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1# 

2# LSST Data Management System 

3# Copyright 2008, 2009, 2010 LSST Corporation. 

4# 

5# This product includes software developed by the 

6# LSST Project (http://www.lsst.org/). 

7# 

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

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

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

11# (at your option) any later version. 

12# 

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

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

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

16# GNU General Public License for more details. 

17# 

18# You should have received a copy of the LSST License Statement and 

19# the GNU General Public License along with this program. If not, 

20# see <http://www.lsstcorp.org/LegalNotices/>. 

21# 

22import math 

23import numpy 

24from deprecated.sphinx import deprecated 

25 

26import lsst.geom 

27import lsst.afw.image as afwImage 

28import lsst.afw.detection as afwDetection 

29import lsst.afw.math as afwMath 

30import lsst.meas.algorithms as measAlg 

31import lsst.afw.cameraGeom as camGeom 

32 

33from lsst.afw.geom.wcsUtils import makeDistortedTanWcs 

34from lsst.meas.algorithms.detection import SourceDetectionTask 

35 

36from contextlib import contextmanager 

37 

38from .overscan import OverscanCorrectionTask, OverscanCorrectionTaskConfig 

39 

40 

41def createPsf(fwhm): 

42 """Make a double Gaussian PSF. 

43 

44 Parameters 

45 ---------- 

46 fwhm : scalar 

47 FWHM of double Gaussian smoothing kernel. 

48 

49 Returns 

50 ------- 

51 psf : `lsst.meas.algorithms.DoubleGaussianPsf` 

52 The created smoothing kernel. 

53 """ 

54 ksize = 4*int(fwhm) + 1 

55 return measAlg.DoubleGaussianPsf(ksize, ksize, fwhm/(2*math.sqrt(2*math.log(2)))) 

56 

57 

58def transposeMaskedImage(maskedImage): 

59 """Make a transposed copy of a masked image. 

60 

61 Parameters 

62 ---------- 

63 maskedImage : `lsst.afw.image.MaskedImage` 

64 Image to process. 

65 

66 Returns 

67 ------- 

68 transposed : `lsst.afw.image.MaskedImage` 

69 The transposed copy of the input image. 

70 """ 

71 transposed = maskedImage.Factory(lsst.geom.Extent2I(maskedImage.getHeight(), maskedImage.getWidth())) 

72 transposed.getImage().getArray()[:] = maskedImage.getImage().getArray().T 

73 transposed.getMask().getArray()[:] = maskedImage.getMask().getArray().T 

74 transposed.getVariance().getArray()[:] = maskedImage.getVariance().getArray().T 

75 return transposed 

76 

77 

78def interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=None): 

79 """Interpolate over defects specified in a defect list. 

80 

81 Parameters 

82 ---------- 

83 maskedImage : `lsst.afw.image.MaskedImage` 

84 Image to process. 

85 defectList : `lsst.meas.algorithms.Defects` 

86 List of defects to interpolate over. 

87 fwhm : scalar 

88 FWHM of double Gaussian smoothing kernel. 

89 fallbackValue : scalar, optional 

90 Fallback value if an interpolated value cannot be determined. 

91 If None, then the clipped mean of the image is used. 

92 """ 

93 psf = createPsf(fwhm) 

94 if fallbackValue is None: 

95 fallbackValue = afwMath.makeStatistics(maskedImage.getImage(), afwMath.MEANCLIP).getValue() 

96 if 'INTRP' not in maskedImage.getMask().getMaskPlaneDict(): 

97 maskedImage.getMask().addMaskPlane('INTRP') 

98 measAlg.interpolateOverDefects(maskedImage, psf, defectList, fallbackValue, True) 

99 return maskedImage 

100 

101 

102def makeThresholdMask(maskedImage, threshold, growFootprints=1, maskName='SAT'): 

103 """Mask pixels based on threshold detection. 

104 

105 Parameters 

106 ---------- 

107 maskedImage : `lsst.afw.image.MaskedImage` 

108 Image to process. Only the mask plane is updated. 

109 threshold : scalar 

110 Detection threshold. 

111 growFootprints : scalar, optional 

112 Number of pixels to grow footprints of detected regions. 

113 maskName : str, optional 

114 Mask plane name, or list of names to convert 

115 

116 Returns 

117 ------- 

118 defectList : `lsst.meas.algorithms.Defects` 

119 Defect list constructed from pixels above the threshold. 

120 """ 

121 # find saturated regions 

122 thresh = afwDetection.Threshold(threshold) 

123 fs = afwDetection.FootprintSet(maskedImage, thresh) 

124 

125 if growFootprints > 0: 

126 fs = afwDetection.FootprintSet(fs, rGrow=growFootprints, isotropic=False) 

127 fpList = fs.getFootprints() 

128 

129 # set mask 

130 mask = maskedImage.getMask() 

131 bitmask = mask.getPlaneBitMask(maskName) 

132 afwDetection.setMaskFromFootprintList(mask, fpList, bitmask) 

133 

134 return measAlg.Defects.fromFootprintList(fpList) 

135 

136 

137def growMasks(mask, radius=0, maskNameList=['BAD'], maskValue="BAD"): 

138 """Grow a mask by an amount and add to the requested plane. 

139 

140 Parameters 

141 ---------- 

142 mask : `lsst.afw.image.Mask` 

143 Mask image to process. 

144 radius : scalar 

145 Amount to grow the mask. 

146 maskNameList : `str` or `list` [`str`] 

147 Mask names that should be grown. 

148 maskValue : `str` 

149 Mask plane to assign the newly masked pixels to. 

150 """ 

151 if radius > 0: 

152 thresh = afwDetection.Threshold(mask.getPlaneBitMask(maskNameList), afwDetection.Threshold.BITMASK) 

153 fpSet = afwDetection.FootprintSet(mask, thresh) 

154 fpSet = afwDetection.FootprintSet(fpSet, rGrow=radius, isotropic=False) 

155 fpSet.setMask(mask, maskValue) 

156 

157 

158def interpolateFromMask(maskedImage, fwhm, growSaturatedFootprints=1, 

159 maskNameList=['SAT'], fallbackValue=None): 

160 """Interpolate over defects identified by a particular set of mask planes. 

161 

162 Parameters 

163 ---------- 

164 maskedImage : `lsst.afw.image.MaskedImage` 

165 Image to process. 

166 fwhm : scalar 

167 FWHM of double Gaussian smoothing kernel. 

168 growSaturatedFootprints : scalar, optional 

169 Number of pixels to grow footprints for saturated pixels. 

170 maskNameList : `List` of `str`, optional 

171 Mask plane name. 

172 fallbackValue : scalar, optional 

173 Value of last resort for interpolation. 

174 """ 

175 mask = maskedImage.getMask() 

176 

177 if growSaturatedFootprints > 0 and "SAT" in maskNameList: 

178 # If we are interpolating over an area larger than the original masked region, we need 

179 # to expand the original mask bit to the full area to explain why we interpolated there. 

180 growMasks(mask, radius=growSaturatedFootprints, maskNameList=['SAT'], maskValue="SAT") 

181 

182 thresh = afwDetection.Threshold(mask.getPlaneBitMask(maskNameList), afwDetection.Threshold.BITMASK) 

183 fpSet = afwDetection.FootprintSet(mask, thresh) 

184 defectList = measAlg.Defects.fromFootprintList(fpSet.getFootprints()) 

185 

186 interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=fallbackValue) 

187 

188 return maskedImage 

189 

190 

191def saturationCorrection(maskedImage, saturation, fwhm, growFootprints=1, interpolate=True, maskName='SAT', 

192 fallbackValue=None): 

193 """Mark saturated pixels and optionally interpolate over them 

194 

195 Parameters 

196 ---------- 

197 maskedImage : `lsst.afw.image.MaskedImage` 

198 Image to process. 

199 saturation : scalar 

200 Saturation level used as the detection threshold. 

201 fwhm : scalar 

202 FWHM of double Gaussian smoothing kernel. 

203 growFootprints : scalar, optional 

204 Number of pixels to grow footprints of detected regions. 

205 interpolate : Bool, optional 

206 If True, saturated pixels are interpolated over. 

207 maskName : str, optional 

208 Mask plane name. 

209 fallbackValue : scalar, optional 

210 Value of last resort for interpolation. 

211 """ 

212 defectList = makeThresholdMask( 

213 maskedImage=maskedImage, 

214 threshold=saturation, 

215 growFootprints=growFootprints, 

216 maskName=maskName, 

217 ) 

218 if interpolate: 

219 interpolateDefectList(maskedImage, defectList, fwhm, fallbackValue=fallbackValue) 

220 

221 return maskedImage 

222 

223 

224def trimToMatchCalibBBox(rawMaskedImage, calibMaskedImage): 

225 """Compute number of edge trim pixels to match the calibration data. 

226 

227 Use the dimension difference between the raw exposure and the 

228 calibration exposure to compute the edge trim pixels. This trim 

229 is applied symmetrically, with the same number of pixels masked on 

230 each side. 

231 

232 Parameters 

233 ---------- 

234 rawMaskedImage : `lsst.afw.image.MaskedImage` 

235 Image to trim. 

236 calibMaskedImage : `lsst.afw.image.MaskedImage` 

237 Calibration image to draw new bounding box from. 

238 

239 Returns 

240 ------- 

241 replacementMaskedImage : `lsst.afw.image.MaskedImage` 

242 ``rawMaskedImage`` trimmed to the appropriate size 

243 Raises 

244 ------ 

245 RuntimeError 

246 Rasied if ``rawMaskedImage`` cannot be symmetrically trimmed to 

247 match ``calibMaskedImage``. 

248 """ 

249 nx, ny = rawMaskedImage.getBBox().getDimensions() - calibMaskedImage.getBBox().getDimensions() 

250 if nx != ny: 

251 raise RuntimeError("Raw and calib maskedImages are trimmed differently in X and Y.") 

252 if nx % 2 != 0: 

253 raise RuntimeError("Calibration maskedImage is trimmed unevenly in X.") 

254 if nx < 0: 

255 raise RuntimeError("Calibration maskedImage is larger than raw data.") 

256 

257 nEdge = nx//2 

258 if nEdge > 0: 

259 replacementMaskedImage = rawMaskedImage[nEdge:-nEdge, nEdge:-nEdge, afwImage.LOCAL] 

260 SourceDetectionTask.setEdgeBits( 

261 rawMaskedImage, 

262 replacementMaskedImage.getBBox(), 

263 rawMaskedImage.getMask().getPlaneBitMask("EDGE") 

264 ) 

265 else: 

266 replacementMaskedImage = rawMaskedImage 

267 

268 return replacementMaskedImage 

269 

270 

271def biasCorrection(maskedImage, biasMaskedImage, trimToFit=False): 

272 """Apply bias correction in place. 

273 

274 Parameters 

275 ---------- 

276 maskedImage : `lsst.afw.image.MaskedImage` 

277 Image to process. The image is modified by this method. 

278 biasMaskedImage : `lsst.afw.image.MaskedImage` 

279 Bias image of the same size as ``maskedImage`` 

280 trimToFit : `Bool`, optional 

281 If True, raw data is symmetrically trimmed to match 

282 calibration size. 

283 

284 Raises 

285 ------ 

286 RuntimeError 

287 Raised if ``maskedImage`` and ``biasMaskedImage`` do not have 

288 the same size. 

289 

290 """ 

291 if trimToFit: 

292 maskedImage = trimToMatchCalibBBox(maskedImage, biasMaskedImage) 

293 

294 if maskedImage.getBBox(afwImage.LOCAL) != biasMaskedImage.getBBox(afwImage.LOCAL): 

295 raise RuntimeError("maskedImage bbox %s != biasMaskedImage bbox %s" % 

296 (maskedImage.getBBox(afwImage.LOCAL), biasMaskedImage.getBBox(afwImage.LOCAL))) 

297 maskedImage -= biasMaskedImage 

298 

299 

300def darkCorrection(maskedImage, darkMaskedImage, expScale, darkScale, invert=False, trimToFit=False): 

301 """Apply dark correction in place. 

302 

303 Parameters 

304 ---------- 

305 maskedImage : `lsst.afw.image.MaskedImage` 

306 Image to process. The image is modified by this method. 

307 darkMaskedImage : `lsst.afw.image.MaskedImage` 

308 Dark image of the same size as ``maskedImage``. 

309 expScale : scalar 

310 Dark exposure time for ``maskedImage``. 

311 darkScale : scalar 

312 Dark exposure time for ``darkMaskedImage``. 

313 invert : `Bool`, optional 

314 If True, re-add the dark to an already corrected image. 

315 trimToFit : `Bool`, optional 

316 If True, raw data is symmetrically trimmed to match 

317 calibration size. 

318 

319 Raises 

320 ------ 

321 RuntimeError 

322 Raised if ``maskedImage`` and ``darkMaskedImage`` do not have 

323 the same size. 

324 

325 Notes 

326 ----- 

327 The dark correction is applied by calculating: 

328 maskedImage -= dark * expScaling / darkScaling 

329 """ 

330 if trimToFit: 

331 maskedImage = trimToMatchCalibBBox(maskedImage, darkMaskedImage) 

332 

333 if maskedImage.getBBox(afwImage.LOCAL) != darkMaskedImage.getBBox(afwImage.LOCAL): 

334 raise RuntimeError("maskedImage bbox %s != darkMaskedImage bbox %s" % 

335 (maskedImage.getBBox(afwImage.LOCAL), darkMaskedImage.getBBox(afwImage.LOCAL))) 

336 

337 scale = expScale / darkScale 

338 if not invert: 

339 maskedImage.scaledMinus(scale, darkMaskedImage) 

340 else: 

341 maskedImage.scaledPlus(scale, darkMaskedImage) 

342 

343 

344def updateVariance(maskedImage, gain, readNoise): 

345 """Set the variance plane based on the image plane. 

346 

347 Parameters 

348 ---------- 

349 maskedImage : `lsst.afw.image.MaskedImage` 

350 Image to process. The variance plane is modified. 

351 gain : scalar 

352 The amplifier gain in electrons/ADU. 

353 readNoise : scalar 

354 The amplifier read nmoise in ADU/pixel. 

355 """ 

356 var = maskedImage.getVariance() 

357 var[:] = maskedImage.getImage() 

358 var /= gain 

359 var += readNoise**2 

360 

361 

362def flatCorrection(maskedImage, flatMaskedImage, scalingType, userScale=1.0, invert=False, trimToFit=False): 

363 """Apply flat correction in place. 

364 

365 Parameters 

366 ---------- 

367 maskedImage : `lsst.afw.image.MaskedImage` 

368 Image to process. The image is modified. 

369 flatMaskedImage : `lsst.afw.image.MaskedImage` 

370 Flat image of the same size as ``maskedImage`` 

371 scalingType : str 

372 Flat scale computation method. Allowed values are 'MEAN', 

373 'MEDIAN', or 'USER'. 

374 userScale : scalar, optional 

375 Scale to use if ``scalingType``='USER'. 

376 invert : `Bool`, optional 

377 If True, unflatten an already flattened image. 

378 trimToFit : `Bool`, optional 

379 If True, raw data is symmetrically trimmed to match 

380 calibration size. 

381 

382 Raises 

383 ------ 

384 RuntimeError 

385 Raised if ``maskedImage`` and ``flatMaskedImage`` do not have 

386 the same size or if ``scalingType`` is not an allowed value. 

387 """ 

388 if trimToFit: 

389 maskedImage = trimToMatchCalibBBox(maskedImage, flatMaskedImage) 

390 

391 if maskedImage.getBBox(afwImage.LOCAL) != flatMaskedImage.getBBox(afwImage.LOCAL): 

392 raise RuntimeError("maskedImage bbox %s != flatMaskedImage bbox %s" % 

393 (maskedImage.getBBox(afwImage.LOCAL), flatMaskedImage.getBBox(afwImage.LOCAL))) 

394 

395 # Figure out scale from the data 

396 # Ideally the flats are normalized by the calibration product pipeline, but this allows some flexibility 

397 # in the case that the flat is created by some other mechanism. 

398 if scalingType in ('MEAN', 'MEDIAN'): 

399 scalingType = afwMath.stringToStatisticsProperty(scalingType) 

400 flatScale = afwMath.makeStatistics(flatMaskedImage.image, scalingType).getValue() 

401 elif scalingType == 'USER': 

402 flatScale = userScale 

403 else: 

404 raise RuntimeError('%s : %s not implemented' % ("flatCorrection", scalingType)) 

405 

406 if not invert: 

407 maskedImage.scaledDivides(1.0/flatScale, flatMaskedImage) 

408 else: 

409 maskedImage.scaledMultiplies(1.0/flatScale, flatMaskedImage) 

410 

411 

412def illuminationCorrection(maskedImage, illumMaskedImage, illumScale, trimToFit=True): 

413 """Apply illumination correction in place. 

414 

415 Parameters 

416 ---------- 

417 maskedImage : `lsst.afw.image.MaskedImage` 

418 Image to process. The image is modified. 

419 illumMaskedImage : `lsst.afw.image.MaskedImage` 

420 Illumination correction image of the same size as ``maskedImage``. 

421 illumScale : scalar 

422 Scale factor for the illumination correction. 

423 trimToFit : `Bool`, optional 

424 If True, raw data is symmetrically trimmed to match 

425 calibration size. 

426 

427 Raises 

428 ------ 

429 RuntimeError 

430 Raised if ``maskedImage`` and ``illumMaskedImage`` do not have 

431 the same size. 

432 """ 

433 if trimToFit: 

434 maskedImage = trimToMatchCalibBBox(maskedImage, illumMaskedImage) 

435 

436 if maskedImage.getBBox(afwImage.LOCAL) != illumMaskedImage.getBBox(afwImage.LOCAL): 

437 raise RuntimeError("maskedImage bbox %s != illumMaskedImage bbox %s" % 

438 (maskedImage.getBBox(afwImage.LOCAL), illumMaskedImage.getBBox(afwImage.LOCAL))) 

439 

440 maskedImage.scaledDivides(1.0/illumScale, illumMaskedImage) 

441 

442 

443def overscanCorrection(ampMaskedImage, overscanImage, fitType='MEDIAN', order=1, collapseRej=3.0, 

444 statControl=None, overscanIsInt=True): 

445 """Apply overscan correction in place. 

446 

447 Parameters 

448 ---------- 

449 ampMaskedImage : `lsst.afw.image.MaskedImage` 

450 Image of amplifier to correct; modified. 

451 overscanImage : `lsst.afw.image.Image` or `lsst.afw.image.MaskedImage` 

452 Image of overscan; modified. 

453 fitType : `str` 

454 Type of fit for overscan correction. May be one of: 

455 

456 - ``MEAN``: use mean of overscan. 

457 - ``MEANCLIP``: use clipped mean of overscan. 

458 - ``MEDIAN``: use median of overscan. 

459 - ``MEDIAN_PER_ROW``: use median per row of overscan. 

460 - ``POLY``: fit with ordinary polynomial. 

461 - ``CHEB``: fit with Chebyshev polynomial. 

462 - ``LEG``: fit with Legendre polynomial. 

463 - ``NATURAL_SPLINE``: fit with natural spline. 

464 - ``CUBIC_SPLINE``: fit with cubic spline. 

465 - ``AKIMA_SPLINE``: fit with Akima spline. 

466 

467 order : `int` 

468 Polynomial order or number of spline knots; ignored unless 

469 ``fitType`` indicates a polynomial or spline. 

470 statControl : `lsst.afw.math.StatisticsControl` 

471 Statistics control object. In particular, we pay attention to numSigmaClip 

472 overscanIsInt : `bool` 

473 Treat the overscan region as consisting of integers, even if it's been 

474 converted to float. E.g. handle ties properly. 

475 

476 Returns 

477 ------- 

478 result : `lsst.pipe.base.Struct` 

479 Result struct with components: 

480 

481 - ``imageFit``: Value(s) removed from image (scalar or 

482 `lsst.afw.image.Image`) 

483 - ``overscanFit``: Value(s) removed from overscan (scalar or 

484 `lsst.afw.image.Image`) 

485 - ``overscanImage``: Overscan corrected overscan region 

486 (`lsst.afw.image.Image`) 

487 Raises 

488 ------ 

489 pexExcept.Exception 

490 Raised if ``fitType`` is not an allowed value. 

491 

492 Notes 

493 ----- 

494 The ``ampMaskedImage`` and ``overscanImage`` are modified, with the fit 

495 subtracted. Note that the ``overscanImage`` should not be a subimage of 

496 the ``ampMaskedImage``, to avoid being subtracted twice. 

497 

498 Debug plots are available for the SPLINE fitTypes by setting the 

499 `debug.display` for `name` == "lsst.ip.isr.isrFunctions". These 

500 plots show the scatter plot of the overscan data (collapsed along 

501 the perpendicular dimension) as a function of position on the CCD 

502 (normalized between +/-1). 

503 """ 

504 ampImage = ampMaskedImage.getImage() 

505 

506 config = OverscanCorrectionTaskConfig() 

507 if fitType: 

508 config.fitType = fitType 

509 if order: 

510 config.order = order 

511 if collapseRej: 

512 config.numSigmaClip = collapseRej 

513 if overscanIsInt: 

514 config.overscanIsInt = True 

515 

516 overscanTask = OverscanCorrectionTask(config=config) 

517 return overscanTask.run(ampImage, overscanImage) 

518 

519 

520def brighterFatterCorrection(exposure, kernel, maxIter, threshold, applyGain, gains=None): 

521 """Apply brighter fatter correction in place for the image. 

522 

523 Parameters 

524 ---------- 

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

526 Exposure to have brighter-fatter correction applied. Modified 

527 by this method. 

528 kernel : `numpy.ndarray` 

529 Brighter-fatter kernel to apply. 

530 maxIter : scalar 

531 Number of correction iterations to run. 

532 threshold : scalar 

533 Convergence threshold in terms of the sum of absolute 

534 deviations between an iteration and the previous one. 

535 applyGain : `Bool` 

536 If True, then the exposure values are scaled by the gain prior 

537 to correction. 

538 gains : `dict` [`str`, `float`] 

539 A dictionary, keyed by amplifier name, of the gains to use. 

540 If gains is None, the nominal gains in the amplifier object are used. 

541 

542 Returns 

543 ------- 

544 diff : `float` 

545 Final difference between iterations achieved in correction. 

546 iteration : `int` 

547 Number of iterations used to calculate correction. 

548 

549 Notes 

550 ----- 

551 This correction takes a kernel that has been derived from flat 

552 field images to redistribute the charge. The gradient of the 

553 kernel is the deflection field due to the accumulated charge. 

554 

555 Given the original image I(x) and the kernel K(x) we can compute 

556 the corrected image Ic(x) using the following equation: 

557 

558 Ic(x) = I(x) + 0.5*d/dx(I(x)*d/dx(int( dy*K(x-y)*I(y)))) 

559 

560 To evaluate the derivative term we expand it as follows: 

561 

562 0.5 * ( d/dx(I(x))*d/dx(int(dy*K(x-y)*I(y))) + I(x)*d^2/dx^2(int(dy* K(x-y)*I(y))) ) 

563 

564 Because we use the measured counts instead of the incident counts 

565 we apply the correction iteratively to reconstruct the original 

566 counts and the correction. We stop iterating when the summed 

567 difference between the current corrected image and the one from 

568 the previous iteration is below the threshold. We do not require 

569 convergence because the number of iterations is too large a 

570 computational cost. How we define the threshold still needs to be 

571 evaluated, the current default was shown to work reasonably well 

572 on a small set of images. For more information on the method see 

573 DocuShare Document-19407. 

574 

575 The edges as defined by the kernel are not corrected because they 

576 have spurious values due to the convolution. 

577 """ 

578 image = exposure.getMaskedImage().getImage() 

579 

580 # The image needs to be units of electrons/holes 

581 with gainContext(exposure, image, applyGain, gains): 

582 

583 kLx = numpy.shape(kernel)[0] 

584 kLy = numpy.shape(kernel)[1] 

585 kernelImage = afwImage.ImageD(kLx, kLy) 

586 kernelImage.getArray()[:, :] = kernel 

587 tempImage = image.clone() 

588 

589 nanIndex = numpy.isnan(tempImage.getArray()) 

590 tempImage.getArray()[nanIndex] = 0. 

591 

592 outImage = afwImage.ImageF(image.getDimensions()) 

593 corr = numpy.zeros_like(image.getArray()) 

594 prev_image = numpy.zeros_like(image.getArray()) 

595 convCntrl = afwMath.ConvolutionControl(False, True, 1) 

596 fixedKernel = afwMath.FixedKernel(kernelImage) 

597 

598 # Define boundary by convolution region. The region that the correction will be 

599 # calculated for is one fewer in each dimension because of the second derivative terms. 

600 # NOTE: these need to use integer math, as we're using start:end as numpy index ranges. 

601 startX = kLx//2 

602 endX = -kLx//2 

603 startY = kLy//2 

604 endY = -kLy//2 

605 

606 for iteration in range(maxIter): 

607 

608 afwMath.convolve(outImage, tempImage, fixedKernel, convCntrl) 

609 tmpArray = tempImage.getArray() 

610 outArray = outImage.getArray() 

611 

612 with numpy.errstate(invalid="ignore", over="ignore"): 

613 # First derivative term 

614 gradTmp = numpy.gradient(tmpArray[startY:endY, startX:endX]) 

615 gradOut = numpy.gradient(outArray[startY:endY, startX:endX]) 

616 first = (gradTmp[0]*gradOut[0] + gradTmp[1]*gradOut[1])[1:-1, 1:-1] 

617 

618 # Second derivative term 

619 diffOut20 = numpy.diff(outArray, 2, 0)[startY:endY, startX + 1:endX - 1] 

620 diffOut21 = numpy.diff(outArray, 2, 1)[startY + 1:endY - 1, startX:endX] 

621 second = tmpArray[startY + 1:endY - 1, startX + 1:endX - 1]*(diffOut20 + diffOut21) 

622 

623 corr[startY + 1:endY - 1, startX + 1:endX - 1] = 0.5*(first + second) 

624 

625 tmpArray[:, :] = image.getArray()[:, :] 

626 tmpArray[nanIndex] = 0. 

627 tmpArray[startY:endY, startX:endX] += corr[startY:endY, startX:endX] 

628 

629 if iteration > 0: 

630 diff = numpy.sum(numpy.abs(prev_image - tmpArray)) 

631 

632 if diff < threshold: 

633 break 

634 prev_image[:, :] = tmpArray[:, :] 

635 

636 image.getArray()[startY + 1:endY - 1, startX + 1:endX - 1] += \ 

637 corr[startY + 1:endY - 1, startX + 1:endX - 1] 

638 

639 return diff, iteration 

640 

641 

642@contextmanager 

643def gainContext(exp, image, apply, gains=None): 

644 """Context manager that applies and removes gain. 

645 

646 Parameters 

647 ---------- 

648 exp : `lsst.afw.image.Exposure` 

649 Exposure to apply/remove gain. 

650 image : `lsst.afw.image.Image` 

651 Image to apply/remove gain. 

652 apply : `Bool` 

653 If True, apply and remove the amplifier gain. 

654 gains : `dict` [`str`, `float`] 

655 A dictionary, keyed by amplifier name, of the gains to use. 

656 If gains is None, the nominal gains in the amplifier object are used. 

657 

658 Yields 

659 ------ 

660 exp : `lsst.afw.image.Exposure` 

661 Exposure with the gain applied. 

662 """ 

663 # check we have all of them if provided because mixing and matching would 

664 # be a real mess 

665 if gains and apply is True: 

666 ampNames = [amp.getName() for amp in exp.getDetector()] 

667 for ampName in ampNames: 

668 if ampName not in gains.keys(): 

669 raise RuntimeError(f"Gains provided to gain context, but no entry found for amp {ampName}") 

670 

671 if apply: 

672 ccd = exp.getDetector() 

673 for amp in ccd: 

674 sim = image.Factory(image, amp.getBBox()) 

675 if gains: 

676 gain = gains[amp.getName()] 

677 else: 

678 gain = amp.getGain() 

679 sim *= gain 

680 

681 try: 

682 yield exp 

683 finally: 

684 if apply: 

685 ccd = exp.getDetector() 

686 for amp in ccd: 

687 sim = image.Factory(image, amp.getBBox()) 

688 if gains: 

689 gain = gains[amp.getName()] 

690 else: 

691 gain = amp.getGain() 

692 sim /= gain 

693 

694 

695def attachTransmissionCurve(exposure, opticsTransmission=None, filterTransmission=None, 

696 sensorTransmission=None, atmosphereTransmission=None): 

697 """Attach a TransmissionCurve to an Exposure, given separate curves for 

698 different components. 

699 

700 Parameters 

701 ---------- 

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

703 Exposure object to modify by attaching the product of all given 

704 ``TransmissionCurves`` in post-assembly trimmed detector coordinates. 

705 Must have a valid ``Detector`` attached that matches the detector 

706 associated with sensorTransmission. 

707 opticsTransmission : `lsst.afw.image.TransmissionCurve` 

708 A ``TransmissionCurve`` that represents the throughput of the optics, 

709 to be evaluated in focal-plane coordinates. 

710 filterTransmission : `lsst.afw.image.TransmissionCurve` 

711 A ``TransmissionCurve`` that represents the throughput of the filter 

712 itself, to be evaluated in focal-plane coordinates. 

713 sensorTransmission : `lsst.afw.image.TransmissionCurve` 

714 A ``TransmissionCurve`` that represents the throughput of the sensor 

715 itself, to be evaluated in post-assembly trimmed detector coordinates. 

716 atmosphereTransmission : `lsst.afw.image.TransmissionCurve` 

717 A ``TransmissionCurve`` that represents the throughput of the 

718 atmosphere, assumed to be spatially constant. 

719 

720 Returns 

721 ------- 

722 combined : `lsst.afw.image.TransmissionCurve` 

723 The TransmissionCurve attached to the exposure. 

724 

725 Notes 

726 ----- 

727 All ``TransmissionCurve`` arguments are optional; if none are provided, the 

728 attached ``TransmissionCurve`` will have unit transmission everywhere. 

729 """ 

730 combined = afwImage.TransmissionCurve.makeIdentity() 

731 if atmosphereTransmission is not None: 

732 combined *= atmosphereTransmission 

733 if opticsTransmission is not None: 

734 combined *= opticsTransmission 

735 if filterTransmission is not None: 

736 combined *= filterTransmission 

737 detector = exposure.getDetector() 

738 fpToPix = detector.getTransform(fromSys=camGeom.FOCAL_PLANE, 

739 toSys=camGeom.PIXELS) 

740 combined = combined.transformedBy(fpToPix) 

741 if sensorTransmission is not None: 

742 combined *= sensorTransmission 

743 exposure.getInfo().setTransmissionCurve(combined) 

744 return combined 

745 

746 

747@deprecated(reason="Camera geometry-based SkyWcs are now set when reading raws. To be removed after v19.", 

748 category=FutureWarning) 

749def addDistortionModel(exposure, camera): 

750 """!Update the WCS in exposure with a distortion model based on camera 

751 geometry. 

752 

753 Parameters 

754 ---------- 

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

756 Exposure to process. Must contain a Detector and WCS. The 

757 exposure is modified. 

758 camera : `lsst.afw.cameraGeom.Camera` 

759 Camera geometry. 

760 

761 Raises 

762 ------ 

763 RuntimeError 

764 Raised if ``exposure`` is lacking a Detector or WCS, or if 

765 ``camera`` is None. 

766 Notes 

767 ----- 

768 Add a model for optical distortion based on geometry found in ``camera`` 

769 and the ``exposure``'s detector. The raw input exposure is assumed 

770 have a TAN WCS that has no compensation for optical distortion. 

771 Two other possibilities are: 

772 - The raw input exposure already has a model for optical distortion, 

773 as is the case for raw DECam data. 

774 In that case you should set config.doAddDistortionModel False. 

775 - The raw input exposure has a model for distortion, but it has known 

776 deficiencies severe enough to be worth fixing (e.g. because they 

777 cause problems for fitting a better WCS). In that case you should 

778 override this method with a version suitable for your raw data. 

779 

780 """ 

781 wcs = exposure.getWcs() 

782 if wcs is None: 

783 raise RuntimeError("exposure has no WCS") 

784 if camera is None: 

785 raise RuntimeError("camera is None") 

786 detector = exposure.getDetector() 

787 if detector is None: 

788 raise RuntimeError("exposure has no Detector") 

789 pixelToFocalPlane = detector.getTransform(camGeom.PIXELS, camGeom.FOCAL_PLANE) 

790 focalPlaneToFieldAngle = camera.getTransformMap().getTransform(camGeom.FOCAL_PLANE, 

791 camGeom.FIELD_ANGLE) 

792 distortedWcs = makeDistortedTanWcs(wcs, pixelToFocalPlane, focalPlaneToFieldAngle) 

793 exposure.setWcs(distortedWcs) 

794 

795 

796def applyGains(exposure, normalizeGains=False): 

797 """Scale an exposure by the amplifier gains. 

798 

799 Parameters 

800 ---------- 

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

802 Exposure to process. The image is modified. 

803 normalizeGains : `Bool`, optional 

804 If True, then amplifiers are scaled to force the median of 

805 each amplifier to equal the median of those medians. 

806 """ 

807 ccd = exposure.getDetector() 

808 ccdImage = exposure.getMaskedImage() 

809 

810 medians = [] 

811 for amp in ccd: 

812 sim = ccdImage.Factory(ccdImage, amp.getBBox()) 

813 sim *= amp.getGain() 

814 

815 if normalizeGains: 

816 medians.append(numpy.median(sim.getImage().getArray())) 

817 

818 if normalizeGains: 

819 median = numpy.median(numpy.array(medians)) 

820 for index, amp in enumerate(ccd): 

821 sim = ccdImage.Factory(ccdImage, amp.getBBox()) 

822 if medians[index] != 0.0: 

823 sim *= median/medians[index] 

824 

825 

826def widenSaturationTrails(mask): 

827 """Grow the saturation trails by an amount dependent on the width of the trail. 

828 

829 Parameters 

830 ---------- 

831 mask : `lsst.afw.image.Mask` 

832 Mask which will have the saturated areas grown. 

833 """ 

834 

835 extraGrowDict = {} 

836 for i in range(1, 6): 

837 extraGrowDict[i] = 0 

838 for i in range(6, 8): 

839 extraGrowDict[i] = 1 

840 for i in range(8, 10): 

841 extraGrowDict[i] = 3 

842 extraGrowMax = 4 

843 

844 if extraGrowMax <= 0: 

845 return 

846 

847 saturatedBit = mask.getPlaneBitMask("SAT") 

848 

849 xmin, ymin = mask.getBBox().getMin() 

850 width = mask.getWidth() 

851 

852 thresh = afwDetection.Threshold(saturatedBit, afwDetection.Threshold.BITMASK) 

853 fpList = afwDetection.FootprintSet(mask, thresh).getFootprints() 

854 

855 for fp in fpList: 

856 for s in fp.getSpans(): 

857 x0, x1 = s.getX0(), s.getX1() 

858 

859 extraGrow = extraGrowDict.get(x1 - x0 + 1, extraGrowMax) 

860 if extraGrow > 0: 

861 y = s.getY() - ymin 

862 x0 -= xmin + extraGrow 

863 x1 -= xmin - extraGrow 

864 

865 if x0 < 0: 

866 x0 = 0 

867 if x1 >= width - 1: 

868 x1 = width - 1 

869 

870 mask.array[y, x0:x1+1] |= saturatedBit 

871 

872 

873def setBadRegions(exposure, badStatistic="MEDIAN"): 

874 """Set all BAD areas of the chip to the average of the rest of the exposure 

875 

876 Parameters 

877 ---------- 

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

879 Exposure to mask. The exposure mask is modified. 

880 badStatistic : `str`, optional 

881 Statistic to use to generate the replacement value from the 

882 image data. Allowed values are 'MEDIAN' or 'MEANCLIP'. 

883 

884 Returns 

885 ------- 

886 badPixelCount : scalar 

887 Number of bad pixels masked. 

888 badPixelValue : scalar 

889 Value substituted for bad pixels. 

890 

891 Raises 

892 ------ 

893 RuntimeError 

894 Raised if `badStatistic` is not an allowed value. 

895 """ 

896 if badStatistic == "MEDIAN": 

897 statistic = afwMath.MEDIAN 

898 elif badStatistic == "MEANCLIP": 

899 statistic = afwMath.MEANCLIP 

900 else: 

901 raise RuntimeError("Impossible method %s of bad region correction" % badStatistic) 

902 

903 mi = exposure.getMaskedImage() 

904 mask = mi.getMask() 

905 BAD = mask.getPlaneBitMask("BAD") 

906 INTRP = mask.getPlaneBitMask("INTRP") 

907 

908 sctrl = afwMath.StatisticsControl() 

909 sctrl.setAndMask(BAD) 

910 value = afwMath.makeStatistics(mi, statistic, sctrl).getValue() 

911 

912 maskArray = mask.getArray() 

913 imageArray = mi.getImage().getArray() 

914 badPixels = numpy.logical_and((maskArray & BAD) > 0, (maskArray & INTRP) == 0) 

915 imageArray[:] = numpy.where(badPixels, value, imageArray) 

916 

917 return badPixels.sum(), value