake KernelCandidate using better variance estimate after first pass? 

346 Primarily useful when convolving a single-depth image, otherwise not necessary.""", 

347 default=False, 

348 ) 

349 constantVarianceWeighting = pexConfig.Field( 

350 dtype=bool, 

351 doc="""Use constant variance weighting in single kernel fitting? 

352 In some cases this is better for bright star residuals.""", 

353 default=True, 

354 ) 

355 calculateKernelUncertainty = pexConfig.Field( 

356 dtype=bool, 

357 doc="""Calculate kernel and background uncertainties for each kernel candidate? 

358 This comes from the inverse of the covariance matrix. 

359 Warning: regularization can cause problems for this step.""", 

360 default=False, 

361 ) 

362 useBicForKernelBasis = pexConfig.Field( 

363 dtype=bool, 

364 doc="""Use Bayesian Information Criterion to select the number of bases going into the kernel""", 

365 default=False, 

366 ) 

367 

368 

369class PsfMatchConfigAL(PsfMatchConfig): 

370 """The parameters specific to the "Alard-Lupton" (sum-of-Gaussian) Psf-matching basis""" 

371 

372 def setDefaults(self): 

373 PsfMatchConfig.setDefaults(self) 

374 self.kernelBasisSet = "alard-lupton" 

375 self.maxConditionNumber = 5.0e7 

376 

377 alardNGauss = pexConfig.Field( 377 ↛ exitline 377 didn't jump to the function exit

378 dtype=int, 

379 doc="Number of base Gaussians in alard-lupton kernel basis function generation.", 

380 default=3, 

381 check=lambda x: x >= 1 

382 ) 

383 alardDegGauss = pexConfig.ListField( 

384 dtype=int, 

385 doc="Polynomial order of spatial modification of base Gaussians. " 

386 "List length must be `alardNGauss`.", 

387 default=(4, 2, 2), 

388 ) 

389 alardSigGauss = pexConfig.ListField( 

390 dtype=float, 

391 doc="Default sigma values in pixels of base Gaussians. " 

392 "List length must be `alardNGauss`.", 

393 default=(0.7, 1.5, 3.0), 

394 ) 

395 alardGaussBeta = pexConfig.Field( 395 ↛ exitline 395 didn't jump to the function exit

396 dtype=float, 

397 doc="Used if `scaleByFwhm==True`, scaling multiplier of base " 

398 "Gaussian sigmas for automated sigma determination", 

399 default=2.0, 

400 check=lambda x: x >= 0.0, 

401 ) 

402 alardMinSig = pexConfig.Field( 402 ↛ exitline 402 didn't jump to the function exit

403 dtype=float, 

404 doc="Used if `scaleByFwhm==True`, minimum sigma (pixels) for base Gaussians", 

405 default=0.7, 

406 check=lambda x: x >= 0.25 

407 ) 

408 alardDegGaussDeconv = pexConfig.Field( 408 ↛ exitline 408 didn't jump to the function exit

409 dtype=int, 

410 doc="Used if `scaleByFwhm==True`, degree of spatial modification of ALL base Gaussians " 

411 "in AL basis during deconvolution", 

412 default=3, 

413 check=lambda x: x >= 1 

414 ) 

415 alardMinSigDeconv = pexConfig.Field( 415 ↛ exitline 415 didn't jump to the function exit

416 dtype=float, 

417 doc="Used if `scaleByFwhm==True`, minimum sigma (pixels) for base Gaussians during deconvolution; " 

418 "make smaller than `alardMinSig` as this is only indirectly used", 

419 default=0.4, 

420 check=lambda x: x >= 0.25 

421 ) 

422 alardNGaussDeconv = pexConfig.Field( 422 ↛ exitline 422 didn't jump to the function exit

423 dtype=int, 

424 doc="Used if `scaleByFwhm==True`, number of base Gaussians in AL basis during deconvolution", 

425 default=3, 

426 check=lambda x: x >= 1 

427 ) 

428 

429 

430class PsfMatchConfigDF(PsfMatchConfig): 

431 """The parameters specific to the delta-function (one basis per-pixel) Psf-matching basis""" 

432 

433 def setDefaults(self): 

434 PsfMatchConfig.setDefaults(self) 

435 self.kernelBasisSet = "delta-function" 

436 self.maxConditionNumber = 5.0e6 

437 self.usePcaForSpatialKernel = True 

438 self.subtractMeanForPca = True 

439 self.useBicForKernelBasis = False 

440 

441 useRegularization = pexConfig.Field( 

442 dtype=bool, 

443 doc="Use regularization to smooth the delta function kernels", 

444 default=True, 

445 ) 

446 regularizationType = pexConfig.ChoiceField( 

447 dtype=str, 

448 doc="Type of regularization.", 

449 default="centralDifference", 

450 allowed={ 

451 "centralDifference": "Penalize second derivative using 2-D stencil of central finite difference", 

452 "forwardDifference": "Penalize first, second, third derivatives using forward finite differeces" 

453 } 

454 ) 

455 centralRegularizationStencil = pexConfig.ChoiceField( 

456 dtype=int, 

457 doc="Type of stencil to approximate central derivative (for centralDifference only)", 

458 default=9, 

459 allowed={ 

460 5: "5-point stencil including only adjacent-in-x,y elements", 

461 9: "9-point stencil including diagonal elements" 

462 } 

463 ) 

464 forwardRegularizationOrders = pexConfig.ListField( 464 ↛ exitline 464 didn't jump to the function exit

465 dtype=int, 

466 doc="Array showing which order derivatives to penalize (for forwardDifference only)", 

467 default=(1, 2), 

468 itemCheck=lambda x: (x > 0) and (x < 4) 

469 ) 

470 regularizationBorderPenalty = pexConfig.Field( 470 ↛ exitline 470 didn't jump to the function exit

471 dtype=float, 

472 doc="Value of the penalty for kernel border pixels", 

473 default=3.0, 

474 check=lambda x: x >= 0.0 

475 ) 

476 lambdaType = pexConfig.ChoiceField( 

477 dtype=str, 

478 doc="How to choose the value of the regularization strength", 

479 default="absolute", 

480 allowed={ 

481 "absolute": "Use lambdaValue as the value of regularization strength", 

482 "relative": "Use lambdaValue as fraction of the default regularization strength (N.R. 18.5.8)", 

483 "minimizeBiasedRisk": "Minimize biased risk estimate", 

484 "minimizeUnbiasedRisk": "Minimize unbiased risk estimate", 

485 } 

486 ) 

487 lambdaValue = pexConfig.Field( 

488 dtype=float, 

489 doc="Value used for absolute determinations of regularization strength", 

490 default=0.2, 

491 ) 

492 lambdaScaling = pexConfig.Field( 

493 dtype=float, 

494 doc="Fraction of the default lambda strength (N.R. 18.5.8) to use. 1e-4 or 1e-5", 

495 default=1e-4, 

496 ) 

497 lambdaStepType = pexConfig.ChoiceField( 

498 dtype=str, 

499 doc="""If a scan through lambda is needed (minimizeBiasedRisk, minimizeUnbiasedRisk), 

500 use log or linear steps""", 

501 default="log", 

502 allowed={ 

503 "log": "Step in log intervals; e.g. lambdaMin, lambdaMax, lambdaStep = -1.0, 2.0, 0.1", 

504 "linear": "Step in linear intervals; e.g. lambdaMin, lambdaMax, lambdaStep = 0.1, 100, 0.1", 

505 } 

506 ) 

507 lambdaMin = pexConfig.Field( 

508 dtype=float, 

509 doc="""If scan through lambda needed (minimizeBiasedRisk, minimizeUnbiasedRisk), 

510 start at this value. If lambdaStepType = log:linear, suggest -1:0.1""", 

511 default=-1.0, 

512 ) 

513 lambdaMax = pexConfig.Field( 

514 dtype=float, 

515 doc="""If scan through lambda needed (minimizeBiasedRisk, minimizeUnbiasedRisk), 

516 stop at this value. If lambdaStepType = log:linear, suggest 2:100""", 

517 default=2.0, 

518 ) 

519 lambdaStep = pexConfig.Field( 

520 dtype=float, 

521 doc="""If scan through lambda needed (minimizeBiasedRisk, minimizeUnbiasedRisk), 

522 step in these increments. If lambdaStepType = log:linear, suggest 0.1:0.1""", 

523 default=0.1, 

524 ) 

525 

526 

527class PsfMatchTask(pipeBase.Task): 

528 """Base class for Psf Matching; should not be called directly 

529 

530 Notes 

531 ----- 

532 PsfMatchTask is a base class that implements the core functionality for matching the 

533 Psfs of two images using a spatially varying Psf-matching `lsst.afw.math.LinearCombinationKernel`. 

534 The Task requires the user to provide an instance of an `lsst.afw.math.SpatialCellSet`, 

535 filled with `lsst.ip.diffim.KernelCandidate` instances, and a list of `lsst.afw.math.Kernels` 

536 of basis shapes that will be used for the decomposition. If requested, the Task 

537 also performs background matching and returns the differential background model as an 

538 `lsst.afw.math.Kernel.SpatialFunction`. 

539 

540 **Invoking the Task** 

541 

542 As a base class, this Task is not directly invoked. However, ``run()`` methods that are 

543 implemented on derived classes will make use of the core ``_solve()`` functionality, 

544 which defines a sequence of `lsst.afw.math.CandidateVisitor` classes that iterate 

545 through the KernelCandidates, first building up a per-candidate solution and then 

546 building up a spatial model from the ensemble of candidates. Sigma clipping is 

547 performed using the mean and standard deviation of all kernel sums (to reject 

548 variable objects), on the per-candidate substamp diffim residuals 

549 (to indicate a bad choice of kernel basis shapes for that particular object), 

550 and on the substamp diffim residuals using the spatial kernel fit (to indicate a bad 

551 choice of spatial kernel order, or poor constraints on the spatial model). The 

552 ``_diagnostic()`` method logs information on the quality of the spatial fit, and also 

553 modifies the Task metadata. 

554 

555 .. list-table:: Quantities set in Metadata 

556 :header-rows: 1 

557 

558 * - Parameter 

559 - Description 

560 * - ``spatialConditionNum`` 

561 - Condition number of the spatial kernel fit 

562 * - ``spatialKernelSum`` 

563 - Kernel sum (10^{-0.4 * ``Delta``; zeropoint}) of the spatial Psf-matching kernel 

564 * - ``ALBasisNGauss`` 

565 - If using sum-of-Gaussian basis, the number of gaussians used 

566 * - ``ALBasisDegGauss`` 

567 - If using sum-of-Gaussian basis, the deg of spatial variation of the Gaussians 

568 * - ``ALBasisSigGauss`` 

569 - If using sum-of-Gaussian basis, the widths (sigma) of the Gaussians 

570 * - ``ALKernelSize`` 

571 - If using sum-of-Gaussian basis, the kernel size 

572 * - ``NFalsePositivesTotal`` 

573 - Total number of diaSources 

574 * - ``NFalsePositivesRefAssociated`` 

575 - Number of diaSources that associate with the reference catalog 

576 * - ``NFalsePositivesRefAssociated`` 

577 - Number of diaSources that associate with the source catalog 

578 * - ``NFalsePositivesUnassociated`` 

579 - Number of diaSources that are orphans 

580 * - ``metric_MEAN`` 

581 - Mean value of substamp diffim quality metrics across all KernelCandidates, 

582 for both the per-candidate (LOCAL) and SPATIAL residuals 

583 * - ``metric_MEDIAN`` 

584 - Median value of substamp diffim quality metrics across all KernelCandidates, 

585 for both the per-candidate (LOCAL) and SPATIAL residuals 

586 * - ``metric_STDEV`` 

587 - Standard deviation of substamp diffim quality metrics across all KernelCandidates, 

588 for both the per-candidate (LOCAL) and SPATIAL residuals 

589 

590 **Debug variables** 

591 

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

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

594 for this Task include: 

595 

596 .. code-block:: py 

597 

598 import sys 

599 import lsstDebug 

600 def DebugInfo(name): 

601 di = lsstDebug.getInfo(name) 

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

603 # enable debug output 

604 di.display = True 

605 # display mask transparency 

606 di.maskTransparency = 80 

607 # show all the candidates and residuals 

608 di.displayCandidates = True 

609 # show kernel basis functions 

610 di.displayKernelBasis = False 

611 # show kernel realized across the image 

612 di.displayKernelMosaic = True 

613 # show coefficients of spatial model 

614 di.plotKernelSpatialModel = False 

615 # show fixed and spatial coefficients and coefficient histograms 

616 di.plotKernelCoefficients = True 

617 # show the bad candidates (red) along with good (green) 

618 di.showBadCandidates = True 

619 return di 

620 lsstDebug.Info = DebugInfo 

621 lsstDebug.frame = 1 

622 

623 Note that if you want additional logging info, you may add to your scripts: 

624 

625 .. code-block:: py 

626 

627 import lsst.log.utils as logUtils 

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

629 """ 

630 ConfigClass = PsfMatchConfig 

631 _DefaultName = "psfMatch" 

632 

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

634 """Create the psf-matching Task 

635 

636 Parameters 

637 ---------- 

638 *args 

639 Arguments to be passed to ``lsst.pipe.base.task.Task.__init__`` 

640 **kwargs 

641 Keyword arguments to be passed to ``lsst.pipe.base.task.Task.__init__`` 

642 

643 Notes 

644 ----- 

645 The initialization sets the Psf-matching kernel configuration using the value of 

646 self.config.kernel.active. If the kernel is requested with regularization to moderate 

647 the bias/variance tradeoff, currently only used when a delta function kernel basis 

648 is provided, it creates a regularization matrix stored as member variable 

649 self.hMat. 

650 """ 

651 pipeBase.Task.__init__(self, *args, **kwargs) 

652 self.kConfig = self.config.kernel.active 

653 

654 if 'useRegularization' in self.kConfig: 

655 self.useRegularization = self.kConfig.useRegularization 

656 else: 

657 self.useRegularization = False 

658 

659 if self.useRegularization: 

660 self.hMat = diffimLib.makeRegularizationMatrix(pexConfig.makePropertySet(self.kConfig)) 

661 

662 def _diagnostic(self, kernelCellSet, spatialSolution, spatialKernel, spatialBg): 

663 """Provide logging diagnostics on quality of spatial kernel fit 

664 

665 Parameters 

666 ---------- 

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

668 Cellset that contains the KernelCandidates used in the fitting 

669 spatialSolution : `lsst.ip.diffim.SpatialKernelSolution` 

670 KernelSolution of best-fit 

671 spatialKernel : `lsst.afw.math.LinearCombinationKernel` 

672 Best-fit spatial Kernel model 

673 spatialBg : `lsst.afw.math.Function2D` 

674 Best-fit spatial background model 

675 """ 

676 # What is the final kernel sum 

677 kImage = afwImage.ImageD(spatialKernel.getDimensions()) 

678 kSum = spatialKernel.computeImage(kImage, False) 

679 self.log.info("Final spatial kernel sum %.3f", kSum) 

680 

681 # Look at how well conditioned the matrix is 

682 conditionNum = spatialSolution.getConditionNumber( 

683 getattr(diffimLib.KernelSolution, self.kConfig.conditionNumberType)) 

684 self.log.info("Spatial model condition number %.3e", conditionNum) 

685 

686 if conditionNum < 0.0: 

687 self.log.warning("Condition number is negative (%.3e)", conditionNum) 

688 if conditionNum > self.kConfig.maxSpatialConditionNumber: 

689 self.log.warning("Spatial solution exceeds max condition number (%.3e > %.3e)", 

690 conditionNum, self.kConfig.maxSpatialConditionNumber) 

691 

692 self.metadata.set("spatialConditionNum", conditionNum) 

693 self.metadata.set("spatialKernelSum", kSum) 

694 

695 # Look at how well the solution is constrained 

696 nBasisKernels = spatialKernel.getNBasisKernels() 

697 nKernelTerms = spatialKernel.getNSpatialParameters() 

698 if nKernelTerms == 0: # order 0 

699 nKernelTerms = 1 

700 

701 # Not fit for 

702 nBgTerms = spatialBg.getNParameters() 

703 if nBgTerms == 1: 

704 if spatialBg.getParameters()[0] == 0.0: 

705 nBgTerms = 0 

706 

707 nGood = 0 

708 nBad = 0 

709 nTot = 0 

710 for cell in kernelCellSet.getCellList(): 

711 for cand in cell.begin(False): # False = include bad candidates 

712 nTot += 1 

713 if cand.getStatus() == afwMath.SpatialCellCandidate.GOOD: 

714 nGood += 1 

715 if cand.getStatus() == afwMath.SpatialCellCandidate.BAD: 

716 nBad += 1 

717 

718 self.log.info("Doing stats of kernel candidates used in the spatial fit.") 

719 

720 # Counting statistics 

721 if nBad > 2*nGood: 

722 self.log.warning("Many more candidates rejected than accepted; %d total, %d rejected, %d used", 

723 nTot, nBad, nGood) 

724 else: 

725 self.log.info("%d candidates total, %d rejected, %d used", nTot, nBad, nGood) 

726 

727 # Some judgements on the quality of the spatial models 

728 if nGood < nKernelTerms: 

729 self.log.warning("Spatial kernel model underconstrained; %d candidates, %d terms, %d bases", 

730 nGood, nKernelTerms, nBasisKernels) 

731 self.log.warning("Consider lowering the spatial order") 

732 elif nGood <= 2*nKernelTerms: 

733 self.log.warning("Spatial kernel model poorly constrained; %d candidates, %d terms, %d bases", 

734 nGood, nKernelTerms, nBasisKernels) 

735 self.log.warning("Consider lowering the spatial order") 

736 else: 

737 self.log.info("Spatial kernel model well constrained; %d candidates, %d terms, %d bases", 

738 nGood, nKernelTerms, nBasisKernels) 

739 

740 if nGood < nBgTerms: 

741 self.log.warning("Spatial background model underconstrained; %d candidates, %d terms", 

742 nGood, nBgTerms) 

743 self.log.warning("Consider lowering the spatial order") 

744 elif nGood <= 2*nBgTerms: 

745 self.log.warning("Spatial background model poorly constrained; %d candidates, %d terms", 

746 nGood, nBgTerms) 

747 self.log.warning("Consider lowering the spatial order") 

748 else: 

749 self.log.info("Spatial background model appears well constrained; %d candidates, %d terms", 

750 nGood, nBgTerms) 

751 

752 def _displayDebug(self, kernelCellSet, spatialKernel, spatialBackground): 

753 """Provide visualization of the inputs and ouputs to the Psf-matching code 

754 

755 Parameters 

756 ---------- 

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

758 The SpatialCellSet used in determining the matching kernel and background 

759 spatialKernel : `lsst.afw.math.LinearCombinationKernel` 

760 Spatially varying Psf-matching kernel 

761 spatialBackground : `lsst.afw.math.Function2D` 

762 Spatially varying background-matching function 

763 """ 

764 import lsstDebug 

765 displayCandidates = lsstDebug.Info(__name__).displayCandidates 

766 displayKernelBasis = lsstDebug.Info(__name__).displayKernelBasis 

767 displayKernelMosaic = lsstDebug.Info(__name__).displayKernelMosaic 

768 plotKernelSpatialModel = lsstDebug.Info(__name__).plotKernelSpatialModel 

769 plotKernelCoefficients = lsstDebug.Info(__name__).plotKernelCoefficients 

770 showBadCandidates = lsstDebug.Info(__name__).showBadCandidates 

771 maskTransparency = lsstDebug.Info(__name__).maskTransparency 

772 if not maskTransparency: 

773 maskTransparency = 0 

774 afwDisplay.setDefaultMaskTransparency(maskTransparency) 

775 

776 if displayCandidates: 

777 diutils.showKernelCandidates(kernelCellSet, kernel=spatialKernel, background=spatialBackground, 

778 frame=lsstDebug.frame, 

779 showBadCandidates=showBadCandidates) 

780 lsstDebug.frame += 1 

781 diutils.showKernelCandidates(kernelCellSet, kernel=spatialKernel, background=spatialBackground, 

782 frame=lsstDebug.frame, 

783 showBadCandidates=showBadCandidates, 

784 kernels=True) 

785 lsstDebug.frame += 1 

786 diutils.showKernelCandidates(kernelCellSet, kernel=spatialKernel, background=spatialBackground, 

787 frame=lsstDebug.frame, 

788 showBadCandidates=showBadCandidates, 

789 resids=True) 

790 lsstDebug.frame += 1 

791 

792 if displayKernelBasis: 

793 diutils.showKernelBasis(spatialKernel, frame=lsstDebug.frame) 

794 lsstDebug.frame += 1 

795 

796 if displayKernelMosaic: 

797 diutils.showKernelMosaic(kernelCellSet.getBBox(), spatialKernel, frame=lsstDebug.frame) 

798 lsstDebug.frame += 1 

799 

800 if plotKernelSpatialModel: 

801 diutils.plotKernelSpatialModel(spatialKernel, kernelCellSet, showBadCandidates=showBadCandidates) 

802 

803 if plotKernelCoefficients: 

804 diutils.plotKernelCoefficients(spatialKernel, kernelCellSet) 

805 

806 def _createPcaBasis(self, kernelCellSet, nStarPerCell, ps): 

807 """Create Principal Component basis 

808 

809 If a principal component analysis is requested, typically when using a delta function basis, 

810 perform the PCA here and return a new basis list containing the new principal components. 

811 

812 Parameters 

813 ---------- 

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

815 a SpatialCellSet containing KernelCandidates, from which components are derived 

816 nStarPerCell : `int` 

817 the number of stars per cell to visit when doing the PCA 

818 ps : `lsst.daf.base.PropertySet` 

819 input property set controlling the single kernel visitor 

820 

821 Returns 

822 ------- 

823 nRejectedPca : `int` 

824 number of KernelCandidates rejected during PCA loop 

825 spatialBasisList : `list` of `lsst.afw.math.kernel.FixedKernel` 

826 basis list containing the principal shapes as Kernels 

827 

828 Raises 

829 ------ 

830 RuntimeError 

831 If the Eigenvalues sum to zero. 

832 """ 

833 nComponents = self.kConfig.numPrincipalComponents 

834 imagePca = diffimLib.KernelPcaD() 

835 importStarVisitor = diffimLib.KernelPcaVisitorF(imagePca) 

836 kernelCellSet.visitCandidates(importStarVisitor, nStarPerCell) 

837 if self.kConfig.subtractMeanForPca: 

838 importStarVisitor.subtractMean() 

839 imagePca.analyze() 

840 

841 eigenValues = imagePca.getEigenValues() 

842 pcaBasisList = importStarVisitor.getEigenKernels() 

843 

844 eSum = np.sum(eigenValues) 

845 if eSum == 0.0: 

846 raise RuntimeError("Eigenvalues sum to zero") 

847 for j in range(len(eigenValues)): 

848 log.log("TRACE5." + self.log.name + "._solve", log.DEBUG, 

849 "Eigenvalue %d : %f (%f)", j, eigenValues[j], eigenValues[j]/eSum) 

850 

851 nToUse = min(nComponents, len(eigenValues)) 

852 trimBasisList = [] 

853 for j in range(nToUse): 

854 # Check for NaNs? 

855 kimage = afwImage.ImageD(pcaBasisList[j].getDimensions()) 

856 pcaBasisList[j].computeImage(kimage, False) 

857 if not (True in np.isnan(kimage.getArray())): 

858 trimBasisList.append(pcaBasisList[j]) 

859 

860 # Put all the power in the first kernel, which will not vary spatially 

861 spatialBasisList = diffimLib.renormalizeKernelList(trimBasisList) 

862 

863 # New Kernel visitor for this new basis list (no regularization explicitly) 

864 singlekvPca = diffimLib.BuildSingleKernelVisitorF(spatialBasisList, ps) 

865 singlekvPca.setSkipBuilt(False) 

866 kernelCellSet.visitCandidates(singlekvPca, nStarPerCell) 

867 singlekvPca.setSkipBuilt(True) 

868 nRejectedPca = singlekvPca.getNRejected() 

869 

870 return nRejectedPca, spatialBasisList 

871 

872 def _buildCellSet(self, *args): 

873 """Fill a SpatialCellSet with KernelCandidates for the Psf-matching process; 

874 override in derived classes""" 

875 return 

876 

877 @pipeBase.timeMethod 

878 def _solve(self, kernelCellSet, basisList, returnOnExcept=False): 

879 """Solve for the PSF matching kernel 

880 

881 Parameters 

882 ---------- 

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

884 a SpatialCellSet to use in determining the matching kernel 

885 (typically as provided by _buildCellSet) 

886 basisList : `list` of `lsst.afw.math.kernel.FixedKernel` 

887 list of Kernels to be used in the decomposition of the spatially varying kernel 

888 (typically as provided by makeKernelBasisList) 

889 returnOnExcept : `bool`, optional 

890 if True then return (None, None) if an error occurs, else raise the exception 

891 

892 Returns 

893 ------- 

894 psfMatchingKernel : `lsst.afw.math.LinearCombinationKernel` 

895 Spatially varying Psf-matching kernel 

896 backgroundModel : `lsst.afw.math.Function2D` 

897 Spatially varying background-matching function 

898 

899 Raises 

900 ------ 

901 RuntimeError : 

902 If unable to determine PSF matching kernel and ``returnOnExcept==False``. 

903 """ 

904 

905 import lsstDebug 

906 display = lsstDebug.Info(__name__).display 

907 

908 maxSpatialIterations = self.kConfig.maxSpatialIterations 

909 nStarPerCell = self.kConfig.nStarPerCell 

910 usePcaForSpatialKernel = self.kConfig.usePcaForSpatialKernel 

911 

912 # Visitor for the single kernel fit 

913 ps = pexConfig.makePropertySet(self.kConfig) 

914 if self.useRegularization: 

915 singlekv = diffimLib.BuildSingleKernelVisitorF(basisList, ps, self.hMat) 

916 else: 

917 singlekv = diffimLib.BuildSingleKernelVisitorF(basisList, ps) 

918 

919 # Visitor for the kernel sum rejection 

920 ksv = diffimLib.KernelSumVisitorF(ps) 

921 

922 # Main loop 

923 t0 = time.time() 

924 try: 

925 totalIterations = 0 

926 thisIteration = 0 

927 while (thisIteration < maxSpatialIterations): 

928 

929 # Make sure there are no uninitialized candidates as active occupants of Cell 

930 nRejectedSkf = -1 

931 while (nRejectedSkf != 0): 

932 log.log("TRACE1." + self.log.name + "._solve", log.DEBUG, 

933 "Building single kernels...") 

934 kernelCellSet.visitCandidates(singlekv, nStarPerCell) 

935 nRejectedSkf = singlekv.getNRejected() 

936 log.log("TRACE1." + self.log.name + "._solve", log.DEBUG, 

937 "Iteration %d, rejected %d candidates due to initial kernel fit", 

938 thisIteration, nRejectedSkf) 

939 

940 # Reject outliers in kernel sum 

941 ksv.resetKernelSum() 

942 ksv.setMode(diffimLib.KernelSumVisitorF.AGGREGATE) 

943 kernelCellSet.visitCandidates(ksv, nStarPerCell) 

944 ksv.processKsumDistribution() 

945 ksv.setMode(diffimLib.KernelSumVisitorF.REJECT) 

946 kernelCellSet.visitCandidates(ksv, nStarPerCell) 

947 

948 nRejectedKsum = ksv.getNRejected() 

949 log.log("TRACE1." + self.log.name + "._solve", log.DEBUG, 

950 "Iteration %d, rejected %d candidates due to kernel sum", 

951 thisIteration, nRejectedKsum) 

952 

953 # Do we jump back to the top without incrementing thisIteration? 

954 if nRejectedKsum > 0: 

955 totalIterations += 1 

956 continue 

957 

958 # At this stage we can either apply the spatial fit to 

959 # the kernels, or we run a PCA, use these as a *new* 

960 # basis set with lower dimensionality, and then apply 

961 # the spatial fit to these kernels 

962 

963 if (usePcaForSpatialKernel): 

964 log.log("TRACE0." + self.log.name + "._solve", log.DEBUG, 

965 "Building Pca basis") 

966 

967 nRejectedPca, spatialBasisList = self._createPcaBasis(kernelCellSet, nStarPerCell, ps) 

968 log.log("TRACE1." + self.log.name + "._solve", log.DEBUG, 

969 "Iteration %d, rejected %d candidates due to Pca kernel fit", 

970 thisIteration, nRejectedPca) 

971 

972 # We don't want to continue on (yet) with the 

973 # spatial modeling, because we have bad objects 

974 # contributing to the Pca basis. We basically 

975 # need to restart from the beginning of this loop, 

976 # since the cell-mates of those objects that were 

977 # rejected need their original Kernels built by 

978 # singleKernelFitter. 

979 

980 # Don't count against thisIteration 

981 if (nRejectedPca > 0): 

982 totalIterations += 1 

983 continue 

984 else: 

985 spatialBasisList = basisList 

986 

987 # We have gotten on to the spatial modeling part 

988 regionBBox = kernelCellSet.getBBox() 

989 spatialkv = diffimLib.BuildSpatialKernelVisitorF(spatialBasisList, regionBBox, ps) 

990 kernelCellSet.visitCandidates(spatialkv, nStarPerCell) 

991 spatialkv.solveLinearEquation() 

992 log.log("TRACE2." + self.log.name + "._solve", log.DEBUG, 

993 "Spatial kernel built with %d candidates", spatialkv.getNCandidates()) 

994 spatialKernel, spatialBackground = spatialkv.getSolutionPair() 

995 

996 # Check the quality of the spatial fit (look at residuals) 

997 assesskv = diffimLib.AssessSpatialKernelVisitorF(spatialKernel, spatialBackground, ps) 

998 kernelCellSet.visitCandidates(assesskv, nStarPerCell) 

999 nRejectedSpatial = assesskv.getNRejected() 

1000 nGoodSpatial = assesskv.getNGood() 

1001 log.log("TRACE1." + self.log.name + "._solve", log.DEBUG, 

1002 "Iteration %d, rejected %d candidates due to spatial kernel fit", 

1003 thisIteration, nRejectedSpatial) 

1004 log.log("TRACE1." + self.log.name + "._solve", log.DEBUG, 

1005 "%d candidates used in fit", nGoodSpatial) 

1006 

1007 # If only nGoodSpatial == 0, might be other candidates in the cells 

1008 if nGoodSpatial == 0 and nRejectedSpatial == 0: 

1009 raise RuntimeError("No kernel candidates for spatial fit") 

1010 

1011 if nRejectedSpatial == 0: 

1012 # Nothing rejected, finished with spatial fit 

1013 break 

1014 

1015 # Otherwise, iterate on... 

1016 thisIteration += 1 

1017 

1018 # Final fit if above did not converge 

1019 if (nRejectedSpatial > 0) and (thisIteration == maxSpatialIterations): 

1020 log.log("TRACE1." + self.log.name + "._solve", log.DEBUG, "Final spatial fit") 

1021 if (usePcaForSpatialKernel): 

1022 nRejectedPca, spatialBasisList = self._createPcaBasis(kernelCellSet, nStarPerCell, ps) 

1023 regionBBox = kernelCellSet.getBBox() 

1024 spatialkv = diffimLib.BuildSpatialKernelVisitorF(spatialBasisList, regionBBox, ps) 

1025 kernelCellSet.visitCandidates(spatialkv, nStarPerCell) 

1026 spatialkv.solveLinearEquation() 

1027 log.log("TRACE2." + self.log.name + "._solve", log.DEBUG, 

1028 "Spatial kernel built with %d candidates", spatialkv.getNCandidates()) 

1029 spatialKernel, spatialBackground = spatialkv.getSolutionPair() 

1030 

1031 spatialSolution = spatialkv.getKernelSolution() 

1032 

1033 except Exception as e: 

1034 self.log.error("ERROR: Unable to calculate psf matching kernel") 

1035 

1036 log.log("TRACE1." + self.log.name + "._solve", log.DEBUG, "%s", e) 

1037 raise e 

1038 

1039 t1 = time.time() 

1040 log.log("TRACE0." + self.log.name + "._solve", log.DEBUG, 

1041 "Total time to compute the spatial kernel : %.2f s", (t1 - t0)) 

1042 

1043 if display: 

1044 self._displayDebug(kernelCellSet, spatialKernel, spatialBackground) 

1045 

1046 self._diagnostic(kernelCellSet, spatialSolution, spatialKernel, spatialBackground) 

1047 

1048 return spatialSolution, spatialKernel, spatialBackground 

1049 

1050 

1051PsfMatch = PsfMatchTask